diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 0000000..092ac30 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,116 @@ +version: 2.1 + +orbs: + codecov: codecov/codecov@3.1.1 + +workflows: + version: 2 + install_and_test: + jobs: + - python_lint + - test_ubuntu + - test_macos + +commands: + install_deps_ubuntu: + steps: + - checkout + - restore_cache: + key: conda-ubuntu-{{ checksum ".circleci/config.yml" }}-{{ checksum "env.common.yml" }}-{{ checksum "env.cpu.yml" }} + - run: + name: Install conda and environment + command: | + if [ ! -d "/home/circleci/miniconda" ]; then + wget https://repo.anaconda.com/miniconda/Miniconda3-py39_22.11.1-1-Linux-x86_64.sh -O miniconda.sh + bash miniconda.sh -b -p "$HOME"/miniconda + source /home/circleci/miniconda/etc/profile.d/conda.sh + conda activate base + # Conda configuration + conda config --set always_yes yes --set auto_update_conda false + # Update conda + conda update conda + conda install mamba -n base -c conda-forge + # Install ocp conda env + conda create --name ocp-models --clone base + source /home/circleci/miniconda/etc/profile.d/conda.sh + conda activate ocp-models + conda install -c conda-forge conda-merge + conda-merge env.common.yml env.cpu.yml > env.yml + mamba env update -n ocp-models --file env.yml + pip install pytest-cov==4.0.0 + fi + - save_cache: + paths: + - /home/circleci/miniconda + key: conda-ubuntu-{{ checksum ".circleci/config.yml" }}-{{ checksum "env.common.yml" }}-{{ checksum "env.cpu.yml" }} + install_deps_macos: + steps: + - checkout + - restore_cache: + key: conda-macos-{{ checksum ".circleci/config.yml" }}-{{ checksum "env.common.yml" }}-{{ checksum "env.cpu.yml" }} + - run: + name: Install conda and environment + command: | + if [[ -d $HOME/miniconda3 ]] ; then + echo "miniconda installed already." + else + curl -o miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_22.11.1-1-MacOSX-x86_64.sh + bash ./miniconda.sh -b + source $HOME/miniconda3/bin/activate + conda config --set always_yes yes --set auto_update_conda false + conda install mamba -n base -c conda-forge + conda create --name ocp-models --clone base + conda activate ocp-models + conda install -c conda-forge conda-merge + conda-merge env.common.yml env.cpu.yml > env.yml + mamba env update -n ocp-models --file env.yml + fi + - save_cache: + paths: + - /Users/distiller/miniconda3 + key: conda-macos-{{ checksum ".circleci/config.yml" }}-{{ checksum "env.common.yml" }}-{{ checksum "env.cpu.yml" }} + +jobs: + python_lint: + docker: + - image: cimg/python:3.9.13 + steps: + - checkout + - run: + name: setup + command: pip install black==22.3.0 + - run: + name: run black + command: black . --check + + test_ubuntu: + docker: + - image: cimg/python:3.9.13 + resource_class: large + steps: + - install_deps_ubuntu + - run: + name: install ocp and run tests + command: | + source /home/circleci/miniconda/etc/profile.d/conda.sh + conda activate ocp-models + pip install -e . + pre-commit install + pytest --cov-report=xml --cov=ocpmodels/ /home/circleci/project/tests + - codecov/upload: + file: coverage.xml + + test_macos: + macos: + xcode: "13.4.1" + resource_class: medium + steps: + - install_deps_macos + - run: + name: install ocp and run tests + command: | + source $HOME/miniconda3/bin/activate + conda activate ocp-models + pip install -e . + pre-commit install + pytest tests diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..1de94f7 --- /dev/null +++ b/.flake8 @@ -0,0 +1,5 @@ +[flake8] +ignore = E203, E266, E501, E731, W503, F403, F401 +max-line-length = 79 +max-complexity = 18 +select = B,C,E,F,W,T4,B9 diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..2f77e91 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.ipynb linguist-documentation diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b61ebc3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,113 @@ +wandb +data +checkpoints +results +logs +*.traj +experimental + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*,cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +docs/source/_build/ + +# PyBuilder +target/ + +# IPython Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# dotenv +.env + +# virtualenv +venv/ +ENV/ + +# Spyder project settings +.spyderproject + +# Rope project settings +.ropeproject + +# User directories +Local + +# .DS_Store +.DS_Store + +# VIM swap files +*.swp + +# PyCharm +.idea/ + +# VS Code +.vscode/ diff --git a/.isort.cfg b/.isort.cfg new file mode 100644 index 0000000..6bde062 --- /dev/null +++ b/.isort.cfg @@ -0,0 +1,6 @@ +[settings] +multi_line_output=3 +include_trailing_comma=True +force_grid_wrap=0 +use_parentheses=True +line_length=79 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..bf7ba03 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,19 @@ +repos: +- repo: https://github.com/ambv/black + rev: 22.3.0 + hooks: + - id: black + language_version: python3.8 + additional_dependencies: ['click==8.0.4'] +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v2.3.0 + hooks: + - id: flake8 + - id: trailing-whitespace + - id: check-added-large-files + - id: end-of-file-fixer +- repo: https://github.com/pre-commit/mirrors-isort + rev: v5.9.1 + hooks: + - id: isort + args: ["--profile", "black", "--filter-files"] diff --git a/DATASET.md b/DATASET.md new file mode 100644 index 0000000..ad95dfc --- /dev/null +++ b/DATASET.md @@ -0,0 +1,469 @@ +# Open Catalyst datasets + +* [Open Catalyst 2020 (OC20)](#open-catalyst-2020-oc20) + * [Scripts to download and preprocess the data](#download-and-preprocess-the-dataset) + * [Structure to Energy and Forces (S2EF)](#structure-to-energy-and-forces-s2ef-task) + * [Initial Structure to Relaxed Structure (IS2RS) / Relaxed Energy (IS2RE)](#initial-structure-to-relaxed-structure-is2rs-and-initial-structure-to-relaxed-energy-is2re-tasks) + * [Relaxation Trajectories](#relaxation-trajectories) + * [Bader charge data](#bader-charge-data) + * [OC20 metadata](#oc20-mappings) + * [Changelog](#dataset-changelog) + * [License and bibtex](#citing-oc20) +* [Open Catalyst 2022 (OC22)](#open-catalyst-2022-oc22) + * [Structure to Total Energy and Forces (S2EF-total)](#structure-to-total-energy-and-forces-s2ef-total-task) + * [Initial Structure to Relaxed Structure (IS2RS) / Relaxed Total Energy (IS2RE-total)](#initial-structure-to-relaxed-structure-is2rs-and-initial-structure-to-relaxed-total-energy-is2re-total-tasks) + * [Relaxation Trajectories](#relaxation-trajectories-1) + * [OC22 metadata](#oc22-mappings) + * [License and bibtex](#citing-oc22) + +* * * + +## Open Catalyst 2020 (OC20) + +*NOTE: Data files for all tasks / splits were updated on Feb 10, 2021 due to minor bugs (affecting < 1% of the data) in earlier versions. If you downloaded data before Feb 10, 2021, please re-download the data.* + + +### Download and preprocess the dataset + +IS2* datasets are stored as LMDB files and are ready to be used upon download. +S2EF train+val datasets require an additional preprocessing step. + +For convenience, a self-contained script can be found [here](https://github.com/Open-Catalyst-Project/ocp/blob/main/scripts/download_data.py) to download, preprocess, and organize the data directories to be readily usable by the existing [configs](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs). + +For IS2*, run the script as: + + + +``` +python scripts/download_data.py --task is2re +``` + + + +For S2EF train/val, run the script as: + + + +``` +python scripts/download_data.py --task s2ef --split SPLIT_SIZE --get-edges --num-workers WORKERS --ref-energy +``` + + + + + +* `--split`: split size to download: `"200k", "2M", "20M", "all", "val_id", "val_ood_ads", "val_ood_cat", or "val_ood_both"`. +* `--get-edges`: includes edge information in LMDBs (~10x storage requirement, ~3-5x slowdown), otherwise, compute edges on the fly (larger GPU memory requirement). +* `--num-workers`: number of workers to parallelize preprocessing across. +* `--ref-energy`: uses referenced energies instead of raw energies. + +For S2EF test, run the script as: + + +``` +python scripts/download_data.py --task s2ef --split test +``` + + + +To download and process the dataset in a directory other than your local `ocp/data` folder, add the following command line argument `--data-path`. + +Note that the baseline [configs](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs) +expect the data to be found in `ocp/data`, make sure you symlink your directory or +modify the paths in the configs accordingly. + +The following sections list dataset download links and sizes for various S2EF +and IS2RE/IS2RS task splits. If you used the above `download_data.py` script to +download and preprocess the data, you are good to go and can stop reading here! + + +### Structure to Energy and Forces (S2EF) task + +For this task’s train and validation sets, we provide compressed trajectory files with the input structures and output energies and forces. We provide precomputed LMDBs for the test sets. To use the train and validation datasets, first download the files and uncompress them. The uncompressed files are used to generate LMDBs, which are in turn used by the dataloaders to train the ML models. Code for the dataloaders and generating the LMDBs may be found in the Github repository. + +Four training datasets are provided with different sizes. Each is a subset of the other, i.e., the 2M dataset is contained in the 20M and all datasets. + +Four datasets are provided for validation set. Each dataset corresponds to a subsplit used to evaluate different types of extrapolation, in domain (id, same distribution as the training dataset), out of domain adsorbate (ood_ads, unseen adsorbate), out of domain catalyst (ood_cat, unseen catalyst composition), and out of domain both (ood_both, unseen adsorbate and catalyst composition). + +For the test sets, we provide precomputed LMDBs for each of the 4 subsplits (In Domain, OOD Adsorbate, OOD Catalyst, OOD Both). + +Each tarball has a README file containing details about file formats, number of structures / trajectories, etc. + +|Splits |Size of compressed version (in bytes) |Size of uncompressed version (in bytes) | MD5 checksum (download link) | +|--- |--- |--- |--- | +|Train | | | | | +|all |225G |1.1T | [12a7087bfd189a06ccbec9bc7add2bcd](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_train_all.tar) | +|20M |34G |165G | [863bc983245ffc0285305a1850e19cf7](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_train_20M.tar) | +|2M |3.4G |17G | [953474cb93f0b08cdc523399f03f7c36](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_train_2M.tar) | +|200K |344M |1.7G | [f8d0909c2623a393148435dede7d3a46](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_train_200K.tar) | +| | | | | | +|Validation | | | | | +|val_id |1.7G |8.3G | [f57f7f5c1302637940f2cc858e789410](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_val_id.tar) | +|val_ood_ads |1.7G |8.2G | [431ab0d7557a4639605ba8b67793f053](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_val_ood_ads.tar) | +|val_ood_cat |1.7G |8.3G | [532d6cd1fe541a0ddb0aa0f99962b7db](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_val_ood_cat.tar) | +|val_ood_both |1.9G |9.5G | [5731862978d80502bbf7017d68c2c729](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_val_ood_both.tar) | +| | | | | | +|Test (LMDBs for all splits) |30G |415G | [bcada432482f6e87b24e14b6b744992a](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_test_lmdbs.tar.gz) | +| | | | | | +|Rattled data |29G |136G | [40431149b27b64ce1fb40cac4e2e064b](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_rattled.tar) | +| | | | | | +|MD data |42G |306G | [9fed845aaab8fb4bf85e3a8db57796e0](https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_md.tar) | +| | | | | + + + + +### Initial Structure to Relaxed Structure (IS2RS) and Initial Structure to Relaxed Energy (IS2RE) tasks + +For the IS2RS and IS2RE tasks, we are providing: + + + +* One `.tar.gz` file with precomputed LMDBs which once downloaded and uncompressed, can be used directly to train ML models. The LMDBs contain the input initial structures and the output relaxed structures and energies. Training datasets are split by size, with each being a subset of the larger splits, similar to S2EF. The validation and test datasets are broken into subsplits based on different extrapolation evaluations (In Domain, OOD Adsorbate, OOD Catalyst, OOD Both). +* underlying ASE relaxation trajectories for the adsorbate+catalyst in the entire training and validation sets for the IS2RE and IS2RS tasks. These are **not** required to download for training ML models, but are available for interested users. + +Each tarball has README file containing details about file formats, number of structures / trajectories, etc. + +|Splits |Size of compressed version (in bytes) |Size of uncompressed version (in bytes) | MD5 checksum (download link) | +|--- |--- |--- |--- | +|Train (all splits) + Validation (all splits) + test (all splits) |8.1G |97G | [cfc04dd2f87b4102ab2f607240d25fb1](https://dl.fbaipublicfiles.com/opencatalystproject/data/is2res_train_val_test_lmdbs.tar.gz) | +|Test-challenge 2021 ([challenge details](https://opencatalystproject.org/challenge.html)) |1.3G |17G | [aed414cdd240fbb5670b5de6887a138b](https://dl.fbaipublicfiles.com/opencatalystproject/data/is2re_test_challenge_2021.tar.gz) | +| | | | | + + + + + + +### Relaxation Trajectories + +#### Adsorbate+catalyst system trajectories (optional download) + +|Split |Size of compressed version (in bytes) |Size of uncompressed version (in bytes) | MD5 checksum (download link) | +|--- |--- |--- |--- | +|All IS2RE/S training (~466k trajectories) |109G |841G | [9e3ed4d1e497bfdce4472ee70455edef](https://dl.fbaipublicfiles.com/opencatalystproject/data/is2res_train_trajectories.tar) | +| | | | | +|IS2RE/S Validation | | | | +|val_id (~25K trajectories) |5.9G |46G | [fcb71363018fb1e7127db2500e39e11a](https://dl.fbaipublicfiles.com/opencatalystproject/data/is2res_val_id_trajectories.tar) | +|val_ood_ads (~25K trajectories) |5.7G |44G | [5ced8ea84584aa229d31e693e0fb090f](https://dl.fbaipublicfiles.com/opencatalystproject/data/is2res_val_ood_ads_trajectories.tar) | +|val_ood_cat (~25K trajectories) |6.0G |46G | [88dcc02fd8c174a72d2c416878fc44ff](https://dl.fbaipublicfiles.com/opencatalystproject/data/is2res_val_ood_cat_trajectories.tar) | +|val_ood_both (~25K trajectories) |4.4G |35G | [bc74b6474a13542cc56eaa97bd51adfc](https://dl.fbaipublicfiles.com/opencatalystproject/data/is2res_val_ood_both_trajectories.tar) | + + + +##### Per-adsorbate trajectories (optional download) + +Adsorbate+catalyst trajectories on a per adsorbate basis are provided [here](./DATASET_PER_ADSORBATE.md) to avoid having to download all systems. Note - a few adsorbates are intentionally left out for the test splits. + + + +#### Catalyst system trajectories (optional download) + +|Number |Size of compressed version (in bytes) |Size of uncompressed version (in bytes) |MD5 checksum (download link) | +|--- |--- |--- |--- | +|294k systems |20G |151G | [347f4183465810e9b384e7a033baefc7](https://dl.fbaipublicfiles.com/opencatalystproject/data/slab_trajectories.tar) | + + +### Bader charge data +We provide Bader charge data for all final frames of our train + validation systems in OC20 (for both S2EF and IS2RE/RS tasks). A `.tar.gz` file, when downloaded and uncompressed will contain several directories with unique system-ids (of the format `random` where `XYZ` is an integer). Each directory will contain raw Bader charge analysis outputs. For more details on the Bader charge analysis, see https://theory.cm.utexas.edu/henkelman/research/bader/. + +Downloadable link: https://dl.fbaipublicfiles.com/opencatalystproject/data/oc20_bader_data.tar (MD5 checksum: `aecc5e23542de49beceb4b7e44c153b9`) + +### OC20 mappings + +#### Data mapping information + +We provide a Python pickle file containing information about the slab and adsorbates for each of the systems in OC20 dataset. Loading the pickle file will load a Python dictionary. The keys of this dictionary are the adsorbate+catalyst system-ids (of the format `random` where `XYZ` is an integer), and the corresponding value of each key is a dictionary with information about: + + + +* `bulk_mpid` : Materials Project ID of the bulk system used corresponding the the catalyst surface +* `bulk_symbols` Chemical composition of the bulk counterpart +* `ads_symbols` Chemical composition of the adsorbate counterpart +* `ads_id` : internal unique identifier, one for each of the 82 adsorbates used in the dataset +* `bulk_id` : internal unique identifier one for each of the 11500 bulks used in the dataset +* `miller_index`: 3-tuple of integers indicating the Miller indices of the surface +* `shift`: c-direction shift used to determine cutoff for the surface (c-direction is following the nomenclature from Pymatgen) +* `top`: boolean indicating whether the chosen surface was at the top or bottom of the originally enumerated surface +* `adsorption_site`: A tuple of 3-tuples containing the Cartesian coordinates of each binding adsorbate atom +* `class`: integer indicating the class of materials the system's slab is part of, where: +* 0 - intermetallics +* 1 - metalloids +* 2 - non-metals +* 3 - halides +* `anomaly`: integer indicating possible anomalies (based off general heuristics, not to be taken as perfect classifications), where: +* 0 - no anomaly +* 1 - adsorbate dissociation +* 2 - adsorbate desorption +* 3 - surface reconstruction +* 4 - incorrect CHCOH placement, appears to be CHCO with a lone, uninteracting, H far off in the unit cell + +Downloadable link: https://dl.fbaipublicfiles.com/opencatalystproject/data/oc20_data_mapping.pkl (MD5 checksum: `01c879067a05b4288055a1fdf821e068`) + +An example entry is + + + +``` +'random2181546': {'bulk_id': 6510, + 'ads_id': 69, + 'bulk_mpid': 'mp-22179', + 'bulk_symbols': 'Si2Ti2Y2', + 'ads_symbols': '*N2', + 'miller_index': (2, 0, 1), + 'shift': 0.145, + 'top': True, + 'adsorption_site': ((4.5, 12.85, 16.13),), + 'class': 1, + 'anomaly': 0} +``` + + + + + +### Adsorbate-catalyst system to catalyst system mapping information + +We provide a Python pickle file containing information about the mapping from adsorbate-catalyst systems to their corresponding catalyst systems. Loading the pickle file will load a Python dictionary. The keys of this dictionary are the adsorbate+catalyst system-ids (of the format `random` where `XYZ` is an integer), and values will be the catalyst system-ids (of the format `random` where `PQR` is an integer). + +Downloadable link: https://dl.fbaipublicfiles.com/opencatalystproject/data/mapping_adslab_slab.pkl (MD5 checksum: `079041076c3f15d18ecb5d17c509cdfe`) + +An example entry is + + + +``` +'random1981709': 'random533137' +``` + + + + + +### Dataset changelog + +#### September 2021 + +* Released IS2RE `test-challenge` data for the [Open Catalyst Challenge 2021](https://opencatalystproject.org/challenge.html) + +#### March 2021 + +* Modified the pickle corresponding to data mapping information. Now the pickle includes extra information about `miller_index`, `shift`, `top` and `adsorption_site`. +* Added Molecular Dynamics (MD) and rattled data for S2EF task. + +#### Version 2, Feb 2021 + +Modifications: + + + +* Removed slab systems which had single frame checkpoints, this led to modifications of reference frame energies of 350k frames out of 130M. +* Fixed stitching of checkpoints in adsorbate+catalyst trajectories. +* Added release of slab trajectories. + +Below are actual updates numbers, of the form `old` → `new` + +Total S2EF frames: + + + +* train: 133953162 → 133934018 +* validation: + * val_id : 1000000 → 999866 + * val_ood_ads: 1000000 → 999838 + * val_ood_cat: 1000000 → 999809 + * val_ood_both: 1000000 → 999944 +* test: + * test_id: 1000000 → 999736 + * test_ood_ads: 1000000 → 999859 + * test_ood_cat: 1000000 → 999826 + * test_ood_both: 1000000 → 999973 + +Total IS2RE and IS2RS systems: + + + +* train: 461313 → 460328 +* validation: + * val_id : 24946 → 24943 + * val_ood_ads: 24966 → 24961 + * val_ood_cat: 24988 → 24963 + * val_ood_both: 24963 → 24987 +* test: + * test_id: 24951 → 24948 + * test_ood_ads: 24931 → 24930 + * test_ood_cat: 24967 → 24965 + * test_ood_both: 24986 → 24985 + +#### Version 1, Oct 2020 + +Total S2EF frames: + + + +* train: 133953162 +* validation: + * val_id : 1000000 + * val_ood_ads: 1000000 + * val_ood_cat: 1000000 + * val_ood_both: 1000000 +* test: + * test_id: 1000000 + * test_ood_ads: 1000000 + * test_ood_cat: 1000000 + * test_ood_both: 1000000 + +Total IS2RE and IS2RS systems: + + + +* train: 461313 +* validation: + * val_id : 24936 + * val_ood_ads: 24966 + * val_ood_cat: 24988 + * val_ood_both: 24963 +* test: + * test_id: 24951 + * test_ood_ads: 24931 + * test_ood_cat: 24967 + * test_ood_both: 24986 + +### Citing OC20 + +The Open Catalyst 2020 (OC20) dataset is licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode). + +Please consider citing the following paper in any research manuscript using the OC20 dataset: + + + +``` +@article{ocp_dataset, + author = {Chanussot*, Lowik and Das*, Abhishek and Goyal*, Siddharth and Lavril*, Thibaut and Shuaibi*, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Palizhati, Aini and Sriram, Anuroop and Wood, Brandon and Yoon, Junwoong and Parikh, Devi and Zitnick, C. Lawrence and Ulissi, Zachary}, + title = {Open Catalyst 2020 (OC20) Dataset and Community Challenges}, + journal = {ACS Catalysis}, + year = {2021}, + doi = {10.1021/acscatal.0c04525}, +} +``` + + + +## Open Catalyst 2022 (OC22) + +### Structure to Total Energy and Forces (S2EF-Total) task + +For this task’s train, validation and test sets, we provide precomputed LMDBs that can be directly used with dataloaders provided in our code. The LMDBs contain input structures from all points in relaxation trajectories along with the energy of the structure and the atomic forces. The validation and test datasets are broken into subsplits based on in-distribution and out-of-distribution materials relative to the training dataset. All LMDBs are compressed into a single `.tar.gz` file. + + +|Splits |Size of compressed version (in bytes) |Size of uncompressed version (in bytes) | MD5 checksum (download link) | +|--- |--- |--- |--- | +|Train (all splits) + Validation (all splits) + test (all splits) | 20G | 71G | [ebea523c6f8d61248a37b4dd660b11e6](https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/s2ef_total_train_val_test_lmdbs.tar.gz) +| | | | | + + + + +### Initial Structure to Relaxed Structure (IS2RS) and Initial Structure to Relaxed Total Energy (IS2RE-Total) tasks + +For IS2RE-Total / IS2RS training, validation and test sets, we provide precomputed LMDBs that can be directly used with dataloaders provided in our code. The LMDBs contain input initial structures and the output relaxed structures and energies. The validation and test datasets are broken into subsplits based on in-distribution and out-of-distribution materials relative to the training dataset. All LMDBs are compressed into a single `.tar.gz` file. + + +|Splits |Size of compressed version (in bytes) |Size of uncompressed version (in bytes) | MD5 checksum (download link) | +|--- |--- |--- |--- | +|Train (all splits) + Validation (all splits) + test (all splits) | 109M | 424M | [b35dc24e99ef3aeaee6c5c949903de94](https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/is2res_total_train_val_test_lmdbs.tar.gz) | +| | | | | + + + + + + + + +### Relaxation Trajectories + +#### System trajectories (optional download) + + +We provide relaxation trajectories for all systems used in train and validation sets of S2EF-Total and IS2RE-Total/RS task: + + +|Number |Size of compressed version (in bytes) |Size of uncompressed version (in bytes) | MD5 checksum (download link) | +|--- |--- |--- |--- | +| S2EF and IS2RE (both train and validation) | 34G | 80G | [977b6be1cbac6864e63c4c7fbf8a3fce](https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc22_trajectories.tar.gz) | +| | | | | + + + + + +### OC22 Mappings + +#### Data mapping information + + + +We provide a Python pickle file containing information about the slab and adsorbates for each of the systems in OC22 dataset. Loading the pickle file will load a Python dictionary. The keys of this dictionary are the system-ids (of the format `XYZ` where `XYZ` is an integer, corresponding to the `sid` in the LMDB Data object), and the corresponding value of each key is a dictionary with information about: + + +* `bulk_id`: Materials Project ID of the bulk system used corresponding the the catalyst surface +* `bulk_symbols`: Chemical composition of the bulk counterpart +* `miller_index`: 3-tuple of integers indicating the Miller indices of the surface +* `traj_id`: Identifier associated with the accompanying raw trajectory (if available) +* `slab_sid`: Identifier associated with the corresponding slab (if available) +* `ads_symbols`: Chemical composition of the adsorbate counterpart (adosrbate+slabs only) +* `nads`: Number of adsorbates present + + + +Downloadable link: https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc22_metadata.pkl (MD5 checksum: `13dc06c6510346d8a7f614d5b26c8ffa` ) + + +An example adsorbate+slab entry: + +``` + 6877: {'bulk_id': 'mp-559112', + 'miller_index': (1, 0, 0), + 'nads': 1, + 'traj_id': 'K2Zn6O7_mp-559112_RyQXa0N0uc_ohyUKozY3G', + 'bulk_symbols': 'K4Zn12O14', + 'slab_sid': 30859, + 'ads_symbols': 'O2'}, +``` + +An example slab entry: + +``` + 34815: {'bulk_id': 'mp-18793', + 'miller_index': (1, 2, 1), + 'nads': 0, + 'traj_id': 'LiCrO2_mp-18793_clean_3HDHBg6TIz', + 'bulk_symbols': 'Li2Cr2O4'}, +``` + +#### + +#### OC20 reference information + + +In order to train OC20 on total energy, we provide a Python pickle file containing the energy necessary to convert adsorption energy values to total energy. Loading the pickle file will load a Python dictionary. The keys of this dictionary are the system-ids (of the format `random` where `XYZ` is an integer, corresponding to the `sid` in the LMDB Data object), and the corresponding value of each key is the energy to be added to OC20 energy values. To train on total energies for OC20, specify the path to this pickle file in your training configs. + +Downloadable link: https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc20_ref.pkl (MD5 checksum: `043e1e0b0cce64c62f01a8563dbc3178`) +#### + +### Citing OC22 + +The Open Catalyst 2022 (OC22) dataset is licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode). + +Please consider citing the following paper in any research manuscript using the OC22 dataset: + + +``` +@article{oc22_dataset, + author = {Tran*, Richard and Lan*, Janice and Shuaibi*, Muhammed and Wood*, Brandon and Goyal*, Siddharth and Das, Abhishek and Heras-Domingo, Javier and Kolluru, Adeesh and Rizvi, Ammar and Shoghi, Nima and Sriram, Anuroop and Ulissi, Zachary and Zitnick, C. Lawrence}, + title = {The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis}, + year = {2022}, + journal={arXiv preprint arXiv:2206.08917}, +} +``` diff --git a/DATASET_PER_ADSORBATE.md b/DATASET_PER_ADSORBATE.md new file mode 100644 index 0000000..be8a967 --- /dev/null +++ b/DATASET_PER_ADSORBATE.md @@ -0,0 +1,112 @@ +# Per-adsorbate trajectories + + +Download links are in the table below: + +|Adsorbate symbol |Downloadable path |size |MD5 checksum | +|--- |--- |--- |--- | +|*O |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/0.tar |1006M |d4151542856b4b6405f276808f75358a | +|*H |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/1.tar |850M |3697f04faf04251a23da8b88a78209f7 | +|*OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/2.tar |1.6G |a21081f3f55eb0c98a91021bbe3dac44 | +|*OH2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/3.tar |1.8G |b12b706854f5d899e02a9ae6578b5d45 | +|*C |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/4.tar |1.1G |e4fe9890764fcf59e01e3ceab089b978 | +|*CH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/6.tar |1.4G |ec9aa2c4c4bd4419359438ba7fbb881d | +|*CHO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/7.tar |1.4G |d32200f74ad5c3bfd42e8835f36d57ab | +|*COH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/8.tar |1.6G |5418a1b331f6c7689a5405cca4cc8d15 | +|*CH2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/9.tar |1.6G |8ee1066149c305d7c17c219b369c5a73 | +|*CH2*O |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/10.tar |1.7G |960c2450814024b66f3c79121179ac60 | +|*CHOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/11.tar |1.8G |60ac9f965f9589a3389483e3d1e58144 | +|*CH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/12.tar |1.7G |7e123e6f4fb10d6897be3f47721dfd4a | +|*OCH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/13.tar |1.8G |0823047bbbe05fa0e63f9d83ec601487 | +|*CH2OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/14.tar |1.9G |9ac71e198d75b1427182cd34abb73e4d | +|*CH4 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/15.tar |1.9G |a405ce403018bf8afbd4425d5c0b34d5 | +|*OHCH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/16.tar |2.1G |d3c829f1952db6e4f428273ee05f59b1 | +|*C*C |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/17.tar |1.5G |d687a151345305897b9245af4b0f9967 | +|*CCO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/18.tar |1.7G |214ca96e620c5ec6e8a6ff8144a22a04 | +|*CCH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/19.tar |1.6G |da2268545e80ca1664026449dd2fdd24 | +|*CHCO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/20.tar |1.7G |386c99407fe63080d26cda525dfdd8cd | +|*CCHO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/21.tar |1.8G |918b20960438494ab160a9dbd9668157 | +|*COCHO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/22.tar |1.8G |84424aa2ad30301e23ece1438ea39923 | +|*CCHOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/23.tar |2.0G |3cc90425ec042a70085ba7eb2916a79a | +|*CCH2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/24.tar |1.8G |9dbcf7566e40965dd7f8a186a75a718e | +|*CH*CH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/25.tar |1.7G |a193b4c72f915ba0b21a41790696b23c | +|CH2*CO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/26.tar |1.8G |de83cf50247f5556fa4f9f64beff1eeb | +|*CHCHO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/27.tar |1.9G |1d140aaa2e7b287124ab38911a711d70 | +|*CH*COH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/28.tar |1.3G |682d8a6b05ca5948b34dc5e5f6bbcd61 | +|*COCH2O |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/29.tar |1.9G |c8742faa8ca40e8edb4110069817fa70 | +|*CHO*CHO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/30.tar |2.0G |8cfbb67beb312b98c40fcb891dfa480a | +|*COHCHO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/31.tar |1.9G |6ffa903a62d8ec3319ecec6a03b06276 | +|*COHCOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/32.tar |2.0G |caca0058b641bfdc9f8de4527e60feb7 | +|*CCH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/33.tar |1.8G |906543aaefc171edab388ff4f0fe8a20 | +|*CHCH2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/34.tar |1.8G |4dfab479495f76179749c1956046fbd8 | +|*COCH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/35.tar |1.9G |29d1b992715054e920e8bb2afe97b393 | +|*CHCHOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/38.tar |2.0G |9e5912df6f7b11706d1046cdb9e3087e | +|*CCH2OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/39.tar |2.1G |7bcae43cee451306e34ec416588a7f09 | +|*CHOCHOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/40.tar |2.0G |f98866d08fe3451ae7ebc47bb51599aa | +|*COCH2OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/41.tar |1.4G |bfaf689e5827fcf26c51e567bb8dd1be | +|*COHCHOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/42.tar |2.0G |236fe4e950aa2fbdde94ef2821fb48d2 | +|*OCHCH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/44.tar |2.1G |66acc5460a999625c3364f0f3bcca871 | +|*COHCH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/45.tar |2.1G |bb4a01956736399c8cee5e219f8c1229 | +|*CHOHCH2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/46.tar |2.1G |e836de4ec146b1b611533f1ef682cace | +|*CHCH2OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/47.tar |2.0G |66df44121806debef6dc038df7115d1d | +|*OCH2CHOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/48.tar |2.2G |ff6981fdbcd2e65d351505c15d218d76 | +|*CHOCH2OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/49.tar |2.1G |448f7d352ab6e32f754e24de64ca302a | +|*COHCH2OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/50.tar |2.1G |8bff6bf3e10cc84acc4a283a375fcc23 | +|*CHOHCHOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/51.tar |2.0G |9c9e4d617d306751760a80f1453e71f1 | +|*CH2CH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/52.tar |2.0G |ec1e964d2ee6f468fa5773743e3994a4 | +|*OCH2CH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/53.tar |2.1G |d297b27b02822f9b6af80bdb64aee819 | +|*CHOHCH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/54.tar |2.1G |368de083dafdc3bbdb560d35e2a102c0 | +|*CH2CH2OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/55.tar |2.1G |3c1aaf790659f7ff89bf1eed8b396b63 | +|*CHOHCH2OH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/56.tar |2.2G |2d71adb9e305e6f3bca49e5df9b5a86a | +|*OHCH2CH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/57.tar |2.3G |cf51128f8522b7b66fc68d79980d6def | +|*NH2N(CH3)2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/58.tar |1.6G |36ba974d80c20ff636431f7c0ad225da | +|*ONN(CH3)2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/59.tar |2.3G |fdc4cd19977496909d61be4aee61c4f1 | +|*OHNNCH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/60.tar |2.1G |50a6ff098f9ba7adbba9ac115726cc5a | +|*ONH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/62.tar |1.8G |47573199c545afe46c554ff756c3e38f | +|*NHNH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/63.tar |1.7G |dd456b7e19ef592d9f0308d911b91d7c | +|*N*NH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/65.tar |1.6G |c05289fd56d64c74306ebf57f1061318 | +|*NO2NO2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/67.tar |2.1G |4822a06f6c5f41bdefd3cbbd8856c11f | +|*N*NO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/68.tar |1.6G |2a27de122d32917cc5b6ac0a21c63c1c | +|*N2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/69.tar |1.5G |cc668fecf679b6edaac8fd8fb9cdd404 | +|*ONNH2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/70.tar |2.1G |dff880f1a5baa7f67b52fd3ed745443d | +|*NH2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/71.tar |1.6G |c7f383b50faa6244e265c9611466cb8f | +|*NH3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/72.tar |1.9G |2b355741f9300445703270e0e4b8c01c | +|*NONH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/73.tar |1.8G |48877a0c6f2994baac82cb722711aaa2 | +|*NH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/74.tar |1.4G |7979b9e7ab557d6979b33e352486f0ef | +|*NO2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/75.tar |1.7G |9f352fbc32bb2b8caf4788aba28b2eb7 | +|*NO |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/76.tar |1.4G |482ee306a5ae2eee78cac40d10059ebc | +|*N |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/77.tar |1.1G |bfb6e03d4a687987ff68976f0793cc46 | +|*NO3 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/78.tar |1.8G |700834326e789a6e38bf3922d9fcb792 | +|*OHNH2 |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/79.tar |2.1G |fa24472e0c02c34d91f3ffe6b77bfb11 | +|*ONOH |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/80.tar |1.4G |4ddcccd62a834a76fe6167461f512529 | +|*CN |https://dl.fbaipublicfiles.com/opencatalystproject/data/per_adsorbate_is2res/81.tar |1.5G |bc7c55330ece006d09496a5ff01d5d50 | + + +Note - A few adsorbates are intentionally left out for the test splits. + +Downloading any of the above and extracting will result in a folder: + +`/` + +* `system.txt` Text file containing information about the different adsorbate+catalyst system names. In total there are N systems. More details described below. +* `/` + * This contains N compressed trajectory files of the format `.extxyz.xz`. + * Files are named as `.extxyz.xz` (where `system_id` is defined below). + + +where, `` can be 0 to 81. N is dependent on which adsorbate index is chosen. + + + +The file `system.txt` has information in the following format: +`system_id,reference_energy` + +where: + +* `system_id `- Internal random ID corresponding to an adsorbate+catalyst system. +* `reference_energy` - Energy used to reference system energies to bare catalyst+gas reference energies. Used for adsorption energy calculations. + + +The `.extxyz.xz` files are LZMA compressed `.extxyz` trajectory files. Each trajectory corresponds to a relaxation trajectory of a different adsorbate+catalyst system. Information about the `.extxyz` trajectory file format may be found at https://wiki.fysik.dtu.dk/ase/ase/io/formatoptions.html#extxyz. + +In order to uncompress the files, `uncompress.py` provides a multi-core implementation which could be used. diff --git a/INSTALL.md b/INSTALL.md new file mode 100644 index 0000000..04df986 --- /dev/null +++ b/INSTALL.md @@ -0,0 +1,51 @@ +## Installation + +- We'll use `conda` to install dependencies and set up the environment. +We recommend using the [Python 3.9 Miniconda installer](https://docs.conda.io/en/latest/miniconda.html#linux-installers). +- After installing `conda`, install [`mamba`](https://mamba.readthedocs.io/en/latest/) to the base environment. `mamba` is a faster, drop-in replacement for `conda`: + ```bash + conda install mamba -n base -c conda-forge + ``` +- Also install `conda-merge` to the base environment: + ```bash + conda install conda-merge -n base -c conda-forge + ``` + +Next, follow the instructions for [GPU](#gpu-machines) or [CPU](#cpu-only-machines) machines depending on your hardware to create a new environment named `ocp-models` and install dependencies. + +### GPU machines + +Instructions are for PyTorch 1.13.1, CUDA 11.6 specifically. + +- First, check that CUDA is in your `PATH` and `LD_LIBRARY_PATH`, e.g. + ```bash + $ echo $PATH | tr ':' '\n' | grep cuda + /public/apps/cuda/11.6/bin + + $ echo $LD_LIBRARY_PATH | tr ':' '\n' | grep cuda + /public/apps/cuda/11.6/lib64 + ``` + The exact paths may differ on your system. +- Then install the dependencies: + ```bash + conda-merge env.common.yml env.gpu.yml > env.yml + mamba env create -f env.yml + ``` + Activate the conda environment with `conda activate ocp-models`. +- Install the `ocp` package with `pip install -e .`. +- Finally, install the pre-commit hooks: + ```bash + pre-commit install + ``` + +### CPU-only machines + +Please skip the following if you completed the with-GPU installation from above. + +```bash +conda-merge env.common.yml env.cpu.yml > env.yml +mamba env create -f env.yml +conda activate ocp-models +pip install -e . +pre-commit install +``` diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..51913d8 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,22 @@ + +MIT License + +Copyright (c) Facebook, Inc. and its affiliates. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/MODELS.md b/MODELS.md new file mode 100644 index 0000000..28669b8 --- /dev/null +++ b/MODELS.md @@ -0,0 +1,124 @@ +# Pretrained OCP models + +This page summarizes all the pretrained models released as part of the [Open Catalyst Project](https://opencatalystproject.org/). All models were trained using this codebase. + +* [Open Catalyst 2020 (OC20)](#open-catalyst-2020-oc20) + * [S2EF models optimized for EFwT](#s2ef-models-optimized-for-efwt) + * [S2EF models optimized for force](#s2ef-models-optimized-for-force-only) + * [IS2RE models](#is2re-models) +* [Open Catalyst 2022 (OC22)](#open-catalyst-2022-oc22) + * [S2EF models](#s2ef-models) + +* * * + + +# Open Catalyst 2020 (OC20) + +* All configurations for these models are available in the [`configs/`](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs) directory. +* All of these models are trained on various splits of the OC20 S2EF / IS2RE datasets. For details, see [https://arxiv.org/abs/2010.09990](https://arxiv.org/abs/2010.09990) and https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md. + +## S2EF models: optimized for EFwT + +|Model |Split |Download |val ID force MAE |val ID EFwT | +|--- |--- |--- |--- |--- | +|CGCNN |200k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/cgcnn_200k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/200k/cgcnn/cgcnn.yml) |0.08 |0% | +|CGCNN |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/cgcnn_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/cgcnn/cgcnn.yml) |0.0673 |0.01% | +|CGCNN |20M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/cgcnn_20M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/20M/cgcnn/cgcnn.yml) |0.065 |0% | +|CGCNN |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/cgcnn_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/cgcnn/cgcnn.yml) |0.0684 |0.01% | +|DimeNet |200k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/dimenet_200k.pt) |0.0693 |0.01% | +|DimeNet |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/dimenet_2M.pt) |0.0576 |0.02% | +|SchNet |200k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/schnet_200k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/200k/schnet/schnet.yml) |0.0743 |0% | +|SchNet |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/schnet_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/schnet/schnet.yml) |0.0737 |0% | +|SchNet |20M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/schnet_20M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/20M/schnet/schnet.yml) |0.0568 |0.03% | +|SchNet |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/schnet_all_large.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/schnet/schnet.yml) |0.0494 |0.12% | +|DimeNet++ |200k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/s2ef/dimenetpp_200k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/200k/dimenet_plus_plus/dpp.yml) |0.0741 |0% | +|DimeNet++ |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/s2ef/dimenetpp_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/dimenet_plus_plus/dpp.yml) |0.0595 |0.01% | +|DimeNet++ |20M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/s2ef/dimenetpp_20M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/20M/dimenet_plus_plus/dpp.yml) |0.0511 |0.06% | +|DimeNet++ |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/s2ef/dimenetpp_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/dimenet_plus_plus/dpp.yml) |0.0444 |0.12% | +|SpinConv |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_12/s2ef/spinconv_force_centric_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/spinconv/spinconv_force.yml) |0.0329 |0.18% | +|SpinConv |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_08/s2ef/spinconv_force_centric_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/spinconv/spinconv_force.yml) |0.0267 |1.02% | +|GemNet-dT |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_12/s2ef/gemnet_t_direct_h512_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/gemnet/gemnet-dT.yml) |0.0257 |1.10% | +|GemNet-dT |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_08/s2ef/gemnet_t_direct_h512_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/gemnet/gemnet-dT.yml) |0.0211 |2.21% | +|PaiNN |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_05/s2ef/painn_h512_s2ef_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/painn/painn_h512.yml) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/painn/painn_nb6_scaling_factors.pt) |0.0294 |0.91% | +|GemNet-OC |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_base_s2ef_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/gemnet/gemnet-oc.yml) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/481f3a5a92dc787384ddae9fe3f50f5d932712fd/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt) |0.0225 |2.12% | +|GemNet-OC |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_base_s2ef_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/gemnet/gemnet-oc.yml) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/481f3a5a92dc787384ddae9fe3f50f5d932712fd/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt) |0.0179 |4.56% | +|GemNet-OC |All+MD |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_03/s2ef/gemnet_oc_base_s2ef_all_md.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/gemnet/gemnet-oc.yml) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/481f3a5a92dc787384ddae9fe3f50f5d932712fd/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt) |0.0173 |4.72% | +|GemNet-OC-Large |All+MD |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_large_s2ef_all_md.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/gemnet/gemnet-oc-large.yml) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/481f3a5a92dc787384ddae9fe3f50f5d932712fd/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt) |0.0164 |5.34% | +|SCN |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_03/s2ef/scn_t1_b1_s2ef_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/scn/scn-t1-b1.yml) |0.0216 |1.68% | +|SCN-t4-b2 |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_03/s2ef/scn_t4_b2_s2ef_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/scn/scn-t4-b2.yml) |0.0193 |2.68% | +|SCN |All+MD |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_03/s2ef/scn_all_md_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/scn/scn-all-md.yml) |0.0160 |5.08% | +|eSCN-L4-M2-Lay12 |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_03/s2ef/escn_l4_m2_lay12_2M_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/escn/eSCN-L4-M2-Lay12.yml) |0.0191 |2.55% | +|eSCN-L6-M2-Lay12 |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_03/s2ef/escn_l6_m2_lay12_2M_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/escn/eSCN-L6-M2-Lay12.yml) |0.0186 |2.66% | +|eSCN-L6-M2-Lay12 |All+MD |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_03/s2ef/escn_l6_m2_lay12_all_md_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/escn/eSCN-L6-M2-Lay12-All-MD.yml) |0.0161 |4.28% | +|eSCN-L6-M3-Lay20 |All+MD |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_03/s2ef/escn_l6_m3_lay20_all_md_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/escn/eSCN-L6-M3-Lay20-All-MD.yml) |0.0139 |6.64% | + +## S2EF models: optimized for force only + +|Model |Split |Download |val ID force MAE | +|--- |--- |--- |--- | +|SchNet |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/schnet_all_forceonly.pt) |0.0443 | +|DimeNet++ |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/s2ef/dimenetpp_all_forceonly.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/dimenet_plus_plus/dpp_forceonly.yml) |0.0334 | +|DimeNet++-Large |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/s2ef/dimenetpp_large_all_forceonly.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/dimenet_plus_plus/dpp10.7M_forceonly.yml) |0.02825 | +|DimeNet++ |20M+Rattled |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/s2ef/dimenetpp_20M_rattled_forceonly.pt) |0.0614 | +|DimeNet++ |20M+MD |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/s2ef/dimenetpp_20M_md_forceonly.pt) |0.0594 | + +## IS2RE models + +|Model |Split |Download |val ID energy MAE | +|--- |--- |--- |--- | +|CGCNN |10k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/cgcnn_10k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/10k/cgcnn/cgcnn.yml) |0.9881 | +|CGCNN |100k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/cgcnn_100k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/100k/cgcnn/cgcnn.yml) |0.682 | +|CGCNN |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/cgcnn_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/all/cgcnn/cgcnn.yml) |0.6199 | +|DimeNet |10k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/is2re/dimenet_10k.pt) |1.0117 | +|DimeNet |100k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/is2re/dimenet_100k.pt) |0.6658 | +|DimeNet |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2020_11/is2re/dimenet_all.pt) |0.5999 | +|SchNet |10k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/schnet_10k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/10k/schnet/schnet.yml) |1.059 | +|SchNet |100k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/schnet_100k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/100k/schnet/schnet.yml) |0.7137 | +|SchNet |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/schnet_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/all/schnet/schnet.yml) |0.6458 | +|DimeNet++ |10k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/dimenetpp_10k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/10k/dimenet_plus_plus/dpp.yml) |0.8837 | +|DimeNet++ |100k |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/dimenetpp_100k.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/100k/dimenet_plus_plus/dpp.yml) |0.6388 | +|DimeNet++ |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_02/is2re/dimenetpp_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/all/dimenet_plus_plus/dpp.yml) |0.5639 | +|PaiNN |All |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_05/is2re/painn_h1024_bs4x8_is2re_all.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/is2re/all/painn/painn_h1024_bs8x4.yml) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/painn/painn_nb6_scaling_factors.pt) |0.5728 | + +The Open Catalyst 2020 (OC20) dataset is licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode). + +Please consider citing the following paper in any research manuscript using the +OC20 dataset or pretrained models, as well as the original paper for each model: + +``` +@article{ocp_dataset, + author = {Chanussot*, Lowik and Das*, Abhishek and Goyal*, Siddharth and Lavril*, Thibaut and Shuaibi*, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Palizhati, Aini and Sriram, Anuroop and Wood, Brandon and Yoon, Junwoong and Parikh, Devi and Zitnick, C. Lawrence and Ulissi, Zachary}, + title = {Open Catalyst 2020 (OC20) Dataset and Community Challenges}, + journal = {ACS Catalysis}, + year = {2021}, + doi = {10.1021/acscatal.0c04525}, +} +``` + +# Open Catalyst 2022 (OC22) + +* All configurations for these models are available in the [`configs/oc22`](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs/oc22) directory. +* All of these models are trained on various splits of the OC22 S2EF / IS2RE datasets. For details, see [https://arxiv.org/abs/2206.08917](https://arxiv.org/abs/2206.08917) and https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md. + +## S2EF models + +|Model |Training |Download |val ID force MAE |val ID energy MAE | +|--- |--- |--- |--- |--- | +|GemNet-dT | OC22 |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_09/oc22/s2ef/gndt_oc22_all_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/oc22/s2ef/gemnet-dt/gemnet-dT.yml) |0.032 |1.127 | +|GemNet-OC | OC22 |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_09/oc22/s2ef/gnoc_oc22_all_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/oc22/s2ef/gemnet-oc/gemnet_oc.yml) |0.030 |0.563 | +|GemNet-OC | OC20+OC22 |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_09/oc22/s2ef/gnoc_oc22_oc20_all_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/oc22/s2ef/gemnet-oc/gemnet_oc_oc20_oc22.yml) |0.027 |0.483 | +|GemNet-OC | OC20->OC22 |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_09/oc22/s2ef/gnoc_finetune_all_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/oc22/s2ef/gemnet-oc/gemnet_oc_finetune.yml) |0.030 |0.417 | + +The Open Catalyst 2022 (OC22) dataset is licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode). + +Please consider citing the following paper in any research manuscript using the +OC22 dataset or pretrained models, as well as the original paper for each model: + +``` +@article{oc22_dataset, + author = {Tran*, Richard and Lan*, Janice and Shuaibi*, Muhammed and Wood*, Brandon and Goyal*, Siddharth and Das, Abhishek and Heras-Domingo, Javier and Kolluru, Adeesh and Rizvi, Ammar and Shoghi, Nima and Sriram, Anuroop and Ulissi, Zachary and Zitnick, C. Lawrence}, + title = {The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis}, + year = {2022}, + journal = {arXiv preprint arXiv:2206.08917}, +} +``` diff --git a/README.md b/README.md new file mode 100644 index 0000000..c2afa3b --- /dev/null +++ b/README.md @@ -0,0 +1,37 @@ +# Gemnet-OC_for_double_provskite + +# Installation + +- need to install 'ocp' + +https://github.com/Open-Catalyst-Project/ocp/tree/main + +# Guide + +- make Graph data and your lmdb dataset +``` +tmp = AtomToData() +tmp.S2EF(Data_list) # Data_list is list of ase Atoms : [[Atoms1, Ad_energy], [Atoms2, Ad_energy] ... ] +tmp.MakeLmdb(name='data', ratio_list=None) # name : file name, default of ratio_list is [0.8, 0.1, 0.1] +``` + +- dataset imformation for train and validation and predict +``` +train_src = 'data/name_train.lmdb' +val_src = 'data/name_validataion.lmdb' +test_src = 'data/name_test.lmdb' + +dataset = [{'src':train_src}, + {'src':val_src}, + {'src':test_src} # recommand do not input test_src for train -> #{'src':test_src} + ] +``` + +- predict GemnetOC +``` +checkpoint_path = '~~~' # checkpoitn file name +pretrained_trainer.load_checkpoint(checkpoint_path=checkpoint_path) +predictions = pretrained_trainer.predict(pretrained_trainer.test_loader, results_file="predict_result", disable_tqdm=False) +``` + +- You can obtain energy prediction values by running run_ocp.py. diff --git a/TRAIN.md b/TRAIN.md new file mode 100644 index 0000000..159f7c2 --- /dev/null +++ b/TRAIN.md @@ -0,0 +1,325 @@ +# Training and evaluating models on OCP datasets + +- [Getting Started](#getting-started) +- [OC20](#oc20) + - [Initial Structure to Relaxed Energy (IS2RE)](#initial-structure-to-relaxed-energy-prediction-is2re) + - [IS2RE Relaxations](#is2re-relaxations) + - [Structure to Energy and Forces (S2EF)](#structure-to-energy-and-forces-s2ef) + - [Training OC20 models with total energies (IS2RE/S2EF)](#training-oc20-models-with-total-energies-is2res2ef) + - [Initial Structure to Relaxed Structure (IS2RS)](#initial-structure-to-relaxed-structure-is2rs) + - [Create EvalAI submission files](#create-evalai-oc20-submission-files) + - [S2EF/IS2RE](#s2efis2re) + - [IS2RS](#is2rs) +- [OC22](#oc22) + - [Initial Structure to Total Relaxed Energy (IS2RE-Total)](#initial-structure-to-total-relaxed-energy-is2re-total) + - [Structure to Total Energy and Forces (S2EF-Total)](#structure-to-total-energy-and-forces-s2ef-total) + - [Joint Training](#joint-training) + - [Create EvalAI submission files](#create-evalai-oc22-submission-files) + - [S2EF-Total/IS2RE-Total](#s2ef-totalis2re-total) + +## Getting Started + +The [Open Catalyst Project](https://opencatalystproject.org/) consists of three +distinct tasks: +- Initial Structure to Relaxed Energy prediction (IS2RE) +- Structure to Energy and Forces (S2EF) +- Initial Structure to Relaxed Structure (IS2RS) + +This document is a tutorial for training and evaluating models +for each of these tasks as well as generating submission files for the +[evaluation server hosted on EvalAI](https://eval.ai/web/challenges/challenge-page/712/overview). + +`main.py` serves as the entry point to run any task. This script requires two command line +arguments at a minimum: +* `--mode MODE`: MODE can be `train`, `predict` or `run-relaxations` to train a model, make predictions +using an existing model, or run machine learning based relaxations using an existing model, respectively. +* `--config-yml PATH`: PATH is the path to a YAML configuration file. We use YAML files to supply all +parameters to the script. The `configs` directory contains a number of example config files. + +Running `main.py` directly runs the model on a single CPU or GPU if one is available: +``` +python main.py --mode train --config-yml configs/TASK/SIZE/MODEL/MODEL.yml +``` +If you have multiple +GPUs, you can use distributed data parallel training by running: +``` +python -u -m torch.distributed.launch --nproc_per_node=8 main.py --distributed --num-gpus 8 [...] +``` +`torch.distributed.launch` launches multiple processes for distributed training. For more details, refer to +https://pytorch.org/docs/stable/distributed.html#launch-utility + +If training with multiple GPUs, GPU load balancing may be used to evenly distribute a batch of variable system sizes across GPUs. Load balancing may either balance by number of atoms or number of neighbors. A `metadata.npz` file must be available in the dataset directory to take advantage of this feature. The following command will generate a `metadata.npz` file and place it in the corresponding directory. +``` +python scripts/make_lmdb_sizes.py --data-path data/s2ef/train/2M --num-workers 8 +``` +Load balancing is activated by default (in atoms mode). To change modes you can specify the following in your config: +``` +optim: + load_balancing: neighbors +``` +For more details, refer to https://github.com/Open-Catalyst-Project/ocp/pull/267. + +If you have access to a slurm cluster, we use the [submitit](https://github.com/facebookincubator/submitit) package to simplify multi-node distributed training: +``` +python main.py --distributed --num-gpus 8 --num-nodes 6 --submit [...] +``` + +In the rest of this tutorial, we explain how to train models for each task. + +# OC20 + +## Initial Structure to Relaxed Energy prediction (IS2RE) + +In the IS2RE tasks, the model takes the initial structure as an input and predicts the structure’s adsorption energy +in the relaxed state. To train a model for the IS2RE task, you can use the `EnergyTrainer` +Trainer and `SinglePointLmdb` dataset by specifying the following in your configuration file: +``` +trainer: energy # Use the EnergyTrainer + +dataset: + # Train data + - src: [Path to training data] + normalize_labels: True + # Mean and standard deviation of energies + target_mean: -0.969171404838562 + target_std: 1.3671793937683105 + # Val data (optional) + - src: [Path to validation data] + # Test data (optional) + - src: [Path to test data] +``` +You can find examples configuration files in [`configs/is2re`](https://github.com/Open-Catalyst-Project/ocp/tree/master/configs/is2re). + +To train a SchNet model for the IS2RE task on the 10k split, run: +``` +python main.py --mode train --config-yml configs/is2re/10k/schnet/schnet.yml +``` + +Training logs are stored in `logs/tensorboard/[TIMESTAMP]` where `[TIMESTAMP]` is +the starting time-stamp of the run. You can monitor the training process by running: +``` +tensorboard --logdir logs/tensorboard/[TIMESTAMP] +``` +At the end of training, the model checkpoint is stored in `checkpoints/[TIMESTAMP]/checkpoint.pt`. + +Next, run this model on the test data: +``` +python main.py --mode predict --config-yml configs/is2re/10k/schnet/schnet.yml \ + --checkpoint checkpoints/[TIMESTAMP]/checkpoint.pt +``` +The predictions are stored in `[RESULTS_DIR]/is2re_predictions.npz` and later used to create a submission file to be uploaded to EvalAI. + +### IS2RE Relaxations + +Alternatively, the IS2RE task may be approached by 2 methods as described in our paper: + +- Single Model: Relaxed energy predictions are extracted from relaxed structures generated via ML relaxations from a single model. + + 1. Train a S2EF model on both energy and forces as described [here](https://github.com/Open-Catalyst-Project/ocp/blob/master/TRAIN.md#structure-to-energy-and-forces-s2ef) + 2. Using the trained S2EF model, run ML relaxations as described [here](https://github.com/Open-Catalyst-Project/ocp/blob/master/TRAIN.md#initial-structure-to-relaxed-structure-is2rs). Ensure `traj_dir` is uniquely specified in the config as to save out the full trajectory. A sample config can be found [here](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/s2ef/2M/dimenet_plus_plus/dpp_relax.yml). ** Note ** Relaxations on the complete val/test set may take upwards of 8hrs depending on your available hardware. + 3. Prepare a submission file by running the following command: + ``` + python scripts/make_submission_file.py --id path/to/id/traj_dir \ + --ood-ads path/to/ood_ads/traj_dir --ood-cat path/to/ood_cat/traj_dir \ + --ood-both path/to/ood_both/traj_dir --out-path submission_file.npz --is2re-relaxations + ``` +- Dual Model: Relaxed energy predictions are extracted from relaxed structures generated via ML relaxations from two models - one for running relaxations and one for making energy predictions. + 1. Train two S2EF models, energy-only and force-only. + 2. Using the trained force-only S2EF model, run ML relaxations as described previously. Ensure `traj_dir` is uniquely specified in the config as to save out the full trajectory. **Note** Relaxations on the complete val/test set may take upwards of 8hrs depending on your available hardware. + 3. In order to make predictions via the energy-only model on the generated trajectories, LMDBs must be constructed via the following command: + ``` + python scripts/preprocess_relaxed.py --id path/to/id/traj_dir \ + --ood-ads path/to/ood_ads/traj_dir --ood-cat path/to/ood_cat/traj_dir \ + --ood-both path/to/ood_both/traj_dir --out-path $DIR --num-workers $NUM_WORKERS + ``` + Where `$DIR` specifies the directory to save generated LMDBs. A sub-directory will be created for each of the 4 splits in `$DIR`. `$NUM_WORKERS` is the number of data preprocessing cpu workers to be used. + 4. Update your energy-only config to point the test set to the newly generated LMDBs. Using the trained energy-only S2EF model, generate predictions via `--mode predict` (as you would do for the general IS2RE/S2EF case). + 5. Prepare a submission file by running the following command: + ``` + python scripts/make_submission_file.py --id path/to/id/s2ef_predictions.npz \ + --ood-ads path/to/ood_ads/s2ef_predictions.npz --ood-cat path/to/ood_cat/s2ef_predictions.npz \ + --ood-both path/to/ood_both/s2ef_predictions.npz --out-path submission_file.npz \ + --is2re-relaxations --hybrid + ``` +## Structure to Energy and Forces (S2EF) + +In the S2EF task, the model takes the positions of the atoms as input and predicts the adsorption energy and per-atom +forces as calculated by DFT. To train a model for the S2EF task, you can use the `ForcesTrainer` Trainer +and `TrajectoryLmdb` dataset by specifying the following in your configuration file: +``` +trainer: forces # Use the ForcesTrainer + +dataset: + # Training data + - src: [Path to training data] + normalize_labels: True + # Mean and standard deviation of energies + target_mean: -0.7586356401443481 + target_std: 2.981738567352295 + # Mean and standard deviation of forces + grad_target_mean: 0.0 + grad_target_std: 2.981738567352295 + # Val data (optional) + - src: [Path to validation data] + # Test data (optional) + - src: [Path to test data] +``` +You can find examples configuration files in [`configs/s2ef`](https://github.com/Open-Catalyst-Project/ocp/tree/master/configs/s2ef). + +To train a SchNet model for the S2EF task on the 2M split using 2 GPUs, run: +``` +python -u -m torch.distributed.launch --nproc_per_node=2 main.py \ + --mode train --config-yml configs/s2ef/2M/schnet/schnet.yml --num-gpus 2 --distributed +``` +Similar to the IS2RE task, tensorboard logs are stored in `logs/tensorboard/[TIMESTAMP]` and the +checkpoint is stored in `checkpoints/[TIMESTAMP]/checkpoint.pt`. + +Next, run this model on the test data: +``` +python main.py --mode predict --config-yml configs/s2ef/2M/schnet/schnet.yml \ + --checkpoint checkpoints/[TIMESTAMP]/checkpoint.pt +``` +The predictions are stored in `[RESULTS_DIR]/s2ef_predictions.npz` and later used to create a submission file to be uploaded to EvalAI. + +## Training OC20 models with total energies (IS2RE/S2EF) + +To train and validate an OC20 IS2RE/S2EF model on total energies targets instead of adsorption energies there are a number of required changes to the config. They include setting: `dataset: oc22_lmdb`, `prediction_dtype: float32`, `train_on_oc20_total_energies: True`, and `oc20_ref: path/to/oc20_ref.pkl` (see example below). Also, please note that our evaluation server does not currently support OC20 total energy models. + +``` +task: + dataset: oc22_lmdb + prediction_dtype: float32 + ... + +dataset: + train: + src: data/oc20/s2ef/train + normalize_labels: False + train_on_oc20_total_energies: True + #download at https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc20_ref.pkl + oc20_ref: path/to/oc20_ref.pkl + val: + src: data/oc20/s2ef/val_id + train_on_oc20_total_energies: True + oc20_ref: path/to/oc20_ref.pkl +``` + + + +## Initial Structure to Relaxed Structure (IS2RS) + +In the IS2RS task the model takes as input an initial structure and predicts the atomic positions in their +final, relaxed state. This can be done by training a model to predict per-atom forces similar to the S2EF +task and then running an iterative relaxation. Although we present an iterative approach, models that directly predict relaxed states are also possible. The iterative approach IS2RS task uses the same configuration files as the S2EF task `configs/s2ef` and follows the same training scheme above. + +To perform an iterative relaxation, ensure the following is added to the configuration files of the models you wish to run relaxations on: +``` +# Relaxation options +relax_dataset: + src: data/is2re/all/val_id/data.lmdb # path to lmdb of systems to be relaxed (uses same lmdbs as is2re) +write_pos: True +relaxation_steps: 300 +relax_opt: + maxstep: 0.04 + memory: 50 + damping: 1.0 + alpha: 70.0 + traj_dir: "trajectories" # specify directory you wish to log the entire relaxations, suppress otherwise +``` + +After training, relaxations can be run by: +``` +python main.py --mode run-relaxations --config-yml configs/s2ef/2M/schnet/schnet.yml \ + --checkpoint checkpoints/[TIMESTAMP]/checkpoint.pt +``` +The relaxed structure positions are stored in `[RESULTS_DIR]/relaxed_positions.npz` and later used to create a submission file to be uploaded to EvalAI. Predicted trajectories are stored in `trajectories` directory for those interested in analyzing the complete relaxation trajectory. + +## Create EvalAI OC20 submission files + +EvalAI expects results to be structured in a specific format for a submission to be successful. A submission must contain results from the 4 different splits - in distribution (id), out of distribution adsorbate (ood ads), out of distribution catalyst (ood cat), and out of distribution adsorbate and catalyst (ood both). Constructing the submission file for each of the above tasks is as follows: + +### S2EF/IS2RE: +1. Run predictions `--mode predict` on all 4 splits, generating `[s2ef/is2re]_predictions.npz` files for each split. +2. Run the following command: + ``` + python make_submission_file.py --id path/to/id/file.npz --ood-ads path/to/ood_ads/file.npz \ + --ood-cat path/to/ood_cat/file.npz --ood-both path/to/ood_both/file.npz --out-path submission_file.npz + ``` + Where `file.npz` corresponds to the respective `[s2ef/is2re]_predictions.npz` files generated for the corresponding task. The final submission file will be written to `submission_file.npz` (rename accordingly). +3. Upload `submission_file.npz` to EvalAI. + + +### IS2RS: +1. Ensure `write_pos: True` is included in your configuration file. Run relaxations `--mode run-relaxations` on all 4 splits, generating `relaxed_positions.npz` files for each split. +2. Run the following command: + ``` + python make_submission_file.py --id path/to/id/relaxed_positions.npz --ood-ads path/to/ood_ads/relaxed_positions.npz \ + --ood-cat path/to/ood_cat/relaxed_positions.npz --ood-both path/to/ood_both/relaxed_positions.npz --out-path is2rs_submission.npz + ``` + The final submission file will be written to `is2rs_submission.npz` (rename accordingly). +3. Upload `is2rs_submission.npz` to EvalAI. + +# OC22 + +## Initial Structure to Total Relaxed Energy (IS2RE-Total) + +For the IS2RE-Total task, the model takes the initial structure as input and predicts the total DFT energy of the relaxed structure. This task is more general and more challenging than the original OC20 IS2RE task that predicts adsorption energy. To train an OC22 IS2RE-Total model use the `EnergyTrainer` with the `OC22LmdbDataset` by including these lines in your configuration file: + +``` +trainer: energy # Use the EnergyTrainer + +task: + dataset: oc22_lmdb # Use the OC22LmdbDataset + ... +``` +You can find examples configuration files in [`configs/oc22/is2re`](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs/oc22/is2re). + +## Structure to Total Energy and Forces (S2EF-Total) + +The S2EF-Total task takes a structure and predicts the total DFT energy and per-atom forces. This differs from the original OC20 S2EF task because it predicts total energy instead of adsorption energy. To train an OC22 S2EF-Total model use the ForcesTrainer with the OC22LmdbDataset by including these lines in your configuration file: + +``` +trainer: forces # Use the ForcesTrainer + +task: + dataset: oc22_lmdb # Use the OC22LmdbDataset + ... +``` +You can find examples configuration files in [`configs/oc22/s2ef`](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs/oc22/s2ef). + +## Joint Training + +Training on OC20 total energies whether independently or jointly with OC22 requires a path to the `oc20_ref` (download link provided below) to be specified in the configuration file. These are necessary to convert OC20 adsorption energies into their corresponding total energies. The following changes in the configuration file capture these changes: + +``` +task: + dataset: oc22_lmdb + ... + +dataset: + train: + src: data/oc20+oc22/s2ef/train + normalize_labels: False + train_on_oc20_total_energies: True + #download at https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc20_ref.pkl + oc20_ref: path/to/oc20_ref.pkl + val: + src: data/oc22/s2ef/val_id + train_on_oc20_total_energies: True + oc20_ref: path/to/oc20_ref.pkl +``` + +You can find an example configuration file at [configs/oc22/s2ef/base_joint.yml](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/oc22/s2ef/base_joint.yml) + +## Create EvalAI OC22 submission files + +EvalAI expects results to be structured in a specific format for a submission to be successful. A submission must contain results from the 2 different splits - in distribution (id) and out of distribution (ood). Construct submission files for the OC22 S2EF-Total/IS2RE-Total tasks as follows: + +### S2EF-Total/IS2RE-Total: +1. Run predictions `--mode predict` on both the id and ood splits, generating `[s2ef/is2re]_predictions.npz` files for each split. +2. Run the following command: + ``` + python make_submission_file.py --dataset OC22 --id path/to/id/file.npz --ood path/to/ood_ads/file.npz --out-path submission_file.npz + ``` + Where `file.npz` corresponds to the respective `[s2ef/is2re]_predictions.npz` files generated for the corresponding task. The final submission file will be written to `submission_file.npz` (rename accordingly). The `dataset` argument specifies which dataset is being considered — this only needs to be set for OC22 predictions because OC20 is the default. +3. Upload `submission_file.npz` to EvalAI. diff --git a/checkpoint/best_checkpoint.7z.001 b/checkpoint/best_checkpoint.7z.001 new file mode 100644 index 0000000..be44d4e Binary files /dev/null and b/checkpoint/best_checkpoint.7z.001 differ diff --git a/checkpoint/best_checkpoint.7z.002 b/checkpoint/best_checkpoint.7z.002 new file mode 100644 index 0000000..33faf59 Binary files /dev/null and b/checkpoint/best_checkpoint.7z.002 differ diff --git a/configs/is2re/100k/base.yml b/configs/is2re/100k/base.yml new file mode 100644 index 0000000..a76c88c --- /dev/null +++ b/configs/is2re/100k/base.yml @@ -0,0 +1,18 @@ +trainer: energy + +dataset: + - src: data/is2re/100k/train/data.lmdb + normalize_labels: True + target_mean: -1.525913953781128 + target_std: 2.279365062713623 + - src: data/is2re/all/val_id/data.lmdb + +logger: tensorboard + +task: + dataset: single_point_lmdb + description: "Relaxed state energy prediction from initial structure." + type: regression + metric: mae + labels: + - relaxed energy diff --git a/configs/is2re/100k/cgcnn/cgcnn.yml b/configs/is2re/100k/cgcnn/cgcnn.yml new file mode 100644 index 0000000..324b385 --- /dev/null +++ b/configs/is2re/100k/cgcnn/cgcnn.yml @@ -0,0 +1,32 @@ +includes: +- configs/is2re/100k/base.yml + +model: + name: cgcnn + atom_embedding_size: 384 + fc_feat_size: 128 + num_fc_layers: 4 + num_graph_conv_layers: 5 + num_gaussians: 100 + cutoff: 6.0 + regress_forces: False + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 1. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 16 + eval_batch_size: 16 + num_workers: 16 + lr_initial: 0.01 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 31250 + - 56250 + - 75000 + warmup_steps: 18750 + warmup_factor: 0.2 + max_epochs: 30 diff --git a/configs/is2re/100k/dimenet_plus_plus/dpp.yml b/configs/is2re/100k/dimenet_plus_plus/dpp.yml new file mode 100644 index 0000000..1bcb1f0 --- /dev/null +++ b/configs/is2re/100k/dimenet_plus_plus/dpp.yml @@ -0,0 +1,35 @@ +includes: +- configs/is2re/100k/base.yml + +model: + name: dimenetplusplus + hidden_channels: 256 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: False + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 1. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 2 + eval_batch_size: 2 + num_workers: 2 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 200000 + - 400000 + - 600000 + warmup_steps: 100000 + warmup_factor: 0.2 + max_epochs: 20 diff --git a/configs/is2re/100k/schnet/schnet.yml b/configs/is2re/100k/schnet/schnet.yml new file mode 100644 index 0000000..19a9205 --- /dev/null +++ b/configs/is2re/100k/schnet/schnet.yml @@ -0,0 +1,31 @@ +includes: +- configs/is2re/100k/base.yml + +model: + name: schnet + hidden_channels: 384 + num_filters: 128 + num_interactions: 4 + num_gaussians: 100 + cutoff: 6.0 + use_pbc: True + regress_forces: False + +# *** Important note *** +# The total number of gpus used for this run was 1. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 32 + eval_batch_size: 32 + num_workers: 16 + lr_initial: 0.0005 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 15625 + - 31250 + - 46875 + warmup_steps: 9375 + warmup_factor: 0.2 + max_epochs: 30 diff --git a/configs/is2re/10k/base.yml b/configs/is2re/10k/base.yml new file mode 100644 index 0000000..07e75c0 --- /dev/null +++ b/configs/is2re/10k/base.yml @@ -0,0 +1,18 @@ +trainer: energy + +dataset: + - src: data/is2re/10k/train/data.lmdb + normalize_labels: True + target_mean: -1.525913953781128 + target_std: 2.279365062713623 + - src: data/is2re/all/val_id/data.lmdb + +logger: tensorboard + +task: + dataset: single_point_lmdb + description: "Relaxed state energy prediction from initial structure." + type: regression + metric: mae + labels: + - relaxed energy diff --git a/configs/is2re/10k/cgcnn/cgcnn.yml b/configs/is2re/10k/cgcnn/cgcnn.yml new file mode 100644 index 0000000..df4bf92 --- /dev/null +++ b/configs/is2re/10k/cgcnn/cgcnn.yml @@ -0,0 +1,32 @@ +includes: +- configs/is2re/10k/base.yml + +model: + name: cgcnn + atom_embedding_size: 128 + fc_feat_size: 256 + num_fc_layers: 4 + num_graph_conv_layers: 5 + num_gaussians: 100 + cutoff: 6.0 + regress_forces: False + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 1. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 64 + eval_batch_size: 64 + num_workers: 16 + lr_initial: 0.01 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 781 + - 1406 + - 2031 + warmup_steps: 468 + warmup_factor: 0.2 + max_epochs: 20 diff --git a/configs/is2re/10k/dimenet_plus_plus/dpp.yml b/configs/is2re/10k/dimenet_plus_plus/dpp.yml new file mode 100644 index 0000000..bc6554e --- /dev/null +++ b/configs/is2re/10k/dimenet_plus_plus/dpp.yml @@ -0,0 +1,35 @@ +includes: +- configs/is2re/10k/base.yml + +model: + name: dimenetplusplus + hidden_channels: 256 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: False + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 1. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 2 + eval_batch_size: 2 + num_workers: 2 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 20000 + - 40000 + - 60000 + warmup_steps: 10000 + warmup_factor: 0.2 + max_epochs: 20 diff --git a/configs/is2re/10k/schnet/schnet.yml b/configs/is2re/10k/schnet/schnet.yml new file mode 100644 index 0000000..5d3418d --- /dev/null +++ b/configs/is2re/10k/schnet/schnet.yml @@ -0,0 +1,31 @@ +includes: +- configs/is2re/10k/base.yml + +model: + name: schnet + hidden_channels: 256 + num_filters: 128 + num_interactions: 3 + num_gaussians: 100 + cutoff: 6.0 + use_pbc: True + regress_forces: False + +# *** Important note *** +# The total number of gpus used for this run was 1. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 64 + eval_batch_size: 64 + num_workers: 16 + lr_initial: 0.005 + lr_gamma: 0.1 + lr_milestones: # epochs at which lr_initial <- lr_initial * lr_gamma + - 1562 + - 2343 + - 3125 + warmup_steps: 468 + warmup_factor: 0.2 + max_epochs: 30 diff --git a/configs/is2re/all/base.yml b/configs/is2re/all/base.yml new file mode 100644 index 0000000..cf61f83 --- /dev/null +++ b/configs/is2re/all/base.yml @@ -0,0 +1,18 @@ +trainer: energy + +dataset: + - src: data/is2re/all/train/data.lmdb + normalize_labels: True + target_mean: -1.525913953781128 + target_std: 2.279365062713623 + - src: data/is2re/all/val_id/data.lmdb + +logger: tensorboard + +task: + dataset: single_point_lmdb + description: "Relaxed state energy prediction from initial structure." + type: regression + metric: mae + labels: + - relaxed energy diff --git a/configs/is2re/all/cgcnn/cgcnn.yml b/configs/is2re/all/cgcnn/cgcnn.yml new file mode 100644 index 0000000..8caeda8 --- /dev/null +++ b/configs/is2re/all/cgcnn/cgcnn.yml @@ -0,0 +1,32 @@ +includes: +- configs/is2re/all/base.yml + +model: + name: cgcnn + atom_embedding_size: 384 + fc_feat_size: 512 + num_fc_layers: 4 + num_graph_conv_layers: 6 + num_gaussians: 100 + cutoff: 6.0 + regress_forces: False + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 4. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 32 + eval_batch_size: 32 + num_workers: 16 + lr_initial: 0.01 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 17981 + - 32366 + - 46752 + warmup_steps: 10788 + warmup_factor: 0.2 + max_epochs: 20 diff --git a/configs/is2re/all/dimenet_plus_plus/dpp.yml b/configs/is2re/all/dimenet_plus_plus/dpp.yml new file mode 100644 index 0000000..c988bcf --- /dev/null +++ b/configs/is2re/all/dimenet_plus_plus/dpp.yml @@ -0,0 +1,35 @@ +includes: +- configs/is2re/all/base.yml + +model: + name: dimenetplusplus + hidden_channels: 256 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: False + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 4. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 4 + eval_batch_size: 4 + num_workers: 4 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 115082 + - 230164 + - 345246 + warmup_steps: 57541 + warmup_factor: 0.2 + max_epochs: 20 diff --git a/configs/is2re/all/painn/painn_h1024_bs8x4.yml b/configs/is2re/all/painn/painn_h1024_bs8x4.yml new file mode 100644 index 0000000..cbc4b92 --- /dev/null +++ b/configs/is2re/all/painn/painn_h1024_bs8x4.yml @@ -0,0 +1,34 @@ +# Run this on 4 GPUs -- so with an effective batch size of 32. + +includes: + - configs/is2re/all/base.yml + +model: + name: painn + hidden_channels: 1024 + num_layers: 6 + num_rbf: 128 + cutoff: 12.0 + max_neighbors: 50 + scale_file: configs/s2ef/all/painn/painn_nb6_scaling_factors.pt + regress_forces: False + use_pbc: True + +optim: + batch_size: 8 + eval_batch_size: 8 + load_balancing: atoms + num_workers: 2 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + lr_initial: 1.e-4 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + weight_decay: 0 # 2e-6 (TF weight decay) / 1e-4 (lr) = 2e-2 diff --git a/configs/is2re/all/schnet/schnet.yml b/configs/is2re/all/schnet/schnet.yml new file mode 100644 index 0000000..90eff1b --- /dev/null +++ b/configs/is2re/all/schnet/schnet.yml @@ -0,0 +1,31 @@ +includes: +- configs/is2re/all/base.yml + +model: + name: schnet + hidden_channels: 384 + num_filters: 128 + num_interactions: 4 + num_gaussians: 100 + cutoff: 6.0 + use_pbc: True + regress_forces: False + +# *** Important note *** +# The total number of gpus used for this run was 4. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 64 + eval_batch_size: 64 + num_workers: 16 + lr_initial: 0.001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 17981 + - 26972 + - 35963 + warmup_steps: 5394 + warmup_factor: 0.2 + max_epochs: 30 diff --git a/configs/is2re/example.yml b/configs/is2re/example.yml new file mode 100644 index 0000000..32a54bd --- /dev/null +++ b/configs/is2re/example.yml @@ -0,0 +1,130 @@ +# Example config for training models for IS2RE. + +trainer: energy # 'energy' or 'forces' + +task: + # The code currently supports 'lmdb' and 'oc22_lmdb' for both IS2RE and S2EF. + # + # To train models on adsorption energy (as in OC20), use `lmdb`. + # To train models on total DFT energy, use `oc22_lmdb`. + # + # Can use 'single_point_lmdb' or 'trajectory_lmdb' for backward compatibility. + # 'single_point_lmdb' was for training IS2RE models, and 'trajectory_lmdb' was + # for training S2EF models. + # To train an oc20 model on total energy use 'oc22_lmdb' + dataset: lmdb # 'lmdb' or 'oc22_lmdb' + # This is an optional parameter specifying the val metric to watch for + # improvement to decide when to save checkpoints. + # By default, this is: + # 'energy_force_within_threshold' for S2EF, + # 'energy_mae' for IS2RE, + # 'average_distance_within_threshold' for IS2RS. + primary_metric: energy_mae + # This is an argument used for checkpoint loading. By default it is True and loads + # checkpoint as it is. If False, it could partially load the checkpoint without giving + # any errors + strict_load: True # True or False + +dataset: + train: + # Path to training set LMDB + src: data/is2re/all/train/data.lmdb + # If we want to normalize each target value, i.e. subtract the mean and + # divide by standard deviation, then those 'target_mean' and 'target_std' + # statistics need to be specified here for the train split. + normalize_labels: True # True or False + # These stats are for OC20 IS2RE. + target_mean: -1.525913953781128 + target_std: 2.279365062713623 + # If we want to train OC20 on total energy, a path to OC20 reference + # energies `oc20_ref` must be specified to unreference existing OC20 data. + # download at https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc20_ref.pkl + # Also, train_on_oc20_total_energies must be set to True + # OC22 defaults to total energy, so these flags are not necessary. + train_on_oc20_total_energies: False # True or False + oc20_ref: None # path to oc20_ref + # If we want to train on total energies and use a linear reference + # normalization scheme, we must specify the path to the per-element + # coefficients in a `.npz` format. + lin_ref: False + val: + # Path to val set LMDB + src: data/is2re/all/val_id/data.lmdb + # If we want to run validation with OC20 total energy val set, `oc20_ref` must be specified and + # train_on_oc20_total_energies set to True + # OC22 defaults to total energy, so these flags are not necessary. + train_on_oc20_total_energies: False # True or False + oc20_ref: None # path to oc20_ref + test: + # Path to test set LMDB + src: data/is2re/all/test_id/data.lmdb + +logger: tensorboard # 'wandb' or 'tensorboard' + +model: + name: gemnet_t + # Model attributes go here, e.g. no. of layers, no. of hidden channels, + # embedding functions, cutoff radius, no. of neighbors, etc. + # This list of params will look different depending on the model. + # + # 'otf_graph' specifies whether graph edges should be computed on the fly + # or they already exist in the preprocessed LMDBs. If unsure, set it to True. + otf_graph: True # True or False + # All models in OCP can be used to predict just energies, or both energies and + # forces. For IS2RE, we don't need forces, so 'regress_forces' is False. + regress_forces: False # True or False + +optim: + # Batch size per GPU for training. + # Note that effective batch size will be 'batch_size' x no. of GPUs. + batch_size: 8 + # Batch size per GPU for evaluation. + # Note that effective batch size will be 'eval_batch_size' x no. of GPUs. + eval_batch_size: 8 + # No. of subprocesses to use for dataloading, pass as an arg to + # https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader. + num_workers: 2 + # After how many updates to run evaluation on val during training. + # If unspecified, defaults to 1 epoch. + eval_every: 5000 + # Loss function to use for energies. Defaults to 'mae'. + loss_energy: mae # 'mae' or 'mse' + # Optimizer to use from torch.optim. + # Default is https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html. + optimizer: AdamW + # Learning rate. Passed as an `lr` argument when initializing the optimizer. + lr_initial: 1.e-4 + # Additional args needed to initialize the optimizer. + optimizer_params: {"amsgrad": True} + # Weight decay to use. Passed as an argument when initializing the optimizer. + weight_decay: 0 + # Learning rate scheduler. Should work for any scheduler specified in + # in torch.optim.lr_scheduler: https://pytorch.org/docs/stable/optim.html + # as long as the relevant args are specified here. + # + # For example, for ReduceLROnPlateau, we specify `mode`, `factor`, `patience`. + # https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html + # + # Note that if task.primary_metric specified earlier in the config is a metric + # where higher is better (e.g. 'energy_force_within_threshold' or + # 'average_distance_within_threshold'), `mode` should be 'max' since we'd want + # to step LR when the metric has stopped increasing. Vice versa for energy_mae + # or forces_mae or loss. + # + # If you don't want to use a scheduler, set it to 'Null' (yes type that out). + # This is for legacy reasons. If scheduler is unspecified, it defaults to + # 'LambdaLR': warming up the learning rate to 'lr_initial' and then stepping + # it at pre-defined set of steps. See the DimeNet++ config for how to do this. + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + # No. of epochs to train for. + max_epochs: 100 + # Exponential moving average of parameters. 'ema_decay' is the decay factor. + ema_decay: 0.999 + # Max norm of gradients for clipping. Uses torch.nn.utils.clip_grad_norm_. + clip_grad_norm: 10 + +slurm: + constraint: "rtx_6000" diff --git a/configs/oc22/is2re/base.yml b/configs/oc22/is2re/base.yml new file mode 100644 index 0000000..8faec3b --- /dev/null +++ b/configs/oc22/is2re/base.yml @@ -0,0 +1,19 @@ +trainer: energy + +dataset: + train: + src: data/oc22/is2re/train + normalize_labels: False + val: + src: data/oc22/is2re/val_id + +logger: wandb + +task: + dataset: oc22_lmdb + description: "Relaxed state energy prediction from initial structure." + type: regression + metric: mae + primary_metric: energy_mae + labels: + - relaxed energy diff --git a/configs/oc22/is2re/base_joint.yml b/configs/oc22/is2re/base_joint.yml new file mode 100644 index 0000000..b72fdca --- /dev/null +++ b/configs/oc22/is2re/base_joint.yml @@ -0,0 +1,22 @@ +trainer: energy + +dataset: + train: + src: data/oc20+oc22/is2re/train + normalize_labels: False + train_on_oc20_total_energies: True + #download at https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc20_ref.pkl + oc20_ref: path/to/oc20_ref.pkl + val: + src: data/oc22/is2re/val_id + +logger: wandb + +task: + dataset: oc22_lmdb + description: "Relaxed state energy prediction from initial structure." + type: regression + metric: mae + primary_metric: energy_mae + labels: + - relaxed energy diff --git a/configs/oc22/is2re/dpp.yml b/configs/oc22/is2re/dpp.yml new file mode 100644 index 0000000..35af51a --- /dev/null +++ b/configs/oc22/is2re/dpp.yml @@ -0,0 +1,28 @@ +includes: + - configs/oc22/is2re/base.yml + +model: + name: dimenetplusplus + hidden_channels: 256 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: False + use_pbc: True + otf_graph: True + +optim: + batch_size: 6 + eval_batch_size: 6 + num_workers: 4 + lr_initial: 0.0001 + max_epochs: 200 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 diff --git a/configs/oc22/is2re/gemnet-dT/gemnet-dT.yml b/configs/oc22/is2re/gemnet-dT/gemnet-dT.yml new file mode 100644 index 0000000..7bd66ff --- /dev/null +++ b/configs/oc22/is2re/gemnet-dT/gemnet-dT.yml @@ -0,0 +1,51 @@ +# Run this on 1 GPU -- so with an effective batch size of 8. +includes: + - configs/oc22/is2re/base.yml + +model: + name: gemnet_t + num_spherical: 7 + num_radial: 64 + num_blocks: 5 + emb_size_atom: 256 + emb_size_edge: 512 + emb_size_trip: 64 + emb_size_rbf: 64 + emb_size_cbf: 16 + emb_size_bil_trip: 64 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 12.0 + max_neighbors: 50 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: True + output_init: HeOrthogonal + activation: silu + scale_file: configs/oc22/scaling_factors/gemnet-dT_c12.json + regress_forces: False + +optim: + batch_size: 8 + eval_batch_size: 8 + num_workers: 2 + lr_initial: 1.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae diff --git a/configs/oc22/is2re/painn/nb6_h1024_n50_c12.pt b/configs/oc22/is2re/painn/nb6_h1024_n50_c12.pt new file mode 100644 index 0000000..3843d7c Binary files /dev/null and b/configs/oc22/is2re/painn/nb6_h1024_n50_c12.pt differ diff --git a/configs/oc22/is2re/painn/painn.yml b/configs/oc22/is2re/painn/painn.yml new file mode 100644 index 0000000..7f941e5 --- /dev/null +++ b/configs/oc22/is2re/painn/painn.yml @@ -0,0 +1,34 @@ +# Run this on 2 GPUs -- so with an effective batch size of 16. +includes: + - configs/oc22/is2re/base.yml + +model: + name: painn + hidden_channels: 1024 + num_layers: 6 + num_rbf: 128 + cutoff: 12.0 + max_neighbors: 50 + scale_file: configs/oc22/is2re/painn/nb6_h1024_n50_c12.pt + regress_forces: False + use_pbc: True + otf_graph: True + +optim: + batch_size: 8 + eval_batch_size: 8 + load_balancing: atoms + num_workers: 2 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + lr_initial: 1.e-4 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + weight_decay: 0 # 2e-6 (TF weight decay) / 1e-4 (lr) = 2e-2 diff --git a/configs/oc22/linref/oc22_linfit_coeffs.npz b/configs/oc22/linref/oc22_linfit_coeffs.npz new file mode 100644 index 0000000..8f81eb7 Binary files /dev/null and b/configs/oc22/linref/oc22_linfit_coeffs.npz differ diff --git a/configs/oc22/s2ef/base.yml b/configs/oc22/s2ef/base.yml new file mode 100644 index 0000000..a7077bc --- /dev/null +++ b/configs/oc22/s2ef/base.yml @@ -0,0 +1,29 @@ +trainer: forces + +dataset: + train: + src: data/oc22/s2ef/train + normalize_labels: False + val: + src: data/oc22/s2ef/val_id + +logger: wandb + +task: + dataset: oc22_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + primary_metric: forces_mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True + prediction_dtype: float32 + +optim: + loss_energy: mae + loss_force: atomwisel2 + force_coefficient: 1 + energy_coefficient: 1 diff --git a/configs/oc22/s2ef/base_joint.yml b/configs/oc22/s2ef/base_joint.yml new file mode 100644 index 0000000..37a83d3 --- /dev/null +++ b/configs/oc22/s2ef/base_joint.yml @@ -0,0 +1,26 @@ +trainer: forces + +dataset: + train: + src: data/oc20+oc22/s2ef/train + normalize_labels: False + train_on_oc20_total_energies: True + #download at https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc20_ref.pkl + oc20_ref: path/to/oc20_ref.pkl + val: + src: data/oc22/s2ef/val_id + +logger: wandb + +task: + dataset: oc22_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + primary_metric: forces_mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True + prediction_dtype: float32 diff --git a/configs/oc22/s2ef/dpp.yml b/configs/oc22/s2ef/dpp.yml new file mode 100644 index 0000000..cf9aac6 --- /dev/null +++ b/configs/oc22/s2ef/dpp.yml @@ -0,0 +1,36 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: dimenetplusplus + hidden_channels: 512 + out_emb_channels: 384 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + otf_graph: True + +optim: + batch_size: 3 + eval_batch_size: 3 + eval_every: 5000 + num_workers: 3 + lr_initial: 0.0001 + warmup_steps: -1 # don't warm-up the learning rate + # warmup_factor: 0.2 + lr_gamma: 0.8 + # Following calculation is for an effective batch size of 3 x 32 GPUs = 96 + # and a dataset size of 8225293 (1 epoch ~ 85500 steps). + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 171000 # ~2 epochs + - 257000 # ~3 epochs + - 343000 # ~4 epochs + - 428000 # ~5 epochs + - 514000 # ~6 epochs + max_epochs: 80 diff --git a/configs/oc22/s2ef/gemnet-dt/gemnet-dT.yml b/configs/oc22/s2ef/gemnet-dt/gemnet-dT.yml new file mode 100644 index 0000000..3d6b16d --- /dev/null +++ b/configs/oc22/s2ef/gemnet-dt/gemnet-dT.yml @@ -0,0 +1,58 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: gemnet_t + num_spherical: 7 + num_radial: 128 + num_blocks: 3 + emb_size_atom: 512 + emb_size_edge: 512 + emb_size_trip: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_bil_trip: 64 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 6.0 + max_neighbors: 50 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: True + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json + + regress_forces: True + direct_forces: True + +optim: + batch_size: 16 + eval_batch_size: 16 + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + warmup_steps: -1 # don't warm-up the learning rate + # warmup_factor: 0.2 + lr_gamma: 0.8 + # Following calculation is for an effective batch size of 16 x 16 GPUs = 256 + # and a dataset size of 8225293 (1 epoch = 32130 steps). + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 64000 # ~2 epochs + - 96000 # ~3 epochs + - 128000 # ~4 epochs + - 160000 # ~5 epochs + - 192000 # ~6 epochs + max_epochs: 80 + ema_decay: 0.999 + clip_grad_norm: 10 diff --git a/configs/oc22/s2ef/gemnet-dt/gemnet_dT_finetune.yml b/configs/oc22/s2ef/gemnet-dt/gemnet_dT_finetune.yml new file mode 100644 index 0000000..c163f39 --- /dev/null +++ b/configs/oc22/s2ef/gemnet-dt/gemnet_dT_finetune.yml @@ -0,0 +1,75 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: gemnet_t + num_spherical: 7 + num_radial: 128 + num_blocks: 3 + emb_size_atom: 512 + emb_size_edge: 512 + emb_size_trip: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_bil_trip: 64 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 6.0 + max_neighbors: 50 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: True + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json + + regress_forces: True + direct_forces: True + +optim: + batch_size: 16 + eval_batch_size: 16 + eval_every: 5000 + num_workers: 2 + lr_initial: 0.0001 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + warmup_steps: -1 # don't warm-up the learning rate + # warmup_factor: 0.2 + lr_gamma: 0.8 + # Following calculation is for an effective batch size of 16 x 16 GPUs = 256 + # and a dataset size of 8225293 (1 epoch ~ 32000 steps). + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 64000 # ~2 epochs + - 96000 # ~3 epochs + - 128000 # ~4 epochs + - 160000 # ~5 epochs + - 192000 # ~6 epochs + - 224000 # ~7 epochs + - 256000 # ~8 epochs + - 288000 # ~9 epochs + - 320000 # ~10 epochs + - 336000 # ~10.5 epochs + - 352000 # ~11 epochs + - 368000 # ~11.5 epochs + - 384000 # ~12 epochs + - 400000 # ~12.5 epochs + - 416000 # ~13 epochs + - 432000 # ~13.5 epochs + - 448000 # ~14 epochs + - 464000 # ~14.5 epochs + max_epochs: 15 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae + force_coefficient: 100 + energy_coefficient: 1 diff --git a/configs/oc22/s2ef/gemnet-oc/gemnet_oc.yml b/configs/oc22/s2ef/gemnet-oc/gemnet_oc.yml new file mode 100644 index 0000000..e0f9995 --- /dev/null +++ b/configs/oc22/s2ef/gemnet-oc/gemnet_oc.yml @@ -0,0 +1,84 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: gemnet_oc + num_spherical: 7 + num_radial: 128 + num_blocks: 4 + emb_size_atom: 256 + emb_size_edge: 512 + emb_size_trip_in: 64 + emb_size_trip_out: 64 + emb_size_quad_in: 32 + emb_size_quad_out: 32 + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_sbf: 32 + num_before_skip: 2 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + num_output_afteratom: 3 + cutoff: 12.0 + cutoff_qint: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + max_neighbors: 30 + max_neighbors_qint: 8 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + sbf: + name: legendre_outer + extensive: True + output_init: HeOrthogonal + activation: silu + + regress_forces: True + direct_forces: True + forces_coupled: False + otf_graph: True + + quad_interaction: True + atom_edge_interaction: True + edge_atom_interaction: True + atom_interaction: True + + num_atom_emb_layers: 2 + num_global_out_layers: 2 + qint_tags: [1, 2] + +optim: + batch_size: 16 + eval_batch_size: 16 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + warmup_steps: -1 # don't warm-up the learning rate + # warmup_factor: 0.2 + lr_gamma: 0.8 + # Following calculation is for an effective batch size of 16 x 16 GPUs = 256 + # and a dataset size of 8225293 (1 epoch = 32130 steps). + # The older dataset had 6140155 points (1 epoch = 23984 steps). + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 64000 # ~2 epochs + - 96000 # ~3 epochs + - 128000 # ~4 epochs + - 160000 # ~5 epochs + - 192000 # ~6 epochs + max_epochs: 80 + ema_decay: 0.999 + clip_grad_norm: 10 + weight_decay: 0 # 2e-6 (TF weight decay) / 1e-4 (lr) = 2e-2 diff --git a/configs/oc22/s2ef/gemnet-oc/gemnet_oc_finetune.yml b/configs/oc22/s2ef/gemnet-oc/gemnet_oc_finetune.yml new file mode 100644 index 0000000..d52902e --- /dev/null +++ b/configs/oc22/s2ef/gemnet-oc/gemnet_oc_finetune.yml @@ -0,0 +1,101 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: gemnet_oc + num_spherical: 7 + num_radial: 128 + num_blocks: 4 + emb_size_atom: 256 + emb_size_edge: 512 + emb_size_trip_in: 64 + emb_size_trip_out: 64 + emb_size_quad_in: 32 + emb_size_quad_out: 32 + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_sbf: 32 + num_before_skip: 2 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + num_output_afteratom: 3 + cutoff: 12.0 + cutoff_qint: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + max_neighbors: 30 + max_neighbors_qint: 8 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + sbf: + name: legendre_outer + extensive: True + output_init: HeOrthogonal + activation: silu + + regress_forces: True + direct_forces: True + forces_coupled: False + otf_graph: True + + quad_interaction: True + atom_edge_interaction: True + edge_atom_interaction: True + atom_interaction: True + + num_atom_emb_layers: 2 + num_global_out_layers: 2 + qint_tags: [1, 2] + +optim: + batch_size: 16 + eval_batch_size: 16 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + lr_initial: 1.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + warmup_steps: -1 # don't warm-up the learning rate + # warmup_factor: 0.2 + lr_gamma: 0.8 + # Following calculation is for an effective batch size of 16 x 16 GPUs = 256 + # and a dataset size of 8225293 (1 epoch = 32130 steps). + # The older dataset had 6140155 points (1 epoch = 23984 steps). + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 64000 # ~2 epochs + - 96000 # ~3 epochs + - 128000 # ~4 epochs + - 160000 # ~5 epochs + - 192000 # ~6 epochs + - 224000 # ~7 epochs + - 256000 # ~8 epochs + - 288000 # ~9 epochs + - 320000 # ~10 epochs + - 336000 # ~10.5 epochs + - 352000 # ~11 epochs + - 368000 # ~11.5 epochs + - 384000 # ~12 epochs + - 400000 # ~12.5 epochs + - 416000 # ~13 epochs + - 432000 # ~13.5 epochs + - 448000 # ~14 epochs + - 464000 # ~14.5 epochs + max_epochs: 15 + ema_decay: 0.999 + clip_grad_norm: 10 + weight_decay: 0 # 2e-6 (TF weight decay) / 1e-4 (lr) = 2e-2 + loss_energy: mae + loss_force: l2mae + force_coefficient: 100 + energy_coefficient: 1 diff --git a/configs/oc22/s2ef/gemnet-oc/gemnet_oc_oc20_oc22.yml b/configs/oc22/s2ef/gemnet-oc/gemnet_oc_oc20_oc22.yml new file mode 100644 index 0000000..8275552 --- /dev/null +++ b/configs/oc22/s2ef/gemnet-oc/gemnet_oc_oc20_oc22.yml @@ -0,0 +1,80 @@ +includes: + - configs/oc22/s2ef/base_joint.yml + +model: + name: gemnet_oc + num_spherical: 7 + num_radial: 128 + num_blocks: 4 + emb_size_atom: 256 + emb_size_edge: 512 + emb_size_trip_in: 64 + emb_size_trip_out: 64 + emb_size_quad_in: 32 + emb_size_quad_out: 32 + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_sbf: 32 + num_before_skip: 2 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + num_output_afteratom: 3 + cutoff: 12.0 + cutoff_qint: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + max_neighbors: 30 + max_neighbors_qint: 8 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + sbf: + name: legendre_outer + extensive: True + output_init: HeOrthogonal + activation: silu + + regress_forces: True + direct_forces: True + forces_coupled: False + otf_graph: True + + quad_interaction: True + atom_edge_interaction: True + edge_atom_interaction: True + atom_interaction: True + + num_atom_emb_layers: 2 + num_global_out_layers: 2 + qint_tags: [1, 2] + +optim: + batch_size: 16 + eval_batch_size: 16 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + ema_decay: 0.999 + clip_grad_norm: 10 + weight_decay: 0 # 2e-6 (TF weight decay) / 1e-4 (lr) = 2e-2 + loss_energy: mae + loss_force: atomwisel2 + force_coefficient: 1 + energy_coefficient: 1 diff --git a/configs/oc22/s2ef/painn/nb6_h512_n50_c12_oc22.pt b/configs/oc22/s2ef/painn/nb6_h512_n50_c12_oc22.pt new file mode 100644 index 0000000..3b19a9a Binary files /dev/null and b/configs/oc22/s2ef/painn/nb6_h512_n50_c12_oc22.pt differ diff --git a/configs/oc22/s2ef/painn/painn.yml b/configs/oc22/s2ef/painn/painn.yml new file mode 100644 index 0000000..a7fa9ba --- /dev/null +++ b/configs/oc22/s2ef/painn/painn.yml @@ -0,0 +1,42 @@ +# Make sure to run without AMP. +includes: + - configs/oc22/s2ef/base.yml + +model: + name: painn + hidden_channels: 512 + num_layers: 6 + num_rbf: 128 + cutoff: 12.0 + max_neighbors: 50 + scale_file: configs/oc22/s2ef/painn/nb6_h512_n50_c12_oc22.pt + regress_forces: True + direct_forces: True + use_pbc: True + otf_graph: True + +optim: + batch_size: 32 + eval_batch_size: 32 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + lr_initial: 1.e-4 + warmup_steps: -1 # don't warm-up the learning rate + # warmup_factor: 0.2 + lr_gamma: 0.8 + # Following calculation is for an effective batch size of 32 x 8 GPUs = 256 + # and a dataset size of 8225293 (1 epoch = 32130 steps). + # The older dataset had 6140155 points (1 epoch = 23984 steps). + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 64000 # ~2 epochs + - 96000 # ~3 epochs + - 128000 # ~4 epochs + - 160000 # ~5 epochs + - 192000 # ~6 epochs + max_epochs: 80 + ema_decay: 0.999 + clip_grad_norm: 10 + weight_decay: 0 # 2e-6 (TF weight decay) / 1e-4 (lr) = 2e-2 diff --git a/configs/oc22/s2ef/schnet.yml b/configs/oc22/s2ef/schnet.yml new file mode 100644 index 0000000..d316d76 --- /dev/null +++ b/configs/oc22/s2ef/schnet.yml @@ -0,0 +1,31 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: schnet + hidden_channels: 1024 + num_filters: 256 + num_interactions: 5 + num_gaussians: 200 + cutoff: 6.0 + use_pbc: True + otf_graph: True + +optim: + batch_size: 20 + eval_batch_size: 20 + eval_every: 5000 + num_workers: 8 + lr_initial: 0.00025 + warmup_steps: -1 # don't warm-up the learning rate + # warmup_factor: 0.2 + lr_gamma: 0.8 + # Following calculation is for an effective batch size of 20 x 16 GPUs = 320 + # and a dataset size of 8225293 (1 epoch ~ 26000 steps). + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 52000 # ~2 epochs + - 77000 # ~3 epochs + - 103000 # ~4 epochs + - 129000 # ~5 epochs + - 154000 # ~6 epochs + max_epochs: 80 diff --git a/configs/oc22/s2ef/spinconv/spinconv.yml b/configs/oc22/s2ef/spinconv/spinconv.yml new file mode 100644 index 0000000..7a7d14d --- /dev/null +++ b/configs/oc22/s2ef/spinconv/spinconv.yml @@ -0,0 +1,43 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: spinconv + model_ref_number: 0 + hidden_channels: 32 + mid_hidden_channels: 256 + num_interactions: 3 + num_basis_functions: 512 + sphere_size_lat: 16 + sphere_size_long: 12 + max_num_neighbors: 40 + cutoff: 6.0 + sphere_message: fullconv + output_message: fullconv + force_estimator: random + regress_forces: True + use_pbc: True + scale_distances: True + basis_width_scalar: 3.0 + otf_graph: True + +optim: + batch_size: 3 + eval_batch_size: 3 + num_workers: 8 + lr_initial: 0.0004 + optimizer: Adam + optimizer_params: {"amsgrad": True} + eval_every: 5000 + warmup_steps: -1 # don't warm-up the learning rate + # warmup_factor: 0.2 + lr_gamma: 0.8 + # Following calculation is for an effective batch size of 3 x 64 GPUs = 192 + # and a dataset size of 8225293 (1 epoch = 32130 steps). + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 86000 # ~2 epochs + - 129000 # ~3 epochs + - 171000 # ~4 epochs + - 214000 # ~5 epochs + - 257000 # ~6 epochs + max_epochs: 80 diff --git a/configs/oc22/s2ef/spinconv/spinconv_finetune.yml b/configs/oc22/s2ef/spinconv/spinconv_finetune.yml new file mode 100644 index 0000000..b94f241 --- /dev/null +++ b/configs/oc22/s2ef/spinconv/spinconv_finetune.yml @@ -0,0 +1,36 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: spinconv + model_ref_number: 0 + hidden_channels: 32 + mid_hidden_channels: 256 + num_interactions: 3 + num_basis_functions: 512 + sphere_size_lat: 16 + sphere_size_long: 12 + max_num_neighbors: 40 + cutoff: 6.0 + sphere_message: fullconv + output_message: fullconv + force_estimator: random + regress_forces: True + use_pbc: True + scale_distances: True + basis_width_scalar: 3.0 + otf_graph: True + +optim: + batch_size: 3 + eval_batch_size: 3 + num_workers: 3 + lr_initial: 0.0001 + optimizer: Adam + optimizer_params: {"amsgrad": True} + eval_every: 5000 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 diff --git a/configs/oc22/s2ef/spinconv/spinconv_joint.yml b/configs/oc22/s2ef/spinconv/spinconv_joint.yml new file mode 100644 index 0000000..8f1a192 --- /dev/null +++ b/configs/oc22/s2ef/spinconv/spinconv_joint.yml @@ -0,0 +1,37 @@ +includes: + - configs/oc22/s2ef/base.yml + +model: + name: spinconv + model_ref_number: 0 + hidden_channels: 32 + mid_hidden_channels: 256 + num_interactions: 3 + num_basis_functions: 512 + sphere_size_lat: 16 + sphere_size_long: 12 + max_num_neighbors: 40 + cutoff: 6.0 + sphere_message: fullconv + output_message: fullconv + force_estimator: random + regress_forces: True + use_pbc: True + scale_distances: True + basis_width_scalar: 3.0 + otf_graph: True + +optim: + batch_size: 3 + eval_batch_size: 3 + num_workers: 8 + lr_initial: 0.0004 + optimizer: Adam + optimizer_params: {"amsgrad": True} + eval_every: 5000 + warmup_steps: -1 # don't warm-up the learning rate + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 diff --git a/configs/oc22/scaling_factors/gemnet-dT_c12.json b/configs/oc22/scaling_factors/gemnet-dT_c12.json new file mode 100644 index 0000000..c4ffb9d --- /dev/null +++ b/configs/oc22/scaling_factors/gemnet-dT_c12.json @@ -0,0 +1,24 @@ +{ + "comment": "debug", + "TripInteraction_1_had_rbf": 5.548952102661133, + "TripInteraction_1_sum_cbf": 2.20351243019104, + "AtomUpdate_1_sum": 0.3556209206581116, + "TripInteraction_2_had_rbf": 5.880437850952148, + "TripInteraction_2_sum_cbf": 2.333744764328003, + "AtomUpdate_2_sum": 0.3611552119255066, + "TripInteraction_3_had_rbf": 5.867868423461914, + "TripInteraction_3_sum_cbf": 2.4541828632354736, + "AtomUpdate_3_sum": 0.35358476638793945, + "TripInteraction_4_had_rbf": 5.782163619995117, + "TripInteraction_4_sum_cbf": 2.6129026412963867, + "AtomUpdate_4_sum": 0.37129640579223633, + "TripInteraction_5_had_rbf": 5.727713584899902, + "TripInteraction_5_sum_cbf": 2.460531711578369, + "AtomUpdate_5_sum": 0.3690475821495056, + "OutBlock_0_sum": 0.366500586271286, + "OutBlock_1_sum": 0.3443125784397125, + "OutBlock_2_sum": 0.35069236159324646, + "OutBlock_3_sum": 0.3590666949748993, + "OutBlock_4_sum": 0.35904067754745483, + "OutBlock_5_sum": 0.3603821396827698 +} diff --git a/configs/s2ef/200k/base.yml b/configs/s2ef/200k/base.yml new file mode 100644 index 0000000..5c59624 --- /dev/null +++ b/configs/s2ef/200k/base.yml @@ -0,0 +1,23 @@ +trainer: forces + +dataset: + - src: data/s2ef/200k/train/ + normalize_labels: True + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + - src: data/s2ef/all/val_id/ + +logger: tensorboard + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True diff --git a/configs/s2ef/200k/cgcnn/cgcnn.yml b/configs/s2ef/200k/cgcnn/cgcnn.yml new file mode 100644 index 0000000..fd27082 --- /dev/null +++ b/configs/s2ef/200k/cgcnn/cgcnn.yml @@ -0,0 +1,31 @@ +includes: +- configs/s2ef/200k/base.yml + +model: + name: cgcnn + atom_embedding_size: 128 + fc_feat_size: 128 + num_fc_layers: 3 + num_graph_conv_layers: 2 + cutoff: 6.0 + num_gaussians: 100 + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 4. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 32 + eval_batch_size: 32 + num_workers: 16 + lr_initial: 0.0005 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 23437 + - 31250 + warmup_steps: 3125 + warmup_factor: 0.2 + max_epochs: 50 + force_coefficient: 10 diff --git a/configs/s2ef/200k/dimenet_plus_plus/dpp.yml b/configs/s2ef/200k/dimenet_plus_plus/dpp.yml new file mode 100644 index 0000000..6dd1ea6 --- /dev/null +++ b/configs/s2ef/200k/dimenet_plus_plus/dpp.yml @@ -0,0 +1,36 @@ +includes: +- configs/s2ef/200k/base.yml + +model: + name: dimenetplusplus + hidden_channels: 192 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 16. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 12 + eval_batch_size: 12 + num_workers: 8 + lr_initial: 0.00001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 5208 + - 8333 + - 10416 + warmup_steps: 3125 + warmup_factor: 0.2 + max_epochs: 30 + force_coefficient: 50 diff --git a/configs/s2ef/200k/forcenet/fn_forceonly.yml b/configs/s2ef/200k/forcenet/fn_forceonly.yml new file mode 100644 index 0000000..e85592d --- /dev/null +++ b/configs/s2ef/200k/forcenet/fn_forceonly.yml @@ -0,0 +1,55 @@ +trainer: forces + +dataset: + - src: data/s2ef/200k/train/ + - src: data/s2ef/all/val_id/ + +model: + name: forcenet + num_interactions: 5 + cutoff: 6 + basis: "sphallmul" + ablation: "none" + depth_mlp_edge: 2 + depth_mlp_node: 1 + activation_str: "swish" + decoder_activation_str: "swish" + feat: "full" + hidden_channels: 512 + decoder_hidden_channels: 512 + max_n: 3 + +# *** Important note *** +# The total number of gpus used for this run was 8. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 8 + eval_batch_size: 8 + eval_every: 10000 + num_workers: 8 + lr_initial: 0.0005 + max_epochs: 20 + energy_coefficient: 0 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 15625 + - 25000 + - 31250 + warmup_steps: 9375 + warmup_factor: 0.2 + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + primary_metric: forces_mae + labels: + - potential energy + grad_input: atomic forces + tag_specific_weights: + - 0.05 + - 1.0 + - 1.0 diff --git a/configs/s2ef/200k/gemnet/gemnet-dT.yml b/configs/s2ef/200k/gemnet/gemnet-dT.yml new file mode 100644 index 0000000..140abd2 --- /dev/null +++ b/configs/s2ef/200k/gemnet/gemnet-dT.yml @@ -0,0 +1,55 @@ +includes: +- configs/s2ef/200k/base.yml + +model: + name: gemnet_t + num_spherical: 7 + num_radial: 128 + num_blocks: 3 + emb_size_atom: 512 + emb_size_edge: 512 + emb_size_trip: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_bil_trip: 64 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 6.0 + max_neighbors: 50 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: False + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json + + regress_forces: True + direct_forces: True + +optim: + batch_size: 32 + eval_batch_size: 32 + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/200k/gemnet/gemnet-oc.yml b/configs/s2ef/200k/gemnet/gemnet-oc.yml new file mode 100644 index 0000000..5207f85 --- /dev/null +++ b/configs/s2ef/200k/gemnet/gemnet-oc.yml @@ -0,0 +1,80 @@ +includes: + - configs/s2ef/200k/base.yml + +model: + name: gemnet_oc + num_spherical: 7 + num_radial: 128 + num_blocks: 4 + emb_size_atom: 256 + emb_size_edge: 512 + emb_size_trip_in: 64 + emb_size_trip_out: 64 + emb_size_quad_in: 32 + emb_size_quad_out: 32 + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_sbf: 32 + num_before_skip: 2 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + num_output_afteratom: 3 + cutoff: 12.0 + cutoff_qint: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + max_neighbors: 30 + max_neighbors_qint: 8 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + sbf: + name: legendre_outer + extensive: True + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt + + regress_forces: True + direct_forces: True + forces_coupled: False + + quad_interaction: True + atom_edge_interaction: True + edge_atom_interaction: True + atom_interaction: True + + num_atom_emb_layers: 2 + num_global_out_layers: 2 + qint_tags: [1, 2] + +optim: + batch_size: 16 + eval_batch_size: 16 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae + weight_decay: 0 diff --git a/configs/s2ef/200k/schnet/schnet.yml b/configs/s2ef/200k/schnet/schnet.yml new file mode 100644 index 0000000..7e97bc2 --- /dev/null +++ b/configs/s2ef/200k/schnet/schnet.yml @@ -0,0 +1,31 @@ +includes: +- configs/s2ef/200k/base.yml + +model: + name: schnet + hidden_channels: 1024 + num_filters: 256 + num_interactions: 3 + num_gaussians: 200 + cutoff: 6.0 + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 4. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 32 + eval_batch_size: 32 + num_workers: 16 + lr_initial: 0.0005 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 7812 + - 12500 + - 15625 + warmup_steps: 4687 + warmup_factor: 0.2 + max_epochs: 30 + force_coefficient: 100 diff --git a/configs/s2ef/200k/spinconv/spinconv_force.yml b/configs/s2ef/200k/spinconv/spinconv_force.yml new file mode 100644 index 0000000..c7c9293 --- /dev/null +++ b/configs/s2ef/200k/spinconv/spinconv_force.yml @@ -0,0 +1,37 @@ +includes: +- configs/s2ef/200k/base.yml + +model: + name: spinconv + model_ref_number: 0 + hidden_channels: 32 + mid_hidden_channels: 256 + num_interactions: 3 + num_basis_functions: 512 + sphere_size_lat: 16 + sphere_size_long: 12 + max_num_neighbors: 40 + cutoff: 6.0 + sphere_message: fullconv + output_message: fullconv + force_estimator: random + regress_forces: True + use_pbc: True + scale_distances: True + basis_width_scalar: 3.0 + +optim: + batch_size: 3 + eval_batch_size: 3 + num_workers: 8 + lr_initial: 0.0004 + optimizer: Adam + optimizer_params: {"amsgrad": True} + eval_every: 5000 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 diff --git a/configs/s2ef/20M/base.yml b/configs/s2ef/20M/base.yml new file mode 100644 index 0000000..2dc86b8 --- /dev/null +++ b/configs/s2ef/20M/base.yml @@ -0,0 +1,23 @@ +trainer: forces + +dataset: + - src: data/s2ef/20M/train/ + normalize_labels: True + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + - src: data/s2ef/all/val_id/ + +logger: tensorboard + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True diff --git a/configs/s2ef/20M/cgcnn/cgcnn.yml b/configs/s2ef/20M/cgcnn/cgcnn.yml new file mode 100644 index 0000000..60aa4be --- /dev/null +++ b/configs/s2ef/20M/cgcnn/cgcnn.yml @@ -0,0 +1,32 @@ +includes: +- configs/s2ef/20M/base.yml + +model: + name: cgcnn + atom_embedding_size: 512 + fc_feat_size: 128 + num_fc_layers: 3 + num_graph_conv_layers: 3 + cutoff: 6.0 + num_gaussians: 100 + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 48. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 24 + eval_batch_size: 24 + num_workers: 16 + lr_initial: 0.0005 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 52083 + - 86805 + - 121527 + warmup_steps: 34722 + warmup_factor: 0.2 + max_epochs: 20 + force_coefficient: 100 diff --git a/configs/s2ef/20M/dimenet_plus_plus/dpp.yml b/configs/s2ef/20M/dimenet_plus_plus/dpp.yml new file mode 100644 index 0000000..f6e4810 --- /dev/null +++ b/configs/s2ef/20M/dimenet_plus_plus/dpp.yml @@ -0,0 +1,37 @@ +includes: +- configs/s2ef/20M/base.yml + +model: + name: dimenetplusplus + hidden_channels: 192 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 64. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 12 + eval_batch_size: 12 + eval_every: 10000 + num_workers: 8 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 78125 + - 130208 + - 208333 + warmup_steps: 52083 + warmup_factor: 0.2 + max_epochs: 15 + force_coefficient: 50 diff --git a/configs/s2ef/20M/gemnet/gemnet-dT.yml b/configs/s2ef/20M/gemnet/gemnet-dT.yml new file mode 100644 index 0000000..440dfdf --- /dev/null +++ b/configs/s2ef/20M/gemnet/gemnet-dT.yml @@ -0,0 +1,55 @@ +includes: +- configs/s2ef/20M/base.yml + +model: + name: gemnet_t + num_spherical: 7 + num_radial: 128 + num_blocks: 3 + emb_size_atom: 512 + emb_size_edge: 512 + emb_size_trip: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_bil_trip: 64 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 6.0 + max_neighbors: 50 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: False + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json + + regress_forces: True + direct_forces: True + +optim: + batch_size: 32 + eval_batch_size: 32 + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/20M/gemnet/gemnet-oc.yml b/configs/s2ef/20M/gemnet/gemnet-oc.yml new file mode 100644 index 0000000..06d5b5d --- /dev/null +++ b/configs/s2ef/20M/gemnet/gemnet-oc.yml @@ -0,0 +1,80 @@ +includes: + - configs/s2ef/20M/base.yml + +model: + name: gemnet_oc + num_spherical: 7 + num_radial: 128 + num_blocks: 4 + emb_size_atom: 256 + emb_size_edge: 512 + emb_size_trip_in: 64 + emb_size_trip_out: 64 + emb_size_quad_in: 32 + emb_size_quad_out: 32 + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_sbf: 32 + num_before_skip: 2 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + num_output_afteratom: 3 + cutoff: 12.0 + cutoff_qint: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + max_neighbors: 30 + max_neighbors_qint: 8 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + sbf: + name: legendre_outer + extensive: True + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt + + regress_forces: True + direct_forces: True + forces_coupled: False + + quad_interaction: True + atom_edge_interaction: True + edge_atom_interaction: True + atom_interaction: True + + num_atom_emb_layers: 2 + num_global_out_layers: 2 + qint_tags: [1, 2] + +optim: + batch_size: 16 + eval_batch_size: 16 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae + weight_decay: 0 diff --git a/configs/s2ef/20M/schnet/schnet.yml b/configs/s2ef/20M/schnet/schnet.yml new file mode 100644 index 0000000..9096f47 --- /dev/null +++ b/configs/s2ef/20M/schnet/schnet.yml @@ -0,0 +1,32 @@ +includes: +- configs/s2ef/20M/base.yml + +model: + name: schnet + hidden_channels: 1024 + num_filters: 256 + num_interactions: 5 + num_gaussians: 200 + cutoff: 6.0 + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 48. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 24 + eval_batch_size: 24 + eval_every: 10000 + num_workers: 16 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 86805 + - 138888 + - 173611 + warmup_steps: 52083 + warmup_factor: 0.2 + max_epochs: 30 + force_coefficient: 50 diff --git a/configs/s2ef/20M/spinconv/spinconv_force.yml b/configs/s2ef/20M/spinconv/spinconv_force.yml new file mode 100644 index 0000000..51deaed --- /dev/null +++ b/configs/s2ef/20M/spinconv/spinconv_force.yml @@ -0,0 +1,37 @@ +includes: +- configs/s2ef/20M/base.yml + +model: + name: spinconv + model_ref_number: 0 + hidden_channels: 32 + mid_hidden_channels: 256 + num_interactions: 3 + num_basis_functions: 512 + sphere_size_lat: 16 + sphere_size_long: 12 + max_num_neighbors: 40 + cutoff: 6.0 + sphere_message: fullconv + output_message: fullconv + force_estimator: random + regress_forces: True + use_pbc: True + scale_distances: True + basis_width_scalar: 3.0 + +optim: + batch_size: 3 + eval_batch_size: 3 + num_workers: 8 + lr_initial: 0.0004 + optimizer: Adam + optimizer_params: {"amsgrad": True} + eval_every: 5000 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 diff --git a/configs/s2ef/2M/base.yml b/configs/s2ef/2M/base.yml new file mode 100644 index 0000000..4953410 --- /dev/null +++ b/configs/s2ef/2M/base.yml @@ -0,0 +1,23 @@ +trainer: forces + +dataset: + - src: data/s2ef/2M/train/ + normalize_labels: True + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + - src: data/s2ef/all/val_id/ + +logger: tensorboard + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True diff --git a/configs/s2ef/2M/cgcnn/cgcnn.yml b/configs/s2ef/2M/cgcnn/cgcnn.yml new file mode 100644 index 0000000..dfc5ba7 --- /dev/null +++ b/configs/s2ef/2M/cgcnn/cgcnn.yml @@ -0,0 +1,32 @@ +includes: +- configs/s2ef/2M/base.yml + +model: + name: cgcnn + atom_embedding_size: 384 + fc_feat_size: 128 + num_fc_layers: 3 + num_graph_conv_layers: 3 + cutoff: 6.0 + num_gaussians: 100 + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 8. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 8 + eval_batch_size: 8 + num_workers: 8 + lr_initial: 0.001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 156250 + - 281250 + - 437500 + warmup_steps: 62500 + warmup_factor: 0.2 + max_epochs: 20 + force_coefficient: 10 diff --git a/configs/s2ef/2M/dimenet_plus_plus/dpp.yml b/configs/s2ef/2M/dimenet_plus_plus/dpp.yml new file mode 100644 index 0000000..ff81185 --- /dev/null +++ b/configs/s2ef/2M/dimenet_plus_plus/dpp.yml @@ -0,0 +1,37 @@ +includes: +- configs/s2ef/2M/base.yml + +model: + name: dimenetplusplus + hidden_channels: 192 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 32. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 12 + eval_batch_size: 12 + eval_every: 10000 + num_workers: 8 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 20833 + - 31250 + - 41666 + warmup_steps: 10416 + warmup_factor: 0.2 + max_epochs: 15 + force_coefficient: 50 diff --git a/configs/s2ef/2M/dimenet_plus_plus/dpp_relax.yml b/configs/s2ef/2M/dimenet_plus_plus/dpp_relax.yml new file mode 100644 index 0000000..521a802 --- /dev/null +++ b/configs/s2ef/2M/dimenet_plus_plus/dpp_relax.yml @@ -0,0 +1,68 @@ +trainer: forces + +dataset: + - src: data/s2ef/2M/train/ + normalize_labels: True + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + - src: data/s2ef/all/val_id/ + +logger: tensorboard + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True + relax_dataset: + src: data/is2re/all/test_id/data.lmdb + write_pos: True + relaxation_steps: 200 + relax_opt: + maxstep: 0.04 + memory: 50 + damping: 1.0 + alpha: 70.0 + traj_dir: "ml-relaxations/dpp-2M-test-id" + +model: + name: dimenetplusplus + hidden_channels: 192 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 32. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 12 + eval_batch_size: 12 + eval_every: 10000 + num_workers: 8 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 20833 + - 31250 + - 41666 + warmup_steps: 10416 + warmup_factor: 0.2 + max_epochs: 15 + force_coefficient: 50 diff --git a/configs/s2ef/2M/escn/eSCN-L4-M2-Lay12.yml b/configs/s2ef/2M/escn/eSCN-L4-M2-Lay12.yml new file mode 100644 index 0000000..285182e --- /dev/null +++ b/configs/s2ef/2M/escn/eSCN-L4-M2-Lay12.yml @@ -0,0 +1,43 @@ +# A total of 16 32GB GPUs were used for training. + +includes: + - configs/s2ef/2M/base.yml + +model: + name: escn + num_layers: 12 + max_neighbors: 20 + cutoff: 12.0 + sphere_channels: 128 + hidden_channels: 256 + lmax_list: [4] + mmax_list: [2] + num_sphere_samples: 128 + distance_function: "gaussian" + regress_forces: True + use_pbc: True + basis_width_scalar: 2.0 + otf_graph: True + +optim: + batch_size: 6 + eval_batch_size: 6 + num_workers: 8 + lr_initial: 0.0008 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + eval_every: 5000 + lr_gamma: 0.3 + lr_milestones: # epochs at which lr_initial <- lr_initial * lr_gamma + - 145833 + - 187500 + - 229166 + warmup_steps: 100 + warmup_factor: 0.2 + max_epochs: 12 + force_coefficient: 100 + energy_coefficient: 2 + clip_grad_norm: 20 + ema_decay: 0.999 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/2M/escn/eSCN-L6-M2-Lay12.yml b/configs/s2ef/2M/escn/eSCN-L6-M2-Lay12.yml new file mode 100644 index 0000000..e2c1810 --- /dev/null +++ b/configs/s2ef/2M/escn/eSCN-L6-M2-Lay12.yml @@ -0,0 +1,43 @@ +# A total of 16 32GB GPUs were used for training. + +includes: + - configs/s2ef/2M/base.yml + +model: + name: escn + num_layers: 12 + max_neighbors: 20 + cutoff: 12.0 + sphere_channels: 128 + hidden_channels: 256 + lmax_list: [6] + mmax_list: [2] + num_sphere_samples: 128 + distance_function: "gaussian" + regress_forces: True + use_pbc: True + basis_width_scalar: 2.0 + otf_graph: True + +optim: + batch_size: 6 + eval_batch_size: 6 + num_workers: 8 + lr_initial: 0.0008 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + eval_every: 5000 + lr_gamma: 0.3 + lr_milestones: # epochs at which lr_initial <- lr_initial * lr_gamma + - 145833 + - 187500 + - 229166 + warmup_steps: 100 + warmup_factor: 0.2 + max_epochs: 12 + force_coefficient: 100 + energy_coefficient: 2 + clip_grad_norm: 100 + ema_decay: 0.999 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/2M/gemnet/gemnet-dT.yml b/configs/s2ef/2M/gemnet/gemnet-dT.yml new file mode 100644 index 0000000..6665e6a --- /dev/null +++ b/configs/s2ef/2M/gemnet/gemnet-dT.yml @@ -0,0 +1,55 @@ +includes: +- configs/s2ef/2M/base.yml + +model: + name: gemnet_t + num_spherical: 7 + num_radial: 128 + num_blocks: 3 + emb_size_atom: 512 + emb_size_edge: 512 + emb_size_trip: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_bil_trip: 64 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 6.0 + max_neighbors: 50 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: False + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json + + regress_forces: True + direct_forces: True + +optim: + batch_size: 32 + eval_batch_size: 32 + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/2M/gemnet/gemnet-oc.yml b/configs/s2ef/2M/gemnet/gemnet-oc.yml new file mode 100644 index 0000000..226ae94 --- /dev/null +++ b/configs/s2ef/2M/gemnet/gemnet-oc.yml @@ -0,0 +1,80 @@ +includes: + - configs/s2ef/2M/base.yml + +model: + name: gemnet_oc + num_spherical: 7 + num_radial: 128 + num_blocks: 4 + emb_size_atom: 256 + emb_size_edge: 512 + emb_size_trip_in: 64 + emb_size_trip_out: 64 + emb_size_quad_in: 32 + emb_size_quad_out: 32 + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_sbf: 32 + num_before_skip: 2 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + num_output_afteratom: 3 + cutoff: 12.0 + cutoff_qint: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + max_neighbors: 30 + max_neighbors_qint: 8 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + sbf: + name: legendre_outer + extensive: True + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt + + regress_forces: True + direct_forces: True + forces_coupled: False + + quad_interaction: True + atom_edge_interaction: True + edge_atom_interaction: True + atom_interaction: True + + num_atom_emb_layers: 2 + num_global_out_layers: 2 + qint_tags: [1, 2] + +optim: + batch_size: 16 + eval_batch_size: 16 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae + weight_decay: 0 diff --git a/configs/s2ef/2M/schnet/schnet.yml b/configs/s2ef/2M/schnet/schnet.yml new file mode 100644 index 0000000..96f74f8 --- /dev/null +++ b/configs/s2ef/2M/schnet/schnet.yml @@ -0,0 +1,31 @@ +includes: +- configs/s2ef/2M/base.yml + +model: + name: schnet + hidden_channels: 1024 + num_filters: 256 + num_interactions: 5 + num_gaussians: 200 + cutoff: 6.0 + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 8. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 24 + eval_batch_size: 24 + num_workers: 16 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 52083 + - 83333 + - 104166 + warmup_steps: 31250 + warmup_factor: 0.2 + max_epochs: 30 + force_coefficient: 100 diff --git a/configs/s2ef/2M/scn/scn-t1-b1.yml b/configs/s2ef/2M/scn/scn-t1-b1.yml new file mode 100644 index 0000000..8bdcfe8 --- /dev/null +++ b/configs/s2ef/2M/scn/scn-t1-b1.yml @@ -0,0 +1,49 @@ +# A total of 16 32GB GPUs were used for training. + +includes: + - configs/s2ef/2M/base.yml + +model: + name: scn + num_interactions: 12 + hidden_channels: 1024 + sphere_channels: 128 + sphere_channels_reduce: 128 + num_sphere_samples: 128 + num_basis_functions: 128 + distance_function: "gaussian" + max_num_neighbors: 40 + cutoff: 8.0 + lmax: 6 + mmax: 1 + use_grid: True + num_bands: 1 + num_taps: 1 + regress_forces: True + use_pbc: True + basis_width_scalar: 2.0 + otf_graph: True + +optim: + batch_size: 4 + eval_batch_size: 4 + num_workers: 8 + lr_initial: 0.0004 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + eval_every: 5000 + lr_gamma: 0.3 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 156250 + - 218750 + - 281250 + - 343750 + warmup_steps: 100 + warmup_factor: 0.2 + max_epochs: 12 + force_coefficient: 100 + energy_coefficient: 2 + clip_grad_norm: 100 + ema_decay: 0.999 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/2M/scn/scn-t4-b2.yml b/configs/s2ef/2M/scn/scn-t4-b2.yml new file mode 100644 index 0000000..18ea98b --- /dev/null +++ b/configs/s2ef/2M/scn/scn-t4-b2.yml @@ -0,0 +1,49 @@ +# A total of 16 32GB GPUs were used for training. + +includes: + - configs/s2ef/2M/base.yml + +model: + name: scn + num_interactions: 12 + hidden_channels: 1024 + sphere_channels: 128 + sphere_channels_reduce: 128 + num_sphere_samples: 128 + num_basis_functions: 128 + distance_function: "gaussian" + max_num_neighbors: 40 + cutoff: 8.0 + lmax: 6 + mmax: 1 + use_grid: True + num_bands: 2 + num_taps: -1 + regress_forces: True + use_pbc: True + basis_width_scalar: 2.0 + otf_graph: True + +optim: + batch_size: 3 + eval_batch_size: 3 + num_workers: 8 + lr_initial: 0.0004 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + eval_every: 5000 + lr_gamma: 0.3 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 208333 + - 291667 + - 375000 + - 458333 + warmup_steps: 100 + warmup_factor: 0.2 + max_epochs: 12 + force_coefficient: 100 + energy_coefficient: 2 + clip_grad_norm: 100 + ema_decay: 0.999 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/2M/spinconv/spinconv_force.yml b/configs/s2ef/2M/spinconv/spinconv_force.yml new file mode 100644 index 0000000..ac25afd --- /dev/null +++ b/configs/s2ef/2M/spinconv/spinconv_force.yml @@ -0,0 +1,37 @@ +includes: +- configs/s2ef/2M/base.yml + +model: + name: spinconv + model_ref_number: 0 + hidden_channels: 32 + mid_hidden_channels: 256 + num_interactions: 3 + num_basis_functions: 512 + sphere_size_lat: 16 + sphere_size_long: 12 + max_num_neighbors: 40 + cutoff: 6.0 + sphere_message: fullconv + output_message: fullconv + force_estimator: random + regress_forces: True + use_pbc: True + scale_distances: True + basis_width_scalar: 3.0 + +optim: + batch_size: 3 + eval_batch_size: 3 + num_workers: 8 + lr_initial: 0.0004 + optimizer: Adam + optimizer_params: {"amsgrad": True} + eval_every: 5000 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 diff --git a/configs/s2ef/all/base.yml b/configs/s2ef/all/base.yml new file mode 100644 index 0000000..3a81152 --- /dev/null +++ b/configs/s2ef/all/base.yml @@ -0,0 +1,23 @@ +trainer: forces + +dataset: + - src: data/s2ef/all/train/ + normalize_labels: True + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + - src: data/s2ef/all/val_id/ + +logger: tensorboard + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True diff --git a/configs/s2ef/all/cgcnn/cgcnn.yml b/configs/s2ef/all/cgcnn/cgcnn.yml new file mode 100644 index 0000000..4b3b4e3 --- /dev/null +++ b/configs/s2ef/all/cgcnn/cgcnn.yml @@ -0,0 +1,32 @@ +includes: +- configs/s2ef/all/base.yml + +model: + name: cgcnn + atom_embedding_size: 512 + fc_feat_size: 128 + num_fc_layers: 3 + num_graph_conv_layers: 3 + cutoff: 6.0 + num_gaussians: 100 + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 32. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 24 + eval_batch_size: 24 + num_workers: 16 + lr_initial: 0.0005 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 523179 + - 871966 + - 1220752 + warmup_steps: 348786 + warmup_factor: 0.2 + max_epochs: 20 + force_coefficient: 10 diff --git a/configs/s2ef/all/dimenet_plus_plus/dpp.yml b/configs/s2ef/all/dimenet_plus_plus/dpp.yml new file mode 100644 index 0000000..b693033 --- /dev/null +++ b/configs/s2ef/all/dimenet_plus_plus/dpp.yml @@ -0,0 +1,37 @@ +includes: +- configs/s2ef/all/base.yml + +model: + name: dimenetplusplus + hidden_channels: 192 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 256. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 8 + eval_batch_size: 8 + eval_every: 10000 + num_workers: 8 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 130794 + - 196192 + - 261589 + warmup_steps: 130794 + warmup_factor: 0.2 + max_epochs: 7 + force_coefficient: 50 diff --git a/configs/s2ef/all/dimenet_plus_plus/dpp10.7M_forceonly.yml b/configs/s2ef/all/dimenet_plus_plus/dpp10.7M_forceonly.yml new file mode 100644 index 0000000..add7539 --- /dev/null +++ b/configs/s2ef/all/dimenet_plus_plus/dpp10.7M_forceonly.yml @@ -0,0 +1,60 @@ +trainer: forces + +dataset: + - src: data/s2ef/all/train/ + normalize_labels: True + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + - src: data/s2ef/all/val_id/ + +logger: tensorboard + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + primary_metric: forces_mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True + +model: + name: dimenetplusplus + hidden_channels: 512 + out_emb_channels: 384 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 256. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 3 + eval_batch_size: 3 + eval_every: 10000 + num_workers: 3 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 174393 + - 348786 + - 523179 + warmup_steps: 174393 + warmup_factor: 0.2 + max_epochs: 5 + energy_coefficient: 0 + force_coefficient: 100 diff --git a/configs/s2ef/all/dimenet_plus_plus/dpp_energyonly.yml b/configs/s2ef/all/dimenet_plus_plus/dpp_energyonly.yml new file mode 100644 index 0000000..c4157c4 --- /dev/null +++ b/configs/s2ef/all/dimenet_plus_plus/dpp_energyonly.yml @@ -0,0 +1,60 @@ +trainer: forces + +dataset: + - src: data/s2ef/all/train/ + normalize_labels: True + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + - src: data/s2ef/all/val_id/ + +logger: tensorboard + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + primary_metric: energy_mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True + +model: + name: dimenetplusplus + hidden_channels: 192 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 256. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 8 + eval_batch_size: 8 + eval_every: 10000 + num_workers: 8 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 130794 + - 196192 + - 261589 + warmup_steps: 130794 + warmup_factor: 0.2 + max_epochs: 7 + energy_coefficient: 100 + force_coefficient: 0 diff --git a/configs/s2ef/all/dimenet_plus_plus/dpp_forceonly.yml b/configs/s2ef/all/dimenet_plus_plus/dpp_forceonly.yml new file mode 100644 index 0000000..75a0b6e --- /dev/null +++ b/configs/s2ef/all/dimenet_plus_plus/dpp_forceonly.yml @@ -0,0 +1,60 @@ +trainer: forces + +dataset: + - src: data/s2ef/all/train/ + normalize_labels: True + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + - src: data/s2ef/all/val_id/ + +logger: tensorboard + +task: + dataset: trajectory_lmdb + description: "Regressing to energies and forces for DFT trajectories from OCP" + type: regression + metric: mae + primary_metric: forces_mae + labels: + - potential energy + grad_input: atomic forces + train_on_free_atoms: True + eval_on_free_atoms: True + +model: + name: dimenetplusplus + hidden_channels: 192 + out_emb_channels: 192 + num_blocks: 3 + cutoff: 6.0 + num_radial: 6 + num_spherical: 7 + num_before_skip: 1 + num_after_skip: 2 + num_output_layers: 3 + regress_forces: True + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 64. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 8 + eval_batch_size: 8 + eval_every: 10000 + num_workers: 8 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 523179 + - 784769 + - 1046359 + warmup_steps: 523179 + warmup_factor: 0.2 + max_epochs: 7 + energy_coefficient: 0 + force_coefficient: 100 diff --git a/configs/s2ef/all/escn/eSCN-L6-M2-Lay12-All-MD.yml b/configs/s2ef/all/escn/eSCN-L6-M2-Lay12-All-MD.yml new file mode 100644 index 0000000..299e235 --- /dev/null +++ b/configs/s2ef/all/escn/eSCN-L6-M2-Lay12-All-MD.yml @@ -0,0 +1,43 @@ +# A total of 16 32GB GPUs were used for training. + +includes: + - configs/s2ef/all/base.yml + +model: + name: escn + num_layers: 12 + max_neighbors: 20 + cutoff: 12.0 + sphere_channels: 128 + hidden_channels: 256 + lmax_list: [6] + mmax_list: [2] + num_sphere_samples: 128 + distance_function: "gaussian" + regress_forces: True + use_pbc: True + basis_width_scalar: 2.0 + otf_graph: True + +optim: + batch_size: 6 + eval_batch_size: 6 + num_workers: 8 + lr_initial: 0.0008 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + eval_every: 5000 + lr_gamma: 0.3 + lr_milestones: # epochs at which lr_initial <- lr_initial * lr_gamma + - 218750 + - 281250 + - 343750 + warmup_steps: 100 + warmup_factor: 0.2 + max_epochs: 24 + force_coefficient: 100 + energy_coefficient: 4 + clip_grad_norm: 20 + ema_decay: 0.999 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/all/escn/eSCN-L6-M3-Lay20-All-MD.yml b/configs/s2ef/all/escn/eSCN-L6-M3-Lay20-All-MD.yml new file mode 100644 index 0000000..5542848 --- /dev/null +++ b/configs/s2ef/all/escn/eSCN-L6-M3-Lay20-All-MD.yml @@ -0,0 +1,43 @@ +# A total of 32 32GB GPUs were used for training. + +includes: + - configs/s2ef/all/base.yml + +model: + name: escn + num_layers: 20 + max_neighbors: 20 + cutoff: 12.0 + sphere_channels: 160 + hidden_channels: 384 + lmax_list: [6] + mmax_list: [3] + num_sphere_samples: 128 + distance_function: "gaussian" + regress_forces: True + use_pbc: True + basis_width_scalar: 2.0 + otf_graph: True + +optim: + batch_size: 2 + eval_batch_size: 2 + num_workers: 8 + lr_initial: 0.0008 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + eval_every: 5000 + lr_gamma: 0.3 + lr_milestones: # epochs at which lr_initial <- lr_initial * lr_gamma + - 433166 + - 541460 + - 649750 + warmup_steps: 100 + warmup_factor: 0.2 + max_epochs: 24 + force_coefficient: 100 + energy_coefficient: 4 + clip_grad_norm: 20 + ema_decay: 0.999 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/all/gemnet/gemnet-dT.yml b/configs/s2ef/all/gemnet/gemnet-dT.yml new file mode 100644 index 0000000..f403018 --- /dev/null +++ b/configs/s2ef/all/gemnet/gemnet-dT.yml @@ -0,0 +1,55 @@ +includes: +- configs/s2ef/all/base.yml + +model: + name: gemnet_t + num_spherical: 7 + num_radial: 128 + num_blocks: 3 + emb_size_atom: 512 + emb_size_edge: 512 + emb_size_trip: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_bil_trip: 64 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 6.0 + max_neighbors: 50 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: False + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json + + regress_forces: True + direct_forces: True + +optim: + batch_size: 32 + eval_batch_size: 32 + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/all/gemnet/gemnet-oc-large.yml b/configs/s2ef/all/gemnet/gemnet-oc-large.yml new file mode 100644 index 0000000..3264863 --- /dev/null +++ b/configs/s2ef/all/gemnet/gemnet-oc-large.yml @@ -0,0 +1,80 @@ +includes: + - configs/s2ef/2M/base.yml + +model: + name: gemnet_oc + num_spherical: 7 + num_radial: 128 + num_blocks: 6 + emb_size_atom: 256 + emb_size_edge: 1024 + emb_size_trip_in: 64 + emb_size_trip_out: 128 + emb_size_quad_in: 64 + emb_size_quad_out: 32 + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_rbf: 32 + emb_size_cbf: 16 + emb_size_sbf: 64 + num_before_skip: 2 + num_after_skip: 2 + num_concat: 4 + num_atom: 3 + num_output_afteratom: 3 + cutoff: 12.0 + cutoff_qint: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + max_neighbors: 30 + max_neighbors_qint: 8 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + sbf: + name: legendre_outer + extensive: True + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt + + regress_forces: True + direct_forces: True + forces_coupled: False + + quad_interaction: True + atom_edge_interaction: True + edge_atom_interaction: True + atom_interaction: True + + num_atom_emb_layers: 2 + num_global_out_layers: 2 + qint_tags: [1, 2] + +optim: + batch_size: 4 + eval_batch_size: 4 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + lr_initial: 2.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae + weight_decay: 0 diff --git a/configs/s2ef/all/gemnet/gemnet-oc.yml b/configs/s2ef/all/gemnet/gemnet-oc.yml new file mode 100644 index 0000000..f720892 --- /dev/null +++ b/configs/s2ef/all/gemnet/gemnet-oc.yml @@ -0,0 +1,80 @@ +includes: + - configs/s2ef/all/base.yml + +model: + name: gemnet_oc + num_spherical: 7 + num_radial: 128 + num_blocks: 4 + emb_size_atom: 256 + emb_size_edge: 512 + emb_size_trip_in: 64 + emb_size_trip_out: 64 + emb_size_quad_in: 32 + emb_size_quad_out: 32 + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_sbf: 32 + num_before_skip: 2 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + num_output_afteratom: 3 + cutoff: 12.0 + cutoff_qint: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + max_neighbors: 30 + max_neighbors_qint: 8 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + sbf: + name: legendre_outer + extensive: True + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt + + regress_forces: True + direct_forces: True + forces_coupled: False + + quad_interaction: True + atom_edge_interaction: True + edge_atom_interaction: True + atom_interaction: True + + num_atom_emb_layers: 2 + num_global_out_layers: 2 + qint_tags: [1, 2] + +optim: + batch_size: 16 + eval_batch_size: 16 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae + weight_decay: 0 diff --git a/configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json b/configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json new file mode 100644 index 0000000..ff3d57b --- /dev/null +++ b/configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json @@ -0,0 +1,20 @@ +{ + "comment": "tri_gaussian128", + "TripInteraction_1_had_rbf": 18.873615264892578, + "TripInteraction_1_sum_cbf": 7.996850490570068, + "AtomUpdate_1_sum": 1.220463752746582, + "TripInteraction_2_had_rbf": 16.10817527770996, + "TripInteraction_2_sum_cbf": 7.614634037017822, + "AtomUpdate_2_sum": 0.9690994620323181, + "TripInteraction_3_had_rbf": 15.01930046081543, + "TripInteraction_3_sum_cbf": 7.025179862976074, + "AtomUpdate_3_sum": 0.8903237581253052, + "OutBlock_0_sum": 1.6437848806381226, + "OutBlock_0_had": 16.161039352416992, + "OutBlock_1_sum": 1.1077653169631958, + "OutBlock_1_had": 13.54678726196289, + "OutBlock_2_sum": 0.9477927684783936, + "OutBlock_2_had": 12.754337310791016, + "OutBlock_3_sum": 0.9059251546859741, + "OutBlock_3_had": 13.484951972961426 +} diff --git a/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt b/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt new file mode 100644 index 0000000..d6572d3 Binary files /dev/null and b/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt differ diff --git a/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt b/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt new file mode 100644 index 0000000..8392b36 Binary files /dev/null and b/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt differ diff --git a/configs/s2ef/all/gp_gemnet/gp-gemnet-dT.yml b/configs/s2ef/all/gp_gemnet/gp-gemnet-dT.yml new file mode 100644 index 0000000..02e4dd9 --- /dev/null +++ b/configs/s2ef/all/gp_gemnet/gp-gemnet-dT.yml @@ -0,0 +1,57 @@ +# Run with `--gp-gpus N` where N = number of GPUs to split the model over. + +includes: +- configs/s2ef/all/base.yml + +model: + name: gp_gemnet_t + num_spherical: 7 + num_radial: 128 + num_blocks: 3 + emb_size_atom: 512 + emb_size_edge: 512 + emb_size_trip: 64 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_bil_trip: 64 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 6.0 + max_neighbors: 50 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: False + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gp_gemnet/scaling_factors/gemnet-dT.json + + regress_forces: True + direct_forces: True + +optim: + batch_size: 32 + eval_batch_size: 32 + eval_every: 5000 + num_workers: 8 + lr_initial: 5.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/all/gp_gemnet/gp-gemnet-xl.yml b/configs/s2ef/all/gp_gemnet/gp-gemnet-xl.yml new file mode 100644 index 0000000..b80bac7 --- /dev/null +++ b/configs/s2ef/all/gp_gemnet/gp-gemnet-xl.yml @@ -0,0 +1,59 @@ +# Run with `--gp-gpus N` where N = number of GPUs to split the model over. +# Do not use `--amp`, as that makes training unstable + +includes: +- configs/s2ef/all/base.yml + +model: + name: gp_gemnet_t + num_spherical: 7 + num_radial: 128 + num_blocks: 6 + emb_size_atom: 128 + emb_size_edge: 1536 + emb_size_trip: 384 + emb_size_rbf: 16 + emb_size_cbf: 16 + emb_size_bil_trip: 192 + num_before_skip: 1 + num_after_skip: 2 + num_concat: 1 + num_atom: 3 + cutoff: 6.0 + rbf: + name: gaussian + envelope: + name: polynomial + exponent: 5 + cbf: + name: spherical_harmonics + extensive: True + otf_graph: False + output_init: HeOrthogonal + activation: silu + scale_file: configs/s2ef/all/gp_gemnet/scaling_factors/gemnet-xl.json + max_neighbors: 50 + regress_forces: True + direct_forces: True + +optim: + batch_size: 2 + eval_batch_size: 2 + eval_every: 5000 + num_workers: 8 + lr_initial: 2.e-4 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae + weight_decay: 0 + load_balancing: neighbors diff --git a/configs/s2ef/all/gp_gemnet/scaling_factors/gemnet-dT.json b/configs/s2ef/all/gp_gemnet/scaling_factors/gemnet-dT.json new file mode 100644 index 0000000..ff3d57b --- /dev/null +++ b/configs/s2ef/all/gp_gemnet/scaling_factors/gemnet-dT.json @@ -0,0 +1,20 @@ +{ + "comment": "tri_gaussian128", + "TripInteraction_1_had_rbf": 18.873615264892578, + "TripInteraction_1_sum_cbf": 7.996850490570068, + "AtomUpdate_1_sum": 1.220463752746582, + "TripInteraction_2_had_rbf": 16.10817527770996, + "TripInteraction_2_sum_cbf": 7.614634037017822, + "AtomUpdate_2_sum": 0.9690994620323181, + "TripInteraction_3_had_rbf": 15.01930046081543, + "TripInteraction_3_sum_cbf": 7.025179862976074, + "AtomUpdate_3_sum": 0.8903237581253052, + "OutBlock_0_sum": 1.6437848806381226, + "OutBlock_0_had": 16.161039352416992, + "OutBlock_1_sum": 1.1077653169631958, + "OutBlock_1_had": 13.54678726196289, + "OutBlock_2_sum": 0.9477927684783936, + "OutBlock_2_had": 12.754337310791016, + "OutBlock_3_sum": 0.9059251546859741, + "OutBlock_3_had": 13.484951972961426 +} diff --git a/configs/s2ef/all/gp_gemnet/scaling_factors/gemnet-xl.json b/configs/s2ef/all/gp_gemnet/scaling_factors/gemnet-xl.json new file mode 100644 index 0000000..7f1a2bf --- /dev/null +++ b/configs/s2ef/all/gp_gemnet/scaling_factors/gemnet-xl.json @@ -0,0 +1,35 @@ +{ + "comment": "", + "TripInteraction_1_had_rbf": 17.294939041137695, + "TripInteraction_1_sum_cbf": 8.488616943359375, + "IntBlock_1_sum": 1.3015538454055786, + "TripInteraction_2_had_rbf": 15.357681274414062, + "TripInteraction_2_sum_cbf": 8.596945762634277, + "IntBlock_2_sum": 1.1383553743362427, + "TripInteraction_3_had_rbf": 14.498085021972656, + "TripInteraction_3_sum_cbf": 8.609837532043457, + "IntBlock_3_sum": 1.0901832580566406, + "TripInteraction_4_had_rbf": 14.31578254699707, + "TripInteraction_4_sum_cbf": 8.489559173583984, + "IntBlock_4_sum": 1.08650541305542, + "TripInteraction_5_had_rbf": 14.452488899230957, + "TripInteraction_5_sum_cbf": 8.417305946350098, + "IntBlock_5_sum": 1.1231739521026611, + "TripInteraction_6_had_rbf": 14.693891525268555, + "TripInteraction_6_sum_cbf": 8.373677253723145, + "IntBlock_6_sum": 1.1754940748214722, + "OutBlock_0_sum": 1.727757215499878, + "OutBlock_0_had": 15.84854507446289, + "OutBlock_1_sum": 1.3406972885131836, + "OutBlock_1_had": 13.932634353637695, + "OutBlock_2_sum": 1.250294804573059, + "OutBlock_2_had": 13.61974811553955, + "OutBlock_3_sum": 1.256105661392212, + "OutBlock_3_had": 13.659594535827637, + "OutBlock_4_sum": 1.2950983047485352, + "OutBlock_4_had": 13.919914245605469, + "OutBlock_5_sum": 1.3295512199401855, + "OutBlock_5_had": 14.4349365234375, + "OutBlock_6_sum": 1.4188448190689087, + "OutBlock_6_had": 14.88949203491211 +} diff --git a/configs/s2ef/all/painn/painn_h512.yml b/configs/s2ef/all/painn/painn_h512.yml new file mode 100644 index 0000000..a7fe4a7 --- /dev/null +++ b/configs/s2ef/all/painn/painn_h512.yml @@ -0,0 +1,37 @@ +includes: + - configs/s2ef/all/base.yml + +model: + name: painn + hidden_channels: 512 + num_layers: 6 + num_rbf: 128 + cutoff: 12.0 + max_neighbors: 50 + scale_file: configs/s2ef/all/painn/painn_nb6_scaling_factors.pt + regress_forces: True + direct_forces: True + use_pbc: True + +optim: + batch_size: 32 + eval_batch_size: 32 + load_balancing: atoms + eval_every: 5000 + num_workers: 2 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + lr_initial: 1.e-4 + lr_gamma: 0.8 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 + ema_decay: 0.999 + clip_grad_norm: 10 + loss_energy: mae + loss_force: l2mae + weight_decay: 0 # 2e-6 (TF weight decay) / 1e-4 (lr) = 2e-2 diff --git a/configs/s2ef/all/painn/painn_nb6_scaling_factors.pt b/configs/s2ef/all/painn/painn_nb6_scaling_factors.pt new file mode 100644 index 0000000..3843d7c Binary files /dev/null and b/configs/s2ef/all/painn/painn_nb6_scaling_factors.pt differ diff --git a/configs/s2ef/all/schnet/schnet.yml b/configs/s2ef/all/schnet/schnet.yml new file mode 100644 index 0000000..46ad056 --- /dev/null +++ b/configs/s2ef/all/schnet/schnet.yml @@ -0,0 +1,32 @@ +includes: +- configs/s2ef/all/base.yml + +model: + name: schnet + hidden_channels: 1024 + num_filters: 256 + num_interactions: 5 + num_gaussians: 200 + cutoff: 6.0 + use_pbc: True + +# *** Important note *** +# The total number of gpus used for this run was 64. +# If the global batch size (num_gpus * batch_size) is modified +# the lr_milestones and warmup_steps need to be adjusted accordingly. + +optim: + batch_size: 20 + eval_batch_size: 20 + eval_every: 10000 + num_workers: 16 + lr_initial: 0.0001 + lr_gamma: 0.1 + lr_milestones: # steps at which lr_initial <- lr_initial * lr_gamma + - 313907 + - 523179 + - 732451 + warmup_steps: 209271 + warmup_factor: 0.2 + max_epochs: 15 + force_coefficient: 30 diff --git a/configs/s2ef/all/scn/scn-all-md.yml b/configs/s2ef/all/scn/scn-all-md.yml new file mode 100644 index 0000000..40f2ce4 --- /dev/null +++ b/configs/s2ef/all/scn/scn-all-md.yml @@ -0,0 +1,50 @@ +# A total of 64 32GB GPUs were used for training. + +includes: + - configs/s2ef/all/base.yml + +model: + name: scn + num_interactions: 16 + hidden_channels: 1024 + sphere_channels: 128 + sphere_channels_reduce: 128 + num_sphere_samples: 128 + num_basis_functions: 128 + distance_function: "gaussian" + show_timing_info: False + max_num_neighbors: 40 + cutoff: 8.0 + lmax: 6 + num_bands: 2 + use_grid: True + regress_forces: True + use_pbc: True + basis_width_scalar: 2.0 + otf_graph: True + +optim: + batch_size: 2 + eval_batch_size: 1 + num_workers: 2 + lr_initial: 0.0004 + optimizer: AdamW + optimizer_params: {"amsgrad": True} + eval_every: 5000 + lr_gamma: 0.3 + lr_milestones: # epochs at which lr_initial <- lr_initial * lr_gamma + - 260000 + - 340000 + - 420000 + - 500000 + - 800000 + - 1000000 + warmup_steps: 100 + warmup_factor: 0.2 + max_epochs: 12 + force_coefficient: 100 + energy_coefficient: 4 + clip_grad_norm: 100 + ema_decay: 0.999 + loss_energy: mae + loss_force: l2mae diff --git a/configs/s2ef/all/spinconv/spinconv_force.yml b/configs/s2ef/all/spinconv/spinconv_force.yml new file mode 100644 index 0000000..da2a934 --- /dev/null +++ b/configs/s2ef/all/spinconv/spinconv_force.yml @@ -0,0 +1,37 @@ +includes: +- configs/s2ef/all/base.yml + +model: + name: spinconv + model_ref_number: 0 + hidden_channels: 32 + mid_hidden_channels: 256 + num_interactions: 3 + num_basis_functions: 512 + sphere_size_lat: 16 + sphere_size_long: 12 + max_num_neighbors: 40 + cutoff: 6.0 + sphere_message: fullconv + output_message: fullconv + force_estimator: random + regress_forces: True + use_pbc: True + scale_distances: True + basis_width_scalar: 3.0 + +optim: + batch_size: 3 + eval_batch_size: 3 + num_workers: 8 + lr_initial: 0.0004 + optimizer: Adam + optimizer_params: {"amsgrad": True} + eval_every: 5000 + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + max_epochs: 80 + force_coefficient: 100 + energy_coefficient: 1 diff --git a/configs/s2ef/example.yml b/configs/s2ef/example.yml new file mode 100644 index 0000000..414a800 --- /dev/null +++ b/configs/s2ef/example.yml @@ -0,0 +1,196 @@ +# Example config for training models for S2EF. + +trainer: forces # 'energy' or 'forces' + +task: + # The code currently supports 'lmdb' and 'oc22_lmdb' for both IS2RE and S2EF. + # + # To train models on adsorption energy (as in OC20), use `lmdb`. + # To train models on total DFT energy, use `oc22_lmdb`. + # + # Can use 'single_point_lmdb' or 'trajectory_lmdb' for backward compatibility. + # 'single_point_lmdb' was for training IS2RE models, and 'trajectory_lmdb' was + # for training S2EF models. + # To train an oc20 model on total energy use 'oc22_lmdb' + dataset: lmdb # 'lmdb' or 'oc22_lmdb' + # This is an optional parameter specifying the val metric to watch for + # improvement to decide when to save checkpoints. + # By default, this is: + # 'energy_force_within_threshold' for S2EF, + # 'energy_mae' for IS2RE, + # 'average_distance_within_threshold' for IS2RS. + primary_metric: forces_mae + # OC20 systems had slab atoms fixed when running DFT calculations. Surface and + # adsorbate atoms were free to move. This info is available for each structure + # in the released LMDBs. + # These args specify whether to train/eval forces on only free atoms or all. + train_on_free_atoms: True # True or False + eval_on_free_atoms: True # True or False + # By default OC20 s2ef predictions are written in float16 to reduce file size + # By default OC22 s2ef predictions are written in float32 + # If training on total energy use float32 + prediction_dtype: float16 # 'float16' or 'float32' + # This is an argument used for checkpoint loading. By default it is True and loads + # checkpoint as it is. If False, it could partially load the checkpoint without giving + # any errors + strict_load: True # True or False + # The following args in the 'task' tree are for running relaxations with an + # S2EF model during training (as additional validation) or testing. + # Totally optional if you're only looking to train an S2EF model. + # + # Whether to evaluate val relaxations when training S2EF models on the + # energy_mae and average_distance_within_threshold metrics. + eval_relaxations: False # True or False + # No. of batches to run relaxations on. Defaults to the full 'relax_dataset'. + num_relaxation_batches: 5 + # Max no. of steps to run relaxations for. + relaxation_steps: 300 + # Whether to save out the positions. + write_pos: True # True or False + # Path to initial structures to run relaxations on. Same as the IS2RE set. + relax_dataset: + src: data/is2re/all/test_id/data.lmdb + # To shard a dataset into smaller subsets, define the total_shards desired + # and the shard a particular process to see. + total_shards: 1 # int (optional) + shard: 0 # int (optional) + relax_opt: + name: lbfgs + maxstep: 0.04 + memory: 50 + damping: 1.0 + alpha: 70.0 + # Directory to save out trajectories (.traj files) in. + traj_dir: path/to/traj/directory + # Whether to save out the full trajectory or just the initial+final frames + save_full_traj: True # True or False + # When set to true, uses "deterministic" CUDA scatter ops if available, + # i.e. given the same input, leads to the same results. Default is false + # since this can be significantly slower. + set_deterministic_scatter: False # True or False + +dataset: + train: + # Directory containing training set LMDBs + src: data/s2ef/all/train/ + # If we want to normalize each target value, i.e. subtract the mean and + # divide by standard deviation, then those 'target_mean' and 'target_std' + # statistics for energies and 'grad_target_mean' and 'grad_target_std' + # statistics for forces need to be specified here for the train split. + normalize_labels: True + # These stats are for OC20 S2EF. + target_mean: -0.7554450631141663 + target_std: 2.887317180633545 + grad_target_mean: 0.0 + grad_target_std: 2.887317180633545 + + # If we want to train OC20 on total energy, a path to OC20 reference + # energies `oc20_ref` must be specified to unreference existing OC20 data. + # download at https://dl.fbaipublicfiles.com/opencatalystproject/data/oc22/oc20_ref.pkl + # Also, train_on_oc20_total_energies must be set to True + # OC22 defaults to total energy, so these flags are not necessary. + train_on_oc20_total_energies: False # True or False + oc20_ref: None # path to oc20_ref + # If we want to train on total energies and use a linear reference + # normalization scheme, we must specify the path to the per-element + # coefficients in a `.npz` format. + lin_ref: False # True or False + val: + # Directory containing val set LMDBs + src: data/s2ef/all/val_id/ + # If we want to run validation with OC20 total energy val set, `oc20_ref` must be specified and + # train_on_oc20_total_energies set to True + # OC22 defaults to total energy, so these flags are not necessary. + train_on_oc20_total_energies: False # True or False + oc20_ref: None # path to oc20_ref + test: + # Directory containing test set LMDBs + src: data/s2ef/all/test_id/ + +logger: tensorboard # 'wandb' or 'tensorboard' + +model: + name: gemnet_t + # Model attributes go here, e.g. no. of layers, no. of hidden channels, + # embedding functions, cutoff radius, no. of neighbors, etc. + # This list of params will look different depending on the model. + # + # 'otf_graph' specifies whether graph edges should be computed on the fly + # or they already exist in the preprocessed LMDBs. If unsure, set it to True. + otf_graph: True # True or False + # All models in OCP can be used to predict just energies, or both energies and + # forces. For S2EF, we need both, so 'regress_forces' is True. + regress_forces: True # True or False + # Whether forces are predicted directly via an independent network (when set + # to True), or as negative gradients of energy wrt positions (when False) + direct_forces: True + +optim: + # Batch size per GPU for training. + # Note that effective batch size will be 'batch_size' x no. of GPUs. + batch_size: 8 + # Batch size per GPU for evaluation. + # Note that effective batch size will be 'eval_batch_size' x no. of GPUs. + eval_batch_size: 8 + # Whether to load balance across GPUs based on no. of 'atoms' or 'neighbors'. + load_balancing: atoms # 'atoms' or 'neighbors' + # No. of subprocesses to use for dataloading, pass as an arg to + # https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader. + num_workers: 2 + # After how many updates to run evaluation on val during training. + # If unspecified, defaults to 1 epoch. + eval_every: 5000 + # Loss function to use for energies. Defaults to 'mae'. + loss_energy: mae # 'mae' or 'mse' + # Loss function to use for forces. Defaults to 'mae'. + # + # 'l2mae' has been working well for us with a force to energy coefficient + # ratio of 100:1. + # + # When training on raw DFT energies, 'atomwisel2' might be a better default + # with a force to energy coefficient ratio of 1:1. 'atomwisel2' scales L2 loss + # for forces by the no. of atoms in the structure. + loss_force: l2mae # 'mae' or 'mse' or 'l2mae' or 'atomwisel2' + # Coefficient to use for the energy loss. + energy_coefficient: 1 + # Coefficient to use for the force loss. + force_coefficient: 100 + # Optimizer to use from torch.optim. + # Default is https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html. + optimizer: AdamW + # Learning rate. Passed as an `lr` argument when initializing the optimizer. + lr_initial: 1.e-4 + # Additional args needed to initialize the optimizer. + optimizer_params: {"amsgrad": True} + # Weight decay to use. Passed as an argument when initializing the optimizer. + weight_decay: 0 + # Learning rate scheduler. Should work for any scheduler specified in + # in torch.optim.lr_scheduler: https://pytorch.org/docs/stable/optim.html + # as long as the relevant args are specified here. + # + # For example, for ReduceLROnPlateau, we specify `mode`, `factor`, `patience`. + # https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html + # + # Note that if task.primary_metric specified earlier in the config is a metric + # where higher is better (e.g. 'energy_force_within_threshold' or + # 'average_distance_within_threshold'), `mode` should be 'max' since we'd want + # to step LR when the metric has stopped increasing. Vice versa for energy_mae + # or forces_mae or loss. + # + # If you don't want to use a scheduler, set it to 'Null' (yes type that out). + # This is for legacy reasons. If scheduler is unspecified, it defaults to + # 'LambdaLR': warming up the learning rate to 'lr_initial' and then stepping + # it at pre-defined set of steps. See the DimeNet++ config for how to do this. + scheduler: ReduceLROnPlateau + mode: min + factor: 0.8 + patience: 3 + # No. of epochs to train for. + max_epochs: 100 + # Exponential moving average of parameters. 'ema_decay' is the decay factor. + ema_decay: 0.999 + # Max norm of gradients for clipping. Uses torch.nn.utils.clip_grad_norm_. + clip_grad_norm: 10 + +slurm: + constraint: "rtx_6000" diff --git a/data_preprocessing.py b/data_preprocessing.py new file mode 100644 index 0000000..4ea2ddd --- /dev/null +++ b/data_preprocessing.py @@ -0,0 +1,132 @@ +from ocpmodels.preprocessing import AtomsToGraphs +from ocpmodels.datasets import SinglePointLmdbDataset +from ocpmodels.common.relaxation.ase_utils import OCPCalculator +import pickle +import pandas as pd +import os +from ase.io import read +from ase.optimize import BFGS +from ase import Atoms, Atom +from tqdm import tqdm +import gc +import numpy as np +from ase.visualize import view +import torch +from math import sqrt + +class traj(object): + def __init__(self, name): + self.a2g = AtomsToGraphs( + max_neigh=50, + radius=6, + r_energy=False, + r_forces=False, + r_distances=True, + r_edges=True, + r_fixed=True) + self.name = name + self.data_list = [] + + def S2EF(self, calc): ## if DFT data -> S2EF(self, ad_E, calc) + if not os.path.exists(os.path.join('traj_file/', self.name)): + #images = [self.i_xdata_f] + # loading init structure + img_tag = self.i_xdata_i + + # reset tags + for i in range(len(img_tag.get_tags())): + img_tag[i].tag = 0 + + # find first layer + atom_positions = img_tag.get_positions() + Atoms_height = np.unique(np.sort(atom_positions[:,2])) + first_layer = np.where(atom_positions == Atoms_height[4]) + + # set tags surface to 1 + for surface in first_layer[0]: + img_tag[surface].tag = 1 + + # set adsorbate tag + ad_height = Atoms_height[5:] + for i in range(len(ad_height)): + ad = np.where(atom_positions == ad_height[i]) + img_tag[ad[0][0]].tag = 2 + + #relaxation - ml_relaxation using cuda:0 + img_tag.calc = calc + image = BFGS(img_tag, trajectory='traj_file/' + self.name) + + image.run(fmax=0.05, steps=100) + + i_xdata = read(os.path.join('traj_file/', self.name)) + forces = i_xdata.get_forces() + fmax = sqrt((forces**2).sum(axis=1).max()) + + if fmax > 0.05: + self.data_list.append(self.name) + else: + images = [i_xdata] + + data_objects = self.a2g.convert_all(images, disable_tqdm=True) + + tags = img_tag.get_tags() + + for fid, data in enumerate(data_objects): + data.sid = torch.LongTensor([0]) + data.fid = torch.LongTensor(fid) + data.tags = torch.LongTensor(tags) + # data.y = calc.get_energy(i_xdata) # if you want to get energy + + return data + + def check_not_relax_data(self, calc): + img_tag = read(os.path.join('traj_file/', self.name)) + + #relaxation - ml_relaxation using cuda:0 + #print(img_tag.get_forces()) + forces = img_tag.get_forces() + fmax = sqrt((forces**2).sum(axis=1).max()) + if fmax > 0.05: + os.remove(os.path.join('traj_file/', self.name)) + + def get_item(self): + return self.data_list + +def ParalIter(processes=32, maxtasksperchild=1): + gc.collect() + if 'get_ipython' in locals().keys(): # it doesnt work in ipython + multiprocessing = None + elif processes == 1: + mapper = map + else: + try: + from multiprocessing import Pool + p = Pool(processes=processes, maxtasksperchild=maxtasksperchild) + mapper = p.imap_unordered + except: + mapper = map + + return mapper + +def ParalProcess(func,inputs, processes=32, maxtasksperchild=1): + mapper = ParalIter(processes, maxtasksperchild) + return list(tqdm(mapper(func,inputs),total = len(inputs))) + +def get_data(name): + data_class = traj(name) + b = data_class.S2EF(calc) + return b + +if __name__ == '__main__': + + #%% using csv data + data_list = 'Enter_your_data_list' # ['name1', 'name2', ...] + + #%% run relaxtion + + checkpoint = 'Enter_your_checkpoint_for_relaxation.pt' + calc = OCPCalculator(checkpoint=checkpoint, cpu=False) + + relaxed_data = ParalProcess(get_data, data_list) + + pickle.dump(relaxed_data, open('data.pkl', 'wb')) \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..9534b01 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..9518256 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1 @@ +nbsphinx diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..a1e791d --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,67 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys + +sys.path.insert(0, os.path.abspath("../../")) + + +# -- Project information ----------------------------------------------------- + +project = "Open Catalyst Project" +copyright = "2020, Facebook, Inc." +author = "Anuroop Sriram" + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.coverage", + "sphinx.ext.napoleon", + "sphinx_rtd_theme", + "nbsphinx", +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "sphinx_rtd_theme" + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] + +master_doc = "index" diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..26aed8d --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,54 @@ +Open Catalyst Project +===================== + +The Open Catalyst Project is a collaborative research effort between Facebook AI +Research (FAIR) and Carnegie Mellon University’s (CMU) Department of Chemical Engineering. +The aim is to use AI to model and discover new catalysts for use in renewable energy +storage to help in addressing climate change. + +Scalable and cost-effective solutions to renewable energy storage are essential to +addressing the world’s rising energy needs while reducing climate change. As we +increase our reliance on renewable energy sources such as wind and solar, which produce +intermittent power, storage is needed to transfer power from times of peak generation to +peak demand. This may require the storage of power for hours, days, or months. One solution +that offers the potential of scaling to nation-sized grids is the conversion of +renewable energy to other fuels, such as hydrogen. To be widely adopted, this +process requires cost-effective solutions to running chemical reactions. + +An open challenge is finding low-cost catalysts to drive these reactions at high rates. +Through the use of quantum mechanical simulations (density functional theory), new +catalyst structures can be tested and evaluated. Unfortunately, the high computational +cost of these simulations limits the number of structures that may be tested. The use of +AI or machine learning may provide a method to efficiently approximate these calculations, +leading to new approaches in finding effective catalysts. + +To enable the broader research community to participate in this important project, +we provide baseline models and code at +`Github page `_. + + +.. toctree:: + :maxdepth: 1 + :caption: Tutorials + + tutorials/getting_started + tutorials/data_playground.ipynb + tutorials/train_s2ef_example.ipynb + tutorials/training + tutorials/submission + +.. + .. toctree:: + :maxdepth: 1 + :caption: Modules + + modules/model + modules/dataset + modules/trainer + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/source/modules/dataset.rst b/docs/source/modules/dataset.rst new file mode 100644 index 0000000..7aff502 --- /dev/null +++ b/docs/source/modules/dataset.rst @@ -0,0 +1,12 @@ +ocpmodels.datasets +================== + +.. .. currentmodule:: ocpmodels.datasets + +.. .. autosummary:: +.. :toctree: generated +.. :nosignatures: + +.. automodule:: ocpmodels.datasets + :members: + :exclude-members: data_list_collater diff --git a/docs/source/modules/model.rst b/docs/source/modules/model.rst new file mode 100644 index 0000000..ba16cb6 --- /dev/null +++ b/docs/source/modules/model.rst @@ -0,0 +1,12 @@ +ocpmodels.models +================ + +.. .. currentmodule:: ocpmodels.models + +.. .. autosummary:: +.. :toctree: generated +.. :nosignatures: + +.. automodule:: ocpmodels.models + :members: + :exclude-members: diff --git a/docs/source/modules/trainer.rst b/docs/source/modules/trainer.rst new file mode 100644 index 0000000..4f3eaa8 --- /dev/null +++ b/docs/source/modules/trainer.rst @@ -0,0 +1,12 @@ +ocpmodels.trainers +================== + +.. .. currentmodule:: ocpmodels.trainers + +.. .. autosummary:: +.. :toctree: generated +.. :nosignatures: + +.. automodule:: ocpmodels.trainers + :members: + :exclude-members: diff --git a/env.common.yml b/env.common.yml new file mode 100644 index 0000000..6937597 --- /dev/null +++ b/env.common.yml @@ -0,0 +1,25 @@ +channels: +- pytorch +- pyg +- conda-forge +- defaults +dependencies: +- ase=3.21.1 +- black==22.3.0 +- matplotlib +- numba +- pip +- pre-commit=2.10.* +- pyg=2.2.0 +- pymatgen=2020.12.31 +- python=3.9.* +- pytorch=1.13.1 +- pyyaml +- tensorboard +- tqdm +- pytest +- python-lmdb +- submitit +- syrupy=3.0.6 +- wandb +name: ocp-models diff --git a/env.cpu.yml b/env.cpu.yml new file mode 100644 index 0000000..ab49b66 --- /dev/null +++ b/env.cpu.yml @@ -0,0 +1,2 @@ +dependencies: + - cpuonly diff --git a/env.gpu.yml b/env.gpu.yml new file mode 100644 index 0000000..5f66d9a --- /dev/null +++ b/env.gpu.yml @@ -0,0 +1,4 @@ +channels: + - nvidia +dependencies: + - pytorch-cuda=11.6 diff --git a/licenses/LICENSE.cgcnn b/licenses/LICENSE.cgcnn new file mode 100644 index 0000000..52dea7f --- /dev/null +++ b/licenses/LICENSE.cgcnn @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 Tian Xie + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/licenses/LICENSE.mmf b/licenses/LICENSE.mmf new file mode 100644 index 0000000..cce2939 --- /dev/null +++ b/licenses/LICENSE.mmf @@ -0,0 +1,30 @@ +BSD License + +For MMF software + +Copyright (c) Facebook, Inc. and its affiliates. All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the name Facebook nor the names of its contributors may be used to + endorse or promote products derived from this software without specific + prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/main.py b/main.py new file mode 100644 index 0000000..d8e9081 --- /dev/null +++ b/main.py @@ -0,0 +1,88 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import copy +import logging + +import submitit + +from ocpmodels.common.flags import flags +from ocpmodels.common.utils import ( + build_config, + create_grid, + new_trainer_context, + save_experiment_log, + setup_logging, +) + + +class Runner(submitit.helpers.Checkpointable): + def __init__(self): + self.config = None + + def __call__(self, config): + with new_trainer_context(args=args, config=config) as ctx: + self.config = ctx.config + self.task = ctx.task + self.trainer = ctx.trainer + + self.task.setup(self.trainer) + self.task.run() + + def checkpoint(self, *args, **kwargs): + new_runner = Runner() + self.trainer.save(checkpoint_file="checkpoint.pt", training_state=True) + self.config["checkpoint"] = self.task.chkpt_path + self.config["timestamp_id"] = self.trainer.timestamp_id + if self.trainer.logger is not None: + self.trainer.logger.mark_preempting() + return submitit.helpers.DelayedSubmission(new_runner, self.config) + + +if __name__ == "__main__": + setup_logging() + + parser = flags.get_parser() + args, override_args = parser.parse_known_args() + config = build_config(args, override_args) + + if args.submit: # Run on cluster + slurm_add_params = config.get( + "slurm", None + ) # additional slurm arguments + if args.sweep_yml: # Run grid search + configs = create_grid(config, args.sweep_yml) + else: + configs = [config] + + logging.info(f"Submitting {len(configs)} jobs") + executor = submitit.AutoExecutor( + folder=args.logdir / "%j", slurm_max_num_timeout=3 + ) + executor.update_parameters( + name=args.identifier, + mem_gb=args.slurm_mem, + timeout_min=args.slurm_timeout * 60, + slurm_partition=args.slurm_partition, + gpus_per_node=args.num_gpus, + cpus_per_task=(config["optim"]["num_workers"] + 1), + tasks_per_node=(args.num_gpus if args.distributed else 1), + nodes=args.num_nodes, + slurm_additional_parameters=slurm_add_params, + ) + for config in configs: + config["slurm"] = copy.deepcopy(executor.parameters) + config["slurm"]["folder"] = str(executor.folder) + jobs = executor.map_array(Runner(), configs) + logging.info( + f"Submitted jobs: {', '.join([job.job_id for job in jobs])}" + ) + log_file = save_experiment_log(args, jobs, configs) + logging.info(f"Experiment log saved to: {log_file}") + + else: # Run locally + Runner()(config) diff --git a/ocpmodels/__init__.py b/ocpmodels/__init__.py new file mode 100644 index 0000000..c17674b --- /dev/null +++ b/ocpmodels/__init__.py @@ -0,0 +1,6 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" diff --git a/ocpmodels/common/__init__.py b/ocpmodels/common/__init__.py new file mode 100644 index 0000000..c17674b --- /dev/null +++ b/ocpmodels/common/__init__.py @@ -0,0 +1,6 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" diff --git a/ocpmodels/common/data_parallel.py b/ocpmodels/common/data_parallel.py new file mode 100644 index 0000000..210efc2 --- /dev/null +++ b/ocpmodels/common/data_parallel.py @@ -0,0 +1,285 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import heapq +import logging +from itertools import chain +from pathlib import Path +from typing import List, Literal, Protocol, Union, runtime_checkable + +import numba +import numpy as np +import torch +from torch.utils.data import BatchSampler, DistributedSampler, Sampler + +from ocpmodels.common import distutils, gp_utils +from ocpmodels.datasets import data_list_collater + + +class OCPDataParallel(torch.nn.DataParallel): + def __init__(self, module, output_device, num_gpus): + if num_gpus < 0: + raise ValueError("# GPUs must be positive.") + if num_gpus > torch.cuda.device_count(): + raise ValueError("# GPUs specified larger than available") + + self.src_device = torch.device(output_device) + + self.cpu = False + if num_gpus == 0: + self.cpu = True + elif num_gpus == 1: + device_ids = [self.src_device] + else: + if ( + self.src_device.type == "cuda" + and self.src_device.index >= num_gpus + ): + raise ValueError("Main device must be less than # of GPUs") + device_ids = list(range(num_gpus)) + + if self.cpu: + super(torch.nn.DataParallel, self).__init__() + self.module = module + + else: + super(OCPDataParallel, self).__init__( + module=module, + device_ids=device_ids, + output_device=self.src_device, + ) + + def forward(self, batch_list, **kwargs): + if self.cpu: + return self.module(batch_list[0]) + + if len(self.device_ids) == 1: + return self.module( + batch_list[0].to(f"cuda:{self.device_ids[0]}"), **kwargs + ) + + for t in chain(self.module.parameters(), self.module.buffers()): + if t.device != self.src_device: + raise RuntimeError( + ( + "Module must have its parameters and buffers on device " + "{} but found one of them on device {}." + ).format(self.src_device, t.device) + ) + + inputs = [ + batch.to(f"cuda:{self.device_ids[i]}") + for i, batch in enumerate(batch_list) + ] + replicas = self.replicate(self.module, self.device_ids[: len(inputs)]) + outputs = self.parallel_apply(replicas, inputs, kwargs) + return self.gather(outputs, self.output_device) + + +class ParallelCollater: + def __init__(self, num_gpus, otf_graph=False): + self.num_gpus = num_gpus + self.otf_graph = otf_graph + + def __call__(self, data_list): + if self.num_gpus in [0, 1]: # adds cpu-only case + batch = data_list_collater(data_list, otf_graph=self.otf_graph) + return [batch] + + else: + num_devices = min(self.num_gpus, len(data_list)) + + count = torch.tensor([data.num_nodes for data in data_list]) + cumsum = count.cumsum(0) + cumsum = torch.cat([cumsum.new_zeros(1), cumsum], dim=0) + device_id = ( + num_devices * cumsum.to(torch.float) / cumsum[-1].item() + ) + device_id = (device_id[:-1] + device_id[1:]) / 2.0 + device_id = device_id.to(torch.long) + split = device_id.bincount().cumsum(0) + split = torch.cat([split.new_zeros(1), split], dim=0) + split = torch.unique(split, sorted=True) + split = split.tolist() + + return [ + data_list_collater(data_list[split[i] : split[i + 1]]) + for i in range(len(split) - 1) + ] + + +@numba.njit +def balanced_partition(sizes, num_parts): + """ + Greedily partition the given set by always inserting + the largest element into the smallest partition. + """ + sort_idx = np.argsort(-sizes) # Sort in descending order + heap = [] + for idx in sort_idx[:num_parts]: + heap.append((sizes[idx], [idx])) + heapq.heapify(heap) + for idx in sort_idx[num_parts:]: + smallest_part = heapq.heappop(heap) + new_size = smallest_part[0] + sizes[idx] + new_idx = smallest_part[1] + [idx] + heapq.heappush(heap, (new_size, new_idx)) + idx_balanced = [part[1] for part in heap] + return idx_balanced + + +@runtime_checkable +class _HasMetadata(Protocol): + @property + def metadata_path(self) -> Path: + ... + + +class BalancedBatchSampler(Sampler): + def _load_dataset(self, dataset, mode: Literal["atoms", "neighbors"]): + errors: List[str] = [] + if not isinstance(dataset, _HasMetadata): + errors.append( + f"Dataset {dataset} does not have a metadata_path attribute." + ) + return None, errors + if not dataset.metadata_path.exists(): + errors.append( + f"Metadata file {dataset.metadata_path} does not exist." + ) + return None, errors + + key = {"atoms": "natoms", "neighbors": "neighbors"}[mode] + sizes = np.load(dataset.metadata_path)[key] + + return sizes, errors + + def __init__( + self, + dataset, + batch_size, + num_replicas, + rank, + device, + mode: Union[str, bool] = "atoms", + shuffle=True, + drop_last=False, + force_balancing=False, + throw_on_error=False, + ): + if mode is True: + mode = "atoms" + + if isinstance(mode, str): + mode = mode.lower() + if mode not in ("atoms", "neighbors"): + raise ValueError( + f"Invalid mode {mode}. Must be one of 'atoms', 'neighbors', or a boolean." + ) + + self.dataset = dataset + self.batch_size = batch_size + self.num_replicas = num_replicas + self.rank = rank + self.device = device + self.mode = mode + self.shuffle = shuffle + self.drop_last = drop_last + + self.single_sampler = DistributedSampler( + self.dataset, + num_replicas=num_replicas, + rank=rank, + shuffle=shuffle, + drop_last=drop_last, + ) + self.batch_sampler = BatchSampler( + self.single_sampler, + batch_size, + drop_last=drop_last, + ) + + self.sizes = None + self.balance_batches = False + + if self.num_replicas <= 1: + logging.info( + "Batch balancing is disabled for single GPU training." + ) + return + + if self.mode is False: + logging.info( + "Batch balancing is disabled because `optim.load_balancing` is `False`" + ) + return + + self.sizes, errors = self._load_dataset(dataset, self.mode) + if self.sizes is None: + self.balance_batches = force_balancing + if force_balancing: + errors.append( + "BalancedBatchSampler has to load the data to determine batch sizes, which incurs significant overhead! " + "You can disable balancing by setting `optim.load_balancing` to `False`." + ) + else: + errors.append( + "Batches will not be balanced, which can incur significant overhead!" + ) + else: + self.balance_batches = True + + if errors: + msg = "BalancedBatchSampler: " + " ".join(errors) + if throw_on_error: + raise RuntimeError(msg) + else: + logging.warning(msg) + + def __len__(self): + return len(self.batch_sampler) + + def set_epoch(self, epoch): + self.single_sampler.set_epoch(epoch) + + def __iter__(self): + if not self.balance_batches: + yield from self.batch_sampler + return + + for batch_idx in self.batch_sampler: + if self.sizes is None: + # Unfortunately, we need to load the data to know the image sizes + data_list = [self.dataset[idx] for idx in batch_idx] + + if self.mode == "atoms": + sizes = [data.num_nodes for data in data_list] + elif self.mode == "neighbors": + sizes = [data.edge_index.shape[1] for data in data_list] + else: + raise NotImplementedError( + f"Unknown load balancing mode: {self.mode}" + ) + else: + sizes = [self.sizes[idx] for idx in batch_idx] + + idx_sizes = torch.stack( + [torch.tensor(batch_idx), torch.tensor(sizes)] + ) + idx_sizes_all = distutils.all_gather(idx_sizes, device=self.device) + idx_sizes_all = torch.cat(idx_sizes_all, dim=-1).cpu() + if gp_utils.initialized(): + idx_sizes_all = torch.unique(input=idx_sizes_all, dim=1) + idx_all = idx_sizes_all[0] + sizes_all = idx_sizes_all[1] + + local_idx_balanced = balanced_partition( + sizes_all.numpy(), num_parts=self.num_replicas + ) + # Since DistributedSampler pads the last batch + # this should always have an entry for each replica. + yield idx_all[local_idx_balanced[self.rank]] diff --git a/ocpmodels/common/distutils.py b/ocpmodels/common/distutils.py new file mode 100644 index 0000000..5e2179d --- /dev/null +++ b/ocpmodels/common/distutils.py @@ -0,0 +1,159 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import os +import subprocess + +import torch +import torch.distributed as dist + + +def setup(config): + if config["submit"]: + node_list = os.environ.get("SLURM_STEP_NODELIST") + if node_list is None: + node_list = os.environ.get("SLURM_JOB_NODELIST") + if node_list is not None: + try: + hostnames = subprocess.check_output( + ["scontrol", "show", "hostnames", node_list] + ) + config["init_method"] = "tcp://{host}:{port}".format( + host=hostnames.split()[0].decode("utf-8"), + port=config["distributed_port"], + ) + nnodes = int(os.environ.get("SLURM_NNODES")) + ntasks_per_node = os.environ.get("SLURM_NTASKS_PER_NODE") + if ntasks_per_node is not None: + ntasks_per_node = int(ntasks_per_node) + else: + ntasks = int(os.environ.get("SLURM_NTASKS")) + nnodes = int(os.environ.get("SLURM_NNODES")) + assert ntasks % nnodes == 0 + ntasks_per_node = int(ntasks / nnodes) + if ntasks_per_node == 1: + assert config["world_size"] % nnodes == 0 + gpus_per_node = config["world_size"] // nnodes + node_id = int(os.environ.get("SLURM_NODEID")) + config["rank"] = node_id * gpus_per_node + config["local_rank"] = 0 + else: + assert ntasks_per_node == config["world_size"] // nnodes + config["rank"] = int(os.environ.get("SLURM_PROCID")) + config["local_rank"] = int(os.environ.get("SLURM_LOCALID")) + + logging.info( + f"Init: {config['init_method']}, {config['world_size']}, {config['rank']}" + ) + + # ensures GPU0 does not have extra context/higher peak memory + torch.cuda.set_device(config["local_rank"]) + + dist.init_process_group( + backend=config["distributed_backend"], + init_method=config["init_method"], + world_size=config["world_size"], + rank=config["rank"], + ) + except subprocess.CalledProcessError as e: # scontrol failed + raise e + except FileNotFoundError: # Slurm is not installed + pass + elif config["summit"]: + world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"]) + world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) + get_master = ( + "echo $(cat {} | sort | uniq | grep -v batch | grep -v login | head -1)" + ).format(os.environ["LSB_DJOB_HOSTFILE"]) + os.environ["MASTER_ADDR"] = str( + subprocess.check_output(get_master, shell=True) + )[2:-3] + os.environ["MASTER_PORT"] = "23456" + os.environ["WORLD_SIZE"] = os.environ["OMPI_COMM_WORLD_SIZE"] + os.environ["RANK"] = os.environ["OMPI_COMM_WORLD_RANK"] + # NCCL and MPI initialization + dist.init_process_group( + backend="nccl", + rank=world_rank, + world_size=world_size, + init_method="env://", + ) + else: + dist.init_process_group( + backend=config["distributed_backend"], init_method="env://" + ) + # TODO: SLURM + + +def cleanup(): + dist.destroy_process_group() + + +def initialized(): + return dist.is_available() and dist.is_initialized() + + +def get_rank(): + return dist.get_rank() if initialized() else 0 + + +def get_world_size(): + return dist.get_world_size() if initialized() else 1 + + +def is_master(): + return get_rank() == 0 + + +def synchronize(): + if get_world_size() == 1: + return + dist.barrier() + + +def broadcast(tensor, src, group=dist.group.WORLD, async_op=False): + if get_world_size() == 1: + return + dist.broadcast(tensor, src, group, async_op) + + +def all_reduce(data, group=dist.group.WORLD, average=False, device=None): + if get_world_size() == 1: + return data + tensor = data + if not isinstance(data, torch.Tensor): + tensor = torch.tensor(data) + if device is not None: + tensor = tensor.cuda(device) + dist.all_reduce(tensor, group=group) + if average: + tensor /= get_world_size() + if not isinstance(data, torch.Tensor): + result = tensor.cpu().numpy() if tensor.numel() > 1 else tensor.item() + else: + result = tensor + return result + + +def all_gather(data, group=dist.group.WORLD, device=None): + if get_world_size() == 1: + return data + tensor = data + if not isinstance(data, torch.Tensor): + tensor = torch.tensor(data) + if device is not None: + tensor = tensor.cuda(device) + tensor_list = [ + tensor.new_zeros(tensor.shape) for _ in range(get_world_size()) + ] + dist.all_gather(tensor_list, tensor, group=group) + if not isinstance(data, torch.Tensor): + result = [tensor.cpu().numpy() for tensor in tensor_list] + else: + result = tensor_list + return result diff --git a/ocpmodels/common/flags.py b/ocpmodels/common/flags.py new file mode 100644 index 0000000..900540a --- /dev/null +++ b/ocpmodels/common/flags.py @@ -0,0 +1,144 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import argparse +from pathlib import Path + + +class Flags: + def __init__(self): + self.parser = argparse.ArgumentParser( + description="Graph Networks for Electrocatalyst Design" + ) + self.add_core_args() + + def get_parser(self): + return self.parser + + def add_core_args(self): + self.parser.add_argument_group("Core Arguments") + self.parser.add_argument( + "--mode", + choices=["train", "predict", "run-relaxations", "validate"], + required=True, + help="Whether to train the model, make predictions, or to run relaxations", + ) + self.parser.add_argument( + "--config-yml", + required=True, + type=Path, + help="Path to a config file listing data, model, optim parameters.", + ) + self.parser.add_argument( + "--identifier", + default="", + type=str, + help="Experiment identifier to append to checkpoint/log/result directory", + ) + self.parser.add_argument( + "--debug", + action="store_true", + help="Whether this is a debugging run or not", + ) + self.parser.add_argument( + "--run-dir", + default="./", + type=str, + help="Directory to store checkpoint/log/result directory", + ) + self.parser.add_argument( + "--print-every", + default=10, + type=int, + help="Log every N iterations (default: 10)", + ) + self.parser.add_argument( + "--seed", default=0, type=int, help="Seed for torch, cuda, numpy" + ) + self.parser.add_argument( + "--amp", action="store_true", help="Use mixed-precision training" + ) + self.parser.add_argument( + "--checkpoint", type=str, help="Model checkpoint to load" + ) + self.parser.add_argument( + "--timestamp-id", + default=None, + type=str, + help="Override time stamp ID. " + "Useful for seamlessly continuing model training in logger.", + ) + # Cluster args + self.parser.add_argument( + "--sweep-yml", + default=None, + type=Path, + help="Path to a config file with parameter sweeps", + ) + self.parser.add_argument( + "--submit", action="store_true", help="Submit job to cluster" + ) + self.parser.add_argument( + "--summit", action="store_true", help="Running on Summit cluster" + ) + self.parser.add_argument( + "--logdir", default="logs", type=Path, help="Where to store logs" + ) + self.parser.add_argument( + "--slurm-partition", + default="ocp", + type=str, + help="Name of partition", + ) + self.parser.add_argument( + "--slurm-mem", default=80, type=int, help="Memory (in gigabytes)" + ) + self.parser.add_argument( + "--slurm-timeout", default=72, type=int, help="Time (in hours)" + ) + self.parser.add_argument( + "--num-gpus", default=1, type=int, help="Number of GPUs to request" + ) + self.parser.add_argument( + "--distributed", action="store_true", help="Run with DDP" + ) + self.parser.add_argument( + "--cpu", action="store_true", help="Run CPU only training" + ) + self.parser.add_argument( + "--num-nodes", + default=1, + type=int, + help="Number of Nodes to request", + ) + self.parser.add_argument( + "--distributed-port", + type=int, + default=13356, + help="Port on master for DDP", + ) + self.parser.add_argument( + "--distributed-backend", + type=str, + default="nccl", + help="Backend for DDP", + ) + self.parser.add_argument( + "--local_rank", default=0, type=int, help="Local rank" + ) + self.parser.add_argument( + "--no-ddp", action="store_true", help="Do not use DDP" + ) + self.parser.add_argument( + "--gp-gpus", + type=int, + default=None, + help="Number of GPUs to split the graph over (only for Graph Parallel training)", + ) + + +flags = Flags() diff --git a/ocpmodels/common/gp_utils.py b/ocpmodels/common/gp_utils.py new file mode 100644 index 0000000..4b3cc1a --- /dev/null +++ b/ocpmodels/common/gp_utils.py @@ -0,0 +1,304 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" +import math +from typing import Any, List + +import torch +from torch import distributed as dist + +""" +Functions to support graph parallel training. +This is based on the Megatron-LM implementation: +https://github.com/facebookresearch/fairscale/blob/main/fairscale/nn/model_parallel/initialize.py +""" + +########## INITIALIZATION ########## + +_GRAPH_PARALLEL_GROUP = None +_DATA_PARALLEL_GROUP = None + + +def ensure_div(a, b): + assert a % b == 0 + + +def divide_and_check_no_remainder(a: int, b: int): + ensure_div(a, b) + return a // b + + +def setup_gp(config): + gp_size = config["gp_gpus"] + backend = config["distributed_backend"] + assert torch.distributed.is_initialized() + world_size = torch.distributed.get_world_size() + + gp_size = min(gp_size, world_size) + ensure_div(world_size, gp_size) + dp_size = world_size // gp_size + rank = dist.get_rank() + + if rank == 0: + print("> initializing graph parallel with size {}".format(gp_size)) + print("> initializing ddp with size {}".format(dp_size)) + + groups = torch.arange(world_size).reshape(dp_size, gp_size) + found = [x.item() for x in torch.where(groups == rank)] + + global _DATA_PARALLEL_GROUP + assert ( + _DATA_PARALLEL_GROUP is None + ), "data parallel group is already initialized" + for j in range(gp_size): + group = dist.new_group(groups[:, j].tolist(), backend=backend) + if j == found[1]: + _DATA_PARALLEL_GROUP = group + global _GRAPH_PARALLEL_GROUP + assert ( + _GRAPH_PARALLEL_GROUP is None + ), "graph parallel group is already initialized" + for i in range(dp_size): + group = dist.new_group(groups[i, :].tolist(), backend=backend) + if i == found[0]: + _GRAPH_PARALLEL_GROUP = group + + +def cleanup_gp(): + dist.destroy_process_group(_DATA_PARALLEL_GROUP) + dist.destroy_process_group(_GRAPH_PARALLEL_GROUP) + + +def initialized(): + return _GRAPH_PARALLEL_GROUP is not None + + +def get_dp_group(): + return _DATA_PARALLEL_GROUP + + +def get_gp_group(): + return _GRAPH_PARALLEL_GROUP + + +def get_dp_rank(): + return dist.get_rank(group=get_dp_group()) + + +def get_gp_rank(): + return dist.get_rank(group=get_gp_group()) + + +def get_dp_world_size(): + return dist.get_world_size(group=get_dp_group()) + + +def get_gp_world_size(): + return ( + 1 if not initialized() else dist.get_world_size(group=get_gp_group()) + ) + + +########## DIST METHODS ########## + + +def pad_tensor(tensor: torch.Tensor, dim: int = -1, target_size: int = None): + size = tensor.size(dim) + if target_size is None: + world_size = get_gp_world_size() + if size % world_size == 0: + pad_size = 0 + else: + pad_size = world_size - size % world_size + else: + pad_size = target_size - size + if pad_size == 0: + return tensor + pad_shape = list(tensor.shape) + pad_shape[dim] = pad_size + padding = torch.empty(pad_shape, device=tensor.device, dtype=tensor.dtype) + return torch.cat([tensor, padding], dim=dim) + + +def trim_tensor( + tensor: torch.Tensor, sizes: torch.Tensor = None, dim: int = 0 +): + size = tensor.size(dim) + world_size = get_gp_world_size() + if size % world_size == 0: + return tensor, sizes + trim_size = size - size % world_size + if dim == 0: + tensor = tensor[:trim_size] + elif dim == 1: + tensor = tensor[:, :trim_size] + else: + raise ValueError + if sizes is not None: + sizes[-1] = sizes[-1] - size % world_size + return tensor, sizes + + +def _split_tensor( + tensor: torch.Tensor, + num_parts: int, + dim: int = -1, + contiguous_chunks: bool = False, +): + part_size = math.ceil(tensor.size(dim) / num_parts) + tensor_list = torch.split(tensor, part_size, dim=dim) + if contiguous_chunks: + return tuple(chunk.contiguous() for chunk in tensor_list) + return tensor_list + + +def _reduce(ctx: Any, input: torch.Tensor) -> torch.Tensor: + group = get_gp_group() + if ctx: + ctx.mark_dirty(input) + if dist.get_world_size(group) == 1: + return input + dist.all_reduce(input, group=group) + return input + + +def _split(input: torch.Tensor, dim: int = -1) -> torch.Tensor: + group = get_gp_group() + rank = get_gp_rank() + world_size = dist.get_world_size(group=group) + if world_size == 1: + return input + input_list = _split_tensor(input, world_size, dim=dim) + return input_list[rank].contiguous() + + +def _gather(input: torch.Tensor, dim: int = -1) -> torch.Tensor: + group = get_gp_group() + rank = get_gp_rank() + world_size = dist.get_world_size(group=group) + if world_size == 1: + return input + tensor_list = [torch.empty_like(input) for _ in range(world_size)] + tensor_list[rank] = input + dist.all_gather(tensor_list, input, group=group) + return torch.cat(tensor_list, dim=dim).contiguous() + + +def _gather_with_padding(input: torch.Tensor, dim: int = -1): + group = get_gp_group() + rank = get_gp_rank() + world_size = dist.get_world_size(group=group) + if world_size == 1: + return input + + # Gather sizes + size_list = [ + torch.empty(1, device=input.device, dtype=torch.long) + for _ in range(world_size) + ] + size = torch.tensor( + [input.size(dim)], device=input.device, dtype=torch.long + ) + size_list[rank] = size + dist.all_gather(size_list, size, group=group) + + # Gather the inputs + max_size = max([size.item() for size in size_list]) + input = pad_tensor(input, dim, max_size) + shape = list(input.shape) + shape[dim] = max_size + tensor_list = [ + torch.empty(shape, device=input.device, dtype=input.dtype) + for _ in range(world_size) + ] + tensor_list[rank] = input + dist.all_gather(tensor_list, input, group=group) + + # Trim and cat + if dim == 0: + tensor_list = [ + tensor[:size] for tensor, size in zip(tensor_list, size_list) + ] + elif dim == 1: + tensor_list = [ + tensor[:, :size] for tensor, size in zip(tensor_list, size_list) + ] + else: + raise ValueError + return torch.cat(tensor_list, dim=dim).contiguous() + + +class CopyToModelParallelRegion(torch.autograd.Function): + @staticmethod + def forward(ctx, input: torch.Tensor): + return input + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + return _reduce(None, grad_output) + + +class ReduceFromModelParallelRegion(torch.autograd.Function): + @staticmethod + def forward(ctx, input: torch.Tensor): + return _reduce(ctx, input) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + world_size = 1 + return grad_output.mul_(world_size) + + +class ScatterToModelParallelRegion(torch.autograd.Function): + @staticmethod + def forward(ctx, input: torch.Tensor, dim: int = -1): + result = _split(input, dim) + ctx.save_for_backward(torch.tensor(dim)) + return result + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + (dim,) = ctx.saved_tensors + world_size = 1 + return ( + _gather_with_padding(grad_output, dim.item()).div_(world_size), + None, + ) + + +class GatherFromModelParallelRegion(torch.autograd.Function): + @staticmethod + def forward(ctx, input: torch.Tensor, dim: int = -1): + ctx.save_for_backward(torch.tensor(dim)) + result = _gather_with_padding(input, dim) + return result + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + (dim,) = ctx.saved_tensors + result = _split(grad_output, dim.item()) + world_size = 1 + return result.mul_(world_size), None + + +def copy_to_model_parallel_region(input: torch.Tensor) -> torch.Tensor: + return CopyToModelParallelRegion.apply(input) + + +def reduce_from_model_parallel_region(input: torch.Tensor) -> torch.Tensor: + return ReduceFromModelParallelRegion.apply(input) + + +def scatter_to_model_parallel_region( + input: torch.Tensor, dim: int = -1 +) -> torch.Tensor: + return ScatterToModelParallelRegion.apply(input, dim) + + +def gather_from_model_parallel_region( + input: torch.Tensor, dim: int = -1 +) -> torch.Tensor: + return GatherFromModelParallelRegion.apply(input, dim) diff --git a/ocpmodels/common/hpo_utils.py b/ocpmodels/common/hpo_utils.py new file mode 100644 index 0000000..239a6f4 --- /dev/null +++ b/ocpmodels/common/hpo_utils.py @@ -0,0 +1,55 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +from ray import tune + + +def tune_reporter( + iters, + train_metrics, + val_metrics, + test_metrics=None, + metric_to_opt="val_loss", + min_max="min", +): + """ + Wrapper function for tune.report() + + Args: + iters(dict): dict with training iteration info (e.g. steps, epochs) + train_metrics(dict): train metrics dict + val_metrics(dict): val metrics dict + test_metrics(dict, optional): test metrics dict, default is None + metric_to_opt(str, optional): str for val metric to optimize, default is val_loss + min_max(str, optional): either "min" or "max", determines whether metric_to_opt is to be minimized or maximized, default is min + + """ + # labels metric dicts + train = label_metric_dict(train_metrics, "train") + val = label_metric_dict(val_metrics, "val") + # this enables tolerance for NaNs assumes val set is used for optimization + if math.isnan(val[metric_to_opt]): + if min_max == "min": + val[metric_to_opt] = 100000.0 + if min_max == "max": + val[metric_to_opt] = 0.0 + if test_metrics: + test = label_metric_dict(test_metrics, "test") + else: + test = {} + # report results to Ray Tune + tune.report(**iters, **train, **val, **test) + + +def label_metric_dict(metric_dict, split): + new_dict = {} + for key in metric_dict: + new_dict["{}_{}".format(split, key)] = metric_dict[key] + metric_dict = new_dict + return metric_dict diff --git a/ocpmodels/common/logger.py b/ocpmodels/common/logger.py new file mode 100644 index 0000000..b6a334b --- /dev/null +++ b/ocpmodels/common/logger.py @@ -0,0 +1,116 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" +import logging +from abc import ABC, abstractmethod + +import torch +import wandb +from torch.utils.tensorboard import SummaryWriter + +from ocpmodels.common.registry import registry + + +class Logger(ABC): + """Generic class to interface with various logging modules, e.g. wandb, + tensorboard, etc. + """ + + def __init__(self, config): + self.config = config + + @abstractmethod + def watch(self, model): + """ + Monitor parameters and gradients. + """ + pass + + def log(self, update_dict, step=None, split=""): + """ + Log some values. + """ + assert step is not None + if split != "": + new_dict = {} + for key in update_dict: + new_dict["{}/{}".format(split, key)] = update_dict[key] + update_dict = new_dict + return update_dict + + @abstractmethod + def log_plots(self, plots): + pass + + @abstractmethod + def mark_preempting(self): + pass + + +@registry.register_logger("wandb") +class WandBLogger(Logger): + def __init__(self, config): + super().__init__(config) + project = ( + self.config["logger"].get("project", None) + if isinstance(self.config["logger"], dict) + else None + ) + + wandb.init( + config=self.config, + id=self.config["cmd"]["timestamp_id"], + name=self.config["cmd"]["identifier"], + dir=self.config["cmd"]["logs_dir"], + project=project, + resume="allow", + ) + + def watch(self, model): + wandb.watch(model) + + def log(self, update_dict, step=None, split=""): + update_dict = super().log(update_dict, step, split) + wandb.log(update_dict, step=int(step)) + + def log_plots(self, plots, caption=""): + assert isinstance(plots, list) + plots = [wandb.Image(x, caption=caption) for x in plots] + wandb.log({"data": plots}) + + def mark_preempting(self): + wandb.mark_preempting() + + +@registry.register_logger("tensorboard") +class TensorboardLogger(Logger): + def __init__(self, config): + super().__init__(config) + self.writer = SummaryWriter(self.config["cmd"]["logs_dir"]) + + # TODO: add a model hook for watching gradients. + def watch(self, model): + logging.warning( + "Model gradient logging to tensorboard not yet supported." + ) + return False + + def log(self, update_dict, step=None, split=""): + update_dict = super().log(update_dict, step, split) + for key in update_dict: + if torch.is_tensor(update_dict[key]): + self.writer.add_scalar(key, update_dict[key].item(), step) + else: + assert isinstance(update_dict[key], int) or isinstance( + update_dict[key], float + ) + self.writer.add_scalar(key, update_dict[key], step) + + def mark_preempting(self): + pass + + def log_plots(self, plots): + pass diff --git a/ocpmodels/common/registry.py b/ocpmodels/common/registry.py new file mode 100644 index 0000000..d60715c --- /dev/null +++ b/ocpmodels/common/registry.py @@ -0,0 +1,313 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +# Copyright (c) Facebook, Inc. and its affiliates. +# Borrowed from https://github.com/facebookresearch/pythia/blob/master/pythia/common/registry.py. +""" +Registry is central source of truth. Inspired from Redux's concept of +global store, Registry maintains mappings of various information to unique +keys. Special functions in registry can be used as decorators to register +different kind of classes. + +Import the global registry object using + +``from ocpmodels.common.registry import registry`` + +Various decorators for registry different kind of classes with unique keys + +- Register a model: ``@registry.register_model`` +""" +import importlib + + +def _get_absolute_mapping(name: str): + # in this case, the `name` should be the fully qualified name of the class + # e.g., `ocpmodels.tasks.base_task.BaseTask` + # we can use importlib to get the module (e.g., `ocpmodels.tasks.base_task`) + # and then import the class (e.g., `BaseTask`) + + module_name = ".".join(name.split(".")[:-1]) + class_name = name.split(".")[-1] + + try: + module = importlib.import_module(module_name) + except (ModuleNotFoundError, ValueError) as e: + raise RuntimeError( + f"Could not import module `{module_name}` for import `{name}`" + ) from e + + try: + return getattr(module, class_name) + except AttributeError as e: + raise RuntimeError( + f"Could not import class `{class_name}` from module `{module_name}`" + ) from e + + +class Registry: + r"""Class for registry object which acts as central source of truth.""" + mapping = { + # Mappings to respective classes. + "task_name_mapping": {}, + "dataset_name_mapping": {}, + "model_name_mapping": {}, + "logger_name_mapping": {}, + "trainer_name_mapping": {}, + "state": {}, + } + + @classmethod + def register_task(cls, name): + r"""Register a new task to registry with key 'name' + Args: + name: Key with which the task will be registered. + Usage:: + from ocpmodels.common.registry import registry + from ocpmodels.tasks import BaseTask + @registry.register_task("train") + class TrainTask(BaseTask): + ... + """ + + def wrap(func): + cls.mapping["task_name_mapping"][name] = func + return func + + return wrap + + @classmethod + def register_dataset(cls, name): + r"""Register a dataset to registry with key 'name' + + Args: + name: Key with which the dataset will be registered. + + Usage:: + + from ocpmodels.common.registry import registry + from ocpmodels.datasets import BaseDataset + + @registry.register_dataset("qm9") + class QM9(BaseDataset): + ... + """ + + def wrap(func): + cls.mapping["dataset_name_mapping"][name] = func + return func + + return wrap + + @classmethod + def register_model(cls, name): + r"""Register a model to registry with key 'name' + + Args: + name: Key with which the model will be registered. + + Usage:: + + from ocpmodels.common.registry import registry + from ocpmodels.modules.layers import CGCNNConv + + @registry.register_model("cgcnn") + class CGCNN(): + ... + """ + + def wrap(func): + cls.mapping["model_name_mapping"][name] = func + return func + + return wrap + + @classmethod + def register_logger(cls, name): + r"""Register a logger to registry with key 'name' + + Args: + name: Key with which the logger will be registered. + + Usage:: + + from ocpmodels.common.registry import registry + + @registry.register_logger("tensorboard") + class WandB(): + ... + """ + + def wrap(func): + from ocpmodels.common.logger import Logger + + assert issubclass( + func, Logger + ), "All loggers must inherit Logger class" + cls.mapping["logger_name_mapping"][name] = func + return func + + return wrap + + @classmethod + def register_trainer(cls, name): + r"""Register a trainer to registry with key 'name' + + Args: + name: Key with which the trainer will be registered. + + Usage:: + + from ocpmodels.common.registry import registry + + @registry.register_trainer("active_discovery") + class ActiveDiscoveryTrainer(): + ... + """ + + def wrap(func): + cls.mapping["trainer_name_mapping"][name] = func + return func + + return wrap + + @classmethod + def register(cls, name, obj): + r"""Register an item to registry with key 'name' + + Args: + name: Key with which the item will be registered. + + Usage:: + + from ocpmodels.common.registry import registry + + registry.register("config", {}) + """ + path = name.split(".") + current = cls.mapping["state"] + + for part in path[:-1]: + if part not in current: + current[part] = {} + current = current[part] + + current[path[-1]] = obj + + @classmethod + def __import_error(cls, name: str, mapping_name: str): + kind = mapping_name[: -len("_name_mapping")] + mapping = cls.mapping.get(mapping_name, {}) + existing_keys = list(mapping.keys()) + + existing_cls_path = ( + mapping.get(existing_keys[-1], None) if existing_keys else None + ) + if existing_cls_path is not None: + existing_cls_path = f"{existing_cls_path.__module__}.{existing_cls_path.__qualname__}" + else: + existing_cls_path = "ocpmodels.trainers.ForcesTrainer" + + existing_keys = [f"'{name}'" for name in existing_keys] + existing_keys = ( + ", ".join(existing_keys[:-1]) + " or " + existing_keys[-1] + ) + existing_keys_str = ( + f" (one of {existing_keys})" if existing_keys else "" + ) + return RuntimeError( + f"Failed to find the {kind} '{name}'. " + f"You may either use a {kind} from the registry{existing_keys_str} " + f"or provide the full import path to the {kind} (e.g., '{existing_cls_path}')." + ) + + @classmethod + def get_class(cls, name: str, mapping_name: str): + existing_mapping = cls.mapping[mapping_name].get(name, None) + if existing_mapping is not None: + return existing_mapping + + # mapping be class path of type `{module_name}.{class_name}` (e.g., `ocpmodels.trainers.ForcesTrainer`) + if name.count(".") < 1: + raise cls.__import_error(name, mapping_name) + + try: + return _get_absolute_mapping(name) + except RuntimeError as e: + raise cls.__import_error(name, mapping_name) from e + + @classmethod + def get_task_class(cls, name): + return cls.get_class(name, "task_name_mapping") + + @classmethod + def get_dataset_class(cls, name): + return cls.get_class(name, "dataset_name_mapping") + + @classmethod + def get_model_class(cls, name): + return cls.get_class(name, "model_name_mapping") + + @classmethod + def get_logger_class(cls, name): + return cls.get_class(name, "logger_name_mapping") + + @classmethod + def get_trainer_class(cls, name): + return cls.get_class(name, "trainer_name_mapping") + + @classmethod + def get(cls, name, default=None, no_warning=False): + r"""Get an item from registry with key 'name' + + Args: + name (string): Key whose value needs to be retreived. + default: If passed and key is not in registry, default value will + be returned with a warning. Default: None + no_warning (bool): If passed as True, warning when key doesn't exist + will not be generated. Useful for cgcnn's + internal operations. Default: False + Usage:: + + from ocpmodels.common.registry import registry + + config = registry.get("config") + """ + original_name = name + name = name.split(".") + value = cls.mapping["state"] + for subname in name: + value = value.get(subname, default) + if value is default: + break + + if ( + "writer" in cls.mapping["state"] + and value == default + and no_warning is False + ): + cls.mapping["state"]["writer"].write( + "Key {} is not present in registry, returning default value " + "of {}".format(original_name, default) + ) + return value + + @classmethod + def unregister(cls, name): + r"""Remove an item from registry with key 'name' + + Args: + name: Key which needs to be removed. + Usage:: + + from ocpmodels.common.registry import registry + + config = registry.unregister("config") + """ + return cls.mapping["state"].pop(name, None) + + +registry = Registry() diff --git a/ocpmodels/common/relaxation/__init__.py b/ocpmodels/common/relaxation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/common/relaxation/ase_utils.py b/ocpmodels/common/relaxation/ase_utils.py new file mode 100644 index 0000000..e44049f --- /dev/null +++ b/ocpmodels/common/relaxation/ase_utils.py @@ -0,0 +1,213 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. + + + +Utilities to interface OCP models/trainers with the Atomic Simulation +Environment (ASE) +""" +import copy +import logging +import os + +import torch +import yaml +from ase import Atoms +from ase.calculators.calculator import Calculator +from ase.calculators.singlepoint import SinglePointCalculator as sp +from ase.constraints import FixAtoms + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + radius_graph_pbc, + setup_imports, + setup_logging, +) +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +def batch_to_atoms(batch): + n_systems = batch.natoms.shape[0] + natoms = batch.natoms.tolist() + numbers = torch.split(batch.atomic_numbers, natoms) + fixed = torch.split(batch.fixed, natoms) + forces = torch.split(batch.force, natoms) + positions = torch.split(batch.pos, natoms) + tags = torch.split(batch.tags, natoms) + cells = batch.cell + energies = batch.y.tolist() + + atoms_objects = [] + for idx in range(n_systems): + atoms = Atoms( + numbers=numbers[idx].tolist(), + positions=positions[idx].cpu().detach().numpy(), + tags=tags[idx].tolist(), + cell=cells[idx].cpu().detach().numpy(), + constraint=FixAtoms(mask=fixed[idx].tolist()), + pbc=[True, True, True], + ) + calc = sp( + atoms=atoms, + energy=energies[idx], + forces=forces[idx].cpu().detach().numpy(), + ) + atoms.set_calculator(calc) + atoms_objects.append(atoms) + + return atoms_objects + + +class OCPCalculator(Calculator): + implemented_properties = ["energy", "forces"] + + def __init__( + self, + config_yml=None, + checkpoint=None, + trainer=None, + cutoff=6, + max_neighbors=50, + cpu=True, + ): + """ + OCP-ASE Calculator + + Args: + config_yml (str): + Path to yaml config or could be a dictionary. + checkpoint (str): + Path to trained checkpoint. + trainer (str): + OCP trainer to be used. "forces" for S2EF, "energy" for IS2RE. + cutoff (int): + Cutoff radius to be used for data preprocessing. + max_neighbors (int): + Maximum amount of neighbors to store for a given atom. + cpu (bool): + Whether to load and run the model on CPU. Set `False` for GPU. + """ + setup_imports() + setup_logging() + Calculator.__init__(self) + + # Either the config path or the checkpoint path needs to be provided + assert config_yml or checkpoint is not None + + if config_yml is not None: + if isinstance(config_yml, str): + config = yaml.safe_load(open(config_yml, "r")) + + if "includes" in config: + for include in config["includes"]: + # Change the path based on absolute path of config_yml + path = os.path.join( + config_yml.split("configs")[0], include + ) + include_config = yaml.safe_load(open(path, "r")) + config.update(include_config) + else: + config = config_yml + # Only keeps the train data that might have normalizer values + if isinstance(config["dataset"], list): + config["dataset"] = config["dataset"][0] + elif isinstance(config["dataset"], dict): + config["dataset"] = config["dataset"].get("train", None) + else: + # Loads the config from the checkpoint directly (always on CPU). + config = torch.load(checkpoint, map_location=torch.device("cpu"))[ + "config" + ] + if trainer is not None: # passing the arg overrides everything else + config["trainer"] = trainer + else: + if "trainer" not in config: # older checkpoint + if config["task"]["dataset"] == "trajectory_lmdb": + config["trainer"] = "forces" + elif config["task"]["dataset"] == "single_point_lmdb": + config["trainer"] = "energy" + else: + logging.warning( + "Unable to identify OCP trainer, defaulting to `forces`. Specify the `trainer` argument into OCPCalculator if otherwise." + ) + config["trainer"] = "forces" + + if "model_attributes" in config: + config["model_attributes"]["name"] = config.pop("model") + config["model"] = config["model_attributes"] + + # for checkpoints with relaxation datasets defined, remove to avoid + # unnecesarily trying to load that dataset + if "relax_dataset" in config["task"]: + del config["task"]["relax_dataset"] + + # Calculate the edge indices on the fly + config["model"]["otf_graph"] = True + + # Save config so obj can be transported over network (pkl) + self.config = copy.deepcopy(config) + self.config["checkpoint"] = checkpoint + + if "normalizer" not in config: + del config["dataset"]["src"] + config["normalizer"] = config["dataset"] + + self.trainer = registry.get_trainer_class( + config.get("trainer", "energy") + )( + task=config["task"], + model=config["model"], + dataset=None, + normalizer=config["normalizer"], + optimizer=config["optim"], + identifier="", + slurm=config.get("slurm", {}), + local_rank=config.get("local_rank", 0), + is_debug=config.get("is_debug", True), + cpu=cpu, + ) + + if checkpoint is not None: + self.load_checkpoint(checkpoint) + + self.a2g = AtomsToGraphs( + max_neigh=max_neighbors, + radius=cutoff, + r_energy=False, + r_forces=False, + r_distances=False, + r_edges=False, + r_pbc=True, + ) + + def load_checkpoint(self, checkpoint_path): + """ + Load existing trained model + + Args: + checkpoint_path: string + Path to trained model + """ + try: + self.trainer.load_checkpoint(checkpoint_path) + except NotImplementedError: + logging.warning("Unable to load checkpoint!") + + def calculate(self, atoms, properties, system_changes): + Calculator.calculate(self, atoms, properties, system_changes) + data_object = self.a2g.convert(atoms) + batch = data_list_collater([data_object], otf_graph=True) + + predictions = self.trainer.predict( + batch, per_image=False, disable_tqdm=True + ) + if self.trainer.name == "s2ef": + self.results["energy"] = predictions["energy"].item() + self.results["forces"] = predictions["forces"].cpu().numpy() + + elif self.trainer.name == "is2re": + self.results["energy"] = predictions["energy"].item() diff --git a/ocpmodels/common/relaxation/ml_relaxation.py b/ocpmodels/common/relaxation/ml_relaxation.py new file mode 100644 index 0000000..4131698 --- /dev/null +++ b/ocpmodels/common/relaxation/ml_relaxation.py @@ -0,0 +1,90 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +from collections import deque +from pathlib import Path + +import torch +from torch_geometric.data import Batch + +from ocpmodels.common.registry import registry +from ocpmodels.datasets.lmdb_dataset import data_list_collater + +from .optimizers.lbfgs_torch import LBFGS, TorchCalc + + +def ml_relax( + batch, + model, + steps, + fmax, + relax_opt, + save_full_traj, + device="cuda:0", + transform=None, + early_stop_batch=False, +): + """ + Runs ML-based relaxations. + Args: + batch: object + model: object + steps: int + Max number of steps in the structure relaxation. + fmax: float + Structure relaxation terminates when the max force + of the system is no bigger than fmax. + relax_opt: str + Optimizer and corresponding parameters to be used for structure relaxations. + save_full_traj: bool + Whether to save out the full ASE trajectory. If False, only save out initial and final frames. + """ + batches = deque([batch[0]]) + relaxed_batches = [] + while batches: + batch = batches.popleft() + oom = False + ids = batch.sid + calc = TorchCalc(model, transform) + + # Run ML-based relaxation + traj_dir = relax_opt.get("traj_dir", None) + optimizer = LBFGS( + batch, + calc, + maxstep=relax_opt.get("maxstep", 0.04), + memory=relax_opt["memory"], + damping=relax_opt.get("damping", 1.0), + alpha=relax_opt.get("alpha", 70.0), + device=device, + save_full_traj=save_full_traj, + traj_dir=Path(traj_dir) if traj_dir is not None else None, + traj_names=ids, + early_stop_batch=early_stop_batch, + ) + try: + relaxed_batch = optimizer.run(fmax=fmax, steps=steps) + relaxed_batches.append(relaxed_batch) + except RuntimeError as e: + oom = True + torch.cuda.empty_cache() + + if oom: + # move OOM recovery code outside of except clause to allow tensors to be freed. + data_list = batch.to_data_list() + if len(data_list) == 1: + raise e + logging.info( + f"Failed to relax batch with size: {len(data_list)}, splitting into two..." + ) + mid = len(data_list) // 2 + batches.appendleft(data_list_collater(data_list[:mid])) + batches.appendleft(data_list_collater(data_list[mid:])) + + relaxed_batch = Batch.from_data_list(relaxed_batches) + return relaxed_batch diff --git a/ocpmodels/common/relaxation/optimizers/__init__.py b/ocpmodels/common/relaxation/optimizers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/common/relaxation/optimizers/lbfgs_torch.py b/ocpmodels/common/relaxation/optimizers/lbfgs_torch.py new file mode 100644 index 0000000..d926268 --- /dev/null +++ b/ocpmodels/common/relaxation/optimizers/lbfgs_torch.py @@ -0,0 +1,240 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +from collections import deque +from pathlib import Path +from typing import Deque, Optional + +import ase +import torch +from torch_geometric.data import Batch +from torch_scatter import scatter + +from ocpmodels.common.relaxation.ase_utils import batch_to_atoms +from ocpmodels.common.utils import radius_graph_pbc + + +class LBFGS: + def __init__( + self, + batch: Batch, + model: "TorchCalc", + maxstep=0.01, + memory=100, + damping=0.25, + alpha=100.0, + force_consistent=None, + device="cuda:0", + save_full_traj=True, + traj_dir: Path = None, + traj_names=None, + early_stop_batch: bool = False, + ): + self.batch = batch + self.model = model + self.maxstep = maxstep + self.memory = memory + self.damping = damping + self.alpha = alpha + self.H0 = 1.0 / self.alpha + self.force_consistent = force_consistent + self.device = device + self.save_full = save_full_traj + self.traj_dir = traj_dir + self.traj_names = traj_names + self.early_stop_batch = early_stop_batch + self.otf_graph = model.model._unwrapped_model.otf_graph + assert not self.traj_dir or ( + traj_dir and len(traj_names) + ), "Trajectory names should be specified to save trajectories" + logging.info("Step Fmax(eV/A)") + + if not self.otf_graph and "edge_index" not in batch: + self.model.update_graph(self.batch) + + def get_energy_and_forces(self, apply_constraint=True): + energy, forces = self.model.get_energy_and_forces( + self.batch, apply_constraint + ) + return energy, forces + + def set_positions(self, update, update_mask): + if not self.early_stop_batch: + update = torch.where(update_mask.unsqueeze(1), update, 0.0) + self.batch.pos += update.to(dtype=torch.float32) + + if not self.otf_graph: + self.model.update_graph(self.batch) + + def check_convergence(self, iteration, forces=None, energy=None): + if forces is None or energy is None: + energy, forces = self.get_energy_and_forces() + forces = forces.to(dtype=torch.float64) + + max_forces_ = scatter( + (forces**2).sum(axis=1).sqrt(), self.batch.batch, reduce="max" + ) + logging.info( + f"{iteration} " + + " ".join(f"{x:0.3f}" for x in max_forces_.tolist()) + ) + + # (batch_size) -> (nAtoms) + max_forces = max_forces_[self.batch.batch] + + return max_forces.ge(self.fmax), energy, forces + + def run(self, fmax, steps): + self.fmax = fmax + self.steps = steps + + self.s = deque(maxlen=self.memory) + self.y = deque(maxlen=self.memory) + self.rho = deque(maxlen=self.memory) + self.r0 = self.f0 = None + + self.trajectories = None + if self.traj_dir: + self.traj_dir.mkdir(exist_ok=True, parents=True) + self.trajectories = [ + ase.io.Trajectory(self.traj_dir / f"{name}.traj_tmp", mode="w") + for name in self.traj_names + ] + + iteration = 0 + converged = False + while iteration < steps and not converged: + update_mask, energy, forces = self.check_convergence(iteration) + converged = torch.all(torch.logical_not(update_mask)) + + if self.trajectories is not None: + if ( + self.save_full + or converged + or iteration == steps - 1 + or iteration == 0 + ): + self.write(energy, forces, update_mask) + + if not converged and iteration < steps - 1: + self.step(iteration, forces, update_mask) + + iteration += 1 + + # GPU memory usage as per nvidia-smi seems to gradually build up as + # batches are processed. This releases unoccupied cached memory. + torch.cuda.empty_cache() + + if self.trajectories is not None: + for traj in self.trajectories: + traj.close() + for name in self.traj_names: + traj_fl = Path(self.traj_dir / f"{name}.traj_tmp", mode="w") + traj_fl.rename(traj_fl.with_suffix(".traj")) + + self.batch.y, self.batch.force = self.get_energy_and_forces( + apply_constraint=False + ) + return self.batch + + def step( + self, + iteration: int, + forces: Optional[torch.Tensor], + update_mask: torch.Tensor, + ): + def determine_step(dr): + steplengths = torch.norm(dr, dim=1) + longest_steps = scatter( + steplengths, self.batch.batch, reduce="max" + ) + longest_steps = longest_steps[self.batch.batch] + maxstep = longest_steps.new_tensor(self.maxstep) + scale = (longest_steps + 1e-7).reciprocal() * torch.min( + longest_steps, maxstep + ) + dr *= scale.unsqueeze(1) + return dr * self.damping + + if forces is None: + _, forces = self.get_energy_and_forces() + + r = self.batch.pos.clone().to(dtype=torch.float64) + + # Update s, y, rho + if iteration > 0: + s0 = (r - self.r0).flatten() + self.s.append(s0) + + y0 = -(forces - self.f0).flatten() + self.y.append(y0) + + self.rho.append(1.0 / torch.dot(y0, s0)) + + loopmax = min(self.memory, iteration) + alpha = forces.new_empty(loopmax) + q = -forces.flatten() + + for i in range(loopmax - 1, -1, -1): + alpha[i] = self.rho[i] * torch.dot(self.s[i], q) # b + q -= alpha[i] * self.y[i] + + z = self.H0 * q + for i in range(loopmax): + beta = self.rho[i] * torch.dot(self.y[i], z) + z += self.s[i] * (alpha[i] - beta) + + # descent direction + p = -z.reshape((-1, 3)) + dr = determine_step(p) + if torch.abs(dr).max() < 1e-7: + # Same configuration again (maybe a restart): + return + + self.set_positions(dr, update_mask) + + self.r0 = r + self.f0 = forces + + def write(self, energy, forces, update_mask): + self.batch.y, self.batch.force = energy, forces + atoms_objects = batch_to_atoms(self.batch) + update_mask_ = torch.split(update_mask, self.batch.natoms.tolist()) + for atm, traj, mask in zip( + atoms_objects, self.trajectories, update_mask_ + ): + if mask[0] or not self.save_full: + traj.write(atm) + + +class TorchCalc: + def __init__(self, model, transform=None): + self.model = model + self.transform = transform + + def get_energy_and_forces(self, atoms, apply_constraint=True): + predictions = self.model.predict( + atoms, per_image=False, disable_tqdm=True + ) + energy = predictions["energy"] + forces = predictions["forces"] + if apply_constraint: + fixed_idx = torch.where(atoms.fixed == 1)[0] + forces[fixed_idx] = 0 + return energy, forces + + def update_graph(self, atoms): + edge_index, cell_offsets, num_neighbors = radius_graph_pbc( + atoms, 6, 50 + ) + atoms.edge_index = edge_index + atoms.cell_offsets = cell_offsets + atoms.neighbors = num_neighbors + if self.transform is not None: + atoms = self.transform(atoms) + return atoms diff --git a/ocpmodels/common/transforms.py b/ocpmodels/common/transforms.py new file mode 100644 index 0000000..d364643 --- /dev/null +++ b/ocpmodels/common/transforms.py @@ -0,0 +1,78 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +# Borrowed from https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/transforms/random_rotate.py +# with changes to keep track of the rotation / inverse rotation matrices. + +import math +import numbers +import random + +import torch +import torch_geometric +from torch_geometric.transforms import LinearTransformation + + +class RandomRotate(object): + r"""Rotates node positions around a specific axis by a randomly sampled + factor within a given interval. + + Args: + degrees (tuple or float): Rotation interval from which the rotation + angle is sampled. If `degrees` is a number instead of a + tuple, the interval is given by :math:`[-\mathrm{degrees}, + \mathrm{degrees}]`. + axes (int, optional): The rotation axes. (default: `[0, 1, 2]`) + """ + + def __init__(self, degrees, axes=[0, 1, 2]): + if isinstance(degrees, numbers.Number): + degrees = (-abs(degrees), abs(degrees)) + assert isinstance(degrees, (tuple, list)) and len(degrees) == 2 + self.degrees = degrees + self.axes = axes + + def __call__(self, data): + if data.pos.size(-1) == 2: + degree = math.pi * random.uniform(*self.degrees) / 180.0 + sin, cos = math.sin(degree), math.cos(degree) + matrix = [[cos, sin], [-sin, cos]] + else: + m1, m2, m3 = torch.eye(3), torch.eye(3), torch.eye(3) + if 0 in self.axes: + degree = math.pi * random.uniform(*self.degrees) / 180.0 + sin, cos = math.sin(degree), math.cos(degree) + m1 = torch.tensor([[1, 0, 0], [0, cos, sin], [0, -sin, cos]]) + if 1 in self.axes: + degree = math.pi * random.uniform(*self.degrees) / 180.0 + sin, cos = math.sin(degree), math.cos(degree) + m2 = torch.tensor([[cos, 0, -sin], [0, 1, 0], [sin, 0, cos]]) + if 2 in self.axes: + degree = math.pi * random.uniform(*self.degrees) / 180.0 + sin, cos = math.sin(degree), math.cos(degree) + m3 = torch.tensor([[cos, sin, 0], [-sin, cos, 0], [0, 0, 1]]) + + matrix = torch.mm(torch.mm(m1, m2), m3) + + data_rotated = LinearTransformation(matrix)(data) + if torch_geometric.__version__.startswith("2."): + matrix = matrix.T + + # LinearTransformation only rotates `.pos`; need to rotate `.cell` too. + if hasattr(data_rotated, "cell"): + data_rotated.cell = torch.matmul(data_rotated.cell, matrix) + + return ( + data_rotated, + matrix, + torch.inverse(matrix), + ) + + def __repr__(self): + return "{}({}, axis={})".format( + self.__class__.__name__, self.degrees, self.axis + ) diff --git a/ocpmodels/common/typing.py b/ocpmodels/common/typing.py new file mode 100644 index 0000000..b177edd --- /dev/null +++ b/ocpmodels/common/typing.py @@ -0,0 +1,18 @@ +from typing import Optional, Type, TypeVar + +_T = TypeVar("_T") + + +def assert_is_instance(obj: object, cls: Type[_T]) -> _T: + if obj and not isinstance(obj, cls): + raise TypeError(f"obj is not an instance of cls: obj={obj}, cls={cls}") + return obj + + +def none_throws(x: Optional[_T], msg: Optional[str] = None) -> _T: + if x is None: + if msg: + raise ValueError(msg) + else: + raise ValueError("x cannot be None") + return x diff --git a/ocpmodels/common/utils.py b/ocpmodels/common/utils.py new file mode 100644 index 0000000..986f3ee --- /dev/null +++ b/ocpmodels/common/utils.py @@ -0,0 +1,1072 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import ast +import collections +import copy +import importlib +import itertools +import json +import logging +import os +import sys +import time +from argparse import Namespace +from bisect import bisect +from contextlib import contextmanager +from dataclasses import dataclass +from functools import wraps +from itertools import product +from pathlib import Path +from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional + +import numpy as np +import torch +import torch.nn as nn +import torch_geometric +import yaml +from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas +from matplotlib.figure import Figure +from torch_geometric.data import Data +from torch_geometric.utils import remove_self_loops +from torch_scatter import scatter, segment_coo, segment_csr + +if TYPE_CHECKING: + from torch.nn.modules.module import _IncompatibleKeys + + +def pyg2_data_transform(data: Data): + """ + if we're on the new pyg (2.0 or later) and if the Data stored is in older format + we need to convert the data to the new format + """ + if torch_geometric.__version__ >= "2.0" and "_store" not in data.__dict__: + return Data( + **{k: v for k, v in data.__dict__.items() if v is not None} + ) + + return data + + +def save_checkpoint( + state, checkpoint_dir="checkpoints/", checkpoint_file="checkpoint.pt" +): + filename = os.path.join(checkpoint_dir, checkpoint_file) + torch.save(state, filename) + return filename + + +class Complete(object): + def __call__(self, data): + device = data.edge_index.device + + row = torch.arange(data.num_nodes, dtype=torch.long, device=device) + col = torch.arange(data.num_nodes, dtype=torch.long, device=device) + + row = row.view(-1, 1).repeat(1, data.num_nodes).view(-1) + col = col.repeat(data.num_nodes) + edge_index = torch.stack([row, col], dim=0) + + edge_attr = None + if data.edge_attr is not None: + idx = data.edge_index[0] * data.num_nodes + data.edge_index[1] + size = list(data.edge_attr.size()) + size[0] = data.num_nodes * data.num_nodes + edge_attr = data.edge_attr.new_zeros(size) + edge_attr[idx] = data.edge_attr + + edge_index, edge_attr = remove_self_loops(edge_index, edge_attr) + data.edge_attr = edge_attr + data.edge_index = edge_index + + return data + + +def warmup_lr_lambda(current_step, optim_config): + """Returns a learning rate multiplier. + Till `warmup_steps`, learning rate linearly increases to `initial_lr`, + and then gets multiplied by `lr_gamma` every time a milestone is crossed. + """ + + # keep this block for older configs that have warmup_epochs instead of warmup_steps + # and lr_milestones are defined in epochs + if ( + any(x < 100 for x in optim_config["lr_milestones"]) + or "warmup_epochs" in optim_config + ): + raise Exception( + "ConfigError: please define lr_milestones in steps not epochs and define warmup_steps instead of warmup_epochs" + ) + + if current_step <= optim_config["warmup_steps"]: + alpha = current_step / float(optim_config["warmup_steps"]) + return optim_config["warmup_factor"] * (1.0 - alpha) + alpha + else: + idx = bisect(optim_config["lr_milestones"], current_step) + return pow(optim_config["lr_gamma"], idx) + + +def print_cuda_usage(): + print("Memory Allocated:", torch.cuda.memory_allocated() / (1024 * 1024)) + print( + "Max Memory Allocated:", + torch.cuda.max_memory_allocated() / (1024 * 1024), + ) + print("Memory Cached:", torch.cuda.memory_cached() / (1024 * 1024)) + print("Max Memory Cached:", torch.cuda.max_memory_cached() / (1024 * 1024)) + + +def conditional_grad(dec): + "Decorator to enable/disable grad depending on whether force/energy predictions are being made" + # Adapted from https://stackoverflow.com/questions/60907323/accessing-class-property-as-decorator-argument + def decorator(func): + @wraps(func) + def cls_method(self, *args, **kwargs): + f = func + if self.regress_forces and not getattr(self, "direct_forces", 0): + f = dec(func) + return f(self, *args, **kwargs) + + return cls_method + + return decorator + + +def plot_histogram(data, xlabel="", ylabel="", title=""): + assert isinstance(data, list) + + # Preset + fig = Figure(figsize=(5, 4), dpi=150) + canvas = FigureCanvas(fig) + ax = fig.gca() + + # Plot + ax.hist(data, bins=20, rwidth=0.9, zorder=3) + + # Axes + ax.grid(color="0.95", zorder=0) + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + fig.tight_layout(pad=2) + + # Return numpy array + canvas.draw() + image_from_plot = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) + image_from_plot = image_from_plot.reshape( + fig.canvas.get_width_height()[::-1] + (3,) + ) + + return image_from_plot + + +# Override the collation method in `pytorch_geometric.data.InMemoryDataset` +def collate(data_list): + keys = data_list[0].keys + data = data_list[0].__class__() + + for key in keys: + data[key] = [] + slices = {key: [0] for key in keys} + + for item, key in product(data_list, keys): + data[key].append(item[key]) + if torch.is_tensor(item[key]): + s = slices[key][-1] + item[key].size( + item.__cat_dim__(key, item[key]) + ) + elif isinstance(item[key], int) or isinstance(item[key], float): + s = slices[key][-1] + 1 + else: + raise ValueError("Unsupported attribute type") + slices[key].append(s) + + if hasattr(data_list[0], "__num_nodes__"): + data.__num_nodes__ = [] + for item in data_list: + data.__num_nodes__.append(item.num_nodes) + + for key in keys: + if torch.is_tensor(data_list[0][key]): + data[key] = torch.cat( + data[key], dim=data.__cat_dim__(key, data_list[0][key]) + ) + else: + data[key] = torch.tensor(data[key]) + slices[key] = torch.tensor(slices[key], dtype=torch.long) + + return data, slices + + +def add_edge_distance_to_graph( + batch, + device="cpu", + dmin=0.0, + dmax=6.0, + num_gaussians=50, +): + # Make sure x has positions. + if not all(batch.pos[0][:] == batch.x[0][-3:]): + batch.x = torch.cat([batch.x, batch.pos.float()], dim=1) + # First set computations to be tracked for positions. + batch.x = batch.x.requires_grad_(True) + # Then compute Euclidean distance between edge endpoints. + pdist = torch.nn.PairwiseDistance(p=2.0) + distances = pdist( + batch.x[batch.edge_index[0]][:, -3:], + batch.x[batch.edge_index[1]][:, -3:], + ) + # Expand it using a gaussian basis filter. + gdf_filter = torch.linspace(dmin, dmax, num_gaussians) + var = gdf_filter[1] - gdf_filter[0] + gdf_filter, var = gdf_filter.to(device), var.to(device) + gdf_distances = torch.exp( + -((distances.view(-1, 1) - gdf_filter) ** 2) / var**2 + ) + # Reassign edge attributes. + batch.edge_weight = distances + batch.edge_attr = gdf_distances.float() + return batch + + +def _import_local_file(path: Path, *, project_root: Path): + """ + Imports a Python file as a module + + :param path: The path to the file to import + :type path: Path + :param project_root: The root directory of the project (i.e., the "ocp" folder) + :type project_root: Path + """ + + path = path.resolve() + project_root = project_root.resolve() + + module_name = ".".join( + path.absolute() + .relative_to(project_root.absolute()) + .with_suffix("") + .parts + ) + logging.debug(f"Resolved module name of {path} to {module_name}") + importlib.import_module(module_name) + + +def setup_experimental_imports(project_root: Path): + experimental_folder = (project_root / "experimental").resolve() + if not experimental_folder.exists() or not experimental_folder.is_dir(): + return + + experimental_files = [ + f.resolve().absolute() for f in experimental_folder.rglob("*.py") + ] + # Ignore certain directories within experimental + ignore_file = experimental_folder / ".ignore" + if ignore_file.exists(): + with open(ignore_file, "r") as f: + for line in f.read().splitlines(): + for ignored_file in (experimental_folder / line).rglob("*.py"): + experimental_files.remove( + ignored_file.resolve().absolute() + ) + + for f in experimental_files: + _import_local_file(f, project_root=project_root) + + +def _get_project_root(): + """ + Gets the root folder of the project (the "ocp" folder) + :return: The absolute path to the project root. + """ + from ocpmodels.common.registry import registry + + # Automatically load all of the modules, so that + # they register with registry + root_folder = registry.get("ocpmodels_root", no_warning=True) + + if root_folder is not None: + assert isinstance(root_folder, str), "ocpmodels_root must be a string" + root_folder = Path(root_folder).resolve().absolute() + assert root_folder.exists(), f"{root_folder} does not exist" + assert root_folder.is_dir(), f"{root_folder} is not a directory" + else: + root_folder = Path(__file__).resolve().absolute().parent.parent + + # root_folder is the "ocpmodes" folder, so we need to go up one more level + return root_folder.parent + + +# Copied from https://github.com/facebookresearch/mmf/blob/master/mmf/utils/env.py#L89. +def setup_imports(config: Optional[dict] = None): + from ocpmodels.common.registry import registry + + skip_experimental_imports = (config or {}).get( + "skip_experimental_imports", None + ) + + # First, check if imports are already setup + has_already_setup = registry.get("imports_setup", no_warning=True) + if has_already_setup: + return + + try: + project_root = _get_project_root() + logging.info(f"Project root: {project_root}") + importlib.import_module("ocpmodels.common.logger") + + import_keys = ["trainers", "datasets", "models", "tasks"] + for key in import_keys: + for f in (project_root / "ocpmodels" / key).rglob("*.py"): + _import_local_file(f, project_root=project_root) + + if not skip_experimental_imports: + setup_experimental_imports(project_root) + finally: + registry.register("imports_setup", True) + + +def dict_set_recursively(dictionary, key_sequence, val): + top_key = key_sequence.pop(0) + if len(key_sequence) == 0: + dictionary[top_key] = val + else: + if top_key not in dictionary: + dictionary[top_key] = {} + dict_set_recursively(dictionary[top_key], key_sequence, val) + + +def parse_value(value): + """ + Parse string as Python literal if possible and fallback to string. + """ + try: + return ast.literal_eval(value) + except (ValueError, SyntaxError): + # Use as string if nothing else worked + return value + + +def create_dict_from_args(args: list, sep: str = "."): + """ + Create a (nested) dictionary from console arguments. + Keys in different dictionary levels are separated by sep. + """ + return_dict = {} + for arg in args: + arg = arg.strip("--") + keys_concat, val = arg.split("=") + val = parse_value(val) + key_sequence = keys_concat.split(sep) + dict_set_recursively(return_dict, key_sequence, val) + return return_dict + + +def load_config(path: str, previous_includes: list = []): + path = Path(path) + if path in previous_includes: + raise ValueError( + f"Cyclic config include detected. {path} included in sequence {previous_includes}." + ) + previous_includes = previous_includes + [path] + + direct_config = yaml.safe_load(open(path, "r")) + + # Load config from included files. + if "includes" in direct_config: + includes = direct_config.pop("includes") + else: + includes = [] + if not isinstance(includes, list): + raise AttributeError( + "Includes must be a list, '{}' provided".format(type(includes)) + ) + + config = {} + duplicates_warning = [] + duplicates_error = [] + + for include in includes: + include_config, inc_dup_warning, inc_dup_error = load_config( + include, previous_includes + ) + duplicates_warning += inc_dup_warning + duplicates_error += inc_dup_error + + # Duplicates between includes causes an error + config, merge_dup_error = merge_dicts(config, include_config) + duplicates_error += merge_dup_error + + # Duplicates between included and main file causes warnings + config, merge_dup_warning = merge_dicts(config, direct_config) + duplicates_warning += merge_dup_warning + + return config, duplicates_warning, duplicates_error + + +def build_config(args, args_override): + config, duplicates_warning, duplicates_error = load_config(args.config_yml) + if len(duplicates_warning) > 0: + logging.warning( + f"Overwritten config parameters from included configs " + f"(non-included parameters take precedence): {duplicates_warning}" + ) + if len(duplicates_error) > 0: + raise ValueError( + f"Conflicting (duplicate) parameters in simultaneously " + f"included configs: {duplicates_error}" + ) + + # Check for overridden parameters. + if args_override != []: + overrides = create_dict_from_args(args_override) + config, _ = merge_dicts(config, overrides) + + # Some other flags. + config["mode"] = args.mode + config["identifier"] = args.identifier + config["timestamp_id"] = args.timestamp_id + config["seed"] = args.seed + config["is_debug"] = args.debug + config["run_dir"] = args.run_dir + config["print_every"] = args.print_every + config["amp"] = args.amp + config["checkpoint"] = args.checkpoint + config["cpu"] = args.cpu + # Submit + config["submit"] = args.submit + config["summit"] = args.summit + # Distributed + config["local_rank"] = args.local_rank + config["distributed_port"] = args.distributed_port + config["world_size"] = args.num_nodes * args.num_gpus + config["distributed_backend"] = args.distributed_backend + config["noddp"] = args.no_ddp + config["gp_gpus"] = args.gp_gpus + + return config + + +def create_grid(base_config, sweep_file): + def _flatten_sweeps(sweeps, root_key="", sep="."): + flat_sweeps = [] + for key, value in sweeps.items(): + new_key = root_key + sep + key if root_key else key + if isinstance(value, collections.MutableMapping): + flat_sweeps.extend(_flatten_sweeps(value, new_key).items()) + else: + flat_sweeps.append((new_key, value)) + return collections.OrderedDict(flat_sweeps) + + def _update_config(config, keys, override_vals, sep="."): + for key, value in zip(keys, override_vals): + key_path = key.split(sep) + child_config = config + for name in key_path[:-1]: + child_config = child_config[name] + child_config[key_path[-1]] = value + return config + + sweeps = yaml.safe_load(open(sweep_file, "r")) + flat_sweeps = _flatten_sweeps(sweeps) + keys = list(flat_sweeps.keys()) + values = list(itertools.product(*flat_sweeps.values())) + + configs = [] + for i, override_vals in enumerate(values): + config = copy.deepcopy(base_config) + config = _update_config(config, keys, override_vals) + config["identifier"] = config["identifier"] + f"_run{i}" + configs.append(config) + return configs + + +def save_experiment_log(args, jobs, configs): + log_file = args.logdir / "exp" / time.strftime("%Y-%m-%d-%I-%M-%S%p.log") + log_file.parent.mkdir(exist_ok=True, parents=True) + with open(log_file, "w") as f: + for job, config in zip(jobs, configs): + print( + json.dumps( + { + "config": config, + "slurm_id": job.job_id, + "timestamp": time.strftime("%I:%M:%S%p %Z %b %d, %Y"), + } + ), + file=f, + ) + return log_file + + +def get_pbc_distances( + pos, + edge_index, + cell, + cell_offsets, + neighbors, + return_offsets=False, + return_distance_vec=False, +): + row, col = edge_index + + distance_vectors = pos[row] - pos[col] + + # correct for pbc + neighbors = neighbors.to(cell.device) + cell = torch.repeat_interleave(cell, neighbors, dim=0) + offsets = cell_offsets.float().view(-1, 1, 3).bmm(cell.float()).view(-1, 3) + distance_vectors += offsets + + # compute distances + distances = distance_vectors.norm(dim=-1) + + # redundancy: remove zero distances + nonzero_idx = torch.arange(len(distances), device=distances.device)[ + distances != 0 + ] + edge_index = edge_index[:, nonzero_idx] + distances = distances[nonzero_idx] + + out = { + "edge_index": edge_index, + "distances": distances, + } + + if return_distance_vec: + out["distance_vec"] = distance_vectors[nonzero_idx] + + if return_offsets: + out["offsets"] = offsets[nonzero_idx] + + return out + + +def radius_graph_pbc( + data, radius, max_num_neighbors_threshold, pbc=[True, True, True] +): + device = data.pos.device + batch_size = len(data.natoms) + + if hasattr(data, "pbc"): + data.pbc = torch.atleast_2d(data.pbc) + for i in range(3): + if not torch.any(data.pbc[:, i]).item(): + pbc[i] = False + elif torch.all(data.pbc[:, i]).item(): + pbc[i] = True + else: + raise RuntimeError( + "Different structures in the batch have different PBC configurations. This is not currently supported." + ) + + # position of the atoms + atom_pos = data.pos + + # Before computing the pairwise distances between atoms, first create a list of atom indices to compare for the entire batch + num_atoms_per_image = data.natoms + num_atoms_per_image_sqr = (num_atoms_per_image**2).long() + + # index offset between images + index_offset = ( + torch.cumsum(num_atoms_per_image, dim=0) - num_atoms_per_image + ) + + index_offset_expand = torch.repeat_interleave( + index_offset, num_atoms_per_image_sqr + ) + num_atoms_per_image_expand = torch.repeat_interleave( + num_atoms_per_image, num_atoms_per_image_sqr + ) + + # Compute a tensor containing sequences of numbers that range from 0 to num_atoms_per_image_sqr for each image + # that is used to compute indices for the pairs of atoms. This is a very convoluted way to implement + # the following (but 10x faster since it removes the for loop) + # for batch_idx in range(batch_size): + # batch_count = torch.cat([batch_count, torch.arange(num_atoms_per_image_sqr[batch_idx], device=device)], dim=0) + num_atom_pairs = torch.sum(num_atoms_per_image_sqr) + index_sqr_offset = ( + torch.cumsum(num_atoms_per_image_sqr, dim=0) - num_atoms_per_image_sqr + ) + index_sqr_offset = torch.repeat_interleave( + index_sqr_offset, num_atoms_per_image_sqr + ) + atom_count_sqr = ( + torch.arange(num_atom_pairs, device=device) - index_sqr_offset + ) + + # Compute the indices for the pairs of atoms (using division and mod) + # If the systems get too large this apporach could run into numerical precision issues + index1 = ( + torch.div( + atom_count_sqr, num_atoms_per_image_expand, rounding_mode="floor" + ) + ) + index_offset_expand + index2 = ( + atom_count_sqr % num_atoms_per_image_expand + ) + index_offset_expand + # Get the positions for each atom + pos1 = torch.index_select(atom_pos, 0, index1) + pos2 = torch.index_select(atom_pos, 0, index2) + + # Calculate required number of unit cells in each direction. + # Smallest distance between planes separated by a1 is + # 1 / ||(a2 x a3) / V||_2, since a2 x a3 is the area of the plane. + # Note that the unit cell volume V = a1 * (a2 x a3) and that + # (a2 x a3) / V is also the reciprocal primitive vector + # (crystallographer's definition). + + cross_a2a3 = torch.cross(data.cell[:, 1], data.cell[:, 2], dim=-1) + cell_vol = torch.sum(data.cell[:, 0] * cross_a2a3, dim=-1, keepdim=True) + + if pbc[0]: + inv_min_dist_a1 = torch.norm(cross_a2a3 / cell_vol, p=2, dim=-1) + rep_a1 = torch.ceil(radius * inv_min_dist_a1) + else: + rep_a1 = data.cell.new_zeros(1) + + if pbc[1]: + cross_a3a1 = torch.cross(data.cell[:, 2], data.cell[:, 0], dim=-1) + inv_min_dist_a2 = torch.norm(cross_a3a1 / cell_vol, p=2, dim=-1) + rep_a2 = torch.ceil(radius * inv_min_dist_a2) + else: + rep_a2 = data.cell.new_zeros(1) + + if pbc[2]: + cross_a1a2 = torch.cross(data.cell[:, 0], data.cell[:, 1], dim=-1) + inv_min_dist_a3 = torch.norm(cross_a1a2 / cell_vol, p=2, dim=-1) + rep_a3 = torch.ceil(radius * inv_min_dist_a3) + else: + rep_a3 = data.cell.new_zeros(1) + + # Take the max over all images for uniformity. This is essentially padding. + # Note that this can significantly increase the number of computed distances + # if the required repetitions are very different between images + # (which they usually are). Changing this to sparse (scatter) operations + # might be worth the effort if this function becomes a bottleneck. + max_rep = [rep_a1.max(), rep_a2.max(), rep_a3.max()] + + # Tensor of unit cells + cells_per_dim = [ + torch.arange(-rep, rep + 1, device=device, dtype=torch.float) + for rep in max_rep + ] + unit_cell = torch.cartesian_prod(*cells_per_dim) + num_cells = len(unit_cell) + unit_cell_per_atom = unit_cell.view(1, num_cells, 3).repeat( + len(index2), 1, 1 + ) + unit_cell = torch.transpose(unit_cell, 0, 1) + unit_cell_batch = unit_cell.view(1, 3, num_cells).expand( + batch_size, -1, -1 + ) + + # Compute the x, y, z positional offsets for each cell in each image + data_cell = torch.transpose(data.cell, 1, 2) + pbc_offsets = torch.bmm(data_cell, unit_cell_batch) + pbc_offsets_per_atom = torch.repeat_interleave( + pbc_offsets, num_atoms_per_image_sqr, dim=0 + ) + + # Expand the positions and indices for the 9 cells + pos1 = pos1.view(-1, 3, 1).expand(-1, -1, num_cells) + pos2 = pos2.view(-1, 3, 1).expand(-1, -1, num_cells) + index1 = index1.view(-1, 1).repeat(1, num_cells).view(-1) + index2 = index2.view(-1, 1).repeat(1, num_cells).view(-1) + # Add the PBC offsets for the second atom + pos2 = pos2 + pbc_offsets_per_atom + + # Compute the squared distance between atoms + atom_distance_sqr = torch.sum((pos1 - pos2) ** 2, dim=1) + atom_distance_sqr = atom_distance_sqr.view(-1) + + # Remove pairs that are too far apart + mask_within_radius = torch.le(atom_distance_sqr, radius * radius) + # Remove pairs with the same atoms (distance = 0.0) + mask_not_same = torch.gt(atom_distance_sqr, 0.0001) + mask = torch.logical_and(mask_within_radius, mask_not_same) + index1 = torch.masked_select(index1, mask) + index2 = torch.masked_select(index2, mask) + unit_cell = torch.masked_select( + unit_cell_per_atom.view(-1, 3), mask.view(-1, 1).expand(-1, 3) + ) + unit_cell = unit_cell.view(-1, 3) + atom_distance_sqr = torch.masked_select(atom_distance_sqr, mask) + + mask_num_neighbors, num_neighbors_image = get_max_neighbors_mask( + natoms=data.natoms, + index=index1, + atom_distance=atom_distance_sqr, + max_num_neighbors_threshold=max_num_neighbors_threshold, + ) + + if not torch.all(mask_num_neighbors): + # Mask out the atoms to ensure each atom has at most max_num_neighbors_threshold neighbors + index1 = torch.masked_select(index1, mask_num_neighbors) + index2 = torch.masked_select(index2, mask_num_neighbors) + unit_cell = torch.masked_select( + unit_cell.view(-1, 3), mask_num_neighbors.view(-1, 1).expand(-1, 3) + ) + unit_cell = unit_cell.view(-1, 3) + + edge_index = torch.stack((index2, index1)) + + return edge_index, unit_cell, num_neighbors_image + + +def get_max_neighbors_mask( + natoms, index, atom_distance, max_num_neighbors_threshold +): + """ + Give a mask that filters out edges so that each atom has at most + `max_num_neighbors_threshold` neighbors. + Assumes that `index` is sorted. + """ + device = natoms.device + num_atoms = natoms.sum() + + # Get number of neighbors + # segment_coo assumes sorted index + ones = index.new_ones(1).expand_as(index) + num_neighbors = segment_coo(ones, index, dim_size=num_atoms) + max_num_neighbors = num_neighbors.max() + num_neighbors_thresholded = num_neighbors.clamp( + max=max_num_neighbors_threshold + ) + + # Get number of (thresholded) neighbors per image + image_indptr = torch.zeros( + natoms.shape[0] + 1, device=device, dtype=torch.long + ) + image_indptr[1:] = torch.cumsum(natoms, dim=0) + num_neighbors_image = segment_csr(num_neighbors_thresholded, image_indptr) + + # If max_num_neighbors is below the threshold, return early + if ( + max_num_neighbors <= max_num_neighbors_threshold + or max_num_neighbors_threshold <= 0 + ): + mask_num_neighbors = torch.tensor( + [True], dtype=bool, device=device + ).expand_as(index) + return mask_num_neighbors, num_neighbors_image + + # Create a tensor of size [num_atoms, max_num_neighbors] to sort the distances of the neighbors. + # Fill with infinity so we can easily remove unused distances later. + distance_sort = torch.full( + [num_atoms * max_num_neighbors], np.inf, device=device + ) + + # Create an index map to map distances from atom_distance to distance_sort + # index_sort_map assumes index to be sorted + index_neighbor_offset = torch.cumsum(num_neighbors, dim=0) - num_neighbors + index_neighbor_offset_expand = torch.repeat_interleave( + index_neighbor_offset, num_neighbors + ) + index_sort_map = ( + index * max_num_neighbors + + torch.arange(len(index), device=device) + - index_neighbor_offset_expand + ) + distance_sort.index_copy_(0, index_sort_map, atom_distance) + distance_sort = distance_sort.view(num_atoms, max_num_neighbors) + + # Sort neighboring atoms based on distance + distance_sort, index_sort = torch.sort(distance_sort, dim=1) + # Select the max_num_neighbors_threshold neighbors that are closest + distance_sort = distance_sort[:, :max_num_neighbors_threshold] + index_sort = index_sort[:, :max_num_neighbors_threshold] + + # Offset index_sort so that it indexes into index + index_sort = index_sort + index_neighbor_offset.view(-1, 1).expand( + -1, max_num_neighbors_threshold + ) + # Remove "unused pairs" with infinite distances + mask_finite = torch.isfinite(distance_sort) + index_sort = torch.masked_select(index_sort, mask_finite) + + # At this point index_sort contains the index into index of the + # closest max_num_neighbors_threshold neighbors per atom + # Create a mask to remove all pairs not in index_sort + mask_num_neighbors = torch.zeros(len(index), device=device, dtype=bool) + mask_num_neighbors.index_fill_(0, index_sort, True) + + return mask_num_neighbors, num_neighbors_image + + +def get_pruned_edge_idx(edge_index, num_atoms=None, max_neigh=1e9): + assert num_atoms is not None + + # removes neighbors > max_neigh + # assumes neighbors are sorted in increasing distance + _nonmax_idx = [] + for i in range(num_atoms): + idx_i = torch.arange(len(edge_index[1]))[(edge_index[1] == i)][ + :max_neigh + ] + _nonmax_idx.append(idx_i) + _nonmax_idx = torch.cat(_nonmax_idx) + + return _nonmax_idx + + +def merge_dicts(dict1: dict, dict2: dict): + """Recursively merge two dictionaries. + Values in dict2 override values in dict1. If dict1 and dict2 contain a dictionary as a + value, this will call itself recursively to merge these dictionaries. + This does not modify the input dictionaries (creates an internal copy). + Additionally returns a list of detected duplicates. + Adapted from https://github.com/TUM-DAML/seml/blob/master/seml/utils.py + + Parameters + ---------- + dict1: dict + First dict. + dict2: dict + Second dict. Values in dict2 will override values from dict1 in case they share the same key. + + Returns + ------- + return_dict: dict + Merged dictionaries. + """ + if not isinstance(dict1, dict): + raise ValueError(f"Expecting dict1 to be dict, found {type(dict1)}.") + if not isinstance(dict2, dict): + raise ValueError(f"Expecting dict2 to be dict, found {type(dict2)}.") + + return_dict = copy.deepcopy(dict1) + duplicates = [] + + for k, v in dict2.items(): + if k not in dict1: + return_dict[k] = v + else: + if isinstance(v, dict) and isinstance(dict1[k], dict): + return_dict[k], duplicates_k = merge_dicts(dict1[k], dict2[k]) + duplicates += [f"{k}.{dup}" for dup in duplicates_k] + else: + return_dict[k] = dict2[k] + duplicates.append(k) + + return return_dict, duplicates + + +class SeverityLevelBetween(logging.Filter): + def __init__(self, min_level, max_level): + super().__init__() + self.min_level = min_level + self.max_level = max_level + + def filter(self, record): + return self.min_level <= record.levelno < self.max_level + + +def setup_logging(): + root = logging.getLogger() + + # Perform setup only if logging has not been configured + if not root.hasHandlers(): + root.setLevel(logging.INFO) + + log_formatter = logging.Formatter( + "%(asctime)s (%(levelname)s): %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + # Send INFO to stdout + handler_out = logging.StreamHandler(sys.stdout) + handler_out.addFilter( + SeverityLevelBetween(logging.INFO, logging.WARNING) + ) + handler_out.setFormatter(log_formatter) + root.addHandler(handler_out) + + # Send WARNING (and higher) to stderr + handler_err = logging.StreamHandler(sys.stderr) + handler_err.setLevel(logging.WARNING) + handler_err.setFormatter(log_formatter) + root.addHandler(handler_err) + + +def compute_neighbors(data, edge_index): + # Get number of neighbors + # segment_coo assumes sorted index + ones = edge_index[1].new_ones(1).expand_as(edge_index[1]) + num_neighbors = segment_coo( + ones, edge_index[1], dim_size=data.natoms.sum() + ) + + # Get number of neighbors per image + image_indptr = torch.zeros( + data.natoms.shape[0] + 1, device=data.pos.device, dtype=torch.long + ) + image_indptr[1:] = torch.cumsum(data.natoms, dim=0) + neighbors = segment_csr(num_neighbors, image_indptr) + return neighbors + + +def check_traj_files(batch, traj_dir): + if traj_dir is None: + return False + traj_dir = Path(traj_dir) + traj_files = [traj_dir / f"{id}.traj" for id in batch[0].sid.tolist()] + return all(fl.exists() for fl in traj_files) + + +@contextmanager +def new_trainer_context(*, config: Dict[str, Any], args: Namespace): + from ocpmodels.common import distutils, gp_utils + from ocpmodels.common.registry import registry + + if TYPE_CHECKING: + from ocpmodels.tasks.task import BaseTask + from ocpmodels.trainers import BaseTrainer + + @dataclass + class _TrainingContext: + config: Dict[str, Any] + task: "BaseTask" + trainer: "BaseTrainer" + + setup_logging() + original_config = config + config = copy.deepcopy(original_config) + + if args.distributed: + distutils.setup(config) + if config["gp_gpus"] is not None: + gp_utils.setup_gp(config) + try: + setup_imports(config) + trainer_cls = registry.get_trainer_class( + config.get("trainer", "energy") + ) + assert trainer_cls is not None, "Trainer not found" + trainer = trainer_cls( + task=config["task"], + model=config["model"], + dataset=config["dataset"], + optimizer=config["optim"], + identifier=config["identifier"], + timestamp_id=config.get("timestamp_id", None), + run_dir=config.get("run_dir", "./"), + is_debug=config.get("is_debug", False), + print_every=config.get("print_every", 10), + seed=config.get("seed", 0), + logger=config.get("logger", "tensorboard"), + local_rank=config["local_rank"], + amp=config.get("amp", False), + cpu=config.get("cpu", False), + slurm=config.get("slurm", {}), + noddp=config.get("noddp", False), + ) + + task_cls = registry.get_task_class(config["mode"]) + assert task_cls is not None, "Task not found" + task = task_cls(config) + start_time = time.time() + ctx = _TrainingContext( + config=original_config, task=task, trainer=trainer + ) + yield ctx + distutils.synchronize() + if distutils.is_master(): + logging.info(f"Total time taken: {time.time() - start_time}") + finally: + if args.distributed: + distutils.cleanup() + + +def _resolve_scale_factor_submodule(model: nn.Module, name: str): + from ocpmodels.modules.scaling.scale_factor import ScaleFactor + + try: + scale = model.get_submodule(name) + if not isinstance(scale, ScaleFactor): + return None + return scale + except AttributeError: + return None + + +def _report_incompat_keys( + model: nn.Module, + keys: "_IncompatibleKeys", + strict: bool = False, +): + # filter out the missing scale factor keys for the new scaling factor module + missing_keys: List[str] = [] + for full_key_name in keys.missing_keys: + parent_module_name, _ = full_key_name.rsplit(".", 1) + scale_factor = _resolve_scale_factor_submodule( + model, parent_module_name + ) + if scale_factor is not None: + continue + missing_keys.append(full_key_name) + + # filter out unexpected scale factor keys that remain from the old scaling modules + unexpected_keys: List[str] = [] + for full_key_name in keys.unexpected_keys: + parent_module_name, _ = full_key_name.rsplit(".", 1) + scale_factor = _resolve_scale_factor_submodule( + model, parent_module_name + ) + if scale_factor is not None: + continue + unexpected_keys.append(full_key_name) + + error_msgs = [] + if len(unexpected_keys) > 0: + error_msgs.insert( + 0, + "Unexpected key(s) in state_dict: {}. ".format( + ", ".join('"{}"'.format(k) for k in unexpected_keys) + ), + ) + if len(missing_keys) > 0: + error_msgs.insert( + 0, + "Missing key(s) in state_dict: {}. ".format( + ", ".join('"{}"'.format(k) for k in missing_keys) + ), + ) + + if len(error_msgs) > 0: + error_msg = "Error(s) in loading state_dict for {}:\n\t{}".format( + model.__class__.__name__, "\n\t".join(error_msgs) + ) + if strict: + raise RuntimeError(error_msg) + else: + logging.warning(error_msg) + + return missing_keys, unexpected_keys + + +def load_state_dict( + module: nn.Module, + state_dict: Mapping[str, torch.Tensor], + strict: bool = True, +): + incompat_keys = module.load_state_dict(state_dict, strict=False) # type: ignore + return _report_incompat_keys(module, incompat_keys, strict=strict) + + +def scatter_det(*args, **kwargs): + from ocpmodels.common.registry import registry + + if registry.get("set_deterministic_scatter", no_warning=True): + torch.use_deterministic_algorithms(mode=True) + + out = scatter(*args, **kwargs) + + if registry.get("set_deterministic_scatter", no_warning=True): + torch.use_deterministic_algorithms(mode=False) + + return out diff --git a/ocpmodels/datasets/__init__.py b/ocpmodels/datasets/__init__.py new file mode 100644 index 0000000..9ed38d8 --- /dev/null +++ b/ocpmodels/datasets/__init__.py @@ -0,0 +1,12 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .lmdb_dataset import ( + LmdbDataset, + SinglePointLmdbDataset, + TrajectoryLmdbDataset, + data_list_collater, +) +from .oc22_lmdb_dataset import OC22LmdbDataset diff --git a/ocpmodels/datasets/ase_datasets.py b/ocpmodels/datasets/ase_datasets.py new file mode 100644 index 0000000..9e4d76b --- /dev/null +++ b/ocpmodels/datasets/ase_datasets.py @@ -0,0 +1,546 @@ +import bisect +import copy +import functools +import glob +import logging +import os +import warnings +from abc import ABC, abstractmethod +from pathlib import Path +from typing import List + +import ase +import numpy as np +from torch import tensor +from torch.utils.data import Dataset +from tqdm import tqdm + +from ocpmodels.common.registry import registry +from ocpmodels.datasets.lmdb_database import LMDBDatabase +from ocpmodels.datasets.target_metadata_guesser import guess_property_metadata +from ocpmodels.preprocessing import AtomsToGraphs + + +def apply_one_tags( + atoms: ase.Atoms, skip_if_nonzero: bool = True, skip_always: bool = False +): + """ + This function will apply tags of 1 to an ASE atoms object. + It is used as an atoms_transform in the datasets contained in this file. + + Certain models will treat atoms differently depending on their tags. + For example, GemNet-OC by default will only compute triplet and quadruplet interactions + for atoms with non-zero tags. This model throws an error if there are no tagged atoms. + For this reason, the default behavior is to tag atoms in structures with no tags. + + args: + skip_if_nonzero (bool): If at least one atom has a nonzero tag, do not tag any atoms + + skip_always (bool): Do not apply any tags. This arg exists so that this function can be disabled + without needing to pass a callable (which is currently difficult to do with main.py) + """ + if skip_always: + return atoms + + if np.all(atoms.get_tags() == 0) or not skip_if_nonzero: + atoms.set_tags(np.ones(len(atoms))) + + return atoms + + +class AseAtomsDataset(Dataset, ABC): + """ + This is an abstract Dataset that includes helpful utilities for turning + ASE atoms objects into OCP-usable data objects. This should not be instantiated directly + as get_atoms_object and load_dataset_get_ids are not implemented in this base class. + + Derived classes must add at least two things: + self.get_atoms_object(id): a function that takes an identifier and returns a corresponding atoms object + + self.load_dataset_get_ids(config: dict): This function is responsible for any initialization/loads + of the dataset and importantly must return a list of all possible identifiers that can be passed into + self.get_atoms_object(id) + + Identifiers need not be any particular type. + """ + + def __init__( + self, config, transform=None, atoms_transform=apply_one_tags + ) -> None: + self.config = config + + a2g_args = config.get("a2g_args", {}) + if a2g_args is None: + a2g_args = {} + + # Make sure we always include PBC info in the resulting atoms objects + a2g_args["r_pbc"] = True + self.a2g = AtomsToGraphs(**a2g_args) + + self.transform = transform + self.atoms_transform = atoms_transform + + if self.config.get("keep_in_memory", False): + self.__getitem__ = functools.cache(self.__getitem__) + + self.ids = self.load_dataset_get_ids(config) + + def __len__(self) -> int: + return len(self.ids) + + def __getitem__(self, idx): + # Handle slicing + if isinstance(idx, slice): + return [self[i] for i in range(*idx.indices(len(self.ids)))] + + # Get atoms object via derived class method + atoms = self.get_atoms_object(self.ids[idx]) + + # Transform atoms object + if self.atoms_transform is not None: + atoms = self.atoms_transform( + atoms, **self.config.get("atoms_transform_args", {}) + ) + + sid = atoms.info.get("sid", self.ids[idx]) + try: + sid = tensor([sid]) + warnings.warn( + "Supplied sid is not numeric (or missing). Using dataset indices instead." + ) + except: + sid = tensor([idx]) + + fid = atoms.info.get("fid", tensor([0])) + + # Convert to data object + data_object = self.a2g.convert(atoms, sid) + data_object.fid = fid + data_object.natoms = len(atoms) + + # Transform data object + if self.transform is not None: + data_object = self.transform( + data_object, **self.config.get("transform_args", {}) + ) + + if self.config.get("include_relaxed_energy", False): + data_object.y_relaxed = self.get_relaxed_energy(self.ids[idx]) + + return data_object + + @abstractmethod + def get_atoms_object(self, identifier): + # This function should return an ASE atoms object. + raise NotImplementedError( + "Returns an ASE atoms object. Derived classes should implement this function." + ) + + @abstractmethod + def load_dataset_get_ids(self, config): + # This function should return a list of ids that can be used to index into the database + raise NotImplementedError( + "Every ASE dataset needs to declare a function to load the dataset and return a list of ids." + ) + + def close_db(self) -> None: + # This method is sometimes called by a trainer + pass + + def guess_target_metadata(self, num_samples: int = 100): + metadata = {} + + if num_samples < len(self): + metadata["targets"] = guess_property_metadata( + [ + self.get_atoms_object(self.ids[idx]) + for idx in np.random.choice( + len(self), size=(num_samples,), replace=False + ) + ] + ) + else: + metadata["targets"] = guess_property_metadata( + [ + self.get_atoms_object(self.ids[idx]) + for idx in range(len(self)) + ] + ) + + return metadata + + def get_metadata(self): + return self.guess_target_metadata() + + +@registry.register_dataset("ase_read") +class AseReadDataset(AseAtomsDataset): + """ + This Dataset uses ase.io.read to load data from a directory on disk. + This is intended for small-scale testing and demonstrations of OCP. + Larger datasets are better served by the efficiency of other dataset types + such as LMDB. + + For a full list of ASE-readable filetypes, see + https://wiki.fysik.dtu.dk/ase/ase/io/io.html + + args: + config (dict): + src (str): The source folder that contains your ASE-readable files + + pattern (str): Filepath matching each file you want to read + ex. "*/POSCAR", "*.cif", "*.xyz" + search recursively with two wildcards: "**/POSCAR" or "**/*.cif" + + a2g_args (dict): Keyword arguments for ocpmodels.preprocessing.AtomsToGraphs() + default options will work for most users + + If you are using this for a training dataset, set + "r_energy":True and/or "r_forces":True as appropriate + In that case, energy/forces must be in the files you read (ex. OUTCAR) + + ase_read_args (dict): Keyword arguments for ase.io.read() + + keep_in_memory (bool): Store data in memory. This helps avoid random reads if you need + to iterate over a dataset many times (e.g. training for many epochs). + Not recommended for large datasets. + + include_relaxed_energy (bool): Include the relaxed energy in the resulting data object. + The relaxed structure is assumed to be the final structure in the file + (e.g. the last frame of a .traj). + + atoms_transform_args (dict): Additional keyword arguments for the atoms_transform callable + + transform_args (dict): Additional keyword arguments for the transform callable + + atoms_transform (callable, optional): Additional preprocessing function applied to the Atoms + object. Useful for applying tags, for example. + + transform (callable, optional): Additional preprocessing function for the Data object + + """ + + def load_dataset_get_ids(self, config) -> List[Path]: + self.ase_read_args = config.get("ase_read_args", {}) + + if ":" in self.ase_read_args.get("index", ""): + raise NotImplementedError( + "To read multiple structures from a single file, please use AseReadMultiStructureDataset." + ) + + self.path = Path(config["src"]) + if self.path.is_file(): + raise Exception("The specified src is not a directory") + + if self.config.get("include_relaxed_energy", False): + self.relaxed_ase_read_args = copy.deepcopy(self.ase_read_args) + self.relaxed_ase_read_args["index"] = "-1" + + return list(self.path.glob(f'{config["pattern"]}')) + + def get_atoms_object(self, identifier): + try: + atoms = ase.io.read(identifier, **self.ase_read_args) + except Exception as err: + warnings.warn(f"{err} occured for: {identifier}") + raise err + + return atoms + + def get_relaxed_energy(self, identifier): + relaxed_atoms = ase.io.read(identifier, **self.relaxed_ase_read_args) + return relaxed_atoms.get_potential_energy(apply_constraint=False) + + +@registry.register_dataset("ase_read_multi") +class AseReadMultiStructureDataset(AseAtomsDataset): + """ + This Dataset can read multiple structures from each file using ase.io.read. + The disadvantage is that all files must be read at startup. + This is a significant cost for large datasets. + + This is intended for small-scale testing and demonstrations of OCP. + Larger datasets are better served by the efficiency of other dataset types + such as LMDB. + + For a full list of ASE-readable filetypes, see + https://wiki.fysik.dtu.dk/ase/ase/io/io.html + + args: + config (dict): + src (str): The source folder that contains your ASE-readable files + + pattern (str): Filepath matching each file you want to read + ex. "*.traj", "*.xyz" + search recursively with two wildcards: "**/POSCAR" or "**/*.cif" + + index_file (str): Filepath to an indexing file, which contains each filename + and the number of structures contained in each file. For instance: + + /path/to/relaxation1.traj 200 + /path/to/relaxation2.traj 150 + + This will overrule the src and pattern that you specify! + + a2g_args (dict): Keyword arguments for ocpmodels.preprocessing.AtomsToGraphs() + default options will work for most users + + If you are using this for a training dataset, set + "r_energy":True and/or "r_forces":True as appropriate + In that case, energy/forces must be in the files you read (ex. OUTCAR) + + ase_read_args (dict): Keyword arguments for ase.io.read() + + keep_in_memory (bool): Store data in memory. This helps avoid random reads if you need + to iterate over a dataset many times (e.g. training for many epochs). + Not recommended for large datasets. + + include_relaxed_energy (bool): Include the relaxed energy in the resulting data object. + The relaxed structure is assumed to be the final structure in the file + (e.g. the last frame of a .traj). + + use_tqdm (bool): Use TQDM progress bar when initializing dataset + + atoms_transform_args (dict): Additional keyword arguments for the atoms_transform callable + + transform_args (dict): Additional keyword arguments for the transform callable + + atoms_transform (callable, optional): Additional preprocessing function applied to the Atoms + object. Useful for applying tags, for example. + + transform (callable, optional): Additional preprocessing function for the Data object + """ + + def load_dataset_get_ids(self, config): + self.ase_read_args = config.get("ase_read_args", {}) + if not hasattr(self.ase_read_args, "index"): + self.ase_read_args["index"] = ":" + + if config.get("index_file", None) is not None: + f = open(config["index_file"], "r") + index = f.readlines() + + ids = [] + for line in index: + filename = line.split(" ")[0] + for i in range(int(line.split(" ")[1])): + ids.append(f"{filename} {i}") + + return ids + + self.path = Path(config["src"]) + if self.path.is_file(): + raise Exception("The specified src is not a directory") + filenames = list(self.path.glob(f'{config["pattern"]}')) + + ids = [] + + if config.get("use_tqdm", True): + filenames = tqdm(filenames) + for filename in filenames: + try: + structures = ase.io.read(filename, **self.ase_read_args) + except Exception as err: + warnings.warn(f"{err} occured for: {filename}") + else: + for i, structure in enumerate(structures): + ids.append(f"{filename} {i}") + + return ids + + def get_atoms_object(self, identifier): + try: + atoms = ase.io.read( + "".join(identifier.split(" ")[:-1]), **self.ase_read_args + )[int(identifier.split(" ")[-1])] + except Exception as err: + warnings.warn(f"{err} occured for: {identifier}") + raise err + + if "sid" not in atoms.info: + atoms.info["sid"] = "".join(identifier.split(" ")[:-1]) + if "fid" not in atoms.info: + atoms.info["fid"] = int(identifier.split(" ")[-1]) + + return atoms + + def get_metadata(self): + return {} + + def get_relaxed_energy(self, identifier): + relaxed_atoms = ase.io.read( + "".join(identifier.split(" ")[:-1]), **self.ase_read_args + )[-1] + return relaxed_atoms.get_potential_energy(apply_constraint=False) + + +class dummy_list(list): + def __init__(self, max) -> None: + self.max = max + return + + def __len__(self): + return self.max + + def __getitem__(self, idx): + # Handle slicing + if isinstance(idx, slice): + return [self[i] for i in range(*idx.indices(self.max))] + + # Cast idx as int since it could be a tensor index + idx = int(idx) + + # Handle negative indices (referenced from end) + if idx < 0: + idx += self.max + + if 0 <= idx < self.max: + return idx + else: + raise IndexError + + +@registry.register_dataset("ase_db") +class AseDBDataset(AseAtomsDataset): + """ + This Dataset connects to an ASE Database, allowing the storage of atoms objects + with a variety of backends including JSON, SQLite, and database server options. + + For more information, see: + https://databases.fysik.dtu.dk/ase/ase/db/db.html + + args: + config (dict): + src (str): Either + - the path an ASE DB, + - the connection address of an ASE DB, + - a folder with multiple ASE DBs, + - a glob string to use to find ASE DBs, or + - a list of ASE db paths/addresses. + If a folder, every file will be attempted as an ASE DB, and warnings + are raised for any files that can't connect cleanly + + Note that for large datasets, ID loading can be slow and there can be many + ids, so it's advised to make loading the id list as easy as possible. There is not + an obvious way to get a full list of ids from most ASE dbs besides simply looping + through the entire dataset. See the AseLMDBDataset which was written with this usecase + in mind. + + connect_args (dict): Keyword arguments for ase.db.connect() + + select_args (dict): Keyword arguments for ase.db.select() + You can use this to query/filter your database + + a2g_args (dict): Keyword arguments for ocpmodels.preprocessing.AtomsToGraphs() + default options will work for most users + + If you are using this for a training dataset, set + "r_energy":True and/or "r_forces":True as appropriate + In that case, energy/forces must be in the database + + keep_in_memory (bool): Store data in memory. This helps avoid random reads if you need + to iterate over a dataset many times (e.g. training for many epochs). + Not recommended for large datasets. + + atoms_transform_args (dict): Additional keyword arguments for the atoms_transform callable + + transform_args (dict): Additional keyword arguments for the transform callable + + atoms_transform (callable, optional): Additional preprocessing function applied to the Atoms + object. Useful for applying tags, for example. + + transform (callable, optional): Additional preprocessing function for the Data object + """ + + def load_dataset_get_ids(self, config) -> dummy_list: + if isinstance(config["src"], list): + filepaths = config["src"] + elif os.path.isfile(config["src"]): + filepaths = [config["src"]] + elif os.path.isdir(config["src"]): + filepaths = glob.glob(f'{config["src"]}/*') + else: + filepaths = glob.glob(config["src"]) + + self.dbs = [] + + for path in filepaths: + try: + self.dbs.append( + self.connect_db(path, config.get("connect_args", {})) + ) + except ValueError: + logging.warning( + f"Tried to connect to {path} but it's not an ASE database!" + ) + + self.select_args = config.get("select_args", {}) + if self.select_args is None: + self.select_args = {} + + # In order to get all of the unique IDs using the default ASE db interface + # we have to load all the data and check ids using a select. This is extremely + # inefficient for large dataset. If the db we're using already presents a list of + # ids and there is no query, we can just use that list instead and save ourselves + # a lot of time! + self.db_ids = [] + for db in self.dbs: + if hasattr(db, "ids") and self.select_args == {}: + self.db_ids.append(db.ids) + else: + self.db_ids.append( + [row.id for row in db.select(**self.select_args)] + ) + + idlens = [len(ids) for ids in self.db_ids] + self._idlen_cumulative = np.cumsum(idlens).tolist() + + return dummy_list(sum(idlens)) + + def get_atoms_object(self, idx): + # Figure out which db this should be indexed from. + db_idx = bisect.bisect(self._idlen_cumulative, idx) + + # Extract index of element within that db + el_idx = idx + if db_idx != 0: + el_idx = idx - self._idlen_cumulative[db_idx - 1] + assert el_idx >= 0 + + atoms_row = self.dbs[db_idx]._get_row(self.db_ids[db_idx][el_idx]) + atoms = atoms_row.toatoms() + + if isinstance(atoms_row.data, dict): + atoms.info.update(atoms_row.data) + + return atoms + + def connect_db(self, address, connect_args={}): + if connect_args is None: + connect_args = {} + db_type = connect_args.get("type", "extract_from_name") + if db_type == "lmdb" or ( + db_type == "extract_from_name" and address.split(".")[-1] == "lmdb" + ): + return LMDBDatabase(address, readonly=True, **connect_args) + else: + return ase.db.connect(address, **connect_args) + + def close_db(self) -> None: + for db in self.dbs: + if hasattr(db, "close"): + db.close() + + def get_metadata(self): + logging.warning( + "You specific a folder of ASE dbs, so it's impossible to know which metadata to use. Using the first!" + ) + if self.dbs[0].metadata == {}: + return self.guess_target_metadata() + else: + return copy.deepcopy(self.dbs[0].metadata) + + def get_relaxed_energy(self, identifier): + raise NotImplementedError( + "IS2RE-Direct training with an ASE DB is not currently supported." + ) diff --git a/ocpmodels/datasets/embeddings/__init__.py b/ocpmodels/datasets/embeddings/__init__.py new file mode 100644 index 0000000..1c41163 --- /dev/null +++ b/ocpmodels/datasets/embeddings/__init__.py @@ -0,0 +1,11 @@ +__all__ = [ + "ATOMIC_RADII", + "KHOT_EMBEDDINGS", + "CONTINUOUS_EMBEDDINGS", + "QMOF_KHOT_EMBEDDINGS", +] + +from .atomic_radii import ATOMIC_RADII +from .continuous_embeddings import CONTINUOUS_EMBEDDINGS +from .khot_embeddings import KHOT_EMBEDDINGS +from .qmof_khot_embeddings import QMOF_KHOT_EMBEDDINGS diff --git a/ocpmodels/datasets/embeddings/atomic_radii.py b/ocpmodels/datasets/embeddings/atomic_radii.py new file mode 100644 index 0000000..857dc3a --- /dev/null +++ b/ocpmodels/datasets/embeddings/atomic_radii.py @@ -0,0 +1,108 @@ +""" +Atomic radii in picometers + +NaN stored for unavailable parameters. +""" +ATOMIC_RADII = { + 0: float("NaN"), + 1: 25.0, + 2: 120.0, + 3: 145.0, + 4: 105.0, + 5: 85.0, + 6: 70.0, + 7: 65.0, + 8: 60.0, + 9: 50.0, + 10: 160.0, + 11: 180.0, + 12: 150.0, + 13: 125.0, + 14: 110.0, + 15: 100.0, + 16: 100.0, + 17: 100.0, + 18: 71.0, + 19: 220.0, + 20: 180.0, + 21: 160.0, + 22: 140.0, + 23: 135.0, + 24: 140.0, + 25: 140.0, + 26: 140.0, + 27: 135.0, + 28: 135.0, + 29: 135.0, + 30: 135.0, + 31: 130.0, + 32: 125.0, + 33: 115.0, + 34: 115.0, + 35: 115.0, + 36: float("NaN"), + 37: 235.0, + 38: 200.0, + 39: 180.0, + 40: 155.0, + 41: 145.0, + 42: 145.0, + 43: 135.0, + 44: 130.0, + 45: 135.0, + 46: 140.0, + 47: 160.0, + 48: 155.0, + 49: 155.0, + 50: 145.0, + 51: 145.0, + 52: 140.0, + 53: 140.0, + 54: float("NaN"), + 55: 260.0, + 56: 215.0, + 57: 195.0, + 58: 185.0, + 59: 185.0, + 60: 185.0, + 61: 185.0, + 62: 185.0, + 63: 185.0, + 64: 180.0, + 65: 175.0, + 66: 175.0, + 67: 175.0, + 68: 175.0, + 69: 175.0, + 70: 175.0, + 71: 175.0, + 72: 155.0, + 73: 145.0, + 74: 135.0, + 75: 135.0, + 76: 130.0, + 77: 135.0, + 78: 135.0, + 79: 135.0, + 80: 150.0, + 81: 190.0, + 82: 180.0, + 83: 160.0, + 84: 190.0, + 85: float("NaN"), + 86: float("NaN"), + 87: float("NaN"), + 88: 215.0, + 89: 195.0, + 90: 180.0, + 91: 180.0, + 92: 175.0, + 93: 175.0, + 94: 175.0, + 95: 175.0, + 96: float("NaN"), + 97: float("NaN"), + 98: float("NaN"), + 99: float("NaN"), + 100: float("NaN"), +} diff --git a/ocpmodels/datasets/embeddings/continuous_embeddings.py b/ocpmodels/datasets/embeddings/continuous_embeddings.py new file mode 100644 index 0000000..c97acfd --- /dev/null +++ b/ocpmodels/datasets/embeddings/continuous_embeddings.py @@ -0,0 +1,1119 @@ +""" +CGCNN-like embeddings using continuous values instead of original k-hot. + +Properties: + Group number + Period number + Electronegativity + Covalent radius + Valence electrons + First ionization energy + Electron affinity + Block + Atomic Volume + +NaN stored for unavaialable parameters. +""" +CONTINUOUS_EMBEDDINGS = { + 0: [ + float("NaN"), + float("NaN"), + float("NaN"), + float("NaN"), + float("NaN"), + float("NaN"), + float("NaN"), + float("NaN"), + float("NaN"), + ], + 1: [ + 1.0, + 1.0, + 2.1877708435058594, + 31.0, + 1.0, + 13.598434448242188, + 0.754194974899292, + 1.0, + 14.100000381469727, + ], + 2: [ + 18.0, + 1.0, + 1.0, + 28.0, + 2.0, + 24.587387084960938, + -19.700000762939453, + 1.0, + 31.799999237060547, + ], + 3: [ + 1.0, + 2.0, + 0.04886792600154877, + 128.0, + 1.0, + 5.391714572906494, + 0.6180490255355835, + 1.0, + 13.100000381469727, + ], + 4: [ + 2.0, + 2.0, + 0.1268472671508789, + 96.0, + 2.0, + 9.322698593139648, + -2.4000000953674316, + 1.0, + 5.0, + ], + 5: [ + 13.0, + 2.0, + 0.25462737679481506, + 84.0, + 3.0, + 8.298019409179688, + 0.27972298860549927, + 2.0, + 4.599999904632568, + ], + 6: [ + 14.0, + 2.0, + 0.42752504348754883, + 73.0, + 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elemental embeddings. +""" + +KHOT_EMBEDDINGS = { + 1: [ + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + ], + 2: [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + ], + 3: [ 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ase.db.core import Database, now, ops +from ase.db.row import AtomsRow + +# These are special keys in the ASE LMDB that hold +# metadata and other info +RESERVED_KEYS = ["nextid", "metadata", "deleted_ids"] + + +class LMDBDatabase(Database): + def __enter__(self) -> "LMDBDatabase": + return self + + def __init__( + self, + filename: Optional[str] = None, + create_indices: bool = True, + use_lock_file: bool = False, + serial: bool = False, + readonly: bool = False, + *args, + **kwargs, + ) -> None: + """ + For the most part, this is identical to the standard ase db initiation + arguments, except that we add a readonly flag. + """ + super().__init__( + filename, create_indices, use_lock_file, serial, *args, **kwargs + ) + + # Add a readonly mode for when we're only training + # to make sure there's no parallel locks + self.readonly = readonly + + if self.readonly: + # Open a new env + self.env = lmdb.open( + self.filename, + subdir=False, + meminit=False, + map_async=True, + readonly=True, + lock=False, + ) + + # Open a transaction and keep it open for fast read/writes! + self.txn = self.env.begin(write=False) + + else: + # Open a new env with write access + self.env = lmdb.open( + self.filename, + map_size=1099511627776 * 2, + subdir=False, + meminit=False, + map_async=True, + ) + + self.txn = self.env.begin(write=True) + + # Load all ids based on keys in the DB. + self._load_ids() + + return + + def __exit__(self, exc_type, exc_value, tb) -> None: + self.close() + + def close(self) -> None: + # Close the lmdb environment and transaction + self.txn.commit() + self.env.close() + + def _write(self, atoms, key_value_pairs, data, id): + Database._write(self, atoms, key_value_pairs, data) + + mtime = now() + + if isinstance(atoms, AtomsRow): + row = atoms + else: + row = AtomsRow(atoms) + row.ctime = mtime + row.user = os.getenv("USER") + + dct = {} + for key in row.__dict__: + if key[0] == "_" or key in row._keys or key == "id": + continue + dct[key] = row[key] + + dct["mtime"] = mtime + + if key_value_pairs: + dct["key_value_pairs"] = key_value_pairs + + if data: + dct["data"] = data + + constraints = row.get("constraints") + if constraints: + dct["constraints"] = [ + constraint.todict() for constraint in constraints + ] + + # json doesn't like Cell objects, so make it a cell + dct["cell"] = np.asarray(dct["cell"]) + + if id is None: + nextid = self._get_nextid() + id = nextid + nextid += 1 + else: + data = self.txn.get("{id}".encode("ascii")) + assert data is not None + + # Add the new entry, then add the id and write the nextid + self.txn.put( + f"{id}".encode("ascii"), + zlib.compress( + orjson.dumps(dct, option=orjson.OPT_SERIALIZE_NUMPY) + ), + ) + self.ids.append(id) + self.txn.put( + "nextid".encode("ascii"), + zlib.compress( + orjson.dumps(nextid, option=orjson.OPT_SERIALIZE_NUMPY) + ), + ) + + return id + + def delete(self, ids) -> None: + for id in ids: + self.txn.delete(f"{id}".encode("ascii")) + self.ids.remove(id) + + self.deleted_ids += ids + self.txn.put( + "deleted_ids".encode("ascii"), + zlib.compress( + orjson.dumps( + self.deleted_ids, option=orjson.OPT_SERIALIZE_NUMPY + ) + ), + ) + + def _get_row(self, id, include_data: bool = True): + if id is None: + assert len(self.ids) == 1 + id = self.ids[0] + data = self.txn.get(f"{id}".encode("ascii")) + + if data is not None: + dct = orjson.loads(zlib.decompress(data)) + else: + raise KeyError(f"Id {id} missing from the database!") + + if not include_data: + dct.pop("data", None) + + dct["id"] = id + return AtomsRow(dct) + + def _get_row_by_index(self, index: int, include_data: bool = True): + """Auxiliary function to get the ith entry, rather than + a specific id + """ + id = self.ids[index] + data = self.txn.get(f"{id}".encode("ascii")) + + if data is not None: + dct = orjson.loads(zlib.decompress(data)) + else: + raise KeyError(f"Id {id} missing from the database!") + + if not include_data: + dct.pop("data", None) + + dct["id"] = id + return AtomsRow(dct) + + def _select( + self, + keys, + cmps, + explain: bool = False, + verbosity: int = 0, + limit=None, + offset: int = 0, + sort=None, + include_data: bool = True, + columns: str = "all", + ): + if explain: + yield {"explain": (0, 0, 0, "scan table")} + return + + if sort: + if sort[0] == "-": + reverse = True + sort = sort[1:] + else: + reverse = False + + def f(row): + return row.get(sort, missing) + + rows = [] + missing = [] + for row in self._select(keys, cmps): + key = row.get(sort) + if key is None: + missing.append((0, row)) + else: + rows.append((key, row)) + + rows.sort(reverse=reverse, key=lambda x: x[0]) + rows += missing + + if limit: + rows = rows[offset : offset + limit] + for key, row in rows: + yield row + return + + if not limit: + limit = -offset - 1 + + cmps = [(key, ops[op], val) for key, op, val in cmps] + n = 0 + for id in self.ids: + if n - offset == limit: + return + row = self._get_row(id, include_data=False) + + for key in keys: + if key not in row: + break + else: + for key, op, val in cmps: + if isinstance(key, int): + value = np.equal(row.numbers, key).sum() + else: + value = row.get(key) + if key == "pbc": + assert op in [ops["="], ops["!="]] + value = "".join("FT"[x] for x in value) + if value is None or not op(value, val): + break + else: + if n >= offset: + yield row + n += 1 + + @property + def metadata(self): + """Load the metadata from the DB if present""" + if self._metadata is None: + metadata = self.txn.get("metadata".encode("ascii")) + if metadata is None: + self._metadata = {} + else: + self._metadata = orjson.loads(zlib.decompress(metadata)) + + return self._metadata.copy() + + @metadata.setter + def metadata(self, dct): + self._metadata = dct + + # Put the updated metadata dictionary + self.txn.put( + "metadata".encode("ascii"), + zlib.compress( + orjson.dumps(dct, option=orjson.OPT_SERIALIZE_NUMPY) + ), + ) + + def _get_nextid(self): + """Get the id of the next row to be written""" + # Get the nextid + nextid_data = self.txn.get("nextid".encode("ascii")) + if nextid_data is not None: + nextid = orjson.loads(zlib.decompress(nextid_data)) + else: + # This db is empty; start at 1! + nextid = 1 + + return nextid + + def count(self, selection=None, **kwargs) -> int: + """Count rows. + + See the select() method for the selection syntax. Use db.count() or + len(db) to count all rows. + """ + if selection is not None: + n = 0 + for row in self.select(selection, **kwargs): + n += 1 + return n + else: + # Fast count if there's no queries! Just get number of ids + return len(self.ids) + + def _load_ids(self) -> None: + """Load ids from the DB + + Since ASE db ids are mostly 1-N integers, but can be missing entries + if ids have been deleted. To save space and operating under the assumption + that there will probably not be many deletions in most OCP datasets, + we just store the deleted ids. + """ + + # Load the deleted ids + deleted_ids_data = self.txn.get("deleted_ids".encode("ascii")) + if deleted_ids_data is None: + self.deleted_ids = [] + else: + self.deleted_ids = orjson.loads(zlib.decompress(deleted_ids_data)) + + # Reconstruct the full id list + self.ids = [ + i + for i in range(1, self._get_nextid()) + if i not in set(self.deleted_ids) + ] diff --git a/ocpmodels/datasets/lmdb_dataset.py b/ocpmodels/datasets/lmdb_dataset.py new file mode 100644 index 0000000..c2020f0 --- /dev/null +++ b/ocpmodels/datasets/lmdb_dataset.py @@ -0,0 +1,183 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import bisect +import logging +import math +import pickle +import random +import warnings +from pathlib import Path + +import lmdb +import numpy as np +import torch +from torch.utils.data import Dataset +from torch_geometric.data import Batch + +from ocpmodels.common import distutils +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import pyg2_data_transform + + +@registry.register_dataset("lmdb") +@registry.register_dataset("single_point_lmdb") +@registry.register_dataset("trajectory_lmdb") +class LmdbDataset(Dataset): + r"""Dataset class to load from LMDB files containing relaxation + trajectories or single point computations. + + Useful for Structure to Energy & Force (S2EF), Initial State to + Relaxed State (IS2RS), and Initial State to Relaxed Energy (IS2RE) tasks. + + Args: + config (dict): Dataset configuration + transform (callable, optional): Data transform function. + (default: :obj:`None`) + """ + + def __init__(self, config, transform=None): + super(LmdbDataset, self).__init__() + self.config = config + + assert not self.config.get( + "train_on_oc20_total_energies", False + ), "For training on total energies set dataset=oc22_lmdb" + + self.path = Path(self.config["src"]) + if not self.path.is_file(): + db_paths = sorted(self.path.glob("*.lmdb")) + assert len(db_paths) > 0, f"No LMDBs found in '{self.path}'" + + self.metadata_path = self.path / "metadata.npz" + + self._keys, self.envs = [], [] + for db_path in db_paths: + self.envs.append(self.connect_db(db_path)) + length = pickle.loads( + self.envs[-1].begin().get("length".encode("ascii")) + ) + self._keys.append(list(range(length))) + + keylens = [len(k) for k in self._keys] + self._keylen_cumulative = np.cumsum(keylens).tolist() + self.num_samples = sum(keylens) + else: + self.metadata_path = self.path.parent / "metadata.npz" + self.env = self.connect_db(self.path) + self._keys = [ + f"{j}".encode("ascii") + for j in range(self.env.stat()["entries"]) + ] + self.num_samples = len(self._keys) + + # If specified, limit dataset to only a portion of the entire dataset + # total_shards: defines total chunks to partition dataset + # shard: defines dataset shard to make visible + self.sharded = False + if "shard" in self.config and "total_shards" in self.config: + self.sharded = True + self.indices = range(self.num_samples) + # split all available indices into 'total_shards' bins + self.shards = np.array_split( + self.indices, self.config.get("total_shards", 1) + ) + # limit each process to see a subset of data based off defined shard + self.available_indices = self.shards[self.config.get("shard", 0)] + self.num_samples = len(self.available_indices) + + self.transform = transform + + def __len__(self): + return self.num_samples + + def __getitem__(self, idx): + # if sharding, remap idx to appropriate idx of the sharded set + if self.sharded: + idx = self.available_indices[idx] + if not self.path.is_file(): + # Figure out which db this should be indexed from. + db_idx = bisect.bisect(self._keylen_cumulative, idx) + # Extract index of element within that db. + el_idx = idx + if db_idx != 0: + el_idx = idx - self._keylen_cumulative[db_idx - 1] + assert el_idx >= 0 + + # Return features. + datapoint_pickled = ( + self.envs[db_idx] + .begin() + .get(f"{self._keys[db_idx][el_idx]}".encode("ascii")) + ) + data_object = pyg2_data_transform(pickle.loads(datapoint_pickled)) + data_object.id = f"{db_idx}_{el_idx}" + else: + datapoint_pickled = self.env.begin().get(self._keys[idx]) + data_object = pyg2_data_transform(pickle.loads(datapoint_pickled)) + + if self.transform is not None: + data_object = self.transform(data_object) + + return data_object + + def connect_db(self, lmdb_path=None): + env = lmdb.open( + str(lmdb_path), + subdir=False, + readonly=True, + lock=False, + readahead=False, + meminit=False, + max_readers=1, + ) + return env + + def close_db(self): + if not self.path.is_file(): + for env in self.envs: + env.close() + else: + self.env.close() + + +class SinglePointLmdbDataset(LmdbDataset): + def __init__(self, config, transform=None): + super(SinglePointLmdbDataset, self).__init__(config, transform) + warnings.warn( + "SinglePointLmdbDataset is deprecated and will be removed in the future." + "Please use 'LmdbDataset' instead.", + stacklevel=3, + ) + + +class TrajectoryLmdbDataset(LmdbDataset): + def __init__(self, config, transform=None): + super(TrajectoryLmdbDataset, self).__init__(config, transform) + warnings.warn( + "TrajectoryLmdbDataset is deprecated and will be removed in the future." + "Please use 'LmdbDataset' instead.", + stacklevel=3, + ) + + +def data_list_collater(data_list, otf_graph=False): + batch = Batch.from_data_list(data_list) + + if not otf_graph: + try: + n_neighbors = [] + for i, data in enumerate(data_list): + n_index = data.edge_index[1, :] + n_neighbors.append(n_index.shape[0]) + batch.neighbors = torch.tensor(n_neighbors) + except (NotImplementedError, TypeError): + logging.warning( + "LMDB does not contain edge index information, set otf_graph=True" + ) + + return batch diff --git a/ocpmodels/datasets/oc22_lmdb_dataset.py b/ocpmodels/datasets/oc22_lmdb_dataset.py new file mode 100644 index 0000000..1170f6a --- /dev/null +++ b/ocpmodels/datasets/oc22_lmdb_dataset.py @@ -0,0 +1,214 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import bisect +import logging +import math +import pickle +import random +import warnings +from pathlib import Path + +import lmdb +import numpy as np +import torch +from torch.utils.data import Dataset +from torch_geometric.data import Batch + +from ocpmodels.common import distutils +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import pyg2_data_transform + + +@registry.register_dataset("oc22_lmdb") +class OC22LmdbDataset(Dataset): + r"""Dataset class to load from LMDB files containing relaxation + trajectories or single point computations. + + Useful for Structure to Energy & Force (S2EF), Initial State to + Relaxed State (IS2RS), and Initial State to Relaxed Energy (IS2RE) tasks. + + Args: + config (dict): Dataset configuration + transform (callable, optional): Data transform function. + (default: :obj:`None`) + """ + + def __init__(self, config, transform=None): + super(OC22LmdbDataset, self).__init__() + self.config = config + + self.path = Path(self.config["src"]) + self.data2train = self.config.get("data2train", "all") + if not self.path.is_file(): + db_paths = sorted(self.path.glob("*.lmdb")) + assert len(db_paths) > 0, f"No LMDBs found in '{self.path}'" + + self.metadata_path = self.path / "metadata.npz" + + self._keys, self.envs = [], [] + for db_path in db_paths: + self.envs.append(self.connect_db(db_path)) + try: + length = pickle.loads( + self.envs[-1].begin().get("length".encode("ascii")) + ) + except TypeError: + length = self.envs[-1].stat()["entries"] + self._keys.append(list(range(length))) + + keylens = [len(k) for k in self._keys] + self._keylen_cumulative = np.cumsum(keylens).tolist() + self.num_samples = sum(keylens) + + if self.data2train != "all": + txt_paths = sorted(self.path.glob("*.txt")) + index = 0 + self.indices = [] + for txt_path in txt_paths: + lines = open(txt_path).read().splitlines() + for line in lines: + if self.data2train == "adslabs": + if "clean" not in line: + self.indices.append(index) + if self.data2train == "slabs": + if "clean" in line: + self.indices.append(index) + index += 1 + self.num_samples = len(self.indices) + else: + self.metadata_path = self.path.parent / "metadata.npz" + self.env = self.connect_db(self.path) + self._keys = [ + f"{j}".encode("ascii") + for j in range(self.env.stat()["entries"]) + ] + self.num_samples = len(self._keys) + + self.transform = transform + self.lin_ref = self.oc20_ref = False + # only needed for oc20 datasets, oc22 is total by default + self.train_on_oc20_total_energies = self.config.get( + "train_on_oc20_total_energies", False + ) + if self.train_on_oc20_total_energies: + self.oc20_ref = pickle.load(open(config["oc20_ref"], "rb")) + if self.config.get("lin_ref", False): + coeff = np.load(self.config["lin_ref"], allow_pickle=True)["coeff"] + self.lin_ref = torch.nn.Parameter( + torch.tensor(coeff), requires_grad=False + ) + self.subsample = self.config.get("subsample", False) + + def __len__(self): + if self.subsample: + return min(self.subsample, self.num_samples) + return self.num_samples + + def __getitem__(self, idx): + if self.data2train != "all": + idx = self.indices[idx] + if not self.path.is_file(): + # Figure out which db this should be indexed from. + db_idx = bisect.bisect(self._keylen_cumulative, idx) + # Extract index of element within that db. + el_idx = idx + if db_idx != 0: + el_idx = idx - self._keylen_cumulative[db_idx - 1] + assert el_idx >= 0 + + # Return features. + datapoint_pickled = ( + self.envs[db_idx] + .begin() + .get(f"{self._keys[db_idx][el_idx]}".encode("ascii")) + ) + data_object = pyg2_data_transform(pickle.loads(datapoint_pickled)) + data_object.id = f"{db_idx}_{el_idx}" + else: + datapoint_pickled = self.env.begin().get(self._keys[idx]) + data_object = pyg2_data_transform(pickle.loads(datapoint_pickled)) + + if self.transform is not None: + data_object = self.transform(data_object) + # make types consistent + sid = data_object.sid + if isinstance(sid, torch.Tensor): + sid = sid.item() + data_object.sid = sid + if "fid" in data_object: + fid = data_object.fid + if isinstance(fid, torch.Tensor): + fid = fid.item() + data_object.fid = fid + + if hasattr(data_object, "y_relaxed"): + attr = "y_relaxed" + elif hasattr(data_object, "y"): + attr = "y" + # if targets are not available, test data is being used + else: + return data_object + + # convert s2ef energies to raw energies + if attr == "y": + # OC20 data + if "oc22" not in data_object: + assert self.config.get( + "train_on_oc20_total_energies", False + ), "To train OC20 or OC22+OC20 on total energies set train_on_oc20_total_energies=True" + randomid = f"random{sid}" + data_object[attr] += self.oc20_ref[randomid] + data_object.nads = 1 + data_object.oc22 = 0 + + # convert is2re energies to raw energies + else: + if "oc22" not in data_object: + assert self.config.get( + "train_on_oc20_total_energies", False + ), "To train OC20 or OC22+OC20 on total energies set train_on_oc20_total_energies=True" + randomid = f"random{sid}" + data_object[attr] += self.oc20_ref[randomid] + del data_object.force + del data_object.y_init + data_object.nads = 1 + data_object.oc22 = 0 + + if self.lin_ref is not False: + lin_energy = sum(self.lin_ref[data_object.atomic_numbers.long()]) + data_object[attr] -= lin_energy + + # to jointly train on oc22+oc20, need to delete these oc20-only attributes + # ensure otf_graph=1 in your model configuration + if "edge_index" in data_object: + del data_object.edge_index + if "cell_offsets" in data_object: + del data_object.cell_offsets + if "distances" in data_object: + del data_object.distances + + return data_object + + def connect_db(self, lmdb_path=None): + env = lmdb.open( + str(lmdb_path), + subdir=False, + readonly=True, + lock=False, + readahead=False, + meminit=False, + max_readers=1, + ) + return env + + def close_db(self): + if not self.path.is_file(): + for env in self.envs: + env.close() + else: + self.env.close() diff --git a/ocpmodels/datasets/target_metadata_guesser.py b/ocpmodels/datasets/target_metadata_guesser.py new file mode 100644 index 0000000..844bd81 --- /dev/null +++ b/ocpmodels/datasets/target_metadata_guesser.py @@ -0,0 +1,197 @@ +import logging + +import numpy as np + + +def uniform_atoms_lengths(atoms_lens) -> bool: + # If all of the structures have the same number of atoms, it's really hard to know + # whether the entries are intensive or extensive, and whether + # some of the entries are per-atom or not + return len(set(atoms_lens)) == 1 + + +def target_constant_shape(atoms_lens, target_samples) -> bool: + # Given a bunch of atoms lengths, and the corresponding samples for the target, + # determine whether the shape is always the same regardless of atom size + return len(set([sample.shape for sample in target_samples])) == 1 + + +def target_per_atom(atoms_lens, target_samples) -> bool: + # Given a bunch of atoms lengths, and the corresponding samples for the target, + # determine whether the target is per-atom (first dimension == # atoms, others constant) + + # If a sample target is just a number/float/etc, it can't be per-atom + if len(np.array(target_samples[0]).shape) == 0: + return False + + first_dim_proportional = all( + [ + np.array(sample).shape[0] == alen + for alen, sample in zip(atoms_lens, target_samples) + ] + ) + + if len(np.array(target_samples[0]).shape) == 1: + other_dim_constant = True + else: + other_dim_constant = ( + len(set([np.array(sample).shape[1:] for sample in target_samples])) + == 1 + ) + + if first_dim_proportional and other_dim_constant: + return True + else: + return False + + +def target_extensive(atoms_lens, target_samples, threshold: float = 0.2): + # Guess whether a property is intensive or extensive. + # We guess by checking whether standard deviation of the per-atom + # properties capture >20% of the variation in the property + # Of course, with a small amount of data! + + # If the targets are all the same shapes, we shouldn't be asking if the property + # is intensive or extensive! + assert target_constant_shape( + atoms_lens, target_samples + ), "The shapes of this target are not constant!" + + # Get the per-atom normalized properties + try: + compiled_target_array = np.array( + [ + sample / atom_len + for sample, atom_len in zip(atoms_lens, target_samples) + ] + ) + except TypeError: + return False + + # Calculate the normalized standard deviation of each element in the property output + target_samples_mean = np.mean(compiled_target_array, axis=0) + target_samples_normalized = compiled_target_array / target_samples_mean + + # If there's not much variation in the per-atom normalized properties, + # guess extensive! + extensive_guess = target_samples_normalized.std(axis=0) < ( + threshold * target_samples_normalized.mean(axis=0) + ) + if extensive_guess.shape == (): + return extensive_guess + elif ( + target_samples_normalized.std(axis=0) + < (threshold * target_samples_normalized.mean(axis=0)) + ).all(): + return True + else: + return False + + +def guess_target_metadata(atoms_len, target_samples): + example_array = np.array(target_samples[0]) + if example_array.dtype == object or example_array.dtype == str: + return { + "shape": None, + "type": "unknown", + "extensive": None, + "units": "unknown", + "comment": "Guessed property metadata. The property didn't seem to be a numpy array with any numeric type, so we dob't know what to do.", + } + elif target_constant_shape(atoms_len, target_samples): + target_shape = np.array(target_samples[0]).shape + + if uniform_atoms_lengths(atoms_len): + if atoms_len[0] > 3 and target_per_atom(atoms_len, target_samples): + target_shape = list(target_samples[0].shape) + target_shape[0] = "N" + return { + "shape": tuple(target_shape), + "type": "per-atom", + "extensive": True, + "units": "unknown", + "comment": "Guessed property metadata. Because all the sampled atoms are the same length, we can't really know if it is per-atom or per-frame, but the first dimension happens to match the number of atoms.", + } + else: + return { + "shape": tuple(target_shape), + "type": "per-image", + "extensive": True, + "units": "unknown", + "comment": "Guessed property metadata. Because all the sampled atoms are the same length, we can't know if this is intensive of extensive, or per-image or per-frame", + } + + elif target_extensive(atoms_len, target_samples): + return { + "shape": tuple(target_shape), + "type": "per-image", + "extensive": True, + "comment": "Guessed property metadata. It appears to be extensive based on a quick correlation with atom sizes", + } + else: + return { + "shape": tuple(target_shape), + "type": "per-image", + "extensive": False, + "units": "unknown", + "comment": "Guess property metadata. It appears to be intensive based on a quick correlation with atom sizes.", + } + elif target_per_atom(atoms_len, target_samples): + target_shape = list(target_samples[0].shape)[1:] + return { + "shape": tuple(target_shape), + "type": "per-atom", + "extensive": True, + "units": "unknown", + "comment": "Guessed property metadata. It appears to be a per-atom property.", + } + else: + return { + "shape": None, + "type": "unknown", + "extensive": None, + "units": "unknown", + "comment": "Guessed property metadata. The property was variable across different samples and didn't seem to be a per-atom property", + } + + +def guess_property_metadata(atoms_list): + atoms = atoms_list[0] + atoms_len = [len(atoms) for atoms in atoms_list] + + targets = {} + + if hasattr(atoms, "info"): + for key in atoms.info: + # Grab the property samples from the list of atoms + target_samples = [ + np.array(atoms.info[key]) for atoms in atoms_list + ] + + # Guess the metadata + targets[f"info.{key}"] = guess_target_metadata( + atoms_len, target_samples + ) + + # Log a warning so the user knows what's happening + logging.warning( + f'Guessed metadata for atoms.info["{key}"]: {str(targets[f"info.{key}"])}' + ) + if hasattr(atoms, "calc") and atoms.calc is not None: + for key in atoms.calc.results: + # Grab the property samples from the list of atoms + target_samples = [ + np.array(atoms.calc.results[key]) for atoms in atoms_list + ] + + # Guess the metadata + targets[f"{key}"] = guess_target_metadata( + atoms_len, target_samples + ) + + # Log a warning so the user knows what's happening + logging.warning( + f'Guessed metadata for ASE calculator property ["{key}"]: {str(targets[key])}' + ) + + return targets diff --git a/ocpmodels/models/__init__.py b/ocpmodels/models/__init__.py new file mode 100644 index 0000000..a5f6b8b --- /dev/null +++ b/ocpmodels/models/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from .base import BaseModel +from .cgcnn import CGCNN +from .dimenet import DimeNetWrap as DimeNet +from .dimenet_plus_plus import DimeNetPlusPlusWrap as DimeNetPlusPlus +from .forcenet import ForceNet +from .gemnet.gemnet import GemNetT +from .gemnet_gp.gemnet import GraphParallelGemNetT as GraphParallelGemNetT +from .gemnet_oc.gemnet_oc import GemNetOC +from .painn.painn import PaiNN +from .schnet import SchNetWrap as SchNet +from .scn.scn import SphericalChannelNetwork +from .spinconv import spinconv diff --git a/ocpmodels/models/base.py b/ocpmodels/models/base.py new file mode 100644 index 0000000..6c6963b --- /dev/null +++ b/ocpmodels/models/base.py @@ -0,0 +1,111 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging + +import torch +import torch.nn as nn +from torch_geometric.nn import radius_graph + +from ocpmodels.common.utils import ( + compute_neighbors, + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) + + +class BaseModel(nn.Module): + def __init__(self, num_atoms=None, bond_feat_dim=None, num_targets=None): + super(BaseModel, self).__init__() + self.num_atoms = num_atoms + self.bond_feat_dim = bond_feat_dim + self.num_targets = num_targets + + def forward(self, data): + raise NotImplementedError + + def generate_graph( + self, + data, + cutoff=None, + max_neighbors=None, + use_pbc=None, + otf_graph=None, + ): + cutoff = cutoff or self.cutoff + max_neighbors = max_neighbors or self.max_neighbors + use_pbc = use_pbc or self.use_pbc + otf_graph = otf_graph or self.otf_graph + + if not otf_graph: + try: + edge_index = data.edge_index + + if use_pbc: + cell_offsets = data.cell_offsets + neighbors = data.neighbors + + except AttributeError: + logging.warning( + "Turning otf_graph=True as required attributes not present in data object" + ) + otf_graph = True + + if use_pbc: + if otf_graph: + edge_index, cell_offsets, neighbors = radius_graph_pbc( + data, cutoff, max_neighbors + ) + + out = get_pbc_distances( + data.pos, + edge_index, + data.cell, + cell_offsets, + neighbors, + return_offsets=True, + return_distance_vec=True, + ) + + edge_index = out["edge_index"] + edge_dist = out["distances"] + cell_offset_distances = out["offsets"] + distance_vec = out["distance_vec"] + else: + if otf_graph: + edge_index = radius_graph( + data.pos, + r=cutoff, + batch=data.batch, + max_num_neighbors=max_neighbors, + ) + + j, i = edge_index + distance_vec = data.pos[j] - data.pos[i] + + edge_dist = distance_vec.norm(dim=-1) + cell_offsets = torch.zeros( + edge_index.shape[1], 3, device=data.pos.device + ) + cell_offset_distances = torch.zeros_like( + cell_offsets, device=data.pos.device + ) + neighbors = compute_neighbors(data, edge_index) + + return ( + edge_index, + edge_dist, + distance_vec, + cell_offsets, + cell_offset_distances, + neighbors, + ) + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) diff --git a/ocpmodels/models/cgcnn.py b/ocpmodels/models/cgcnn.py new file mode 100644 index 0000000..46606dd --- /dev/null +++ b/ocpmodels/models/cgcnn.py @@ -0,0 +1,232 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.nn as nn +from torch_geometric.nn import MessagePassing, global_mean_pool, radius_graph +from torch_geometric.nn.models.schnet import GaussianSmearing + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.datasets.embeddings import KHOT_EMBEDDINGS, QMOF_KHOT_EMBEDDINGS +from ocpmodels.models.base import BaseModel + + +@registry.register_model("cgcnn") +class CGCNN(BaseModel): + r"""Implementation of the Crystal Graph CNN model from the + `"Crystal Graph Convolutional Neural Networks for an Accurate + and Interpretable Prediction of Material Properties" + `_ paper. + + Args: + num_atoms (int): Number of atoms. + bond_feat_dim (int): Dimension of bond features. + num_targets (int): Number of targets to predict. + use_pbc (bool, optional): If set to :obj:`True`, account for periodic boundary conditions. + (default: :obj:`True`) + regress_forces (bool, optional): If set to :obj:`True`, predict forces by differentiating + energy with respect to positions. + (default: :obj:`True`) + atom_embedding_size (int, optional): Size of atom embeddings. + (default: :obj:`64`) + num_graph_conv_layers (int, optional): Number of graph convolutional layers. + (default: :obj:`6`) + fc_feat_size (int, optional): Size of fully connected layers. + (default: :obj:`128`) + num_fc_layers (int, optional): Number of fully connected layers. + (default: :obj:`4`) + otf_graph (bool, optional): If set to :obj:`True`, compute graph edges on the fly. + (default: :obj:`False`) + cutoff (float, optional): Cutoff distance for interatomic interactions. + (default: :obj:`10.0`) + num_gaussians (int, optional): Number of Gaussians used for smearing. + (default: :obj:`50.0`) + """ + + def __init__( + self, + num_atoms, + bond_feat_dim, + num_targets, + use_pbc=True, + regress_forces=True, + atom_embedding_size=64, + num_graph_conv_layers=6, + fc_feat_size=128, + num_fc_layers=4, + otf_graph=False, + cutoff=6.0, + num_gaussians=50, + embeddings="khot", + ): + super(CGCNN, self).__init__(num_atoms, bond_feat_dim, num_targets) + self.regress_forces = regress_forces + self.use_pbc = use_pbc + self.cutoff = cutoff + self.otf_graph = otf_graph + self.max_neighbors = 50 + # Get CGCNN atom embeddings + if embeddings == "khot": + embeddings = KHOT_EMBEDDINGS + elif embeddings == "qmof": + embeddings = QMOF_KHOT_EMBEDDINGS + else: + raise ValueError( + 'embedding mnust be either "khot" for original CGCNN K-hot elemental embeddings or "qmof" for QMOF K-hot elemental embeddings' + ) + self.embedding = torch.zeros(100, len(embeddings[1])) + for i in range(100): + self.embedding[i] = torch.tensor(embeddings[i + 1]) + self.embedding_fc = nn.Linear(len(embeddings[1]), atom_embedding_size) + + self.convs = nn.ModuleList( + [ + CGCNNConv( + node_dim=atom_embedding_size, + edge_dim=bond_feat_dim, + cutoff=cutoff, + ) + for _ in range(num_graph_conv_layers) + ] + ) + + self.conv_to_fc = nn.Sequential( + nn.Linear(atom_embedding_size, fc_feat_size), nn.Softplus() + ) + + if num_fc_layers > 1: + layers = [] + for _ in range(num_fc_layers - 1): + layers.append(nn.Linear(fc_feat_size, fc_feat_size)) + layers.append(nn.Softplus()) + self.fcs = nn.Sequential(*layers) + self.fc_out = nn.Linear(fc_feat_size, self.num_targets) + + self.cutoff = cutoff + self.distance_expansion = GaussianSmearing(0.0, cutoff, num_gaussians) + + @conditional_grad(torch.enable_grad()) + def _forward(self, data): + # Get node features + if self.embedding.device != data.atomic_numbers.device: + self.embedding = self.embedding.to(data.atomic_numbers.device) + data.x = self.embedding[data.atomic_numbers.long() - 1] + + ( + edge_index, + distances, + distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + + data.edge_index = edge_index + data.edge_attr = self.distance_expansion(distances) + # Forward pass through the network + mol_feats = self._convolve(data) + mol_feats = self.conv_to_fc(mol_feats) + if hasattr(self, "fcs"): + mol_feats = self.fcs(mol_feats) + + energy = self.fc_out(mol_feats) + return energy + + def forward(self, data): + if self.regress_forces: + data.pos.requires_grad_(True) + energy = self._forward(data) + + if self.regress_forces: + forces = -1 * ( + torch.autograd.grad( + energy, + data.pos, + grad_outputs=torch.ones_like(energy), + create_graph=True, + )[0] + ) + return energy, forces + else: + return energy + + def _convolve(self, data): + """ + Returns the output of the convolution layers before they are passed + into the dense layers. + """ + node_feats = self.embedding_fc(data.x) + for f in self.convs: + node_feats = f(node_feats, data.edge_index, data.edge_attr) + mol_feats = global_mean_pool(node_feats, data.batch) + return mol_feats + + +class CGCNNConv(MessagePassing): + """Implements the message passing layer from + `"Crystal Graph Convolutional Neural Networks for an + Accurate and Interpretable Prediction of Material Properties" + `. + """ + + def __init__(self, node_dim, edge_dim, cutoff=6.0, **kwargs): + super(CGCNNConv, self).__init__(aggr="add") + self.node_feat_size = node_dim + self.edge_feat_size = edge_dim + self.cutoff = cutoff + + self.lin1 = nn.Linear( + 2 * self.node_feat_size + self.edge_feat_size, + 2 * self.node_feat_size, + ) + self.bn1 = nn.BatchNorm1d(2 * self.node_feat_size) + self.ln1 = nn.LayerNorm(self.node_feat_size) + + self.reset_parameters() + + def reset_parameters(self): + torch.nn.init.xavier_uniform_(self.lin1.weight) + + self.lin1.bias.data.fill_(0) + + self.bn1.reset_parameters() + self.ln1.reset_parameters() + + def forward(self, x, edge_index, edge_attr): + """ + Arguments: + x has shape [num_nodes, node_feat_size] + edge_index has shape [2, num_edges] + edge_attr is [num_edges, edge_feat_size] + """ + out = self.propagate( + edge_index, x=x, edge_attr=edge_attr, size=(x.size(0), x.size(0)) + ) + out = nn.Softplus()(self.ln1(out) + x) + return out + + def message(self, x_i, x_j, edge_attr): + """ + Arguments: + x_i has shape [num_edges, node_feat_size] + x_j has shape [num_edges, node_feat_size] + edge_attr has shape [num_edges, edge_feat_size] + + Returns: + tensor of shape [num_edges, node_feat_size] + """ + z = self.lin1(torch.cat([x_i, x_j, edge_attr], dim=1)) + z = self.bn1(z) + z1, z2 = z.chunk(2, dim=1) + z1 = nn.Sigmoid()(z1) + z2 = nn.Softplus()(z2) + return z1 * z2 diff --git a/ocpmodels/models/dimenet.py b/ocpmodels/models/dimenet.py new file mode 100644 index 0000000..ece70fb --- /dev/null +++ b/ocpmodels/models/dimenet.py @@ -0,0 +1,230 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +from torch import nn +from torch_geometric.nn import DimeNet, radius_graph +from torch_scatter import scatter +from torch_sparse import SparseTensor + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.models.base import BaseModel + + +@registry.register_model("dimenet") +class DimeNetWrap(DimeNet, BaseModel): + r"""Wrapper around the directional message passing neural network (DimeNet) from the + `"Directional Message Passing for Molecular Graphs" + `_ paper. + + DimeNet transforms messages based on the angle between them in a + rotation-equivariant fashion. + + Args: + num_atoms (int): Unused argument + bond_feat_dim (int): Unused argument + num_targets (int): Number of targets to predict. + use_pbc (bool, optional): If set to :obj:`True`, account for periodic boundary conditions. + (default: :obj:`True`) + regress_forces (bool, optional): If set to :obj:`True`, predict forces by differentiating + energy with respect to positions. + (default: :obj:`True`) + hidden_channels (int, optional): Number of hidden channels. + (default: :obj:`128`) + num_blocks (int, optional): Number of building blocks. + (default: :obj:`6`) + num_bilinear (int, optional): Size of the bilinear layer tensor. + (default: :obj:`8`) + num_spherical (int, optional): Number of spherical harmonics. + (default: :obj:`7`) + num_radial (int, optional): Number of radial basis functions. + (default: :obj:`6`) + otf_graph (bool, optional): If set to :obj:`True`, compute graph edges on the fly. + (default: :obj:`False`) + cutoff (float, optional): Cutoff distance for interatomic interactions. + (default: :obj:`10.0`) + envelope_exponent (int, optional): Shape of the smooth cutoff. + (default: :obj:`5`) + num_before_skip: (int, optional): Number of residual layers in the + interaction blocks before the skip connection. (default: :obj:`1`) + num_after_skip: (int, optional): Number of residual layers in the + interaction blocks after the skip connection. (default: :obj:`2`) + num_output_layers: (int, optional): Number of linear layers for the + output blocks. (default: :obj:`3`) + max_angles_per_image (int, optional): The maximum number of angles used + per image. This can be used to reduce memory usage at the cost of + model performance. (default: :obj:`1e6`) + """ + + def __init__( + self, + num_atoms, + bond_feat_dim, # not used + num_targets, + use_pbc=True, + regress_forces=True, + hidden_channels=128, + num_blocks=6, + num_bilinear=8, + num_spherical=7, + num_radial=6, + otf_graph=False, + cutoff=10.0, + envelope_exponent=5, + num_before_skip=1, + num_after_skip=2, + num_output_layers=3, + max_angles_per_image=int(1e6), + ): + self.num_targets = num_targets + self.regress_forces = regress_forces + self.use_pbc = use_pbc + self.cutoff = cutoff + self.otf_graph = otf_graph + self.max_angles_per_image = max_angles_per_image + self.max_neighbors = 50 + + super(DimeNetWrap, self).__init__( + hidden_channels=hidden_channels, + out_channels=num_targets, + num_blocks=num_blocks, + num_bilinear=num_bilinear, + num_spherical=num_spherical, + num_radial=num_radial, + cutoff=cutoff, + envelope_exponent=envelope_exponent, + num_before_skip=num_before_skip, + num_after_skip=num_after_skip, + num_output_layers=num_output_layers, + ) + + def triplets(self, edge_index, cell_offsets, num_nodes): + row, col = edge_index # j->i + + value = torch.arange(row.size(0), device=row.device) + adj_t = SparseTensor( + row=col, col=row, value=value, sparse_sizes=(num_nodes, num_nodes) + ) + adj_t_row = adj_t[row] + num_triplets = adj_t_row.set_value(None).sum(dim=1).to(torch.long) + + # Node indices (k->j->i) for triplets. + idx_i = col.repeat_interleave(num_triplets) + idx_j = row.repeat_interleave(num_triplets) + idx_k = adj_t_row.storage.col() + + # Edge indices (k->j, j->i) for triplets. + idx_kj = adj_t_row.storage.value() + idx_ji = adj_t_row.storage.row() + + # Remove self-loop triplets d->b->d + # Check atom as well as cell offset + cell_offset_kji = cell_offsets[idx_kj] + cell_offsets[idx_ji] + mask = (idx_i != idx_k) | torch.any(cell_offset_kji != 0, dim=-1) + + idx_i, idx_j, idx_k = idx_i[mask], idx_j[mask], idx_k[mask] + idx_kj, idx_ji = idx_kj[mask], idx_ji[mask] + + return col, row, idx_i, idx_j, idx_k, idx_kj, idx_ji + + @conditional_grad(torch.enable_grad()) + def _forward(self, data): + pos = data.pos + batch = data.batch + ( + edge_index, + dist, + _, + cell_offsets, + offsets, + neighbors, + ) = self.generate_graph(data) + + data.edge_index = edge_index + data.cell_offsets = cell_offsets + data.neighbors = neighbors + j, i = edge_index + + _, _, idx_i, idx_j, idx_k, idx_kj, idx_ji = self.triplets( + edge_index, + data.cell_offsets, + num_nodes=data.atomic_numbers.size(0), + ) + + # Cap no. of triplets during training. + if self.training: + sub_ix = torch.randperm(idx_i.size(0))[ + : self.max_angles_per_image * data.natoms.size(0) + ] + idx_i, idx_j, idx_k = ( + idx_i[sub_ix], + idx_j[sub_ix], + idx_k[sub_ix], + ) + idx_kj, idx_ji = idx_kj[sub_ix], idx_ji[sub_ix] + + # Calculate angles. + pos_i = pos[idx_i].detach() + pos_j = pos[idx_j].detach() + if self.use_pbc: + pos_ji, pos_kj = ( + pos[idx_j].detach() - pos_i + offsets[idx_ji], + pos[idx_k].detach() - pos_j + offsets[idx_kj], + ) + else: + pos_ji, pos_kj = ( + pos[idx_j].detach() - pos_i, + pos[idx_k].detach() - pos_j, + ) + + a = (pos_ji * pos_kj).sum(dim=-1) + b = torch.cross(pos_ji, pos_kj).norm(dim=-1) + angle = torch.atan2(b, a) + + rbf = self.rbf(dist) + sbf = self.sbf(dist, angle, idx_kj) + + # Embedding block. + x = self.emb(data.atomic_numbers.long(), rbf, i, j) + P = self.output_blocks[0](x, rbf, i, num_nodes=pos.size(0)) + + # Interaction blocks. + for interaction_block, output_block in zip( + self.interaction_blocks, self.output_blocks[1:] + ): + x = interaction_block(x, rbf, sbf, idx_kj, idx_ji) + P += output_block(x, rbf, i, num_nodes=pos.size(0)) + + energy = P.sum(dim=0) if batch is None else scatter(P, batch, dim=0) + return energy + + def forward(self, data): + if self.regress_forces: + data.pos.requires_grad_(True) + energy = self._forward(data) + + if self.regress_forces: + forces = -1 * ( + torch.autograd.grad( + energy, + data.pos, + grad_outputs=torch.ones_like(energy), + create_graph=True, + )[0] + ) + return energy, forces + else: + return energy + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) diff --git a/ocpmodels/models/dimenet_plus_plus.py b/ocpmodels/models/dimenet_plus_plus.py new file mode 100644 index 0000000..a973fa6 --- /dev/null +++ b/ocpmodels/models/dimenet_plus_plus.py @@ -0,0 +1,471 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. + +--- + +This code borrows heavily from the DimeNet implementation as part of +pytorch-geometric: https://github.com/rusty1s/pytorch_geometric. License: + +--- + +Copyright (c) 2020 Matthias Fey + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. +""" + +import torch +from torch import nn +from torch_geometric.nn import radius_graph +from torch_geometric.nn.inits import glorot_orthogonal + +from torch_geometric.nn.models.dimenet import ( + BesselBasisLayer, + EmbeddingBlock, + Envelope, + ResidualLayer, + SphericalBasisLayer, +) +from torch_geometric.nn.resolver import activation_resolver +from torch_scatter import scatter +from torch_sparse import SparseTensor + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.models.base import BaseModel + +try: + import sympy as sym +except ImportError: + sym = None + + +class InteractionPPBlock(torch.nn.Module): + def __init__( + self, + hidden_channels, + int_emb_size, + basis_emb_size, + num_spherical, + num_radial, + num_before_skip, + num_after_skip, + act="silu", + ): + act = activation_resolver(act) + super(InteractionPPBlock, self).__init__() + self.act = act + + # Transformations of Bessel and spherical basis representations. + self.lin_rbf1 = nn.Linear(num_radial, basis_emb_size, bias=False) + self.lin_rbf2 = nn.Linear(basis_emb_size, hidden_channels, bias=False) + self.lin_sbf1 = nn.Linear( + num_spherical * num_radial, basis_emb_size, bias=False + ) + self.lin_sbf2 = nn.Linear(basis_emb_size, int_emb_size, bias=False) + + # Dense transformations of input messages. + self.lin_kj = nn.Linear(hidden_channels, hidden_channels) + self.lin_ji = nn.Linear(hidden_channels, hidden_channels) + + # Embedding projections for interaction triplets. + self.lin_down = nn.Linear(hidden_channels, int_emb_size, bias=False) + self.lin_up = nn.Linear(int_emb_size, hidden_channels, bias=False) + + # Residual layers before and after skip connection. + self.layers_before_skip = torch.nn.ModuleList( + [ + ResidualLayer(hidden_channels, act) + for _ in range(num_before_skip) + ] + ) + self.lin = nn.Linear(hidden_channels, hidden_channels) + self.layers_after_skip = torch.nn.ModuleList( + [ + ResidualLayer(hidden_channels, act) + for _ in range(num_after_skip) + ] + ) + + self.reset_parameters() + + def reset_parameters(self): + glorot_orthogonal(self.lin_rbf1.weight, scale=2.0) + glorot_orthogonal(self.lin_rbf2.weight, scale=2.0) + glorot_orthogonal(self.lin_sbf1.weight, scale=2.0) + glorot_orthogonal(self.lin_sbf2.weight, scale=2.0) + + glorot_orthogonal(self.lin_kj.weight, scale=2.0) + self.lin_kj.bias.data.fill_(0) + glorot_orthogonal(self.lin_ji.weight, scale=2.0) + self.lin_ji.bias.data.fill_(0) + + glorot_orthogonal(self.lin_down.weight, scale=2.0) + glorot_orthogonal(self.lin_up.weight, scale=2.0) + + for res_layer in self.layers_before_skip: + res_layer.reset_parameters() + glorot_orthogonal(self.lin.weight, scale=2.0) + self.lin.bias.data.fill_(0) + for res_layer in self.layers_after_skip: + res_layer.reset_parameters() + + def forward(self, x, rbf, sbf, idx_kj, idx_ji): + # Initial transformations. + x_ji = self.act(self.lin_ji(x)) + x_kj = self.act(self.lin_kj(x)) + + # Transformation via Bessel basis. + rbf = self.lin_rbf1(rbf) + rbf = self.lin_rbf2(rbf) + x_kj = x_kj * rbf + + # Down-project embeddings and generate interaction triplet embeddings. + x_kj = self.act(self.lin_down(x_kj)) + + # Transform via 2D spherical basis. + sbf = self.lin_sbf1(sbf) + sbf = self.lin_sbf2(sbf) + x_kj = x_kj[idx_kj] * sbf + + # Aggregate interactions and up-project embeddings. + x_kj = scatter(x_kj, idx_ji, dim=0, dim_size=x.size(0)) + x_kj = self.act(self.lin_up(x_kj)) + + h = x_ji + x_kj + for layer in self.layers_before_skip: + h = layer(h) + h = self.act(self.lin(h)) + x + for layer in self.layers_after_skip: + h = layer(h) + + return h + + +class OutputPPBlock(torch.nn.Module): + def __init__( + self, + num_radial, + hidden_channels, + out_emb_channels, + out_channels, + num_layers, + act="silu", + ): + act = activation_resolver(act) + super(OutputPPBlock, self).__init__() + self.act = act + + self.lin_rbf = nn.Linear(num_radial, hidden_channels, bias=False) + self.lin_up = nn.Linear(hidden_channels, out_emb_channels, bias=True) + self.lins = torch.nn.ModuleList() + for _ in range(num_layers): + self.lins.append(nn.Linear(out_emb_channels, out_emb_channels)) + self.lin = nn.Linear(out_emb_channels, out_channels, bias=False) + + self.reset_parameters() + + def reset_parameters(self): + glorot_orthogonal(self.lin_rbf.weight, scale=2.0) + glorot_orthogonal(self.lin_up.weight, scale=2.0) + for lin in self.lins: + glorot_orthogonal(lin.weight, scale=2.0) + lin.bias.data.fill_(0) + self.lin.weight.data.fill_(0) + + def forward(self, x, rbf, i, num_nodes=None): + x = self.lin_rbf(rbf) * x + x = scatter(x, i, dim=0, dim_size=num_nodes) + x = self.lin_up(x) + for lin in self.lins: + x = self.act(lin(x)) + return self.lin(x) + + +class DimeNetPlusPlus(torch.nn.Module): + r"""DimeNet++ implementation based on https://github.com/klicperajo/dimenet. + + Args: + hidden_channels (int): Hidden embedding size. + out_channels (int): Size of each output sample. + num_blocks (int): Number of building blocks. + int_emb_size (int): Embedding size used for interaction triplets + basis_emb_size (int): Embedding size used in the basis transformation + out_emb_channels(int): Embedding size used for atoms in the output block + num_spherical (int): Number of spherical harmonics. + num_radial (int): Number of radial basis functions. + cutoff: (float, optional): Cutoff distance for interatomic + interactions. (default: :obj:`5.0`) + envelope_exponent (int, optional): Shape of the smooth cutoff. + (default: :obj:`5`) + num_before_skip: (int, optional): Number of residual layers in the + interaction blocks before the skip connection. (default: :obj:`1`) + num_after_skip: (int, optional): Number of residual layers in the + interaction blocks after the skip connection. (default: :obj:`2`) + num_output_layers: (int, optional): Number of linear layers for the + output blocks. (default: :obj:`3`) + act: (function, optional): The activation funtion. + (default: :obj:`silu`) + """ + + url = "https://github.com/klicperajo/dimenet/raw/master/pretrained" + + def __init__( + self, + hidden_channels, + out_channels, + num_blocks, + int_emb_size, + basis_emb_size, + out_emb_channels, + num_spherical, + num_radial, + cutoff=5.0, + envelope_exponent=5, + num_before_skip=1, + num_after_skip=2, + num_output_layers=3, + act="silu", + ): + + act = activation_resolver(act) + + super(DimeNetPlusPlus, self).__init__() + + self.cutoff = cutoff + + if sym is None: + raise ImportError("Package `sympy` could not be found.") + + self.num_blocks = num_blocks + + self.rbf = BesselBasisLayer(num_radial, cutoff, envelope_exponent) + self.sbf = SphericalBasisLayer( + num_spherical, num_radial, cutoff, envelope_exponent + ) + + self.emb = EmbeddingBlock(num_radial, hidden_channels, act) + + self.output_blocks = torch.nn.ModuleList( + [ + OutputPPBlock( + num_radial, + hidden_channels, + out_emb_channels, + out_channels, + num_output_layers, + act, + ) + for _ in range(num_blocks + 1) + ] + ) + + self.interaction_blocks = torch.nn.ModuleList( + [ + InteractionPPBlock( + hidden_channels, + int_emb_size, + basis_emb_size, + num_spherical, + num_radial, + num_before_skip, + num_after_skip, + act, + ) + for _ in range(num_blocks) + ] + ) + + self.reset_parameters() + + def reset_parameters(self): + self.rbf.reset_parameters() + self.emb.reset_parameters() + for out in self.output_blocks: + out.reset_parameters() + for interaction in self.interaction_blocks: + interaction.reset_parameters() + + def triplets(self, edge_index, cell_offsets, num_nodes): + row, col = edge_index # j->i + + value = torch.arange(row.size(0), device=row.device) + adj_t = SparseTensor( + row=col, col=row, value=value, sparse_sizes=(num_nodes, num_nodes) + ) + adj_t_row = adj_t[row] + num_triplets = adj_t_row.set_value(None).sum(dim=1).to(torch.long) + + # Node indices (k->j->i) for triplets. + idx_i = col.repeat_interleave(num_triplets) + idx_j = row.repeat_interleave(num_triplets) + idx_k = adj_t_row.storage.col() + + # Edge indices (k->j, j->i) for triplets. + idx_kj = adj_t_row.storage.value() + idx_ji = adj_t_row.storage.row() + + # Remove self-loop triplets d->b->d + # Check atom as well as cell offset + cell_offset_kji = cell_offsets[idx_kj] + cell_offsets[idx_ji] + mask = (idx_i != idx_k) | torch.any(cell_offset_kji != 0, dim=-1) + + idx_i, idx_j, idx_k = idx_i[mask], idx_j[mask], idx_k[mask] + idx_kj, idx_ji = idx_kj[mask], idx_ji[mask] + + return col, row, idx_i, idx_j, idx_k, idx_kj, idx_ji + + def forward(self, z, pos, batch=None): + """ """ + raise NotImplementedError + + +@registry.register_model("dimenetplusplus") +class DimeNetPlusPlusWrap(DimeNetPlusPlus, BaseModel): + def __init__( + self, + num_atoms, + bond_feat_dim, # not used + num_targets, + use_pbc=True, + regress_forces=True, + hidden_channels=128, + num_blocks=4, + int_emb_size=64, + basis_emb_size=8, + out_emb_channels=256, + num_spherical=7, + num_radial=6, + otf_graph=False, + cutoff=10.0, + envelope_exponent=5, + num_before_skip=1, + num_after_skip=2, + num_output_layers=3, + ): + self.num_targets = num_targets + self.regress_forces = regress_forces + self.use_pbc = use_pbc + self.cutoff = cutoff + self.otf_graph = otf_graph + self.max_neighbors = 50 + + super(DimeNetPlusPlusWrap, self).__init__( + hidden_channels=hidden_channels, + out_channels=num_targets, + num_blocks=num_blocks, + int_emb_size=int_emb_size, + basis_emb_size=basis_emb_size, + out_emb_channels=out_emb_channels, + num_spherical=num_spherical, + num_radial=num_radial, + cutoff=cutoff, + envelope_exponent=envelope_exponent, + num_before_skip=num_before_skip, + num_after_skip=num_after_skip, + num_output_layers=num_output_layers, + ) + + @conditional_grad(torch.enable_grad()) + def _forward(self, data): + pos = data.pos + batch = data.batch + ( + edge_index, + dist, + _, + cell_offsets, + offsets, + neighbors, + ) = self.generate_graph(data) + + data.edge_index = edge_index + data.cell_offsets = cell_offsets + data.neighbors = neighbors + j, i = edge_index + + _, _, idx_i, idx_j, idx_k, idx_kj, idx_ji = self.triplets( + edge_index, + data.cell_offsets, + num_nodes=data.atomic_numbers.size(0), + ) + + # Calculate angles. + pos_i = pos[idx_i].detach() + pos_j = pos[idx_j].detach() + if self.use_pbc: + pos_ji, pos_kj = ( + pos[idx_j].detach() - pos_i + offsets[idx_ji], + pos[idx_k].detach() - pos_j + offsets[idx_kj], + ) + else: + pos_ji, pos_kj = ( + pos[idx_j].detach() - pos_i, + pos[idx_k].detach() - pos_j, + ) + + a = (pos_ji * pos_kj).sum(dim=-1) + b = torch.cross(pos_ji, pos_kj).norm(dim=-1) + angle = torch.atan2(b, a) + + rbf = self.rbf(dist) + sbf = self.sbf(dist, angle, idx_kj) + + # Embedding block. + x = self.emb(data.atomic_numbers.long(), rbf, i, j) + P = self.output_blocks[0](x, rbf, i, num_nodes=pos.size(0)) + + # Interaction blocks. + for interaction_block, output_block in zip( + self.interaction_blocks, self.output_blocks[1:] + ): + x = interaction_block(x, rbf, sbf, idx_kj, idx_ji) + P += output_block(x, rbf, i, num_nodes=pos.size(0)) + + energy = P.sum(dim=0) if batch is None else scatter(P, batch, dim=0) + + return energy + + def forward(self, data): + if self.regress_forces: + data.pos.requires_grad_(True) + energy = self._forward(data) + + if self.regress_forces: + forces = -1 * ( + torch.autograd.grad( + energy, + data.pos, + grad_outputs=torch.ones_like(energy), + create_graph=True, + )[0] + ) + return energy, forces + else: + return energy + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) diff --git a/ocpmodels/models/equiformer_v2/Jd.pt b/ocpmodels/models/equiformer_v2/Jd.pt new file mode 100644 index 0000000..01ed13e Binary files /dev/null and b/ocpmodels/models/equiformer_v2/Jd.pt differ diff --git a/ocpmodels/models/equiformer_v2/LICENSE b/ocpmodels/models/equiformer_v2/LICENSE new file mode 100644 index 0000000..fa84361 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Yi-Lun Liao + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/ocpmodels/models/equiformer_v2/README.md b/ocpmodels/models/equiformer_v2/README.md new file mode 100644 index 0000000..ea13864 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/README.md @@ -0,0 +1,92 @@ +# EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations + +Yi-Lun Liao, Brandon Wood, Abhishek Das*, Tess Smidt* + +[[`arXiv:2306.12059`](https://arxiv.org/abs/2306.12059)] + +NOTE: Please refer to the [official EquiformerV2 codebase](https://github.com/atomicarchitects/equiformer_v2) +for installation instructions and for up-to-date code that reproduces numbers in +the paper. + +The version of EquiformerV2 code within this OCP repository is meant to make it +easier to use EquiformerV2 as part of the OCP toolkit and to ease future +development. + +## OC20 checkpoints and configs + +We provide model weights for EquiformerV2 trained on S2EF-2M dataset for 30 epochs, +EquiformerV2 (31M) trained on S2EF-All+MD, and EquiformerV2 (153M) trained on S2EF-All+MD. + +| Model | Training Split | Download | S2EF val force MAE (meV / Å) | S2EF val energy MAE (meV) | Test results | +| ----- | -------------- | -------- | ---------------------------- | ------------------------- | ------------ | +|EquiformerV2 (83M) |2M |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_06/oc20/s2ef/eq2_83M_2M.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/equiformer_v2/equiformer_v2_N@12_L@6_M@2.yml) |19.4 | 278 | - | +|EquiformerV2 (31M)|All+MD |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_06/oc20/s2ef/eq2_31M_ec4_allmd.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/equiformer_v2/equiformer_v2_N@8_L@4_M@2_31M.yml) |16.3 | 232 | [S2EF](https://evalai.s3.amazonaws.com/media/submission_files/submission_289655/7208829e-f32b-4b61-aab3-a1c26b3e67da.json) \| [IS2RE](https://evalai.s3.amazonaws.com/media/submission_files/submission_289660/4b4da09a-9d67-4e83-9a3a-8e9c0e4b763f.json) \| [IS2RS](https://evalai.s3.amazonaws.com/media/submission_files/submission_289662/d38ac10a-e692-4354-a8c1-5af169f35640.json) | +|EquiformerV2 (153M) |All+MD | [checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_06/oc20/s2ef/eq2_153M_ec4_allmd.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/equiformer_v2/equiformer_v2_N@20_L@6_M@3_153M.yml) |15.0 | 227 | [S2EF](https://evalai.s3.amazonaws.com/media/submission_files/submission_277316/064d8657-4901-4c8b-89d2-5b13a171188d.json) \| [IS2RE](https://evalai.s3.amazonaws.com/media/submission_files/submission_277553/61652a78-539b-457d-927d-43a1f756d3a5.json) \| [IS2RS](https://evalai.s3.amazonaws.com/media/submission_files/submission_277562/c573bba6-156e-48c6-8a4e-e1293e1ce99b.json) | + +## OC22 checkpoints and configs + +| Model | Download | S2EF-Total val force MAE (meV / Å) | S2EF-Total val energy MAE (meV) | Test results | +| ----- | -------- | ---------------------------- | ------------------------- | ------------ | +|EquiformerV2 ($\lambda_E$=4, $\lambda_F$=100, 121M) |[checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_10/oc22/s2ef/eq2_121M_e4_f100_oc22_s2ef.pt) \| [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/oc22/s2ef/equiformer_v2/equiformer_v2_N@18_L@6_M@2_e4_f100_121M.yml) |26.9 |547 |[S2EF-Total](https://evalai.s3.amazonaws.com/media/submission_files/submission_309299/fbcc2a91-b21a-4bcd-a0a1-757fff48a5ea.json) | + +### OC22 energy prediction + +For the energy targets, instead of using the total DFT energies directly, we +reference them using per-element linear fit reference energies, followed by +normalizing the referenced energy distribution. + +That is, during training, target $E = \frac{E_{DFT} - E_{ref} - E_{mean}}{E_{std}}$, and during testing/inference, the total DFT energy prediction $\hat{E_{DFT}}$ is given as $\hat{E} \times E_{std} + E_{ref} + E_{mean}$ where +$E_{DFT}$ = raw DFT energy, +$E_{ref}$ = reference energy ([per-element reference energies available here for OC22](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/oc22/linref/oc22_linfit_coeffs.npz)), +$E_{mean}$ = normalizer mean, computed after subtracting per-element references (=0 for OC22), +$E_{std}$ = normalizer standard deviation, computed after subtracting per-element references (=25.12 for OC22), +$\hat{E}$ = predicted energy, +$\hat{E_{DFT}}$ = predicted total DFT energy. + +We can also write this as +$\hat{E_{DFT}} = E_{std} \times (\hat{E} + \frac{E_{ref}}{E_{std}}) + E_{mean}$, +which makes it a little easier to handle it in the current version of the code. + +$\frac{E_{ref}}{E_{std}}$ comes packaged as part of the checkpoint above and +can be used during inference using the `use_energy_lin_ref` flag in the config. + +During training / finetuning, the OC22 dataloader handles the energy referencing, +so set `use_energy_lin_ref=False`. + +## Running EquiformerV2 + +* If you haven't trained OCP models before and are specifically interested in EquiformerV2, +the training / validation scripts provided in the [official EquiformerV2 codebase](https://github.com/atomicarchitects/equiformer_v2/tree/main) +might be easier to get started. +* We provide a [slightly modified trainer](https://github.com/Open-Catalyst-Project/ocp/blob/main/ocpmodels/models/equiformer_v2/trainers/forces_trainer.py) and LR scheduler. The differences +from the parent `forces` trainer are the following: + - Support for cosine LR scheduler. + - When using the LR scheduler, it first converts the epochs into number of + steps and then passes it to the scheduler. That way in the config + everything can be specified in terms of epochs. +* To run training ([similar workflow as other OCP models](https://github.com/Open-Catalyst-Project/ocp/blob/main/TRAIN.md#structure-to-energy-and-forces-s2ef)): + ```bash + python main.py \ + --config-yml configs/s2ef/2M/equiformer_v2/equiformer_v2_N@12_L@6_M@2.yml \ + --mode train + ``` +* To run validation with a pretrained model checkpoint: + ```bash + python main.py \ + --config-yml configs/s2ef/2M/equiformer_v2/equiformer_v2_N@12_L@6_M@2.yml \ + --checkpoint path/to/checkpoint.pt \ + --mode validate + ``` + +## Citing + +If you use EquiformerV2 in your work, please consider citing: + +```bibtex +@article{equiformer_v2, + title={{EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations}}, + author={Yi-Lun Liao and Brandon Wood and Abhishek Das* and Tess Smidt*}, + journal={arxiv preprint arxiv:2306.12059}, + year={2023}, +} +``` diff --git a/ocpmodels/models/equiformer_v2/__init__.py b/ocpmodels/models/equiformer_v2/__init__.py new file mode 100644 index 0000000..74cbb33 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/__init__.py @@ -0,0 +1 @@ +from .equiformer_v2_oc20 import EquiformerV2_OC20 as EquiformerV2 diff --git a/ocpmodels/models/equiformer_v2/activation.py b/ocpmodels/models/equiformer_v2/activation.py new file mode 100644 index 0000000..7aee6a8 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/activation.py @@ -0,0 +1,202 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ScaledSiLU(nn.Module): + def __init__(self, inplace: bool = False) -> None: + super(ScaledSiLU, self).__init__() + self.inplace = inplace + self.scale_factor = 1.6791767923989418 + + def forward(self, inputs): + return F.silu(inputs, inplace=self.inplace) * self.scale_factor + + def extra_repr(self): + str = "scale_factor={}".format(self.scale_factor) + if self.inplace: + str = str + ", inplace=True" + return str + + +# Reference: https://github.com/facebookresearch/llama/blob/main/llama/model.py#L175 +class ScaledSwiGLU(nn.Module): + def __init__( + self, in_channels: int, out_channels: int, bias: bool = True + ) -> None: + super(ScaledSwiGLU, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.w = torch.nn.Linear(in_channels, 2 * out_channels, bias=bias) + self.act = ScaledSiLU() + + def forward(self, inputs): + w = self.w(inputs) + w_1 = w.narrow(-1, 0, self.out_channels) + w_1 = self.act(w_1) + w_2 = w.narrow(-1, self.out_channels, self.out_channels) + out = w_1 * w_2 + return out + + +# Reference: https://github.com/facebookresearch/llama/blob/main/llama/model.py#L175 +class SwiGLU(nn.Module): + def __init__( + self, in_channels: int, out_channels: int, bias: bool = True + ) -> None: + super(SwiGLU, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.w = torch.nn.Linear(in_channels, 2 * out_channels, bias=bias) + self.act = torch.nn.SiLU() + + def forward(self, inputs): + w = self.w(inputs) + w_1 = w.narrow(-1, 0, self.out_channels) + w_1 = self.act(w_1) + w_2 = w.narrow(-1, self.out_channels, self.out_channels) + out = w_1 * w_2 + return out + + +class SmoothLeakyReLU(torch.nn.Module): + def __init__(self, negative_slope: float = 0.2) -> None: + super().__init__() + self.alpha = negative_slope + + def forward(self, x): + x1 = ((1 + self.alpha) / 2) * x + x2 = ((1 - self.alpha) / 2) * x * (2 * torch.sigmoid(x) - 1) + return x1 + x2 + + def extra_repr(self): + return "negative_slope={}".format(self.alpha) + + +class ScaledSmoothLeakyReLU(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.act = SmoothLeakyReLU(0.2) + self.scale_factor = 1.531320475574866 + + def forward(self, x): + return self.act(x) * self.scale_factor + + def extra_repr(self): + return "negative_slope={}, scale_factor={}".format( + self.act.alpha, self.scale_factor + ) + + +class ScaledSigmoid(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.scale_factor = 1.8467055342154763 + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.sigmoid(x) * self.scale_factor + + +class GateActivation(torch.nn.Module): + def __init__(self, lmax: int, mmax: int, num_channels: int) -> None: + super().__init__() + + self.lmax = lmax + self.mmax = mmax + self.num_channels = num_channels + + # compute `expand_index` based on `lmax` and `mmax` + num_components = 0 + for lval in range(1, self.lmax + 1): + num_m_components = min((2 * lval + 1), (2 * self.mmax + 1)) + num_components = num_components + num_m_components + expand_index = torch.zeros([num_components]).long() + start_idx = 0 + for lval in range(1, self.lmax + 1): + length = min((2 * lval + 1), (2 * self.mmax + 1)) + expand_index[start_idx : (start_idx + length)] = lval - 1 + start_idx = start_idx + length + self.register_buffer("expand_index", expand_index) + + self.scalar_act = ( + torch.nn.SiLU() + ) # SwiGLU(self.num_channels, self.num_channels) # # + self.gate_act = torch.nn.Sigmoid() # torch.nn.SiLU() # # + + def forward(self, gating_scalars, input_tensors): + """ + `gating_scalars`: shape [N, lmax * num_channels] + `input_tensors`: shape [N, (lmax + 1) ** 2, num_channels] + """ + + gating_scalars = self.gate_act(gating_scalars) + gating_scalars = gating_scalars.reshape( + gating_scalars.shape[0], self.lmax, self.num_channels + ) + gating_scalars = torch.index_select( + gating_scalars, dim=1, index=self.expand_index + ) + + input_tensors_scalars = input_tensors.narrow(1, 0, 1) + input_tensors_scalars = self.scalar_act(input_tensors_scalars) + + input_tensors_vectors = input_tensors.narrow( + 1, 1, input_tensors.shape[1] - 1 + ) + input_tensors_vectors = input_tensors_vectors * gating_scalars + + output_tensors = torch.cat( + (input_tensors_scalars, input_tensors_vectors), dim=1 + ) + + return output_tensors + + +class S2Activation(torch.nn.Module): + """ + Assume we only have one resolution + """ + + def __init__(self, lmax: int, mmax: int) -> None: + super().__init__() + self.lmax = lmax + self.mmax = mmax + self.act = torch.nn.SiLU() + + def forward(self, inputs, SO3_grid): + to_grid_mat = SO3_grid[self.lmax][self.mmax].get_to_grid_mat( + device=None + ) # `device` is not used + from_grid_mat = SO3_grid[self.lmax][self.mmax].get_from_grid_mat( + device=None + ) + x_grid = torch.einsum("bai, zic -> zbac", to_grid_mat, inputs) + x_grid = self.act(x_grid) + outputs = torch.einsum("bai, zbac -> zic", from_grid_mat, x_grid) + return outputs + + +class SeparableS2Activation(torch.nn.Module): + def __init__(self, lmax: int, mmax: int) -> None: + super().__init__() + + self.lmax = lmax + self.mmax = mmax + + self.scalar_act = torch.nn.SiLU() + self.s2_act = S2Activation(self.lmax, self.mmax) + + def forward(self, input_scalars, input_tensors, SO3_grid): + output_scalars = self.scalar_act(input_scalars) + output_scalars = output_scalars.reshape( + output_scalars.shape[0], 1, output_scalars.shape[-1] + ) + output_tensors = self.s2_act(input_tensors, SO3_grid) + outputs = torch.cat( + ( + output_scalars, + output_tensors.narrow(1, 1, output_tensors.shape[1] - 1), + ), + dim=1, + ) + return outputs diff --git a/ocpmodels/models/equiformer_v2/drop.py b/ocpmodels/models/equiformer_v2/drop.py new file mode 100644 index 0000000..f30ec1f --- /dev/null +++ b/ocpmodels/models/equiformer_v2/drop.py @@ -0,0 +1,151 @@ +""" + Add `extra_repr` into DropPath implemented by timm + for displaying more info. +""" + + +import torch +import torch.nn as nn +import torch.nn.functional as F +from e3nn import o3 + + +def drop_path( + x: torch.Tensor, drop_prob: float = 0.0, training: bool = False +) -> torch.Tensor: + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand( + shape, dtype=x.dtype, device=x.device + ) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float) -> None: + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "drop_prob={}".format(self.drop_prob) + + +class GraphDropPath(nn.Module): + """ + Consider batch for graph data when dropping paths. + """ + + def __init__(self, drop_prob: float) -> None: + super(GraphDropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x: torch.Tensor, batch) -> torch.Tensor: + batch_size = batch.max() + 1 + shape = (batch_size,) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + ones = torch.ones(shape, dtype=x.dtype, device=x.device) + drop = drop_path(ones, self.drop_prob, self.training) + out = x * drop[batch] + return out + + def extra_repr(self) -> str: + return "drop_prob={}".format(self.drop_prob) + + +class EquivariantDropout(nn.Module): + def __init__(self, irreps, drop_prob: float) -> None: + super(EquivariantDropout, self).__init__() + self.irreps = irreps + self.num_irreps = irreps.num_irreps + self.drop_prob = drop_prob + self.drop = torch.nn.Dropout(drop_prob, True) + self.mul = o3.ElementwiseTensorProduct( + irreps, o3.Irreps("{}x0e".format(self.num_irreps)) + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if not self.training or self.drop_prob == 0.0: + return x + shape = (x.shape[0], self.num_irreps) + mask = torch.ones(shape, dtype=x.dtype, device=x.device) + mask = self.drop(mask) + out = self.mul(x, mask) + return out + + +class EquivariantScalarsDropout(nn.Module): + def __init__(self, irreps, drop_prob: float) -> None: + super(EquivariantScalarsDropout, self).__init__() + self.irreps = irreps + self.drop_prob = drop_prob + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if not self.training or self.drop_prob == 0.0: + return x + out = [] + start_idx = 0 + for mul, ir in self.irreps: + temp = x.narrow(-1, start_idx, mul * ir.dim) + start_idx += mul * ir.dim + if ir.is_scalar(): + temp = F.dropout( + temp, p=self.drop_prob, training=self.training + ) + out.append(temp) + out = torch.cat(out, dim=-1) + return out + + def extra_repr(self) -> str: + return "irreps={}, drop_prob={}".format(self.irreps, self.drop_prob) + + +class EquivariantDropoutArraySphericalHarmonics(nn.Module): + def __init__(self, drop_prob: float, drop_graph: bool = False) -> None: + super(EquivariantDropoutArraySphericalHarmonics, self).__init__() + self.drop_prob = drop_prob + self.drop = torch.nn.Dropout(drop_prob, True) + self.drop_graph = drop_graph + + def forward(self, x: torch.Tensor, batch=None) -> torch.Tensor: + if not self.training or self.drop_prob == 0.0: + return x + assert len(x.shape) == 3 + + if self.drop_graph: + assert batch is not None + batch_size = batch.max() + 1 + shape = (batch_size, 1, x.shape[2]) + mask = torch.ones(shape, dtype=x.dtype, device=x.device) + mask = self.drop(mask) + out = x * mask[batch] + else: + shape = (x.shape[0], 1, x.shape[2]) + mask = torch.ones(shape, dtype=x.dtype, device=x.device) + mask = self.drop(mask) + out = x * mask + + return out + + def extra_repr(self) -> str: + return "drop_prob={}, drop_graph={}".format( + self.drop_prob, self.drop_graph + ) diff --git a/ocpmodels/models/equiformer_v2/edge_rot_mat.py b/ocpmodels/models/equiformer_v2/edge_rot_mat.py new file mode 100644 index 0000000..c190f58 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/edge_rot_mat.py @@ -0,0 +1,63 @@ +import logging + +import torch + + +def init_edge_rot_mat(edge_distance_vec): + edge_vec_0 = edge_distance_vec + edge_vec_0_distance = torch.sqrt(torch.sum(edge_vec_0**2, dim=1)) + + # Make sure the atoms are far enough apart + # assert torch.min(edge_vec_0_distance) < 0.0001 + if torch.min(edge_vec_0_distance) < 0.0001: + logging.error( + "Error edge_vec_0_distance: {}".format( + torch.min(edge_vec_0_distance) + ) + ) + + norm_x = edge_vec_0 / (edge_vec_0_distance.view(-1, 1)) + + edge_vec_2 = torch.rand_like(edge_vec_0) - 0.5 + edge_vec_2 = edge_vec_2 / ( + torch.sqrt(torch.sum(edge_vec_2**2, dim=1)).view(-1, 1) + ) + # Create two rotated copys of the random vectors in case the random vector is aligned with norm_x + # With two 90 degree rotated vectors, at least one should not be aligned with norm_x + edge_vec_2b = edge_vec_2.clone() + edge_vec_2b[:, 0] = -edge_vec_2[:, 1] + edge_vec_2b[:, 1] = edge_vec_2[:, 0] + edge_vec_2c = edge_vec_2.clone() + edge_vec_2c[:, 1] = -edge_vec_2[:, 2] + edge_vec_2c[:, 2] = edge_vec_2[:, 1] + vec_dot_b = torch.abs(torch.sum(edge_vec_2b * norm_x, dim=1)).view(-1, 1) + vec_dot_c = torch.abs(torch.sum(edge_vec_2c * norm_x, dim=1)).view(-1, 1) + + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)).view(-1, 1) + edge_vec_2 = torch.where( + torch.gt(vec_dot, vec_dot_b), edge_vec_2b, edge_vec_2 + ) + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)).view(-1, 1) + edge_vec_2 = torch.where( + torch.gt(vec_dot, vec_dot_c), edge_vec_2c, edge_vec_2 + ) + + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)) + # Check the vectors aren't aligned + assert torch.max(vec_dot) < 0.99 + + norm_z = torch.cross(norm_x, edge_vec_2, dim=1) + norm_z = norm_z / (torch.sqrt(torch.sum(norm_z**2, dim=1, keepdim=True))) + norm_z = norm_z / (torch.sqrt(torch.sum(norm_z**2, dim=1)).view(-1, 1)) + norm_y = torch.cross(norm_x, norm_z, dim=1) + norm_y = norm_y / (torch.sqrt(torch.sum(norm_y**2, dim=1, keepdim=True))) + + # Construct the 3D rotation matrix + norm_x = norm_x.view(-1, 3, 1) + norm_y = -norm_y.view(-1, 3, 1) + norm_z = norm_z.view(-1, 3, 1) + + edge_rot_mat_inv = torch.cat([norm_z, norm_x, norm_y], dim=2) + edge_rot_mat = torch.transpose(edge_rot_mat_inv, 1, 2) + + return edge_rot_mat.detach() diff --git a/ocpmodels/models/equiformer_v2/equiformer_v2_oc20.py b/ocpmodels/models/equiformer_v2/equiformer_v2_oc20.py new file mode 100644 index 0000000..93598b6 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/equiformer_v2_oc20.py @@ -0,0 +1,607 @@ +import logging +import math +from typing import List, Optional + +import torch +import torch.nn as nn + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import conditional_grad +from ocpmodels.models.base import BaseModel +from ocpmodels.models.scn.smearing import GaussianSmearing + +try: + pass +except ImportError: + pass + + +from .edge_rot_mat import init_edge_rot_mat +from .gaussian_rbf import GaussianRadialBasisLayer +from .input_block import EdgeDegreeEmbedding +from .layer_norm import ( + EquivariantLayerNormArray, + EquivariantLayerNormArraySphericalHarmonics, + EquivariantRMSNormArraySphericalHarmonics, + EquivariantRMSNormArraySphericalHarmonicsV2, + get_normalization_layer, +) +from .module_list import ModuleListInfo +from .radial_function import RadialFunction +from .so3 import ( + CoefficientMappingModule, + SO3_Embedding, + SO3_Grid, + SO3_LinearV2, + SO3_Rotation, +) +from .transformer_block import ( + FeedForwardNetwork, + SO2EquivariantGraphAttention, + TransBlockV2, +) + +# Statistics of IS2RE 100K +_AVG_NUM_NODES = 77.81317 +_AVG_DEGREE = ( + 23.395238876342773 # IS2RE: 100k, max_radius = 5, max_neighbors = 100 +) + + +@registry.register_model("equiformer_v2") +class EquiformerV2_OC20(BaseModel): + """ + Equiformer with graph attention built upon SO(2) convolution and feedforward network built upon S2 activation + + Args: + use_pbc (bool): Use periodic boundary conditions + regress_forces (bool): Compute forces + otf_graph (bool): Compute graph On The Fly (OTF) + max_neighbors (int): Maximum number of neighbors per atom + max_radius (float): Maximum distance between nieghboring atoms in Angstroms + max_num_elements (int): Maximum atomic number + + num_layers (int): Number of layers in the GNN + sphere_channels (int): Number of spherical channels (one set per resolution) + attn_hidden_channels (int): Number of hidden channels used during SO(2) graph attention + num_heads (int): Number of attention heads + attn_alpha_head (int): Number of channels for alpha vector in each attention head + attn_value_head (int): Number of channels for value vector in each attention head + ffn_hidden_channels (int): Number of hidden channels used during feedforward network + norm_type (str): Type of normalization layer (['layer_norm', 'layer_norm_sh', 'rms_norm_sh']) + + lmax_list (int): List of maximum degree of the spherical harmonics (1 to 10) + mmax_list (int): List of maximum order of the spherical harmonics (0 to lmax) + grid_resolution (int): Resolution of SO3_Grid + + num_sphere_samples (int): Number of samples used to approximate the integration of the sphere in the output blocks + + edge_channels (int): Number of channels for the edge invariant features + use_atom_edge_embedding (bool): Whether to use atomic embedding along with relative distance for edge scalar features + share_atom_edge_embedding (bool): Whether to share `atom_edge_embedding` across all blocks + use_m_share_rad (bool): Whether all m components within a type-L vector of one channel share radial function weights + distance_function ("gaussian", "sigmoid", "linearsigmoid", "silu"): Basis function used for distances + + attn_activation (str): Type of activation function for SO(2) graph attention + use_s2_act_attn (bool): Whether to use attention after S2 activation. Otherwise, use the same attention as Equiformer + use_attn_renorm (bool): Whether to re-normalize attention weights + ffn_activation (str): Type of activation function for feedforward network + use_gate_act (bool): If `True`, use gate activation. Otherwise, use S2 activation + use_grid_mlp (bool): If `True`, use projecting to grids and performing MLPs for FFNs. + use_sep_s2_act (bool): If `True`, use separable S2 activation when `use_gate_act` is False. + + alpha_drop (float): Dropout rate for attention weights + drop_path_rate (float): Drop path rate + proj_drop (float): Dropout rate for outputs of attention and FFN in Transformer blocks + + weight_init (str): ['normal', 'uniform'] initialization of weights of linear layers except those in radial functions + enforce_max_neighbors_strictly (bool): When edges are subselected based on the `max_neighbors` arg, arbitrarily select amongst equidistant / degenerate edges to have exactly the correct number. + avg_num_nodes (float): Average number of nodes per graph + avg_degree (float): Average degree of nodes in the graph + + use_energy_lin_ref (bool): Whether to add the per-atom energy references during prediction. + During training and validation, this should be kept `False` since we use the `lin_ref` parameter in the OC22 dataloader to subtract the per-atom linear references from the energy targets. + During prediction (where we don't have energy targets), this can be set to `True` to add the per-atom linear references to the predicted energies. + load_energy_lin_ref (bool): Whether to add nn.Parameters for the per-element energy references. + This additional flag is there to ensure compatibility when strict-loading checkpoints, since the `use_energy_lin_ref` flag can be either True or False even if the model is trained with linear references. + You can't have use_energy_lin_ref = True and load_energy_lin_ref = False, since the model will not have the parameters for the linear references. All other combinations are fine. + """ + + def __init__( + self, + num_atoms: int, # not used + bond_feat_dim: int, # not used + num_targets: int, # not used + use_pbc: bool = True, + regress_forces: bool = True, + otf_graph: bool = True, + max_neighbors: int = 500, + max_radius: float = 5.0, + max_num_elements: int = 90, + num_layers: int = 12, + sphere_channels: int = 128, + attn_hidden_channels: int = 128, + num_heads: int = 8, + attn_alpha_channels: int = 32, + attn_value_channels: int = 16, + ffn_hidden_channels: int = 512, + norm_type: str = "rms_norm_sh", + lmax_list: List[int] = [6], + mmax_list: List[int] = [2], + grid_resolution: Optional[int] = None, + num_sphere_samples: int = 128, + edge_channels: int = 128, + use_atom_edge_embedding: bool = True, + share_atom_edge_embedding: bool = False, + use_m_share_rad: bool = False, + distance_function: str = "gaussian", + num_distance_basis: int = 512, + attn_activation: str = "scaled_silu", + use_s2_act_attn: bool = False, + use_attn_renorm: bool = True, + ffn_activation: str = "scaled_silu", + use_gate_act: bool = False, + use_grid_mlp: bool = False, + use_sep_s2_act: bool = True, + alpha_drop: float = 0.1, + drop_path_rate: float = 0.05, + proj_drop: float = 0.0, + weight_init: str = "normal", + enforce_max_neighbors_strictly: bool = True, + avg_num_nodes: Optional[float] = None, + avg_degree: Optional[float] = None, + use_energy_lin_ref: Optional[bool] = False, + load_energy_lin_ref: Optional[bool] = False, + ): + super().__init__() + + import sys + + if "e3nn" not in sys.modules: + logging.error( + "You need to install e3nn==0.4.4 to use EquiformerV2." + ) + raise ImportError + + self.use_pbc = use_pbc + self.regress_forces = regress_forces + self.otf_graph = otf_graph + self.max_neighbors = max_neighbors + self.max_radius = max_radius + self.cutoff = max_radius + self.max_num_elements = max_num_elements + + self.num_layers = num_layers + self.sphere_channels = sphere_channels + self.attn_hidden_channels = attn_hidden_channels + self.num_heads = num_heads + self.attn_alpha_channels = attn_alpha_channels + self.attn_value_channels = attn_value_channels + self.ffn_hidden_channels = ffn_hidden_channels + self.norm_type = norm_type + + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.grid_resolution = grid_resolution + + self.num_sphere_samples = num_sphere_samples + + self.edge_channels = edge_channels + self.use_atom_edge_embedding = use_atom_edge_embedding + self.share_atom_edge_embedding = share_atom_edge_embedding + if self.share_atom_edge_embedding: + assert self.use_atom_edge_embedding + self.block_use_atom_edge_embedding = False + else: + self.block_use_atom_edge_embedding = self.use_atom_edge_embedding + self.use_m_share_rad = use_m_share_rad + self.distance_function = distance_function + self.num_distance_basis = num_distance_basis + + self.attn_activation = attn_activation + self.use_s2_act_attn = use_s2_act_attn + self.use_attn_renorm = use_attn_renorm + self.ffn_activation = ffn_activation + self.use_gate_act = use_gate_act + self.use_grid_mlp = use_grid_mlp + self.use_sep_s2_act = use_sep_s2_act + + self.alpha_drop = alpha_drop + self.drop_path_rate = drop_path_rate + self.proj_drop = proj_drop + + self.avg_num_nodes = avg_num_nodes or _AVG_NUM_NODES + self.avg_degree = avg_degree or _AVG_DEGREE + + self.use_energy_lin_ref = use_energy_lin_ref + self.load_energy_lin_ref = load_energy_lin_ref + assert not ( + self.use_energy_lin_ref and not self.load_energy_lin_ref + ), "You can't have use_energy_lin_ref = True and load_energy_lin_ref = False, since the model will not have the parameters for the linear references. All other combinations are fine." + + self.weight_init = weight_init + assert self.weight_init in ["normal", "uniform"] + + self.enforce_max_neighbors_strictly = enforce_max_neighbors_strictly + + self.device = "cpu" # torch.cuda.current_device() + + self.grad_forces = False + self.num_resolutions: int = len(self.lmax_list) + self.sphere_channels_all: int = ( + self.num_resolutions * self.sphere_channels + ) + + # Weights for message initialization + self.sphere_embedding = nn.Embedding( + self.max_num_elements, self.sphere_channels_all + ) + + # Initialize the function used to measure the distances between atoms + assert self.distance_function in [ + "gaussian", + ] + if self.distance_function == "gaussian": + self.distance_expansion = GaussianSmearing( + 0.0, + self.cutoff, + 600, + 2.0, + ) + # self.distance_expansion = GaussianRadialBasisLayer(num_basis=self.num_distance_basis, cutoff=self.max_radius) + else: + raise ValueError + + # Initialize the sizes of radial functions (input channels and 2 hidden channels) + self.edge_channels_list = [int(self.distance_expansion.num_output)] + [ + self.edge_channels + ] * 2 + + # Initialize atom edge embedding + if self.share_atom_edge_embedding and self.use_atom_edge_embedding: + self.source_embedding = nn.Embedding( + self.max_num_elements, self.edge_channels_list[-1] + ) + self.target_embedding = nn.Embedding( + self.max_num_elements, self.edge_channels_list[-1] + ) + self.edge_channels_list[0] = ( + self.edge_channels_list[0] + 2 * self.edge_channels_list[-1] + ) + else: + self.source_embedding, self.target_embedding = None, None + + # Initialize the module that compute WignerD matrices and other values for spherical harmonic calculations + self.SO3_rotation = nn.ModuleList() + for i in range(self.num_resolutions): + self.SO3_rotation.append(SO3_Rotation(self.lmax_list[i])) + + # Initialize conversion between degree l and order m layouts + self.mappingReduced = CoefficientMappingModule( + self.lmax_list, self.mmax_list + ) + + # Initialize the transformations between spherical and grid representations + self.SO3_grid = ModuleListInfo( + "({}, {})".format(max(self.lmax_list), max(self.lmax_list)) + ) + for lval in range(max(self.lmax_list) + 1): + SO3_m_grid = nn.ModuleList() + for m in range(max(self.lmax_list) + 1): + SO3_m_grid.append( + SO3_Grid( + lval, + m, + resolution=self.grid_resolution, + normalization="component", + ) + ) + self.SO3_grid.append(SO3_m_grid) + + # Edge-degree embedding + self.edge_degree_embedding = EdgeDegreeEmbedding( + self.sphere_channels, + self.lmax_list, + self.mmax_list, + self.SO3_rotation, + self.mappingReduced, + self.max_num_elements, + self.edge_channels_list, + self.block_use_atom_edge_embedding, + rescale_factor=self.avg_degree, + ) + + # Initialize the blocks for each layer of EquiformerV2 + self.blocks = nn.ModuleList() + for i in range(self.num_layers): + block = TransBlockV2( + self.sphere_channels, + self.attn_hidden_channels, + self.num_heads, + self.attn_alpha_channels, + self.attn_value_channels, + self.ffn_hidden_channels, + self.sphere_channels, + self.lmax_list, + self.mmax_list, + self.SO3_rotation, + self.mappingReduced, + self.SO3_grid, + self.max_num_elements, + self.edge_channels_list, + self.block_use_atom_edge_embedding, + self.use_m_share_rad, + self.attn_activation, + self.use_s2_act_attn, + self.use_attn_renorm, + self.ffn_activation, + self.use_gate_act, + self.use_grid_mlp, + self.use_sep_s2_act, + self.norm_type, + self.alpha_drop, + self.drop_path_rate, + self.proj_drop, + ) + self.blocks.append(block) + + # Output blocks for energy and forces + self.norm = get_normalization_layer( + self.norm_type, + lmax=max(self.lmax_list), + num_channels=self.sphere_channels, + ) + self.energy_block = FeedForwardNetwork( + self.sphere_channels, + self.ffn_hidden_channels, + 1, + self.lmax_list, + self.mmax_list, + self.SO3_grid, + self.ffn_activation, + self.use_gate_act, + self.use_grid_mlp, + self.use_sep_s2_act, + ) + if self.regress_forces: + self.force_block = SO2EquivariantGraphAttention( + self.sphere_channels, + self.attn_hidden_channels, + self.num_heads, + self.attn_alpha_channels, + self.attn_value_channels, + 1, + self.lmax_list, + self.mmax_list, + self.SO3_rotation, + self.mappingReduced, + self.SO3_grid, + self.max_num_elements, + self.edge_channels_list, + self.block_use_atom_edge_embedding, + self.use_m_share_rad, + self.attn_activation, + self.use_s2_act_attn, + self.use_attn_renorm, + self.use_gate_act, + self.use_sep_s2_act, + alpha_drop=0.0, + ) + + if self.load_energy_lin_ref: + self.energy_lin_ref = nn.Parameter( + torch.zeros(self.max_num_elements), + requires_grad=False, + ) + + self.apply(self._init_weights) + self.apply(self._uniform_init_rad_func_linear_weights) + + @conditional_grad(torch.enable_grad()) + def forward(self, data): + self.batch_size = len(data.natoms) + self.dtype = data.pos.dtype + self.device = data.pos.device + + atomic_numbers = data.atomic_numbers.long() + num_atoms = len(atomic_numbers) + + ( + edge_index, + edge_distance, + edge_distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph( + data, + enforce_max_neighbors_strictly=self.enforce_max_neighbors_strictly, + ) + + ############################################################### + # Initialize data structures + ############################################################### + + # Compute 3x3 rotation matrix per edge + edge_rot_mat = self._init_edge_rot_mat( + data, edge_index, edge_distance_vec + ) + + # Initialize the WignerD matrices and other values for spherical harmonic calculations + for i in range(self.num_resolutions): + self.SO3_rotation[i].set_wigner(edge_rot_mat) + + ############################################################### + # Initialize node embeddings + ############################################################### + + # Init per node representations using an atomic number based embedding + offset = 0 + x = SO3_Embedding( + num_atoms, + self.lmax_list, + self.sphere_channels, + self.device, + self.dtype, + ) + + offset_res = 0 + offset = 0 + # Initialize the l = 0, m = 0 coefficients for each resolution + for i in range(self.num_resolutions): + if self.num_resolutions == 1: + x.embedding[:, offset_res, :] = self.sphere_embedding( + atomic_numbers + ) + else: + x.embedding[:, offset_res, :] = self.sphere_embedding( + atomic_numbers + )[:, offset : offset + self.sphere_channels] + offset = offset + self.sphere_channels + offset_res = offset_res + int((self.lmax_list[i] + 1) ** 2) + + # Edge encoding (distance and atom edge) + edge_distance = self.distance_expansion(edge_distance) + if self.share_atom_edge_embedding and self.use_atom_edge_embedding: + source_element = atomic_numbers[ + edge_index[0] + ] # Source atom atomic number + target_element = atomic_numbers[ + edge_index[1] + ] # Target atom atomic number + source_embedding = self.source_embedding(source_element) + target_embedding = self.target_embedding(target_element) + edge_distance = torch.cat( + (edge_distance, source_embedding, target_embedding), dim=1 + ) + + # Edge-degree embedding + edge_degree = self.edge_degree_embedding( + atomic_numbers, edge_distance, edge_index + ) + x.embedding = x.embedding + edge_degree.embedding + + ############################################################### + # Update spherical node embeddings + ############################################################### + + for i in range(self.num_layers): + x = self.blocks[i]( + x, # SO3_Embedding + atomic_numbers, + edge_distance, + edge_index, + batch=data.batch, # for GraphDropPath + ) + + # Final layer norm + x.embedding = self.norm(x.embedding) + + ############################################################### + # Energy estimation + ############################################################### + node_energy = self.energy_block(x) + node_energy = node_energy.embedding.narrow(1, 0, 1) + energy = torch.zeros( + len(data.natoms), + device=node_energy.device, + dtype=node_energy.dtype, + ) + energy.index_add_(0, data.batch, node_energy.view(-1)) + energy = energy / self.avg_num_nodes + + # Add the per-atom linear references to the energy. + if self.use_energy_lin_ref and self.load_energy_lin_ref: + # During training, target E = (E_DFT - E_ref - E_mean) / E_std, and + # during inference, \hat{E_DFT} = \hat{E} * E_std + E_ref + E_mean + # where + # + # E_DFT = raw DFT energy, + # E_ref = reference energy, + # E_mean = normalizer mean, + # E_std = normalizer std, + # \hat{E} = predicted energy, + # \hat{E_DFT} = predicted DFT energy. + # + # We can also write this as + # \hat{E_DFT} = E_std * (\hat{E} + E_ref / E_std) + E_mean, + # which is why we save E_ref / E_std as the linear reference. + with torch.cuda.amp.autocast(False): + energy = energy.to(self.energy_lin_ref.dtype).index_add( + 0, + data.batch, + self.energy_lin_ref[atomic_numbers], + ) + + outputs = {"energy": energy} + ############################################################### + # Force estimation + ############################################################### + if self.regress_forces: + forces = self.force_block( + x, atomic_numbers, edge_distance, edge_index + ) + forces = forces.embedding.narrow(1, 1, 3) + forces = forces.view(-1, 3) + outputs["forces"] = forces + + return outputs + + # Initialize the edge rotation matrics + def _init_edge_rot_mat(self, data, edge_index, edge_distance_vec): + return init_edge_rot_mat(edge_distance_vec) + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) + + def _init_weights(self, m): + if isinstance(m, torch.nn.Linear) or isinstance(m, SO3_LinearV2): + if m.bias is not None: + torch.nn.init.constant_(m.bias, 0) + if self.weight_init == "normal": + std = 1 / math.sqrt(m.in_features) + torch.nn.init.normal_(m.weight, 0, std) + + elif isinstance(m, torch.nn.LayerNorm): + torch.nn.init.constant_(m.bias, 0) + torch.nn.init.constant_(m.weight, 1.0) + + def _uniform_init_rad_func_linear_weights(self, m): + if isinstance(m, RadialFunction): + m.apply(self._uniform_init_linear_weights) + + def _uniform_init_linear_weights(self, m): + if isinstance(m, torch.nn.Linear): + if m.bias is not None: + torch.nn.init.constant_(m.bias, 0) + std = 1 / math.sqrt(m.in_features) + torch.nn.init.uniform_(m.weight, -std, std) + + @torch.jit.ignore + def no_weight_decay(self) -> set: + no_wd_list = [] + named_parameters_list = [name for name, _ in self.named_parameters()] + for module_name, module in self.named_modules(): + if isinstance( + module, + ( + torch.nn.Linear, + SO3_LinearV2, + torch.nn.LayerNorm, + EquivariantLayerNormArray, + EquivariantLayerNormArraySphericalHarmonics, + EquivariantRMSNormArraySphericalHarmonics, + EquivariantRMSNormArraySphericalHarmonicsV2, + GaussianRadialBasisLayer, + ), + ): + for parameter_name, _ in module.named_parameters(): + if isinstance(module, (torch.nn.Linear, SO3_LinearV2)): + if "weight" in parameter_name: + continue + global_parameter_name = module_name + "." + parameter_name + assert global_parameter_name in named_parameters_list + no_wd_list.append(global_parameter_name) + + return set(no_wd_list) diff --git a/ocpmodels/models/equiformer_v2/gaussian_rbf.py b/ocpmodels/models/equiformer_v2/gaussian_rbf.py new file mode 100644 index 0000000..140c1a0 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/gaussian_rbf.py @@ -0,0 +1,51 @@ +import torch + + +@torch.jit.script +def gaussian(x: torch.Tensor, mean, std) -> torch.Tensor: + pi = 3.14159 + a = (2 * pi) ** 0.5 + return torch.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std) + + +# From Graphormer +class GaussianRadialBasisLayer(torch.nn.Module): + def __init__(self, num_basis: int, cutoff: float) -> None: + super().__init__() + self.num_basis = num_basis + self.cutoff = cutoff + 0.0 + self.mean = torch.nn.Parameter(torch.zeros(1, self.num_basis)) + self.std = torch.nn.Parameter(torch.zeros(1, self.num_basis)) + self.weight = torch.nn.Parameter(torch.ones(1, 1)) + self.bias = torch.nn.Parameter(torch.zeros(1, 1)) + + self.std_init_max = 1.0 + self.std_init_min = 1.0 / self.num_basis + self.mean_init_max = 1.0 + self.mean_init_min = 0 + torch.nn.init.uniform_( + self.mean, self.mean_init_min, self.mean_init_max + ) + torch.nn.init.uniform_(self.std, self.std_init_min, self.std_init_max) + torch.nn.init.constant_(self.weight, 1) + torch.nn.init.constant_(self.bias, 0) + + def forward( + self, dist: torch.Tensor, node_atom=None, edge_src=None, edge_dst=None + ): + x = dist / self.cutoff + x = x.unsqueeze(-1) + x = self.weight * x + self.bias + x = x.expand(-1, self.num_basis) + mean = self.mean + std = self.std.abs() + 1e-5 + x = gaussian(x, mean, std) + return x + + def extra_repr(self): + return "mean_init_max={}, mean_init_min={}, std_init_max={}, std_init_min={}".format( + self.mean_init_max, + self.mean_init_min, + self.std_init_max, + self.std_init_min, + ) diff --git a/ocpmodels/models/equiformer_v2/input_block.py b/ocpmodels/models/equiformer_v2/input_block.py new file mode 100644 index 0000000..62fe590 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/input_block.py @@ -0,0 +1,138 @@ +import copy +from typing import List + +import torch +import torch.nn as nn + +from .radial_function import RadialFunction +from .so3 import SO3_Embedding + + +class EdgeDegreeEmbedding(torch.nn.Module): + """ + + Args: + sphere_channels (int): Number of spherical channels + + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + + SO3_rotation (list:SO3_Rotation): Class to calculate Wigner-D matrices and rotate embeddings + mappingReduced (CoefficientMappingModule): Class to convert l and m indices once node embedding is rotated + + max_num_elements (int): Maximum number of atomic numbers + edge_channels_list (list:int): List of sizes of invariant edge embedding. For example, [input_channels, hidden_channels, hidden_channels]. + The last one will be used as hidden size when `use_atom_edge_embedding` is `True`. + use_atom_edge_embedding (bool): Whether to use atomic embedding along with relative distance for edge scalar features + + rescale_factor (float): Rescale the sum aggregation + """ + + def __init__( + self, + sphere_channels: int, + lmax_list: List[int], + mmax_list: List[int], + SO3_rotation, + mappingReduced, + max_num_elements: int, + edge_channels_list, + use_atom_edge_embedding: bool, + rescale_factor, + ): + super(EdgeDegreeEmbedding, self).__init__() + self.sphere_channels = sphere_channels + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions = len(self.lmax_list) + self.SO3_rotation = SO3_rotation + self.mappingReduced = mappingReduced + + self.m_0_num_coefficients: int = self.mappingReduced.m_size[0] + self.m_all_num_coefficents: int = len(self.mappingReduced.l_harmonic) + + # Create edge scalar (invariant to rotations) features + # Embedding function of the atomic numbers + self.max_num_elements = max_num_elements + self.edge_channels_list = copy.deepcopy(edge_channels_list) + self.use_atom_edge_embedding = use_atom_edge_embedding + + if self.use_atom_edge_embedding: + self.source_embedding = nn.Embedding( + self.max_num_elements, self.edge_channels_list[-1] + ) + self.target_embedding = nn.Embedding( + self.max_num_elements, self.edge_channels_list[-1] + ) + nn.init.uniform_(self.source_embedding.weight.data, -0.001, 0.001) + nn.init.uniform_(self.target_embedding.weight.data, -0.001, 0.001) + self.edge_channels_list[0] = ( + self.edge_channels_list[0] + 2 * self.edge_channels_list[-1] + ) + else: + self.source_embedding, self.target_embedding = None, None + + # Embedding function of distance + self.edge_channels_list.append( + self.m_0_num_coefficients * self.sphere_channels + ) + self.rad_func = RadialFunction(self.edge_channels_list) + + self.rescale_factor = rescale_factor + + def forward(self, atomic_numbers, edge_distance, edge_index): + + if self.use_atom_edge_embedding: + source_element = atomic_numbers[ + edge_index[0] + ] # Source atom atomic number + target_element = atomic_numbers[ + edge_index[1] + ] # Target atom atomic number + source_embedding = self.source_embedding(source_element) + target_embedding = self.target_embedding(target_element) + x_edge = torch.cat( + (edge_distance, source_embedding, target_embedding), dim=1 + ) + else: + x_edge = edge_distance + + x_edge_m_0 = self.rad_func(x_edge) + x_edge_m_0 = x_edge_m_0.reshape( + -1, self.m_0_num_coefficients, self.sphere_channels + ) + x_edge_m_pad = torch.zeros( + ( + x_edge_m_0.shape[0], + (self.m_all_num_coefficents - self.m_0_num_coefficients), + self.sphere_channels, + ), + device=x_edge_m_0.device, + ) + x_edge_m_all = torch.cat((x_edge_m_0, x_edge_m_pad), dim=1) + + x_edge_embedding = SO3_Embedding( + 0, + self.lmax_list.copy(), + self.sphere_channels, + device=x_edge_m_all.device, + dtype=x_edge_m_all.dtype, + ) + x_edge_embedding.set_embedding(x_edge_m_all) + x_edge_embedding.set_lmax_mmax( + self.lmax_list.copy(), self.mmax_list.copy() + ) + + # Reshape the spherical harmonics based on l (degree) + x_edge_embedding._l_primary(self.mappingReduced) + + # Rotate back the irreps + x_edge_embedding._rotate_inv(self.SO3_rotation, self.mappingReduced) + + # Compute the sum of the incoming neighboring messages for each target node + x_edge_embedding._reduce_edge(edge_index[1], atomic_numbers.shape[0]) + x_edge_embedding.embedding = ( + x_edge_embedding.embedding / self.rescale_factor + ) + + return x_edge_embedding diff --git a/ocpmodels/models/equiformer_v2/layer_norm.py b/ocpmodels/models/equiformer_v2/layer_norm.py new file mode 100644 index 0000000..b5a0c03 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/layer_norm.py @@ -0,0 +1,477 @@ +""" + 1. Normalize features of shape (N, sphere_basis, C), + with sphere_basis = (lmax + 1) ** 2. + + 2. The difference from `layer_norm.py` is that all type-L vectors have + the same number of channels and input features are of shape (N, sphere_basis, C). +""" + +import math + +import torch +import torch.nn as nn + + +def get_normalization_layer( + norm_type: str, + lmax: int, + num_channels: int, + eps: float = 1e-5, + affine: bool = True, + normalization: str = "component", +): + assert norm_type in ["layer_norm", "layer_norm_sh", "rms_norm_sh"] + if norm_type == "layer_norm": + norm_class = EquivariantLayerNormArray + elif norm_type == "layer_norm_sh": + norm_class = EquivariantLayerNormArraySphericalHarmonics + elif norm_type == "rms_norm_sh": + norm_class = EquivariantRMSNormArraySphericalHarmonicsV2 + else: + raise ValueError + return norm_class(lmax, num_channels, eps, affine, normalization) + + +def get_l_to_all_m_expand_index(lmax: int): + expand_index = torch.zeros([(lmax + 1) ** 2]).long() + for lval in range(lmax + 1): + start_idx = lval**2 + length = 2 * lval + 1 + expand_index[start_idx : (start_idx + length)] = lval + return expand_index + + +class EquivariantLayerNormArray(nn.Module): + def __init__( + self, + lmax: int, + num_channels: int, + eps: float = 1e-5, + affine: bool = True, + normalization: str = "component", + ): + super().__init__() + + self.lmax = lmax + self.num_channels = num_channels + self.eps = eps + self.affine = affine + + if affine: + self.affine_weight = nn.Parameter( + torch.ones(lmax + 1, num_channels) + ) + self.affine_bias = nn.Parameter(torch.zeros(num_channels)) + else: + self.register_parameter("affine_weight", None) + self.register_parameter("affine_bias", None) + + assert normalization in ["norm", "component"] + self.normalization = normalization + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(lmax={self.lmax}, num_channels={self.num_channels}, eps={self.eps})" + + @torch.cuda.amp.autocast(enabled=False) + def forward(self, node_input): + """ + Assume input is of shape [N, sphere_basis, C] + """ + + out = [] + + for lval in range(self.lmax + 1): + start_idx = lval**2 + length = 2 * lval + 1 + + feature = node_input.narrow(1, start_idx, length) + + # For scalars, first compute and subtract the mean + if lval == 0: + feature_mean = torch.mean(feature, dim=2, keepdim=True) + feature = feature - feature_mean + + # Then compute the rescaling factor (norm of each feature vector) + # Rescaling of the norms themselves based on the option "normalization" + if self.normalization == "norm": + feature_norm = feature.pow(2).sum( + dim=1, keepdim=True + ) # [N, 1, C] + elif self.normalization == "component": + feature_norm = feature.pow(2).mean( + dim=1, keepdim=True + ) # [N, 1, C] + + feature_norm = torch.mean( + feature_norm, dim=2, keepdim=True + ) # [N, 1, 1] + feature_norm = (feature_norm + self.eps).pow(-0.5) + + if self.affine: + weight = self.affine_weight.narrow(0, lval, 1) # [1, C] + weight = weight.view(1, 1, -1) # [1, 1, C] + feature_norm = feature_norm * weight # [N, 1, C] + + feature = feature * feature_norm + + if self.affine and lval == 0: + bias = self.affine_bias + bias = bias.view(1, 1, -1) + feature = feature + bias + + out.append(feature) + + out = torch.cat(out, dim=1) + + return out + + +class EquivariantLayerNormArraySphericalHarmonics(nn.Module): + """ + 1. Normalize over L = 0. + 2. Normalize across all m components from degrees L > 0. + 3. Do not normalize separately for different L (L > 0). + """ + + def __init__( + self, + lmax: int, + num_channels: int, + eps: float = 1e-5, + affine: bool = True, + normalization: str = "component", + std_balance_degrees: bool = True, + ): + super().__init__() + + self.lmax = lmax + self.num_channels = num_channels + self.eps = eps + self.affine = affine + self.std_balance_degrees = std_balance_degrees + + # for L = 0 + self.norm_l0 = torch.nn.LayerNorm( + self.num_channels, eps=self.eps, elementwise_affine=self.affine + ) + + # for L > 0 + if self.affine: + self.affine_weight = nn.Parameter( + torch.ones(self.lmax, self.num_channels) + ) + else: + self.register_parameter("affine_weight", None) + + assert normalization in ["norm", "component"] + self.normalization = normalization + + if self.std_balance_degrees: + balance_degree_weight = torch.zeros((self.lmax + 1) ** 2 - 1, 1) + for lval in range(1, self.lmax + 1): + start_idx = lval**2 - 1 + length = 2 * lval + 1 + balance_degree_weight[start_idx : (start_idx + length), :] = ( + 1.0 / length + ) + balance_degree_weight = balance_degree_weight / self.lmax + self.register_buffer( + "balance_degree_weight", balance_degree_weight + ) + else: + self.balance_degree_weight = None + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(lmax={self.lmax}, num_channels={self.num_channels}, eps={self.eps}, std_balance_degrees={self.std_balance_degrees})" + + @torch.cuda.amp.autocast(enabled=False) + def forward(self, node_input): + """ + Assume input is of shape [N, sphere_basis, C] + """ + + out = [] + + # for L = 0 + feature = node_input.narrow(1, 0, 1) + feature = self.norm_l0(feature) + out.append(feature) + + # for L > 0 + if self.lmax > 0: + num_m_components = (self.lmax + 1) ** 2 + feature = node_input.narrow(1, 1, num_m_components - 1) + + # Then compute the rescaling factor (norm of each feature vector) + # Rescaling of the norms themselves based on the option "normalization" + if self.normalization == "norm": + feature_norm = feature.pow(2).sum( + dim=1, keepdim=True + ) # [N, 1, C] + elif self.normalization == "component": + if self.std_balance_degrees: + feature_norm = feature.pow( + 2 + ) # [N, (L_max + 1)**2 - 1, C], without L = 0 + feature_norm = torch.einsum( + "nic, ia -> nac", + feature_norm, + self.balance_degree_weight, + ) # [N, 1, C] + else: + feature_norm = feature.pow(2).mean( + dim=1, keepdim=True + ) # [N, 1, C] + + feature_norm = torch.mean( + feature_norm, dim=2, keepdim=True + ) # [N, 1, 1] + feature_norm = (feature_norm + self.eps).pow(-0.5) + + for lval in range(1, self.lmax + 1): + start_idx = lval**2 + length = 2 * lval + 1 + feature = node_input.narrow( + 1, start_idx, length + ) # [N, (2L + 1), C] + if self.affine: + weight = self.affine_weight.narrow( + 0, (lval - 1), 1 + ) # [1, C] + weight = weight.view(1, 1, -1) # [1, 1, C] + feature_scale = feature_norm * weight # [N, 1, C] + else: + feature_scale = feature_norm + feature = feature * feature_scale + out.append(feature) + + out = torch.cat(out, dim=1) + return out + + +class EquivariantRMSNormArraySphericalHarmonics(nn.Module): + """ + 1. Normalize across all m components from degrees L >= 0. + """ + + def __init__( + self, + lmax: int, + num_channels: int, + eps: float = 1e-5, + affine: bool = True, + normalization: str = "component", + ): + super().__init__() + + self.lmax = lmax + self.num_channels = num_channels + self.eps = eps + self.affine = affine + + # for L >= 0 + if self.affine: + self.affine_weight = nn.Parameter( + torch.ones((self.lmax + 1), self.num_channels) + ) + else: + self.register_parameter("affine_weight", None) + + assert normalization in ["norm", "component"] + self.normalization = normalization + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(lmax={self.lmax}, num_channels={self.num_channels}, eps={self.eps})" + + @torch.cuda.amp.autocast(enabled=False) + def forward(self, node_input): + """ + Assume input is of shape [N, sphere_basis, C] + """ + + out = [] + + # for L >= 0 + feature = node_input + if self.normalization == "norm": + feature_norm = feature.pow(2).sum(dim=1, keepdim=True) # [N, 1, C] + elif self.normalization == "component": + feature_norm = feature.pow(2).mean( + dim=1, keepdim=True + ) # [N, 1, C] + + feature_norm = torch.mean( + feature_norm, dim=2, keepdim=True + ) # [N, 1, 1] + feature_norm = (feature_norm + self.eps).pow(-0.5) + + for lval in range(0, self.lmax + 1): + start_idx = lval**2 + length = 2 * lval + 1 + feature = node_input.narrow( + 1, start_idx, length + ) # [N, (2L + 1), C] + if self.affine: + weight = self.affine_weight.narrow(0, lval, 1) # [1, C] + weight = weight.view(1, 1, -1) # [1, 1, C] + feature_scale = feature_norm * weight # [N, 1, C] + else: + feature_scale = feature_norm + feature = feature * feature_scale + out.append(feature) + + out = torch.cat(out, dim=1) + return out + + +class EquivariantRMSNormArraySphericalHarmonicsV2(nn.Module): + """ + 1. Normalize across all m components from degrees L >= 0. + 2. Expand weights and multiply with normalized feature to prevent slicing and concatenation. + """ + + def __init__( + self, + lmax: int, + num_channels: int, + eps: float = 1e-5, + affine: bool = True, + normalization: str = "component", + centering: bool = True, + std_balance_degrees: bool = True, + ): + super().__init__() + + self.lmax = lmax + self.num_channels = num_channels + self.eps = eps + self.affine = affine + self.centering = centering + self.std_balance_degrees = std_balance_degrees + + # for L >= 0 + if self.affine: + self.affine_weight = nn.Parameter( + torch.ones((self.lmax + 1), self.num_channels) + ) + if self.centering: + self.affine_bias = nn.Parameter(torch.zeros(self.num_channels)) + else: + self.register_parameter("affine_bias", None) + else: + self.register_parameter("affine_weight", None) + self.register_parameter("affine_bias", None) + + assert normalization in ["norm", "component"] + self.normalization = normalization + + expand_index = get_l_to_all_m_expand_index(self.lmax) + self.register_buffer("expand_index", expand_index) + + if self.std_balance_degrees: + balance_degree_weight = torch.zeros((self.lmax + 1) ** 2, 1) + for lval in range(self.lmax + 1): + start_idx = lval**2 + length = 2 * lval + 1 + balance_degree_weight[start_idx : (start_idx + length), :] = ( + 1.0 / length + ) + balance_degree_weight = balance_degree_weight / (self.lmax + 1) + self.register_buffer( + "balance_degree_weight", balance_degree_weight + ) + else: + self.balance_degree_weight = None + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(lmax={self.lmax}, num_channels={self.num_channels}, eps={self.eps}, centering={self.centering}, std_balance_degrees={self.std_balance_degrees})" + + @torch.cuda.amp.autocast(enabled=False) + def forward(self, node_input): + """ + Assume input is of shape [N, sphere_basis, C] + """ + + feature = node_input + + if self.centering: + feature_l0 = feature.narrow(1, 0, 1) + feature_l0_mean = feature_l0.mean(dim=2, keepdim=True) # [N, 1, 1] + feature_l0 = feature_l0 - feature_l0_mean + feature = torch.cat( + (feature_l0, feature.narrow(1, 1, feature.shape[1] - 1)), dim=1 + ) + + # for L >= 0 + if self.normalization == "norm": + assert not self.std_balance_degrees + feature_norm = feature.pow(2).sum(dim=1, keepdim=True) # [N, 1, C] + elif self.normalization == "component": + if self.std_balance_degrees: + feature_norm = feature.pow(2) # [N, (L_max + 1)**2, C] + feature_norm = torch.einsum( + "nic, ia -> nac", feature_norm, self.balance_degree_weight + ) # [N, 1, C] + else: + feature_norm = feature.pow(2).mean( + dim=1, keepdim=True + ) # [N, 1, C] + + feature_norm = torch.mean( + feature_norm, dim=2, keepdim=True + ) # [N, 1, 1] + feature_norm = (feature_norm + self.eps).pow(-0.5) + + if self.affine: + weight = self.affine_weight.view( + 1, (self.lmax + 1), self.num_channels + ) # [1, L_max + 1, C] + weight = torch.index_select( + weight, dim=1, index=self.expand_index + ) # [1, (L_max + 1)**2, C] + feature_norm = feature_norm * weight # [N, (L_max + 1)**2, C] + + out = feature * feature_norm + + if self.affine and self.centering: + out[:, 0:1, :] = out.narrow(1, 0, 1) + self.affine_bias.view( + 1, 1, self.num_channels + ) + + return out + + +class EquivariantDegreeLayerScale(nn.Module): + """ + 1. Similar to Layer Scale used in CaiT (Going Deeper With Image Transformers (ICCV'21)), we scale the output of both attention and FFN. + 2. For degree L > 0, we scale down the square root of 2 * L, which is to emulate halving the number of channels when using higher L. + """ + + def __init__( + self, lmax: int, num_channels: int, scale_factor: float = 2.0 + ) -> None: + super().__init__() + + self.lmax = lmax + self.num_channels = num_channels + self.scale_factor = scale_factor + + self.affine_weight = nn.Parameter( + torch.ones(1, (self.lmax + 1), self.num_channels) + ) + for lval in range(1, self.lmax + 1): + self.affine_weight.data[0, lval, :].mul_( + 1.0 / math.sqrt(self.scale_factor * lval) + ) + expand_index = get_l_to_all_m_expand_index(self.lmax) + self.register_buffer("expand_index", expand_index) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(lmax={self.lmax}, num_channels={self.num_channels}, scale_factor={self.scale_factor})" + + def forward(self, node_input): + weight = torch.index_select( + self.affine_weight, dim=1, index=self.expand_index + ) # [1, (L_max + 1)**2, C] + node_input = node_input * weight # [N, (L_max + 1)**2, C] + return node_input diff --git a/ocpmodels/models/equiformer_v2/module_list.py b/ocpmodels/models/equiformer_v2/module_list.py new file mode 100644 index 0000000..30d5bc8 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/module_list.py @@ -0,0 +1,10 @@ +import torch + + +class ModuleListInfo(torch.nn.ModuleList): + def __init__(self, info_str, modules=None) -> None: + super().__init__(modules) + self.info_str = str(info_str) + + def __repr__(self) -> str: + return self.info_str diff --git a/ocpmodels/models/equiformer_v2/radial_function.py b/ocpmodels/models/equiformer_v2/radial_function.py new file mode 100644 index 0000000..a7c86c4 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/radial_function.py @@ -0,0 +1,32 @@ +import torch +import torch.nn as nn + + +class RadialFunction(nn.Module): + """ + Contruct a radial function (linear layers + layer normalization + SiLU) given a list of channels + """ + + def __init__(self, channels_list) -> None: + super().__init__() + modules = [] + input_channels = channels_list[0] + for i in range(len(channels_list)): + if i == 0: + continue + + modules.append( + nn.Linear(input_channels, channels_list[i], bias=True) + ) + input_channels = channels_list[i] + + if i == len(channels_list) - 1: + break + + modules.append(nn.LayerNorm(channels_list[i])) + modules.append(torch.nn.SiLU()) + + self.net = nn.Sequential(*modules) + + def forward(self, inputs): + return self.net(inputs) diff --git a/ocpmodels/models/equiformer_v2/so2_ops.py b/ocpmodels/models/equiformer_v2/so2_ops.py new file mode 100644 index 0000000..55f7613 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/so2_ops.py @@ -0,0 +1,384 @@ +import copy +import math +from typing import List, Optional + +import torch +import torch.nn as nn +from torch.nn import Linear + +from .radial_function import RadialFunction +from .so3 import SO3_Embedding + + +class SO2_m_Convolution(torch.nn.Module): + """ + SO(2) Conv: Perform an SO(2) convolution on features corresponding to +- m + + Args: + m (int): Order of the spherical harmonic coefficients + sphere_channels (int): Number of spherical channels + m_output_channels (int): Number of output channels used during the SO(2) conv + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + """ + + def __init__( + self, + m: int, + sphere_channels: int, + m_output_channels: int, + lmax_list: List[int], + mmax_list: List[int], + ) -> None: + super(SO2_m_Convolution, self).__init__() + + self.m = m + self.sphere_channels = sphere_channels + self.m_output_channels = m_output_channels + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions: int = len(self.lmax_list) + + num_channels = 0 + for i in range(self.num_resolutions): + num_coefficents = 0 + if self.mmax_list[i] >= self.m: + num_coefficents = self.lmax_list[i] - self.m + 1 + num_channels = ( + num_channels + num_coefficents * self.sphere_channels + ) + assert num_channels > 0 + + self.fc = Linear( + num_channels, + 2 + * self.m_output_channels + * (num_channels // self.sphere_channels), + bias=False, + ) + self.fc.weight.data.mul_(1 / math.sqrt(2)) + + def forward(self, x_m): + x_m = self.fc(x_m) + x_r = x_m.narrow(2, 0, self.fc.out_features // 2) + x_i = x_m.narrow( + 2, self.fc.out_features // 2, self.fc.out_features // 2 + ) + x_m_r = x_r.narrow(1, 0, 1) - x_i.narrow( + 1, 1, 1 + ) # x_r[:, 0] - x_i[:, 1] + x_m_i = x_r.narrow(1, 1, 1) + x_i.narrow( + 1, 0, 1 + ) # x_r[:, 1] + x_i[:, 0] + x_out = torch.cat((x_m_r, x_m_i), dim=1) + + return x_out + + +class SO2_Convolution(torch.nn.Module): + """ + SO(2) Block: Perform SO(2) convolutions for all m (orders) + + Args: + sphere_channels (int): Number of spherical channels + m_output_channels (int): Number of output channels used during the SO(2) conv + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + mappingReduced (CoefficientMappingModule): Used to extract a subset of m components + internal_weights (bool): If True, not using radial function to multiply inputs features + edge_channels_list (list:int): List of sizes of invariant edge embedding. For example, [input_channels, hidden_channels, hidden_channels]. + extra_m0_output_channels (int): If not None, return `out_embedding` (SO3_Embedding) and `extra_m0_features` (Tensor). + """ + + def __init__( + self, + sphere_channels: int, + m_output_channels: int, + lmax_list: List[int], + mmax_list: List[int], + mappingReduced, + internal_weights: bool = True, + edge_channels_list: Optional[List[int]] = None, + extra_m0_output_channels: Optional[int] = None, + ): + super(SO2_Convolution, self).__init__() + self.sphere_channels = sphere_channels + self.m_output_channels = m_output_channels + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.mappingReduced = mappingReduced + self.num_resolutions = len(lmax_list) + self.internal_weights = internal_weights + self.edge_channels_list = copy.deepcopy(edge_channels_list) + self.extra_m0_output_channels = extra_m0_output_channels + + num_channels_rad = 0 # for radial function + + num_channels_m0 = 0 + for i in range(self.num_resolutions): + num_coefficients = self.lmax_list[i] + 1 + num_channels_m0 = ( + num_channels_m0 + num_coefficients * self.sphere_channels + ) + + # SO(2) convolution for m = 0 + m0_output_channels = self.m_output_channels * ( + num_channels_m0 // self.sphere_channels + ) + if self.extra_m0_output_channels is not None: + m0_output_channels = ( + m0_output_channels + self.extra_m0_output_channels + ) + self.fc_m0 = Linear(num_channels_m0, m0_output_channels) + num_channels_rad = num_channels_rad + self.fc_m0.in_features + + # SO(2) convolution for non-zero m + self.so2_m_conv = nn.ModuleList() + for m in range(1, max(self.mmax_list) + 1): + self.so2_m_conv.append( + SO2_m_Convolution( + m, + self.sphere_channels, + self.m_output_channels, + self.lmax_list, + self.mmax_list, + ) + ) + num_channels_rad = ( + num_channels_rad + self.so2_m_conv[-1].fc.in_features + ) + + # Embedding function of distance + self.rad_func = None + if not self.internal_weights: + assert self.edge_channels_list is not None + self.edge_channels_list.append(int(num_channels_rad)) + self.rad_func = RadialFunction(self.edge_channels_list) + + def forward(self, x, x_edge): + + num_edges = len(x_edge) + out = [] + + # Reshape the spherical harmonics based on m (order) + x._m_primary(self.mappingReduced) + + # radial function + if self.rad_func is not None: + x_edge = self.rad_func(x_edge) + offset_rad = 0 + + # Compute m=0 coefficients separately since they only have real values (no imaginary) + x_0 = x.embedding.narrow(1, 0, self.mappingReduced.m_size[0]) + x_0 = x_0.reshape(num_edges, -1) + if self.rad_func is not None: + x_edge_0 = x_edge.narrow(1, 0, self.fc_m0.in_features) + x_0 = x_0 * x_edge_0 + x_0 = self.fc_m0(x_0) + + x_0_extra = None + # extract extra m0 features + if self.extra_m0_output_channels is not None: + x_0_extra = x_0.narrow(-1, 0, self.extra_m0_output_channels) + x_0 = x_0.narrow( + -1, + self.extra_m0_output_channels, + (self.fc_m0.out_features - self.extra_m0_output_channels), + ) + + x_0 = x_0.view(num_edges, -1, self.m_output_channels) + # x.embedding[:, 0 : self.mappingReduced.m_size[0]] = x_0 + out.append(x_0) + offset_rad = offset_rad + self.fc_m0.in_features + + # Compute the values for the m > 0 coefficients + offset = self.mappingReduced.m_size[0] + for m in range(1, max(self.mmax_list) + 1): + # Get the m order coefficients + x_m = x.embedding.narrow( + 1, offset, 2 * self.mappingReduced.m_size[m] + ) + x_m = x_m.reshape(num_edges, 2, -1) + + # Perform SO(2) convolution + if self.rad_func is not None: + x_edge_m = x_edge.narrow( + 1, offset_rad, self.so2_m_conv[m - 1].fc.in_features + ) + x_edge_m = x_edge_m.reshape( + num_edges, 1, self.so2_m_conv[m - 1].fc.in_features + ) + x_m = x_m * x_edge_m + x_m = self.so2_m_conv[m - 1](x_m) + x_m = x_m.view(num_edges, -1, self.m_output_channels) + # x.embedding[:, offset : offset + 2 * self.mappingReduced.m_size[m]] = x_m + out.append(x_m) + offset = offset + 2 * self.mappingReduced.m_size[m] + offset_rad = offset_rad + self.so2_m_conv[m - 1].fc.in_features + + out = torch.cat(out, dim=1) + out_embedding = SO3_Embedding( + 0, + x.lmax_list.copy(), + self.m_output_channels, + device=x.device, + dtype=x.dtype, + ) + out_embedding.set_embedding(out) + out_embedding.set_lmax_mmax( + self.lmax_list.copy(), self.mmax_list.copy() + ) + + # Reshape the spherical harmonics based on l (degree) + out_embedding._l_primary(self.mappingReduced) + + if self.extra_m0_output_channels is not None: + return out_embedding, x_0_extra + else: + return out_embedding + + +class SO2_Linear(torch.nn.Module): + """ + SO(2) Linear: Perform SO(2) linear for all m (orders). + + Args: + sphere_channels (int): Number of spherical channels + m_output_channels (int): Number of output channels used during the SO(2) conv + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + mappingReduced (CoefficientMappingModule): Used to extract a subset of m components + internal_weights (bool): If True, not using radial function to multiply inputs features + edge_channels_list (list:int): List of sizes of invariant edge embedding. For example, [input_channels, hidden_channels, hidden_channels]. + """ + + def __init__( + self, + sphere_channels: int, + m_output_channels: int, + lmax_list: List[int], + mmax_list: List[int], + mappingReduced, + internal_weights: bool = False, + edge_channels_list: Optional[List[int]] = None, + ): + super(SO2_Linear, self).__init__() + self.sphere_channels = sphere_channels + self.m_output_channels = m_output_channels + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.mappingReduced = mappingReduced + self.internal_weights = internal_weights + self.edge_channels_list = copy.deepcopy(edge_channels_list) + self.num_resolutions = len(lmax_list) + + num_channels_rad = 0 + + num_channels_m0 = 0 + for i in range(self.num_resolutions): + num_coefficients = self.lmax_list[i] + 1 + num_channels_m0 = ( + num_channels_m0 + num_coefficients * self.sphere_channels + ) + + # SO(2) linear for m = 0 + self.fc_m0 = Linear( + num_channels_m0, + self.m_output_channels * (num_channels_m0 // self.sphere_channels), + ) + num_channels_rad = num_channels_rad + self.fc_m0.in_features + + # SO(2) linear for non-zero m + self.so2_m_fc = nn.ModuleList() + for m in range(1, max(self.mmax_list) + 1): + num_in_channels = 0 + for i in range(self.num_resolutions): + num_coefficents = 0 + if self.mmax_list[i] >= m: + num_coefficents = self.lmax_list[i] - m + 1 + num_in_channels = ( + num_in_channels + num_coefficents * self.sphere_channels + ) + assert num_in_channels > 0 + fc = Linear( + num_in_channels, + self.m_output_channels + * (num_in_channels // self.sphere_channels), + bias=False, + ) + num_channels_rad = num_channels_rad + fc.in_features + self.so2_m_fc.append(fc) + + # Embedding function of distance + self.rad_func = None + if not self.internal_weights: + assert self.edge_channels_list is not None + self.edge_channels_list.append(int(num_channels_rad)) + self.rad_func = RadialFunction(self.edge_channels_list) + + def forward(self, x, x_edge): + + batch_size = x.embedding.shape[0] + out = [] + + # Reshape the spherical harmonics based on m (order) + x._m_primary(self.mappingReduced) + + # radial function + if self.rad_func is not None: + x_edge = self.rad_func(x_edge) + offset_rad = 0 + + # Compute m=0 coefficients separately since they only have real values (no imaginary) + x_0 = x.embedding.narrow(1, 0, self.mappingReduced.m_size[0]) + x_0 = x_0.reshape(batch_size, -1) + if self.rad_func is not None: + x_edge_0 = x_edge.narrow(1, 0, self.fc_m0.in_features) + x_0 = x_0 * x_edge_0 + x_0 = self.fc_m0(x_0) + x_0 = x_0.view(batch_size, -1, self.m_output_channels) + out.append(x_0) + offset_rad = offset_rad + self.fc_m0.in_features + + # Compute the values for the m > 0 coefficients + offset = self.mappingReduced.m_size[0] + for m in range(1, max(self.mmax_list) + 1): + # Get the m order coefficients + x_m = x.embedding.narrow( + 1, offset, 2 * self.mappingReduced.m_size[m] + ) + x_m = x_m.reshape(batch_size, 2, -1) + if self.rad_func is not None: + x_edge_m = x_edge.narrow( + 1, offset_rad, self.so2_m_fc[m - 1].in_features + ) + x_edge_m = x_edge_m.reshape( + batch_size, 1, self.so2_m_fc[m - 1].in_features + ) + x_m = x_m * x_edge_m + + # Perform SO(2) linear + x_m = self.so2_m_fc[m - 1](x_m) + x_m = x_m.view(batch_size, -1, self.m_output_channels) + out.append(x_m) + + offset = offset + 2 * self.mappingReduced.m_size[m] + offset_rad = offset_rad + self.so2_m_fc[m - 1].in_features + + out = torch.cat(out, dim=1) + out_embedding = SO3_Embedding( + 0, + x.lmax_list.copy(), + self.m_output_channels, + device=x.device, + dtype=x.dtype, + ) + out_embedding.set_embedding(out) + out_embedding.set_lmax_mmax( + self.lmax_list.copy(), self.mmax_list.copy() + ) + + # Reshape the spherical harmonics based on l (degree) + out_embedding._l_primary(self.mappingReduced) + + return out_embedding diff --git a/ocpmodels/models/equiformer_v2/so3.py b/ocpmodels/models/equiformer_v2/so3.py new file mode 100644 index 0000000..eaafad4 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/so3.py @@ -0,0 +1,748 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. + + +TODO: + 1. Simplify the case when `num_resolutions` == 1. + 2. Remove indexing when the shape is the same. + 3. Move some functions outside classes and to separate files. +""" + +import math +from typing import List, Optional + +import torch + +try: + from e3nn import o3 + from e3nn.o3 import FromS2Grid, ToS2Grid +except ImportError: + pass + +from torch.nn import Linear + +from .wigner import wigner_D + + +class CoefficientMappingModule(torch.nn.Module): + """ + Helper module for coefficients used to reshape lval <--> m and to get coefficients of specific degree or order + + Args: + lmax_list (list:int): List of maximum degree of the spherical harmonics + mmax_list (list:int): List of maximum order of the spherical harmonics + """ + + def __init__( + self, + lmax_list: List[int], + mmax_list: List[int], + ): + super().__init__() + + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions = len(lmax_list) + + # Temporarily use `cpu` as device and this will be overwritten. + self.device = "cpu" + + # Compute the degree (lval) and order (m) for each entry of the embedding + l_harmonic = torch.tensor([], device=self.device).long() + m_harmonic = torch.tensor([], device=self.device).long() + m_complex = torch.tensor([], device=self.device).long() + + res_size = torch.zeros( + [self.num_resolutions], device=self.device + ).long() + + offset = 0 + for i in range(self.num_resolutions): + for lval in range(0, self.lmax_list[i] + 1): + mmax = min(self.mmax_list[i], lval) + m = torch.arange(-mmax, mmax + 1, device=self.device).long() + m_complex = torch.cat([m_complex, m], dim=0) + m_harmonic = torch.cat( + [m_harmonic, torch.abs(m).long()], dim=0 + ) + l_harmonic = torch.cat( + [l_harmonic, m.fill_(lval).long()], dim=0 + ) + res_size[i] = len(l_harmonic) - offset + offset = len(l_harmonic) + + num_coefficients = len(l_harmonic) + # `self.to_m` moves m components from different L to contiguous index + to_m = torch.zeros( + [num_coefficients, num_coefficients], device=self.device + ) + m_size = torch.zeros( + [max(self.mmax_list) + 1], device=self.device + ).long() + + # The following is implemented poorly - very slow. It only gets called + # a few times so haven't optimized. + offset = 0 + for m in range(max(self.mmax_list) + 1): + idx_r, idx_i = self.complex_idx(m, -1, m_complex, l_harmonic) + + for idx_out, idx_in in enumerate(idx_r): + to_m[idx_out + offset, idx_in] = 1.0 + offset = offset + len(idx_r) + + m_size[m] = int(len(idx_r)) + + for idx_out, idx_in in enumerate(idx_i): + to_m[idx_out + offset, idx_in] = 1.0 + offset = offset + len(idx_i) + + to_m = to_m.detach() + + # save tensors and they will be moved to GPU + self.register_buffer("l_harmonic", l_harmonic) + self.register_buffer("m_harmonic", m_harmonic) + self.register_buffer("m_complex", m_complex) + self.register_buffer("res_size", res_size) + self.register_buffer("to_m", to_m) + self.register_buffer("m_size", m_size) + + # for caching the output of `coefficient_idx` + self.lmax_cache, self.mmax_cache = None, None + self.mask_indices_cache = None + self.rotate_inv_rescale_cache = None + + # Return mask containing coefficients of order m (real and imaginary parts) + def complex_idx(self, m: int, lmax: int, m_complex, l_harmonic): + """ + Add `m_complex` and `l_harmonic` to the input arguments + since we cannot use `self.m_complex`. + """ + if lmax == -1: + lmax = max(self.lmax_list) + + indices = torch.arange(len(l_harmonic), device=self.device) + # Real part + mask_r = torch.bitwise_and(l_harmonic.le(lmax), m_complex.eq(m)) + mask_idx_r = torch.masked_select(indices, mask_r) + + mask_idx_i = torch.tensor([], device=self.device).long() + # Imaginary part + if m != 0: + mask_i = torch.bitwise_and(l_harmonic.le(lmax), m_complex.eq(-m)) + mask_idx_i = torch.masked_select(indices, mask_i) + + return mask_idx_r, mask_idx_i + + # Return mask containing coefficients less than or equal to degree (lval) and order (m) + def coefficient_idx(self, lmax: int, mmax: int): + + if (self.lmax_cache is not None) and (self.mmax_cache is not None): + if (self.lmax_cache == lmax) and (self.mmax_cache == mmax): + if self.mask_indices_cache is not None: + return self.mask_indices_cache + + mask = torch.bitwise_and( + self.l_harmonic.le(lmax), self.m_harmonic.le(mmax) + ) + self.device = mask.device + indices = torch.arange(len(mask), device=self.device) + mask_indices = torch.masked_select(indices, mask) + self.lmax_cache, self.mmax_cache = lmax, mmax + self.mask_indices_cache = mask_indices + return self.mask_indices_cache + + # Return the re-scaling for rotating back to original frame + # this is required since we only use a subset of m components for SO(2) convolution + def get_rotate_inv_rescale(self, lmax: int, mmax: int): + + if (self.lmax_cache is not None) and (self.mmax_cache is not None): + if (self.lmax_cache == lmax) and (self.mmax_cache == mmax): + if self.rotate_inv_rescale_cache is not None: + return self.rotate_inv_rescale_cache + + if self.mask_indices_cache is None: + self.coefficient_idx(lmax, mmax) + + rotate_inv_rescale = torch.ones( + (1, (lmax + 1) ** 2, (lmax + 1) ** 2), device=self.device + ) + for lval in range(lmax + 1): + if lval <= mmax: + continue + start_idx = lval**2 + length = 2 * lval + 1 + rescale_factor = math.sqrt(length / (2 * mmax + 1)) + rotate_inv_rescale[ + :, + start_idx : (start_idx + length), + start_idx : (start_idx + length), + ] = rescale_factor + rotate_inv_rescale = rotate_inv_rescale[:, :, self.mask_indices_cache] + self.rotate_inv_rescale_cache = rotate_inv_rescale + return self.rotate_inv_rescale_cache + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(lmax_list={self.lmax_list}, mmax_list={self.mmax_list})" + + +class SO3_Embedding: + """ + Helper functions for performing operations on irreps embedding + + Args: + length (int): Batch size + lmax_list (list:int): List of maximum degree of the spherical harmonics + num_channels (int): Number of channels + device: Device of the output + dtype: type of the output tensors + """ + + def __init__( + self, + length: int, + lmax_list: List[int], + num_channels: int, + device: torch.device, + dtype: torch.dtype, + ): + super().__init__() + self.num_channels = num_channels + self.device = device + self.dtype = dtype + self.num_resolutions = len(lmax_list) + + self.num_coefficients = 0 + for i in range(self.num_resolutions): + self.num_coefficients = self.num_coefficients + int( + (lmax_list[i] + 1) ** 2 + ) + + embedding = torch.zeros( + length, + self.num_coefficients, + self.num_channels, + device=self.device, + dtype=self.dtype, + ) + + self.set_embedding(embedding) + self.set_lmax_mmax(lmax_list, lmax_list.copy()) + + # Clone an embedding of irreps + def clone(self) -> "SO3_Embedding": + clone = SO3_Embedding( + 0, + self.lmax_list.copy(), + self.num_channels, + self.device, + self.dtype, + ) + clone.set_embedding(self.embedding.clone()) + return clone + + # Initialize an embedding of irreps + def set_embedding(self, embedding) -> None: + self.length = len(embedding) + self.embedding = embedding + + # Set the maximum order to be the maximum degree + def set_lmax_mmax( + self, lmax_list: List[int], mmax_list: List[int] + ) -> None: + self.lmax_list = lmax_list + self.mmax_list = mmax_list + + # Expand the node embeddings to the number of edges + def _expand_edge(self, edge_index: torch.Tensor) -> None: + embedding = self.embedding[edge_index] + self.set_embedding(embedding) + + # Initialize an embedding of irreps of a neighborhood + def expand_edge(self, edge_index: torch.Tensor): + x_expand = SO3_Embedding( + 0, + self.lmax_list.copy(), + self.num_channels, + self.device, + self.dtype, + ) + x_expand.set_embedding(self.embedding[edge_index]) + return x_expand + + # Compute the sum of the embeddings of the neighborhood + def _reduce_edge(self, edge_index: torch.Tensor, num_nodes: int): + new_embedding = torch.zeros( + num_nodes, + self.num_coefficients, + self.num_channels, + device=self.embedding.device, + dtype=self.embedding.dtype, + ) + new_embedding.index_add_(0, edge_index, self.embedding) + self.set_embedding(new_embedding) + + # Reshape the embedding lval -> m + def _m_primary(self, mapping): + self.embedding = torch.einsum( + "nac, ba -> nbc", self.embedding, mapping.to_m + ) + + # Reshape the embedding m -> lval + def _l_primary(self, mapping): + self.embedding = torch.einsum( + "nac, ab -> nbc", self.embedding, mapping.to_m + ) + + # Rotate the embedding + def _rotate( + self, SO3_rotation, lmax_list: List[int], mmax_list: List[int] + ): + if self.num_resolutions == 1: + embedding_rotate = SO3_rotation[0].rotate( + self.embedding, lmax_list[0], mmax_list[0] + ) + else: + offset = 0 + embedding_rotate = torch.tensor( + [], device=self.device, dtype=self.dtype + ) + for i in range(self.num_resolutions): + num_coefficients = int((self.lmax_list[i] + 1) ** 2) + embedding_i = self.embedding[ + :, offset : offset + num_coefficients + ] + embedding_rotate = torch.cat( + [ + embedding_rotate, + SO3_rotation[i].rotate( + embedding_i, lmax_list[i], mmax_list[i] + ), + ], + dim=1, + ) + offset = offset + num_coefficients + + self.embedding = embedding_rotate + self.set_lmax_mmax(lmax_list.copy(), mmax_list.copy()) + + # Rotate the embedding by the inverse of the rotation matrix + def _rotate_inv(self, SO3_rotation, mappingReduced): + + if self.num_resolutions == 1: + embedding_rotate = SO3_rotation[0].rotate_inv( + self.embedding, self.lmax_list[0], self.mmax_list[0] + ) + else: + offset = 0 + embedding_rotate = torch.tensor( + [], device=self.device, dtype=self.dtype + ) + for i in range(self.num_resolutions): + num_coefficients = mappingReduced.res_size[i] + embedding_i = self.embedding[ + :, offset : offset + num_coefficients + ] + embedding_rotate = torch.cat( + [ + embedding_rotate, + SO3_rotation[i].rotate_inv( + embedding_i, self.lmax_list[i], self.mmax_list[i] + ), + ], + dim=1, + ) + offset = offset + num_coefficients + self.embedding = embedding_rotate + + # Assume mmax = lmax when rotating back + for i in range(self.num_resolutions): + self.mmax_list[i] = int(self.lmax_list[i]) + self.set_lmax_mmax(self.lmax_list, self.mmax_list) + + # Compute point-wise spherical non-linearity + def _grid_act(self, SO3_grid, act, mappingReduced): + offset = 0 + for i in range(self.num_resolutions): + + num_coefficients = mappingReduced.res_size[i] + + if self.num_resolutions == 1: + x_res = self.embedding + else: + x_res = self.embedding[ + :, offset : offset + num_coefficients + ].contiguous() + to_grid_mat = SO3_grid[self.lmax_list[i]][ + self.mmax_list[i] + ].get_to_grid_mat(self.device) + from_grid_mat = SO3_grid[self.lmax_list[i]][ + self.mmax_list[i] + ].get_from_grid_mat(self.device) + + x_grid = torch.einsum("bai, zic -> zbac", to_grid_mat, x_res) + x_grid = act(x_grid) + x_res = torch.einsum("bai, zbac -> zic", from_grid_mat, x_grid) + if self.num_resolutions == 1: + self.embedding = x_res + else: + self.embedding[:, offset : offset + num_coefficients] = x_res + offset = offset + num_coefficients + + # Compute a sample of the grid + def to_grid(self, SO3_grid, lmax=-1): + if lmax == -1: + lmax = max(self.lmax_list) + + to_grid_mat_lmax = SO3_grid[lmax][lmax].get_to_grid_mat(self.device) + grid_mapping = SO3_grid[lmax][lmax].mapping + + offset = 0 + x_grid = torch.tensor([], device=self.device) + + for i in range(self.num_resolutions): + num_coefficients = int((self.lmax_list[i] + 1) ** 2) + if self.num_resolutions == 1: + x_res = self.embedding + else: + x_res = self.embedding[ + :, offset : offset + num_coefficients + ].contiguous() + to_grid_mat = to_grid_mat_lmax[ + :, + :, + grid_mapping.coefficient_idx( + self.lmax_list[i], self.lmax_list[i] + ), + ] + x_grid = torch.cat( + [x_grid, torch.einsum("bai, zic -> zbac", to_grid_mat, x_res)], + dim=3, + ) + offset = offset + num_coefficients + + return x_grid + + # Compute irreps from grid representation + def _from_grid(self, x_grid, SO3_grid, lmax: int = -1): + if lmax == -1: + lmax = max(self.lmax_list) + + from_grid_mat_lmax = SO3_grid[lmax][lmax].get_from_grid_mat( + self.device + ) + grid_mapping = SO3_grid[lmax][lmax].mapping + + offset = 0 + offset_channel = 0 + for i in range(self.num_resolutions): + from_grid_mat = from_grid_mat_lmax[ + :, + :, + grid_mapping.coefficient_idx( + self.lmax_list[i], self.lmax_list[i] + ), + ] + if self.num_resolutions == 1: + temp = x_grid + else: + temp = x_grid[ + :, + :, + :, + offset_channel : offset_channel + self.num_channels, + ] + x_res = torch.einsum("bai, zbac -> zic", from_grid_mat, temp) + num_coefficients = int((self.lmax_list[i] + 1) ** 2) + + if self.num_resolutions == 1: + self.embedding = x_res + else: + self.embedding[:, offset : offset + num_coefficients] = x_res + + offset = offset + num_coefficients + offset_channel = offset_channel + self.num_channels + + +class SO3_Rotation(torch.nn.Module): + """ + Helper functions for Wigner-D rotations + + Args: + lmax_list (list:int): List of maximum degree of the spherical harmonics + """ + + def __init__( + self, + lmax: int, + ): + super().__init__() + self.lmax = lmax + self.mapping = CoefficientMappingModule([self.lmax], [self.lmax]) + + def set_wigner(self, rot_mat3x3): + self.device, self.dtype = rot_mat3x3.device, rot_mat3x3.dtype + self.wigner = self.RotationToWignerDMatrix(rot_mat3x3, 0, self.lmax) + self.wigner_inv = torch.transpose(self.wigner, 1, 2).contiguous() + self.wigner = self.wigner.detach() + self.wigner_inv = self.wigner_inv.detach() + + # Rotate the embedding + def rotate(self, embedding, out_lmax: int, out_mmax: int): + out_mask = self.mapping.coefficient_idx(out_lmax, out_mmax) + wigner = self.wigner[:, out_mask, :] + return torch.bmm(wigner, embedding) + + # Rotate the embedding by the inverse of the rotation matrix + def rotate_inv(self, embedding, in_lmax: int, in_mmax: int): + in_mask = self.mapping.coefficient_idx(in_lmax, in_mmax) + wigner_inv = self.wigner_inv[:, :, in_mask] + wigner_inv_rescale = self.mapping.get_rotate_inv_rescale( + in_lmax, in_mmax + ) + wigner_inv = wigner_inv * wigner_inv_rescale + return torch.bmm(wigner_inv, embedding) + + # Compute Wigner matrices from rotation matrix + def RotationToWignerDMatrix( + self, edge_rot_mat, start_lmax: int, end_lmax: int + ) -> torch.Tensor: + x = edge_rot_mat @ edge_rot_mat.new_tensor([0.0, 1.0, 0.0]) + alpha, beta = o3.xyz_to_angles(x) + R = ( + o3.angles_to_matrix( + alpha, beta, torch.zeros_like(alpha) + ).transpose(-1, -2) + @ edge_rot_mat + ) + gamma = torch.atan2(R[..., 0, 2], R[..., 0, 0]) + + size = (end_lmax + 1) ** 2 - (start_lmax) ** 2 + wigner = torch.zeros(len(alpha), size, size, device=self.device) + start = 0 + for lmax in range(start_lmax, end_lmax + 1): + block = wigner_D(lmax, alpha, beta, gamma) + end = start + block.size()[1] + wigner[:, start:end, start:end] = block + start = end + + return wigner.detach() + + +class SO3_Grid(torch.nn.Module): + """ + Helper functions for grid representation of the irreps + + Args: + lmax (int): Maximum degree of the spherical harmonics + mmax (int): Maximum order of the spherical harmonics + """ + + def __init__( + self, + lmax: int, + mmax: int, + normalization: str = "integral", + resolution: Optional[int] = None, + ): + super().__init__() + self.lmax = lmax + self.mmax = mmax + self.lat_resolution = 2 * (self.lmax + 1) + if lmax == mmax: + self.long_resolution = 2 * (self.mmax + 1) + 1 + else: + self.long_resolution = 2 * (self.mmax) + 1 + if resolution is not None: + self.lat_resolution = resolution + self.long_resolution = resolution + + self.mapping = CoefficientMappingModule([self.lmax], [self.lmax]) + + device = "cpu" + + to_grid = ToS2Grid( + self.lmax, + (self.lat_resolution, self.long_resolution), + normalization=normalization, # normalization="integral", + device=device, + ) + to_grid_mat = torch.einsum( + "mbi, am -> bai", to_grid.shb, to_grid.sha + ).detach() + # rescale based on mmax + if lmax != mmax: + for lval in range(lmax + 1): + if lval <= mmax: + continue + start_idx = lval**2 + length = 2 * lval + 1 + rescale_factor = math.sqrt(length / (2 * mmax + 1)) + to_grid_mat[:, :, start_idx : (start_idx + length)] = ( + to_grid_mat[:, :, start_idx : (start_idx + length)] + * rescale_factor + ) + to_grid_mat = to_grid_mat[ + :, :, self.mapping.coefficient_idx(self.lmax, self.mmax) + ] + + from_grid = FromS2Grid( + (self.lat_resolution, self.long_resolution), + self.lmax, + normalization=normalization, # normalization="integral", + device=device, + ) + from_grid_mat = torch.einsum( + "am, mbi -> bai", from_grid.sha, from_grid.shb + ).detach() + # rescale based on mmax + if lmax != mmax: + for lval in range(lmax + 1): + if lval <= mmax: + continue + start_idx = lval**2 + length = 2 * lval + 1 + rescale_factor = math.sqrt(length / (2 * mmax + 1)) + from_grid_mat[:, :, start_idx : (start_idx + length)] = ( + from_grid_mat[:, :, start_idx : (start_idx + length)] + * rescale_factor + ) + from_grid_mat = from_grid_mat[ + :, :, self.mapping.coefficient_idx(self.lmax, self.mmax) + ] + + # save tensors and they will be moved to GPU + self.register_buffer("to_grid_mat", to_grid_mat) + self.register_buffer("from_grid_mat", from_grid_mat) + + # Compute matrices to transform irreps to grid + def get_to_grid_mat(self, device): + return self.to_grid_mat + + # Compute matrices to transform grid to irreps + def get_from_grid_mat(self, device): + return self.from_grid_mat + + # Compute grid from irreps representation + def to_grid(self, embedding, lmax: int, mmax: int): + to_grid_mat = self.to_grid_mat[ + :, :, self.mapping.coefficient_idx(lmax, mmax) + ] + grid = torch.einsum("bai, zic -> zbac", to_grid_mat, embedding) + return grid + + # Compute irreps from grid representation + def from_grid(self, grid, lmax: int, mmax: int): + from_grid_mat = self.from_grid_mat[ + :, :, self.mapping.coefficient_idx(lmax, mmax) + ] + embedding = torch.einsum("bai, zbac -> zic", from_grid_mat, grid) + return embedding + + +class SO3_Linear(torch.nn.Module): + def __init__( + self, in_features: int, out_features: int, lmax: int, bias: bool = True + ) -> None: + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.lmax = lmax + self.linear_list = torch.nn.ModuleList() + for lval in range(lmax + 1): + if lval == 0: + self.linear_list.append( + Linear(in_features, out_features, bias=bias) + ) + else: + self.linear_list.append( + Linear(in_features, out_features, bias=False) + ) + + def forward(self, input_embedding, output_scale=None): + out = [] + for lval in range(self.lmax + 1): + start_idx = lval**2 + length = 2 * lval + 1 + features = input_embedding.embedding.narrow(1, start_idx, length) + features = self.linear_list[lval](features) + if output_scale is not None: + scale = output_scale.narrow(1, lval, 1) + features = features * scale + out.append(features) + out = torch.cat(out, dim=1) + + out_embedding = SO3_Embedding( + 0, + input_embedding.lmax_list.copy(), + self.out_features, + device=input_embedding.device, + dtype=input_embedding.dtype, + ) + out_embedding.set_embedding(out) + out_embedding.set_lmax_mmax( + input_embedding.lmax_list.copy(), input_embedding.lmax_list.copy() + ) + + return out_embedding + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(in_features={self.in_features}, out_features={self.out_features}, lmax={self.lmax})" + + +class SO3_LinearV2(torch.nn.Module): + def __init__( + self, in_features: int, out_features: int, lmax: int, bias: bool = True + ) -> None: + """ + 1. Use `torch.einsum` to prevent slicing and concatenation + 2. Need to specify some behaviors in `no_weight_decay` and weight initialization. + """ + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.lmax = lmax + + self.weight = torch.nn.Parameter( + torch.randn((self.lmax + 1), out_features, in_features) + ) + bound = 1 / math.sqrt(self.in_features) + torch.nn.init.uniform_(self.weight, -bound, bound) + self.bias = torch.nn.Parameter(torch.zeros(out_features)) + + expand_index = torch.zeros([(lmax + 1) ** 2]).long() + for lval in range(lmax + 1): + start_idx = lval**2 + length = 2 * lval + 1 + expand_index[start_idx : (start_idx + length)] = lval + self.register_buffer("expand_index", expand_index) + + def forward(self, input_embedding): + + weight = torch.index_select( + self.weight, dim=0, index=self.expand_index + ) # [(L_max + 1) ** 2, C_out, C_in] + out = torch.einsum( + "bmi, moi -> bmo", input_embedding.embedding, weight + ) # [N, (L_max + 1) ** 2, C_out] + bias = self.bias.view(1, 1, self.out_features) + out[:, 0:1, :] = out.narrow(1, 0, 1) + bias + + out_embedding = SO3_Embedding( + 0, + input_embedding.lmax_list.copy(), + self.out_features, + device=input_embedding.device, + dtype=input_embedding.dtype, + ) + out_embedding.set_embedding(out) + out_embedding.set_lmax_mmax( + input_embedding.lmax_list.copy(), input_embedding.lmax_list.copy() + ) + + return out_embedding + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(in_features={self.in_features}, out_features={self.out_features}, lmax={self.lmax})" diff --git a/ocpmodels/models/equiformer_v2/trainers/__init__.py b/ocpmodels/models/equiformer_v2/trainers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/equiformer_v2/trainers/energy_trainer.py b/ocpmodels/models/equiformer_v2/trainers/energy_trainer.py new file mode 100644 index 0000000..f868dcf --- /dev/null +++ b/ocpmodels/models/equiformer_v2/trainers/energy_trainer.py @@ -0,0 +1,59 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + + +from ocpmodels.common.registry import registry +from ocpmodels.modules.exponential_moving_average import ( + ExponentialMovingAverage, +) +from ocpmodels.trainers import OCPTrainer + +from .lr_scheduler import LRScheduler + + +@registry.register_trainer("equiformerv2_energy") +class EquiformerV2EnergyTrainer(OCPTrainer): + # This trainer does a few things differently from the parent energy trainer: + # - When using the scheduler, it first converts the epochs into number of + # steps and then passes it to the scheduler. That way in the config + # everything can be specified in terms of epochs. + def load_extras(self): + def multiply(obj, num): + if isinstance(obj, list): + for i in range(len(obj)): + obj[i] = obj[i] * num + else: + obj = obj * num + return obj + + self.config["optim"]["scheduler_params"]["epochs"] = self.config[ + "optim" + ]["max_epochs"] + self.config["optim"]["scheduler_params"]["lr"] = self.config["optim"][ + "lr_initial" + ] + + # convert epochs into number of steps + n_iter_per_epoch = len(self.train_loader) + scheduler_params = self.config["optim"]["scheduler_params"] + for k in scheduler_params.keys(): + if "epochs" in k: + if isinstance(scheduler_params[k], (int, float, list)): + scheduler_params[k] = multiply( + scheduler_params[k], n_iter_per_epoch + ) + + self.scheduler = LRScheduler(self.optimizer, self.config["optim"]) + self.clip_grad_norm = self.config["optim"].get("clip_grad_norm") + self.ema_decay = self.config["optim"].get("ema_decay") + if self.ema_decay: + self.ema = ExponentialMovingAverage( + self.model.parameters(), + self.ema_decay, + ) + else: + self.ema = None diff --git a/ocpmodels/models/equiformer_v2/trainers/forces_trainer.py b/ocpmodels/models/equiformer_v2/trainers/forces_trainer.py new file mode 100644 index 0000000..44dc981 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/trainers/forces_trainer.py @@ -0,0 +1,72 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging + +from ocpmodels.common.registry import registry +from ocpmodels.modules.exponential_moving_average import ( + ExponentialMovingAverage, +) +from ocpmodels.trainers import OCPTrainer + +from .lr_scheduler import LRScheduler + + +@registry.register_trainer("equiformerv2_forces") +class EquiformerV2ForcesTrainer(OCPTrainer): + # This trainer does a few things differently from the parent forces trainer: + # - Support for cosine LR scheduler. + # - When using the LR scheduler, it first converts the epochs into number of + # steps and then passes it to the scheduler. That way in the config + # everything can be specified in terms of epochs. + def load_extras(self) -> None: + def multiply(obj, num): + if isinstance(obj, list): + for i in range(len(obj)): + obj[i] = obj[i] * num + else: + obj = obj * num + return obj + + self.config["optim"]["scheduler_params"]["epochs"] = self.config[ + "optim" + ]["max_epochs"] + self.config["optim"]["scheduler_params"]["lr"] = self.config["optim"][ + "lr_initial" + ] + + # convert epochs into number of steps + if self.train_loader is None: + logging.warning("Skipping scheduler setup. No training set found.") + self.scheduler = None + else: + n_iter_per_epoch = len(self.train_loader) + scheduler_params = self.config["optim"]["scheduler_params"] + for k in scheduler_params.keys(): + if "epochs" in k: + if isinstance(scheduler_params[k], (int, float)): + scheduler_params[k] = int( + multiply(scheduler_params[k], n_iter_per_epoch) + ) + elif isinstance(scheduler_params[k], list): + scheduler_params[k] = [ + int(x) + for x in multiply( + scheduler_params[k], n_iter_per_epoch + ) + ] + self.scheduler = LRScheduler(self.optimizer, self.config["optim"]) + + self.clip_grad_norm = self.config["optim"].get("clip_grad_norm") + self.ema_decay = self.config["optim"].get("ema_decay") + if self.ema_decay: + self.ema = ExponentialMovingAverage( + self.model.parameters(), + self.ema_decay, + ) + else: + self.ema = None diff --git a/ocpmodels/models/equiformer_v2/trainers/lr_scheduler.py b/ocpmodels/models/equiformer_v2/trainers/lr_scheduler.py new file mode 100644 index 0000000..f392621 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/trainers/lr_scheduler.py @@ -0,0 +1,178 @@ +import inspect +import math +from bisect import bisect +from typing import List, Optional + +import torch + +from ocpmodels.common.typing import assert_is_instance as aii + + +def multiply(obj, num): + if isinstance(obj, list): + for i in range(len(obj)): + obj[i] = obj[i] * num + else: + obj = obj * num + return obj + + +def cosine_lr_lambda(current_step: int, scheduler_params): + warmup_epochs = aii(scheduler_params["warmup_epochs"], int) + lr_warmup_factor = aii(scheduler_params["warmup_factor"], float) + max_epochs = aii(scheduler_params["epochs"], int) + lr_min_factor = aii(scheduler_params["lr_min_factor"], float) + + # `warmup_epochs` is already multiplied with the num of iterations + if current_step <= warmup_epochs: + alpha = current_step / float(warmup_epochs) + return lr_warmup_factor * (1.0 - alpha) + alpha + else: + if current_step >= max_epochs: + return lr_min_factor + lr_scale = lr_min_factor + 0.5 * (1 - lr_min_factor) * ( + 1 + math.cos(math.pi * (current_step / max_epochs)) + ) + return lr_scale + + +class CosineLRLambda: + def __init__(self, scheduler_params) -> None: + self.warmup_epochs = aii(scheduler_params["warmup_epochs"], int) + self.lr_warmup_factor = aii(scheduler_params["warmup_factor"], float) + self.max_epochs = aii(scheduler_params["epochs"], int) + self.lr_min_factor = aii(scheduler_params["lr_min_factor"], float) + + def __call__(self, current_step: int): + # `warmup_epochs` is already multiplied with the num of iterations + if current_step <= self.warmup_epochs: + alpha = current_step / float(self.warmup_epochs) + return self.lr_warmup_factor * (1.0 - alpha) + alpha + else: + if current_step >= self.max_epochs: + return self.lr_min_factor + lr_scale = self.lr_min_factor + 0.5 * (1 - self.lr_min_factor) * ( + 1 + math.cos(math.pi * (current_step / self.max_epochs)) + ) + return lr_scale + + +def multistep_lr_lambda(current_step: int, scheduler_params) -> float: + warmup_epochs = aii(scheduler_params["warmup_epochs"], int) + lr_warmup_factor = aii(scheduler_params["warmup_factor"], float) + lr_decay_epochs: List[int] = scheduler_params["decay_epochs"] + lr_gamma = aii(scheduler_params["decay_rate"], float) + + if current_step <= warmup_epochs: + alpha = current_step / float(warmup_epochs) + return lr_warmup_factor * (1.0 - alpha) + alpha + else: + idx = bisect(lr_decay_epochs, current_step) + return pow(lr_gamma, idx) + + +class MultistepLRLambda: + def __init__(self, scheduler_params) -> None: + self.warmup_epochs = aii(scheduler_params["warmup_epochs"], int) + self.lr_warmup_factor = aii(scheduler_params["warmup_factor"], float) + self.lr_decay_epochs = aii(scheduler_params["decay_epochs"], list) + self.lr_gamma = aii(scheduler_params["decay_rate"], float) + + def __call__(self, current_step: int) -> float: + if current_step <= self.warmup_epochs: + alpha = current_step / float(self.warmup_epochs) + return self.lr_warmup_factor * (1.0 - alpha) + alpha + else: + idx = bisect(self.lr_decay_epochs, current_step) + return pow(self.lr_gamma, idx) + + +class LRScheduler: + """ + Notes: + 1. scheduler.step() is called for every step for OC20 training. + 2. We use "scheduler_params" in .yml to specify scheduler parameters. + 3. For cosine learning rate, we use LambdaLR with lambda function being cosine: + scheduler: LambdaLR + scheduler_params: + lambda_type: cosine + ... + 4. Following 3., if `cosine` is used, `scheduler_params` in .yml looks like: + scheduler: LambdaLR + scheduler_params: + lambda_type: cosine + warmup_epochs: ... + warmup_factor: ... + lr_min_factor: ... + 5. Following 3., if `multistep` is used, `scheduler_params` in .yml looks like: + scheduler: LambdaLR + scheduler_params: + lambda_type: multistep + warmup_epochs: ... + warmup_factor: ... + decay_epochs: ... (list) + decay_rate: ... + + Args: + optimizer (obj): torch optim object + config (dict): Optim dict from the input config + """ + + def __init__(self, optimizer, config) -> None: + self.optimizer = optimizer + self.config = config.copy() + + assert "scheduler" in self.config.keys() + assert "scheduler_params" in self.config.keys() + self.scheduler_type = aii(self.config["scheduler"], str) + self.scheduler_params = self.config["scheduler_params"].copy() + + # Use `LambdaLR` for multi-step and cosine learning rate + if self.scheduler_type == "LambdaLR": + scheduler_lambda_fn = None + self.lambda_type = self.scheduler_params["lambda_type"] + + if self.lambda_type == "cosine": + scheduler_lambda_fn = CosineLRLambda(self.scheduler_params) + elif self.lambda_type == "multistep": + scheduler_lambda_fn = MultistepLRLambda(self.scheduler_params) + else: + raise ValueError + self.scheduler_params["lr_lambda"] = scheduler_lambda_fn + + if self.scheduler_type != "Null": + self.scheduler = getattr( + torch.optim.lr_scheduler, self.scheduler_type + ) + scheduler_args = self.filter_kwargs(self.scheduler_params) + self.scheduler = self.scheduler(optimizer, **scheduler_args) + + def step(self, metrics=None, epoch=None): + if self.scheduler_type == "Null": + return + if self.scheduler_type == "ReduceLROnPlateau": + if metrics is None: + raise Exception( + "Validation set required for ReduceLROnPlateau." + ) + self.scheduler.step(metrics) + else: + self.scheduler.step() + + def filter_kwargs(self, config): + # adapted from https://stackoverflow.com/questions/26515595/ + sig = inspect.signature(self.scheduler) + filter_keys = [ + param.name + for param in sig.parameters.values() + if param.kind == param.POSITIONAL_OR_KEYWORD + ] + filter_keys.remove("optimizer") + scheduler_args = { + arg: config[arg] for arg in config if arg in filter_keys + } + return scheduler_args + + def get_lr(self) -> Optional[float]: + for group in self.optimizer.param_groups: + return aii(group["lr"], float) diff --git a/ocpmodels/models/equiformer_v2/transformer_block.py b/ocpmodels/models/equiformer_v2/transformer_block.py new file mode 100644 index 0000000..ebfeab0 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/transformer_block.py @@ -0,0 +1,728 @@ +import copy +import math +from typing import List + +import torch +import torch.nn as nn +import torch_geometric + +from .activation import ( + GateActivation, + S2Activation, + SeparableS2Activation, + SmoothLeakyReLU, +) +from .drop import EquivariantDropoutArraySphericalHarmonics, GraphDropPath +from .layer_norm import get_normalization_layer +from .radial_function import RadialFunction +from .so2_ops import SO2_Convolution +from .so3 import SO3_Embedding, SO3_LinearV2 + + +class SO2EquivariantGraphAttention(torch.nn.Module): + """ + SO2EquivariantGraphAttention: Perform MLP attention + non-linear message passing + SO(2) Convolution with radial function -> S2 Activation -> SO(2) Convolution -> attention weights and non-linear messages + attention weights * non-linear messages -> Linear + + Args: + sphere_channels (int): Number of spherical channels + hidden_channels (int): Number of hidden channels used during the SO(2) conv + num_heads (int): Number of attention heads + attn_alpha_head (int): Number of channels for alpha vector in each attention head + attn_value_head (int): Number of channels for value vector in each attention head + output_channels (int): Number of output channels + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + + SO3_rotation (list:SO3_Rotation): Class to calculate Wigner-D matrices and rotate embeddings + mappingReduced (CoefficientMappingModule): Class to convert l and m indices once node embedding is rotated + SO3_grid (SO3_grid): Class used to convert from grid the spherical harmonic representations + + max_num_elements (int): Maximum number of atomic numbers + edge_channels_list (list:int): List of sizes of invariant edge embedding. For example, [input_channels, hidden_channels, hidden_channels]. + The last one will be used as hidden size when `use_atom_edge_embedding` is `True`. + use_atom_edge_embedding (bool): Whether to use atomic embedding along with relative distance for edge scalar features + use_m_share_rad (bool): Whether all m components within a type-L vector of one channel share radial function weights + + activation (str): Type of activation function + use_s2_act_attn (bool): Whether to use attention after S2 activation. Otherwise, use the same attention as Equiformer + use_attn_renorm (bool): Whether to re-normalize attention weights + use_gate_act (bool): If `True`, use gate activation. Otherwise, use S2 activation. + use_sep_s2_act (bool): If `True`, use separable S2 activation when `use_gate_act` is False. + + alpha_drop (float): Dropout rate for attention weights + """ + + def __init__( + self, + sphere_channels: int, + hidden_channels: int, + num_heads: int, + attn_alpha_channels: int, + attn_value_channels: int, + output_channels: int, + lmax_list: List[int], + mmax_list: List[int], + SO3_rotation, + mappingReduced, + SO3_grid, + max_num_elements: int, + edge_channels_list, + use_atom_edge_embedding: bool = True, + use_m_share_rad: bool = False, + activation="scaled_silu", + use_s2_act_attn: bool = False, + use_attn_renorm: bool = True, + use_gate_act: bool = False, + use_sep_s2_act: bool = True, + alpha_drop: float = 0.0, + ): + super(SO2EquivariantGraphAttention, self).__init__() + + self.sphere_channels = sphere_channels + self.hidden_channels = hidden_channels + self.num_heads = num_heads + self.attn_alpha_channels = attn_alpha_channels + self.attn_value_channels = attn_value_channels + self.output_channels = output_channels + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions = len(self.lmax_list) + + self.SO3_rotation = SO3_rotation + self.mappingReduced = mappingReduced + self.SO3_grid = SO3_grid + + # Create edge scalar (invariant to rotations) features + # Embedding function of the atomic numbers + self.max_num_elements = max_num_elements + self.edge_channels_list = copy.deepcopy(edge_channels_list) + self.use_atom_edge_embedding = use_atom_edge_embedding + self.use_m_share_rad = use_m_share_rad + + if self.use_atom_edge_embedding: + self.source_embedding = nn.Embedding( + self.max_num_elements, self.edge_channels_list[-1] + ) + self.target_embedding = nn.Embedding( + self.max_num_elements, self.edge_channels_list[-1] + ) + nn.init.uniform_(self.source_embedding.weight.data, -0.001, 0.001) + nn.init.uniform_(self.target_embedding.weight.data, -0.001, 0.001) + self.edge_channels_list[0] = ( + self.edge_channels_list[0] + 2 * self.edge_channels_list[-1] + ) + else: + self.source_embedding, self.target_embedding = None, None + + self.use_s2_act_attn = use_s2_act_attn + self.use_attn_renorm = use_attn_renorm + self.use_gate_act = use_gate_act + self.use_sep_s2_act = use_sep_s2_act + + assert not self.use_s2_act_attn # since this is not used + + # Create SO(2) convolution blocks + extra_m0_output_channels = None + if not self.use_s2_act_attn: + extra_m0_output_channels = ( + self.num_heads * self.attn_alpha_channels + ) + if self.use_gate_act: + extra_m0_output_channels = ( + extra_m0_output_channels + + max(self.lmax_list) * self.hidden_channels + ) + else: + if self.use_sep_s2_act: + extra_m0_output_channels = ( + extra_m0_output_channels + self.hidden_channels + ) + + if self.use_m_share_rad: + self.edge_channels_list = self.edge_channels_list + [ + 2 * self.sphere_channels * (max(self.lmax_list) + 1) + ] + self.rad_func = RadialFunction(self.edge_channels_list) + expand_index = torch.zeros([(max(self.lmax_list) + 1) ** 2]).long() + for lval in range(max(self.lmax_list) + 1): + start_idx = lval**2 + length = 2 * lval + 1 + expand_index[start_idx : (start_idx + length)] = lval + self.register_buffer("expand_index", expand_index) + + self.so2_conv_1 = SO2_Convolution( + 2 * self.sphere_channels, + self.hidden_channels, + self.lmax_list, + self.mmax_list, + self.mappingReduced, + internal_weights=(False if not self.use_m_share_rad else True), + edge_channels_list=( + self.edge_channels_list if not self.use_m_share_rad else None + ), + extra_m0_output_channels=extra_m0_output_channels, # for attention weights and/or gate activation + ) + + if self.use_s2_act_attn: + self.alpha_norm = None + self.alpha_act = None + self.alpha_dot = None + else: + if self.use_attn_renorm: + self.alpha_norm = torch.nn.LayerNorm(self.attn_alpha_channels) + else: + self.alpha_norm = torch.nn.Identity() + self.alpha_act = SmoothLeakyReLU() + self.alpha_dot = torch.nn.Parameter( + torch.randn(self.num_heads, self.attn_alpha_channels) + ) + # torch_geometric.nn.inits.glorot(self.alpha_dot) # Following GATv2 + std = 1.0 / math.sqrt(self.attn_alpha_channels) + torch.nn.init.uniform_(self.alpha_dot, -std, std) + + self.alpha_dropout = None + if alpha_drop != 0.0: + self.alpha_dropout = torch.nn.Dropout(alpha_drop) + + if self.use_gate_act: + self.gate_act = GateActivation( + lmax=max(self.lmax_list), + mmax=max(self.mmax_list), + num_channels=self.hidden_channels, + ) + else: + if self.use_sep_s2_act: + # separable S2 activation + self.s2_act = SeparableS2Activation( + lmax=max(self.lmax_list), mmax=max(self.mmax_list) + ) + else: + # S2 activation + self.s2_act = S2Activation( + lmax=max(self.lmax_list), mmax=max(self.mmax_list) + ) + + self.so2_conv_2 = SO2_Convolution( + self.hidden_channels, + self.num_heads * self.attn_value_channels, + self.lmax_list, + self.mmax_list, + self.mappingReduced, + internal_weights=True, + edge_channels_list=None, + extra_m0_output_channels=( + self.num_heads if self.use_s2_act_attn else None + ), # for attention weights + ) + + self.proj = SO3_LinearV2( + self.num_heads * self.attn_value_channels, + self.output_channels, + lmax=self.lmax_list[0], + ) + + def forward( + self, + x: torch.Tensor, + atomic_numbers, + edge_distance: torch.Tensor, + edge_index, + ): + + # Compute edge scalar features (invariant to rotations) + # Uses atomic numbers and edge distance as inputs + if self.use_atom_edge_embedding: + source_element = atomic_numbers[ + edge_index[0] + ] # Source atom atomic number + target_element = atomic_numbers[ + edge_index[1] + ] # Target atom atomic number + source_embedding = self.source_embedding(source_element) + target_embedding = self.target_embedding(target_element) + x_edge = torch.cat( + (edge_distance, source_embedding, target_embedding), dim=1 + ) + else: + x_edge = edge_distance + + x_source = x.clone() + x_target = x.clone() + x_source._expand_edge(edge_index[0, :]) + x_target._expand_edge(edge_index[1, :]) + + x_message_data = torch.cat( + (x_source.embedding, x_target.embedding), dim=2 + ) + x_message = SO3_Embedding( + 0, + x_target.lmax_list.copy(), + x_target.num_channels * 2, + device=x_target.device, + dtype=x_target.dtype, + ) + x_message.set_embedding(x_message_data) + x_message.set_lmax_mmax(self.lmax_list.copy(), self.mmax_list.copy()) + + # radial function (scale all m components within a type-L vector of one channel with the same weight) + if self.use_m_share_rad: + x_edge_weight = self.rad_func(x_edge) + x_edge_weight = x_edge_weight.reshape( + -1, (max(self.lmax_list) + 1), 2 * self.sphere_channels + ) + x_edge_weight = torch.index_select( + x_edge_weight, dim=1, index=self.expand_index + ) # [E, (L_max + 1) ** 2, C] + x_message.embedding = x_message.embedding * x_edge_weight + + # Rotate the irreps to align with the edge + x_message._rotate(self.SO3_rotation, self.lmax_list, self.mmax_list) + + # First SO(2)-convolution + if self.use_s2_act_attn: + x_message = self.so2_conv_1(x_message, x_edge) + else: + x_message, x_0_extra = self.so2_conv_1(x_message, x_edge) + + # Activation + x_alpha_num_channels = self.num_heads * self.attn_alpha_channels + if self.use_gate_act: + # Gate activation + x_0_gating = x_0_extra.narrow( + 1, + x_alpha_num_channels, + x_0_extra.shape[1] - x_alpha_num_channels, + ) # for activation + x_0_alpha = x_0_extra.narrow( + 1, 0, x_alpha_num_channels + ) # for attention weights + x_message.embedding = self.gate_act( + x_0_gating, x_message.embedding + ) + else: + if self.use_sep_s2_act: + x_0_gating = x_0_extra.narrow( + 1, + x_alpha_num_channels, + x_0_extra.shape[1] - x_alpha_num_channels, + ) # for activation + x_0_alpha = x_0_extra.narrow( + 1, 0, x_alpha_num_channels + ) # for attention weights + x_message.embedding = self.s2_act( + x_0_gating, x_message.embedding, self.SO3_grid + ) + else: + x_0_alpha = x_0_extra + x_message.embedding = self.s2_act( + x_message.embedding, self.SO3_grid + ) + # x_message._grid_act(self.SO3_grid, self.value_act, self.mappingReduced) + + # Second SO(2)-convolution + if self.use_s2_act_attn: + x_message, x_0_extra = self.so2_conv_2(x_message, x_edge) + else: + x_message = self.so2_conv_2(x_message, x_edge) + + # Attention weights + if self.use_s2_act_attn: + alpha = x_0_extra + else: + x_0_alpha = x_0_alpha.reshape( + -1, self.num_heads, self.attn_alpha_channels + ) + x_0_alpha = self.alpha_norm(x_0_alpha) + x_0_alpha = self.alpha_act(x_0_alpha) + alpha = torch.einsum("bik, ik -> bi", x_0_alpha, self.alpha_dot) + alpha = torch_geometric.utils.softmax(alpha, edge_index[1]) + alpha = alpha.reshape(alpha.shape[0], 1, self.num_heads, 1) + if self.alpha_dropout is not None: + alpha = self.alpha_dropout(alpha) + + # Attention weights * non-linear messages + attn = x_message.embedding + attn = attn.reshape( + attn.shape[0], + attn.shape[1], + self.num_heads, + self.attn_value_channels, + ) + attn = attn * alpha + attn = attn.reshape( + attn.shape[0], + attn.shape[1], + self.num_heads * self.attn_value_channels, + ) + x_message.embedding = attn + + # Rotate back the irreps + x_message._rotate_inv(self.SO3_rotation, self.mappingReduced) + + # Compute the sum of the incoming neighboring messages for each target node + x_message._reduce_edge(edge_index[1], len(x.embedding)) + + # Project + out_embedding = self.proj(x_message) + + return out_embedding + + +class FeedForwardNetwork(torch.nn.Module): + """ + FeedForwardNetwork: Perform feedforward network with S2 activation or gate activation + + Args: + sphere_channels (int): Number of spherical channels + hidden_channels (int): Number of hidden channels used during feedforward network + output_channels (int): Number of output channels + + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + + SO3_grid (SO3_grid): Class used to convert from grid the spherical harmonic representations + + activation (str): Type of activation function + use_gate_act (bool): If `True`, use gate activation. Otherwise, use S2 activation + use_grid_mlp (bool): If `True`, use projecting to grids and performing MLPs. + use_sep_s2_act (bool): If `True`, use separable grid MLP when `use_grid_mlp` is True. + """ + + def __init__( + self, + sphere_channels: int, + hidden_channels: int, + output_channels: int, + lmax_list: List[int], + mmax_list: List[int], + SO3_grid, + activation: str = "scaled_silu", + use_gate_act: bool = False, + use_grid_mlp: bool = False, + use_sep_s2_act: bool = True, + ): + super(FeedForwardNetwork, self).__init__() + self.sphere_channels = sphere_channels + self.hidden_channels = hidden_channels + self.output_channels = output_channels + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions = len(lmax_list) + self.sphere_channels_all = self.num_resolutions * self.sphere_channels + self.SO3_grid = SO3_grid + self.use_gate_act = use_gate_act + self.use_grid_mlp = use_grid_mlp + self.use_sep_s2_act = use_sep_s2_act + + self.max_lmax = max(self.lmax_list) + + self.so3_linear_1 = SO3_LinearV2( + self.sphere_channels_all, self.hidden_channels, lmax=self.max_lmax + ) + if self.use_grid_mlp: + if self.use_sep_s2_act: + self.scalar_mlp = nn.Sequential( + nn.Linear( + self.sphere_channels_all, + self.hidden_channels, + bias=True, + ), + nn.SiLU(), + ) + else: + self.scalar_mlp = None + self.grid_mlp = nn.Sequential( + nn.Linear( + self.hidden_channels, self.hidden_channels, bias=False + ), + nn.SiLU(), + nn.Linear( + self.hidden_channels, self.hidden_channels, bias=False + ), + nn.SiLU(), + nn.Linear( + self.hidden_channels, self.hidden_channels, bias=False + ), + ) + else: + if self.use_gate_act: + self.gating_linear = torch.nn.Linear( + self.sphere_channels_all, + self.max_lmax * self.hidden_channels, + ) + self.gate_act = GateActivation( + self.max_lmax, self.max_lmax, self.hidden_channels + ) + else: + if self.use_sep_s2_act: + self.gating_linear = torch.nn.Linear( + self.sphere_channels_all, self.hidden_channels + ) + self.s2_act = SeparableS2Activation( + self.max_lmax, self.max_lmax + ) + else: + self.gating_linear = None + self.s2_act = S2Activation(self.max_lmax, self.max_lmax) + self.so3_linear_2 = SO3_LinearV2( + self.hidden_channels, self.output_channels, lmax=self.max_lmax + ) + + def forward(self, input_embedding): + + gating_scalars = None + if self.use_grid_mlp: + if self.use_sep_s2_act: + gating_scalars = self.scalar_mlp( + input_embedding.embedding.narrow(1, 0, 1) + ) + else: + if self.gating_linear is not None: + gating_scalars = self.gating_linear( + input_embedding.embedding.narrow(1, 0, 1) + ) + + input_embedding = self.so3_linear_1(input_embedding) + + if self.use_grid_mlp: + # Project to grid + input_embedding_grid = input_embedding.to_grid( + self.SO3_grid, lmax=self.max_lmax + ) + # Perform point-wise operations + input_embedding_grid = self.grid_mlp(input_embedding_grid) + # Project back to spherical harmonic coefficients + input_embedding._from_grid( + input_embedding_grid, self.SO3_grid, lmax=self.max_lmax + ) + + if self.use_sep_s2_act: + input_embedding.embedding = torch.cat( + ( + gating_scalars, + input_embedding.embedding.narrow( + 1, 1, input_embedding.embedding.shape[1] - 1 + ), + ), + dim=1, + ) + else: + if self.use_gate_act: + input_embedding.embedding = self.gate_act( + gating_scalars, input_embedding.embedding + ) + else: + if self.use_sep_s2_act: + input_embedding.embedding = self.s2_act( + gating_scalars, + input_embedding.embedding, + self.SO3_grid, + ) + else: + input_embedding.embedding = self.s2_act( + input_embedding.embedding, self.SO3_grid + ) + + input_embedding = self.so3_linear_2(input_embedding) + + return input_embedding + + +class TransBlockV2(torch.nn.Module): + """ + + Args: + sphere_channels (int): Number of spherical channels + attn_hidden_channels (int): Number of hidden channels used during SO(2) graph attention + num_heads (int): Number of attention heads + attn_alpha_head (int): Number of channels for alpha vector in each attention head + attn_value_head (int): Number of channels for value vector in each attention head + ffn_hidden_channels (int): Number of hidden channels used during feedforward network + output_channels (int): Number of output channels + + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + + SO3_rotation (list:SO3_Rotation): Class to calculate Wigner-D matrices and rotate embeddings + mappingReduced (CoefficientMappingModule): Class to convert l and m indices once node embedding is rotated + SO3_grid (SO3_grid): Class used to convert from grid the spherical harmonic representations + + max_num_elements (int): Maximum number of atomic numbers + edge_channels_list (list:int): List of sizes of invariant edge embedding. For example, [input_channels, hidden_channels, hidden_channels]. + The last one will be used as hidden size when `use_atom_edge_embedding` is `True`. + use_atom_edge_embedding (bool): Whether to use atomic embedding along with relative distance for edge scalar features + use_m_share_rad (bool): Whether all m components within a type-L vector of one channel share radial function weights + + attn_activation (str): Type of activation function for SO(2) graph attention + use_s2_act_attn (bool): Whether to use attention after S2 activation. Otherwise, use the same attention as Equiformer + use_attn_renorm (bool): Whether to re-normalize attention weights + ffn_activation (str): Type of activation function for feedforward network + use_gate_act (bool): If `True`, use gate activation. Otherwise, use S2 activation + use_grid_mlp (bool): If `True`, use projecting to grids and performing MLPs for FFN. + use_sep_s2_act (bool): If `True`, use separable S2 activation when `use_gate_act` is False. + + norm_type (str): Type of normalization layer (['layer_norm', 'layer_norm_sh']) + + alpha_drop (float): Dropout rate for attention weights + drop_path_rate (float): Drop path rate + proj_drop (float): Dropout rate for outputs of attention and FFN + """ + + def __init__( + self, + sphere_channels: int, + attn_hidden_channels: int, + num_heads: int, + attn_alpha_channels: int, + attn_value_channels: int, + ffn_hidden_channels: int, + output_channels: int, + lmax_list: List[int], + mmax_list: List[int], + SO3_rotation, + mappingReduced, + SO3_grid, + max_num_elements: int, + edge_channels_list: List[int], + use_atom_edge_embedding: bool = True, + use_m_share_rad: bool = False, + attn_activation: str = "silu", + use_s2_act_attn: bool = False, + use_attn_renorm: bool = True, + ffn_activation: str = "silu", + use_gate_act: bool = False, + use_grid_mlp: bool = False, + use_sep_s2_act: bool = True, + norm_type: str = "rms_norm_sh", + alpha_drop: float = 0.0, + drop_path_rate: float = 0.0, + proj_drop: float = 0.0, + ) -> None: + super(TransBlockV2, self).__init__() + + max_lmax = max(lmax_list) + self.norm_1 = get_normalization_layer( + norm_type, lmax=max_lmax, num_channels=sphere_channels + ) + + self.ga = SO2EquivariantGraphAttention( + sphere_channels=sphere_channels, + hidden_channels=attn_hidden_channels, + num_heads=num_heads, + attn_alpha_channels=attn_alpha_channels, + attn_value_channels=attn_value_channels, + output_channels=sphere_channels, + lmax_list=lmax_list, + mmax_list=mmax_list, + SO3_rotation=SO3_rotation, + mappingReduced=mappingReduced, + SO3_grid=SO3_grid, + max_num_elements=max_num_elements, + edge_channels_list=edge_channels_list, + use_atom_edge_embedding=use_atom_edge_embedding, + use_m_share_rad=use_m_share_rad, + activation=attn_activation, + use_s2_act_attn=use_s2_act_attn, + use_attn_renorm=use_attn_renorm, + use_gate_act=use_gate_act, + use_sep_s2_act=use_sep_s2_act, + alpha_drop=alpha_drop, + ) + + self.drop_path = ( + GraphDropPath(drop_path_rate) if drop_path_rate > 0.0 else None + ) + self.proj_drop = ( + EquivariantDropoutArraySphericalHarmonics( + proj_drop, drop_graph=False + ) + if proj_drop > 0.0 + else None + ) + + self.norm_2 = get_normalization_layer( + norm_type, lmax=max_lmax, num_channels=sphere_channels + ) + + self.ffn = FeedForwardNetwork( + sphere_channels=sphere_channels, + hidden_channels=ffn_hidden_channels, + output_channels=output_channels, + lmax_list=lmax_list, + mmax_list=mmax_list, + SO3_grid=SO3_grid, + activation=ffn_activation, + use_gate_act=use_gate_act, + use_grid_mlp=use_grid_mlp, + use_sep_s2_act=use_sep_s2_act, + ) + + if sphere_channels != output_channels: + self.ffn_shortcut = SO3_LinearV2( + sphere_channels, output_channels, lmax=max_lmax + ) + else: + self.ffn_shortcut = None + + def forward( + self, + x, # SO3_Embedding + atomic_numbers, + edge_distance, + edge_index, + batch, # for GraphDropPath + ): + + output_embedding = x + + x_res = output_embedding.embedding + output_embedding.embedding = self.norm_1(output_embedding.embedding) + output_embedding = self.ga( + output_embedding, atomic_numbers, edge_distance, edge_index + ) + + if self.drop_path is not None: + output_embedding.embedding = self.drop_path( + output_embedding.embedding, batch + ) + if self.proj_drop is not None: + output_embedding.embedding = self.proj_drop( + output_embedding.embedding, batch + ) + + output_embedding.embedding = output_embedding.embedding + x_res + + x_res = output_embedding.embedding + output_embedding.embedding = self.norm_2(output_embedding.embedding) + output_embedding = self.ffn(output_embedding) + + if self.drop_path is not None: + output_embedding.embedding = self.drop_path( + output_embedding.embedding, batch + ) + if self.proj_drop is not None: + output_embedding.embedding = self.proj_drop( + output_embedding.embedding, batch + ) + + if self.ffn_shortcut is not None: + shortcut_embedding = SO3_Embedding( + 0, + output_embedding.lmax_list.copy(), + self.ffn_shortcut.in_features, + device=output_embedding.device, + dtype=output_embedding.dtype, + ) + shortcut_embedding.set_embedding(x_res) + shortcut_embedding.set_lmax_mmax( + output_embedding.lmax_list.copy(), + output_embedding.lmax_list.copy(), + ) + shortcut_embedding = self.ffn_shortcut(shortcut_embedding) + x_res = shortcut_embedding.embedding + + output_embedding.embedding = output_embedding.embedding + x_res + + return output_embedding diff --git a/ocpmodels/models/equiformer_v2/wigner.py b/ocpmodels/models/equiformer_v2/wigner.py new file mode 100644 index 0000000..0490636 --- /dev/null +++ b/ocpmodels/models/equiformer_v2/wigner.py @@ -0,0 +1,40 @@ +import os + +import torch + +# Borrowed from e3nn @ 0.4.0: +# https://github.com/e3nn/e3nn/blob/0.4.0/e3nn/o3/_wigner.py#L10 +# _Jd is a list of tensors of shape (2l+1, 2l+1) +_Jd = torch.load(os.path.join(os.path.dirname(__file__), "Jd.pt")) + + +# Borrowed from e3nn @ 0.4.0: +# https://github.com/e3nn/e3nn/blob/0.4.0/e3nn/o3/_wigner.py#L37 +# +# In 0.5.0, e3nn shifted to torch.matrix_exp which is significantly slower: +# https://github.com/e3nn/e3nn/blob/0.5.0/e3nn/o3/_wigner.py#L92 +def wigner_D( + lv: int, alpha: torch.Tensor, beta: torch.Tensor, gamma: torch.Tensor +) -> torch.Tensor: + if not lv < len(_Jd): + raise NotImplementedError( + f"wigner D maximum l implemented is {len(_Jd) - 1}, send us an email to ask for more" + ) + + alpha, beta, gamma = torch.broadcast_tensors(alpha, beta, gamma) + J = _Jd[lv].to(dtype=alpha.dtype, device=alpha.device) + Xa = _z_rot_mat(alpha, lv) + Xb = _z_rot_mat(beta, lv) + Xc = _z_rot_mat(gamma, lv) + return Xa @ J @ Xb @ J @ Xc + + +def _z_rot_mat(angle: torch.Tensor, lv: int) -> torch.Tensor: + shape, device, dtype = angle.shape, angle.device, angle.dtype + M = angle.new_zeros((*shape, 2 * lv + 1, 2 * lv + 1)) + inds = torch.arange(0, 2 * lv + 1, 1, device=device) + reversed_inds = torch.arange(2 * lv, -1, -1, device=device) + frequencies = torch.arange(lv, -lv - 1, -1, dtype=dtype, device=device) + M[..., inds, reversed_inds] = torch.sin(frequencies * angle[..., None]) + M[..., inds, inds] = torch.cos(frequencies * angle[..., None]) + return M diff --git a/ocpmodels/models/escn/Jd.pt b/ocpmodels/models/escn/Jd.pt new file mode 100644 index 0000000..01ed13e Binary files /dev/null and b/ocpmodels/models/escn/Jd.pt differ diff --git a/ocpmodels/models/escn/__init__.py b/ocpmodels/models/escn/__init__.py new file mode 100644 index 0000000..4ee2f5c --- /dev/null +++ b/ocpmodels/models/escn/__init__.py @@ -0,0 +1 @@ +from .escn import eSCN diff --git a/ocpmodels/models/escn/escn.py b/ocpmodels/models/escn/escn.py new file mode 100644 index 0000000..2e73348 --- /dev/null +++ b/ocpmodels/models/escn/escn.py @@ -0,0 +1,1000 @@ +""" +Copyright (c) Meta, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import time + +import numpy as np +import torch +import torch.nn as nn +from pyexpat.model import XML_CQUANT_OPT + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import conditional_grad +from ocpmodels.models.base import BaseModel +from ocpmodels.models.escn.so3 import ( + CoefficientMapping, + SO3_Embedding, + SO3_Grid, + SO3_Rotation, +) +from ocpmodels.models.scn.sampling import CalcSpherePoints +from ocpmodels.models.scn.smearing import ( + GaussianSmearing, + LinearSigmoidSmearing, + SigmoidSmearing, + SiLUSmearing, +) + +try: + from e3nn import o3 +except ImportError: + pass + + +@registry.register_model("escn") +class eSCN(BaseModel): + """Equivariant Spherical Channel Network + Paper: Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs + + + Args: + use_pbc (bool): Use periodic boundary conditions + regress_forces (bool): Compute forces + otf_graph (bool): Compute graph On The Fly (OTF) + max_neighbors (int): Maximum number of neighbors per atom + cutoff (float): Maximum distance between nieghboring atoms in Angstroms + max_num_elements (int): Maximum atomic number + + num_layers (int): Number of layers in the GNN + lmax_list (int): List of maximum degree of the spherical harmonics (1 to 10) + mmax_list (int): List of maximum order of the spherical harmonics (0 to lmax) + sphere_channels (int): Number of spherical channels (one set per resolution) + hidden_channels (int): Number of hidden units in message passing + num_sphere_samples (int): Number of samples used to approximate the integration of the sphere in the output blocks + edge_channels (int): Number of channels for the edge invariant features + distance_function ("gaussian", "sigmoid", "linearsigmoid", "silu"): Basis function used for distances + basis_width_scalar (float): Width of distance basis function + distance_resolution (float): Distance between distance basis functions in Angstroms + show_timing_info (bool): Show timing and memory info + """ + + def __init__( + self, + num_atoms, # not used + bond_feat_dim, # not used + num_targets, # not used + use_pbc=True, + regress_forces=True, + otf_graph=False, + max_neighbors=40, + cutoff=8.0, + max_num_elements=90, + num_layers=8, + lmax_list=[6], + mmax_list=[2], + sphere_channels=128, + hidden_channels=256, + edge_channels=128, + use_grid=True, + num_sphere_samples=128, + distance_function="gaussian", + basis_width_scalar=1.0, + distance_resolution=0.02, + show_timing_info=False, + ): + super().__init__() + + import sys + + if "e3nn" not in sys.modules: + logging.error( + "You need to install the e3nn library to use the SCN model" + ) + raise ImportError + + self.regress_forces = regress_forces + self.use_pbc = use_pbc + self.cutoff = cutoff + self.otf_graph = otf_graph + self.show_timing_info = show_timing_info + self.max_num_elements = max_num_elements + self.hidden_channels = hidden_channels + self.num_layers = num_layers + self.num_atoms = 0 + self.num_sphere_samples = num_sphere_samples + self.sphere_channels = sphere_channels + self.max_neighbors = max_neighbors + self.edge_channels = edge_channels + self.distance_resolution = distance_resolution + self.grad_forces = False + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions = len(self.lmax_list) + self.sphere_channels_all = self.num_resolutions * self.sphere_channels + self.basis_width_scalar = basis_width_scalar + self.distance_function = distance_function + self.device = torch.cuda.current_device() + + # variables used for display purposes + self.counter = 0 + + # non-linear activation function used throughout the network + self.act = nn.SiLU() + + # Weights for message initialization + self.sphere_embedding = nn.Embedding( + self.max_num_elements, self.sphere_channels_all + ) + + # Initialize the function used to measure the distances between atoms + assert self.distance_function in [ + "gaussian", + "sigmoid", + "linearsigmoid", + "silu", + ] + self.num_gaussians = int(cutoff / self.distance_resolution) + if self.distance_function == "gaussian": + self.distance_expansion = GaussianSmearing( + 0.0, + cutoff, + self.num_gaussians, + basis_width_scalar, + ) + if self.distance_function == "sigmoid": + self.distance_expansion = SigmoidSmearing( + 0.0, + cutoff, + self.num_gaussians, + basis_width_scalar, + ) + if self.distance_function == "linearsigmoid": + self.distance_expansion = LinearSigmoidSmearing( + 0.0, + cutoff, + self.num_gaussians, + basis_width_scalar, + ) + if self.distance_function == "silu": + self.distance_expansion = SiLUSmearing( + 0.0, + cutoff, + self.num_gaussians, + basis_width_scalar, + ) + + # Initialize the transformations between spherical and grid representations + self.SO3_grid = nn.ModuleList() + for l in range(max(self.lmax_list) + 1): + SO3_m_grid = nn.ModuleList() + for m in range(max(self.lmax_list) + 1): + SO3_m_grid.append(SO3_Grid(l, m)) + + self.SO3_grid.append(SO3_m_grid) + + # Initialize the blocks for each layer of the GNN + self.layer_blocks = nn.ModuleList() + for i in range(self.num_layers): + block = LayerBlock( + i, + self.sphere_channels, + self.hidden_channels, + self.edge_channels, + self.lmax_list, + self.mmax_list, + self.distance_expansion, + self.max_num_elements, + self.SO3_grid, + self.act, + ) + self.layer_blocks.append(block) + + # Output blocks for energy and forces + self.energy_block = EnergyBlock( + self.sphere_channels_all, self.num_sphere_samples, self.act + ) + if self.regress_forces: + self.force_block = ForceBlock( + self.sphere_channels_all, self.num_sphere_samples, self.act + ) + + # Create a roughly evenly distributed point sampling of the sphere for the output blocks + self.sphere_points = nn.Parameter( + CalcSpherePoints(self.num_sphere_samples), requires_grad=False + ) + + # For each spherical point, compute the spherical harmonic coefficient weights + self.sphharm_weights = [] + for i in range(self.num_resolutions): + self.sphharm_weights.append( + nn.Parameter( + o3.spherical_harmonics( + torch.arange(0, self.lmax_list[i] + 1).tolist(), + self.sphere_points, + False, + ), + requires_grad=False, + ) + ) + self.sphharm_weights = nn.ParameterList(self.sphharm_weights) + + @conditional_grad(torch.enable_grad()) + def forward(self, data): + self.batch_size = len(data.natoms) + self.dtype = data.pos.dtype + + start_time = time.time() + atomic_numbers = data.atomic_numbers.long() + num_atoms = len(atomic_numbers) + pos = data.pos + + ( + edge_index, + edge_distance, + edge_distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + + ############################################################### + # Initialize data structures + ############################################################### + + # Compute 3x3 rotation matrix per edge + edge_rot_mat = self._init_edge_rot_mat( + data, edge_index, edge_distance_vec + ) + + # Initialize the WignerD matrices and other values for spherical harmonic calculations + self.SO3_edge_rot = nn.ModuleList() + for i in range(self.num_resolutions): + self.SO3_edge_rot.append( + SO3_Rotation(edge_rot_mat, self.lmax_list[i]) + ) + + ############################################################### + # Initialize node embeddings + ############################################################### + + # Init per node representations using an atomic number based embedding + offset = 0 + x = SO3_Embedding( + num_atoms, + self.lmax_list, + self.sphere_channels, + self.device, + self.dtype, + ) + + offset_res = 0 + offset = 0 + # Initialize the l=0,m=0 coefficients for each resolution + for i in range(self.num_resolutions): + x.embedding[:, offset_res, :] = self.sphere_embedding( + atomic_numbers + )[:, offset : offset + self.sphere_channels] + offset = offset + self.sphere_channels + offset_res = offset_res + int((self.lmax_list[i] + 1) ** 2) + + # This can be expensive to compute (not implemented efficiently), so only do it once and pass it along to each layer + mappingReduced = CoefficientMapping( + self.lmax_list, self.mmax_list, self.device + ) + + ############################################################### + # Update spherical node embeddings + ############################################################### + + for i in range(self.num_layers): + if i > 0: + x_message = self.layer_blocks[i]( + x, + atomic_numbers, + edge_distance, + edge_index, + self.SO3_edge_rot, + mappingReduced, + ) + + # Residual layer for all layers past the first + x.embedding = x.embedding + x_message.embedding + + else: + # No residual for the first layer + x = self.layer_blocks[i]( + x, + atomic_numbers, + edge_distance, + edge_index, + self.SO3_edge_rot, + mappingReduced, + ) + + # Sample the spherical channels (node embeddings) at evenly distributed points on the sphere. + # These values are fed into the output blocks. + x_pt = torch.tensor([], device=self.device) + offset = 0 + # Compute the embedding values at every sampled point on the sphere + for i in range(self.num_resolutions): + num_coefficients = int((x.lmax_list[i] + 1) ** 2) + x_pt = torch.cat( + [ + x_pt, + torch.einsum( + "abc, pb->apc", + x.embedding[:, offset : offset + num_coefficients], + self.sphharm_weights[i], + ).contiguous(), + ], + dim=2, + ) + offset = offset + num_coefficients + + x_pt = x_pt.view(-1, self.sphere_channels_all) + + ############################################################### + # Energy estimation + ############################################################### + node_energy = self.energy_block(x_pt) + energy = torch.zeros(len(data.natoms), device=pos.device) + energy.index_add_(0, data.batch, node_energy.view(-1)) + # Scale energy to help balance numerical precision w.r.t. forces + energy = energy * 0.001 + + ############################################################### + # Force estimation + ############################################################### + if self.regress_forces: + forces = self.force_block(x_pt, self.sphere_points) + + if self.show_timing_info is True: + torch.cuda.synchronize() + print( + "{} Time: {}\tMemory: {}\t{}".format( + self.counter, + time.time() - start_time, + len(data.pos), + torch.cuda.max_memory_allocated() / 1000000, + ) + ) + + self.counter = self.counter + 1 + + if not self.regress_forces: + return energy + else: + return energy, forces + + # Initialize the edge rotation matrics + def _init_edge_rot_mat(self, data, edge_index, edge_distance_vec): + edge_vec_0 = edge_distance_vec + edge_vec_0_distance = torch.sqrt(torch.sum(edge_vec_0**2, dim=1)) + + # Make sure the atoms are far enough apart + if torch.min(edge_vec_0_distance) < 0.0001: + print( + "Error edge_vec_0_distance: {}".format( + torch.min(edge_vec_0_distance) + ) + ) + (minval, minidx) = torch.min(edge_vec_0_distance, 0) + print( + "Error edge_vec_0_distance: {} {} {} {} {}".format( + minidx, + edge_index[0, minidx], + edge_index[1, minidx], + data.pos[edge_index[0, minidx]], + data.pos[edge_index[1, minidx]], + ) + ) + + norm_x = edge_vec_0 / (edge_vec_0_distance.view(-1, 1)) + + edge_vec_2 = torch.rand_like(edge_vec_0) - 0.5 + edge_vec_2 = edge_vec_2 / ( + torch.sqrt(torch.sum(edge_vec_2**2, dim=1)).view(-1, 1) + ) + # Create two rotated copys of the random vectors in case the random vector is aligned with norm_x + # With two 90 degree rotated vectors, at least one should not be aligned with norm_x + edge_vec_2b = edge_vec_2.clone() + edge_vec_2b[:, 0] = -edge_vec_2[:, 1] + edge_vec_2b[:, 1] = edge_vec_2[:, 0] + edge_vec_2c = edge_vec_2.clone() + edge_vec_2c[:, 1] = -edge_vec_2[:, 2] + edge_vec_2c[:, 2] = edge_vec_2[:, 1] + vec_dot_b = torch.abs(torch.sum(edge_vec_2b * norm_x, dim=1)).view( + -1, 1 + ) + vec_dot_c = torch.abs(torch.sum(edge_vec_2c * norm_x, dim=1)).view( + -1, 1 + ) + + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)).view(-1, 1) + edge_vec_2 = torch.where( + torch.gt(vec_dot, vec_dot_b), edge_vec_2b, edge_vec_2 + ) + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)).view(-1, 1) + edge_vec_2 = torch.where( + torch.gt(vec_dot, vec_dot_c), edge_vec_2c, edge_vec_2 + ) + + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)) + # Check the vectors aren't aligned + assert torch.max(vec_dot) < 0.99 + + norm_z = torch.cross(norm_x, edge_vec_2, dim=1) + norm_z = norm_z / ( + torch.sqrt(torch.sum(norm_z**2, dim=1, keepdim=True)) + ) + norm_z = norm_z / ( + torch.sqrt(torch.sum(norm_z**2, dim=1)).view(-1, 1) + ) + norm_y = torch.cross(norm_x, norm_z, dim=1) + norm_y = norm_y / ( + torch.sqrt(torch.sum(norm_y**2, dim=1, keepdim=True)) + ) + + # Construct the 3D rotation matrix + norm_x = norm_x.view(-1, 3, 1) + norm_y = -norm_y.view(-1, 3, 1) + norm_z = norm_z.view(-1, 3, 1) + + edge_rot_mat_inv = torch.cat([norm_z, norm_x, norm_y], dim=2) + edge_rot_mat = torch.transpose(edge_rot_mat_inv, 1, 2) + + return edge_rot_mat.detach() + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) + + +class LayerBlock(torch.nn.Module): + """ + Layer block: Perform one layer (message passing and aggregation) of the GNN + + Args: + layer_idx (int): Layer number + sphere_channels (int): Number of spherical channels + hidden_channels (int): Number of hidden channels used during the SO(2) conv + edge_channels (int): Size of invariant edge embedding + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + distance_expansion (func): Function used to compute distance embedding + max_num_elements (int): Maximum number of atomic numbers + SO3_grid (SO3_grid): Class used to convert from grid the spherical harmonic representations + act (function): Non-linear activation function + """ + + def __init__( + self, + layer_idx, + sphere_channels, + hidden_channels, + edge_channels, + lmax_list, + mmax_list, + distance_expansion, + max_num_elements, + SO3_grid, + act, + ): + super(LayerBlock, self).__init__() + self.layer_idx = layer_idx + self.act = act + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions = len(lmax_list) + self.sphere_channels = sphere_channels + self.sphere_channels_all = self.num_resolutions * self.sphere_channels + self.SO3_grid = SO3_grid + + # Message block + self.message_block = MessageBlock( + self.layer_idx, + self.sphere_channels, + hidden_channels, + edge_channels, + self.lmax_list, + self.mmax_list, + distance_expansion, + max_num_elements, + self.SO3_grid, + self.act, + ) + + # Non-linear point-wise comvolution for the aggregated messages + self.fc1_sphere = nn.Linear( + 2 * self.sphere_channels_all, self.sphere_channels_all, bias=False + ) + + self.fc2_sphere = nn.Linear( + self.sphere_channels_all, self.sphere_channels_all, bias=False + ) + + self.fc3_sphere = nn.Linear( + self.sphere_channels_all, self.sphere_channels_all, bias=False + ) + + def forward( + self, + x, + atomic_numbers, + edge_distance, + edge_index, + SO3_edge_rot, + mappingReduced, + ): + + # Compute messages by performing message block + x_message = self.message_block( + x, + atomic_numbers, + edge_distance, + edge_index, + SO3_edge_rot, + mappingReduced, + ) + + # Compute point-wise spherical non-linearity on aggregated messages + max_lmax = max(self.lmax_list) + + # Project to grid + x_grid_message = x_message.to_grid(self.SO3_grid, lmax=max_lmax) + x_grid = x.to_grid(self.SO3_grid, lmax=max_lmax) + x_grid = torch.cat([x_grid, x_grid_message], dim=3) + + # Perform point-wise convolution + x_grid = self.act(self.fc1_sphere(x_grid)) + x_grid = self.act(self.fc2_sphere(x_grid)) + x_grid = self.fc3_sphere(x_grid) + + # Project back to spherical harmonic coefficients + x_message._from_grid(x_grid, self.SO3_grid, lmax=max_lmax) + + # Return aggregated messages + return x_message + + +class MessageBlock(torch.nn.Module): + """ + Message block: Perform message passing + + Args: + layer_idx (int): Layer number + sphere_channels (int): Number of spherical channels + hidden_channels (int): Number of hidden channels used during the SO(2) conv + edge_channels (int): Size of invariant edge embedding + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + distance_expansion (func): Function used to compute distance embedding + max_num_elements (int): Maximum number of atomic numbers + SO3_grid (SO3_grid): Class used to convert from grid the spherical harmonic representations + act (function): Non-linear activation function + """ + + def __init__( + self, + layer_idx, + sphere_channels, + hidden_channels, + edge_channels, + lmax_list, + mmax_list, + distance_expansion, + max_num_elements, + SO3_grid, + act, + ): + super(MessageBlock, self).__init__() + self.layer_idx = layer_idx + self.act = act + self.hidden_channels = hidden_channels + self.sphere_channels = sphere_channels + self.SO3_grid = SO3_grid + self.num_resolutions = len(lmax_list) + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.edge_channels = edge_channels + + # Create edge scalar (invariant to rotations) features + self.edge_block = EdgeBlock( + self.edge_channels, + distance_expansion, + max_num_elements, + self.act, + ) + + # Create SO(2) convolution blocks + self.so2_block_source = SO2Block( + self.sphere_channels, + self.hidden_channels, + self.edge_channels, + self.lmax_list, + self.mmax_list, + self.act, + ) + self.so2_block_target = SO2Block( + self.sphere_channels, + self.hidden_channels, + self.edge_channels, + self.lmax_list, + self.mmax_list, + self.act, + ) + + def forward( + self, + x, + atomic_numbers, + edge_distance, + edge_index, + SO3_edge_rot, + mappingReduced, + ): + ############################################################### + # Compute messages + ############################################################### + + # Compute edge scalar features (invariant to rotations) + # Uses atomic numbers and edge distance as inputs + x_edge = self.edge_block( + edge_distance, + atomic_numbers[edge_index[0]], # Source atom atomic number + atomic_numbers[edge_index[1]], # Target atom atomic number + ) + + # Copy embeddings for each edge's source and target nodes + x_source = x.clone() + x_target = x.clone() + x_source._expand_edge(edge_index[0, :]) + x_target._expand_edge(edge_index[1, :]) + + # Rotate the irreps to align with the edge + x_source._rotate(SO3_edge_rot, self.lmax_list, self.mmax_list) + x_target._rotate(SO3_edge_rot, self.lmax_list, self.mmax_list) + + # Compute messages + x_source = self.so2_block_source(x_source, x_edge, mappingReduced) + x_target = self.so2_block_target(x_target, x_edge, mappingReduced) + + # Add together the source and target results + x_target.embedding = x_source.embedding + x_target.embedding + + # Point-wise spherical non-linearity + x_target._grid_act(self.SO3_grid, self.act, mappingReduced) + + # Rotate back the irreps + x_target._rotate_inv(SO3_edge_rot, mappingReduced) + + # Compute the sum of the incoming neighboring messages for each target node + x_target._reduce_edge(edge_index[1], len(x.embedding)) + + return x_target + + +class SO2Block(torch.nn.Module): + """ + SO(2) Block: Perform SO(2) convolutions for all m (orders) + + Args: + sphere_channels (int): Number of spherical channels + hidden_channels (int): Number of hidden channels used during the SO(2) conv + edge_channels (int): Size of invariant edge embedding + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + act (function): Non-linear activation function + """ + + def __init__( + self, + sphere_channels, + hidden_channels, + edge_channels, + lmax_list, + mmax_list, + act, + ): + super(SO2Block, self).__init__() + self.sphere_channels = sphere_channels + self.hidden_channels = hidden_channels + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions = len(lmax_list) + self.act = act + + num_channels_m0 = 0 + for i in range(self.num_resolutions): + num_coefficents = self.lmax_list[i] + 1 + num_channels_m0 = ( + num_channels_m0 + num_coefficents * self.sphere_channels + ) + + # SO(2) convolution for m=0 + self.fc1_dist0 = nn.Linear(edge_channels, self.hidden_channels) + self.fc1_m0 = nn.Linear( + num_channels_m0, self.hidden_channels, bias=False + ) + self.fc2_m0 = nn.Linear( + self.hidden_channels, num_channels_m0, bias=False + ) + + # SO(2) convolution for non-zero m + self.so2_conv = nn.ModuleList() + for m in range(1, max(self.mmax_list) + 1): + so2_conv = SO2Conv( + m, + self.sphere_channels, + self.hidden_channels, + edge_channels, + self.lmax_list, + self.mmax_list, + self.act, + ) + self.so2_conv.append(so2_conv) + + def forward( + self, + x, + x_edge, + mappingReduced, + ): + + num_edges = len(x_edge) + + # Reshape the spherical harmonics based on m (order) + x._m_primary(mappingReduced) + + # Compute m=0 coefficients separately since they only have real values (no imaginary) + + # Compute edge scalar features for m=0 + x_edge_0 = self.act(self.fc1_dist0(x_edge)) + + x_0 = x.embedding[:, 0 : mappingReduced.m_size[0]].contiguous() + x_0 = x_0.view(num_edges, -1) + + x_0 = self.fc1_m0(x_0) + x_0 = x_0 * x_edge_0 + x_0 = self.fc2_m0(x_0) + x_0 = x_0.view(num_edges, -1, x.num_channels) + + # Update the m=0 coefficients + x.embedding[:, 0 : mappingReduced.m_size[0]] = x_0 + + # Compute the values for the m > 0 coefficients + offset = mappingReduced.m_size[0] + for m in range(1, max(self.mmax_list) + 1): + # Get the m order coefficients + x_m = x.embedding[ + :, offset : offset + 2 * mappingReduced.m_size[m] + ].contiguous() + x_m = x_m.view(num_edges, 2, -1) + # Perform SO(2) convolution + x_m = self.so2_conv[m - 1](x_m, x_edge) + x_m = x_m.view(num_edges, -1, x.num_channels) + x.embedding[ + :, offset : offset + 2 * mappingReduced.m_size[m] + ] = x_m + + offset = offset + 2 * mappingReduced.m_size[m] + + # Reshape the spherical harmonics based on l (degree) + x._l_primary(mappingReduced) + + return x + + +class SO2Conv(torch.nn.Module): + """ + SO(2) Conv: Perform an SO(2) convolution + + Args: + m (int): Order of the spherical harmonic coefficients + sphere_channels (int): Number of spherical channels + hidden_channels (int): Number of hidden channels used during the SO(2) conv + edge_channels (int): Size of invariant edge embedding + lmax_list (list:int): List of degrees (l) for each resolution + mmax_list (list:int): List of orders (m) for each resolution + act (function): Non-linear activation function + """ + + def __init__( + self, + m, + sphere_channels, + hidden_channels, + edge_channels, + lmax_list, + mmax_list, + act, + ): + super(SO2Conv, self).__init__() + self.hidden_channels = hidden_channels + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.sphere_channels = sphere_channels + self.num_resolutions = len(self.lmax_list) + self.m = m + self.act = act + + num_channels = 0 + for i in range(self.num_resolutions): + num_coefficents = 0 + if self.mmax_list[i] >= m: + num_coefficents = self.lmax_list[i] - m + 1 + + num_channels = ( + num_channels + num_coefficents * self.sphere_channels + ) + + assert num_channels > 0 + + # Embedding function of the distance + self.fc1_dist = nn.Linear(edge_channels, 2 * self.hidden_channels) + + # Real weights of SO(2) convolution + self.fc1_r = nn.Linear(num_channels, self.hidden_channels, bias=False) + self.fc2_r = nn.Linear(self.hidden_channels, num_channels, bias=False) + + # Imaginary weights of SO(2) convolution + self.fc1_i = nn.Linear(num_channels, self.hidden_channels, bias=False) + self.fc2_i = nn.Linear(self.hidden_channels, num_channels, bias=False) + + def forward(self, x_m, x_edge): + # Compute edge scalar features + x_edge = self.act(self.fc1_dist(x_edge)) + x_edge = x_edge.view(-1, 2, self.hidden_channels) + + # Perform the complex weight multiplication + x_r = self.fc1_r(x_m) + x_r = x_r * x_edge[:, 0:1, :] + x_r = self.fc2_r(x_r) + + x_i = self.fc1_i(x_m) + x_i = x_i * x_edge[:, 1:2, :] + x_i = self.fc2_i(x_i) + + x_m_r = x_r[:, 0] - x_i[:, 1] + x_m_i = x_r[:, 1] + x_i[:, 0] + + return torch.stack((x_m_r, x_m_i), dim=1).contiguous() + + +class EdgeBlock(torch.nn.Module): + """ + Edge Block: Compute invariant edge representation from edge diatances and atomic numbers + + Args: + edge_channels (int): Size of invariant edge embedding + distance_expansion (func): Function used to compute distance embedding + max_num_elements (int): Maximum number of atomic numbers + act (function): Non-linear activation function + """ + + def __init__( + self, + edge_channels, + distance_expansion, + max_num_elements, + act, + ): + super(EdgeBlock, self).__init__() + self.in_channels = distance_expansion.num_output + self.distance_expansion = distance_expansion + self.act = act + self.edge_channels = edge_channels + self.max_num_elements = max_num_elements + + # Embedding function of the distance + self.fc1_dist = nn.Linear(self.in_channels, self.edge_channels) + + # Embedding function of the atomic numbers + self.source_embedding = nn.Embedding( + self.max_num_elements, self.edge_channels + ) + self.target_embedding = nn.Embedding( + self.max_num_elements, self.edge_channels + ) + nn.init.uniform_(self.source_embedding.weight.data, -0.001, 0.001) + nn.init.uniform_(self.target_embedding.weight.data, -0.001, 0.001) + + # Embedding function of the edge + self.fc1_edge_attr = nn.Linear( + self.edge_channels, + self.edge_channels, + ) + + def forward(self, edge_distance, source_element, target_element): + + # Compute distance embedding + x_dist = self.distance_expansion(edge_distance) + x_dist = self.fc1_dist(x_dist) + + # Compute atomic number embeddings + source_embedding = self.source_embedding(source_element) + target_embedding = self.target_embedding(target_element) + + # Compute invariant edge embedding + x_edge = self.act(source_embedding + target_embedding + x_dist) + x_edge = self.act(self.fc1_edge_attr(x_edge)) + + return x_edge + + +class EnergyBlock(torch.nn.Module): + """ + Energy Block: Output block computing the energy + + Args: + num_channels (int): Number of channels + num_sphere_samples (int): Number of samples used to approximate the integral on the sphere + act (function): Non-linear activation function + """ + + def __init__( + self, + num_channels, + num_sphere_samples, + act, + ): + super(EnergyBlock, self).__init__() + self.num_channels = num_channels + self.num_sphere_samples = num_sphere_samples + self.act = act + + self.fc1 = nn.Linear(self.num_channels, self.num_channels) + self.fc2 = nn.Linear(self.num_channels, self.num_channels) + self.fc3 = nn.Linear(self.num_channels, 1, bias=False) + + def forward(self, x_pt): + # x_pt are the values of the channels sampled at different points on the sphere + x_pt = self.act(self.fc1(x_pt)) + x_pt = self.act(self.fc2(x_pt)) + x_pt = self.fc3(x_pt) + x_pt = x_pt.view(-1, self.num_sphere_samples, 1) + node_energy = torch.sum(x_pt, dim=1) / self.num_sphere_samples + + return node_energy + + +class ForceBlock(torch.nn.Module): + """ + Force Block: Output block computing the per atom forces + + Args: + num_channels (int): Number of channels + num_sphere_samples (int): Number of samples used to approximate the integral on the sphere + act (function): Non-linear activation function + """ + + def __init__( + self, + num_channels, + num_sphere_samples, + act, + ): + super(ForceBlock, self).__init__() + self.num_channels = num_channels + self.num_sphere_samples = num_sphere_samples + self.act = act + + self.fc1 = nn.Linear(self.num_channels, self.num_channels) + self.fc2 = nn.Linear(self.num_channels, self.num_channels) + self.fc3 = nn.Linear(self.num_channels, 1, bias=False) + + def forward(self, x_pt, sphere_points): + # x_pt are the values of the channels sampled at different points on the sphere + x_pt = self.act(self.fc1(x_pt)) + x_pt = self.act(self.fc2(x_pt)) + x_pt = self.fc3(x_pt) + x_pt = x_pt.view(-1, self.num_sphere_samples, 1) + forces = x_pt * sphere_points.view(1, self.num_sphere_samples, 3) + forces = torch.sum(forces, dim=1) / self.num_sphere_samples + + return forces diff --git a/ocpmodels/models/escn/so3.py b/ocpmodels/models/escn/so3.py new file mode 100644 index 0000000..02d2ff2 --- /dev/null +++ b/ocpmodels/models/escn/so3.py @@ -0,0 +1,568 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os + +import torch +import torch.nn as nn + +try: + from e3nn import o3 + from e3nn.o3 import FromS2Grid, ToS2Grid +except ImportError: + pass + +# Borrowed from e3nn @ 0.4.0: +# https://github.com/e3nn/e3nn/blob/0.4.0/e3nn/o3/_wigner.py#L10 +# _Jd is a list of tensors of shape (2l+1, 2l+1) +_Jd = torch.load(os.path.join(os.path.dirname(__file__), "Jd.pt")) + + +class CoefficientMapping: + """ + Helper functions for coefficients used to reshape l<-->m and to get coefficients of specific degree or order + + Args: + lmax_list (list:int): List of maximum degree of the spherical harmonics + mmax_list (list:int): List of maximum order of the spherical harmonics + device: Device of the output + """ + + def __init__( + self, + lmax_list, + mmax_list, + device, + ): + super().__init__() + + self.lmax_list = lmax_list + self.mmax_list = mmax_list + self.num_resolutions = len(lmax_list) + self.device = device + + # Compute the degree (l) and order (m) for each + # entry of the embedding + + self.l_harmonic = torch.tensor([], device=self.device).long() + self.m_harmonic = torch.tensor([], device=self.device).long() + self.m_complex = torch.tensor([], device=self.device).long() + + self.res_size = torch.zeros( + [self.num_resolutions], device=self.device + ).long() + offset = 0 + for i in range(self.num_resolutions): + for l in range(0, self.lmax_list[i] + 1): + mmax = min(self.mmax_list[i], l) + m = torch.arange(-mmax, mmax + 1, device=self.device).long() + self.m_complex = torch.cat([self.m_complex, m], dim=0) + self.m_harmonic = torch.cat( + [self.m_harmonic, torch.abs(m).long()], dim=0 + ) + self.l_harmonic = torch.cat( + [self.l_harmonic, m.fill_(l).long()], dim=0 + ) + self.res_size[i] = len(self.l_harmonic) - offset + offset = len(self.l_harmonic) + + num_coefficients = len(self.l_harmonic) + self.to_m = torch.zeros( + [num_coefficients, num_coefficients], device=self.device + ) + self.m_size = torch.zeros( + [max(self.mmax_list) + 1], device=self.device + ).long() + + # The following is implemented poorly - very slow. It only gets called + # a few times so haven't optimized. + offset = 0 + for m in range(max(self.mmax_list) + 1): + idx_r, idx_i = self.complex_idx(m) + + for idx_out, idx_in in enumerate(idx_r): + self.to_m[idx_out + offset, idx_in] = 1.0 + offset = offset + len(idx_r) + self.m_size[m] = int(len(idx_r)) + + for idx_out, idx_in in enumerate(idx_i): + self.to_m[idx_out + offset, idx_in] = 1.0 + offset = offset + len(idx_i) + + self.to_m = self.to_m.detach() + + # Return mask containing coefficients of order m (real and imaginary parts) + def complex_idx(self, m, lmax=-1): + if lmax == -1: + lmax = max(self.lmax_list) + + indices = torch.arange(len(self.l_harmonic), device=self.device) + # Real part + mask_r = torch.bitwise_and( + self.l_harmonic.le(lmax), self.m_complex.eq(m) + ) + mask_idx_r = torch.masked_select(indices, mask_r) + + mask_idx_i = torch.tensor([], device=self.device).long() + # Imaginary part + if m != 0: + mask_i = torch.bitwise_and( + self.l_harmonic.le(lmax), self.m_complex.eq(-m) + ) + mask_idx_i = torch.masked_select(indices, mask_i) + + return mask_idx_r, mask_idx_i + + # Return mask containing coefficients less than or equal to degree (l) and order (m) + def coefficient_idx(self, lmax, mmax): + mask = torch.bitwise_and( + self.l_harmonic.le(lmax), self.m_harmonic.le(mmax) + ) + indices = torch.arange(len(mask), device=self.device) + + return torch.masked_select(indices, mask) + + +class SO3_Embedding(torch.nn.Module): + """ + Helper functions for irreps embedding + + Args: + length (int): Batch size + lmax_list (list:int): List of maximum degree of the spherical harmonics + num_channels (int): Number of channels + device: Device of the output + dtype: type of the output tensors + """ + + def __init__( + self, + length, + lmax_list, + num_channels, + device, + dtype, + ): + super().__init__() + self.num_channels = num_channels + self.device = device + self.dtype = dtype + self.num_resolutions = len(lmax_list) + + self.num_coefficients = 0 + for i in range(self.num_resolutions): + self.num_coefficients = self.num_coefficients + int( + (lmax_list[i] + 1) ** 2 + ) + + embedding = torch.zeros( + length, + self.num_coefficients, + self.num_channels, + device=self.device, + dtype=self.dtype, + ) + + self.set_embedding(embedding) + self.set_lmax_mmax(lmax_list, lmax_list.copy()) + + # Clone an embedding of irreps + def clone(self): + clone = SO3_Embedding( + 0, + self.lmax_list.copy(), + self.num_channels, + self.device, + self.dtype, + ) + + clone.set_embedding(self.embedding.clone()) + + return clone + + # Initialize an embedding of irreps + def set_embedding(self, embedding): + self.length = len(embedding) + self.embedding = embedding + + # Set the maximum order to be the maximum degree + def set_lmax_mmax(self, lmax_list, mmax_list): + self.lmax_list = lmax_list + self.mmax_list = mmax_list + + # Expand the node embeddings to the number of edges + def _expand_edge(self, edge_index): + embedding = self.embedding[edge_index] + self.set_embedding(embedding) + + # Initialize an embedding of irreps of a neighborhood + def expand_edge(self, edge_index): + x_expand = SO3_Embedding( + 0, + self.lmax_list.copy(), + self.num_channels, + self.device, + self.dtype, + ) + x_expand.set_embedding(self.embedding[edge_index]) + return x_expand + + # Compute the sum of the embeddings of the neighborhood + def _reduce_edge(self, edge_index, num_nodes): + new_embedding = torch.zeros( + num_nodes, + self.num_coefficients, + self.num_channels, + device=self.embedding.device, + dtype=self.embedding.dtype, + ) + new_embedding.index_add_(0, edge_index, self.embedding) + self.set_embedding(new_embedding) + + # Reshape the embedding l-->m + def _m_primary(self, mapping): + self.embedding = torch.einsum( + "nac,ba->nbc", self.embedding, mapping.to_m + ) + + # Reshape the embedding m-->l + def _l_primary(self, mapping): + self.embedding = torch.einsum( + "nac,ab->nbc", self.embedding, mapping.to_m + ) + + # Rotate the embedding + def _rotate(self, SO3_rotation, lmax_list, mmax_list): + embedding_rotate = torch.tensor( + [], device=self.device, dtype=self.dtype + ) + + offset = 0 + for i in range(self.num_resolutions): + num_coefficients = int((self.lmax_list[i] + 1) ** 2) + embedding_i = self.embedding[:, offset : offset + num_coefficients] + embedding_rotate = torch.cat( + [ + embedding_rotate, + SO3_rotation[i].rotate( + embedding_i, lmax_list[i], mmax_list[i] + ), + ], + dim=1, + ) + offset = offset + num_coefficients + + self.embedding = embedding_rotate + self.set_lmax_mmax(lmax_list.copy(), mmax_list.copy()) + + # Rotate the embedding by the inverse of the rotation matrix + def _rotate_inv(self, SO3_rotation, mappingReduced): + embedding_rotate = torch.tensor( + [], device=self.device, dtype=self.dtype + ) + + offset = 0 + for i in range(self.num_resolutions): + num_coefficients = mappingReduced.res_size[i] + embedding_i = self.embedding[:, offset : offset + num_coefficients] + embedding_rotate = torch.cat( + [ + embedding_rotate, + SO3_rotation[i].rotate_inv( + embedding_i, self.lmax_list[i], self.mmax_list[i] + ), + ], + dim=1, + ) + offset = offset + num_coefficients + + self.embedding = embedding_rotate + + # Assume mmax = lmax when rotating back + for i in range(self.num_resolutions): + self.mmax_list[i] = int(self.lmax_list[i]) + + self.set_lmax_mmax(self.lmax_list, self.mmax_list) + + # Compute point-wise spherical non-linearity + def _grid_act(self, SO3_grid, act, mappingReduced): + offset = 0 + for i in range(self.num_resolutions): + + num_coefficients = mappingReduced.res_size[i] + + x_res = self.embedding[ + :, offset : offset + num_coefficients + ].contiguous() + to_grid_mat = SO3_grid[self.lmax_list[i]][ + self.mmax_list[i] + ].get_to_grid_mat(self.device) + from_grid_mat = SO3_grid[self.lmax_list[i]][ + self.mmax_list[i] + ].get_from_grid_mat(self.device) + + x_grid = torch.einsum("bai,zic->zbac", to_grid_mat, x_res) + x_grid = act(x_grid) + x_res = torch.einsum("bai,zbac->zic", from_grid_mat, x_grid) + + self.embedding[:, offset : offset + num_coefficients] = x_res + offset = offset + num_coefficients + + # Compute a sample of the grid + def to_grid(self, SO3_grid, lmax=-1): + if lmax == -1: + lmax = max(self.lmax_list) + + to_grid_mat_lmax = SO3_grid[lmax][lmax].get_to_grid_mat(self.device) + grid_mapping = SO3_grid[lmax][lmax].mapping + + offset = 0 + x_grid = torch.tensor([], device=self.device) + + for i in range(self.num_resolutions): + num_coefficients = int((self.lmax_list[i] + 1) ** 2) + x_res = self.embedding[ + :, offset : offset + num_coefficients + ].contiguous() + to_grid_mat = to_grid_mat_lmax[ + :, + :, + grid_mapping.coefficient_idx( + self.lmax_list[i], self.lmax_list[i] + ), + ] + x_grid = torch.cat( + [x_grid, torch.einsum("bai,zic->zbac", to_grid_mat, x_res)], + dim=3, + ) + offset = offset + num_coefficients + + return x_grid + + # Compute irreps from grid representation + def _from_grid(self, x_grid, SO3_grid, lmax=-1): + if lmax == -1: + lmax = max(self.lmax_list) + + from_grid_mat_lmax = SO3_grid[lmax][lmax].get_from_grid_mat( + self.device + ) + grid_mapping = SO3_grid[lmax][lmax].mapping + + offset = 0 + offset_channel = 0 + for i in range(self.num_resolutions): + from_grid_mat = from_grid_mat_lmax[ + :, + :, + grid_mapping.coefficient_idx( + self.lmax_list[i], self.lmax_list[i] + ), + ] + x_res = torch.einsum( + "bai,zbac->zic", + from_grid_mat, + x_grid[ + :, + :, + :, + offset_channel : offset_channel + self.num_channels, + ], + ) + num_coefficients = int((self.lmax_list[i] + 1) ** 2) + self.embedding[:, offset : offset + num_coefficients] = x_res + offset = offset + num_coefficients + offset_channel = offset_channel + self.num_channels + + +class SO3_Rotation(torch.nn.Module): + """ + Helper functions for Wigner-D rotations + + Args: + rot_mat3x3 (tensor): Rotation matrix + lmax_list (list:int): List of maximum degree of the spherical harmonics + """ + + def __init__( + self, + rot_mat3x3, + lmax, + ): + super().__init__() + self.device = rot_mat3x3.device + self.dtype = rot_mat3x3.dtype + + length = len(rot_mat3x3) + + self.wigner = self.RotationToWignerDMatrix(rot_mat3x3, 0, lmax) + self.wigner_inv = torch.transpose(self.wigner, 1, 2).contiguous() + + self.wigner = self.wigner.detach() + self.wigner_inv = self.wigner_inv.detach() + + self.set_lmax(lmax) + + # Initialize coefficients for reshape l<-->m + def set_lmax(self, lmax): + self.lmax = lmax + self.mapping = CoefficientMapping( + [self.lmax], [self.lmax], self.device + ) + + # Rotate the embedding + def rotate(self, embedding, out_lmax, out_mmax): + out_mask = self.mapping.coefficient_idx(out_lmax, out_mmax) + wigner = self.wigner[:, out_mask, :] + return torch.bmm(wigner, embedding) + + # Rotate the embedding by the inverse of the rotation matrix + def rotate_inv(self, embedding, in_lmax, in_mmax): + in_mask = self.mapping.coefficient_idx(in_lmax, in_mmax) + wigner_inv = self.wigner_inv[:, :, in_mask] + + return torch.bmm(wigner_inv, embedding) + + # Compute Wigner matrices from rotation matrix + def RotationToWignerDMatrix(self, edge_rot_mat, start_lmax, end_lmax): + x = edge_rot_mat @ edge_rot_mat.new_tensor([0.0, 1.0, 0.0]) + alpha, beta = o3.xyz_to_angles(x) + R = ( + o3.angles_to_matrix( + alpha, beta, torch.zeros_like(alpha) + ).transpose(-1, -2) + @ edge_rot_mat + ) + gamma = torch.atan2(R[..., 0, 2], R[..., 0, 0]) + + size = (end_lmax + 1) ** 2 - (start_lmax) ** 2 + wigner = torch.zeros(len(alpha), size, size, device=self.device) + start = 0 + for lmax in range(start_lmax, end_lmax + 1): + block = self.wigner_D(lmax, alpha, beta, gamma) + end = start + block.size()[1] + wigner[:, start:end, start:end] = block + start = end + + return wigner.detach() + + # Borrowed from e3nn @ 0.4.0: + # https://github.com/e3nn/e3nn/blob/0.4.0/e3nn/o3/_wigner.py#L37 + # + # In 0.5.0, e3nn shifted to torch.matrix_exp which is significantly slower: + # https://github.com/e3nn/e3nn/blob/0.5.0/e3nn/o3/_wigner.py#L92 + def wigner_D(self, l, alpha, beta, gamma): + if not l < len(_Jd): + raise NotImplementedError( + f"wigner D maximum l implemented is {len(_Jd) - 1}, send us an email to ask for more" + ) + + alpha, beta, gamma = torch.broadcast_tensors(alpha, beta, gamma) + J = _Jd[l].to(dtype=alpha.dtype, device=alpha.device) + Xa = self._z_rot_mat(alpha, l) + Xb = self._z_rot_mat(beta, l) + Xc = self._z_rot_mat(gamma, l) + return Xa @ J @ Xb @ J @ Xc + + def _z_rot_mat(self, angle, l): + shape, device, dtype = angle.shape, angle.device, angle.dtype + M = angle.new_zeros((*shape, 2 * l + 1, 2 * l + 1)) + inds = torch.arange(0, 2 * l + 1, 1, device=device) + reversed_inds = torch.arange(2 * l, -1, -1, device=device) + frequencies = torch.arange(l, -l - 1, -1, dtype=dtype, device=device) + M[..., inds, reversed_inds] = torch.sin(frequencies * angle[..., None]) + M[..., inds, inds] = torch.cos(frequencies * angle[..., None]) + return M + + +class SO3_Grid(torch.nn.Module): + """ + Helper functions for grid representation of the irreps + + Args: + lmax (int): Maximum degree of the spherical harmonics + mmax (int): Maximum order of the spherical harmonics + """ + + def __init__( + self, + lmax, + mmax, + ): + super().__init__() + self.lmax = lmax + self.mmax = mmax + self.lat_resolution = 2 * (self.lmax + 1) + if lmax == mmax: + self.long_resolution = 2 * (self.mmax + 1) + 1 + else: + self.long_resolution = 2 * (self.mmax) + 1 + + self.initialized = False + + def _initialize(self, device): + if self.initialized is True: + return + self.mapping = CoefficientMapping([self.lmax], [self.lmax], device) + + to_grid = ToS2Grid( + self.lmax, + (self.lat_resolution, self.long_resolution), + normalization="integral", + device=device, + ) + + self.to_grid_mat = torch.einsum( + "mbi,am->bai", to_grid.shb, to_grid.sha + ).detach() + self.to_grid_mat = self.to_grid_mat[ + :, :, self.mapping.coefficient_idx(self.lmax, self.mmax) + ] + + from_grid = FromS2Grid( + (self.lat_resolution, self.long_resolution), + self.lmax, + normalization="integral", + device=device, + ) + + self.from_grid_mat = torch.einsum( + "am,mbi->bai", from_grid.sha, from_grid.shb + ).detach() + self.from_grid_mat = self.from_grid_mat[ + :, :, self.mapping.coefficient_idx(self.lmax, self.mmax) + ] + + self.initialized = True + + # Compute matrices to transform irreps to grid + def get_to_grid_mat(self, device): + self._initialize(device) + return self.to_grid_mat + + # Compute matrices to transform grid to irreps + def get_from_grid_mat(self, device): + self._initialize(device) + return self.from_grid_mat + + # Compute grid from irreps representation + def to_grid(self, embedding, lmax, mmax): + self._initialize(embedding.device) + to_grid_mat = self.to_grid_mat[ + :, :, self.mapping.coefficient_idx(lmax, mmax) + ] + grid = torch.einsum("bai,zic->zbac", to_grid_mat, embedding) + return grid + + # Compute irreps from grid representation + def from_grid(self, grid, lmax, mmax): + self._initialize(grid.device) + from_grid_mat = self.from_grid_mat[ + :, :, self.mapping.coefficient_idx(lmax, mmax) + ] + embedding = torch.einsum("bai,zbac->zic", from_grid_mat, grid) + return embedding diff --git a/ocpmodels/models/forcenet.py b/ocpmodels/models/forcenet.py new file mode 100644 index 0000000..42fee8b --- /dev/null +++ b/ocpmodels/models/forcenet.py @@ -0,0 +1,517 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os +from math import pi as PI + +import numpy as np +import torch +import torch.nn as nn +from torch_geometric.nn import MessagePassing +from torch_scatter import scatter + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import get_pbc_distances, radius_graph_pbc +from ocpmodels.datasets.embeddings import ATOMIC_RADII, CONTINUOUS_EMBEDDINGS +from ocpmodels.models.base import BaseModel +from ocpmodels.models.utils.activations import Act +from ocpmodels.models.utils.basis import Basis, SphericalSmearing + + +class FNDecoder(nn.Module): + def __init__(self, decoder_type, decoder_activation_str, output_dim): + super(FNDecoder, self).__init__() + self.decoder_type = decoder_type + self.decoder_activation = Act(decoder_activation_str) + self.output_dim = output_dim + + if self.decoder_type == "linear": + self.decoder = nn.Sequential(nn.Linear(self.output_dim, 3)) + elif self.decoder_type == "mlp": + self.decoder = nn.Sequential( + nn.Linear(self.output_dim, self.output_dim), + nn.BatchNorm1d(self.output_dim), + self.decoder_activation, + nn.Linear(self.output_dim, 3), + ) + else: + raise ValueError(f"Undefined force decoder: {self.decoder_type}") + + self.reset_parameters() + + def reset_parameters(self): + for m in self.decoder: + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0) + + def forward(self, x): + return self.decoder(x) + + +class InteractionBlock(MessagePassing): + def __init__( + self, + hidden_channels, + mlp_basis_dim, + basis_type, + depth_mlp_edge=2, + depth_mlp_trans=1, + activation_str="ssp", + ablation="none", + ): + super(InteractionBlock, self).__init__(aggr="add") + + self.activation = Act(activation_str) + self.ablation = ablation + self.basis_type = basis_type + + # basis function assumes input is in the range of [-1,1] + if self.basis_type != "rawcat": + self.lin_basis = torch.nn.Linear(mlp_basis_dim, hidden_channels) + + if self.ablation == "nocond": + # the edge filter only depends on edge_attr + in_features = ( + mlp_basis_dim + if self.basis_type == "rawcat" + else hidden_channels + ) + else: + # edge filter depends on edge_attr and current node embedding + in_features = ( + mlp_basis_dim + 2 * hidden_channels + if self.basis_type == "rawcat" + else 3 * hidden_channels + ) + + if depth_mlp_edge > 0: + mlp_edge = [torch.nn.Linear(in_features, hidden_channels)] + for i in range(depth_mlp_edge): + mlp_edge.append(self.activation) + mlp_edge.append( + torch.nn.Linear(hidden_channels, hidden_channels) + ) + else: + ## need batch normalization afterwards. Otherwise training is unstable. + mlp_edge = [ + torch.nn.Linear(in_features, hidden_channels), + torch.nn.BatchNorm1d(hidden_channels), + ] + self.mlp_edge = torch.nn.Sequential(*mlp_edge) + + if not self.ablation == "nofilter": + self.lin = torch.nn.Linear(hidden_channels, hidden_channels) + + if depth_mlp_trans > 0: + mlp_trans = [torch.nn.Linear(hidden_channels, hidden_channels)] + for i in range(depth_mlp_trans): + mlp_trans.append(torch.nn.BatchNorm1d(hidden_channels)) + mlp_trans.append(self.activation) + mlp_trans.append( + torch.nn.Linear(hidden_channels, hidden_channels) + ) + else: + # need batch normalization afterwards. Otherwise, becomes NaN + mlp_trans = [ + torch.nn.Linear(hidden_channels, hidden_channels), + torch.nn.BatchNorm1d(hidden_channels), + ] + + self.mlp_trans = torch.nn.Sequential(*mlp_trans) + + if not self.ablation == "noself": + self.center_W = torch.nn.Parameter( + torch.Tensor(1, hidden_channels) + ) + + self.reset_parameters() + + def reset_parameters(self): + if self.basis_type != "rawcat": + torch.nn.init.xavier_uniform_(self.lin_basis.weight) + self.lin_basis.bias.data.fill_(0) + + for m in self.mlp_trans: + if isinstance(m, torch.nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0) + + for m in self.mlp_edge: + if isinstance(m, torch.nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + m.bias.data.fill_(0) + + if not self.ablation == "nofilter": + torch.nn.init.xavier_uniform_(self.lin.weight) + self.lin.bias.data.fill_(0) + + if not self.ablation == "noself": + torch.nn.init.xavier_uniform_(self.center_W) + + def forward(self, x, edge_index, edge_attr, edge_weight): + if self.basis_type != "rawcat": + edge_emb = self.lin_basis(edge_attr) + else: + # for rawcat, we directly use the raw feature + edge_emb = edge_attr + + if self.ablation == "nocond": + emb = edge_emb + else: + emb = torch.cat( + [edge_emb, x[edge_index[0]], x[edge_index[1]]], dim=1 + ) + + W = self.mlp_edge(emb) * edge_weight.view(-1, 1) + if self.ablation == "nofilter": + x = self.propagate(edge_index, x=x, W=W) + self.center_W + else: + x = self.lin(x) + if self.ablation == "noself": + x = self.propagate(edge_index, x=x, W=W) + else: + x = self.propagate(edge_index, x=x, W=W) + self.center_W * x + x = self.mlp_trans(x) + + return x + + def message(self, x_j, W): + if self.ablation == "nofilter": + return W + else: + return x_j * W + + +# flake8: noqa: C901 +@registry.register_model("forcenet") +class ForceNet(BaseModel): + r"""Implementation of ForceNet architecture. + + Args: + num_atoms (int): Unused argument + bond_feat_dim (int): Unused argument + num_targets (int): Unused argumebt + hidden_channels (int, optional): Number of hidden channels. + (default: :obj:`512`) + num_iteractions (int, optional): Number of interaction blocks. + (default: :obj:`5`) + cutoff (float, optional): Cutoff distance for interatomic interactions. + (default: :obj:`6.0`) + feat (str, optional): Input features to be used + (default: :obj:`full`) + num_freqs (int, optional): Number of frequencies for basis function. + (default: :obj:`50`) + max_n (int, optional): Maximum order of spherical harmonics. + (default: :obj:`6`) + basis (str, optional): Basis function to be used. + (default: :obj:`full`) + depth_mlp_edge (int, optional): Depth of MLP for edges in interaction blocks. + (default: :obj:`2`) + depth_mlp_node (int, optional): Depth of MLP for nodes in interaction blocks. + (default: :obj:`1`) + activation_str (str, optional): Activation function used post linear layer in all message passing MLPs. + (default: :obj:`swish`) + ablation (str, optional): Type of ablation to be performed. + (default: :obj:`none`) + decoder_hidden_channels (int, optional): Number of hidden channels in the decoder. + (default: :obj:`512`) + decoder_type (str, optional): Type of decoder: linear or MLP. + (default: :obj:`mlp`) + decoder_activation_str (str, optional): Activation function used post linear layer in decoder. + (default: :obj:`swish`) + training (bool, optional): If set to :obj:`True`, specify training phase. + (default: :obj:`True`) + otf_graph (bool, optional): If set to :obj:`True`, compute graph edges on the fly. + (default: :obj:`False`) + """ + + def __init__( + self, + num_atoms, # not used + bond_feat_dim, # not used + num_targets, # not used + hidden_channels=512, + num_interactions=5, + cutoff=6.0, + feat="full", + num_freqs=50, + max_n=3, + basis="sphallmul", + depth_mlp_edge=2, + depth_mlp_node=1, + activation_str="swish", + ablation="none", + decoder_hidden_channels=512, + decoder_type="mlp", + decoder_activation_str="swish", + training=True, + otf_graph=False, + use_pbc=True, + ): + + super(ForceNet, self).__init__() + self.training = training + self.ablation = ablation + if self.ablation not in [ + "none", + "nofilter", + "nocond", + "nodistlist", + "onlydist", + "nodelinear", + "edgelinear", + "noself", + ]: + raise ValueError(f"Unknown ablation called {ablation}.") + + """ + Descriptions of ablations: + - none: base ForceNet model + - nofilter: no element-wise filter parameterization in message modeling + - nocond: convolutional filter is only conditioned on edge features, not node embeddings + - nodistlist: no atomic radius information in edge features + - onlydist: edge features only contains distance information. Orientation information is ommited. + - nodelinear: node update MLP function is replaced with linear function followed by batch normalization + - edgelinear: edge MLP transformation function is replaced with linear function followed by batch normalization. + - noself: no self edge of m_t. + """ + + self.otf_graph = otf_graph + self.cutoff = cutoff + self.output_dim = decoder_hidden_channels + self.feat = feat + self.num_freqs = num_freqs + self.num_layers = num_interactions + self.max_n = max_n + self.activation_str = activation_str + self.use_pbc = use_pbc + self.max_neighbors = 50 + + if self.ablation == "edgelinear": + depth_mlp_edge = 0 + + if self.ablation == "nodelinear": + depth_mlp_node = 0 + + # read atom map and atom radii + atom_map = torch.zeros(101, 9) + for i in range(101): + atom_map[i] = torch.tensor(CONTINUOUS_EMBEDDINGS[i]) + + atom_radii = torch.zeros(101) + for i in range(101): + atom_radii[i] = ATOMIC_RADII[i] + atom_radii = atom_radii / 100 + + self.atom_radii = nn.Parameter(atom_radii, requires_grad=False) + self.basis_type = basis + + self.pbc_apply_sph_harm = "sph" in self.basis_type + self.pbc_sph_option = None + + # for spherical harmonics for PBC + if "sphall" in self.basis_type: + self.pbc_sph_option = "all" + elif "sphsine" in self.basis_type: + self.pbc_sph_option = "sine" + elif "sphcosine" in self.basis_type: + self.pbc_sph_option = "cosine" + + self.pbc_sph = None + if self.pbc_apply_sph_harm: + self.pbc_sph = SphericalSmearing( + max_n=self.max_n, option=self.pbc_sph_option + ) + + # self.feat can be "simple" or "full" + if self.feat == "simple": + self.embedding = nn.Embedding(100, hidden_channels) + + # set up dummy atom_map that only contains atomic_number information + atom_map = torch.linspace(0, 1, 101).view(-1, 1).repeat(1, 9) + self.atom_map = nn.Parameter(atom_map, requires_grad=False) + + elif self.feat == "full": + # Normalize along each dimaension + atom_map[0] = np.nan + atom_map_notnan = atom_map[atom_map[:, 0] == atom_map[:, 0]] + atom_map_min = torch.min(atom_map_notnan, dim=0)[0] + atom_map_max = torch.max(atom_map_notnan, dim=0)[0] + atom_map_gap = atom_map_max - atom_map_min + + ## squash to [0,1] + atom_map = ( + atom_map - atom_map_min.view(1, -1) + ) / atom_map_gap.view(1, -1) + + self.atom_map = torch.nn.Parameter(atom_map, requires_grad=False) + + in_features = 9 + # first apply basis function and then linear function + if "sph" in self.basis_type: + # spherical basis is only meaningful for edge feature, so use powersine instead + node_basis_type = "powersine" + else: + node_basis_type = self.basis_type + basis = Basis( + in_features, + num_freqs=num_freqs, + basis_type=node_basis_type, + act=self.activation_str, + ) + self.embedding = torch.nn.Sequential( + basis, torch.nn.Linear(basis.out_dim, hidden_channels) + ) + + else: + raise ValueError("Undefined feature type for atom") + + # process basis function for edge feature + if self.ablation == "nodistlist": + # do not consider additional distance edge features + # normalized (x,y,z) + distance + in_feature = 4 + elif self.ablation == "onlydist": + # only consider distance-based edge features + # ignore normalized (x,y,z) + in_feature = 4 + + # if basis_type is spherical harmonics, then reduce to powersine + if "sph" in self.basis_type: + logging.info( + "Under onlydist ablation, spherical basis is reduced to powersine basis." + ) + self.basis_type = "powersine" + self.pbc_sph = None + + else: + in_feature = 7 + self.basis_fun = Basis( + in_feature, + num_freqs, + self.basis_type, + self.activation_str, + sph=self.pbc_sph, + ) + + # process interaction blocks + self.interactions = torch.nn.ModuleList() + for _ in range(num_interactions): + block = InteractionBlock( + hidden_channels, + self.basis_fun.out_dim, + self.basis_type, + depth_mlp_edge=depth_mlp_edge, + depth_mlp_trans=depth_mlp_node, + activation_str=self.activation_str, + ablation=ablation, + ) + self.interactions.append(block) + + self.lin = torch.nn.Linear(hidden_channels, self.output_dim) + self.activation = Act(activation_str) + + # ForceNet decoder + self.decoder = FNDecoder( + decoder_type, decoder_activation_str, self.output_dim + ) + + # Projection layer for energy prediction + self.energy_mlp = nn.Linear(self.output_dim, 1) + + def forward(self, data): + z = data.atomic_numbers.long() + + pos = data.pos + batch = data.batch + + if self.feat == "simple": + h = self.embedding(z) + elif self.feat == "full": + h = self.embedding(self.atom_map[z]) + else: + raise RuntimeError("Undefined feature type for atom") + + ( + edge_index, + edge_dist, + edge_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + + data.edge_index = edge_index + data.cell_offsets = cell_offsets + data.neighbors = neighbors + + if self.pbc_apply_sph_harm: + edge_vec_normalized = edge_vec / edge_dist.view(-1, 1) + edge_attr_sph = self.pbc_sph(edge_vec_normalized) + + # calculate the edge weight according to the dist + edge_weight = torch.cos(0.5 * edge_dist * PI / self.cutoff) + + # normalized edge vectors + edge_vec_normalized = edge_vec / edge_dist.view(-1, 1) + + # edge distance, taking the atom_radii into account + # each element lies in [0,1] + edge_dist_list = ( + torch.stack( + [ + edge_dist, + edge_dist - self.atom_radii[z[edge_index[0]]], + edge_dist - self.atom_radii[z[edge_index[1]]], + edge_dist + - self.atom_radii[z[edge_index[0]]] + - self.atom_radii[z[edge_index[1]]], + ] + ).transpose(0, 1) + / self.cutoff + ) + + if self.ablation == "nodistlist": + edge_dist_list = edge_dist_list[:, 0].view(-1, 1) + + # make sure distance is positive + edge_dist_list[edge_dist_list < 1e-3] = 1e-3 + + # squash to [0,1] for gaussian basis + if self.basis_type == "gauss": + edge_vec_normalized = (edge_vec_normalized + 1) / 2.0 + + # process raw_edge_attributes to generate edge_attributes + if self.ablation == "onlydist": + raw_edge_attr = edge_dist_list + else: + raw_edge_attr = torch.cat( + [edge_vec_normalized, edge_dist_list], dim=1 + ) + + if "sph" in self.basis_type: + edge_attr = self.basis_fun(raw_edge_attr, edge_attr_sph) + else: + edge_attr = self.basis_fun(raw_edge_attr) + + # pass edge_attributes through interaction blocks + for i, interaction in enumerate(self.interactions): + h = h + interaction(h, edge_index, edge_attr, edge_weight) + + h = self.lin(h) + h = self.activation(h) + + out = scatter(h, batch, dim=0, reduce="add") + + force = self.decoder(h) + energy = self.energy_mlp(out) + return energy, force + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) diff --git a/ocpmodels/models/gemnet/__init__.py b/ocpmodels/models/gemnet/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/gemnet/gemnet.py b/ocpmodels/models/gemnet/gemnet.py new file mode 100644 index 0000000..758d4d2 --- /dev/null +++ b/ocpmodels/models/gemnet/gemnet.py @@ -0,0 +1,601 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from typing import Optional + +import numpy as np +import torch +from torch_geometric.nn import radius_graph +from torch_scatter import scatter +from torch_sparse import SparseTensor + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + compute_neighbors, + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.models.base import BaseModel +from ocpmodels.modules.scaling.compat import load_scales_compat + +from .layers.atom_update_block import OutputBlock +from .layers.base_layers import Dense +from .layers.efficient import EfficientInteractionDownProjection +from .layers.embedding_block import AtomEmbedding, EdgeEmbedding +from .layers.interaction_block import InteractionBlockTripletsOnly +from .layers.radial_basis import RadialBasis +from .layers.spherical_basis import CircularBasisLayer +from .utils import ( + inner_product_normalized, + mask_neighbors, + ragged_range, + repeat_blocks, +) + + +@registry.register_model("gemnet_t") +class GemNetT(BaseModel): + """ + GemNet-T, triplets-only variant of GemNet + + Parameters + ---------- + num_atoms (int): Unused argument + bond_feat_dim (int): Unused argument + num_targets: int + Number of prediction targets. + + num_spherical: int + Controls maximum frequency. + num_radial: int + Controls maximum frequency. + num_blocks: int + Number of building blocks to be stacked. + + emb_size_atom: int + Embedding size of the atoms. + emb_size_edge: int + Embedding size of the edges. + emb_size_trip: int + (Down-projected) Embedding size in the triplet message passing block. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + emb_size_bil_trip: int + Embedding size of the edge embeddings in the triplet-based message passing block after the bilinear layer. + + num_before_skip: int + Number of residual blocks before the first skip connection. + num_after_skip: int + Number of residual blocks after the first skip connection. + num_concat: int + Number of residual blocks after the concatenation. + num_atom: int + Number of residual blocks in the atom embedding blocks. + + regress_forces: bool + Whether to predict forces. Default: True + direct_forces: bool + If True predict forces based on aggregation of interatomic directions. + If False predict forces based on negative gradient of energy potential. + + cutoff: float + Embedding cutoff for interactomic directions in Angstrom. + rbf: dict + Name and hyperparameters of the radial basis function. + envelope: dict + Name and hyperparameters of the envelope function. + cbf: dict + Name and hyperparameters of the cosine basis function. + extensive: bool + Whether the output should be extensive (proportional to the number of atoms) + output_init: str + Initialization method for the final dense layer. + activation: str + Name of the activation function. + scale_file: str + Path to the json file containing the scaling factors. + """ + + def __init__( + self, + num_atoms: Optional[int], + bond_feat_dim: int, + num_targets: int, + num_spherical: int, + num_radial: int, + num_blocks: int, + emb_size_atom: int, + emb_size_edge: int, + emb_size_trip: int, + emb_size_rbf: int, + emb_size_cbf: int, + emb_size_bil_trip: int, + num_before_skip: int, + num_after_skip: int, + num_concat: int, + num_atom: int, + regress_forces: bool = True, + direct_forces: bool = False, + cutoff: float = 6.0, + max_neighbors: int = 50, + rbf: dict = {"name": "gaussian"}, + envelope: dict = {"name": "polynomial", "exponent": 5}, + cbf: dict = {"name": "spherical_harmonics"}, + extensive: bool = True, + otf_graph: bool = False, + use_pbc: bool = True, + output_init: str = "HeOrthogonal", + activation: str = "swish", + num_elements: int = 83, + scale_file: Optional[str] = None, + ): + super().__init__() + self.num_targets = num_targets + assert num_blocks > 0 + self.num_blocks = num_blocks + self.extensive = extensive + + self.cutoff = cutoff + assert self.cutoff <= 6 or otf_graph + + self.max_neighbors = max_neighbors + assert self.max_neighbors == 50 or otf_graph + + self.regress_forces = regress_forces + self.otf_graph = otf_graph + self.use_pbc = use_pbc + + # GemNet variants + self.direct_forces = direct_forces + + ### ---------------------------------- Basis Functions ---------------------------------- ### + self.radial_basis = RadialBasis( + num_radial=num_radial, + cutoff=cutoff, + rbf=rbf, + envelope=envelope, + ) + + radial_basis_cbf3 = RadialBasis( + num_radial=num_radial, + cutoff=cutoff, + rbf=rbf, + envelope=envelope, + ) + self.cbf_basis3 = CircularBasisLayer( + num_spherical, + radial_basis=radial_basis_cbf3, + cbf=cbf, + efficient=True, + ) + ### ------------------------------------------------------------------------------------- ### + + ### ------------------------------- Share Down Projections ------------------------------ ### + # Share down projection across all interaction blocks + self.mlp_rbf3 = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_cbf3 = EfficientInteractionDownProjection( + num_spherical, num_radial, emb_size_cbf + ) + + # Share the dense Layer of the atom embedding block accross the interaction blocks + self.mlp_rbf_h = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_rbf_out = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + ### ------------------------------------------------------------------------------------- ### + + # Embedding block + self.atom_emb = AtomEmbedding(emb_size_atom, num_elements) + self.edge_emb = EdgeEmbedding( + emb_size_atom, num_radial, emb_size_edge, activation=activation + ) + + out_blocks = [] + int_blocks = [] + + # Interaction Blocks + interaction_block = InteractionBlockTripletsOnly # GemNet-(d)T + for i in range(num_blocks): + int_blocks.append( + interaction_block( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_trip=emb_size_trip, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + emb_size_bil_trip=emb_size_bil_trip, + num_before_skip=num_before_skip, + num_after_skip=num_after_skip, + num_concat=num_concat, + num_atom=num_atom, + activation=activation, + name=f"IntBlock_{i+1}", + ) + ) + + for i in range(num_blocks + 1): + out_blocks.append( + OutputBlock( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=num_atom, + num_targets=num_targets, + activation=activation, + output_init=output_init, + direct_forces=direct_forces, + name=f"OutBlock_{i}", + ) + ) + + self.out_blocks = torch.nn.ModuleList(out_blocks) + self.int_blocks = torch.nn.ModuleList(int_blocks) + + self.shared_parameters = [ + (self.mlp_rbf3.linear.weight, self.num_blocks), + (self.mlp_cbf3.weight, self.num_blocks), + (self.mlp_rbf_h.linear.weight, self.num_blocks), + (self.mlp_rbf_out.linear.weight, self.num_blocks + 1), + ] + + load_scales_compat(self, scale_file) + + def get_triplets(self, edge_index, num_atoms): + """ + Get all b->a for each edge c->a. + It is possible that b=c, as long as the edges are distinct. + + Returns + ------- + id3_ba: torch.Tensor, shape (num_triplets,) + Indices of input edge b->a of each triplet b->a<-c + id3_ca: torch.Tensor, shape (num_triplets,) + Indices of output edge c->a of each triplet b->a<-c + id3_ragged_idx: torch.Tensor, shape (num_triplets,) + Indices enumerating the copies of id3_ca for creating a padded matrix + """ + idx_s, idx_t = edge_index # c->a (source=c, target=a) + + value = torch.arange( + idx_s.size(0), device=idx_s.device, dtype=idx_s.dtype + ) + # Possibly contains multiple copies of the same edge (for periodic interactions) + adj = SparseTensor( + row=idx_t, + col=idx_s, + value=value, + sparse_sizes=(num_atoms, num_atoms), + ) + adj_edges = adj[idx_t] + + # Edge indices (b->a, c->a) for triplets. + id3_ba = adj_edges.storage.value() + id3_ca = adj_edges.storage.row() + + # Remove self-loop triplets + # Compare edge indices, not atom indices to correctly handle periodic interactions + mask = id3_ba != id3_ca + id3_ba = id3_ba[mask] + id3_ca = id3_ca[mask] + + # Get indices to reshape the neighbor indices b->a into a dense matrix. + # id3_ca has to be sorted for this to work. + num_triplets = torch.bincount(id3_ca, minlength=idx_s.size(0)) + id3_ragged_idx = ragged_range(num_triplets) + + return id3_ba, id3_ca, id3_ragged_idx + + def select_symmetric_edges(self, tensor, mask, reorder_idx, inverse_neg): + # Mask out counter-edges + tensor_directed = tensor[mask] + # Concatenate counter-edges after normal edges + sign = 1 - 2 * inverse_neg + tensor_cat = torch.cat([tensor_directed, sign * tensor_directed]) + # Reorder everything so the edges of every image are consecutive + tensor_ordered = tensor_cat[reorder_idx] + return tensor_ordered + + def reorder_symmetric_edges( + self, edge_index, cell_offsets, neighbors, edge_dist, edge_vector + ): + """ + Reorder edges to make finding counter-directional edges easier. + + Some edges are only present in one direction in the data, + since every atom has a maximum number of neighbors. Since we only use i->j + edges here, we lose some j->i edges and add others by + making it symmetric. + We could fix this by merging edge_index with its counter-edges, + including the cell_offsets, and then running torch.unique. + But this does not seem worth it. + """ + + # Generate mask + mask_sep_atoms = edge_index[0] < edge_index[1] + # Distinguish edges between the same (periodic) atom by ordering the cells + cell_earlier = ( + (cell_offsets[:, 0] < 0) + | ((cell_offsets[:, 0] == 0) & (cell_offsets[:, 1] < 0)) + | ( + (cell_offsets[:, 0] == 0) + & (cell_offsets[:, 1] == 0) + & (cell_offsets[:, 2] < 0) + ) + ) + mask_same_atoms = edge_index[0] == edge_index[1] + mask_same_atoms &= cell_earlier + mask = mask_sep_atoms | mask_same_atoms + + # Mask out counter-edges + edge_index_new = edge_index[mask[None, :].expand(2, -1)].view(2, -1) + + # Concatenate counter-edges after normal edges + edge_index_cat = torch.cat( + [ + edge_index_new, + torch.stack([edge_index_new[1], edge_index_new[0]], dim=0), + ], + dim=1, + ) + + # Count remaining edges per image + batch_edge = torch.repeat_interleave( + torch.arange(neighbors.size(0), device=edge_index.device), + neighbors, + ) + batch_edge = batch_edge[mask] + neighbors_new = 2 * torch.bincount( + batch_edge, minlength=neighbors.size(0) + ) + + # Create indexing array + edge_reorder_idx = repeat_blocks( + neighbors_new // 2, + repeats=2, + continuous_indexing=True, + repeat_inc=edge_index_new.size(1), + ) + + # Reorder everything so the edges of every image are consecutive + edge_index_new = edge_index_cat[:, edge_reorder_idx] + cell_offsets_new = self.select_symmetric_edges( + cell_offsets, mask, edge_reorder_idx, True + ) + edge_dist_new = self.select_symmetric_edges( + edge_dist, mask, edge_reorder_idx, False + ) + edge_vector_new = self.select_symmetric_edges( + edge_vector, mask, edge_reorder_idx, True + ) + + return ( + edge_index_new, + cell_offsets_new, + neighbors_new, + edge_dist_new, + edge_vector_new, + ) + + def select_edges( + self, + data, + edge_index, + cell_offsets, + neighbors, + edge_dist, + edge_vector, + cutoff=None, + ): + if cutoff is not None: + edge_mask = edge_dist <= cutoff + + edge_index = edge_index[:, edge_mask] + cell_offsets = cell_offsets[edge_mask] + neighbors = mask_neighbors(neighbors, edge_mask) + edge_dist = edge_dist[edge_mask] + edge_vector = edge_vector[edge_mask] + + empty_image = neighbors == 0 + if torch.any(empty_image): + raise ValueError( + f"An image has no neighbors: id={data.id[empty_image]}, " + f"sid={data.sid[empty_image]}, fid={data.fid[empty_image]}" + ) + return edge_index, cell_offsets, neighbors, edge_dist, edge_vector + + def generate_interaction_graph(self, data): + num_atoms = data.atomic_numbers.size(0) + + ( + edge_index, + D_st, + distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + # These vectors actually point in the opposite direction. + # But we want to use col as idx_t for efficient aggregation. + V_st = -distance_vec / D_st[:, None] + + # Mask interaction edges if required + if self.otf_graph or np.isclose(self.cutoff, 6): + select_cutoff = None + else: + select_cutoff = self.cutoff + (edge_index, cell_offsets, neighbors, D_st, V_st,) = self.select_edges( + data=data, + edge_index=edge_index, + cell_offsets=cell_offsets, + neighbors=neighbors, + edge_dist=D_st, + edge_vector=V_st, + cutoff=select_cutoff, + ) + + ( + edge_index, + cell_offsets, + neighbors, + D_st, + V_st, + ) = self.reorder_symmetric_edges( + edge_index, cell_offsets, neighbors, D_st, V_st + ) + + # Indices for swapping c->a and a->c (for symmetric MP) + block_sizes = neighbors // 2 + id_swap = repeat_blocks( + block_sizes, + repeats=2, + continuous_indexing=False, + start_idx=block_sizes[0], + block_inc=block_sizes[:-1] + block_sizes[1:], + repeat_inc=-block_sizes, + ) + + id3_ba, id3_ca, id3_ragged_idx = self.get_triplets( + edge_index, num_atoms=num_atoms + ) + + return ( + edge_index, + neighbors, + D_st, + V_st, + id_swap, + id3_ba, + id3_ca, + id3_ragged_idx, + ) + + @conditional_grad(torch.enable_grad()) + def forward(self, data): + pos = data.pos + batch = data.batch + atomic_numbers = data.atomic_numbers.long() + + if self.regress_forces and not self.direct_forces: + pos.requires_grad_(True) + + ( + edge_index, + neighbors, + D_st, + V_st, + id_swap, + id3_ba, + id3_ca, + id3_ragged_idx, + ) = self.generate_interaction_graph(data) + idx_s, idx_t = edge_index + + # Calculate triplet angles + cosφ_cab = inner_product_normalized(V_st[id3_ca], V_st[id3_ba]) + rad_cbf3, cbf3 = self.cbf_basis3(D_st, cosφ_cab, id3_ca) + + rbf = self.radial_basis(D_st) + + # Embedding block + h = self.atom_emb(atomic_numbers) + # (nAtoms, emb_size_atom) + m = self.edge_emb(h, rbf, idx_s, idx_t) # (nEdges, emb_size_edge) + + rbf3 = self.mlp_rbf3(rbf) + cbf3 = self.mlp_cbf3(rad_cbf3, cbf3, id3_ca, id3_ragged_idx) + + rbf_h = self.mlp_rbf_h(rbf) + rbf_out = self.mlp_rbf_out(rbf) + + E_t, F_st = self.out_blocks[0](h, m, rbf_out, idx_t) + # (nAtoms, num_targets), (nEdges, num_targets) + + for i in range(self.num_blocks): + # Interaction block + h, m = self.int_blocks[i]( + h=h, + m=m, + rbf3=rbf3, + cbf3=cbf3, + id3_ragged_idx=id3_ragged_idx, + id_swap=id_swap, + id3_ba=id3_ba, + id3_ca=id3_ca, + rbf_h=rbf_h, + idx_s=idx_s, + idx_t=idx_t, + ) # (nAtoms, emb_size_atom), (nEdges, emb_size_edge) + + E, F = self.out_blocks[i + 1](h, m, rbf_out, idx_t) + # (nAtoms, num_targets), (nEdges, num_targets) + F_st += F + E_t += E + + nMolecules = torch.max(batch) + 1 + if self.extensive: + E_t = scatter( + E_t, batch, dim=0, dim_size=nMolecules, reduce="add" + ) # (nMolecules, num_targets) + else: + E_t = scatter( + E_t, batch, dim=0, dim_size=nMolecules, reduce="mean" + ) # (nMolecules, num_targets) + + if self.regress_forces: + if self.direct_forces: + # map forces in edge directions + F_st_vec = F_st[:, :, None] * V_st[:, None, :] + # (nEdges, num_targets, 3) + F_t = scatter( + F_st_vec, + idx_t, + dim=0, + dim_size=data.atomic_numbers.size(0), + reduce="add", + ) # (nAtoms, num_targets, 3) + F_t = F_t.squeeze(1) # (nAtoms, 3) + else: + if self.num_targets > 1: + forces = [] + for i in range(self.num_targets): + # maybe this can be solved differently + forces += [ + -torch.autograd.grad( + E_t[:, i].sum(), pos, create_graph=True + )[0] + ] + F_t = torch.stack(forces, dim=1) + # (nAtoms, num_targets, 3) + else: + F_t = -torch.autograd.grad( + E_t.sum(), pos, create_graph=True + )[0] + # (nAtoms, 3) + + return E_t, F_t # (nMolecules, num_targets), (nAtoms, 3) + else: + return E_t + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) diff --git a/ocpmodels/models/gemnet/initializers.py b/ocpmodels/models/gemnet/initializers.py new file mode 100644 index 0000000..39a07ce --- /dev/null +++ b/ocpmodels/models/gemnet/initializers.py @@ -0,0 +1,47 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch + + +def _standardize(kernel): + """ + Makes sure that N*Var(W) = 1 and E[W] = 0 + """ + eps = 1e-6 + + if len(kernel.shape) == 3: + axis = [0, 1] # last dimension is output dimension + else: + axis = 1 + + var, mean = torch.var_mean(kernel, dim=axis, unbiased=True, keepdim=True) + kernel = (kernel - mean) / (var + eps) ** 0.5 + return kernel + + +def he_orthogonal_init(tensor): + """ + Generate a weight matrix with variance according to He (Kaiming) initialization. + Based on a random (semi-)orthogonal matrix neural networks + are expected to learn better when features are decorrelated + (stated by eg. "Reducing overfitting in deep networks by decorrelating representations", + "Dropout: a simple way to prevent neural networks from overfitting", + "Exact solutions to the nonlinear dynamics of learning in deep linear neural networks") + """ + tensor = torch.nn.init.orthogonal_(tensor) + + if len(tensor.shape) == 3: + fan_in = tensor.shape[:-1].numel() + else: + fan_in = tensor.shape[1] + + with torch.no_grad(): + tensor.data = _standardize(tensor.data) + tensor.data *= (1 / fan_in) ** 0.5 + + return tensor diff --git a/ocpmodels/models/gemnet/layers/__init__.py b/ocpmodels/models/gemnet/layers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/gemnet/layers/atom_update_block.py b/ocpmodels/models/gemnet/layers/atom_update_block.py new file mode 100644 index 0000000..b7d8735 --- /dev/null +++ b/ocpmodels/models/gemnet/layers/atom_update_block.py @@ -0,0 +1,206 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +from torch_scatter import scatter + +from ocpmodels.modules.scaling import ScaleFactor + +from ..initializers import he_orthogonal_init +from .base_layers import Dense, ResidualLayer + + +class AtomUpdateBlock(torch.nn.Module): + """ + Aggregate the message embeddings of the atoms + + Parameters + ---------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_atom: int + Embedding size of the edges. + nHidden: int + Number of residual blocks. + activation: callable/str + Name of the activation function to use in the dense layers. + """ + + def __init__( + self, + emb_size_atom: int, + emb_size_edge: int, + emb_size_rbf: int, + nHidden: int, + activation=None, + name: str = "atom_update", + ): + super().__init__() + self.name = name + + self.dense_rbf = Dense( + emb_size_rbf, emb_size_edge, activation=None, bias=False + ) + self.scale_sum = ScaleFactor(name + "_sum") + + self.layers = self.get_mlp( + emb_size_edge, emb_size_atom, nHidden, activation + ) + + def get_mlp(self, units_in, units, nHidden, activation): + dense1 = Dense(units_in, units, activation=activation, bias=False) + mlp = [dense1] + res = [ + ResidualLayer(units, nLayers=2, activation=activation) + for i in range(nHidden) + ] + mlp += res + return torch.nn.ModuleList(mlp) + + def forward(self, h, m, rbf, id_j): + """ + Returns + ------- + h: torch.Tensor, shape=(nAtoms, emb_size_atom) + Atom embedding. + """ + nAtoms = h.shape[0] + + mlp_rbf = self.dense_rbf(rbf) # (nEdges, emb_size_edge) + x = m * mlp_rbf + + x2 = scatter(x, id_j, dim=0, dim_size=nAtoms, reduce="sum") + # (nAtoms, emb_size_edge) + x = self.scale_sum(x2, ref=m) + + for layer in self.layers: + x = layer(x) # (nAtoms, emb_size_atom) + + return x + + +class OutputBlock(AtomUpdateBlock): + """ + Combines the atom update block and subsequent final dense layer. + + Parameters + ---------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_atom: int + Embedding size of the edges. + nHidden: int + Number of residual blocks. + num_targets: int + Number of targets. + activation: str + Name of the activation function to use in the dense layers except for the final dense layer. + direct_forces: bool + If true directly predict forces without taking the gradient of the energy potential. + output_init: int + Kernel initializer of the final dense layer. + """ + + def __init__( + self, + emb_size_atom: int, + emb_size_edge: int, + emb_size_rbf: int, + nHidden: int, + num_targets: int, + activation=None, + direct_forces=True, + output_init="HeOrthogonal", + name: str = "output", + **kwargs, + ): + + super().__init__( + name=name, + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=nHidden, + activation=activation, + **kwargs, + ) + + assert isinstance(output_init, str) + self.output_init = output_init.lower() + self.direct_forces = direct_forces + + self.seq_energy = self.layers # inherited from parent class + self.out_energy = Dense( + emb_size_atom, num_targets, bias=False, activation=None + ) + + if self.direct_forces: + self.scale_rbf_F = ScaleFactor(name + "_had") + self.seq_forces = self.get_mlp( + emb_size_edge, emb_size_edge, nHidden, activation + ) + self.out_forces = Dense( + emb_size_edge, num_targets, bias=False, activation=None + ) + self.dense_rbf_F = Dense( + emb_size_rbf, emb_size_edge, activation=None, bias=False + ) + + self.reset_parameters() + + def reset_parameters(self): + if self.output_init == "heorthogonal": + self.out_energy.reset_parameters(he_orthogonal_init) + if self.direct_forces: + self.out_forces.reset_parameters(he_orthogonal_init) + elif self.output_init == "zeros": + self.out_energy.reset_parameters(torch.nn.init.zeros_) + if self.direct_forces: + self.out_forces.reset_parameters(torch.nn.init.zeros_) + else: + raise UserWarning(f"Unknown output_init: {self.output_init}") + + def forward(self, h, m, rbf, id_j): + """ + Returns + ------- + (E, F): tuple + - E: torch.Tensor, shape=(nAtoms, num_targets) + - F: torch.Tensor, shape=(nEdges, num_targets) + Energy and force prediction + """ + nAtoms = h.shape[0] + + # -------------------------------------- Energy Prediction -------------------------------------- # + rbf_emb_E = self.dense_rbf(rbf) # (nEdges, emb_size_edge) + x = m * rbf_emb_E + + x_E = scatter(x, id_j, dim=0, dim_size=nAtoms, reduce="sum") + # (nAtoms, emb_size_edge) + x_E = self.scale_sum(x_E, ref=m) + + for layer in self.seq_energy: + x_E = layer(x_E) # (nAtoms, emb_size_atom) + + x_E = self.out_energy(x_E) # (nAtoms, num_targets) + + # --------------------------------------- Force Prediction -------------------------------------- # + if self.direct_forces: + x_F = m + for i, layer in enumerate(self.seq_forces): + x_F = layer(x_F) # (nEdges, emb_size_edge) + + rbf_emb_F = self.dense_rbf_F(rbf) # (nEdges, emb_size_edge) + x_F_rbf = x_F * rbf_emb_F + x_F = self.scale_rbf_F(x_F_rbf, ref=x_F) + + x_F = self.out_forces(x_F) # (nEdges, num_targets) + else: + x_F = 0 + # ----------------------------------------------------------------------------------------------- # + + return x_E, x_F diff --git a/ocpmodels/models/gemnet/layers/base_layers.py b/ocpmodels/models/gemnet/layers/base_layers.py new file mode 100644 index 0000000..4c6aa07 --- /dev/null +++ b/ocpmodels/models/gemnet/layers/base_layers.py @@ -0,0 +1,113 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import torch + +from ..initializers import he_orthogonal_init + + +class Dense(torch.nn.Module): + """ + Combines dense layer with scaling for swish activation. + + Parameters + ---------- + units: int + Output embedding size. + activation: str + Name of the activation function to use. + bias: bool + True if use bias. + """ + + def __init__(self, in_features, out_features, bias=False, activation=None): + super().__init__() + + self.linear = torch.nn.Linear(in_features, out_features, bias=bias) + self.reset_parameters() + + if isinstance(activation, str): + activation = activation.lower() + if activation in ["swish", "silu"]: + self._activation = ScaledSiLU() + elif activation == "siqu": + self._activation = SiQU() + elif activation is None: + self._activation = torch.nn.Identity() + else: + raise NotImplementedError( + "Activation function not implemented for GemNet (yet)." + ) + + def reset_parameters(self, initializer=he_orthogonal_init): + initializer(self.linear.weight) + if self.linear.bias is not None: + self.linear.bias.data.fill_(0) + + def forward(self, x): + x = self.linear(x) + x = self._activation(x) + return x + + +class ScaledSiLU(torch.nn.Module): + def __init__(self): + super().__init__() + self.scale_factor = 1 / 0.6 + self._activation = torch.nn.SiLU() + + def forward(self, x): + return self._activation(x) * self.scale_factor + + +class SiQU(torch.nn.Module): + def __init__(self): + super().__init__() + self._activation = torch.nn.SiLU() + + def forward(self, x): + return x * self._activation(x) + + +class ResidualLayer(torch.nn.Module): + """ + Residual block with output scaled by 1/sqrt(2). + + Parameters + ---------- + units: int + Output embedding size. + nLayers: int + Number of dense layers. + layer_kwargs: str + Keyword arguments for initializing the layers. + """ + + def __init__( + self, units: int, nLayers: int = 2, layer=Dense, **layer_kwargs + ): + super().__init__() + self.dense_mlp = torch.nn.Sequential( + *[ + layer( + in_features=units, + out_features=units, + bias=False, + **layer_kwargs + ) + for _ in range(nLayers) + ] + ) + self.inv_sqrt_2 = 1 / math.sqrt(2) + + def forward(self, input): + x = self.dense_mlp(input) + x = input + x + x = x * self.inv_sqrt_2 + return x diff --git a/ocpmodels/models/gemnet/layers/basis_utils.py b/ocpmodels/models/gemnet/layers/basis_utils.py new file mode 100644 index 0000000..b623a40 --- /dev/null +++ b/ocpmodels/models/gemnet/layers/basis_utils.py @@ -0,0 +1,288 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import sympy as sym +from scipy import special as sp +from scipy.optimize import brentq + + +def Jn(r, n): + """ + numerical spherical bessel functions of order n + """ + return sp.spherical_jn(n, r) + + +def Jn_zeros(n, k): + """ + Compute the first k zeros of the spherical bessel functions up to order n (excluded) + """ + zerosj = np.zeros((n, k), dtype="float32") + zerosj[0] = np.arange(1, k + 1) * np.pi + points = np.arange(1, k + n) * np.pi + racines = np.zeros(k + n - 1, dtype="float32") + for i in range(1, n): + for j in range(k + n - 1 - i): + foo = brentq(Jn, points[j], points[j + 1], (i,)) + racines[j] = foo + points = racines + zerosj[i][:k] = racines[:k] + + return zerosj + + +def spherical_bessel_formulas(n): + """ + Computes the sympy formulas for the spherical bessel functions up to order n (excluded) + """ + x = sym.symbols("x") + # j_i = (-x)^i * (1/x * d/dx)^î * sin(x)/x + j = [sym.sin(x) / x] # j_0 + a = sym.sin(x) / x + for i in range(1, n): + b = sym.diff(a, x) / x + j += [sym.simplify(b * (-x) ** i)] + a = sym.simplify(b) + return j + + +def bessel_basis(n, k): + """ + Compute the sympy formulas for the normalized and rescaled spherical bessel functions up to + order n (excluded) and maximum frequency k (excluded). + + Returns: + bess_basis: list + Bessel basis formulas taking in a single argument x. + Has length n where each element has length k. -> In total n*k many. + """ + zeros = Jn_zeros(n, k) + normalizer = [] + for order in range(n): + normalizer_tmp = [] + for i in range(k): + normalizer_tmp += [0.5 * Jn(zeros[order, i], order + 1) ** 2] + normalizer_tmp = ( + 1 / np.array(normalizer_tmp) ** 0.5 + ) # sqrt(2/(j_l+1)**2) , sqrt(1/c**3) not taken into account yet + normalizer += [normalizer_tmp] + + f = spherical_bessel_formulas(n) + x = sym.symbols("x") + bess_basis = [] + for order in range(n): + bess_basis_tmp = [] + for i in range(k): + bess_basis_tmp += [ + sym.simplify( + normalizer[order][i] + * f[order].subs(x, zeros[order, i] * x) + ) + ] + bess_basis += [bess_basis_tmp] + return bess_basis + + +def sph_harm_prefactor(l_degree, m_order): + """Computes the constant pre-factor for the spherical harmonic of degree l and order m. + + Parameters + ---------- + l_degree: int + Degree of the spherical harmonic. l >= 0 + m_order: int + Order of the spherical harmonic. -l <= m <= l + + Returns + ------- + factor: float + + """ + # sqrt((2*l+1)/4*pi * (l-m)!/(l+m)! ) + return ( + (2 * l_degree + 1) + / (4 * np.pi) + * np.math.factorial(l_degree - abs(m_order)) + / np.math.factorial(l_degree + abs(m_order)) + ) ** 0.5 + + +def associated_legendre_polynomials( + L_maxdegree, zero_m_only=True, pos_m_only=True +): + """Computes string formulas of the associated legendre polynomials up to degree L (excluded). + + Parameters + ---------- + L_maxdegree: int + Degree up to which to calculate the associated legendre polynomials (degree L is excluded). + zero_m_only: bool + If True only calculate the polynomials for the polynomials where m=0. + pos_m_only: bool + If True only calculate the polynomials for the polynomials where m>=0. Overwritten by zero_m_only. + + Returns + ------- + polynomials: list + Contains the sympy functions of the polynomials (in total L many if zero_m_only is True else L^2 many). + """ + # calculations from http://web.cmb.usc.edu/people/alber/Software/tomominer/docs/cpp/group__legendre__polynomials.html + z = sym.symbols("z") + P_l_m = [ + [0] * (2 * l_degree + 1) for l_degree in range(L_maxdegree) + ] # for order l: -l <= m <= l + + P_l_m[0][0] = 1 + if L_maxdegree > 0: + if zero_m_only: + # m = 0 + P_l_m[1][0] = z + for l_degree in range(2, L_maxdegree): + P_l_m[l_degree][0] = sym.simplify( + ( + (2 * l_degree - 1) * z * P_l_m[l_degree - 1][0] + - (l_degree - 1) * P_l_m[l_degree - 2][0] + ) + / l_degree + ) + return P_l_m + else: + # for m >= 0 + for l_degree in range(1, L_maxdegree): + P_l_m[l_degree][l_degree] = sym.simplify( + (1 - 2 * l_degree) + * (1 - z**2) ** 0.5 + * P_l_m[l_degree - 1][l_degree - 1] + ) # P_00, P_11, P_22, P_33 + + for m_order in range(0, L_maxdegree - 1): + P_l_m[m_order + 1][m_order] = sym.simplify( + (2 * m_order + 1) * z * P_l_m[m_order][m_order] + ) # P_10, P_21, P_32, P_43 + + for l_degree in range(2, L_maxdegree): + for m_order in range(l_degree - 1): # P_20, P_30, P_31 + P_l_m[l_degree][m_order] = sym.simplify( + ( + (2 * l_degree - 1) + * z + * P_l_m[l_degree - 1][m_order] + - (l_degree + m_order - 1) + * P_l_m[l_degree - 2][m_order] + ) + / (l_degree - m_order) + ) + + if not pos_m_only: + # for m < 0: P_l(-m) = (-1)^m * (l-m)!/(l+m)! * P_lm + for l_degree in range(1, L_maxdegree): + for m_order in range( + 1, l_degree + 1 + ): # P_1(-1), P_2(-1) P_2(-2) + P_l_m[l_degree][-m_order] = sym.simplify( + (-1) ** m_order + * np.math.factorial(l_degree - m_order) + / np.math.factorial(l_degree + m_order) + * P_l_m[l_degree][m_order] + ) + + return P_l_m + + +def real_sph_harm(L_maxdegree, use_theta, use_phi=True, zero_m_only=True): + """ + Computes formula strings of the the real part of the spherical harmonics up to degree L (excluded). + Variables are either spherical coordinates phi and theta (or cartesian coordinates x,y,z) on the UNIT SPHERE. + + Parameters + ---------- + L_maxdegree: int + Degree up to which to calculate the spherical harmonics (degree L is excluded). + use_theta: bool + - True: Expects the input of the formula strings to contain theta. + - False: Expects the input of the formula strings to contain z. + use_phi: bool + - True: Expects the input of the formula strings to contain phi. + - False: Expects the input of the formula strings to contain x and y. + Does nothing if zero_m_only is True + zero_m_only: bool + If True only calculate the harmonics where m=0. + + Returns + ------- + Y_lm_real: list + Computes formula strings of the the real part of the spherical harmonics up + to degree L (where degree L is not excluded). + In total L^2 many sph harm exist up to degree L (excluded). However, if zero_m_only only is True then + the total count is reduced to be only L many. + """ + z = sym.symbols("z") + P_l_m = associated_legendre_polynomials(L_maxdegree, zero_m_only) + if zero_m_only: + # for all m != 0: Y_lm = 0 + Y_l_m = [[0] for l_degree in range(L_maxdegree)] + else: + Y_l_m = [ + [0] * (2 * l_degree + 1) for l_degree in range(L_maxdegree) + ] # for order l: -l <= m <= l + + # convert expressions to spherical coordiantes + if use_theta: + # replace z by cos(theta) + theta = sym.symbols("theta") + for l_degree in range(L_maxdegree): + for m_order in range(len(P_l_m[l_degree])): + if not isinstance(P_l_m[l_degree][m_order], int): + P_l_m[l_degree][m_order] = P_l_m[l_degree][m_order].subs( + z, sym.cos(theta) + ) + + ## calculate Y_lm + # Y_lm = N * P_lm(cos(theta)) * exp(i*m*phi) + # { sqrt(2) * (-1)^m * N * P_l|m| * sin(|m|*phi) if m < 0 + # Y_lm_real = { Y_lm if m = 0 + # { sqrt(2) * (-1)^m * N * P_lm * cos(m*phi) if m > 0 + + for l_degree in range(L_maxdegree): + Y_l_m[l_degree][0] = sym.simplify( + sph_harm_prefactor(l_degree, 0) * P_l_m[l_degree][0] + ) # Y_l0 + + if not zero_m_only: + phi = sym.symbols("phi") + for l_degree in range(1, L_maxdegree): + # m > 0 + for m_order in range(1, l_degree + 1): + Y_l_m[l_degree][m_order] = sym.simplify( + 2**0.5 + * (-1) ** m_order + * sph_harm_prefactor(l_degree, m_order) + * P_l_m[l_degree][m_order] + * sym.cos(m_order * phi) + ) + # m < 0 + for m_order in range(1, l_degree + 1): + Y_l_m[l_degree][-m_order] = sym.simplify( + 2**0.5 + * (-1) ** m_order + * sph_harm_prefactor(l_degree, -m_order) + * P_l_m[l_degree][m_order] + * sym.sin(m_order * phi) + ) + + # convert expressions to cartesian coordinates + if not use_phi: + # replace phi by atan2(y,x) + x = sym.symbols("x") + y = sym.symbols("y") + for l_degree in range(L_maxdegree): + for m_order in range(len(Y_l_m[l_degree])): + Y_l_m[l_degree][m_order] = sym.simplify( + Y_l_m[l_degree][m_order].subs(phi, sym.atan2(y, x)) + ) + return Y_l_m diff --git a/ocpmodels/models/gemnet/layers/efficient.py b/ocpmodels/models/gemnet/layers/efficient.py new file mode 100644 index 0000000..f50e721 --- /dev/null +++ b/ocpmodels/models/gemnet/layers/efficient.py @@ -0,0 +1,173 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch + +from ..initializers import he_orthogonal_init + + +class EfficientInteractionDownProjection(torch.nn.Module): + """ + Down projection in the efficient reformulation. + + Parameters + ---------- + emb_size_interm: int + Intermediate embedding size (down-projection size). + kernel_initializer: callable + Initializer of the weight matrix. + """ + + def __init__( + self, + num_spherical: int, + num_radial: int, + emb_size_interm: int, + ): + super().__init__() + + self.num_spherical = num_spherical + self.num_radial = num_radial + self.emb_size_interm = emb_size_interm + + self.reset_parameters() + + def reset_parameters(self): + self.weight = torch.nn.Parameter( + torch.empty( + (self.num_spherical, self.num_radial, self.emb_size_interm) + ), + requires_grad=True, + ) + he_orthogonal_init(self.weight) + + def forward(self, rbf, sph, id_ca, id_ragged_idx): + """ + + Arguments + --------- + rbf: torch.Tensor, shape=(1, nEdges, num_radial) + sph: torch.Tensor, shape=(nEdges, Kmax, num_spherical) + id_ca + id_ragged_idx + + Returns + ------- + rbf_W1: torch.Tensor, shape=(nEdges, emb_size_interm, num_spherical) + sph: torch.Tensor, shape=(nEdges, Kmax, num_spherical) + Kmax = maximum number of neighbors of the edges + """ + num_edges = rbf.shape[1] + + # MatMul: mul + sum over num_radial + rbf_W1 = torch.matmul(rbf, self.weight) + # (num_spherical, nEdges , emb_size_interm) + rbf_W1 = rbf_W1.permute(1, 2, 0) + # (nEdges, emb_size_interm, num_spherical) + + # Zero padded dense matrix + # maximum number of neighbors, catch empty id_ca with maximum + if sph.shape[0] == 0: + Kmax = 0 + else: + Kmax = torch.max( + torch.max(id_ragged_idx + 1), + torch.tensor(0).to(id_ragged_idx.device), + ) + + sph2 = sph.new_zeros(num_edges, Kmax, self.num_spherical) + sph2[id_ca, id_ragged_idx] = sph + + sph2 = torch.transpose(sph2, 1, 2) + # (nEdges, num_spherical/emb_size_interm, Kmax) + + return rbf_W1, sph2 + + +class EfficientInteractionBilinear(torch.nn.Module): + """ + Efficient reformulation of the bilinear layer and subsequent summation. + + Parameters + ---------- + units_out: int + Embedding output size of the bilinear layer. + kernel_initializer: callable + Initializer of the weight matrix. + """ + + def __init__( + self, + emb_size: int, + emb_size_interm: int, + units_out: int, + ): + super().__init__() + self.emb_size = emb_size + self.emb_size_interm = emb_size_interm + self.units_out = units_out + + self.reset_parameters() + + def reset_parameters(self): + self.weight = torch.nn.Parameter( + torch.empty( + (self.emb_size, self.emb_size_interm, self.units_out), + requires_grad=True, + ) + ) + he_orthogonal_init(self.weight) + + def forward( + self, + basis, + m, + id_reduce, + id_ragged_idx, + ): + """ + + Arguments + --------- + basis + m: quadruplets: m = m_db , triplets: m = m_ba + id_reduce + id_ragged_idx + + Returns + ------- + m_ca: torch.Tensor, shape=(nEdges, units_out) + Edge embeddings. + """ + # num_spherical is actually num_spherical**2 for quadruplets + (rbf_W1, sph) = basis + # (nEdges, emb_size_interm, num_spherical), (nEdges, num_spherical, Kmax) + nEdges = rbf_W1.shape[0] + + # Create (zero-padded) dense matrix of the neighboring edge embeddings. + Kmax = torch.max( + torch.max(id_ragged_idx) + 1, + torch.tensor(0).to(id_ragged_idx.device), + ) + # maximum number of neighbors, catch empty id_reduce_ji with maximum + m2 = m.new_zeros(nEdges, Kmax, self.emb_size) + m2[id_reduce, id_ragged_idx] = m + # (num_quadruplets or num_triplets, emb_size) -> (nEdges, Kmax, emb_size) + + sum_k = torch.matmul(sph, m2) # (nEdges, num_spherical, emb_size) + + # MatMul: mul + sum over num_spherical + rbf_W1_sum_k = torch.matmul(rbf_W1, sum_k) + # (nEdges, emb_size_interm, emb_size) + + # Bilinear: Sum over emb_size_interm and emb_size + m_ca = torch.matmul(rbf_W1_sum_k.permute(2, 0, 1), self.weight) + # (emb_size, nEdges, units_out) + m_ca = torch.sum(m_ca, dim=0) + # (nEdges, units_out) + + return m_ca diff --git a/ocpmodels/models/gemnet/layers/embedding_block.py b/ocpmodels/models/gemnet/layers/embedding_block.py new file mode 100644 index 0000000..b70c622 --- /dev/null +++ b/ocpmodels/models/gemnet/layers/embedding_block.py @@ -0,0 +1,99 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch + +from .base_layers import Dense + + +class AtomEmbedding(torch.nn.Module): + """ + Initial atom embeddings based on the atom type + + Parameters + ---------- + emb_size: int + Atom embeddings size + """ + + def __init__(self, emb_size, num_elements): + super().__init__() + self.emb_size = emb_size + + self.embeddings = torch.nn.Embedding(num_elements, emb_size) + # init by uniform distribution + torch.nn.init.uniform_( + self.embeddings.weight, a=-np.sqrt(3), b=np.sqrt(3) + ) + + def forward(self, Z): + """ + Returns + ------- + h: torch.Tensor, shape=(nAtoms, emb_size) + Atom embeddings. + """ + h = self.embeddings(Z - 1) # -1 because Z.min()=1 (==Hydrogen) + return h + + +class EdgeEmbedding(torch.nn.Module): + """ + Edge embedding based on the concatenation of atom embeddings and subsequent dense layer. + + Parameters + ---------- + emb_size: int + Embedding size after the dense layer. + activation: str + Activation function used in the dense layer. + """ + + def __init__( + self, + atom_features, + edge_features, + out_features, + activation=None, + ): + super().__init__() + in_features = 2 * atom_features + edge_features + self.dense = Dense( + in_features, out_features, activation=activation, bias=False + ) + + def forward( + self, + h, + m_rbf, + idx_s, + idx_t, + ): + """ + + Arguments + --------- + h + m_rbf: shape (nEdges, nFeatures) + in embedding block: m_rbf = rbf ; In interaction block: m_rbf = m_st + idx_s + idx_t + + Returns + ------- + m_st: torch.Tensor, shape=(nEdges, emb_size) + Edge embeddings. + """ + h_s = h[idx_s] # shape=(nEdges, emb_size) + h_t = h[idx_t] # shape=(nEdges, emb_size) + + m_st = torch.cat( + [h_s, h_t, m_rbf], dim=-1 + ) # (nEdges, 2*emb_size+nFeatures) + m_st = self.dense(m_st) # (nEdges, emb_size) + return m_st diff --git a/ocpmodels/models/gemnet/layers/interaction_block.py b/ocpmodels/models/gemnet/layers/interaction_block.py new file mode 100644 index 0000000..033e25e --- /dev/null +++ b/ocpmodels/models/gemnet/layers/interaction_block.py @@ -0,0 +1,341 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import torch + +from ocpmodels.modules.scaling.scale_factor import ScaleFactor + +from .atom_update_block import AtomUpdateBlock +from .base_layers import Dense, ResidualLayer +from .efficient import EfficientInteractionBilinear +from .embedding_block import EdgeEmbedding + + +class InteractionBlockTripletsOnly(torch.nn.Module): + """ + Interaction block for GemNet-T/dT. + + Parameters + ---------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_edge: int + Embedding size of the edges. + emb_size_trip: int + (Down-projected) Embedding size in the triplet message passing block. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + + emb_size_bil_trip: int + Embedding size of the edge embeddings in the triplet-based message passing block after the bilinear layer. + num_before_skip: int + Number of residual blocks before the first skip connection. + num_after_skip: int + Number of residual blocks after the first skip connection. + num_concat: int + Number of residual blocks after the concatenation. + num_atom: int + Number of residual blocks in the atom embedding blocks. + + activation: str + Name of the activation function to use in the dense layers except for the final dense layer. + """ + + def __init__( + self, + emb_size_atom, + emb_size_edge, + emb_size_trip, + emb_size_rbf, + emb_size_cbf, + emb_size_bil_trip, + num_before_skip, + num_after_skip, + num_concat, + num_atom, + activation=None, + name="Interaction", + ): + super().__init__() + self.name = name + + block_nr = name.split("_")[-1] + + ## -------------------------------------------- Message Passing ------------------------------------------- ## + # Dense transformation of skip connection + self.dense_ca = Dense( + emb_size_edge, + emb_size_edge, + activation=activation, + bias=False, + ) + + # Triplet Interaction + self.trip_interaction = TripletInteraction( + emb_size_edge=emb_size_edge, + emb_size_trip=emb_size_trip, + emb_size_bilinear=emb_size_bil_trip, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + activation=activation, + name=f"TripInteraction_{block_nr}", + ) + + ## ---------------------------------------- Update Edge Embeddings ---------------------------------------- ## + # Residual layers before skip connection + self.layers_before_skip = torch.nn.ModuleList( + [ + ResidualLayer( + emb_size_edge, + activation=activation, + ) + for i in range(num_before_skip) + ] + ) + + # Residual layers after skip connection + self.layers_after_skip = torch.nn.ModuleList( + [ + ResidualLayer( + emb_size_edge, + activation=activation, + ) + for i in range(num_after_skip) + ] + ) + + ## ---------------------------------------- Update Atom Embeddings ---------------------------------------- ## + self.atom_update = AtomUpdateBlock( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=num_atom, + activation=activation, + name=f"AtomUpdate_{block_nr}", + ) + + ## ------------------------------ Update Edge Embeddings with Atom Embeddings ----------------------------- ## + self.concat_layer = EdgeEmbedding( + emb_size_atom, + emb_size_edge, + emb_size_edge, + activation=activation, + ) + self.residual_m = torch.nn.ModuleList( + [ + ResidualLayer(emb_size_edge, activation=activation) + for _ in range(num_concat) + ] + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + + def forward( + self, + h, + m, + rbf3, + cbf3, + id3_ragged_idx, + id_swap, + id3_ba, + id3_ca, + rbf_h, + idx_s, + idx_t, + ): + """ + Returns + ------- + h: torch.Tensor, shape=(nEdges, emb_size_atom) + Atom embeddings. + m: torch.Tensor, shape=(nEdges, emb_size_edge) + Edge embeddings (c->a). + """ + + # Initial transformation + x_ca_skip = self.dense_ca(m) # (nEdges, emb_size_edge) + + x3 = self.trip_interaction( + m, + rbf3, + cbf3, + id3_ragged_idx, + id_swap, + id3_ba, + id3_ca, + ) + + ## ----------------------------- Merge Embeddings after Triplet Interaction ------------------------------ ## + x = x_ca_skip + x3 # (nEdges, emb_size_edge) + x = x * self.inv_sqrt_2 + + ## ---------------------------------------- Update Edge Embeddings --------------------------------------- ## + # Transformations before skip connection + for i, layer in enumerate(self.layers_before_skip): + x = layer(x) # (nEdges, emb_size_edge) + + # Skip connection + m = m + x # (nEdges, emb_size_edge) + m = m * self.inv_sqrt_2 + + # Transformations after skip connection + for i, layer in enumerate(self.layers_after_skip): + m = layer(m) # (nEdges, emb_size_edge) + + ## ---------------------------------------- Update Atom Embeddings --------------------------------------- ## + h2 = self.atom_update(h, m, rbf_h, idx_t) + + # Skip connection + h = h + h2 # (nAtoms, emb_size_atom) + h = h * self.inv_sqrt_2 + + ## ----------------------------- Update Edge Embeddings with Atom Embeddings ----------------------------- ## + m2 = self.concat_layer(h, m, idx_s, idx_t) # (nEdges, emb_size_edge) + + for i, layer in enumerate(self.residual_m): + m2 = layer(m2) # (nEdges, emb_size_edge) + + # Skip connection + m = m + m2 # (nEdges, emb_size_edge) + m = m * self.inv_sqrt_2 + return h, m + + +class TripletInteraction(torch.nn.Module): + """ + Triplet-based message passing block. + + Parameters + ---------- + emb_size_edge: int + Embedding size of the edges. + emb_size_trip: int + (Down-projected) Embedding size of the edge embeddings after the hadamard product with rbf. + emb_size_bilinear: int + Embedding size of the edge embeddings after the bilinear layer. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + + activation: str + Name of the activation function to use in the dense layers except for the final dense layer. + """ + + def __init__( + self, + emb_size_edge, + emb_size_trip, + emb_size_bilinear, + emb_size_rbf, + emb_size_cbf, + activation=None, + name="TripletInteraction", + **kwargs, + ): + super().__init__() + self.name = name + + # Dense transformation + self.dense_ba = Dense( + emb_size_edge, + emb_size_edge, + activation=activation, + bias=False, + ) + + # Up projections of basis representations, bilinear layer and scaling factors + self.mlp_rbf = Dense( + emb_size_rbf, + emb_size_edge, + activation=None, + bias=False, + ) + self.scale_rbf = ScaleFactor(name + "_had_rbf") + + self.mlp_cbf = EfficientInteractionBilinear( + emb_size_trip, emb_size_cbf, emb_size_bilinear + ) + + # combines scaling for bilinear layer and summation + self.scale_cbf_sum = ScaleFactor(name + "_sum_cbf") + + # Down and up projections + self.down_projection = Dense( + emb_size_edge, + emb_size_trip, + activation=activation, + bias=False, + ) + self.up_projection_ca = Dense( + emb_size_bilinear, + emb_size_edge, + activation=activation, + bias=False, + ) + self.up_projection_ac = Dense( + emb_size_bilinear, + emb_size_edge, + activation=activation, + bias=False, + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + + def forward( + self, + m, + rbf3, + cbf3, + id3_ragged_idx, + id_swap, + id3_ba, + id3_ca, + ): + """ + Returns + ------- + m: torch.Tensor, shape=(nEdges, emb_size_edge) + Edge embeddings (c->a). + """ + + # Dense transformation + x_ba = self.dense_ba(m) # (nEdges, emb_size_edge) + + # Transform via radial bessel basis + rbf_emb = self.mlp_rbf(rbf3) # (nEdges, emb_size_edge) + x_ba2 = x_ba * rbf_emb + x_ba = self.scale_rbf(x_ba2, ref=x_ba) + + x_ba = self.down_projection(x_ba) # (nEdges, emb_size_trip) + + # Transform via circular spherical basis + x_ba = x_ba[id3_ba] + + # Efficient bilinear layer + x = self.mlp_cbf(cbf3, x_ba, id3_ca, id3_ragged_idx) + # (nEdges, emb_size_quad) + x = self.scale_cbf_sum(x, ref=x_ba) + + # => + # rbf(d_ba) + # cbf(d_ca, angle_cab) + + # Up project embeddings + x_ca = self.up_projection_ca(x) # (nEdges, emb_size_edge) + x_ac = self.up_projection_ac(x) # (nEdges, emb_size_edge) + + # Merge interaction of c->a and a->c + x_ac = x_ac[id_swap] # swap to add to edge a->c and not c->a + x3 = x_ca + x_ac + x3 = x3 * self.inv_sqrt_2 + return x3 diff --git a/ocpmodels/models/gemnet/layers/radial_basis.py b/ocpmodels/models/gemnet/layers/radial_basis.py new file mode 100644 index 0000000..3f030fc --- /dev/null +++ b/ocpmodels/models/gemnet/layers/radial_basis.py @@ -0,0 +1,206 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import numpy as np +import torch +from scipy.special import binom +from torch_geometric.nn.models.schnet import GaussianSmearing + + +class PolynomialEnvelope(torch.nn.Module): + """ + Polynomial envelope function that ensures a smooth cutoff. + + Parameters + ---------- + exponent: int + Exponent of the envelope function. + """ + + def __init__(self, exponent): + super().__init__() + assert exponent > 0 + self.p = exponent + self.a = -(self.p + 1) * (self.p + 2) / 2 + self.b = self.p * (self.p + 2) + self.c = -self.p * (self.p + 1) / 2 + + def forward(self, d_scaled): + env_val = ( + 1 + + self.a * d_scaled**self.p + + self.b * d_scaled ** (self.p + 1) + + self.c * d_scaled ** (self.p + 2) + ) + return torch.where(d_scaled < 1, env_val, torch.zeros_like(d_scaled)) + + +class ExponentialEnvelope(torch.nn.Module): + """ + Exponential envelope function that ensures a smooth cutoff, + as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021. + SpookyNet: Learning Force Fields with Electronic Degrees of Freedom + and Nonlocal Effects + """ + + def __init__(self): + super().__init__() + + def forward(self, d_scaled): + env_val = torch.exp( + -(d_scaled**2) / ((1 - d_scaled) * (1 + d_scaled)) + ) + return torch.where(d_scaled < 1, env_val, torch.zeros_like(d_scaled)) + + +class SphericalBesselBasis(torch.nn.Module): + """ + 1D spherical Bessel basis + + Parameters + ---------- + num_radial: int + Controls maximum frequency. + cutoff: float + Cutoff distance in Angstrom. + """ + + def __init__( + self, + num_radial: int, + cutoff: float, + ): + super().__init__() + self.norm_const = math.sqrt(2 / (cutoff**3)) + # cutoff ** 3 to counteract dividing by d_scaled = d / cutoff + + # Initialize frequencies at canonical positions + self.frequencies = torch.nn.Parameter( + data=torch.tensor( + np.pi * np.arange(1, num_radial + 1, dtype=np.float32) + ), + requires_grad=True, + ) + + def forward(self, d_scaled): + return ( + self.norm_const + / d_scaled[:, None] + * torch.sin(self.frequencies * d_scaled[:, None]) + ) # (num_edges, num_radial) + + +class BernsteinBasis(torch.nn.Module): + """ + Bernstein polynomial basis, + as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021. + SpookyNet: Learning Force Fields with Electronic Degrees of Freedom + and Nonlocal Effects + + Parameters + ---------- + num_radial: int + Controls maximum frequency. + pregamma_initial: float + Initial value of exponential coefficient gamma. + Default: gamma = 0.5 * a_0**-1 = 0.94486, + inverse softplus -> pregamma = log e**gamma - 1 = 0.45264 + """ + + def __init__( + self, + num_radial: int, + pregamma_initial: float = 0.45264, + ): + super().__init__() + prefactor = binom(num_radial - 1, np.arange(num_radial)) + self.register_buffer( + "prefactor", + torch.tensor(prefactor, dtype=torch.float), + persistent=False, + ) + + self.pregamma = torch.nn.Parameter( + data=torch.tensor(pregamma_initial, dtype=torch.float), + requires_grad=True, + ) + self.softplus = torch.nn.Softplus() + + exp1 = torch.arange(num_radial) + self.register_buffer("exp1", exp1[None, :], persistent=False) + exp2 = num_radial - 1 - exp1 + self.register_buffer("exp2", exp2[None, :], persistent=False) + + def forward(self, d_scaled): + gamma = self.softplus(self.pregamma) # constrain to positive + exp_d = torch.exp(-gamma * d_scaled)[:, None] + return ( + self.prefactor * (exp_d**self.exp1) * ((1 - exp_d) ** self.exp2) + ) + + +class RadialBasis(torch.nn.Module): + """ + + Parameters + ---------- + num_radial: int + Controls maximum frequency. + cutoff: float + Cutoff distance in Angstrom. + rbf: dict = {"name": "gaussian"} + Basis function and its hyperparameters. + envelope: dict = {"name": "polynomial", "exponent": 5} + Envelope function and its hyperparameters. + """ + + def __init__( + self, + num_radial: int, + cutoff: float, + rbf: dict = {"name": "gaussian"}, + envelope: dict = {"name": "polynomial", "exponent": 5}, + ): + super().__init__() + self.inv_cutoff = 1 / cutoff + + env_name = envelope["name"].lower() + env_hparams = envelope.copy() + del env_hparams["name"] + + if env_name == "polynomial": + self.envelope = PolynomialEnvelope(**env_hparams) + elif env_name == "exponential": + self.envelope = ExponentialEnvelope(**env_hparams) + else: + raise ValueError(f"Unknown envelope function '{env_name}'.") + + rbf_name = rbf["name"].lower() + rbf_hparams = rbf.copy() + del rbf_hparams["name"] + + # RBFs get distances scaled to be in [0, 1] + if rbf_name == "gaussian": + self.rbf = GaussianSmearing( + start=0, stop=1, num_gaussians=num_radial, **rbf_hparams + ) + elif rbf_name == "spherical_bessel": + self.rbf = SphericalBesselBasis( + num_radial=num_radial, cutoff=cutoff, **rbf_hparams + ) + elif rbf_name == "bernstein": + self.rbf = BernsteinBasis(num_radial=num_radial, **rbf_hparams) + else: + raise ValueError(f"Unknown radial basis function '{rbf_name}'.") + + def forward(self, d): + d_scaled = d * self.inv_cutoff + + env = self.envelope(d_scaled) + return env[:, None] * self.rbf(d_scaled) # (nEdges, num_radial) diff --git a/ocpmodels/models/gemnet/layers/spherical_basis.py b/ocpmodels/models/gemnet/layers/spherical_basis.py new file mode 100644 index 0000000..21add78 --- /dev/null +++ b/ocpmodels/models/gemnet/layers/spherical_basis.py @@ -0,0 +1,95 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import sympy as sym +import torch +from torch_geometric.nn.models.schnet import GaussianSmearing + +from .basis_utils import real_sph_harm +from .radial_basis import RadialBasis + + +class CircularBasisLayer(torch.nn.Module): + """ + 2D Fourier Bessel Basis + + Parameters + ---------- + num_spherical: int + Controls maximum frequency. + radial_basis: RadialBasis + Radial basis functions + cbf: dict + Name and hyperparameters of the cosine basis function + efficient: bool + Whether to use the "efficient" summation order + """ + + def __init__( + self, + num_spherical: int, + radial_basis: RadialBasis, + cbf: str, + efficient: bool = False, + ): + super().__init__() + + self.radial_basis = radial_basis + self.efficient = efficient + + cbf_name = cbf["name"].lower() + cbf_hparams = cbf.copy() + del cbf_hparams["name"] + + if cbf_name == "gaussian": + self.cosφ_basis = GaussianSmearing( + start=-1, stop=1, num_gaussians=num_spherical, **cbf_hparams + ) + elif cbf_name == "spherical_harmonics": + Y_lm = real_sph_harm( + num_spherical, use_theta=False, zero_m_only=True + ) + sph_funcs = [] # (num_spherical,) + + # convert to tensorflow functions + z = sym.symbols("z") + modules = {"sin": torch.sin, "cos": torch.cos, "sqrt": torch.sqrt} + m_order = 0 # only single angle + for l_degree in range(len(Y_lm)): # num_spherical + if ( + l_degree == 0 + ): # Y_00 is only a constant -> function returns value and not tensor + first_sph = sym.lambdify( + [z], Y_lm[l_degree][m_order], modules + ) + sph_funcs.append( + lambda z: torch.zeros_like(z) + first_sph(z) + ) + else: + sph_funcs.append( + sym.lambdify([z], Y_lm[l_degree][m_order], modules) + ) + self.cosφ_basis = lambda cosφ: torch.stack( + [f(cosφ) for f in sph_funcs], dim=1 + ) + else: + raise ValueError(f"Unknown cosine basis function '{cbf_name}'.") + + def forward(self, D_ca, cosφ_cab, id3_ca): + rbf = self.radial_basis(D_ca) # (num_edges, num_radial) + cbf = self.cosφ_basis(cosφ_cab) # (num_triplets, num_spherical) + + if not self.efficient: + rbf = rbf[id3_ca] # (num_triplets, num_radial) + out = (rbf[:, None, :] * cbf[:, :, None]).view( + -1, rbf.shape[-1] * cbf.shape[-1] + ) + return (out,) + # (num_triplets, num_radial * num_spherical) + else: + return (rbf[None, :, :], cbf) + # (1, num_edges, num_radial), (num_edges, num_spherical) diff --git a/ocpmodels/models/gemnet/utils.py b/ocpmodels/models/gemnet/utils.py new file mode 100644 index 0000000..42208ae --- /dev/null +++ b/ocpmodels/models/gemnet/utils.py @@ -0,0 +1,279 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import json + +import torch +from torch_scatter import segment_csr + + +def read_json(path): + """""" + if not path.endswith(".json"): + raise UserWarning(f"Path {path} is not a json-path.") + + with open(path, "r") as f: + content = json.load(f) + return content + + +def update_json(path, data): + """""" + if not path.endswith(".json"): + raise UserWarning(f"Path {path} is not a json-path.") + + content = read_json(path) + content.update(data) + write_json(path, content) + + +def write_json(path, data): + """""" + if not path.endswith(".json"): + raise UserWarning(f"Path {path} is not a json-path.") + + with open(path, "w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=4) + + +def read_value_json(path, key): + """""" + content = read_json(path) + + if key in content.keys(): + return content[key] + else: + return None + + +def ragged_range(sizes): + """Multiple concatenated ranges. + + Examples + -------- + sizes = [1 4 2 3] + Return: [0 0 1 2 3 0 1 0 1 2] + """ + assert sizes.dim() == 1 + if sizes.sum() == 0: + return sizes.new_empty(0) + + # Remove 0 sizes + sizes_nonzero = sizes > 0 + if not torch.all(sizes_nonzero): + sizes = torch.masked_select(sizes, sizes_nonzero) + + # Initialize indexing array with ones as we need to setup incremental indexing + # within each group when cumulatively summed at the final stage. + id_steps = torch.ones(sizes.sum(), dtype=torch.long, device=sizes.device) + id_steps[0] = 0 + insert_index = sizes[:-1].cumsum(0) + insert_val = (1 - sizes)[:-1] + + # Assign index-offsetting values + id_steps[insert_index] = insert_val + + # Finally index into input array for the group repeated o/p + res = id_steps.cumsum(0) + return res + + +def repeat_blocks( + sizes, + repeats, + continuous_indexing=True, + start_idx=0, + block_inc=0, + repeat_inc=0, +): + """Repeat blocks of indices. + Adapted from https://stackoverflow.com/questions/51154989/numpy-vectorized-function-to-repeat-blocks-of-consecutive-elements + + continuous_indexing: Whether to keep increasing the index after each block + start_idx: Starting index + block_inc: Number to increment by after each block, + either global or per block. Shape: len(sizes) - 1 + repeat_inc: Number to increment by after each repetition, + either global or per block + + Examples + -------- + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = False + Return: [0 0 0 0 1 2 0 1 2 0 1 0 1 0 1] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True + Return: [0 0 0 1 2 3 1 2 3 4 5 4 5 4 5] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + repeat_inc = 4 + Return: [0 4 8 1 2 3 5 6 7 4 5 8 9 12 13] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + start_idx = 5 + Return: [5 5 5 6 7 8 6 7 8 9 10 9 10 9 10] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + block_inc = 1 + Return: [0 0 0 2 3 4 2 3 4 6 7 6 7 6 7] + sizes = [0,3,2] ; repeats = [3,2,3] ; continuous_indexing = True + Return: [0 1 2 0 1 2 3 4 3 4 3 4] + sizes = [2,3,2] ; repeats = [2,0,2] ; continuous_indexing = True + Return: [0 1 0 1 5 6 5 6] + """ + assert sizes.dim() == 1 + assert all(sizes >= 0) + + # Remove 0 sizes + sizes_nonzero = sizes > 0 + if not torch.all(sizes_nonzero): + assert block_inc == 0 # Implementing this is not worth the effort + sizes = torch.masked_select(sizes, sizes_nonzero) + if isinstance(repeats, torch.Tensor): + repeats = torch.masked_select(repeats, sizes_nonzero) + if isinstance(repeat_inc, torch.Tensor): + repeat_inc = torch.masked_select(repeat_inc, sizes_nonzero) + + if isinstance(repeats, torch.Tensor): + assert all(repeats >= 0) + insert_dummy = repeats[0] == 0 + if insert_dummy: + one = sizes.new_ones(1) + zero = sizes.new_zeros(1) + sizes = torch.cat((one, sizes)) + repeats = torch.cat((one, repeats)) + if isinstance(block_inc, torch.Tensor): + block_inc = torch.cat((zero, block_inc)) + if isinstance(repeat_inc, torch.Tensor): + repeat_inc = torch.cat((zero, repeat_inc)) + else: + assert repeats >= 0 + insert_dummy = False + + # Get repeats for each group using group lengths/sizes + r1 = torch.repeat_interleave( + torch.arange(len(sizes), device=sizes.device), repeats + ) + + # Get total size of output array, as needed to initialize output indexing array + N = (sizes * repeats).sum() + + # Initialize indexing array with ones as we need to setup incremental indexing + # within each group when cumulatively summed at the final stage. + # Two steps here: + # 1. Within each group, we have multiple sequences, so setup the offsetting + # at each sequence lengths by the seq. lengths preceding those. + id_ar = torch.ones(N, dtype=torch.long, device=sizes.device) + id_ar[0] = 0 + insert_index = sizes[r1[:-1]].cumsum(0) + insert_val = (1 - sizes)[r1[:-1]] + + if isinstance(repeats, torch.Tensor) and torch.any(repeats == 0): + diffs = r1[1:] - r1[:-1] + indptr = torch.cat((sizes.new_zeros(1), diffs.cumsum(0))) + if continuous_indexing: + # If a group was skipped (repeats=0) we need to add its size + insert_val += segment_csr(sizes[: r1[-1]], indptr, reduce="sum") + + # Add block increments + if isinstance(block_inc, torch.Tensor): + insert_val += segment_csr( + block_inc[: r1[-1]], indptr, reduce="sum" + ) + else: + insert_val += block_inc * (indptr[1:] - indptr[:-1]) + if insert_dummy: + insert_val[0] -= block_inc + else: + idx = r1[1:] != r1[:-1] + if continuous_indexing: + # 2. For each group, make sure the indexing starts from the next group's + # first element. So, simply assign 1s there. + insert_val[idx] = 1 + + # Add block increments + insert_val[idx] += block_inc + + # Add repeat_inc within each group + if isinstance(repeat_inc, torch.Tensor): + insert_val += repeat_inc[r1[:-1]] + if isinstance(repeats, torch.Tensor): + repeat_inc_inner = repeat_inc[repeats > 0][:-1] + else: + repeat_inc_inner = repeat_inc[:-1] + else: + insert_val += repeat_inc + repeat_inc_inner = repeat_inc + + # Subtract the increments between groups + if isinstance(repeats, torch.Tensor): + repeats_inner = repeats[repeats > 0][:-1] + else: + repeats_inner = repeats + insert_val[r1[1:] != r1[:-1]] -= repeat_inc_inner * repeats_inner + + # Assign index-offsetting values + id_ar[insert_index] = insert_val + + if insert_dummy: + id_ar = id_ar[1:] + if continuous_indexing: + id_ar[0] -= 1 + + # Set start index now, in case of insertion due to leading repeats=0 + id_ar[0] += start_idx + + # Finally index into input array for the group repeated o/p + res = id_ar.cumsum(0) + return res + + +def calculate_interatomic_vectors(R, id_s, id_t, offsets_st): + """ + Calculate the vectors connecting the given atom pairs, + considering offsets from periodic boundary conditions (PBC). + + Parameters + ---------- + R: Tensor, shape = (nAtoms, 3) + Atom positions. + id_s: Tensor, shape = (nEdges,) + Indices of the source atom of the edges. + id_t: Tensor, shape = (nEdges,) + Indices of the target atom of the edges. + offsets_st: Tensor, shape = (nEdges,) + PBC offsets of the edges. + Subtract this from the correct direction. + + Returns + ------- + (D_st, V_st): tuple + D_st: Tensor, shape = (nEdges,) + Distance from atom t to s. + V_st: Tensor, shape = (nEdges,) + Unit direction from atom t to s. + """ + Rs = R[id_s] + Rt = R[id_t] + # ReLU prevents negative numbers in sqrt + if offsets_st is None: + V_st = Rt - Rs # s -> t + else: + V_st = Rt - Rs + offsets_st # s -> t + D_st = torch.sqrt(torch.sum(V_st**2, dim=1)) + V_st = V_st / D_st[..., None] + return D_st, V_st + + +def inner_product_normalized(x, y): + """ + Calculate the inner product between the given normalized vectors, + giving a result between -1 and 1. + """ + return torch.sum(x * y, dim=-1).clamp(min=-1, max=1) + + +def mask_neighbors(neighbors, edge_mask): + neighbors_old_indptr = torch.cat([neighbors.new_zeros(1), neighbors]) + neighbors_old_indptr = torch.cumsum(neighbors_old_indptr, dim=0) + neighbors = segment_csr(edge_mask.long(), neighbors_old_indptr) + return neighbors diff --git a/ocpmodels/models/gemnet_gp/README.md b/ocpmodels/models/gemnet_gp/README.md new file mode 100644 index 0000000..1b47420 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/README.md @@ -0,0 +1,30 @@ +# Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations + +Anuroop Sriram, Abhishek Das, Brandon M. Wood, Siddharth Goyal, C. Lawrence Zitnick + +[[`arXiv:2203.09697`](https://arxiv.org/abs/2203.09697)] + + +To use graph parallel training, add `--gp-gpus N` to your command line, where N = number of GPUs to split the model over. This flag works for all tasks (`train`, `predict`, `validate` & `run-relaxations`). + +As an example, the Gemnet-XL model can be trained using: +```bash +python main.py --mode train --config-yml configs/s2ef/all/gp_gemnet/gp-gemnet-xl.yml \ + --distributed --num-nodes 32 --num-gpus 8 --gp-gpus 4 +``` +This trains the model on 256 GPUs (32 nodes x 8 GPUs each) with 4-way graph parallelism (i.e. the graph is distributed over 4 GPUs) and 64-way data parallelism (64 == 256 / 4). + +The Gemnet-XL model was trained without AMP as it led to unstable training. + +## Citing + +If you use Graph Parallelism in your work, please consider citing: + +```bibtex +@inproceedings{sriram_graphparallel_2022, + title={{Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations}}, + author={Sriram, Anuroop and Das, Abhishek and Wood, Brandon M. and Goyal, Siddharth and Zitnick, C. Lawrence}, + booktitle={International Conference on Learning Representations (ICLR)}, + year={2022} +} +``` diff --git a/ocpmodels/models/gemnet_gp/__init__.py b/ocpmodels/models/gemnet_gp/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/gemnet_gp/gemnet.py b/ocpmodels/models/gemnet_gp/gemnet.py new file mode 100644 index 0000000..1abf5e3 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/gemnet.py @@ -0,0 +1,649 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from typing import Optional + +import numpy as np +import torch +from torch_cluster import radius_graph +from torch_scatter import scatter +from torch_sparse import SparseTensor + +from ocpmodels.common import distutils, gp_utils +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + compute_neighbors, + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.models.base import BaseModel +from ocpmodels.modules.scaling.compat import load_scales_compat + +from .layers.atom_update_block import OutputBlock +from .layers.base_layers import Dense +from .layers.efficient import EfficientInteractionDownProjection +from .layers.embedding_block import AtomEmbedding, EdgeEmbedding +from .layers.interaction_block import InteractionBlockTripletsOnly +from .layers.radial_basis import RadialBasis +from .layers.spherical_basis import CircularBasisLayer +from .utils import ( + inner_product_normalized, + mask_neighbors, + ragged_range, + repeat_blocks, +) + + +@registry.register_model("gp_gemnet_t") +class GraphParallelGemNetT(BaseModel): + """ + GemNet-T, triplets-only variant of GemNet + + Parameters + ---------- + num_atoms (int): Unused argument + bond_feat_dim (int): Unused argument + num_targets: int + Number of prediction targets. + + num_spherical: int + Controls maximum frequency. + num_radial: int + Controls maximum frequency. + num_blocks: int + Number of building blocks to be stacked. + + emb_size_atom: int + Embedding size of the atoms. + emb_size_edge: int + Embedding size of the edges. + emb_size_trip: int + (Down-projected) Embedding size in the triplet message passing block. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + emb_size_bil_trip: int + Embedding size of the edge embeddings in the triplet-based message passing block after the bilinear layer. + + num_before_skip: int + Number of residual blocks before the first skip connection. + num_after_skip: int + Number of residual blocks after the first skip connection. + num_concat: int + Number of residual blocks after the concatenation. + num_atom: int + Number of residual blocks in the atom embedding blocks. + + regress_forces: bool + Whether to predict forces. Default: True + direct_forces: bool + If True predict forces based on aggregation of interatomic directions. + If False predict forces based on negative gradient of energy potential. + + cutoff: float + Embedding cutoff for interactomic directions in Angstrom. + rbf: dict + Name and hyperparameters of the radial basis function. + envelope: dict + Name and hyperparameters of the envelope function. + cbf: dict + Name and hyperparameters of the cosine basis function. + extensive: bool + Whether the output should be extensive (proportional to the number of atoms) + output_init: str + Initialization method for the final dense layer. + activation: str + Name of the activation function. + scale_file: str + Path to the json file containing the scaling factors. + """ + + def __init__( + self, + num_atoms: Optional[int], + bond_feat_dim: int, + num_targets: int, + num_spherical: int, + num_radial: int, + num_blocks: int, + emb_size_atom: int, + emb_size_edge: int, + emb_size_trip: int, + emb_size_rbf: int, + emb_size_cbf: int, + emb_size_bil_trip: int, + num_before_skip: int, + num_after_skip: int, + num_concat: int, + num_atom: int, + regress_forces: bool = True, + direct_forces: bool = False, + cutoff: float = 6.0, + max_neighbors: int = 50, + rbf: dict = {"name": "gaussian"}, + envelope: dict = {"name": "polynomial", "exponent": 5}, + cbf: dict = {"name": "spherical_harmonics"}, + extensive: bool = True, + otf_graph: bool = False, + use_pbc: bool = True, + output_init: str = "HeOrthogonal", + activation: str = "swish", + scale_num_blocks: bool = False, + scatter_atoms: bool = True, + scale_file: Optional[str] = None, + ): + super().__init__() + self.num_targets = num_targets + assert num_blocks > 0 + self.num_blocks = num_blocks + self.extensive = extensive + self.scale_num_blocks = scale_num_blocks + self.scatter_atoms = scatter_atoms + + self.cutoff = cutoff + assert self.cutoff <= 6 or otf_graph + + self.max_neighbors = max_neighbors + assert self.max_neighbors == 50 or otf_graph + + self.regress_forces = regress_forces + self.otf_graph = otf_graph + self.use_pbc = use_pbc + + # GemNet variants + self.direct_forces = direct_forces + + ### ---------------------------------- Basis Functions ---------------------------------- ### + self.radial_basis = RadialBasis( + num_radial=num_radial, + cutoff=cutoff, + rbf=rbf, + envelope=envelope, + ) + + radial_basis_cbf3 = RadialBasis( + num_radial=num_radial, + cutoff=cutoff, + rbf=rbf, + envelope=envelope, + ) + self.cbf_basis3 = CircularBasisLayer( + num_spherical, + radial_basis=radial_basis_cbf3, + cbf=cbf, + efficient=True, + ) + ### ------------------------------------------------------------------------------------- ### + + ### ------------------------------- Share Down Projections ------------------------------ ### + # Share down projection across all interaction blocks + self.mlp_rbf3 = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_cbf3 = EfficientInteractionDownProjection( + num_spherical, num_radial, emb_size_cbf + ) + + # Share the dense Layer of the atom embedding block accross the interaction blocks + self.mlp_rbf_h = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_rbf_out = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + ### ------------------------------------------------------------------------------------- ### + + # Embedding block + self.atom_emb = AtomEmbedding(emb_size_atom) + self.edge_emb = EdgeEmbedding( + emb_size_atom, num_radial, emb_size_edge, activation=activation + ) + + out_blocks = [] + int_blocks = [] + + # Interaction Blocks + interaction_block = InteractionBlockTripletsOnly # GemNet-(d)T + for i in range(num_blocks): + int_blocks.append( + interaction_block( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_trip=emb_size_trip, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + emb_size_bil_trip=emb_size_bil_trip, + num_before_skip=num_before_skip, + num_after_skip=num_after_skip, + num_concat=num_concat, + num_atom=num_atom, + activation=activation, + name=f"IntBlock_{i+1}", + ) + ) + + for i in range(num_blocks + 1): + out_blocks.append( + OutputBlock( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=num_atom, + num_targets=num_targets, + activation=activation, + output_init=output_init, + direct_forces=direct_forces, + name=f"OutBlock_{i}", + ) + ) + + self.out_blocks = torch.nn.ModuleList(out_blocks) + self.int_blocks = torch.nn.ModuleList(int_blocks) + + load_scales_compat(self, scale_file) + + def get_triplets(self, edge_index, num_atoms): + """ + Get all b->a for each edge c->a. + It is possible that b=c, as long as the edges are distinct. + + Returns + ------- + id3_ba: torch.Tensor, shape (num_triplets,) + Indices of input edge b->a of each triplet b->a<-c + id3_ca: torch.Tensor, shape (num_triplets,) + Indices of output edge c->a of each triplet b->a<-c + id3_ragged_idx: torch.Tensor, shape (num_triplets,) + Indices enumerating the copies of id3_ca for creating a padded matrix + """ + idx_s, idx_t = edge_index # c->a (source=c, target=a) + + value = torch.arange( + idx_s.size(0), device=idx_s.device, dtype=idx_s.dtype + ) + # Possibly contains multiple copies of the same edge (for periodic interactions) + adj = SparseTensor( + row=idx_t, + col=idx_s, + value=value, + sparse_sizes=(num_atoms, num_atoms), + ) + adj_edges = adj[idx_t] + + # Edge indices (b->a, c->a) for triplets. + id3_ba = adj_edges.storage.value() + id3_ca = adj_edges.storage.row() + + # Remove self-loop triplets + # Compare edge indices, not atom indices to correctly handle periodic interactions + mask = id3_ba != id3_ca + id3_ba = id3_ba[mask] + id3_ca = id3_ca[mask] + + # Get indices to reshape the neighbor indices b->a into a dense matrix. + # id3_ca has to be sorted for this to work. + num_triplets = torch.bincount(id3_ca, minlength=idx_s.size(0)) + id3_ragged_idx = ragged_range(num_triplets) + + return id3_ba, id3_ca, id3_ragged_idx + + def select_symmetric_edges(self, tensor, mask, reorder_idx, inverse_neg): + # Mask out counter-edges + tensor_directed = tensor[mask] + # Concatenate counter-edges after normal edges + sign = 1 - 2 * inverse_neg + tensor_cat = torch.cat([tensor_directed, sign * tensor_directed]) + # Reorder everything so the edges of every image are consecutive + tensor_ordered = tensor_cat[reorder_idx] + return tensor_ordered + + def reorder_symmetric_edges( + self, edge_index, cell_offsets, neighbors, edge_dist, edge_vector + ): + """ + Reorder edges to make finding counter-directional edges easier. + + Some edges are only present in one direction in the data, + since every atom has a maximum number of neighbors. Since we only use i->j + edges here, we lose some j->i edges and add others by + making it symmetric. + We could fix this by merging edge_index with its counter-edges, + including the cell_offsets, and then running torch.unique. + But this does not seem worth it. + """ + + # Generate mask + mask_sep_atoms = edge_index[0] < edge_index[1] + # Distinguish edges between the same (periodic) atom by ordering the cells + cell_earlier = ( + (cell_offsets[:, 0] < 0) + | ((cell_offsets[:, 0] == 0) & (cell_offsets[:, 1] < 0)) + | ( + (cell_offsets[:, 0] == 0) + & (cell_offsets[:, 1] == 0) + & (cell_offsets[:, 2] < 0) + ) + ) + mask_same_atoms = edge_index[0] == edge_index[1] + mask_same_atoms &= cell_earlier + mask = mask_sep_atoms | mask_same_atoms + + # Mask out counter-edges + edge_index_new = edge_index[mask[None, :].expand(2, -1)].view(2, -1) + + # Concatenate counter-edges after normal edges + edge_index_cat = torch.cat( + [ + edge_index_new, + torch.stack([edge_index_new[1], edge_index_new[0]], dim=0), + ], + dim=1, + ) + + # Count remaining edges per image + neighbors = neighbors.to(edge_index.device) + batch_edge = torch.repeat_interleave( + torch.arange(neighbors.size(0), device=edge_index.device), + neighbors, + ) + batch_edge = batch_edge[mask] + neighbors_new = 2 * torch.bincount( + batch_edge, minlength=neighbors.size(0) + ) + + # Create indexing array + edge_reorder_idx = repeat_blocks( + neighbors_new // 2, + repeats=2, + continuous_indexing=True, + repeat_inc=edge_index_new.size(1), + ) + + # Reorder everything so the edges of every image are consecutive + edge_index_new = edge_index_cat[:, edge_reorder_idx] + cell_offsets_new = self.select_symmetric_edges( + cell_offsets, mask, edge_reorder_idx, True + ) + edge_dist_new = self.select_symmetric_edges( + edge_dist, mask, edge_reorder_idx, False + ) + edge_vector_new = self.select_symmetric_edges( + edge_vector, mask, edge_reorder_idx, True + ) + + return ( + edge_index_new, + cell_offsets_new, + neighbors_new, + edge_dist_new, + edge_vector_new, + ) + + def select_edges( + self, + data, + edge_index, + cell_offsets, + neighbors, + edge_dist, + edge_vector, + cutoff=None, + ): + if cutoff is not None: + edge_mask = edge_dist <= cutoff + + edge_index = edge_index[:, edge_mask] + cell_offsets = cell_offsets[edge_mask] + neighbors = mask_neighbors(neighbors, edge_mask) + edge_dist = edge_dist[edge_mask] + edge_vector = edge_vector[edge_mask] + + empty_image = neighbors == 0 + if torch.any(empty_image): + raise ValueError( + f"An image has no neighbors: id={data.id[empty_image]}, " + f"sid={data.sid[empty_image]}, fid={data.fid[empty_image]}" + ) + return edge_index, cell_offsets, neighbors, edge_dist, edge_vector + + def generate_interaction_graph(self, data): + num_atoms = data.atomic_numbers.size(0) + + ( + edge_index, + D_st, + distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + # These vectors actually point in the opposite direction. + # But we want to use col as idx_t for efficient aggregation. + V_st = -distance_vec / D_st[:, None] + + # Mask interaction edges if required + if self.otf_graph or np.isclose(self.cutoff, 6): + select_cutoff = None + else: + select_cutoff = self.cutoff + (edge_index, cell_offsets, neighbors, D_st, V_st,) = self.select_edges( + data=data, + edge_index=edge_index, + cell_offsets=cell_offsets, + neighbors=neighbors, + edge_dist=D_st, + edge_vector=V_st, + cutoff=select_cutoff, + ) + + ( + edge_index, + cell_offsets, + neighbors, + D_st, + V_st, + ) = self.reorder_symmetric_edges( + edge_index, cell_offsets, neighbors, D_st, V_st + ) + + # Indices for swapping c->a and a->c (for symmetric MP) + block_sizes = neighbors // 2 + id_swap = repeat_blocks( + block_sizes, + repeats=2, + continuous_indexing=False, + start_idx=block_sizes[0], + block_inc=block_sizes[:-1] + block_sizes[1:], + repeat_inc=-block_sizes, + ) + + id3_ba, id3_ca, id3_ragged_idx = self.get_triplets( + edge_index, num_atoms=num_atoms + ) + + return ( + edge_index, + neighbors, + D_st, + V_st, + id_swap, + id3_ba, + id3_ca, + id3_ragged_idx, + ) + + @conditional_grad(torch.enable_grad()) + def forward(self, data): + pos = data.pos + batch = data.batch + atomic_numbers = data.atomic_numbers.long() + + if self.regress_forces and not self.direct_forces: + pos.requires_grad_(True) + + ( + edge_index, + neighbors, + D_st, + V_st, + id_swap, + id3_ba, + id3_ca, + id3_ragged_idx, + ) = self.generate_interaction_graph(data) + idx_s, idx_t = edge_index + + # Graph Parallel: Precompute Kmax so all processes have the same value + Kmax = torch.max( + torch.max(id3_ragged_idx) + 1, + torch.tensor(0).to(id3_ragged_idx.device), + ) + + # Graph Parallel: Scatter triplets (consistent with edge splits) + edge_partition = gp_utils.scatter_to_model_parallel_region( + torch.arange(edge_index.size(1)) + ) + triplet_partition = torch.where( + torch.logical_and( + id3_ca >= edge_partition.min(), id3_ca <= edge_partition.max() + ) + )[0] + id3_ba = id3_ba[triplet_partition] + id3_ca = id3_ca[triplet_partition] + id3_ragged_idx = id3_ragged_idx[triplet_partition] + edge_offset = edge_partition.min() + + # Calculate triplet angles + cosφ_cab = inner_product_normalized(V_st[id3_ca], V_st[id3_ba]) + rad_cbf3, cbf3 = self.cbf_basis3(D_st, cosφ_cab, id3_ca) + + # TODO: Only do this for the partitioned edges + cbf3 = self.mlp_cbf3(rad_cbf3, cbf3, id3_ca, id3_ragged_idx, Kmax) + + # Graph Paralllel: Scatter edges + D_st = gp_utils.scatter_to_model_parallel_region(D_st, dim=0) + cbf3 = ( + gp_utils.scatter_to_model_parallel_region(cbf3[0], dim=0), + gp_utils.scatter_to_model_parallel_region(cbf3[1], dim=0), + ) + idx_s = gp_utils.scatter_to_model_parallel_region(idx_s, dim=0) + idx_t_full = idx_t + idx_t = gp_utils.scatter_to_model_parallel_region(idx_t, dim=0) + + rbf = self.radial_basis(D_st) + + # Graph Paralllel: Scatter Nodes + nAtoms = atomic_numbers.shape[0] + if self.scatter_atoms: + atomic_numbers = gp_utils.scatter_to_model_parallel_region( + atomic_numbers, dim=0 + ) + + # Embedding block + h = self.atom_emb(atomic_numbers) + # (nAtoms, emb_size_atom) + m = self.edge_emb(h, rbf, idx_s, idx_t) # (nEdges, emb_size_edge) + + rbf3 = self.mlp_rbf3(rbf) + + rbf_h = self.mlp_rbf_h(rbf) + rbf_out = self.mlp_rbf_out(rbf) + + E_t, F_st = self.out_blocks[0](nAtoms, m, rbf_out, idx_t) + # (nAtoms, num_targets), (nEdges, num_targets) + + for i in range(self.num_blocks): + # Interaction block + h, m = self.int_blocks[i]( + h=h, + m=m, + rbf3=rbf3, + cbf3=cbf3, + id3_ragged_idx=id3_ragged_idx, + id_swap=id_swap, + id3_ba=id3_ba, + id3_ca=id3_ca, + rbf_h=rbf_h, + idx_s=idx_s, + idx_t=idx_t, + edge_offset=edge_offset, + Kmax=Kmax, + nAtoms=nAtoms, + ) # (nAtoms, emb_size_atom), (nEdges, emb_size_edge) + + E, F = self.out_blocks[i + 1](nAtoms, m, rbf_out, idx_t) + # (nAtoms, num_targets), (nEdges, num_targets) + F_st += F + E_t += E + + if self.scale_num_blocks: + F_st = F_st / (self.num_blocks + 1) + E_t = E_t / (self.num_blocks + 1) + + # Graph Parallel: Gather F_st + F_st = gp_utils.gather_from_model_parallel_region(F_st, dim=0) + + nMolecules = torch.max(batch) + 1 + if self.extensive: + E_t = gp_utils.gather_from_model_parallel_region(E_t, dim=0) + E_t = scatter( + E_t, batch, dim=0, dim_size=nMolecules, reduce="add" + ) # (nMolecules, num_targets) + else: + E_t = scatter( + E_t, batch, dim=0, dim_size=nMolecules, reduce="mean" + ) # (nMolecules, num_targets) + + if self.regress_forces: + if self.direct_forces: + # map forces in edge directions + F_st_vec = F_st[:, :, None] * V_st[:, None, :] + # (nEdges, num_targets, 3) + F_t = scatter( + F_st_vec, + idx_t_full, + dim=0, + dim_size=data.atomic_numbers.size(0), + reduce="add", + ) # (nAtoms, num_targets, 3) + F_t = F_t.squeeze(1) # (nAtoms, 3) + else: + if self.num_targets > 1: + forces = [] + for i in range(self.num_targets): + # maybe this can be solved differently + forces += [ + -torch.autograd.grad( + E_t[:, i].sum(), pos, create_graph=True + )[0] + ] + F_t = torch.stack(forces, dim=1) + # (nAtoms, num_targets, 3) + else: + F_t = -torch.autograd.grad( + E_t.sum(), pos, create_graph=True + )[0] + # (nAtoms, 3) + + return E_t, F_t # (nMolecules, num_targets), (nAtoms, 3) + else: + return E_t + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) diff --git a/ocpmodels/models/gemnet_gp/initializers.py b/ocpmodels/models/gemnet_gp/initializers.py new file mode 100644 index 0000000..39a07ce --- /dev/null +++ b/ocpmodels/models/gemnet_gp/initializers.py @@ -0,0 +1,47 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch + + +def _standardize(kernel): + """ + Makes sure that N*Var(W) = 1 and E[W] = 0 + """ + eps = 1e-6 + + if len(kernel.shape) == 3: + axis = [0, 1] # last dimension is output dimension + else: + axis = 1 + + var, mean = torch.var_mean(kernel, dim=axis, unbiased=True, keepdim=True) + kernel = (kernel - mean) / (var + eps) ** 0.5 + return kernel + + +def he_orthogonal_init(tensor): + """ + Generate a weight matrix with variance according to He (Kaiming) initialization. + Based on a random (semi-)orthogonal matrix neural networks + are expected to learn better when features are decorrelated + (stated by eg. "Reducing overfitting in deep networks by decorrelating representations", + "Dropout: a simple way to prevent neural networks from overfitting", + "Exact solutions to the nonlinear dynamics of learning in deep linear neural networks") + """ + tensor = torch.nn.init.orthogonal_(tensor) + + if len(tensor.shape) == 3: + fan_in = tensor.shape[:-1].numel() + else: + fan_in = tensor.shape[1] + + with torch.no_grad(): + tensor.data = _standardize(tensor.data) + tensor.data *= (1 / fan_in) ** 0.5 + + return tensor diff --git a/ocpmodels/models/gemnet_gp/layers/__init__.py b/ocpmodels/models/gemnet_gp/layers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/gemnet_gp/layers/atom_update_block.py b/ocpmodels/models/gemnet_gp/layers/atom_update_block.py new file mode 100644 index 0000000..40593c8 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/layers/atom_update_block.py @@ -0,0 +1,244 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from typing import Optional + +import torch +from torch_scatter import scatter +from torch_scatter.utils import broadcast + +from ocpmodels.common import gp_utils +from ocpmodels.modules.scaling import ScaleFactor + +from ..initializers import he_orthogonal_init +from .base_layers import Dense, ResidualLayer + + +def scatter_sum( + src: torch.Tensor, + index: torch.Tensor, + dim: int = -1, + out: Optional[torch.Tensor] = None, + dim_size: Optional[int] = None, +) -> torch.Tensor: + """ + Clone of torch_scatter.scatter_sum but without in-place operations + """ + index = broadcast(index, src, dim) + if out is None: + size = list(src.size()) + if dim_size is not None: + size[dim] = dim_size + elif index.numel() == 0: + size[dim] = 0 + else: + size[dim] = int(index.max()) + 1 + + out = torch.zeros(size, dtype=src.dtype, device=src.device) + return torch.scatter_add(out, dim, index, src) + else: + return out.scatter_add(dim, index, src) + + +class AtomUpdateBlock(torch.nn.Module): + """ + Aggregate the message embeddings of the atoms + + Parameters + ---------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_atom: int + Embedding size of the edges. + nHidden: int + Number of residual blocks. + activation: callable/str + Name of the activation function to use in the dense layers. + """ + + def __init__( + self, + emb_size_atom: int, + emb_size_edge: int, + emb_size_rbf: int, + nHidden: int, + activation=None, + name: str = "atom_update", + ): + super().__init__() + self.name = name + + self.dense_rbf = Dense( + emb_size_rbf, emb_size_edge, activation=None, bias=False + ) + self.scale_sum = ScaleFactor(name + "_sum") + + self.layers = self.get_mlp( + emb_size_edge, emb_size_atom, nHidden, activation + ) + + def get_mlp(self, units_in, units, nHidden, activation): + dense1 = Dense(units_in, units, activation=activation, bias=False) + mlp = [dense1] + res = [ + ResidualLayer(units, nLayers=2, activation=activation) + for i in range(nHidden) + ] + mlp = mlp + res + return torch.nn.ModuleList(mlp) + + def forward(self, nAtoms, m, rbf, id_j): + """ + Returns + ------- + h: torch.Tensor, shape=(nAtoms, emb_size_atom) + Atom embedding. + """ + mlp_rbf = self.dense_rbf(rbf) # (nEdges, emb_size_edge) + x = m * mlp_rbf + + # Graph Parallel: Local node aggregation + x2 = scatter(x, id_j, dim=0, dim_size=nAtoms, reduce="sum") + + # Graph Parallel: Global node aggregation + x2 = gp_utils.reduce_from_model_parallel_region(x2) + x2 = gp_utils.scatter_to_model_parallel_region(x2, dim=0) + + # (nAtoms, emb_size_edge) + x = self.scale_sum(x2, ref=m) + + for layer in self.layers: + x = layer(x) # (nAtoms, emb_size_atom) + + return x + + +class OutputBlock(AtomUpdateBlock): + """ + Combines the atom update block and subsequent final dense layer. + + Parameters + ---------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_atom: int + Embedding size of the edges. + nHidden: int + Number of residual blocks. + num_targets: int + Number of targets. + activation: str + Name of the activation function to use in the dense layers except for the final dense layer. + direct_forces: bool + If true directly predict forces without taking the gradient of the energy potential. + output_init: int + Kernel initializer of the final dense layer. + """ + + def __init__( + self, + emb_size_atom: int, + emb_size_edge: int, + emb_size_rbf: int, + nHidden: int, + num_targets: int, + activation=None, + direct_forces=True, + output_init="HeOrthogonal", + name: str = "output", + **kwargs, + ): + + super().__init__( + name=name, + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=nHidden, + activation=activation, + **kwargs, + ) + + assert isinstance(output_init, str) + self.output_init = output_init.lower() + self.direct_forces = direct_forces + + self.seq_energy = self.layers # inherited from parent class + self.out_energy = Dense( + emb_size_atom, num_targets, bias=False, activation=None + ) + + if self.direct_forces: + self.scale_rbf_F = ScaleFactor(name + "_had") + self.seq_forces = self.get_mlp( + emb_size_edge, emb_size_edge, nHidden, activation + ) + self.out_forces = Dense( + emb_size_edge, num_targets, bias=False, activation=None + ) + self.dense_rbf_F = Dense( + emb_size_rbf, emb_size_edge, activation=None, bias=False + ) + + self.reset_parameters() + + def reset_parameters(self): + if self.output_init == "heorthogonal": + self.out_energy.reset_parameters(he_orthogonal_init) + if self.direct_forces: + self.out_forces.reset_parameters(he_orthogonal_init) + elif self.output_init == "zeros": + self.out_energy.reset_parameters(torch.nn.init.zeros_) + if self.direct_forces: + self.out_forces.reset_parameters(torch.nn.init.zeros_) + else: + raise UserWarning(f"Unknown output_init: {self.output_init}") + + def forward(self, nAtoms, m, rbf, id_j): + """ + Returns + ------- + (E, F): tuple + - E: torch.Tensor, shape=(nAtoms, num_targets) + - F: torch.Tensor, shape=(nEdges, num_targets) + Energy and force prediction + """ + + # -------------------------------------- Energy Prediction -------------------------------------- # + rbf_emb_E = self.dense_rbf(rbf) # (nEdges, emb_size_edge) + x = m * rbf_emb_E + + # Graph Parallel: Local Node aggregation + x_E = scatter(x, id_j, dim=0, dim_size=nAtoms, reduce="sum") + # Graph Parallel: Global Node aggregation + x_E = gp_utils.reduce_from_model_parallel_region(x_E) + x_E = gp_utils.scatter_to_model_parallel_region(x_E, dim=0) + + # (nAtoms, emb_size_edge) + x_E = self.scale_sum(x_E, ref=m) + + for layer in self.seq_energy: + x_E = layer(x_E) # (nAtoms, emb_size_atom) + + x_E = self.out_energy(x_E) # (nAtoms, num_targets) + + # --------------------------------------- Force Prediction -------------------------------------- # + if self.direct_forces: + x_F = m + for i, layer in enumerate(self.seq_forces): + x_F = layer(x_F) # (nEdges, emb_size_edge) + + rbf_emb_F = self.dense_rbf_F(rbf) # (nEdges, emb_size_edge) + x_F_rbf = x_F * rbf_emb_F + x_F = self.scale_rbf_F(x_F_rbf, ref=x_F) + + x_F = self.out_forces(x_F) # (nEdges, num_targets) + else: + x_F = 0 + # ----------------------------------------------------------------------------------------------- # + + return x_E, x_F diff --git a/ocpmodels/models/gemnet_gp/layers/base_layers.py b/ocpmodels/models/gemnet_gp/layers/base_layers.py new file mode 100644 index 0000000..4c6aa07 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/layers/base_layers.py @@ -0,0 +1,113 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import torch + +from ..initializers import he_orthogonal_init + + +class Dense(torch.nn.Module): + """ + Combines dense layer with scaling for swish activation. + + Parameters + ---------- + units: int + Output embedding size. + activation: str + Name of the activation function to use. + bias: bool + True if use bias. + """ + + def __init__(self, in_features, out_features, bias=False, activation=None): + super().__init__() + + self.linear = torch.nn.Linear(in_features, out_features, bias=bias) + self.reset_parameters() + + if isinstance(activation, str): + activation = activation.lower() + if activation in ["swish", "silu"]: + self._activation = ScaledSiLU() + elif activation == "siqu": + self._activation = SiQU() + elif activation is None: + self._activation = torch.nn.Identity() + else: + raise NotImplementedError( + "Activation function not implemented for GemNet (yet)." + ) + + def reset_parameters(self, initializer=he_orthogonal_init): + initializer(self.linear.weight) + if self.linear.bias is not None: + self.linear.bias.data.fill_(0) + + def forward(self, x): + x = self.linear(x) + x = self._activation(x) + return x + + +class ScaledSiLU(torch.nn.Module): + def __init__(self): + super().__init__() + self.scale_factor = 1 / 0.6 + self._activation = torch.nn.SiLU() + + def forward(self, x): + return self._activation(x) * self.scale_factor + + +class SiQU(torch.nn.Module): + def __init__(self): + super().__init__() + self._activation = torch.nn.SiLU() + + def forward(self, x): + return x * self._activation(x) + + +class ResidualLayer(torch.nn.Module): + """ + Residual block with output scaled by 1/sqrt(2). + + Parameters + ---------- + units: int + Output embedding size. + nLayers: int + Number of dense layers. + layer_kwargs: str + Keyword arguments for initializing the layers. + """ + + def __init__( + self, units: int, nLayers: int = 2, layer=Dense, **layer_kwargs + ): + super().__init__() + self.dense_mlp = torch.nn.Sequential( + *[ + layer( + in_features=units, + out_features=units, + bias=False, + **layer_kwargs + ) + for _ in range(nLayers) + ] + ) + self.inv_sqrt_2 = 1 / math.sqrt(2) + + def forward(self, input): + x = self.dense_mlp(input) + x = input + x + x = x * self.inv_sqrt_2 + return x diff --git a/ocpmodels/models/gemnet_gp/layers/basis_utils.py b/ocpmodels/models/gemnet_gp/layers/basis_utils.py new file mode 100644 index 0000000..b623a40 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/layers/basis_utils.py @@ -0,0 +1,288 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import sympy as sym +from scipy import special as sp +from scipy.optimize import brentq + + +def Jn(r, n): + """ + numerical spherical bessel functions of order n + """ + return sp.spherical_jn(n, r) + + +def Jn_zeros(n, k): + """ + Compute the first k zeros of the spherical bessel functions up to order n (excluded) + """ + zerosj = np.zeros((n, k), dtype="float32") + zerosj[0] = np.arange(1, k + 1) * np.pi + points = np.arange(1, k + n) * np.pi + racines = np.zeros(k + n - 1, dtype="float32") + for i in range(1, n): + for j in range(k + n - 1 - i): + foo = brentq(Jn, points[j], points[j + 1], (i,)) + racines[j] = foo + points = racines + zerosj[i][:k] = racines[:k] + + return zerosj + + +def spherical_bessel_formulas(n): + """ + Computes the sympy formulas for the spherical bessel functions up to order n (excluded) + """ + x = sym.symbols("x") + # j_i = (-x)^i * (1/x * d/dx)^î * sin(x)/x + j = [sym.sin(x) / x] # j_0 + a = sym.sin(x) / x + for i in range(1, n): + b = sym.diff(a, x) / x + j += [sym.simplify(b * (-x) ** i)] + a = sym.simplify(b) + return j + + +def bessel_basis(n, k): + """ + Compute the sympy formulas for the normalized and rescaled spherical bessel functions up to + order n (excluded) and maximum frequency k (excluded). + + Returns: + bess_basis: list + Bessel basis formulas taking in a single argument x. + Has length n where each element has length k. -> In total n*k many. + """ + zeros = Jn_zeros(n, k) + normalizer = [] + for order in range(n): + normalizer_tmp = [] + for i in range(k): + normalizer_tmp += [0.5 * Jn(zeros[order, i], order + 1) ** 2] + normalizer_tmp = ( + 1 / np.array(normalizer_tmp) ** 0.5 + ) # sqrt(2/(j_l+1)**2) , sqrt(1/c**3) not taken into account yet + normalizer += [normalizer_tmp] + + f = spherical_bessel_formulas(n) + x = sym.symbols("x") + bess_basis = [] + for order in range(n): + bess_basis_tmp = [] + for i in range(k): + bess_basis_tmp += [ + sym.simplify( + normalizer[order][i] + * f[order].subs(x, zeros[order, i] * x) + ) + ] + bess_basis += [bess_basis_tmp] + return bess_basis + + +def sph_harm_prefactor(l_degree, m_order): + """Computes the constant pre-factor for the spherical harmonic of degree l and order m. + + Parameters + ---------- + l_degree: int + Degree of the spherical harmonic. l >= 0 + m_order: int + Order of the spherical harmonic. -l <= m <= l + + Returns + ------- + factor: float + + """ + # sqrt((2*l+1)/4*pi * (l-m)!/(l+m)! ) + return ( + (2 * l_degree + 1) + / (4 * np.pi) + * np.math.factorial(l_degree - abs(m_order)) + / np.math.factorial(l_degree + abs(m_order)) + ) ** 0.5 + + +def associated_legendre_polynomials( + L_maxdegree, zero_m_only=True, pos_m_only=True +): + """Computes string formulas of the associated legendre polynomials up to degree L (excluded). + + Parameters + ---------- + L_maxdegree: int + Degree up to which to calculate the associated legendre polynomials (degree L is excluded). + zero_m_only: bool + If True only calculate the polynomials for the polynomials where m=0. + pos_m_only: bool + If True only calculate the polynomials for the polynomials where m>=0. Overwritten by zero_m_only. + + Returns + ------- + polynomials: list + Contains the sympy functions of the polynomials (in total L many if zero_m_only is True else L^2 many). + """ + # calculations from http://web.cmb.usc.edu/people/alber/Software/tomominer/docs/cpp/group__legendre__polynomials.html + z = sym.symbols("z") + P_l_m = [ + [0] * (2 * l_degree + 1) for l_degree in range(L_maxdegree) + ] # for order l: -l <= m <= l + + P_l_m[0][0] = 1 + if L_maxdegree > 0: + if zero_m_only: + # m = 0 + P_l_m[1][0] = z + for l_degree in range(2, L_maxdegree): + P_l_m[l_degree][0] = sym.simplify( + ( + (2 * l_degree - 1) * z * P_l_m[l_degree - 1][0] + - (l_degree - 1) * P_l_m[l_degree - 2][0] + ) + / l_degree + ) + return P_l_m + else: + # for m >= 0 + for l_degree in range(1, L_maxdegree): + P_l_m[l_degree][l_degree] = sym.simplify( + (1 - 2 * l_degree) + * (1 - z**2) ** 0.5 + * P_l_m[l_degree - 1][l_degree - 1] + ) # P_00, P_11, P_22, P_33 + + for m_order in range(0, L_maxdegree - 1): + P_l_m[m_order + 1][m_order] = sym.simplify( + (2 * m_order + 1) * z * P_l_m[m_order][m_order] + ) # P_10, P_21, P_32, P_43 + + for l_degree in range(2, L_maxdegree): + for m_order in range(l_degree - 1): # P_20, P_30, P_31 + P_l_m[l_degree][m_order] = sym.simplify( + ( + (2 * l_degree - 1) + * z + * P_l_m[l_degree - 1][m_order] + - (l_degree + m_order - 1) + * P_l_m[l_degree - 2][m_order] + ) + / (l_degree - m_order) + ) + + if not pos_m_only: + # for m < 0: P_l(-m) = (-1)^m * (l-m)!/(l+m)! * P_lm + for l_degree in range(1, L_maxdegree): + for m_order in range( + 1, l_degree + 1 + ): # P_1(-1), P_2(-1) P_2(-2) + P_l_m[l_degree][-m_order] = sym.simplify( + (-1) ** m_order + * np.math.factorial(l_degree - m_order) + / np.math.factorial(l_degree + m_order) + * P_l_m[l_degree][m_order] + ) + + return P_l_m + + +def real_sph_harm(L_maxdegree, use_theta, use_phi=True, zero_m_only=True): + """ + Computes formula strings of the the real part of the spherical harmonics up to degree L (excluded). + Variables are either spherical coordinates phi and theta (or cartesian coordinates x,y,z) on the UNIT SPHERE. + + Parameters + ---------- + L_maxdegree: int + Degree up to which to calculate the spherical harmonics (degree L is excluded). + use_theta: bool + - True: Expects the input of the formula strings to contain theta. + - False: Expects the input of the formula strings to contain z. + use_phi: bool + - True: Expects the input of the formula strings to contain phi. + - False: Expects the input of the formula strings to contain x and y. + Does nothing if zero_m_only is True + zero_m_only: bool + If True only calculate the harmonics where m=0. + + Returns + ------- + Y_lm_real: list + Computes formula strings of the the real part of the spherical harmonics up + to degree L (where degree L is not excluded). + In total L^2 many sph harm exist up to degree L (excluded). However, if zero_m_only only is True then + the total count is reduced to be only L many. + """ + z = sym.symbols("z") + P_l_m = associated_legendre_polynomials(L_maxdegree, zero_m_only) + if zero_m_only: + # for all m != 0: Y_lm = 0 + Y_l_m = [[0] for l_degree in range(L_maxdegree)] + else: + Y_l_m = [ + [0] * (2 * l_degree + 1) for l_degree in range(L_maxdegree) + ] # for order l: -l <= m <= l + + # convert expressions to spherical coordiantes + if use_theta: + # replace z by cos(theta) + theta = sym.symbols("theta") + for l_degree in range(L_maxdegree): + for m_order in range(len(P_l_m[l_degree])): + if not isinstance(P_l_m[l_degree][m_order], int): + P_l_m[l_degree][m_order] = P_l_m[l_degree][m_order].subs( + z, sym.cos(theta) + ) + + ## calculate Y_lm + # Y_lm = N * P_lm(cos(theta)) * exp(i*m*phi) + # { sqrt(2) * (-1)^m * N * P_l|m| * sin(|m|*phi) if m < 0 + # Y_lm_real = { Y_lm if m = 0 + # { sqrt(2) * (-1)^m * N * P_lm * cos(m*phi) if m > 0 + + for l_degree in range(L_maxdegree): + Y_l_m[l_degree][0] = sym.simplify( + sph_harm_prefactor(l_degree, 0) * P_l_m[l_degree][0] + ) # Y_l0 + + if not zero_m_only: + phi = sym.symbols("phi") + for l_degree in range(1, L_maxdegree): + # m > 0 + for m_order in range(1, l_degree + 1): + Y_l_m[l_degree][m_order] = sym.simplify( + 2**0.5 + * (-1) ** m_order + * sph_harm_prefactor(l_degree, m_order) + * P_l_m[l_degree][m_order] + * sym.cos(m_order * phi) + ) + # m < 0 + for m_order in range(1, l_degree + 1): + Y_l_m[l_degree][-m_order] = sym.simplify( + 2**0.5 + * (-1) ** m_order + * sph_harm_prefactor(l_degree, -m_order) + * P_l_m[l_degree][m_order] + * sym.sin(m_order * phi) + ) + + # convert expressions to cartesian coordinates + if not use_phi: + # replace phi by atan2(y,x) + x = sym.symbols("x") + y = sym.symbols("y") + for l_degree in range(L_maxdegree): + for m_order in range(len(Y_l_m[l_degree])): + Y_l_m[l_degree][m_order] = sym.simplify( + Y_l_m[l_degree][m_order].subs(phi, sym.atan2(y, x)) + ) + return Y_l_m diff --git a/ocpmodels/models/gemnet_gp/layers/efficient.py b/ocpmodels/models/gemnet_gp/layers/efficient.py new file mode 100644 index 0000000..e049d01 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/layers/efficient.py @@ -0,0 +1,158 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch + +from ..initializers import he_orthogonal_init + + +class EfficientInteractionDownProjection(torch.nn.Module): + """ + Down projection in the efficient reformulation. + + Parameters + ---------- + emb_size_interm: int + Intermediate embedding size (down-projection size). + kernel_initializer: callable + Initializer of the weight matrix. + """ + + def __init__( + self, + num_spherical: int, + num_radial: int, + emb_size_interm: int, + ): + super().__init__() + + self.num_spherical = num_spherical + self.num_radial = num_radial + self.emb_size_interm = emb_size_interm + + self.reset_parameters() + + def reset_parameters(self): + self.weight = torch.nn.Parameter( + torch.empty( + (self.num_spherical, self.num_radial, self.emb_size_interm) + ), + requires_grad=True, + ) + he_orthogonal_init(self.weight) + + def forward(self, rbf, sph, id_ca, id_ragged_idx, Kmax): + """ + + Arguments + --------- + rbf: torch.Tensor, shape=(1, nEdges, num_radial) + sph: torch.Tensor, shape=(nEdges, Kmax, num_spherical) + id_ca + id_ragged_idx + + Returns + ------- + rbf_W1: torch.Tensor, shape=(nEdges, emb_size_interm, num_spherical) + sph: torch.Tensor, shape=(nEdges, Kmax, num_spherical) + Kmax = maximum number of neighbors of the edges + """ + num_edges = rbf.shape[1] + + # MatMul: mul + sum over num_radial + rbf_W1 = torch.matmul(rbf, self.weight) + # (num_spherical, nEdges , emb_size_interm) + rbf_W1 = rbf_W1.permute(1, 2, 0) + # (nEdges, emb_size_interm, num_spherical) + + # Zero padded dense matrix + # maximum number of neighbors, catch empty id_ca with maximum + if sph.shape[0] == 0: + Kmax = 0 + + sph2 = sph.new_zeros(num_edges, Kmax, self.num_spherical) + sph2[id_ca, id_ragged_idx] = sph + + sph2 = torch.transpose(sph2, 1, 2) + # (nEdges, num_spherical/emb_size_interm, Kmax) + + return rbf_W1, sph2 + + +class EfficientInteractionBilinear(torch.nn.Module): + """ + Efficient reformulation of the bilinear layer and subsequent summation. + + Parameters + ---------- + units_out: int + Embedding output size of the bilinear layer. + kernel_initializer: callable + Initializer of the weight matrix. + """ + + def __init__( + self, + emb_size: int, + emb_size_interm: int, + units_out: int, + ): + super().__init__() + self.emb_size = emb_size + self.emb_size_interm = emb_size_interm + self.units_out = units_out + + self.reset_parameters() + + def reset_parameters(self): + self.weight = torch.nn.Parameter( + torch.empty( + (self.emb_size, self.emb_size_interm, self.units_out), + requires_grad=True, + ) + ) + he_orthogonal_init(self.weight) + + def forward(self, basis, m, id_reduce, id_ragged_idx, edge_offset, Kmax): + """ + + Arguments + --------- + basis + m: quadruplets: m = m_db , triplets: m = m_ba + id_reduce + id_ragged_idx + + Returns + ------- + m_ca: torch.Tensor, shape=(nEdges, units_out) + Edge embeddings. + """ + # num_spherical is actually num_spherical**2 for quadruplets + (rbf_W1, sph) = basis + # (nEdges, emb_size_interm, num_spherical), (nEdges, num_spherical, Kmax) + nEdges = rbf_W1.shape[0] + + # Create (zero-padded) dense matrix of the neighboring edge embeddings. + # maximum number of neighbors, catch empty id_reduce_ji with maximum + m2 = m.new_zeros(nEdges, Kmax, self.emb_size) + m2[id_reduce - edge_offset, id_ragged_idx] = m + # (num_quadruplets or num_triplets, emb_size) -> (nEdges, Kmax, emb_size) + + sum_k = torch.matmul(sph, m2) # (nEdges, num_spherical, emb_size) + + # MatMul: mul + sum over num_spherical + rbf_W1_sum_k = torch.matmul(rbf_W1, sum_k) + # (nEdges, emb_size_interm, emb_size) + + # Bilinear: Sum over emb_size_interm and emb_size + m_ca = torch.matmul(rbf_W1_sum_k.permute(2, 0, 1), self.weight) + # (emb_size, nEdges, units_out) + m_ca = torch.sum(m_ca, dim=0) + # (nEdges, units_out) + + return m_ca diff --git a/ocpmodels/models/gemnet_gp/layers/embedding_block.py b/ocpmodels/models/gemnet_gp/layers/embedding_block.py new file mode 100644 index 0000000..2791619 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/layers/embedding_block.py @@ -0,0 +1,103 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch + +from ocpmodels.common import gp_utils + +from .base_layers import Dense + + +class AtomEmbedding(torch.nn.Module): + """ + Initial atom embeddings based on the atom type + + Parameters + ---------- + emb_size: int + Atom embeddings size + """ + + def __init__(self, emb_size): + super().__init__() + self.emb_size = emb_size + + # Atom embeddings: We go up to Bi (83). + self.embeddings = torch.nn.Embedding(83, emb_size) + # init by uniform distribution + torch.nn.init.uniform_( + self.embeddings.weight, a=-np.sqrt(3), b=np.sqrt(3) + ) + + def forward(self, Z): + """ + Returns + ------- + h: torch.Tensor, shape=(nAtoms, emb_size) + Atom embeddings. + """ + h = self.embeddings(Z - 1) # -1 because Z.min()=1 (==Hydrogen) + return h + + +class EdgeEmbedding(torch.nn.Module): + """ + Edge embedding based on the concatenation of atom embeddings and subsequent dense layer. + + Parameters + ---------- + emb_size: int + Embedding size after the dense layer. + activation: str + Activation function used in the dense layer. + """ + + def __init__( + self, + atom_features, + edge_features, + out_features, + activation=None, + ): + super().__init__() + in_features = 2 * atom_features + edge_features + self.dense = Dense( + in_features, out_features, activation=activation, bias=False + ) + + def forward( + self, + h, + m_rbf, + idx_s, + idx_t, + ): + """ + + Arguments + --------- + h + m_rbf: shape (nEdges, nFeatures) + in embedding block: m_rbf = rbf ; In interaction block: m_rbf = m_st + idx_s + idx_t + + Returns + ------- + m_st: torch.Tensor, shape=(nEdges, emb_size) + Edge embeddings. + """ + h = gp_utils.gather_from_model_parallel_region(h, dim=0) + h_s = h[idx_s] # shape=(nEdges, emb_size) + h_t = h[idx_t] # shape=(nEdges, emb_size) + + m_st = torch.cat( + [h_s, h_t, m_rbf], dim=-1 + ) # (nEdges, 2*emb_size+nFeatures) + m_st = self.dense(m_st) # (nEdges, emb_size) + return m_st diff --git a/ocpmodels/models/gemnet_gp/layers/interaction_block.py b/ocpmodels/models/gemnet_gp/layers/interaction_block.py new file mode 100644 index 0000000..b77da76 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/layers/interaction_block.py @@ -0,0 +1,360 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import torch + +from ocpmodels.common import gp_utils +from ocpmodels.modules.scaling import ScaleFactor + +from .atom_update_block import AtomUpdateBlock +from .base_layers import Dense, ResidualLayer +from .efficient import EfficientInteractionBilinear +from .embedding_block import EdgeEmbedding + + +class InteractionBlockTripletsOnly(torch.nn.Module): + """ + Interaction block for GemNet-T/dT. + + Parameters + ---------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_edge: int + Embedding size of the edges. + emb_size_trip: int + (Down-projected) Embedding size in the triplet message passing block. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + + emb_size_bil_trip: int + Embedding size of the edge embeddings in the triplet-based message passing block after the bilinear layer. + num_before_skip: int + Number of residual blocks before the first skip connection. + num_after_skip: int + Number of residual blocks after the first skip connection. + num_concat: int + Number of residual blocks after the concatenation. + num_atom: int + Number of residual blocks in the atom embedding blocks. + + activation: str + Name of the activation function to use in the dense layers except for the final dense layer. + """ + + def __init__( + self, + emb_size_atom, + emb_size_edge, + emb_size_trip, + emb_size_rbf, + emb_size_cbf, + emb_size_bil_trip, + num_before_skip, + num_after_skip, + num_concat, + num_atom, + activation=None, + name="Interaction", + ): + super().__init__() + self.name = name + + block_nr = name.split("_")[-1] + + ## -------------------------------------------- Message Passing ------------------------------------------- ## + # Dense transformation of skip connection + self.dense_ca = Dense( + emb_size_edge, + emb_size_edge, + activation=activation, + bias=False, + ) + + # Triplet Interaction + self.trip_interaction = TripletInteraction( + emb_size_edge=emb_size_edge, + emb_size_trip=emb_size_trip, + emb_size_bilinear=emb_size_bil_trip, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + activation=activation, + name=f"TripInteraction_{block_nr}", + ) + + ## ---------------------------------------- Update Edge Embeddings ---------------------------------------- ## + # Residual layers before skip connection + self.layers_before_skip = torch.nn.ModuleList( + [ + ResidualLayer( + emb_size_edge, + activation=activation, + ) + for i in range(num_before_skip) + ] + ) + + # Residual layers after skip connection + self.layers_after_skip = torch.nn.ModuleList( + [ + ResidualLayer( + emb_size_edge, + activation=activation, + ) + for i in range(num_after_skip) + ] + ) + + ## ---------------------------------------- Update Atom Embeddings ---------------------------------------- ## + self.atom_update = AtomUpdateBlock( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=num_atom, + activation=activation, + name=f"AtomUpdate_{block_nr}", + ) + + ## ------------------------------ Update Edge Embeddings with Atom Embeddings ----------------------------- ## + self.concat_layer = EdgeEmbedding( + emb_size_atom, + emb_size_edge, + emb_size_edge, + activation=activation, + ) + self.residual_m = torch.nn.ModuleList( + [ + ResidualLayer(emb_size_edge, activation=activation) + for _ in range(num_concat) + ] + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + + def forward( + self, + h, + m, + rbf3, + cbf3, + id3_ragged_idx, + id_swap, + id3_ba, + id3_ca, + rbf_h, + idx_s, + idx_t, + edge_offset, + Kmax, + nAtoms, + ): + """ + Returns + ------- + h: torch.Tensor, shape=(nEdges, emb_size_atom) + Atom embeddings. + m: torch.Tensor, shape=(nEdges, emb_size_edge) + Edge embeddings (c->a). + Node: h + Edge: m, rbf3, id_swap, rbf_h, idx_s, idx_t, cbf3[0], cbf3[1] (dense) + Triplet: id3_ragged_idx, id3_ba, id3_ca + """ + # Initial transformation + x_ca_skip = self.dense_ca(m) # (nEdges, emb_size_edge) + + x3 = self.trip_interaction( + m, + rbf3, + cbf3, + id3_ragged_idx, + id_swap, + id3_ba, + id3_ca, + edge_offset, + Kmax, + ) + + ## ----------------------------- Merge Embeddings after Triplet Interaction ------------------------------ ## + x = x_ca_skip + x3 # (nEdges, emb_size_edge) + x = x * self.inv_sqrt_2 + + ## ---------------------------------------- Update Edge Embeddings --------------------------------------- ## + # Transformations before skip connection + for i, layer in enumerate(self.layers_before_skip): + x = layer(x) # (nEdges, emb_size_edge) + + # Skip connection + m = m + x # (nEdges, emb_size_edge) + m = m * self.inv_sqrt_2 + + # Transformations after skip connection + for i, layer in enumerate(self.layers_after_skip): + m = layer(m) # (nEdges, emb_size_edge) + + ## ---------------------------------------- Update Atom Embeddings --------------------------------------- ## + h2 = self.atom_update(nAtoms, m, rbf_h, idx_t) + + # Skip connection + h = h + h2 # (nAtoms, emb_size_atom) + h = h * self.inv_sqrt_2 + + ## ----------------------------- Update Edge Embeddings with Atom Embeddings ----------------------------- ## + m2 = self.concat_layer(h, m, idx_s, idx_t) # (nEdges, emb_size_edge) + + for i, layer in enumerate(self.residual_m): + m2 = layer(m2) # (nEdges, emb_size_edge) + + # Skip connection + m = m + m2 # (nEdges, emb_size_edge) + m = m * self.inv_sqrt_2 + return h, m + + +class TripletInteraction(torch.nn.Module): + """ + Triplet-based message passing block. + + Parameters + ---------- + emb_size_edge: int + Embedding size of the edges. + emb_size_trip: int + (Down-projected) Embedding size of the edge embeddings after the hadamard product with rbf. + emb_size_bilinear: int + Embedding size of the edge embeddings after the bilinear layer. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + + activation: str + Name of the activation function to use in the dense layers except for the final dense layer. + """ + + def __init__( + self, + emb_size_edge, + emb_size_trip, + emb_size_bilinear, + emb_size_rbf, + emb_size_cbf, + activation=None, + name="TripletInteraction", + **kwargs, + ): + super().__init__() + self.name = name + + # Dense transformation + self.dense_ba = Dense( + emb_size_edge, + emb_size_edge, + activation=activation, + bias=False, + ) + + # Up projections of basis representations, bilinear layer and scaling factors + self.mlp_rbf = Dense( + emb_size_rbf, + emb_size_edge, + activation=None, + bias=False, + ) + self.scale_rbf = ScaleFactor(name + "_had_rbf") + + self.mlp_cbf = EfficientInteractionBilinear( + emb_size_trip, emb_size_cbf, emb_size_bilinear + ) + + # combines scaling for bilinear layer and summation + self.scale_cbf_sum = ScaleFactor(name + "_sum_cbf") + + # Down and up projections + self.down_projection = Dense( + emb_size_edge, + emb_size_trip, + activation=activation, + bias=False, + ) + self.up_projection_ca = Dense( + emb_size_bilinear, + emb_size_edge, + activation=activation, + bias=False, + ) + self.up_projection_ac = Dense( + emb_size_bilinear, + emb_size_edge, + activation=activation, + bias=False, + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + + def forward( + self, + m, + rbf3, + cbf3, + id3_ragged_idx, + id_swap, + id3_ba, + id3_ca, + edge_offset, + Kmax, + ): + """ + Returns + ------- + m: torch.Tensor, shape=(nEdges, emb_size_edge) + Edge embeddings (c->a). + """ + + # Dense transformation + x_ba = self.dense_ba(m) # (nEdges, emb_size_edge) + + # Transform via radial bessel basis + rbf_emb = self.mlp_rbf(rbf3) # (nEdges, emb_size_edge) + x_ba2 = x_ba * rbf_emb + x_ba = self.scale_rbf(x_ba2, ref=x_ba) + + x_ba = self.down_projection(x_ba) # (nEdges, emb_size_trip) + + # Graph Parallel: Gather x_ba from all nodes + x_ba = gp_utils.gather_from_model_parallel_region(x_ba, dim=0) + + # Transform via circular spherical basis + x_ba = x_ba[id3_ba] + + # Efficient bilinear layer + x = self.mlp_cbf(cbf3, x_ba, id3_ca, id3_ragged_idx, edge_offset, Kmax) + # (nEdges, emb_size_quad) + x = self.scale_cbf_sum(x, ref=x_ba) + + # => + # rbf(d_ba) + # cbf(d_ca, angle_cab) + + # Up project embeddings + x_ca = self.up_projection_ca(x) # (nEdges, emb_size_edge) + x_ac = self.up_projection_ac(x) # (nEdges, emb_size_edge) + + # Graph Parallel: Gather x_ac from all nodes + x_ac = gp_utils.gather_from_model_parallel_region(x_ac, dim=0) + + # Merge interaction of c->a and a->c + x_ac = x_ac[id_swap] # swap to add to edge a->c and not c->a + x_ac = gp_utils.scatter_to_model_parallel_region(x_ac, dim=0) + + x3 = x_ca + x_ac + x3 = x3 * self.inv_sqrt_2 + + return x3 diff --git a/ocpmodels/models/gemnet_gp/layers/radial_basis.py b/ocpmodels/models/gemnet_gp/layers/radial_basis.py new file mode 100644 index 0000000..3f030fc --- /dev/null +++ b/ocpmodels/models/gemnet_gp/layers/radial_basis.py @@ -0,0 +1,206 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import numpy as np +import torch +from scipy.special import binom +from torch_geometric.nn.models.schnet import GaussianSmearing + + +class PolynomialEnvelope(torch.nn.Module): + """ + Polynomial envelope function that ensures a smooth cutoff. + + Parameters + ---------- + exponent: int + Exponent of the envelope function. + """ + + def __init__(self, exponent): + super().__init__() + assert exponent > 0 + self.p = exponent + self.a = -(self.p + 1) * (self.p + 2) / 2 + self.b = self.p * (self.p + 2) + self.c = -self.p * (self.p + 1) / 2 + + def forward(self, d_scaled): + env_val = ( + 1 + + self.a * d_scaled**self.p + + self.b * d_scaled ** (self.p + 1) + + self.c * d_scaled ** (self.p + 2) + ) + return torch.where(d_scaled < 1, env_val, torch.zeros_like(d_scaled)) + + +class ExponentialEnvelope(torch.nn.Module): + """ + Exponential envelope function that ensures a smooth cutoff, + as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021. + SpookyNet: Learning Force Fields with Electronic Degrees of Freedom + and Nonlocal Effects + """ + + def __init__(self): + super().__init__() + + def forward(self, d_scaled): + env_val = torch.exp( + -(d_scaled**2) / ((1 - d_scaled) * (1 + d_scaled)) + ) + return torch.where(d_scaled < 1, env_val, torch.zeros_like(d_scaled)) + + +class SphericalBesselBasis(torch.nn.Module): + """ + 1D spherical Bessel basis + + Parameters + ---------- + num_radial: int + Controls maximum frequency. + cutoff: float + Cutoff distance in Angstrom. + """ + + def __init__( + self, + num_radial: int, + cutoff: float, + ): + super().__init__() + self.norm_const = math.sqrt(2 / (cutoff**3)) + # cutoff ** 3 to counteract dividing by d_scaled = d / cutoff + + # Initialize frequencies at canonical positions + self.frequencies = torch.nn.Parameter( + data=torch.tensor( + np.pi * np.arange(1, num_radial + 1, dtype=np.float32) + ), + requires_grad=True, + ) + + def forward(self, d_scaled): + return ( + self.norm_const + / d_scaled[:, None] + * torch.sin(self.frequencies * d_scaled[:, None]) + ) # (num_edges, num_radial) + + +class BernsteinBasis(torch.nn.Module): + """ + Bernstein polynomial basis, + as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021. + SpookyNet: Learning Force Fields with Electronic Degrees of Freedom + and Nonlocal Effects + + Parameters + ---------- + num_radial: int + Controls maximum frequency. + pregamma_initial: float + Initial value of exponential coefficient gamma. + Default: gamma = 0.5 * a_0**-1 = 0.94486, + inverse softplus -> pregamma = log e**gamma - 1 = 0.45264 + """ + + def __init__( + self, + num_radial: int, + pregamma_initial: float = 0.45264, + ): + super().__init__() + prefactor = binom(num_radial - 1, np.arange(num_radial)) + self.register_buffer( + "prefactor", + torch.tensor(prefactor, dtype=torch.float), + persistent=False, + ) + + self.pregamma = torch.nn.Parameter( + data=torch.tensor(pregamma_initial, dtype=torch.float), + requires_grad=True, + ) + self.softplus = torch.nn.Softplus() + + exp1 = torch.arange(num_radial) + self.register_buffer("exp1", exp1[None, :], persistent=False) + exp2 = num_radial - 1 - exp1 + self.register_buffer("exp2", exp2[None, :], persistent=False) + + def forward(self, d_scaled): + gamma = self.softplus(self.pregamma) # constrain to positive + exp_d = torch.exp(-gamma * d_scaled)[:, None] + return ( + self.prefactor * (exp_d**self.exp1) * ((1 - exp_d) ** self.exp2) + ) + + +class RadialBasis(torch.nn.Module): + """ + + Parameters + ---------- + num_radial: int + Controls maximum frequency. + cutoff: float + Cutoff distance in Angstrom. + rbf: dict = {"name": "gaussian"} + Basis function and its hyperparameters. + envelope: dict = {"name": "polynomial", "exponent": 5} + Envelope function and its hyperparameters. + """ + + def __init__( + self, + num_radial: int, + cutoff: float, + rbf: dict = {"name": "gaussian"}, + envelope: dict = {"name": "polynomial", "exponent": 5}, + ): + super().__init__() + self.inv_cutoff = 1 / cutoff + + env_name = envelope["name"].lower() + env_hparams = envelope.copy() + del env_hparams["name"] + + if env_name == "polynomial": + self.envelope = PolynomialEnvelope(**env_hparams) + elif env_name == "exponential": + self.envelope = ExponentialEnvelope(**env_hparams) + else: + raise ValueError(f"Unknown envelope function '{env_name}'.") + + rbf_name = rbf["name"].lower() + rbf_hparams = rbf.copy() + del rbf_hparams["name"] + + # RBFs get distances scaled to be in [0, 1] + if rbf_name == "gaussian": + self.rbf = GaussianSmearing( + start=0, stop=1, num_gaussians=num_radial, **rbf_hparams + ) + elif rbf_name == "spherical_bessel": + self.rbf = SphericalBesselBasis( + num_radial=num_radial, cutoff=cutoff, **rbf_hparams + ) + elif rbf_name == "bernstein": + self.rbf = BernsteinBasis(num_radial=num_radial, **rbf_hparams) + else: + raise ValueError(f"Unknown radial basis function '{rbf_name}'.") + + def forward(self, d): + d_scaled = d * self.inv_cutoff + + env = self.envelope(d_scaled) + return env[:, None] * self.rbf(d_scaled) # (nEdges, num_radial) diff --git a/ocpmodels/models/gemnet_gp/layers/spherical_basis.py b/ocpmodels/models/gemnet_gp/layers/spherical_basis.py new file mode 100644 index 0000000..21add78 --- /dev/null +++ b/ocpmodels/models/gemnet_gp/layers/spherical_basis.py @@ -0,0 +1,95 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import sympy as sym +import torch +from torch_geometric.nn.models.schnet import GaussianSmearing + +from .basis_utils import real_sph_harm +from .radial_basis import RadialBasis + + +class CircularBasisLayer(torch.nn.Module): + """ + 2D Fourier Bessel Basis + + Parameters + ---------- + num_spherical: int + Controls maximum frequency. + radial_basis: RadialBasis + Radial basis functions + cbf: dict + Name and hyperparameters of the cosine basis function + efficient: bool + Whether to use the "efficient" summation order + """ + + def __init__( + self, + num_spherical: int, + radial_basis: RadialBasis, + cbf: str, + efficient: bool = False, + ): + super().__init__() + + self.radial_basis = radial_basis + self.efficient = efficient + + cbf_name = cbf["name"].lower() + cbf_hparams = cbf.copy() + del cbf_hparams["name"] + + if cbf_name == "gaussian": + self.cosφ_basis = GaussianSmearing( + start=-1, stop=1, num_gaussians=num_spherical, **cbf_hparams + ) + elif cbf_name == "spherical_harmonics": + Y_lm = real_sph_harm( + num_spherical, use_theta=False, zero_m_only=True + ) + sph_funcs = [] # (num_spherical,) + + # convert to tensorflow functions + z = sym.symbols("z") + modules = {"sin": torch.sin, "cos": torch.cos, "sqrt": torch.sqrt} + m_order = 0 # only single angle + for l_degree in range(len(Y_lm)): # num_spherical + if ( + l_degree == 0 + ): # Y_00 is only a constant -> function returns value and not tensor + first_sph = sym.lambdify( + [z], Y_lm[l_degree][m_order], modules + ) + sph_funcs.append( + lambda z: torch.zeros_like(z) + first_sph(z) + ) + else: + sph_funcs.append( + sym.lambdify([z], Y_lm[l_degree][m_order], modules) + ) + self.cosφ_basis = lambda cosφ: torch.stack( + [f(cosφ) for f in sph_funcs], dim=1 + ) + else: + raise ValueError(f"Unknown cosine basis function '{cbf_name}'.") + + def forward(self, D_ca, cosφ_cab, id3_ca): + rbf = self.radial_basis(D_ca) # (num_edges, num_radial) + cbf = self.cosφ_basis(cosφ_cab) # (num_triplets, num_spherical) + + if not self.efficient: + rbf = rbf[id3_ca] # (num_triplets, num_radial) + out = (rbf[:, None, :] * cbf[:, :, None]).view( + -1, rbf.shape[-1] * cbf.shape[-1] + ) + return (out,) + # (num_triplets, num_radial * num_spherical) + else: + return (rbf[None, :, :], cbf) + # (1, num_edges, num_radial), (num_edges, num_spherical) diff --git a/ocpmodels/models/gemnet_gp/utils.py b/ocpmodels/models/gemnet_gp/utils.py new file mode 100644 index 0000000..42208ae --- /dev/null +++ b/ocpmodels/models/gemnet_gp/utils.py @@ -0,0 +1,279 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import json + +import torch +from torch_scatter import segment_csr + + +def read_json(path): + """""" + if not path.endswith(".json"): + raise UserWarning(f"Path {path} is not a json-path.") + + with open(path, "r") as f: + content = json.load(f) + return content + + +def update_json(path, data): + """""" + if not path.endswith(".json"): + raise UserWarning(f"Path {path} is not a json-path.") + + content = read_json(path) + content.update(data) + write_json(path, content) + + +def write_json(path, data): + """""" + if not path.endswith(".json"): + raise UserWarning(f"Path {path} is not a json-path.") + + with open(path, "w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=4) + + +def read_value_json(path, key): + """""" + content = read_json(path) + + if key in content.keys(): + return content[key] + else: + return None + + +def ragged_range(sizes): + """Multiple concatenated ranges. + + Examples + -------- + sizes = [1 4 2 3] + Return: [0 0 1 2 3 0 1 0 1 2] + """ + assert sizes.dim() == 1 + if sizes.sum() == 0: + return sizes.new_empty(0) + + # Remove 0 sizes + sizes_nonzero = sizes > 0 + if not torch.all(sizes_nonzero): + sizes = torch.masked_select(sizes, sizes_nonzero) + + # Initialize indexing array with ones as we need to setup incremental indexing + # within each group when cumulatively summed at the final stage. + id_steps = torch.ones(sizes.sum(), dtype=torch.long, device=sizes.device) + id_steps[0] = 0 + insert_index = sizes[:-1].cumsum(0) + insert_val = (1 - sizes)[:-1] + + # Assign index-offsetting values + id_steps[insert_index] = insert_val + + # Finally index into input array for the group repeated o/p + res = id_steps.cumsum(0) + return res + + +def repeat_blocks( + sizes, + repeats, + continuous_indexing=True, + start_idx=0, + block_inc=0, + repeat_inc=0, +): + """Repeat blocks of indices. + Adapted from https://stackoverflow.com/questions/51154989/numpy-vectorized-function-to-repeat-blocks-of-consecutive-elements + + continuous_indexing: Whether to keep increasing the index after each block + start_idx: Starting index + block_inc: Number to increment by after each block, + either global or per block. Shape: len(sizes) - 1 + repeat_inc: Number to increment by after each repetition, + either global or per block + + Examples + -------- + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = False + Return: [0 0 0 0 1 2 0 1 2 0 1 0 1 0 1] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True + Return: [0 0 0 1 2 3 1 2 3 4 5 4 5 4 5] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + repeat_inc = 4 + Return: [0 4 8 1 2 3 5 6 7 4 5 8 9 12 13] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + start_idx = 5 + Return: [5 5 5 6 7 8 6 7 8 9 10 9 10 9 10] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + block_inc = 1 + Return: [0 0 0 2 3 4 2 3 4 6 7 6 7 6 7] + sizes = [0,3,2] ; repeats = [3,2,3] ; continuous_indexing = True + Return: [0 1 2 0 1 2 3 4 3 4 3 4] + sizes = [2,3,2] ; repeats = [2,0,2] ; continuous_indexing = True + Return: [0 1 0 1 5 6 5 6] + """ + assert sizes.dim() == 1 + assert all(sizes >= 0) + + # Remove 0 sizes + sizes_nonzero = sizes > 0 + if not torch.all(sizes_nonzero): + assert block_inc == 0 # Implementing this is not worth the effort + sizes = torch.masked_select(sizes, sizes_nonzero) + if isinstance(repeats, torch.Tensor): + repeats = torch.masked_select(repeats, sizes_nonzero) + if isinstance(repeat_inc, torch.Tensor): + repeat_inc = torch.masked_select(repeat_inc, sizes_nonzero) + + if isinstance(repeats, torch.Tensor): + assert all(repeats >= 0) + insert_dummy = repeats[0] == 0 + if insert_dummy: + one = sizes.new_ones(1) + zero = sizes.new_zeros(1) + sizes = torch.cat((one, sizes)) + repeats = torch.cat((one, repeats)) + if isinstance(block_inc, torch.Tensor): + block_inc = torch.cat((zero, block_inc)) + if isinstance(repeat_inc, torch.Tensor): + repeat_inc = torch.cat((zero, repeat_inc)) + else: + assert repeats >= 0 + insert_dummy = False + + # Get repeats for each group using group lengths/sizes + r1 = torch.repeat_interleave( + torch.arange(len(sizes), device=sizes.device), repeats + ) + + # Get total size of output array, as needed to initialize output indexing array + N = (sizes * repeats).sum() + + # Initialize indexing array with ones as we need to setup incremental indexing + # within each group when cumulatively summed at the final stage. + # Two steps here: + # 1. Within each group, we have multiple sequences, so setup the offsetting + # at each sequence lengths by the seq. lengths preceding those. + id_ar = torch.ones(N, dtype=torch.long, device=sizes.device) + id_ar[0] = 0 + insert_index = sizes[r1[:-1]].cumsum(0) + insert_val = (1 - sizes)[r1[:-1]] + + if isinstance(repeats, torch.Tensor) and torch.any(repeats == 0): + diffs = r1[1:] - r1[:-1] + indptr = torch.cat((sizes.new_zeros(1), diffs.cumsum(0))) + if continuous_indexing: + # If a group was skipped (repeats=0) we need to add its size + insert_val += segment_csr(sizes[: r1[-1]], indptr, reduce="sum") + + # Add block increments + if isinstance(block_inc, torch.Tensor): + insert_val += segment_csr( + block_inc[: r1[-1]], indptr, reduce="sum" + ) + else: + insert_val += block_inc * (indptr[1:] - indptr[:-1]) + if insert_dummy: + insert_val[0] -= block_inc + else: + idx = r1[1:] != r1[:-1] + if continuous_indexing: + # 2. For each group, make sure the indexing starts from the next group's + # first element. So, simply assign 1s there. + insert_val[idx] = 1 + + # Add block increments + insert_val[idx] += block_inc + + # Add repeat_inc within each group + if isinstance(repeat_inc, torch.Tensor): + insert_val += repeat_inc[r1[:-1]] + if isinstance(repeats, torch.Tensor): + repeat_inc_inner = repeat_inc[repeats > 0][:-1] + else: + repeat_inc_inner = repeat_inc[:-1] + else: + insert_val += repeat_inc + repeat_inc_inner = repeat_inc + + # Subtract the increments between groups + if isinstance(repeats, torch.Tensor): + repeats_inner = repeats[repeats > 0][:-1] + else: + repeats_inner = repeats + insert_val[r1[1:] != r1[:-1]] -= repeat_inc_inner * repeats_inner + + # Assign index-offsetting values + id_ar[insert_index] = insert_val + + if insert_dummy: + id_ar = id_ar[1:] + if continuous_indexing: + id_ar[0] -= 1 + + # Set start index now, in case of insertion due to leading repeats=0 + id_ar[0] += start_idx + + # Finally index into input array for the group repeated o/p + res = id_ar.cumsum(0) + return res + + +def calculate_interatomic_vectors(R, id_s, id_t, offsets_st): + """ + Calculate the vectors connecting the given atom pairs, + considering offsets from periodic boundary conditions (PBC). + + Parameters + ---------- + R: Tensor, shape = (nAtoms, 3) + Atom positions. + id_s: Tensor, shape = (nEdges,) + Indices of the source atom of the edges. + id_t: Tensor, shape = (nEdges,) + Indices of the target atom of the edges. + offsets_st: Tensor, shape = (nEdges,) + PBC offsets of the edges. + Subtract this from the correct direction. + + Returns + ------- + (D_st, V_st): tuple + D_st: Tensor, shape = (nEdges,) + Distance from atom t to s. + V_st: Tensor, shape = (nEdges,) + Unit direction from atom t to s. + """ + Rs = R[id_s] + Rt = R[id_t] + # ReLU prevents negative numbers in sqrt + if offsets_st is None: + V_st = Rt - Rs # s -> t + else: + V_st = Rt - Rs + offsets_st # s -> t + D_st = torch.sqrt(torch.sum(V_st**2, dim=1)) + V_st = V_st / D_st[..., None] + return D_st, V_st + + +def inner_product_normalized(x, y): + """ + Calculate the inner product between the given normalized vectors, + giving a result between -1 and 1. + """ + return torch.sum(x * y, dim=-1).clamp(min=-1, max=1) + + +def mask_neighbors(neighbors, edge_mask): + neighbors_old_indptr = torch.cat([neighbors.new_zeros(1), neighbors]) + neighbors_old_indptr = torch.cumsum(neighbors_old_indptr, dim=0) + neighbors = segment_csr(edge_mask.long(), neighbors_old_indptr) + return neighbors diff --git a/ocpmodels/models/gemnet_oc/README.md b/ocpmodels/models/gemnet_oc/README.md new file mode 100644 index 0000000..a5f4704 --- /dev/null +++ b/ocpmodels/models/gemnet_oc/README.md @@ -0,0 +1,29 @@ +# GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets + +Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C. Lawrence Zitnick, Abhishek Das + +[[`arXiv:2204.02782`](https://arxiv.org/abs/2204.02782)] + +When running inference with a pretrained GemNet-OC model, make sure that the +`scale_file` path is correct in the config, otherwise predictions will be inaccurate. + +| Model | Val ID 30k Force MAE | Val ID 30k Energy MAE | Val ID 30k Force cos | Test metrics | Download | +| ----- | -------------------- | --------------------- | -------------------- | ------------ | -------- | +| gemnet_oc_2M | 0.0225 | 0.2299 | 0.6174 | [S2EF](https://evalai.s3.amazonaws.com/media/submission_files/submission_179229/062c037e-4f1f-49c2-9eeb-8e14681a70ee.json) \| [IS2RE](https://evalai.s3.amazonaws.com/media/submission_files/submission_179296/6688f44f-9d5a-4020-beca-8b804e0212fb.json) \| [IS2RS](https://evalai.s3.amazonaws.com/media/submission_files/submission_179257/0d02a349-0abe-44c0-a65c-29a9df75c886.json) | [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/2M/gemnet/gemnet-oc.yml) \| [checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_base_s2ef_2M.pt) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/481f3a5a92dc787384ddae9fe3f50f5d932712fd/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt) | +| gemnet_oc_all | 0.0179 | 0.1668 | 0.6879 | [S2EF](https://evalai.s3.amazonaws.com/media/submission_files/submission_179008/6e731f20-17cf-417e-b0ad-97352be8cc37.json) \| [IS2RE]() \| [IS2RS](https://evalai.s3.amazonaws.com/media/submission_files/submission_160550/72a65a42-1fa9-44c5-8546-9eb691df8d2e.json) | [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/gemnet/gemnet-oc.yml) \| [checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_base_s2ef_all.pt) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/481f3a5a92dc787384ddae9fe3f50f5d932712fd/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt) | +| gemnet_oc_large_all_md_energy | 0.0178 | 0.1504 | 0.6906 | [S2EF](https://evalai.s3.amazonaws.com/media/submission_files/submission_179143/40940149-6a4a-49a4-a2ce-38486215990f.json) | - | +| gemnet_oc_large_all_md_force | 0.0164 | 0.1665 | 0.7139 | [S2EF](https://evalai.s3.amazonaws.com/media/submission_files/submission_179042/ba160459-0de3-4583-a98b-12102138c61e.json) \| [IS2RS](https://evalai.s3.amazonaws.com/media/submission_files/submission_169243/10bc7c8d-5124-4338-aaf3-04a7d015c4a0.json) | [config](https://github.com/Open-Catalyst-Project/ocp/blob/main/configs/s2ef/all/gemnet/gemnet-oc-large.yml) \| [checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_large_s2ef_all_md.pt) \| [scale file](https://github.com/Open-Catalyst-Project/ocp/blob/481f3a5a92dc787384ddae9fe3f50f5d932712fd/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt) | +| gemnet_oc_large_all_md_energy + gemnet_oc_large_all_md_force | - | - | - | [IS2RE](https://evalai.s3.amazonaws.com/media/submission_files/submission_212962/6acc7cf7-e18b-4d6a-9082-b4a114110dbf.json) | - | + +## Citing + +If you use GemNet-OC in your work, please consider citing: + +```bibtex +@article{gasteiger_gemnet_oc_2022, + title = {{GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets}}, + author = {Gasteiger, Johannes and Shuaibi, Muhammed and Sriram, Anuroop and G{\"u}nnemann, Stephan and Ulissi, Zachary and Zitnick, C Lawrence and Das, Abhishek}, + journal = {Transactions on Machine Learning Research (TMLR)}, + year = {2022}, +} +``` diff --git a/ocpmodels/models/gemnet_oc/__init__.py b/ocpmodels/models/gemnet_oc/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/gemnet_oc/gemnet_oc.py b/ocpmodels/models/gemnet_oc/gemnet_oc.py new file mode 100644 index 0000000..b5ddb42 --- /dev/null +++ b/ocpmodels/models/gemnet_oc/gemnet_oc.py @@ -0,0 +1,1359 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import os +from typing import Optional + +import numpy as np +import torch +from torch_geometric.nn import radius_graph +from torch_scatter import scatter, segment_coo + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + compute_neighbors, + conditional_grad, + get_max_neighbors_mask, + get_pbc_distances, + radius_graph_pbc, + scatter_det, +) +from ocpmodels.models.base import BaseModel +from ocpmodels.modules.scaling.compat import load_scales_compat + +from .initializers import get_initializer +from .interaction_indices import ( + get_mixed_triplets, + get_quadruplets, + get_triplets, +) +from .layers.atom_update_block import OutputBlock +from .layers.base_layers import Dense, ResidualLayer +from .layers.efficient import BasisEmbedding +from .layers.embedding_block import AtomEmbedding, EdgeEmbedding +from .layers.force_scaler import ForceScaler +from .layers.interaction_block import InteractionBlock +from .layers.radial_basis import RadialBasis +from .layers.spherical_basis import CircularBasisLayer, SphericalBasisLayer +from .utils import ( + get_angle, + get_edge_id, + get_inner_idx, + inner_product_clamped, + mask_neighbors, + repeat_blocks, +) + + +@registry.register_model("gemnet_oc") +class GemNetOC(BaseModel): + """ + Arguments + --------- + num_atoms (int): Unused argument + bond_feat_dim (int): Unused argument + num_targets: int + Number of prediction targets. + + num_spherical: int + Controls maximum frequency. + num_radial: int + Controls maximum frequency. + num_blocks: int + Number of building blocks to be stacked. + + emb_size_atom: int + Embedding size of the atoms. + emb_size_edge: int + Embedding size of the edges. + emb_size_trip_in: int + (Down-projected) embedding size of the quadruplet edge embeddings + before the bilinear layer. + emb_size_trip_out: int + (Down-projected) embedding size of the quadruplet edge embeddings + after the bilinear layer. + emb_size_quad_in: int + (Down-projected) embedding size of the quadruplet edge embeddings + before the bilinear layer. + emb_size_quad_out: int + (Down-projected) embedding size of the quadruplet edge embeddings + after the bilinear layer. + emb_size_aint_in: int + Embedding size in the atom interaction before the bilinear layer. + emb_size_aint_out: int + Embedding size in the atom interaction after the bilinear layer. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + emb_size_sbf: int + Embedding size of the spherical basis transformation (two angles). + + num_before_skip: int + Number of residual blocks before the first skip connection. + num_after_skip: int + Number of residual blocks after the first skip connection. + num_concat: int + Number of residual blocks after the concatenation. + num_atom: int + Number of residual blocks in the atom embedding blocks. + num_output_afteratom: int + Number of residual blocks in the output blocks + after adding the atom embedding. + num_atom_emb_layers: int + Number of residual blocks for transforming atom embeddings. + num_global_out_layers: int + Number of final residual blocks before the output. + + regress_forces: bool + Whether to predict forces. Default: True + direct_forces: bool + If True predict forces based on aggregation of interatomic directions. + If False predict forces based on negative gradient of energy potential. + use_pbc: bool + Whether to use periodic boundary conditions. + scale_backprop_forces: bool + Whether to scale up the energy and then scales down the forces + to prevent NaNs and infs in backpropagated forces. + + cutoff: float + Embedding cutoff for interatomic connections and embeddings in Angstrom. + cutoff_qint: float + Quadruplet interaction cutoff in Angstrom. + Optional. Uses cutoff per default. + cutoff_aeaint: float + Edge-to-atom and atom-to-edge interaction cutoff in Angstrom. + Optional. Uses cutoff per default. + cutoff_aint: float + Atom-to-atom interaction cutoff in Angstrom. + Optional. Uses maximum of all other cutoffs per default. + max_neighbors: int + Maximum number of neighbors for interatomic connections and embeddings. + max_neighbors_qint: int + Maximum number of quadruplet interactions per embedding. + Optional. Uses max_neighbors per default. + max_neighbors_aeaint: int + Maximum number of edge-to-atom and atom-to-edge interactions per embedding. + Optional. Uses max_neighbors per default. + max_neighbors_aint: int + Maximum number of atom-to-atom interactions per atom. + Optional. Uses maximum of all other neighbors per default. + + rbf: dict + Name and hyperparameters of the radial basis function. + rbf_spherical: dict + Name and hyperparameters of the radial basis function used as part of the + circular and spherical bases. + Optional. Uses rbf per default. + envelope: dict + Name and hyperparameters of the envelope function. + cbf: dict + Name and hyperparameters of the circular basis function. + sbf: dict + Name and hyperparameters of the spherical basis function. + extensive: bool + Whether the output should be extensive (proportional to the number of atoms) + forces_coupled: bool + If True, enforce that |F_st| = |F_ts|. No effect if direct_forces is False. + output_init: str + Initialization method for the final dense layer. + activation: str + Name of the activation function. + scale_file: str + Path to the pytorch file containing the scaling factors. + + quad_interaction: bool + Whether to use quadruplet interactions (with dihedral angles) + atom_edge_interaction: bool + Whether to use atom-to-edge interactions + edge_atom_interaction: bool + Whether to use edge-to-atom interactions + atom_interaction: bool + Whether to use atom-to-atom interactions + + scale_basis: bool + Whether to use a scaling layer in the raw basis function for better + numerical stability. + qint_tags: list + Which atom tags to use quadruplet interactions for. + 0=sub-surface bulk, 1=surface, 2=adsorbate atoms. + """ + + def __init__( + self, + num_atoms: Optional[int], + bond_feat_dim: int, + num_targets: int, + num_spherical: int, + num_radial: int, + num_blocks: int, + emb_size_atom: int, + emb_size_edge: int, + emb_size_trip_in: int, + emb_size_trip_out: int, + emb_size_quad_in: int, + emb_size_quad_out: int, + emb_size_aint_in: int, + emb_size_aint_out: int, + emb_size_rbf: int, + emb_size_cbf: int, + emb_size_sbf: int, + num_before_skip: int, + num_after_skip: int, + num_concat: int, + num_atom: int, + num_output_afteratom: int, + num_atom_emb_layers: int = 0, + num_global_out_layers: int = 2, + regress_forces: bool = True, + direct_forces: bool = False, + use_pbc: bool = True, + scale_backprop_forces: bool = False, + cutoff: float = 6.0, + cutoff_qint: Optional[float] = None, + cutoff_aeaint: Optional[float] = None, + cutoff_aint: Optional[float] = None, + max_neighbors: int = 50, + max_neighbors_qint: Optional[int] = None, + max_neighbors_aeaint: Optional[int] = None, + max_neighbors_aint: Optional[int] = None, + rbf: dict = {"name": "gaussian"}, + rbf_spherical: Optional[dict] = None, + envelope: dict = {"name": "polynomial", "exponent": 5}, + cbf: dict = {"name": "spherical_harmonics"}, + sbf: dict = {"name": "spherical_harmonics"}, + extensive: bool = True, + forces_coupled: bool = False, + output_init: str = "HeOrthogonal", + activation: str = "silu", + quad_interaction: bool = False, + atom_edge_interaction: bool = False, + edge_atom_interaction: bool = False, + atom_interaction: bool = False, + scale_basis: bool = False, + qint_tags: list = [0, 1, 2], + num_elements: int = 83, + otf_graph: bool = False, + scale_file: Optional[str] = None, + **kwargs, # backwards compatibility with deprecated arguments + ): + super().__init__() + if len(kwargs) > 0: + logging.warning(f"Unrecognized arguments: {list(kwargs.keys())}") + self.num_targets = num_targets + assert num_blocks > 0 + self.num_blocks = num_blocks + self.extensive = extensive + + self.atom_edge_interaction = atom_edge_interaction + self.edge_atom_interaction = edge_atom_interaction + self.atom_interaction = atom_interaction + self.quad_interaction = quad_interaction + self.qint_tags = torch.tensor(qint_tags) + self.otf_graph = otf_graph + if not rbf_spherical: + rbf_spherical = rbf + + self.set_cutoffs(cutoff, cutoff_qint, cutoff_aeaint, cutoff_aint) + self.set_max_neighbors( + max_neighbors, + max_neighbors_qint, + max_neighbors_aeaint, + max_neighbors_aint, + ) + self.use_pbc = use_pbc + + self.direct_forces = direct_forces + self.forces_coupled = forces_coupled + self.regress_forces = regress_forces + self.force_scaler = ForceScaler(enabled=scale_backprop_forces) + + self.init_basis_functions( + num_radial, + num_spherical, + rbf, + rbf_spherical, + envelope, + cbf, + sbf, + scale_basis, + ) + self.init_shared_basis_layers( + num_radial, num_spherical, emb_size_rbf, emb_size_cbf, emb_size_sbf + ) + + # Embedding blocks + self.atom_emb = AtomEmbedding(emb_size_atom, num_elements) + self.edge_emb = EdgeEmbedding( + emb_size_atom, num_radial, emb_size_edge, activation=activation + ) + + # Interaction Blocks + int_blocks = [] + for _ in range(num_blocks): + int_blocks.append( + InteractionBlock( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_trip_in=emb_size_trip_in, + emb_size_trip_out=emb_size_trip_out, + emb_size_quad_in=emb_size_quad_in, + emb_size_quad_out=emb_size_quad_out, + emb_size_a2a_in=emb_size_aint_in, + emb_size_a2a_out=emb_size_aint_out, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + emb_size_sbf=emb_size_sbf, + num_before_skip=num_before_skip, + num_after_skip=num_after_skip, + num_concat=num_concat, + num_atom=num_atom, + num_atom_emb_layers=num_atom_emb_layers, + quad_interaction=quad_interaction, + atom_edge_interaction=atom_edge_interaction, + edge_atom_interaction=edge_atom_interaction, + atom_interaction=atom_interaction, + activation=activation, + ) + ) + self.int_blocks = torch.nn.ModuleList(int_blocks) + + out_blocks = [] + for _ in range(num_blocks + 1): + out_blocks.append( + OutputBlock( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=num_atom, + nHidden_afteratom=num_output_afteratom, + activation=activation, + direct_forces=direct_forces, + ) + ) + self.out_blocks = torch.nn.ModuleList(out_blocks) + + out_mlp_E = [ + Dense( + emb_size_atom * (num_blocks + 1), + emb_size_atom, + activation=activation, + ) + ] + out_mlp_E += [ + ResidualLayer( + emb_size_atom, + activation=activation, + ) + for _ in range(num_global_out_layers) + ] + self.out_mlp_E = torch.nn.Sequential(*out_mlp_E) + self.out_energy = Dense( + emb_size_atom, num_targets, bias=False, activation=None + ) + if direct_forces: + out_mlp_F = [ + Dense( + emb_size_edge * (num_blocks + 1), + emb_size_edge, + activation=activation, + ) + ] + out_mlp_F += [ + ResidualLayer( + emb_size_edge, + activation=activation, + ) + for _ in range(num_global_out_layers) + ] + self.out_mlp_F = torch.nn.Sequential(*out_mlp_F) + self.out_forces = Dense( + emb_size_edge, num_targets, bias=False, activation=None + ) + + out_initializer = get_initializer(output_init) + self.out_energy.reset_parameters(out_initializer) + if direct_forces: + self.out_forces.reset_parameters(out_initializer) + + load_scales_compat(self, scale_file) + + def set_cutoffs(self, cutoff, cutoff_qint, cutoff_aeaint, cutoff_aint): + self.cutoff = cutoff + + if ( + not (self.atom_edge_interaction or self.edge_atom_interaction) + or cutoff_aeaint is None + ): + self.cutoff_aeaint = self.cutoff + else: + self.cutoff_aeaint = cutoff_aeaint + if not self.quad_interaction or cutoff_qint is None: + self.cutoff_qint = self.cutoff + else: + self.cutoff_qint = cutoff_qint + if not self.atom_interaction or cutoff_aint is None: + self.cutoff_aint = max( + self.cutoff, + self.cutoff_aeaint, + self.cutoff_qint, + ) + else: + self.cutoff_aint = cutoff_aint + + assert self.cutoff <= self.cutoff_aint + assert self.cutoff_aeaint <= self.cutoff_aint + assert self.cutoff_qint <= self.cutoff_aint + + def set_max_neighbors( + self, + max_neighbors, + max_neighbors_qint, + max_neighbors_aeaint, + max_neighbors_aint, + ): + self.max_neighbors = max_neighbors + + if ( + not (self.atom_edge_interaction or self.edge_atom_interaction) + or max_neighbors_aeaint is None + ): + self.max_neighbors_aeaint = self.max_neighbors + else: + self.max_neighbors_aeaint = max_neighbors_aeaint + if not self.quad_interaction or max_neighbors_qint is None: + self.max_neighbors_qint = self.max_neighbors + else: + self.max_neighbors_qint = max_neighbors_qint + if not self.atom_interaction or max_neighbors_aint is None: + self.max_neighbors_aint = max( + self.max_neighbors, + self.max_neighbors_aeaint, + self.max_neighbors_qint, + ) + else: + self.max_neighbors_aint = max_neighbors_aint + + assert self.max_neighbors <= self.max_neighbors_aint + assert self.max_neighbors_aeaint <= self.max_neighbors_aint + assert self.max_neighbors_qint <= self.max_neighbors_aint + + def init_basis_functions( + self, + num_radial, + num_spherical, + rbf, + rbf_spherical, + envelope, + cbf, + sbf, + scale_basis, + ): + self.radial_basis = RadialBasis( + num_radial=num_radial, + cutoff=self.cutoff, + rbf=rbf, + envelope=envelope, + scale_basis=scale_basis, + ) + radial_basis_spherical = RadialBasis( + num_radial=num_radial, + cutoff=self.cutoff, + rbf=rbf_spherical, + envelope=envelope, + scale_basis=scale_basis, + ) + if self.quad_interaction: + radial_basis_spherical_qint = RadialBasis( + num_radial=num_radial, + cutoff=self.cutoff_qint, + rbf=rbf_spherical, + envelope=envelope, + scale_basis=scale_basis, + ) + self.cbf_basis_qint = CircularBasisLayer( + num_spherical, + radial_basis=radial_basis_spherical_qint, + cbf=cbf, + scale_basis=scale_basis, + ) + + self.sbf_basis_qint = SphericalBasisLayer( + num_spherical, + radial_basis=radial_basis_spherical, + sbf=sbf, + scale_basis=scale_basis, + ) + if self.atom_edge_interaction: + self.radial_basis_aeaint = RadialBasis( + num_radial=num_radial, + cutoff=self.cutoff_aeaint, + rbf=rbf, + envelope=envelope, + scale_basis=scale_basis, + ) + self.cbf_basis_aeint = CircularBasisLayer( + num_spherical, + radial_basis=radial_basis_spherical, + cbf=cbf, + scale_basis=scale_basis, + ) + if self.edge_atom_interaction: + self.radial_basis_aeaint = RadialBasis( + num_radial=num_radial, + cutoff=self.cutoff_aeaint, + rbf=rbf, + envelope=envelope, + scale_basis=scale_basis, + ) + radial_basis_spherical_aeaint = RadialBasis( + num_radial=num_radial, + cutoff=self.cutoff_aeaint, + rbf=rbf_spherical, + envelope=envelope, + scale_basis=scale_basis, + ) + self.cbf_basis_eaint = CircularBasisLayer( + num_spherical, + radial_basis=radial_basis_spherical_aeaint, + cbf=cbf, + scale_basis=scale_basis, + ) + if self.atom_interaction: + self.radial_basis_aint = RadialBasis( + num_radial=num_radial, + cutoff=self.cutoff_aint, + rbf=rbf, + envelope=envelope, + scale_basis=scale_basis, + ) + + self.cbf_basis_tint = CircularBasisLayer( + num_spherical, + radial_basis=radial_basis_spherical, + cbf=cbf, + scale_basis=scale_basis, + ) + + def init_shared_basis_layers( + self, + num_radial, + num_spherical, + emb_size_rbf, + emb_size_cbf, + emb_size_sbf, + ): + # Share basis down projections across all interaction blocks + if self.quad_interaction: + self.mlp_rbf_qint = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_cbf_qint = BasisEmbedding( + num_radial, emb_size_cbf, num_spherical + ) + self.mlp_sbf_qint = BasisEmbedding( + num_radial, emb_size_sbf, num_spherical**2 + ) + + if self.atom_edge_interaction: + self.mlp_rbf_aeint = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_cbf_aeint = BasisEmbedding( + num_radial, emb_size_cbf, num_spherical + ) + if self.edge_atom_interaction: + self.mlp_rbf_eaint = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_cbf_eaint = BasisEmbedding( + num_radial, emb_size_cbf, num_spherical + ) + if self.atom_interaction: + self.mlp_rbf_aint = BasisEmbedding(num_radial, emb_size_rbf) + + self.mlp_rbf_tint = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_cbf_tint = BasisEmbedding( + num_radial, emb_size_cbf, num_spherical + ) + + # Share the dense Layer of the atom embedding block accross the interaction blocks + self.mlp_rbf_h = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + self.mlp_rbf_out = Dense( + num_radial, + emb_size_rbf, + activation=None, + bias=False, + ) + + # Set shared parameters for better gradients + self.shared_parameters = [ + (self.mlp_rbf_tint.linear.weight, self.num_blocks), + (self.mlp_cbf_tint.weight, self.num_blocks), + (self.mlp_rbf_h.linear.weight, self.num_blocks), + (self.mlp_rbf_out.linear.weight, self.num_blocks + 1), + ] + if self.quad_interaction: + self.shared_parameters += [ + (self.mlp_rbf_qint.linear.weight, self.num_blocks), + (self.mlp_cbf_qint.weight, self.num_blocks), + (self.mlp_sbf_qint.weight, self.num_blocks), + ] + if self.atom_edge_interaction: + self.shared_parameters += [ + (self.mlp_rbf_aeint.linear.weight, self.num_blocks), + (self.mlp_cbf_aeint.weight, self.num_blocks), + ] + if self.edge_atom_interaction: + self.shared_parameters += [ + (self.mlp_rbf_eaint.linear.weight, self.num_blocks), + (self.mlp_cbf_eaint.weight, self.num_blocks), + ] + if self.atom_interaction: + self.shared_parameters += [ + (self.mlp_rbf_aint.weight, self.num_blocks), + ] + + def calculate_quad_angles( + self, + V_st, + V_qint_st, + quad_idx, + ): + """Calculate angles for quadruplet-based message passing. + + Arguments + --------- + V_st: Tensor, shape = (nAtoms, 3) + Normalized directions from s to t + V_qint_st: Tensor, shape = (nAtoms, 3) + Normalized directions from s to t for the quadruplet + interaction graph + quad_idx: dict of torch.Tensor + Indices relevant for quadruplet interactions. + + Returns + ------- + cosφ_cab: Tensor, shape = (num_triplets_inint,) + Cosine of angle between atoms c -> a <- b. + cosφ_abd: Tensor, shape = (num_triplets_qint,) + Cosine of angle between atoms a -> b -> d. + angle_cabd: Tensor, shape = (num_quadruplets,) + Dihedral angle between atoms c <- a-b -> d. + """ + # ---------------------------------- d -> b -> a ---------------------------------- # + V_ba = V_qint_st[quad_idx["triplet_in"]["out"]] + # (num_triplets_qint, 3) + V_db = V_st[quad_idx["triplet_in"]["in"]] + # (num_triplets_qint, 3) + cosφ_abd = inner_product_clamped(V_ba, V_db) + # (num_triplets_qint,) + + # Project for calculating dihedral angle + # Cross product is the same as projection, just 90° rotated + V_db_cross = torch.cross(V_db, V_ba, dim=-1) # a - b -| d + V_db_cross = V_db_cross[quad_idx["trip_in_to_quad"]] + # (num_quadruplets,) + + # --------------------------------- c -> a <- b ---------------------------------- # + V_ca = V_st[quad_idx["triplet_out"]["out"]] # (num_triplets_in, 3) + V_ba = V_qint_st[quad_idx["triplet_out"]["in"]] # (num_triplets_in, 3) + cosφ_cab = inner_product_clamped(V_ca, V_ba) # (n4Triplets,) + + # Project for calculating dihedral angle + # Cross product is the same as projection, just 90° rotated + V_ca_cross = torch.cross(V_ca, V_ba, dim=-1) # c |- a - b + V_ca_cross = V_ca_cross[quad_idx["trip_out_to_quad"]] + # (num_quadruplets,) + + # -------------------------------- c -> a - b <- d -------------------------------- # + half_angle_cabd = get_angle(V_ca_cross, V_db_cross) + # (num_quadruplets,) + angle_cabd = half_angle_cabd + # Ignore parity and just use the half angle. + + return cosφ_cab, cosφ_abd, angle_cabd + + def select_symmetric_edges(self, tensor, mask, reorder_idx, opposite_neg): + """Use a mask to remove values of removed edges and then + duplicate the values for the correct edge direction. + + Arguments + --------- + tensor: torch.Tensor + Values to symmetrize for the new tensor. + mask: torch.Tensor + Mask defining which edges go in the correct direction. + reorder_idx: torch.Tensor + Indices defining how to reorder the tensor values after + concatenating the edge values of both directions. + opposite_neg: bool + Whether the edge in the opposite direction should use the + negative tensor value. + + Returns + ------- + tensor_ordered: torch.Tensor + A tensor with symmetrized values. + """ + # Mask out counter-edges + tensor_directed = tensor[mask] + # Concatenate counter-edges after normal edges + sign = 1 - 2 * opposite_neg + tensor_cat = torch.cat([tensor_directed, sign * tensor_directed]) + # Reorder everything so the edges of every image are consecutive + tensor_ordered = tensor_cat[reorder_idx] + return tensor_ordered + + def symmetrize_edges( + self, + graph, + batch_idx, + ): + """ + Symmetrize edges to ensure existence of counter-directional edges. + + Some edges are only present in one direction in the data, + since every atom has a maximum number of neighbors. + We only use i->j edges here. So we lose some j->i edges + and add others by making it symmetric. + """ + num_atoms = batch_idx.shape[0] + new_graph = {} + + # Generate mask + mask_sep_atoms = graph["edge_index"][0] < graph["edge_index"][1] + # Distinguish edges between the same (periodic) atom by ordering the cells + cell_earlier = ( + (graph["cell_offset"][:, 0] < 0) + | ( + (graph["cell_offset"][:, 0] == 0) + & (graph["cell_offset"][:, 1] < 0) + ) + | ( + (graph["cell_offset"][:, 0] == 0) + & (graph["cell_offset"][:, 1] == 0) + & (graph["cell_offset"][:, 2] < 0) + ) + ) + mask_same_atoms = graph["edge_index"][0] == graph["edge_index"][1] + mask_same_atoms &= cell_earlier + mask = mask_sep_atoms | mask_same_atoms + + # Mask out counter-edges + edge_index_directed = graph["edge_index"][ + mask[None, :].expand(2, -1) + ].view(2, -1) + + # Concatenate counter-edges after normal edges + edge_index_cat = torch.cat( + [edge_index_directed, edge_index_directed.flip(0)], + dim=1, + ) + + # Count remaining edges per image + batch_edge = torch.repeat_interleave( + torch.arange( + graph["num_neighbors"].size(0), + device=graph["edge_index"].device, + ), + graph["num_neighbors"], + ) + batch_edge = batch_edge[mask] + # segment_coo assumes sorted batch_edge + # Factor 2 since this is only one half of the edges + ones = batch_edge.new_ones(1).expand_as(batch_edge) + new_graph["num_neighbors"] = 2 * segment_coo( + ones, batch_edge, dim_size=graph["num_neighbors"].size(0) + ) + + # Create indexing array + edge_reorder_idx = repeat_blocks( + torch.div(new_graph["num_neighbors"], 2, rounding_mode="floor"), + repeats=2, + continuous_indexing=True, + repeat_inc=edge_index_directed.size(1), + ) + + # Reorder everything so the edges of every image are consecutive + new_graph["edge_index"] = edge_index_cat[:, edge_reorder_idx] + new_graph["cell_offset"] = self.select_symmetric_edges( + graph["cell_offset"], mask, edge_reorder_idx, True + ) + new_graph["distance"] = self.select_symmetric_edges( + graph["distance"], mask, edge_reorder_idx, False + ) + new_graph["vector"] = self.select_symmetric_edges( + graph["vector"], mask, edge_reorder_idx, True + ) + + # Indices for swapping c->a and a->c (for symmetric MP) + # To obtain these efficiently and without any index assumptions, + # we get order the counter-edge IDs and then + # map this order back to the edge IDs. + # Double argsort gives the desired mapping + # from the ordered tensor to the original tensor. + edge_ids = get_edge_id( + new_graph["edge_index"], new_graph["cell_offset"], num_atoms + ) + order_edge_ids = torch.argsort(edge_ids) + inv_order_edge_ids = torch.argsort(order_edge_ids) + edge_ids_counter = get_edge_id( + new_graph["edge_index"].flip(0), + -new_graph["cell_offset"], + num_atoms, + ) + order_edge_ids_counter = torch.argsort(edge_ids_counter) + id_swap = order_edge_ids_counter[inv_order_edge_ids] + + return new_graph, id_swap + + def subselect_edges( + self, + data, + graph, + cutoff=None, + max_neighbors=None, + ): + """Subselect edges using a stricter cutoff and max_neighbors.""" + subgraph = graph.copy() + + if cutoff is not None: + edge_mask = subgraph["distance"] <= cutoff + + subgraph["edge_index"] = subgraph["edge_index"][:, edge_mask] + subgraph["cell_offset"] = subgraph["cell_offset"][edge_mask] + subgraph["num_neighbors"] = mask_neighbors( + subgraph["num_neighbors"], edge_mask + ) + subgraph["distance"] = subgraph["distance"][edge_mask] + subgraph["vector"] = subgraph["vector"][edge_mask] + + if max_neighbors is not None: + edge_mask, subgraph["num_neighbors"] = get_max_neighbors_mask( + natoms=data.natoms, + index=subgraph["edge_index"][1], + atom_distance=subgraph["distance"], + max_num_neighbors_threshold=max_neighbors, + ) + if not torch.all(edge_mask): + subgraph["edge_index"] = subgraph["edge_index"][:, edge_mask] + subgraph["cell_offset"] = subgraph["cell_offset"][edge_mask] + subgraph["distance"] = subgraph["distance"][edge_mask] + subgraph["vector"] = subgraph["vector"][edge_mask] + + empty_image = subgraph["num_neighbors"] == 0 + if torch.any(empty_image): + raise ValueError( + f"An image has no neighbors: id={data.id[empty_image]}, " + f"sid={data.sid[empty_image]}, fid={data.fid[empty_image]}" + ) + return subgraph + + def generate_graph_dict(self, data, cutoff, max_neighbors): + """Generate a radius/nearest neighbor graph.""" + otf_graph = cutoff > 6 or max_neighbors > 50 or self.otf_graph + + ( + edge_index, + edge_dist, + distance_vec, + cell_offsets, + _, # cell offset distances + num_neighbors, + ) = self.generate_graph( + data, + cutoff=cutoff, + max_neighbors=max_neighbors, + otf_graph=otf_graph, + ) + # These vectors actually point in the opposite direction. + # But we want to use col as idx_t for efficient aggregation. + edge_vector = -distance_vec / edge_dist[:, None] + cell_offsets = -cell_offsets # a - c + offset + + graph = { + "edge_index": edge_index, + "distance": edge_dist, + "vector": edge_vector, + "cell_offset": cell_offsets, + "num_neighbors": num_neighbors, + } + + # Mask interaction edges if required + if otf_graph or np.isclose(cutoff, 6): + select_cutoff = None + else: + select_cutoff = cutoff + if otf_graph or max_neighbors == 50: + select_neighbors = None + else: + select_neighbors = max_neighbors + graph = self.subselect_edges( + data=data, + graph=graph, + cutoff=select_cutoff, + max_neighbors=select_neighbors, + ) + + return graph + + def subselect_graph( + self, + data, + graph, + cutoff, + max_neighbors, + cutoff_orig, + max_neighbors_orig, + ): + """If the new cutoff and max_neighbors is different from the original, + subselect the edges of a given graph. + """ + # Check if embedding edges are different from interaction edges + if np.isclose(cutoff, cutoff_orig): + select_cutoff = None + else: + select_cutoff = cutoff + if max_neighbors == max_neighbors_orig: + select_neighbors = None + else: + select_neighbors = max_neighbors + + return self.subselect_edges( + data=data, + graph=graph, + cutoff=select_cutoff, + max_neighbors=select_neighbors, + ) + + def get_graphs_and_indices(self, data): + """ "Generate embedding and interaction graphs and indices.""" + num_atoms = data.atomic_numbers.size(0) + + # Atom interaction graph is always the largest + if ( + self.atom_edge_interaction + or self.edge_atom_interaction + or self.atom_interaction + ): + a2a_graph = self.generate_graph_dict( + data, self.cutoff_aint, self.max_neighbors_aint + ) + main_graph = self.subselect_graph( + data, + a2a_graph, + self.cutoff, + self.max_neighbors, + self.cutoff_aint, + self.max_neighbors_aint, + ) + a2ee2a_graph = self.subselect_graph( + data, + a2a_graph, + self.cutoff_aeaint, + self.max_neighbors_aeaint, + self.cutoff_aint, + self.max_neighbors_aint, + ) + else: + main_graph = self.generate_graph_dict( + data, self.cutoff, self.max_neighbors + ) + a2a_graph = {} + a2ee2a_graph = {} + if self.quad_interaction: + if ( + self.atom_edge_interaction + or self.edge_atom_interaction + or self.atom_interaction + ): + qint_graph = self.subselect_graph( + data, + a2a_graph, + self.cutoff_qint, + self.max_neighbors_qint, + self.cutoff_aint, + self.max_neighbors_aint, + ) + else: + assert self.cutoff_qint <= self.cutoff + assert self.max_neighbors_qint <= self.max_neighbors + qint_graph = self.subselect_graph( + data, + main_graph, + self.cutoff_qint, + self.max_neighbors_qint, + self.cutoff, + self.max_neighbors, + ) + + # Only use quadruplets for certain tags + self.qint_tags = self.qint_tags.to(qint_graph["edge_index"].device) + tags_s = data.tags[qint_graph["edge_index"][0]] + tags_t = data.tags[qint_graph["edge_index"][1]] + qint_tag_mask_s = (tags_s[..., None] == self.qint_tags).any(dim=-1) + qint_tag_mask_t = (tags_t[..., None] == self.qint_tags).any(dim=-1) + qint_tag_mask = qint_tag_mask_s | qint_tag_mask_t + qint_graph["edge_index"] = qint_graph["edge_index"][ + :, qint_tag_mask + ] + qint_graph["cell_offset"] = qint_graph["cell_offset"][ + qint_tag_mask, : + ] + qint_graph["distance"] = qint_graph["distance"][qint_tag_mask] + qint_graph["vector"] = qint_graph["vector"][qint_tag_mask, :] + del qint_graph["num_neighbors"] + else: + qint_graph = {} + + # Symmetrize edges for swapping in symmetric message passing + main_graph, id_swap = self.symmetrize_edges(main_graph, data.batch) + + trip_idx_e2e = get_triplets(main_graph, num_atoms=num_atoms) + + # Additional indices for quadruplets + if self.quad_interaction: + quad_idx = get_quadruplets( + main_graph, + qint_graph, + num_atoms, + ) + else: + quad_idx = {} + + if self.atom_edge_interaction: + trip_idx_a2e = get_mixed_triplets( + a2ee2a_graph, + main_graph, + num_atoms=num_atoms, + return_agg_idx=True, + ) + else: + trip_idx_a2e = {} + if self.edge_atom_interaction: + trip_idx_e2a = get_mixed_triplets( + main_graph, + a2ee2a_graph, + num_atoms=num_atoms, + return_agg_idx=True, + ) + # a2ee2a_graph['edge_index'][1] has to be sorted for this + a2ee2a_graph["target_neighbor_idx"] = get_inner_idx( + a2ee2a_graph["edge_index"][1], dim_size=num_atoms + ) + else: + trip_idx_e2a = {} + if self.atom_interaction: + # a2a_graph['edge_index'][1] has to be sorted for this + a2a_graph["target_neighbor_idx"] = get_inner_idx( + a2a_graph["edge_index"][1], dim_size=num_atoms + ) + + return ( + main_graph, + a2a_graph, + a2ee2a_graph, + qint_graph, + id_swap, + trip_idx_e2e, + trip_idx_a2e, + trip_idx_e2a, + quad_idx, + ) + + def get_bases( + self, + main_graph, + a2a_graph, + a2ee2a_graph, + qint_graph, + trip_idx_e2e, + trip_idx_a2e, + trip_idx_e2a, + quad_idx, + num_atoms, + ): + """Calculate and transform basis functions.""" + basis_rad_main_raw = self.radial_basis(main_graph["distance"]) + + # Calculate triplet angles + cosφ_cab = inner_product_clamped( + main_graph["vector"][trip_idx_e2e["out"]], + main_graph["vector"][trip_idx_e2e["in"]], + ) + basis_rad_cir_e2e_raw, basis_cir_e2e_raw = self.cbf_basis_tint( + main_graph["distance"], cosφ_cab + ) + + if self.quad_interaction: + # Calculate quadruplet angles + cosφ_cab_q, cosφ_abd, angle_cabd = self.calculate_quad_angles( + main_graph["vector"], + qint_graph["vector"], + quad_idx, + ) + + basis_rad_cir_qint_raw, basis_cir_qint_raw = self.cbf_basis_qint( + qint_graph["distance"], cosφ_abd + ) + basis_rad_sph_qint_raw, basis_sph_qint_raw = self.sbf_basis_qint( + main_graph["distance"], + cosφ_cab_q[quad_idx["trip_out_to_quad"]], + angle_cabd, + ) + if self.atom_edge_interaction: + basis_rad_a2ee2a_raw = self.radial_basis_aeaint( + a2ee2a_graph["distance"] + ) + cosφ_cab_a2e = inner_product_clamped( + main_graph["vector"][trip_idx_a2e["out"]], + a2ee2a_graph["vector"][trip_idx_a2e["in"]], + ) + basis_rad_cir_a2e_raw, basis_cir_a2e_raw = self.cbf_basis_aeint( + main_graph["distance"], cosφ_cab_a2e + ) + if self.edge_atom_interaction: + cosφ_cab_e2a = inner_product_clamped( + a2ee2a_graph["vector"][trip_idx_e2a["out"]], + main_graph["vector"][trip_idx_e2a["in"]], + ) + basis_rad_cir_e2a_raw, basis_cir_e2a_raw = self.cbf_basis_eaint( + a2ee2a_graph["distance"], cosφ_cab_e2a + ) + if self.atom_interaction: + basis_rad_a2a_raw = self.radial_basis_aint(a2a_graph["distance"]) + + # Shared Down Projections + bases_qint = {} + if self.quad_interaction: + bases_qint["rad"] = self.mlp_rbf_qint(basis_rad_main_raw) + bases_qint["cir"] = self.mlp_cbf_qint( + rad_basis=basis_rad_cir_qint_raw, + sph_basis=basis_cir_qint_raw, + idx_sph_outer=quad_idx["triplet_in"]["out"], + ) + bases_qint["sph"] = self.mlp_sbf_qint( + rad_basis=basis_rad_sph_qint_raw, + sph_basis=basis_sph_qint_raw, + idx_sph_outer=quad_idx["out"], + idx_sph_inner=quad_idx["out_agg"], + ) + + bases_a2e = {} + if self.atom_edge_interaction: + bases_a2e["rad"] = self.mlp_rbf_aeint(basis_rad_a2ee2a_raw) + bases_a2e["cir"] = self.mlp_cbf_aeint( + rad_basis=basis_rad_cir_a2e_raw, + sph_basis=basis_cir_a2e_raw, + idx_sph_outer=trip_idx_a2e["out"], + idx_sph_inner=trip_idx_a2e["out_agg"], + ) + bases_e2a = {} + if self.edge_atom_interaction: + bases_e2a["rad"] = self.mlp_rbf_eaint(basis_rad_main_raw) + bases_e2a["cir"] = self.mlp_cbf_eaint( + rad_basis=basis_rad_cir_e2a_raw, + sph_basis=basis_cir_e2a_raw, + idx_rad_outer=a2ee2a_graph["edge_index"][1], + idx_rad_inner=a2ee2a_graph["target_neighbor_idx"], + idx_sph_outer=trip_idx_e2a["out"], + idx_sph_inner=trip_idx_e2a["out_agg"], + num_atoms=num_atoms, + ) + if self.atom_interaction: + basis_a2a_rad = self.mlp_rbf_aint( + rad_basis=basis_rad_a2a_raw, + idx_rad_outer=a2a_graph["edge_index"][1], + idx_rad_inner=a2a_graph["target_neighbor_idx"], + num_atoms=num_atoms, + ) + else: + basis_a2a_rad = None + + bases_e2e = {} + bases_e2e["rad"] = self.mlp_rbf_tint(basis_rad_main_raw) + bases_e2e["cir"] = self.mlp_cbf_tint( + rad_basis=basis_rad_cir_e2e_raw, + sph_basis=basis_cir_e2e_raw, + idx_sph_outer=trip_idx_e2e["out"], + idx_sph_inner=trip_idx_e2e["out_agg"], + ) + + basis_atom_update = self.mlp_rbf_h(basis_rad_main_raw) + basis_output = self.mlp_rbf_out(basis_rad_main_raw) + + return ( + basis_rad_main_raw, + basis_atom_update, + basis_output, + bases_qint, + bases_e2e, + bases_a2e, + bases_e2a, + basis_a2a_rad, + ) + + @conditional_grad(torch.enable_grad()) + def forward(self, data): + pos = data.pos + batch = data.batch + atomic_numbers = data.atomic_numbers.long() + num_atoms = atomic_numbers.shape[0] + + if self.regress_forces and not self.direct_forces: + pos.requires_grad_(True) + + ( + main_graph, + a2a_graph, + a2ee2a_graph, + qint_graph, + id_swap, + trip_idx_e2e, + trip_idx_a2e, + trip_idx_e2a, + quad_idx, + ) = self.get_graphs_and_indices(data) + _, idx_t = main_graph["edge_index"] + + ( + basis_rad_raw, + basis_atom_update, + basis_output, + bases_qint, + bases_e2e, + bases_a2e, + bases_e2a, + basis_a2a_rad, + ) = self.get_bases( + main_graph=main_graph, + a2a_graph=a2a_graph, + a2ee2a_graph=a2ee2a_graph, + qint_graph=qint_graph, + trip_idx_e2e=trip_idx_e2e, + trip_idx_a2e=trip_idx_a2e, + trip_idx_e2a=trip_idx_e2a, + quad_idx=quad_idx, + num_atoms=num_atoms, + ) + + # Embedding block + h = self.atom_emb(atomic_numbers) + # (nAtoms, emb_size_atom) + m = self.edge_emb(h, basis_rad_raw, main_graph["edge_index"]) + # (nEdges, emb_size_edge) + + x_E, x_F = self.out_blocks[0](h, m, basis_output, idx_t) + # (nAtoms, emb_size_atom), (nEdges, emb_size_edge) + xs_E, xs_F = [x_E], [x_F] + + for i in range(self.num_blocks): + # Interaction block + h, m = self.int_blocks[i]( + h=h, + m=m, + bases_qint=bases_qint, + bases_e2e=bases_e2e, + bases_a2e=bases_a2e, + bases_e2a=bases_e2a, + basis_a2a_rad=basis_a2a_rad, + basis_atom_update=basis_atom_update, + edge_index_main=main_graph["edge_index"], + a2ee2a_graph=a2ee2a_graph, + a2a_graph=a2a_graph, + id_swap=id_swap, + trip_idx_e2e=trip_idx_e2e, + trip_idx_a2e=trip_idx_a2e, + trip_idx_e2a=trip_idx_e2a, + quad_idx=quad_idx, + ) # (nAtoms, emb_size_atom), (nEdges, emb_size_edge) + + x_E, x_F = self.out_blocks[i + 1](h, m, basis_output, idx_t) + # (nAtoms, emb_size_atom), (nEdges, emb_size_edge) + xs_E.append(x_E) + xs_F.append(x_F) + + # Global output block for final predictions + x_E = self.out_mlp_E(torch.cat(xs_E, dim=-1)) + if self.direct_forces: + x_F = self.out_mlp_F(torch.cat(xs_F, dim=-1)) + with torch.cuda.amp.autocast(False): + E_t = self.out_energy(x_E.float()) + if self.direct_forces: + F_st = self.out_forces(x_F.float()) + + nMolecules = torch.max(batch) + 1 + if self.extensive: + E_t = scatter_det( + E_t, batch, dim=0, dim_size=nMolecules, reduce="add" + ) # (nMolecules, num_targets) + else: + E_t = scatter_det( + E_t, batch, dim=0, dim_size=nMolecules, reduce="mean" + ) # (nMolecules, num_targets) + + if self.regress_forces: + if self.direct_forces: + if self.forces_coupled: # enforce F_st = F_ts + nEdges = idx_t.shape[0] + id_undir = repeat_blocks( + main_graph["num_neighbors"] // 2, + repeats=2, + continuous_indexing=True, + ) + F_st = scatter_det( + F_st, + id_undir, + dim=0, + dim_size=int(nEdges / 2), + reduce="mean", + ) # (nEdges/2, num_targets) + F_st = F_st[id_undir] # (nEdges, num_targets) + + # map forces in edge directions + F_st_vec = F_st[:, :, None] * main_graph["vector"][:, None, :] + # (nEdges, num_targets, 3) + F_t = scatter_det( + F_st_vec, + idx_t, + dim=0, + dim_size=num_atoms, + reduce="add", + ) # (nAtoms, num_targets, 3) + else: + F_t = self.force_scaler.calc_forces_and_update(E_t, pos) + + E_t = E_t.squeeze(1) # (num_molecules) + F_t = F_t.squeeze(1) # (num_atoms, 3) + return E_t, F_t + else: + E_t = E_t.squeeze(1) # (num_molecules) + return E_t + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) diff --git a/ocpmodels/models/gemnet_oc/initializers.py b/ocpmodels/models/gemnet_oc/initializers.py new file mode 100644 index 0000000..badbca1 --- /dev/null +++ b/ocpmodels/models/gemnet_oc/initializers.py @@ -0,0 +1,95 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from functools import partial + +import torch + + +def _standardize(kernel): + """ + Makes sure that N*Var(W) = 1 and E[W] = 0 + """ + eps = 1e-6 + + if len(kernel.shape) == 3: + axis = [0, 1] # last dimension is output dimension + else: + axis = 1 + + var, mean = torch.var_mean(kernel, dim=axis, unbiased=True, keepdim=True) + kernel = (kernel - mean) / (var + eps) ** 0.5 + return kernel + + +def he_orthogonal_init(tensor): + """ + Generate a weight matrix with variance according to He (Kaiming) initialization. + Based on a random (semi-)orthogonal matrix neural networks + are expected to learn better when features are decorrelated + (stated by eg. "Reducing overfitting in deep networks by decorrelating representations", + "Dropout: a simple way to prevent neural networks from overfitting", + "Exact solutions to the nonlinear dynamics of learning in deep linear neural networks") + """ + tensor = torch.nn.init.orthogonal_(tensor) + + if len(tensor.shape) == 3: + fan_in = tensor.shape[:-1].numel() + else: + fan_in = tensor.shape[1] + + with torch.no_grad(): + tensor.data = _standardize(tensor.data) + tensor.data *= (1 / fan_in) ** 0.5 + + return tensor + + +def grid_init(tensor, start=-1, end=1): + """ + Generate a weight matrix so that each input value corresponds to one value on a regular grid between start and end. + """ + fan_in = tensor.shape[1] + + with torch.no_grad(): + data = torch.linspace( + start, end, fan_in, device=tensor.device, dtype=tensor.dtype + ).expand_as(tensor) + tensor.copy_(data) + + return tensor + + +def log_grid_init(tensor, start=-4, end=0): + """ + Generate a weight matrix so that each input value corresponds to one value on a regular logarithmic grid between 10^start and 10^end. + """ + fan_in = tensor.shape[1] + + with torch.no_grad(): + data = torch.logspace( + start, end, fan_in, device=tensor.device, dtype=tensor.dtype + ).expand_as(tensor) + tensor.copy_(data) + + return tensor + + +def get_initializer(name, **init_kwargs): + name = name.lower() + if name == "heorthogonal": + initializer = he_orthogonal_init + elif name == "zeros": + initializer = torch.nn.init.zeros_ + elif name == "grid": + initializer = grid_init + elif name == "loggrid": + initializer = log_grid_init + else: + raise UserWarning(f"Unknown initializer: {name}") + + initializer = partial(initializer, **init_kwargs) + return initializer diff --git a/ocpmodels/models/gemnet_oc/interaction_indices.py b/ocpmodels/models/gemnet_oc/interaction_indices.py new file mode 100644 index 0000000..3721b0c --- /dev/null +++ b/ocpmodels/models/gemnet_oc/interaction_indices.py @@ -0,0 +1,310 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +from torch_scatter import segment_coo +from torch_sparse import SparseTensor + +from .utils import get_inner_idx, masked_select_sparsetensor_flat + + +def get_triplets(graph, num_atoms): + """ + Get all input edges b->a for each output edge c->a. + It is possible that b=c, as long as the edges are distinct + (i.e. atoms b and c stem from different unit cells). + + Arguments + --------- + graph: dict of torch.Tensor + Contains the graph's edge_index. + num_atoms: int + Total number of atoms. + + Returns + ------- + Dictionary containing the entries: + in: torch.Tensor, shape (num_triplets,) + Indices of input edge b->a of each triplet b->a<-c + out: torch.Tensor, shape (num_triplets,) + Indices of output edge c->a of each triplet b->a<-c + out_agg: torch.Tensor, shape (num_triplets,) + Indices enumerating the intermediate edges of each output edge. + Used for creating a padded matrix and aggregating via matmul. + """ + idx_s, idx_t = graph["edge_index"] # c->a (source=c, target=a) + num_edges = idx_s.size(0) + + value = torch.arange(num_edges, device=idx_s.device, dtype=idx_s.dtype) + # Possibly contains multiple copies of the same edge (for periodic interactions) + adj = SparseTensor( + row=idx_t, + col=idx_s, + value=value, + sparse_sizes=(num_atoms, num_atoms), + ) + adj_edges = adj[idx_t] + + # Edge indices (b->a, c->a) for triplets. + idx = {} + idx["in"] = adj_edges.storage.value() + idx["out"] = adj_edges.storage.row() + + # Remove self-loop triplets + # Compare edge indices, not atom indices to correctly handle periodic interactions + mask = idx["in"] != idx["out"] + idx["in"] = idx["in"][mask] + idx["out"] = idx["out"][mask] + + # idx['out'] has to be sorted for this + idx["out_agg"] = get_inner_idx(idx["out"], dim_size=num_edges) + + return idx + + +def get_mixed_triplets( + graph_in, + graph_out, + num_atoms, + to_outedge=False, + return_adj=False, + return_agg_idx=False, +): + """ + Get all output edges (ingoing or outgoing) for each incoming edge. + It is possible that in atom=out atom, as long as the edges are distinct + (i.e. they stem from different unit cells). In edges and out edges stem + from separate graphs (hence "mixed") with shared atoms. + + Arguments + --------- + graph_in: dict of torch.Tensor + Contains the input graph's edge_index and cell_offset. + graph_out: dict of torch.Tensor + Contains the output graph's edge_index and cell_offset. + Input and output graphs use the same atoms, but different edges. + num_atoms: int + Total number of atoms. + to_outedge: bool + Whether to map the output to the atom's outgoing edges a->c + instead of the ingoing edges c->a. + return_adj: bool + Whether to output the adjacency (incidence) matrix between output + edges and atoms adj_edges. + return_agg_idx: bool + Whether to output the indices enumerating the intermediate edges + of each output edge. + + Returns + ------- + Dictionary containing the entries: + in: torch.Tensor, shape (num_triplets,) + Indices of input edges + out: torch.Tensor, shape (num_triplets,) + Indices of output edges + adj_edges: SparseTensor, shape (num_edges, num_atoms) + Adjacency (incidence) matrix between output edges and atoms, + with values specifying the input edges. + Only returned if return_adj is True. + out_agg: torch.Tensor, shape (num_triplets,) + Indices enumerating the intermediate edges of each output edge. + Used for creating a padded matrix and aggregating via matmul. + Only returned if return_agg_idx is True. + """ + idx_out_s, idx_out_t = graph_out["edge_index"] + # c->a (source=c, target=a) + idx_in_s, idx_in_t = graph_in["edge_index"] + num_edges = idx_out_s.size(0) + + value_in = torch.arange( + idx_in_s.size(0), device=idx_in_s.device, dtype=idx_in_s.dtype + ) + # This exploits that SparseTensor can have multiple copies of the same edge! + adj_in = SparseTensor( + row=idx_in_t, + col=idx_in_s, + value=value_in, + sparse_sizes=(num_atoms, num_atoms), + ) + if to_outedge: + adj_edges = adj_in[idx_out_s] + else: + adj_edges = adj_in[idx_out_t] + + # Edge indices (b->a, c->a) for triplets. + idx_in = adj_edges.storage.value() + idx_out = adj_edges.storage.row() + + # Remove self-loop triplets c->a<-c or c<-a<-c + # Check atom as well as cell offset + if to_outedge: + idx_atom_in = idx_in_s[idx_in] + idx_atom_out = idx_out_t[idx_out] + cell_offsets_sum = ( + graph_out["cell_offset"][idx_out] + graph_in["cell_offset"][idx_in] + ) + else: + idx_atom_in = idx_in_s[idx_in] + idx_atom_out = idx_out_s[idx_out] + cell_offsets_sum = ( + graph_out["cell_offset"][idx_out] - graph_in["cell_offset"][idx_in] + ) + mask = (idx_atom_in != idx_atom_out) | torch.any( + cell_offsets_sum != 0, dim=-1 + ) + + idx = {} + if return_adj: + idx["adj_edges"] = masked_select_sparsetensor_flat(adj_edges, mask) + idx["in"] = idx["adj_edges"].storage.value().clone() + idx["out"] = idx["adj_edges"].storage.row() + else: + idx["in"] = idx_in[mask] + idx["out"] = idx_out[mask] + + if return_agg_idx: + # idx['out'] has to be sorted + idx["out_agg"] = get_inner_idx(idx["out"], dim_size=num_edges) + + return idx + + +def get_quadruplets( + main_graph, + qint_graph, + num_atoms, +): + """ + Get all d->b for each edge c->a and connection b->a + Careful about periodic images! + Separate interaction cutoff not supported. + + Arguments + --------- + main_graph: dict of torch.Tensor + Contains the main graph's edge_index and cell_offset. + The main graph defines which edges are embedded. + qint_graph: dict of torch.Tensor + Contains the quadruplet interaction graph's edge_index and + cell_offset. main_graph and qint_graph use the same atoms, + but different edges. + num_atoms: int + Total number of atoms. + + Returns + ------- + Dictionary containing the entries: + triplet_in['in']: torch.Tensor, shape (nTriplets,) + Indices of input edge d->b in triplet d->b->a. + triplet_in['out']: torch.Tensor, shape (nTriplets,) + Interaction indices of output edge b->a in triplet d->b->a. + triplet_out['in']: torch.Tensor, shape (nTriplets,) + Interaction indices of input edge b->a in triplet c->a<-b. + triplet_out['out']: torch.Tensor, shape (nTriplets,) + Indices of output edge c->a in triplet c->a<-b. + out: torch.Tensor, shape (nQuadruplets,) + Indices of output edge c->a in quadruplet + trip_in_to_quad: torch.Tensor, shape (nQuadruplets,) + Indices to map from input triplet d->b->a + to quadruplet d->b->a<-c. + trip_out_to_quad: torch.Tensor, shape (nQuadruplets,) + Indices to map from output triplet c->a<-b + to quadruplet d->b->a<-c. + out_agg: torch.Tensor, shape (num_triplets,) + Indices enumerating the intermediate edges of each output edge. + Used for creating a padded matrix and aggregating via matmul. + """ + idx_s, _ = main_graph["edge_index"] + idx_qint_s, _ = qint_graph["edge_index"] + # c->a (source=c, target=a) + num_edges = idx_s.size(0) + idx = {} + + idx["triplet_in"] = get_mixed_triplets( + main_graph, + qint_graph, + num_atoms, + to_outedge=True, + return_adj=True, + ) + # Input triplets d->b->a + + idx["triplet_out"] = get_mixed_triplets( + qint_graph, + main_graph, + num_atoms, + to_outedge=False, + ) + # Output triplets c->a<-b + + # ---------------- Quadruplets ----------------- + # Repeat indices by counting the number of input triplets per + # intermediate edge ba. segment_coo assumes sorted idx['triplet_in']['out'] + ones = ( + idx["triplet_in"]["out"] + .new_ones(1) + .expand_as(idx["triplet_in"]["out"]) + ) + num_trip_in_per_inter = segment_coo( + ones, idx["triplet_in"]["out"], dim_size=idx_qint_s.size(0) + ) + + num_trip_out_per_inter = num_trip_in_per_inter[idx["triplet_out"]["in"]] + idx["out"] = torch.repeat_interleave( + idx["triplet_out"]["out"], num_trip_out_per_inter + ) + idx_inter = torch.repeat_interleave( + idx["triplet_out"]["in"], num_trip_out_per_inter + ) + idx["trip_out_to_quad"] = torch.repeat_interleave( + torch.arange( + len(idx["triplet_out"]["out"]), + device=idx_s.device, + dtype=idx_s.dtype, + ), + num_trip_out_per_inter, + ) + + # Generate input indices by using the adjacency + # matrix idx['triplet_in']['adj_edges'] + idx["triplet_in"]["adj_edges"].set_value_( + torch.arange( + len(idx["triplet_in"]["in"]), + device=idx_s.device, + dtype=idx_s.dtype, + ), + layout="coo", + ) + adj_trip_in_per_trip_out = idx["triplet_in"]["adj_edges"][ + idx["triplet_out"]["in"] + ] + # Rows in adj_trip_in_per_trip_out are intermediate edges ba + idx["trip_in_to_quad"] = adj_trip_in_per_trip_out.storage.value() + idx_in = idx["triplet_in"]["in"][idx["trip_in_to_quad"]] + + # Remove quadruplets with c == d + # Triplets should already ensure that a != d and b != c + # Compare atom indices and cell offsets + idx_atom_c = idx_s[idx["out"]] + idx_atom_d = idx_s[idx_in] + + cell_offset_cd = ( + main_graph["cell_offset"][idx_in] + + qint_graph["cell_offset"][idx_inter] + - main_graph["cell_offset"][idx["out"]] + ) + mask_cd = (idx_atom_c != idx_atom_d) | torch.any( + cell_offset_cd != 0, dim=-1 + ) + + idx["out"] = idx["out"][mask_cd] + idx["trip_out_to_quad"] = idx["trip_out_to_quad"][mask_cd] + idx["trip_in_to_quad"] = idx["trip_in_to_quad"][mask_cd] + + # idx['out'] has to be sorted for this + idx["out_agg"] = get_inner_idx(idx["out"], dim_size=num_edges) + + return idx diff --git a/ocpmodels/models/gemnet_oc/layers/__init__.py b/ocpmodels/models/gemnet_oc/layers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/gemnet_oc/layers/atom_update_block.py b/ocpmodels/models/gemnet_oc/layers/atom_update_block.py new file mode 100644 index 0000000..c49758e --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/atom_update_block.py @@ -0,0 +1,196 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import torch +from torch_scatter import scatter + +from ocpmodels.common.utils import scatter_det +from ocpmodels.modules.scaling import ScaleFactor + +from ..initializers import get_initializer +from .base_layers import Dense, ResidualLayer + + +class AtomUpdateBlock(torch.nn.Module): + """ + Aggregate the message embeddings of the atoms + + Arguments + --------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_edge: int + Embedding size of the edges. + emb_size_rbf: int + Embedding size of the radial basis. + nHidden: int + Number of residual blocks. + activation: callable/str + Name of the activation function to use in the dense layers. + """ + + def __init__( + self, + emb_size_atom: int, + emb_size_edge: int, + emb_size_rbf: int, + nHidden: int, + activation=None, + ): + super().__init__() + + self.dense_rbf = Dense( + emb_size_rbf, emb_size_edge, activation=None, bias=False + ) + self.scale_sum = ScaleFactor() + + self.layers = self.get_mlp( + emb_size_edge, emb_size_atom, nHidden, activation + ) + + def get_mlp(self, units_in, units, nHidden, activation): + if units_in != units: + dense1 = Dense(units_in, units, activation=activation, bias=False) + mlp = [dense1] + else: + mlp = [] + res = [ + ResidualLayer(units, nLayers=2, activation=activation) + for i in range(nHidden) + ] + mlp += res + return torch.nn.ModuleList(mlp) + + def forward(self, h, m, basis_rad, idx_atom): + """ + Returns + ------- + h: torch.Tensor, shape=(nAtoms, emb_size_atom) + Atom embedding. + """ + nAtoms = h.shape[0] + + bases_emb = self.dense_rbf(basis_rad) # (nEdges, emb_size_edge) + x = m * bases_emb + + x2 = scatter_det( + x, idx_atom, dim=0, dim_size=nAtoms, reduce="sum" + ) # (nAtoms, emb_size_edge) + x = self.scale_sum(x2, ref=m) + + for layer in self.layers: + x = layer(x) # (nAtoms, emb_size_atom) + + return x + + +class OutputBlock(AtomUpdateBlock): + """ + Combines the atom update block and subsequent final dense layer. + + Arguments + --------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_edge: int + Embedding size of the edges. + emb_size_rbf: int + Embedding size of the radial basis. + nHidden: int + Number of residual blocks before adding the atom embedding. + nHidden_afteratom: int + Number of residual blocks after adding the atom embedding. + activation: str + Name of the activation function to use in the dense layers. + direct_forces: bool + If true directly predict forces, i.e. without taking the gradient + of the energy potential. + """ + + def __init__( + self, + emb_size_atom: int, + emb_size_edge: int, + emb_size_rbf: int, + nHidden: int, + nHidden_afteratom: int, + activation=None, + direct_forces=True, + ): + super().__init__( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=nHidden, + activation=activation, + ) + + self.direct_forces = direct_forces + + self.seq_energy_pre = self.layers # inherited from parent class + if nHidden_afteratom >= 1: + self.seq_energy2 = self.get_mlp( + emb_size_atom, emb_size_atom, nHidden_afteratom, activation + ) + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + else: + self.seq_energy2 = None + + if self.direct_forces: + self.scale_rbf_F = ScaleFactor() + self.seq_forces = self.get_mlp( + emb_size_edge, emb_size_edge, nHidden, activation + ) + self.dense_rbf_F = Dense( + emb_size_rbf, emb_size_edge, activation=None, bias=False + ) + + def forward(self, h, m, basis_rad, idx_atom): + """ + Returns + ------- + torch.Tensor, shape=(nAtoms, emb_size_atom) + Output atom embeddings. + torch.Tensor, shape=(nEdges, emb_size_edge) + Output edge embeddings. + """ + nAtoms = h.shape[0] + + # ------------------------ Atom embeddings ------------------------ # + basis_emb_E = self.dense_rbf(basis_rad) # (nEdges, emb_size_edge) + x = m * basis_emb_E + + x_E = scatter_det( + x, idx_atom, dim=0, dim_size=nAtoms, reduce="sum" + ) # (nAtoms, emb_size_edge) + x_E = self.scale_sum(x_E, ref=m) + + for layer in self.seq_energy_pre: + x_E = layer(x_E) # (nAtoms, emb_size_atom) + + if self.seq_energy2 is not None: + x_E = x_E + h + x_E = x_E * self.inv_sqrt_2 + for layer in self.seq_energy2: + x_E = layer(x_E) # (nAtoms, emb_size_atom) + + # ------------------------- Edge embeddings ------------------------ # + if self.direct_forces: + x_F = m + for i, layer in enumerate(self.seq_forces): + x_F = layer(x_F) # (nEdges, emb_size_edge) + + basis_emb_F = self.dense_rbf_F(basis_rad) + # (nEdges, emb_size_edge) + x_F_basis = x_F * basis_emb_F + x_F = self.scale_rbf_F(x_F_basis, ref=x_F) + else: + x_F = 0 + # ------------------------------------------------------------------ # + + return x_E, x_F diff --git a/ocpmodels/models/gemnet_oc/layers/base_layers.py b/ocpmodels/models/gemnet_oc/layers/base_layers.py new file mode 100644 index 0000000..2e3c9ac --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/base_layers.py @@ -0,0 +1,105 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import torch + +from ..initializers import he_orthogonal_init + + +class Dense(torch.nn.Module): + """ + Combines dense layer with scaling for silu activation. + + Arguments + --------- + in_features: int + Input embedding size. + out_features: int + Output embedding size. + bias: bool + True if use bias. + activation: str + Name of the activation function to use. + """ + + def __init__(self, in_features, out_features, bias=False, activation=None): + super().__init__() + + self.linear = torch.nn.Linear(in_features, out_features, bias=bias) + self.reset_parameters() + + if isinstance(activation, str): + activation = activation.lower() + if activation in ["silu", "swish"]: + self._activation = ScaledSiLU() + elif activation is None: + self._activation = torch.nn.Identity() + else: + raise NotImplementedError( + "Activation function not implemented for GemNet (yet)." + ) + + def reset_parameters(self, initializer=he_orthogonal_init): + initializer(self.linear.weight) + if self.linear.bias is not None: + self.linear.bias.data.fill_(0) + + def forward(self, x): + x = self.linear(x) + x = self._activation(x) + return x + + +class ScaledSiLU(torch.nn.Module): + def __init__(self): + super().__init__() + self.scale_factor = 1 / 0.6 + self._activation = torch.nn.SiLU() + + def forward(self, x): + return self._activation(x) * self.scale_factor + + +class ResidualLayer(torch.nn.Module): + """ + Residual block with output scaled by 1/sqrt(2). + + Arguments + --------- + units: int + Input and output embedding size. + nLayers: int + Number of dense layers. + layer: torch.nn.Module + Class for the layers inside the residual block. + layer_kwargs: str + Keyword arguments for initializing the layers. + """ + + def __init__( + self, units: int, nLayers: int = 2, layer=Dense, **layer_kwargs + ): + super().__init__() + self.dense_mlp = torch.nn.Sequential( + *[ + layer( + in_features=units, + out_features=units, + bias=False, + **layer_kwargs + ) + for _ in range(nLayers) + ] + ) + self.inv_sqrt_2 = 1 / math.sqrt(2) + + def forward(self, input): + x = self.dense_mlp(input) + x = input + x + x = x * self.inv_sqrt_2 + return x diff --git a/ocpmodels/models/gemnet_oc/layers/basis_utils.py b/ocpmodels/models/gemnet_oc/layers/basis_utils.py new file mode 100644 index 0000000..66c5447 --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/basis_utils.py @@ -0,0 +1,327 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import sympy as sym +import torch +from scipy import special as sp +from scipy.optimize import brentq + + +def Jn(r, n): + """ + numerical spherical bessel functions of order n + """ + return sp.spherical_jn(n, r) + + +def Jn_zeros(n, k): + """ + Compute the first k zeros of the spherical bessel functions + up to order n (excluded) + """ + zerosj = np.zeros((n, k), dtype="float32") + zerosj[0] = np.arange(1, k + 1) * np.pi + points = np.arange(1, k + n) * np.pi + racines = np.zeros(k + n - 1, dtype="float32") + for i in range(1, n): + for j in range(k + n - 1 - i): + foo = brentq(Jn, points[j], points[j + 1], (i,)) + racines[j] = foo + points = racines + zerosj[i][:k] = racines[:k] + + return zerosj + + +def spherical_bessel_formulas(n): + """ + Computes the sympy formulas for the spherical bessel functions + up to order n (excluded) + """ + x = sym.symbols("x", real=True) + # j_i = (-x)^i * (1/x * d/dx)^î * sin(x)/x + j = [sym.sin(x) / x] # j_0 + a = sym.sin(x) / x + for i in range(1, n): + b = sym.diff(a, x) / x + j += [sym.simplify(b * (-x) ** i)] + a = sym.simplify(b) + return j + + +def bessel_basis(n, k): + """ + Compute the sympy formulas for the normalized and rescaled spherical bessel + functions up to order n (excluded) and maximum frequency k (excluded). + + Returns + ------- + bess_basis: list + Bessel basis formulas taking in a single argument x. + Has length n where each element has length k. -> In total n*k many. + """ + zeros = Jn_zeros(n, k) + normalizer = [] + for order in range(n): + normalizer_tmp = [] + for i in range(k): + normalizer_tmp += [0.5 * Jn(zeros[order, i], order + 1) ** 2] + normalizer_tmp = ( + 1 / np.array(normalizer_tmp) ** 0.5 + ) # sqrt(2/(j_l+1)**2) , sqrt(1/c**3) not taken into account yet + normalizer += [normalizer_tmp] + + f = spherical_bessel_formulas(n) + x = sym.symbols("x", real=True) + bess_basis = [] + for order in range(n): + bess_basis_tmp = [] + for i in range(k): + bess_basis_tmp += [ + sym.simplify( + normalizer[order][i] + * f[order].subs(x, zeros[order, i] * x) + ) + ] + bess_basis += [bess_basis_tmp] + return bess_basis + + +def sph_harm_prefactor(l_degree, m_order): + """ + Computes the constant pre-factor for the spherical harmonic + of degree l and order m. + + Arguments + --------- + l_degree: int + Degree of the spherical harmonic. l >= 0 + m_order: int + Order of the spherical harmonic. -l <= m <= l + + Returns + ------- + factor: float + + """ + # sqrt((2*l+1)/4*pi * (l-m)!/(l+m)! ) + return ( + (2 * l_degree + 1) + / (4 * np.pi) + * np.math.factorial(l_degree - abs(m_order)) + / np.math.factorial(l_degree + abs(m_order)) + ) ** 0.5 + + +def associated_legendre_polynomials( + L_maxdegree, zero_m_only=True, pos_m_only=True +): + """ + Computes string formulas of the associated legendre polynomials + up to degree L (excluded). + + Arguments + --------- + L_maxdegree: int + Degree up to which to calculate the associated legendre polynomials + (degree L is excluded). + zero_m_only: bool + If True only calculate the polynomials for the polynomials where m=0. + pos_m_only: bool + If True only calculate the polynomials for the polynomials where m>=0. + Overwritten by zero_m_only. + + Returns + ------- + polynomials: list + Contains the sympy functions of the polynomials + (in total L many if zero_m_only is True else L^2 many). + """ + # calculations from http://web.cmb.usc.edu/people/alber/Software/tomominer/docs/cpp/group__legendre__polynomials.html + z = sym.symbols("z", real=True) + P_l_m = [ + [0] * (2 * l_degree + 1) for l_degree in range(L_maxdegree) + ] # for order l: -l <= m <= l + + P_l_m[0][0] = 1 + if L_maxdegree > 1: + if zero_m_only: + # m = 0 + P_l_m[1][0] = z + for l_degree in range(2, L_maxdegree): + P_l_m[l_degree][0] = sym.simplify( + ( + (2 * l_degree - 1) * z * P_l_m[l_degree - 1][0] + - (l_degree - 1) * P_l_m[l_degree - 2][0] + ) + / l_degree + ) + return P_l_m + else: + # for m >= 0 + for l_degree in range(1, L_maxdegree): + P_l_m[l_degree][l_degree] = sym.simplify( + (1 - 2 * l_degree) + * (1 - z**2) ** 0.5 + * P_l_m[l_degree - 1][l_degree - 1] + ) # P_00, P_11, P_22, P_33 + + for m_order in range(0, L_maxdegree - 1): + P_l_m[m_order + 1][m_order] = sym.simplify( + (2 * m_order + 1) * z * P_l_m[m_order][m_order] + ) # P_10, P_21, P_32, P_43 + + for l_degree in range(2, L_maxdegree): + for m_order in range(l_degree - 1): # P_20, P_30, P_31 + P_l_m[l_degree][m_order] = sym.simplify( + ( + (2 * l_degree - 1) + * z + * P_l_m[l_degree - 1][m_order] + - (l_degree + m_order - 1) + * P_l_m[l_degree - 2][m_order] + ) + / (l_degree - m_order) + ) + + if not pos_m_only: + # for m < 0: P_l(-m) = (-1)^m * (l-m)!/(l+m)! * P_lm + for l_degree in range(1, L_maxdegree): + for m_order in range( + 1, l_degree + 1 + ): # P_1(-1), P_2(-1) P_2(-2) + P_l_m[l_degree][-m_order] = sym.simplify( + (-1) ** m_order + * np.math.factorial(l_degree - m_order) + / np.math.factorial(l_degree + m_order) + * P_l_m[l_degree][m_order] + ) + + return P_l_m + + +def real_sph_harm(L_maxdegree, use_theta, use_phi=True, zero_m_only=True): + """ + Computes formula strings of the the real part of the spherical harmonics + up to degree L (excluded). Variables are either spherical coordinates phi + and theta (or cartesian coordinates x,y,z) on the UNIT SPHERE. + + Arguments + --------- + L_maxdegree: int + Degree up to which to calculate the spherical harmonics + (degree L is excluded). + use_theta: bool + - True: Expects the input of the formula strings to contain theta. + - False: Expects the input of the formula strings to contain z. + use_phi: bool + - True: Expects the input of the formula strings to contain phi. + - False: Expects the input of the formula strings to contain x and y. + Does nothing if zero_m_only is True + zero_m_only: bool + If True only calculate the harmonics where m=0. + + Returns + ------- + Y_lm_real: list + Computes formula strings of the the real part of the spherical + harmonics up to degree L (where degree L is not excluded). + In total L^2 many sph harm exist up to degree L (excluded). + However, if zero_m_only only is True then the total count + is reduced to L. + """ + z = sym.symbols("z", real=True) + P_l_m = associated_legendre_polynomials(L_maxdegree, zero_m_only) + if zero_m_only: + # for all m != 0: Y_lm = 0 + Y_l_m = [[0] for l_degree in range(L_maxdegree)] + else: + Y_l_m = [ + [0] * (2 * l_degree + 1) for l_degree in range(L_maxdegree) + ] # for order l: -l <= m <= l + + # convert expressions to spherical coordiantes + if use_theta: + # replace z by cos(theta) + theta = sym.symbols("theta", real=True) + for l_degree in range(L_maxdegree): + for m_order in range(len(P_l_m[l_degree])): + if not isinstance(P_l_m[l_degree][m_order], int): + P_l_m[l_degree][m_order] = P_l_m[l_degree][m_order].subs( + z, sym.cos(theta) + ) + + ## calculate Y_lm + # Y_lm = N * P_lm(cos(theta)) * exp(i*m*phi) + # { sqrt(2) * (-1)^m * N * P_l|m| * sin(|m|*phi) if m < 0 + # Y_lm_real = { Y_lm if m = 0 + # { sqrt(2) * (-1)^m * N * P_lm * cos(m*phi) if m > 0 + + for l_degree in range(L_maxdegree): + Y_l_m[l_degree][0] = sym.simplify( + sph_harm_prefactor(l_degree, 0) * P_l_m[l_degree][0] + ) # Y_l0 + + if not zero_m_only: + phi = sym.symbols("phi", real=True) + for l_degree in range(1, L_maxdegree): + # m > 0 + for m_order in range(1, l_degree + 1): + Y_l_m[l_degree][m_order] = sym.simplify( + 2**0.5 + * (-1) ** m_order + * sph_harm_prefactor(l_degree, m_order) + * P_l_m[l_degree][m_order] + * sym.cos(m_order * phi) + ) + # m < 0 + for m_order in range(1, l_degree + 1): + Y_l_m[l_degree][-m_order] = sym.simplify( + 2**0.5 + * (-1) ** m_order + * sph_harm_prefactor(l_degree, -m_order) + * P_l_m[l_degree][m_order] + * sym.sin(m_order * phi) + ) + + # convert expressions to cartesian coordinates + if not use_phi: + # replace phi by atan2(y,x) + x, y = sym.symbols("x y", real=True) + for l_degree in range(L_maxdegree): + for m_order in range(len(Y_l_m[l_degree])): + Y_l_m[l_degree][m_order] = sym.simplify( + Y_l_m[l_degree][m_order].subs(phi, sym.atan2(y, x)) + ) + return Y_l_m + + +def get_sph_harm_basis(L_maxdegree, zero_m_only=True): + """Get a function calculating the spherical harmonics basis from z and phi.""" + # retrieve equations + Y_lm = real_sph_harm( + L_maxdegree, use_theta=False, use_phi=True, zero_m_only=zero_m_only + ) + Y_lm_flat = [Y for Y_l in Y_lm for Y in Y_l] + + # convert to pytorch functions + z = sym.symbols("z", real=True) + variables = [z] + if not zero_m_only: + variables.append(sym.symbols("phi", real=True)) + + modules = {"sin": torch.sin, "cos": torch.cos, "sqrt": torch.sqrt} + sph_funcs = sym.lambdify(variables, Y_lm_flat, modules) + + # Return as a single function + # args are either [cosφ] or [cosφ, ϑ] + def basis_fn(*args): + basis = sph_funcs(*args) + basis[0] = args[0].new_tensor(basis[0]).expand_as(args[0]) + return torch.stack(basis, dim=1) + + return basis_fn diff --git a/ocpmodels/models/gemnet_oc/layers/efficient.py b/ocpmodels/models/gemnet_oc/layers/efficient.py new file mode 100644 index 0000000..1697aeb --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/efficient.py @@ -0,0 +1,269 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from typing import Optional + +import torch +from torch_scatter import scatter + +from ..initializers import he_orthogonal_init +from .base_layers import Dense + + +class BasisEmbedding(torch.nn.Module): + """ + Embed a basis (CBF, SBF), optionally using the efficient reformulation. + + Arguments + --------- + num_radial: int + Number of radial basis functions. + emb_size_interm: int + Intermediate embedding size of triplets/quadruplets. + num_spherical: int + Number of circular/spherical basis functions. + Only required if there is a circular/spherical basis. + """ + + def __init__( + self, + num_radial: int, + emb_size_interm: int, + num_spherical: Optional[int] = None, + ): + super().__init__() + self.num_radial = num_radial + self.num_spherical = num_spherical + if num_spherical is None: + self.weight = torch.nn.Parameter( + torch.empty(emb_size_interm, num_radial), + requires_grad=True, + ) + else: + self.weight = torch.nn.Parameter( + torch.empty(num_radial, num_spherical, emb_size_interm), + requires_grad=True, + ) + self.reset_parameters() + + def reset_parameters(self): + he_orthogonal_init(self.weight) + + def forward( + self, + rad_basis, + sph_basis=None, + idx_rad_outer=None, + idx_rad_inner=None, + idx_sph_outer=None, + idx_sph_inner=None, + num_atoms=None, + ): + """ + + Arguments + --------- + rad_basis: torch.Tensor, shape=(num_edges, num_radial or num_orders * num_radial) + Raw radial basis. + sph_basis: torch.Tensor, shape=(num_triplets or num_quadruplets, num_spherical) + Raw spherical or circular basis. + idx_rad_outer: torch.Tensor, shape=(num_edges) + Atom associated with each radial basis value. + Optional, used for efficient edge aggregation. + idx_rad_inner: torch.Tensor, shape=(num_edges) + Enumerates radial basis values per atom. + Optional, used for efficient edge aggregation. + idx_sph_outer: torch.Tensor, shape=(num_triplets or num_quadruplets) + Edge associated with each circular/spherical basis value. + Optional, used for efficient triplet/quadruplet aggregation. + idx_sph_inner: torch.Tensor, shape=(num_triplets or num_quadruplets) + Enumerates circular/spherical basis values per edge. + Optional, used for efficient triplet/quadruplet aggregation. + num_atoms: int + Total number of atoms. + Optional, used for efficient edge aggregation. + + Returns + ------- + rad_W1: torch.Tensor, shape=(num_edges, emb_size_interm, num_spherical) + sph: torch.Tensor, shape=(num_edges, Kmax, num_spherical) + Kmax = maximum number of neighbors of the edges + """ + num_edges = rad_basis.shape[0] + + if self.num_spherical is not None: + # MatMul: mul + sum over num_radial + rad_W1 = rad_basis @ self.weight.reshape(self.weight.shape[0], -1) + # (num_edges, emb_size_interm * num_spherical) + rad_W1 = rad_W1.reshape(num_edges, -1, sph_basis.shape[-1]) + # (num_edges, emb_size_interm, num_spherical) + else: + # MatMul: mul + sum over num_radial + rad_W1 = rad_basis @ self.weight.T + # (num_edges, emb_size_interm) + + if idx_rad_inner is not None: + # Zero padded dense matrix + # maximum number of neighbors + if idx_rad_outer.shape[0] == 0: + # catch empty idx_rad_outer + Kmax = 0 + else: + Kmax = torch.max(idx_rad_inner) + 1 + + rad_W1_padded = rad_W1.new_zeros( + [num_atoms, Kmax] + list(rad_W1.shape[1:]) + ) + rad_W1_padded[idx_rad_outer, idx_rad_inner] = rad_W1 + # (num_atoms, Kmax, emb_size_interm, ...) + rad_W1_padded = torch.transpose(rad_W1_padded, 1, 2) + # (num_atoms, emb_size_interm, Kmax, ...) + rad_W1_padded = rad_W1_padded.reshape( + num_atoms, rad_W1.shape[1], -1 + ) + # (num_atoms, emb_size_interm, Kmax2 * ...) + rad_W1 = rad_W1_padded + + if idx_sph_inner is not None: + # Zero padded dense matrix + # maximum number of neighbors + if idx_sph_outer.shape[0] == 0: + # catch empty idx_sph_outer + Kmax = 0 + else: + Kmax = torch.max(idx_sph_inner) + 1 + + sph2 = sph_basis.new_zeros(num_edges, Kmax, sph_basis.shape[-1]) + sph2[idx_sph_outer, idx_sph_inner] = sph_basis + # (num_edges, Kmax, num_spherical) + sph2 = torch.transpose(sph2, 1, 2) + # (num_edges, num_spherical, Kmax) + + if sph_basis is None: + return rad_W1 + else: + if idx_sph_inner is None: + rad_W1 = rad_W1[idx_sph_outer] + # (num_triplets, emb_size_interm, num_spherical) + + sph_W1 = rad_W1 @ sph_basis[:, :, None] + # (num_triplets, emb_size_interm, num_spherical) + return sph_W1.squeeze(-1) + else: + return rad_W1, sph2 + + +class EfficientInteractionBilinear(torch.nn.Module): + """ + Efficient reformulation of the bilinear layer and subsequent summation. + + Arguments + --------- + emb_size_in: int + Embedding size of input triplets/quadruplets. + emb_size_interm: int + Intermediate embedding size of the basis transformation. + emb_size_out: int + Embedding size of output triplets/quadruplets. + """ + + def __init__( + self, + emb_size_in: int, + emb_size_interm: int, + emb_size_out: int, + ): + super().__init__() + self.emb_size_in = emb_size_in + self.emb_size_interm = emb_size_interm + self.emb_size_out = emb_size_out + + self.bilinear = Dense( + self.emb_size_in * self.emb_size_interm, + self.emb_size_out, + bias=False, + activation=None, + ) + + def forward( + self, + basis, + m, + idx_agg_outer, + idx_agg_inner, + idx_agg2_outer=None, + idx_agg2_inner=None, + agg2_out_size=None, + ): + """ + + Arguments + --------- + basis: Tuple (torch.Tensor, torch.Tensor), + shapes=((num_edges, emb_size_interm, num_spherical), + (num_edges, num_spherical, Kmax)) + First element: Radial basis multiplied with weight matrix + Second element: Circular/spherical basis + m: torch.Tensor, shape=(num_edges, emb_size_in) + Input edge embeddings + idx_agg_outer: torch.Tensor, shape=(num_triplets or num_quadruplets) + Output edge aggregating this intermediate triplet/quadruplet edge. + idx_agg_inner: torch.Tensor, shape=(num_triplets or num_quadruplets) + Enumerates intermediate edges per output edge. + idx_agg2_outer: torch.Tensor, shape=(num_edges) + Output atom aggregating this edge. + idx_agg2_inner: torch.Tensor, shape=(num_edges) + Enumerates edges per output atom. + agg2_out_size: int + Number of output embeddings when aggregating twice. Typically + the number of atoms. + + Returns + ------- + m_ca: torch.Tensor, shape=(num_edges, emb_size) + Aggregated edge/atom embeddings. + """ + # num_spherical is actually num_spherical**2 for quadruplets + (rad_W1, sph) = basis + # (num_edges, emb_size_interm, num_spherical), + # (num_edges, num_spherical, Kmax) + num_edges = sph.shape[0] + + # Create (zero-padded) dense matrix of the neighboring edge embeddings. + Kmax = torch.max(idx_agg_inner) + 1 + m_padded = m.new_zeros(num_edges, Kmax, self.emb_size_in) + m_padded[idx_agg_outer, idx_agg_inner] = m + # (num_quadruplets/num_triplets, emb_size_in) -> (num_edges, Kmax, emb_size_in) + + sph_m = torch.matmul(sph, m_padded) + # (num_edges, num_spherical, emb_size_in) + + if idx_agg2_outer is not None: + Kmax2 = torch.max(idx_agg2_inner) + 1 + sph_m_padded = sph_m.new_zeros( + agg2_out_size, Kmax2, sph_m.shape[1], sph_m.shape[2] + ) + sph_m_padded[idx_agg2_outer, idx_agg2_inner] = sph_m + # (num_atoms, Kmax2, num_spherical, emb_size_in) + sph_m_padded = sph_m_padded.reshape( + agg2_out_size, -1, sph_m.shape[-1] + ) + # (num_atoms, Kmax2 * num_spherical, emb_size_in) + + rad_W1_sph_m = rad_W1 @ sph_m_padded + # (num_atoms, emb_size_interm, emb_size_in) + else: + # MatMul: mul + sum over num_spherical + rad_W1_sph_m = torch.matmul(rad_W1, sph_m) + # (num_edges, emb_size_interm, emb_size_in) + + # Bilinear: Sum over emb_size_interm and emb_size_in + m_ca = self.bilinear( + rad_W1_sph_m.reshape(-1, rad_W1_sph_m.shape[1:].numel()) + ) + # (num_edges/num_atoms, emb_size_out) + + return m_ca diff --git a/ocpmodels/models/gemnet_oc/layers/embedding_block.py b/ocpmodels/models/gemnet_oc/layers/embedding_block.py new file mode 100644 index 0000000..2e9b638 --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/embedding_block.py @@ -0,0 +1,101 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch + +from .base_layers import Dense + + +class AtomEmbedding(torch.nn.Module): + """ + Initial atom embeddings based on the atom type + + Arguments + --------- + emb_size: int + Atom embeddings size + """ + + def __init__(self, emb_size, num_elements): + super().__init__() + self.emb_size = emb_size + + self.embeddings = torch.nn.Embedding(num_elements, emb_size) + # init by uniform distribution + torch.nn.init.uniform_( + self.embeddings.weight, a=-np.sqrt(3), b=np.sqrt(3) + ) + + def forward(self, Z): + """ + Returns + ------- + h: torch.Tensor, shape=(nAtoms, emb_size) + Atom embeddings. + """ + h = self.embeddings(Z - 1) # -1 because Z.min()=1 (==Hydrogen) + return h + + +class EdgeEmbedding(torch.nn.Module): + """ + Edge embedding based on the concatenation of atom embeddings + and a subsequent dense layer. + + Arguments + --------- + atom_features: int + Embedding size of the atom embedding. + edge_features: int + Embedding size of the input edge embedding. + out_features: int + Embedding size after the dense layer. + activation: str + Activation function used in the dense layer. + """ + + def __init__( + self, + atom_features, + edge_features, + out_features, + activation=None, + ): + super().__init__() + in_features = 2 * atom_features + edge_features + self.dense = Dense( + in_features, out_features, activation=activation, bias=False + ) + + def forward( + self, + h, + m, + edge_index, + ): + """ + Arguments + --------- + h: torch.Tensor, shape (num_atoms, atom_features) + Atom embeddings. + m: torch.Tensor, shape (num_edges, edge_features) + Radial basis in embedding block, + edge embedding in interaction block. + + Returns + ------- + m_st: torch.Tensor, shape=(nEdges, emb_size) + Edge embeddings. + """ + h_s = h[edge_index[0]] # shape=(nEdges, emb_size) + h_t = h[edge_index[1]] # shape=(nEdges, emb_size) + + m_st = torch.cat( + [h_s, h_t, m], dim=-1 + ) # (nEdges, 2*emb_size+nFeatures) + m_st = self.dense(m_st) # (nEdges, emb_size) + return m_st diff --git a/ocpmodels/models/gemnet_oc/layers/force_scaler.py b/ocpmodels/models/gemnet_oc/layers/force_scaler.py new file mode 100644 index 0000000..f444542 --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/force_scaler.py @@ -0,0 +1,94 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging + +import torch + + +class ForceScaler: + """ + Scales up the energy and then scales down the forces + to prevent NaNs and infs in calculations using AMP. + Inspired by torch.cuda.amp.GradScaler. + """ + + def __init__( + self, + init_scale=2.0**8, + growth_factor=2.0, + backoff_factor=0.5, + growth_interval=2000, + max_force_iters=50, + enabled=True, + ): + self.scale_factor = init_scale + self.growth_factor = growth_factor + self.backoff_factor = backoff_factor + self.growth_interval = growth_interval + self.max_force_iters = max_force_iters + self.enabled = enabled + self.finite_force_results = 0 + + def scale(self, energy): + return energy * self.scale_factor if self.enabled else energy + + def unscale(self, forces): + return forces / self.scale_factor if self.enabled else forces + + def calc_forces(self, energy, pos): + energy_scaled = self.scale(energy) + forces_scaled = -torch.autograd.grad( + energy_scaled, + pos, + grad_outputs=torch.ones_like(energy_scaled), + create_graph=True, + )[0] + # (nAtoms, 3) + forces = self.unscale(forces_scaled) + return forces + + def calc_forces_and_update(self, energy, pos): + if self.enabled: + found_nans_or_infs = True + force_iters = 0 + + # Re-calculate forces until everything is nice and finite. + while found_nans_or_infs: + forces = self.calc_forces(energy, pos) + + found_nans_or_infs = not torch.all(forces.isfinite()) + if found_nans_or_infs: + self.finite_force_results = 0 + + # Prevent infinite loop + force_iters += 1 + if force_iters == self.max_force_iters: + logging.warning( + "Too many non-finite force results in a batch. " + "Breaking scaling loop." + ) + break + else: + # Delete graph to save memory + del forces + else: + self.finite_force_results += 1 + self.update() + else: + forces = self.calc_forces(energy, pos) + return forces + + def update(self): + if self.finite_force_results == 0: + self.scale_factor *= self.backoff_factor + + if self.finite_force_results == self.growth_interval: + self.scale_factor *= self.growth_factor + self.finite_force_results = 0 + + logging.info(f"finite force step count: {self.finite_force_results}") + logging.info(f"scaling factor: {self.scale_factor}") diff --git a/ocpmodels/models/gemnet_oc/layers/interaction_block.py b/ocpmodels/models/gemnet_oc/layers/interaction_block.py new file mode 100644 index 0000000..6c61670 --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/interaction_block.py @@ -0,0 +1,758 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import torch + +from ocpmodels.modules.scaling import ScaleFactor + +from .atom_update_block import AtomUpdateBlock +from .base_layers import Dense, ResidualLayer +from .efficient import EfficientInteractionBilinear +from .embedding_block import EdgeEmbedding + + +class InteractionBlock(torch.nn.Module): + """ + Interaction block for GemNet-Q/dQ. + + Arguments + --------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_edge: int + Embedding size of the edges. + emb_size_trip_in: int + (Down-projected) embedding size of the quadruplet edge embeddings + before the bilinear layer. + emb_size_trip_out: int + (Down-projected) embedding size of the quadruplet edge embeddings + after the bilinear layer. + emb_size_quad_in: int + (Down-projected) embedding size of the quadruplet edge embeddings + before the bilinear layer. + emb_size_quad_out: int + (Down-projected) embedding size of the quadruplet edge embeddings + after the bilinear layer. + emb_size_a2a_in: int + Embedding size in the atom interaction before the bilinear layer. + emb_size_a2a_out: int + Embedding size in the atom interaction after the bilinear layer. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + emb_size_sbf: int + Embedding size of the spherical basis transformation (two angles). + num_before_skip: int + Number of residual blocks before the first skip connection. + num_after_skip: int + Number of residual blocks after the first skip connection. + num_concat: int + Number of residual blocks after the concatenation. + num_atom: int + Number of residual blocks in the atom embedding blocks. + num_atom_emb_layers: int + Number of residual blocks for transforming atom embeddings. + quad_interaction: bool + Whether to use quadruplet interactions. + atom_edge_interaction: bool + Whether to use atom-to-edge interactions. + edge_atom_interaction: bool + Whether to use edge-to-atom interactions. + atom_interaction: bool + Whether to use atom-to-atom interactions. + activation: str + Name of the activation function to use in the dense layers. + """ + + def __init__( + self, + emb_size_atom, + emb_size_edge, + emb_size_trip_in, + emb_size_trip_out, + emb_size_quad_in, + emb_size_quad_out, + emb_size_a2a_in, + emb_size_a2a_out, + emb_size_rbf, + emb_size_cbf, + emb_size_sbf, + num_before_skip, + num_after_skip, + num_concat, + num_atom, + num_atom_emb_layers=0, + quad_interaction=False, + atom_edge_interaction=False, + edge_atom_interaction=False, + atom_interaction=False, + activation=None, + ): + super().__init__() + + ## ------------------------ Message Passing ----------------------- ## + # Dense transformation of skip connection + self.dense_ca = Dense( + emb_size_edge, + emb_size_edge, + activation=activation, + bias=False, + ) + + # Triplet Interaction + self.trip_interaction = TripletInteraction( + emb_size_in=emb_size_edge, + emb_size_out=emb_size_edge, + emb_size_trip_in=emb_size_trip_in, + emb_size_trip_out=emb_size_trip_out, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + symmetric_mp=True, + swap_output=True, + activation=activation, + ) + + # Quadruplet Interaction + if quad_interaction: + self.quad_interaction = QuadrupletInteraction( + emb_size_edge=emb_size_edge, + emb_size_quad_in=emb_size_quad_in, + emb_size_quad_out=emb_size_quad_out, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + emb_size_sbf=emb_size_sbf, + symmetric_mp=True, + activation=activation, + ) + else: + self.quad_interaction = None + + if atom_edge_interaction: + self.atom_edge_interaction = TripletInteraction( + emb_size_in=emb_size_atom, + emb_size_out=emb_size_edge, + emb_size_trip_in=emb_size_trip_in, + emb_size_trip_out=emb_size_trip_out, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + symmetric_mp=True, + swap_output=True, + activation=activation, + ) + else: + self.atom_edge_interaction = None + if edge_atom_interaction: + self.edge_atom_interaction = TripletInteraction( + emb_size_in=emb_size_edge, + emb_size_out=emb_size_atom, + emb_size_trip_in=emb_size_trip_in, + emb_size_trip_out=emb_size_trip_out, + emb_size_rbf=emb_size_rbf, + emb_size_cbf=emb_size_cbf, + symmetric_mp=False, + swap_output=False, + activation=activation, + ) + else: + self.edge_atom_interaction = None + if atom_interaction: + self.atom_interaction = PairInteraction( + emb_size_atom=emb_size_atom, + emb_size_pair_in=emb_size_a2a_in, + emb_size_pair_out=emb_size_a2a_out, + emb_size_rbf=emb_size_rbf, + activation=activation, + ) + else: + self.atom_interaction = None + + ## -------------------- Update Edge Embeddings -------------------- ## + # Residual layers before skip connection + self.layers_before_skip = torch.nn.ModuleList( + [ + ResidualLayer( + emb_size_edge, + activation=activation, + ) + for i in range(num_before_skip) + ] + ) + + # Residual layers after skip connection + self.layers_after_skip = torch.nn.ModuleList( + [ + ResidualLayer( + emb_size_edge, + activation=activation, + ) + for i in range(num_after_skip) + ] + ) + + ## -------------------- Update Atom Embeddings -------------------- ## + self.atom_emb_layers = torch.nn.ModuleList( + [ + ResidualLayer( + emb_size_atom, + activation=activation, + ) + for _ in range(num_atom_emb_layers) + ] + ) + + self.atom_update = AtomUpdateBlock( + emb_size_atom=emb_size_atom, + emb_size_edge=emb_size_edge, + emb_size_rbf=emb_size_rbf, + nHidden=num_atom, + activation=activation, + ) + + ## ---------- Update Edge Embeddings with Atom Embeddings --------- ## + self.concat_layer = EdgeEmbedding( + emb_size_atom, + emb_size_edge, + emb_size_edge, + activation=activation, + ) + self.residual_m = torch.nn.ModuleList( + [ + ResidualLayer(emb_size_edge, activation=activation) + for _ in range(num_concat) + ] + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + num_eint = 2.0 + quad_interaction + atom_edge_interaction + self.inv_sqrt_num_eint = 1 / math.sqrt(num_eint) + num_aint = 1.0 + edge_atom_interaction + atom_interaction + self.inv_sqrt_num_aint = 1 / math.sqrt(num_aint) + + def forward( + self, + h, + m, + bases_qint, + bases_e2e, + bases_a2e, + bases_e2a, + basis_a2a_rad, + basis_atom_update, + edge_index_main, + a2ee2a_graph, + a2a_graph, + id_swap, + trip_idx_e2e, + trip_idx_a2e, + trip_idx_e2a, + quad_idx, + ): + """ + Returns + ------- + h: torch.Tensor, shape=(nEdges, emb_size_atom) + Atom embeddings. + m: torch.Tensor, shape=(nEdges, emb_size_edge) + Edge embeddings (c->a). + """ + num_atoms = h.shape[0] + + # Initial transformation + x_ca_skip = self.dense_ca(m) # (nEdges, emb_size_edge) + + x_e2e = self.trip_interaction( + m, + bases_e2e, + trip_idx_e2e, + id_swap, + ) + if self.quad_interaction is not None: + x_qint = self.quad_interaction( + m, + bases_qint, + quad_idx, + id_swap, + ) + if self.atom_edge_interaction is not None: + x_a2e = self.atom_edge_interaction( + h, + bases_a2e, + trip_idx_a2e, + id_swap, + expand_idx=a2ee2a_graph["edge_index"][0], + ) + if self.edge_atom_interaction is not None: + h_e2a = self.edge_atom_interaction( + m, + bases_e2a, + trip_idx_e2a, + id_swap, + idx_agg2=a2ee2a_graph["edge_index"][1], + idx_agg2_inner=a2ee2a_graph["target_neighbor_idx"], + agg2_out_size=num_atoms, + ) + if self.atom_interaction is not None: + h_a2a = self.atom_interaction( + h, + basis_a2a_rad, + a2a_graph["edge_index"], + a2a_graph["target_neighbor_idx"], + ) + + ## -------------- Merge Embeddings after interactions ------------- ## + x = x_ca_skip + x_e2e # (nEdges, emb_size_edge) + if self.quad_interaction is not None: + x += x_qint # (nEdges, emb_size_edge) + if self.atom_edge_interaction is not None: + x += x_a2e # (nEdges, emb_size_edge) + x = x * self.inv_sqrt_num_eint + + # Merge atom embeddings after interactions + if self.edge_atom_interaction is not None: + h = h + h_e2a # (nEdges, emb_size_edge) + if self.atom_interaction is not None: + h = h + h_a2a # (nEdges, emb_size_edge) + h = h * self.inv_sqrt_num_aint + + ## -------------------- Update Edge Embeddings -------------------- ## + # Transformations before skip connection + for i, layer in enumerate(self.layers_before_skip): + x = layer(x) # (nEdges, emb_size_edge) + + # Skip connection + m = m + x # (nEdges, emb_size_edge) + m = m * self.inv_sqrt_2 + + # Transformations after skip connection + for i, layer in enumerate(self.layers_after_skip): + m = layer(m) # (nEdges, emb_size_edge) + + ## -------------------- Update Atom Embeddings -------------------- ## + for layer in self.atom_emb_layers: + h = layer(h) # (nAtoms, emb_size_atom) + + h2 = self.atom_update(h, m, basis_atom_update, edge_index_main[1]) + + # Skip connection + h = h + h2 # (nAtoms, emb_size_atom) + h = h * self.inv_sqrt_2 + + ## ---------- Update Edge Embeddings with Atom Embeddings --------- ## + m2 = self.concat_layer(h, m, edge_index_main) + # (nEdges, emb_size_edge) + + for i, layer in enumerate(self.residual_m): + m2 = layer(m2) # (nEdges, emb_size_edge) + + # Skip connection + m = m + m2 # (nEdges, emb_size_edge) + m = m * self.inv_sqrt_2 + return h, m + + +class QuadrupletInteraction(torch.nn.Module): + """ + Quadruplet-based message passing block. + + Arguments + --------- + emb_size_edge: int + Embedding size of the edges. + emb_size_quad_in: int + (Down-projected) embedding size of the quadruplet edge embeddings + before the bilinear layer. + emb_size_quad_out: int + (Down-projected) embedding size of the quadruplet edge embeddings + after the bilinear layer. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + emb_size_sbf: int + Embedding size of the spherical basis transformation (two angles). + symmetric_mp: bool + Whether to use symmetric message passing and + update the edges in both directions. + activation: str + Name of the activation function to use in the dense layers. + """ + + def __init__( + self, + emb_size_edge, + emb_size_quad_in, + emb_size_quad_out, + emb_size_rbf, + emb_size_cbf, + emb_size_sbf, + symmetric_mp=True, + activation=None, + ): + super().__init__() + self.symmetric_mp = symmetric_mp + + # Dense transformation + self.dense_db = Dense( + emb_size_edge, + emb_size_edge, + activation=activation, + bias=False, + ) + + # Up projections of basis representations, + # bilinear layer and scaling factors + self.mlp_rbf = Dense( + emb_size_rbf, + emb_size_edge, + activation=None, + bias=False, + ) + self.scale_rbf = ScaleFactor() + + self.mlp_cbf = Dense( + emb_size_cbf, + emb_size_quad_in, + activation=None, + bias=False, + ) + self.scale_cbf = ScaleFactor() + + self.mlp_sbf = EfficientInteractionBilinear( + emb_size_quad_in, emb_size_sbf, emb_size_quad_out + ) + self.scale_sbf_sum = ScaleFactor() + # combines scaling for bilinear layer and summation + + # Down and up projections + self.down_projection = Dense( + emb_size_edge, + emb_size_quad_in, + activation=activation, + bias=False, + ) + self.up_projection_ca = Dense( + emb_size_quad_out, + emb_size_edge, + activation=activation, + bias=False, + ) + if self.symmetric_mp: + self.up_projection_ac = Dense( + emb_size_quad_out, + emb_size_edge, + activation=activation, + bias=False, + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + + def forward( + self, + m, + bases, + idx, + id_swap, + ): + """ + Returns + ------- + m: torch.Tensor, shape=(nEdges, emb_size_edge) + Edge embeddings (c->a). + """ + + x_db = self.dense_db(m) # (nEdges, emb_size_edge) + + # Transform via radial basis + x_db2 = x_db * self.mlp_rbf(bases["rad"]) # (nEdges, emb_size_edge) + x_db = self.scale_rbf(x_db2, ref=x_db) + + # Down project embeddings + x_db = self.down_projection(x_db) # (nEdges, emb_size_quad_in) + + # Transform via circular basis + x_db = x_db[idx["triplet_in"]["in"]] + # (num_triplets_int, emb_size_quad_in) + + x_db2 = x_db * self.mlp_cbf(bases["cir"]) + # (num_triplets_int, emb_size_quad_in) + x_db = self.scale_cbf(x_db2, ref=x_db) + + # Transform via spherical basis + x_db = x_db[idx["trip_in_to_quad"]] + # (num_quadruplets, emb_size_quad_in) + x = self.mlp_sbf(bases["sph"], x_db, idx["out"], idx["out_agg"]) + # (nEdges, emb_size_quad_out) + x = self.scale_sbf_sum(x, ref=x_db) + + # => + # rbf(d_db) + # cbf(d_ba, angle_abd) + # sbf(d_ca, angle_cab, angle_cabd) + + if self.symmetric_mp: + # Upproject embeddings + x_ca = self.up_projection_ca(x) # (nEdges, emb_size_edge) + x_ac = self.up_projection_ac(x) # (nEdges, emb_size_edge) + + # Merge interaction of c->a and a->c + x_ac = x_ac[id_swap] # swap to add to edge a->c and not c->a + x_res = x_ca + x_ac + x_res = x_res * self.inv_sqrt_2 + return x_res + else: + x_res = self.up_projection_ca(x) + return x_res + + +class TripletInteraction(torch.nn.Module): + """ + Triplet-based message passing block. + + Arguments + --------- + emb_size_in: int + Embedding size of the input embeddings. + emb_size_out: int + Embedding size of the output embeddings. + emb_size_trip_in: int + (Down-projected) embedding size of the quadruplet edge embeddings + before the bilinear layer. + emb_size_trip_out: int + (Down-projected) embedding size of the quadruplet edge embeddings + after the bilinear layer. + emb_size_rbf: int + Embedding size of the radial basis transformation. + emb_size_cbf: int + Embedding size of the circular basis transformation (one angle). + symmetric_mp: bool + Whether to use symmetric message passing and + update the edges in both directions. + swap_output: bool + Whether to swap the output embedding directions. + Only relevant if symmetric_mp is False. + activation: str + Name of the activation function to use in the dense layers. + """ + + def __init__( + self, + emb_size_in, + emb_size_out, + emb_size_trip_in, + emb_size_trip_out, + emb_size_rbf, + emb_size_cbf, + symmetric_mp=True, + swap_output=True, + activation=None, + ): + super().__init__() + self.symmetric_mp = symmetric_mp + self.swap_output = swap_output + + # Dense transformation + self.dense_ba = Dense( + emb_size_in, + emb_size_in, + activation=activation, + bias=False, + ) + + # Up projections of basis representations, bilinear layer and scaling factors + self.mlp_rbf = Dense( + emb_size_rbf, + emb_size_in, + activation=None, + bias=False, + ) + self.scale_rbf = ScaleFactor() + + self.mlp_cbf = EfficientInteractionBilinear( + emb_size_trip_in, emb_size_cbf, emb_size_trip_out + ) + self.scale_cbf_sum = ScaleFactor() + # combines scaling for bilinear layer and summation + + # Down and up projections + self.down_projection = Dense( + emb_size_in, + emb_size_trip_in, + activation=activation, + bias=False, + ) + self.up_projection_ca = Dense( + emb_size_trip_out, + emb_size_out, + activation=activation, + bias=False, + ) + if self.symmetric_mp: + self.up_projection_ac = Dense( + emb_size_trip_out, + emb_size_out, + activation=activation, + bias=False, + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + + def forward( + self, + m, + bases, + idx, + id_swap, + expand_idx=None, + idx_agg2=None, + idx_agg2_inner=None, + agg2_out_size=None, + ): + """ + Returns + ------- + m: torch.Tensor, shape=(nEdges, emb_size_edge) + Edge embeddings. + """ + + # Dense transformation + x_ba = self.dense_ba(m) # (nEdges, emb_size_edge) + + if expand_idx is not None: + x_ba = x_ba[expand_idx] + + # Transform via radial basis + rad_emb = self.mlp_rbf(bases["rad"]) # (nEdges, emb_size_edge) + x_ba2 = x_ba * rad_emb + x_ba = self.scale_rbf(x_ba2, ref=x_ba) + + x_ba = self.down_projection(x_ba) # (nEdges, emb_size_trip_in) + + # Transform via circular spherical basis + x_ba = x_ba[idx["in"]] + + # Efficient bilinear layer + x = self.mlp_cbf( + basis=bases["cir"], + m=x_ba, + idx_agg_outer=idx["out"], + idx_agg_inner=idx["out_agg"], + idx_agg2_outer=idx_agg2, + idx_agg2_inner=idx_agg2_inner, + agg2_out_size=agg2_out_size, + ) + # (num_atoms, emb_size_trip_out) + x = self.scale_cbf_sum(x, ref=x_ba) + + # => + # rbf(d_ba) + # cbf(d_ca, angle_cab) + + if self.symmetric_mp: + # Up project embeddings + x_ca = self.up_projection_ca(x) # (nEdges, emb_size_edge) + x_ac = self.up_projection_ac(x) # (nEdges, emb_size_edge) + + # Merge interaction of c->a and a->c + x_ac = x_ac[id_swap] # swap to add to edge a->c and not c->a + x_res = x_ca + x_ac + x_res = x_res * self.inv_sqrt_2 + return x_res + else: + if self.swap_output: + x = x[id_swap] + x_res = self.up_projection_ca(x) # (nEdges, emb_size_edge) + return x_res + + +class PairInteraction(torch.nn.Module): + """ + Pair-based message passing block. + + Arguments + --------- + emb_size_atom: int + Embedding size of the atoms. + emb_size_pair_in: int + Embedding size of the atom pairs before the bilinear layer. + emb_size_pair_out: int + Embedding size of the atom pairs after the bilinear layer. + emb_size_rbf: int + Embedding size of the radial basis transformation. + activation: str + Name of the activation function to use in the dense layers. + """ + + def __init__( + self, + emb_size_atom, + emb_size_pair_in, + emb_size_pair_out, + emb_size_rbf, + activation=None, + ): + super().__init__() + + # Bilinear layer and scaling factor + self.bilinear = Dense( + emb_size_rbf * emb_size_pair_in, + emb_size_pair_out, + activation=None, + bias=False, + ) + self.scale_rbf_sum = ScaleFactor() + + # Down and up projections + self.down_projection = Dense( + emb_size_atom, + emb_size_pair_in, + activation=activation, + bias=False, + ) + self.up_projection = Dense( + emb_size_pair_out, + emb_size_atom, + activation=activation, + bias=False, + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + + def forward( + self, + h, + rad_basis, + edge_index, + target_neighbor_idx, + ): + """ + Returns + ------- + h: torch.Tensor, shape=(num_atoms, emb_size_atom) + Atom embeddings. + """ + num_atoms = h.shape[0] + + x_b = self.down_projection(h) # (num_atoms, emb_size_edge) + x_ba = x_b[edge_index[0]] # (num_edges, emb_size_edge) + + Kmax = torch.max(target_neighbor_idx) + 1 + x2 = x_ba.new_zeros(num_atoms, Kmax, x_ba.shape[-1]) + x2[edge_index[1], target_neighbor_idx] = x_ba + # (num_atoms, Kmax, emb_size_edge) + + x_ba2 = rad_basis @ x2 + # (num_atoms, emb_size_interm, emb_size_edge) + h_out = self.bilinear(x_ba2.reshape(num_atoms, -1)) + + h_out = self.scale_rbf_sum(h_out, ref=x_ba) + # (num_atoms, emb_size_edge) + + h_out = self.up_projection(h_out) # (num_atoms, emb_size_atom) + + return h_out diff --git a/ocpmodels/models/gemnet_oc/layers/radial_basis.py b/ocpmodels/models/gemnet_oc/layers/radial_basis.py new file mode 100644 index 0000000..a963cce --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/radial_basis.py @@ -0,0 +1,237 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math + +import numpy as np +import sympy as sym +import torch +from scipy.special import binom + +from ocpmodels.modules.scaling import ScaleFactor + +from .basis_utils import bessel_basis + + +class PolynomialEnvelope(torch.nn.Module): + """ + Polynomial envelope function that ensures a smooth cutoff. + + Arguments + --------- + exponent: int + Exponent of the envelope function. + """ + + def __init__(self, exponent): + super().__init__() + assert exponent > 0 + self.p = exponent + self.a = -(self.p + 1) * (self.p + 2) / 2 + self.b = self.p * (self.p + 2) + self.c = -self.p * (self.p + 1) / 2 + + def forward(self, d_scaled): + env_val = ( + 1 + + self.a * d_scaled**self.p + + self.b * d_scaled ** (self.p + 1) + + self.c * d_scaled ** (self.p + 2) + ) + return torch.where(d_scaled < 1, env_val, torch.zeros_like(d_scaled)) + + +class ExponentialEnvelope(torch.nn.Module): + """ + Exponential envelope function that ensures a smooth cutoff, + as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021. + SpookyNet: Learning Force Fields with Electronic Degrees of Freedom + and Nonlocal Effects + """ + + def __init__(self): + super().__init__() + + def forward(self, d_scaled): + env_val = torch.exp( + -(d_scaled**2) / ((1 - d_scaled) * (1 + d_scaled)) + ) + return torch.where(d_scaled < 1, env_val, torch.zeros_like(d_scaled)) + + +class GaussianBasis(torch.nn.Module): + def __init__(self, start=0.0, stop=5.0, num_gaussians=50, trainable=False): + super().__init__() + offset = torch.linspace(start, stop, num_gaussians) + if trainable: + self.offset = torch.nn.Parameter(offset, requires_grad=True) + else: + self.register_buffer("offset", offset) + self.coeff = -0.5 / ((stop - start) / (num_gaussians - 1)) ** 2 + + def forward(self, dist): + dist = dist[:, None] - self.offset[None, :] + return torch.exp(self.coeff * torch.pow(dist, 2)) + + +class SphericalBesselBasis(torch.nn.Module): + """ + First-order spherical Bessel basis + + Arguments + --------- + num_radial: int + Number of basis functions. Controls the maximum frequency. + cutoff: float + Cutoff distance in Angstrom. + """ + + def __init__( + self, + num_radial: int, + cutoff: float, + ): + super().__init__() + self.norm_const = math.sqrt(2 / (cutoff**3)) + # cutoff ** 3 to counteract dividing by d_scaled = d / cutoff + + # Initialize frequencies at canonical positions + self.frequencies = torch.nn.Parameter( + data=torch.tensor( + np.pi * np.arange(1, num_radial + 1, dtype=np.float32) + ), + requires_grad=True, + ) + + def forward(self, d_scaled): + return ( + self.norm_const + / d_scaled[:, None] + * torch.sin(self.frequencies * d_scaled[:, None]) + ) # (num_edges, num_radial) + + +class BernsteinBasis(torch.nn.Module): + """ + Bernstein polynomial basis, + as proposed in Unke, Chmiela, Gastegger, Schütt, Sauceda, Müller 2021. + SpookyNet: Learning Force Fields with Electronic Degrees of Freedom + and Nonlocal Effects + + Arguments + --------- + num_radial: int + Number of basis functions. Controls the maximum frequency. + pregamma_initial: float + Initial value of exponential coefficient gamma. + Default: gamma = 0.5 * a_0**-1 = 0.94486, + inverse softplus -> pregamma = log e**gamma - 1 = 0.45264 + """ + + def __init__( + self, + num_radial: int, + pregamma_initial: float = 0.45264, + ): + super().__init__() + prefactor = binom(num_radial - 1, np.arange(num_radial)) + self.register_buffer( + "prefactor", + torch.tensor(prefactor, dtype=torch.float), + persistent=False, + ) + + self.pregamma = torch.nn.Parameter( + data=torch.tensor(pregamma_initial, dtype=torch.float), + requires_grad=True, + ) + self.softplus = torch.nn.Softplus() + + exp1 = torch.arange(num_radial) + self.register_buffer("exp1", exp1[None, :], persistent=False) + exp2 = num_radial - 1 - exp1 + self.register_buffer("exp2", exp2[None, :], persistent=False) + + def forward(self, d_scaled): + gamma = self.softplus(self.pregamma) # constrain to positive + exp_d = torch.exp(-gamma * d_scaled)[:, None] + return ( + self.prefactor * (exp_d**self.exp1) * ((1 - exp_d) ** self.exp2) + ) + + +class RadialBasis(torch.nn.Module): + """ + + Arguments + --------- + num_radial: int + Number of basis functions. Controls the maximum frequency. + cutoff: float + Cutoff distance in Angstrom. + rbf: dict = {"name": "gaussian"} + Basis function and its hyperparameters. + envelope: dict = {"name": "polynomial", "exponent": 5} + Envelope function and its hyperparameters. + scale_basis: bool + Whether to scale the basis values for better numerical stability. + """ + + def __init__( + self, + num_radial: int, + cutoff: float, + rbf: dict = {"name": "gaussian"}, + envelope: dict = {"name": "polynomial", "exponent": 5}, + scale_basis: bool = False, + ): + super().__init__() + self.inv_cutoff = 1 / cutoff + + self.scale_basis = scale_basis + if self.scale_basis: + self.scale_rbf = ScaleFactor() + + env_name = envelope["name"].lower() + env_hparams = envelope.copy() + del env_hparams["name"] + + if env_name == "polynomial": + self.envelope = PolynomialEnvelope(**env_hparams) + elif env_name == "exponential": + self.envelope = ExponentialEnvelope(**env_hparams) + else: + raise ValueError(f"Unknown envelope function '{env_name}'.") + + rbf_name = rbf["name"].lower() + rbf_hparams = rbf.copy() + del rbf_hparams["name"] + + # RBFs get distances scaled to be in [0, 1] + if rbf_name == "gaussian": + self.rbf = GaussianBasis( + start=0, stop=1, num_gaussians=num_radial, **rbf_hparams + ) + elif rbf_name == "spherical_bessel": + self.rbf = SphericalBesselBasis( + num_radial=num_radial, cutoff=cutoff, **rbf_hparams + ) + elif rbf_name == "bernstein": + self.rbf = BernsteinBasis(num_radial=num_radial, **rbf_hparams) + else: + raise ValueError(f"Unknown radial basis function '{rbf_name}'.") + + def forward(self, d): + d_scaled = d * self.inv_cutoff + + env = self.envelope(d_scaled) + res = env[:, None] * self.rbf(d_scaled) + + if self.scale_basis: + res = self.scale_rbf(res) + + return res + # (num_edges, num_radial) or (num_edges, num_orders * num_radial) diff --git a/ocpmodels/models/gemnet_oc/layers/spherical_basis.py b/ocpmodels/models/gemnet_oc/layers/spherical_basis.py new file mode 100644 index 0000000..7f2cb48 --- /dev/null +++ b/ocpmodels/models/gemnet_oc/layers/spherical_basis.py @@ -0,0 +1,143 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch + +from ocpmodels.modules.scaling import ScaleFactor + +from .basis_utils import get_sph_harm_basis +from .radial_basis import GaussianBasis, RadialBasis + + +class CircularBasisLayer(torch.nn.Module): + """ + 2D Fourier Bessel Basis + + Arguments + --------- + num_spherical: int + Number of basis functions. Controls the maximum frequency. + radial_basis: RadialBasis + Radial basis function. + cbf: dict + Name and hyperparameters of the circular basis function. + scale_basis: bool + Whether to scale the basis values for better numerical stability. + """ + + def __init__( + self, + num_spherical: int, + radial_basis: RadialBasis, + cbf: dict, + scale_basis: bool = False, + ): + super().__init__() + + self.radial_basis = radial_basis + + self.scale_basis = scale_basis + if self.scale_basis: + self.scale_cbf = ScaleFactor() + + cbf_name = cbf["name"].lower() + cbf_hparams = cbf.copy() + del cbf_hparams["name"] + + if cbf_name == "gaussian": + self.cosφ_basis = GaussianBasis( + start=-1, stop=1, num_gaussians=num_spherical, **cbf_hparams + ) + elif cbf_name == "spherical_harmonics": + self.cosφ_basis = get_sph_harm_basis( + num_spherical, zero_m_only=True + ) + else: + raise ValueError(f"Unknown cosine basis function '{cbf_name}'.") + + def forward(self, D_ca, cosφ_cab): + rad_basis = self.radial_basis(D_ca) # (num_edges, num_radial) + cir_basis = self.cosφ_basis(cosφ_cab) # (num_triplets, num_spherical) + + if self.scale_basis: + cir_basis = self.scale_cbf(cir_basis) + + return rad_basis, cir_basis + # (num_edges, num_radial), (num_triplets, num_spherical) + + +class SphericalBasisLayer(torch.nn.Module): + """ + 3D Fourier Bessel Basis + + Arguments + --------- + num_spherical: int + Number of basis functions. Controls the maximum frequency. + radial_basis: RadialBasis + Radial basis functions. + sbf: dict + Name and hyperparameters of the spherical basis function. + scale_basis: bool + Whether to scale the basis values for better numerical stability. + """ + + def __init__( + self, + num_spherical: int, + radial_basis: RadialBasis, + sbf: dict, + scale_basis: bool = False, + ): + super().__init__() + + self.num_spherical = num_spherical + self.radial_basis = radial_basis + + self.scale_basis = scale_basis + if self.scale_basis: + self.scale_sbf = ScaleFactor() + + sbf_name = sbf["name"].lower() + sbf_hparams = sbf.copy() + del sbf_hparams["name"] + + if sbf_name == "spherical_harmonics": + self.spherical_basis = get_sph_harm_basis( + num_spherical, zero_m_only=False + ) + + elif sbf_name == "legendre_outer": + circular_basis = get_sph_harm_basis( + num_spherical, zero_m_only=True + ) + self.spherical_basis = lambda cosφ, ϑ: ( + circular_basis(cosφ)[:, :, None] + * circular_basis(torch.cos(ϑ))[:, None, :] + ).reshape(cosφ.shape[0], -1) + + elif sbf_name == "gaussian_outer": + self.circular_basis = GaussianBasis( + start=-1, stop=1, num_gaussians=num_spherical, **sbf_hparams + ) + self.spherical_basis = lambda cosφ, ϑ: ( + self.circular_basis(cosφ)[:, :, None] + * self.circular_basis(torch.cos(ϑ))[:, None, :] + ).reshape(cosφ.shape[0], -1) + + else: + raise ValueError(f"Unknown spherical basis function '{sbf_name}'.") + + def forward(self, D_ca, cosφ_cab, θ_cabd): + rad_basis = self.radial_basis(D_ca) + sph_basis = self.spherical_basis(cosφ_cab, θ_cabd) + # (num_quadruplets, num_spherical**2) + + if self.scale_basis: + sph_basis = self.scale_sbf(sph_basis) + + return rad_basis, sph_basis + # (num_edges, num_radial), (num_quadruplets, num_spherical**2) diff --git a/ocpmodels/models/gemnet_oc/utils.py b/ocpmodels/models/gemnet_oc/utils.py new file mode 100644 index 0000000..8bf1c4a --- /dev/null +++ b/ocpmodels/models/gemnet_oc/utils.py @@ -0,0 +1,424 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch +from torch_scatter import segment_coo, segment_csr +from torch_sparse import SparseTensor + + +def ragged_range(sizes): + """Multiple concatenated ranges. + + Examples + -------- + sizes = [1 4 2 3] + Return: [0 0 1 2 3 0 1 0 1 2] + """ + assert sizes.dim() == 1 + if sizes.sum() == 0: + return sizes.new_empty(0) + + # Remove 0 sizes + sizes_nonzero = sizes > 0 + if not torch.all(sizes_nonzero): + sizes = torch.masked_select(sizes, sizes_nonzero) + + # Initialize indexing array with ones as we need to setup incremental indexing + # within each group when cumulatively summed at the final stage. + id_steps = torch.ones(sizes.sum(), dtype=torch.long, device=sizes.device) + id_steps[0] = 0 + insert_index = sizes[:-1].cumsum(0) + insert_val = (1 - sizes)[:-1] + + # Assign index-offsetting values + id_steps[insert_index] = insert_val + + # Finally index into input array for the group repeated o/p + res = id_steps.cumsum(0) + return res + + +def repeat_blocks( + sizes, + repeats, + continuous_indexing=True, + start_idx=0, + block_inc=0, + repeat_inc=0, +): + """Repeat blocks of indices. + Adapted from https://stackoverflow.com/questions/51154989/numpy-vectorized-function-to-repeat-blocks-of-consecutive-elements + + continuous_indexing: Whether to keep increasing the index after each block + start_idx: Starting index + block_inc: Number to increment by after each block, + either global or per block. Shape: len(sizes) - 1 + repeat_inc: Number to increment by after each repetition, + either global or per block + + Examples + -------- + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = False + Return: [0 0 0 0 1 2 0 1 2 0 1 0 1 0 1] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True + Return: [0 0 0 1 2 3 1 2 3 4 5 4 5 4 5] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + repeat_inc = 4 + Return: [0 4 8 1 2 3 5 6 7 4 5 8 9 12 13] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + start_idx = 5 + Return: [5 5 5 6 7 8 6 7 8 9 10 9 10 9 10] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + block_inc = 1 + Return: [0 0 0 2 3 4 2 3 4 6 7 6 7 6 7] + sizes = [0,3,2] ; repeats = [3,2,3] ; continuous_indexing = True + Return: [0 1 2 0 1 2 3 4 3 4 3 4] + sizes = [2,3,2] ; repeats = [2,0,2] ; continuous_indexing = True + Return: [0 1 0 1 5 6 5 6] + """ + assert sizes.dim() == 1 + assert all(sizes >= 0) + + # Remove 0 sizes + sizes_nonzero = sizes > 0 + if not torch.all(sizes_nonzero): + assert block_inc == 0 # Implementing this is not worth the effort + sizes = torch.masked_select(sizes, sizes_nonzero) + if isinstance(repeats, torch.Tensor): + repeats = torch.masked_select(repeats, sizes_nonzero) + if isinstance(repeat_inc, torch.Tensor): + repeat_inc = torch.masked_select(repeat_inc, sizes_nonzero) + + if isinstance(repeats, torch.Tensor): + assert all(repeats >= 0) + insert_dummy = repeats[0] == 0 + if insert_dummy: + one = sizes.new_ones(1) + zero = sizes.new_zeros(1) + sizes = torch.cat((one, sizes)) + repeats = torch.cat((one, repeats)) + if isinstance(block_inc, torch.Tensor): + block_inc = torch.cat((zero, block_inc)) + if isinstance(repeat_inc, torch.Tensor): + repeat_inc = torch.cat((zero, repeat_inc)) + else: + assert repeats >= 0 + insert_dummy = False + + # Get repeats for each group using group lengths/sizes + r1 = torch.repeat_interleave( + torch.arange(len(sizes), device=sizes.device), repeats + ) + + # Get total size of output array, as needed to initialize output indexing array + N = (sizes * repeats).sum() + + # Initialize indexing array with ones as we need to setup incremental indexing + # within each group when cumulatively summed at the final stage. + # Two steps here: + # 1. Within each group, we have multiple sequences, so setup the offsetting + # at each sequence lengths by the seq. lengths preceding those. + id_ar = torch.ones(N, dtype=torch.long, device=sizes.device) + id_ar[0] = 0 + insert_index = sizes[r1[:-1]].cumsum(0) + insert_val = (1 - sizes)[r1[:-1]] + + if isinstance(repeats, torch.Tensor) and torch.any(repeats == 0): + diffs = r1[1:] - r1[:-1] + indptr = torch.cat((sizes.new_zeros(1), diffs.cumsum(0))) + if continuous_indexing: + # If a group was skipped (repeats=0) we need to add its size + insert_val += segment_csr(sizes[: r1[-1]], indptr, reduce="sum") + + # Add block increments + if isinstance(block_inc, torch.Tensor): + insert_val += segment_csr( + block_inc[: r1[-1]], indptr, reduce="sum" + ) + else: + insert_val += block_inc * (indptr[1:] - indptr[:-1]) + if insert_dummy: + insert_val[0] -= block_inc + else: + idx = r1[1:] != r1[:-1] + if continuous_indexing: + # 2. For each group, make sure the indexing starts from the next group's + # first element. So, simply assign 1s there. + insert_val[idx] = 1 + + # Add block increments + insert_val[idx] += block_inc + + # Add repeat_inc within each group + if isinstance(repeat_inc, torch.Tensor): + insert_val += repeat_inc[r1[:-1]] + if isinstance(repeats, torch.Tensor): + repeat_inc_inner = repeat_inc[repeats > 0][:-1] + else: + repeat_inc_inner = repeat_inc[:-1] + else: + insert_val += repeat_inc + repeat_inc_inner = repeat_inc + + # Subtract the increments between groups + if isinstance(repeats, torch.Tensor): + repeats_inner = repeats[repeats > 0][:-1] + else: + repeats_inner = repeats + insert_val[r1[1:] != r1[:-1]] -= repeat_inc_inner * repeats_inner + + # Assign index-offsetting values + id_ar[insert_index] = insert_val + + if insert_dummy: + id_ar = id_ar[1:] + if continuous_indexing: + id_ar[0] -= 1 + + # Set start index now, in case of insertion due to leading repeats=0 + id_ar[0] += start_idx + + # Finally index into input array for the group repeated o/p + res = id_ar.cumsum(0) + return res + + +def masked_select_sparsetensor_flat(src, mask): + row, col, value = src.coo() + row = row[mask] + col = col[mask] + value = value[mask] + return SparseTensor( + row=row, col=col, value=value, sparse_sizes=src.sparse_sizes() + ) + + +def calculate_interatomic_vectors(R, id_s, id_t, offsets_st): + """ + Calculate the vectors connecting the given atom pairs, + considering offsets from periodic boundary conditions (PBC). + + Arguments + --------- + R: Tensor, shape = (nAtoms, 3) + Atom positions. + id_s: Tensor, shape = (nEdges,) + Indices of the source atom of the edges. + id_t: Tensor, shape = (nEdges,) + Indices of the target atom of the edges. + offsets_st: Tensor, shape = (nEdges,) + PBC offsets of the edges. + Subtract this from the correct direction. + + Returns + ------- + (D_st, V_st): tuple + D_st: Tensor, shape = (nEdges,) + Distance from atom t to s. + V_st: Tensor, shape = (nEdges,) + Unit direction from atom t to s. + """ + Rs = R[id_s] + Rt = R[id_t] + # ReLU prevents negative numbers in sqrt + if offsets_st is None: + V_st = Rt - Rs # s -> t + else: + V_st = Rt - Rs + offsets_st # s -> t + D_st = torch.sqrt(torch.sum(V_st**2, dim=1)) + V_st = V_st / D_st[..., None] + return D_st, V_st + + +def inner_product_clamped(x, y): + """ + Calculate the inner product between the given normalized vectors, + giving a result between -1 and 1. + """ + return torch.sum(x * y, dim=-1).clamp(min=-1, max=1) + + +def get_angle(R_ac, R_ab): + """Calculate angles between atoms c -> a <- b. + + Arguments + --------- + R_ac: Tensor, shape = (N, 3) + Vector from atom a to c. + R_ab: Tensor, shape = (N, 3) + Vector from atom a to b. + + Returns + ------- + angle_cab: Tensor, shape = (N,) + Angle between atoms c <- a -> b. + """ + # cos(alpha) = (u * v) / (|u|*|v|) + x = torch.sum(R_ac * R_ab, dim=-1) # shape = (N,) + # sin(alpha) = |u x v| / (|u|*|v|) + y = torch.cross(R_ac, R_ab, dim=-1).norm(dim=-1) # shape = (N,) + y = y.clamp(min=1e-9) # Avoid NaN gradient for y = (0,0,0) + + angle = torch.atan2(y, x) + return angle + + +def vector_rejection(R_ab, P_n): + """ + Project the vector R_ab onto a plane with normal vector P_n. + + Arguments + --------- + R_ab: Tensor, shape = (N, 3) + Vector from atom a to b. + P_n: Tensor, shape = (N, 3) + Normal vector of a plane onto which to project R_ab. + + Returns + ------- + R_ab_proj: Tensor, shape = (N, 3) + Projected vector (orthogonal to P_n). + """ + a_x_b = torch.sum(R_ab * P_n, dim=-1) + b_x_b = torch.sum(P_n * P_n, dim=-1) + return R_ab - (a_x_b / b_x_b)[:, None] * P_n + + +def get_projected_angle(R_ab, P_n, eps=1e-4): + """ + Project the vector R_ab onto a plane with normal vector P_n, + then calculate the angle w.r.t. the (x [cross] P_n), + or (y [cross] P_n) if the former would be ill-defined/numerically unstable. + + Arguments + --------- + R_ab: Tensor, shape = (N, 3) + Vector from atom a to b. + P_n: Tensor, shape = (N, 3) + Normal vector of a plane onto which to project R_ab. + eps: float + Norm of projection below which to use the y-axis instead of x. + + Returns + ------- + angle_ab: Tensor, shape = (N) + Angle on plane w.r.t. x- or y-axis. + """ + R_ab_proj = torch.cross(R_ab, P_n, dim=-1) + + # Obtain axis defining the angle=0 + x = P_n.new_tensor([[1, 0, 0]]).expand_as(P_n) + zero_angle = torch.cross(x, P_n, dim=-1) + + use_y = torch.norm(zero_angle, dim=-1) < eps + P_n_y = P_n[use_y] + y = P_n_y.new_tensor([[0, 1, 0]]).expand_as(P_n_y) + y_cross = torch.cross(y, P_n_y, dim=-1) + zero_angle[use_y] = y_cross + + angle = get_angle(zero_angle, R_ab_proj) + + # Flip sign of angle if necessary to obtain clock-wise angles + cross = torch.cross(zero_angle, R_ab_proj, dim=-1) + flip_sign = torch.sum(cross * P_n, dim=-1) < 0 + angle[flip_sign] = -angle[flip_sign] + + return angle + + +def mask_neighbors(neighbors, edge_mask): + neighbors_old_indptr = torch.cat([neighbors.new_zeros(1), neighbors]) + neighbors_old_indptr = torch.cumsum(neighbors_old_indptr, dim=0) + neighbors = segment_csr(edge_mask.long(), neighbors_old_indptr) + return neighbors + + +def get_neighbor_order(num_atoms, index, atom_distance): + """ + Give a mask that filters out edges so that each atom has at most + `max_num_neighbors_threshold` neighbors. + """ + device = index.device + + # Get sorted index and inverse sorting + # Necessary for index_sort_map + index_sorted, index_order = torch.sort(index) + index_order_inverse = torch.argsort(index_order) + + # Get number of neighbors + ones = index_sorted.new_ones(1).expand_as(index_sorted) + num_neighbors = segment_coo(ones, index_sorted, dim_size=num_atoms) + max_num_neighbors = num_neighbors.max() + + # Create a tensor of size [num_atoms, max_num_neighbors] to sort the distances of the neighbors. + # Fill with infinity so we can easily remove unused distances later. + distance_sort = torch.full( + [num_atoms * max_num_neighbors], np.inf, device=device + ) + + # Create an index map to map distances from atom_distance to distance_sort + index_neighbor_offset = torch.cumsum(num_neighbors, dim=0) - num_neighbors + index_neighbor_offset_expand = torch.repeat_interleave( + index_neighbor_offset, num_neighbors + ) + index_sort_map = ( + index_sorted * max_num_neighbors + + torch.arange(len(index_sorted), device=device) + - index_neighbor_offset_expand + ) + distance_sort.index_copy_(0, index_sort_map, atom_distance) + distance_sort = distance_sort.view(num_atoms, max_num_neighbors) + + # Sort neighboring atoms based on distance + distance_sort, index_sort = torch.sort(distance_sort, dim=1) + + # Offset index_sort so that it indexes into index_sorted + index_sort = index_sort + index_neighbor_offset.view(-1, 1).expand( + -1, max_num_neighbors + ) + # Remove "unused pairs" with infinite distances + mask_finite = torch.isfinite(distance_sort) + index_sort = torch.masked_select(index_sort, mask_finite) + + # Create indices specifying the order in index_sort + order_peratom = torch.arange(max_num_neighbors, device=device)[ + None, : + ].expand_as(mask_finite) + order_peratom = torch.masked_select(order_peratom, mask_finite) + + # Re-index to obtain order value of each neighbor in index_sorted + order = torch.zeros(len(index), device=device, dtype=torch.long) + order[index_sort] = order_peratom + + return order[index_order_inverse] + + +def get_inner_idx(idx, dim_size): + """ + Assign an inner index to each element (neighbor) with the same index. + For example, with idx=[0 0 0 1 1 1 1 2 2] this returns [0 1 2 0 1 2 3 0 1]. + These indices allow reshape neighbor indices into a dense matrix. + idx has to be sorted for this to work. + """ + ones = idx.new_ones(1).expand_as(idx) + num_neighbors = segment_coo(ones, idx, dim_size=dim_size) + inner_idx = ragged_range(num_neighbors) + return inner_idx + + +def get_edge_id(edge_idx, cell_offsets, num_atoms): + cell_basis = cell_offsets.max() - cell_offsets.min() + 1 + cell_id = ( + ( + cell_offsets + * cell_offsets.new_tensor([[1, cell_basis, cell_basis**2]]) + ) + .sum(-1) + .long() + ) + edge_id = edge_idx[0] + edge_idx[1] * num_atoms + cell_id * num_atoms**2 + return edge_id diff --git a/ocpmodels/models/painn/README.md b/ocpmodels/models/painn/README.md new file mode 100644 index 0000000..8e127dc --- /dev/null +++ b/ocpmodels/models/painn/README.md @@ -0,0 +1,39 @@ +# Polarizable Atom Interaction Neural Network (PaiNN) + +Kristof T. Schütt, Oliver T. Unke, Michael Gastegger + +[[`arXiv:2102.03150`](https://arxiv.org/abs/2102.03150)] + +This is our independent reimplementation of the original PaiNN architecture +with the difference that forces are predicted directly from vectorial features +via a gated equivariant block instead of gradients of the energy output. +This breaks energy conservation but is essential for good performance on OC20. + +All PaiNN models were trained without AMP, as using AMP led to unstable training. + +## IS2RE + +Trained only using IS2RE data, no auxiliary losses and/or S2EF data. + +| Model | Val ID Energy MAE | Test metrics | Download | +| ----- | ----------------- | ------------ | -------- | +| painn_h1024_bs4x8 | 0.5728 | [IS2RE](https://evalai.s3.amazonaws.com/media/submission_files/submission_200972/45d289fc-8de9-45cc-aed4-6cd1753cb56d.json) | [config](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs/is2re/all/painn/painn_h1024_bs8x4.yml) \| [checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_05/is2re/painn_h1024_bs4x8_is2re_all.pt) | + +## S2EF + +| Model | Val ID 30k Force MAE | Val ID 30k Energy MAE | Val ID 30k Force cos | Test metrics | Download | +| ----- | -------------------- | --------------------- | -------------------- | ------------ | -------- | +| painn_h512 | 0.02945 | 0.2459 | 0.5143 | [S2EF](https://evalai.s3.amazonaws.com/media/submission_files/submission_200711/2f487981-051d-445e-a7cd-6eb00ebe0735.json) \| [IS2RE](https://evalai.s3.amazonaws.com/media/submission_files/submission_200710/7fe29c4c-c203-434d-a6d4-9ea992d3bb5c.json) \| [IS2RS](https://evalai.s3.amazonaws.com/media/submission_files/submission_200700/8fd419e6-bab3-49be-a936-ae31979b4866.json) | [config](https://github.com/Open-Catalyst-Project/ocp/tree/main/configs/s2ef/all/painn/painn_h512.yml) \| [checkpoint](https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_05/s2ef/painn_h512_s2ef_all.pt) | + +## Citing + +If you use PaiNN in your work, please consider citing the original paper: + +```bibtex +@inproceedings{schutt_painn_2021, + title = {Equivariant message passing for the prediction of tensorial properties and molecular spectra}, + author = {Sch{\"u}tt, Kristof and Unke, Oliver and Gastegger, Michael}, + booktitle = {Proceedings of the International Conference on Machine Learning (ICML)}, + year = {2021}, +} +``` diff --git a/ocpmodels/models/painn/__init__.py b/ocpmodels/models/painn/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/painn/painn.py b/ocpmodels/models/painn/painn.py new file mode 100644 index 0000000..5cd6283 --- /dev/null +++ b/ocpmodels/models/painn/painn.py @@ -0,0 +1,650 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. + +--- + +MIT License + +Copyright (c) 2021 www.compscience.org + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +""" + +import logging +import math +import os +from typing import Optional, Tuple + +import torch +from torch import nn +from torch_geometric.nn import MessagePassing, radius_graph +from torch_scatter import scatter, segment_coo + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + compute_neighbors, + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.models.base import BaseModel +from ocpmodels.models.gemnet.layers.base_layers import ScaledSiLU +from ocpmodels.models.gemnet.layers.embedding_block import AtomEmbedding +from ocpmodels.models.gemnet.layers.radial_basis import RadialBasis +from ocpmodels.modules.scaling import ScaleFactor +from ocpmodels.modules.scaling.compat import load_scales_compat + +from .utils import get_edge_id, repeat_blocks + + +@registry.register_model("painn") +class PaiNN(BaseModel): + r"""PaiNN model based on the description in Schütt et al. (2021): + Equivariant message passing for the prediction of tensorial properties + and molecular spectra, https://arxiv.org/abs/2102.03150. + """ + + def __init__( + self, + num_atoms, + bond_feat_dim, + num_targets, + hidden_channels=512, + num_layers=6, + num_rbf=128, + cutoff=12.0, + max_neighbors=50, + rbf: dict = {"name": "gaussian"}, + envelope: dict = {"name": "polynomial", "exponent": 5}, + regress_forces=True, + direct_forces=True, + use_pbc=True, + otf_graph=True, + num_elements=83, + scale_file: Optional[str] = None, + ): + super(PaiNN, self).__init__() + + self.hidden_channels = hidden_channels + self.num_layers = num_layers + self.num_rbf = num_rbf + self.cutoff = cutoff + self.max_neighbors = max_neighbors + self.regress_forces = regress_forces + self.direct_forces = direct_forces + self.otf_graph = otf_graph + self.use_pbc = use_pbc + + # Borrowed from GemNet. + self.symmetric_edge_symmetrization = False + + #### Learnable parameters ############################################# + + self.atom_emb = AtomEmbedding(hidden_channels, num_elements) + + self.radial_basis = RadialBasis( + num_radial=num_rbf, + cutoff=self.cutoff, + rbf=rbf, + envelope=envelope, + ) + + self.message_layers = nn.ModuleList() + self.update_layers = nn.ModuleList() + + for i in range(num_layers): + self.message_layers.append( + PaiNNMessage(hidden_channels, num_rbf).jittable() + ) + self.update_layers.append(PaiNNUpdate(hidden_channels)) + setattr(self, "upd_out_scalar_scale_%d" % i, ScaleFactor()) + + self.out_energy = nn.Sequential( + nn.Linear(hidden_channels, hidden_channels // 2), + ScaledSiLU(), + nn.Linear(hidden_channels // 2, 1), + ) + + if self.regress_forces is True and self.direct_forces is True: + self.out_forces = PaiNNOutput(hidden_channels) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + + self.reset_parameters() + + load_scales_compat(self, scale_file) + + def reset_parameters(self): + nn.init.xavier_uniform_(self.out_energy[0].weight) + self.out_energy[0].bias.data.fill_(0) + nn.init.xavier_uniform_(self.out_energy[2].weight) + self.out_energy[2].bias.data.fill_(0) + + # Borrowed from GemNet. + def select_symmetric_edges(self, tensor, mask, reorder_idx, inverse_neg): + # Mask out counter-edges + tensor_directed = tensor[mask] + # Concatenate counter-edges after normal edges + sign = 1 - 2 * inverse_neg + tensor_cat = torch.cat([tensor_directed, sign * tensor_directed]) + # Reorder everything so the edges of every image are consecutive + tensor_ordered = tensor_cat[reorder_idx] + return tensor_ordered + + # Borrowed from GemNet. + def symmetrize_edges( + self, + edge_index, + cell_offsets, + neighbors, + batch_idx, + reorder_tensors, + reorder_tensors_invneg, + ): + """ + Symmetrize edges to ensure existence of counter-directional edges. + + Some edges are only present in one direction in the data, + since every atom has a maximum number of neighbors. + If `symmetric_edge_symmetrization` is False, + we only use i->j edges here. So we lose some j->i edges + and add others by making it symmetric. + If `symmetric_edge_symmetrization` is True, + we always use both directions. + """ + num_atoms = batch_idx.shape[0] + + if self.symmetric_edge_symmetrization: + edge_index_bothdir = torch.cat( + [edge_index, edge_index.flip(0)], + dim=1, + ) + cell_offsets_bothdir = torch.cat( + [cell_offsets, -cell_offsets], + dim=0, + ) + + # Filter for unique edges + edge_ids = get_edge_id( + edge_index_bothdir, cell_offsets_bothdir, num_atoms + ) + unique_ids, unique_inv = torch.unique( + edge_ids, return_inverse=True + ) + perm = torch.arange( + unique_inv.size(0), + dtype=unique_inv.dtype, + device=unique_inv.device, + ) + unique_idx = scatter( + perm, + unique_inv, + dim=0, + dim_size=unique_ids.shape[0], + reduce="min", + ) + edge_index_new = edge_index_bothdir[:, unique_idx] + + # Order by target index + edge_index_order = torch.argsort(edge_index_new[1]) + edge_index_new = edge_index_new[:, edge_index_order] + unique_idx = unique_idx[edge_index_order] + + # Subindex remaining tensors + cell_offsets_new = cell_offsets_bothdir[unique_idx] + reorder_tensors = [ + self.symmetrize_tensor(tensor, unique_idx, False) + for tensor in reorder_tensors + ] + reorder_tensors_invneg = [ + self.symmetrize_tensor(tensor, unique_idx, True) + for tensor in reorder_tensors_invneg + ] + + # Count edges per image + # segment_coo assumes sorted edge_index_new[1] and batch_idx + ones = edge_index_new.new_ones(1).expand_as(edge_index_new[1]) + neighbors_per_atom = segment_coo( + ones, edge_index_new[1], dim_size=num_atoms + ) + neighbors_per_image = segment_coo( + neighbors_per_atom, batch_idx, dim_size=neighbors.shape[0] + ) + else: + # Generate mask + mask_sep_atoms = edge_index[0] < edge_index[1] + # Distinguish edges between the same (periodic) atom by ordering the cells + cell_earlier = ( + (cell_offsets[:, 0] < 0) + | ((cell_offsets[:, 0] == 0) & (cell_offsets[:, 1] < 0)) + | ( + (cell_offsets[:, 0] == 0) + & (cell_offsets[:, 1] == 0) + & (cell_offsets[:, 2] < 0) + ) + ) + mask_same_atoms = edge_index[0] == edge_index[1] + mask_same_atoms &= cell_earlier + mask = mask_sep_atoms | mask_same_atoms + + # Mask out counter-edges + edge_index_new = edge_index[mask[None, :].expand(2, -1)].view( + 2, -1 + ) + + # Concatenate counter-edges after normal edges + edge_index_cat = torch.cat( + [edge_index_new, edge_index_new.flip(0)], + dim=1, + ) + + # Count remaining edges per image + batch_edge = torch.repeat_interleave( + torch.arange(neighbors.size(0), device=edge_index.device), + neighbors, + ) + batch_edge = batch_edge[mask] + # segment_coo assumes sorted batch_edge + # Factor 2 since this is only one half of the edges + ones = batch_edge.new_ones(1).expand_as(batch_edge) + neighbors_per_image = 2 * segment_coo( + ones, batch_edge, dim_size=neighbors.size(0) + ) + + # Create indexing array + edge_reorder_idx = repeat_blocks( + torch.div(neighbors_per_image, 2, rounding_mode="floor"), + repeats=2, + continuous_indexing=True, + repeat_inc=edge_index_new.size(1), + ) + + # Reorder everything so the edges of every image are consecutive + edge_index_new = edge_index_cat[:, edge_reorder_idx] + cell_offsets_new = self.select_symmetric_edges( + cell_offsets, mask, edge_reorder_idx, True + ) + reorder_tensors = [ + self.select_symmetric_edges( + tensor, mask, edge_reorder_idx, False + ) + for tensor in reorder_tensors + ] + reorder_tensors_invneg = [ + self.select_symmetric_edges( + tensor, mask, edge_reorder_idx, True + ) + for tensor in reorder_tensors_invneg + ] + + # Indices for swapping c->a and a->c (for symmetric MP) + # To obtain these efficiently and without any index assumptions, + # we get order the counter-edge IDs and then + # map this order back to the edge IDs. + # Double argsort gives the desired mapping + # from the ordered tensor to the original tensor. + edge_ids = get_edge_id(edge_index_new, cell_offsets_new, num_atoms) + order_edge_ids = torch.argsort(edge_ids) + inv_order_edge_ids = torch.argsort(order_edge_ids) + edge_ids_counter = get_edge_id( + edge_index_new.flip(0), -cell_offsets_new, num_atoms + ) + order_edge_ids_counter = torch.argsort(edge_ids_counter) + id_swap = order_edge_ids_counter[inv_order_edge_ids] + + return ( + edge_index_new, + cell_offsets_new, + neighbors_per_image, + reorder_tensors, + reorder_tensors_invneg, + id_swap, + ) + + def generate_graph_values(self, data): + ( + edge_index, + edge_dist, + distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + + # Unit vectors pointing from edge_index[1] to edge_index[0], + # i.e., edge_index[0] - edge_index[1] divided by the norm. + # make sure that the distances are not close to zero before dividing + mask_zero = torch.isclose(edge_dist, torch.tensor(0.0), atol=1e-6) + edge_dist[mask_zero] = 1.0e-6 + edge_vector = distance_vec / edge_dist[:, None] + + empty_image = neighbors == 0 + if torch.any(empty_image): + raise ValueError( + f"An image has no neighbors: id={data.id[empty_image]}, " + f"sid={data.sid[empty_image]}, fid={data.fid[empty_image]}" + ) + + # Symmetrize edges for swapping in symmetric message passing + ( + edge_index, + cell_offsets, + neighbors, + [edge_dist], + [edge_vector], + id_swap, + ) = self.symmetrize_edges( + edge_index, + cell_offsets, + neighbors, + data.batch, + [edge_dist], + [edge_vector], + ) + + return ( + edge_index, + neighbors, + edge_dist, + edge_vector, + id_swap, + ) + + @conditional_grad(torch.enable_grad()) + def forward(self, data): + pos = data.pos + batch = data.batch + z = data.atomic_numbers.long() + + if self.regress_forces and not self.direct_forces: + pos = pos.requires_grad_(True) + + ( + edge_index, + neighbors, + edge_dist, + edge_vector, + id_swap, + ) = self.generate_graph_values(data) + + assert z.dim() == 1 and z.dtype == torch.long + + edge_rbf = self.radial_basis(edge_dist) # rbf * envelope + + x = self.atom_emb(z) + vec = torch.zeros(x.size(0), 3, x.size(1), device=x.device) + + #### Interaction blocks ############################################### + + for i in range(self.num_layers): + dx, dvec = self.message_layers[i]( + x, vec, edge_index, edge_rbf, edge_vector + ) + + x = x + dx + vec = vec + dvec + x = x * self.inv_sqrt_2 + + dx, dvec = self.update_layers[i](x, vec) + + x = x + dx + vec = vec + dvec + x = getattr(self, "upd_out_scalar_scale_%d" % i)(x) + + #### Output block ##################################################### + + per_atom_energy = self.out_energy(x).squeeze(1) + energy = scatter(per_atom_energy, batch, dim=0) + + if self.regress_forces: + if self.direct_forces: + forces = self.out_forces(x, vec) + return energy, forces + else: + forces = ( + -1 + * torch.autograd.grad( + x, + pos, + grad_outputs=torch.ones_like(x), + create_graph=True, + )[0] + ) + return energy, forces + else: + return energy + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) + + def __repr__(self): + return ( + f"{self.__class__.__name__}(" + f"hidden_channels={self.hidden_channels}, " + f"num_layers={self.num_layers}, " + f"num_rbf={self.num_rbf}, " + f"max_neighbors={self.max_neighbors}, " + f"cutoff={self.cutoff})" + ) + + +class PaiNNMessage(MessagePassing): + def __init__( + self, + hidden_channels, + num_rbf, + ): + super(PaiNNMessage, self).__init__(aggr="add", node_dim=0) + + self.hidden_channels = hidden_channels + + self.x_proj = nn.Sequential( + nn.Linear(hidden_channels, hidden_channels), + ScaledSiLU(), + nn.Linear(hidden_channels, hidden_channels * 3), + ) + self.rbf_proj = nn.Linear(num_rbf, hidden_channels * 3) + + self.inv_sqrt_3 = 1 / math.sqrt(3.0) + self.inv_sqrt_h = 1 / math.sqrt(hidden_channels) + self.x_layernorm = nn.LayerNorm(hidden_channels) + + self.reset_parameters() + + def reset_parameters(self): + nn.init.xavier_uniform_(self.x_proj[0].weight) + self.x_proj[0].bias.data.fill_(0) + nn.init.xavier_uniform_(self.x_proj[2].weight) + self.x_proj[2].bias.data.fill_(0) + nn.init.xavier_uniform_(self.rbf_proj.weight) + self.rbf_proj.bias.data.fill_(0) + self.x_layernorm.reset_parameters() + + def forward(self, x, vec, edge_index, edge_rbf, edge_vector): + xh = self.x_proj(self.x_layernorm(x)) + + # TODO(@abhshkdz): Nans out with AMP here during backprop. Debug / fix. + rbfh = self.rbf_proj(edge_rbf) + + # propagate_type: (xh: Tensor, vec: Tensor, rbfh_ij: Tensor, r_ij: Tensor) + dx, dvec = self.propagate( + edge_index, + xh=xh, + vec=vec, + rbfh_ij=rbfh, + r_ij=edge_vector, + size=None, + ) + + return dx, dvec + + def message(self, xh_j, vec_j, rbfh_ij, r_ij): + x, xh2, xh3 = torch.split(xh_j * rbfh_ij, self.hidden_channels, dim=-1) + xh2 = xh2 * self.inv_sqrt_3 + + vec = vec_j * xh2.unsqueeze(1) + xh3.unsqueeze(1) * r_ij.unsqueeze(2) + vec = vec * self.inv_sqrt_h + + return x, vec + + def aggregate( + self, + features: Tuple[torch.Tensor, torch.Tensor], + index: torch.Tensor, + ptr: Optional[torch.Tensor], + dim_size: Optional[int], + ) -> Tuple[torch.Tensor, torch.Tensor]: + x, vec = features + x = scatter(x, index, dim=self.node_dim, dim_size=dim_size) + vec = scatter(vec, index, dim=self.node_dim, dim_size=dim_size) + return x, vec + + def update( + self, inputs: Tuple[torch.Tensor, torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor]: + return inputs + + +class PaiNNUpdate(nn.Module): + def __init__(self, hidden_channels): + super().__init__() + self.hidden_channels = hidden_channels + + self.vec_proj = nn.Linear( + hidden_channels, hidden_channels * 2, bias=False + ) + self.xvec_proj = nn.Sequential( + nn.Linear(hidden_channels * 2, hidden_channels), + ScaledSiLU(), + nn.Linear(hidden_channels, hidden_channels * 3), + ) + + self.inv_sqrt_2 = 1 / math.sqrt(2.0) + self.inv_sqrt_h = 1 / math.sqrt(hidden_channels) + + self.reset_parameters() + + def reset_parameters(self): + nn.init.xavier_uniform_(self.vec_proj.weight) + nn.init.xavier_uniform_(self.xvec_proj[0].weight) + self.xvec_proj[0].bias.data.fill_(0) + nn.init.xavier_uniform_(self.xvec_proj[2].weight) + self.xvec_proj[2].bias.data.fill_(0) + + def forward(self, x, vec): + vec1, vec2 = torch.split( + self.vec_proj(vec), self.hidden_channels, dim=-1 + ) + vec_dot = (vec1 * vec2).sum(dim=1) * self.inv_sqrt_h + + # NOTE: Can't use torch.norm because the gradient is NaN for input = 0. + # Add an epsilon offset to make sure sqrt is always positive. + x_vec_h = self.xvec_proj( + torch.cat( + [x, torch.sqrt(torch.sum(vec2**2, dim=-2) + 1e-8)], dim=-1 + ) + ) + xvec1, xvec2, xvec3 = torch.split( + x_vec_h, self.hidden_channels, dim=-1 + ) + + dx = xvec1 + xvec2 * vec_dot + dx = dx * self.inv_sqrt_2 + + dvec = xvec3.unsqueeze(1) * vec1 + + return dx, dvec + + +class PaiNNOutput(nn.Module): + def __init__(self, hidden_channels): + super().__init__() + self.hidden_channels = hidden_channels + + self.output_network = nn.ModuleList( + [ + GatedEquivariantBlock( + hidden_channels, + hidden_channels // 2, + ), + GatedEquivariantBlock(hidden_channels // 2, 1), + ] + ) + + self.reset_parameters() + + def reset_parameters(self): + for layer in self.output_network: + layer.reset_parameters() + + def forward(self, x, vec): + for layer in self.output_network: + x, vec = layer(x, vec) + return vec.squeeze() + + +# Borrowed from TorchMD-Net +class GatedEquivariantBlock(nn.Module): + """Gated Equivariant Block as defined in Schütt et al. (2021): + Equivariant message passing for the prediction of tensorial properties and molecular spectra + """ + + def __init__( + self, + hidden_channels, + out_channels, + ): + super(GatedEquivariantBlock, self).__init__() + self.out_channels = out_channels + + self.vec1_proj = nn.Linear( + hidden_channels, hidden_channels, bias=False + ) + self.vec2_proj = nn.Linear(hidden_channels, out_channels, bias=False) + + self.update_net = nn.Sequential( + nn.Linear(hidden_channels * 2, hidden_channels), + ScaledSiLU(), + nn.Linear(hidden_channels, out_channels * 2), + ) + + self.act = ScaledSiLU() + + def reset_parameters(self): + nn.init.xavier_uniform_(self.vec1_proj.weight) + nn.init.xavier_uniform_(self.vec2_proj.weight) + nn.init.xavier_uniform_(self.update_net[0].weight) + self.update_net[0].bias.data.fill_(0) + nn.init.xavier_uniform_(self.update_net[2].weight) + self.update_net[2].bias.data.fill_(0) + + def forward(self, x, v): + vec1 = torch.norm(self.vec1_proj(v), dim=-2) + vec2 = self.vec2_proj(v) + + x = torch.cat([x, vec1], dim=-1) + x, v = torch.split(self.update_net(x), self.out_channels, dim=-1) + v = v.unsqueeze(1) * vec2 + + x = self.act(x) + return x, v diff --git a/ocpmodels/models/painn/utils.py b/ocpmodels/models/painn/utils.py new file mode 100644 index 0000000..19f3d6a --- /dev/null +++ b/ocpmodels/models/painn/utils.py @@ -0,0 +1,168 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +from torch_scatter import segment_csr + + +def repeat_blocks( + sizes, + repeats, + continuous_indexing=True, + start_idx=0, + block_inc=0, + repeat_inc=0, +): + """Repeat blocks of indices. + Adapted from https://stackoverflow.com/questions/51154989/numpy-vectorized-function-to-repeat-blocks-of-consecutive-elements + + continuous_indexing: Whether to keep increasing the index after each block + start_idx: Starting index + block_inc: Number to increment by after each block, + either global or per block. Shape: len(sizes) - 1 + repeat_inc: Number to increment by after each repetition, + either global or per block + + Examples + -------- + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = False + Return: [0 0 0 0 1 2 0 1 2 0 1 0 1 0 1] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True + Return: [0 0 0 1 2 3 1 2 3 4 5 4 5 4 5] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + repeat_inc = 4 + Return: [0 4 8 1 2 3 5 6 7 4 5 8 9 12 13] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + start_idx = 5 + Return: [5 5 5 6 7 8 6 7 8 9 10 9 10 9 10] + sizes = [1,3,2] ; repeats = [3,2,3] ; continuous_indexing = True ; + block_inc = 1 + Return: [0 0 0 2 3 4 2 3 4 6 7 6 7 6 7] + sizes = [0,3,2] ; repeats = [3,2,3] ; continuous_indexing = True + Return: [0 1 2 0 1 2 3 4 3 4 3 4] + sizes = [2,3,2] ; repeats = [2,0,2] ; continuous_indexing = True + Return: [0 1 0 1 5 6 5 6] + """ + assert sizes.dim() == 1 + assert all(sizes >= 0) + + # Remove 0 sizes + sizes_nonzero = sizes > 0 + if not torch.all(sizes_nonzero): + assert block_inc == 0 # Implementing this is not worth the effort + sizes = torch.masked_select(sizes, sizes_nonzero) + if isinstance(repeats, torch.Tensor): + repeats = torch.masked_select(repeats, sizes_nonzero) + if isinstance(repeat_inc, torch.Tensor): + repeat_inc = torch.masked_select(repeat_inc, sizes_nonzero) + + if isinstance(repeats, torch.Tensor): + assert all(repeats >= 0) + insert_dummy = repeats[0] == 0 + if insert_dummy: + one = sizes.new_ones(1) + zero = sizes.new_zeros(1) + sizes = torch.cat((one, sizes)) + repeats = torch.cat((one, repeats)) + if isinstance(block_inc, torch.Tensor): + block_inc = torch.cat((zero, block_inc)) + if isinstance(repeat_inc, torch.Tensor): + repeat_inc = torch.cat((zero, repeat_inc)) + else: + assert repeats >= 0 + insert_dummy = False + + # Get repeats for each group using group lengths/sizes + r1 = torch.repeat_interleave( + torch.arange(len(sizes), device=sizes.device), repeats + ) + + # Get total size of output array, as needed to initialize output indexing array + N = (sizes * repeats).sum() + + # Initialize indexing array with ones as we need to setup incremental indexing + # within each group when cumulatively summed at the final stage. + # Two steps here: + # 1. Within each group, we have multiple sequences, so setup the offsetting + # at each sequence lengths by the seq. lengths preceding those. + id_ar = torch.ones(N, dtype=torch.long, device=sizes.device) + id_ar[0] = 0 + insert_index = sizes[r1[:-1]].cumsum(0) + insert_val = (1 - sizes)[r1[:-1]] + + if isinstance(repeats, torch.Tensor) and torch.any(repeats == 0): + diffs = r1[1:] - r1[:-1] + indptr = torch.cat((sizes.new_zeros(1), diffs.cumsum(0))) + if continuous_indexing: + # If a group was skipped (repeats=0) we need to add its size + insert_val += segment_csr(sizes[: r1[-1]], indptr, reduce="sum") + + # Add block increments + if isinstance(block_inc, torch.Tensor): + insert_val += segment_csr( + block_inc[: r1[-1]], indptr, reduce="sum" + ) + else: + insert_val += block_inc * (indptr[1:] - indptr[:-1]) + if insert_dummy: + insert_val[0] -= block_inc + else: + idx = r1[1:] != r1[:-1] + if continuous_indexing: + # 2. For each group, make sure the indexing starts from the next group's + # first element. So, simply assign 1s there. + insert_val[idx] = 1 + + # Add block increments + insert_val[idx] += block_inc + + # Add repeat_inc within each group + if isinstance(repeat_inc, torch.Tensor): + insert_val += repeat_inc[r1[:-1]] + if isinstance(repeats, torch.Tensor): + repeat_inc_inner = repeat_inc[repeats > 0][:-1] + else: + repeat_inc_inner = repeat_inc[:-1] + else: + insert_val += repeat_inc + repeat_inc_inner = repeat_inc + + # Subtract the increments between groups + if isinstance(repeats, torch.Tensor): + repeats_inner = repeats[repeats > 0][:-1] + else: + repeats_inner = repeats + insert_val[r1[1:] != r1[:-1]] -= repeat_inc_inner * repeats_inner + + # Assign index-offsetting values + id_ar[insert_index] = insert_val + + if insert_dummy: + id_ar = id_ar[1:] + if continuous_indexing: + id_ar[0] -= 1 + + # Set start index now, in case of insertion due to leading repeats=0 + id_ar[0] += start_idx + + # Finally index into input array for the group repeated o/p + res = id_ar.cumsum(0) + return res + + +def get_edge_id(edge_idx, cell_offsets, num_atoms): + cell_basis = cell_offsets.max() - cell_offsets.min() + 1 + cell_id = ( + ( + cell_offsets + * cell_offsets.new_tensor([[1, cell_basis, cell_basis**2]]) + ) + .sum(-1) + .long() + ) + edge_id = edge_idx[0] + edge_idx[1] * num_atoms + cell_id * num_atoms**2 + return edge_id diff --git a/ocpmodels/models/schnet.py b/ocpmodels/models/schnet.py new file mode 100644 index 0000000..81e012c --- /dev/null +++ b/ocpmodels/models/schnet.py @@ -0,0 +1,142 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +from torch_geometric.nn import SchNet +from torch_scatter import scatter + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.models.base import BaseModel + + +@registry.register_model("schnet") +class SchNetWrap(SchNet, BaseModel): + r"""Wrapper around the continuous-filter convolutional neural network SchNet from the + `"SchNet: A Continuous-filter Convolutional Neural Network for Modeling + Quantum Interactions" `_. Each layer uses interaction + block of the form: + + .. math:: + \mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j \odot + h_{\mathbf{\Theta}} ( \exp(-\gamma(\mathbf{e}_{j,i} - \mathbf{\mu}))), + + Args: + num_atoms (int): Unused argument + bond_feat_dim (int): Unused argument + num_targets (int): Number of targets to predict. + use_pbc (bool, optional): If set to :obj:`True`, account for periodic boundary conditions. + (default: :obj:`True`) + regress_forces (bool, optional): If set to :obj:`True`, predict forces by differentiating + energy with respect to positions. + (default: :obj:`True`) + otf_graph (bool, optional): If set to :obj:`True`, compute graph edges on the fly. + (default: :obj:`False`) + hidden_channels (int, optional): Number of hidden channels. + (default: :obj:`128`) + num_filters (int, optional): Number of filters to use. + (default: :obj:`128`) + num_interactions (int, optional): Number of interaction blocks + (default: :obj:`6`) + num_gaussians (int, optional): The number of gaussians :math:`\mu`. + (default: :obj:`50`) + cutoff (float, optional): Cutoff distance for interatomic interactions. + (default: :obj:`10.0`) + readout (string, optional): Whether to apply :obj:`"add"` or + :obj:`"mean"` global aggregation. (default: :obj:`"add"`) + """ + + def __init__( + self, + num_atoms, # not used + bond_feat_dim, # not used + num_targets, + use_pbc=True, + regress_forces=True, + otf_graph=False, + hidden_channels=128, + num_filters=128, + num_interactions=6, + num_gaussians=50, + cutoff=10.0, + readout="add", + ): + self.num_targets = num_targets + self.regress_forces = regress_forces + self.use_pbc = use_pbc + self.cutoff = cutoff + self.otf_graph = otf_graph + self.max_neighbors = 50 + self.reduce = readout + super(SchNetWrap, self).__init__( + hidden_channels=hidden_channels, + num_filters=num_filters, + num_interactions=num_interactions, + num_gaussians=num_gaussians, + cutoff=cutoff, + readout=readout, + ) + + @conditional_grad(torch.enable_grad()) + def _forward(self, data): + z = data.atomic_numbers.long() + pos = data.pos + batch = data.batch + + ( + edge_index, + edge_weight, + distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + + if self.use_pbc: + assert z.dim() == 1 and z.dtype == torch.long + + edge_attr = self.distance_expansion(edge_weight) + + h = self.embedding(z) + for interaction in self.interactions: + h = h + interaction(h, edge_index, edge_weight, edge_attr) + + h = self.lin1(h) + h = self.act(h) + h = self.lin2(h) + + batch = torch.zeros_like(z) if batch is None else batch + energy = scatter(h, batch, dim=0, reduce=self.reduce) + else: + energy = super(SchNetWrap, self).forward(z, pos, batch) + return energy + + def forward(self, data): + if self.regress_forces: + data.pos.requires_grad_(True) + energy = self._forward(data) + + if self.regress_forces: + forces = -1 * ( + torch.autograd.grad( + energy, + data.pos, + grad_outputs=torch.ones_like(energy), + create_graph=True, + )[0] + ) + return energy, forces + else: + return energy + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) diff --git a/ocpmodels/models/scn/Jd.pt b/ocpmodels/models/scn/Jd.pt new file mode 100644 index 0000000..01ed13e Binary files /dev/null and b/ocpmodels/models/scn/Jd.pt differ diff --git a/ocpmodels/models/scn/README.md b/ocpmodels/models/scn/README.md new file mode 100644 index 0000000..0fbfe9e --- /dev/null +++ b/ocpmodels/models/scn/README.md @@ -0,0 +1,22 @@ +# Spherical Channels for Modeling Atomic Interactions + +C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, Brandon Wood + +[[`arXiv:2206.14331`](https://arxiv.org/abs/2206.14331)] + +To run the Spherical Channel Network (SCN), install [e3nn](https://github.com/e3nn/e3nn/) with `pip install e3nn==0.2.6`. + +SCN was developed with e3nn v0.2.6, and might run slower with later versions [[1](https://github.com/Open-Catalyst-Project/ocp/issues/397), [2](https://github.com/Open-Catalyst-Project/ocp/pull/402)]. + +## Citing + +If you use SCN in your work, please consider citing: + +```bibtex +@inproceedings{zitnick_scn_2022, + title = {{Spherical Channels for Modeling Atomic Interactions}}, + author = {Zitnick, C. Lawrence and Das, Abhishek and Kolluru, Adeesh and Lan, Janice and Shuaibi, Muhammed and Sriram, Anuroop and Ulissi, Zachary and Wood, Brandon}, + booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, + year = {2022}, +} +``` diff --git a/ocpmodels/models/scn/__init__.py b/ocpmodels/models/scn/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ocpmodels/models/scn/sampling.py b/ocpmodels/models/scn/sampling.py new file mode 100644 index 0000000..67a1ed8 --- /dev/null +++ b/ocpmodels/models/scn/sampling.py @@ -0,0 +1,46 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" +import math + +import torch + +### Methods for sample points on a sphere + + +def CalcSpherePoints(num_points, device="cpu"): + goldenRatio = (1 + 5**0.5) / 2 + i = torch.arange(num_points, device=device).view(-1, 1) + theta = 2 * math.pi * i / goldenRatio + phi = torch.arccos(1 - 2 * (i + 0.5) / num_points) + points = torch.cat( + [ + torch.cos(theta) * torch.sin(phi), + torch.sin(theta) * torch.sin(phi), + torch.cos(phi), + ], + dim=1, + ) + + # weight the points by their density + pt_cross = points.view(1, -1, 3) - points.view(-1, 1, 3) + pt_cross = torch.sum(pt_cross**2, dim=2) + pt_cross = torch.exp(-pt_cross / (0.5 * 0.3)) + scalar = 1.0 / torch.sum(pt_cross, dim=1) + scalar = num_points * scalar / torch.sum(scalar) + return points * (scalar.view(-1, 1)) + + +def CalcSpherePointsRandom(num_points, device): + pts = 2.0 * (torch.rand(num_points, 3, device=device) - 0.5) + radius = torch.sum(pts**2, dim=1) + while torch.max(radius) > 1.0: + replace_pts = 2.0 * (torch.rand(num_points, 3, device=device) - 0.5) + replace_mask = radius.gt(0.99) + pts.masked_scatter_(replace_mask.view(-1, 1).repeat(1, 3), replace_pts) + radius = torch.sum(pts**2, dim=1) + + return pts / radius.view(-1, 1) diff --git a/ocpmodels/models/scn/scn.py b/ocpmodels/models/scn/scn.py new file mode 100644 index 0000000..770b8fc --- /dev/null +++ b/ocpmodels/models/scn/scn.py @@ -0,0 +1,839 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import sys +import time + +import numpy as np +import torch +import torch.nn as nn +from torch_geometric.nn import radius_graph + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import ( + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.models.base import BaseModel +from ocpmodels.models.scn.sampling import CalcSpherePoints +from ocpmodels.models.scn.smearing import ( + GaussianSmearing, + LinearSigmoidSmearing, + SigmoidSmearing, + SiLUSmearing, +) +from ocpmodels.models.scn.spherical_harmonics import SphericalHarmonicsHelper + +try: + import e3nn + from e3nn import o3 +except ImportError: + pass + + +@registry.register_model("scn") +class SphericalChannelNetwork(BaseModel): + """Spherical Channel Network + Paper: Spherical Channels for Modeling Atomic Interactions + + Args: + use_pbc (bool): Use periodic boundary conditions + regress_forces (bool): Compute forces + otf_graph (bool): Compute graph On The Fly (OTF) + max_num_neighbors (int): Maximum number of neighbors per atom + cutoff (float): Maximum distance between nieghboring atoms in Angstroms + max_num_elements (int): Maximum atomic number + + num_interactions (int): Number of layers in the GNN + lmax (int): Maximum degree of the spherical harmonics (1 to 10) + mmax (int): Maximum order of the spherical harmonics (0 or 1) + num_resolutions (int): Number of resolutions used to compute messages, further away atoms has lower resolution (1 or 2) + sphere_channels (int): Number of spherical channels + sphere_channels_reduce (int): Number of spherical channels used during message passing (downsample or upsample) + hidden_channels (int): Number of hidden units in message passing + num_taps (int): Number of taps or rotations used during message passing (1 or otherwise set automatically based on mmax) + + use_grid (bool): Use non-linear pointwise convolution during aggregation + num_bands (int): Number of bands used during message aggregation for the 1x1 pointwise convolution (1 or 2) + + num_sphere_samples (int): Number of samples used to approximate the integration of the sphere in the output blocks + num_basis_functions (int): Number of basis functions used for distance and atomic number blocks + distance_function ("gaussian", "sigmoid", "linearsigmoid", "silu"): Basis function used for distances + basis_width_scalar (float): Width of distance basis function + distance_resolution (float): Distance between distance basis functions in Angstroms + + show_timing_info (bool): Show timing and memory info + """ + + def __init__( + self, + num_atoms, # not used + bond_feat_dim, # not used + num_targets, # not used + use_pbc=True, + regress_forces=True, + otf_graph=False, + max_num_neighbors=20, + cutoff=8.0, + max_num_elements=90, + num_interactions=8, + lmax=6, + mmax=1, + num_resolutions=2, + sphere_channels=128, + sphere_channels_reduce=128, + hidden_channels=256, + num_taps=-1, + use_grid=True, + num_bands=1, + num_sphere_samples=128, + num_basis_functions=128, + distance_function="gaussian", + basis_width_scalar=1.0, + distance_resolution=0.02, + show_timing_info=False, + direct_forces=True, + ): + super().__init__() + + if "e3nn" not in sys.modules: + logging.error( + "You need to install e3nn v0.2.6 to use the SCN model" + ) + raise ImportError + + assert e3nn.__version__ == "0.2.6" + + self.regress_forces = regress_forces + self.use_pbc = use_pbc + self.cutoff = cutoff + self.otf_graph = otf_graph + self.show_timing_info = show_timing_info + self.max_num_elements = max_num_elements + self.hidden_channels = hidden_channels + self.num_interactions = num_interactions + self.num_atoms = 0 + self.num_sphere_samples = num_sphere_samples + self.sphere_channels = sphere_channels + self.sphere_channels_reduce = sphere_channels_reduce + self.max_num_neighbors = self.max_neighbors = max_num_neighbors + self.num_basis_functions = num_basis_functions + self.distance_resolution = distance_resolution + self.grad_forces = False + self.lmax = lmax + self.mmax = mmax + self.basis_width_scalar = basis_width_scalar + self.sphere_basis = (self.lmax + 1) ** 2 + self.use_grid = use_grid + self.distance_function = distance_function + + # variables used for display purposes + self.counter = 0 + + self.act = nn.SiLU() + + # Weights for message initialization + self.sphere_embedding = nn.Embedding( + self.max_num_elements, self.sphere_channels + ) + + assert self.distance_function in [ + "gaussian", + "sigmoid", + "linearsigmoid", + "silu", + ] + + self.num_gaussians = int(cutoff / self.distance_resolution) + if self.distance_function == "gaussian": + self.distance_expansion = GaussianSmearing( + 0.0, + cutoff, + self.num_gaussians, + basis_width_scalar, + ) + if self.distance_function == "sigmoid": + self.distance_expansion = SigmoidSmearing( + 0.0, + cutoff, + self.num_gaussians, + basis_width_scalar, + ) + if self.distance_function == "linearsigmoid": + self.distance_expansion = LinearSigmoidSmearing( + 0.0, + cutoff, + self.num_gaussians, + basis_width_scalar, + ) + + if self.distance_function == "silu": + self.distance_expansion = SiLUSmearing( + 0.0, + cutoff, + self.num_gaussians, + basis_width_scalar, + ) + + if num_resolutions == 1: + self.num_resolutions = 1 + self.hidden_channels_list = torch.tensor([self.hidden_channels]) + self.lmax_list = torch.tensor( + [self.lmax, -1] + ) # always end with -1 + self.cutoff_list = torch.tensor([self.max_num_neighbors - 0.01]) + if num_resolutions == 2: + self.num_resolutions = 2 + self.hidden_channels_list = torch.tensor( + [self.hidden_channels, self.hidden_channels // 4] + ) + self.lmax_list = torch.tensor([self.lmax, max(4, self.lmax - 2)]) + self.cutoff_list = torch.tensor( + [12 - 0.01, self.max_num_neighbors - 0.01] + ) + + self.sphharm_list = [] + for i in range(self.num_resolutions): + self.sphharm_list.append( + SphericalHarmonicsHelper( + self.lmax_list[i], + self.mmax, + num_taps, + num_bands, + ) + ) + + self.edge_blocks = nn.ModuleList() + for _ in range(self.num_interactions): + block = EdgeBlock( + self.num_resolutions, + self.sphere_channels_reduce, + self.hidden_channels_list, + self.cutoff_list, + self.sphharm_list, + self.sphere_channels, + self.distance_expansion, + self.max_num_elements, + self.num_basis_functions, + self.num_gaussians, + self.use_grid, + self.act, + ) + self.edge_blocks.append(block) + + # Energy estimation + self.energy_fc1 = nn.Linear(self.sphere_channels, self.sphere_channels) + self.energy_fc2 = nn.Linear( + self.sphere_channels, self.sphere_channels_reduce + ) + self.energy_fc3 = nn.Linear(self.sphere_channels_reduce, 1) + + # Force estimation + if self.regress_forces: + self.force_fc1 = nn.Linear( + self.sphere_channels, self.sphere_channels + ) + self.force_fc2 = nn.Linear( + self.sphere_channels, self.sphere_channels_reduce + ) + self.force_fc3 = nn.Linear(self.sphere_channels_reduce, 1) + + @conditional_grad(torch.enable_grad()) + def forward(self, data): + self.device = data.pos.device + self.num_atoms = len(data.batch) + self.batch_size = len(data.natoms) + # torch.autograd.set_detect_anomaly(True) + + start_time = time.time() + + outputs = self._forward_helper( + data, + ) + + if self.show_timing_info is True: + torch.cuda.synchronize() + print( + "{} Time: {}\tMemory: {}\t{}".format( + self.counter, + time.time() - start_time, + len(data.pos), + torch.cuda.max_memory_allocated() / 1000000, + ) + ) + + self.counter = self.counter + 1 + + return outputs + + # restructure forward helper for conditional grad + def _forward_helper(self, data): + atomic_numbers = data.atomic_numbers.long() + num_atoms = len(atomic_numbers) + pos = data.pos + + ( + edge_index, + edge_distance, + edge_distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + + ############################################################### + # Initialize data structures + ############################################################### + + # Calculate which message block each edge should use. Based on edge distance rank. + edge_rank = self._rank_edge_distances( + edge_distance, edge_index, self.max_num_neighbors + ) + + # Reorder edges so that they are grouped by distance rank (lowest to highest) + last_cutoff = -0.1 + message_block_idx = torch.zeros(len(edge_distance), device=pos.device) + edge_distance_reorder = torch.tensor([], device=self.device) + edge_index_reorder = torch.tensor([], device=self.device) + edge_distance_vec_reorder = torch.tensor([], device=self.device) + cutoff_index = torch.tensor([0], device=self.device) + for i in range(self.num_resolutions): + mask = torch.logical_and( + edge_rank.gt(last_cutoff), edge_rank.le(self.cutoff_list[i]) + ) + last_cutoff = self.cutoff_list[i] + message_block_idx.masked_fill_(mask, i) + edge_distance_reorder = torch.cat( + [ + edge_distance_reorder, + torch.masked_select(edge_distance, mask), + ], + dim=0, + ) + edge_index_reorder = torch.cat( + [ + edge_index_reorder, + torch.masked_select( + edge_index, mask.view(1, -1).repeat(2, 1) + ).view(2, -1), + ], + dim=1, + ) + edge_distance_vec_mask = torch.masked_select( + edge_distance_vec, mask.view(-1, 1).repeat(1, 3) + ).view(-1, 3) + edge_distance_vec_reorder = torch.cat( + [edge_distance_vec_reorder, edge_distance_vec_mask], dim=0 + ) + cutoff_index = torch.cat( + [ + cutoff_index, + torch.tensor( + [len(edge_distance_reorder)], device=self.device + ), + ], + dim=0, + ) + + edge_index = edge_index_reorder.long() + edge_distance = edge_distance_reorder + edge_distance_vec = edge_distance_vec_reorder + + # Compute 3x3 rotation matrix per edge + edge_rot_mat = self._init_edge_rot_mat( + data, edge_index, edge_distance_vec + ) + + # Initialize the WignerD matrices and other values for spherical harmonic calculations + for i in range(self.num_resolutions): + self.sphharm_list[i].InitWignerDMatrix( + edge_rot_mat[cutoff_index[i] : cutoff_index[i + 1]], + ) + + ############################################################### + # Initialize node embeddings + ############################################################### + + # Init per node representations using an atomic number based embedding + x = torch.zeros( + num_atoms, + self.sphere_basis, + self.sphere_channels, + device=pos.device, + ) + x[:, 0, :] = self.sphere_embedding(atomic_numbers) + + ############################################################### + # Update spherical node embeddings + ############################################################### + for i, interaction in enumerate(self.edge_blocks): + if i > 0: + x = x + interaction( + x, atomic_numbers, edge_distance, edge_index, cutoff_index + ) + else: + x = interaction( + x, atomic_numbers, edge_distance, edge_index, cutoff_index + ) + + ############################################################### + # Estimate energy and forces using the node embeddings + ############################################################### + + # Create a roughly evenly distributed point sampling of the sphere + sphere_points = CalcSpherePoints( + self.num_sphere_samples, x.device + ).detach() + sphharm_weights = o3.spherical_harmonics( + torch.arange(0, self.lmax + 1).tolist(), sphere_points, False + ).detach() + + # Energy estimation + node_energy = torch.einsum( + "abc, pb->apc", x, sphharm_weights + ).contiguous() + node_energy = node_energy.view(-1, self.sphere_channels) + node_energy = self.act(self.energy_fc1(node_energy)) + node_energy = self.act(self.energy_fc2(node_energy)) + node_energy = self.energy_fc3(node_energy) + node_energy = node_energy.view(-1, self.num_sphere_samples, 1) + node_energy = torch.sum(node_energy, dim=1) / self.num_sphere_samples + energy = torch.zeros(len(data.natoms), device=pos.device) + energy.index_add_(0, data.batch, node_energy.view(-1)) + + # Force estimation + if self.regress_forces: + forces = torch.einsum( + "abc, pb->apc", x, sphharm_weights + ).contiguous() + forces = forces.view(-1, self.sphere_channels) + forces = self.act(self.force_fc1(forces)) + forces = self.act(self.force_fc2(forces)) + forces = self.force_fc3(forces) + forces = forces.view(-1, self.num_sphere_samples, 1) + forces = forces * sphere_points.view(1, self.num_sphere_samples, 3) + forces = torch.sum(forces, dim=1) / self.num_sphere_samples + + if not self.regress_forces: + return energy + else: + return energy, forces + + def _init_edge_rot_mat(self, data, edge_index, edge_distance_vec): + edge_vec_0 = edge_distance_vec + edge_vec_0_distance = torch.sqrt(torch.sum(edge_vec_0**2, dim=1)) + + if torch.min(edge_vec_0_distance) < 0.0001: + print( + "Error edge_vec_0_distance: {}".format( + torch.min(edge_vec_0_distance) + ) + ) + (minval, minidx) = torch.min(edge_vec_0_distance, 0) + print( + "Error edge_vec_0_distance: {} {} {} {} {}".format( + minidx, + edge_index[0, minidx], + edge_index[1, minidx], + data.pos[edge_index[0, minidx]], + data.pos[edge_index[1, minidx]], + ) + ) + + norm_x = edge_vec_0 / (edge_vec_0_distance.view(-1, 1)) + + edge_vec_2 = torch.rand_like(edge_vec_0) - 0.5 + edge_vec_2 = edge_vec_2 / ( + torch.sqrt(torch.sum(edge_vec_2**2, dim=1)).view(-1, 1) + ) + # Create two rotated copys of the random vectors in case the random vector is aligned with norm_x + # With two 90 degree rotated vectors, at least one should not be aligned with norm_x + edge_vec_2b = edge_vec_2.clone() + edge_vec_2b[:, 0] = -edge_vec_2[:, 1] + edge_vec_2b[:, 1] = edge_vec_2[:, 0] + edge_vec_2c = edge_vec_2.clone() + edge_vec_2c[:, 1] = -edge_vec_2[:, 2] + edge_vec_2c[:, 2] = edge_vec_2[:, 1] + vec_dot_b = torch.abs(torch.sum(edge_vec_2b * norm_x, dim=1)).view( + -1, 1 + ) + vec_dot_c = torch.abs(torch.sum(edge_vec_2c * norm_x, dim=1)).view( + -1, 1 + ) + + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)).view(-1, 1) + edge_vec_2 = torch.where( + torch.gt(vec_dot, vec_dot_b), edge_vec_2b, edge_vec_2 + ) + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)).view(-1, 1) + edge_vec_2 = torch.where( + torch.gt(vec_dot, vec_dot_c), edge_vec_2c, edge_vec_2 + ) + + vec_dot = torch.abs(torch.sum(edge_vec_2 * norm_x, dim=1)) + # Check the vectors aren't aligned + assert torch.max(vec_dot) < 0.99 + + norm_z = torch.cross(norm_x, edge_vec_2, dim=1) + norm_z = norm_z / ( + torch.sqrt(torch.sum(norm_z**2, dim=1, keepdim=True)) + ) + norm_z = norm_z / ( + torch.sqrt(torch.sum(norm_z**2, dim=1)).view(-1, 1) + ) + norm_y = torch.cross(norm_x, norm_z, dim=1) + norm_y = norm_y / ( + torch.sqrt(torch.sum(norm_y**2, dim=1, keepdim=True)) + ) + + norm_x = norm_x.view(-1, 3, 1) + norm_y = -norm_y.view(-1, 3, 1) + norm_z = norm_z.view(-1, 3, 1) + + edge_rot_mat_inv = torch.cat([norm_z, norm_x, norm_y], dim=2) + edge_rot_mat = torch.transpose(edge_rot_mat_inv, 1, 2) + + return edge_rot_mat.detach() + + def _rank_edge_distances( + self, edge_distance, edge_index, max_num_neighbors + ): + device = edge_distance.device + # Create an index map to map distances from atom_distance to distance_sort + # index_sort_map assumes index to be sorted + output, num_neighbors = torch.unique(edge_index[1], return_counts=True) + index_neighbor_offset = ( + torch.cumsum(num_neighbors, dim=0) - num_neighbors + ) + index_neighbor_offset_expand = torch.repeat_interleave( + index_neighbor_offset, num_neighbors + ) + + index_sort_map = ( + edge_index[1] * max_num_neighbors + + torch.arange(len(edge_distance), device=device) + - index_neighbor_offset_expand + ) + + num_atoms = torch.max(edge_index) + 1 + distance_sort = torch.full( + [num_atoms * max_num_neighbors], np.inf, device=device + ) + distance_sort.index_copy_(0, index_sort_map, edge_distance) + distance_sort = distance_sort.view(num_atoms, max_num_neighbors) + no_op, index_sort = torch.sort(distance_sort, dim=1) + + index_map = ( + torch.arange(max_num_neighbors, device=device) + .view(1, -1) + .repeat(num_atoms, 1) + .view(-1) + ) + index_sort = index_sort + ( + torch.arange(num_atoms, device=device) * max_num_neighbors + ).view(-1, 1).repeat(1, max_num_neighbors) + edge_rank = torch.zeros_like(index_map) + edge_rank.index_copy_(0, index_sort.view(-1), index_map) + edge_rank = edge_rank.view(num_atoms, max_num_neighbors) + + index_sort_mask = distance_sort.lt(1000.0) + edge_rank = torch.masked_select(edge_rank, index_sort_mask) + + return edge_rank + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) + + +class EdgeBlock(torch.nn.Module): + def __init__( + self, + num_resolutions, + sphere_channels_reduce, + hidden_channels_list, + cutoff_list, + sphharm_list, + sphere_channels, + distance_expansion, + max_num_elements, + num_basis_functions, + num_gaussians, + use_grid, + act, + ): + super(EdgeBlock, self).__init__() + self.num_resolutions = num_resolutions + self.act = act + self.hidden_channels_list = hidden_channels_list + self.sphere_channels = sphere_channels + self.sphere_channels_reduce = sphere_channels_reduce + self.distance_expansion = distance_expansion + self.cutoff_list = cutoff_list + self.sphharm_list = sphharm_list + self.max_num_elements = max_num_elements + self.num_basis_functions = num_basis_functions + self.use_grid = use_grid + self.num_gaussians = num_gaussians + + # Edge features + self.dist_block = DistanceBlock( + self.num_gaussians, + self.num_basis_functions, + self.distance_expansion, + self.max_num_elements, + self.act, + ) + + # Create a message block for each cutoff + self.message_blocks = nn.ModuleList() + for i in range(self.num_resolutions): + block = MessageBlock( + self.sphere_channels_reduce, + int(self.hidden_channels_list[i]), + self.num_basis_functions, + self.sphharm_list[i], + self.act, + ) + self.message_blocks.append(block) + + # Downsampling number of sphere channels + # Make sure bias is false unless equivariance is lost + if self.sphere_channels != self.sphere_channels_reduce: + self.downsample = nn.Linear( + self.sphere_channels, + self.sphere_channels_reduce, + bias=False, + ) + self.upsample = nn.Linear( + self.sphere_channels_reduce, + self.sphere_channels, + bias=False, + ) + + # Use non-linear message aggregation? + if self.use_grid: + # Network for each node to combine edge messages + self.fc1_sphere = nn.Linear( + self.sphharm_list[0].num_bands + * 2 + * self.sphere_channels_reduce, + self.sphharm_list[0].num_bands + * 2 + * self.sphere_channels_reduce, + ) + + self.fc2_sphere = nn.Linear( + self.sphharm_list[0].num_bands + * 2 + * self.sphere_channels_reduce, + 2 * self.sphere_channels_reduce, + ) + + self.fc3_sphere = nn.Linear( + 2 * self.sphere_channels_reduce, self.sphere_channels_reduce + ) + + def forward( + self, + x, + atomic_numbers, + edge_distance, + edge_index, + cutoff_index, + ): + + ############################################################### + # Update spherical node embeddings + ############################################################### + + x_edge = self.dist_block( + edge_distance, + atomic_numbers[edge_index[0]], + atomic_numbers[edge_index[1]], + ) + x_new = torch.zeros( + len(x), + self.sphharm_list[0].sphere_basis, + self.sphere_channels_reduce, + dtype=x.dtype, + device=x.device, + ) + + if self.sphere_channels != self.sphere_channels_reduce: + x_down = self.downsample(x.view(-1, self.sphere_channels)) + else: + x_down = x + x_down = x_down.view( + -1, self.sphharm_list[0].sphere_basis, self.sphere_channels_reduce + ) + + for i, interaction in enumerate(self.message_blocks): + start_idx = cutoff_index[i] + end_idx = cutoff_index[i + 1] + + x_message = interaction( + x_down[:, 0 : self.sphharm_list[i].sphere_basis, :], + x_edge[start_idx:end_idx], + edge_index[:, start_idx:end_idx], + ) + + # Sum all incoming edges to the target nodes + x_new[:, 0 : self.sphharm_list[i].sphere_basis, :].index_add_( + 0, edge_index[1, start_idx:end_idx], x_message.to(x_new.dtype) + ) + + if self.use_grid: + # Feed in the spherical functions from the previous time step + x_grid = self.sphharm_list[0].ToGrid( + x_down, self.sphere_channels_reduce + ) + x_grid = torch.cat( + [ + x_grid, + self.sphharm_list[0].ToGrid( + x_new, self.sphere_channels_reduce + ), + ], + dim=1, + ) + + x_grid = self.act(self.fc1_sphere(x_grid)) + x_grid = self.act(self.fc2_sphere(x_grid)) + x_grid = self.fc3_sphere(x_grid) + x_new = self.sphharm_list[0].FromGrid( + x_grid, self.sphere_channels_reduce + ) + + if self.sphere_channels != self.sphere_channels_reduce: + x_new = x_new.view(-1, self.sphere_channels_reduce) + x_new = self.upsample(x_new) + x_new = x_new.view( + -1, self.sphharm_list[0].sphere_basis, self.sphere_channels + ) + + return x_new + + +class MessageBlock(torch.nn.Module): + def __init__( + self, + sphere_channels_reduce, + hidden_channels, + num_basis_functions, + sphharm, + act, + ): + super(MessageBlock, self).__init__() + self.act = act + self.hidden_channels = hidden_channels + self.sphere_channels_reduce = sphere_channels_reduce + self.sphharm = sphharm + + self.fc1_dist = nn.Linear(num_basis_functions, self.hidden_channels) + + # Network for each edge to compute edge messages + self.fc1_edge_proj = nn.Linear( + 2 * self.sphharm.sphere_basis_reduce * self.sphere_channels_reduce, + self.hidden_channels, + ) + + self.fc1_edge = nn.Linear(self.hidden_channels, self.hidden_channels) + + self.fc2_edge = nn.Linear( + self.hidden_channels, + self.sphharm.sphere_basis_reduce * self.sphere_channels_reduce, + ) + + def forward( + self, + x, + x_edge, + edge_index, + ): + + ############################################################### + # Compute messages + ############################################################### + x_edge = self.act(self.fc1_dist(x_edge)) + + x_source = x[edge_index[0, :]] + x_target = x[edge_index[1, :]] + + # Rotate the spherical harmonic basis functions to align with the edge + x_msg_source = self.sphharm.Rotate(x_source) + x_msg_target = self.sphharm.Rotate(x_target) + + # Compute messages + x_message = torch.cat([x_msg_source, x_msg_target], dim=1) + x_message = self.act(self.fc1_edge_proj(x_message)) + x_message = ( + x_message.view( + -1, self.sphharm.num_y_rotations, self.hidden_channels + ) + ) * x_edge.view(-1, 1, self.hidden_channels) + x_message = x_message.view(-1, self.hidden_channels) + + x_message = self.act(self.fc1_edge(x_message)) + x_message = self.act(self.fc2_edge(x_message)) + + # Combine the rotated versions of the messages + x_message = x_message.view(-1, self.sphere_channels_reduce) + x_message = self.sphharm.CombineYRotations(x_message) + + # Rotate the spherical harmonic basis functions back to global coordinate frame + x_message = self.sphharm.RotateInv(x_message) + + return x_message + + +class DistanceBlock(torch.nn.Module): + def __init__( + self, + in_channels, + num_basis_functions, + distance_expansion, + max_num_elements, + act, + ): + super(DistanceBlock, self).__init__() + self.in_channels = in_channels + self.distance_expansion = distance_expansion + self.act = act + self.num_basis_functions = num_basis_functions + self.max_num_elements = max_num_elements + self.num_edge_channels = self.num_basis_functions + + self.fc1_dist = nn.Linear(self.in_channels, self.num_basis_functions) + + self.source_embedding = nn.Embedding( + self.max_num_elements, self.num_basis_functions + ) + self.target_embedding = nn.Embedding( + self.max_num_elements, self.num_basis_functions + ) + nn.init.uniform_(self.source_embedding.weight.data, -0.001, 0.001) + nn.init.uniform_(self.target_embedding.weight.data, -0.001, 0.001) + + self.fc1_edge_attr = nn.Linear( + self.num_edge_channels, + self.num_edge_channels, + ) + + def forward(self, edge_distance, source_element, target_element): + x_dist = self.distance_expansion(edge_distance) + x_dist = self.fc1_dist(x_dist) + + source_embedding = self.source_embedding(source_element) + target_embedding = self.target_embedding(target_element) + + x_edge = self.act(source_embedding + target_embedding + x_dist) + x_edge = self.act(self.fc1_edge_attr(x_edge)) + + return x_edge diff --git a/ocpmodels/models/scn/smearing.py b/ocpmodels/models/scn/smearing.py new file mode 100644 index 0000000..cd83b0f --- /dev/null +++ b/ocpmodels/models/scn/smearing.py @@ -0,0 +1,74 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.nn as nn + + +# Different encodings for the atom distance embeddings +class GaussianSmearing(torch.nn.Module): + def __init__( + self, start=-5.0, stop=5.0, num_gaussians=50, basis_width_scalar=1.0 + ): + super(GaussianSmearing, self).__init__() + self.num_output = num_gaussians + offset = torch.linspace(start, stop, num_gaussians) + self.coeff = ( + -0.5 / (basis_width_scalar * (offset[1] - offset[0])).item() ** 2 + ) + self.register_buffer("offset", offset) + + def forward(self, dist): + dist = dist.view(-1, 1) - self.offset.view(1, -1) + return torch.exp(self.coeff * torch.pow(dist, 2)) + + +class SigmoidSmearing(torch.nn.Module): + def __init__( + self, start=-5.0, stop=5.0, num_sigmoid=50, basis_width_scalar=1.0 + ): + super(SigmoidSmearing, self).__init__() + self.num_output = num_sigmoid + offset = torch.linspace(start, stop, num_sigmoid) + self.coeff = (basis_width_scalar / (offset[1] - offset[0])).item() + self.register_buffer("offset", offset) + + def forward(self, dist): + exp_dist = self.coeff * (dist.view(-1, 1) - self.offset.view(1, -1)) + return torch.sigmoid(exp_dist) + + +class LinearSigmoidSmearing(torch.nn.Module): + def __init__( + self, start=-5.0, stop=5.0, num_sigmoid=50, basis_width_scalar=1.0 + ): + super(LinearSigmoidSmearing, self).__init__() + self.num_output = num_sigmoid + offset = torch.linspace(start, stop, num_sigmoid) + self.coeff = (basis_width_scalar / (offset[1] - offset[0])).item() + self.register_buffer("offset", offset) + + def forward(self, dist): + exp_dist = self.coeff * (dist.view(-1, 1) - self.offset.view(1, -1)) + x_dist = torch.sigmoid(exp_dist) + 0.001 * exp_dist + return x_dist + + +class SiLUSmearing(torch.nn.Module): + def __init__( + self, start=-5.0, stop=5.0, num_output=50, basis_width_scalar=1.0 + ): + super(SiLUSmearing, self).__init__() + self.num_output = num_output + self.fc1 = nn.Linear(2, num_output) + self.act = nn.SiLU() + + def forward(self, dist): + x_dist = dist.view(-1, 1) + x_dist = torch.cat([x_dist, torch.ones_like(x_dist)], dim=1) + x_dist = self.act(self.fc1(x_dist)) + return x_dist diff --git a/ocpmodels/models/scn/spherical_harmonics.py b/ocpmodels/models/scn/spherical_harmonics.py new file mode 100644 index 0000000..1540cde --- /dev/null +++ b/ocpmodels/models/scn/spherical_harmonics.py @@ -0,0 +1,382 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import math +import os + +import torch + +try: + from e3nn import o3 + from e3nn.o3 import FromS2Grid, ToS2Grid +except ImportError: + pass + +# Borrowed from e3nn @ 0.4.0: +# https://github.com/e3nn/e3nn/blob/0.4.0/e3nn/o3/_wigner.py#L10 +# _Jd is a list of tensors of shape (2l+1, 2l+1) +_Jd = torch.load(os.path.join(os.path.dirname(__file__), "Jd.pt")) + + +class SphericalHarmonicsHelper: + """ + Helper functions for spherical harmonics calculations and representations + + Args: + lmax (int): Maximum degree of the spherical harmonics + mmax (int): Maximum order of the spherical harmonics + num_taps (int): Number of taps or rotations (1 or otherwise set automatically based on mmax) + num_bands (int): Number of bands used during message aggregation for the 1x1 pointwise convolution (1 or 2) + """ + + def __init__( + self, + lmax, + mmax, + num_taps, + num_bands, + ): + import sys + + if "e3nn" not in sys.modules: + logging.error( + "You need to install the e3nn library to use Spherical Harmonics" + ) + raise ImportError + + super().__init__() + self.lmax = lmax + self.mmax = mmax + self.num_taps = num_taps + self.num_bands = num_bands + + # Make sure lmax is large enough to support the num_bands + assert self.lmax - (self.num_bands - 1) >= 0 + + self.sphere_basis = (self.lmax + 1) ** 2 + self.sphere_basis = int(self.sphere_basis) + + self.sphere_basis_reduce = self.lmax + 1 + for i in range(1, self.mmax + 1): + self.sphere_basis_reduce = self.sphere_basis_reduce + 2 * ( + self.lmax + 1 - i + ) + self.sphere_basis_reduce = int(self.sphere_basis_reduce) + + def InitWignerDMatrix(self, edge_rot_mat): + self.device = edge_rot_mat.device + + # Initialize matrix to combine the y-axis rotations during message passing + self.mapping_y_rot, self.y_rotations = self.InitYRotMapping() + self.num_y_rotations = len(self.y_rotations) + + # Conversion from basis to grid respresentations + self.grid_res = (self.lmax + 1) * 2 + self.to_grid_shb = torch.tensor([], device=self.device) + self.to_grid_sha = torch.tensor([], device=self.device) + + for b in range(self.num_bands): + l = self.lmax - b # noqa: E741 + togrid = ToS2Grid( + l, + (self.grid_res, self.grid_res + 1), + normalization="integral", + device=self.device, + ) + shb = togrid.shb + sha = togrid.sha + + padding = torch.zeros( + shb.size()[0], + shb.size()[1], + self.sphere_basis - shb.size()[2], + device=self.device, + ) + shb = torch.cat([shb, padding], dim=2) + self.to_grid_shb = torch.cat([self.to_grid_shb, shb], dim=0) + if b == 0: + self.to_grid_sha = sha + else: + self.to_grid_sha = torch.block_diag(self.to_grid_sha, sha) + + self.to_grid_sha = self.to_grid_sha.view( + self.num_bands, self.grid_res + 1, -1 + ) + self.to_grid_sha = torch.transpose(self.to_grid_sha, 0, 1).contiguous() + self.to_grid_sha = self.to_grid_sha.view( + (self.grid_res + 1) * self.num_bands, -1 + ) + + self.to_grid_shb = self.to_grid_shb.detach() + self.to_grid_sha = self.to_grid_sha.detach() + + self.from_grid = FromS2Grid( + (self.grid_res, self.grid_res + 1), + self.lmax, + normalization="integral", + device=self.device, + ) + for p in self.from_grid.parameters(): + p.detach() + + # Compute subsets of Wigner matrices to use for messages + wigner = torch.tensor([], device=self.device) + wigner_inv = torch.tensor([], device=self.device) + + for y_rot in self.y_rotations: + + # Compute rotation about y-axis + y_rot_mat = self.RotationMatrix(0, y_rot, 0) + y_rot_mat = y_rot_mat.repeat(len(edge_rot_mat), 1, 1) + # Add additional rotation about y-axis + rot_mat = torch.bmm(y_rot_mat, edge_rot_mat) + + # Compute Wigner matrices corresponding to the 3x3 rotation matrices + wignerD = self.RotationToWignerDMatrix(rot_mat, 0, self.lmax) + + basis_in = torch.tensor([], device=self.device) + basis_out = torch.tensor([], device=self.device) + start_l = 0 + end_l = self.lmax + 1 + for l in range(start_l, end_l): # noqa: E741 + offset = l**2 + basis_in = torch.cat( + [ + basis_in, + torch.arange(2 * l + 1, device=self.device) + offset, + ], + dim=0, + ) + m_max = min(l, self.mmax) + basis_out = torch.cat( + [ + basis_out, + torch.arange(-m_max, m_max + 1, device=self.device) + + offset + + l, + ], + dim=0, + ) + + # Only keep the rows/columns of the matrices used given lmax and mmax + wignerD_reduce = wignerD[:, basis_out.long(), :] + wignerD_reduce = wignerD_reduce[:, :, basis_in.long()] + + if y_rot == 0.0: + wigner_inv = ( + torch.transpose(wignerD_reduce, 1, 2).contiguous().detach() + ) + + wigner = torch.cat([wigner, wignerD_reduce.unsqueeze(1)], dim=1) + + wigner = wigner.view(-1, self.sphere_basis_reduce, self.sphere_basis) + + self.wigner = wigner.detach() + self.wigner_inv = wigner_inv.detach() + + # If num_taps is greater than 1, calculate how to combine the different samples. + # Note the e3nn code flips the y-axis with the z-axis in the SCN paper description. + def InitYRotMapping(self): + + if self.mmax == 0: + y_rotations = torch.tensor([0.0], device=self.device) + num_y_rotations = 1 + mapping_y_rot = torch.eye( + self.sphere_basis_reduce, device=self.device + ) + + if self.mmax == 1: + + if self.num_taps == 1: + y_rotations = torch.tensor([0.0], device=self.device) + num_y_rotations = len(y_rotations) + mapping_y_rot = torch.eye( + len(y_rotations) * self.sphere_basis_reduce, + self.sphere_basis_reduce, + device=self.device, + ) + else: + y_rotations = torch.tensor( + [0.0, 0.5 * math.pi, math.pi, 1.5 * math.pi], + device=self.device, + ) + num_y_rotations = len(y_rotations) + mapping_y_rot = torch.zeros( + len(y_rotations) * self.sphere_basis_reduce, + self.sphere_basis_reduce, + device=self.device, + ) + + # m = 0 + for l in range(0, self.lmax + 1): # noqa: E741 + offset = (l - 1) * 3 + 2 + if l == 0: # noqa: E741 + offset = 0 + for y in range(num_y_rotations): + mapping_y_rot[ + offset + y * self.sphere_basis_reduce, offset + ] = (1.0 / num_y_rotations) + + # m = -1 + for l in range(1, self.lmax + 1): # noqa: E741 + offset = (l - 1) * 3 + 1 + for y in range(num_y_rotations): + mapping_y_rot[ + offset + y * self.sphere_basis_reduce, offset + ] = (math.cos(y_rotations[y]) / num_y_rotations) + mapping_y_rot[ + (offset + 2) + y * self.sphere_basis_reduce, offset + ] = (math.sin(y_rotations[y]) / num_y_rotations) + + # m = 1 + for l in range(1, self.lmax + 1): # noqa: E741 + offset = (l - 1) * 3 + 3 + for y in range(num_y_rotations): + mapping_y_rot[ + offset + y * self.sphere_basis_reduce, offset + ] = (math.cos(y_rotations[y]) / num_y_rotations) + mapping_y_rot[ + offset - 2 + y * self.sphere_basis_reduce, offset + ] = (-math.sin(y_rotations[y]) / num_y_rotations) + + return mapping_y_rot.detach(), y_rotations + + # Simplified version of function from e3nn + def ToGrid(self, x, channels): + x = x.view(-1, self.sphere_basis, channels) + x_grid = torch.einsum("mbi,zic->zbmc", self.to_grid_shb, x) + x_grid = torch.einsum( + "am,zbmc->zbac", self.to_grid_sha, x_grid + ).contiguous() + x_grid = x_grid.view(-1, self.num_bands * channels) + return x_grid + + # Simplified version of function from e3nn + def FromGrid(self, x_grid, channels): + x_grid = x_grid.view(-1, self.grid_res, (self.grid_res + 1), channels) + x = torch.einsum("am,zbac->zbmc", self.from_grid.sha, x_grid) + x = torch.einsum("mbi,zbmc->zic", self.from_grid.shb, x).contiguous() + x = x.view(-1, channels) + return x + + def CombineYRotations(self, x): + num_channels = x.size()[-1] + x = x.view( + -1, self.num_y_rotations * self.sphere_basis_reduce, num_channels + ) + x = torch.einsum("abc, bd->adc", x, self.mapping_y_rot).contiguous() + return x + + def Rotate(self, x): + num_channels = x.size()[2] + x = x.view(-1, 1, self.sphere_basis, num_channels).repeat( + 1, self.num_y_rotations, 1, 1 + ) + x = x.view(-1, self.sphere_basis, num_channels) + # print('{} {}'.format(self.wigner.size(), x.size())) + x_rot = torch.bmm(self.wigner, x) + x_rot = x_rot.view(-1, self.sphere_basis_reduce * num_channels) + return x_rot + + def FlipGrid(self, grid, num_channels): + # lat long + long_res = self.grid_res + grid = grid.view(-1, self.grid_res, self.grid_res, num_channels) + grid = torch.roll(grid, int(long_res // 2), 2) + flip_grid = torch.flip(grid, [1]) + return flip_grid.view(-1, num_channels) + + def RotateInv(self, x): + x_rot = torch.bmm(self.wigner_inv, x) + return x_rot + + def RotateWigner(self, x, wigner): + x_rot = torch.bmm(wigner, x) + return x_rot + + def RotationMatrix(self, rot_x, rot_y, rot_z): + m1, m2, m3 = ( + torch.eye(3, device=self.device), + torch.eye(3, device=self.device), + torch.eye(3, device=self.device), + ) + if rot_x: + degree = rot_x + sin, cos = math.sin(degree), math.cos(degree) + m1 = torch.tensor( + [[1, 0, 0], [0, cos, sin], [0, -sin, cos]], device=self.device + ) + if rot_y: + degree = rot_y + sin, cos = math.sin(degree), math.cos(degree) + m2 = torch.tensor( + [[cos, 0, -sin], [0, 1, 0], [sin, 0, cos]], device=self.device + ) + if rot_z: + degree = rot_z + sin, cos = math.sin(degree), math.cos(degree) + m3 = torch.tensor( + [[cos, sin, 0], [-sin, cos, 0], [0, 0, 1]], device=self.device + ) + + matrix = torch.mm(torch.mm(m1, m2), m3) + matrix = matrix.view(1, 3, 3) + + return matrix + + def RotationToWignerDMatrix(self, edge_rot_mat, start_lmax, end_lmax): + x = edge_rot_mat @ edge_rot_mat.new_tensor([0.0, 1.0, 0.0]) + alpha, beta = o3.xyz_to_angles(x) + R = ( + o3.angles_to_matrix( + alpha, beta, torch.zeros_like(alpha) + ).transpose(-1, -2) + @ edge_rot_mat + ) + gamma = torch.atan2(R[..., 0, 2], R[..., 0, 0]) + + size = (end_lmax + 1) ** 2 - (start_lmax) ** 2 + wigner = torch.zeros(len(alpha), size, size, device=self.device) + start = 0 + for lmax in range(start_lmax, end_lmax + 1): + block = wigner_D(lmax, alpha, beta, gamma) + end = start + block.size()[1] + wigner[:, start:end, start:end] = block + start = end + + return wigner.detach() + + +# Borrowed from e3nn @ 0.4.0: +# https://github.com/e3nn/e3nn/blob/0.4.0/e3nn/o3/_wigner.py#L37 +# +# In 0.5.0, e3nn shifted to torch.matrix_exp which is significantly slower: +# https://github.com/e3nn/e3nn/blob/0.5.0/e3nn/o3/_wigner.py#L92 +def wigner_D(l, alpha, beta, gamma): + if not l < len(_Jd): + raise NotImplementedError( + f"wigner D maximum l implemented is {len(_Jd) - 1}, send us an email to ask for more" + ) + + alpha, beta, gamma = torch.broadcast_tensors(alpha, beta, gamma) + J = _Jd[l].to(dtype=alpha.dtype, device=alpha.device) + Xa = _z_rot_mat(alpha, l) + Xb = _z_rot_mat(beta, l) + Xc = _z_rot_mat(gamma, l) + return Xa @ J @ Xb @ J @ Xc + + +def _z_rot_mat(angle, l): + shape, device, dtype = angle.shape, angle.device, angle.dtype + M = angle.new_zeros((*shape, 2 * l + 1, 2 * l + 1)) + inds = torch.arange(0, 2 * l + 1, 1, device=device) + reversed_inds = torch.arange(2 * l, -1, -1, device=device) + frequencies = torch.arange(l, -l - 1, -1, dtype=dtype, device=device) + M[..., inds, reversed_inds] = torch.sin(frequencies * angle[..., None]) + M[..., inds, inds] = torch.cos(frequencies * angle[..., None]) + return M diff --git a/ocpmodels/models/spinconv.py b/ocpmodels/models/spinconv.py new file mode 100644 index 0000000..8da86fe --- /dev/null +++ b/ocpmodels/models/spinconv.py @@ -0,0 +1,1264 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" +import math +import time +from math import pi as PI + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import Embedding, Linear, ModuleList, Sequential +from torch_geometric.nn import MessagePassing, SchNet, radius_graph +from torch_scatter import scatter + +from ocpmodels.common.registry import registry +from ocpmodels.common.transforms import RandomRotate +from ocpmodels.common.utils import ( + compute_neighbors, + conditional_grad, + get_pbc_distances, + radius_graph_pbc, +) +from ocpmodels.models.base import BaseModel + +try: + from e3nn import o3 + from e3nn.io import SphericalTensor + from e3nn.o3 import FromS2Grid, SphericalHarmonics, ToS2Grid +except Exception: + pass + + +@registry.register_model("spinconv") +class spinconv(BaseModel): + def __init__( + self, + num_atoms, # not used + bond_feat_dim, # not used + num_targets, + use_pbc=True, + regress_forces=True, + otf_graph=False, + hidden_channels=32, + mid_hidden_channels=200, + num_interactions=1, + num_basis_functions=200, + basis_width_scalar=1.0, + max_num_neighbors=20, + sphere_size_lat=15, + sphere_size_long=9, + cutoff=10.0, + distance_block_scalar_max=2.0, + max_num_elements=90, + embedding_size=32, + show_timing_info=False, + sphere_message="fullconv", # message block sphere representation + output_message="fullconv", # output block sphere representation + lmax=False, + force_estimator="random", + model_ref_number=0, + readout="add", + num_rand_rotations=5, + scale_distances=True, + ): + super(spinconv, self).__init__() + + self.num_targets = num_targets + self.num_random_rotations = num_rand_rotations + self.regress_forces = regress_forces + self.use_pbc = use_pbc + self.cutoff = cutoff + self.otf_graph = otf_graph + self.show_timing_info = show_timing_info + self.max_num_elements = max_num_elements + self.mid_hidden_channels = mid_hidden_channels + self.sphere_size_lat = sphere_size_lat + self.sphere_size_long = sphere_size_long + self.num_atoms = 0 + self.hidden_channels = hidden_channels + self.embedding_size = embedding_size + self.max_num_neighbors = self.max_neighbors = max_num_neighbors + self.sphere_message = sphere_message + self.output_message = output_message + self.force_estimator = force_estimator + self.num_basis_functions = num_basis_functions + self.distance_block_scalar_max = distance_block_scalar_max + self.grad_forces = False + self.num_embedding_basis = 8 + self.lmax = lmax + self.scale_distances = scale_distances + self.basis_width_scalar = basis_width_scalar + + if self.sphere_message in ["spharm", "rotspharmroll", "rotspharmwd"]: + assert self.lmax, "lmax must be defined for spherical harmonics" + if self.output_message in ["spharm", "rotspharmroll", "rotspharmwd"]: + assert self.lmax, "lmax must be defined for spherical harmonics" + + # variables used for display purposes + self.counter = 0 + self.start_time = time.time() + self.total_time = 0 + self.model_ref_number = model_ref_number + + if self.force_estimator == "grad": + self.grad_forces = True + + # self.act = ShiftedSoftplus() + self.act = Swish() + + self.distance_expansion_forces = GaussianSmearing( + 0.0, + cutoff, + num_basis_functions, + basis_width_scalar, + ) + + # Weights for message initialization + self.embeddingblock2 = EmbeddingBlock( + self.mid_hidden_channels, + self.hidden_channels, + self.mid_hidden_channels, + self.embedding_size, + self.num_embedding_basis, + self.max_num_elements, + self.act, + ) + self.distfc1 = nn.Linear( + self.mid_hidden_channels, self.mid_hidden_channels + ) + self.distfc2 = nn.Linear( + self.mid_hidden_channels, self.mid_hidden_channels + ) + + self.dist_block = DistanceBlock( + self.num_basis_functions, + self.mid_hidden_channels, + self.max_num_elements, + self.distance_block_scalar_max, + self.distance_expansion_forces, + self.scale_distances, + ) + + self.message_blocks = ModuleList() + for _ in range(num_interactions): + block = MessageBlock( + hidden_channels, + hidden_channels, + mid_hidden_channels, + embedding_size, + self.sphere_size_lat, + self.sphere_size_long, + self.max_num_elements, + self.sphere_message, + self.act, + self.lmax, + ) + self.message_blocks.append(block) + + self.energyembeddingblock = EmbeddingBlock( + hidden_channels, + 1, + mid_hidden_channels, + embedding_size, + 8, + self.max_num_elements, + self.act, + ) + + if force_estimator == "random": + self.force_output_block = ForceOutputBlock( + hidden_channels, + 2, + mid_hidden_channels, + embedding_size, + self.sphere_size_lat, + self.sphere_size_long, + self.max_num_elements, + self.output_message, + self.act, + self.lmax, + ) + + @conditional_grad(torch.enable_grad()) + def forward(self, data): + self.device = data.pos.device + self.num_atoms = len(data.batch) + self.batch_size = len(data.natoms) + + pos = data.pos + if self.regress_forces: + pos = pos.requires_grad_(True) + + ( + edge_index, + edge_distance, + edge_distance_vec, + cell_offsets, + _, # cell offset distances + neighbors, + ) = self.generate_graph(data) + + edge_index, edge_distance, edge_distance_vec = self._filter_edges( + edge_index, + edge_distance, + edge_distance_vec, + self.max_num_neighbors, + ) + + outputs = self._forward_helper( + data, edge_index, edge_distance, edge_distance_vec + ) + if self.show_timing_info is True: + torch.cuda.synchronize() + print( + "Memory: {}\t{}\t{}".format( + len(edge_index[0]), + torch.cuda.memory_allocated() + / (1000 * len(edge_index[0])), + torch.cuda.max_memory_allocated() / 1000000, + ) + ) + + return outputs + + # restructure forward helper for conditional grad + def _forward_helper( + self, data, edge_index, edge_distance, edge_distance_vec + ): + ############################################################### + # Initialize messages + ############################################################### + + source_element = data.atomic_numbers[edge_index[0, :]].long() + target_element = data.atomic_numbers[edge_index[1, :]].long() + + x_dist = self.dist_block(edge_distance, source_element, target_element) + + x = x_dist + x = self.distfc1(x) + x = self.act(x) + x = self.distfc2(x) + x = self.act(x) + x = self.embeddingblock2(x, source_element, target_element) + + ############################################################### + # Update messages using block interactions + ############################################################### + + edge_rot_mat = self._init_edge_rot_mat( + data, edge_index, edge_distance_vec + ) + ( + proj_edges_index, + proj_edges_delta, + proj_edges_src_index, + ) = self._project2D_edges_init( + edge_rot_mat, edge_index, edge_distance_vec + ) + + for block_index, interaction in enumerate(self.message_blocks): + x_out = interaction( + x, + x_dist, + source_element, + target_element, + proj_edges_index, + proj_edges_delta, + proj_edges_src_index, + ) + + if block_index > 0: + x = x + x_out + else: + x = x_out + + ############################################################### + # Decoder + # Compute the forces and energies from the messages + ############################################################### + assert self.force_estimator in ["random", "grad"] + + energy = scatter(x, edge_index[1], dim=0, dim_size=data.num_nodes) / ( + self.max_num_neighbors / 2.0 + 1.0 + ) + atomic_numbers = data.atomic_numbers.long() + energy = self.energyembeddingblock( + energy, atomic_numbers, atomic_numbers + ) + energy = scatter(energy, data.batch, dim=0) + + if self.regress_forces: + if self.force_estimator == "grad": + forces = -1 * ( + torch.autograd.grad( + energy, + data.pos, + grad_outputs=torch.ones_like(energy), + create_graph=True, + )[0] + ) + if self.force_estimator == "random": + forces = self._compute_forces_random_rotations( + x, + self.num_random_rotations, + data.atomic_numbers.long(), + edge_index, + edge_distance_vec, + data.batch, + ) + + if not self.regress_forces: + return energy + else: + return energy, forces + + def _compute_forces_random_rotations( + self, + x, + num_random_rotations, + target_element, + edge_index, + edge_distance_vec, + batch, + ): + # Compute the forces and energy by randomly rotating the system and taking the average + + device = x.device + + rot_mat_x = torch.zeros(3, 3, device=device) + rot_mat_x[0][0] = 1.0 + rot_mat_x[1][1] = 1.0 + rot_mat_x[2][2] = 1.0 + + rot_mat_y = torch.zeros(3, 3, device=device) + rot_mat_y[0][1] = 1.0 + rot_mat_y[1][0] = -1.0 + rot_mat_y[2][2] = 1.0 + + rot_mat_z = torch.zeros(3, 3, device=device) + rot_mat_z[0][2] = 1.0 + rot_mat_z[1][1] = 1.0 + rot_mat_z[2][0] = -1.0 + + rot_mat_x = rot_mat_x.view(-1, 3, 3).repeat(self.num_atoms, 1, 1) + rot_mat_y = rot_mat_y.view(-1, 3, 3).repeat(self.num_atoms, 1, 1) + rot_mat_z = rot_mat_z.view(-1, 3, 3).repeat(self.num_atoms, 1, 1) + + # compute the random rotations + random_rot_mat = self._random_rot_mat( + self.num_atoms * num_random_rotations, device + ) + random_rot_mat = random_rot_mat.view( + num_random_rotations, self.num_atoms, 3, 3 + ) + + # the first matrix is the identity with the rest being random + # atom_rot_mat = torch.cat([torch.eye(3, device=device).view(1, 1, 3, 3).repeat(1, self.num_atoms, 1, 1), random_rot_mat], dim=0) + # or they are all random + atom_rot_mat = random_rot_mat + + forces = torch.zeros(self.num_atoms, 3, device=device) + + for rot_index in range(num_random_rotations): + + rot_mat_x_perturb = torch.bmm(rot_mat_x, atom_rot_mat[rot_index]) + rot_mat_y_perturb = torch.bmm(rot_mat_y, atom_rot_mat[rot_index]) + rot_mat_z_perturb = torch.bmm(rot_mat_z, atom_rot_mat[rot_index]) + + # project neighbors using the random rotations + ( + proj_nodes_index_x, + proj_nodes_delta_x, + proj_nodes_src_index_x, + ) = self._project2D_nodes_init( + rot_mat_x_perturb, edge_index, edge_distance_vec + ) + ( + proj_nodes_index_y, + proj_nodes_delta_y, + proj_nodes_src_index_y, + ) = self._project2D_nodes_init( + rot_mat_y_perturb, edge_index, edge_distance_vec + ) + ( + proj_nodes_index_z, + proj_nodes_delta_z, + proj_nodes_src_index_z, + ) = self._project2D_nodes_init( + rot_mat_z_perturb, edge_index, edge_distance_vec + ) + + # estimate the force in each perpendicular direction + force_x = self.force_output_block( + x, + self.num_atoms, + target_element, + proj_nodes_index_x, + proj_nodes_delta_x, + proj_nodes_src_index_x, + ) + force_y = self.force_output_block( + x, + self.num_atoms, + target_element, + proj_nodes_index_y, + proj_nodes_delta_y, + proj_nodes_src_index_y, + ) + force_z = self.force_output_block( + x, + self.num_atoms, + target_element, + proj_nodes_index_z, + proj_nodes_delta_z, + proj_nodes_src_index_z, + ) + forces_perturb = torch.cat( + [force_x[:, 0:1], force_y[:, 0:1], force_z[:, 0:1]], dim=1 + ) + + # rotate the predicted forces back into the global reference frame + rot_mat_inv = torch.transpose(rot_mat_x_perturb, 1, 2) + forces_perturb = torch.bmm( + rot_mat_inv, forces_perturb.view(-1, 3, 1) + ).view(-1, 3) + + forces = forces + forces_perturb + + forces = forces / (num_random_rotations) + + return forces + + def _filter_edges( + self, edge_index, edge_distance, edge_distance_vec, max_num_neighbors + ): + # Remove edges that aren't within the closest max_num_neighbors from either the target or source atom. + # This ensures all edges occur in pairs, i.e., if X -> Y exists then Y -> X is included. + # However, if both X -> Y and Y -> X don't both exist in the original list, this isn't guaranteed. + # Since some edges may have exactly the same distance, this function is not deterministic + device = edge_index.device + length = len(edge_distance) + + # Assuming the edges are consecutive based on the target index + target_node_index, neigh_count = torch.unique_consecutive( + edge_index[1], return_counts=True + ) + max_neighbors = torch.max(neigh_count) + + # handle special case where an atom doesn't have any neighbors + target_neigh_count = torch.zeros(self.num_atoms, device=device).long() + target_neigh_count.index_copy_( + 0, target_node_index.long(), neigh_count + ) + + # Create a list of edges for each atom + index_offset = ( + torch.cumsum(target_neigh_count, dim=0) - target_neigh_count + ) + neigh_index = torch.arange(length, device=device) + neigh_index = neigh_index - index_offset[edge_index[1]] + + edge_map_index = (edge_index[1] * max_neighbors + neigh_index).long() + target_lookup = ( + torch.zeros(self.num_atoms * max_neighbors, device=device) - 1 + ).long() + target_lookup.index_copy_( + 0, edge_map_index, torch.arange(length, device=device).long() + ) + + # Get the length of each edge + distance_lookup = ( + torch.zeros(self.num_atoms * max_neighbors, device=device) + + 1000000.0 + ) + distance_lookup.index_copy_(0, edge_map_index, edge_distance) + distance_lookup = distance_lookup.view(self.num_atoms, max_neighbors) + + # Sort the distances + distance_sorted_no_op, indices = torch.sort(distance_lookup, dim=1) + + # Create a hash that maps edges that go from X -> Y and Y -> X in the same bin + edge_index_min, no_op = torch.min(edge_index, dim=0) + edge_index_max, no_op = torch.max(edge_index, dim=0) + edge_index_hash = edge_index_min * self.num_atoms + edge_index_max + edge_count_start = torch.zeros( + self.num_atoms * self.num_atoms, device=device + ) + edge_count_start.index_add_( + 0, edge_index_hash, torch.ones(len(edge_index_hash), device=device) + ) + + # Find index into the original edge_index + indices = indices + ( + torch.arange(len(indices), device=device) * max_neighbors + ).view(-1, 1).repeat(1, max_neighbors) + indices = indices.view(-1) + target_lookup_sorted = ( + torch.zeros(self.num_atoms * max_neighbors, device=device) - 1 + ).long() + target_lookup_sorted = target_lookup[indices] + target_lookup_sorted = target_lookup_sorted.view( + self.num_atoms, max_neighbors + ) + + # Select the closest max_num_neighbors for each edge and remove the unused entries + target_lookup_below_thres = ( + target_lookup_sorted[:, 0:max_num_neighbors].contiguous().view(-1) + ) + target_lookup_below_thres = target_lookup_below_thres.view(-1) + mask_unused = target_lookup_below_thres.ge(0) + target_lookup_below_thres = torch.masked_select( + target_lookup_below_thres, mask_unused + ) + + # Find edges that are used at least once and create a mask to keep + edge_count = torch.zeros( + self.num_atoms * self.num_atoms, device=device + ) + edge_count.index_add_( + 0, + edge_index_hash[target_lookup_below_thres], + torch.ones(len(target_lookup_below_thres), device=device), + ) + edge_count_mask = edge_count.ne(0) + edge_keep = edge_count_mask[edge_index_hash] + + # Finally remove all edges that are too long in distance as indicated by the mask + edge_index_mask = edge_keep.view(1, -1).repeat(2, 1) + edge_index = torch.masked_select(edge_index, edge_index_mask).view( + 2, -1 + ) + edge_distance = torch.masked_select(edge_distance, edge_keep) + edge_distance_vec_mask = edge_keep.view(-1, 1).repeat(1, 3) + edge_distance_vec = torch.masked_select( + edge_distance_vec, edge_distance_vec_mask + ).view(-1, 3) + + return edge_index, edge_distance, edge_distance_vec + + def _random_rot_mat(self, num_matrices, device): + ang_a = 2.0 * math.pi * torch.rand(num_matrices, device=device) + ang_b = 2.0 * math.pi * torch.rand(num_matrices, device=device) + ang_c = 2.0 * math.pi * torch.rand(num_matrices, device=device) + + cos_a = torch.cos(ang_a) + cos_b = torch.cos(ang_b) + cos_c = torch.cos(ang_c) + sin_a = torch.sin(ang_a) + sin_b = torch.sin(ang_b) + sin_c = torch.sin(ang_c) + + rot_a = ( + torch.eye(3, device=device) + .view(1, 3, 3) + .repeat(num_matrices, 1, 1) + ) + rot_b = ( + torch.eye(3, device=device) + .view(1, 3, 3) + .repeat(num_matrices, 1, 1) + ) + rot_c = ( + torch.eye(3, device=device) + .view(1, 3, 3) + .repeat(num_matrices, 1, 1) + ) + + rot_a[:, 1, 1] = cos_a + rot_a[:, 1, 2] = sin_a + rot_a[:, 2, 1] = -sin_a + rot_a[:, 2, 2] = cos_a + + rot_b[:, 0, 0] = cos_b + rot_b[:, 0, 2] = -sin_b + rot_b[:, 2, 0] = sin_b + rot_b[:, 2, 2] = cos_b + + rot_c[:, 0, 0] = cos_c + rot_c[:, 0, 1] = sin_c + rot_c[:, 1, 0] = -sin_c + rot_c[:, 1, 1] = cos_c + + return torch.bmm(torch.bmm(rot_a, rot_b), rot_c) + + def _init_edge_rot_mat(self, data, edge_index, edge_distance_vec): + device = data.pos.device + num_atoms = len(data.batch) + + edge_vec_0 = edge_distance_vec + edge_vec_0_distance = torch.sqrt(torch.sum(edge_vec_0**2, dim=1)) + + if torch.min(edge_vec_0_distance) < 0.0001: + print( + "Error edge_vec_0_distance: {}".format( + torch.min(edge_vec_0_distance) + ) + ) + (minval, minidx) = torch.min(edge_vec_0_distance, 0) + print( + "Error edge_vec_0_distance: {} {} {} {} {}".format( + minidx, + edge_index[0, minidx], + edge_index[1, minidx], + data.pos[edge_index[0, minidx]], + data.pos[edge_index[1, minidx]], + ) + ) + + avg_vector = torch.zeros(num_atoms, 3, device=device) + weight = 0.5 * ( + torch.cos(edge_vec_0_distance * PI / self.cutoff) + 1.0 + ) + avg_vector.index_add_( + 0, edge_index[1, :], edge_vec_0 * weight.view(-1, 1).expand(-1, 3) + ) + + edge_vec_2 = avg_vector[edge_index[1, :]] + 0.0001 + edge_vec_2_distance = torch.sqrt(torch.sum(edge_vec_2**2, dim=1)) + + if torch.min(edge_vec_2_distance) < 0.000001: + print( + "Error edge_vec_2_distance: {}".format( + torch.min(edge_vec_2_distance) + ) + ) + + norm_x = edge_vec_0 / (edge_vec_0_distance.view(-1, 1)) + norm_0_2 = edge_vec_2 / (edge_vec_2_distance.view(-1, 1)) + norm_z = torch.cross(norm_x, norm_0_2, dim=1) + norm_z = norm_z / ( + torch.sqrt(torch.sum(norm_z**2, dim=1, keepdim=True)) + 0.0000001 + ) + norm_y = torch.cross(norm_x, norm_z, dim=1) + norm_y = norm_y / ( + torch.sqrt(torch.sum(norm_y**2, dim=1, keepdim=True)) + 0.0000001 + ) + + norm_x = norm_x.view(-1, 3, 1) + norm_y = norm_y.view(-1, 3, 1) + norm_z = norm_z.view(-1, 3, 1) + + edge_rot_mat_inv = torch.cat([norm_x, norm_y, norm_z], dim=2) + edge_rot_mat = torch.transpose(edge_rot_mat_inv, 1, 2) + + return edge_rot_mat + + def _project2D_edges_init(self, rot_mat, edge_index, edge_distance_vec): + torch.set_printoptions(sci_mode=False) + length = len(edge_distance_vec) + device = edge_distance_vec.device + + # Assuming the edges are consecutive based on the target index + target_node_index, neigh_count = torch.unique_consecutive( + edge_index[1], return_counts=True + ) + max_neighbors = torch.max(neigh_count) + target_neigh_count = torch.zeros(self.num_atoms, device=device).long() + target_neigh_count.index_copy_( + 0, target_node_index.long(), neigh_count + ) + + index_offset = ( + torch.cumsum(target_neigh_count, dim=0) - target_neigh_count + ) + neigh_index = torch.arange(length, device=device) + neigh_index = neigh_index - index_offset[edge_index[1]] + + edge_map_index = edge_index[1] * max_neighbors + neigh_index + target_lookup = ( + torch.zeros(self.num_atoms * max_neighbors, device=device) - 1 + ).long() + target_lookup.index_copy_( + 0, + edge_map_index.long(), + torch.arange(length, device=device).long(), + ) + target_lookup = target_lookup.view(self.num_atoms, max_neighbors) + + # target_lookup - For each target node, a list of edge indices + # target_neigh_count - number of neighbors for each target node + source_edge = target_lookup[edge_index[0]] + target_edge = ( + torch.arange(length, device=device) + .long() + .view(-1, 1) + .repeat(1, max_neighbors) + ) + + source_edge = source_edge.view(-1) + target_edge = target_edge.view(-1) + + mask_unused = source_edge.ge(0) + source_edge = torch.masked_select(source_edge, mask_unused) + target_edge = torch.masked_select(target_edge, mask_unused) + + return self._project2D_init( + source_edge, target_edge, rot_mat, edge_distance_vec + ) + + def _project2D_nodes_init(self, rot_mat, edge_index, edge_distance_vec): + torch.set_printoptions(sci_mode=False) + length = len(edge_distance_vec) + device = edge_distance_vec.device + + target_node = edge_index[1] + source_edge = torch.arange(length, device=device) + + return self._project2D_init( + source_edge, target_node, rot_mat, edge_distance_vec + ) + + def _project2D_init( + self, source_edge, target_edge, rot_mat, edge_distance_vec + ): + edge_distance_norm = F.normalize(edge_distance_vec) + source_edge_offset = edge_distance_norm[source_edge] + + source_edge_offset_rot = torch.bmm( + rot_mat[target_edge], source_edge_offset.view(-1, 3, 1) + ) + + source_edge_X = torch.atan2( + source_edge_offset_rot[:, 1], source_edge_offset_rot[:, 2] + ).view(-1) + + # source_edge_X ranges from -pi to pi + source_edge_X = (source_edge_X + math.pi) / (2.0 * math.pi) + + # source_edge_Y ranges from -1 to 1 + source_edge_Y = source_edge_offset_rot[:, 0].view(-1) + source_edge_Y = torch.clamp(source_edge_Y, min=-1.0, max=1.0) + source_edge_Y = (source_edge_Y.asin() + (math.pi / 2.0)) / ( + math.pi + ) # bin by angle + # source_edge_Y = (source_edge_Y + 1.0) / 2.0 # bin by sin + source_edge_Y = 0.99 * (source_edge_Y) + 0.005 + + source_edge_X = source_edge_X * self.sphere_size_long + source_edge_Y = source_edge_Y * ( + self.sphere_size_lat - 1.0 + ) # not circular so pad by one + + source_edge_X_0 = torch.floor(source_edge_X).long() + source_edge_X_del = source_edge_X - source_edge_X_0 + source_edge_X_0 = source_edge_X_0 % self.sphere_size_long + source_edge_X_1 = (source_edge_X_0 + 1) % self.sphere_size_long + + source_edge_Y_0 = torch.floor(source_edge_Y).long() + source_edge_Y_del = source_edge_Y - source_edge_Y_0 + source_edge_Y_0 = source_edge_Y_0 % self.sphere_size_lat + source_edge_Y_1 = (source_edge_Y_0 + 1) % self.sphere_size_lat + + # Compute the values needed to bilinearly splat the values onto the spheres + index_0_0 = ( + target_edge * self.sphere_size_lat * self.sphere_size_long + + source_edge_Y_0 * self.sphere_size_long + + source_edge_X_0 + ) + index_0_1 = ( + target_edge * self.sphere_size_lat * self.sphere_size_long + + source_edge_Y_0 * self.sphere_size_long + + source_edge_X_1 + ) + index_1_0 = ( + target_edge * self.sphere_size_lat * self.sphere_size_long + + source_edge_Y_1 * self.sphere_size_long + + source_edge_X_0 + ) + index_1_1 = ( + target_edge * self.sphere_size_lat * self.sphere_size_long + + source_edge_Y_1 * self.sphere_size_long + + source_edge_X_1 + ) + + delta_0_0 = (1.0 - source_edge_X_del) * (1.0 - source_edge_Y_del) + delta_0_1 = (source_edge_X_del) * (1.0 - source_edge_Y_del) + delta_1_0 = (1.0 - source_edge_X_del) * (source_edge_Y_del) + delta_1_1 = (source_edge_X_del) * (source_edge_Y_del) + + index_0_0 = index_0_0.view(1, -1) + index_0_1 = index_0_1.view(1, -1) + index_1_0 = index_1_0.view(1, -1) + index_1_1 = index_1_1.view(1, -1) + + # NaNs otherwise + if self.grad_forces: + with torch.no_grad(): + delta_0_0 = delta_0_0.view(1, -1) + delta_0_1 = delta_0_1.view(1, -1) + delta_1_0 = delta_1_0.view(1, -1) + delta_1_1 = delta_1_1.view(1, -1) + else: + delta_0_0 = delta_0_0.view(1, -1) + delta_0_1 = delta_0_1.view(1, -1) + delta_1_0 = delta_1_0.view(1, -1) + delta_1_1 = delta_1_1.view(1, -1) + + return ( + torch.cat([index_0_0, index_0_1, index_1_0, index_1_1]), + torch.cat([delta_0_0, delta_0_1, delta_1_0, delta_1_1]), + source_edge, + ) + + @property + def num_params(self): + return sum(p.numel() for p in self.parameters()) + + +class MessageBlock(torch.nn.Module): + def __init__( + self, + in_hidden_channels, + out_hidden_channels, + mid_hidden_channels, + embedding_size, + sphere_size_lat, + sphere_size_long, + max_num_elements, + sphere_message, + act, + lmax, + ): + super(MessageBlock, self).__init__() + self.in_hidden_channels = in_hidden_channels + self.out_hidden_channels = out_hidden_channels + self.act = act + self.lmax = lmax + self.embedding_size = embedding_size + self.mid_hidden_channels = mid_hidden_channels + self.sphere_size_lat = sphere_size_lat + self.sphere_size_long = sphere_size_long + self.sphere_message = sphere_message + self.max_num_elements = max_num_elements + self.num_embedding_basis = 8 + + self.spinconvblock = SpinConvBlock( + self.in_hidden_channels, + self.mid_hidden_channels, + self.sphere_size_lat, + self.sphere_size_long, + self.sphere_message, + self.act, + self.lmax, + ) + + self.embeddingblock1 = EmbeddingBlock( + self.mid_hidden_channels, + self.mid_hidden_channels, + self.mid_hidden_channels, + self.embedding_size, + self.num_embedding_basis, + self.max_num_elements, + self.act, + ) + self.embeddingblock2 = EmbeddingBlock( + self.mid_hidden_channels, + self.out_hidden_channels, + self.mid_hidden_channels, + self.embedding_size, + self.num_embedding_basis, + self.max_num_elements, + self.act, + ) + + self.distfc1 = nn.Linear( + self.mid_hidden_channels, self.mid_hidden_channels + ) + self.distfc2 = nn.Linear( + self.mid_hidden_channels, self.mid_hidden_channels + ) + + def forward( + self, + x, + x_dist, + source_element, + target_element, + proj_index, + proj_delta, + proj_src_index, + ): + out_size = len(x) + + x = self.spinconvblock( + x, out_size, proj_index, proj_delta, proj_src_index + ) + + x = self.embeddingblock1(x, source_element, target_element) + + x_dist = self.distfc1(x_dist) + x_dist = self.act(x_dist) + x_dist = self.distfc2(x_dist) + x = x + x_dist + + x = self.act(x) + x = self.embeddingblock2(x, source_element, target_element) + + return x + + +class ForceOutputBlock(torch.nn.Module): + def __init__( + self, + in_hidden_channels, + out_hidden_channels, + mid_hidden_channels, + embedding_size, + sphere_size_lat, + sphere_size_long, + max_num_elements, + sphere_message, + act, + lmax, + ): + super(ForceOutputBlock, self).__init__() + self.in_hidden_channels = in_hidden_channels + self.out_hidden_channels = out_hidden_channels + self.act = act + self.lmax = lmax + self.embedding_size = embedding_size + self.mid_hidden_channels = mid_hidden_channels + self.sphere_size_lat = sphere_size_lat + self.sphere_size_long = sphere_size_long + self.sphere_message = sphere_message + self.max_num_elements = max_num_elements + self.num_embedding_basis = 8 + + self.spinconvblock = SpinConvBlock( + self.in_hidden_channels, + self.mid_hidden_channels, + self.sphere_size_lat, + self.sphere_size_long, + self.sphere_message, + self.act, + self.lmax, + ) + + self.block1 = EmbeddingBlock( + self.mid_hidden_channels, + self.mid_hidden_channels, + self.mid_hidden_channels, + self.embedding_size, + self.num_embedding_basis, + self.max_num_elements, + self.act, + ) + self.block2 = EmbeddingBlock( + self.mid_hidden_channels, + self.out_hidden_channels, + self.mid_hidden_channels, + self.embedding_size, + self.num_embedding_basis, + self.max_num_elements, + self.act, + ) + + def forward( + self, + x, + out_size, + target_element, + proj_index, + proj_delta, + proj_src_index, + ): + x = self.spinconvblock( + x, out_size, proj_index, proj_delta, proj_src_index + ) + + x = self.block1(x, target_element, target_element) + x = self.act(x) + x = self.block2(x, target_element, target_element) + + return x + + +class SpinConvBlock(torch.nn.Module): + def __init__( + self, + in_hidden_channels, + mid_hidden_channels, + sphere_size_lat, + sphere_size_long, + sphere_message, + act, + lmax, + ): + super(SpinConvBlock, self).__init__() + self.in_hidden_channels = in_hidden_channels + self.mid_hidden_channels = mid_hidden_channels + self.sphere_size_lat = sphere_size_lat + self.sphere_size_long = sphere_size_long + self.sphere_message = sphere_message + self.act = act + self.lmax = lmax + self.num_groups = self.in_hidden_channels // 8 + + self.ProjectLatLongSphere = ProjectLatLongSphere( + sphere_size_lat, sphere_size_long + ) + assert self.sphere_message in [ + "fullconv", + "rotspharmwd", + ] + if self.sphere_message in ["rotspharmwd"]: + self.sph_froms2grid = FromS2Grid( + (self.sphere_size_lat, self.sphere_size_long), self.lmax + ) + self.mlp = nn.Linear( + self.in_hidden_channels * (self.lmax + 1) ** 2, + self.mid_hidden_channels, + ) + self.sphlength = (self.lmax + 1) ** 2 + rotx = torch.zeros(self.sphere_size_long) + ( + 2 * math.pi / self.sphere_size_long + ) + roty = torch.zeros(self.sphere_size_long) + rotz = torch.zeros(self.sphere_size_long) + + self.wigner = [] + for xrot, yrot, zrot in zip(rotx, roty, rotz): + _blocks = [] + for l_degree in range(self.lmax + 1): + _blocks.append(o3.wigner_D(l_degree, xrot, yrot, zrot)) + self.wigner.append(torch.block_diag(*_blocks)) + + if self.sphere_message == "fullconv": + padding = self.sphere_size_long // 2 + self.conv1 = nn.Conv1d( + self.in_hidden_channels * self.sphere_size_lat, + self.mid_hidden_channels, + self.sphere_size_long, + groups=self.in_hidden_channels // 8, + padding=padding, + padding_mode="circular", + ) + self.pool = nn.AvgPool1d(sphere_size_long) + + self.GroupNorm = nn.GroupNorm( + self.num_groups, self.mid_hidden_channels + ) + + def forward(self, x, out_size, proj_index, proj_delta, proj_src_index): + x = self.ProjectLatLongSphere( + x, out_size, proj_index, proj_delta, proj_src_index + ) + if self.sphere_message == "rotspharmwd": + sph_harm_calc = torch.zeros( + ((x.shape[0], self.mid_hidden_channels)), + device=x.device, + ) + + sph_harm = self.sph_froms2grid(x) + sph_harm = sph_harm.view(-1, self.sphlength, 1) + for wD_diag in self.wigner: + wD_diag = wD_diag.to(x.device) + sph_harm_calc += self.act( + self.mlp(sph_harm.reshape(x.shape[0], -1)) + ) + wd = wD_diag.view(1, self.sphlength, self.sphlength).expand( + len(x) * self.in_hidden_channels, -1, -1 + ) + sph_harm = torch.bmm(wd, sph_harm) + x = sph_harm_calc + + if self.sphere_message in ["fullconv"]: + x = x.view( + -1, + self.in_hidden_channels * self.sphere_size_lat, + self.sphere_size_long, + ) + x = self.conv1(x) + x = self.act(x) + # Pool in the longitudal direction + x = self.pool(x[:, :, 0 : self.sphere_size_long]) + x = x.view(out_size, -1) + + x = self.GroupNorm(x) + + return x + + +class EmbeddingBlock(torch.nn.Module): + def __init__( + self, + in_hidden_channels, + out_hidden_channels, + mid_hidden_channels, + embedding_size, + num_embedding_basis, + max_num_elements, + act, + ): + super(EmbeddingBlock, self).__init__() + self.in_hidden_channels = in_hidden_channels + self.out_hidden_channels = out_hidden_channels + self.act = act + self.embedding_size = embedding_size + self.mid_hidden_channels = mid_hidden_channels + self.num_embedding_basis = num_embedding_basis + self.max_num_elements = max_num_elements + + self.fc1 = nn.Linear(self.in_hidden_channels, self.mid_hidden_channels) + self.fc2 = nn.Linear( + self.mid_hidden_channels, + self.num_embedding_basis * self.mid_hidden_channels, + ) + self.fc3 = nn.Linear( + self.mid_hidden_channels, self.out_hidden_channels + ) + + self.source_embedding = nn.Embedding( + max_num_elements, self.embedding_size + ) + self.target_embedding = nn.Embedding( + max_num_elements, self.embedding_size + ) + nn.init.uniform_(self.source_embedding.weight.data, -0.0001, 0.0001) + nn.init.uniform_(self.target_embedding.weight.data, -0.0001, 0.0001) + + self.embed_fc1 = nn.Linear( + 2 * self.embedding_size, self.num_embedding_basis + ) + + self.softmax = nn.Softmax(dim=1) + + def forward(self, x, source_element, target_element): + source_embedding = self.source_embedding(source_element) + target_embedding = self.target_embedding(target_element) + embedding = torch.cat([source_embedding, target_embedding], dim=1) + embedding = self.embed_fc1(embedding) + embedding = self.softmax(embedding) + + x = self.fc1(x) + x = self.act(x) + x = self.fc2(x) + x = self.act(x) + x = ( + x.view(-1, self.num_embedding_basis, self.mid_hidden_channels) + ) * (embedding.view(-1, self.num_embedding_basis, 1)) + x = torch.sum(x, dim=1) + x = self.fc3(x) + + return x + + +class DistanceBlock(torch.nn.Module): + def __init__( + self, + in_channels, + out_channels, + max_num_elements, + scalar_max, + distance_expansion, + scale_distances, + ): + super(DistanceBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.max_num_elements = max_num_elements + self.distance_expansion = distance_expansion + self.scalar_max = scalar_max + self.scale_distances = scale_distances + + if self.scale_distances: + self.dist_scalar = nn.Embedding( + self.max_num_elements * self.max_num_elements, 1 + ) + self.dist_offset = nn.Embedding( + self.max_num_elements * self.max_num_elements, 1 + ) + nn.init.uniform_(self.dist_scalar.weight.data, -0.0001, 0.0001) + nn.init.uniform_(self.dist_offset.weight.data, -0.0001, 0.0001) + + self.fc1 = nn.Linear(self.in_channels, self.out_channels) + + def forward(self, edge_distance, source_element, target_element): + if self.scale_distances: + embedding_index = ( + source_element * self.max_num_elements + target_element + ) + + # Restrict the scalar to range from 1 / self.scalar_max to self.scalar_max + scalar_max = math.log(self.scalar_max) + scalar = ( + 2.0 * torch.sigmoid(self.dist_scalar(embedding_index).view(-1)) + - 1.0 + ) + scalar = torch.exp(scalar_max * scalar) + offset = self.dist_offset(embedding_index).view(-1) + x = self.distance_expansion(scalar * edge_distance + offset) + else: + x = self.distance_expansion(edge_distance) + + x = self.fc1(x) + + return x + + +class ProjectLatLongSphere(torch.nn.Module): + def __init__(self, sphere_size_lat, sphere_size_long): + super(ProjectLatLongSphere, self).__init__() + self.sphere_size_lat = sphere_size_lat + self.sphere_size_long = sphere_size_long + + def forward(self, x, length, index, delta, source_edge_index): + device = x.device + hidden_channels = len(x[0]) + + x_proj = torch.zeros( + length * self.sphere_size_lat * self.sphere_size_long, + hidden_channels, + device=device, + ) + splat_values = x[source_edge_index] + + # Perform bilinear splatting + x_proj.index_add_(0, index[0], splat_values * (delta[0].view(-1, 1))) + x_proj.index_add_(0, index[1], splat_values * (delta[1].view(-1, 1))) + x_proj.index_add_(0, index[2], splat_values * (delta[2].view(-1, 1))) + x_proj.index_add_(0, index[3], splat_values * (delta[3].view(-1, 1))) + + x_proj = x_proj.view( + length, + self.sphere_size_lat * self.sphere_size_long, + hidden_channels, + ) + x_proj = torch.transpose(x_proj, 1, 2).contiguous() + x_proj = x_proj.view( + length, + hidden_channels, + self.sphere_size_lat, + self.sphere_size_long, + ) + + return x_proj + + +class Swish(torch.nn.Module): + def __init__(self): + super(Swish, self).__init__() + + def forward(self, x): + return x * torch.sigmoid(x) + + +class GaussianSmearing(torch.nn.Module): + def __init__( + self, start=-5.0, stop=5.0, num_gaussians=50, basis_width_scalar=1.0 + ): + super(GaussianSmearing, self).__init__() + offset = torch.linspace(start, stop, num_gaussians) + self.coeff = ( + -0.5 / (basis_width_scalar * (offset[1] - offset[0])).item() ** 2 + ) + self.register_buffer("offset", offset) + + def forward(self, dist): + dist = dist.view(-1, 1) - self.offset.view(1, -1) + return torch.exp(self.coeff * torch.pow(dist, 2)) diff --git a/ocpmodels/models/utils/__init__.py b/ocpmodels/models/utils/__init__.py new file mode 100644 index 0000000..6264236 --- /dev/null +++ b/ocpmodels/models/utils/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/ocpmodels/models/utils/activations.py b/ocpmodels/models/utils/activations.py new file mode 100644 index 0000000..7f243bc --- /dev/null +++ b/ocpmodels/models/utils/activations.py @@ -0,0 +1,47 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch +import torch.nn.functional as F + + +class Act(torch.nn.Module): + def __init__(self, act, slope=0.05): + super(Act, self).__init__() + self.act = act + self.slope = slope + self.shift = torch.log(torch.tensor(2.0)).item() + + def forward(self, input): + if self.act == "relu": + return F.relu(input) + elif self.act == "leaky_relu": + return F.leaky_relu(input) + elif self.act == "sp": + return F.softplus(input, beta=1) + elif self.act == "leaky_sp": + return F.softplus(input, beta=1) - self.slope * F.relu(-input) + elif self.act == "elu": + return F.elu(input, alpha=1) + elif self.act == "leaky_elu": + return F.elu(input, alpha=1) - self.slope * F.relu(-input) + elif self.act == "ssp": + return F.softplus(input, beta=1) - self.shift + elif self.act == "leaky_ssp": + return ( + F.softplus(input, beta=1) + - self.slope * F.relu(-input) + - self.shift + ) + elif self.act == "tanh": + return torch.tanh(input) + elif self.act == "leaky_tanh": + return torch.tanh(input) + self.slope * input + elif self.act == "swish": + return torch.sigmoid(input) * input + else: + raise RuntimeError(f"Undefined activation called {self.act}") diff --git a/ocpmodels/models/utils/basis.py b/ocpmodels/models/utils/basis.py new file mode 100644 index 0000000..6ee5b63 --- /dev/null +++ b/ocpmodels/models/utils/basis.py @@ -0,0 +1,289 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import math +from math import pi as PI +from typing import List + +import numpy as np +import torch +import torch.nn as nn +from scipy.special import sph_harm +from torch.nn.init import _calculate_correct_fan + +from .activations import Act + + +class Sine(nn.Module): + def __init__(self, w0: float = 30.0): + + super(Sine, self).__init__() + self.w0 = w0 + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.sin(self.w0 * x) + + +class SIREN(nn.Module): + def __init__( + self, + layers: List[int], + in_features: int, + out_features: int, + w0: float = 30.0, + initializer: str = "siren", + c: float = 6, + ): + + super(SIREN, self).__init__() + self.layers = [nn.Linear(in_features, layers[0]), Sine(w0=w0)] + + for index in range(len(layers) - 1): + self.layers.extend( + [nn.Linear(layers[index], layers[index + 1]), Sine(w0=1)] + ) + + self.layers.append(nn.Linear(layers[-1], out_features)) + self.network = nn.Sequential(*self.layers) + + if initializer is not None and initializer == "siren": + for m in self.network: + if isinstance(m, nn.Linear): + num_input = float(m.weight.size(-1)) + with torch.no_grad(): + m.weight.uniform_( + -math.sqrt(6.0 / num_input), + math.sqrt(6.0 / num_input), + ) + + def forward(self, X): + return self.network(X) + + +class SINESmearing(nn.Module): + def __init__(self, in_features, num_freqs=40, use_cosine=False): + + super(SINESmearing, self).__init__() + + self.num_freqs = num_freqs + self.out_dim = in_features * self.num_freqs + self.use_cosine = use_cosine + + freq = torch.arange(num_freqs).float() + freq = torch.pow(torch.ones_like(freq) * 1.1, freq) + self.freq_filter = nn.Parameter( + freq.view(-1, 1).repeat(1, in_features).view(1, -1), + requires_grad=False, + ) + + def forward(self, x): + x = x.repeat(1, self.num_freqs) + x = x * self.freq_filter + + if self.use_cosine: + return torch.cos(x) + else: + return torch.sin(x) + + +class GaussianSmearing(nn.Module): + def __init__(self, in_features, start=0, end=1, num_freqs=50): + super(GaussianSmearing, self).__init__() + self.num_freqs = num_freqs + offset = torch.linspace(start, end, num_freqs) + self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2 + self.offset = nn.Parameter( + offset.view(-1, 1).repeat(1, in_features).view(1, -1), + requires_grad=False, + ) + + def forward(self, x): + x = x.repeat(1, self.num_freqs) + x = x - self.offset + return torch.exp(self.coeff * torch.pow(x, 2)) + + +class FourierSmearing(nn.Module): + def __init__(self, in_features, num_freqs=40, use_cosine=False): + + super(FourierSmearing, self).__init__() + + self.num_freqs = num_freqs + self.out_dim = in_features * self.num_freqs + self.use_cosine = use_cosine + + freq = torch.arange(num_freqs).to(torch.float32) + self.freq_filter = nn.Parameter( + freq.view(-1, 1).repeat(1, in_features).view(1, -1), + requires_grad=False, + ) + + def forward(self, x): + x = x.repeat(1, self.num_freqs) + x = x * self.freq_filter + + if self.use_cosine: + return torch.cos(x) + else: + return torch.sin(x) + + +class Basis(nn.Module): + def __init__( + self, + in_features, + num_freqs=50, + basis_type="powersine", + act="ssp", + sph=None, + ): + super(Basis, self).__init__() + + self.num_freqs = num_freqs + self.basis_type = basis_type + + if basis_type == "powersine": + self.smearing = SINESmearing(in_features, num_freqs) + self.out_dim = in_features * num_freqs + elif basis_type == "powercosine": + self.smearing = SINESmearing( + in_features, num_freqs, use_cosine=True + ) + self.out_dim = in_features * num_freqs + elif basis_type == "fouriersine": + self.smearing = FourierSmearing(in_features, num_freqs) + self.out_dim = in_features * num_freqs + elif basis_type == "gauss": + self.smearing = GaussianSmearing( + in_features, start=0, end=1, num_freqs=num_freqs + ) + self.out_dim = in_features * num_freqs + elif basis_type == "linact": + self.smearing = torch.nn.Sequential( + torch.nn.Linear(in_features, num_freqs * in_features), Act(act) + ) + self.out_dim = in_features * num_freqs + elif basis_type == "raw" or basis_type == "rawcat": + self.out_dim = in_features + elif "sph" in basis_type: + # by default, we use sine function to encode distance + # sph must be given here + assert sph is not None + # assumes the first three columns are normalizaed xyz + # the rest of the columns are distances + if "cat" in basis_type: + # concatenate + self.smearing_sine = SINESmearing(in_features - 3, num_freqs) + self.out_dim = sph.out_dim + (in_features - 3) * num_freqs + elif "mul" in basis_type: + self.smearing_sine = SINESmearing(in_features - 3, num_freqs) + self.lin = torch.nn.Linear( + self.smearing_sine.out_dim, in_features - 3 + ) + self.out_dim = (in_features - 3) * sph.out_dim + elif "m40" in basis_type: + dim = 40 + self.smearing_sine = SINESmearing(in_features - 3, num_freqs) + self.lin = torch.nn.Linear( + self.smearing_sine.out_dim, dim + ) # make the output dimensionality comparable. + self.out_dim = dim * sph.out_dim + elif "nosine" in basis_type: + # does not use sine smearing for encoding distance + self.out_dim = (in_features - 3) * sph.out_dim + else: + raise ValueError( + "cat or mul not specified for spherical harnomics." + ) + else: + raise RuntimeError("Undefined basis type.") + + def forward(self, x, edge_attr_sph=None): + if "sph" in self.basis_type: + if "nosine" not in self.basis_type: + x_sine = self.smearing_sine( + x[:, 3:] + ) # the first three features correspond to edge_vec_normalized, so we ignore + if "cat" in self.basis_type: + # just concatenate spherical edge feature and sined node features + return torch.cat([edge_attr_sph, x_sine], dim=1) + elif "mul" in self.basis_type or "m40" in self.basis_type: + # multiply sined node features into spherical edge feature (inspired by theory in spherical harmonics) + r = self.lin(x_sine) + outer = torch.einsum("ik,ij->ikj", edge_attr_sph, r) + return torch.flatten(outer, start_dim=1) + else: + raise RuntimeError( + f"Unknown basis type called {self.basis_type}" + ) + else: + outer = torch.einsum("ik,ij->ikj", edge_attr_sph, x[:, 3:]) + return torch.flatten(outer, start_dim=1) + + elif "raw" in self.basis_type: + # do nothing, just return node features + pass + else: + x = self.smearing(x) + return x + + +class SphericalSmearing(nn.Module): + def __init__(self, max_n=10, option="all"): + super(SphericalSmearing, self).__init__() + + self.max_n = max_n + + m = [] + n = [] + for i in range(max_n): + for j in range(0, i + 1): + n.append(i) + m.append(j) + + m = np.array(m) + n = np.array(n) + + if option == "all": + self.m = m + self.n = n + elif option == "sine": + self.m = m[n % 2 == 1] + self.n = n[n % 2 == 1] + elif option == "cosine": + self.m = m[n % 2 == 0] + self.n = n[n % 2 == 0] + + self.out_dim = int(np.sum(self.m == 0) + 2 * np.sum(self.m != 0)) + + def forward(self, xyz): + # assuming input is already normalized + assert xyz.size(1) == 3 + + xyz = xyz / xyz.norm(dim=-1).view(-1, 1) + + phi = torch.acos(xyz[:, 2]) + theta = torch.atan2(-xyz[:, 1], -xyz[:, 0]) + math.pi + + phi = phi.cpu().numpy() + theta = theta.cpu().numpy() + + m_tile = np.tile(self.m, (len(xyz), 1)) + n_tile = np.tile(self.n, (len(xyz), 1)) + theta_tile = np.tile(theta.reshape(len(xyz), 1), (1, len(self.m))) + phi_tile = np.tile(phi.reshape(len(xyz), 1), (1, len(self.m))) + + harm = sph_harm(m_tile, n_tile, theta_tile, phi_tile) + + harm_mzero = harm[:, self.m == 0] + harm_mnonzero = harm[:, self.m != 0] + + harm_real = np.concatenate( + [harm_mzero.real, harm_mnonzero.real, harm_mnonzero.imag], axis=1 + ) + + return torch.from_numpy(harm_real).to(torch.float32).to(xyz.device) diff --git a/ocpmodels/modules/__init__.py b/ocpmodels/modules/__init__.py new file mode 100644 index 0000000..c17674b --- /dev/null +++ b/ocpmodels/modules/__init__.py @@ -0,0 +1,6 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" diff --git a/ocpmodels/modules/evaluator.py b/ocpmodels/modules/evaluator.py new file mode 100644 index 0000000..655f981 --- /dev/null +++ b/ocpmodels/modules/evaluator.py @@ -0,0 +1,298 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import torch + + +""" +An evaluation module for use with the OCP dataset and suite of tasks. It should +be possible to import this independently of the rest of the codebase, e.g: + +``` +from ocpmodels.modules import Evaluator + +evaluator = Evaluator(task="is2re") +perf = evaluator.eval(prediction, target) +``` + +task: "s2ef", "is2rs", "is2re". + +We specify a default set of metrics for each task, but should be easy to extend +to add more metrics. `evaluator.eval` takes as input two dictionaries, one for +predictions and another for targets to check against. It returns a dictionary +with the relevant metrics computed. +""" + + +class Evaluator: + task_metrics = { + "s2ef": [ + "forcesx_mae", + "forcesy_mae", + "forcesz_mae", + "forces_mae", + "forces_cos", + "forces_magnitude", + "energy_mae", + "energy_force_within_threshold", + ], + "is2rs": [ + "average_distance_within_threshold", + "positions_mae", + "positions_mse", + ], + "is2re": ["energy_mae", "energy_mse", "energy_within_threshold"], + } + + task_attributes = { + "s2ef": ["energy", "forces", "natoms"], + "is2rs": ["positions", "cell", "pbc", "natoms"], + "is2re": ["energy"], + } + + task_primary_metric = { + "s2ef": "energy_force_within_threshold", + "is2rs": "average_distance_within_threshold", + "is2re": "energy_mae", + } + + def __init__(self, task=None): + assert task in ["s2ef", "is2rs", "is2re"] + self.task = task + self.metric_fn = self.task_metrics[task] + + def eval(self, prediction, target, prev_metrics={}): + for attr in self.task_attributes[self.task]: + assert attr in prediction + assert attr in target + assert prediction[attr].shape == target[attr].shape + + metrics = prev_metrics + + for fn in self.task_metrics[self.task]: + res = eval(fn)(prediction, target) + metrics = self.update(fn, res, metrics) + + return metrics + + def update(self, key, stat, metrics): + if key not in metrics: + metrics[key] = { + "metric": None, + "total": 0, + "numel": 0, + } + + if isinstance(stat, dict): + # If dictionary, we expect it to have `metric`, `total`, `numel`. + metrics[key]["total"] += stat["total"] + metrics[key]["numel"] += stat["numel"] + metrics[key]["metric"] = ( + metrics[key]["total"] / metrics[key]["numel"] + ) + elif isinstance(stat, float) or isinstance(stat, int): + # If float or int, just add to the total and increment numel by 1. + metrics[key]["total"] += stat + metrics[key]["numel"] += 1 + metrics[key]["metric"] = ( + metrics[key]["total"] / metrics[key]["numel"] + ) + elif torch.is_tensor(stat): + raise NotImplementedError + + return metrics + + +def energy_mae(prediction, target): + return absolute_error(prediction["energy"], target["energy"]) + + +def energy_mse(prediction, target): + return squared_error(prediction["energy"], target["energy"]) + + +def forcesx_mae(prediction, target): + return absolute_error(prediction["forces"][:, 0], target["forces"][:, 0]) + + +def forcesx_mse(prediction, target): + return squared_error(prediction["forces"][:, 0], target["forces"][:, 0]) + + +def forcesy_mae(prediction, target): + return absolute_error(prediction["forces"][:, 1], target["forces"][:, 1]) + + +def forcesy_mse(prediction, target): + return squared_error(prediction["forces"][:, 1], target["forces"][:, 1]) + + +def forcesz_mae(prediction, target): + return absolute_error(prediction["forces"][:, 2], target["forces"][:, 2]) + + +def forcesz_mse(prediction, target): + return squared_error(prediction["forces"][:, 2], target["forces"][:, 2]) + + +def forces_mae(prediction, target): + return absolute_error(prediction["forces"], target["forces"]) + + +def forces_mse(prediction, target): + return squared_error(prediction["forces"], target["forces"]) + + +def forces_cos(prediction, target): + return cosine_similarity(prediction["forces"], target["forces"]) + + +def forces_magnitude(prediction, target): + return magnitude_error(prediction["forces"], target["forces"], p=2) + + +def positions_mae(prediction, target): + return absolute_error(prediction["positions"], target["positions"]) + + +def positions_mse(prediction, target): + return squared_error(prediction["positions"], target["positions"]) + + +def energy_force_within_threshold(prediction, target): + # Note that this natoms should be the count of free atoms we evaluate over. + assert target["natoms"].sum() == prediction["forces"].size(0) + assert target["natoms"].size(0) == prediction["energy"].size(0) + + # compute absolute error on per-atom forces and energy per system. + # then count the no. of systems where max force error is < 0.03 and max + # energy error is < 0.02. + f_thresh = 0.03 + e_thresh = 0.02 + + success, total = 0.0, target["natoms"].size(0) + + error_forces = torch.abs(target["forces"] - prediction["forces"]) + error_energy = torch.abs(target["energy"] - prediction["energy"]) + + start_idx = 0 + for i, n in enumerate(target["natoms"]): + if ( + error_energy[i] < e_thresh + and error_forces[start_idx : start_idx + n].max() < f_thresh + ): + success += 1 + start_idx += n + + return { + "metric": success / total, + "total": success, + "numel": total, + } + + +def energy_within_threshold(prediction, target): + # compute absolute error on energy per system. + # then count the no. of systems where max energy error is < 0.02. + e_thresh = 0.02 + error_energy = torch.abs(target["energy"] - prediction["energy"]) + + success = (error_energy < e_thresh).sum().item() + total = target["energy"].size(0) + + return { + "metric": success / total, + "total": success, + "numel": total, + } + + +def average_distance_within_threshold(prediction, target): + pred_pos = torch.split( + prediction["positions"], prediction["natoms"].tolist() + ) + target_pos = torch.split(target["positions"], target["natoms"].tolist()) + + mean_distance = [] + for idx, ml_pos in enumerate(pred_pos): + mean_distance.append( + np.mean( + np.linalg.norm( + min_diff( + ml_pos.detach().cpu().numpy(), + target_pos[idx].detach().cpu().numpy(), + target["cell"][idx].detach().cpu().numpy(), + target["pbc"].tolist(), + ), + axis=1, + ) + ) + ) + + success = 0 + intv = np.arange(0.01, 0.5, 0.001) + for i in intv: + success += sum(np.array(mean_distance) < i) + + total = len(mean_distance) * len(intv) + + return {"metric": success / total, "total": success, "numel": total} + + +def min_diff(pred_pos, dft_pos, cell, pbc): + pos_diff = pred_pos - dft_pos + fractional = np.linalg.solve(cell.T, pos_diff.T).T + + for i, periodic in enumerate(pbc): + # Yes, we need to do it twice + if periodic: + fractional[:, i] %= 1.0 + fractional[:, i] %= 1.0 + + fractional[fractional > 0.5] -= 1 + + return np.matmul(fractional, cell) + + +def cosine_similarity(prediction, target): + error = torch.cosine_similarity(prediction, target) + return { + "metric": torch.mean(error).item(), + "total": torch.sum(error).item(), + "numel": error.numel(), + } + + +def absolute_error(prediction, target): + error = torch.abs(target - prediction) + return { + "metric": torch.mean(error).item(), + "total": torch.sum(error).item(), + "numel": prediction.numel(), + } + + +def squared_error(prediction, target): + error = (target - prediction) ** 2 + return { + "metric": torch.mean(error).item(), + "total": torch.sum(error).item(), + "numel": prediction.numel(), + } + + +def magnitude_error(prediction, target, p=2): + assert prediction.shape[1] > 1 + error = torch.abs( + torch.norm(prediction, p=p, dim=-1) - torch.norm(target, p=p, dim=-1) + ) + return { + "metric": torch.mean(error).item(), + "total": torch.sum(error).item(), + "numel": error.numel(), + } diff --git a/ocpmodels/modules/exponential_moving_average.py b/ocpmodels/modules/exponential_moving_average.py new file mode 100644 index 0000000..22c7e08 --- /dev/null +++ b/ocpmodels/modules/exponential_moving_average.py @@ -0,0 +1,202 @@ +""" +Copied (and improved) from: +https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py (MIT license) +""" + +from __future__ import division, unicode_literals + +import copy +import weakref +from typing import Iterable, Optional + +import torch + + +# Partially based on: +# https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/training/moving_averages.py +class ExponentialMovingAverage: + """ + Maintains (exponential) moving average of a set of parameters. + + Args: + parameters: Iterable of `torch.nn.Parameter` (typically from + `model.parameters()`). + decay: The exponential decay. + use_num_updates: Whether to use number of updates when computing + averages. + """ + + def __init__( + self, + parameters: Iterable[torch.nn.Parameter], + decay: float, + use_num_updates: bool = False, + ): + if decay < 0.0 or decay > 1.0: + raise ValueError("Decay must be between 0 and 1") + self.decay = decay + self.num_updates = 0 if use_num_updates else None + parameters = list(parameters) + self.shadow_params = [ + p.clone().detach() for p in parameters if p.requires_grad + ] + self.collected_params = [] + # By maintaining only a weakref to each parameter, + # we maintain the old GC behaviour of ExponentialMovingAverage: + # if the model goes out of scope but the ExponentialMovingAverage + # is kept, no references to the model or its parameters will be + # maintained, and the model will be cleaned up. + self._params_refs = [ + weakref.ref(p) for p in parameters if p.requires_grad + ] + + def _get_parameters( + self, parameters: Optional[Iterable[torch.nn.Parameter]] + ) -> Iterable[torch.nn.Parameter]: + if parameters is None: + parameters = [p() for p in self._params_refs] + if any(p is None for p in parameters): + raise ValueError( + "(One of) the parameters with which this " + "ExponentialMovingAverage " + "was initialized no longer exists (was garbage collected);" + " please either provide `parameters` explicitly or keep " + "the model to which they belong from being garbage " + "collected." + ) + return parameters + else: + return [p for p in parameters if p.requires_grad] + + def update( + self, parameters: Optional[Iterable[torch.nn.Parameter]] = None + ) -> None: + """ + Update currently maintained parameters. + + Call this every time the parameters are updated, such as the result of + the `optimizer.step()` call. + + Args: + parameters: Iterable of `torch.nn.Parameter`; usually the same set of + parameters used to initialize this object. If `None`, the + parameters with which this `ExponentialMovingAverage` was + initialized will be used. + """ + parameters = self._get_parameters(parameters) + decay = self.decay + if self.num_updates is not None: + self.num_updates += 1 + decay = min( + decay, (1 + self.num_updates) / (10 + self.num_updates) + ) + one_minus_decay = 1.0 - decay + with torch.no_grad(): + for s_param, param in zip(self.shadow_params, parameters): + tmp = param - s_param + s_param.add_(tmp, alpha=one_minus_decay) + + def copy_to( + self, parameters: Optional[Iterable[torch.nn.Parameter]] = None + ) -> None: + """ + Copy current parameters into given collection of parameters. + + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored moving averages. If `None`, the + parameters with which this `ExponentialMovingAverage` was + initialized will be used. + """ + parameters = self._get_parameters(parameters) + for s_param, param in zip(self.shadow_params, parameters): + param.data.copy_(s_param.data) + + def store( + self, parameters: Optional[Iterable[torch.nn.Parameter]] = None + ) -> None: + """ + Save the current parameters for restoring later. + + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. If `None`, the parameters of with which this + `ExponentialMovingAverage` was initialized will be used. + """ + parameters = self._get_parameters(parameters) + self.collected_params = [param.clone() for param in parameters] + + def restore( + self, parameters: Optional[Iterable[torch.nn.Parameter]] = None + ) -> None: + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. If `None`, the + parameters with which this `ExponentialMovingAverage` was + initialized will be used. + """ + parameters = self._get_parameters(parameters) + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) + + def state_dict(self) -> dict: + r"""Returns the state of the ExponentialMovingAverage as a dict.""" + # Following PyTorch conventions, references to tensors are returned: + # "returns a reference to the state and not its copy!" - + # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict + return { + "decay": self.decay, + "num_updates": self.num_updates, + "shadow_params": self.shadow_params, + "collected_params": self.collected_params, + } + + def load_state_dict(self, state_dict: dict) -> None: + r"""Loads the ExponentialMovingAverage state. + + Args: + state_dict (dict): EMA state. Should be an object returned + from a call to :meth:`state_dict`. + """ + # deepcopy, to be consistent with module API + state_dict = copy.deepcopy(state_dict) + + self.decay = state_dict["decay"] + if self.decay < 0.0 or self.decay > 1.0: + raise ValueError("Decay must be between 0 and 1") + + self.num_updates = state_dict["num_updates"] + assert self.num_updates is None or isinstance( + self.num_updates, int + ), "Invalid num_updates" + + assert isinstance( + state_dict["shadow_params"], list + ), "shadow_params must be a list" + self.shadow_params = [ + p.to(self.shadow_params[i].device) + for i, p in enumerate(state_dict["shadow_params"]) + ] + assert all( + isinstance(p, torch.Tensor) for p in self.shadow_params + ), "shadow_params must all be Tensors" + + assert isinstance( + state_dict["collected_params"], list + ), "collected_params must be a list" + # collected_params is empty at initialization, + # so use shadow_params for device instead + self.collected_params = [ + p.to(self.shadow_params[i].device) + for i, p in enumerate(state_dict["collected_params"]) + ] + assert all( + isinstance(p, torch.Tensor) for p in self.collected_params + ), "collected_params must all be Tensors" diff --git a/ocpmodels/modules/loss.py b/ocpmodels/modules/loss.py new file mode 100644 index 0000000..184e6bb --- /dev/null +++ b/ocpmodels/modules/loss.py @@ -0,0 +1,83 @@ +import logging + +import torch +from torch import nn + +from ocpmodels.common import distutils + + +class L2MAELoss(nn.Module): + def __init__(self, reduction="mean"): + super().__init__() + self.reduction = reduction + assert reduction in ["mean", "sum"] + + def forward(self, input: torch.Tensor, target: torch.Tensor): + dists = torch.norm(input - target, p=2, dim=-1) + if self.reduction == "mean": + return torch.mean(dists) + elif self.reduction == "sum": + return torch.sum(dists) + + +class AtomwiseL2Loss(nn.Module): + def __init__(self, reduction="mean"): + super().__init__() + self.reduction = reduction + assert reduction in ["mean", "sum"] + + def forward( + self, + input: torch.Tensor, + target: torch.Tensor, + natoms: torch.Tensor, + ): + assert natoms.shape[0] == input.shape[0] == target.shape[0] + assert len(natoms.shape) == 1 # (nAtoms, ) + + dists = torch.norm(input - target, p=2, dim=-1) + loss = natoms * dists + + if self.reduction == "mean": + return torch.mean(loss) + elif self.reduction == "sum": + return torch.sum(loss) + + +class DDPLoss(nn.Module): + def __init__(self, loss_fn, reduction="mean"): + super().__init__() + self.loss_fn = loss_fn + self.loss_fn.reduction = "sum" + self.reduction = reduction + assert reduction in ["mean", "sum"] + + def forward( + self, + input: torch.Tensor, + target: torch.Tensor, + natoms: torch.Tensor = None, + batch_size: int = None, + ): + # zero out nans, if any + found_nans_or_infs = not torch.all(input.isfinite()) + if found_nans_or_infs is True: + logging.warning("Found nans while computing loss") + input = torch.nan_to_num(input, nan=0.0) + + if natoms is None: + loss = self.loss_fn(input, target) + else: # atom-wise loss + loss = self.loss_fn(input, target, natoms) + if self.reduction == "mean": + num_samples = ( + batch_size if batch_size is not None else input.shape[0] + ) + num_samples = distutils.all_reduce( + num_samples, device=input.device + ) + # Multiply by world size since gradients are averaged + # across DDP replicas + return loss * distutils.get_world_size() / num_samples + else: + return loss diff --git a/ocpmodels/modules/normalizer.py b/ocpmodels/modules/normalizer.py new file mode 100644 index 0000000..302f0d6 --- /dev/null +++ b/ocpmodels/modules/normalizer.py @@ -0,0 +1,46 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import torch + + +class Normalizer(object): + """Normalize a Tensor and restore it later.""" + + def __init__(self, tensor=None, mean=None, std=None, device=None): + """tensor is taken as a sample to calculate the mean and std""" + if tensor is None and mean is None: + return + + if device is None: + device = "cpu" + + if tensor is not None: + self.mean = torch.mean(tensor, dim=0).to(device) + self.std = torch.std(tensor, dim=0).to(device) + return + + if mean is not None and std is not None: + self.mean = torch.tensor(mean).to(device) + self.std = torch.tensor(std).to(device) + + def to(self, device): + self.mean = self.mean.to(device) + self.std = self.std.to(device) + + def norm(self, tensor): + return (tensor - self.mean) / self.std + + def denorm(self, normed_tensor): + return normed_tensor * self.std + self.mean + + def state_dict(self): + return {"mean": self.mean, "std": self.std} + + def load_state_dict(self, state_dict): + self.mean = state_dict["mean"].to(self.mean.device) + self.std = state_dict["std"].to(self.mean.device) diff --git a/ocpmodels/modules/scaling/__init__.py b/ocpmodels/modules/scaling/__init__.py new file mode 100644 index 0000000..807416b --- /dev/null +++ b/ocpmodels/modules/scaling/__init__.py @@ -0,0 +1,3 @@ +from .scale_factor import ScaleFactor + +__all__ = ["ScaleFactor"] diff --git a/ocpmodels/modules/scaling/compat.py b/ocpmodels/modules/scaling/compat.py new file mode 100644 index 0000000..4240db0 --- /dev/null +++ b/ocpmodels/modules/scaling/compat.py @@ -0,0 +1,76 @@ +import json +import logging +from pathlib import Path +from typing import Dict, Optional, Union + +import torch +import torch.nn as nn + +from .scale_factor import ScaleFactor + +ScaleDict = Union[Dict[str, float], Dict[str, torch.Tensor]] + + +def _load_scale_dict(scale_file: Optional[Union[str, ScaleDict]]): + """ + Loads scale factors from either: + - a JSON file mapping scale factor names to scale values + - a python dictionary pickled object (loaded using `torch.load`) mapping scale factor names to scale values + - a dictionary mapping scale factor names to scale values + """ + if not scale_file: + return None + + if isinstance(scale_file, dict): + if not scale_file: + logging.warning("Empty scale dictionary provided to model.") + return scale_file + + path = Path(scale_file) + if not path.exists(): + raise ValueError(f"Scale file {path} does not exist.") + + scale_dict: Optional[ScaleDict] = None + if path.suffix == ".pt": + scale_dict = torch.load(path) + elif path.suffix == ".json": + with open(path, "r") as f: + scale_dict = json.load(f) + + if isinstance(scale_dict, dict): + # old json scale factors have a comment field that has the model name + scale_dict.pop("comment", None) + else: + raise ValueError(f"Unsupported scale file extension: {path.suffix}") + + if not scale_dict: + return None + + return scale_dict + + +def load_scales_compat( + module: nn.Module, scale_file: Optional[Union[str, ScaleDict]] +): + scale_dict = _load_scale_dict(scale_file) + if not scale_dict: + return + + scale_factors = { + module.name or name: (module, name) + for name, module in module.named_modules() + if isinstance(module, ScaleFactor) + } + logging.debug( + f"Found the following scale factors: {[(k, name) for k, (_, name) in scale_factors.items()]}" + ) + for name, scale in scale_dict.items(): + if name not in scale_factors: + logging.warning(f"Scale factor {name} not found in model") + continue + + scale_module, module_name = scale_factors[name] + logging.debug( + f"Loading scale factor {scale} for ({name} => {module_name})" + ) + scale_module.set_(scale) diff --git a/ocpmodels/modules/scaling/fit.py b/ocpmodels/modules/scaling/fit.py new file mode 100644 index 0000000..83f1f72 --- /dev/null +++ b/ocpmodels/modules/scaling/fit.py @@ -0,0 +1,241 @@ +import logging +import math +import readline +import sys +from itertools import islice +from pathlib import Path +from typing import TYPE_CHECKING, Dict, Literal + +import torch +import torch.nn as nn +from torch.nn.parallel.distributed import DistributedDataParallel + +from ocpmodels.common.data_parallel import OCPDataParallel +from ocpmodels.common.flags import flags +from ocpmodels.common.utils import ( + build_config, + new_trainer_context, + setup_logging, +) +from ocpmodels.modules.scaling import ScaleFactor +from ocpmodels.modules.scaling.compat import load_scales_compat + +if TYPE_CHECKING: + from ocpmodels.trainers.base_trainer import BaseTrainer + + +def _prefilled_input(prompt: str, prefill: str = ""): + readline.set_startup_hook(lambda: readline.insert_text(prefill)) + try: + return input(prompt) + finally: + readline.set_startup_hook() + + +def _train_batch(trainer: "BaseTrainer", batch): + with torch.no_grad(): + with torch.cuda.amp.autocast(enabled=trainer.scaler is not None): + out = trainer._forward(batch) + loss = trainer._compute_loss(out, batch) + del out, loss + + +def main(*, num_batches: int = 16): + # region args/config setup + setup_logging() + + parser = flags.get_parser() + args, override_args = parser.parse_known_args() + _config = build_config(args, override_args) + _config["logger"] = "tensorboard" + # endregion + + assert not args.distributed, "This doesn't work with DDP" + with new_trainer_context(args=args, config=_config) as ctx: + config = ctx.config + trainer = ctx.trainer + + ckpt_file = config.get("checkpoint", None) + assert ( + ckpt_file is not None + ), "Checkpoint file not specified. Please specify --checkpoint " + ckpt_file = Path(ckpt_file) + + logging.info( + f"Input checkpoint path: {ckpt_file}, {ckpt_file.exists()=}" + ) + + model: nn.Module = trainer.model + val_loader = trainer.val_loader + assert ( + val_loader is not None + ), "Val dataset is required for making predictions" + + if ckpt_file.exists(): + trainer.load_checkpoint(str(ckpt_file)) + + # region reoad scale file contents if necessary + # unwrap module from DP/DDP + unwrapped_model = model + while isinstance( + unwrapped_model, (DistributedDataParallel, OCPDataParallel) + ): + unwrapped_model = unwrapped_model.module + assert isinstance( + unwrapped_model, nn.Module + ), "Model is not a nn.Module" + load_scales_compat(unwrapped_model, config.get("scale_file", None)) + # endregion + + model.eval() + + # recursively go through the submodules and get the ScaleFactor modules + scale_factors: Dict[str, ScaleFactor] = { + name: module + for name, module in model.named_modules() + if isinstance(module, ScaleFactor) + } + + mode: Literal["all", "unfitted"] = "all" + + # region detect fitted/unfitted factors + fitted_scale_factors = [ + f"{name}: {module.scale_factor.item():.3f}" + for name, module in scale_factors.items() + if module.fitted + ] + unfitted_scale_factors = [ + name for name, module in scale_factors.items() if not module.fitted + ] + fitted_scale_factors_str = ", ".join(fitted_scale_factors) + logging.info(f"Fitted scale factors: [{fitted_scale_factors_str}]") + unfitted_scale_factors_str = ", ".join(unfitted_scale_factors) + logging.info(f"Unfitted scale factors: [{unfitted_scale_factors_str}]") + + if fitted_scale_factors: + flag = input( + "Do you want to continue and fit all scale factors (1), " + "only fit the variables not fitted yet (2), or exit (3)? " + ) + if str(flag) == "1": + mode = "all" + logging.info("Fitting all scale factors.") + elif str(flag) == "2": + mode = "unfitted" + logging.info("Only fitting unfitted variables.") + else: + print(flag) + logging.info("Exiting script") + sys.exit() + # endregion + + # region get the output path + out_path = Path( + _prefilled_input( + "Enter output path for fitted scale factors: ", + prefill=str(ckpt_file), + ) + ) + if out_path.exists(): + logging.warning(f"Already found existing file: {out_path}") + flag = input( + "Do you want to continue and overwrite existing file (1), " + "or exit (2)? " + ) + if str(flag) == "1": + logging.info("Overwriting existing file.") + else: + logging.info("Exiting script") + sys.exit() + + logging.info( + f"Output path for fitted scale factors: {out_path}, {out_path.exists()=}" + ) + # endregion + + # region reset the scale factors if mode == "all" + if mode == "all": + logging.info("Fitting all scale factors.") + for name, scale_factor in scale_factors.items(): + if scale_factor.fitted: + logging.info( + f"{name} is already fitted in the checkpoint, resetting it. {scale_factor.scale_factor}" + ) + scale_factor.reset_() + # endregion + + # region we do a single pass through the network to get the correct execution order of the scale factors + scale_factor_indices: Dict[str, int] = {} + max_idx = 0 + + # initialize all scale factors + for name, module in scale_factors.items(): + + def index_fn(name=name): + nonlocal max_idx + assert name is not None + if name not in scale_factor_indices: + scale_factor_indices[name] = max_idx + logging.debug(f"Scale factor for {name} = {max_idx}") + max_idx += 1 + + module.initialize_(index_fn=index_fn) + + # single pass through network + _train_batch(trainer, next(iter(val_loader))) + + # sort the scale factors by their computation order + sorted_factors = sorted( + scale_factors.items(), + key=lambda x: scale_factor_indices.get(x[0], math.inf), + ) + + logging.info("Sorted scale factors by computation order:") + for name, _ in sorted_factors: + logging.info(f"{name}: {scale_factor_indices[name]}") + + # endregion + + # loop over the scale factors in the computation order + # and fit them one by one + logging.info("Start fitting") + + for name, module in sorted_factors: + if mode == "unfitted" and module.fitted: + logging.info(f"Skipping {name} (already fitted)") + continue + + logging.info(f"Fitting {name}...") + with module.fit_context_(): + for batch in islice(val_loader, num_batches): + _train_batch(trainer, batch) + stats, ratio, value = module.fit_() + + logging.info( + f"Variable: {name}, " + f"Var_in: {stats['variance_in']:.3f}, " + f"Var_out: {stats['variance_out']:.3f}, " + f"Ratio: {ratio:.3f} => Scaling factor: {value:.3f}" + ) + + # make sure all scale factors are fitted + for name, module in sorted_factors: + assert module.fitted, f"{name} is not fitted" + + # region save the scale factors to the checkpoint file + trainer.config["cmd"]["checkpoint_dir"] = out_path.parent + trainer.is_debug = False + out_file = trainer.save( + metrics=None, + checkpoint_file=out_path.name, + training_state=False, + ) + assert out_file is not None, "Failed to save checkpoint" + out_file = Path(out_file) + assert out_file.exists(), f"Failed to save checkpoint to {out_file}" + # endregion + logging.info(f"Saved results to: {out_file}") + + +if __name__ == "__main__": + main() diff --git a/ocpmodels/modules/scaling/scale_factor.py b/ocpmodels/modules/scaling/scale_factor.py new file mode 100644 index 0000000..8a8d5a5 --- /dev/null +++ b/ocpmodels/modules/scaling/scale_factor.py @@ -0,0 +1,170 @@ +import itertools +import logging +import math +from contextlib import contextmanager +from typing import Callable, Optional, TypedDict, Union + +import torch +import torch.nn as nn + + +class _Stats(TypedDict): + variance_in: float + variance_out: float + n_samples: int + + +IndexFn = Callable[[], None] + + +def _check_consistency(old: torch.Tensor, new: torch.Tensor, key: str): + if not torch.allclose(old, new): + raise ValueError( + f"Scale factor parameter {key} is inconsistent with the loaded state dict.\n" + f"Old: {old}\n" + f"Actual: {new}" + ) + + +class ScaleFactor(nn.Module): + scale_factor: torch.Tensor + + name: Optional[str] = None + index_fn: Optional[IndexFn] = None + stats: Optional[_Stats] = None + + def __init__( + self, + name: Optional[str] = None, + enforce_consistency: bool = True, + ): + super().__init__() + + self.name = name + self.index_fn = None + self.stats = None + + self.scale_factor = nn.parameter.Parameter( + torch.tensor(0.0), requires_grad=False + ) + if enforce_consistency: + self._register_load_state_dict_pre_hook(self._enforce_consistency) + + def _enforce_consistency( + self, + state_dict, + prefix, + _local_metadata, + _strict, + _missing_keys, + _unexpected_keys, + _error_msgs, + ): + if not self.fitted: + return + + persistent_buffers = { + k: v + for k, v in self._buffers.items() + if k not in self._non_persistent_buffers_set + } + local_name_params = itertools.chain( + self._parameters.items(), persistent_buffers.items() + ) + local_state = {k: v for k, v in local_name_params if v is not None} + + for name, param in local_state.items(): + key = prefix + name + if key not in state_dict: + continue + + input_param = state_dict[key] + _check_consistency(old=param, new=input_param, key=key) + + @property + def fitted(self): + return bool((self.scale_factor != 0.0).item()) + + @torch.jit.unused + def reset_(self): + self.scale_factor.zero_() + + @torch.jit.unused + def set_(self, scale: Union[float, torch.Tensor]): + if self.fitted: + _check_consistency( + old=self.scale_factor, + new=torch.tensor(scale) if isinstance(scale, float) else scale, + key="scale_factor", + ) + self.scale_factor.fill_(scale) + + @torch.jit.unused + def initialize_(self, *, index_fn: Optional[IndexFn] = None): + self.index_fn = index_fn + + @contextmanager + @torch.jit.unused + def fit_context_(self): + self.stats = _Stats(variance_in=0.0, variance_out=0.0, n_samples=0) + yield + del self.stats + self.stats = None + + @torch.jit.unused + def fit_(self): + assert self.stats, "Stats not set" + for k, v in self.stats.items(): + assert v > 0, f"{k} is {v}" + + self.stats["variance_in"] = ( + self.stats["variance_in"] / self.stats["n_samples"] + ) + self.stats["variance_out"] = ( + self.stats["variance_out"] / self.stats["n_samples"] + ) + + ratio = self.stats["variance_out"] / self.stats["variance_in"] + value = math.sqrt(1 / ratio) + + self.set_(value) + + stats = dict(**self.stats) + return stats, ratio, value + + @torch.no_grad() + @torch.jit.unused + def _observe(self, x: torch.Tensor, ref: Optional[torch.Tensor] = None): + if self.stats is None: + logging.debug("Observer not initialized but self.observe() called") + return + + n_samples = x.shape[0] + self.stats["variance_out"] += ( + torch.mean(torch.var(x, dim=0)).item() * n_samples + ) + + if ref is None: + self.stats["variance_in"] += n_samples + else: + self.stats["variance_in"] += ( + torch.mean(torch.var(ref, dim=0)).item() * n_samples + ) + self.stats["n_samples"] += n_samples + + def forward( + self, + x: torch.Tensor, + *, + ref: Optional[torch.Tensor] = None, + ): + if self.index_fn is not None: + self.index_fn() + + if self.fitted: + x = x * self.scale_factor + + if not torch.jit.is_scripting(): + self._observe(x, ref=ref) + + return x diff --git a/ocpmodels/modules/scaling/util.py b/ocpmodels/modules/scaling/util.py new file mode 100644 index 0000000..15c58b5 --- /dev/null +++ b/ocpmodels/modules/scaling/util.py @@ -0,0 +1,23 @@ +import logging + +import torch.nn as nn + +from .scale_factor import ScaleFactor + + +def ensure_fitted(module: nn.Module, warn: bool = False): + for name, child in module.named_modules(): + if not isinstance(child, ScaleFactor) or child.fitted: + continue + if child.name is not None: + name = f"{child.name} ({name})" + msg = ( + f"Scale factor {name} is not fitted. " + "Please make sure that you either (1) load a checkpoint with fitted scale factors, " + "(2) explicitly load scale factors using the `model.scale_file` attribute, or " + "(3) fit the scale factors using the `fit.py` script." + ) + if warn: + logging.warning(msg) + else: + raise ValueError(msg) diff --git a/ocpmodels/modules/scheduler.py b/ocpmodels/modules/scheduler.py new file mode 100644 index 0000000..afd2473 --- /dev/null +++ b/ocpmodels/modules/scheduler.py @@ -0,0 +1,66 @@ +import inspect + +import torch.optim.lr_scheduler as lr_scheduler + +from ocpmodels.common.utils import warmup_lr_lambda + + +class LRScheduler: + """ + Learning rate scheduler class for torch.optim learning rate schedulers + + Notes: + If no learning rate scheduler is specified in the config the default + scheduler is warmup_lr_lambda (ocpmodels.common.utils) not no scheduler, + this is for backward-compatibility reasons. To run without a lr scheduler + specify scheduler: "Null" in the optim section of the config. + + Args: + config (dict): Optim dict from the input config + optimizer (obj): torch optim object + """ + + def __init__(self, optimizer, config): + self.optimizer = optimizer + self.config = config.copy() + if "scheduler" in self.config: + self.scheduler_type = self.config["scheduler"] + else: + self.scheduler_type = "LambdaLR" + scheduler_lambda_fn = lambda x: warmup_lr_lambda(x, self.config) + self.config["lr_lambda"] = scheduler_lambda_fn + + if self.scheduler_type != "Null": + self.scheduler = getattr(lr_scheduler, self.scheduler_type) + scheduler_args = self.filter_kwargs(config) + self.scheduler = self.scheduler(optimizer, **scheduler_args) + + def step(self, metrics=None, epoch=None): + if self.scheduler_type == "Null": + return + if self.scheduler_type == "ReduceLROnPlateau": + if metrics is None: + raise Exception( + "Validation set required for ReduceLROnPlateau." + ) + self.scheduler.step(metrics) + else: + self.scheduler.step() + + def filter_kwargs(self, config): + # adapted from https://stackoverflow.com/questions/26515595/ + sig = inspect.signature(self.scheduler) + filter_keys = [ + param.name + for param in sig.parameters.values() + if param.kind == param.POSITIONAL_OR_KEYWORD + ] + filter_keys.remove("optimizer") + scheduler_args = { + arg: self.config[arg] for arg in self.config if arg in filter_keys + } + return scheduler_args + + def get_lr(self): + for group in self.optimizer.param_groups: + return group["lr"] diff --git a/ocpmodels/modules/transforms.py b/ocpmodels/modules/transforms.py new file mode 100644 index 0000000..a9ecbc4 --- /dev/null +++ b/ocpmodels/modules/transforms.py @@ -0,0 +1,50 @@ +import torch +from torch_geometric.data import Data + +from ocpmodels.common.utils import cg_change_mat, irreps_sum + + +class DataTransforms: + def __init__(self, config) -> None: + self.config = config + + def __call__(self, data_object): + if not self.config: + return data_object + + for transform_fn in self.config: + # TODO: Normalization information used in the trainers. Ignore here + # for now. + if transform_fn == "normalizer": + continue + data_object = eval(transform_fn)( + data_object, self.config[transform_fn] + ) + + return data_object + + +def decompose_tensor(data_object, config) -> Data: + tensor_key = config["tensor"] + rank = config["rank"] + + if tensor_key not in data_object: + return data_object + + if rank != 2: + raise NotImplementedError + + tensor_decomposition = torch.einsum( + "ab, cb->ca", + cg_change_mat(rank), + data_object[tensor_key].reshape(1, irreps_sum(rank)), + ) + + for decomposition_key in config["decomposition"]: + irrep_dim = config["decomposition"][decomposition_key]["irrep_dim"] + data_object[decomposition_key] = tensor_decomposition[ + :, + max(0, irreps_sum(irrep_dim - 1)) : irreps_sum(irrep_dim), + ] + + return data_object diff --git a/ocpmodels/preprocessing/__init__.py b/ocpmodels/preprocessing/__init__.py new file mode 100644 index 0000000..1139688 --- /dev/null +++ b/ocpmodels/preprocessing/__init__.py @@ -0,0 +1,8 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from .atoms_to_graphs import AtomsToGraphs diff --git a/ocpmodels/preprocessing/atoms_to_graphs.py b/ocpmodels/preprocessing/atoms_to_graphs.py new file mode 100644 index 0000000..7434a6f --- /dev/null +++ b/ocpmodels/preprocessing/atoms_to_graphs.py @@ -0,0 +1,245 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import ase.db.sqlite +import ase.io.trajectory +import numpy as np +import torch +from torch_geometric.data import Data + +from ocpmodels.common.utils import collate + +try: + from pymatgen.io.ase import AseAtomsAdaptor +except Exception: + pass + + +try: + shell = get_ipython().__class__.__name__ + if shell == "ZMQInteractiveShell": + from tqdm.notebook import tqdm + else: + from tqdm import tqdm +except NameError: + from tqdm import tqdm + + +class AtomsToGraphs: + """A class to help convert periodic atomic structures to graphs. + + The AtomsToGraphs class takes in periodic atomic structures in form of ASE atoms objects and converts + them into graph representations for use in PyTorch. The primary purpose of this class is to determine the + nearest neighbors within some radius around each individual atom, taking into account PBC, and set the + pair index and distance between atom pairs appropriately. Lastly, atomic properties and the graph information + are put into a PyTorch geometric data object for use with PyTorch. + + Args: + max_neigh (int): Maximum number of neighbors to consider. + radius (int or float): Cutoff radius in Angstroms to search for neighbors. + r_energy (bool): Return the energy with other properties. Default is False, so the energy will not be returned. + r_forces (bool): Return the forces with other properties. Default is False, so the forces will not be returned. + r_distances (bool): Return the distances with other properties. + Default is False, so the distances will not be returned. + r_edges (bool): Return interatomic edges with other properties. Default is True, so edges will be returned. + r_fixed (bool): Return a binary vector with flags for fixed (1) vs free (0) atoms. + Default is True, so the fixed indices will be returned. + r_pbc (bool): Return the periodic boundary conditions with other properties. + Default is False, so the periodic boundary conditions will not be returned. + + Attributes: + max_neigh (int): Maximum number of neighbors to consider. + radius (int or float): Cutoff radius in Angstoms to search for neighbors. + r_energy (bool): Return the energy with other properties. Default is False, so the energy will not be returned. + r_forces (bool): Return the forces with other properties. Default is False, so the forces will not be returned. + r_distances (bool): Return the distances with other properties. + Default is False, so the distances will not be returned. + r_edges (bool): Return interatomic edges with other properties. Default is True, so edges will be returned. + r_fixed (bool): Return a binary vector with flags for fixed (1) vs free (0) atoms. + Default is True, so the fixed indices will be returned. + r_pbc (bool): Return the periodic boundary conditions with other properties. + Default is False, so the periodic boundary conditions will not be returned. + + """ + + def __init__( + self, + max_neigh=200, + radius=6, + r_energy=False, + r_forces=False, + r_distances=False, + r_edges=True, + r_fixed=True, + r_pbc=False, + ): + self.max_neigh = max_neigh + self.radius = radius + self.r_energy = r_energy + self.r_forces = r_forces + self.r_distances = r_distances + self.r_fixed = r_fixed + self.r_edges = r_edges + self.r_pbc = r_pbc + + def _get_neighbors_pymatgen(self, atoms): + """Preforms nearest neighbor search and returns edge index, distances, + and cell offsets""" + struct = AseAtomsAdaptor.get_structure(atoms) + _c_index, _n_index, _offsets, n_distance = struct.get_neighbor_list( + r=self.radius, numerical_tol=0, exclude_self=True + ) + + _nonmax_idx = [] + for i in range(len(atoms)): + idx_i = (_c_index == i).nonzero()[0] + # sort neighbors by distance, remove edges larger than max_neighbors + idx_sorted = np.argsort(n_distance[idx_i])[: self.max_neigh] + _nonmax_idx.append(idx_i[idx_sorted]) + _nonmax_idx = np.concatenate(_nonmax_idx) + + _c_index = _c_index[_nonmax_idx] + _n_index = _n_index[_nonmax_idx] + n_distance = n_distance[_nonmax_idx] + _offsets = _offsets[_nonmax_idx] + + return _c_index, _n_index, n_distance, _offsets + + def _reshape_features(self, c_index, n_index, n_distance, offsets): + """Stack center and neighbor index and reshapes distances, + takes in np.arrays and returns torch tensors""" + edge_index = torch.LongTensor(np.vstack((n_index, c_index))) + edge_distances = torch.FloatTensor(n_distance) + cell_offsets = torch.LongTensor(offsets) + + # remove distances smaller than a tolerance ~ 0. The small tolerance is + # needed to correct for pymatgen's neighbor_list returning self atoms + # in a few edge cases. + nonzero = torch.where(edge_distances >= 1e-8)[0] + edge_index = edge_index[:, nonzero] + edge_distances = edge_distances[nonzero] + cell_offsets = cell_offsets[nonzero] + + return edge_index, edge_distances, cell_offsets + + def convert( + self, + atoms, + ): + """Convert a single atomic stucture to a graph. + + Args: + atoms (ase.atoms.Atoms): An ASE atoms object. + + Returns: + data (torch_geometric.data.Data): A torch geometic data object with positions, atomic_numbers, tags, + and optionally, energy, forces, distances, edges, and periodic boundary conditions. + Optional properties can included by setting r_property=True when constructing the class. + """ + + # set the atomic numbers, positions, and cell + atomic_numbers = torch.Tensor(atoms.get_atomic_numbers()) + positions = torch.Tensor(atoms.get_positions()) + cell = torch.Tensor(atoms.get_cell()).view(1, 3, 3) + natoms = positions.shape[0] + # initialized to torch.zeros(natoms) if tags missing. + # https://wiki.fysik.dtu.dk/ase/_modules/ase/atoms.html#Atoms.get_tags + tags = torch.Tensor(atoms.get_tags()) + + # put the minimum data in torch geometric data object + data = Data( + cell=cell, + pos=positions, + atomic_numbers=atomic_numbers, + natoms=natoms, + tags=tags, + ) + + # optionally include other properties + if self.r_edges: + # run internal functions to get padded indices and distances + split_idx_dist = self._get_neighbors_pymatgen(atoms) + edge_index, edge_distances, cell_offsets = self._reshape_features( + *split_idx_dist + ) + + data.edge_index = edge_index + data.cell_offsets = cell_offsets + if self.r_energy: + energy = atoms.get_potential_energy(apply_constraint=False) + data.y = energy + if self.r_forces: + forces = torch.Tensor(atoms.get_forces(apply_constraint=False)) + data.force = forces + if self.r_distances and self.r_edges: + data.distances = edge_distances + if self.r_fixed: + fixed_idx = torch.zeros(natoms) + if hasattr(atoms, "constraints"): + from ase.constraints import FixAtoms + + for constraint in atoms.constraints: + if isinstance(constraint, FixAtoms): + fixed_idx[constraint.index] = 1 + data.fixed = fixed_idx + if self.r_pbc: + data.pbc = torch.tensor(atoms.pbc) + + return data + + def convert_all( + self, + atoms_collection, + processed_file_path=None, + collate_and_save=False, + disable_tqdm=False, + ): + """Convert all atoms objects in a list or in an ase.db to graphs. + + Args: + atoms_collection (list of ase.atoms.Atoms or ase.db.sqlite.SQLite3Database): + Either a list of ASE atoms objects or an ASE database. + processed_file_path (str): + A string of the path to where the processed file will be written. Default is None. + collate_and_save (bool): A boolean to collate and save or not. Default is False, so will not write a file. + + Returns: + data_list (list of torch_geometric.data.Data): + A list of torch geometric data objects containing molecular graph info and properties. + """ + + # list for all data + data_list = [] + if isinstance(atoms_collection, list): + atoms_iter = atoms_collection + elif isinstance(atoms_collection, ase.db.sqlite.SQLite3Database): + atoms_iter = atoms_collection.select() + elif isinstance( + atoms_collection, ase.io.trajectory.SlicedTrajectory + ) or isinstance(atoms_collection, ase.io.trajectory.TrajectoryReader): + atoms_iter = atoms_collection + else: + raise NotImplementedError + + for atoms in tqdm( + atoms_iter, + desc="converting ASE atoms collection to graphs", + total=len(atoms_collection), + unit=" systems", + disable=disable_tqdm, + ): + # check if atoms is an ASE Atoms object this for the ase.db case + if not isinstance(atoms, ase.atoms.Atoms): + atoms = atoms.toatoms() + data = self.convert(atoms) + data_list.append(data) + + if collate_and_save: + data, slices = collate(data_list) + torch.save((data, slices), processed_file_path) + + return data_list diff --git a/ocpmodels/tasks/__init__.py b/ocpmodels/tasks/__init__.py new file mode 100644 index 0000000..0e69e1d --- /dev/null +++ b/ocpmodels/tasks/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +__all__ = ["TrainTask", "PredictTask", "ValidateTask", "RelxationTask"] + +from .task import PredictTask, RelxationTask, TrainTask, ValidateTask diff --git a/ocpmodels/tasks/task.py b/ocpmodels/tasks/task.py new file mode 100644 index 0000000..14d5cb4 --- /dev/null +++ b/ocpmodels/tasks/task.py @@ -0,0 +1,98 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import os + +from ocpmodels.common.registry import registry +from ocpmodels.trainers.forces_trainer import ForcesTrainer + + +class BaseTask: + def __init__(self, config): + self.config = config + + def setup(self, trainer): + self.trainer = trainer + if self.config["checkpoint"] is not None: + self.trainer.load_checkpoint(self.config["checkpoint"]) + + # save checkpoint path to runner state for slurm resubmissions + self.chkpt_path = os.path.join( + self.trainer.config["cmd"]["checkpoint_dir"], "checkpoint.pt" + ) + + def run(self): + raise NotImplementedError + + +@registry.register_task("train") +class TrainTask(BaseTask): + def _process_error(self, e: RuntimeError): + e_str = str(e) + if ( + "find_unused_parameters" in e_str + and "torch.nn.parallel.DistributedDataParallel" in e_str + ): + for name, parameter in self.trainer.model.named_parameters(): + if parameter.requires_grad and parameter.grad is None: + logging.warning( + f"Parameter {name} has no gradient. Consider removing it from the model." + ) + + def run(self): + try: + self.trainer.train( + disable_eval_tqdm=self.config.get( + "hide_eval_progressbar", False + ) + ) + except RuntimeError as e: + self._process_error(e) + raise e + + +@registry.register_task("predict") +class PredictTask(BaseTask): + def run(self): + assert ( + self.trainer.test_loader is not None + ), "Test dataset is required for making predictions" + assert self.config["checkpoint"] + results_file = "predictions" + self.trainer.predict( + self.trainer.test_loader, + results_file=results_file, + disable_tqdm=self.config.get("hide_eval_progressbar", False), + ) + + +@registry.register_task("validate") +class ValidateTask(BaseTask): + def run(self): + # Note that the results won't be precise on multi GPUs due to padding of extra images (although the difference should be minor) + assert ( + self.trainer.val_loader is not None + ), "Val dataset is required for making predictions" + assert self.config["checkpoint"] + self.trainer.validate( + split="val", + disable_tqdm=self.config.get("hide_eval_progressbar", False), + ) + + +@registry.register_task("run-relaxations") +class RelxationTask(BaseTask): + def run(self): + assert isinstance( + self.trainer, ForcesTrainer + ), "Relaxations are only possible for ForcesTrainer" + assert ( + self.trainer.relax_dataset is not None + ), "Relax dataset is required for making predictions" + assert self.config["checkpoint"] + self.trainer.run_relaxations() diff --git a/ocpmodels/trainers/BE_trainer.py b/ocpmodels/trainers/BE_trainer.py new file mode 100644 index 0000000..a39b4c2 --- /dev/null +++ b/ocpmodels/trainers/BE_trainer.py @@ -0,0 +1,335 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging + +import torch +import torch_geometric +from tqdm import tqdm + +from ocpmodels.common import distutils +from ocpmodels.common.registry import registry +from ocpmodels.modules.scaling.util import ensure_fitted +from ocpmodels.trainers.base_trainer import BaseTrainer + + +@registry.register_trainer("energy") +class BETrainer(BaseTrainer): + """ + Trainer class for the Initial Structure to Relaxed Energy (IS2RE) task. + + .. note:: + + Examples of configurations for task, model, dataset and optimizer + can be found in `configs/ocp_is2re `_. + + + Args: + task (dict): Task configuration. + model (dict): Model configuration. + dataset (dict): Dataset configuration. The dataset needs to be a SinglePointLMDB dataset. + optimizer (dict): Optimizer configuration. + identifier (str): Experiment identifier that is appended to log directory. + run_dir (str, optional): Path to the run directory where logs are to be saved. + (default: :obj:`None`) + is_debug (bool, optional): Run in debug mode. + (default: :obj:`False`) + is_hpo (bool, optional): Run hyperparameter optimization with Ray Tune. + (default: :obj:`False`) + print_every (int, optional): Frequency of printing logs. + (default: :obj:`100`) + seed (int, optional): Random number seed. + (default: :obj:`None`) + logger (str, optional): Type of logger to be used. + (default: :obj:`tensorboard`) + local_rank (int, optional): Local rank of the process, only applicable for distributed training. + (default: :obj:`0`) + amp (bool, optional): Run using automatic mixed precision. + (default: :obj:`False`) + slurm (dict): Slurm configuration. Currently just for keeping track. + (default: :obj:`{}`) + """ + + def __init__( + self, + task, + model, + dataset, + optimizer, + identifier, + normalizer=None, + timestamp_id=None, + run_dir=None, + is_debug=False, + is_hpo=False, + print_every=100, + seed=None, + logger="tensorboard", + local_rank=0, + amp=False, + cpu=False, + slurm={}, + noddp=False, + ): + super().__init__( + task=task, + model=model, + dataset=dataset, + optimizer=optimizer, + identifier=identifier, + normalizer=normalizer, + timestamp_id=timestamp_id, + run_dir=run_dir, + is_debug=is_debug, + is_hpo=is_hpo, + print_every=print_every, + seed=seed, + logger=logger, + local_rank=local_rank, + amp=amp, + cpu=cpu, + name="is2re", + slurm=slurm, + noddp=noddp, + ) + + def load_task(self): + logging.info(f"Loading dataset: {self.config['task']['dataset']}") + self.num_targets = 1 + + @torch.no_grad() + def predict( + self, loader, per_image=True, results_file=None, disable_tqdm=False + ): + ensure_fitted(self._unwrapped_model) + + if distutils.is_master() and not disable_tqdm: + logging.info("Predicting on test.") + assert isinstance( + loader, + ( + torch.utils.data.dataloader.DataLoader, + torch_geometric.data.Batch, + ), + ) + rank = distutils.get_rank() + + if isinstance(loader, torch_geometric.data.Batch): + loader = [[loader]] + + self.model.eval() + if self.ema: + self.ema.store() + self.ema.copy_to() + + if self.normalizers is not None and "target" in self.normalizers: + self.normalizers["target"].to(self.device) + predictions = {"id": [], "energy": []} + + for i, batch in tqdm( + enumerate(loader), + total=len(loader), + position=rank, + desc="device {}".format(rank), + disable=disable_tqdm, + ): + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch) + + if self.normalizers is not None and "target" in self.normalizers: + out["energy"] = self.normalizers["target"].denorm( + out["energy"] + ) + + if per_image: + predictions["id"].extend( + [str(i) for i in batch[0].sid.tolist()] + ) + predictions["energy"].extend( + out["energy"].cpu().detach().numpy() + ) + else: + predictions["energy"] = out["energy"].detach() + return predictions + + self.save_results(predictions, results_file, keys=["energy"]) + + if self.ema: + self.ema.restore() + + return predictions + + def train(self, disable_eval_tqdm=False): + ensure_fitted(self._unwrapped_model, warn=True) + + eval_every = self.config["optim"].get( + "eval_every", len(self.train_loader) + ) + primary_metric = self.config["task"].get( + "primary_metric", self.evaluator.task_primary_metric[self.name] + ) + self.best_val_metric = 1e9 + + # Calculate start_epoch from step instead of loading the epoch number + # to prevent inconsistencies due to different batch size in checkpoint. + start_epoch = self.step // len(self.train_loader) + + for epoch_int in range( + start_epoch, self.config["optim"]["max_epochs"] + ): + self.train_sampler.set_epoch(epoch_int) + skip_steps = self.step % len(self.train_loader) + train_loader_iter = iter(self.train_loader) + + for i in range(skip_steps, len(self.train_loader)): + self.epoch = epoch_int + (i + 1) / len(self.train_loader) + self.step = epoch_int * len(self.train_loader) + i + 1 + self.model.train() + + # Get a batch. + batch = next(train_loader_iter) + + # Forward, loss, backward. + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch) + loss = self._compute_loss(out, batch) + loss = self.scaler.scale(loss) if self.scaler else loss + self._backward(loss) + scale = self.scaler.get_scale() if self.scaler else 1.0 + + # Compute metrics. + self.metrics = self._compute_metrics( + out, + batch, + self.evaluator, + metrics={}, + ) + self.metrics = self.evaluator.update( + "loss", loss.item() / scale, self.metrics + ) + + # Log metrics. + log_dict = {k: self.metrics[k]["metric"] for k in self.metrics} + log_dict.update( + { + "lr": self.scheduler.get_lr(), + "epoch": self.epoch, + "step": self.step, + } + ) + if ( + self.step % self.config["cmd"]["print_every"] == 0 + and distutils.is_master() + and not self.is_hpo + ): + log_str = [ + "{}: {:.2e}".format(k, v) for k, v in log_dict.items() + ] + print(", ".join(log_str)) + self.metrics = {} + + if self.logger is not None: + self.logger.log( + log_dict, + step=self.step, + split="train", + ) + + # Evaluate on val set after every `eval_every` iterations. + if self.step % eval_every == 0: + self.save( + checkpoint_file="checkpoint.pt", training_state=True + ) + + if self.val_loader is not None: + val_metrics = self.validate( + split="val", + disable_tqdm=disable_eval_tqdm, + ) + if ( + val_metrics[ + self.evaluator.task_primary_metric[self.name] + ]["metric"] + < self.best_val_metric + ): + self.best_val_metric = val_metrics[ + self.evaluator.task_primary_metric[self.name] + ]["metric"] + self.save( + metrics=val_metrics, + checkpoint_file="best_checkpoint.pt", + training_state=False, + ) + if self.test_loader is not None: + self.predict( + self.test_loader, + results_file="predictions", + disable_tqdm=False, + ) + + if self.is_hpo: + self.hpo_update( + self.epoch, + self.step, + self.metrics, + val_metrics, + ) + + if self.scheduler.scheduler_type == "ReduceLROnPlateau": + if self.step % eval_every == 0: + self.scheduler.step( + metrics=val_metrics[primary_metric]["metric"], + ) + else: + self.scheduler.step() + + torch.cuda.empty_cache() + + self.train_dataset.close_db() + if self.config.get("val_dataset", False): + self.val_dataset.close_db() + if self.config.get("test_dataset", False): + self.test_dataset.close_db() + + def _forward(self, batch_list): + output = self.model(batch_list) + + if output.shape[-1] == 1: + output = output.view(-1) + + return { + "energy": output, + } + + def _compute_loss(self, out, batch_list): + energy_target = torch.cat( + [batch.y.to(self.device) for batch in batch_list], dim=0 + ) + + if self.normalizer.get("normalize_labels", False): + target_normed = self.normalizers["target"].norm(energy_target) + else: + target_normed = energy_target + + loss = self.loss_fn["energy"](out["energy"], target_normed) + return loss + + def _compute_metrics(self, out, batch_list, evaluator, metrics={}): + energy_target = torch.cat( + [batch.y.to(self.device) for batch in batch_list], dim=0 + ) + + if self.normalizer.get("normalize_labels", False): + out["energy"] = self.normalizers["target"].denorm(out["energy"]) + + metrics = evaluator.eval( + out, + {"energy": energy_target}, + prev_metrics=metrics, + ) + + return metrics diff --git a/ocpmodels/trainers/__init__.py b/ocpmodels/trainers/__init__.py new file mode 100644 index 0000000..f5fce57 --- /dev/null +++ b/ocpmodels/trainers/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +__all__ = [ + "BaseTrainer", + "ForcesTrainer", + "EnergyTrainer", + "BETrainer", +] + +from .base_trainer import BaseTrainer +from .energy_trainer import EnergyTrainer +from .forces_trainer import ForcesTrainer +from .BE_trainer import BETrainer diff --git a/ocpmodels/trainers/base_trainer.py b/ocpmodels/trainers/base_trainer.py new file mode 100644 index 0000000..a59f333 --- /dev/null +++ b/ocpmodels/trainers/base_trainer.py @@ -0,0 +1,796 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" +import datetime +import errno +import logging +import os +import random +import subprocess +from abc import ABC, abstractmethod +from collections import defaultdict + +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +import yaml +from torch.nn.parallel.distributed import DistributedDataParallel +from torch.utils.data import DataLoader +from tqdm import tqdm + +import ocpmodels +from ocpmodels.common import distutils, gp_utils +from ocpmodels.common.data_parallel import ( + BalancedBatchSampler, + OCPDataParallel, + ParallelCollater, +) +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import load_state_dict, save_checkpoint +from ocpmodels.modules.evaluator import Evaluator +from ocpmodels.modules.exponential_moving_average import ( + ExponentialMovingAverage, +) +from ocpmodels.modules.loss import AtomwiseL2Loss, DDPLoss, L2MAELoss +from ocpmodels.modules.normalizer import Normalizer +from ocpmodels.modules.scaling.compat import load_scales_compat +from ocpmodels.modules.scaling.util import ensure_fitted +from ocpmodels.modules.scheduler import LRScheduler + + +@registry.register_trainer("base") +class BaseTrainer(ABC): + @property + def _unwrapped_model(self): + module = self.model + while isinstance(module, (OCPDataParallel, DistributedDataParallel)): + module = module.module + return module + + def __init__( + self, + task, + model, + dataset, + optimizer, + identifier, + normalizer=None, + timestamp_id=None, + run_dir=None, + is_debug=False, + is_hpo=False, + print_every=100, + seed=None, + logger="tensorboard", + local_rank=0, + amp=False, + cpu=False, + name="base_trainer", + slurm={}, + noddp=False, + ): + self.name = name + self.cpu = cpu + self.epoch = 0 + self.step = 0 + + if torch.cuda.is_available() and not self.cpu: + self.device = torch.device(f"cuda:{local_rank}") + else: + self.device = torch.device("cpu") + self.cpu = True # handle case when `--cpu` isn't specified + # but there are no gpu devices available + if run_dir is None: + run_dir = os.getcwd() + + if timestamp_id is None: + timestamp = torch.tensor(datetime.datetime.now().timestamp()).to( + self.device + ) + # create directories from master rank only + distutils.broadcast(timestamp, 0) + timestamp = datetime.datetime.fromtimestamp( + timestamp.int() + ).strftime("%Y-%m-%d-%H-%M-%S") + if identifier: + self.timestamp_id = f"{timestamp}-{identifier}" + else: + self.timestamp_id = timestamp + else: + self.timestamp_id = timestamp_id + + try: + commit_hash = ( + subprocess.check_output( + [ + "git", + "-C", + ocpmodels.__path__[0], + "describe", + "--always", + ] + ) + .strip() + .decode("ascii") + ) + # catch instances where code is not being run from a git repo + except Exception: + commit_hash = None + + logger_name = logger if isinstance(logger, str) else logger["name"] + self.config = { + "task": task, + "trainer": "forces" if name == "s2ef" else "energy", + "model": model.pop("name"), + "model_attributes": model, + "optim": optimizer, + "logger": logger, + "amp": amp, + "gpus": distutils.get_world_size() if not self.cpu else 0, + "cmd": { + "identifier": identifier, + "print_every": print_every, + "seed": seed, + "timestamp_id": self.timestamp_id, + "commit": commit_hash, + "checkpoint_dir": os.path.join( + run_dir, "checkpoints", self.timestamp_id + ), + "results_dir": os.path.join( + run_dir, "results", self.timestamp_id + ), + "logs_dir": os.path.join( + run_dir, "logs", logger_name, self.timestamp_id + ), + }, + "slurm": slurm, + "noddp": noddp, + } + # AMP Scaler + self.scaler = torch.cuda.amp.GradScaler() if amp else None + + if "SLURM_JOB_ID" in os.environ and "folder" in self.config["slurm"]: + if "SLURM_ARRAY_JOB_ID" in os.environ: + self.config["slurm"]["job_id"] = "%s_%s" % ( + os.environ["SLURM_ARRAY_JOB_ID"], + os.environ["SLURM_ARRAY_TASK_ID"], + ) + else: + self.config["slurm"]["job_id"] = os.environ["SLURM_JOB_ID"] + self.config["slurm"]["folder"] = self.config["slurm"][ + "folder" + ].replace("%j", self.config["slurm"]["job_id"]) + if isinstance(dataset, list): + if len(dataset) > 0: + self.config["dataset"] = dataset[0] + if len(dataset) > 1: + self.config["val_dataset"] = dataset[1] + if len(dataset) > 2: + self.config["test_dataset"] = dataset[2] + elif isinstance(dataset, dict): + self.config["dataset"] = dataset.get("train", None) + self.config["val_dataset"] = dataset.get("val", None) + self.config["test_dataset"] = dataset.get("test", None) + else: + self.config["dataset"] = dataset + + self.normalizer = normalizer + # This supports the legacy way of providing norm parameters in dataset + if self.config.get("dataset", None) is not None and normalizer is None: + self.normalizer = self.config["dataset"] + + if not is_debug and distutils.is_master() and not is_hpo: + os.makedirs(self.config["cmd"]["checkpoint_dir"], exist_ok=True) + os.makedirs(self.config["cmd"]["results_dir"], exist_ok=True) + os.makedirs(self.config["cmd"]["logs_dir"], exist_ok=True) + + self.is_debug = is_debug + self.is_hpo = is_hpo + + if self.is_hpo: + # conditional import is necessary for checkpointing + + # sets the hpo checkpoint frequency + # default is no checkpointing + self.hpo_checkpoint_every = self.config["optim"].get( + "checkpoint_every", -1 + ) + + if distutils.is_master(): + print(yaml.dump(self.config, default_flow_style=False)) + self.load() + + self.evaluator = Evaluator(task=name) + + def load(self): + self.load_seed_from_config() + self.load_logger() + self.load_datasets() + self.load_task() + self.load_model() + self.load_loss() + self.load_optimizer() + self.load_extras() + + def load_seed_from_config(self): + # https://pytorch.org/docs/stable/notes/randomness.html + seed = self.config["cmd"]["seed"] + if seed is None: + return + + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + def load_logger(self): + self.logger = None + if not self.is_debug and distutils.is_master() and not self.is_hpo: + assert ( + self.config["logger"] is not None + ), "Specify logger in config" + + logger = self.config["logger"] + logger_name = logger if isinstance(logger, str) else logger["name"] + assert logger_name, "Specify logger name" + + self.logger = registry.get_logger_class(logger_name)(self.config) + + def get_sampler(self, dataset, batch_size, shuffle): + if "load_balancing" in self.config["optim"]: + balancing_mode = self.config["optim"]["load_balancing"] + force_balancing = True + else: + balancing_mode = "atoms" + force_balancing = False + + if gp_utils.initialized(): + num_replicas = gp_utils.get_dp_world_size() + rank = gp_utils.get_dp_rank() + else: + num_replicas = distutils.get_world_size() + rank = distutils.get_rank() + sampler = BalancedBatchSampler( + dataset, + batch_size=batch_size, + num_replicas=num_replicas, + rank=rank, + device=self.device, + mode=balancing_mode, + shuffle=shuffle, + force_balancing=force_balancing, + ) + return sampler + + def get_dataloader(self, dataset, sampler): + loader = DataLoader( + dataset, + collate_fn=self.parallel_collater, + num_workers=self.config["optim"]["num_workers"], + pin_memory=True, + batch_sampler=sampler, + ) + return loader + + def load_datasets(self): + self.parallel_collater = ParallelCollater( + 0 if self.cpu else 1, + self.config["model_attributes"].get("otf_graph", False), + ) + + self.train_loader = self.val_loader = self.test_loader = None + + if self.config.get("dataset", None): + self.train_dataset = registry.get_dataset_class( + self.config["task"]["dataset"] + )(self.config["dataset"]) + self.train_sampler = self.get_sampler( + self.train_dataset, + self.config["optim"]["batch_size"], + shuffle=True, + ) + self.train_loader = self.get_dataloader( + self.train_dataset, + self.train_sampler, + ) + + if self.config.get("val_dataset", None): + self.val_dataset = registry.get_dataset_class( + self.config["task"]["dataset"] + )(self.config["val_dataset"]) + self.val_sampler = self.get_sampler( + self.val_dataset, + self.config["optim"].get( + "eval_batch_size", self.config["optim"]["batch_size"] + ), + shuffle=False, + ) + self.val_loader = self.get_dataloader( + self.val_dataset, + self.val_sampler, + ) + + if self.config.get("test_dataset", None): + self.test_dataset = registry.get_dataset_class( + self.config["task"]["dataset"] + )(self.config["test_dataset"]) + self.test_sampler = self.get_sampler( + self.test_dataset, + self.config["optim"].get( + "eval_batch_size", self.config["optim"]["batch_size"] + ), + shuffle=False, + ) + self.test_loader = self.get_dataloader( + self.test_dataset, + self.test_sampler, + ) + + # Normalizer for the dataset. + # Compute mean, std of training set labels. + self.normalizers = {} + if self.normalizer.get("normalize_labels", False): + if "target_mean" in self.normalizer: + self.normalizers["target"] = Normalizer( + mean=self.normalizer["target_mean"], + std=self.normalizer["target_std"], + device=self.device, + ) + else: + self.normalizers["target"] = Normalizer( + tensor=self.train_loader.dataset.data.y[ + self.train_loader.dataset.__indices__ + ], + device=self.device, + ) + + @abstractmethod + def load_task(self): + """Initialize task-specific information. Derived classes should implement this function.""" + + def load_model(self): + # Build model + if distutils.is_master(): + logging.info(f"Loading model: {self.config['model']}") + + # TODO: depreicated, remove. + bond_feat_dim = None + bond_feat_dim = self.config["model_attributes"].get( + "num_gaussians", 50 + ) + + loader = self.train_loader or self.val_loader or self.test_loader + self.model = registry.get_model_class(self.config["model"])( + loader.dataset[0].x.shape[-1] + if loader + and hasattr(loader.dataset[0], "x") + and loader.dataset[0].x is not None + else None, + bond_feat_dim, + self.num_targets, + **self.config["model_attributes"], + ).to(self.device) + + if distutils.is_master(): + logging.info( + f"Loaded {self.model.__class__.__name__} with " + f"{self.model.num_params} parameters." + ) + + if self.logger is not None: + self.logger.watch(self.model) + + self.model = OCPDataParallel( + self.model, + output_device=self.device, + num_gpus=1 if not self.cpu else 0, + ) + if distutils.initialized() and not self.config["noddp"]: + self.model = DistributedDataParallel( + self.model, device_ids=[self.device] + ) + + def load_checkpoint(self, checkpoint_path): + if not os.path.isfile(checkpoint_path): + raise FileNotFoundError( + errno.ENOENT, "Checkpoint file not found", checkpoint_path + ) + + logging.info(f"Loading checkpoint from: {checkpoint_path}") + map_location = torch.device("cpu") if self.cpu else self.device + checkpoint = torch.load(checkpoint_path, map_location=map_location) + self.epoch = checkpoint.get("epoch", 0) + self.step = checkpoint.get("step", 0) + self.best_val_metric = checkpoint.get("best_val_metric", None) + self.primary_metric = checkpoint.get("primary_metric", None) + + # Match the "module." count in the keys of model and checkpoint state_dict + # DataParallel model has 1 "module.", DistributedDataParallel has 2 "module." + # Not using either of the above two would have no "module." + + ckpt_key_count = next(iter(checkpoint["state_dict"])).count("module") + mod_key_count = next(iter(self.model.state_dict())).count("module") + key_count_diff = mod_key_count - ckpt_key_count + + if key_count_diff > 0: + new_dict = { + key_count_diff * "module." + k: v + for k, v in checkpoint["state_dict"].items() + } + elif key_count_diff < 0: + new_dict = { + k[len("module.") * abs(key_count_diff) :]: v + for k, v in checkpoint["state_dict"].items() + } + else: + new_dict = checkpoint["state_dict"] + + strict = self.config["task"].get("strict_load", True) + load_state_dict(self.model, new_dict, strict=strict) + + if "optimizer" in checkpoint: + self.optimizer.load_state_dict(checkpoint["optimizer"]) + if "scheduler" in checkpoint and checkpoint["scheduler"] is not None: + self.scheduler.scheduler.load_state_dict(checkpoint["scheduler"]) + if "ema" in checkpoint and checkpoint["ema"] is not None: + self.ema.load_state_dict(checkpoint["ema"]) + else: + self.ema = None + + scale_dict = checkpoint.get("scale_dict", None) + if scale_dict: + logging.info( + "Overwriting scaling factors with those loaded from checkpoint. " + "If you're generating predictions with a pretrained checkpoint, this is the correct behavior. " + "To disable this, delete `scale_dict` from the checkpoint. " + ) + load_scales_compat(self._unwrapped_model, scale_dict) + + for key in checkpoint["normalizers"]: + if key in self.normalizers: + self.normalizers[key].load_state_dict( + checkpoint["normalizers"][key] + ) + if self.scaler and checkpoint["amp"]: + self.scaler.load_state_dict(checkpoint["amp"]) + + def load_loss(self): + self.loss_fn = {} + self.loss_fn["energy"] = self.config["optim"].get("loss_energy", "mae") + self.loss_fn["force"] = self.config["optim"].get("loss_force", "mae") + for loss, loss_name in self.loss_fn.items(): + if loss_name in ["l1", "mae"]: + self.loss_fn[loss] = nn.L1Loss() + elif loss_name == "mse": + self.loss_fn[loss] = nn.MSELoss() + elif loss_name == "l2mae": + self.loss_fn[loss] = L2MAELoss() + elif loss_name == "atomwisel2": + self.loss_fn[loss] = AtomwiseL2Loss() + else: + raise NotImplementedError( + f"Unknown loss function name: {loss_name}" + ) + self.loss_fn[loss] = DDPLoss(self.loss_fn[loss]) + + def load_optimizer(self): + optimizer = self.config["optim"].get("optimizer", "AdamW") + optimizer = getattr(optim, optimizer) + + if self.config["optim"].get("weight_decay", 0) > 0: + + # Do not regularize bias etc. + params_decay = [] + params_no_decay = [] + for name, param in self.model.named_parameters(): + if param.requires_grad: + if "embedding" in name: + params_no_decay += [param] + elif "frequencies" in name: + params_no_decay += [param] + elif "bias" in name: + params_no_decay += [param] + else: + params_decay += [param] + + self.optimizer = optimizer( + [ + {"params": params_no_decay, "weight_decay": 0}, + { + "params": params_decay, + "weight_decay": self.config["optim"]["weight_decay"], + }, + ], + lr=self.config["optim"]["lr_initial"], + **self.config["optim"].get("optimizer_params", {}), + ) + else: + self.optimizer = optimizer( + params=self.model.parameters(), + lr=self.config["optim"]["lr_initial"], + **self.config["optim"].get("optimizer_params", {}), + ) + + def load_extras(self): + self.scheduler = LRScheduler(self.optimizer, self.config["optim"]) + self.clip_grad_norm = self.config["optim"].get("clip_grad_norm") + self.ema_decay = self.config["optim"].get("ema_decay") + if self.ema_decay: + self.ema = ExponentialMovingAverage( + self.model.parameters(), + self.ema_decay, + ) + else: + self.ema = None + + def save( + self, + metrics=None, + checkpoint_file="checkpoint.pt", + training_state=True, + ): + if not self.is_debug and distutils.is_master(): + if training_state: + return save_checkpoint( + { + "epoch": self.epoch, + "step": self.step, + "state_dict": self.model.state_dict(), + "optimizer": self.optimizer.state_dict(), + "scheduler": self.scheduler.scheduler.state_dict() + if self.scheduler.scheduler_type != "Null" + else None, + "normalizers": { + key: value.state_dict() + for key, value in self.normalizers.items() + }, + "config": self.config, + "val_metrics": metrics, + "ema": self.ema.state_dict() if self.ema else None, + "amp": self.scaler.state_dict() + if self.scaler + else None, + "best_val_metric": self.best_val_metric, + "primary_metric": self.config["task"].get( + "primary_metric", + self.evaluator.task_primary_metric[self.name], + ), + }, + checkpoint_dir=self.config["cmd"]["checkpoint_dir"], + checkpoint_file=checkpoint_file, + ) + else: + if self.ema: + self.ema.store() + self.ema.copy_to() + ckpt_path = save_checkpoint( + { + "state_dict": self.model.state_dict(), + "normalizers": { + key: value.state_dict() + for key, value in self.normalizers.items() + }, + "config": self.config, + "val_metrics": metrics, + "amp": self.scaler.state_dict() + if self.scaler + else None, + }, + checkpoint_dir=self.config["cmd"]["checkpoint_dir"], + checkpoint_file=checkpoint_file, + ) + if self.ema: + self.ema.restore() + return ckpt_path + return None + + def save_hpo(self, epoch, step, metrics, checkpoint_every): + # default is no checkpointing + # checkpointing frequency can be adjusted by setting checkpoint_every in steps + # to checkpoint every time results are communicated to Ray Tune set checkpoint_every=1 + if checkpoint_every != -1 and step % checkpoint_every == 0: + with tune.checkpoint_dir( # noqa: F821 + step=step + ) as checkpoint_dir: + path = os.path.join(checkpoint_dir, "checkpoint") + torch.save(self.save_state(epoch, step, metrics), path) + + def hpo_update( + self, epoch, step, train_metrics, val_metrics, test_metrics=None + ): + progress = { + "steps": step, + "epochs": epoch, + "act_lr": self.optimizer.param_groups[0]["lr"], + } + # checkpointing must occur before reporter + # default is no checkpointing + self.save_hpo( + epoch, + step, + val_metrics, + self.hpo_checkpoint_every, + ) + # report metrics to tune + tune_reporter( # noqa: F821 + iters=progress, + train_metrics={ + k: train_metrics[k]["metric"] for k in self.metrics + }, + val_metrics={k: val_metrics[k]["metric"] for k in val_metrics}, + test_metrics=test_metrics, + ) + + @abstractmethod + def train(self): + """Derived classes should implement this function.""" + + @torch.no_grad() + def validate(self, split="val", disable_tqdm=False): + ensure_fitted(self._unwrapped_model, warn=True) + + if distutils.is_master(): + logging.info(f"Evaluating on {split}.") + if self.is_hpo: + disable_tqdm = True + + self.model.eval() + if self.ema: + self.ema.store() + self.ema.copy_to() + + evaluator, metrics = Evaluator(task=self.name), {} + rank = distutils.get_rank() + + loader = self.val_loader if split == "val" else self.test_loader + + for i, batch in tqdm( + enumerate(loader), + total=len(loader), + position=rank, + desc="device {}".format(rank), + disable=disable_tqdm, + ): + # Forward. + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch) + loss = self._compute_loss(out, batch) + + # Compute metrics. + metrics = self._compute_metrics(out, batch, evaluator, metrics) + metrics = evaluator.update("loss", loss.item(), metrics) + + aggregated_metrics = {} + for k in metrics: + aggregated_metrics[k] = { + "total": distutils.all_reduce( + metrics[k]["total"], average=False, device=self.device + ), + "numel": distutils.all_reduce( + metrics[k]["numel"], average=False, device=self.device + ), + } + aggregated_metrics[k]["metric"] = ( + aggregated_metrics[k]["total"] / aggregated_metrics[k]["numel"] + ) + metrics = aggregated_metrics + + log_dict = {k: metrics[k]["metric"] for k in metrics} + log_dict.update({"epoch": self.epoch}) + if distutils.is_master(): + log_str = ["{}: {:.4f}".format(k, v) for k, v in log_dict.items()] + logging.info(", ".join(log_str)) + + # Make plots. + if self.logger is not None: + self.logger.log( + log_dict, + step=self.step, + split=split, + ) + + if self.ema: + self.ema.restore() + + return metrics + + @abstractmethod + def _forward(self, batch_list): + """Derived classes should implement this function.""" + + @abstractmethod + def _compute_loss(self, out, batch_list): + """Derived classes should implement this function.""" + + def _backward(self, loss): + self.optimizer.zero_grad() + loss.backward() + # Scale down the gradients of shared parameters + if hasattr(self.model.module, "shared_parameters"): + for p, factor in self.model.module.shared_parameters: + if hasattr(p, "grad") and p.grad is not None: + p.grad.detach().div_(factor) + else: + if not hasattr(self, "warned_shared_param_no_grad"): + self.warned_shared_param_no_grad = True + logging.warning( + "Some shared parameters do not have a gradient. " + "Please check if all shared parameters are used " + "and point to PyTorch parameters." + ) + if self.clip_grad_norm: + if self.scaler: + self.scaler.unscale_(self.optimizer) + grad_norm = torch.nn.utils.clip_grad_norm_( + self.model.parameters(), + max_norm=self.clip_grad_norm, + ) + if self.logger is not None: + self.logger.log( + {"grad_norm": grad_norm}, step=self.step, split="train" + ) + if self.scaler: + self.scaler.step(self.optimizer) + self.scaler.update() + else: + self.optimizer.step() + if self.ema: + self.ema.update() + + def save_results(self, predictions, results_file, keys): + if results_file is None: + return + + results_file_path = os.path.join( + self.config["cmd"]["results_dir"], + f"{self.name}_{results_file}_{distutils.get_rank()}.npz", + ) + np.savez_compressed( + results_file_path, + ids=predictions["id"], + **{key: predictions[key] for key in keys}, + ) + + distutils.synchronize() + if distutils.is_master(): + gather_results = defaultdict(list) + full_path = os.path.join( + self.config["cmd"]["results_dir"], + f"{self.name}_{results_file}.npz", + ) + + for i in range(distutils.get_world_size()): + rank_path = os.path.join( + self.config["cmd"]["results_dir"], + f"{self.name}_{results_file}_{i}.npz", + ) + rank_results = np.load(rank_path, allow_pickle=True) + gather_results["ids"].extend(rank_results["ids"]) + for key in keys: + gather_results[key].extend(rank_results[key]) + os.remove(rank_path) + + # Because of how distributed sampler works, some system ids + # might be repeated to make no. of samples even across GPUs. + _, idx = np.unique(gather_results["ids"], return_index=True) + gather_results["ids"] = np.array(gather_results["ids"])[idx] + for k in keys: + if k == "forces": + gather_results[k] = np.concatenate( + np.array(gather_results[k])[idx] + ) + elif k == "chunk_idx": + gather_results[k] = np.cumsum( + np.array(gather_results[k])[idx] + )[:-1] + else: + gather_results[k] = np.array(gather_results[k])[idx] + + logging.info(f"Writing results to {full_path}") + np.savez_compressed(full_path, **gather_results) diff --git a/ocpmodels/trainers/energy_trainer.py b/ocpmodels/trainers/energy_trainer.py new file mode 100644 index 0000000..08e5ac4 --- /dev/null +++ b/ocpmodels/trainers/energy_trainer.py @@ -0,0 +1,335 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging + +import torch +import torch_geometric +from tqdm import tqdm + +from ocpmodels.common import distutils +from ocpmodels.common.registry import registry +from ocpmodels.modules.scaling.util import ensure_fitted +from ocpmodels.trainers.base_trainer import BaseTrainer + + +@registry.register_trainer("energy") +class EnergyTrainer(BaseTrainer): + """ + Trainer class for the Initial Structure to Relaxed Energy (IS2RE) task. + + .. note:: + + Examples of configurations for task, model, dataset and optimizer + can be found in `configs/ocp_is2re `_. + + + Args: + task (dict): Task configuration. + model (dict): Model configuration. + dataset (dict): Dataset configuration. The dataset needs to be a SinglePointLMDB dataset. + optimizer (dict): Optimizer configuration. + identifier (str): Experiment identifier that is appended to log directory. + run_dir (str, optional): Path to the run directory where logs are to be saved. + (default: :obj:`None`) + is_debug (bool, optional): Run in debug mode. + (default: :obj:`False`) + is_hpo (bool, optional): Run hyperparameter optimization with Ray Tune. + (default: :obj:`False`) + print_every (int, optional): Frequency of printing logs. + (default: :obj:`100`) + seed (int, optional): Random number seed. + (default: :obj:`None`) + logger (str, optional): Type of logger to be used. + (default: :obj:`tensorboard`) + local_rank (int, optional): Local rank of the process, only applicable for distributed training. + (default: :obj:`0`) + amp (bool, optional): Run using automatic mixed precision. + (default: :obj:`False`) + slurm (dict): Slurm configuration. Currently just for keeping track. + (default: :obj:`{}`) + """ + + def __init__( + self, + task, + model, + dataset, + optimizer, + identifier, + normalizer=None, + timestamp_id=None, + run_dir=None, + is_debug=False, + is_hpo=False, + print_every=100, + seed=None, + logger="tensorboard", + local_rank=0, + amp=False, + cpu=False, + slurm={}, + noddp=False, + ): + super().__init__( + task=task, + model=model, + dataset=dataset, + optimizer=optimizer, + identifier=identifier, + normalizer=normalizer, + timestamp_id=timestamp_id, + run_dir=run_dir, + is_debug=is_debug, + is_hpo=is_hpo, + print_every=print_every, + seed=seed, + logger=logger, + local_rank=local_rank, + amp=amp, + cpu=cpu, + name="is2re", + slurm=slurm, + noddp=noddp, + ) + + def load_task(self): + logging.info(f"Loading dataset: {self.config['task']['dataset']}") + self.num_targets = 1 + + @torch.no_grad() + def predict( + self, loader, per_image=True, results_file=None, disable_tqdm=False + ): + ensure_fitted(self._unwrapped_model) + + if distutils.is_master() and not disable_tqdm: + logging.info("Predicting on test.") + assert isinstance( + loader, + ( + torch.utils.data.dataloader.DataLoader, + torch_geometric.data.Batch, + ), + ) + rank = distutils.get_rank() + + if isinstance(loader, torch_geometric.data.Batch): + loader = [[loader]] + + self.model.eval() + if self.ema: + self.ema.store() + self.ema.copy_to() + + if self.normalizers is not None and "target" in self.normalizers: + self.normalizers["target"].to(self.device) + predictions = {"id": [], "energy": []} + + for i, batch in tqdm( + enumerate(loader), + total=len(loader), + position=rank, + desc="device {}".format(rank), + disable=disable_tqdm, + ): + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch) + + if self.normalizers is not None and "target" in self.normalizers: + out["energy"] = self.normalizers["target"].denorm( + out["energy"] + ) + + if per_image: + predictions["id"].extend( + [str(i) for i in batch[0].sid.tolist()] + ) + predictions["energy"].extend( + out["energy"].cpu().detach().numpy() + ) + else: + predictions["energy"] = out["energy"].detach() + return predictions + + self.save_results(predictions, results_file, keys=["energy"]) + + if self.ema: + self.ema.restore() + + return predictions + + def train(self, disable_eval_tqdm=False): + ensure_fitted(self._unwrapped_model, warn=True) + + eval_every = self.config["optim"].get( + "eval_every", len(self.train_loader) + ) + primary_metric = self.config["task"].get( + "primary_metric", self.evaluator.task_primary_metric[self.name] + ) + self.best_val_metric = 1e9 + + # Calculate start_epoch from step instead of loading the epoch number + # to prevent inconsistencies due to different batch size in checkpoint. + start_epoch = self.step // len(self.train_loader) + + for epoch_int in range( + start_epoch, self.config["optim"]["max_epochs"] + ): + self.train_sampler.set_epoch(epoch_int) + skip_steps = self.step % len(self.train_loader) + train_loader_iter = iter(self.train_loader) + + for i in range(skip_steps, len(self.train_loader)): + self.epoch = epoch_int + (i + 1) / len(self.train_loader) + self.step = epoch_int * len(self.train_loader) + i + 1 + self.model.train() + + # Get a batch. + batch = next(train_loader_iter) + + # Forward, loss, backward. + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch) + loss = self._compute_loss(out, batch) + loss = self.scaler.scale(loss) if self.scaler else loss + self._backward(loss) + scale = self.scaler.get_scale() if self.scaler else 1.0 + + # Compute metrics. + self.metrics = self._compute_metrics( + out, + batch, + self.evaluator, + metrics={}, + ) + self.metrics = self.evaluator.update( + "loss", loss.item() / scale, self.metrics + ) + + # Log metrics. + log_dict = {k: self.metrics[k]["metric"] for k in self.metrics} + log_dict.update( + { + "lr": self.scheduler.get_lr(), + "epoch": self.epoch, + "step": self.step, + } + ) + if ( + self.step % self.config["cmd"]["print_every"] == 0 + and distutils.is_master() + and not self.is_hpo + ): + log_str = [ + "{}: {:.2e}".format(k, v) for k, v in log_dict.items() + ] + print(", ".join(log_str)) + self.metrics = {} + + if self.logger is not None: + self.logger.log( + log_dict, + step=self.step, + split="train", + ) + + # Evaluate on val set after every `eval_every` iterations. + if self.step % eval_every == 0: + self.save( + checkpoint_file="checkpoint.pt", training_state=True + ) + + if self.val_loader is not None: + val_metrics = self.validate( + split="val", + disable_tqdm=disable_eval_tqdm, + ) + if ( + val_metrics[ + self.evaluator.task_primary_metric[self.name] + ]["metric"] + < self.best_val_metric + ): + self.best_val_metric = val_metrics[ + self.evaluator.task_primary_metric[self.name] + ]["metric"] + self.save( + metrics=val_metrics, + checkpoint_file="best_checkpoint.pt", + training_state=False, + ) + if self.test_loader is not None: + self.predict( + self.test_loader, + results_file="predictions", + disable_tqdm=False, + ) + + if self.is_hpo: + self.hpo_update( + self.epoch, + self.step, + self.metrics, + val_metrics, + ) + + if self.scheduler.scheduler_type == "ReduceLROnPlateau": + if self.step % eval_every == 0: + self.scheduler.step( + metrics=val_metrics[primary_metric]["metric"], + ) + else: + self.scheduler.step() + + torch.cuda.empty_cache() + + self.train_dataset.close_db() + if self.config.get("val_dataset", False): + self.val_dataset.close_db() + if self.config.get("test_dataset", False): + self.test_dataset.close_db() + + def _forward(self, batch_list): + output = self.model(batch_list) + + if output.shape[-1] == 1: + output = output.view(-1) + + return { + "energy": output, + } + + def _compute_loss(self, out, batch_list): + energy_target = torch.cat( + [batch.y_relaxed.to(self.device) for batch in batch_list], dim=0 + ) + + if self.normalizer.get("normalize_labels", False): + target_normed = self.normalizers["target"].norm(energy_target) + else: + target_normed = energy_target + + loss = self.loss_fn["energy"](out["energy"], target_normed) + return loss + + def _compute_metrics(self, out, batch_list, evaluator, metrics={}): + energy_target = torch.cat( + [batch.y_relaxed.to(self.device) for batch in batch_list], dim=0 + ) + + if self.normalizer.get("normalize_labels", False): + out["energy"] = self.normalizers["target"].denorm(out["energy"]) + + metrics = evaluator.eval( + out, + {"energy": energy_target}, + prev_metrics=metrics, + ) + + return metrics diff --git a/ocpmodels/trainers/forces_trainer.py b/ocpmodels/trainers/forces_trainer.py new file mode 100644 index 0000000..c2c6c36 --- /dev/null +++ b/ocpmodels/trainers/forces_trainer.py @@ -0,0 +1,826 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import os +import pathlib +from collections import defaultdict +from pathlib import Path + +import numpy as np +import torch +import torch_geometric +from tqdm import tqdm + +from ocpmodels.common import distutils +from ocpmodels.common.registry import registry +from ocpmodels.common.relaxation.ml_relaxation import ml_relax +from ocpmodels.common.utils import check_traj_files +from ocpmodels.modules.evaluator import Evaluator +from ocpmodels.modules.normalizer import Normalizer +from ocpmodels.modules.scaling.util import ensure_fitted +from ocpmodels.trainers.base_trainer import BaseTrainer + + +@registry.register_trainer("forces") +class ForcesTrainer(BaseTrainer): + """ + Trainer class for the Structure to Energy & Force (S2EF) and Initial State to + Relaxed State (IS2RS) tasks. + + .. note:: + + Examples of configurations for task, model, dataset and optimizer + can be found in `configs/ocp_s2ef `_ + and `configs/ocp_is2rs `_. + + Args: + task (dict): Task configuration. + model (dict): Model configuration. + dataset (dict): Dataset configuration. The dataset needs to be a SinglePointLMDB dataset. + optimizer (dict): Optimizer configuration. + identifier (str): Experiment identifier that is appended to log directory. + run_dir (str, optional): Path to the run directory where logs are to be saved. + (default: :obj:`None`) + is_debug (bool, optional): Run in debug mode. + (default: :obj:`False`) + is_hpo (bool, optional): Run hyperparameter optimization with Ray Tune. + (default: :obj:`False`) + print_every (int, optional): Frequency of printing logs. + (default: :obj:`100`) + seed (int, optional): Random number seed. + (default: :obj:`None`) + logger (str, optional): Type of logger to be used. + (default: :obj:`tensorboard`) + local_rank (int, optional): Local rank of the process, only applicable for distributed training. + (default: :obj:`0`) + amp (bool, optional): Run using automatic mixed precision. + (default: :obj:`False`) + slurm (dict): Slurm configuration. Currently just for keeping track. + (default: :obj:`{}`) + """ + + def __init__( + self, + task, + model, + dataset, + optimizer, + identifier, + normalizer=None, + timestamp_id=None, + run_dir=None, + is_debug=False, + is_hpo=False, + print_every=100, + seed=None, + logger="tensorboard", + local_rank=0, + amp=False, + cpu=False, + slurm={}, + noddp=False, + ): + super().__init__( + task=task, + model=model, + dataset=dataset, + optimizer=optimizer, + identifier=identifier, + normalizer=normalizer, + timestamp_id=timestamp_id, + run_dir=run_dir, + is_debug=is_debug, + is_hpo=is_hpo, + print_every=print_every, + seed=seed, + logger=logger, + local_rank=local_rank, + amp=amp, + cpu=cpu, + name="s2ef", + slurm=slurm, + noddp=noddp, + ) + + def load_task(self): + logging.info(f"Loading dataset: {self.config['task']['dataset']}") + + if "relax_dataset" in self.config["task"]: + self.relax_dataset = registry.get_dataset_class("lmdb")( + self.config["task"]["relax_dataset"] + ) + self.relax_sampler = self.get_sampler( + self.relax_dataset, + self.config["optim"].get( + "eval_batch_size", self.config["optim"]["batch_size"] + ), + shuffle=False, + ) + self.relax_loader = self.get_dataloader( + self.relax_dataset, + self.relax_sampler, + ) + + self.num_targets = 1 + + # If we're computing gradients wrt input, set mean of normalizer to 0 -- + # since it is lost when compute dy / dx -- and std to forward target std + if self.config["model_attributes"].get("regress_forces", True): + if self.normalizer.get("normalize_labels", False): + if "grad_target_mean" in self.normalizer: + self.normalizers["grad_target"] = Normalizer( + mean=self.normalizer["grad_target_mean"], + std=self.normalizer["grad_target_std"], + device=self.device, + ) + else: + self.normalizers["grad_target"] = Normalizer( + tensor=self.train_loader.dataset.data.y[ + self.train_loader.dataset.__indices__ + ], + device=self.device, + ) + self.normalizers["grad_target"].mean.fill_(0) + + # Takes in a new data source and generates predictions on it. + @torch.no_grad() + def predict( + self, + data_loader, + per_image=True, + results_file=None, + disable_tqdm=False, + ): + ensure_fitted(self._unwrapped_model, warn=True) + + if distutils.is_master() and not disable_tqdm: + logging.info("Predicting on test.") + assert isinstance( + data_loader, + ( + torch.utils.data.dataloader.DataLoader, + torch_geometric.data.Batch, + ), + ) + rank = distutils.get_rank() + + if isinstance(data_loader, torch_geometric.data.Batch): + data_loader = [[data_loader]] + + self.model.eval() + if self.ema: + self.ema.store() + self.ema.copy_to() + + if self.normalizers is not None and "target" in self.normalizers: + self.normalizers["target"].to(self.device) + self.normalizers["grad_target"].to(self.device) + + predictions = {"id": [], "energy": [], "forces": [], "chunk_idx": []} + + for i, batch_list in tqdm( + enumerate(data_loader), + total=len(data_loader), + position=rank, + desc="device {}".format(rank), + disable=disable_tqdm, + ): + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch_list) + + if self.normalizers is not None and "target" in self.normalizers: + out["energy"] = self.normalizers["target"].denorm( + out["energy"] + ) + out["forces"] = self.normalizers["grad_target"].denorm( + out["forces"] + ) + if per_image: + systemids = [ + str(i) + "_" + str(j) + for i, j in zip( + batch_list[0].sid.tolist(), batch_list[0].fid.tolist() + ) + ] + predictions["id"].extend(systemids) + batch_natoms = torch.cat( + [batch.natoms for batch in batch_list] + ) + batch_fixed = torch.cat([batch.fixed for batch in batch_list]) + # total energy target requires predictions to be saved in float32 + # default is float16 + if ( + self.config["task"].get("prediction_dtype", "float16") + == "float32" + or self.config["task"]["dataset"] == "oc22_lmdb" + ): + predictions["energy"].extend( + out["energy"].cpu().detach().to(torch.float32).numpy() + ) + forces = out["forces"].cpu().detach().to(torch.float32) + else: + predictions["energy"].extend( + out["energy"].cpu().detach().to(torch.float16).numpy() + ) + forces = out["forces"].cpu().detach().to(torch.float16) + per_image_forces = torch.split(forces, batch_natoms.tolist()) + per_image_forces = [ + force.numpy() for force in per_image_forces + ] + # evalAI only requires forces on free atoms + if results_file is not None: + _per_image_fixed = torch.split( + batch_fixed, batch_natoms.tolist() + ) + _per_image_free_forces = [ + force[(fixed == 0).tolist()] + for force, fixed in zip( + per_image_forces, _per_image_fixed + ) + ] + _chunk_idx = np.array( + [ + free_force.shape[0] + for free_force in _per_image_free_forces + ] + ) + per_image_forces = _per_image_free_forces + predictions["chunk_idx"].extend(_chunk_idx) + predictions["forces"].extend(per_image_forces) + else: + predictions["energy"] = out["energy"].detach() + predictions["forces"] = out["forces"].detach() + if self.ema: + self.ema.restore() + return predictions + + predictions["forces"] = np.array(predictions["forces"]) + predictions["chunk_idx"] = np.array(predictions["chunk_idx"]) + predictions["energy"] = np.array(predictions["energy"]) + predictions["id"] = np.array(predictions["id"]) + self.save_results( + predictions, results_file, keys=["energy", "forces", "chunk_idx"] + ) + + if self.ema: + self.ema.restore() + + return predictions + + def update_best( + self, + primary_metric, + val_metrics, + disable_eval_tqdm=True, + ): + if ( + "mae" in primary_metric + and val_metrics[primary_metric]["metric"] < self.best_val_metric + ) or ( + "mae" not in primary_metric + and val_metrics[primary_metric]["metric"] > self.best_val_metric + ): + self.best_val_metric = val_metrics[primary_metric]["metric"] + self.save( + metrics=val_metrics, + checkpoint_file="best_checkpoint.pt", + training_state=False, + ) + if self.test_loader is not None: + self.predict( + self.test_loader, + results_file="predictions", + disable_tqdm=disable_eval_tqdm, + ) + + def train(self, disable_eval_tqdm=False): + ensure_fitted(self._unwrapped_model, warn=True) + + eval_every = self.config["optim"].get( + "eval_every", len(self.train_loader) + ) + checkpoint_every = self.config["optim"].get( + "checkpoint_every", eval_every + ) + primary_metric = self.config["task"].get( + "primary_metric", self.evaluator.task_primary_metric[self.name] + ) + if ( + not hasattr(self, "primary_metric") + or self.primary_metric != primary_metric + ): + self.best_val_metric = 1e9 if "mae" in primary_metric else -1.0 + else: + primary_metric = self.primary_metric + self.metrics = {} + + # Calculate start_epoch from step instead of loading the epoch number + # to prevent inconsistencies due to different batch size in checkpoint. + start_epoch = self.step // len(self.train_loader) + + for epoch_int in range( + start_epoch, self.config["optim"]["max_epochs"] + ): + self.train_sampler.set_epoch(epoch_int) + skip_steps = self.step % len(self.train_loader) + train_loader_iter = iter(self.train_loader) + + for i in range(skip_steps, len(self.train_loader)): + self.epoch = epoch_int + (i + 1) / len(self.train_loader) + self.step = epoch_int * len(self.train_loader) + i + 1 + self.model.train() + + # Get a batch. + batch = next(train_loader_iter) + + # Forward, loss, backward. + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch) + loss = self._compute_loss(out, batch) + loss = self.scaler.scale(loss) if self.scaler else loss + self._backward(loss) + scale = self.scaler.get_scale() if self.scaler else 1.0 + + # Compute metrics. + self.metrics = self._compute_metrics( + out, + batch, + self.evaluator, + self.metrics, + ) + self.metrics = self.evaluator.update( + "loss", loss.item() / scale, self.metrics + ) + + # Log metrics. + log_dict = {k: self.metrics[k]["metric"] for k in self.metrics} + log_dict.update( + { + "lr": self.scheduler.get_lr(), + "epoch": self.epoch, + "step": self.step, + } + ) + if ( + self.step % self.config["cmd"]["print_every"] == 0 + and distutils.is_master() + and not self.is_hpo + ): + log_str = [ + "{}: {:.2e}".format(k, v) for k, v in log_dict.items() + ] + logging.info(", ".join(log_str)) + self.metrics = {} + + if self.logger is not None: + self.logger.log( + log_dict, + step=self.step, + split="train", + ) + + if ( + checkpoint_every != -1 + and self.step % checkpoint_every == 0 + ): + self.save( + checkpoint_file="checkpoint.pt", training_state=True + ) + + # Evaluate on val set every `eval_every` iterations. + if self.step % eval_every == 0: + if self.val_loader is not None: + val_metrics = self.validate( + split="val", + disable_tqdm=disable_eval_tqdm, + ) + self.update_best( + primary_metric, + val_metrics, + disable_eval_tqdm=disable_eval_tqdm, + ) + if self.is_hpo: + self.hpo_update( + self.epoch, + self.step, + self.metrics, + val_metrics, + ) + + if self.config["task"].get("eval_relaxations", False): + if "relax_dataset" not in self.config["task"]: + logging.warning( + "Cannot evaluate relaxations, relax_dataset not specified" + ) + else: + self.run_relaxations() + + if self.scheduler.scheduler_type == "ReduceLROnPlateau": + if self.step % eval_every == 0: + self.scheduler.step( + metrics=val_metrics[primary_metric]["metric"], + ) + else: + self.scheduler.step() + + torch.cuda.empty_cache() + + if checkpoint_every == -1: + self.save(checkpoint_file="checkpoint.pt", training_state=True) + + self.train_dataset.close_db() + if self.config.get("val_dataset", False): + self.val_dataset.close_db() + if self.config.get("test_dataset", False): + self.test_dataset.close_db() + + def _forward(self, batch_list): + # forward pass. + if self.config["model_attributes"].get("regress_forces", True): + out_energy, out_forces = self.model(batch_list) + else: + out_energy = self.model(batch_list) + + if out_energy.shape[-1] == 1: + out_energy = out_energy.view(-1) + + out = { + "energy": out_energy, + } + + if self.config["model_attributes"].get("regress_forces", True): + out["forces"] = out_forces + + return out + + def _compute_loss(self, out, batch_list): + loss = [] + + # Energy loss. + energy_target = torch.cat( + [batch.y.to(self.device) for batch in batch_list], dim=0 + ) + if self.normalizer.get("normalize_labels", False): + energy_target = self.normalizers["target"].norm(energy_target) + energy_mult = self.config["optim"].get("energy_coefficient", 1) + loss.append( + energy_mult * self.loss_fn["energy"](out["energy"], energy_target) + ) + + # Force loss. + if self.config["model_attributes"].get("regress_forces", True): + force_target = torch.cat( + [batch.force.to(self.device) for batch in batch_list], dim=0 + ) + if self.normalizer.get("normalize_labels", False): + force_target = self.normalizers["grad_target"].norm( + force_target + ) + + tag_specific_weights = self.config["task"].get( + "tag_specific_weights", [] + ) + if tag_specific_weights != []: + # handle tag specific weights as introduced in forcenet + assert len(tag_specific_weights) == 3 + + batch_tags = torch.cat( + [ + batch.tags.float().to(self.device) + for batch in batch_list + ], + dim=0, + ) + weight = torch.zeros_like(batch_tags) + weight[batch_tags == 0] = tag_specific_weights[0] + weight[batch_tags == 1] = tag_specific_weights[1] + weight[batch_tags == 2] = tag_specific_weights[2] + + if self.config["optim"].get("loss_force", "l2mae") == "l2mae": + # zero out nans, if any + found_nans_or_infs = not torch.all( + out["forces"].isfinite() + ) + if found_nans_or_infs is True: + logging.warning("Found nans while computing loss") + out["forces"] = torch.nan_to_num( + out["forces"], nan=0.0 + ) + + dists = torch.norm( + out["forces"] - force_target, p=2, dim=-1 + ) + weighted_dists_sum = (dists * weight).sum() + + num_samples = out["forces"].shape[0] + num_samples = distutils.all_reduce( + num_samples, device=self.device + ) + weighted_dists_sum = ( + weighted_dists_sum + * distutils.get_world_size() + / num_samples + ) + + force_mult = self.config["optim"].get( + "force_coefficient", 30 + ) + loss.append(force_mult * weighted_dists_sum) + else: + raise NotImplementedError + else: + # Force coefficient = 30 has been working well for us. + force_mult = self.config["optim"].get("force_coefficient", 30) + if self.config["task"].get("train_on_free_atoms", False): + fixed = torch.cat( + [batch.fixed.to(self.device) for batch in batch_list] + ) + mask = fixed == 0 + if ( + self.config["optim"] + .get("loss_force", "mae") + .startswith("atomwise") + ): + force_mult = self.config["optim"].get( + "force_coefficient", 1 + ) + natoms = torch.cat( + [ + batch.natoms.to(self.device) + for batch in batch_list + ] + ) + natoms = torch.repeat_interleave(natoms, natoms) + force_loss = force_mult * self.loss_fn["force"]( + out["forces"][mask], + force_target[mask], + natoms=natoms[mask], + batch_size=batch_list[0].natoms.shape[0], + ) + loss.append(force_loss) + else: + loss.append( + force_mult + * self.loss_fn["force"]( + out["forces"][mask], force_target[mask] + ) + ) + else: + loss.append( + force_mult + * self.loss_fn["force"](out["forces"], force_target) + ) + + # Sanity check to make sure the compute graph is correct. + for lc in loss: + assert hasattr(lc, "grad_fn") + + loss = sum(loss) + return loss + + def _compute_metrics(self, out, batch_list, evaluator, metrics={}): + natoms = torch.cat( + [batch.natoms.to(self.device) for batch in batch_list], dim=0 + ) + + target = { + "energy": torch.cat( + [batch.y.to(self.device) for batch in batch_list], dim=0 + ), + "forces": torch.cat( + [batch.force.to(self.device) for batch in batch_list], dim=0 + ), + "natoms": natoms, + } + + out["natoms"] = natoms + + if self.config["task"].get("eval_on_free_atoms", True): + fixed = torch.cat( + [batch.fixed.to(self.device) for batch in batch_list] + ) + mask = fixed == 0 + out["forces"] = out["forces"][mask] + target["forces"] = target["forces"][mask] + + s_idx = 0 + natoms_free = [] + for natoms in target["natoms"]: + natoms_free.append( + torch.sum(mask[s_idx : s_idx + natoms]).item() + ) + s_idx += natoms + target["natoms"] = torch.LongTensor(natoms_free).to(self.device) + out["natoms"] = torch.LongTensor(natoms_free).to(self.device) + + if self.normalizer.get("normalize_labels", False): + out["energy"] = self.normalizers["target"].denorm(out["energy"]) + out["forces"] = self.normalizers["grad_target"].denorm( + out["forces"] + ) + + metrics = evaluator.eval(out, target, prev_metrics=metrics) + return metrics + + def run_relaxations(self, split="val"): + ensure_fitted(self._unwrapped_model) + + # When set to true, uses deterministic CUDA scatter ops, if available. + # https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms + # Only implemented for GemNet-OC currently. + registry.register( + "set_deterministic_scatter", + self.config["task"].get("set_deterministic_scatter", False), + ) + + logging.info("Running ML-relaxations") + self.model.eval() + if self.ema: + self.ema.store() + self.ema.copy_to() + + evaluator_is2rs, metrics_is2rs = Evaluator(task="is2rs"), {} + evaluator_is2re, metrics_is2re = Evaluator(task="is2re"), {} + + # Need both `pos_relaxed` and `y_relaxed` to compute val IS2R* metrics. + # Else just generate predictions. + if ( + hasattr(self.relax_dataset[0], "pos_relaxed") + and self.relax_dataset[0].pos_relaxed is not None + ) and ( + hasattr(self.relax_dataset[0], "y_relaxed") + and self.relax_dataset[0].y_relaxed is not None + ): + split = "val" + else: + split = "test" + + ids = [] + relaxed_positions = [] + chunk_idx = [] + for i, batch in tqdm( + enumerate(self.relax_loader), total=len(self.relax_loader) + ): + if i >= self.config["task"].get("num_relaxation_batches", 1e9): + break + + # If all traj files already exist, then skip this batch + if check_traj_files( + batch, self.config["task"]["relax_opt"].get("traj_dir", None) + ): + logging.info(f"Skipping batch: {batch[0].sid.tolist()}") + continue + + relaxed_batch = ml_relax( + batch=batch, + model=self, + steps=self.config["task"].get("relaxation_steps", 200), + fmax=self.config["task"].get("relaxation_fmax", 0.0), + relax_opt=self.config["task"]["relax_opt"], + save_full_traj=self.config["task"].get("save_full_traj", True), + device=self.device, + transform=None, + ) + + if self.config["task"].get("write_pos", False): + systemids = [str(i) for i in relaxed_batch.sid.tolist()] + natoms = relaxed_batch.natoms.tolist() + positions = torch.split(relaxed_batch.pos, natoms) + batch_relaxed_positions = [pos.tolist() for pos in positions] + + relaxed_positions += batch_relaxed_positions + chunk_idx += natoms + ids += systemids + + if split == "val": + mask = relaxed_batch.fixed == 0 + s_idx = 0 + natoms_free = [] + for natoms in relaxed_batch.natoms: + natoms_free.append( + torch.sum(mask[s_idx : s_idx + natoms]).item() + ) + s_idx += natoms + + target = { + "energy": relaxed_batch.y_relaxed, + "positions": relaxed_batch.pos_relaxed[mask], + "cell": relaxed_batch.cell, + "pbc": torch.tensor([True, True, True]), + "natoms": torch.LongTensor(natoms_free), + } + + prediction = { + "energy": relaxed_batch.y, + "positions": relaxed_batch.pos[mask], + "cell": relaxed_batch.cell, + "pbc": torch.tensor([True, True, True]), + "natoms": torch.LongTensor(natoms_free), + } + + metrics_is2rs = evaluator_is2rs.eval( + prediction, + target, + metrics_is2rs, + ) + metrics_is2re = evaluator_is2re.eval( + {"energy": prediction["energy"]}, + {"energy": target["energy"]}, + metrics_is2re, + ) + + if self.config["task"].get("write_pos", False): + rank = distutils.get_rank() + pos_filename = os.path.join( + self.config["cmd"]["results_dir"], f"relaxed_pos_{rank}.npz" + ) + np.savez_compressed( + pos_filename, + ids=ids, + pos=np.array(relaxed_positions, dtype=object), + chunk_idx=chunk_idx, + ) + + distutils.synchronize() + if distutils.is_master(): + gather_results = defaultdict(list) + full_path = os.path.join( + self.config["cmd"]["results_dir"], + "relaxed_positions.npz", + ) + + for i in range(distutils.get_world_size()): + rank_path = os.path.join( + self.config["cmd"]["results_dir"], + f"relaxed_pos_{i}.npz", + ) + rank_results = np.load(rank_path, allow_pickle=True) + gather_results["ids"].extend(rank_results["ids"]) + gather_results["pos"].extend(rank_results["pos"]) + gather_results["chunk_idx"].extend( + rank_results["chunk_idx"] + ) + os.remove(rank_path) + + # Because of how distributed sampler works, some system ids + # might be repeated to make no. of samples even across GPUs. + _, idx = np.unique(gather_results["ids"], return_index=True) + gather_results["ids"] = np.array(gather_results["ids"])[idx] + gather_results["pos"] = np.concatenate( + np.array(gather_results["pos"])[idx] + ) + gather_results["chunk_idx"] = np.cumsum( + np.array(gather_results["chunk_idx"])[idx] + )[ + :-1 + ] # np.split does not need last idx, assumes n-1:end + + logging.info(f"Writing results to {full_path}") + np.savez_compressed(full_path, **gather_results) + + if split == "val": + for task in ["is2rs", "is2re"]: + metrics = eval(f"metrics_{task}") + aggregated_metrics = {} + for k in metrics: + aggregated_metrics[k] = { + "total": distutils.all_reduce( + metrics[k]["total"], + average=False, + device=self.device, + ), + "numel": distutils.all_reduce( + metrics[k]["numel"], + average=False, + device=self.device, + ), + } + aggregated_metrics[k]["metric"] = ( + aggregated_metrics[k]["total"] + / aggregated_metrics[k]["numel"] + ) + metrics = aggregated_metrics + + # Make plots. + log_dict = { + f"{task}_{k}": metrics[k]["metric"] for k in metrics + } + if self.logger is not None: + self.logger.log( + log_dict, + step=self.step, + split=split, + ) + + if distutils.is_master(): + logging.info(metrics) + + if self.ema: + self.ema.restore() + + registry.unregister("set_deterministic_scatter") diff --git a/ocpmodels/trainers/ocp_trainer.py b/ocpmodels/trainers/ocp_trainer.py new file mode 100644 index 0000000..1ef82ba --- /dev/null +++ b/ocpmodels/trainers/ocp_trainer.py @@ -0,0 +1,733 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import os +from collections import defaultdict +from typing import Optional + +import numpy as np +import torch +import torch_geometric +from tqdm import tqdm + +from ocpmodels.common import distutils +from ocpmodels.common.registry import registry +from ocpmodels.common.relaxation.ml_relaxation import ml_relax +from ocpmodels.common.utils import cg_change_mat, check_traj_files, irreps_sum +from ocpmodels.modules.evaluator import Evaluator +from ocpmodels.modules.scaling.util import ensure_fitted +from ocpmodels.trainers.base_trainer import BaseTrainer + + +@registry.register_trainer("ocp") +@registry.register_trainer("energy") +@registry.register_trainer("forces") +class OCPTrainer(BaseTrainer): + """ + Trainer class for the Structure to Energy & Force (S2EF) and Initial State to + Relaxed State (IS2RS) tasks. + + .. note:: + + Examples of configurations for task, model, dataset and optimizer + can be found in `configs/ocp_s2ef `_ + and `configs/ocp_is2rs `_. + + Args: + task (dict): Task configuration. + model (dict): Model configuration. + outputs (dict): Output property configuration. + dataset (dict): Dataset configuration. The dataset needs to be a SinglePointLMDB dataset. + optimizer (dict): Optimizer configuration. + loss_fns (dict): Loss function configuration. + eval_metrics (dict): Evaluation metrics configuration. + identifier (str): Experiment identifier that is appended to log directory. + run_dir (str, optional): Path to the run directory where logs are to be saved. + (default: :obj:`None`) + is_debug (bool, optional): Run in debug mode. + (default: :obj:`False`) + print_every (int, optional): Frequency of printing logs. + (default: :obj:`100`) + seed (int, optional): Random number seed. + (default: :obj:`None`) + logger (str, optional): Type of logger to be used. + (default: :obj:`tensorboard`) + local_rank (int, optional): Local rank of the process, only applicable for distributed training. + (default: :obj:`0`) + amp (bool, optional): Run using automatic mixed precision. + (default: :obj:`False`) + slurm (dict): Slurm configuration. Currently just for keeping track. + (default: :obj:`{}`) + noddp (bool, optional): Run model without DDP. + """ + + def __init__( + self, + task, + model, + outputs, + dataset, + optimizer, + loss_fns, + eval_metrics, + identifier, + timestamp_id=None, + run_dir=None, + is_debug=False, + print_every=100, + seed=None, + logger="tensorboard", + local_rank=0, + amp=False, + cpu=False, + slurm={}, + noddp=False, + name="ocp", + ): + super().__init__( + task=task, + model=model, + outputs=outputs, + dataset=dataset, + optimizer=optimizer, + loss_fns=loss_fns, + eval_metrics=eval_metrics, + identifier=identifier, + timestamp_id=timestamp_id, + run_dir=run_dir, + is_debug=is_debug, + print_every=print_every, + seed=seed, + logger=logger, + local_rank=local_rank, + amp=amp, + cpu=cpu, + slurm=slurm, + noddp=noddp, + name=name, + ) + + def train(self, disable_eval_tqdm: bool = False) -> None: + ensure_fitted(self._unwrapped_model, warn=True) + + eval_every = self.config["optim"].get( + "eval_every", len(self.train_loader) + ) + checkpoint_every = self.config["optim"].get( + "checkpoint_every", eval_every + ) + primary_metric = self.evaluation_metrics.get( + "primary_metric", self.evaluator.task_primary_metric[self.name] + ) + if ( + not hasattr(self, "primary_metric") + or self.primary_metric != primary_metric + ): + self.best_val_metric = 1e9 if "mae" in primary_metric else -1.0 + else: + primary_metric = self.primary_metric + self.metrics = {} + + # Calculate start_epoch from step instead of loading the epoch number + # to prevent inconsistencies due to different batch size in checkpoint. + start_epoch = self.step // len(self.train_loader) + + for epoch_int in range( + start_epoch, self.config["optim"]["max_epochs"] + ): + self.train_sampler.set_epoch(epoch_int) + skip_steps = self.step % len(self.train_loader) + train_loader_iter = iter(self.train_loader) + + for i in range(skip_steps, len(self.train_loader)): + self.epoch = epoch_int + (i + 1) / len(self.train_loader) + self.step = epoch_int * len(self.train_loader) + i + 1 + self.model.train() + + # Get a batch. + batch = next(train_loader_iter) + + # Forward, loss, backward. + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch) + loss = self._compute_loss(out, batch) + + # Compute metrics. + self.metrics = self._compute_metrics( + out, + batch, + self.evaluator, + self.metrics, + ) + self.metrics = self.evaluator.update( + "loss", loss.item(), self.metrics + ) + + loss = self.scaler.scale(loss) if self.scaler else loss + self._backward(loss) + + # Log metrics. + log_dict = {k: self.metrics[k]["metric"] for k in self.metrics} + log_dict.update( + { + "lr": self.scheduler.get_lr(), + "epoch": self.epoch, + "step": self.step, + } + ) + if ( + self.step % self.config["cmd"]["print_every"] == 0 + and distutils.is_master() + ): + log_str = [ + "{}: {:.2e}".format(k, v) for k, v in log_dict.items() + ] + logging.info(", ".join(log_str)) + self.metrics = {} + + if self.logger is not None: + self.logger.log( + log_dict, + step=self.step, + split="train", + ) + + if ( + checkpoint_every != -1 + and self.step % checkpoint_every == 0 + ): + self.save( + checkpoint_file="checkpoint.pt", training_state=True + ) + + # Evaluate on val set every `eval_every` iterations. + if self.step % eval_every == 0: + if self.val_loader is not None: + val_metrics = self.validate( + split="val", + disable_tqdm=disable_eval_tqdm, + ) + self.update_best( + primary_metric, + val_metrics, + disable_eval_tqdm=disable_eval_tqdm, + ) + + if self.config["task"].get("eval_relaxations", False): + if "relax_dataset" not in self.config["task"]: + logging.warning( + "Cannot evaluate relaxations, relax_dataset not specified" + ) + else: + self.run_relaxations() + + if self.scheduler.scheduler_type == "ReduceLROnPlateau": + if self.step % eval_every == 0: + self.scheduler.step( + metrics=val_metrics[primary_metric]["metric"], + ) + else: + self.scheduler.step() + + torch.cuda.empty_cache() + + if checkpoint_every == -1: + self.save(checkpoint_file="checkpoint.pt", training_state=True) + + self.train_dataset.close_db() + if self.config.get("val_dataset", False): + self.val_dataset.close_db() + if self.config.get("test_dataset", False): + self.test_dataset.close_db() + + def _forward(self, batch): + out = self.model(batch.to(self.device)) + + ### TODO: Move into BaseModel in OCP 2.0 + outputs = {} + batch_size = batch.natoms.numel() + num_atoms_in_batch = batch.natoms.sum() + for target_key in self.output_targets: + ### Target property is a direct output of the model + if target_key in out: + pred = out[target_key] + ## Target property is a derived output of the model. Construct the + ## parent property + else: + _max_rank = 0 + for subtarget_key in self.output_targets[target_key][ + "decomposition" + ]: + _max_rank = max( + _max_rank, + self.output_targets[subtarget_key]["irrep_dim"], + ) + + pred_irreps = torch.zeros( + (batch_size, irreps_sum(_max_rank)), device=self.device + ) + + for subtarget_key in self.output_targets[target_key][ + "decomposition" + ]: + irreps = self.output_targets[subtarget_key]["irrep_dim"] + _pred = out[subtarget_key] + + if self.normalizers.get(subtarget_key, False): + _pred = self.normalizers[subtarget_key].denorm(_pred) + + ## Fill in the corresponding irreps prediction + ## Reshape irrep prediction to (batch_size, irrep_dim) + pred_irreps[ + :, + max(0, irreps_sum(irreps - 1)) : irreps_sum(irreps), + ] = _pred.view(batch_size, -1) + + pred = torch.einsum( + "ba, cb->ca", + cg_change_mat(_max_rank, self.device), + pred_irreps, + ) + + ### not all models are consistent with the output shape + ### reshape accordingly: num_atoms_in_batch, -1 or num_systems_in_batch, -1 + if self.output_targets[target_key]["level"] == "atom": + pred = pred.view(num_atoms_in_batch, -1) + else: + pred = pred.view(batch_size, -1) + + outputs[target_key] = pred + + return outputs + + def _compute_loss(self, out, batch): + batch_size = batch.natoms.numel() + fixed = batch.fixed + mask = fixed == 0 + + loss = [] + for loss_fn in self.loss_fns: + target_name, loss_info = loss_fn + + target = batch[target_name] + pred = out[target_name] + natoms = batch.natoms + natoms = torch.repeat_interleave(natoms, natoms) + + if ( + self.output_targets[target_name]["level"] == "atom" + and self.output_targets[target_name]["train_on_free_atoms"] + ): + target = target[mask] + pred = pred[mask] + natoms = natoms[mask] + + num_atoms_in_batch = natoms.numel() + if self.normalizers.get(target_name, False): + target = self.normalizers[target_name].norm(target) + + ### reshape accordingly: num_atoms_in_batch, -1 or num_systems_in_batch, -1 + if self.output_targets[target_name]["level"] == "atom": + target = target.view(num_atoms_in_batch, -1) + else: + target = target.view(batch_size, -1) + + mult = loss_info["coefficient"] + loss.append( + mult + * loss_info["fn"]( + pred, + target, + natoms=natoms, + batch_size=batch_size, + ) + ) + + # Sanity check to make sure the compute graph is correct. + for lc in loss: + assert hasattr(lc, "grad_fn") + + loss = sum(loss) + return loss + + def _compute_metrics(self, out, batch, evaluator, metrics={}): + natoms = batch.natoms + batch_size = natoms.numel() + + ### Retrieve free atoms + fixed = batch.fixed + mask = fixed == 0 + + s_idx = 0 + natoms_free = [] + for _natoms in natoms: + natoms_free.append(torch.sum(mask[s_idx : s_idx + _natoms]).item()) + s_idx += _natoms + natoms = torch.LongTensor(natoms_free).to(self.device) + + targets = {} + for target_name in self.output_targets: + target = batch[target_name] + num_atoms_in_batch = batch.natoms.sum() + + if ( + self.output_targets[target_name]["level"] == "atom" + and self.output_targets[target_name]["eval_on_free_atoms"] + ): + target = target[mask] + out[target_name] = out[target_name][mask] + num_atoms_in_batch = natoms.sum() + + ### reshape accordingly: num_atoms_in_batch, -1 or num_systems_in_batch, -1 + if self.output_targets[target_name]["level"] == "atom": + target = target.view(num_atoms_in_batch, -1) + else: + target = target.view(batch_size, -1) + + targets[target_name] = target + if self.normalizers.get(target_name, False): + out[target_name] = self.normalizers[target_name].denorm( + out[target_name] + ) + + targets["natoms"] = natoms + out["natoms"] = natoms + + metrics = evaluator.eval(out, targets, prev_metrics=metrics) + return metrics + + # Takes in a new data source and generates predictions on it. + @torch.no_grad() + def predict( + self, + data_loader, + per_image: bool = True, + results_file: Optional[str] = None, + disable_tqdm: bool = False, + ): + ensure_fitted(self._unwrapped_model, warn=True) + + if distutils.is_master() and not disable_tqdm: + logging.info("Predicting on test.") + assert isinstance( + data_loader, + ( + torch.utils.data.dataloader.DataLoader, + torch_geometric.data.Batch, + ), + ) + rank = distutils.get_rank() + + if isinstance(data_loader, torch_geometric.data.Batch): + data_loader = [data_loader] + + self.model.eval() + if self.ema is not None: + self.ema.store() + self.ema.copy_to() + + predictions = defaultdict(list) + + for i, batch in tqdm( + enumerate(data_loader), + total=len(data_loader), + position=rank, + desc="device {}".format(rank), + disable=disable_tqdm, + ): + + with torch.cuda.amp.autocast(enabled=self.scaler is not None): + out = self._forward(batch) + + for target_key in self.config["outputs"]: + pred = out[target_key] + if self.normalizers.get(target_key, False): + pred = self.normalizers[target_key].denorm(pred) + + if per_image: + ### Save outputs in desired precision, default float16 + if ( + self.config["outputs"][target_key].get( + "prediction_dtype", "float16" + ) + == "float32" + or self.config["task"].get( + "prediction_dtype", "float16" + ) + == "float32" + or self.config["task"].get("dataset", "lmdb") + == "oc22_lmdb" + ): + dtype = torch.float32 + else: + dtype = torch.float16 + + pred = pred.cpu().detach().to(dtype) + ### Split predictions into per-image predictions + if self.config["outputs"][target_key]["level"] == "atom": + batch_natoms = batch.natoms + batch_fixed = batch.fixed + per_image_pred = torch.split( + pred, batch_natoms.tolist() + ) + + ### Save out only free atom, EvalAI does not need fixed atoms + _per_image_fixed = torch.split( + batch_fixed, batch_natoms.tolist() + ) + _per_image_free_preds = [ + _pred[(fixed == 0).tolist()].numpy() + for _pred, fixed in zip( + per_image_pred, _per_image_fixed + ) + ] + _chunk_idx = np.array( + [ + free_pred.shape[0] + for free_pred in _per_image_free_preds + ] + ) + per_image_pred = _per_image_free_preds + ### Assumes system level properties are of the same dimension + else: + per_image_pred = pred.numpy() + _chunk_idx = None + + predictions[f"{target_key}"].extend(per_image_pred) + ### Backwards compatibility, retain 'chunk_idx' for forces. + if _chunk_idx is not None: + if target_key == "forces": + predictions["chunk_idx"].extend(_chunk_idx) + else: + predictions[f"{target_key}_chunk_idx"].extend( + _chunk_idx + ) + else: + predictions[f"{target_key}"] = pred.detach() + + if not per_image: + return predictions + + ### Get unique system identifiers + sids = batch.sid.tolist() + ## Support naming structure for OC20 S2EF + if "fid" in batch: + fids = batch.fid.tolist() + systemids = [f"{sid}_{fid}" for sid, fid in zip(sids, fids)] + else: + systemids = [f"{sid}" for sid in sids] + + predictions["ids"].extend(systemids) + + for key in predictions: + predictions[key] = np.array(predictions[key]) + + self.save_results(predictions, results_file) + + if self.ema: + self.ema.restore() + + return predictions + + def run_relaxations(self, split="val"): + ensure_fitted(self._unwrapped_model) + + # When set to true, uses deterministic CUDA scatter ops, if available. + # https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms + # Only implemented for GemNet-OC currently. + registry.register( + "set_deterministic_scatter", + self.config["task"].get("set_deterministic_scatter", False), + ) + + logging.info("Running ML-relaxations") + self.model.eval() + if self.ema: + self.ema.store() + self.ema.copy_to() + + evaluator_is2rs, metrics_is2rs = Evaluator(task="is2rs"), {} + evaluator_is2re, metrics_is2re = Evaluator(task="is2re"), {} + + # Need both `pos_relaxed` and `y_relaxed` to compute val IS2R* metrics. + # Else just generate predictions. + if ( + hasattr(self.relax_dataset[0], "pos_relaxed") + and self.relax_dataset[0].pos_relaxed is not None + ) and ( + hasattr(self.relax_dataset[0], "y_relaxed") + and self.relax_dataset[0].y_relaxed is not None + ): + split = "val" + else: + split = "test" + + ids = [] + relaxed_positions = [] + chunk_idx = [] + for i, batch in tqdm( + enumerate(self.relax_loader), total=len(self.relax_loader) + ): + if i >= self.config["task"].get("num_relaxation_batches", 1e9): + break + + # If all traj files already exist, then skip this batch + if check_traj_files( + batch, self.config["task"]["relax_opt"].get("traj_dir", None) + ): + logging.info(f"Skipping batch: {batch[0].sid.tolist()}") + continue + + relaxed_batch = ml_relax( + batch=batch, + model=self, + steps=self.config["task"].get("relaxation_steps", 200), + fmax=self.config["task"].get("relaxation_fmax", 0.0), + relax_opt=self.config["task"]["relax_opt"], + save_full_traj=self.config["task"].get("save_full_traj", True), + device=self.device, + transform=None, + ) + + if self.config["task"].get("write_pos", False): + systemids = [str(i) for i in relaxed_batch.sid.tolist()] + natoms = relaxed_batch.natoms.tolist() + positions = torch.split(relaxed_batch.pos, natoms) + batch_relaxed_positions = [pos.tolist() for pos in positions] + + relaxed_positions += batch_relaxed_positions + chunk_idx += natoms + ids += systemids + + if split == "val": + mask = relaxed_batch.fixed == 0 + s_idx = 0 + natoms_free = [] + for natoms in relaxed_batch.natoms: + natoms_free.append( + torch.sum(mask[s_idx : s_idx + natoms]).item() + ) + s_idx += natoms + + target = { + "energy": relaxed_batch.y_relaxed, + "positions": relaxed_batch.pos_relaxed[mask], + "cell": relaxed_batch.cell, + "pbc": torch.tensor([True, True, True]), + "natoms": torch.LongTensor(natoms_free), + } + + prediction = { + "energy": relaxed_batch.y, + "positions": relaxed_batch.pos[mask], + "cell": relaxed_batch.cell, + "pbc": torch.tensor([True, True, True]), + "natoms": torch.LongTensor(natoms_free), + } + + metrics_is2rs = evaluator_is2rs.eval( + prediction, + target, + metrics_is2rs, + ) + metrics_is2re = evaluator_is2re.eval( + {"energy": prediction["energy"]}, + {"energy": target["energy"]}, + metrics_is2re, + ) + + if self.config["task"].get("write_pos", False): + rank = distutils.get_rank() + pos_filename = os.path.join( + self.config["cmd"]["results_dir"], f"relaxed_pos_{rank}.npz" + ) + np.savez_compressed( + pos_filename, + ids=ids, + pos=np.array(relaxed_positions, dtype=object), + chunk_idx=chunk_idx, + ) + + distutils.synchronize() + if distutils.is_master(): + gather_results = defaultdict(list) + full_path = os.path.join( + self.config["cmd"]["results_dir"], + "relaxed_positions.npz", + ) + + for i in range(distutils.get_world_size()): + rank_path = os.path.join( + self.config["cmd"]["results_dir"], + f"relaxed_pos_{i}.npz", + ) + rank_results = np.load(rank_path, allow_pickle=True) + gather_results["ids"].extend(rank_results["ids"]) + gather_results["pos"].extend(rank_results["pos"]) + gather_results["chunk_idx"].extend( + rank_results["chunk_idx"] + ) + os.remove(rank_path) + + # Because of how distributed sampler works, some system ids + # might be repeated to make no. of samples even across GPUs. + _, idx = np.unique(gather_results["ids"], return_index=True) + gather_results["ids"] = np.array(gather_results["ids"])[idx] + gather_results["pos"] = np.concatenate( + np.array(gather_results["pos"])[idx] + ) + gather_results["chunk_idx"] = np.cumsum( + np.array(gather_results["chunk_idx"])[idx] + )[ + :-1 + ] # np.split does not need last idx, assumes n-1:end + + logging.info(f"Writing results to {full_path}") + np.savez_compressed(full_path, **gather_results) + + if split == "val": + for task in ["is2rs", "is2re"]: + metrics = eval(f"metrics_{task}") + aggregated_metrics = {} + for k in metrics: + aggregated_metrics[k] = { + "total": distutils.all_reduce( + metrics[k]["total"], + average=False, + device=self.device, + ), + "numel": distutils.all_reduce( + metrics[k]["numel"], + average=False, + device=self.device, + ), + } + aggregated_metrics[k]["metric"] = ( + aggregated_metrics[k]["total"] + / aggregated_metrics[k]["numel"] + ) + metrics = aggregated_metrics + + # Make plots. + log_dict = { + f"{task}_{k}": metrics[k]["metric"] for k in metrics + } + if self.logger is not None: + self.logger.log( + log_dict, + step=self.step, + split=split, + ) + + if distutils.is_master(): + logging.info(metrics) + + if self.ema: + self.ema.restore() + + registry.unregister("set_deterministic_scatter") diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..dfab025 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,16 @@ +[tool.black] +line-length = 79 +include = '\.pyi?$' +exclude = ''' +/( + \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | _build + | buck-out + | build + | dist +)/ +''' diff --git a/run_OCP.py b/run_OCP.py new file mode 100644 index 0000000..b2f1b12 --- /dev/null +++ b/run_OCP.py @@ -0,0 +1,150 @@ +from sklearn.metrics import mean_absolute_error +import numpy as np +import pickle +import torch +import os +import lmdb +from tqdm import tqdm +import matplotlib.pyplot as plt +from ocpmodels.trainers import ForcesTrainer, BaseTrainer, BETrainer +from ocpmodels.datasets import SinglePointLmdbDataset +from ocpmodels import models +from ocpmodels.common import logger +from ocpmodels.common.utils import setup_logging + +# data_list = 'data.pkl' + +# with open(data_list, 'rb') as f: +# Data = pickle.load(f) + +# print('create LMDB database') +# print('create data set') + +# db = lmdb.open( +# "data/test_set.lmdb", +# #map_size=10e1, +# subdir=False, +# meminit=False, +# map_async=True, +# map_size = (10**9) # 10 gb +# ) + +# idx = 0 +# for tmp in tqdm(Data[int(len(Data)*0.9):]): +# txn = db.begin(write=True) +# txn.put(f"{idx}".encode("ascii"), pickle.dumps(tmp, protocol=-1)) +# txn.commit() +# db.sync() +# idx += 1 +# db.close() + +task = { + 'dataset': 'single_point_lmdb', # dataset used for the S2EF task + 'description': 'Relaxed state energy prediction from initial structure.', + 'type': 'regression', + 'metric': 'mae', + 'labels': ['relaxed energy'], + #'grad_input': 'atomic forces', + #'train_on_free_atoms': True, + #'eval_on_free_atoms': True +} + +test_src = 'data/all_double_perv_te.lmdb' + +# Dataset +dataset = [{'src': test_src}, # train set + {'src': test_src}, # val_set + {'src': test_src} +] + +# Optimizer +optimizer = { + 'batch_size': 16, # if hitting GPU memory issues, lower this + 'eval_batch_size': 16, + 'load_balancing': 'atoms', + 'eval_every': 100, + 'num_workers': 0, + 'lr_initial': 5.e-5, + 'optimizer' : 'AdamW', + 'optimizer_params' : {'amsgrad':True}, + 'scheduler': "ReduceLROnPlateau", + 'mode': "min", + 'factor': 0.8, + 'patience': 3, + 'max_epochs': 5000, + 'force_coefficient': 100, + 'energy_coefficient':1, + 'ema_decay': 0.999, + 'clip_grad_norm':10, + 'loss_energy': 'mae', + 'loss_force':'l2mae', + 'weight_decay':0, +} + +model_params = { + 'name': 'gemnet_oc', + 'num_spherical': 7, + 'num_radial': 128, + 'num_blocks': 4, + 'emb_size_atom': 256, + 'emb_size_edge': 512, + 'emb_size_trip_in': 64, + 'emb_size_trip_out': 64, + 'emb_size_quad_in': 32, + 'emb_size_quad_out': 32, + 'emb_size_aint_in': 64, + 'emb_size_aint_out': 64, + 'emb_size_rbf': 16, + 'emb_size_cbf': 16, + 'emb_size_sbf': 32, + 'num_before_skip': 2, + 'num_after_skip': 2, + 'num_concat': 1, + 'num_atom': 3, + 'num_output_afteratom':3, + 'cutoff': 12.0, + 'cutoff_qint': 12.0, + 'cutoff_aeaint': 12.0, + 'cutoff_aint': 12.0, + 'max_neighbors': 30, + 'max_neighbors_qint': 8, + 'max_neighbors_aeaint': 20, + 'max_neighbors_aint': 1000, + 'rbf': {'name': 'gaussian'}, + 'envelope': {'name': 'polynomial', 'exponent': 5}, + 'cbf': {'name': 'spherical_harmonics'}, + 'extensive': True, + 'output_init': 'HeOrthogonal', + 'activation': 'silu', + 'scale_file': 'configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt', + 'regress_forces': False, + 'direct_forces': True, + 'forces_coupled':False, + 'quad_interaction':True, + 'atom_edge_interaction':True, + 'edge_atom_interaction':True, + 'atom_interaction':True, + 'num_atom_emb_layers':2, + 'num_global_out_layers':2, + 'qint_tags': [1,2], +} + +pretrained_trainer = BETrainer( + task=task, + model=model_params, + dataset=dataset, + optimizer=optimizer, + identifier="gemnet-oc-double_perov", + run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override! + is_debug=False, # if True, do not save checkpoint, logs, or results + #is_vis=False, + print_every=20, + seed=0, # random seed to use + logger="tensorboard", # logger of choice (tensorboard and wandb supported) + local_rank=0, +) + +checkpoint_path='checkpoint/best_checkpoint.pt' +pretrained_trainer.load_checkpoint(checkpoint_path=checkpoint_path) +predictions = pretrained_trainer.predict(pretrained_trainer.test_loader, results_file="predict_result", disable_tqdm=False) + diff --git a/scripts/__init__.py b/scripts/__init__.py new file mode 100644 index 0000000..c17674b --- /dev/null +++ b/scripts/__init__.py @@ -0,0 +1,6 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" diff --git a/scripts/download_data.py b/scripts/download_data.py new file mode 100644 index 0000000..4211d40 --- /dev/null +++ b/scripts/download_data.py @@ -0,0 +1,170 @@ +import argparse +import glob +import logging +import os + +import ocpmodels + +""" +This script provides users with an automated way to download, preprocess (where +applicable), and organize data to readily be used by the existing config files. +""" + +DOWNLOAD_LINKS = { + "s2ef": { + "200k": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_train_200K.tar", + "2M": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_train_2M.tar", + "20M": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_train_20M.tar", + "all": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_train_all.tar", + "val_id": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_val_id.tar", + "val_ood_ads": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_val_ood_ads.tar", + "val_ood_cat": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_val_ood_cat.tar", + "val_ood_both": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_val_ood_both.tar", + "test": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_test_lmdbs.tar.gz", + "rattled": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_rattled.tar", + "md": "https://dl.fbaipublicfiles.com/opencatalystproject/data/s2ef_md.tar", + }, + "is2re": "https://dl.fbaipublicfiles.com/opencatalystproject/data/is2res_train_val_test_lmdbs.tar.gz", +} + +S2EF_COUNTS = { + "s2ef": { + "200k": 200000, + "2M": 2000000, + "20M": 20000000, + "all": 133934018, + "val_id": 999866, + "val_ood_ads": 999838, + "val_ood_cat": 999809, + "val_ood_both": 999944, + "rattled": 16677031, + "md": 38315405, + }, +} + + +def get_data(datadir, task, split, del_intmd_files): + os.makedirs(datadir, exist_ok=True) + + if task == "s2ef" and split is None: + raise NotImplementedError("S2EF requires a split to be defined.") + + if task == "s2ef": + assert ( + split in DOWNLOAD_LINKS[task] + ), f'S2EF "{split}" split not defined, please specify one of the following: {list(DOWNLOAD_LINKS["s2ef"].keys())}' + download_link = DOWNLOAD_LINKS[task][split] + + elif task == "is2re": + download_link = DOWNLOAD_LINKS[task] + + os.system(f"wget {download_link} -P {datadir}") + filename = os.path.join(datadir, os.path.basename(download_link)) + logging.info("Extracting contents...") + os.system(f"tar -xvf {filename} -C {datadir}") + dirname = os.path.join( + datadir, + os.path.basename(filename).split(".")[0], + ) + if task == "s2ef" and split != "test": + compressed_dir = os.path.join(dirname, os.path.basename(dirname)) + if split in ["200k", "2M", "20M", "all", "rattled", "md"]: + output_path = os.path.join(datadir, task, split, "train") + else: + output_path = os.path.join(datadir, task, "all", split) + uncompressed_dir = uncompress_data(compressed_dir) + preprocess_data(uncompressed_dir, output_path) + + verify_count(output_path, task, split) + if task == "s2ef" and split == "test": + os.system(f"mv {dirname}/test_data/s2ef/all/test_* {datadir}/s2ef/all") + elif task == "is2re": + os.system(f"mv {dirname}/data/is2re {datadir}") + + if del_intmd_files: + cleanup(filename, dirname) + + +def uncompress_data(compressed_dir): + import uncompress + + parser = uncompress.get_parser() + args, _ = parser.parse_known_args() + args.ipdir = compressed_dir + args.opdir = os.path.dirname(compressed_dir) + "_uncompressed" + uncompress.main(args) + return args.opdir + + +def preprocess_data(uncompressed_dir, output_path): + import preprocess_ef as preprocess + + parser = preprocess.get_parser() + args, _ = parser.parse_known_args() + args.data_path = uncompressed_dir + args.out_path = output_path + preprocess.main(args) + + +def verify_count(output_path, task, split): + paths = glob.glob(os.path.join(output_path, "*.txt")) + count = 0 + for path in paths: + lines = open(path, "r").read().splitlines() + count += len(lines) + assert ( + count == S2EF_COUNTS[task][split] + ), f"S2EF {split} count incorrect, verify preprocessing has completed successfully." + + +def cleanup(filename, dirname): + import shutil + + if os.path.exists(filename): + os.remove(filename) + if os.path.exists(dirname): + shutil.rmtree(dirname) + if os.path.exists(dirname + "_uncompressed"): + shutil.rmtree(dirname + "_uncompressed") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--task", type=str, help="Task to download") + parser.add_argument( + "--split", type=str, help="Corresponding data split to download" + ) + parser.add_argument( + "--keep", + action="store_true", + help="Keep intermediate directories and files upon data retrieval/processing", + ) + # Flags for S2EF train/val set preprocessing: + parser.add_argument( + "--get-edges", + action="store_true", + help="Store edge indices in LMDB, ~10x storage requirement. Default: compute edge indices on-the-fly.", + ) + parser.add_argument( + "--num-workers", + type=int, + default=1, + help="No. of feature-extracting processes or no. of dataset chunks", + ) + parser.add_argument( + "--ref-energy", action="store_true", help="Subtract reference energies" + ) + parser.add_argument( + "--data-path", + type=str, + default=os.path.join(os.path.dirname(ocpmodels.__path__[0]), "data"), + help="Specify path to save dataset. Defaults to 'ocpmodels/data'", + ) + + args, _ = parser.parse_known_args() + get_data( + datadir=args.data_path, + task=args.task, + split=args.split, + del_intmd_files=not args.keep, + ) diff --git a/scripts/gif_maker_parallelized.py b/scripts/gif_maker_parallelized.py new file mode 100644 index 0000000..6aab180 --- /dev/null +++ b/scripts/gif_maker_parallelized.py @@ -0,0 +1,127 @@ +""" +Script to generate gifs from traj + +Note: +This is just a quick way to generate gifs and visalizations from traj, there are many parameters and settings in the code that people can vary to make visualizations better. We have chosen these settings as this seem to work fine for most of our systems. + +Requirements: + +povray +ffmpeg +ase==3.21 + +""" +import argparse +import copy +import multiprocessing as mp +import os + +import ase.io +import numpy as np +from ase.data import covalent_radii +from ase.io.pov import get_bondpairs + + +def pov_from_atoms(mp_args): + + atoms, idx, out_path = mp_args + # how many extra repeats to generate on either side to look infinite + extra_cells = 2 + # try and guess which atoms are adsorbates since the tags aren't correct after running in vasp + # ideally this would be fixed by getting the right adsorbate atoms from the initial configurations + atoms_organic = np.array( + [atom.symbol in set(["C", "H", "O", "N"]) for atom in atoms] + ) + # get the bare surface (note: this will not behave correctly for nitrides/hydrides/carbides/etc) + atoms_surface = atoms[~atoms_organic].copy() + # replicate the bare surface + atoms_surface = atoms_surface.repeat( + (extra_cells * 2 + 1, extra_cells * 2 + 1, 1) + ) + # make an image of the adsorbate in the center of the slab + atoms_adsorbate = atoms[atoms_organic] + atoms_adsorbate.positions += extra_cells * ( + atoms.cell[0, :] + atoms.cell[1, :] + ) + # add the adsorbate to the replicated surface, then center the positions on the adsorbate + num_surface_atoms = len(atoms_surface) + atoms_surface += atoms_adsorbate + atoms_surface.positions -= atoms_adsorbate.positions.mean(axis=0) + # only include bonds for the adsorbate atoms + bondpairs = get_bondpairs(atoms_surface) + bondpairs = [ + bond + for bond in bondpairs + if bond[0] >= num_surface_atoms and bond[1] >= num_surface_atoms + ] + # write the image with povray + bbox = (-6.4, -4, 6.4, 4) # clip to a small region around the adsorbate + os.chdir(f"{out_path}") + renderer = ase.io.write( + "snapshot_%04i.pov" % idx, + atoms_surface, + povray_settings={ + "celllinewidth": 0, + "canvas_height": 300, + "textures": ["intermediate"] * len(atoms_surface), + "bondatoms": bondpairs, + }, + bbox=bbox, + rotation="-40x", + radii=covalent_radii[atoms_surface.numbers], + ) + renderer.render() + print(f"image {idx} completed!") + + +def parallelize_generation(traj_path, out_path, n_procs): + + # make the covalent radii for O/C/N a little smaller to make bonds visible + covalent_radii[6] = covalent_radii[6] * 0.7 + covalent_radii[7] = covalent_radii[7] * 0.7 + covalent_radii[8] = covalent_radii[8] * 0.7 + + # name of the folder containing images and gif + file_name = os.path.basename(traj_path).split(".")[0] + out_path = os.path.join(out_path, file_name) + out_path = os.path.abspath(out_path) + os.makedirs(out_path, exist_ok=True) + + atoms_list = ase.io.read(traj_path, ":") + + # parallelizing image generation + mp_args_list = [ + (atoms, idx, out_path) for idx, atoms in enumerate(atoms_list) + ] + pool = mp.Pool(processes=n_procs) + pool.map(pov_from_atoms, mp_args_list) + + # creating gif + os.system( + f"ffmpeg -pattern_type glob -i '{out_path}/*.png' {out_path}/{file_name}.gif" + ) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--traj-path", required=True, help="Path to traj file") + parser.add_argument( + "--out-path", + required=True, + help="Directory to save generated images and gif", + ) + parser.add_argument( + "--num-workers", + type=int, + default=1, + help="Number of processes to be used", + ) + return parser + + +if __name__ == "__main__": + + parser = get_parser() + args = parser.parse_args() + + parallelize_generation(args.traj_path, args.out_path, args.num_workers) diff --git a/scripts/hpo/README.md b/scripts/hpo/README.md new file mode 100644 index 0000000..8524b74 --- /dev/null +++ b/scripts/hpo/README.md @@ -0,0 +1,54 @@ +# Running Hyperparameter Optimization with Ray Tune + +# Installation +`pip install ray ray[tune]` + +## Model config considerations + +The current Ray Tune implementation uses the standard OCP config. However, there are a number of config settings that require additional consideration. + +``` +logger: None +is_hpo: True + +optim: + … + eval_every: (int) number of steps + checkpoint_every: (int: optional) number of steps +``` +The first two are easily set. The logger is set to None because Ray Tune internally handles the logging. + +The `eval_every` setting is case specific and will likely require some experimentation. The `eval_every` flag sets how often the validation set is run in number of steps. Depending on the OCP model and dataset of interest, training for a single epoch can take a substantial amount of time. However, to take full advantage of HPO methods that minimize compute by terminating trials that are not promising, such as successive halving, communication of train and val metrics need to happen on shorter timescales. Paraphrasing the Ray Tune docs, `eval_every` should be set large enough to avoid overheads but short enough to report progress periodically — minutes timescale recommended. + +The `eval_every` setting is only available for the force trainer so when using the energy trainer validation will be run and reporting to Ray Tune will occur on a per epoch basis. + +The `checkpoint_every` setting determines how frequently, in steps, Ray Tune will write a checkpoint. Checkpointing can create a lot of overhead for certain HPO methods so do not do it too frequently. The default behavior is no checkpointing. + +## Usage with Slurm + +1. Make necessary changes to `run_tune.py` and `slurm/submit-ray-cluster.sbatch` + + Example `run_tune.py` updates + - choose search and scheduler algorithms and set associated parameters (see [Ray Tune docs](https://docs.ray.io/en/master/tune/index.html) for details) + - set the resources to use per individual trial + + Example `slurm/submit-ray-cluster.sbatch` updates + - load modules or set conda env + - change the total run time and resources to use + +2. submit using `sbatch slurm/submit-ray-cluster.sbatch` + +Slurm scripts taken from https://github.com/NERSC/slurm-ray-cluster + +For usage with other cluster managers or cloud resources please refer to the +[Distributed Ray Docs](https://docs.ray.io/en/master/cluster/index.html#) + +## Examples + +1. Asynchronous Successive Halving — `ocp/scripts/hpo/run_tune.py` +2. Population Based Training — `ocp/scripts/hpo/run_tune_pbt.py` + +## Testing/Debugging Ray Tune + +- In `run_tune.py` set `ray.init(local_mode=True)` +- run `python path_to/run_tune.py --mode train --config-yml path_to/config --run_dir path_to_run_dir` diff --git a/scripts/hpo/__init__.py b/scripts/hpo/__init__.py new file mode 100644 index 0000000..c17674b --- /dev/null +++ b/scripts/hpo/__init__.py @@ -0,0 +1,6 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" diff --git a/scripts/hpo/run_tune.py b/scripts/hpo/run_tune.py new file mode 100644 index 0000000..2d3854c --- /dev/null +++ b/scripts/hpo/run_tune.py @@ -0,0 +1,111 @@ +import os + +import ray +from ray import tune +from ray.tune import CLIReporter +from ray.tune.schedulers import ASHAScheduler + +from ocpmodels.common.flags import flags +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import build_config, setup_imports + + +# this function is general and should work for any ocp trainer +def ocp_trainable(config, checkpoint_dir=None): + setup_imports() + # trainer defaults are changed to run HPO + trainer = registry.get_trainer_class(config.get("trainer", "energy"))( + task=config["task"], + model=config["model"], + dataset=config["dataset"], + optimizer=config["optim"], + identifier=config["identifier"], + run_dir=config.get("run_dir", "./"), + is_debug=config.get("is_debug", False), + is_vis=config.get("is_vis", False), + is_hpo=config.get("is_hpo", True), # hpo + print_every=config.get("print_every", 10), + seed=config.get("seed", 0), + logger=config.get("logger", None), # hpo + local_rank=config["local_rank"], + amp=config.get("amp", False), + cpu=config.get("cpu", False), + ) + # add checkpoint here + if checkpoint_dir: + checkpoint = os.path.join(checkpoint_dir, "checkpoint") + trainer.load_pretrained(checkpoint) + # start training + trainer.train() + + +# this section defines the hyperparameters to tune and all the Ray Tune settings +# current params/settings are an example for ForceNet +def main(): + # parse config + parser = flags.get_parser() + args, override_args = parser.parse_known_args() + config = build_config(args, override_args) + # add parameters to tune using grid or random search + config["model"].update( + hidden_channels=tune.choice([256, 384, 512, 640, 704]), + decoder_hidden_channels=tune.choice([256, 384, 512, 640, 704]), + depth_mlp_edge=tune.choice([1, 2, 3, 4, 5]), + depth_mlp_node=tune.choice([1, 2, 3, 4, 5]), + num_interactions=tune.choice([3, 4, 5, 6]), + ) + # define scheduler + scheduler = ASHAScheduler( + time_attr="steps", + metric="val_loss", + mode="min", + max_t=100000, + grace_period=2000, + reduction_factor=4, + brackets=1, + ) + # ray init + # for debug + # ray.init(local_mode=True) + # for slurm cluster + ray.init( + address="auto", + _node_ip_address=os.environ["ip_head"].split(":")[0], + _redis_password=os.environ["redis_password"], + ) + # define command line reporter + reporter = CLIReporter( + print_intermediate_tables=True, + metric="val_loss", + mode="min", + metric_columns={ + "steps": "steps", + "epochs": "epochs", + "training_iteration": "training_iteration", + "val_loss": "val_loss", + "val_forces_mae": "val_forces_mae", + }, + ) + + # define run parameters + analysis = tune.run( + ocp_trainable, + resources_per_trial={"cpu": 8, "gpu": 1}, + config=config, + fail_fast=False, + local_dir=config.get("run_dir", "./"), + num_samples=500, + progress_reporter=reporter, + scheduler=scheduler, + ) + + print( + "Best config is:", + analysis.get_best_config( + metric="val_forces_mae", mode="min", scope="last" + ), + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/hpo/run_tune_pbt.py b/scripts/hpo/run_tune_pbt.py new file mode 100644 index 0000000..c70d72c --- /dev/null +++ b/scripts/hpo/run_tune_pbt.py @@ -0,0 +1,109 @@ +import logging +import os + +import ray +from ray import tune +from ray.tune import CLIReporter +from ray.tune.schedulers import PopulationBasedTraining + +from ocpmodels.common.flags import flags +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import build_config, setup_imports + + +# this function is general and should work for any ocp trainer +def ocp_trainable(config, checkpoint_dir=None): + setup_imports() + # update config for PBT learning rate + config["optim"].update(lr_initial=config["lr"]) + # trainer defaults are changed to run HPO + trainer = registry.get_trainer_class(config.get("trainer", "energy"))( + task=config["task"], + model=config["model"], + dataset=config["dataset"], + optimizer=config["optim"], + identifier=config["identifier"], + run_dir=config.get("run_dir", "./"), + is_debug=config.get("is_debug", False), + is_vis=config.get("is_vis", False), + is_hpo=config.get("is_hpo", True), # hpo + print_every=config.get("print_every", 10), + seed=config.get("seed", 0), + logger=config.get("logger", None), # hpo + local_rank=config["local_rank"], + amp=config.get("amp", False), + cpu=config.get("cpu", False), + ) + # add checkpoint here + if checkpoint_dir: + checkpoint = os.path.join(checkpoint_dir, "checkpoint") + trainer.load_pretrained(checkpoint) + # set learning rate + for g in trainer.optimizer.param_groups: + g["lr"] = config["lr"] + # start training + trainer.train() + + +# this section defines all the Ray Tune run parameters +def main(): + # parse config + parser = flags.get_parser() + args, override_args = parser.parse_known_args() + config = build_config(args, override_args) + # add parameters to tune using grid or random search + config["lr"] = tune.loguniform(0.0001, 0.01) + # define scheduler + scheduler = PopulationBasedTraining( + time_attr="training_iteration", + metric="val_loss", + mode="min", + perturbation_interval=1, + hyperparam_mutations={ + "lr": tune.loguniform(0.000001, 0.01), + }, + ) + # ray init + ray.init( + address="auto", + _node_ip_address=os.environ["ip_head"].split(":")[0], + _redis_password=os.environ["redis_password"], + ) + # define command line reporter + reporter = CLIReporter( + print_intermediate_tables=True, + metric="val_loss", + mode="min", + metric_columns={ + "act_lr": "act_lr", + "steps": "steps", + "epochs": "epochs", + "training_iteration": "training_iteration", + "val_loss": "val_loss", + "val_forces_mae": "val_forces_mae", + }, + ) + # define run parameters + analysis = tune.run( + ocp_trainable, + resources_per_trial={"cpu": 8, "gpu": 1}, + config=config, + stop={"epochs": 12}, + # time_budget_s=28200, + fail_fast=False, + local_dir=config.get("run_dir", "./"), + num_samples=8, + progress_reporter=reporter, + scheduler=scheduler, + ) + + print( + "Best config is:", + analysis.get_best_config( + metric="val_forces_mae", mode="min", scope="last" + ), + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/hpo/slurm/start-head.sh b/scripts/hpo/slurm/start-head.sh new file mode 100644 index 0000000..012cd20 --- /dev/null +++ b/scripts/hpo/slurm/start-head.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +export LC_ALL=C.UTF-8 +export LANG=C.UTF-8 + +echo "starting ray head node" +# Launch the head node +ray start --head --node-ip-address=$1 --port=6379 --redis-password=$2 +sleep infinity diff --git a/scripts/hpo/slurm/start-worker.sh b/scripts/hpo/slurm/start-worker.sh new file mode 100644 index 0000000..d225d44 --- /dev/null +++ b/scripts/hpo/slurm/start-worker.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +export LC_ALL=C.UTF-8 +export LANG=C.UTF-8 + +echo "starting ray worker node" +ray start --address $1 --redis-password=$2 +sleep infinity diff --git a/scripts/hpo/slurm/submit-ray-cluster.sbatch b/scripts/hpo/slurm/submit-ray-cluster.sbatch new file mode 100644 index 0000000..601233b --- /dev/null +++ b/scripts/hpo/slurm/submit-ray-cluster.sbatch @@ -0,0 +1,50 @@ +#!/bin/bash + +#SBATCH -C gpu +#SBATCH --time=00:10:00 + +### This script works for any number of nodes, Ray will find and manage all resources +#SBATCH --nodes=1 + +### Give all resources to a single Ray task, ray can manage the resources internally +#SBATCH --ntasks-per-node=1 +#SBATCH --gpus-per-task=8 +#SBATCH --cpus-per-task=80 + + +# Load modules or your own conda environment here +# e.g. conda activate ocp-models + +################# DON NOT CHANGE THINGS HERE UNLESS YOU KNOW WHAT YOU ARE DOING ############### +# This script is a modification to the implementation suggest by gregSchwartz18 here: +# https://github.com/ray-project/ray/issues/826#issuecomment-522116599 +redis_password=$(uuidgen) +export redis_password + +nodes=$(scontrol show hostnames $SLURM_JOB_NODELIST) # Getting the node names +nodes_array=( $nodes ) + +node_1=${nodes_array[0]} +ip=$(srun --nodes=1 --ntasks=1 -w $node_1 hostname --ip-address) # making redis-address +port=6379 +ip_head=$ip:$port +export ip_head +echo "IP Head: $ip_head" + +echo "STARTING HEAD at $node_1" +srun --nodes=1 --ntasks=1 -w $node_1 start-head.sh $ip $redis_password & +sleep 45 + +worker_num=$(($SLURM_JOB_NUM_NODES - 1)) #number of nodes other than the head node +for (( i=1; i<=$worker_num; i++ )) +do + node_i=${nodes_array[$i]} + echo "STARTING WORKER $i at $node_i" + srun --nodes=1 --ntasks=1 -w $node_i start-worker.sh $ip_head $redis_password & + sleep 5 +done +############################################################################################## + +#### call your code below +# e.g. python path_to/run_tune.py --mode train --config-yml path_to/configs/s2ef/200k/forcenet/fn_forceonly.yml --run_dir path_to_run_dir +exit diff --git a/scripts/make_challenge_submission_file.py b/scripts/make_challenge_submission_file.py new file mode 100644 index 0000000..dfed5c0 --- /dev/null +++ b/scripts/make_challenge_submission_file.py @@ -0,0 +1,118 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. + + +ONLY for use in the NeurIPS 2021 Open Catalyst Challenge. For all other submissions +please use make_submission_file.py. +""" + +import argparse +import glob +import os + +import numpy as np + + +def write_is2re_relaxations(path, filename, hybrid): + import ase.io + from tqdm import tqdm + + submission_file = {} + + if not hybrid: + ids = [] + energies = [] + systems = glob.glob(os.path.join(path, "*.traj")) + for system in tqdm(systems): + sid, _ = os.path.splitext(os.path.basename(system)) + ids.append(str(sid)) + traj = ase.io.read(system, "-1") + energies.append(traj.get_potential_energy()) + + submission_file["challenge_ids"] = np.array(ids) + submission_file["challenge_energy"] = np.array(energies) + + else: + preds = np.load(path) + ids = [] + energies = [] + for sid, energy in zip(preds["ids"], preds["energy"]): + sid = sid.split("_")[0] + ids.append(sid) + energies.append(energy) + + submission_file["challenge_ids"] = np.array(ids) + submission_file["challenge_energy"] = np.array(energies) + + np.savez_compressed(filename, **submission_file) + + +def write_predictions(path, filename): + submission_file = {} + + res = np.load(path, allow_pickle=True) + contents = res.files + for i in contents: + key = "_".join(["challenge", i]) + submission_file[key] = res[i] + + np.savez_compressed(filename, **submission_file) + + +def main(args): + path = args.path + + if not args.out_path.endswith(".npz"): + args.out_path = args.out_path + ".npz" + + if not args.is2re_relaxations: + write_predictions(path, filename=args.out_path) + else: + write_is2re_relaxations( + path, filename=args.out_path, hybrid=args.hybrid + ) + print(f"Results saved to {args.out_path} successfully.") + + +if __name__ == "__main__": + """ + Create a submission file for the NeurIPS 2021 Open Catalyst Challenge. + + Results file can be obtained as follows for the various tasks: + + S2EF: config["mode"] = "predict" + IS2RE: config["mode"] = "predict" + IS2RS: config["mode"] = "run-relaxations" and config["task"]["write_pos"] = True + + Use this script to write your results files in the format evalAI expects + submissions. + + If writing IS2RE predictions from relaxations, the path specified must be a + directory containg trajectory (.traj) files. Additionally, --is2re-relaxations must be + provided as a command line argument. + + If writing IS2RE predictions from hybrid relaxations (force only model + + energy only model), paths must be the .npz S2EF prediction files. + Additionally, --is2re-relaxations and --hybrid must be provided as a + command line argument. + """ + + parser = argparse.ArgumentParser() + parser.add_argument("--path", help="Path to results") + parser.add_argument("--out-path", help="Path to write predictions to.") + parser.add_argument( + "--is2re-relaxations", + action="store_true", + help="Write IS2RE results from trajectories. Path specified must be a directory containing .traj files.", + ) + parser.add_argument( + "--hybrid", + action="store_true", + help="Write IS2RE results from S2EF prediction files. Path specified must be a S2EF NPZ file.", + ) + + args = parser.parse_args() + main(args) diff --git a/scripts/make_lmdb_sizes.py b/scripts/make_lmdb_sizes.py new file mode 100644 index 0000000..884b43b --- /dev/null +++ b/scripts/make_lmdb_sizes.py @@ -0,0 +1,77 @@ +""" +This script provides the functionality to generate metadata.npz files necessary +for load_balancing the DataLoader. + +""" +import argparse +import multiprocessing as mp +import os +import warnings + +import numpy as np +from tqdm import tqdm + +from ocpmodels.datasets import SinglePointLmdbDataset, TrajectoryLmdbDataset + + +def get_data(index): + data = dataset[index] + natoms = data.natoms + neighbors = None + if hasattr(data, "edge_index"): + neighbors = data.edge_index.shape[1] + + return index, natoms, neighbors + + +def main(args): + path = args.data_path + global dataset + if os.path.isdir(path): + dataset = TrajectoryLmdbDataset({"src": path}) + outpath = os.path.join(path, "metadata.npz") + elif os.path.isfile(path): + dataset = SinglePointLmdbDataset({"src": path}) + outpath = os.path.join(os.path.dirname(path), "metadata.npz") + + indices = range(len(dataset)) + + pool = mp.Pool(args.num_workers) + outputs = list(tqdm(pool.imap(get_data, indices), total=len(indices))) + + indices = [] + natoms = [] + neighbors = [] + for i in outputs: + indices.append(i[0]) + natoms.append(i[1]) + neighbors.append(i[2]) + + _sort = np.argsort(indices) + sorted_natoms = np.array(natoms, dtype=np.int32)[_sort] + if None in neighbors: + warnings.warn( + f"edge_index information not found, {outpath} only supports atom-wise load balancing." + ) + np.savez(outpath, natoms=sorted_natoms) + else: + sorted_neighbors = np.array(neighbors, dtype=np.int32)[_sort] + np.savez(outpath, natoms=sorted_natoms, neighbors=sorted_neighbors) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--data-path", + required=True, + type=str, + help="Path to S2EF directory or IS2R* .lmdb file", + ) + parser.add_argument( + "--num-workers", + default=1, + type=int, + help="Num of workers to parallelize across", + ) + args = parser.parse_args() + main(args) diff --git a/scripts/make_submission_file.py b/scripts/make_submission_file.py new file mode 100644 index 0000000..f46c579 --- /dev/null +++ b/scripts/make_submission_file.py @@ -0,0 +1,148 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import argparse +import glob +import os + +import numpy as np + +SPLITS = { + "OC20": ["id", "ood_ads", "ood_cat", "ood_both"], + "OC22": ["id", "ood"], +} + + +def write_is2re_relaxations(args): + import ase.io + from tqdm import tqdm + + submission_file = {} + + if not args.hybrid: + for split in SPLITS[args.dataset]: + ids = [] + energies = [] + systems = glob.glob(os.path.join(vars(args)[split], "*.traj")) + for system in tqdm(systems): + sid, _ = os.path.splitext(os.path.basename(system)) + ids.append(str(sid)) + # Read the last frame in the ML trajectory. Modify "-1" if you wish to modify which frame to use. + traj = ase.io.read(system, "-1") + energies.append(traj.get_potential_energy()) + + submission_file[f"{split}_ids"] = np.array(ids) + submission_file[f"{split}_energy"] = np.array(energies) + + else: + for split in SPLITS[args.dataset]: + preds = np.load(vars(args)[split]) + ids = [] + energies = [] + for sid, energy in zip(preds["ids"], preds["energy"]): + sid = sid.split("_")[0] + ids.append(sid) + energies.append(energy) + + submission_file[f"{split}_ids"] = np.array(ids) + submission_file[f"{split}_energy"] = np.array(energies) + + np.savez_compressed(args.out_path, **submission_file) + + +def write_predictions(args): + if args.is2re_relaxations: + write_is2re_relaxations(args) + else: + submission_file = {} + + for split in SPLITS[args.dataset]: + res = np.load(vars(args)[split], allow_pickle=True) + contents = res.files + for i in contents: + key = "_".join([split, i]) + submission_file[key] = res[i] + + np.savez_compressed(args.out_path, **submission_file) + + +def main(args): + for split in SPLITS[args.dataset]: + assert vars(args).get( + split + ), f"Missing {split} split for {args.dataset}" + + if not args.out_path.endswith(".npz"): + args.out_path = args.out_path + ".npz" + + write_predictions(args) + print(f"Results saved to {args.out_path} successfully.") + + +if __name__ == "__main__": + """ + Create a submission file for evalAI. Ensure that for the task you are + submitting for you have generated results files on each of the splits: + OC20: id, ood_ads, ood_cat, ood_both + OC22: id, ood + + Results file can be obtained as follows for the various tasks: + + S2EF: config["mode"] = "predict" + IS2RE: config["mode"] = "predict" + IS2RS: config["mode"] = "run-relaxations" and config["task"]["write_pos"] = True + + Use this script to join the results files (4 for OC20, 2 for OC22) in the format evalAI expects + submissions. + + If writing IS2RE predictions from relaxations, paths must be directories + containg trajectory files. Additionally, --is2re-relaxations must be + provided as a command line argument. + + If writing IS2RE predictions from hybrid relaxations (force only model + + energy only model), paths must be the .npz S2EF prediction files. + Additionally, --is2re-relaxations and --hybrid must be provided as a + command line argument. + """ + + parser = argparse.ArgumentParser() + parser.add_argument( + "--id", help="Path to ID results. Required for OC20 and OC22." + ) + parser.add_argument( + "--ood-ads", help="Path to OOD-Ads results. Required only for OC20." + ) + parser.add_argument( + "--ood-cat", help="Path to OOD-Cat results. Required only for OC20." + ) + parser.add_argument( + "--ood-both", help="Path to OOD-Both results. Required only for OC20." + ) + parser.add_argument( + "--ood", help="Path to OOD OC22 results. Required only for OC22." + ) + parser.add_argument("--out-path", help="Path to write predictions to.") + parser.add_argument( + "--is2re-relaxations", + action="store_true", + help="Write IS2RE results from trajectories. Paths specified correspond to directories containing .traj files.", + ) + parser.add_argument( + "--hybrid", + action="store_true", + help="Write IS2RE results from S2EF prediction files. Paths specified correspond to S2EF NPZ files.", + ) + parser.add_argument( + "--dataset", + type=str, + default="OC20", + choices=["OC20", "OC22"], + help="Which dataset to write a prediction file for, OC20 or OC22.", + ) + + args = parser.parse_args() + main(args) diff --git a/scripts/preprocess_ef.py b/scripts/preprocess_ef.py new file mode 100644 index 0000000..91e4062 --- /dev/null +++ b/scripts/preprocess_ef.py @@ -0,0 +1,171 @@ +""" +Creates LMDB files with extracted graph features from provided *.extxyz files +for the S2EF task. +""" + +import argparse +import glob +import multiprocessing as mp +import os +import pickle +import random +import sys + +import ase.io +import lmdb +import numpy as np +import torch +from tqdm import tqdm + +from ocpmodels.preprocessing import AtomsToGraphs + + +def write_images_to_lmdb(mp_arg): + a2g, db_path, samples, sampled_ids, idx, pid, args = mp_arg + db = lmdb.open( + db_path, + map_size=1099511627776 * 2, + subdir=False, + meminit=False, + map_async=True, + ) + + pbar = tqdm( + total=5000 * len(samples), + position=pid, + desc="Preprocessing data into LMDBs", + ) + for sample in samples: + traj_logs = open(sample, "r").read().splitlines() + xyz_idx = os.path.splitext(os.path.basename(sample))[0] + traj_path = os.path.join(args.data_path, f"{xyz_idx}.extxyz") + traj_frames = ase.io.read(traj_path, ":") + + for i, frame in enumerate(traj_frames): + frame_log = traj_logs[i].split(",") + sid = int(frame_log[0].split("random")[1]) + fid = int(frame_log[1].split("frame")[1]) + data_object = a2g.convert(frame) + # add atom tags + data_object.tags = torch.LongTensor(frame.get_tags()) + data_object.sid = sid + data_object.fid = fid + # subtract off reference energy + if args.ref_energy and not args.test_data: + ref_energy = float(frame_log[2]) + data_object.y -= ref_energy + + txn = db.begin(write=True) + txn.put( + f"{idx}".encode("ascii"), + pickle.dumps(data_object, protocol=-1), + ) + txn.commit() + idx += 1 + sampled_ids.append(",".join(frame_log[:2]) + "\n") + pbar.update(1) + + # Save count of objects in lmdb. + txn = db.begin(write=True) + txn.put("length".encode("ascii"), pickle.dumps(idx, protocol=-1)) + txn.commit() + + db.sync() + db.close() + + return sampled_ids, idx + + +def main(args): + xyz_logs = glob.glob(os.path.join(args.data_path, "*.txt")) + if not xyz_logs: + raise RuntimeError("No *.txt files found. Did you uncompress?") + if args.num_workers > len(xyz_logs): + args.num_workers = len(xyz_logs) + + # Initialize feature extractor. + a2g = AtomsToGraphs( + max_neigh=50, + radius=6, + r_energy=not args.test_data, + r_forces=not args.test_data, + r_fixed=True, + r_distances=False, + r_edges=args.get_edges, + ) + + # Create output directory if it doesn't exist. + os.makedirs(os.path.join(args.out_path), exist_ok=True) + + # Initialize lmdb paths + db_paths = [ + os.path.join(args.out_path, "data.%04d.lmdb" % i) + for i in range(args.num_workers) + ] + + # Chunk the trajectories into args.num_workers splits + chunked_txt_files = np.array_split(xyz_logs, args.num_workers) + + # Extract features + sampled_ids, idx = [[]] * args.num_workers, [0] * args.num_workers + + pool = mp.Pool(args.num_workers) + mp_args = [ + ( + a2g, + db_paths[i], + chunked_txt_files[i], + sampled_ids[i], + idx[i], + i, + args, + ) + for i in range(args.num_workers) + ] + op = list(zip(*pool.imap(write_images_to_lmdb, mp_args))) + sampled_ids, idx = list(op[0]), list(op[1]) + + # Log sampled image, trajectory trace + for j, i in enumerate(range(args.num_workers)): + ids_log = open( + os.path.join(args.out_path, "data_log.%04d.txt" % i), "w" + ) + ids_log.writelines(sampled_ids[j]) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--data-path", + help="Path to dir containing *.extxyz and *.txt files", + ) + parser.add_argument( + "--out-path", + help="Directory to save extracted features. Will create if doesn't exist", + ) + parser.add_argument( + "--get-edges", + action="store_true", + help="Store edge indices in LMDB, ~10x storage requirement. Default: compute edge indices on-the-fly.", + ) + parser.add_argument( + "--num-workers", + type=int, + default=1, + help="No. of feature-extracting processes or no. of dataset chunks", + ) + parser.add_argument( + "--ref-energy", action="store_true", help="Subtract reference energies" + ) + parser.add_argument( + "--test-data", + action="store_true", + help="Is data being processed test data?", + ) + return parser + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + main(args) diff --git a/scripts/preprocess_relaxed.py b/scripts/preprocess_relaxed.py new file mode 100644 index 0000000..26b88cb --- /dev/null +++ b/scripts/preprocess_relaxed.py @@ -0,0 +1,145 @@ +""" +Creates LMDB files with extracted graph features from provided *.extxyz files +for the S2EF task. +""" + +import argparse +import glob +import multiprocessing as mp +import os +import pickle +import random +import sys + +import ase.io +import lmdb +import numpy as np +import torch +from tqdm import tqdm + +from ocpmodels.preprocessing import AtomsToGraphs + + +def write_images_to_lmdb(mp_arg): + a2g, db_path, samples, pid = mp_arg + db = lmdb.open( + db_path, + map_size=1099511627776 * 2, + subdir=False, + meminit=False, + map_async=True, + ) + + pbar = tqdm( + total=len(samples), + position=pid, + desc="Preprocessing data into LMDBs", + ) + idx = 0 + for sample in samples: + ml_relaxed = ase.io.read(sample, "-1") + data_object = a2g.convert(ml_relaxed) + + sid, _ = os.path.splitext(os.path.basename(sample)) + fid = -1 + # add atom tags + data_object.tags = torch.LongTensor(ml_relaxed.get_tags()) + data_object.sid = int(sid) + data_object.fid = fid + + txn = db.begin(write=True) + txn.put( + f"{idx}".encode("ascii"), + pickle.dumps(data_object, protocol=-1), + ) + txn.commit() + idx += 1 + pbar.update(1) + + # Save count of objects in lmdb. + txn = db.begin(write=True) + txn.put("length".encode("ascii"), pickle.dumps(idx, protocol=-1)) + txn.commit() + + db.sync() + db.close() + + +def main(args, split): + systems = glob.glob(f"{eval(f'args.{split}')}/*.traj") + + systems_chunked = np.array_split(systems, args.num_workers) + + # Initialize feature extractor. + a2g = AtomsToGraphs( + max_neigh=50, + radius=6, + r_energy=False, + r_forces=False, + r_distances=False, + r_fixed=True, + r_edges=True, + ) + + # Create output directory if it doesn't exist. + out_path = f"{args.out_path}_{split}" + os.makedirs(out_path, exist_ok=True) + + # Initialize lmdb paths + db_paths = [ + os.path.join(out_path, "data.%04d.lmdb" % i) + for i in range(args.num_workers) + ] + + pool = mp.Pool(args.num_workers) + mp_args = [ + ( + a2g, + db_paths[i], + systems_chunked[i], + i, + ) + for i in range(args.num_workers) + ] + list(pool.imap(write_images_to_lmdb, mp_args)) + pool.close() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--id", + required=True, + help="Path to ID trajectories", + ) + parser.add_argument( + "--ood-ads", + required=True, + help="Path to OOD-Ads trajectories", + ) + parser.add_argument( + "--ood-cat", + required=True, + help="Path to OOD-Cat trajectories", + ) + parser.add_argument( + "--ood-both", + required=True, + help="Path to OOD-Both trajectories", + ) + parser.add_argument( + "--out-path", + required=True, + help="Directory to save extracted features. Will create if doesn't exist", + ) + parser.add_argument( + "--num-workers", + type=int, + default=1, + help="No. of feature-extracting processes.", + ) + + args = parser.parse_args() + + for split in ["id", "ood_ads", "ood_cat", "ood_both"]: + main(args, split) diff --git a/scripts/uncompress.py b/scripts/uncompress.py new file mode 100644 index 0000000..bda727b --- /dev/null +++ b/scripts/uncompress.py @@ -0,0 +1,65 @@ +""" +Uncompresses downloaded S2EF datasets to be used by the LMDB preprocessing +script - preprocess_ef.py +""" + +import argparse +import glob +import lzma +import multiprocessing as mp +import os + +from tqdm import tqdm + + +def read_lzma(inpfile, outfile): + with open(inpfile, "rb") as f: + contents = lzma.decompress(f.read()) + with open(outfile, "wb") as op: + op.write(contents) + + +def decompress_list_of_files(ip_op_pair): + ip_file, op_file = ip_op_pair + read_lzma(ip_file, op_file) + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--ipdir", type=str, help="Path to compressed dataset directory" + ) + parser.add_argument( + "--opdir", type=str, help="Directory path to uncompress files to" + ) + parser.add_argument( + "--num-workers", type=int, help="# of processes to parallelize across" + ) + return parser + + +def main(args): + os.makedirs(args.opdir, exist_ok=True) + + filelist = glob.glob(os.path.join(args.ipdir, "*txt.xz")) + glob.glob( + os.path.join(args.ipdir, "*extxyz.xz") + ) + ip_op_pairs = [] + for i in filelist: + fname_base = os.path.basename(i) + ip_op_pairs.append((i, os.path.join(args.opdir, fname_base[:-3]))) + + pool = mp.Pool(args.num_workers) + list( + tqdm( + pool.imap(decompress_list_of_files, ip_op_pairs), + total=len(ip_op_pairs), + desc=f"Uncompressing {args.ipdir}", + ) + ) + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + main(args) diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..bf2daae --- /dev/null +++ b/setup.py @@ -0,0 +1,17 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from setuptools import find_packages, setup + +setup( + name="ocp-models", + version="0.0.3", + description="Machine learning models for use in catalysis as part of the Open Catalyst Project", + url="https://github.com/Open-Catalyst-Project/ocp", + packages=find_packages(), + include_package_data=True, +) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..c17674b --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,6 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" diff --git a/tests/common/test_data_parallel_batch_sampler.py b/tests/common/test_data_parallel_batch_sampler.py new file mode 100644 index 0000000..795ac9a --- /dev/null +++ b/tests/common/test_data_parallel_batch_sampler.py @@ -0,0 +1,236 @@ +import tempfile +from contextlib import contextmanager +from pathlib import Path + +import numpy as np +import pytest +from torch.utils.data import Dataset + +from ocpmodels.common.data_parallel import BalancedBatchSampler + +DATA = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +SIZE_ATOMS = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +SIZE_NEIGHBORS = [4, 4, 4, 4, 4, 4, 4, 4, 4, 4] + + +@contextmanager +def _temp_file(name: str): + with tempfile.TemporaryDirectory() as tmpdir: + yield Path(tmpdir) / name + + +@pytest.fixture +def valid_path_dataset(): + class _Dataset(Dataset): + def __init__(self, data, fpath: Path): + self.data = data + self.metadata_path = fpath + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + with _temp_file("metadata.npz") as file: + np.savez( + natoms=np.array(SIZE_ATOMS), + neighbors=np.array(SIZE_NEIGHBORS), + file=file, + ) + yield _Dataset(DATA, file) + + +@pytest.fixture +def invalid_path_dataset(): + class _Dataset(Dataset): + def __init__(self, data): + self.data = data + self.metadata_path = Path("/tmp/does/not/exist.np") + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + return _Dataset(DATA) + + +@pytest.fixture +def invalid_dataset(): + class _Dataset(Dataset): + def __init__(self, data): + self.data = data + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + return self.data[idx] + + return _Dataset(DATA) + + +def test_lowercase(invalid_dataset): + sampler = BalancedBatchSampler( + dataset=invalid_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="ATOMS", + throw_on_error=False, + ) + assert sampler.mode == "atoms" + + sampler = BalancedBatchSampler( + dataset=invalid_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="NEIGHBORS", + throw_on_error=False, + ) + assert sampler.mode == "neighbors" + + +def test_invalid_mode(invalid_dataset): + with pytest.raises( + ValueError, match="Must be one of 'atoms', 'neighbors', or a boolean." + ): + BalancedBatchSampler( + dataset=invalid_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="natoms", + throw_on_error=True, + ) + + with pytest.raises( + ValueError, match="Must be one of 'atoms', 'neighbors', or a boolean." + ): + BalancedBatchSampler( + dataset=invalid_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="nneighbors", + throw_on_error=True, + ) + + +def test_invalid_dataset(invalid_dataset): + with pytest.raises( + RuntimeError, + match="does not have a metadata_path attribute. BalancedBatchSampler has to load the data to determine batch sizes, which incurs significant overhead!", + ): + BalancedBatchSampler( + dataset=invalid_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="atoms", + throw_on_error=True, + force_balancing=True, + ) + with pytest.raises( + RuntimeError, + match="does not have a metadata_path attribute. Batches will not be balanced, which can incur significant overhead!", + ): + BalancedBatchSampler( + dataset=invalid_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="atoms", + throw_on_error=True, + force_balancing=False, + ) + + +def test_invalid_path_dataset(invalid_path_dataset): + with pytest.raises( + RuntimeError, + match="Metadata file .+ does not exist. BalancedBatchSampler has to load the data to determine batch sizes, which incurs significant overhead!", + ): + BalancedBatchSampler( + dataset=invalid_path_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="atoms", + throw_on_error=True, + force_balancing=True, + ) + with pytest.raises( + RuntimeError, + match="Metadata file .+ does not exist. Batches will not be balanced, which can incur significant overhead!", + ): + BalancedBatchSampler( + dataset=invalid_path_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="atoms", + throw_on_error=True, + force_balancing=False, + ) + + +def test_valid_dataset(valid_path_dataset): + sampler = BalancedBatchSampler( + dataset=valid_path_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="atoms", + throw_on_error=True, + ) + assert (sampler.sizes == np.array(SIZE_ATOMS)).all() + + sampler = BalancedBatchSampler( + dataset=valid_path_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode="neighbors", + throw_on_error=True, + ) + assert (sampler.sizes == np.array(SIZE_NEIGHBORS)).all() + + +def test_disabled(valid_path_dataset): + sampler = BalancedBatchSampler( + dataset=valid_path_dataset, + batch_size=1, + rank=0, + num_replicas=2, + device=None, + mode=False, + throw_on_error=True, + ) + assert sampler.balance_batches is False + + +def test_single_node(valid_path_dataset): + sampler = BalancedBatchSampler( + dataset=valid_path_dataset, + batch_size=1, + rank=0, + num_replicas=1, + device=None, + mode="atoms", + throw_on_error=True, + ) + assert sampler.balance_batches is False diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..bd08075 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,150 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +from typing import TYPE_CHECKING, Optional, Union + +import numpy as np +import pytest +from syrupy.extensions.amber import AmberSnapshotExtension + +if TYPE_CHECKING: + from syrupy.types import SerializableData, SerializedData, SnapshotIndex + +DEFAULT_RTOL = 1.0e-03 +DEFAULT_ATOL = 1.0e-03 + + +class Approx: + """ + Wrapper object for approximately compared numpy arrays. + """ + + def __init__( + self, + data: Union[np.ndarray, list], + *, + rtol: Optional[float] = None, + atol: Optional[float] = None, + ): + if isinstance(data, list): + self.data = np.array(data) + elif isinstance(data, np.ndarray): + self.data = data + else: + raise TypeError(f"Cannot convert {type(data)} to np.array") + + self.rtol = rtol if rtol is not None else DEFAULT_RTOL + self.atol = atol if atol is not None else DEFAULT_ATOL + self.tol_repr = True + + def __repr__(self): + data = np.array_repr(self.data) + data = "\n".join(f"\t{line}" for line in data.splitlines()) + tol_repr = "" + if self.tol_repr: + tol_repr = f", \n\trtol={self.rtol}, \n\tatol={self.atol}" + return f"Approx(\n{data}{tol_repr}\n)" + + +class _ApproxNumpyFormatter: + def __init__(self, data): + self.data = data + + def __repr__(self): + return Approx( + self.data.expected, + rtol=self.data.rel, + atol=self.data.abs, + ).__repr__() + + +def _try_parse_approx(data: "SerializableData"): + """ + Parse the string representation of an Approx object. + We can just use eval here, since we know the string is safe. + """ + if not isinstance(data, str): + return None + + data = data.strip() + if not data.startswith("Approx("): + return None + + approx = eval( + data.replace("dtype=", "dtype=np."), + {"Approx": Approx, "np": np}, + {"array": np.array}, + ) + if not isinstance(approx, Approx): + return None + + return approx + + +class ApproxExtension(AmberSnapshotExtension): + """ + By default, syrupy uses the __repr__ of the expected (snapshot) and actual values + to serialize them into strings. Then, it compares the strings to see if they match. + + However, this behavior is not ideal for comparing floats/ndarrays. For example, + if we have a snapshot with a float value of 0.1, and the actual value is 0.10000000000000001, + then the strings will not match, even though the values are effectively equal. + + To work around this, we override the serialize method to seralize the expected value + into a special representation. Then, we override the matches function (which originally does a + simple string comparison) to parse the expected and actual values into numpy arrays. + Finally, we compare the arrays using np.allclose. + """ + + def matches( + self, + *, + serialized_data: "SerializableData", + snapshot_data: "SerializableData", + ) -> bool: + # if both serialized_data and snapshot_data are serialized Approx objects, + # then we can load them as numpy arrays and compare them using np.allclose + serialized_approx = _try_parse_approx(serialized_data) + snapshot_approx = _try_parse_approx(snapshot_data) + if serialized_approx is not None and snapshot_approx is not None: + return np.allclose( + snapshot_approx.data, + serialized_approx.data, + rtol=serialized_approx.rtol, + atol=serialized_approx.atol, + ) + + return super().matches( + serialized_data=serialized_data, snapshot_data=snapshot_data + ) + + def serialize(self, data, **kwargs): + # we override the existing serialization behavior + # of the `pytest.approx()` object to serialize it into a special string. + if isinstance(data, type(pytest.approx(np.array(0.0)))): + return super().serialize(_ApproxNumpyFormatter(data), **kwargs) + elif isinstance(data, type(pytest.approx(0.0))): + raise NotImplementedError("Scalar approx not implemented yet") + return super().serialize(data, **kwargs) + + def write_snapshot( + self, *, data: "SerializedData", index: "SnapshotIndex" + ) -> None: + # Right before writing to file, we update the serialized snapshot data + # and remove the atol/rtol from the string representation. + # This is an implementation detail, and is not necessary for the extension to work. + # It just makes the snapshot files a bit cleaner. + approx = _try_parse_approx(data) + if approx is not None: + approx.tol_repr = False + data = self.serialize(approx) + return super().write_snapshot(data=data, index=index) + + +@pytest.fixture +def snapshot(snapshot): + return snapshot.use_extension(ApproxExtension) diff --git a/tests/evaluator/test_evaluator.py b/tests/evaluator/test_evaluator.py new file mode 100644 index 0000000..8f3a995 --- /dev/null +++ b/tests/evaluator/test_evaluator.py @@ -0,0 +1,107 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import numpy as np +import pytest +import torch + +from ocpmodels.modules.evaluator import ( + Evaluator, + cosine_similarity, + magnitude_error, +) + + +@pytest.fixture(scope="class") +def load_evaluator_s2ef(request): + request.cls.evaluator = Evaluator(task="s2ef") + prediction = { + "energy": torch.randn(6), + "forces": torch.randn(1000000, 3), + "natoms": torch.tensor( + (100000, 200000, 300000, 200000, 100000, 100000) + ), + } + target = { + "energy": torch.randn(6), + "forces": torch.randn(1000000, 3), + "natoms": torch.tensor( + (100000, 200000, 300000, 200000, 100000, 100000) + ), + } + request.cls.metrics = request.cls.evaluator.eval(prediction, target) + + +@pytest.fixture(scope="class") +def load_evaluator_is2rs(request): + request.cls.evaluator = Evaluator(task="is2rs") + prediction = { + "positions": torch.randn(50, 3), + "natoms": torch.tensor((5, 5, 10, 12, 18)), + "cell": torch.randn(5, 3, 3), + "pbc": torch.tensor([True, True, True]), + } + target = { + "positions": torch.randn(50, 3), + "cell": torch.randn(5, 3, 3), + "natoms": torch.tensor((5, 5, 10, 12, 18)), + "pbc": torch.tensor([True, True, True]), + } + request.cls.metrics = request.cls.evaluator.eval(prediction, target) + + +@pytest.fixture(scope="class") +def load_evaluator_is2re(request): + request.cls.evaluator = Evaluator(task="is2re") + prediction = { + "energy": torch.randn(50), + } + target = { + "energy": torch.randn(50), + } + request.cls.metrics = request.cls.evaluator.eval(prediction, target) + + +class TestMetrics: + def test_cosine_similarity(self): + v1, v2 = torch.randn(1000000, 3), torch.randn(1000000, 3) + res = cosine_similarity(v1, v2) + np.testing.assert_almost_equal(res["metric"], 0, decimal=2) + np.testing.assert_almost_equal( + res["total"] / res["numel"], res["metric"] + ) + + def test_magnitude_error(self): + v1, v2 = ( + torch.tensor([[0.0, 1], [-1, 0]]), + torch.tensor([[0.0, 0], [0, 0]]), + ) + res = magnitude_error(v1, v2) + np.testing.assert_equal(res["metric"], 1.0) + + +@pytest.mark.usefixtures("load_evaluator_s2ef") +class TestS2EFEval: + def test_metrics_exist(self): + assert "energy_mae" in self.metrics + assert "forces_mae" in self.metrics + assert "forces_cos" in self.metrics + assert "energy_force_within_threshold" in self.metrics + + +@pytest.mark.usefixtures("load_evaluator_is2rs") +class TestIS2RSEval: + def test_metrics_exist(self): + assert "average_distance_within_threshold" in self.metrics + + +@pytest.mark.usefixtures("load_evaluator_is2re") +class TestIS2REEval: + def test_metrics_exist(self): + assert "energy_mae" in self.metrics + assert "energy_mse" in self.metrics + assert "energy_within_threshold" in self.metrics diff --git a/tests/models/__snapshots__/test_cgcnn.ambr b/tests/models/__snapshots__/test_cgcnn.ambr new file mode 100644 index 0000000..15e5adc --- /dev/null +++ b/tests/models/__snapshots__/test_cgcnn.ambr @@ -0,0 +1,55 @@ +# name: TestCGCNN.test_energy_force_shape + Size( + 1, + 1, + ) +# --- +# name: TestCGCNN.test_energy_force_shape.1 + Approx( + array([[-0.6980915]], dtype=float32) + ) +# --- +# name: TestCGCNN.test_energy_force_shape.2 + Size( + 34, + 3, + ) +# --- +# name: TestCGCNN.test_energy_force_shape.3 + Approx( + array([[ 1.29042644e-04, -1.03389888e-04, -2.33838844e-04], + [ 1.52390960e-04, -3.90381210e-05, -2.31300713e-04], + [-2.48247583e-04, -7.56867666e-06, 5.06239303e-04], + [-8.81828601e-06, -2.43573140e-05, -2.53080367e-03], + [-1.40011718e-04, -3.32475203e-04, 1.00107107e-04], + [-1.09475950e-04, -2.65165028e-04, -1.11474330e-03], + [ 1.30597677e-04, -8.98349172e-05, 3.13808996e-04], + [ 1.28627900e-04, 6.72513852e-05, -2.45574955e-03], + [-7.20664684e-05, -3.13092547e-04, 9.14076518e-05], + [-1.26512998e-04, 2.68732780e-04, -1.09959091e-03], + [-1.11122208e-04, -1.31082925e-04, -2.16848264e-03], + [ 6.11276046e-05, 3.13131430e-04, -1.33579900e-03], + [ 3.69807298e-04, 5.76094666e-04, 1.29816309e-03], + [ 9.49630412e-05, 2.87347677e-04, -1.33989743e-05], + [-2.71516095e-04, -2.59785447e-04, 6.44381391e-04], + [-1.77162394e-04, 5.58188403e-05, -2.30204663e-03], + [ 5.45579416e-04, 1.47891289e-04, 3.03564681e-04], + [ 5.30245889e-05, -3.20222753e-04, -1.39251526e-03], + [ 1.46420571e-04, 4.65057936e-04, 3.19033628e-04], + [ 3.29618313e-04, -6.22679945e-04, 2.59055360e-03], + [-5.35642961e-04, -1.43381127e-04, -3.89138120e-04], + [-1.42356555e-04, -5.93290679e-05, 3.29975854e-04], + [ 6.70946756e-05, -4.98041394e-04, 3.86157626e-04], + [ 2.27281547e-04, 6.65286614e-04, 2.59183953e-03], + [ 1.72746732e-05, -5.10961865e-04, -1.80649557e-04], + [ 1.79107155e-05, 2.37802436e-04, 4.32887988e-04], + [-2.03807416e-04, 5.21945010e-04, 3.83861130e-04], + [-1.84430392e-04, -4.46439662e-04, 2.01875577e-03], + [ 5.46962081e-04, 9.63673301e-05, 1.49810367e-04], + [-8.05375108e-04, 1.44214282e-04, -1.72352011e-04], + [ 2.72394507e-04, 2.14465399e-04, 9.35536169e-04], + [-1.06148575e-04, -4.89991682e-04, 3.53208045e-04], + [ 1.38049887e-04, 1.89953673e-04, -7.36689180e-05], + [-1.85473749e-04, 4.05476603e-04, 1.94478617e-03]], dtype=float32) + ) +# --- diff --git a/tests/models/__snapshots__/test_dimenet.ambr b/tests/models/__snapshots__/test_dimenet.ambr new file mode 100644 index 0000000..cc79e28 --- /dev/null +++ b/tests/models/__snapshots__/test_dimenet.ambr @@ -0,0 +1,55 @@ +# name: TestDimeNet.test_energy_force_shape + Size( + 1, + 1, + ) +# --- +# name: TestDimeNet.test_energy_force_shape.1 + Approx( + array([[0.]], dtype=float32) + ) +# --- +# name: TestDimeNet.test_energy_force_shape.2 + Size( + 34, + 3, + ) +# --- +# name: TestDimeNet.test_energy_force_shape.3 + Approx( + array([[-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.]], dtype=float32) + ) +# --- diff --git a/tests/models/__snapshots__/test_dimenetpp.ambr b/tests/models/__snapshots__/test_dimenetpp.ambr new file mode 100644 index 0000000..cc79e28 --- /dev/null +++ b/tests/models/__snapshots__/test_dimenetpp.ambr @@ -0,0 +1,55 @@ +# name: TestDimeNet.test_energy_force_shape + Size( + 1, + 1, + ) +# --- +# name: TestDimeNet.test_energy_force_shape.1 + Approx( + array([[0.]], dtype=float32) + ) +# --- +# name: TestDimeNet.test_energy_force_shape.2 + Size( + 34, + 3, + ) +# --- +# name: TestDimeNet.test_energy_force_shape.3 + Approx( + array([[-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.], + [-0., -0., -0.]], dtype=float32) + ) +# --- diff --git a/tests/models/__snapshots__/test_forcenet.ambr b/tests/models/__snapshots__/test_forcenet.ambr new file mode 100644 index 0000000..6700511 --- /dev/null +++ b/tests/models/__snapshots__/test_forcenet.ambr @@ -0,0 +1,55 @@ +# name: TestForceNet.test_energy_force_shape + Size( + 1, + 1, + ) +# --- +# name: TestForceNet.test_energy_force_shape.1 + Approx( + array([[5.4464083]], dtype=float32) + ) +# --- +# name: TestForceNet.test_energy_force_shape.2 + Size( + 34, + 3, + ) +# --- +# name: TestForceNet.test_energy_force_shape.3 + Approx( + array([[-1.30544817e+00, -1.49980092e+00, 2.58076763e+00], + [-1.56487048e-01, 1.21984780e-01, 5.30002713e-01], + [ 2.38139129e+00, 4.72385049e-01, 2.54773617e+00], + [-4.32623863e-01, -4.87135053e-01, 2.76770496e+00], + [ 1.39428461e+00, 6.48864388e-01, 6.26993179e-01], + [ 4.07347471e-01, 1.23799689e-01, -5.07026792e-01], + [ 2.25082827e+00, 9.96077657e-02, 1.82430422e+00], + [-3.89873087e-01, -5.24788022e-01, 2.83203650e+00], + [-7.49948323e-02, 1.06079209e+00, 5.90661526e-01], + [ 3.96642715e-01, 1.45493388e-01, -4.97148573e-01], + [-2.75852084e-02, -1.35263979e+00, 3.46320987e-01], + [ 1.14955910e-01, 1.54354393e-01, 7.60127604e-01], + [ 9.49162841e-01, -9.56393898e-01, -1.52243674e-01], + [ 6.18018210e-01, -8.03342938e-01, 4.35517371e-01], + [ 1.03898096e+00, -7.93570936e-01, -1.10788167e-01], + [-9.31364298e-02, -1.40183270e+00, 2.39522368e-01], + [ 6.57854915e-01, -4.57121521e-01, 3.02878112e-01], + [ 1.14108153e-01, 1.36927634e-01, 7.50466406e-01], + [ 5.74506104e-01, -2.99378604e-01, -1.67183876e-01], + [ 1.42032638e-01, -1.20734669e-01, 7.83950210e-01], + [-1.45273298e-01, 1.05403699e-01, 1.37604153e+00], + [ 2.53411889e-01, -2.43154675e-01, 3.51213545e-01], + [ 5.22605896e-01, -3.22370619e-01, -1.23302817e-01], + [ 1.19088799e-01, -1.06448963e-01, 7.81443238e-01], + [-4.69261706e-02, -2.09370628e-03, 1.46875441e+00], + [ 2.96553016e-01, -3.83107215e-01, 5.76262951e-01], + [ 6.58208549e-01, -1.03099659e-01, -1.22475326e-01], + [ 1.00546025e-01, -8.82540047e-02, 9.23259616e-01], + [-1.48815066e-01, -2.76506990e-01, 1.26217115e+00], + [ 3.05881441e-01, -2.15618253e-01, 2.10024521e-01], + [ 1.36593431e-01, -3.22298646e-01, 1.27883935e+00], + [ 6.13107383e-01, -5.51185012e-02, -2.86964476e-02], + [ 2.06872270e-01, -1.47157997e-01, 2.15230256e-01], + [ 7.88176805e-02, -8.68521333e-02, 9.16256189e-01]], dtype=float32) + ) +# --- diff --git a/tests/models/__snapshots__/test_gemnet.ambr b/tests/models/__snapshots__/test_gemnet.ambr new file mode 100644 index 0000000..b0a3d80 --- /dev/null +++ b/tests/models/__snapshots__/test_gemnet.ambr @@ -0,0 +1,55 @@ +# name: TestGemNetT.test_energy_force_shape + Size( + 1, + 1, + ) +# --- +# name: TestGemNetT.test_energy_force_shape.1 + Approx( + array([[-2.8309784]], dtype=float32) + ) +# --- +# name: TestGemNetT.test_energy_force_shape.2 + Size( + 34, + 3, + ) +# --- +# name: TestGemNetT.test_energy_force_shape.3 + Approx( + array([[-1.9063300e-01, -4.7955269e-01, -4.1163838e-01], + [ 3.2963447e-02, -4.1976303e-02, 1.4681098e-01], + [ 4.5310814e-02, -1.5647057e-02, -3.9135024e-02], + [ 1.4961853e-03, -1.4203560e-02, 3.3883080e-03], + [ 4.6693304e-01, 5.3721493e-01, -3.1852049e-01], + [-8.8010393e-02, -4.9002431e-02, 8.4329851e-02], + [-1.8078296e-02, -5.1812166e-01, -6.4160776e-01], + [ 3.8833320e-03, 3.1418726e-03, -1.2101825e-02], + [-4.2179075e-01, -3.1736892e-01, -8.3664578e-01], + [-8.8227496e-02, 4.9139053e-02, 8.4563583e-02], + [ 9.9142501e-04, 6.5464345e-03, -3.8683496e-02], + [ 1.1314953e-02, 4.9042784e-02, -4.6410507e-01], + [-2.5108865e-01, 3.4118420e-01, 1.9485345e+00], + [-2.9001984e-01, -9.5232567e-03, 6.8808216e-01], + [ 3.1730324e-01, 7.3230848e-02, 9.9207681e-01], + [-2.8140317e-03, 5.3735077e-04, -1.3953466e-02], + [ 3.3537340e-01, -2.2644079e-01, -2.9799128e-01], + [ 1.1551380e-02, -4.9458712e-02, -4.6380383e-01], + [-2.6666662e-01, 4.4628358e-01, -3.6511219e-01], + [-1.9638909e-01, 3.9656147e-01, 8.2290068e-02], + [-2.4544127e-01, 4.1645682e-01, -3.9824256e-01], + [-3.1365227e-02, 3.2322398e-01, -5.5612624e-01], + [-2.6242790e-01, -4.5097196e-01, -3.6955750e-01], + [-1.9644189e-01, -3.9658454e-01, 8.2599446e-02], + [-1.1141943e-03, -1.8397301e-01, 5.0364542e-01], + [ 2.2267769e-01, -4.4530220e-02, -5.3146845e-01], + [ 2.5451428e-01, 4.5904759e-01, 3.7856877e-01], + [ 3.1834924e-01, 3.8487628e-01, 2.8591937e-01], + [ 2.1277699e-01, 4.8441634e-01, 4.5796084e-01], + [-1.1093411e-01, 3.2475430e-01, -5.6391889e-01], + [ 2.6626289e-01, -5.2011424e-01, 7.8410697e-01], + [ 2.5859460e-01, -4.4564921e-01, 3.8762924e-01], + [ 3.4471753e-01, -4.1330525e-01, -2.7253401e-01], + [ 3.1812382e-01, -3.8451308e-01, 2.8622845e-01]], dtype=float32) + ) +# --- diff --git a/tests/models/__snapshots__/test_gemnet_oc.ambr b/tests/models/__snapshots__/test_gemnet_oc.ambr new file mode 100644 index 0000000..5b56f0d --- /dev/null +++ b/tests/models/__snapshots__/test_gemnet_oc.ambr @@ -0,0 +1,54 @@ +# name: TestGemNetOC.test_energy_force_shape + Size( + 1, + ) +# --- +# name: TestGemNetOC.test_energy_force_shape.1 + Approx( + array([0.05976763], dtype=float32) + ) +# --- +# name: TestGemNetOC.test_energy_force_shape.2 + Size( + 34, + 3, + ) +# --- +# name: TestGemNetOC.test_energy_force_shape.3 + Approx( + array([[-0.01565197, -0.00906339, 0.02557305], + [-0.0276756 , -0.00339622, -0.01670166], + [-0.00513399, -0.01340215, -0.02776764], + [ 0.0167798 , 0.02109457, -0.07046591], + [ 0.00189061, 0.01565336, -0.03865878], + [ 0.02498426, 0.04165749, -0.07992381], + [ 0.00588416, 0.01703149, -0.02247796], + [ 0.01662521, -0.02046375, -0.0946758 ], + [ 0.05743103, -0.00689543, 0.02919763], + [ 0.02535299, -0.04247025, -0.07936587], + [-0.00362939, -0.0010459 , -0.02357624], + [-0.02289579, -0.03455767, -0.07460248], + [ 0.01777059, 0.00949161, 0.04692052], + [ 0.00743628, -0.02891731, -0.00174833], + [-0.01541256, 0.03000411, 0.10616676], + [-0.00328496, 0.00335687, -0.02452255], + [ 0.01408599, -0.01194764, -0.00303976], + [-0.02339223, 0.03420214, -0.07335933], + [-0.02742678, 0.02457595, 0.08851591], + [-0.00465523, 0.01404552, 0.01092728], + [ 0.00078778, 0.00438715, 0.02250567], + [-0.01212098, 0.00118569, 0.00698295], + [-0.02876237, -0.02612069, 0.08894789], + [-0.00365539, -0.01491504, 0.01382371], + [-0.02721602, -0.01951971, 0.02940147], + [-0.01804939, -0.00540495, 0.01060654], + [ 0.01114192, 0.00849383, 0.03842851], + [ 0.00670466, 0.01588664, -0.00815901], + [ 0.00648732, -0.00378032, -0.00089826], + [-0.02769482, 0.03015776, 0.00436287], + [ 0.0106617 , -0.00929041, 0.00956215], + [ 0.00833678, -0.01173691, 0.04838438], + [ 0.03862672, 0.03938209, -0.03861057], + [ 0.00708717, -0.01711058, -0.00938919]], dtype=float32) + ) +# --- diff --git a/tests/models/__snapshots__/test_schnet.ambr b/tests/models/__snapshots__/test_schnet.ambr new file mode 100644 index 0000000..93ae1d7 --- /dev/null +++ b/tests/models/__snapshots__/test_schnet.ambr @@ -0,0 +1,55 @@ +# name: TestSchNet.test_energy_force_shape + Size( + 1, + 1, + ) +# --- +# name: TestSchNet.test_energy_force_shape.1 + Approx( + array([[-5.483429]], dtype=float32) + ) +# --- +# name: TestSchNet.test_energy_force_shape.2 + Size( + 34, + 3, + ) +# --- +# name: TestSchNet.test_energy_force_shape.3 + Approx( + array([[ 1.92584217e-01, -2.41746485e-01, 2.98944712e-01], + [-1.70239031e-01, -2.61918530e-02, -3.37327063e-01], + [ 1.66453853e-01, -3.17063838e-01, 7.55078435e-01], + [-7.08544767e-03, -1.82398688e-02, -6.90884739e-02], + [-4.18034524e-01, 5.01857579e-01, -1.95073575e-01], + [-2.61729881e-02, -6.70452714e-02, -1.02554381e-01], + [-3.09667796e-01, -3.46787483e-01, 1.92245036e-01], + [ 4.20085154e-03, -3.75020877e-03, -3.52602452e-04], + [ 2.94753909e-03, 9.95900929e-02, 1.00515410e-02], + [-2.62446217e-02, 6.74669966e-02, -1.03242710e-01], + [ 1.11400196e-02, 1.67459548e-02, 1.95101649e-03], + [ 3.65287401e-02, 9.20087546e-02, -2.15489998e-01], + [ 7.73691311e-02, 2.89178759e-01, 1.39776349e+00], + [-3.43479544e-01, 7.32435226e-01, -4.46479976e-01], + [ 5.30380122e-02, -5.29454529e-01, 5.80309868e-01], + [-2.56470032e-03, 1.57785993e-02, -3.35297398e-02], + [ 3.03548455e-01, 1.73613280e-01, 1.42041072e-01], + [ 3.64362709e-02, -9.21925306e-02, -2.15180919e-01], + [-9.22280550e-03, 1.87577158e-02, 6.13273829e-02], + [-2.30593234e-03, -3.43042985e-02, 5.71594000e-01], + [-2.17402920e-01, -2.50463843e-01, -9.74546790e-01], + [ 1.71874315e-01, 3.36009264e-02, -2.36117661e-01], + [-1.37480348e-03, -2.40753442e-02, 6.24244735e-02], + [-2.23489851e-03, 3.39953303e-02, 5.71841240e-01], + [-8.38690847e-02, -1.14767045e-01, -2.62930393e-02], + [ 3.26499701e-01, 2.09019512e-01, -1.54383302e-01], + [ 3.98817658e-03, 2.42945552e-02, 6.77623898e-02], + [ 5.88913262e-03, -4.05097082e-02, 6.04023695e-01], + [ 3.34081471e-01, -2.30947137e-02, -9.03338194e-01], + [-5.38587511e-01, -9.75838453e-02, -8.72324824e-01], + [ 1.67467088e-01, 2.76862085e-03, -3.66581380e-02], + [ 5.77144325e-03, -1.91735476e-02, 7.28204548e-02], + [ 2.52768815e-01, -1.05491355e-01, -1.07253861e+00], + [ 5.89928776e-03, 4.08237502e-02, 6.04341447e-01]], dtype=float32) + ) +# --- diff --git a/tests/models/atoms.json b/tests/models/atoms.json new file mode 100644 index 0000000..97c6c47 --- /dev/null +++ b/tests/models/atoms.json @@ -0,0 +1,20 @@ +{"1": { + "calculator": "unknown", + "calculator_parameters": {}, + "cell": {"array": {"__ndarray__": [[3, 3], "float64", [0.0, -8.07194878, 0.0, 6.93127032, 0.0, 0.08307657, 0.0, 0.0, 39.37850739]]}, "pbc": {"__ndarray__": [[3], "bool", [true, true, true]]}, "__ase_objtype__": "cell"}, + "constraints": [{"name": "FixAtoms", "kwargs": {"indices": [2, 3, 5, 6, 7, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 26, 27, 28, 30, 31, 33]}}], + "ctime": 20.460198850701047, + "energy": -135.66393572, + "forces": {"__ndarray__": [[34, 3], "float64", [0.05011766, -0.01973735, 0.23846654, -0.12013861, -0.05240431, -0.22395961, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10578597, 0.01361956, -0.05699137, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03172177, 0.00066391, -0.01049754, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00908246, -0.09729627, 0.00726873, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02260358, -0.09508909, -0.01036104, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03928853, -0.04423657, 0.04053315, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.02912151, 0.05899768, -0.01100117, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.09680946, 0.06950572, 0.05602877, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03057741, 0.10594487, -0.04712197, 0.0, 0.0, 0.0]]}, + "initial_charges": {"__ndarray__": [[34], "float64", [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]}, + "initial_magmoms": {"__ndarray__": [[34], "float64", [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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{"__ndarray__": [[34, 3], "float64", [-0.3289066593614256, -3.0340615866893037, 27.073342845551938, -0.0750331499077992, -2.8712314914365584, 28.205836912191387, 6.2092629718957655, -4.771209055418616, 21.953210855443853, 3.8988395550000003, -0.735234665418617, 18.643976120697392, 1.636610785518665, -1.2302542698255066, 23.72823397486728, 1.5884161381042343, -4.771209055418616, 15.334741779736007, 2.7436278118957658, -6.789196250418616, 21.91167257044385, 0.433204395, -2.7532218604186167, 18.602437835697394, 5.33707967127947, -3.0430981333485136, 25.502246117362063, 5.054051298104235, -6.789196250418616, 15.376280064736006, 3.8988395550000003, -4.771209055418616, 18.643976120697392, 1.5884161381042343, -0.735234665418617, 15.334741779736007, 6.2092629718957655, -0.735234665418617, 21.953210855443853, 1.7024669335227842, -4.898430878701221, 24.462466125364735, 2.7436278118957658, -2.7532218604186167, 21.91167257044385, 0.433204395, -6.789196250418616, 18.602437835697394, 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24.661108015400288, 6.213507335276218, -4.778623221092535, 14.575819653667754]]}, + "tags": {"__ndarray__": [[34], "int64", [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, + "unique_id": "77df5102462860280bfa6b622c880125", + "user": "bwood"}, +"ids": [1], +"nextid": 2} diff --git a/tests/models/gemnet-dT-scales.json b/tests/models/gemnet-dT-scales.json new file mode 100644 index 0000000..ff3d57b --- /dev/null +++ b/tests/models/gemnet-dT-scales.json @@ -0,0 +1,20 @@ +{ + "comment": "tri_gaussian128", + "TripInteraction_1_had_rbf": 18.873615264892578, + "TripInteraction_1_sum_cbf": 7.996850490570068, + "AtomUpdate_1_sum": 1.220463752746582, + "TripInteraction_2_had_rbf": 16.10817527770996, + "TripInteraction_2_sum_cbf": 7.614634037017822, + "AtomUpdate_2_sum": 0.9690994620323181, + "TripInteraction_3_had_rbf": 15.01930046081543, + "TripInteraction_3_sum_cbf": 7.025179862976074, + "AtomUpdate_3_sum": 0.8903237581253052, + "OutBlock_0_sum": 1.6437848806381226, + "OutBlock_0_had": 16.161039352416992, + "OutBlock_1_sum": 1.1077653169631958, + "OutBlock_1_had": 13.54678726196289, + "OutBlock_2_sum": 0.9477927684783936, + "OutBlock_2_had": 12.754337310791016, + "OutBlock_3_sum": 0.9059251546859741, + "OutBlock_3_had": 13.484951972961426 +} diff --git a/tests/models/test_cgcnn.py b/tests/models/test_cgcnn.py new file mode 100644 index 0000000..bcf4122 --- /dev/null +++ b/tests/models/test_cgcnn.py @@ -0,0 +1,97 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os +import random + +import numpy as np +import pytest +import torch +from ase.io import read + +from ocpmodels.common.registry import registry +from ocpmodels.common.transforms import RandomRotate +from ocpmodels.common.utils import setup_imports +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +@pytest.fixture(scope="class") +def load_model(request): + torch.manual_seed(4) + setup_imports() + + num_gaussians = 50 + model = registry.get_model_class("cgcnn")( + None, + num_gaussians, + 1, + cutoff=6.0, + num_gaussians=num_gaussians, + regress_forces=True, + use_pbc=True, + ) + request.cls.model = model + + +@pytest.mark.usefixtures("load_data") +@pytest.mark.usefixtures("load_model") +class TestCGCNN: + def test_rotation_invariance(self): + random.seed(1) + data = self.data + + # Sampling a random rotation within [-180, 180] for all axes. + transform = RandomRotate([-180, 180], [0, 1, 2]) + data_rotated, rot, inv_rot = transform(data.clone()) + assert not np.array_equal(data.pos, data_rotated.pos) + + # Pass it through the model. + batch = data_list_collater([data, data_rotated]) + out = self.model(batch) + + # Compare predicted energies and forces (after inv-rotation). + energies = out[0].detach() + np.testing.assert_almost_equal(energies[0], energies[1], decimal=5) + + forces = out[1].detach() + np.testing.assert_array_almost_equal( + forces[: forces.shape[0] // 2], + torch.matmul(forces[forces.shape[0] // 2 :], inv_rot), + decimal=5, + ) + + def test_energy_force_shape(self, snapshot): + # Recreate the Data object to only keep the necessary features. + data = self.data + + # Pass it through the model. + energy, forces = self.model(data_list_collater([data])) + + assert snapshot == energy.shape + assert snapshot == pytest.approx(energy.detach()) + + assert snapshot == forces.shape + assert snapshot == pytest.approx(forces.detach()) diff --git a/tests/models/test_dimenet.py b/tests/models/test_dimenet.py new file mode 100644 index 0000000..d711676 --- /dev/null +++ b/tests/models/test_dimenet.py @@ -0,0 +1,95 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os +import random + +import numpy as np +import pytest +import torch +from ase.io import read + +from ocpmodels.common.registry import registry +from ocpmodels.common.transforms import RandomRotate +from ocpmodels.common.utils import setup_imports +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +@pytest.fixture(scope="class") +def load_model(request): + torch.manual_seed(4) + setup_imports() + + model = registry.get_model_class("dimenet")( + None, + 32, + 1, + cutoff=6.0, + regress_forces=True, + use_pbc=False, + ) + request.cls.model = model + + +@pytest.mark.usefixtures("load_data") +@pytest.mark.usefixtures("load_model") +class TestDimeNet: + def test_rotation_invariance(self): + random.seed(1) + data = self.data + + # Sampling a random rotation within [-180, 180] for all axes. + transform = RandomRotate([-180, 180], [0, 1, 2]) + data_rotated, rot, inv_rot = transform(data.clone()) + assert not np.array_equal(data.pos, data_rotated.pos) + + # Pass it through the model. + batch = data_list_collater([data, data_rotated]) + out = self.model(batch) + + # Compare predicted energies and forces (after inv-rotation). + energies = out[0].detach() + np.testing.assert_almost_equal(energies[0], energies[1], decimal=5) + + forces = out[1].detach() + np.testing.assert_array_almost_equal( + forces[: forces.shape[0] // 2], + torch.matmul(forces[forces.shape[0] // 2 :], inv_rot), + decimal=5, + ) + + def test_energy_force_shape(self, snapshot): + # Recreate the Data object to only keep the necessary features. + data = self.data + + # Pass it through the model. + energy, forces = self.model(data_list_collater([data])) + + assert snapshot == energy.shape + assert snapshot == pytest.approx(energy.detach()) + + assert snapshot == forces.shape + assert snapshot == pytest.approx(forces.detach()) diff --git a/tests/models/test_dimenetpp.py b/tests/models/test_dimenetpp.py new file mode 100644 index 0000000..f12d90d --- /dev/null +++ b/tests/models/test_dimenetpp.py @@ -0,0 +1,97 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import os +import random + +import numpy as np +import pytest +import torch +from ase.io import read + +from ocpmodels.common.registry import registry +from ocpmodels.common.transforms import RandomRotate +from ocpmodels.common.utils import setup_imports +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +@pytest.fixture(scope="class") +def load_model(request): + torch.manual_seed(4) + setup_imports() + + model = registry.get_model_class("dimenetplusplus")( + None, + 32, + 1, + cutoff=6.0, + regress_forces=True, + use_pbc=False, + ) + request.cls.model = model + + +@pytest.mark.usefixtures("load_data") +@pytest.mark.usefixtures("load_model") +class TestDimeNet: + def test_rotation_invariance(self): + random.seed(1) + data = self.data + + # Sampling a random rotation within [-180, 180] for all axes. + transform = RandomRotate([-180, 180], [0, 1, 2]) + data_rotated, rot, inv_rot = transform(data.clone()) + assert not np.array_equal(data.pos, data_rotated.pos) + + # Pass it through the model. + batch = data_list_collater([data, data_rotated]) + out = self.model(batch) + + # Compare predicted energies and forces (after inv-rotation). + energies = out[0].detach() + np.testing.assert_almost_equal(energies[0], energies[1], decimal=5) + + forces = out[1].detach() + logging.info(forces) + np.testing.assert_array_almost_equal( + forces[: forces.shape[0] // 2], + torch.matmul(forces[forces.shape[0] // 2 :], inv_rot), + decimal=5, + ) + + def test_energy_force_shape(self, snapshot): + # Recreate the Data object to only keep the necessary features. + data = self.data + + # Pass it through the model. + energy, forces = self.model(data_list_collater([data])) + + assert snapshot == energy.shape + assert snapshot == pytest.approx(energy.detach()) + + assert snapshot == forces.shape + assert snapshot == pytest.approx(forces.detach()) diff --git a/tests/models/test_forcenet.py b/tests/models/test_forcenet.py new file mode 100644 index 0000000..5c07f59 --- /dev/null +++ b/tests/models/test_forcenet.py @@ -0,0 +1,65 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os + +import numpy as np +import pytest +from ase.io import read + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import setup_imports +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +@pytest.fixture(scope="class") +def load_model(request): + setup_imports() + + model = registry.get_model_class("forcenet")( + None, + 32, + 1, + cutoff=6.0, + ) + request.cls.model = model + + +@pytest.mark.usefixtures("load_data") +@pytest.mark.usefixtures("load_model") +class TestForceNet: + def test_energy_force_shape(self, snapshot): + # Recreate the Data object to only keep the necessary features. + data = self.data + + # Pass it through the model. + energy, forces = self.model(data_list_collater([data])) + + assert snapshot == energy.shape + assert snapshot == pytest.approx(energy.detach()) + + assert snapshot == forces.shape + assert snapshot == pytest.approx(forces.detach()) diff --git a/tests/models/test_gemnet.py b/tests/models/test_gemnet.py new file mode 100644 index 0000000..1e31aec --- /dev/null +++ b/tests/models/test_gemnet.py @@ -0,0 +1,113 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import logging +import os +import random + +import numpy as np +import pytest +import torch +from ase.io import read + +from ocpmodels.common.registry import registry +from ocpmodels.common.transforms import RandomRotate +from ocpmodels.common.utils import setup_imports +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +@pytest.fixture(scope="class") +def load_model(request): + torch.manual_seed(4) + setup_imports() + + model = registry.get_model_class("gemnet_t")( + None, + -1, + 1, + cutoff=6.0, + num_spherical=7, + num_radial=128, + num_blocks=3, + emb_size_atom=16, + emb_size_edge=16, + emb_size_trip=16, + emb_size_rbf=16, + emb_size_cbf=16, + emb_size_bil_trip=64, + num_before_skip=1, + num_after_skip=2, + num_concat=1, + num_atom=3, + regress_forces=True, + direct_forces=True, + scale_file=os.path.join( + os.path.dirname(os.path.abspath(__file__)), "gemnet-dT-scales.json" + ), + ) + request.cls.model = model + + +@pytest.mark.usefixtures("load_data") +@pytest.mark.usefixtures("load_model") +class TestGemNetT: + def test_rotation_invariance(self): + random.seed(1) + data = self.data + + # Sampling a random rotation within [-180, 180] for all axes. + transform = RandomRotate([-180, 180], [0, 1, 2]) + data_rotated, rot, inv_rot = transform(data.clone()) + assert not np.array_equal(data.pos, data_rotated.pos) + + # Pass it through the model. + batch = data_list_collater([data, data_rotated]) + out = self.model(batch) + + # Compare predicted energies and forces (after inv-rotation). + energies = out[0].detach() + np.testing.assert_almost_equal(energies[0], energies[1], decimal=5) + + forces = out[1].detach() + logging.info(forces) + np.testing.assert_array_almost_equal( + forces[: forces.shape[0] // 2], + torch.matmul(forces[forces.shape[0] // 2 :], inv_rot), + decimal=4, + ) + + def test_energy_force_shape(self, snapshot): + # Recreate the Data object to only keep the necessary features. + data = self.data + + # Pass it through the model. + energy, forces = self.model(data_list_collater([data])) + + assert snapshot == energy.shape + assert snapshot == pytest.approx(energy.detach()) + + assert snapshot == forces.shape + assert snapshot == pytest.approx(forces.detach()) diff --git a/tests/models/test_gemnet_oc.py b/tests/models/test_gemnet_oc.py new file mode 100644 index 0000000..ef33033 --- /dev/null +++ b/tests/models/test_gemnet_oc.py @@ -0,0 +1,159 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import io +import logging +import os +import random + +import numpy as np +import pytest +import requests +import torch +from ase.io import read + +from ocpmodels.common.registry import registry +from ocpmodels.common.transforms import RandomRotate +from ocpmodels.common.utils import load_state_dict, setup_imports +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +@pytest.fixture(scope="class") +def load_model(request): + torch.manual_seed(4) + setup_imports() + + # download and load weights. + checkpoint_url = "https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_base_s2ef_all.pt" + + # load buffer into memory as a stream + # and then load it with torch.load + r = requests.get(checkpoint_url, stream=True) + r.raise_for_status() + checkpoint = torch.load( + io.BytesIO(r.content), map_location=torch.device("cpu") + ) + + model = registry.get_model_class("gemnet_oc")( + None, + -1, + 1, + num_spherical=7, + num_radial=128, + num_blocks=4, + emb_size_atom=256, + emb_size_edge=512, + emb_size_trip_in=64, + emb_size_trip_out=64, + emb_size_quad_in=32, + emb_size_quad_out=32, + emb_size_aint_in=64, + emb_size_aint_out=64, + emb_size_rbf=16, + emb_size_cbf=16, + emb_size_sbf=32, + num_before_skip=2, + num_after_skip=2, + num_concat=1, + num_atom=3, + num_output_afteratom=3, + num_atom_emb_layers=2, + num_global_out_layers=2, + regress_forces=True, + direct_forces=True, + use_pbc=True, + cutoff=12.0, + cutoff_qint=12.0, + cutoff_aeaint=12.0, + cutoff_aint=12.0, + max_neighbors=30, + max_neighbors_qint=8, + max_neighbors_aeaint=20, + max_neighbors_aint=1000, + rbf={"name": "gaussian"}, + envelope={"name": "polynomial", "exponent": 5}, + cbf={"name": "spherical_harmonics"}, + sbf={"name": "legendre_outer"}, + extensive=True, + forces_coupled=False, + output_init="HeOrthogonal", + activation="silu", + quad_interaction=True, + atom_edge_interaction=True, + edge_atom_interaction=True, + atom_interaction=True, + qint_tags=[1, 2], + scale_file=checkpoint["scale_dict"], + ) + + new_dict = { + k[len("module.") * 2 :]: v for k, v in checkpoint["state_dict"].items() + } + load_state_dict(model, new_dict) + + request.cls.model = model + + +@pytest.mark.usefixtures("load_data") +@pytest.mark.usefixtures("load_model") +class TestGemNetOC: + def test_rotation_invariance(self): + random.seed(1) + data = self.data + + # Sampling a random rotation within [-180, 180] for all axes. + transform = RandomRotate([-180, 180], [0, 1, 2]) + data_rotated, rot, inv_rot = transform(data.clone()) + assert not np.array_equal(data.pos, data_rotated.pos) + + # Pass it through the model. + batch = data_list_collater([data, data_rotated]) + out = self.model(batch) + + # Compare predicted energies and forces (after inv-rotation). + energies = out[0].detach() + np.testing.assert_almost_equal(energies[0], energies[1], decimal=3) + + forces = out[1].detach() + logging.info(forces) + np.testing.assert_array_almost_equal( + forces[: forces.shape[0] // 2], + torch.matmul(forces[forces.shape[0] // 2 :], inv_rot), + decimal=3, + ) + + def test_energy_force_shape(self, snapshot): + # Recreate the Data object to only keep the necessary features. + data = self.data + + # Pass it through the model. + energy, forces = self.model(data_list_collater([data])) + + assert snapshot == energy.shape + assert snapshot == pytest.approx(energy.detach()) + + assert snapshot == forces.shape + assert snapshot == pytest.approx(forces.detach()) diff --git a/tests/models/test_gemnet_oc_scaling_mismatch.py b/tests/models/test_gemnet_oc_scaling_mismatch.py new file mode 100644 index 0000000..f69140d --- /dev/null +++ b/tests/models/test_gemnet_oc_scaling_mismatch.py @@ -0,0 +1,302 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import io + +import pytest +import requests +import torch + +from ocpmodels.common.registry import registry +from ocpmodels.common.utils import load_state_dict, setup_imports +from ocpmodels.modules.scaling import ScaleFactor +from ocpmodels.modules.scaling.compat import load_scales_compat +from ocpmodels.modules.scaling.util import ensure_fitted + + +class TestGemNetOC: + def test_no_scaling_mismatch(self): + torch.manual_seed(4) + setup_imports() + + # download and load weights. + checkpoint_url = "https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_base_s2ef_all.pt" + + # load buffer into memory as a stream + # and then load it with torch.load + r = requests.get(checkpoint_url, stream=True) + r.raise_for_status() + checkpoint = torch.load( + io.BytesIO(r.content), map_location=torch.device("cpu") + ) + + model = registry.get_model_class("gemnet_oc")( + None, + -1, + 1, + num_spherical=7, + num_radial=128, + num_blocks=4, + emb_size_atom=256, + emb_size_edge=512, + emb_size_trip_in=64, + emb_size_trip_out=64, + emb_size_quad_in=32, + emb_size_quad_out=32, + emb_size_aint_in=64, + emb_size_aint_out=64, + emb_size_rbf=16, + emb_size_cbf=16, + emb_size_sbf=32, + num_before_skip=2, + num_after_skip=2, + num_concat=1, + num_atom=3, + num_output_afteratom=3, + num_atom_emb_layers=2, + num_global_out_layers=2, + regress_forces=True, + direct_forces=True, + use_pbc=True, + cutoff=12.0, + cutoff_qint=12.0, + cutoff_aeaint=12.0, + cutoff_aint=12.0, + max_neighbors=30, + max_neighbors_qint=8, + max_neighbors_aeaint=20, + max_neighbors_aint=1000, + rbf={"name": "gaussian"}, + envelope={"name": "polynomial", "exponent": 5}, + cbf={"name": "spherical_harmonics"}, + sbf={"name": "legendre_outer"}, + extensive=True, + forces_coupled=False, + output_init="HeOrthogonal", + activation="silu", + quad_interaction=True, + atom_edge_interaction=True, + edge_atom_interaction=True, + atom_interaction=True, + qint_tags=[1, 2], + scale_file=checkpoint["scale_dict"], + ) + + new_dict = { + k[len("module.") * 2 :]: v + for k, v in checkpoint["state_dict"].items() + } + + try: + load_state_dict(model, new_dict) + except ValueError as e: + assert False, f"'load_state_dict' raised an exception {e}" + + def test_scaling_mismatch(self): + torch.manual_seed(4) + setup_imports() + + # download and load weights. + checkpoint_url = "https://dl.fbaipublicfiles.com/opencatalystproject/models/2022_07/s2ef/gemnet_oc_base_s2ef_all.pt" + + # load buffer into memory as a stream + # and then load it with torch.load + r = requests.get(checkpoint_url, stream=True) + r.raise_for_status() + checkpoint = torch.load( + io.BytesIO(r.content), map_location=torch.device("cpu") + ) + + model = registry.get_model_class("gemnet_oc")( + None, + -1, + 1, + num_spherical=7, + num_radial=128, + num_blocks=4, + emb_size_atom=256, + emb_size_edge=512, + emb_size_trip_in=64, + emb_size_trip_out=64, + emb_size_quad_in=32, + emb_size_quad_out=32, + emb_size_aint_in=64, + emb_size_aint_out=64, + emb_size_rbf=16, + emb_size_cbf=16, + emb_size_sbf=32, + num_before_skip=2, + num_after_skip=2, + num_concat=1, + num_atom=3, + num_output_afteratom=3, + num_atom_emb_layers=2, + num_global_out_layers=2, + regress_forces=True, + direct_forces=True, + use_pbc=True, + cutoff=12.0, + cutoff_qint=12.0, + cutoff_aeaint=12.0, + cutoff_aint=12.0, + max_neighbors=30, + max_neighbors_qint=8, + max_neighbors_aeaint=20, + max_neighbors_aint=1000, + rbf={"name": "gaussian"}, + envelope={"name": "polynomial", "exponent": 5}, + cbf={"name": "spherical_harmonics"}, + sbf={"name": "legendre_outer"}, + extensive=True, + forces_coupled=False, + output_init="HeOrthogonal", + activation="silu", + quad_interaction=True, + atom_edge_interaction=True, + edge_atom_interaction=True, + atom_interaction=True, + qint_tags=[1, 2], + scale_file=checkpoint["scale_dict"], + ) + + for key in checkpoint["scale_dict"]: + for submodule in model.modules(): + if not isinstance(submodule, ScaleFactor): + continue + + submodule.reset_() + + load_scales_compat(model, checkpoint["scale_dict"]) + + new_dict = { + k[len("module.") * 2 :]: v + for k, v in checkpoint["state_dict"].items() + } + param_key = f"{key}.scale_factor" + new_dict[param_key] = checkpoint["scale_dict"][key] - 10.0 + + with pytest.raises( + ValueError, + match=f"Scale factor parameter {param_key} is inconsistent with the loaded state dict.", + ): + load_state_dict(model, new_dict) + + def test_no_file_exists(self): + torch.manual_seed(4) + setup_imports() + + with pytest.raises(ValueError): + registry.get_model_class("gemnet_oc")( + None, + -1, + 1, + num_spherical=7, + num_radial=128, + num_blocks=4, + emb_size_atom=256, + emb_size_edge=512, + emb_size_trip_in=64, + emb_size_trip_out=64, + emb_size_quad_in=32, + emb_size_quad_out=32, + emb_size_aint_in=64, + emb_size_aint_out=64, + emb_size_rbf=16, + emb_size_cbf=16, + emb_size_sbf=32, + num_before_skip=2, + num_after_skip=2, + num_concat=1, + num_atom=3, + num_output_afteratom=3, + num_atom_emb_layers=2, + num_global_out_layers=2, + regress_forces=True, + direct_forces=True, + use_pbc=True, + cutoff=12.0, + cutoff_qint=12.0, + cutoff_aeaint=12.0, + cutoff_aint=12.0, + max_neighbors=30, + max_neighbors_qint=8, + max_neighbors_aeaint=20, + max_neighbors_aint=1000, + rbf={"name": "gaussian"}, + envelope={"name": "polynomial", "exponent": 5}, + cbf={"name": "spherical_harmonics"}, + sbf={"name": "legendre_outer"}, + extensive=True, + forces_coupled=False, + output_init="HeOrthogonal", + activation="silu", + quad_interaction=True, + atom_edge_interaction=True, + edge_atom_interaction=True, + atom_interaction=True, + qint_tags=[1, 2], + scale_file="/tmp/this/file/does/not/exist.pt", + ) + + def test_not_fitted(self): + torch.manual_seed(4) + setup_imports() + + model = registry.get_model_class("gemnet_oc")( + None, + -1, + 1, + num_spherical=7, + num_radial=128, + num_blocks=4, + emb_size_atom=256, + emb_size_edge=512, + emb_size_trip_in=64, + emb_size_trip_out=64, + emb_size_quad_in=32, + emb_size_quad_out=32, + emb_size_aint_in=64, + emb_size_aint_out=64, + emb_size_rbf=16, + emb_size_cbf=16, + emb_size_sbf=32, + num_before_skip=2, + num_after_skip=2, + num_concat=1, + num_atom=3, + num_output_afteratom=3, + num_atom_emb_layers=2, + num_global_out_layers=2, + regress_forces=True, + direct_forces=True, + use_pbc=True, + cutoff=12.0, + cutoff_qint=12.0, + cutoff_aeaint=12.0, + cutoff_aint=12.0, + max_neighbors=30, + max_neighbors_qint=8, + max_neighbors_aeaint=20, + max_neighbors_aint=1000, + rbf={"name": "gaussian"}, + envelope={"name": "polynomial", "exponent": 5}, + cbf={"name": "spherical_harmonics"}, + sbf={"name": "legendre_outer"}, + extensive=True, + forces_coupled=False, + output_init="HeOrthogonal", + activation="silu", + quad_interaction=True, + atom_edge_interaction=True, + edge_atom_interaction=True, + atom_interaction=True, + qint_tags=[1, 2], + scale_file=None, + ) + + with pytest.raises(ValueError): + ensure_fitted(model) diff --git a/tests/models/test_schnet.py b/tests/models/test_schnet.py new file mode 100644 index 0000000..59e1e5d --- /dev/null +++ b/tests/models/test_schnet.py @@ -0,0 +1,90 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os +import random + +import numpy as np +import pytest +import torch +from ase.io import read + +from ocpmodels.common.registry import registry +from ocpmodels.common.transforms import RandomRotate +from ocpmodels.common.utils import setup_imports +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +@pytest.fixture(scope="class") +def load_model(request): + torch.manual_seed(4) + setup_imports() + + model = registry.get_model_class("schnet")( + None, 32, 1, cutoff=6.0, regress_forces=True, use_pbc=True + ) + request.cls.model = model + + +@pytest.mark.usefixtures("load_data") +@pytest.mark.usefixtures("load_model") +class TestSchNet: + def test_rotation_invariance(self): + random.seed(1) + data = self.data + + # Sampling a random rotation within [-180, 180] for all axes. + transform = RandomRotate([-180, 180], [0, 1, 2]) + data_rotated, rot, inv_rot = transform(data.clone()) + assert not np.array_equal(data.pos, data_rotated.pos) + + # Pass it through the model. + batch = data_list_collater([data, data_rotated]) + out = self.model(batch) + + # Compare predicted energies and forces (after inv-rotation). + energies = out[0].detach() + np.testing.assert_almost_equal(energies[0], energies[1], decimal=5) + + forces = out[1].detach() + np.testing.assert_array_almost_equal( + forces[: forces.shape[0] // 2], + torch.matmul(forces[forces.shape[0] // 2 :], inv_rot), + decimal=4, + ) + + def test_energy_force_shape(self, snapshot): + # Recreate the Data object to only keep the necessary features. + data = self.data + + # Pass it through the model. + energy, forces = self.model(data_list_collater([data])) + + assert snapshot == energy.shape + assert snapshot == pytest.approx(energy.detach()) + + assert snapshot == forces.shape + assert snapshot == pytest.approx(forces.detach()) diff --git a/tests/preprocessing/__init__.py b/tests/preprocessing/__init__.py new file mode 100644 index 0000000..c17674b --- /dev/null +++ b/tests/preprocessing/__init__.py @@ -0,0 +1,6 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" diff --git a/tests/preprocessing/atoms.json b/tests/preprocessing/atoms.json new file mode 100644 index 0000000..97c6c47 --- /dev/null +++ b/tests/preprocessing/atoms.json @@ -0,0 +1,20 @@ +{"1": { + "calculator": "unknown", + "calculator_parameters": {}, + "cell": {"array": {"__ndarray__": [[3, 3], "float64", [0.0, -8.07194878, 0.0, 6.93127032, 0.0, 0.08307657, 0.0, 0.0, 39.37850739]]}, "pbc": {"__ndarray__": [[3], "bool", [true, true, true]]}, "__ase_objtype__": "cell"}, + "constraints": [{"name": "FixAtoms", "kwargs": {"indices": [2, 3, 5, 6, 7, 9, 10, 11, 12, 14, 15, 17, 18, 19, 20, 22, 23, 24, 26, 27, 28, 30, 31, 33]}}], + "ctime": 20.460198850701047, + "energy": -135.66393572, + "forces": {"__ndarray__": [[34, 3], "float64", [0.05011766, -0.01973735, 0.23846654, -0.12013861, -0.05240431, -0.22395961, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10578597, 0.01361956, -0.05699137, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03172177, 0.00066391, -0.01049754, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00908246, -0.09729627, 0.00726873, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02260358, -0.09508909, -0.01036104, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03928853, -0.04423657, 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-6.789196250418616, 21.91167257044385, 0.433204395, -2.7532218604186167, 18.602437835697394, 5.33707967127947, -3.0430981333485136, 25.502246117362063, 5.054051298104235, -6.789196250418616, 15.376280064736006, 3.8988395550000003, -4.771209055418616, 18.643976120697392, 1.5884161381042343, -0.735234665418617, 15.334741779736007, 6.2092629718957655, -0.735234665418617, 21.953210855443853, 1.7024669335227842, -4.898430878701221, 24.462466125364735, 2.7436278118957658, -2.7532218604186167, 21.91167257044385, 0.433204395, -6.789196250418616, 18.602437835697394, 5.0596241087542175, -7.073912126493459, 24.329534869886448, 5.054051298104235, -2.7532218604186167, 15.376280064736006, 1.5841717747237825, -4.763794809025211, 17.789819163977032, 6.205018677828017, -0.7278204190252113, 14.563661393015645, 3.8945952609322516, -0.7278204190252113, 21.09905389955426, 6.2730609484910635, -5.008717107687484, 24.37936591790035, 5.049806934723782, -6.796610416092535, 17.831357448977034, 2.739383517828017, -2.7606360260925347, 14.522123108015645, 0.4289601009322512, -2.7606360260925347, 21.05751561455426, 2.7016609108638554, -7.122213699359126, 24.33216256212159, 5.058295592171984, -2.7458076140252117, 17.84351570962914, 2.747872175276218, -6.781782004025211, 14.534281368667754, 0.43744868906774886, -6.781782004025211, 21.069673874375603, 3.0271987649116516, -2.983072135599385, 24.66107410517354, 3.903083849067749, -4.778623221092535, 21.111212159375604, 1.5926604321719833, -0.7426488310925348, 17.801977424629143, 6.319541839318875, -0.99856463967624, 24.661108015400288, 6.213507335276218, -4.778623221092535, 14.575819653667754]]}, + "tags": {"__ndarray__": [[34], "int64", [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, + "unique_id": "77df5102462860280bfa6b622c880125", + "user": "bwood"}, +"ids": [1], +"nextid": 2} diff --git a/tests/preprocessing/test_atoms_to_graphs.py b/tests/preprocessing/test_atoms_to_graphs.py new file mode 100644 index 0000000..4184c52 --- /dev/null +++ b/tests/preprocessing/test_atoms_to_graphs.py @@ -0,0 +1,131 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os + +import numpy as np +import pytest +from ase.io import read +from ase.neighborlist import NeighborList, NewPrimitiveNeighborList + +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def atoms_to_graphs_internals(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + test_object = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + request.cls.atg = test_object + request.cls.atoms = atoms + + +@pytest.mark.usefixtures("atoms_to_graphs_internals") +class TestAtomsToGraphs: + def test_gen_neighbors_pymatgen(self): + # call the internal function + ( + c_index, + n_index, + n_distances, + offsets, + ) = self.atg._get_neighbors_pymatgen(self.atoms) + edge_index, edge_distances, cell_offsets = self.atg._reshape_features( + c_index, n_index, n_distances, offsets + ) + + # use ase to compare distances and indices + n = NeighborList( + cutoffs=[self.atg.radius / 2.0] * len(self.atoms), + self_interaction=False, + skin=0, + bothways=True, + primitive=NewPrimitiveNeighborList, + ) + n.update(self.atoms) + ase_neighbors = [ + n.get_neighbors(index) for index in range(len(self.atoms)) + ] + ase_s_index = [] + ase_n_index = [] + ase_offsets = [] + for i, n in enumerate(ase_neighbors): + nidx = n[0] + ncount = len(nidx) + ase_s_index += [i] * ncount + ase_n_index += nidx.tolist() + ase_offsets.append(n[1]) + ase_s_index = np.array(ase_s_index) + ase_n_index = np.array(ase_n_index) + ase_offsets = np.concatenate(np.array(ase_offsets)) + # compute ase distance + cell = self.atoms.cell + positions = self.atoms.positions + distance_vec = positions[ase_s_index] - positions[ase_n_index] + _offsets = np.dot(ase_offsets, cell) + distance_vec -= _offsets + act_dist = np.linalg.norm(distance_vec, axis=-1) + + act_dist = np.sort(act_dist) + act_index = np.sort(ase_n_index) + test_dist = np.sort(edge_distances) + test_index = np.sort(edge_index[0, :]) + # check that the distance and neighbor index values are correct + np.testing.assert_allclose(act_dist, test_dist) + np.testing.assert_array_equal(act_index, test_index) + + def test_convert(self): + # run convert on a single atoms obj + data = self.atg.convert(self.atoms) + # atomic numbers + act_atomic_numbers = self.atoms.get_atomic_numbers() + atomic_numbers = data.atomic_numbers.numpy() + np.testing.assert_equal(act_atomic_numbers, atomic_numbers) + # positions + act_positions = self.atoms.get_positions() + positions = data.pos.numpy() + np.testing.assert_allclose(act_positions, positions) + # check energy value + act_energy = self.atoms.get_potential_energy(apply_constraint=False) + test_energy = data.y + np.testing.assert_equal(act_energy, test_energy) + # forces + act_forces = self.atoms.get_forces(apply_constraint=False) + forces = data.force.numpy() + np.testing.assert_allclose(act_forces, forces) + + def test_convert_all(self): + # run convert_all on a list with one atoms object + # this does not test the atoms.db functionality + atoms_list = [self.atoms] + data_list = self.atg.convert_all(atoms_list) + # check shape/values of features + # atomic numbers + act_atomic_nubmers = self.atoms.get_atomic_numbers() + atomic_numbers = data_list[0].atomic_numbers.numpy() + np.testing.assert_equal(act_atomic_nubmers, atomic_numbers) + # positions + act_positions = self.atoms.get_positions() + positions = data_list[0].pos.numpy() + np.testing.assert_allclose(act_positions, positions) + # check energy value + act_energy = self.atoms.get_potential_energy(apply_constraint=False) + test_energy = data_list[0].y + np.testing.assert_equal(act_energy, test_energy) + # forces + act_forces = self.atoms.get_forces(apply_constraint=False) + forces = data_list[0].force.numpy() + np.testing.assert_allclose(act_forces, forces) diff --git a/tests/preprocessing/test_pbc.py b/tests/preprocessing/test_pbc.py new file mode 100644 index 0000000..d16bcbe --- /dev/null +++ b/tests/preprocessing/test_pbc.py @@ -0,0 +1,55 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os + +import numpy as np +import pytest +from ase.io import read + +from ocpmodels.common.utils import get_pbc_distances +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=12, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +@pytest.mark.usefixtures("load_data") +class TestPBC: + def test_pbc_distances(self): + data = self.data + batch = data_list_collater([data] * 5) + out = get_pbc_distances( + batch.pos, + batch.edge_index, + batch.cell, + batch.cell_offsets, + batch.neighbors, + ) + edge_index, pbc_distances = out["edge_index"], out["distances"] + + np.testing.assert_array_equal( + batch.edge_index, + edge_index, + ) + np.testing.assert_array_almost_equal(batch.distances, pbc_distances) diff --git a/tests/preprocessing/test_radius_graph_pbc.py b/tests/preprocessing/test_radius_graph_pbc.py new file mode 100644 index 0000000..1e83f42 --- /dev/null +++ b/tests/preprocessing/test_radius_graph_pbc.py @@ -0,0 +1,264 @@ +""" +Copyright (c) Facebook, Inc. and its affiliates. + +This source code is licensed under the MIT license found in the +LICENSE file in the root directory of this source tree. +""" + +import os + +import ase +import numpy as np +import pytest +import torch +from ase.io import read +from ase.lattice.cubic import FaceCenteredCubic +from ase.build import molecule +from pymatgen.io.ase import AseAtomsAdaptor +from torch_geometric.transforms.radius_graph import RadiusGraph +from torch_geometric.utils.sort_edge_index import sort_edge_index + +from ocpmodels.common.utils import get_pbc_distances, radius_graph_pbc +from ocpmodels.datasets import data_list_collater +from ocpmodels.preprocessing import AtomsToGraphs + + +@pytest.fixture(scope="class") +def load_data(request): + atoms = read( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"), + index=0, + format="json", + ) + a2g = AtomsToGraphs( + max_neigh=200, + radius=6, + r_energy=True, + r_forces=True, + r_distances=True, + ) + data_list = a2g.convert_all([atoms]) + request.cls.data = data_list[0] + + +def check_features_match( + edge_index_1, cell_offsets_1, edge_index_2, cell_offsets_2 +): + # Combine both edge indices and offsets to one tensor + features_1 = torch.cat((edge_index_1, cell_offsets_1.T), dim=0).T + features_2 = torch.cat((edge_index_2, cell_offsets_2.T), dim=0).T.long() + + # Convert rows of tensors to sets. The order of edges is not guaranteed + features_1 = {tuple(x.tolist()) for x in features_1} + features_2 = {tuple(x.tolist()) for x in features_2} + + # Ensure sets are not empty + assert len(features_1) > 0 + assert len(features_2) > 0 + + # Ensure sets are the same + assert features_1 == features_2 + + return True + + +@pytest.mark.usefixtures("load_data") +class TestRadiusGraphPBC: + def test_radius_graph_pbc(self): + data = self.data + batch = data_list_collater([data] * 5) + + out = radius_graph_pbc( + batch, + radius=6, + max_num_neighbors_threshold=2000, + pbc=[True, True, False], + ) + + edge_index, cell_offsets, neighbors = out + + assert check_features_match( + batch.edge_index, batch.cell_offsets, out[0], out[1] + ) + + def test_bulk(self): + radius = 10 + + # Must be sufficiently large to ensure all edges are retained + max_neigh = 2000 + + a2g = AtomsToGraphs(radius=radius, max_neigh=max_neigh) + structure = FaceCenteredCubic("Pt", size=[1, 2, 3]) + data = a2g.convert(structure) + batch = data_list_collater([data]) + + # Ensure adequate distance between repeated cells + structure.cell[0] *= radius + structure.cell[1] *= radius + structure.cell[2] *= radius + + # [False, False, False] + data = a2g.convert(structure) + non_pbc = data.edge_index.shape[1] + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[False, False, False], + ) + + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) + + # [True, False, False] + structure.cell[0] /= radius + data = a2g.convert(structure) + pbc_x = data.edge_index.shape[1] + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[True, False, False], + ) + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) + + # [True, True, False] + structure.cell[1] /= radius + data = a2g.convert(structure) + pbc_xy = data.edge_index.shape[1] + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[True, True, False], + ) + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) + + # [False, True, False] + structure.cell[0] *= radius + data = a2g.convert(structure) + pbc_y = data.edge_index.shape[1] + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[False, True, False], + ) + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) + + # [False, True, True] + structure.cell[2] /= radius + data = a2g.convert(structure) + pbc_yz = data.edge_index.shape[1] + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[False, True, True], + ) + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) + + # [False, False, True] + structure.cell[1] *= radius + data = a2g.convert(structure) + pbc_z = data.edge_index.shape[1] + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[False, False, True], + ) + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) + + # [True, False, True] + structure.cell[0] /= radius + data = a2g.convert(structure) + pbc_xz = data.edge_index.shape[1] + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[True, False, True], + ) + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) + + # [True, True, True] + structure.cell[1] /= radius + data = a2g.convert(structure) + pbc_all = data.edge_index.shape[1] + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[True, True, True], + ) + + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) + + # Ensure edges are actually found + assert non_pbc > 0 + assert pbc_x > non_pbc + assert pbc_y > non_pbc + assert pbc_z > non_pbc + assert pbc_xy > max(pbc_x, pbc_y) + assert pbc_yz > max(pbc_y, pbc_z) + assert pbc_xz > max(pbc_x, pbc_z) + assert pbc_all > max(pbc_xy, pbc_yz, pbc_xz) + + structure = FaceCenteredCubic("Pt", size=[1, 2, 3]) + + # Ensure radius_graph_pbc matches radius_graph for non-PBC condition + RG = RadiusGraph(r=radius, max_num_neighbors=max_neigh) + radgraph = RG(batch) + + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[False, False, False], + ) + assert ( + sort_edge_index(out[0]) == sort_edge_index(radgraph.edge_index) + ).all() + + def test_molecule(self): + radius = 6 + max_neigh = 1000 + a2g = AtomsToGraphs(radius=radius, max_neigh=max_neigh) + structure = molecule("CH3COOH") + structure.cell = [[20, 0, 0], [0, 20, 0], [0, 0, 20]] + data = a2g.convert(structure) + batch = data_list_collater([data]) + out = radius_graph_pbc( + batch, + radius=radius, + max_num_neighbors_threshold=max_neigh, + pbc=[False, False, False], + ) + + assert check_features_match( + data.edge_index, data.cell_offsets, out[0], out[1] + ) diff --git a/tutorials/OCP_Tutorial.ipynb b/tutorials/OCP_Tutorial.ipynb new file mode 100644 index 0000000..9930cfa --- /dev/null +++ b/tutorials/OCP_Tutorial.ipynb @@ -0,0 +1,4927 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "CCAI - OCP Tutorial", + "provenance": [], + "collapsed_sections": [ + "PoF-BxSM5Jkc", + "bSt6h_Q-oqjK", + "pto2SpJPwlz1", + "gaauxWdNw_-4", + "TcUvAI81xoSt", + "TUH5BaaXo-ca" + ], + "toc_visible": true, + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dzeHYa5GCxN7" + }, + "source": [ + "# MIT License\n", + "#\n", + "#@title Copyright (c) 2021 CCAI Community Authors { display-mode: \"form\" }\n", + "#\n", + "# Permission is hereby granted, free of charge, to any person obtaining a\n", + "# copy of this software and associated documentation files (the \"Software\"),\n", + "# to deal in the Software without restriction, including without limitation\n", + "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n", + "# and/or sell copies of the Software, and to permit persons to whom the\n", + "# Software is furnished to do so, subject to the following conditions:\n", + "#\n", + "# The above copyright notice and this permission notice shall be included in\n", + "# all copies or substantial portions of the Software.\n", + "#\n", + "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n", + "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n", + "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n", + "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n", + "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n", + "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n", + "# DEALINGS IN THE SOFTWARE." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "13i7KQ9t-CV8" + }, + "source": [ + "# Open Catalyst Project Tutorial Notebook\n", + "Author(s):\n", + "* [Muhammed Shuaibi](https://mshuaibii.github.io/), CMU, mshuaibi@andrew.cmu.edu\n", + "* [Abhishek Das](https://abhishekdas.com/), FAIR, abhshkdz@fb.com \n", + "* [Adeesh Kolluru](https://adeeshkolluru.github.io/), CMU, akolluru@andrew.cmu.edu\n", + "* [Brandon Wood](https://wood-b.github.io/), NERSC, bwood@lbl.gov \n", + "* [Janice Lan](https://www.linkedin.com/in/janice-lan), FAIR, janlan@fb.com\n", + "* [Anuroop Sriram](https://www.linkedin.com/in/anuroopsriram), FAIR, anuroops@fb.com\n", + "* [Zachary Ulissi](https://ulissigroup.cheme.cmu.edu/), CMU, zulissi@andrew.cmu.edu\n", + "* [Larry Zitnick](http://larryzitnick.org/), FAIR, zitnick@fb.com\n", + "\n", + "FAIR - Facebook AI Research\n", + "\n", + "CMU - Carnegie Mellon University\n", + "\n", + "NERSC - National Energy Research Scientific Computing Center\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E_qIKf8erkfC" + }, + "source": [ + "## Table of Contents\n", + "\n", + "* [Background](#background)\n", + "* [Objective](#objective)\n", + "* [Climate Impact](#climate-impact)\n", + "* [Target Audience](#target-audience)\n", + "* [Background & Prerequisites](#background-and-prereqs)\n", + "* [Software Requirements](#software-requirements)\n", + "* [Dataset Overview & Visualization](#data-description)\n", + " * [Download](#download)\n", + " * [Visualization](#visual)\n", + " * [Data contents](#contents)\n", + "* [Tasks](#tasks)\n", + " * [S2EF](#s2ef)\n", + " * [IS2RE](#is2re)\n", + " * [IS2RS](#is2rs)\n", + "* [OCP Calculator](#calc)\n", + "* [Model development](#model-dev)\n", + "* [Running on command line](#cmd)\n", + "* [Limitations](#limit)\n", + "* [Next steps](#steps)\n", + "* [References](#references)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JkjKcVJ47hSN" + }, + "source": [ + "## Background \n", + "The discovery of efficient and economic catalysts (materials) are needed to enable the widespread use of renewable energy technologies. A common approach in discovering high performance catalysts is using molecular simulations. Specifically, each simulation models the interaction of a catalyst surface with molecules that are commonly seen in electrochemical reactions. By predicting these interactions accurately, the catalyst's impact on the overall rate of a chemical reaction may be estimated.\n", + "\n", + "An important quantity in screening catalysts is their adsorption energy for the molecules, referred to as `adsorbates', involved in the reaction of interest. The adsorption energy may be found by simulating the interaction of the adsorbate molecule on the surface of the catalyst to find their resting or relaxed energy, i.e., how tightly the adsorbate binds to the catalyst's surface (visualized below). The rate of the chemical reaction, a value of high practical importance, is then commonly approximated using simple functions of the adsorption energy. The goal of this tutorial specifically and the project overall is to encourage research and benchmark progress towards training ML models to approximate this relaxation.\n", + "\n", + "Specifically, during the course of a relaxation, given an initial set of atoms and their positions, the task is to iteratively estimate atomic forces and update atomic positions until a relaxed state is reached. The energy corresponding to the relaxed state is the structure's 'relaxed energy'.\n", + "\n", + "As part of the [Open Catalyst Project](https://github.com/Open-Catalyst-Project/ocp) (OCP), we identify three key tasks ML models need to perform well on in\n", + "order to effectively approximate DFT --\n", + "\n", + " 1) Given an **I**nitial **S**tructure, predict the **R**elaxed **E**nergy of the relaxed strucutre (**IS2RE**),\n", + "\n", + " 2) Given an **I**nitial **S**tructure, predict the **R**elaxed **S**tructure (**IS2RS**),\n", + "\n", + " 3) Given any **S**tructure, predict the structure **E**nergy and per-atom **F**orces (**S2EF**)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FPeCifZbtiKJ" + }, + "source": [ + 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+ ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PvjO99jp7xnh" + }, + "source": [ + "## Objective \n", + "This notebook serves as a tutorial for interacting with the Open Catalyst Project.\n", + "\n", + "By the end of this tutorial, users will have gained:\n", + "* Intuition to the dataset and it's properties\n", + "* Knowledge of the various OCP tasks: IS2RE, IS2RS, S2EF\n", + "* Steps to train, validate, and predict a model on the various tasks\n", + "* A walkthrough on creating your own model\n", + "* (Optional) Creating your own dataset for other molecular/catalyst applications \n", + "* (Optional) Using pretrained models directly with an [ASE](https://wiki.fysik.dtu.dk/ase/#:~:text=The%20Atomic%20Simulation%20Environment%20(ASE,under%20the%20GNU%20LGPL%20license.)-style calculator." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "99jkSa_KmrDH" + }, + "source": [ + "\n", + "# Climate Impact\n", + "\n", + "Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world’s rising energy needs while reducing climate change. As illustrated in the figure below, as we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand. This may require the storage of power for hours, days, or months. One solution that offers the potential of scaling to nation-sized grids is the conversion of renewable energy to other fuels, such as hydrogen. To be widely adopted, this process requires cost-effective solutions to running chemical reactions.\n", + "\n", + "An open challenge is finding low-cost catalysts to drive these reactions at high rates. Through the use of quantum mechanical simulations (Density Functional Theory, DFT), new catalyst structures can be tested and evaluated. Unfortunately, the high computational cost of these simulations limits the number of structures that may be tested. The use of AI or machine learning may provide a method to efficiently approximate these calculations; reducing the time required from 24} hours to a second. This capability would transform the search for new catalysts from the present day practice of evaluating O(1,000) of handpicked candidates to the brute force search over millions or even billions of candidates.\n", + "\n", + "As part of OCP, we publicly released the world's largest quantum mechanical simulation dataset -- [OC20](https://github.com/Open-Catalyst-Project/ocp/blob/master/DATASET.md) -- in the Fall of 2020 along with a suite of baselines and evaluation metrics. The creation of the dataset required over 70 million hours of compute. This dataset enables the exploration of techniques that will generalize across different catalyst materials and adsorbates. If successful, models trained on the dataset could enable the computational testing of millions of catalyst materials for a wide variety of chemical reactions. However, techniques that achieve the accuracies required** for practical impact are still beyond reach and remain an open area for research, thus encouraging research in this important area to help in meeting the world's energy needs in the decades ahead.\n", + "\n", + "** The computational catalysis community often aims for an adsorption energy MAE of 0.1-0.2 eV for practical relevance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jcpOlBcTsYVa" + }, + "source": [ + 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o5sbM_JPpdMR" + }, + "source": [ + "\n", + "# Target Audience\n", + "\n", + "This tutorial is designed for those interested in application of ML towards climate change. More specifically, those interested in material/catalyst discovery and Graph Nueral Networks (GNNs) will find lots of benefit here. Little to no domain chemistry knowledge is necessary as it will be covered in the tutorial. Experience with GNNs is a plus but not required. \n", + "\n", + "We have designed this notebook in a manner to get the ML communnity up to speed as far as background knowledge is concerned, and the catalysis community to better understand how to use the OCP's state-of-the-art models in their everyday workflows.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gQgijl46pYzn" + }, + "source": [ + "\n", + "# Background & Prerequisites\n", + "\n", + "Basic experience training ML models. Familiarity with PyTorch. Familiarity with Pytorch-Geometric could be helpful for development, but not required.\n", + "No background in chemistry is assumed.\n", + "\n", + "For those looking to apply our pretrained models on their datasets, familiarity with the [Atomic Simulation Environment](https://wiki.fysik.dtu.dk/ase/#:~:text=The%20Atomic%20Simulation%20Environment%20(ASE,under%20the%20GNU%20LGPL%20license.) is useful." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7BpQklEEIFDD" + }, + "source": [ + "\n", + "## Background References\n", + "\n", + "To gain an even better understanding of the Open Catalyst Project and the problems it seeks to address, we strongly recommend the following resources:\n", + "\n", + "* To learn more about electrocatalysis, see our [white paper](https://arxiv.org/pdf/2010.09435.pdf).\n", + "* To learn about the OC20 dataset and the associated tasks, please see the [OC20 dataset paper](https://arxiv.org/pdf/2010.09990.pdf).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rSRCNgYzUwaf" + }, + "source": [ + "\n", + "# Software Requirements\n", + "\n", + "All required dependencies can be found here - https://github.com/Open-Catalyst-Project/ocp#installation.\n", + "\n", + "For the following Colab Notebook, we manually install the dependencies below.\n", + "\n", + "For the purpose of the demo, we hihgly recommend you use a GPU. Google Colab provides access to 1 GPU (Runtime -> Change runtime type -> select GPU). The tutorial will function without a GPU, but will be slower for training times." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "58AKzWydvkVu" + }, + "source": [ + "# %%bash\n", + "pip install torch==1.7.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html \n", + "pip install demjson==2.2.4 lmdb==1.1.1 ase==3.21 pymatgen==2020.12.31 pyyaml==5.4 tensorboard==2.4 wandb==0.11.2\n", + "pip install torch-scatter==2.0.6 torch-sparse==0.6.9 torch-cluster==1.5.9 torch-spline-conv==1.2.1 torch-geometric==1.6.3 -f https://data.pyg.org/whl/torch-1.7.1+cu110.html\n", + "git clone https://github.com/Open-Catalyst-Project/ocp.git" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0NDOYuyAvmtO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e3508b8f-8ade-4000-cdd8-7c5f75865a96" + }, + "source": [ + "%cd ocp\n", + "!pip install -e ." + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/ocp\n", + "Obtaining file:///content/ocp\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n", + "Installing collected packages: ocp-models\n", + " Running setup.py develop for ocp-models\n", + "Successfully installed ocp-models-0.0.3\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LS0Tllp95tSu", + "outputId": "c2821fbe-093a-4a8d-ad43-6f2e61a9499a" + }, + "source": [ + "import torch\n", + "torch.cuda.is_available()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jXoiLncsU3pe" + }, + "source": [ + "\n", + "# Dataset Overview\n", + "\n", + "The Open Catalyst 2020 Dataset (OC20) will be used throughout this tutorial. More details can be found [here](https://github.com/Open-Catalyst-Project/ocp/blob/master/DATASET.md) and the corresponding [paper](https://arxiv.org/abs/2010.09990). Data is stored in PyTorch Geometric [Data](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html) objects and stored in LMDB files. For each task we include several sized training splits. Validation/Test splits are broken into several subsplits: In Domain (ID), Out of Domain Adsorbate (OOD-Ads), Out of Domain Catalyast (OOD-Cat) and Out of Domain Adsorbate and Catalyst (OOD-Both). Split sizes are summarized below:\n", + "\n", + "Train\n", + "* S2EF - 200k, 2M, 20M, 134M(All)\n", + "* IS2RE/IS2RS - 10k, 100k, 460k(All)\n", + "\n", + "Val/Test\n", + "* S2EF - ~1M across all subsplits\n", + "* IS2RE/IS2RS - ~25k across all splits\n", + "\n", + "#### **Tutorial Use**\n", + "\n", + "For the sake of this tutorial we provide much smaller splits (100 train, 20 val for all tasks) to allow users to easily store, train, and predict across the various tasks. Please refer [here](https://github.com/Open-Catalyst-Project/ocp#download-data) for details on how to download the full datasets for general use.\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FIiwpALzBKaH" + }, + "source": [ + "![oc20.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PoF-BxSM5Jkc" + }, + "source": [ + "## Data Download [~1min] \n", + "FOR TUTORIAL USE ONLY" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LEITxr5no8kh" + }, + "source": [ + "%%bash\n", + "mkdir data\n", + "cd data\n", + "wget -q http://dl.fbaipublicfiles.com/opencatalystproject/data/tutorial_data.tar.gz -O tutorial_data.tar.gz\n", + "tar -xzvf tutorial_data.tar.gz\n", + "rm tutorial_data.tar.gz" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bSt6h_Q-oqjK" + }, + "source": [ + "## Data Visualization " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HodnfJpE8D0u" + }, + "source": [ + "import matplotlib\n", + "matplotlib.use('Agg')\n", + "\n", + "import os\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "params = {\n", + " 'axes.labelsize': 14,\n", + " 'font.size': 14,\n", + " 'font.family': ' DejaVu Sans',\n", + " 'legend.fontsize': 20,\n", + " 'xtick.labelsize': 20,\n", + " 'ytick.labelsize': 20,\n", + " 'axes.labelsize': 25,\n", + " 'axes.titlesize': 25,\n", + " 'text.usetex': False,\n", + " 'figure.figsize': [12, 12]\n", + "}\n", + "matplotlib.rcParams.update(params)\n", + "\n", + "\n", + "import ase.io\n", + "from ase.io.trajectory import Trajectory\n", + "from ase.io import extxyz\n", + "from ase.calculators.emt import EMT\n", + "from ase.build import fcc100, add_adsorbate, molecule\n", + "from ase.constraints import FixAtoms\n", + "from ase.optimize import LBFGS\n", + "from ase.visualize.plot import plot_atoms\n", + "from ase import Atoms\n", + "from IPython.display import Image" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VRR5C88U8mH1" + }, + "source": [ + "### Understanding the data\n", + "We use the Atomic Simulation Environment (ASE) library to interact with our data. This notebook will provide you with some intuition on how atomic data is generated, how the data is structured, how to visualize the data, and the specific properties that are passed on to our models." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hEDcCSGD86Hg" + }, + "source": [ + "### Generating sample data\n", + "\n", + "The OC20 dataset was generated using density functional theory (DFT), a quantum chemistry method for modeling atomistic environments. For more details, please see our dataset paper. In this notebook, we generate sample data in the same format as the OC20 dataset; however, we use a faster method that is less accurate called effective-medium theory (EMT) because our DFT calculations are too computationally expensive to run here. EMT is great for demonstration purposes but not accurate enough for our actual catalysis applications. Below is a structural relaxation of a catalyst system, a propane (C3H8) adsorbate on a copper (Cu) surface. Throughout this tutorial a surface may be referred to as a slab and the combination of an adsorbate and a surface as an adslab." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y6Hx8JtXEbW-" + }, + "source": [ + "### Structural relaxations\n", + "\n", + "A structural relaxation or structure optimization is the process of iteratively updating atom positions to find the atom positions that minimize the energy of the structure. Standard optimization methods are used in structural relaxations — below we use the Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm. The step number, time, energy, and force max are printed at each optimization step. Each step is considered one example because it provides all the information we need to train models for the S2EF task and the entire set of steps is referred to as a trajectory. Visualizing intermediate structures or viewing the entire trajectory can be illuminating to understand what is physically happening and to look for problems in the simulation, especially when we run ML-driven relaxations. Common problems one may look out for - atoms excessively overlapping/colliding with each other and atoms flying off into random directions." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GEpQz9In9GrX", + "outputId": "96cd7bc8-2877-4b35-e133-80a10ad81b61" + }, + "source": [ + "###DATA GENERATION - FEEL FREE TO SKIP###\n", + "\n", + "# This cell sets up and runs a structural relaxation \n", + "# of a propane (C3H8) adsorbate on a copper (Cu) surface\n", + "\n", + "adslab = fcc100(\"Cu\", size=(3, 3, 3))\n", + "adsorbate = molecule(\"C3H8\")\n", + "add_adsorbate(adslab, adsorbate, 3, offset=(1, 1)) # adslab = adsorbate + slab\n", + "\n", + "# tag all slab atoms below surface as 0, surface as 1, adsorbate as 2\n", + "tags = np.zeros(len(adslab))\n", + "tags[18:27] = 1\n", + "tags[27:] = 2\n", + "\n", + "adslab.set_tags(tags)\n", + "\n", + "# Fixed atoms are prevented from moving during a structure relaxation. \n", + "# We fix all slab atoms beneath the surface. \n", + "cons= FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 0)])\n", + "adslab.set_constraint(cons)\n", + "adslab.center(vacuum=13.0, axis=2)\n", + "adslab.set_pbc(True)\n", + "adslab.set_calculator(EMT())\n", + "\n", + "os.makedirs('data', exist_ok=True)\n", + "\n", + "# Define structure optimizer - LBFGS. Run for 100 steps, \n", + "# or if the max force on all atoms (fmax) is below 0 ev/A.\n", + "# fmax is typically set to 0.01-0.05 eV/A, \n", + "# for this demo however we run for the full 100 steps.\n", + "\n", + "dyn = LBFGS(adslab, trajectory=\"data/toy_c3h8_relax.traj\")\n", + "dyn.run(fmax=0, steps=100)\n", + "\n", + "traj = ase.io.read(\"data/toy_c3h8_relax.traj\", \":\")\n", + "\n", + "# convert traj format to extxyz format (used by OC20 dataset)\n", + "columns = (['symbols','positions', 'move_mask', 'tags'])\n", + "with open('data/toy_c3h8_relax.extxyz','w') as f:\n", + " extxyz.write_xyz(f, traj, columns=columns)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Step Time Energy fmax\n", + "*Force-consistent energies used in optimization.\n", + "LBFGS: 0 01:59:21 15.804700* 6.7764\n", + "LBFGS: 1 01:59:21 12.190607* 4.3232\n", + "LBFGS: 2 01:59:21 10.240169* 2.2655\n", + "LBFGS: 3 01:59:22 9.779223* 0.9372\n", + "LBFGS: 4 01:59:22 9.671525* 0.7702\n", + "LBFGS: 5 01:59:22 9.574461* 0.6635\n", + "LBFGS: 6 01:59:22 9.537502* 0.5718\n", + "LBFGS: 7 01:59:22 9.516673* 0.4466\n", + "LBFGS: 8 01:59:22 9.481330* 0.4611\n", + "LBFGS: 9 01:59:22 9.462255* 0.2931\n", + "LBFGS: 10 01:59:22 9.448937* 0.2490\n", + "LBFGS: 11 01:59:22 9.433813* 0.2371\n", + "LBFGS: 12 01:59:22 9.418884* 0.2602\n", + "LBFGS: 13 01:59:23 9.409649* 0.2532\n", + "LBFGS: 14 01:59:23 9.404838* 0.1624\n", + "LBFGS: 15 01:59:23 9.401753* 0.1823\n", + "LBFGS: 16 01:59:23 9.397314* 0.2592\n", + "LBFGS: 17 01:59:23 9.387947* 0.3450\n", + "LBFGS: 18 01:59:23 9.370825* 0.4070\n", + "LBFGS: 19 01:59:23 9.342222* 0.4333\n", + "LBFGS: 20 01:59:23 9.286822* 0.5002\n", + "LBFGS: 21 01:59:23 9.249910* 0.5241\n", + "LBFGS: 22 01:59:23 9.187179* 0.5120\n", + "LBFGS: 23 01:59:24 9.124811* 0.5718\n", + "LBFGS: 24 01:59:24 9.066185* 0.5409\n", + "LBFGS: 25 01:59:24 9.000116* 1.0798\n", + "LBFGS: 26 01:59:24 8.893632* 0.7528\n", + "LBFGS: 27 01:59:24 8.845939* 0.3321\n", + "LBFGS: 28 01:59:24 8.815173* 0.2512\n", + "LBFGS: 29 01:59:24 8.808721* 0.2143\n", + "LBFGS: 30 01:59:24 8.794643* 0.1546\n", + "LBFGS: 31 01:59:24 8.789162* 0.2014\n", + "LBFGS: 32 01:59:24 8.782320* 0.1755\n", + "LBFGS: 33 01:59:25 8.780394* 0.1037\n", + "LBFGS: 34 01:59:25 8.778410* 0.1076\n", + "LBFGS: 35 01:59:25 8.775079* 0.1797\n", + "LBFGS: 36 01:59:25 8.766987* 0.3334\n", + "LBFGS: 37 01:59:25 8.750249* 0.5307\n", + "LBFGS: 38 01:59:25 8.725928* 0.6851\n", + "LBFGS: 39 01:59:25 8.702312* 0.5823\n", + "LBFGS: 40 01:59:25 8.661515* 0.3996\n", + "LBFGS: 41 01:59:25 8.643432* 0.5585\n", + "LBFGS: 42 01:59:25 8.621201* 0.3673\n", + "LBFGS: 43 01:59:26 8.614414* 0.1394\n", + "LBFGS: 44 01:59:26 8.610785* 0.1372\n", + "LBFGS: 45 01:59:26 8.608134* 0.1464\n", + "LBFGS: 46 01:59:26 8.604928* 0.1196\n", + "LBFGS: 47 01:59:26 8.599151* 0.1354\n", + "LBFGS: 48 01:59:26 8.594063* 0.1479\n", + "LBFGS: 49 01:59:26 8.589493* 0.1538\n", + "LBFGS: 50 01:59:26 8.587274* 0.0885\n", + "LBFGS: 51 01:59:26 8.584633* 0.0938\n", + "LBFGS: 52 01:59:26 8.580239* 0.1409\n", + "LBFGS: 53 01:59:27 8.572938* 0.2543\n", + "LBFGS: 54 01:59:27 8.563343* 0.2919\n", + "LBFGS: 55 01:59:27 8.554117* 0.1966\n", + "LBFGS: 56 01:59:27 8.547597* 0.1291\n", + "LBFGS: 57 01:59:27 8.542086* 0.1280\n", + "LBFGS: 58 01:59:27 8.535432* 0.0982\n", + "LBFGS: 59 01:59:27 8.533622* 0.1277\n", + "LBFGS: 60 01:59:27 8.527487* 0.1167\n", + "LBFGS: 61 01:59:27 8.523863* 0.1218\n", + "LBFGS: 62 01:59:28 8.519229* 0.1305\n", + "LBFGS: 63 01:59:28 8.515424* 0.1019\n", + "LBFGS: 64 01:59:28 8.511240* 0.2122\n", + "LBFGS: 65 01:59:28 8.507967* 0.2666\n", + "LBFGS: 66 01:59:28 8.503903* 0.2377\n", + "LBFGS: 67 01:59:28 8.497575* 0.1623\n", + "LBFGS: 68 01:59:28 8.485434* 0.2022\n", + "LBFGS: 69 01:59:28 8.466738* 0.2159\n", + "LBFGS: 70 01:59:28 8.467607* 0.3348\n", + "LBFGS: 71 01:59:29 8.454037* 0.1063\n", + "LBFGS: 72 01:59:29 8.448980* 0.1197\n", + "LBFGS: 73 01:59:29 8.446550* 0.0992\n", + "LBFGS: 74 01:59:29 8.444705* 0.0562\n", + "LBFGS: 75 01:59:29 8.443403* 0.0388\n", + "LBFGS: 76 01:59:29 8.442646* 0.0548\n", + "LBFGS: 77 01:59:29 8.442114* 0.0614\n", + "LBFGS: 78 01:59:29 8.440960* 0.0588\n", + "LBFGS: 79 01:59:29 8.439820* 0.0482\n", + "LBFGS: 80 01:59:29 8.438600* 0.0513\n", + "LBFGS: 81 01:59:30 8.437429* 0.0541\n", + "LBFGS: 82 01:59:30 8.435695* 0.0672\n", + "LBFGS: 83 01:59:30 8.431957* 0.0857\n", + "LBFGS: 84 01:59:30 8.423485* 0.1332\n", + "LBFGS: 85 01:59:30 8.413846* 0.2078\n", + "LBFGS: 86 01:59:30 8.404849* 0.1787\n", + "LBFGS: 87 01:59:30 8.385339* 0.1690\n", + "LBFGS: 88 01:59:30 8.386849* 0.1876\n", + "LBFGS: 89 01:59:30 8.371078* 0.1181\n", + "LBFGS: 90 01:59:31 8.368801* 0.0942\n", + "LBFGS: 91 01:59:31 8.366226* 0.0670\n", + "LBFGS: 92 01:59:31 8.361680* 0.0550\n", + "LBFGS: 93 01:59:31 8.360631* 0.0473\n", + "LBFGS: 94 01:59:31 8.359692* 0.0242\n", + "LBFGS: 95 01:59:31 8.359361* 0.0155\n", + "LBFGS: 96 01:59:31 8.359163* 0.0143\n", + "LBFGS: 97 01:59:31 8.359102* 0.0156\n", + "LBFGS: 98 01:59:31 8.359048* 0.0155\n", + "LBFGS: 99 01:59:31 8.358986* 0.0142\n", + "LBFGS: 100 01:59:32 8.358921* 0.0132\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ase/io/extxyz.py:302: UserWarning: Skipping unhashable information adsorbate_info\n", + " '{0}'.format(key))\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kb77jRtz9fws" + }, + "source": [ + "### Reading a trajectory" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mUbvcij59d6I" + }, + "source": [ + "identifier = \"toy_c3h8_relax.extxyz\"\n", + "\n", + "# the `index` argument corresponds to what frame of the trajectory to read in, specifiying \":\" reads in the full trajectory.\n", + "traj = ase.io.read(f\"data/{identifier}\", index=\":\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b_e6zDVx9pTC" + }, + "source": [ + "### Viewing a trajectory\n", + "\n", + "Below we visualize the initial, middle, and final steps in the structural relaxation trajectory from above. Copper atoms in the surface are colored orange, the propane adsorbate on the surface has grey colored carbon atoms and white colored hydrogen atoms. The adsorbate’s structure changes during the simulation and you can see how it relaxes on the surface. In this case, the relaxation looks normal; however, there can be instances where the adsorbate flies away (desorbs) from the surface or the adsorbate can break apart (dissociation), which are hard to detect without visualization. Additionally, visualizations can be used as a quick sanity check to ensure the initial system is set up correctly and there are no major issues with the simulation.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 680 + }, + "id": "CV5qe6IP9vZg", + "outputId": "256f97d6-daa7-40fa-ef50-7ba0ca005f9d" + }, + "source": [ + "fig, ax = plt.subplots(1, 3)\n", + "labels = ['initial', 'middle', 'final']\n", + "for i in range(3):\n", + " ax[i].axis('off')\n", + " ax[i].set_title(labels[i])\n", + "ase.visualize.plot.plot_atoms(traj[0], \n", + " ax[0], \n", + " radii=0.8, \n", + " rotation=(\"-75x, 45y, 10z\"))\n", + "ase.visualize.plot.plot_atoms(traj[50], \n", + " ax[1], \n", + " radii=0.8, \n", + " rotation=(\"-75x, 45y, 10z\"))\n", + "ase.visualize.plot.plot_atoms(traj[-1], \n", + " ax[2], \n", + " radii=0.8, \n", + " rotation=(\"-75x, 45y, 10z\"))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SSR1vQZ1_Ojq" + }, + "source": [ + "### Data contents \n", + "\n", + "Here we take a closer look at what information is contained within these trajectories." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9x8w3o17_May", + "outputId": "a6ed3414-774f-4e9c-f211-73379999f6a0" + }, + "source": [ + "i_structure = traj[0]\n", + "i_structure" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Atoms(symbols='Cu27C3H8', pbc=True, cell=[7.65796644025031, 7.65796644025031, 33.266996999999996], energies=..., forces=..., tags=..., constraint=FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), calculator=SinglePointCalculator(...))" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4CgeShkN_bdJ" + }, + "source": [ + "#### Atomic numbers" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cMGTQRIz_f2c", + "outputId": "20442973-b999-4723-ec66-ac169203dfbe" + }, + "source": [ + "numbers = i_structure.get_atomic_numbers()\n", + "print(numbers)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29\n", + " 29 29 29 6 6 6 1 1 1 1 1 1 1 1]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ol4Zi2Gh_qU_" + }, + "source": [ + "#### Atomic symbols" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cwbxks-i_uVq", + "outputId": "4960d233-b6c8-42bb-979d-879b6a20cfd4" + }, + "source": [ + "symbols = np.array(i_structure.get_chemical_symbols())\n", + "print(symbols)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu'\n", + " 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'C' 'C'\n", + " 'C' 'H' 'H' 'H' 'H' 'H' 'H' 'H' 'H']\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x57XplOw_yNw" + }, + "source": [ + "#### Unit cell\n", + "\n", + "The unit cell is the volume containing our system of interest. Express as a 3x3 array representing the directional vectors that make up the volume. Illustrated as the dashed box in the above visuals." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VWMMzn_i_0vM", + "outputId": "9fd0343a-9599-4fcb-911d-87ac48974bc0" + }, + "source": [ + "cell = np.array(i_structure.cell)\n", + "print(cell)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[ 7.65796644 0. 0. ]\n", + " [ 0. 7.65796644 0. ]\n", + " [ 0. 0. 33.266997 ]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XHRbOyaA_97r" + }, + "source": [ + "#### Periodic boundary conditions (PBC)\n", + "\n", + "x,y,z boolean representing whether a unit cell repeats in the corresponding directions. The OC20 dataset sets this to [True, True, True], with a large enough vacuum layer above the surface such that a unit cell does not see itself in the z direction. Although the original structure shown above is what get's passed into our models, the presence of PBC allows it to effectively repeat infinitely in the x and y directions. Below we visualize the same structure with a periodicity of 2 in all directions, what the model may effectively see." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "htvwgCuFAOSB", + "outputId": "578202d3-f9c5-4857-c2c1-86ee6aaf5aa0" + }, + "source": [ + "pbc = i_structure.pbc\n", + "print(pbc)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[ True True True]\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 400 + }, + "id": "Flzo7aO-RgyA", + "outputId": "36835a5f-cc91-48d1-ee8b-8fc5112c0cb6" + }, + "source": [ + "fig, ax = plt.subplots(1, 3)\n", + "labels = ['initial', 'middle', 'final']\n", + "for i in range(3):\n", + " ax[i].axis('off')\n", + " ax[i].set_title(labels[i])\n", + "\n", + "ase.visualize.plot.plot_atoms(traj[0].repeat((2,2,1)), \n", + " ax[0], \n", + " radii=0.8, \n", + " rotation=(\"-75x, 45y, 10z\"))\n", + "ase.visualize.plot.plot_atoms(traj[50].repeat((2,2,1)), \n", + " ax[1], \n", + " radii=0.8, \n", + " rotation=(\"-75x, 45y, 10z\"))\n", + "ase.visualize.plot.plot_atoms(traj[-1].repeat((2,2,1)), \n", + " ax[2], \n", + " radii=0.8, \n", + " rotation=(\"-75x, 45y, 10z\"))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 13 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TWGXcH7AARpy" + }, + "source": [ + "#### Tags\n", + "\n", + "The OC20 dataset consists of systems with several different types of atoms. To help with identifying the index of certain atoms, we tag each atom according to where it is found in the system. There are three categories of atoms: \n", + "- *sub-surface slab atoms*: these are atoms in the bottom layers of the catalyst, furthest away from the adsorbate\n", + "- *surface slab atoms*: these are atoms in the top layers of the catalyst, close to where the adsorbate will be placed \n", + "- *adsorbate atoms*: atoms that make up the adsorbate molecule on top of the catalyst.\n", + "\n", + "Tag:\n", + "\n", + "0 - Sub-surface slab atoms\n", + "\n", + "1 - Surface slab atoms\n", + "\n", + "2 - Adsorbate atoms\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SGZzFhsrB5A2", + "outputId": "3b2e4e3e-b82f-4e1a-ed88-e53e3040240b" + }, + "source": [ + "tags = i_structure.get_tags()\n", + "print(tags)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2\n", + " 2]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0zVhbDL2B8cd" + }, + "source": [ + "#### Fixed atoms constraint\n", + "\n", + "In reality, surfaces contain many, many more atoms beneath what we've illustrated as the surface. At an infinite depth, these subsurface atoms would look just like the bulk structure. We approximate a true surface by fixing the subsurface atoms into their “bulk” locations. This ensures that they cannot move at the “bottom” of the surface. If they could, this would throw off our calculations. Consistent with the above, we fix all atoms with tags=0, and denote them as \"fixed\". All other atoms are considered \"free\"." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FBMUmGrrCD_h", + "outputId": "4d0aad44-f6bd-491b-d734-5edf5be04031" + }, + "source": [ + "cons = i_structure.constraints[0]\n", + "print(cons, '\\n')\n", + "\n", + "# indices of fixed atoms\n", + "indices = cons.index\n", + "print(indices, '\\n')\n", + "\n", + "# fixed atoms correspond to tags = 0\n", + "print(tags[indices])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]) \n", + "\n", + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17] \n", + "\n", + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_DHAYeBUCHbN" + }, + "source": [ + "#### Adsorption energy\n", + "\n", + "The energy of the system is one of the properties of interest in the OC20 dataset. It's important to note that absolute energies provide little value to researchers and must be referenced properly to be useful. The OC20 dataset references all it's energies to the bare slab + gas references to arrive at adsorption energies. Adsorption energies are important in studying catalysts and their corresponding reaction rates. In addition to the structure relaxations of the OC20 dataset, bare slab and gas (N2, H2, H2O, CO) relaxations were carried out with DFT in order to calculate adsorption energies." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5XxYqdM7CMdd", + "outputId": "c2f5ea9c-1614-42ef-fbc0-75fddd7c976f" + }, + "source": [ + "final_structure = traj[-1]\n", + "relaxed_energy = final_structure.get_potential_energy()\n", + "print(f'Relaxed absolute energy = {relaxed_energy} eV')\n", + "\n", + "# Corresponding raw slab used in original adslab (adsorbate+slab) system. \n", + "raw_slab = fcc100(\"Cu\", size=(3, 3, 3))\n", + "raw_slab.set_calculator(EMT())\n", + "raw_slab_energy = raw_slab.get_potential_energy()\n", + "print(f'Raw slab energy = {raw_slab_energy} eV')\n", + "\n", + "\n", + "adsorbate = Atoms(\"C3H8\").get_chemical_symbols()\n", + "# For clarity, we define arbitrary gas reference energies here.\n", + "# A more detailed discussion of these calculations can be found in the corresponding paper's SI. \n", + "gas_reference_energies = {'H': .3, 'O': .45, 'C': .35, 'N': .50}\n", + "\n", + "adsorbate_reference_energy = 0\n", + "for ads in adsorbate:\n", + " adsorbate_reference_energy += gas_reference_energies[ads]\n", + "\n", + "print(f'Adsorbate reference energy = {adsorbate_reference_energy} eV\\n')\n", + "\n", + "adsorption_energy = relaxed_energy - raw_slab_energy - adsorbate_reference_energy\n", + "print(f'Adsorption energy: {adsorption_energy} eV')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Relaxed absolute energy = 8.358921451420816 eV\n", + "Raw slab energy = 8.127167122751231 eV\n", + "Adsorbate reference energy = 3.4499999999999993 eV\n", + "\n", + "Adsorption energy: -3.218245671330415 eV\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EchgyYxXCUit" + }, + "source": [ + "#### Plot energy profile of toy trajectory\n", + "\n", + "Plotting the energy profile of our trajectory is a good way to ensure nothing strange has occured. We expect to see a decreasing monotonic function. If the energy is consistently increasing or there's multiple large spikes this could be a sign of some issues in the optimization. This is particularly useful for when analyzing ML-driven relaxations and whether they make general physical sense." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 482 + }, + "id": "WffoTL5pCSrg", + "outputId": "86e7a0fb-7a34-42ee-db58-edd30323eb54" + }, + "source": [ + "energies = [image.get_potential_energy() - raw_slab_energy - adsorbate_reference_energy for image in traj]\n", + "\n", + "plt.figure(figsize=(7, 7))\n", + "plt.plot(range(len(energies)), energies, lw=3)\n", + "plt.xlabel(\"Step\", fontsize=24)\n", + "plt.ylabel(\"Energy, eV\", fontsize=24)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Energy, eV')" + ] + }, + "metadata": {}, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "erpOSowgCeuS" + }, + "source": [ + "#### Force\n", + "\n", + "Forces are another important property of the OC20 dataset. Unlike datasets like QM9 which contain only ground state properties, the OC20 dataset contains per-atom forces necessary to carry out atomistic simulations. Physically, forces are the negative gradient of energy w.r.t atomic positions: $F = -\\frac{dE}{dx}$. Although not mandatory (depending on the application), maintaining this energy-force consistency is important for models that seek to make predictions on both properties.\n", + "\n", + "The \"apply_constraint\" argument controls whether to apply system constraints to the forces. In the OC20 dataset, this controls whether to return forces for fixed atoms (apply_constraint=False) or return 0s (apply_constraint=True)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NtgLDiT2Cmff", + "outputId": "61a720bd-4117-4403-eb07-4d49fd5ddc22" + }, + "source": [ + "# Returning forces for all atoms - regardless of whether \"fixed\" or \"free\"\n", + "i_structure.get_forces(apply_constraint=False)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[-1.07900000e-05, -3.80000000e-06, 1.13560540e-01],\n", + " [-0.00000000e+00, -4.29200000e-05, 1.13302410e-01],\n", + " [ 1.07900000e-05, -3.80000000e-06, 1.13560540e-01],\n", + " [-1.84600000e-05, 0.00000000e+00, 1.13543430e-01],\n", + " [ 0.00000000e+00, -0.00000000e+00, 1.13047800e-01],\n", + " [ 1.84600000e-05, 0.00000000e+00, 1.13543430e-01],\n", + " [-1.07900000e-05, 3.80000000e-06, 1.13560540e-01],\n", + " [-0.00000000e+00, 4.29200000e-05, 1.13302410e-01],\n", + " [ 1.07900000e-05, 3.80000000e-06, 1.13560540e-01],\n", + " [-1.10430500e-02, -2.53094000e-03, -4.84573700e-02],\n", + " [ 1.10430500e-02, -2.53094000e-03, -4.84573700e-02],\n", + " [ 0.00000000e+00, -2.20890000e-04, -2.07827000e-03],\n", + " [-1.10430500e-02, 2.53094000e-03, -4.84573700e-02],\n", + " [ 1.10430500e-02, 2.53094000e-03, -4.84573700e-02],\n", + " [-0.00000000e+00, 2.20890000e-04, -2.07827000e-03],\n", + " [-3.49808000e-03, -0.00000000e+00, -7.85544000e-03],\n", + " [ 3.49808000e-03, -0.00000000e+00, -7.85544000e-03],\n", + " [-0.00000000e+00, -0.00000000e+00, -5.97640000e-04],\n", + " [-3.18144370e-01, -2.36420450e-01, -3.97089230e-01],\n", + " [ 0.00000000e+00, -2.18895316e+00, -2.74768262e+00],\n", + " [ 3.18144370e-01, -2.36420450e-01, -3.97089230e-01],\n", + " [-5.65980520e-01, 0.00000000e+00, -6.16046990e-01],\n", + " [ 0.00000000e+00, 0.00000000e+00, -4.47152822e+00],\n", + " [ 5.65980520e-01, -0.00000000e+00, -6.16046990e-01],\n", + " [-3.18144370e-01, 2.36420450e-01, -3.97089230e-01],\n", + " [ 0.00000000e+00, 2.18895316e+00, -2.74768262e+00],\n", + " [ 3.18144370e-01, 2.36420450e-01, -3.97089230e-01],\n", + " [-0.00000000e+00, 0.00000000e+00, -3.96835355e+00],\n", + " [-0.00000000e+00, -3.64190926e+00, 5.71458646e+00],\n", + " [-0.00000000e+00, 3.64190926e+00, 5.71458646e+00],\n", + " [-2.18178516e+00, -0.00000000e+00, 1.67589182e+00],\n", + " [ 2.18178516e+00, 0.00000000e+00, 1.67589182e+00],\n", + " [-0.00000000e+00, 2.46333681e+00, 1.78299828e+00],\n", + " [-0.00000000e+00, -2.46333681e+00, 1.78299828e+00],\n", + " [ 6.18714050e+00, 2.26336330e-01, -5.99485570e-01],\n", + " [-6.18714050e+00, 2.26336330e-01, -5.99485570e-01],\n", + " [-6.18714050e+00, -2.26336330e-01, -5.99485570e-01],\n", + " [ 6.18714050e+00, -2.26336330e-01, -5.99485570e-01]])" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QVgvU-OgCqzx", + "outputId": "1a4bed0b-3554-4b42-b41e-7ca84741d66e" + }, + "source": [ + "# Applying the fixed atoms constraint to the forces\n", + "i_structure.get_forces(apply_constraint=True)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [-0.31814437, -0.23642045, -0.39708923],\n", + " [ 0. , -2.18895316, -2.74768262],\n", + " [ 0.31814437, -0.23642045, -0.39708923],\n", + " [-0.56598052, 0. , -0.61604699],\n", + " [ 0. , 0. , -4.47152822],\n", + " [ 0.56598052, -0. , -0.61604699],\n", + " [-0.31814437, 0.23642045, -0.39708923],\n", + " [ 0. , 2.18895316, -2.74768262],\n", + " [ 0.31814437, 0.23642045, -0.39708923],\n", + " [-0. , 0. , -3.96835355],\n", + " [-0. , -3.64190926, 5.71458646],\n", + " [-0. , 3.64190926, 5.71458646],\n", + " [-2.18178516, -0. , 1.67589182],\n", + " [ 2.18178516, 0. , 1.67589182],\n", + " [-0. , 2.46333681, 1.78299828],\n", + " [-0. , -2.46333681, 1.78299828],\n", + " [ 6.1871405 , 0.22633633, -0.59948557],\n", + " [-6.1871405 , 0.22633633, -0.59948557],\n", + " [-6.1871405 , -0.22633633, -0.59948557],\n", + " [ 6.1871405 , -0.22633633, -0.59948557]])" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uzDp10XsoHdo" + }, + "source": [ + "### Interacting with the OC20 datasets\n", + "\n", + "The OC20 datasets are stored in LMDBs. Here we show how to interact with the datasets directly in order to better understand the data. We use two seperate classes to read in the approriate datasets:\n", + "\n", + "*S2EF* - We use the [TrajectoryLmdbDataset](https://github.com/Open-Catalyst-Project/ocp/blob/master/ocpmodels/datasets/trajectory_lmdb.py) object to read in a **directory** of LMDB files containing the dataset.\n", + "\n", + "*IS2RE/IS2RS* - We use the [SinglePointLmdbDataset](https://github.com/Open-Catalyst-Project/ocp/blob/master/ocpmodels/datasets/single_point_lmdb.py) class to read in a **single LMDB file** containing the dataset.\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "7F7BjxNQoGLn", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "36fcd255-facc-43dd-efda-c238cac9c5d9" + }, + "source": [ + "from ocpmodels.datasets import TrajectoryLmdbDataset, SinglePointLmdbDataset\n", + "\n", + "# TrajectoryLmdbDataset is our custom Dataset method to read the lmdbs as Data objects. Note that we need to give the path to the folder containing lmdbs for S2EF\n", + "dataset = TrajectoryLmdbDataset({\"src\": \"data/s2ef/train_100/\"})\n", + "\n", + "print(\"Size of the dataset created:\", len(dataset))\n", + "print(dataset[0])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Size of the dataset created: 100\n", + "Data(atomic_numbers=[86], cell=[1, 3, 3], cell_offsets=[2964, 3], edge_index=[2, 2964], fid=[1], fixed=[86], force=[86, 3], id=\"0_0\", natoms=86, pos=[86, 3], sid=[1], tags=[86], total_frames=74, y=6.282500615000004)\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pD5B_TymoJ8S", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "72b21c2a-9472-4b08-afe9-c1bd28a5b399" + }, + "source": [ + "data = dataset[0]\n", + "data" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Data(atomic_numbers=[86], cell=[1, 3, 3], cell_offsets=[2964, 3], edge_index=[2, 2964], fid=[1], fixed=[86], force=[86, 3], id=\"0_0\", natoms=86, pos=[86, 3], sid=[1], tags=[86], total_frames=74, y=6.282500615000004)" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rL4u0glIoL8h", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a29c8dfc-617f-48fa-9195-e851b23033e1" + }, + "source": [ + "energies = torch.tensor([data.y for data in dataset])\n", + "energies" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensor([ 6.2825e+00, 4.1290e+00, 3.1451e+00, 3.0260e+00, 1.7921e+00,\n", + " 1.6451e+00, 1.2257e+00, 1.2161e+00, 1.0712e+00, 7.4727e-01,\n", + " 5.9575e-01, 5.7016e-01, 4.2819e-01, 3.1616e-01, 2.5283e-01,\n", + " 2.2425e-01, 2.2346e-01, 2.0530e-01, 1.6090e-01, 1.1807e-01,\n", + " 1.1691e-01, 9.1254e-02, 7.4997e-02, 6.3274e-02, 5.2782e-02,\n", + " 4.8892e-02, 3.9609e-02, 3.1746e-02, 2.7179e-02, 2.7007e-02,\n", + " 2.3709e-02, 1.8005e-02, 1.7676e-02, 1.4129e-02, 1.3162e-02,\n", + " 1.1374e-02, 7.4124e-03, 7.7525e-03, 6.1224e-03, 5.2787e-03,\n", + " 2.8587e-03, 1.1835e-04, -1.1200e-03, -1.3011e-03, -2.6812e-03,\n", + " -5.9202e-03, -6.1644e-03, -6.9261e-03, -9.1364e-03, -9.2114e-03,\n", + " -1.0665e-02, -1.3760e-02, -1.3588e-02, -1.4895e-02, -1.6190e-02,\n", + " -1.8660e-02, -1.4980e-02, -1.8880e-02, -2.0218e-02, -2.0559e-02,\n", + " -2.1013e-02, -2.2129e-02, -2.2748e-02, -2.3322e-02, -2.3382e-02,\n", + " -2.3865e-02, -2.3973e-02, -2.4196e-02, -2.4755e-02, -2.4951e-02,\n", + " -2.5078e-02, -2.5148e-02, -2.5257e-02, -2.5550e-02, 5.9721e+00,\n", + " 9.5081e+00, 2.6373e+00, 4.0946e+00, 1.4385e+00, 1.2700e+00,\n", + " 1.0081e+00, 5.3797e-01, 5.1462e-01, 2.8812e-01, 1.2429e-01,\n", + " -1.1352e-02, -2.2293e-01, -3.9102e-01, -4.3574e-01, -5.3142e-01,\n", + " -5.4777e-01, -6.3948e-01, -7.3816e-01, -8.2163e-01, -8.2526e-01,\n", + " -8.8313e-01, -8.8615e-01, -9.3446e-01, -9.5100e-01, -9.5168e-01])" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mkOm2roAoNY2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 737 + }, + "outputId": "aed9b4de-99de-49ab-a21c-3a372166747a" + }, + "source": [ + "plt.hist(energies, bins = 50)\n", + "plt.yscale(\"log\")\n", + "plt.xlabel(\"Energies\")\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RtECvWIPCu0b" + }, + "source": [ + "### Additional Resources\n", + "\n", + "More helpful resources, tutorials, and documentation can be found at ASE's webpage: https://wiki.fysik.dtu.dk/ase/index.html. We point to specific pages that may be of interest:\n", + "\n", + "* Interacting with Atoms Object: https://wiki.fysik.dtu.dk/ase/ase/atoms.html\n", + "* Visualization: https://wiki.fysik.dtu.dk/ase/ase/visualize/visualize.html\n", + "* Structure optimization: https://wiki.fysik.dtu.dk/ase/ase/optimize.html\n", + "* More ASE Tutorials: https://wiki.fysik.dtu.dk/ase/tutorials/tutorials.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qa9Iuu2GU52Z" + }, + "source": [ + "\n", + "# Tasks\n", + "\n", + "In this section, we cover the different types of tasks the OC20 dataset presents and how to train and predict their corresponding models.\n", + "\n", + "1. Structure to Energy and Forces (S2EF)\n", + "2. Initial Structure to Relaxed Energy (IS2RE)\n", + "3. Initial Structure to Relaxed Structure (IS2RS)\n", + "\n", + "Tasks can be interrelated. The figure below illustrates several approaches to solving the IS2RE task:\n", + "\n", + "(a) the traditional approach uses DFT along with an optimizer,\n", + "such as BFGS or conjugate gradient, to iteratively update\n", + "the atom positions until the relaxed structure and energy are found.\n", + "\n", + "(b) using ML models trained to predict the energy and forces of a\n", + "structure, S2EF can be used as a direct replacement for DFT. \n", + "\n", + "(c) the relaxed structure could potentially be directly regressed from\n", + "the initial structure and S2EF used to find the energy.\n", + "\n", + "(d) directly compute the relaxed energy from the initial state.\n", + "\n", + "\n", + "**NOTE** The following sections are intended to demonstrate the inner workings of our codebase and what goes into running the various tasks. We do not recommend training to completion within a notebook setting. Please see the [running on command line](#cmd) section for the preferred way to train/evaluate models." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W7aZpLzmuNra" + }, + "source": [ + "![tasks.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yWXsiZ5freTG" + }, + "source": [ + "## Structure to Energy and Forces (S2EF) \n", + "\n", + "The S2EF task takes an atomic system as input and predicts the energy of the entire system and forces on each atom. This is our most general task, ultimately serving as a surrogate to DFT. A model that can perform well on this task can accelerate other applications like molecular dynamics and transitions tate calculations.\n", + "\n", + "### Steps for training an S2EF model\n", + "1) Define or load a configuration (config), which includes the following\n", + "* task\n", + "* model\n", + "* optimizer\n", + "* dataset\n", + "* trainer\n", + "\n", + "2) Create a ForcesTrainer object\n", + "\n", + "3) Train the model\n", + "\n", + "4) Validate the model\n", + "\n", + "**For storage and compute reasons we use a very small subset of the OC20 S2EF dataset for this tutorial. Results will be considerably worse than presented in our paper.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2snWOAxnPPyd" + }, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "l-1rNyuk_1Mo" + }, + "source": [ + "from ocpmodels.trainers import ForcesTrainer\n", + "from ocpmodels.datasets import TrajectoryLmdbDataset\n", + "from ocpmodels import models\n", + "from ocpmodels.common import logger\n", + "from ocpmodels.common.utils import setup_logging\n", + "setup_logging()\n", + "\n", + "import numpy as np\n", + "import copy\n", + "import os" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OmkUDMQgP5he" + }, + "source": [ + "### Dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1SHl_1eQP4mW" + }, + "source": [ + "train_src = \"data/s2ef/train_100\"\n", + "val_src = \"data/s2ef/val_20\"" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZUpFFV2OWyYJ" + }, + "source": [ + "### Normalize data\n", + "\n", + "If you wish to normalize the targets we must compute the mean and standard deviation for our energy values. Because forces are physically related by the negative gradient of energy, we use the same multiplicative energy factor for forces." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HAJ3x4SnXE1o" + }, + "source": [ + "train_dataset = TrajectoryLmdbDataset({\"src\": train_src})\n", + "\n", + "energies = []\n", + "for data in train_dataset:\n", + " energies.append(data.y)\n", + "\n", + "mean = np.mean(energies)\n", + "stdev = np.std(energies)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ruspSf6CQIk4" + }, + "source": [ + "### Define the Config" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6R6IkYLCQPpH" + }, + "source": [ + "For this example, we will explicitly define the config; however, a set of default configs can be found [here](https://github.com/Open-Catalyst-Project/ocp/tree/master/configs). Default config yaml files can easily be loaded with the following [utility](https://github.com/Open-Catalyst-Project/ocp/blob/aa8e44d50229fce887b3a94a5661c4f85cd73eed/ocpmodels/common/utils.py#L361-L400). Loading a yaml config is preferrable when launching jobs from the command line. We have included our best models' config files here for reference. \n", + "\n", + "**Note** - we only train for a single epoch with a reduced batch size (GPU memory constraints) for demonstration purposes, modify accordingly for full convergence." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "j6Z_XbkiPGR9" + }, + "source": [ + "# Task\n", + "task = {\n", + " 'dataset': 'trajectory_lmdb', # dataset used for the S2EF task\n", + " 'description': 'Regressing to energies and forces for DFT trajectories from OCP',\n", + " 'type': 'regression',\n", + " 'metric': 'mae',\n", + " 'labels': ['potential energy'],\n", + " 'grad_input': 'atomic forces',\n", + " 'train_on_free_atoms': True,\n", + " 'eval_on_free_atoms': True\n", + "}\n", + "# Model\n", + "model = {\n", + " 'name': 'gemnet_t',\n", + " \"num_spherical\": 7,\n", + " \"num_radial\": 128,\n", + " \"num_blocks\": 3,\n", + " \"emb_size_atom\": 512,\n", + " \"emb_size_edge\": 512,\n", + " \"emb_size_trip\": 64,\n", + " \"emb_size_rbf\": 16,\n", + " \"emb_size_cbf\": 16,\n", + " \"emb_size_bil_trip\": 64,\n", + " \"num_before_skip\": 1,\n", + " \"num_after_skip\": 2,\n", + " \"num_concat\": 1,\n", + " \"num_atom\": 3,\n", + " \"cutoff\": 6.0,\n", + " \"max_neighbors\": 50,\n", + " \"rbf\": {\"name\": \"gaussian\"},\n", + " \"envelope\": {\n", + " \"name\": \"polynomial\",\n", + " \"exponent\": 5,\n", + " },\n", + " \"cbf\": {\"name\": \"spherical_harmonics\"},\n", + " \"extensive\": True,\n", + " \"otf_graph\": False,\n", + " \"output_init\": \"HeOrthogonal\",\n", + " \"activation\": \"silu\",\n", + " \"scale_file\": \"configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\",\n", + " \"regress_forces\": True,\n", + " \"direct_forces\": True,\n", + "}\n", + "# Optimizer\n", + "optimizer = {\n", + " 'batch_size': 1, # originally 32\n", + " 'eval_batch_size': 1, # originally 32\n", + " 'num_workers': 2,\n", + " 'lr_initial': 5.e-4,\n", + " 'optimizer': 'AdamW',\n", + " 'optimizer_params': {\"amsgrad\": True},\n", + " 'scheduler': \"ReduceLROnPlateau\",\n", + " 'mode': \"min\",\n", + " 'factor': 0.8,\n", + " 'patience': 3,\n", + " 'max_epochs': 1, # used for demonstration purposes\n", + " 'force_coefficient': 100,\n", + " 'ema_decay': 0.999,\n", + " 'clip_grad_norm': 10,\n", + " 'loss_energy': 'mae',\n", + " 'loss_force': 'l2mae',\n", + "}\n", + "# Dataset\n", + "dataset = [\n", + " {'src': train_src,\n", + " 'normalize_labels': True,\n", + " \"target_mean\": mean,\n", + " \"target_std\": stdev,\n", + " \"grad_target_mean\": 0.0,\n", + " \"grad_target_std\": stdev\n", + " }, # train set \n", + " {'src': val_src}, # val set (optional)\n", + "]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8AsZpLjIQg-W" + }, + "source": [ + "### Create the trainer" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0it4gs6gPGGz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e7a98c1d-6d4f-425b-878f-4a3a7b42b2ed" + }, + "source": [ + "trainer = ForcesTrainer(\n", + " task=task,\n", + " model=copy.deepcopy(model), # copied for later use, not necessary in practice.\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"S2EF-example\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=5,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + " amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),\n", + ")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "amp: true\n", + "cmd:\n", + " checkpoint_dir: ./checkpoints/2021-11-22-17-14-40-S2EF-example\n", + " commit: bc04a90\n", + " identifier: S2EF-example\n", + " logs_dir: ./logs/tensorboard/2021-11-22-17-14-40-S2EF-example\n", + " print_every: 5\n", + " results_dir: ./results/2021-11-22-17-14-40-S2EF-example\n", + " seed: 0\n", + " timestamp_id: 2021-11-22-17-14-40-S2EF-example\n", + "dataset:\n", + " grad_target_mean: 0.0\n", + " grad_target_std: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - &id001 !!python/object/apply:numpy.dtype\n", + " args:\n", + " - f8\n", + " - false\n", + " - true\n", + " state: !!python/tuple\n", + " - 3\n", + " - <\n", + " - null\n", + " - null\n", + " - null\n", + " - -1\n", + " - -1\n", + " - 0\n", + " - !!binary |\n", + " dPVlWhRA+D8=\n", + " normalize_labels: true\n", + " src: data/s2ef/train_100\n", + " target_mean: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - *id001\n", + " - !!binary |\n", + " zSXlDMrm3D8=\n", + " target_std: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - *id001\n", + " - !!binary |\n", + " dPVlWhRA+D8=\n", + "gpus: 1\n", + "logger: tensorboard\n", + "model: gemnet_t\n", + "model_attributes:\n", + " activation: silu\n", + " cbf:\n", + " name: spherical_harmonics\n", + " cutoff: 6.0\n", + " direct_forces: true\n", + " emb_size_atom: 512\n", + " emb_size_bil_trip: 64\n", + " emb_size_cbf: 16\n", + " emb_size_edge: 512\n", + " emb_size_rbf: 16\n", + " emb_size_trip: 64\n", + " envelope:\n", + " exponent: 5\n", + " name: polynomial\n", + " extensive: true\n", + " max_neighbors: 50\n", + " num_after_skip: 2\n", + " num_atom: 3\n", + " num_before_skip: 1\n", + " num_blocks: 3\n", + " num_concat: 1\n", + " num_radial: 128\n", + " num_spherical: 7\n", + " otf_graph: false\n", + " output_init: HeOrthogonal\n", + " rbf:\n", + " name: gaussian\n", + " regress_forces: true\n", + " scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n", + "optim:\n", + " batch_size: 1\n", + " clip_grad_norm: 10\n", + " ema_decay: 0.999\n", + " eval_batch_size: 1\n", + " factor: 0.8\n", + " force_coefficient: 100\n", + " loss_energy: mae\n", + " loss_force: l2mae\n", + " lr_initial: 0.0005\n", + " max_epochs: 1\n", + " mode: min\n", + " num_workers: 2\n", + " optimizer: AdamW\n", + " optimizer_params:\n", + " amsgrad: true\n", + " patience: 3\n", + " scheduler: ReduceLROnPlateau\n", + "slurm: {}\n", + "task:\n", + " dataset: trajectory_lmdb\n", + " description: Regressing to energies and forces for DFT trajectories from OCP\n", + " eval_on_free_atoms: true\n", + " grad_input: atomic forces\n", + " labels:\n", + " - potential energy\n", + " metric: mae\n", + " train_on_free_atoms: true\n", + " type: regression\n", + "val_dataset:\n", + " src: data/s2ef/val_20\n", + "\n", + "2021-11-22 17:15:16 (INFO): Loading dataset: trajectory_lmdb\n", + "2021-11-22 17:15:16 (INFO): Loading model: gemnet_t\n", + "2021-11-22 17:15:20 (INFO): Loaded GemNetT with 31671825 parameters.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2021-11-22 17:15:20 (WARNING): Model gradient logging to tensorboard not yet supported.\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4yGWsRq3PF8R" + }, + "source": [ + "trainer.model" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vA8nDKt4QqkO" + }, + "source": [ + "### Train the model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WFmssq5oPFd_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a80e93f3-637a-4394-9ec8-4c38bac27461" + }, + "source": [ + "trainer.train()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:15:33 (INFO): forcesx_mae: 2.37e+00, forcesy_mae: 3.27e+00, forcesz_mae: 3.07e+00, forces_mae: 2.90e+00, forces_cos: -4.09e-02, forces_magnitude: 5.73e+00, energy_mae: 4.82e+01, energy_force_within_threshold: 0.00e+00, loss: 8.53e+02, lr: 5.00e-04, epoch: 5.00e-02, step: 5.00e+00\n", + "2021-11-22 17:15:39 (INFO): forcesx_mae: 2.42e+00, forcesy_mae: 3.28e+00, forcesz_mae: 3.03e+00, forces_mae: 2.91e+00, forces_cos: -1.82e-02, forces_magnitude: 5.77e+00, energy_mae: 4.96e+01, energy_force_within_threshold: 0.00e+00, loss: 7.71e+02, lr: 5.00e-04, epoch: 1.00e-01, step: 1.00e+01\n", + "2021-11-22 17:15:46 (INFO): forcesx_mae: 1.78e+01, forcesy_mae: 8.20e+01, forcesz_mae: 2.61e+01, forces_mae: 4.20e+01, forces_cos: -1.39e-02, forces_magnitude: 9.52e+01, energy_mae: 2.10e+03, energy_force_within_threshold: 0.00e+00, loss: 1.45e+04, lr: 5.00e-04, epoch: 1.50e-01, step: 1.50e+01\n", + "2021-11-22 17:15:53 (INFO): forcesx_mae: 1.17e+01, forcesy_mae: 4.24e+01, forcesz_mae: 1.78e+01, forces_mae: 2.40e+01, forces_cos: -2.96e-02, forces_magnitude: 5.36e+01, energy_mae: 1.12e+03, energy_force_within_threshold: 0.00e+00, loss: 3.92e+03, lr: 5.00e-04, epoch: 2.00e-01, step: 2.00e+01\n", + "2021-11-22 17:15:59 (INFO): forcesx_mae: 1.40e+01, forcesy_mae: 3.46e+01, forcesz_mae: 1.56e+01, forces_mae: 2.14e+01, forces_cos: 9.12e-03, forces_magnitude: 4.50e+01, energy_mae: 4.24e+02, energy_force_within_threshold: 0.00e+00, loss: 4.87e+03, lr: 5.00e-04, epoch: 2.50e-01, step: 2.50e+01\n", + "2021-11-22 17:16:06 (INFO): forcesx_mae: 9.72e+01, forcesy_mae: 2.24e+02, forcesz_mae: 1.05e+02, forces_mae: 1.42e+02, forces_cos: -4.17e-02, forces_magnitude: 3.00e+02, energy_mae: 4.30e+03, energy_force_within_threshold: 0.00e+00, loss: 3.78e+04, lr: 5.00e-04, epoch: 3.00e-01, step: 3.00e+01\n", + "2021-11-22 17:16:12 (INFO): forcesx_mae: 1.33e+00, forcesy_mae: 1.43e+00, forcesz_mae: 1.35e+00, forces_mae: 1.37e+00, forces_cos: 6.92e-03, forces_magnitude: 2.72e+00, energy_mae: 2.62e+01, energy_force_within_threshold: 0.00e+00, loss: 2.00e+02, lr: 5.00e-04, epoch: 3.50e-01, step: 3.50e+01\n", + "2021-11-22 17:16:19 (INFO): forcesx_mae: 1.05e+02, forcesy_mae: 2.08e+02, forcesz_mae: 1.16e+02, forces_mae: 1.43e+02, forces_cos: -2.02e-02, forces_magnitude: 2.95e+02, energy_mae: 3.29e+03, energy_force_within_threshold: 0.00e+00, loss: 3.36e+04, lr: 5.00e-04, epoch: 4.00e-01, step: 4.00e+01\n", + "2021-11-22 17:16:25 (INFO): forcesx_mae: 2.25e+02, forcesy_mae: 5.61e+02, forcesz_mae: 2.86e+02, forces_mae: 3.57e+02, forces_cos: 7.29e-02, forces_magnitude: 7.71e+02, energy_mae: 7.83e+03, energy_force_within_threshold: 0.00e+00, loss: 7.47e+04, lr: 5.00e-04, epoch: 4.50e-01, step: 4.50e+01\n", + "2021-11-22 17:16:32 (INFO): forcesx_mae: 6.88e-01, forcesy_mae: 7.65e-01, forcesz_mae: 6.54e-01, forces_mae: 7.03e-01, forces_cos: -7.49e-02, forces_magnitude: 1.25e+00, energy_mae: 1.88e+01, energy_force_within_threshold: 0.00e+00, loss: 1.05e+02, lr: 5.00e-04, epoch: 5.00e-01, step: 5.00e+01\n", + "2021-11-22 17:16:38 (INFO): forcesx_mae: 5.71e-01, forcesy_mae: 6.43e-01, forcesz_mae: 6.73e-01, forces_mae: 6.29e-01, forces_cos: 1.62e-01, forces_magnitude: 9.06e-01, energy_mae: 2.06e+01, energy_force_within_threshold: 0.00e+00, loss: 9.64e+01, lr: 5.00e-04, epoch: 5.50e-01, step: 5.50e+01\n", + "2021-11-22 17:16:45 (INFO): forcesx_mae: 4.86e-01, forcesy_mae: 4.93e-01, forcesz_mae: 5.01e-01, forces_mae: 4.93e-01, forces_cos: -2.05e-02, forces_magnitude: 9.57e-01, energy_mae: 1.11e+01, energy_force_within_threshold: 0.00e+00, loss: 7.26e+01, lr: 5.00e-04, epoch: 6.00e-01, step: 6.00e+01\n", + "2021-11-22 17:16:51 (INFO): forcesx_mae: 9.37e-01, forcesy_mae: 2.66e+00, forcesz_mae: 1.30e+00, forces_mae: 1.63e+00, forces_cos: 2.07e-01, forces_magnitude: 2.77e+00, energy_mae: 8.03e+00, energy_force_within_threshold: 0.00e+00, loss: 2.04e+02, lr: 5.00e-04, epoch: 6.50e-01, step: 6.50e+01\n", + "2021-11-22 17:16:58 (INFO): forcesx_mae: 4.89e-01, forcesy_mae: 4.57e-01, forcesz_mae: 4.84e-01, forces_mae: 4.77e-01, forces_cos: 1.22e-01, forces_magnitude: 6.81e-01, energy_mae: 6.36e+00, energy_force_within_threshold: 0.00e+00, loss: 6.72e+01, lr: 5.00e-04, epoch: 7.00e-01, step: 7.00e+01\n", + "2021-11-22 17:17:04 (INFO): forcesx_mae: 1.61e+00, forcesy_mae: 1.96e+00, forcesz_mae: 1.58e+00, forces_mae: 1.72e+00, forces_cos: 5.39e-02, forces_magnitude: 3.33e+00, energy_mae: 1.70e+01, energy_force_within_threshold: 0.00e+00, loss: 1.97e+02, lr: 5.00e-04, epoch: 7.50e-01, step: 7.50e+01\n", + "2021-11-22 17:17:11 (INFO): forcesx_mae: 9.00e-01, forcesy_mae: 1.00e+00, forcesz_mae: 1.10e+00, forces_mae: 1.00e+00, forces_cos: 2.08e-02, forces_magnitude: 1.65e+00, energy_mae: 1.93e+01, energy_force_within_threshold: 0.00e+00, loss: 1.34e+02, lr: 5.00e-04, epoch: 8.00e-01, step: 8.00e+01\n", + "2021-11-22 17:17:17 (INFO): forcesx_mae: 6.05e-01, forcesy_mae: 1.65e+00, forcesz_mae: 8.28e-01, forces_mae: 1.03e+00, forces_cos: 5.95e-02, forces_magnitude: 1.87e+00, energy_mae: 1.63e+01, energy_force_within_threshold: 0.00e+00, loss: 1.30e+02, lr: 5.00e-04, epoch: 8.50e-01, step: 8.50e+01\n", + "2021-11-22 17:17:24 (INFO): forcesx_mae: 5.26e-01, forcesy_mae: 7.32e-01, forcesz_mae: 5.05e-01, forces_mae: 5.88e-01, forces_cos: 5.29e-04, forces_magnitude: 1.07e+00, energy_mae: 4.13e+00, energy_force_within_threshold: 0.00e+00, loss: 7.10e+01, lr: 5.00e-04, epoch: 9.00e-01, step: 9.00e+01\n", + "2021-11-22 17:17:30 (INFO): forcesx_mae: 4.01e-01, forcesy_mae: 4.67e-01, forcesz_mae: 3.45e-01, forces_mae: 4.04e-01, forces_cos: 6.19e-02, forces_magnitude: 7.39e-01, energy_mae: 3.07e+00, energy_force_within_threshold: 0.00e+00, loss: 5.64e+01, lr: 5.00e-04, epoch: 9.50e-01, step: 9.50e+01\n", + "2021-11-22 17:17:37 (INFO): forcesx_mae: 4.27e-01, forcesy_mae: 7.22e-01, forcesz_mae: 4.27e-01, forces_mae: 5.25e-01, forces_cos: 4.71e-02, forces_magnitude: 9.01e-01, energy_mae: 8.72e+00, energy_force_within_threshold: 0.00e+00, loss: 6.92e+01, lr: 5.00e-04, epoch: 1.00e+00, step: 1.00e+02\n", + "2021-11-22 17:17:39 (INFO): Evaluating on val.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "device 0: 100%|██████████| 20/20 [00:02<00:00, 7.13it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:17:42 (INFO): forcesx_mae: 1.4760, forcesy_mae: 1.1875, forcesz_mae: 1.6235, forces_mae: 1.4290, forces_cos: -0.2961, forces_magnitude: 2.5544, energy_mae: 7.8576, energy_force_within_threshold: 0.0000, loss: 193.1406, epoch: 1.0000\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZHkrkULBQ1Xy" + }, + "source": [ + "### Validate the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "paYx3_FBQ8OE" + }, + "source": [ + "#### Load the best checkpoint\n", + "\n", + "The `checkpoints` directory contains two checkpoint files:\n", + "\n", + "\n", + "\n", + "* `best_checkpoint.pt` - Model parameters corresponding to the best val performance during training. Used for predictions.\n", + "* `checkpoint.pt` - Model parameters and optimizer settings for the latest checkpoint. Used to continue training.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UW4ihgBdQ0Yt", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "8226c4d2-041d-46d3-c0d9-02ce85f8fc93" + }, + "source": [ + "# The `best_checpoint.pt` file contains the checkpoint with the best val performance\n", + "checkpoint_path = os.path.join(trainer.config[\"cmd\"][\"checkpoint_dir\"], \"best_checkpoint.pt\")\n", + "checkpoint_path" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'./checkpoints/2021-11-22-17-14-40-S2EF-example/best_checkpoint.pt'" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6jppgncMTivj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a15e13a5-4c1d-4fd4-c2c3-ef9fa210a9dd" + }, + "source": [ + "# Append the dataset with the test set. We use the same val set for demonstration.\n", + "\n", + "# Dataset\n", + "dataset.append(\n", + " {'src': val_src}, # test set (optional)\n", + ")\n", + "dataset" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'grad_target_mean': 0.0,\n", + " 'grad_target_std': 1.5156444102461508,\n", + " 'normalize_labels': True,\n", + " 'src': 'data/s2ef/train_100',\n", + " 'target_mean': 0.45158625849998374,\n", + " 'target_std': 1.5156444102461508},\n", + " {'src': 'data/s2ef/val_20'},\n", + " {'src': 'data/s2ef/val_20'}]" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MaVROfxzRLaj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0f143c63-1e1d-44c4-c641-34bac1706c2c" + }, + "source": [ + "pretrained_trainer = ForcesTrainer(\n", + " task=task,\n", + " model=model,\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"S2EF-val-example\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=10,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + " amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n", + ")\n", + "\n", + "pretrained_trainer.load_checkpoint(checkpoint_path=checkpoint_path)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "amp: true\n", + "cmd:\n", + " checkpoint_dir: ./checkpoints/2021-11-22-17-16-48-S2EF-val-example\n", + " commit: bc04a90\n", + " identifier: S2EF-val-example\n", + " logs_dir: ./logs/tensorboard/2021-11-22-17-16-48-S2EF-val-example\n", + " print_every: 10\n", + " results_dir: ./results/2021-11-22-17-16-48-S2EF-val-example\n", + " seed: 0\n", + " timestamp_id: 2021-11-22-17-16-48-S2EF-val-example\n", + "dataset:\n", + " grad_target_mean: 0.0\n", + " grad_target_std: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - &id001 !!python/object/apply:numpy.dtype\n", + " args:\n", + " - f8\n", + " - false\n", + " - true\n", + " state: !!python/tuple\n", + " - 3\n", + " - <\n", + " - null\n", + " - null\n", + " - null\n", + " - -1\n", + " - -1\n", + " - 0\n", + " - !!binary |\n", + " dPVlWhRA+D8=\n", + " normalize_labels: true\n", + " src: data/s2ef/train_100\n", + " target_mean: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - *id001\n", + " - !!binary |\n", + " zSXlDMrm3D8=\n", + " target_std: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - *id001\n", + " - !!binary |\n", + " dPVlWhRA+D8=\n", + "gpus: 1\n", + "logger: tensorboard\n", + "model: gemnet_t\n", + "model_attributes:\n", + " activation: silu\n", + " cbf:\n", + " name: spherical_harmonics\n", + " cutoff: 6.0\n", + " direct_forces: true\n", + " emb_size_atom: 512\n", + " emb_size_bil_trip: 64\n", + " emb_size_cbf: 16\n", + " emb_size_edge: 512\n", + " emb_size_rbf: 16\n", + " emb_size_trip: 64\n", + " envelope:\n", + " exponent: 5\n", + " name: polynomial\n", + " extensive: true\n", + " max_neighbors: 50\n", + " num_after_skip: 2\n", + " num_atom: 3\n", + " num_before_skip: 1\n", + " num_blocks: 3\n", + " num_concat: 1\n", + " num_radial: 128\n", + " num_spherical: 7\n", + " otf_graph: false\n", + " output_init: HeOrthogonal\n", + " rbf:\n", + " name: gaussian\n", + " regress_forces: true\n", + " scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n", + "optim:\n", + " batch_size: 1\n", + " clip_grad_norm: 10\n", + " ema_decay: 0.999\n", + " eval_batch_size: 1\n", + " factor: 0.8\n", + " force_coefficient: 100\n", + " loss_energy: mae\n", + " loss_force: l2mae\n", + " lr_initial: 0.0005\n", + " max_epochs: 1\n", + " mode: min\n", + " num_workers: 2\n", + " optimizer: AdamW\n", + " optimizer_params:\n", + " amsgrad: true\n", + " patience: 3\n", + " scheduler: ReduceLROnPlateau\n", + "slurm: {}\n", + "task:\n", + " dataset: trajectory_lmdb\n", + " description: Regressing to energies and forces for DFT trajectories from OCP\n", + " eval_on_free_atoms: true\n", + " grad_input: atomic forces\n", + " labels:\n", + " - potential energy\n", + " metric: mae\n", + " train_on_free_atoms: true\n", + " type: regression\n", + "test_dataset:\n", + " src: data/s2ef/val_20\n", + "val_dataset:\n", + " src: data/s2ef/val_20\n", + "\n", + "2021-11-22 17:17:43 (INFO): Loading dataset: trajectory_lmdb\n", + "2021-11-22 17:17:43 (INFO): Loading model: gemnet_t\n", + "2021-11-22 17:17:46 (INFO): Loaded GemNetT with 31671825 parameters.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2021-11-22 17:17:46 (WARNING): Model gradient logging to tensorboard not yet supported.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:17:46 (INFO): Loading checkpoint from: ./checkpoints/2021-11-22-17-14-40-S2EF-example/best_checkpoint.pt\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kWetMgsmRBZS" + }, + "source": [ + "#### Run on the test set" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jbiPZNeJQ0WK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dd346bcd-f30a-4333-a1ca-e18c057cb238" + }, + "source": [ + "# make predictions on the existing test_loader\n", + "predictions = pretrained_trainer.predict(pretrained_trainer.test_loader, results_file=\"s2ef_results\", disable_tqdm=False)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:17:46 (INFO): Predicting on test.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "device 0: 100%|██████████| 20/20 [00:02<00:00, 7.47it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:17:49 (INFO): Writing results to ./results/2021-11-22-17-16-48-S2EF-val-example/s2ef_s2ef_results.npz\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zaZGqeyqNCXz" + }, + "source": [ + "energies = predictions[\"energy\"]\n", + "forces = predictions[\"forces\"]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o8L28axZ4NVj" + }, + "source": [ + "## Initial Structure to Relaxed Energy (IS2RE) \n", + "The IS2RE task predicts the relaxed energy (energy of the relaxed state) given the initial state of a system. One approach to this is by training a regression model mapping the initial structure to the relaxed energy. We call this the *direct* approach to the IS2RE task. \n", + "\n", + "An alternative is to perform a structure relaxation using an S2EF model to obtain the relaxed state and compute the energy of that state (see the IS2RS task below for details about relaxation).\n", + "\n", + "### Steps for training an IS2RE model\n", + "1) Define or load a configuration (config), which includes the following\n", + "* task\n", + "* model\n", + "* optimizer\n", + "* dataset\n", + "* trainer\n", + "\n", + "2) Create an EnergyTrainer object\n", + "\n", + "3) Train the model\n", + "\n", + "4) Validate the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kEPPcr0YYHpH" + }, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "d-0GsaGDW16G" + }, + "source": [ + "from ocpmodels.trainers import EnergyTrainer\n", + "from ocpmodels.datasets import SinglePointLmdbDataset\n", + "from ocpmodels import models\n", + "from ocpmodels.common import logger\n", + "from ocpmodels.common.utils import setup_logging\n", + "setup_logging()\n", + "\n", + "import numpy as np\n", + "import copy\n", + "import os" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w20BJZ_GYWat" + }, + "source": [ + "### Dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BlL5gGPQW1te" + }, + "source": [ + "train_src = \"data/is2re/train_100/data.lmdb\"\n", + "val_src = \"data/is2re/val_20/data.lmdb\"" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yT5qHT2wamPh" + }, + "source": [ + "### Normalize data\n", + "\n", + "If you wish to normalize the targets we must compute the mean and standard deviation for our energy values." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vaY-ZUMaamPh" + }, + "source": [ + "train_dataset = SinglePointLmdbDataset({\"src\": train_src})\n", + "\n", + "energies = []\n", + "for data in train_dataset:\n", + " energies.append(data.y_relaxed)\n", + "\n", + "mean = np.mean(energies)\n", + "stdev = np.std(energies)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K4SSW0UGYeYM" + }, + "source": [ + "### Define the Config\n", + "\n", + "For this example, we will explicitly define the config; however, a set of default configs can be found [here](https://github.com/Open-Catalyst-Project/ocp/tree/master/configs). Default config yaml files can easily be loaded with the following [utility](https://github.com/Open-Catalyst-Project/ocp/blob/aa8e44d50229fce887b3a94a5661c4f85cd73eed/ocpmodels/common/utils.py#L361-L400). Loading a yaml config is preferrable when launching jobs from the command line. We have included our best models' config files here for reference. \n", + "\n", + "**Note** - we only train for a single epoch with a reduced batch size (GPU memory constraints) for demonstration purposes, modify accordingly for full convergence." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TiHmkTm6W1do" + }, + "source": [ + "# Task\n", + "task = {\n", + " \"dataset\": \"single_point_lmdb\",\n", + " \"description\": \"Relaxed state energy prediction from initial structure.\",\n", + " \"type\": \"regression\",\n", + " \"metric\": \"mae\",\n", + " \"labels\": [\"relaxed energy\"],\n", + "}\n", + "# Model\n", + "model = {\n", + " 'name': 'gemnet_t',\n", + " \"num_spherical\": 7,\n", + " \"num_radial\": 64,\n", + " \"num_blocks\": 5,\n", + " \"emb_size_atom\": 256,\n", + " \"emb_size_edge\": 512,\n", + " \"emb_size_trip\": 64,\n", + " \"emb_size_rbf\": 16,\n", + " \"emb_size_cbf\": 16,\n", + " \"emb_size_bil_trip\": 64,\n", + " \"num_before_skip\": 1,\n", + " \"num_after_skip\": 2,\n", + " \"num_concat\": 1,\n", + " \"num_atom\": 3,\n", + " \"cutoff\": 6.0,\n", + " \"max_neighbors\": 50,\n", + " \"rbf\": {\"name\": \"gaussian\"},\n", + " \"envelope\": {\n", + " \"name\": \"polynomial\",\n", + " \"exponent\": 5,\n", + " },\n", + " \"cbf\": {\"name\": \"spherical_harmonics\"},\n", + " \"extensive\": True,\n", + " \"otf_graph\": False,\n", + " \"output_init\": \"HeOrthogonal\",\n", + " \"activation\": \"silu\",\n", + " \"scale_file\": \"configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\",\n", + " \"regress_forces\": False,\n", + " \"direct_forces\": False,\n", + "}\n", + "# Optimizer\n", + "optimizer = {\n", + " 'batch_size': 1, # originally 32\n", + " 'eval_batch_size': 1, # originally 32\n", + " 'num_workers': 2,\n", + " 'lr_initial': 1.e-4,\n", + " 'optimizer': 'AdamW',\n", + " 'optimizer_params': {\"amsgrad\": True},\n", + " 'scheduler': \"ReduceLROnPlateau\",\n", + " 'mode': \"min\",\n", + " 'factor': 0.8,\n", + " 'patience': 3,\n", + " 'max_epochs': 1, # used for demonstration purposes\n", + " 'ema_decay': 0.999,\n", + " 'clip_grad_norm': 10,\n", + " 'loss_energy': 'mae',\n", + "}\n", + "# Dataset\n", + "dataset = [\n", + " {'src': train_src,\n", + " 'normalize_labels': True,\n", + " 'target_mean': mean,\n", + " 'target_std': stdev,\n", + " }, # train set \n", + " {'src': val_src}, # val set (optional)\n", + "]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oG5w1sk-v1LI" + }, + "source": [ + "###Create EnergyTrainer" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ExmkV2K1W07H", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4e875ed0-258b-43eb-e191-d00274400128" + }, + "source": [ + "energy_trainer = EnergyTrainer(\n", + " task=task,\n", + " model=copy.deepcopy(model), # copied for later use, not necessary in practice.\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"IS2RE-example\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=5,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + " amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage) \n", + ")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "amp: true\n", + "cmd:\n", + " checkpoint_dir: ./checkpoints/2021-11-22-17-21-04-IS2RE-example\n", + " commit: bc04a90\n", + " identifier: IS2RE-example\n", + " logs_dir: ./logs/tensorboard/2021-11-22-17-21-04-IS2RE-example\n", + " print_every: 5\n", + " results_dir: ./results/2021-11-22-17-21-04-IS2RE-example\n", + " seed: 0\n", + " timestamp_id: 2021-11-22-17-21-04-IS2RE-example\n", + "dataset:\n", + " normalize_labels: true\n", + " src: data/is2re/train_100/data.lmdb\n", + " target_mean: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - &id001 !!python/object/apply:numpy.dtype\n", + " args:\n", + " - f8\n", + " - false\n", + " - true\n", + " state: !!python/tuple\n", + " - 3\n", + " - <\n", + " - null\n", + " - null\n", + " - null\n", + " - -1\n", + " - -1\n", + " - 0\n", + " - !!binary |\n", + " MjyJzgpQ978=\n", + " target_std: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - *id001\n", + " - !!binary |\n", + " PnyyzMtk/T8=\n", + "gpus: 1\n", + "logger: tensorboard\n", + "model: gemnet_t\n", + "model_attributes:\n", + " activation: silu\n", + " cbf:\n", + " name: spherical_harmonics\n", + " cutoff: 6.0\n", + " direct_forces: false\n", + " emb_size_atom: 256\n", + " emb_size_bil_trip: 64\n", + " emb_size_cbf: 16\n", + " emb_size_edge: 512\n", + " emb_size_rbf: 16\n", + " emb_size_trip: 64\n", + " envelope:\n", + " exponent: 5\n", + " name: polynomial\n", + " extensive: true\n", + " max_neighbors: 50\n", + " num_after_skip: 2\n", + " num_atom: 3\n", + " num_before_skip: 1\n", + " num_blocks: 5\n", + " num_concat: 1\n", + " num_radial: 64\n", + " num_spherical: 7\n", + " otf_graph: false\n", + " output_init: HeOrthogonal\n", + " rbf:\n", + " name: gaussian\n", + " regress_forces: false\n", + " scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n", + "optim:\n", + " batch_size: 1\n", + " clip_grad_norm: 10\n", + " ema_decay: 0.999\n", + " eval_batch_size: 1\n", + " factor: 0.8\n", + " loss_energy: mae\n", + " lr_initial: 0.0001\n", + " max_epochs: 1\n", + " mode: min\n", + " num_workers: 2\n", + " optimizer: AdamW\n", + " optimizer_params:\n", + " amsgrad: true\n", + " patience: 3\n", + " scheduler: ReduceLROnPlateau\n", + "slurm: {}\n", + "task:\n", + " dataset: single_point_lmdb\n", + " description: Relaxed state energy prediction from initial structure.\n", + " labels:\n", + " - relaxed energy\n", + " metric: mae\n", + " type: regression\n", + "val_dataset:\n", + " src: data/is2re/val_20/data.lmdb\n", + "\n", + "2021-11-22 17:20:24 (INFO): Loading dataset: single_point_lmdb\n", + "2021-11-22 17:20:24 (INFO): Loading model: gemnet_t\n", + "2021-11-22 17:20:26 (INFO): Loaded GemNetT with 22774037 parameters.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2021-11-22 17:20:26 (WARNING): Model gradient logging to tensorboard not yet supported.\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tnJer5rGwjwi" + }, + "source": [ + "energy_trainer.model" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pto2SpJPwlz1" + }, + "source": [ + "### Train the Model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "iHMRkFplwsky", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "df58e36a-6bb9-411a-ce4a-b9258fc06a55" + }, + "source": [ + "energy_trainer.train()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "energy_mae: 6.21e+01, energy_mse: 3.86e+03, energy_within_threshold: 0.00e+00, loss: 6.76e+01, lr: 1.00e-04, epoch: 5.00e-02, step: 5.00e+00\n", + "energy_mae: 1.86e+02, energy_mse: 3.46e+04, energy_within_threshold: 0.00e+00, loss: 2.03e+02, lr: 1.00e-04, epoch: 1.00e-01, step: 1.00e+01\n", + "energy_mae: 2.88e+03, energy_mse: 8.31e+06, energy_within_threshold: 0.00e+00, loss: 3.14e+03, lr: 1.00e-04, epoch: 1.50e-01, step: 1.50e+01\n", + "energy_mae: 5.92e+02, energy_mse: 3.51e+05, energy_within_threshold: 0.00e+00, loss: 3.22e+02, lr: 1.00e-04, epoch: 2.00e-01, step: 2.00e+01\n", + "energy_mae: 4.49e+03, energy_mse: 2.02e+07, energy_within_threshold: 0.00e+00, loss: 2.45e+03, lr: 1.00e-04, epoch: 2.50e-01, step: 2.50e+01\n", + "energy_mae: 4.48e+01, energy_mse: 2.01e+03, energy_within_threshold: 0.00e+00, loss: 2.44e+01, lr: 1.00e-04, epoch: 3.00e-01, step: 3.00e+01\n", + "energy_mae: 1.29e+02, energy_mse: 1.68e+04, energy_within_threshold: 0.00e+00, loss: 7.05e+01, lr: 1.00e-04, epoch: 3.50e-01, step: 3.50e+01\n", + "energy_mae: 2.21e+02, energy_mse: 4.90e+04, energy_within_threshold: 0.00e+00, loss: 1.21e+02, lr: 1.00e-04, epoch: 4.00e-01, step: 4.00e+01\n", + "energy_mae: 2.20e+02, energy_mse: 4.84e+04, energy_within_threshold: 0.00e+00, loss: 1.20e+02, lr: 1.00e-04, epoch: 4.50e-01, step: 4.50e+01\n", + "energy_mae: 1.82e+01, energy_mse: 3.32e+02, energy_within_threshold: 0.00e+00, loss: 9.91e+00, lr: 1.00e-04, epoch: 5.00e-01, step: 5.00e+01\n", + "energy_mae: 2.80e+03, energy_mse: 7.84e+06, energy_within_threshold: 0.00e+00, loss: 1.52e+03, lr: 1.00e-04, epoch: 5.50e-01, step: 5.50e+01\n", + "energy_mae: 5.37e+01, energy_mse: 2.88e+03, energy_within_threshold: 0.00e+00, loss: 2.92e+01, lr: 1.00e-04, epoch: 6.00e-01, step: 6.00e+01\n", + "energy_mae: 4.53e+00, energy_mse: 2.05e+01, energy_within_threshold: 0.00e+00, loss: 2.46e+00, lr: 1.00e-04, epoch: 6.50e-01, step: 6.50e+01\n", + "energy_mae: 2.54e+03, energy_mse: 6.47e+06, energy_within_threshold: 0.00e+00, loss: 1.38e+03, lr: 1.00e-04, epoch: 7.00e-01, step: 7.00e+01\n", + "energy_mae: 5.55e+02, energy_mse: 3.08e+05, energy_within_threshold: 0.00e+00, loss: 3.02e+02, lr: 1.00e-04, epoch: 7.50e-01, step: 7.50e+01\n", + "energy_mae: 1.72e+02, energy_mse: 2.95e+04, energy_within_threshold: 0.00e+00, loss: 9.35e+01, lr: 1.00e-04, epoch: 8.00e-01, step: 8.00e+01\n", + "energy_mae: 1.04e+02, energy_mse: 1.08e+04, energy_within_threshold: 0.00e+00, loss: 5.67e+01, lr: 1.00e-04, epoch: 8.50e-01, step: 8.50e+01\n", + "energy_mae: 1.68e+02, energy_mse: 2.81e+04, energy_within_threshold: 0.00e+00, loss: 9.13e+01, lr: 1.00e-04, epoch: 9.00e-01, step: 9.00e+01\n", + "energy_mae: 4.73e+02, energy_mse: 2.24e+05, energy_within_threshold: 0.00e+00, loss: 2.58e+02, lr: 1.00e-04, epoch: 9.50e-01, step: 9.50e+01\n", + "energy_mae: 2.12e+01, energy_mse: 4.49e+02, energy_within_threshold: 0.00e+00, loss: 1.15e+01, lr: 1.00e-04, epoch: 1.00e+00, step: 1.00e+02\n", + "2021-11-22 17:23:24 (INFO): Evaluating on val.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "device 0: 100%|██████████| 20/20 [00:10<00:00, 1.86it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:23:35 (INFO): energy_mae: 1028.9198, energy_mse: 3489562.4455, energy_within_threshold: 0.0000, loss: 560.1051, epoch: 1.0000\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MkAd2MBmw8wO" + }, + "source": [ + "### Validate the Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gaauxWdNw_-4" + }, + "source": [ + "#### Load the best checkpoint" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xkj0Bslqws_N", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "2680bf59-c13e-4113-b3bd-15aa62c9007e" + }, + "source": [ + "# The `best_checpoint.pt` file contains the checkpoint with the best val performance\n", + "checkpoint_path = os.path.join(energy_trainer.config[\"cmd\"][\"checkpoint_dir\"], \"best_checkpoint.pt\")\n", + "checkpoint_path" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'./checkpoints/2021-11-22-17-21-04-IS2RE-example/best_checkpoint.pt'" + ] + }, + "metadata": {}, + "execution_count": 29 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BqmCqaFlbMZC", + "outputId": "fd9f2409-1b51-4b6a-90ca-0a00a40d2dfe" + }, + "source": [ + "# Append the dataset with the test set. We use the same val set for demonstration.\n", + "\n", + "# Dataset\n", + "dataset.append(\n", + " {'src': val_src}, # test set (optional)\n", + ")\n", + "dataset" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'normalize_labels': True,\n", + " 'src': 'data/is2re/train_100/data.lmdb',\n", + " 'target_mean': -1.4570415561499996,\n", + " 'target_std': 1.8371084209427546},\n", + " {'src': 'data/is2re/val_20/data.lmdb'},\n", + " {'src': 'data/is2re/val_20/data.lmdb'}]" + ] + }, + "metadata": {}, + "execution_count": 30 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IkcqadZIxXP-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5a07d5c7-cbdf-4901-80db-1fcf19c1c42b" + }, + "source": [ + "pretrained_energy_trainer = EnergyTrainer(\n", + " task=task,\n", + " model=model,\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"IS2RE-val-example\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=10,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + " amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n", + ")\n", + "\n", + "pretrained_energy_trainer.load_checkpoint(checkpoint_path=checkpoint_path)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "amp: true\n", + "cmd:\n", + " checkpoint_dir: ./checkpoints/2021-11-22-17-23-12-IS2RE-val-example\n", + " commit: bc04a90\n", + " identifier: IS2RE-val-example\n", + " logs_dir: ./logs/tensorboard/2021-11-22-17-23-12-IS2RE-val-example\n", + " print_every: 10\n", + " results_dir: ./results/2021-11-22-17-23-12-IS2RE-val-example\n", + " seed: 0\n", + " timestamp_id: 2021-11-22-17-23-12-IS2RE-val-example\n", + "dataset:\n", + " normalize_labels: true\n", + " src: data/is2re/train_100/data.lmdb\n", + " target_mean: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - &id001 !!python/object/apply:numpy.dtype\n", + " args:\n", + " - f8\n", + " - false\n", + " - true\n", + " state: !!python/tuple\n", + " - 3\n", + " - <\n", + " - null\n", + " - null\n", + " - null\n", + " - -1\n", + " - -1\n", + " - 0\n", + " - !!binary |\n", + " MjyJzgpQ978=\n", + " target_std: !!python/object/apply:numpy.core.multiarray.scalar\n", + " - *id001\n", + " - !!binary |\n", + " PnyyzMtk/T8=\n", + "gpus: 1\n", + "logger: tensorboard\n", + "model: gemnet_t\n", + "model_attributes:\n", + " activation: silu\n", + " cbf:\n", + " name: spherical_harmonics\n", + " cutoff: 6.0\n", + " direct_forces: false\n", + " emb_size_atom: 256\n", + " emb_size_bil_trip: 64\n", + " emb_size_cbf: 16\n", + " emb_size_edge: 512\n", + " emb_size_rbf: 16\n", + " emb_size_trip: 64\n", + " envelope:\n", + " exponent: 5\n", + " name: polynomial\n", + " extensive: true\n", + " max_neighbors: 50\n", + " num_after_skip: 2\n", + " num_atom: 3\n", + " num_before_skip: 1\n", + " num_blocks: 5\n", + " num_concat: 1\n", + " num_radial: 64\n", + " num_spherical: 7\n", + " otf_graph: false\n", + " output_init: HeOrthogonal\n", + " rbf:\n", + " name: gaussian\n", + " regress_forces: false\n", + " scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n", + "optim:\n", + " batch_size: 1\n", + " clip_grad_norm: 10\n", + " ema_decay: 0.999\n", + " eval_batch_size: 1\n", + " factor: 0.8\n", + " loss_energy: mae\n", + " lr_initial: 0.0001\n", + " max_epochs: 1\n", + " mode: min\n", + " num_workers: 2\n", + " optimizer: AdamW\n", + " optimizer_params:\n", + " amsgrad: true\n", + " patience: 3\n", + " scheduler: ReduceLROnPlateau\n", + "slurm: {}\n", + "task:\n", + " dataset: single_point_lmdb\n", + " description: Relaxed state energy prediction from initial structure.\n", + " labels:\n", + " - relaxed energy\n", + " metric: mae\n", + " type: regression\n", + "test_dataset:\n", + " src: data/is2re/val_20/data.lmdb\n", + "val_dataset:\n", + " src: data/is2re/val_20/data.lmdb\n", + "\n", + "2021-11-22 17:23:36 (INFO): Loading dataset: single_point_lmdb\n", + "2021-11-22 17:23:36 (INFO): Loading model: gemnet_t\n", + "2021-11-22 17:23:38 (INFO): Loaded GemNetT with 22774037 parameters.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2021-11-22 17:23:38 (WARNING): Model gradient logging to tensorboard not yet supported.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:23:38 (INFO): Loading checkpoint from: ./checkpoints/2021-11-22-17-21-04-IS2RE-example/best_checkpoint.pt\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TcUvAI81xoSt" + }, + "source": [ + "#### Test the model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VtCEFtXxxr3u", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eadd2568-ac65-4d3a-b234-cafe99cee575" + }, + "source": [ + "# make predictions on the existing test_loader\n", + "predictions = pretrained_energy_trainer.predict(pretrained_trainer.test_loader, results_file=\"is2re_results\", disable_tqdm=False)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:23:38 (INFO): Predicting on test.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "device 0: 100%|██████████| 20/20 [00:03<00:00, 5.80it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:23:42 (INFO): Writing results to ./results/2021-11-22-17-23-12-IS2RE-val-example/is2re_is2re_results.npz\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1UcfxFi4x4aD" + }, + "source": [ + "energies = predictions[\"energy\"]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gM9Wqk0GIxyU" + }, + "source": [ + "## Initial Structure to Relaxed Structure (IS2RS) \n", + "\n", + "We approach the IS2RS task by using a pre-trained S2EF model to iteratively run a structure optimization to arrive at a relaxed structure. While the majority of approaches for this task do this iteratively, we note it's possible to train a model to directly predict relaxed structures.\n", + "\n", + "## Steps for making IS2RS predictions\n", + "1) Define or load a configuration (config), which includes the following\n", + "* task with relaxation dataset information\n", + "* model\n", + "* optimizer\n", + "* dataset\n", + "* trainer\n", + "\n", + "2) Create a ForcesTrainer object\n", + "\n", + "3) Train a S2EF model or load an existing S2EF checkpoint\n", + "\n", + "4) Run relaxations\n", + "\n", + "**Note** For this task we'll be using a publicly released pre-trained checkpoint of our best model to perform relaxations." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tNSI3hUAJAWc" + }, + "source": [ + "#### Imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z-WZXuRiI6Vo" + }, + "source": [ + "from ocpmodels.trainers import ForcesTrainer\n", + "from ocpmodels.datasets import TrajectoryLmdbDataset\n", + "from ocpmodels import models\n", + "from ocpmodels.common import logger\n", + "from ocpmodels.common.utils import setup_logging\n", + "setup_logging()\n", + "\n", + "import numpy as np" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XFLZTpRvldZE" + }, + "source": [ + "### Dataset\n", + "\n", + "The IS2RS task requires an additional realxation dataset to be defined - `relax_dataset`. This dataset is read in similar to the IS2RE dataset - requiring an LMDB file. The same datasets are used for the IS2RE and IS2RS tasks." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "irrPcbs4ldZF" + }, + "source": [ + "train_src = \"data/s2ef/train_100\"\n", + "val_src = \"data/s2ef/val_20\"\n", + "relax_dataset = \"data/is2re/val_20/data.lmdb\"" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7gJ01gabd6BR" + }, + "source": [ + "### Download pretrained checkpoint" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MiOeqFN-d-7K" + }, + "source": [ + "!wget -q https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_08/s2ef/gemnet_t_direct_h512_all.pt\n", + "checkpoint_path = \"/content/ocp/gemnet_t_direct_h512_all.pt\"" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fp1Ab8TGltP6" + }, + "source": [ + "### Define the Config" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JLOydGsmltP7" + }, + "source": [ + "Running an iterative S2EF model for the IS2RS task can be run from any S2EF config given the following additions to the `task` portion of the config:\n", + "\n", + "* relax_dataset - IS2RE LMDB dataset\n", + "* *write_pos* - Whether to save out relaxed positions\n", + "* *relaxation_steps* - Number of optimization steps to run\n", + "* *relax_opt* - Dictionary of optimizer settings. Currently only LBFGS supported\n", + " * *maxstep* - Maximum distance an optimization is allowed to make\n", + " * *memory* - Memory history to use for LBFGS\n", + " * *damping* - Calculated step is multiplied by this factor before updating positions\n", + " * *alpha* - Initial guess for the Hessian\n", + " * *traj_dir* - If specified, directory to save out the full ML relaxation as an ASE trajectory. Useful for debugging or visualizing results.\n", + "* *num_relaxation_batches* - If specified, relaxations will only be run for a subset of the relaxation dataset. Useful for debugging or wanting to visualize a few systems.\n", + "\n", + "A sample relaxation config can be found [here](https://github.com/Open-Catalyst-Project/ocp/blob/1044e311182c1120c6e6d137ce6db3f445148973/configs/s2ef/2M/dimenet_plus_plus/dpp_relax.yml#L24-L33).\n", + " " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XU9DisuyltP8" + }, + "source": [ + "# Task\n", + "task = {\n", + " 'dataset': 'trajectory_lmdb', # dataset used for the S2EF task\n", + " 'description': 'Regressing to energies and forces for DFT trajectories from OCP',\n", + " 'type': 'regression',\n", + " 'metric': 'mae',\n", + " 'labels': ['potential energy'],\n", + " 'grad_input': 'atomic forces',\n", + " 'train_on_free_atoms': True,\n", + " 'eval_on_free_atoms': True,\n", + " 'relax_dataset': {\"src\": relax_dataset},\n", + " 'write_pos': True,\n", + " 'relaxation_steps': 200,\n", + " 'num_relaxation_batches': 1,\n", + " 'relax_opt': {\n", + " 'maxstep': 0.04,\n", + " 'memory': 50,\n", + " 'damping': 1.0,\n", + " 'alpha': 70.0,\n", + " 'traj_dir': \"ml-relaxations/is2rs-test\", \n", + " }\n", + "}\n", + "# Model\n", + "model = {\n", + " 'name': 'gemnet_t',\n", + " \"num_spherical\": 7,\n", + " \"num_radial\": 128,\n", + " \"num_blocks\": 3,\n", + " \"emb_size_atom\": 512,\n", + " \"emb_size_edge\": 512,\n", + " \"emb_size_trip\": 64,\n", + " \"emb_size_rbf\": 16,\n", + " \"emb_size_cbf\": 16,\n", + " \"emb_size_bil_trip\": 64,\n", + " \"num_before_skip\": 1,\n", + " \"num_after_skip\": 2,\n", + " \"num_concat\": 1,\n", + " \"num_atom\": 3,\n", + " \"cutoff\": 6.0,\n", + " \"max_neighbors\": 50,\n", + " \"rbf\": {\"name\": \"gaussian\"},\n", + " \"envelope\": {\n", + " \"name\": \"polynomial\",\n", + " \"exponent\": 5,\n", + " },\n", + " \"cbf\": {\"name\": \"spherical_harmonics\"},\n", + " \"extensive\": True,\n", + " \"otf_graph\": False,\n", + " \"output_init\": \"HeOrthogonal\",\n", + " \"activation\": \"silu\",\n", + " \"scale_file\": \"configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\",\n", + " \"regress_forces\": True,\n", + " \"direct_forces\": True,\n", + "}\n", + "# Optimizer\n", + "optimizer = {\n", + " 'batch_size': 1, # originally 32\n", + " 'eval_batch_size': 1, # originally 32\n", + " 'num_workers': 2,\n", + " 'lr_initial': 5.e-4,\n", + " 'optimizer': 'AdamW',\n", + " 'optimizer_params': {\"amsgrad\": True},\n", + " 'scheduler': \"ReduceLROnPlateau\",\n", + " 'mode': \"min\",\n", + " 'factor': 0.8,\n", + " 'ema_decay': 0.999,\n", + " 'clip_grad_norm': 10,\n", + " 'patience': 3,\n", + " 'max_epochs': 1, # used for demonstration purposes\n", + " 'force_coefficient': 100,\n", + "}\n", + "# Dataset\n", + "dataset = [\n", + " {'src': train_src, 'normalize_labels': False}, # train set \n", + " {'src': val_src}, # val set (optional)\n", + "]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IsOqQIjnogkQ" + }, + "source": [ + "### Create the trainer" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5KZvPu4hogkR", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fdbbfa5c-0d7c-449f-8be5-ef2e5d17860d" + }, + "source": [ + "trainer = ForcesTrainer(\n", + " task=task,\n", + " model=model,\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"is2rs-example\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=5,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + " amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n", + ")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "amp: true\n", + "cmd:\n", + " checkpoint_dir: ./checkpoints/2021-11-22-17-42-24-is2rs-example\n", + " commit: bc04a90\n", + " identifier: is2rs-example\n", + " logs_dir: ./logs/tensorboard/2021-11-22-17-42-24-is2rs-example\n", + " print_every: 5\n", + " results_dir: ./results/2021-11-22-17-42-24-is2rs-example\n", + " seed: 0\n", + " timestamp_id: 2021-11-22-17-42-24-is2rs-example\n", + "dataset:\n", + " normalize_labels: false\n", + " src: data/s2ef/train_100\n", + "gpus: 1\n", + "logger: tensorboard\n", + "model: gemnet_t\n", + "model_attributes:\n", + " activation: silu\n", + " cbf:\n", + " name: spherical_harmonics\n", + " cutoff: 6.0\n", + " direct_forces: true\n", + " emb_size_atom: 512\n", + " emb_size_bil_trip: 64\n", + " emb_size_cbf: 16\n", + " emb_size_edge: 512\n", + " emb_size_rbf: 16\n", + " emb_size_trip: 64\n", + " envelope:\n", + " exponent: 5\n", + " name: polynomial\n", + " extensive: true\n", + " max_neighbors: 50\n", + " num_after_skip: 2\n", + " num_atom: 3\n", + " num_before_skip: 1\n", + " num_blocks: 3\n", + " num_concat: 1\n", + " num_radial: 128\n", + " num_spherical: 7\n", + " otf_graph: false\n", + " output_init: HeOrthogonal\n", + " rbf:\n", + " name: gaussian\n", + " regress_forces: true\n", + " scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n", + "optim:\n", + " batch_size: 1\n", + " clip_grad_norm: 10\n", + " ema_decay: 0.999\n", + " eval_batch_size: 1\n", + " factor: 0.8\n", + " force_coefficient: 100\n", + " lr_initial: 0.0005\n", + " max_epochs: 1\n", + " mode: min\n", + " num_workers: 2\n", + " optimizer: AdamW\n", + " optimizer_params:\n", + " amsgrad: true\n", + " patience: 3\n", + " scheduler: ReduceLROnPlateau\n", + "slurm: {}\n", + "task:\n", + " dataset: trajectory_lmdb\n", + " description: Regressing to energies and forces for DFT trajectories from OCP\n", + " eval_on_free_atoms: true\n", + " grad_input: atomic forces\n", + " labels:\n", + " - potential energy\n", + " metric: mae\n", + " num_relaxation_batches: 1\n", + " relax_dataset:\n", + " src: data/is2re/val_20/data.lmdb\n", + " relax_opt:\n", + " alpha: 70.0\n", + " damping: 1.0\n", + " maxstep: 0.04\n", + " memory: 50\n", + " traj_dir: ml-relaxations/is2rs-test\n", + " relaxation_steps: 200\n", + " train_on_free_atoms: true\n", + " type: regression\n", + " write_pos: true\n", + "val_dataset:\n", + " src: data/s2ef/val_20\n", + "\n", + "2021-11-22 17:42:56 (INFO): Loading dataset: trajectory_lmdb\n", + "2021-11-22 17:42:56 (INFO): Loading model: gemnet_t\n", + "2021-11-22 17:43:00 (INFO): Loaded GemNetT with 31671825 parameters.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2021-11-22 17:43:00 (WARNING): Model gradient logging to tensorboard not yet supported.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wtMn792WpC4X" + }, + "source": [ + "### Load the best checkpoint\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jFXQJBYxpC4Y", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f35be368-a350-465d-fb32-5a5795317bac" + }, + "source": [ + "trainer.load_checkpoint(checkpoint_path=checkpoint_path)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:43:00 (INFO): Loading checkpoint from: /content/ocp/gemnet_t_direct_h512_all.pt\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2rtga4JPot6i" + }, + "source": [ + "### Run relaxations\n", + "\n", + "We run a full relaxation for a single batch of our relaxation dataset (`num_relaxation_batches=1`)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aQG-HEpuot6k", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f91a9a2a-4ea8-4b60-c6a1-a1255e482119" + }, + "source": [ + "trainer.run_relaxations()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2021-11-22 17:43:19 (INFO): Running ML-relaxations\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\r 0%| | 0/20 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CN9RC25hxLlp" + }, + "source": [ + "Qualitatively, the ML relaxation is behaving as expected - decreasing energies over the course of the relaxation." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 198 + }, + "id": "6kxJBkV1wZUw", + "outputId": "f1f39a5f-feac-42bc-c208-c6c14aff88ef" + }, + "source": [ + "fig, ax = plt.subplots(1, 3)\n", + "labels = ['ml-initial', 'ml-middle', 'ml-final']\n", + "for i in range(3):\n", + " ax[i].axis('off')\n", + " ax[i].set_title(labels[i])\n", + "\n", + "ase.visualize.plot.plot_atoms(\n", + " ml_trajectory[0], \n", + " ax[0], \n", + " radii=0.8,\n", + " # rotation=(\"-75x, 45y, 10z\")) # uncomment to visualize at different angles\n", + ")\n", + "ase.visualize.plot.plot_atoms(\n", + " ml_trajectory[100], \n", + " ax[1], \n", + " radii=0.8, \n", + " # rotation=(\"-75x, 45y, 10z\") # uncomment to visualize at different angles\n", + ")\n", + "ase.visualize.plot.plot_atoms(\n", + " ml_trajectory[-1], \n", + " ax[2], \n", + " radii=0.8,\n", + " # rotation=(\"-75x, 45y, 10z\"), # uncomment to visualize at different angles\n", + ")\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 99 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8LE2lrJwyblQ" + }, + "source": [ + "Qualitatively, the generated structures seem reasonable with no obvious issues we had previously mentioned to look out for." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MymFuumcRd8r" + }, + "source": [ + "# Model development \n", + "\n", + "In this section, we will walk through how to develop a simple Graph Neural Network model on the S2EF-200k dataset.\n", + "\n", + "Let's begin by setting up some imports and boilerplate config parameters." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mk71_j2i96X4" + }, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vK49MKgd9ufL" + }, + "source": [ + "import torch\n", + "\n", + "from typing import Optional\n", + "\n", + "from ocpmodels.trainers import ForcesTrainer\n", + "from ocpmodels import models\n", + "from ocpmodels.common import logger\n", + "from ocpmodels.common.utils import setup_logging, get_pbc_distances\n", + "from ocpmodels.common.registry import registry\n", + "\n", + "from ocpmodels.models.gemnet.layers.radial_basis import PolynomialEnvelope\n", + "\n", + "from torch_geometric.nn.models.schnet import GaussianSmearing\n", + "from torch_scatter import scatter" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xj9QvWby-AI6" + }, + "source": [ + "setup_logging()\n", + "\n", + "# Dataset paths\n", + "train_src = \"data/s2ef/train_200k\"\n", + "val_src = \"data/s2ef/val\"\n", + "\n", + "# Configs\n", + "task = {\n", + " 'dataset': 'trajectory_lmdb', # dataset used for the S2EF task\n", + " 'description': 'Regressing to energies and forces for DFT trajectories from OCP',\n", + " 'type': 'regression',\n", + " 'metric': 'mae',\n", + " 'labels': ['potential energy'],\n", + " 'grad_input': 'atomic forces',\n", + " 'train_on_free_atoms': True,\n", + " 'eval_on_free_atoms': True\n", + "}\n", + "\n", + "# Optimizer\n", + "optimizer = {\n", + " 'batch_size': 16, # if hitting GPU memory issues, lower this\n", + " 'eval_batch_size': 8,\n", + " 'num_workers': 8,\n", + " 'lr_initial': 0.0001,\n", + " 'scheduler': \"ReduceLROnPlateau\",\n", + " 'mode': \"min\",\n", + " 'factor': 0.8,\n", + " 'patience': 3,\n", + " 'max_epochs': 80,\n", + " 'max_epochs': 5,\n", + " 'force_coefficient': 100,\n", + "}\n", + "\n", + "# Dataset\n", + "dataset = [\n", + " {'src': train_src, 'normalize_labels': True, 'target_mean': -0.7554450631141663, 'target_std': 2.887317180633545, 'grad_target_mean': 0.0, 'grad_target_std': 2.887317180633545}, # train set\n", + " {'src': val_src},\n", + "]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bzp-Cyrm-JOE" + }, + "source": [ + "## Atom and Edge Embeddings\n", + "\n", + "Each atom is represented as a node with its features computed using a simple `torch.nn.Embedding` layer on the atomic number.\n", + "\n", + "All pairs of atoms with a defined cutoff radius (=6A) are assumed to have edges between them, with their features computed as the concatenation of 1) a Gaussian expansion of the distance between the atoms, and the 2) source and 3) target\n", + "node features.\n", + "\n", + "We will use the `GaussianSmearing` layer (reproduced below) from the PyTorch Geometric library for computing distance features:\n", + "\n", + "```\n", + "class GaussianSmearing(torch.nn.Module):\n", + " def __init__(self, start=0.0, stop=5.0, num_gaussians=50):\n", + " super(GaussianSmearing, self).__init__()\n", + " offset = torch.linspace(start, stop, num_gaussians)\n", + " self.coeff = -0.5 / (offset[1] - offset[0]).item()**2\n", + " self.register_buffer('offset', offset)\n", + "\n", + " def forward(self, dist):\n", + " dist = dist.view(-1, 1) - self.offset.view(1, -1)\n", + " return torch.exp(self.coeff * torch.pow(dist, 2))\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dfMCS-pL-2X5" + }, + "source": [ + "class AtomEmbedding(torch.nn.Module):\n", + " def __init__(self, emb_size):\n", + " super().__init__()\n", + " self.embeddings = torch.nn.Embedding(83, emb_size) # We go up to Bi (83).\n", + "\n", + " def forward(self, Z):\n", + " h = self.embeddings(Z - 1) # -1 because Z.min()=1 (==Hydrogen)\n", + " return h\n", + "\n", + "class EdgeEmbedding(torch.nn.Module):\n", + " def __init__(self, atom_emb_size, edge_emb_size, out_size):\n", + " super().__init__()\n", + " in_features = 2 * atom_emb_size + edge_emb_size\n", + " self.dense = torch.nn.Sequential(\n", + " torch.nn.Linear(in_features, out_size, bias=False),\n", + " torch.nn.SiLU()\n", + " )\n", + "\n", + " def forward(self, h, m_rbf, idx_s, idx_t,\n", + " ):\n", + " h_s = h[idx_s] # indexing source node, shape=(num_edges, emb_size)\n", + " h_t = h[idx_t] # indexing target node, shape=(num_edges, emb_size)\n", + "\n", + " m_st = torch.cat([h_s, h_t, m_rbf], dim=-1) # (num_edges, 2 * atom_emb_size + edge_emb_size)\n", + " m_st = self.dense(m_st) # (num_edges, out_size)\n", + " return m_st\n", + "\n", + "class RadialBasis(torch.nn.Module):\n", + " def __init__(self, num_radial: int, cutoff: float, env_exponent: int = 5):\n", + " super().__init__()\n", + " self.inv_cutoff = 1 / cutoff\n", + " self.envelope = PolynomialEnvelope(env_exponent)\n", + " self.rbf = GaussianSmearing(start=0, stop=1, num_gaussians=num_radial)\n", + "\n", + " def forward(self, d):\n", + " d_scaled = d * self.inv_cutoff\n", + " env = self.envelope(d_scaled)\n", + " return env[:, None] * self.rbf(d_scaled) # (num_edges, num_radial)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nhvCP4wzAE_K" + }, + "source": [ + "## Message passing \n", + "\n", + "We start by implementing a very simple message-passing scheme to predict system energy and forces.\n", + "\n", + "Given the node and edge features, we sum up edge features for all edges $e_{ij}$ connecting node $i$ to its neighbors $j$, and pass the resultant vector through a fully-connected layer to project it down to a scalar. This gives us a scalar energy contribution for each node $i$ in the structure. We then sum up all node energy contributions to predict the overall system energy.\n", + "\n", + "Similarly, to predict forces, we pass edge features through a fully-connected layer to project it down to a scalar representing the force magnitude per edge $e_{ij}$. We can then sum up these force magnitudes based on the original edge directions to predict the resultant force vector per node $i$." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QMjBCLcSAQSp" + }, + "source": [ + "@registry.register_model(\"simple\")\n", + "class SimpleAtomEdgeModel(torch.nn.Module):\n", + " def __init__(self, num_atoms, bond_feat_dim, num_targets, emb_size=64, num_radial=64, cutoff=6.0, env_exponent=5):\n", + " super().__init__()\n", + "\n", + " self.radial_basis = RadialBasis(\n", + " num_radial=num_radial,\n", + " cutoff=cutoff,\n", + " env_exponent=env_exponent,\n", + " )\n", + "\n", + " self.atom_emb = AtomEmbedding(emb_size)\n", + " self.edge_emb = EdgeEmbedding(emb_size, num_radial, emb_size)\n", + "\n", + " self.out_energy = torch.nn.Linear(emb_size, 1)\n", + " self.out_forces = torch.nn.Linear(emb_size, 1)\n", + "\n", + " def forward(self, data):\n", + " batch = data.batch\n", + " atomic_numbers = data.atomic_numbers.long()\n", + " edge_index = data.edge_index\n", + " cell_offsets = data.cell_offsets\n", + " neighbors = data.neighbors\n", + "\n", + " # computing edges and distances taking periodic boundary conditions into account\n", + " out = get_pbc_distances(\n", + " data.pos,\n", + " edge_index,\n", + " data.cell,\n", + " cell_offsets,\n", + " neighbors,\n", + " return_offsets=True,\n", + " return_distance_vec=True,\n", + " )\n", + "\n", + " edge_index = out[\"edge_index\"]\n", + " D_st = out[\"distances\"]\n", + " V_st = -out[\"distance_vec\"] / D_st[:, None]\n", + "\n", + " idx_s, idx_t = edge_index\n", + "\n", + " # embed atoms\n", + " h_atom = self.atom_emb(atomic_numbers)\n", + "\n", + " # gaussian expansion of distances D_st\n", + " m_rbf = self.radial_basis(D_st)\n", + " # embed edges\n", + " m = self.edge_emb(h_atom, m_rbf, idx_s, idx_t)\n", + "\n", + " # read out energy\n", + " # \n", + " # x_E_i = \\sum_j m_ji -- summing up edge features m_ji for all neighbors j\n", + " # of node i to predict node i's energy contribution.\n", + " x_E = scatter(m, idx_t, dim=0, dim_size=h_atom.shape[0], reduce=\"sum\")\n", + " x_E = self.out_energy(x_E)\n", + "\n", + " # E = \\sum_i x_E_i\n", + " num_systems = torch.max(batch)+1\n", + " E = scatter(x_E, batch, dim=0, dim_size=num_systems, reduce=\"add\")\n", + " # (num_systems, 1)\n", + "\n", + " # read out forces\n", + " # \n", + " # x_F is the force magnitude per edge, we multiply that by the direction of each edge ji,\n", + " # and sum up all the vectors to predict the resultant force on node i\n", + " x_F = self.out_forces(m)\n", + " F_st_vec = x_F[:, :, None] * V_st[:, None, :]\n", + " F = scatter(F_st_vec, idx_t, dim=0, dim_size=atomic_numbers.size(0), reduce=\"add\")\n", + " # (num_atoms, num_targets, 3)\n", + " F = F.squeeze(1)\n", + "\n", + " return E, F\n", + "\n", + " @property\n", + " def num_params(self):\n", + " return sum(p.numel() for p in self.parameters())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Vl3WEqVAith" + }, + "source": [ + "## Training the model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u7E7pLiqAmnL" + }, + "source": [ + "model_params = {\n", + " 'name': 'simple',\n", + " 'emb_size': 256,\n", + " 'num_radial': 128,\n", + " 'cutoff': 6.0,\n", + " 'env_exponent': 5,\n", + "}\n", + "\n", + "trainer = ForcesTrainer(\n", + " task=task,\n", + " model=model_params,\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"S2EF-simple\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=20,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + ")\n", + "\n", + "trainer.train()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "thF9lWK9Ay90" + }, + "source": [ + "If you've wired everything up correctly, this model should be relatively small (~185k params) and achieve a force MAE of 0.0815, force cosine of 0.0321, energy MAE of 2.2772 in 2 epochs.\n", + "\n", + "We encourage the reader to try playing with the embedding size, cutoff radius, number of gaussian basis functions, and polynomial envelope exponent to see how it affects performance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PSqVJXsxArvu" + }, + "source": [ + "## Incorporating triplets and training GemNet-T\n", + "\n", + "Recall how this model computes edge embeddings based only on a Gaussian expansion of edge distances.\n", + "\n", + "To better capture 3D geometry, we should also embed angles formed by triplets or quadruplets of atoms. A model that incorporates this idea and works quite well is GemNet (Klicpera et al., NeurIPS 2021); see the following figure.\n", + "\n", + "![Screen Shot 2021-11-22 at 3.58.24 PM.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Twh6yIC5GTrW" + }, + "source": [ + "You can train a GemNet-T (T = triplets) on S2EF-200k using the following config.\n", + "\n", + "Note that this is a significantly bulkier model (~3.4M params) than the one we developed above and will take longer to train." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LVbM_S0sGlOr" + }, + "source": [ + "model_params = {\n", + " 'name': 'gemnet_t',\n", + " 'num_spherical': 7,\n", + " 'num_radial': 128,\n", + " 'num_blocks': 1,\n", + " 'emb_size_atom': 256,\n", + " 'emb_size_edge': 256,\n", + " 'emb_size_trip': 64,\n", + " 'emb_size_rbf': 16,\n", + " 'emb_size_cbf': 16,\n", + " 'emb_size_bil_trip': 64,\n", + " 'num_before_skip': 1,\n", + " 'num_after_skip': 1,\n", + " 'num_concat': 1,\n", + " 'num_atom': 3,\n", + " 'cutoff': 6.0,\n", + " 'max_neighbors': 50,\n", + " 'rbf': {'name': 'gaussian'},\n", + " 'envelope': {'name': 'polynomial', 'exponent': 5},\n", + " 'cbf': {'name': 'spherical_harmonics'},\n", + " 'extensive': True,\n", + " 'otf_graph': False,\n", + " 'output_init': 'HeOrthogonal',\n", + " 'activation': 'silu',\n", + " 'scale_file': 'configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json',\n", + " 'regress_forces': True,\n", + " 'direct_forces': True,\n", + "}\n", + "\n", + "trainer = ForcesTrainer(\n", + " task=task,\n", + " model=model_params,\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"S2EF-gemnet-t\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=20,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + ")\n", + "\n", + "trainer.train()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F-Pw3GCVHAwA" + }, + "source": [ + "This model should achieve a force MAE of 0.0668, a force cosine of 0.1180, and an energy MAE of 0.8106 in 2 epochs, significantly better than our simple model.\n", + "\n", + "Again, we encourage the reader to try playing with no. of blocks, choice of basis functions, the various embedding sizes to develop intuition for the interplay between these hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rzx0lArZJ6r0" + }, + "source": [ + "# (Optional) OCP Calculator \n", + "\n", + "For those interested in using our pretrained models for other applications, we provide an [ASE](https://wiki.fysik.dtu.dk/ase/#:~:text=The%20Atomic%20Simulation%20Environment%20(ASE,under%20the%20GNU%20LGPL%20license.)-compatible Calculator to interface with ASE's functionality." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QGaXyeS_8yHp" + }, + "source": [ + "## Download pretrained checkpoint\n", + "\n", + "We have released checkpoints of all the models on the leaderboard [here](https://github.com/Open-Catalyst-Project/ocp/blob/master/MODELS.md). These trained models can be used as an ASE calculator for various calculations.\n", + "\n", + "For this tutorial we download our current best model checkpoint: GemNet-T" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MBCRi69284Ve" + }, + "source": [ + "!wget -q https://dl.fbaipublicfiles.com/opencatalystproject/models/2021_08/s2ef/gemnet_t_direct_h512_all.pt\n", + "checkpoint_path = \"/content/ocp/gemnet_t_direct_h512_all.pt\"" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TNQ1dNVG93kH" + }, + "source": [ + "## Using the OCP Calculator\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "o_MHpzbhPKN_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fa4336cf-ba85-43b6-e608-551ffcf3763a" + }, + "source": [ + "from ocpmodels.common.relaxation.ase_utils import OCPCalculator\n", + "import ase.io\n", + "from ase.optimize import BFGS\n", + "from ase.build import fcc100, add_adsorbate, molecule\n", + "import os\n", + "from ase.constraints import FixAtoms\n", + "\n", + "# Construct a sample structure\n", + "adslab = fcc100(\"Cu\", size=(3, 3, 3))\n", + "adsorbate = molecule(\"C3H8\")\n", + "add_adsorbate(adslab, adsorbate, 3, offset=(1, 1))\n", + "tags = np.zeros(len(adslab))\n", + "tags[18:27] = 1\n", + "tags[27:] = 2\n", + "adslab.set_tags(tags)\n", + "cons= FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 0)])\n", + "adslab.set_constraint(cons)\n", + "adslab.center(vacuum=13.0, axis=2)\n", + "adslab.set_pbc(True)\n", + "\n", + "config_yml_path = \"configs/s2ef/all/gemnet/gemnet-dT.yml\"\n", + "\n", + "# Define the calculator\n", + "calc = OCPCalculator(config_yml=config_yml_path, checkpoint=checkpoint_path)\n", + "\n", + "# Set up the calculator\n", + "adslab.calc = calc\n", + "\n", + "os.makedirs(\"data/sample_ml_relax\", exist_ok=True)\n", + "opt = BFGS(adslab, trajectory=\"data/sample_ml_relax/toy_c3h8_relax.traj\")\n", + "\n", + "opt.run(fmax=0.05, steps=100)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "amp: false\n", + "cmd:\n", + " checkpoint_dir: /content/ocp/checkpoints/2021-11-22-18-03-44\n", + " commit: bc04a90\n", + " identifier: ''\n", + " logs_dir: /content/ocp/logs/tensorboard/2021-11-22-18-03-44\n", + " print_every: 100\n", + " results_dir: /content/ocp/results/2021-11-22-18-03-44\n", + " seed: null\n", + " timestamp_id: 2021-11-22-18-03-44\n", + "dataset: null\n", + "gpus: 0\n", + "logger: tensorboard\n", + "model: gemnet_t\n", + "model_attributes:\n", + " activation: silu\n", + " cbf:\n", + " name: spherical_harmonics\n", + " cutoff: 6.0\n", + " direct_forces: true\n", + " emb_size_atom: 512\n", + " emb_size_bil_trip: 64\n", + " emb_size_cbf: 16\n", + " emb_size_edge: 512\n", + " emb_size_rbf: 16\n", + " emb_size_trip: 64\n", + " envelope:\n", + " exponent: 5\n", + " name: polynomial\n", + " extensive: true\n", + " max_neighbors: 50\n", + " num_after_skip: 2\n", + " num_atom: 3\n", + " num_before_skip: 1\n", + " num_blocks: 3\n", + " num_concat: 1\n", + " num_radial: 128\n", + " num_spherical: 7\n", + " otf_graph: true\n", + " output_init: HeOrthogonal\n", + " rbf:\n", + " name: gaussian\n", + " regress_forces: true\n", + " scale_file: configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json\n", + "optim:\n", + " batch_size: 32\n", + " clip_grad_norm: 10\n", + " ema_decay: 0.999\n", + " energy_coefficient: 1\n", + " eval_batch_size: 32\n", + " eval_every: 5000\n", + " factor: 0.8\n", + " force_coefficient: 100\n", + " loss_energy: mae\n", + " loss_force: l2mae\n", + " lr_initial: 0.0005\n", + " max_epochs: 80\n", + " mode: min\n", + " num_workers: 2\n", + " optimizer: AdamW\n", + " optimizer_params:\n", + " amsgrad: true\n", + " patience: 3\n", + " scheduler: ReduceLROnPlateau\n", + "slurm: {}\n", + "task:\n", + " dataset: trajectory_lmdb\n", + " description: Regressing to energies and forces for DFT trajectories from OCP\n", + " eval_on_free_atoms: true\n", + " grad_input: atomic forces\n", + " labels:\n", + " - potential energy\n", + " metric: mae\n", + " train_on_free_atoms: true\n", + " type: regression\n", + "\n", + "2021-11-22 18:03:35 (INFO): Loading dataset: trajectory_lmdb\n", + "2021-11-22 18:03:35 (INFO): Loading model: gemnet_t\n", + "2021-11-22 18:03:38 (INFO): Loaded GemNetT with 31671825 parameters.\n", + "2021-11-22 18:03:38 (INFO): Loading checkpoint from: /content/ocp/gemnet_t_direct_h512_all.pt\n", + " Step Time Energy fmax\n", + "BFGS: 0 18:03:41 -4.099784 1.5675\n", + "BFGS: 1 18:03:43 -4.244461 1.1370\n", + "BFGS: 2 18:03:44 -4.403120 0.7635\n", + "BFGS: 3 18:03:46 -4.503653 0.8364\n", + "BFGS: 4 18:03:48 -4.558208 0.7339\n", + "BFGS: 5 18:03:49 -4.592069 0.4095\n", + "BFGS: 6 18:03:51 -4.619362 0.7312\n", + "BFGS: 7 18:03:53 -4.671468 0.9712\n", + "BFGS: 8 18:03:54 -4.796430 0.9211\n", + "BFGS: 9 18:03:56 -4.957961 0.9762\n", + "BFGS: 10 18:03:57 -5.109433 1.0384\n", + "BFGS: 11 18:03:59 -5.295604 1.2247\n", + "BFGS: 12 18:04:00 -5.498977 1.1271\n", + "BFGS: 13 18:04:02 -5.618095 1.0669\n", + "BFGS: 14 18:04:04 -5.737120 0.9509\n", + "BFGS: 15 18:04:05 -5.901926 0.9260\n", + "BFGS: 16 18:04:07 -6.076125 1.2738\n", + "BFGS: 17 18:04:08 -6.198373 1.2029\n", + "BFGS: 18 18:04:10 -6.250323 0.6851\n", + "BFGS: 19 18:04:11 -6.254094 0.2008\n", + "BFGS: 20 18:04:13 -6.293966 0.1779\n", + "BFGS: 21 18:04:14 -6.326333 0.2294\n", + "BFGS: 22 18:04:16 -6.324431 0.1700\n", + "BFGS: 23 18:04:17 -6.321288 0.1016\n", + "BFGS: 24 18:04:19 -6.328468 0.0847\n", + "BFGS: 25 18:04:20 -6.331809 0.0587\n", + "BFGS: 26 18:04:22 -6.332153 0.0444\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "execution_count": 106 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TUH5BaaXo-ca" + }, + "source": [ + "\n", + "# (Optional) Creating your own LMDBs for use in the OCP repository \n", + "\n", + "In order to interface with our repository, the data mustbe structured and organized in a specific format. Below we walk you through on how to create such datasets with your own non-OC20 data that may help with your research.\n", + "\n", + "For this tutorial we use the toy C3H8 trajectory we previously generated [here](#data-description)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o7cG3WhLnuqg" + }, + "source": [ + "\n", + "\n", + "#### Initial Structure to Relaxed Energy (IS2RE) LMDBs\n", + "IS2RE/IS2RS LMDBs utilize the SinglePointLmdb dataset. This dataset expects the data to be contained in a **single** LMDB file. In addition to the attributes defined by AtomsToGraph, the following attributes must be added for the IS2RE/IS2RS tasks:\n", + "\n", + "- pos_relaxed: Relaxed adslab positions\n", + "- sid: Unique system identifier, arbitrary\n", + "- y_init: Initial adslab energy, formerly Data.y\n", + "- y_relaxed: Relaxed adslab energy\n", + "- tags (optional): 0 - subsurface, 1 - surface, 2 - adsorbate\n", + "\n", + "\n", + "As a demo, we will use the above generated data to create an IS2R* LMDB file.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nweCG0y5nxlw" + }, + "source": [ + "from ocpmodels.preprocessing import AtomsToGraphs\n", + "\n", + "\"\"\"\n", + "args description:\n", + "\n", + "max neigh (int): maximum number of neighors to be considered while constructing a graph\n", + "radius (int): Neighbors are considered only within this radius cutoff in Angstrom\n", + "r_energy (bool): Stored energy value in the Data object; False for test data\n", + "r_forces (bool): Stores forces value in the Data object; False for test data\n", + "r_distances (bool): pre-calculates distances taking into account PBC and max neigh/radius\n", + " If you set it to False, make sure to add \"otf_graph = True\" under models in config for runs\n", + "r_fixed (bools): True if you want to fix the subsurface atoms\n", + "\"\"\"\n", + "\n", + "a2g = AtomsToGraphs(\n", + " max_neigh=50,\n", + " radius=6,\n", + " r_energy=True, \n", + " r_forces=True,\n", + " r_distances=False, \n", + " r_fixed=True,\n", + ")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "K16pPnQdnzro" + }, + "source": [ + "import lmdb\n", + "\n", + "\"\"\"\n", + "For most cases one just needs to change the name of the lmdb as they require.\n", + "Make sure to give the entire path in the config (with .lmdb) for IS2RE tasks\n", + "\"\"\"\n", + "\n", + "db = lmdb.open(\n", + " \"data/toy_C3H8.lmdb\",\n", + " map_size=1099511627776 * 2,\n", + " subdir=False,\n", + " meminit=False,\n", + " map_async=True,\n", + ")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "t_8oaE5qn1Za" + }, + "source": [ + "\"\"\"\n", + "This method converts extracts all features from trajectory file and convert to Data Object\n", + "\"\"\"\n", + "\n", + "def read_trajectory_extract_features(a2g, traj_path):\n", + " # Read the traj file\n", + " traj = ase.io.read(traj_path, \":\")\n", + "\n", + " # Get tags if you had defined those in the atoms object, if not skip this line\n", + " tags = traj[0].get_tags()\n", + "\n", + " # Collect only initial and final image as this is IS2RS task\n", + " images = [traj[0], traj[-1]]\n", + "\n", + " # Converts a list of atoms object to a list of Data object using a2g defined above\n", + " data_objects = a2g.convert_all(images, disable_tqdm=True)\n", + "\n", + " # Add tags to the data objects if you have them (we would suggest to do so), if not skip this\n", + " data_objects[0].tags = torch.LongTensor(tags)\n", + " data_objects[1].tags = torch.LongTensor(tags)\n", + "\n", + " return data_objects" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "qSfOagphn7yy" + }, + "source": [ + "import torch\n", + "import pickle\n", + "system_paths = [\"data/toy_c3h8_relax.traj\"] # specify list of trajectory files you wish to write to LMDBs\n", + "idx = 0\n", + "\n", + "for system in system_paths:\n", + " # Extract Data object\n", + " data_objects = read_trajectory_extract_features(a2g, system)\n", + " initial_struc = data_objects[0]\n", + " relaxed_struc = data_objects[1]\n", + " \n", + " initial_struc.y_init = initial_struc.y # subtract off reference energy, if applicable\n", + " del initial_struc.y\n", + " initial_struc.y_relaxed = relaxed_struc.y # subtract off reference energy, if applicable\n", + " initial_struc.pos_relaxed = relaxed_struc.pos\n", + " \n", + " # Filter data if necessary\n", + " # OCP filters adsorption energies > |10| eV\n", + " \n", + " initial_struc.sid = idx # arbitrary unique identifier \n", + " \n", + " # no neighbor edge case check\n", + " if initial_struc.edge_index.shape[1] == 0:\n", + " print(\"no neighbors\", traj_path)\n", + " continue\n", + " \n", + " # Write to LMDB\n", + " txn = db.begin(write=True)\n", + " txn.put(f\"{idx}\".encode(\"ascii\"), pickle.dumps(initial_struc, protocol=-1))\n", + " txn.commit()\n", + " db.sync()\n", + " idx += 1\n", + "\n", + "db.close()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "p8ftTehrn9pG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "74c95b8a-e260-4b6f-92c4-3544f28deda5" + }, + "source": [ + "from ocpmodels.datasets import SinglePointLmdbDataset\n", + "\n", + "# SinglePointLmdbDataset is out custom Dataset method to read the lmdbs as Data objects. Note that we need to give the entire path (including lmdb) for IS2RE\n", + "dataset = SinglePointLmdbDataset({\"src\": \"data/toy_C3H8.lmdb\"})\n", + "\n", + "print(\"Size of the dataset created:\", len(dataset))\n", + "print(dataset[0])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Size of the dataset created: 1\n", + "Data(atomic_numbers=[38], cell=[1, 3, 3], cell_offsets=[1733, 3], edge_index=[2, 1733], fixed=[38], force=[38, 3], natoms=38, pos=[38, 3], pos_relaxed=[38, 3], sid=0, tags=[38], y_init=15.80469962027714, y_relaxed=8.358921451420816)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UWYBEis2n_ye" + }, + "source": [ + "#### Structure to Energy and Forces (S2EF) LMDBs\n", + "\n", + "S2EF LMDBs utilize the TrajectoryLmdb dataset. This dataset expects a directory of LMDB files. In addition to the attributes defined by AtomsToGraph, the following attributes must be added for the S2EF task:\n", + "\n", + "- tags (optional): 0 - subsurface, 1 - surface, 2 - adsorbate\n", + "- fid: Frame index along the trajcetory\n", + "- sid- sid: Unique system identifier, arbitrary\n", + "\n", + "Additionally, a \"length\" key must be added to each LMDB file.\n", + "\n", + "As a demo, we will use the above generated data to create an S2EF LMDB dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "k74bbQJuoBwy" + }, + "source": [ + "os.makedirs(\"data/s2ef\", exist_ok=True)\n", + "db = lmdb.open(\n", + " \"data/s2ef/toy_C3H8.lmdb\",\n", + " map_size=1099511627776 * 2,\n", + " subdir=False,\n", + " meminit=False,\n", + " map_async=True,\n", + ")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "-6VuR1lBoDfY", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0c3e104b-d22f-4376-85f3-0cd505c8914d" + }, + "source": [ + "from tqdm import tqdm\n", + "tags = traj[0].get_tags()\n", + "data_objects = a2g.convert_all(traj, disable_tqdm=True)\n", + "\n", + "\n", + "for fid, data in tqdm(enumerate(data_objects), total=len(data_objects)):\n", + " #assign sid\n", + " data.sid = torch.LongTensor([0])\n", + " \n", + " #assign fid\n", + " data.fid = torch.LongTensor([fid])\n", + " \n", + " #assign tags, if available\n", + " data.tags = torch.LongTensor(tags)\n", + " \n", + " # Filter data if necessary\n", + " # OCP filters adsorption energies > |10| eV and forces > |50| eV/A\n", + "\n", + " # no neighbor edge case check\n", + " if data.edge_index.shape[1] == 0:\n", + " print(\"no neighbors\", traj_path)\n", + " continue\n", + "\n", + " txn = db.begin(write=True)\n", + " txn.put(f\"{fid}\".encode(\"ascii\"), pickle.dumps(data, protocol=-1))\n", + " txn.commit()\n", + " \n", + "txn = db.begin(write=True)\n", + "txn.put(f\"length\".encode(\"ascii\"), pickle.dumps(len(data_objects), protocol=-1))\n", + "txn.commit()\n", + "\n", + "\n", + "db.sync()\n", + "db.close()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 101/101 [00:00<00:00, 129.56it/s]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rJ2ZXuBMH8xt" + }, + "source": [ + "# Running on command line [Preferred way to train models] " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aj8HsmxjISED" + }, + "source": [ + "The previous sections of this notebook are intended to demonstrate the inner workings of our codebase. For regular training, we suggest that you train and evaluate on command line.\n", + "\n", + "1. Clone our repo at https://github.com/Open-Catalyst-Project/ocp and set up the environment according to the readme.\n", + "2. Download relevant data ([see above for info](https://colab.research.google.com/drive/1oGZcrakB4Pbj8Xq74lSvcRDUHw9L-Dh5#scrollTo=jXoiLncsU3pe)).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lAdwlMNOKwYj" + }, + "source": [ + "3. In the config file, modify the path of the data [train](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/base.yml#L4) [val](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/base.yml#L8), [normalization parameters](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/base.yml#L5-L7) as well as any other [model](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/dimenet_plus_plus/dpp.yml#L4-L16) or [training](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/is2re/10k/dimenet_plus_plus/dpp.yml#L23-L35) args. \n", + "\n", + "For a simple example, we'll train DimeNet++ on IS2RE demo data: \\\n", + "a. Modify the train data path in `/contents/ocp/configs/is2re/10k/base.yml` in \n", + "Line 4 to `/contents/ocp/data/is2re/train_10k/data.lmdb` and val data path in Line 8 to `/contents/ocp/data/is2re/val_2k/data.lmdb`. \\\n", + "b. Calculate the mean and std for train data and modify Lines 6-7 respectively \\\n", + "c. We can change the model parameters in `/contents/ocp/configs/is2re/10k/dimenet_plus_plus/dpp.yml` and we suggest you to change the lr_milestones and warmup_steps as the data here is smaller (these need to be tuned for every dataset).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HjWsAaojKzpH" + }, + "source": [ + "4. Train: `python main.py --mode train --config-yml configs/is2re/10k/dimenet_plus_plus/dpp.yml --identifier dpp_is2re_sample`\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mCgs4eGSO-HM" + }, + "source": [ + "# Optional block to try command line training \n", + "# Note that config args can be added in the command line. For example, --optim.batch_size=1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q1xRtYWTO8Xb" + }, + "source": [ + "5. Add a data path as a test set to `configs/is2re/10k/base.yml`\n", + "6. Run predictions with the trained model: \n", + "`python main.py --mode predict --config-yml configs/is2re/10k/dimenet_plus_plus/dpp.yml --checkpoint checkpoints/[datetime]-dpp_is2re_sample/checkpoint.pt`\n", + "7. View energy predictions at `results/[datetime]/is2re_predictions.npz`\n", + "\n", + "For more information on how to train and evaluate, see [this readme](https://github.com/Open-Catalyst-Project/ocp/blob/master/TRAIN.md). For checkpoints of publicly available trained models, see [MODELS.md](https://github.com/Open-Catalyst-Project/ocp/blob/master/MODELS.md)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oHIjM6eMwlXY" + }, + "source": [ + "# Limitations \n", + "The OpenCatalyst project is motivated by the problems we face due to climate change, many of which require innovative solutions to reduce energy usage and replace traditional chemical feedstocks with renewable alternatives. For example, one of the most energy intensive chemical processes is the development of new electrochemical catalysts for ammonia fertilizer production that helped to feed the world’s growing population during the 20th century. This is also an illustrative example of possible unintended consequences as advancements in chemistry and materials may be used for numerous purposes. As ammonia fertilization increased in use, its overuse in today’s farming has led to ocean “dead zones” and its production is very carbon intensive. Knowledge and techniques used to create ammonia were also transferred to the creation of explosives during wartime. We hope to steer the use of ML for atomic simulations to societally-beneficial uses by training and testing our approaches on datasets, such as OC20, that were specifically designed to address chemical reactions useful for addressing climate change." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CLLCQpv14Gsx" + }, + "source": [ + "# Next Steps \n", + "\n", + "While progress has been well underway - https://opencatalystproject.org/leaderboard.html, a considerable gap still exists between state-of-the-art models and our target goals. We offer some some general thoughts as to next steps for the readers to ponder on or explore:\n", + "\n", + "* GNN depth has consistenly improved model performance. What limitations to depth are there? How far can we push deeper models for OC20? \n", + "* Our best performing models have little to no physical biases encoded. Can we incorporate such biases to improve our models? Experiments with physically inspired embeddings have had no advantage vs. random initializations, are there better ways to incorporate this information into the models?\n", + "* Uncertainty estimation will play an important role in later stages of the project when it comes to large scale screening. How can we get reliable uncertainty estimates from large scale GNNs?\n", + "* Are we limited to message-passing GNNs? Can we leverage alternative architectures for similiar or better performance?\n", + "* Trajectories are nothing more than sequential data points. How can we use sequential modeling techniques to model the full trajectory?\n", + "\n", + "OC20 is a large and diverse dataset with many splits. For those with limited resources but unsure where to start, we provide some general recommendations:\n", + "\n", + "* The IS2RE-direct task is a great place to start. With the largest training set containing ~460k data points, this task is easily accesible for those with even just a single GPU.\n", + "* Those interested in the more general S2EF task don't need to train on the All set to get meaningful performance.\n", + " * Results on the 2M dataset are often sufficient to highlight model improvements.\n", + " * For a fixed compute budget (e.g. fixed number of steps), training on the All set often leads to better performance.\n", + "* The S2EF 200k dataset is fairly noisy, trying to find meaningful trends using this dataset can be difficult.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PkKqewK_-ZLD" + }, + "source": [ + "\n", + "# References\n", + "\n", + "* Open Catalyst codebase: https://github.com/Open-Catalyst-Project/ocp/\n", + "* Open Catalyst webpage: https://opencatalystproject.org/\n", + "* [Electrocatalysis white paper](https://arxiv.org/pdf/2010.09435.pdf): C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Riviere, Muhammed Shuaibi, Anuroop Sriram, Kevin Tran, Brandon Wood, Junwoong Yoon, Devi Parikh, Zachary Ulissi: “An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage”, 2020; arXiv:2010.09435.\n", + "* [OC20 dataset paper](https://arxiv.org/pdf/2010.09990.pdf): L. Chanussot, A. Das, S. Goyal, T. Lavril, M. Shuaibi, M. Riviere, K. Tran, J. Heras-Domingo, C. Ho, W. Hu, A. Palizhati, A. Sriram, B. Wood, J. Yoon, D. Parikh, C. L. Zitnick, and Z. Ulissi. The Open Catalyst 2020 (oc20) dataset and community challenges. ACS Catalysis, 2021.\n", + "* [Gemnet model:](https://arxiv.org/abs/2106.08903) Johannes Klicpera, Florian Becker, and Stephan Günnemann. Gemnet: Universal directional graph neural networks for molecules, 2021.\n", + "\n", + "\n" + ] + } + ] +} \ No newline at end of file diff --git a/tutorials/README.md b/tutorials/README.md new file mode 100644 index 0000000..d33b1b5 --- /dev/null +++ b/tutorials/README.md @@ -0,0 +1,19 @@ +### Tutorials + +As part of the [NeurIPS 2021 Climate Change AI Workshop](https://www.climatechange.ai/papers/neurips2021/79), a comprehensive, interactive Google-Colab tutorial notebook can be found [here](https://colab.research.google.com/github/Open-Catalyst-Project/ocp/blob/master/tutorials/OCP_Tutorial.ipynb). This notebook is designed for those new to OC20 and interested in how to get started. Topics include: + * Background + * Software Requirements + * Dataset overview & Visualization + * OCP Tasks - Train, Validate, Predict + * IS2RE + * S2EF + * IS2RS + * OCP Calculator + * Model Development + + + Additionally, we provide several Jupyter notebooks: + * [Data preprocessing](https://github.com/Open-Catalyst-Project/ocp/blob/master/tutorials/data_preprocessing.ipynb) - preprocessing raw ASE atoms object to OCP graph Data objects. + * [LMDB dataset creation](https://github.com/Open-Catalyst-Project/ocp/blob/master/tutorials/lmdb_dataset_creation.ipynb) - creating your own OCP-comatible LMDB datasets from ASE-compatible Atoms objects. + * [Data visualization](https://github.com/Open-Catalyst-Project/ocp/blob/master/tutorials/data_visualization.ipynb) - understanding the raw data and its contents. (same contents found in the above google colab notebook) + * [S2EF training example](https://github.com/Open-Catalyst-Project/ocp/blob/master/tutorials/train_s2ef_example.ipynb) - training a SchNet S2EF model, loading a trained model, and making predictions. (same contents found in the above google colab notebook). diff --git a/tutorials/data_preprocessing.ipynb b/tutorials/data_preprocessing.ipynb new file mode 100644 index 0000000..90b777f --- /dev/null +++ b/tutorials/data_preprocessing.ipynb @@ -0,0 +1,486 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6222a144", + "metadata": {}, + "source": [ + "### OCP Data Preprocessing Tutorial\n", + "\n", + "\n", + "This notebook provides an overview of converting ASE Atoms objects to PyTorch Geometric Data objects. To better understand the raw data contained within OC20, check out the following tutorial first: https://github.com/Open-Catalyst-Project/ocp/blob/master/docs/source/tutorials/data_visualization.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "10f841e5", + "metadata": {}, + "outputs": [], + "source": [ + "from ocpmodels.preprocessing import AtomsToGraphs\n", + "import ase.io\n", + "from ase.build import bulk\n", + "from ase.build import fcc100, add_adsorbate, molecule\n", + "from ase.constraints import FixAtoms\n", + "from ase.calculators.emt import EMT\n", + "from ase.optimize import BFGS" + ] + }, + { + "cell_type": "markdown", + "id": "c565ef44", + "metadata": {}, + "source": [ + "### Generate toy dataset: Relaxation of CO on Cu" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e98686c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adslab = fcc100(\"Cu\", size=(2, 2, 3))\n", + "ads = molecule(\"CO\")\n", + "add_adsorbate(adslab, ads, 3, offset=(1, 1))\n", + "cons = FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 3)])\n", + "adslab.set_constraint(cons)\n", + "adslab.center(vacuum=13.0, axis=2)\n", + "adslab.set_pbc(True)\n", + "adslab.set_calculator(EMT())\n", + "dyn = BFGS(adslab, trajectory=\"CuCO_adslab.traj\", logfile=None)\n", + "dyn.run(fmax=0, steps=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6975a24a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1001\n" + ] + } + ], + "source": [ + "raw_data = ase.io.read(\"CuCO_adslab.traj\", \":\")\n", + "print(len(raw_data))" + ] + }, + { + "cell_type": "markdown", + "id": "064b96bc", + "metadata": {}, + "source": [ + "### Convert Atoms object to Data object\n", + "\n", + "The AtomsToGraphs class takes in several arguments to control how Data objects created:\n", + "\n", + "- max_neigh (int): Maximum number of neighbors a given atom is allowed to have, discarding the furthest\n", + "- radius (float): Cutoff radius to compute nearest neighbors around\n", + "- r_energy (bool): Write energy to Data object\n", + "- r_forces (bool): Write forces to Data object\n", + "- r_distances (bool): Write distances between neighbors to Data object\n", + "- r_edges (bool): Write neigbhor edge indices to Data object\n", + "- r_fixed (bool): Write indices of fixed atoms to Data object" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f269f5db", + "metadata": {}, + "outputs": [], + "source": [ + "a2g = AtomsToGraphs(\n", + " max_neigh=50,\n", + " radius=6,\n", + " r_energy=True,\n", + " r_forces=True,\n", + " r_distances=False,\n", + " r_edges=True,\n", + " r_fixed=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f9db4b2c", + "metadata": {}, + "outputs": [], + "source": [ + "data_objects = a2g.convert_all(raw_data, disable_tqdm=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1c20684e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(atomic_numbers=[14], cell=[1, 3, 3], cell_offsets=[636, 3], edge_index=[2, 636], fixed=[14], force=[14, 3], natoms=14, pos=[14, 3], y=3.989314410668539)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data_objects[0]\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a3d2aa78", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 8., 6.])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.atomic_numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fba3f218", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[ 5.1053, 0.0000, 0.0000],\n", + " [ 0.0000, 5.1053, 0.0000],\n", + " [ 0.0000, 0.0000, 32.6100]]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.cell" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d9f97a2c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 1, 2, 2, ..., 4, 6, 3],\n", + " [ 0, 0, 0, ..., 13, 13, 13]])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.edge_index #neighbor idx, source idx" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d134ee03", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([45., 45., 45., 46., 49., 49., 49., 49., 50., 49., 49., 50., 26., 35.])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from torch_geometric.utils import degree\n", + "# Degree corresponds to the number of neighbors a given node has. Note there is no more than max_neigh neighbors for\n", + "# any given node.\n", + "\n", + "degree(data.edge_index[1]) " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8a5d356f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.fixed" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6a06a5f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 9.9356e-16, 4.5465e-15, 1.1354e-01],\n", + " [ 2.6749e-15, 3.7696e-15, 1.1344e-01],\n", + " [ 8.4481e-16, 2.7062e-16, 1.1344e-01],\n", + " [-6.6623e-18, 6.6196e-17, 1.1294e-01],\n", + " [-8.5221e-03, -8.5221e-03, -1.1496e-02],\n", + " [ 8.5221e-03, -8.5221e-03, -1.1496e-02],\n", + " [-8.5221e-03, 8.5221e-03, -1.1496e-02],\n", + " [ 8.5221e-03, 8.5221e-03, -1.1496e-02],\n", + " [ 1.9082e-17, 9.6277e-16, -1.0431e-01],\n", + " [-2.0583e-15, -4.3021e-16, -6.6610e-02],\n", + " [-5.5511e-17, -2.3592e-15, -6.6610e-02],\n", + " [-2.9409e-17, -4.3038e-15, -3.3250e-01],\n", + " [ 3.3204e-19, 6.7763e-21, -3.4247e-01],\n", + " [-4.5103e-17, -5.2042e-17, 5.0512e-01]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.force" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40786dcb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.0000, 0.0000, 13.0000],\n", + " [ 2.5527, 0.0000, 13.0000],\n", + " [ 0.0000, 2.5527, 13.0000],\n", + " [ 2.5527, 2.5527, 13.0000],\n", + " [ 1.2763, 1.2763, 14.8050],\n", + " [ 3.8290, 1.2763, 14.8050],\n", + " [ 1.2763, 3.8290, 14.8050],\n", + " [ 3.8290, 3.8290, 14.8050],\n", + " [ 0.0000, 0.0000, 16.6100],\n", + " [ 2.5527, 0.0000, 16.6100],\n", + " [ 0.0000, 2.5527, 16.6100],\n", + " [ 2.5527, 2.5527, 16.6100],\n", + " [ 2.5527, 2.5527, 19.6100],\n", + " [ 2.5527, 2.5527, 18.4597]])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.pos" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5ebc9a93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.989314410668539" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.y" + ] + }, + { + "cell_type": "markdown", + "id": "879a7964", + "metadata": {}, + "source": [ + "### Adding additional info to your Data objects\n", + "\n", + "In addition to the above information, the OCP repo requires several other pieces of information for your data to work\n", + "with the provided trainers:\n", + "\n", + "- sid (int): A unique identifier for a particular system. Does not affect your model performance, used for prediction saving \n", + "- fid (int) (S2EF only): If training for the S2EF task, your data must also contain a unique frame identifier for atoms objects coming from the same system.\n", + "- tags (tensor): Tag information - 0 for subsurface, 1 for surface, 2 for adsorbate. Optional, can be used for training.\n", + "\n", + "\n", + "Other information may be added her as well if you choose to incorporate other information in your models/frameworks" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "87f2c8c8", + "metadata": {}, + "outputs": [], + "source": [ + "data_objects = []\n", + "for idx, system in enumerate(raw_data):\n", + " data = a2g.convert(system)\n", + " data.fid = idx\n", + " data.sid = 0 # All data points come from the same system, arbitrarly define this as 0\n", + " data_objects.append(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "15ce371b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(atomic_numbers=[14], cell=[1, 3, 3], cell_offsets=[636, 3], edge_index=[2, 636], fid=100, fixed=[14], force=[14, 3], natoms=14, pos=[14, 3], sid=0, y=3.968355893395719)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data_objects[100]\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "6be76f6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.sid" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "3c38f65f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.fid" + ] + }, + { + "cell_type": "markdown", + "id": "e3f6a061", + "metadata": {}, + "source": [ + "Resources:\n", + "\n", + "- https://github.com/Open-Catalyst-Project/ocp/blob/6604e7130ea41fabff93c229af2486433093e3b4/ocpmodels/preprocessing/atoms_to_graphs.py\n", + "- https://github.com/Open-Catalyst-Project/ocp/blob/master/scripts/preprocess_ef.py" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ocp-models", + "language": "python", + "name": "ocp-models" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/data_visualization.ipynb b/tutorials/data_visualization.ipynb new file mode 100644 index 0000000..a9341b2 --- /dev/null +++ b/tutorials/data_visualization.ipynb @@ -0,0 +1,822 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.use('Agg')\n", + "\n", + "import os\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "params = {\n", + " 'axes.labelsize': 14,\n", + " 'font.size': 14,\n", + " 'font.family': ' DejaVu Sans',\n", + " 'legend.fontsize': 20,\n", + " 'xtick.labelsize': 20,\n", + " 'ytick.labelsize': 20,\n", + " 'axes.labelsize': 25,\n", + " 'axes.titlesize': 25,\n", + " 'text.usetex': False,\n", + " 'figure.figsize': [12, 12]\n", + "}\n", + "matplotlib.rcParams.update(params)\n", + "\n", + "\n", + "import ase.io\n", + "from ase.io.trajectory import Trajectory\n", + "from ase.io import extxyz\n", + "from ase.calculators.emt import EMT\n", + "from ase.build import fcc100, add_adsorbate, molecule\n", + "from ase.constraints import FixAtoms\n", + "from ase.optimize import LBFGS\n", + "from ase.visualize.plot import plot_atoms\n", + "from ase import Atoms\n", + "from IPython.display import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "videos_dir = \"videos/\"\n", + "os.makedirs(videos_dir, exist_ok=True)\n", + "\n", + "config = {\n", + " \"num_procs\": 1,\n", + " \"fps\": 30,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding the data\n", + "\n", + "We use the Atomic Simulation Environment (ASE) to interact with our data. This notebook will provide you with some intuition on what the data looks like, how to visualize it, and the specific properties that are passed on to our models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating sample data\n", + "For simplicity, we generate sample data in the same format as the OC20 dataset. A toy relaxation (or trajectory) of propane (C3H8) on a copper (Cu) surface is used with a classical-like potential (EMT). Unlike DFT, EMT is extremely fast but limited in accuracy and applicability to certain elements, making it great for demos and tests. You are free to explore alternative systems below, however, you may skip the data construction and move on to \"Reading a trajectory\"." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Step Time Energy fmax\n", + "*Force-consistent energies used in optimization.\n", + "LBFGS: 0 04:31:37 15.804700* 6.7764\n", + "LBFGS: 1 04:31:37 14.018888* 5.5932\n", + "LBFGS: 2 04:31:37 12.806356* 4.8521\n", + "LBFGS: 3 04:31:37 11.942190* 4.2650\n", + "LBFGS: 4 04:31:37 11.288882* 3.7258\n", + "LBFGS: 5 04:31:37 10.778343* 3.1760\n", + "LBFGS: 6 04:31:37 10.376493* 2.5830\n", + "LBFGS: 7 04:31:37 10.066492* 1.9348\n", + "LBFGS: 8 04:31:37 9.839944* 1.2476\n", + "LBFGS: 9 04:31:37 9.690873* 0.6301\n", + "LBFGS: 10 04:31:37 9.608610* 0.5065\n", + "LBFGS: 11 04:31:37 9.575756* 0.4808\n", + "LBFGS: 12 04:31:37 9.540397* 0.4147\n", + "LBFGS: 13 04:31:37 9.505960* 0.6106\n", + "LBFGS: 14 04:31:37 9.464765* 0.5971\n", + "LBFGS: 15 04:31:37 9.429512* 0.3517\n", + "LBFGS: 16 04:31:37 9.415173* 0.2134\n", + "LBFGS: 17 04:31:37 9.409989* 0.1426\n", + "LBFGS: 18 04:31:37 9.405735* 0.2088\n", + "LBFGS: 19 04:31:37 9.400485* 0.2673\n", + "LBFGS: 20 04:31:37 9.392300* 0.2800\n", + "LBFGS: 21 04:31:38 9.383425* 0.2144\n", + "LBFGS: 22 04:31:38 9.376064* 0.2322\n", + "LBFGS: 23 04:31:38 9.368306* 0.2685\n", + "LBFGS: 24 04:31:38 9.353821* 0.4335\n", + "LBFGS: 25 04:31:38 9.337132* 0.5630\n", + "LBFGS: 26 04:31:38 9.319439* 0.6642\n", + "LBFGS: 27 04:31:38 9.300718* 0.7506\n", + "LBFGS: 28 04:31:38 9.281012* 0.8256\n", + "LBFGS: 29 04:31:38 9.260247* 0.8892\n", + "LBFGS: 30 04:31:38 9.238220* 0.9400\n", + "LBFGS: 31 04:31:38 9.214585* 0.9759\n", + "LBFGS: 32 04:31:38 9.188875* 0.9950\n", + "LBFGS: 33 04:31:38 9.160629* 0.9964\n", + "LBFGS: 34 04:31:38 9.129637* 1.0123\n", + "LBFGS: 35 04:31:38 9.096151* 1.0754\n", + "LBFGS: 36 04:31:38 9.060833* 1.1179\n", + "LBFGS: 37 04:31:38 9.024484* 1.1350\n", + "LBFGS: 38 04:31:38 8.987881* 1.1232\n", + "LBFGS: 39 04:31:38 8.951815* 1.0792\n", + "LBFGS: 40 04:31:38 8.917153* 0.9984\n", + "LBFGS: 41 04:31:38 8.884930* 0.8732\n", + "LBFGS: 42 04:31:38 8.856457* 0.6900\n", + "LBFGS: 43 04:31:38 8.833630* 0.4172\n", + "LBFGS: 44 04:31:38 8.821747* 0.1922\n", + "LBFGS: 45 04:31:38 8.816462* 0.1456\n", + "LBFGS: 46 04:31:38 8.811231* 0.1742\n", + "LBFGS: 47 04:31:39 8.808922* 0.1459\n", + "LBFGS: 48 04:31:39 8.805457* 0.1324\n", + "LBFGS: 49 04:31:39 8.802116* 0.1357\n", + "LBFGS: 50 04:31:39 8.799266* 0.1469\n", + "LBFGS: 51 04:31:39 8.797205* 0.1113\n", + "LBFGS: 52 04:31:39 8.795876* 0.0868\n", + "LBFGS: 53 04:31:39 8.790077* 0.1435\n", + "LBFGS: 54 04:31:39 8.782248* 0.2200\n", + "LBFGS: 55 04:31:39 8.773097* 0.2756\n", + "LBFGS: 56 04:31:39 8.763281* 0.3048\n", + "LBFGS: 57 04:31:39 8.752938* 0.3311\n", + "LBFGS: 58 04:31:39 8.742241* 0.3544\n", + "LBFGS: 59 04:31:39 8.731305* 0.3690\n", + "LBFGS: 60 04:31:39 8.720213* 0.3768\n", + "LBFGS: 61 04:31:39 8.709033* 0.3784\n", + "LBFGS: 62 04:31:39 8.697841* 0.3743\n", + "LBFGS: 63 04:31:39 8.686722* 0.3643\n", + "LBFGS: 64 04:31:39 8.675773* 0.3478\n", + "LBFGS: 65 04:31:39 8.665096* 0.3233\n", + "LBFGS: 66 04:31:39 8.654766* 0.2884\n", + "LBFGS: 67 04:31:39 8.644762* 0.2386\n", + "LBFGS: 68 04:31:39 8.634793* 0.1708\n", + "LBFGS: 69 04:31:39 8.623881* 0.2239\n", + "LBFGS: 70 04:31:39 8.616408* 0.2303\n", + "LBFGS: 71 04:31:39 8.609319* 0.1846\n", + "LBFGS: 72 04:31:39 8.605403* 0.1570\n", + "LBFGS: 73 04:31:39 8.601855* 0.1751\n", + "LBFGS: 74 04:31:40 8.596478* 0.1552\n", + "LBFGS: 75 04:31:40 8.591722* 0.1303\n", + "LBFGS: 76 04:31:40 8.586444* 0.1078\n", + "LBFGS: 77 04:31:40 8.583936* 0.1202\n", + "LBFGS: 78 04:31:40 8.578482* 0.1231\n", + "LBFGS: 79 04:31:40 8.572623* 0.1416\n", + "LBFGS: 80 04:31:40 8.566533* 0.1426\n", + "LBFGS: 81 04:31:40 8.560680* 0.1243\n", + "LBFGS: 82 04:31:40 8.555352* 0.1201\n", + "LBFGS: 83 04:31:40 8.550508* 0.1256\n", + "LBFGS: 84 04:31:40 8.545922* 0.1318\n", + "LBFGS: 85 04:31:40 8.541385* 0.1385\n", + "LBFGS: 86 04:31:40 8.536941* 0.1470\n", + "LBFGS: 87 04:31:40 8.533030* 0.1741\n", + "LBFGS: 88 04:31:40 8.535256* 0.4591\n", + "LBFGS: 89 04:31:40 8.534573* 0.2915\n", + "LBFGS: 90 04:31:40 8.535688* 0.3416\n", + "LBFGS: 91 04:31:40 8.529157* 0.1414\n", + "LBFGS: 92 04:31:40 8.524230* 0.1399\n", + "LBFGS: 93 04:31:40 8.519336* 0.1442\n", + "LBFGS: 94 04:31:40 8.514342* 0.1491\n", + "LBFGS: 95 04:31:40 8.509192* 0.1549\n", + "LBFGS: 96 04:31:40 8.503941* 0.1588\n", + "LBFGS: 97 04:31:40 8.498586* 0.1621\n", + "LBFGS: 98 04:31:40 8.493160* 0.1621\n", + "LBFGS: 99 04:31:40 8.487599* 0.1598\n", + "LBFGS: 100 04:31:41 8.481898* 0.1504\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mshuaibi/miniconda3/envs/ocp-models/lib/python3.6/site-packages/ase/io/jsonio.py:122: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " a = np.array(obj)\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "###DATA GENERATION - FEEL FREE TO SKIP###\n", + "\n", + "adslab = fcc100(\"Cu\", size=(3, 3, 3))\n", + "adsorbate = molecule(\"C3H8\")\n", + "add_adsorbate(adslab, adsorbate, 3, offset=(1, 1)) # adslab = adsorbate + slab\n", + "\n", + "# tag all slab atoms below surface as 0, surface as 1, adsorbate as 2\n", + "tags = np.zeros(len(adslab))\n", + "tags[18:27] = 1\n", + "tags[27:] = 2\n", + "\n", + "adslab.set_tags(tags)\n", + "\n", + "# Fixed atoms are prevented from moving during a structure relaxation. We fix all slab atoms beneath the surface \n", + "cons= FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 0)])\n", + "adslab.set_constraint(cons)\n", + "adslab.center(vacuum=13.0, axis=2)\n", + "adslab.set_pbc(True)\n", + "adslab.set_calculator(EMT())\n", + "\n", + "os.makedirs('data', exist_ok=True)\n", + "\n", + "# Define structure optimizer - LBFGS. Run for 100 steps, or if the max force on all atoms (fmax) is below 0 ev/A.\n", + "# fmax is typically set to 0.01-0.05 eV/A, for this demo however we run for the full 100 steps.\n", + "\n", + "dyn = LBFGS(adslab, trajectory=\"data/toy_c3h8_relax.traj\")\n", + "dyn.run(fmax=0, steps=100)\n", + "\n", + "traj = ase.io.read(\"data/toy_c3h8_relax.traj\", \":\")\n", + "\n", + "# convert traj format to extxyz format (used by OC20 dataset)\n", + "columns = (['symbols','positions', 'move_mask', 'tags'])\n", + "with open('data/toy_c3h8_relax.extxyz','w') as f:\n", + " extxyz.write_xyz(f, traj, columns=columns)\n", + " \n", + "os.system(\"rm data/toy_c3h8_relax.traj\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading a trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "identifier = \"toy_c3h8_relax.extxyz\"\n", + "traj = ase.io.read(\"data/%s\" % identifier, index=\":\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Viewing a trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Tcl0rYXt86ySx/RPbG9l+kXQ3eALQmrlZn1Qs8h0ASctJWjWKRUInyMUQy+YbOiR9StLDwL75JXMDD5Eeqz9qe07b+0PKK7V9Y01PFbYkpQBVphWn7b/b3tr2uflb55OKRd5QKrp8StJ/ci77LJI2jGKR0CkkzSNpE0nTS5pZ0q2k9BuAWUjX2dbEzLq2V7H9ev5zedWD1Wxu4JdVHrDH+fqC7d1t75O/dQfwZ9LkEMCJSgXUqwFI+phSAbX+96iD16CeYW3NHObN7wEz2N5P0qeBQ4AjbZ+Tc1Vesf1sqVgBJN0M7Gr75gI/exZghO0nJe1CGgx82/aFkg4E3gJ+2ZfcvhD6I795T5ufCGwNbAYcBDxLKnD6u+0v59zRWUgFT8WeCkj6Fqlw8sACP3taYKGc17cM8AvgHtt7SPo4qZjk3FxsEkItWjOHkpYDvkRKaTlHqVPGoqQbuqeADwF32x5bMNYlgYttVzYp1MbPFrAgKbVwrKQzSekEy5Ley74NXGX7jwMdWycZNDOskmaS9PFcOIGkH5Jy1VbOF7XXgKsAbF+Y7+jOydv3lh6sZtsB95T4wbZfsf1k/vwU2yvavjB/+xHSm887+W75YUm/AMjbC8adYmhXfsqxdf78o8CLpJxwSDMwVwFjbL9texnbXwaw/VyeOS2dwnIKcFyJH5x/J/fnz++2vY7tPfK3nyO9988KIOlcpe4HC+TtpXMBSgh9JmkRSdtKmjPP7N9NmkkEGA88CPwXIM82bmD7SSf/KTlYzR4jdRIYcPl38Gjrd2D7c7aXdOoB+26ObX4ASTtJukXSNnl7IUkzlYh7oDVuhjX/w71j+3VJPwKWI/0n/CDwE+AM26crtY4Z0wEnSaPkx5GLA3PZ/qekDYHfkn7vX8/bc5Lac9XxWCd0kTwwmsn2i0r9hHcCDrP9b0l/JlXF7ylpBKkdTSfcODZKnpH+IKk93njSbPV0theTND/wadLM2O0FwwwdIE88zOlUVLQoqTjxDttHSPoGqc3iwU4FjQuRzt/SN46NImk4qaj75fwE5WhgD1LqxI2SvkTqLnJpTamJxXT1gFVpJajNSInM50v6JulR/lY5kXk70mzClU34h8uJ2v+y/doUX9xhlKs3JW1J6mt3tO3rJZ1O6k6wP2lBhOG2X5/csUL3ysVCK5HysZcGbiYVHnxJqY3UPKTK9xcLhlkJScuTbp6LPBWZGj3O1yVIXRQesP1jSbsBGwDH5PN3hjhfmyv/+69POifvlnQTqb3awqRH1Z8Errd9d7koq5HT3lZxhUVXA6VHeuO7pOKuJYGtSE8+TwQutH1irjt5u1vHQx0/YM13dPORBqXP50f5m5Ia/s4A/By4xPYpeRbmzabe0Ul6APh461FfE+RBykqkf8e5SY+NLrO9haQFSSfezbZfKhhmaEN+yjGH7QclfYK0CMZxtn+fZwMAvgWMI83kNXLAo9SX8TXbPywdS1WU2gp9BPiP7XskXQEsA6wCPE2ajb3d9kPFggxtyU85liStojg7cCqp3/fuOSVnY+AE2zdLmrEbJ0z6QtKHgF/ZXrl0LFXJk3rrAtj+s6S9gCOAfW2fllMk3yLNkhdpB9aOjhuwKvUL3YlUXHGcpF2BHwBftf07SeuR8k1v9SBb41ep8nn9Jl8McrHInE6tedYhPXK6OM/u7AwsBJxm+5GScYaJ8sz/OqQ3wiGk7hJ/sL1jnqFZELjJ9isFwxxwkn4MvGj7iNKx1KVHscjjwPTAWaTi1Q0lfQDYFfiz7b8VDDP0kM/JLUk9Qa+SdCmwCOlGZDTpqeUtth8uGOaAU6rQP8H2qqVjqVOeSR5i+yVJB5EWJtnO9l2SjgMeAH7mThscUqjoStI0Sq2Rlsjb35H0SK5mfYdUMdjKLT3V9ly2fwdg+8qcoD2oBqvZxkxsNdVIuVjkifz51blYpLWU5QOk/7PTAEi6SdKVkqbL/6dWiGKR6uUCivmVlyCUtI6k23IKDsBSpLv0oU49gme0vSOA09LCVwy2wWp2NGm2qrF6FIu869Ry6NO2N8zffoVULNIq7josF4usm7eXHizFIgNNqch4LUkj83vj1Ur9TCH1652LlIoFsKntpWw/n99//zjYBqvZnaR0tUZzKqB+KX/+A6cC6rty/cmtwGy2rdS68mFNbGc5r6QF8k1qEbXOsOa/mGy/q9SYfh6nZt0bAceTljw8Ked6vQU81NTH+aF6uVhkOdtXKlU4/w14PM/urERqMH257XuLBtpFJA21PT7Pmn4KOIbUw/ReUmrGNvn3viBwV1Mf54fq9SgWecz2M5LOIc3mLUQa3H4DuDFmY/uux/m6CLAbaQGM30k6HlgN2MFpBai1gXsdha6hjzSxgHq47dvyE87DSe0sT5L0ufzSi9yH5eKrUNkMq6Thkj4qaY28/TXgJVKCPsB0wO0Atv/q1LLhpLx9Z56JicHqZEjaXNGsfwKn9kVX5s8ft700sEn+9jBgRdKa0q3ZnXM0cTGImUvE3EkkrShpix6fP0pK0IeUH34/MDbPni1hexuY8Hv/TwxWJ0/SKkqrXQVSE3XbN9h+Jm9vQ+oQ8QIwnFTEsxmApPUkXSZp27w9Y76ADlr5KccnJM2bt68CnsgTQ0PIkz4Atve2vVrrZt32P2OwOnmS5lIqCg1Aft+/z/ZteftUUp3Jyfklw4HNgRFKi7dcJekYSKl9SiuHVaqyGVZJJ5AaA//L9keUimlmJVUXRuuoCkh6gbSm8vOlY+k2Of1kDVJB13OSHiQNahclDc42JuVZDopHYTlf62pSCs68gEhVpZc599sNU0epF/Fttn9eOpZuI2lWUk7li7b/JekI0hLYW9i+XNKmpA4wtwyGiQ6lKvCHSAOGb9o+WqkLzt2kgpnG/w7qJml94CDbMWhtk9Iy72sB89s+S9KHSSuXnWT7K0rL7c5BeoIyur8/p8o71vmB7YHWNPGcpIKZHQAk7SXpgPw4MfTPUFKfxNCmPIP/mx6zDIsDa+Y3+tlI/08PhAlLe56r1Be0qZYE/khq4/Ia6W55e/IMq6Q1JB3eemIS+iXO136y/bLti2z/K28fQEpDuS6/ZGPSwgzT5XzNCyX9oFC4A2EY6Zq6MHBa/trqwHnAKElDJZ0gqdKlgAeZOF/7yWmZ93/aPitvXwvMTCqYh3S9+TapRRqSfizpt+2OB6scsL5Muht+FN6zWtQv8vcfIyV6D8vTx3dL+mMu6BguabGSybxdYlVSFWeYSrkC8rl8Z/g0aUWW1trViwAfZuLj8SZ6C3i+xyPDZ51Wnvl0/v4rpKKMOQEk/UzStZI+mLeXUOroESbtYODc0kE0yCukJwKQBq63ATOR0s1WB3ZRWhGtiYaQ8vOfyoWN2P6K7cWdFtOYlpRnviCApI0k/VfSl/P2vJJmKxV8l7gW+GLpIJrCqU1WK7f1HtL52rISsBHwvXaOWaStVR6YLgQsZvsKSSsAFwPX2N4uz+osDVxh+/EBDzA0jlI18lrAS7ZvkHQksDdpTfXbSGvS32b7ovz6WUlFgLOWirmT5N/HB0m/o5eUKo5XAkaR2hntTGogfn3BMEND5GvEGsC8tv+QcwvPA463/W2l/qAzktqnvZL3uQL4oe0rigXeIZTaAy5HWrjiDqVVqA4GdrR9gaQdSYOJS22/WTLW0Ay58G814ArS5M/dwDjby+XC+o1IKWf/za//ErCC7S/19WcUSWJ38kjrjcX27bYXAFqPM0YAG5IuiEg6WtKpkubJ25Un83YDSdvnGcEwCbk4Y3j+/EhJf8jfWpq0Ys+SeftoYJTtW/P/x8Nag9VsHPCbAQu8w+VHtFd6YjuUdUi/v7dJ6QRLAK0Crk/npyeb5O0ZBuPTE0kfyW/iYRIkDWvN/EnaWtKfJK2cn4B8F2jlE94ALAt8B8D2ubZP8XvbpV0GRP41E9oD3mL7jrz9Y1JR28X5JXMDuwNDlFpfXaHUk7NVMDOsSOAFSVpQqfd3mIT8RLz11G0JSb+R9NX87e1JPV1ny6l2GwMrwITC+qNbg9XsXtKsdt9/foVFVwcAf3INS7QptShaHfgdafnOF0gtOlaXNDtpVH+zG77OuKR3SC0mxpWOpRNImpG06tnrti+SdDApD3Vz23+TtAPpcf8VLvEooYPlGav5bZ9Rw7HnIS0k8KDT2tank/6dNrJ9Sy6YeRi4p8n/LpLOJi2JeFbpWDpF/n/3Ads/zU/WrgdOtr2vUuulOUlLacfKdj3kJ0SH2d63hmNPB6wHzGz7HEmfJi0AcajtwyWtTEq7uNX2G5M7VjfLNQufs71l6Vg6hVJXnfVI5+R9km4n3fgsTDpXNyUV2g/I8tNVzrB+gpzvVrV8p/gL2686LRgwijSSh1ThvB9p+UckbZGLRZarI5bCBl1SeL6jm0/SqLx9hKSbldp7zQhsQ/r/AHAsqU3O3wBykdXlTR4UTYXlSDd6lbP9tO2zbd+Yv7QTqcXY3XmmdXvSBVE5t+7nkpp4kRh05ytMeMqxSP78U0pN6zfP394SWFhpgY+7SEv47gsTWi/9IQarvRrBxGtepWy/ZftS2+fk7QtJ9San5JesChxHSqlqtQj8RutJVoMMZWKO9KChtLDEsvlaO3d+ynF8/vaqpMnC1lPttW0v6NTy6hnbpw7UYBWqHbA+S5r9rJ3td3oUd91he0Pb++VvP0oqFhkBIOliSdf0SCdYsYuLRRaz/W7pIOokaXpJu+f8FoA9gVuY+Gjwb8AepNysp2xvafs0ANtjXFF7F6WehzdO+ZVdawwwIO3RcsrFU7bfzJ9/3vYH8//lt5i4hnmrm8idkrbK24ure4tF9gYuKR1E3SRtIukHkkbkx4XPkCqCAe4DDgWuggn9Qb9qe1x+H6+s5WF+r1+pquN1GAOPDNgPs19z7qhi+ySnAupWbvCdpMmpt/MN5xOtAY6k2SUt2qUpQJcA+5QOom6Slsw3HB/JX7oUuIA0c/oKcDpwFIDtM23vbPvWvF1Z0Xd+rz+4rX2aPvmkicUi15Bag9xAWkJyWUmLklbzuar1DxLql2dXlgTesP2QpFb7sw1I3SR+QSrAO015JZcCMS5KSiWIHMQBlG8mlwVetv2IpJ+SKnfXdlptZT/gQVL6UbPfvDpEHnzMR1qp8EalfpVHAafbPib/m8xEesIxmrROeYlz9iZgd9s3DfTPHqzy/42FSDPlN0r6BOn9+zSn4rhPktoGXuLoHz5gcgrJ8qTJHpPyu9+yvVHO090cOMupALnUNfZA0hPRA/u8z2B8z9fE5eyWAvYF7rZ9vKR9gXWBH+V/yJmBVzvhwqiUBL99HTmHdclvZiKdMF8jFekckN/UjgWOsH1yzmV7k5Tz2BGPUPNM0b6227oDDNXTe1c4+j6woO0dJS1LWuL5PNsnShpBelPuiMd6kjYkvbc8UTqWvpI0xGkp7U1JaV5HAo+TbhL+ZXt7SXMDCwD/dQetdpZna86w/VjpWAa7HtfYrUhpID+yfbuk80kV5HuS0mVmcOoDXZzS4jJz2b6mdCx91eN8XQzYhZRnfK6kX5Em6ra1/aCkdUnvRR1T55PfY6a1fUGf96lqLCbpMFLy/COVHLAASQuS8nRuzP/IV5Nme5YFXiTlEN3iAmvTSxoJPGt7xED/7L7IOaUrk9pY3Cjpm6S84i1sXyXpO8DD3TTgbjKlwoppbf++dCz9lc+JjwDjnYrs9gV+BOxh+zeS1iPdCN1s+60C8V0K/NT2Xwb6Z/dFHvAvbvtPkj4E/IG0Lvg+kj5LmlU9q5MucoNVTmnbz/bXS8cyNSStDixn+9T8FOsO4GLbWystY7wE6fr7QoHYdiUtJrPLQP/svpA0F/Ah0oqMz0q6hnTzvqBScdR2pDZl/y4aaI2qHLDeDuzgvO5sE+QZwnmBp0gFPqcCw2xvLmkV0h3NH/PFUnXOxObZ3sdtz1TXz+hjHNOQUirezIPSD5EG8suRfj9n5Nnq+YG3HetXdyRJ3wamsX1I6ViqpNQ5Yojt0XnGbQvSkrOPkIpI7siPsWs9X3MsfwOOtP3XOn/OFGIQMML2GEkbk1JvTnRa7vQyUs7atqSiinlJvYc74ilHmEjSMsD5tpcuHUuV8pPD2fMAbH3gWzEAfaMAACAASURBVKTeuidK2geYB/i57ccH4Bq7B/Ah27vX9TP6GMfIfL7OR/p9PGz7J0q9dDcEDrB9c/4/8Ygb3Lnh/aosunqcVOzUGLlA5Mn88VXbW9luVbs+R1q9oVUpeVQuFlkbQNIKFReLvEaa6R0wSgVQH5e0Wd7ei9Rs+lP5Jc+Sqr2HOvVZW8328QC2n+jmwaqk5SRdVTqOGr3MABVdDSSnYpHR+fPDbH/I9sOkCuCrSe15ANbOxSKHwIQejFUXi3wO+GeFx5siSatI2kNpNcGFgZeAX+VvjyU19X4EwPbGtrfJ729jnZYv7trBqqTr8qPRJnoHeKh0EFVzKrx7Nn9+he31bbdWGLyNVJTZcr9Sx4mh+dq0sqotoD4T+GaFx5sipeLe7fLgs3WT+3R+YvkGcD/pfQvbP3YqML85b9/dzYNVSQdJ+kpb+3RAemYjaGKxyOO2X5B0Lqlx7gLA26QT4TrblxUMs1c5QXuY0wpG3wA2IyVlvwv8npQwf0ye5R3XSXlrdVHqPXiy7ZVLxxKqlwemCwPT275b0m6kpvQH2/61pJ1JOXZ/7JQcu5b8lGNO20/m3LT9gd869dA8jjRb+hVS15bZu/nGsR2SHiT1+n2wdCyhenkCaCnb1+V80/OBB/ITzzVJbfouLZGyNzn5vWZ+0pPakcAJpNqYvXLqzTbAsbavybUTL7jh3YAgtagkrTx5RF/3KbLSVRM59bK7pZV7Y3trYFbbr5JmdaYhNVNH0ieUVhbZJm/PqimsLKLUMuZzUxun0soy2+XHH+QYnmJij7/bSOv7jrX9Sr6jOyb/nUYPhsFq9iJwTukgQj3yrOLDzgud2P6V7fmBVo61SEsJTqO08s+/JR0DE548zDiln6HUg3SuqY1V0lqSDpA0m1Lvy1eAk/K3nwZOY+IszD62d8kzzeMHy2A1O5P0uwkNZPsl29flz++3vTypoAvS5MqSwFIAko6V9AdN7Ac8akpPTyR9QGlZ+KmSZ033lPTR/KXfAzeScsLHAJeTOilg+/f5ye01efu5wTBYza4H2uroUWUO63GklTEG0xtkv0iahdSM9wXbN0k6ilQ1uantqyVtQboQ3dh6RCdpAdIM7fx9OH7rjm6oU2ugr5BaA+0I3E5acvQW20cprTk9vpsfBYb2Sfo8MNrvXY429EJpOeRVSG2dLsizmheTcuu+ppTPPgq43j2WCpV0PfDV1kV2Cj9jZmBhp9Zdq5KWDr7Y9o+Ulj5cgJQP+7Sk6R3rvw8qSgVJX7R9UOlYukFODVmV1GN0NCkN5l1gUWBW0s3ojT1n4yV9jXSO79+H409LGhw/THqScT4w0vb6edC7G3Cm7b/H+VqdKgesj5F6JT5ayQEHmTxjM86pmOlYYA1gbVJ/w+OAW4Evky5q7rGfbFvSLqSFBb6l1GftbOBo20cqNdMeSio4GfBq6dB5JB0NPGn7qNKxdKP8RGRG2y/npxS7Az+x/RdJPyJdHDch3Yje2Dpne5yvHyG1jfq17XslPUwqeFo/PxZcHrjN9osl/n6hs+RH3sfYnuoZwMEoT+LMntP1FgOOAB61vX+eINqQlAL0Bqmo6f3n6zzAF0hFTmfnCboNSW2jbpW0CSk94YESf7/BYrKPodv0IO9NkA5t6JknZ3tCIrKkt0l3idOQ7hh3knQoqSHwCqQ+sheSmjc/k0/Mf9iep8fxbhmQv0SD5AHFgbY/UTqWmjwPDHjrmKZw6vX6cv78HN6bPnIl6YZzM9J74iuSbgbmIC1csjNp6cs3mPieuWjrIpmfUv19AP4ajSLpAWANF2iJNADeIl1jQz/kc6uVrvcgqXNIy13AgqRJoVuAi5Rabj0PLJGfbk5HOn//k/fZt+fEke1La/9LNEx+sny37ZP7vE8UXXWXPCBdjNShYClSy4vBkvMyYJRarBxk+2NTfHEIk5GLRf4IXERqKdW1lb2dTNLLpKdML5WOJXSvXED9deDDpFadTbwBKk7SL0k9ZU+a4ouzKLrqErngYrt8V/cEKe/0wRis1uZx4LzSQYTulYsbZ8sDqNeA+2KwWqtf0LDWimHgSFpN0qo5be5x4LkYrNbqClJNTZ9VmcN6OvBl22MqOWB4D0kfIK06s3wuAtnE9sWl4wrdSdKXgPttX146lqaSdA+wue17JK1GypmLVaNC2yR9EPik7UNLx9JUSqt1vmX70JwGMCrS6TpLlTOsW5AKe0I9hpKSwsntamKwGqbGqqS851CfYUw8Z2+IwWqYCvOSlg0P9el5jX08Bqudp8oB613AuAqPF97rTmBdmNBLNRra10jSZpLOLh1HjZ4kiq7qthp5dSJJSyst0BFqIullpUUVmmgMUXRVt8OAYwEkzaW0UlyoiaRTJW095VdOVNmA1fYaHjxN5Qec0xJ2L+fNWYFi65MPEtMB05YOoi62D7F9Yek4miw3Om/1N/4F8KGS8QwCs5BnyJrG9j9s7106jiZzWp64NYb5LKnwKtRnZLs7RNFVl5A0b4+7kaGktaVDfe4HLigdROheknaV1HpTjnO2Rrl7yk+iCDX0l6R1cq4wxPk6EC4idTvqs0qKrvKbxQWkAoPok1UDSWsDh9v+iKTpgbVsR6/G0C+SDgCusX1t6ViaStKzwIq2n8mr39zb4ylJCH2W3//XsH1k6ViaStLxpHP0uJwOMNx56ebQGapaOGAIqYIxBqv16ZkQ/ibRWDxMndWBWJWlXj3P2esLxxK62wJA1C3Uq+f5+kjZUEJvqkoJEBNXgAj1uIa0lCOShkuKfLgaSdpBUp9X4OhCDwPRYL1eiwIvQmpLJGlE4XgaS9IISU1exvYVouiqbvsBpwBIWkBSdFGpkaQ/Sdq4nX0qGbDmgqDVqjhW6F3+Hbd63M7Pe5eCDNWblgbneNve3/aVpeNoMtuv9sipPB1YomQ8DTeMtHx1I9m+xPbBpeNoMttv5EUDAL4A7F4ynkFgONDWU/nGXpCbRtKikrbMm5EQXr/bgeh1G/pN0v/1aLMU52y93gZ+UjqI0L0kbSpp2bwZ52v9zia3/eurqoquRgKn2f7sVB8s9ErSJ4E9bG+WHy2uYPu60nGF7iTpB8AfbN9cOpamkvQWMLPtNyWtCdwRKwGG/pC0KbCY7eNKx9JUks4E/mL7t5IWBbDd1oAq1KuqoqvpgPUrOlboXc+E8LFADFbD1FiTtJZzqE/PczbO1zA1FgaWndKLwlTpeb7GQLUDVZUS8C5wY0XHCr37M7ANgKQZo+iqXvlx7tGl46jRvaRCjlCfmcmPFSWtLml44XgaS9J8kh4rHUeNnqfNx6ehbV8AzgOQtJikBQrH02iSrs3t/vqskhnW3FuwrWqv0B7b7zAxp2Zp4OfAKuUiarxpaDMhvJvY/lLpGJouPwlpORdYB3ikTDSNV9XTwo5k+/elY2i6HgVXAF8CniHyous0LWmys8+i6KpLSFpO0qfy5jAiIbxu1xHL34Z+kjRUUs+lHeOcrder5HXgQ+gPSVtJanXyiPO1fqcAT7azQ1VFV3OTlsX7/FQfLPRK0g7ARrZ3kDQTKQH/ltJxhe4k6QTgp7bvKx1LE+XH/y/bnj5vrwnc/L5ZnBD6RNI2wIy2m9wbuihJFwM/t/1nSYsDb9p+onRcYaKqZlhHAGtVdKzQu2FMTAh/NQar9VJWOo4arQWMnOKrQn9NKOCAVHQVg9X6DILzdbH8J9SnZ9HVAzFYrVd/zteqBqxvAzdUdKzQuzOA3QAkzSYplumr13eB75QOoka3Aa+VDqLBxgKztTYkrdujJ2uo3nLAnaWDqNGTpNXpQn0+SU4Dk7SspPkKx9N0d0tapp0dqlrp6nHb21ZxrNA72+Ntj8ubHwKOKBnPIPCeGbKmsb2T7ftLx9FUTnrOqF4IzFAqnkGg0Y3ebZ9u+5el42gy2+Nst97z9wc2KRnPIND2NTaKrrqEpFUltU6gCekBoTZXAFeVDiJ0p7y2/X49vhTnbL2eBY4vHUToXpK+IGnBvBnna/1+CrzQzg5VFV0tDnzT9q5TfbDQK0n7AkvY3kfSrMA8tu8qHVfoTpJ+SzpnI0+rBpLmAW6xPXfeXgO4sccMTgh9Jml3YIzt35WOpakkXQscYPua3C1gtO3nSscVJqqqd93MQORU1qtnQvjLwMtlw2k2ScNIT3abOsD4MKkPXqjH+4uuri8YS+NJGgIM7ZE21TRLkBYPCPXpeY2NdKmaSZoWGOc2Zk2rSgkYSxRd1e1YYD9IbcQkrVQ4nqY7CtindBA1+jfweukgmirPXE9YKUfSJg2vYi/tw8CVpYOo0UPAo6WDaLg1gesBJK2U23WG+jwGtPU7rqro6h7be1ZxrNC7XMTRWhXio8CBJeMZBJpedLWt7WdKx9FkrfM1z/79pXA4Tdf08/Xnts8pHUeT5Wtsa7bvYNJNUKhPFF01laR1JK2fNyMhvH5/Av5VOojQnSSNktSaoR8KjG/n0Vdo28PASaWDCN1L0l6S5sqbcY2t32HAmHZ2qKroaiVgF9t7T/XBQq8kfRuYxvYhkmYHZrH9YOm4QnfKq7p8zvYrpWNpIknLAn+wvUyeYf2Q7RtLxxW6U17m937bF5SOpakk/RfYxvaduejqhVwvEjpEVUVXswFtNYANbRO5z6DtF4EXy4bTbJKmI82KNbW341rEE5Y6DQHGwYTUgBis1igXSQ5t8GpiSwKjSwcxCETR1QCRNAPwRomiq1eIN+Ra2f6e7e8BSFpI0oqlY2q4XwKfLx1Eja4GmnpxL872nbZXAJA0jaSPl46p4T4O/KF0EDW6G3i8dBBNZns523cDSFpT0hylY2q4l4Hp2tmhqqKrm2x/s4pjhT7ZGPhy6SAarulFHJvbHls6jkFiRiD6Z9ar6efr0bajcG/gHAasUDqIhouiq6aStKmktfNmJITX7yzgptJBhO4kaUFJX8qbjV42tEP8Fzi1dBChe0k6QNIseTOusfX7Bm3+jqsquloX+LjtA6b6YKFXko4GnrR9VH5UMdz2Y6XjCt0pr+qyToNzdIuStBZwlO01JU0DLGP79tJxhe4k6TDgatt/Kx1LU0l6EljN9pOSlgSetv1a6bjCRFXNsI4CFq/oWKF37wJvA9h+Pgar9cprwU9TOo4arUX6PxXqIeANANvjYrBaL0nT5iKOploKmLV0EA03nomFzffFYLU+SmZud7+qBqzPEo9Pa2X7a7aPA5C0pKQPlI6p4X4HfKJ0EHXIKy79pcdCFKFitq+1/TGYcPOzSemYGm4bUqFkU90KPFU6iCazvaDtZwEkrScpbhDqMz1p3NiWqoqu/mn7h1UcK/TJFsAOpYNouMbmHeYFXTYtHccgMjdwYukgGq7pRVc/sH1N6TgGkaOARUsH0WD9Ol+j6KpLSNpK0up5s9Fvzh3iZFIhRwhtk7SUpF3zZpyv9bsROLN0EKF7STo099+GOGfr9jbw9XZ3qqro6lPAyra/O9UHC72SdCpwre1TctHV0FgLPvRHzvW7zPbaU3xx6BdJmwL72P54vgguZPu+0nGF7iTpOOAs27FcdE0kvQ7MYXtsXunqCdtvlI4rTFTVSldzAgtUdKzQu3HkRu+2ny8cS+PlhPDXbY8rHUsNpiF6DNbNwFiAvPpSDFZrJGk4MKTBvYWXBkaWDqLh3iBWuhoQebnqWWy/1M5+VaUEPA7cUtGxQi9s72H7twCSPpDXKg/1uYhUSd9E7wIXlw6iyWz/xfZWAJJmlbRR6ZgablfgiNJB1Oh6+lGkEvrO9uy234QJfc/jBqE+cwF3tbtTVUVXl9k+vopjhT7ZHti8dBAN19gcJtuv2d6+dByDyKLAj0oH0XCNPV8BbB9i+7bScQwiJ5CeHId6RNFVk0n6oqQV82aj35w7xNHAg6WDCN1J0kqSdsybcb7W70rg96WDCN1J0hBJR/X4Upyz9RoNfKvdnaoqutoBmNv2kVN9sNArSecDv7H9R0lzAuNtv1g6rtB9JM0NnGE7HlPXRNL2wCdtb5fzK+ey/UjhsEKXkvRb4MexAEU9JE0LjLU9Td5eHHi0oTUMXauqoqu5SL0GQ33ezH+w/VzhWBpP0ihgdEPfsKYnrZwT6jMeeA0gVxo/UjSahmvlG9oeUzqWmiwFTDfFV4X+GgpMmACy/UDBWBpP0jBS0dUL7exXVUrAA0Dk19TI9na2LwGQtGpe6zjU5+9AUwvb3iCKrmpl+xzbuwNImkvSBqVjarj9gANKB1GjK+kxoArVsv2G7QmTbrnvedwg1GcJoO2FMCqZYbV9QRXHCX22M3AH0SqnTo3NYcrLD+5VOo5BZHlSvtblpQNpsMaerwC2v1E6hkHmV8Bi5FaSoXJRdNVkkvaR1HqM29hlQzvI94AnSwcRupOktSVtmzcbPZjqEBflPyG0TdJIST3bosU5W6+ngO+0u1NVRVd7k5YoP2GqDxZ6JelK4Pu2r8xFV2/ZHl06rtB9cjrJkbY/XTqWppL0ZWA523vllcVmsf1U6bhCd5J0MbC37YdLx9JE+Zp6p+058/aiwCO23y0bWeipqqKruYmp87q9xsSVrqLoqmaS5gWes93EmewZgIVLB9Fwb5Fat2D7deD1suE0m6RZgXdsv1Y6lposRXXX6/C/BEy4rtp+qGAsjZfzg2fJ6Wl9VlVKwJ35T6iJ7U+11pGW9NF8Bxjqcz0wX+kgajIa+EvpIJrM9sm2DwSQtJCk9UrH1HCHALuXDqJGlwCvlg6iqWw/a3v51rakL+TlQ0M9VgYubHenqla6Otv2+VUcK/TJXsDqpYNouMbmMNl+2PY3S8cxiKwC7FM6iIZr7PkKYHvfdmejwlT5dekAGm4Y/ajDiTuILiHpW5IWypuNfnPuEPsDL5UOInQnSZtI2iJvxvlav7OAv5YOInQnSXNIOjR/PgQg8ldrdT/ww3Z3qqro6hDgMdunT/XBQq8k3QLsbPsWSXMAr9seWzqu0H0krQLsb3u70rE0laSDgBlsHyRpRP78+dJxhe4k6TrSymltNVoPfZMLUf9se0lJAha0/WjpuMJ7VTXDOjcwY0XHCr17iYlFV8/HYLVekhaWNLR0HDUZCcxbOoiGe52JRVdjY7BarzxD1uRr0BKkwqBQDwPPQGp3FIPVekmaIS8R3paqBqw3AvdUdKzQC9vr274LQNLGkhYoHVPD3U4a2DXRc8BlpYNoMtvH2P4xgKSlJH20dEwNdwSwdekganQeaYW6UAPb99v+KICkaSR9oXRMDbcucFq7O1VVdPVr27GKy8D5Kmn1nFCfxuYd2r7Ldtv5Q6Hf1gbiAlivxp6vALb3tD2mdByDxAzAcaWDaLhY6arJJB2emxtDw9+cO8SuwJulgwjdKa9FvmnejPO1fr8E/lE6iNCdJC2Sa3EgzteBcAtwVLs7VVV0dTRwje0/TvXBQq8kPQx8zPbDkkYBY2zHgCq0TdIGwLa2dy0dS1NJOpK08MSRuehqWtsvl44rdCdJ9wIr2I4FemogaXXgZ7ZXz10C5rL9dOm4wntVNcM6F2kaPdTnGeBtANsvxGC1PkqWLB1HjUYCo0oH0XCvAq/AhKKrGKzWSNK8DS+6WgyINkv1eYe0vj22343Bar0kzSRpnnb3q2rA+k/ggYqOFXphe03bTwJI2qI/FXahz4bQ7CLCx4HIOa+R7UNt/wpA0oqSPlw6poY7HtiodBA1OoN4TF0b2zfZ3gJA0khJO5aOqeE2A45ud6eqiq5+Yfv6Ko4V+uQAYJHSQTRYo3OY8pvz8aXjGEQ2BLYsHUTDNf2c3Tka2Q+Y2YFDSwfRcFF01WSSTuzxyKvRb84dYDzwudJBhO4l6YuSPpY343yt31HAf0oHEbqTpOUlfSNvDiPO17pdA5zQ7k5VFV2dBpxlO5bGq4mkV4BFbL8saVZS0dW40nGF7iNpK2Bt2/9XOpamknQy8G/bv5I0AzAk2hKF/pA0PXCn7cVLx9JUkjYBvmp747xgzCy2XywdV3ivqmZY5wSmqehYoXePAuMAbL8cg9X6SBoqaenScdRoRmCm0kE03AtMLLp6PQar9cor0zV1oY9hpNUkQ33eBJ4AsD0+Bqv1kjSbpLZXW6xqwPpX4LGKjhV6YXvF1kVP0uckzV46pgabBfhX6SBqdD9wVekgmsz2N23/HkDSGpJWKx1Tw50KNPV3PJ709ws1sX2V7V0AJI2StH3pmBpuW+CQKb7qfaoquvqp7TuqOFbok0NIs9qhHo3OObR9je3TS8cxiGwGbFw6iIZrbN6h7Tds71s6jkFkflJhc6hPFF01maQzc0NjaPiAqgO8CnyxdBChe0naV9KaeTPO1/p9G/hv6SBCd8pPQVo5/XG+1u8vwMnt7lRV0dUFwBG2r5vqg4X/IUmkptFDbFvSzKSiqzipQtsk7Uoq4DuodCxNJek84Bzbv5c0HNJMWeGwQheSNBfwV9srlo6lqSRtC2xhextJw4AZbL9aOq7wXlXNsM5R4bHC/xoC3O18d2F7dAxW6yNpOknLlI6jRjMSK9PV7WlgNEx4pBuD1RpJWjJ3Y2iiaYHZSgfRcGOYWHT1TgxW6yVprpIrXV1AeoMONchVi8u2tiXt3uCK2E4wP3Bx6SBqdDupD16oie19Wm3+JK0naaXSMTXcOUBTO3uMBSLnvEa2/2x7fwBJ80vapnRMDbcL0HZedlVFV0fafqiKY4U++T5pPfhQj0bnMNm+wvYfSscxiHwG+EjpIBqusees7ZdsH1w6jkFkcWCv0kE0XBRdNZWkGSSd0eNLja2I7RBPA3uWDiJ0L0kHS2rlHDZ2MNVBvgI8XDqI0J0kbSBpj7wZ52v9zgXObHenqoqu/gF8yXZUadYgr2z1sO1Z8vZI4PVYWzr0h6SvA8NsH146lqaSdAVwuO3LJU0H2PbbpeMK3UfSUsCvba85xReHfpG0J7CS7T3ySlfT2X69dFzhvaqaYY0m9vUycOeEDXtMDFbrI2mEpGWn/MquNZJUyBHq8yipPRq234rBar0kfSAvYdpE0xIpYHV7hfeudBWD1RrlPOG2V2+rasB6JhBLmdXE9iu2J+TASdpPUiyFW5+lgd+WDqJG/85/Qk1s72z7BgBJn5C0fOmYGu5PQNtLPXaJl2j2+1Fxts+2fSiApMUlfaZ0TA33f8CO7e5UVdHVD20/U8WxQp8cTsqzCfUYCrxTOoi62L7E9qWl4xhEtgM+WDqIhmvsOWv7SdtHlI5jEFmefgymQlv6db5G0VUXkDSHpF/1+FIkhdfrPuCrpYMI3UvSjyUtkTfjfK3fTsDzpYMI3UnS5pK+kDfjfK3fycD57e5UyYBV0t2S5q/iWKFXI4ENe2yPoKGzCZ0gp2BcWzqOukg6XNLepeNouPWBmfPnO5KqYkNNbP+9qYsz5GVD/1Y6joZbGmgtFnM+sG3BWBrP9l222+7qUdUM6yykpUNDPd4B7mht5CKOqW/vEHolaeaGF12NIFJK6nYfafUcbI+LlenqJWnVBuf1Tws0taCsUzzPxKKrd6NIsl6SFs1LDrelqgHrScBrFR0rvI/tx21/EkDSUEnfLB1Tw60CHFc6iBpdCdxUOogms72d7XsAJG0tacnSMTXcX0lLDjfRk8BZpYNoMtun2D4eJnSc+FTpmBruQODT7e5UVdHVd23HgHVgTAt8t3QQDdfohRlsn287lmYdOF8AYsBaryYXXT1o+8TScQwiHyKtThfqE0VXTSVpEUnH581ICK/fTUDMYod+k3SSpPnyZpyz9dsciN6ZoV8kfUHS1nkzztf6HQVc1u5OVRVdPSepqY9jOsEsTFyLfCwwU8FYGs/2C7ZvLh1HXfJgKtq21GsDYHj+fFP68eYc+i4XXTVyhlXSJpLarqgObVkWWDR/fiqwW8FYGs/2f20/2e5+Vc2wzkjckdTpTXLRlZP4XddI0qiGF10Nn/JLwlS6A3gDJhRxRFFqjSStLUml46jJtKQ0pVCfJ/OfuMYOAEnLSJqj3f2qGrAeBURVXU1s3217BwBJI/Na8KE+6wKHlg6iRhfTo+tEqJ7tzVszCJJ2lrRQ6Zga7h9AUwesDwC/Lx1Ek9n+me3fAEhaXdLHS8fUcN8H1mt3p6qKrg5u6uOYDjQjsH/pIBqusQUcALbPsX1L6TgGkV2ABUoH0VSShpAmxho5i517Vp5ROo5BZE1g49JBNFwUXTVVbrPxk7zZ6MFUh7iKdAcYQr9IOkfSrK1NImWqTgY+VjqI0L0k7SPpk3kzrrH1+zbwz3Z3muq8GElDgbG2o7FxfUaRWm1g+wkgVhWrke1ngWdLx1EXSecBv7F9YelYGmwD8uIMttcqHEuj5UVUriodR10kbQds2koLC7VYHhgHYPuowrE0nu07+7NfFTOswyo6Tpi0MUTO4YCRNK+kZab8yq41HWlWKtTnRiKvf0BIGiZpndJx1KipK3h1koeAp0sHMVhI+qCk2dvdr4qB5rvADyo4TpgE2zfa3hcmVLB/tXRMDbcpzc4TPge4t3QQTWZ7E9uvwoTHjXOXjqnBRgJNflpwB3BB6SCazPYRrSdOkj4maf3SMTXckcDK7e401QPWvE7296b2OKHP5gB2Lx1EwzV9pavf2o4B68DZk5TWE+rR9PP1Ftt/KB3HILIOsHbpIBquX+fsVA9YlU3tccKkSfqwpFabpSGkvqyhPheS7gAbKc7X+km6TFLrUe54ooijTqOBT5QOoi5xvtZP0kE9ZlVNpPPUbV+g7cV5lPLV+0/SKOBe223nI4S+kbQlsIPtLUrHErqfpKuA79m+snQsTSVpPDBtNCAPU0vS3sAytr9cOpamknQOcL7ts0vHEiatihzWWHe3fi8C/aqqC+2TtLCkpUrHUaM4Z+t3DSm/P9RM0nBJHy0dR43ifK3fPTS4M0ynkbSGpJnb3a+KAetY4IgKjhMmwfbVtg8BkLRgvuMO9dmKZq8lfSrwSOkgmsz2OrndEpIOvkhlyAAAIABJREFUlDRL6ZgabC6gyY31/w1cUjqIJrP9ndYTJ0mflBQ5rPU6Hliy3Z2qKLoaE33LBtT8wOdKB9FwTS/iOM32Y6XjGET2JlWyh3o0/Xy93valpeMYRDYg9z0PtSmz0lWuuZrqBQjCpEnaRNK3enxpbLFgBoczgBNKB1GX3LcyCjlqIml6SX/t8aW3iKKrOj0BfKZ0EHWRNDQvPxtqIunHktbMm++QztlQnx2B+9rdqYqiq8WBy2wvNlUHCpMkaWdgbdtfLB1L6H6SbgW+aPuW0rE0kaSZgCdtz1g6ltD9JB0MDLd9UOlYmkrSZcAxMZPd2aoquorZg3o9A/y3dBCDhaSlJC1ROo4aRRFHvd4F/l46iMFC0kxRdBWm0m3AC6WDGCwkrSup7TSpKgasLwKRw1oj+//Zu+/wqMq0DeD3Pb2kEoqU0KQJKL2IUgVBsRfARUVdFXQtWHcVCyuyfqusa1dEsbcFLKBYAFFZRAGxItK7CFJCep3n+2NO2AgJSWbm5E3eeX/XlevKZOac3IOeyZkz7/M8Ml9EpgEAyQ4kzeAAe10CYLTqEDZ6DGYMoW2sdf1nl94m+QBJn8pMmmuN8P/TuvoUwALVIXQmIreLyEoAIDmGZG/VmTT3HIBqT/+LRdHVXhF5Ntr9GFXWGoDpx2ov3Ys4ZojI76pzxJEbEb5KZthD9+N1iYgsUZ0jjpwGoKPqEJpTNunKQdIb7X6MipEcRXKidTMEIFNlnjjwBIAXVYewi1UUZIo4bEKyAcm5ZX6UBY1PqGqBNQAuVh3CLiQ9prDZXiSnkzzeulkAM03SbmcC+LW6G8Xij1YPAEtjsB+jYs0AtAAAEflIRHT+uFo5EdkhIjp/ZP4LrP+fDFv4AXQrvSEijUTE/AG0iYjkiMga1Tls9A8AN6kOobmuAAIAICJXm4lX9hKRH0Wk2p0YzKSrumErgJ9Vh4gXJE8g2Vp1DhuZY9ZeBTBFVzWGZH3NG72b49V+XwM4oDpEvCB5WiSfzMfihHU7gMdjsB+jAiIyR0RmAADJ7iQvUxxJdxMQXsekq3/AvDjbRkR2i8g44NCSqUdUZ9Lc8QCmqA5ho3kAvlAdQmcicoOIrAMAkleS7KI6k+ZeBVDttn+xKLraKSKvRrsfo8o6ADhVdQjNaX1FQ0SeFpEs1TnihAvAtapDaE734/XT0gp2o0acCaCl6hCai+iYjUXRlYtkINr9GBUjeRXJ8dbNYgAHVeaJA1MAzFIdwi4kE82kK/uQbE3yLeumE8AelXniwNcArlIdwi4k/STdqnPojOQskq2smzkA8lTmiQMnI4Li8VgsCRgMYG6ljzKikQ6rZ5mI/EdErlGcR2tW0dU+1TlstANAsuoQGkuA1RZHRPJEpJniPFoTkSwR2aQ6h40eB3CZ6hCa6wLAAwAi8icR+aSSxxtREJGfRKTmr7BC849jaon1ANaqDhEvSPYmqXMVvTlm7ZUNYLHqEPGCZBOSJ6nOYSNzvNrvM4Tbzxk1gOT5kbRWpIhE+4tbAugmIu9EtSOjSqxq2HQReV11Fl2RfB3AByLymuosdiB5HYDpIlKkOovurPGDk0XkVtVZdEXyXADjROQc1VnsQPJUADtFxIznrgEkb0L49X+d6iy6IlkCwCsixdXZLhZFV1vMyWqNOh7h9R+GfbS+oiEiT5iT1RoTAHCp6hCacyK8tl9LIvKJOVmtUecigrGhRtVY9RMOKCq68lpXEQybkLyN5FjrZgGADJV54sAtAD5UHcIuJOuZoiv7kOxK8sXSmwB0HkJRG3wCjRvrW0WSHtU5dEZyAckG1s0MmElXduskEXy8H4txb2cBGA3gghjsyyhfc4RPVCEizyvOoj0R2aE6g12sE9V9iM36daN8SQCOBcI9WREu6DBsIiKZ0Htc9QsA3oLGnUtqgS6wXhNF5CzFWbRmnahGNAjJFF3VDT8A2KA6RLwgOZBkU9U5bOIEEIrk3a1RZfsRLuIwaoDVRqyv6hw20nrJQy3xIUwrqxphtUIdFcm2sThhXQVgZgz2Y1RARGaIyHwAIHk6yfNVZ9LcXxGeLa0jAXC96hA6s1q23A0AJBuS/IfqTJobBGB8ZQ+qw54D8L3qEDoTkXHWlXqQvIdkc9WZNOZHhOeMsSi6WiciH0e7H6PKugHooTqE5rT91EBESkTkKdU54kgKzHIpu2l7vAKAiHygeZ/Z2uYChI9bwx4RH6+xKLoKkEyKdj9GxUhOJVnasiUb4Y8cDftcAWCJ6hB2sGbbN1SdQ2fWkpLSNwUCYKfKPHHgPwAmqQ5hF5KppujKXiS/Iemzbv4Oq2bEsEUWIrzoFoslAWMBPByD/RgVa45wIQdE5FERmaY4j9ZEZKeI5KjOYZNkAKa/oL1SADQDABFZLyKDFefRmogctIrbdDUbwADVITR3PMJvLiEip4iIGdRjE+tTvohqckzRVd2wHMBm1SHiBckRGl+FNMer/XZB0yv0tRHJ40j2Up3DRuaYtd8cmMK2GkEySDKiZVKxOGFdAkDLiUC1hYg8LiJLAIDkKJIjVWfS3D0A2qoOYZMcADerDqEzEVkuIg8BAMlWJCcrjqS70wD8SXUIGz0MM5rbViJyUelse5LTyvRkNWIvDcC/I9kwFkVXq0Xki2j3Y1RZbwAdVYfQnLZXNEQkT0ReUp0jjjQEcLrqEJrT9ngFABGZKyK/qs4RR0YjXMlu2ENp0VUiyeRo92NUjOSTJIdaNw9YX4Z9zgXwneoQdrB64JmxgzYieRbJh6ybhQC2q8wTB54FoG3rMKs1mim6sglJD8mfyvxoB8LHrWGPHQD6R7JhLCZdXQOgAYDbYrAvo3zNEZ5JDhGZqjiL9jS/mpEOYDGAlopz6CwVQCMAEJFvAZi+yTYSkYOqM9jsQ4T7zK5UHURTLliT6QBARE5UmEV7IlKECN/Em6KruuELmKs0NYbkuSRTVeewiTle7bcFwJeqQ8QLkt1IdlOdw0bmmLVXCMAbqkPEC6tN23kRbRvthEaSXQC4RcS8+6sBJP8MYIuILFKdRVckfwZwoYisVp0l1qzlO6eKiJlLXgNIHg/gDBF5QHUWXZG8D0CJiPxddRY7kDwXwBcisk91lnhAcgaAiRq3NlSK5AkAXhOR46u7bSyKrr43J6s16kQArVWH0Jy2s7utnpXmZLXmNEV4dKhhH62vQIrIO+ZktUaNQfj/KcMeSouuUkmaMWY2IvkGyb7WzT0wRVd2GwhAy1GIVoFBE9U5dEby0jKtrPIAbFMYJx48COAx1SHsQrIZSbfqHLoi2YDk12V+tBGaXrCoJVYDGB7JhrFYw3obgL/EYD9GxZoB8ACAiNwpIrMV59GaiPxmLQzXUScAH6gOoblUAPUAQEQ+F5GrFOfRmvWpQabqHDb6HEAL1SE05kG4GBUAICJdRSRXYR6tiUhhpJPpTNFV3fAxgN9Uh4gXJC8mmaA6h01cMMer3X4B8JXqEPGCZD9rXZyuzN9Ye+UDeEt1iHhB8hiSZ0e0bQyKrnoAKBCRnyp9sBE1khMBLBcRU4VsE5K7APTQsb0VyfoA+oiIucpaA0ieCKCviEQ02cWoHMnHAGwUkUdVZ7EDyfMBfCwi2aqz6I4kAbwJYIxEe3JklIvkAABTRaTavVhjUXT1jTlZrVEnI1zIYdhH2ysaIrLXnKzWqBYA+lb6KCMa2h6vACAic8zJao1xINwhxpys2kdp0VVDU3RlL5Ifkexk3dwJIENlnjjQGcBe1SHsQDJAspnqHDojeQPJ0kEqWTBFV3a7A8BM1SHsQrI1SVO1bhOSbUgutm46APygMk8cWAogoj6ssVjDOhnA2Bjsx6hYM1j/rUTkRhFZoDiP1kRkj4joesWmL4BXVIfQXCqABAAQkQ9ExEwBtJGIZGpeJLMC4f+nDHt4ATQEwlOYRKSr4jxas4qu9keyrSm6qhveAWD68NUQkldr3EbGHK/2+w7AN6pDxAuSp5DsoDqHjcwxa6+DAEznnRpCsiXJMyLaNgZFV70B7BeRDVHtyKgSkvcAmGfNKDdsQDIPQJqOV21INgbQQUQWV/pgI2okhwFoKyJPqc6iK5IvA1goIi+rzmIHkhcAmCsihaqz6M7qDvO0iFyiOouuSJ4F4CoRObO627qi/eUisjzafRjVMgDAMtUhNKftFQ0R2QVgl+occeRYAF1Uh9Cc1q3aTN/tGuUDcLrqEJpTWnTV1JpPbtiE5AqSpY2NNwPQuUl2bdAYgJZXM0gmmaIre5G8l+Q11s39MEVXdrsaGn+kS/I4q92SYQOSPUiWdk4JwRRd2e0DAJdGsmHUV1gBPGQFeC0G+zLK1xiAAICZmmM/zed2DwVwCYBzVQfRWAqsN5Ui8h/FWbQXBy2fVgNwQ+OryIr5ED5mYRUDDVYbR2/W0paILgiZoqu64VUAur8o1wokHSRvUJ3DRuZ4td8yAN+rDhEvSJ5F8ljVOexgXVklwlf+DHvsQbiw2agBJDuQHBHRtjEouuoLYKeIbI9qR0aVkPwXgOkisk51Fh2R9ALIEhGP6ix2sJaWNBMRsw66BpA8D0CyiLygOouuSM4DMENE5qrOEmvWCesFIjJLdZZ4QLIRwlOYrlSdRVckLwUwVESqvSwgFkVXZmZ2zRoE4A3VITSm9RVI642leXNZc9rB9NC0m7bHrDVxyZys1pxEmCUBdlNadNWKZFK0+zEqRnJjmcK2X2CWB9gpD0Aj1SHsQrJemQI+wwYkHyFZOkxlN0zRld0uBPCJ6hB2IOkk2Vl1Dp2RHEKydK15IcxyHru9BuAvkWwYi6KrJwE8AWB+DPZllK8RrHckImKmitnIuqKhcxeGcwD0B3C56iAaS0G4SAZmKYD9RCRHdQYbBRFeE52oOojG/Aj/O0NEtiHCsaFG1aguutK6B14t8Sw0bbNU25AMkIzo3V8doe3Hp7XIIgBrVIeIFyTHaPypgTle7bcNgHbrn2srkt1JnhLRtjEoujoJwHoR2RPVjowqIfkcgMkiskN1Fh1Zi+5/FJGGqrPYgWRLAKlmUlrNIDkOQIGIvKk6i65ILgEwSUS+UJ0l1kh6AAwTkQ8qfbARNavbxE0icp3qLLoieSOAY0Wk2t14or7CKiJLzclqjRqMcN84wx5aX9EQkS3mZLVGHQegpeoQmtP2mBWRQnOyWqNSAZyoOoTmlBZdtbfm7xo2sPqCZpSZdPIdAO1m3NciuwC0Vh3CLiQbmUlX9iL5ijUvGwB2WF+GfQYD0LJbDUkPyeNV59AZyfNJPm/dzIUpurLbYwD+GsmGsVjD+iKAE2KwH6N8TgAJVjEQROR8EflVcSZtSVie6hw2GgvgFtUhNJcE67VVRJ4QkVcV59GaiBSIiJZXWAEcA1PQbDc/rE8tReRnEblCcR6tiUixVXhVbbGadFUcg/0Y5RMA/1YdIl5YbZ8mqM5hI3O82m8egA2qQ8QLkn+21p7ryByv9lsL86agxpA8ieSAiLaNQdHVyQB+EpGMqHZkVAnJ2QCuEpEDqrPoiGR7AHNFpL3qLHawigo8ImKq2GuAVWCwWccpTLUFyR8BjBWRH1RniTWSQQB9RORT1VniAcmuAC4REfMplE1I3gvAKSL3VHfbWEy6+m+0+zCqZTDC77oNe2jdpk1ENqrOEGc6AihQHUJzOhdd5QAwJ6s1pz6ArqpDaE5p0dUJJAPR7scoH8lUkmWLNr6G+QNop58BdFEdwi4km5FsqjqHzkh+QHKQdXMjgJ0K48SDE6Bp31urL7SpEbERyatIPmLdPAhTdGW3vwO4P5INYzHp6i0A5yP8h96IPRfKtLESkdMVZtGeVdxWpDqHjcYj/PzuUx1EY4e6pojIgyqDxAMR0XmNZxuER1maTgH28cE6FxKRFQBWqI2jt2gKJE3RVe2Xh3AbCKMGkGxK8krVOWxkjlf7vYnw9ByjBpC8kWSK6hw2Mcer/VYBWKA6RLwgeSrJiHrdxqLoqj+AVZrPc641SC4EcJqI6HwVUBmSfQE8KiJ9VGexA8l2AIpFZJPqLPGA5N0AlonIQtVZdEVyO4CTrDnwWiGZBKCTiCxTnSUeWNXrw0TkbtVZdEXy3wC2i8jD1d02FkVXS6Ldh1EtgxFudWXYQ9sCDgAQkXWqM8SZzgDWqw6hOW2PWRHJBGBOVmtOI4Sn0xn2UVp01ZukN9r9GOUj2ZrkL2V+9Bk0fXGuDURkKYCTVOewC8ljSTZRnUNnJL8i2d26+TOA31TmiQNNAWg5TIVksim6shfJ20lOsW7uBaBde7TaRERuQITLHGOxhvU9AGkx2I9RPhfKtLESkVMk2nUcxlFp/u87EcAFqkNozg8gBAAi8ncR+UxtHL1Z0+l0PWa7AHhCdQjNeQEQAERksYiYglSbRXq8mqKr2u8AgKdUh4gXJNuQHKc6h43M8Wq/52CuqtYYkpNI+ip/ZJ3kgjle7fZfhD+5NGoAyXNI9oho2xgVXX0d6WxYo+qsF+X5IjJEdRZdkRwB4CYRGa46ix2sSV45IrKj0gcbUSM5DcAcUzRjH5LZABqLSJbqLLFGsh6AFiLyreos8YDkGQBOEJF/qM6iK5IvAFgiIjOru60puqpb3AB6qQ6hOQLQ9s2XiKxVnSHOHA/AdAiwVwiarusXkf0A9qvOEUcaA2itOkQciOhTg1gUXQ0gaUaF2oRkd5IrrZsC0y/OViLyoYicqTqHXUgeR/IY1Tl0RnIDyWOtm98C+F1lHt2JSJKI5KrOYQeS9UmaoQE2IvlPkrdbN38F8JPKPLoTkctF5OVIto3FpKuPES660vIFoxZwwWpjJSLZAM5TG8eo4+5E+E1PRC8YRpW4YV1BEJG/Kc5i1G0nAfgzgLNUB9GYC/8rkvxAcRbjKGJRdGUWhdvrVwDPqg4RL0ieQPJPqnPYyByv9nsUQIbqEPGAYTqvNzTHq/0+AfCl6hDxguRYkp0j2jZGRVf/1bitSK1BsiGAF0RkpOosuiI5BsC5IjJadRY7kOwAYL+I7FGdJR6QfA7AYyJiejvagKQLQIGIaLksjWR9AA1F5GfVWeKBdbGioYg8ojqLrki+A+BlEXmnutuaoqtajiQRfmMRAuADYJpI2y9fdQC7iMgvlT/KiAZJB6z2oAgXXQUUR9KZE4C2Y8FFZC/CzewNmxx2vDZFeNqVYZ8QgIhGy0e1JICkg+TQaPZhVGoI/ldlXABTdGUrEXlTRLTtw0qyK8kGqnNobh+AVOv7L2GqvG0jIgUikqQ6h11INon041Ojyp4BcLX1/WYAqxVm0Z6InC8i70eybbRrWD0AIvrFRpUdmrsrIrtF5ArFeYy6bQqAvqpDaK7sMXuTiKxTnMeou4YBuE11CM2VPV5ni8gLivMYFYj2hNUFTfvf1SIbAZgDqIaQ7EvyQtU5bGSOWftNgcbLSmoTkoEyc+B1ZI5X+70DYGWljzJiguQEkm0j2jaaWimr/2pPEfk64p0YVWb1dvyniJhZ8DYhOQFANxEZrzqLHUgeB+A3ETmgOks8IDkHwG0iskl1Fh1ZRUlrRSRNdRY7kGwEIFFENqjOEg9IXgOgRERMZx6bkPwMwN9FZHF1t42q6EpESgCYk1UbWW8KHCJSBCAIoL3iSLorgd5FHGtUZ9AdSS+AwjJFV27FkXTmAHBQdQi7iMhuALtV59AZSTeAkHU+0wxAnuJIustHhNMkoy268pI8JZp9GJU6B8Bb1vdZABYpzKI9EZkhIjerzmEXa8lDPdU5NJeP8IhfIHy8ZirMojUR2SMi2o7SJNmSZEfVOTT3OoDzre9/sb4Mm4jICBFZGsm20a5hTQXwWpT7MI6u7ILwzSIyUXEeo26bBqCT6hC6slrkwGpDBxG5RkR2qU1l1GFnA9ByeVItUvZv7CsiMltxHqMCpuiq9vsRwKuqQ8QLkqeQ1HkMohNmco7d/qo6QLwgmUbyHtU5bGT+xtrvVYT/zho1gOStJNMj2jbKoisPgONE5PuId2JUGcluAG4WkUtUZ9EVyTsAJOs6A94qutouItmqs8QDkp8CGC0iv6vOoiOSbQB8LCLHqs5iB5LHAHCLyHbVWeKB9fq/TUTMJ8c2IfkdgMtF5Nvqbhtt0VUhAHOyaiNrQbhDRAoAJAJoqTaR9goBaHsyZ4qu7GVNpguISGnhXmdE/0mWcXTaDmYQkd9UZ9AdST/CRZKlRVdmzbm9sqGo6CrBFF3Z7lIAT1vf7wPwqcIs2hORf4nI/apz2IXkIJLaTgaqBZIAlF2z+j5M1bFtRGSDiPRSncMuJNuT7KA6h+bmAig9j/kewHqFWbQnIieLSETTxKJ9598MwJNR7sM4ukNrDkVktYjcqziPUbc9AaC56hAaO1TAAQAicoWImCs2RqTGALhIdQjNlS26elZEPlGcx6iAKbqq/b4GMEt1iHhB8mySw1XnsJE5Zu2VB2CS6hDxgmRzklquN7eY49V+z8BcVa0xJP9OskFE20ZZdOUH0EJETN+yGkByEICLdJ3CVBuQ/CeA/SLyT9VZ7GAVXW0WETM6tAZYBQZ9zb+3PUj2AvC0iPRUncUOJBsDELOWtWaQfBDAUhF5T3UWXZHcAmCQiGyp7rbRFl3lwTTZtRVJH8JvLPIQXh/XRHEk3eXBFF0ZEbL6sCaUWQbQEUDkVwWMyoQAaNuBwfTwtR/JRAB5IlKM8DLHBMWRdHcAQFEkG0ZbdFWP5JBo9mFU6joApUVAvwL4TF0U/YnIZBHRdl02ydNJBlTn0Fg6gJ/K3P4PTN9b24jINyJymuocdiHZhWRb1Tk09ymA7tb3ywBsVphFeyLSTUR2RrJttGtY2wP4R5T7MI6u7ILwlSLyL8V5jLptBgAzmtU+hxddXWy1yzGMSFwBYKTqEJor+zf2cRH5UnEeowLRnrD+4cXZsMUiAGY9TQ0hOZbkQNU5bGSOWXvtAzBZdYh4QbIDyZtV57CROV7t9xAAM5ihhpD8N8mIll1EW3QVBNBIRDZFvBOjykieDWCAiNyiOouuSE4HsEpEpqvOYgerp+MGa72WYSNrEuB3ItJRdRZdkRwG4HYRGaY6ix2soqtCEdmnOks8IPksgLdEZJHqLLoimQGgpYhkVHfbaIuucgCYk1UbWW8KRERyAaQAiKgdhFFlmQCyVIewi+noYS+SLgCJInIA4dfXVooj6a4IwB7VIexiiq7sR7IegMwyRVc+xZF0txsRruuPtuiqseYfn9YGdwAovaK6CcAShVm0JyK3icjrqnPYheT51rhfwx6d8b/CyBCAV9VF0Z+IfCYiY1XnsAvJPiTNmx57fQ2gtfX9pzDLA2wlIu1FJKJOPNGuYe2K8AmVYZ+yC8KXiMgMxXmMuu1lAF7VITRW9njNF5GrFOcx6rZrAZiLQvYqe8xOE5EfFOcxKhCLoiuzFs5e7wH4SHWIeEFyPMm+qnPYyBRx2Gs7TOeUGkOyB8nrVOewkTle7XcPNO7lW9uQfN7qV139baMsukoAkBxpTy2jekiOA3CsiNyjOouuSL4F4G0ReUt1FjtYPR03ikhIdRbdWeMHPxKRHqqz6IrkhQBGiciFqrPYwSq6yikziMKwEclZAB4SkeWqs+jIOlEtAeCQCE4+oy26yobGU4FqA5LJAIqtArcUAMmKI+luH/QuujIzs21E0ovwpKt9ADwAGiuOpLt8hIs4tGSKruxH8hgAv1v9kpsiyvMi46gcADZFcrJaunHESLYk2T+afRiVmopw82gAWIPwJA7DJiJyrYjMV53DDgzTtkClljgRwNvW93kAtC3gqw1EZJ6IaLskgORAkumqc2juRwBp1vcfQOM3QKqJSLGIHBvp9tGuYe2H8KJwwz5lF4R/IiJvKs5j1F0uAC+pDqG5ssfrfhG5VXEeo267GYBZUmKvQ7U4IjJVRDYqzmNUwEy6qv1eA7BYdYh4QfI2kl1U57CJOV7t9wuAaapDxAvrCuSVqnPYyByz9psIIEd1iHhAMmANZ4hs+yiLrhIBBETEXEKvASRvBOATkX+qzqIrkp8A+JeIfKw6S6xZC95bmsl0NYNkGwAzRWSA6iy6Ink1gF66tg+ziq4yrRoGw2YkFwG4TkTWqM6iI2tIw0YRSY1k+2iLrrKgcYFKbUCyPoC8MkVXFV4Vt8ZujkD4XfkXIrKiZlJq5TdoWkhodQYwJ6s2IhkAEBSR3xHud5tWySZGdLIQPma1ZIqu7EeyOYDtViFQYwBUHEl3GyLdMKoTVusEqZ6IfBnNfoyjegTAxwBeAbAKwBGXxEkmJScnz05OTj75wgsvdHi9Xs6ePbsoOTl5fWZm5kgR+bWmQ9dVInKp6gx2IekDcJ7Ok7xqgVMBXA7gbAAZALRsj1ZbiMgbqjPYieQIAN+bE1dbbQLgR3jM7ywA+9XG0ZeI7AfQK9Lto13DOgTAJVHuwzi6skUc80Tk/cMfkJSU9P7ZZ589YM+ePf4ZM2Z4n3jiCc+vv/4avOmmmzonJCQsIemp8dRGbZQE4FHVITRX9njdKSL3Kc5j1G13AGivOoTmyh6z94qItlfs6zpTdFX7PQPgq4ruJNnL7/d3nzlzptfj+d95qcPhwL333uvq2LFjAwDn1EBOLZCcYjXX15E5Xu33DYDHK3sQSTfJ/iRPM22LIkdyJMmLVeewkTlmbUSSAK4wg1RqBskGJCt9faxItCesLyI81syoBqsf5hmpqamfJyUl7UlNTd3g8XjuIHnEQmQR+by0SIbkZJJ/KXu/2+0+78orr/Q7nc7yfg8mTJiQmJqaaq6CV90IABEtCK8D9sC0yLGViGwRkcXAobGhHx7+GJ/PNyEQCOxp167d+/369XsjGAyuS0lJ+cRqYG5UTycAunb1AIDzAZhaBJtI2Ault0l+R7KJykyaSwRwRqQbR3XCKiJZ1poEo4pIMiEhYUbLli3ffOSRRwZ8++23DebNm3fsOeecc3cwGFxNstlhj29MMmjdTEZ4es5rtDyNAAAgAElEQVQhTqczkJycXOF/x8TERDgcjkDsn4m2tkLTFiciUmLGKNuLZCLJhtZNH8LLMA7x+/03NGzY8F9Lly5NWbt2bdLSpUuTd+/e7ZswYcKgYDD4NcmkI/dqHMUB6F10tVtE8lXn0BVJB8nWZX7UCOXUiRgxUwxgXcRbi0jEXwC6AugdzT7i7QvAhccee2x2ZmamHO7vf/97UXJy8heHPf4DAGdY318AYPBh94/u27fvkTuzXHLJJblut/tO1c/bfKn/QvjkabTqHHX9C+FuHQ1gtQU87L7LALxkfd8GwE1l7gv4fL7stWvXSnnOOuusHKfTeVNFv9d8xd8XgPMA1FedQ9cvAAkAcsrcfgBAkupc5qv8r2iXBJwO4Nwo9xFXUlJS/vrAAw8EExMTj7jv9ttvd4VCoV4kj7Wu1Fzt9Xp7+ny+Fzwez0GPxzM9EAhMc7vdD5Ms/Wj3nR9++KHwnXfeOWJ/X3/9NWbPnh0qKip6zuanZdQNxwC4X3WIuorkyNTU1O+9Xu+eQCCwPTExcYfb7b7O6m9bqmwBxwYR+XeZ+0Z07969pF27duXuf+LEiYHExMQJ9j0Dow66F+H59oY9/rBGWETuEJFMhXmMo4iqrRXMgvBqy8/PP27QoEHl3ufz+dCrV6/Czz777CGHwzGiXbt2oS5dugSbNWuGhIQEFBcXY8+ePfU2b97c9csvvxzv9/s3A7gsNzd3+MUXX7zo/PPP91x66aV+j8eD2bNnFzz//PMleXl5o0RkT40+yTqM5KMAHhQ9Pzo3x2uEvF7vVfXr13/k2WefDZx55plwOp1YtmxZk2uvvfb/Nm7c2JPk5RK+RPM5gNUV7Ca1RYsWRy42tzRt2hQlJSXJtjwBTZG8CECRiMxWncUmLphj1k55AHSelFarkGwJ4HoRuSWi7a3L4JH+8gRrH2Z4QBUlJibuXb58edpxxx1X7v2dOnUKZWdnF15xxRW+lJSUP9w3Z84cNG/eHL169UIoFMI333wjs2fPzg+FQg8VFRU94Xa7r0pMTBwNwJmXl/dRXl7e4yKytQaeljZIrgcwUkQiX2dTS5F0AUiVcFN7o4pIpvh8vl+///57/+FXR7OystCuXbuc33777VQ5rB81yaEAJojIBdbtk9LT0z/aunVrQrg4+Y9efvllTJw48Yv9+/cPtPHpaIXkAwCyROQfqrPYgWQDABkiUqQ6SzwguQNABxHRcniMaiS7A3heRLpFsn20RVfZ5mS1ekTk9WeffbbcF5/Vq1djx44djuuvv/6Ik1UAyM/PRygU7r7hcDjQq1cv3nnnnf569erd4vF47ioqKnpg3759Xfbt29c5Nzf3VnOyGpH1CL/r1o6IFJuT1YiMHj58eKi8j/ITExNxyy23+JOSkq4DAJKphxVd+cs8/MuMjIzf33jjjSOuEmRnZ2Py5Mk5Bw4cmGbHE9DYHgDajgYXkd/Nyap9SLpIHlvmR/VhrmjbqQDA2kg3juqEleSJ1hlzXLJ6KaZb83GrJCcn56Fnn302d+bMmVJ68gkA69evx5lnnonhw4fD7XaXu2379u3RuHHjP/wsOTkZN9xwQzAQCPwZwOjInolRSkROF5HtqnPYgWRDkheqzlHXOByOFj169AhWdP/xxx/vcLvdbayb4wDcaX2/GcC7pY8TEcnKyjr/yiuvzP7b3/5WvGnTJmRmZuLdd99Fz549c/bu3TsbwBGDQYyKici/ReR51TnsQvIS0znCVscA+KLM7ScQnnhl2EBEVovImEi3j7bo6lwAw6LcR51D0hcIBB7w+/17U1NTf/F6vbtSU1OXk6z0ozwR2Z6bm3vyxIkT16enp+eMGjUqu2/fvoVdu3ZF9+7dcdJJJ1W4bc+ePdG8efMjfh4MBnH55ZcH3W73syTrR/fsDI21BHC76hB1TSgU2vnjjz/mVnT/2rVrpbi4uPTTjENrDq0X5xllHysi3+bl5XV98sknX+jcuXNm/fr1Cy6//PIf165de3VWVlbpOljDKDUV+vaFrg0OL7q6VUSKFeYxjsJMuqomku7ExMSFgwcPvnHVqlVJ+/fvD2RmZnqeeOKJXomJiR+SrLQproj8lJWV1eHXX38dMmvWrFtXrlwpEydORP/+/SPO1aJFC3Tp0sXldDrHR7wTAyRfLG+Agybi7niNkbfmzZvn2LZt2xF35OXlYdq0abkHDx580vrR+wBeOdrORGRTVlbW1bm5ucmFhYW+AwcOnCAir5uT1eojeTXJkapz2Mgcs/baC+Ba1SHiBcnOJCNebx7tCes9AJ6Kch91zUXt27fvOnfuXH+HDh0AAB6PB2PHjsUHH3zgDwQCL1rFLUclYcsBlLRr1664YcOGlW2CV155BT/++GOF9w8cONDvdDpvZHkVHUZVjQRQ/pqMum8lwpO8jGoQkb0A7urXr1/up59+WtqvET/99BOGDRuWe/DgwU8Q7g4AhNdAF5EcRvLvJN8+rO2VEVvdALRQHcJGxwPYpTqErkQkR0TeBw6tZz2gOpPmGgLoG+nGUbW1EhEtJwIdTWpq6sS77rorWN4o1P79+6N169aen376aSiAj6qyP7/ff1qXLl0qXB9XVkFBAY52EaZZs2ZwOBwJANIBHHk5yKiKHwEUqg5hB6t4I0N1jrooLy/vXw6H49dzzz33AYfD0cDj8YSys7OLQ6HQv/Pz86cCGBYIBG5zOp0Dg8FgYUpKSnF2drY7MzPTIyK5fr9/UX5+/kMAPjdXUmNqB/QuujKTJG1E0gcgXUTWI3w+ZKZC2isHURRdRXXCSnIIgN9FpOLLfpopKSlp0rFjxwrvP+GEExw//fRTswofcBgR6dm0adX6Qh9//PFo0KBBhfeTRNOmTQs3btzYDeaENSIiMkR1BruQbA6gh4gcOWXCqFQoFHqD5JsIX9FzA9gCoKnP51saCAQ6DR06NNi1a1cGAgE3AGzfvh179uxBhw4dXKtWrTpt0aJFA/Lz81eSvFjTPr81TkSmqs5gJ5ITALwoZjyrXdoAeAtAJwAhAP9SG0dvIvI1gK8j3T7awQFjEP6YMW5OWJ1O5641a9Y0atu2bbn3//jjjyUAqvzHKBQKJSYkJFTpsX369Kn0MUlJSS4AVe5aYMSVDgiv1zInrBGyro5uAQCSg9xu97yhQ4f6Bg8e7Dr8U5f09HSkp6cDAPr3789+/folfPLJJyd9+umnP5M81XrxNoyjeRDAGwDMCas9yk6mK8T/OnwYtVC0a6vibgrHgQMHHpk6dWpOScmRT3vp0qXYuHFjMYAFAECylcPhmBQMBj/1+/27vF7vPr/fvy0QCMwleR3JeiRLyttXpIqKikIwbTkiRvIdkh7VOWxiCjhihGRPt9v9wVVXXZUwdOjQI05Wy+N0OnHaaae5L7vssiS3272QZKcaiKo1krdan/Tpyhyz9toK4CbVIeKF1Qr17ki3j/YK63WIv4PpjTVr1ow/99xzu02bNs3Xrl07FBYW4u2338b48eNzc3NzrwDQ1O/3z/B4PP179erlaNOmjadx48Zwu93Iy8urt3PnzvSff/75lNWrVz/kdDqzdu/ejfr1K+9G9cwzz+CUU05BRVd3AWDXrl0CQLspTTWo0i4PddhCAEtUh6jrSPo9Hs/bY8aMCZQ3TKDUl19+iV27duH888//w887deqE8847L/juu+/OIXm8aQwflR4AflUdwkZNEF73Z9hARDIALAIAqyXk9yJStTV6RiSaIFwoGZFoi64q7E2oKxEpJHnKokWL7uvatev4QCDgyM3NdXu93p8yMzNvdTgcjZ1O5+ohQ4Z4Bw0a5PJ4jrxY16xZM/Tp0yeQnZ2Np556yrVp0yZ06lT5xZaioqP/XcvPz8eBAwf8AH6I9PkZWAlN34RZJ0Zxe3Jkdc84MSkp6Tq3292muLh428GDB58CsLg6hVAul+uvbdu2Teve/egzU4qLiysskuzbty9XrlzZbPPmzdcCeLQaT8P4o00AtJ3eJiIHVWfQmTVevok1ituFap4TkXQC8ADIN8WUVZIBhZOuziDZIZp91EUikpeTk3NbXl5e/X379nXKy8trduDAgR4Oh6O13+9/7qabbgqeeuqp5Z6slpWQkICxY8e6VqxYgaosC+jRowfq1at4eeqKFSvE4/EsiHSBPslWVjuebvHaGktEThQRLU9YSbYjeZbqHCqQZGJi4sxGjRp9cvfdd49+7bXXek2ZMuW85s2bv5eYmPhOVVrRWftxk7xx5MiRlVYTp6eno7T1XTn7wRlnnBF0u91/NW2vIicik0RkgeocdiF5S7y+FteQbgBmWt/noopvHkl2TElJedvlcuU7nc6shISEXW63+3aSurZEjAkRWSQid0S6fbQvlOMAnBDlPuosESkUka0i8ru1pu3xG2+80d+kSZMq76Np06aoX78+VqxYUelj+/Xrh7S0tHLvKywsxCeffJKbn5//YJV/uYVkm5SUlC+TkpJW9+7de1bjxo2/SExM3ELS9OzUS1cAF6sOoYLL5bq2VatWF65fvz546623OoYPH47rr7+ea9euTejSpcswn883qYq7GpSWluaoyjHeqlUrdO7cucL7W7ZsiUAgkACgVxV/txFHrDcy08yVO1s5ARQDgIhkikilTe1J9g4EAl/feeedZ+/Zs8dVVFTkXLRoUaOTTjrp3qSkpI+q+ubXqD4z6SoGSLq9Xu9/LrzwQl9VBgAc7rzzzsP777+PjIzIW2TOnTu3oLCwcIGIfF725ySTSLYg2aS8Kzkk0wOBwPLJkyf32bNnj//rr79O3rlzZ8KsWbOaJyUlzYmnk1aSXpI6z3KPy+OVJP1+/51PPfVUMDEx8Q/3+Xw+PPPMMwGHwzGxKldHSPZu3769P0a50LZtWxfMCWvESE4hGXEj8lpOy+OVpJNke+ur8mpFe/2EanQGsD6pefPFF19MuP322x2pqakgid69e2PhwoWBtm3b9kGcXhSoCpKnkrw10u2jPWG9CMB7Ue5DB2fVr1+/fo8ePSL66KZZs2YYPHgwpk+fjuzs7Aof969//Qvbt28/4udLliwJLV++PKOgoOBKhvX1+/1v+ny+3U6nc28gEFjt8/k2OJ3O3GAw+C3Ja0gmAkAwGJx0zTXXJEycONHh9XoBhP+QDh8+HG+88UYgKSnpyTj6SMoNYLDqEDaaBeBy1SEUSCsuLk7r169fuXd26tQJiYmJLgCtKtuRz+fr3qRJkyp97Ldw4UJ88MEHR31M06ZN/V6vN+IiBAM9oW8bv2IAyapDxApJer3eicFg8LeGDRuubNiw4cpgMPibz+e7WdXfGBHZKyJfWvnakKxsfWXvpKSkBhdccMERd7hcLkyePDmYkpJysx1ZNZEOoOJG9pWItuiqIJrtdeH3+28dMmRIYjTH3JAhQ5Cfn49p06Zh3LhxaNXqyL+dJSUlKPs7CgsLMXfu3ILly5dnFBYW9gOQ7PP55ns8nk6DBg3yd+zY0dGwYUM4HA43AOTk5GDLli1dly5d+tC6desecjgcN3s8nouvv/76cv8AjxgxAn6/v1FmZmZnxEevXQGwVHUIu4hIMayPv+JMUUlJiaO4uBhu95H/q4sICgoKHKhCQRpJf3n7KE9V1qV7PB44HI6YXLGNU2sA7FMdwg7WUgBtOgQkJCQ80bJly3EvvfRSsLRgcdWqVQmXXXbZfZs3b24L4JpofwfJJISnVe21Xu8qe3wKgIZliq4qW37Rulu3blLR3/pu3bqhsLCweTVjx5PfEUUXo2gnXY0GsEJENkWzn7qMpMvpdHY/2vSrKu4HI0eOhNPpxFNPPRXq3LlzwcCBA/0tWrQ4dJJ64oknIikpCbm5uVi5cqUsWLAgt6io6OPCwsKrAZzkdrvfGDZsmHfw4MFOh+PIi+fBYBCdOnVCp06dgrt27cILL7zw8J49e4LNmpU/mMvhcKBx48ZFu3fv1vUKxh9Yo4ZPVZ3DLiS7AmgsIh+qzlKTRORgamrqz3Pnzu1yeIspAPjss88QCoX2whoIcDShUGh/bm7VmqO0bt36qKOUASAnJ0eKi4u1POGqCSKi7dUsa2zoNSLyb9VZokWyQ1JS0uVLlizxp6SkHPp59+7d8cUXXwRbtmw5juRjIrImwv33SUlJecjj8fT1er3FJSUlhX6//6n8/Pz7KipCJpkM4G8Oh+PCYDC41ufzBYqKijJIXoVw55AN5Wy2Z+PGjRUe1Js2bYLb7TbjdCsgInMBzI10+2gXB18J4ADCrUXiVbtgMFjo9/tj0mx+4MCB+Pjjj4u///77yT///PNNInJMkyZNMpOSkhzFxcWyePFiOXjwoN/tdn+cn5//oIgsIXm61+t98y9/+Yu/efOqvblr3Lgx/vrXvwanTJkiK1asYN++Ry4Dy83Nxfr1630ANsbiuRnK9UG4b2VcnbACQEZGxp0TJkyY1blz50D79u0P/Xzbtm249NJLc3Nycu4sr7jF+qiyA4BeTqezs4g037JlS/HJJ59c6Wvn0foll9qyZUtOUVFR5RWXRjwKALgHQJ0/YfX5fH8eP368q+zJaqmUlBSMHz/e/fjjj18J4Jbq7pvkKcFgcO5DDz0UGDt2LPx+v/vnn3/233777RO/+OKLQSQHWVOsSh/f2Ov1/p/L5bqwRYsWaNOmja9hw4atHQ4HMjMzsWnTpp6//PKL+P3+n/Lz868Xkf+W+XWfbdu2rWjZsmU48cQT/5BDRDBt2rS87OzsJ6v7HIyqifaEVctF4dVULxgMxuzfwO/3g6QzFAo9np+f/yDJi7Zs2ZIFIBXhj3PXAfipuLi4AABIHuN2u98cP358lU9WSzmdTgwYMIB33HEHFi5ciMOn9TzyyCMlLpfrSxHZEZtnV7uRTAMwXUSOXKCkh7g9XkVkvtvtvqFr166Pn3rqqaGePXsGf/zxx9x58+Y5ANxbXFz8WtnHWwWKl/h8vklOp7NJq1atJD09PZiXl8dvv/0WoVAI5X2KcdjvLN1XufcXFxdjw4YNTgBfxuI5xiOSjwGYISI6LlnS5nj1+XzN27dvX+Famg4dOrh8Pl+L6uzTejPZOhgM/mfOnDmB4cOHH7qvY8eOeO+99/wnn3zyCV999dUlAJ4HAIfDMcrtdj/Xr18/3+DBg91JSUmHtik9XgcOHOgsLi7GqlWrur7zzjuLvV7vc4WFhTdaXYFKnE7n+NNPP/2V119/3T98+HA4HA7s3bsX9957b+Gnn376a0lJybPV+seJIyQvANBIRCI6qY/2hHU4NDmgolAUy9GqoVAIIuLA/1ptvFF6H8lNAIaVXTvs8/me6devn7d169YR/b4BAwZgxowZGDJkCKZOnYpevXph69atePTRRwtffPHFg9bkrnjhB3BipY+qu6Yj+kLLOquoqOh5krPnzp07+v33328ZCoV2AnhTRP7wkTzJll6vd1ZaWtpxZ555ZrB9+/aHTk5FBBs3bsSaNWsqHfYxb948BAIBDB06tNz7f/jhB5BcXcFHjxUi2RDAeQgX5KwBML8q6/U01QvAG5U+qm7ai/BkoDovNzd33bfffluIcJP9I3z77beFubm5v1RlXyS7eL3em10u1/lOp9N5zDHH+E499ciVXE6nE3fddVfw4osvngjgebfbPTEYDE69+uqrA+Vd3Nm2bRtmz56NW265BS6XC71790anTp1cL7/88hVbtmw5juRwESkoKSmZQzJ/zJgxj7rd7mPq1atXvH37dq/H43kvOzv7LyKSVb1/nbjSAkDEk8SiLbqK26k5ZWzYv3+/vypXXKri999/h8fj2Zufn19kvYPsKyLLrLv/8I6bZDOPxzN8+PDhES9HcLlcuOqqqzB9+vSS008/PS8nJ8fv9XqzAbyQl5f3TxH5LbpnVKcUQe+iqxLE+RtMa3JQhVdASHZyu91Lhg0bljRkyJAj1oKTxJAhQ/D++++jffv2cLkqfgk92mtCQUEB3n333dy8vLwqz9UmyYSEhAf9fv91Z5xxRqh58+aehQsX5q1fv76A5Dkiou3/u0fxHcLTc7RjLVEprPSBdUBhYeHzL7744q2TJk1C48aN/3Dfb7/9hpkzZ4YKCgqeP9o+SCZ5vd7H/X7/hQMHDvT06dPHuWHDBhQUFFT4KUbnzp1RVFTUhOTpwWBw6s033xyoaPhOecdrMBjE1Vdf7Xnuued6b9q06VmEe89DRD4gOR9Au3379iUB2JCXl3egiv8c8WwnwgMaIhLtpKsrSMb13F0R2ed0Og/u3r07JvvbunUrHA7HKuumG8AXZe5+GOE1wwAAkpd2796dpe2oIuVyuTBmzBhnXl4eQ6GQJzc3NyU3N/emODtZhYjsFpFRqnPYheSJJMu/3GeAZH232/356NGjU4YOHVpu4SIAdO3aFWlpaXj//aO37O3QoQPK++SjqKgIM2fOLMrJydng8/nG+P3+11wu14MkLyTZuJxdAQACgcA9rVq1umbr1q2+//znP4Fp06a5vvvuu8TZs2fXDwQCH5OsfNGsZkTkmkgLdWo7kikkr1edIxZEZEsoFJrap0+f3Pnz56OkpAQlJSWYP38++vTpkxsKhR4Qkc0VbU+yucfjWX3CCSeMuueee/wjRoxwpqamIjk5GWvXVtyJau3atXC5XMUej+eVyy67rMKTVSC8lra81ndOpxPjxo3zu93uC0geupQrYWtFZIWImJPVKhCRN0Xk6Ui3j/aS4DWI4vKuLkKh0KvLli2LyTvhJUuW5Obl5S2yKkQPTeEAABF5tOxsab/fP7RDhw7Rna1a0tLSYJ34tonF/oxaaQA07oIQLa/X+/yJJ56Y0LNnz6P2pyOJMWPGYM2aNZg/fz5CoVC5j+vQoQNatmx56HZ+fj4++OAD3H333cjIyHD27du3w8iRI8edddZZfxo6dOgt7dq1e87tdm/y+/0fkux+2O8MhEKh2957771ggwYN/vB7TjvtNNx8883ehISEv0b63I1aqR6Am1SHiJXc3Nz7t2/ffvmf/vSntYFAoCgQCBT96U9/Wrdt27YrcnNz76toO5L1PB7PshEjRjQeO3asz+//Xxe4Nm3aYPfu3fj888+P2C4UCuH//u//kJOTk9apU6fEyoogU1NT0adPn3Lv8/l8GDVqVMDn8z1W1edrxJ6ZdBUDRUVFj3/11VclmZmZUe1n8+bN2L17d6B58+Z3+/3+3wOBwGQAh2a/k/SUbbBcUlLS6fCPV6LRtGnTEgDHx2yHdQzJliRfVZ3DRuZ4rQDJXi6Xa+gZZ5xRpTeACQkJuO6667B27Vo8/fTT2Lt37xGPKSkpOXQyu379ejzwwAPYu3cvJk6ciDvuuMNx4YUXevr3749+/frhtNNOc1x77bVJU6ZM8Z1++unDvV7vfz0ez4Nlxjz279ixY0l5/ZkB4LLLLnOFQqEje3ZpjuTLJCsd+FBHuaDZ8Soi/8nIyOhQWFjYoLCwsMHBgwc7AyjxeDwPOp3Ov1ut9/7A6/XO6NWrV9qQIUOOmIrlcDhw9tln47zzzsNbb72FoqLwKsWtW7di7Nix2LFjB/x+v/OUU06ptHlyKBQ6au/kzp07w+VyNSMZs8l0JJO9Xu89SUlJuzweT0FycvI2l8t1C8lArH5HbWJ9Kh9xXUy0J6w9AKyq9FGaE5HNIvLk66+/nhvp2OfCwkK89dZbeP3117F169bEH374IaFv377XJSYm3ljmJHU3wt0CAAChUMjn8/li8AzCfD6fA0AwZjusexIA6Dx16AEAk1SHsAvJBJLjSN5F8nKriXiV+Hy+WwYPHuz1eKq+HDwxMRE33HAD2rdvj4cffhgzZ87EqlWrsHfvXpSUlGDWrFlYsGAB5syZg5kzZ2LMmDEYN24cjjnmmKPlwIABAzhp0iR/enr6X7xe7/vWyFhfUlJShS8uiYmJCIVCh/4ok/STHERyKMnUirbTQG8AsXsRrEWsZvbtK31gHWR9UnhsIBDY2bNnz5mTJ0++7ZZbbpmUlpa2NDk5eQHJIACQHOzxeIafffbZFb6R7NSpEy666CJMnjwZ9evXR/PmzXHCCSdgz549OOOMM+Dz+VBRr/Gy1q1bh+nTp1d4v8PhQM+ePT2M0bhykmkJCQmrzjrrrDsWL158zN69ez3z589PHzp06JTExMRlpf8GmmkFoPL/GBWItugqsrMzDRUVFd21efPmkfPmzTv2zDPP9FRn6lVxcTFeffVVnHLKKShtbN6mTRvMnz8/0KZNmyFZWVkDAXyGw66QORyOgoKC2A0bKygoCEGjySoRyIXeRVeCyie51Ekej2ecz+d7asCAAaEePXoEvvvuu9zFixc/6fF4biosLKz4rxDCxUwul+uMnj17VnuuudPpxNChQ3HyySfjm2++wapVq/D2228jJycHIoKEhASUlJTg+uuvR5MmVS/4TkpKwrXXXht49tln+2/duvWZgoKCe5cvX+7NyclBMHjk37GPPvoIPp9vFUlHIBCY7Pf7bzr22GNDXq9XVq9e7U1KSnojKyvrLyKSV93nWMt9BUDbqmwRKX+9SR1HsqHf71/80ksvJZUZc+qcOnVq4NJLLz35ww8/fAvAGT6f728jRowIVPZGsm3btmjbti0yMzNRUFCA5ORkeDweLF++HOnp6VXKVJXC6RYtWrj9fv8gAFNIOgGM8Hg8Z7pcrpNLSkoaARCn0/lrUVHRkqKioncQHkBQ7mtuYmLiv8eOHdvs6aefPvTkTjrpJHz44Yf+UaNGtXv//ffvAaDbMp9NiKKQkNGcc1oLwt8QkSM/D9McyWOcTuc4v9/ftqCgYHtRUdFLAHK8Xu9/O3TokD5q1Ch/eX9YDvf777/jlVdeQbdu3TBr1qwjRkc+/PDDuO+++97MyMi4iOQtAB4vbYIcDAY/GzVq1MCuXY/4FCUid911V052dnYPEalsnrJRB5E8BUCxiBy54KsOI3l6WlrarC+++CJQduLcunXr0KIiMRkAACAASURBVL9//9w9e/aMFZF3j7J9S7/fv/qBBx6Iycdwe/fuxRNPPIHbb78d06ZNw1lnnYVIj9G8vDzcf//9uTk5OWcnJyffdMkllwx97LHH/vCGeM+ePejatWvOrl27LkhMTDy7bdu2l8yaNStYWvD1+++/Y8KECXkLFixYmZWVNUjXkyDdWAV450RTpFJbeb3eu0ePHn3Hyy+/fMRY4vz8fDRq1CgvMzPzRKfTuWLq1KnuSD9JXLBgAfLz83HmmWdW+ti9e/di+/bt6Nat4g/ZtmzZgunTp6/Nz8+f4na7H65Xr56/d+/eienp6UhNDX+QsW/fPmzbtk2WLVuWk5OTk1FQUHCtiMwrux+SiT6fb/eWLVv8jRo1OuL3rFu3Dl27ds3My8tLi+OWdUeIdknADQgvDI8rPp9vos/n23zxxRdP/sc//vHnq6666s5gMLgmGAz+taCgoPuaNWtevf/++3MXLlxYkp2dXe4+9u7di3feeafwwQcfLD7++OMxZ86ccuect23bFk6n8ziSXQC8UHZiR35+/qJ169bF5BLr/v37kZ+fTwDrY7E/o1YaCuAk1SFiLSUl5f9mzJgROHw8crt27fDCCy8EkpOT/1nJLprXq1cvZi360tLSkJGRgSlTpvyWlpZWEs0bSr/fj9GjRwd8Pt9TBw8evOSll17a3L9//+zZs2dj6dKlePDBB0MdO3bMPXjw4MMA1oZCocsWLVoULNudoEGDBnjrrbf8jRo16gZgWPTP0KghTQBcpTqEHRISEkaNGzfuiJNVILws5vzzzyeAKxo0aJAbzbI3kpWORy5Vv379o56sAuF2dCLSrF69etOvueaahn/7298ShwwZgrZt26J+/fqoX78+2rdvj2HDhvHuu+9OuPzyy5slJSW96ff73yRZdllDelpaWnF5J6uZmZlYvnw5SkpK/ADOt67kGoh+cACg2aLwypA8+5hjjpn61Vdf+Vq0ODSYw3PffffhpJNOmrB58+YtBQUFV5OcvmDBgts//PDDc5KSkgqaNm0Kr9fryMnJKdmxY4czPz9fSD5XVFS0z+123+F0Osu9uvPNypVgSf5xjVM8X+zNKvQFPI6DbpdjVVZ+yScimLdy5co7zz77bETb2mrp0qVFJD8EMITkOhHZFtUO6yCSxwO4UUSuVJ3FJkSZrhM6IJnm8/naV3QFZcSIESgpKWlOsqmI7KxoN3ZkC5WUpAwdOjTqPzadO3eG2+1ukp+f3y4rK6vr0qVLR61Zs2Y8gNTi4uLvMjMz/y0iK5xO552XXHKJo7zxly6XCxMnTgxOmjTpKgAfR5uptiA5D8CfRWSP6iw2cCDcG1pHrqP9zbLqKRo3bdq00mKpo0lJScGOHVUb1FhSUgIRqbC3ckFBAd5++2107tzZP2bMGMfRejAD4ZPlDh06YNKkSYGXX375rA0bNnxM8lTrotOBjIwMd2FhIUqXO4gIHn74Ydx///3o378/Lr74YveyZctmbNu27TGSF4jIkmo9+VqI5M0AdorIW5FsH+0a1rjr+5eSkjLlqaeeCpQ5WQUQvqryyiuvBIcMGXIPyadF5BsAo0l6Dhw40PnAgQMdES4OyALwPYB1IhIi2erDDz+895dffkGHDh3+sM/9+/dj+tNP4OExLTztGwc8fSd/g5cnHNdg3a684V+uPzjgszUH6CAyFi5cWH/kyJER/7fMyMjAf//7X1f9+vVHpKenD129erU3JSVl2cGDB8eJyPZI91sHJQM4TnUIu4jI31RnsIHX4/GUVPTHw+FwwOfzlWRnZx/tHd1vBw8ejNlVjIMHD8LvdaOouMSXkxP9knCHw4E+ffr4Fi9efEZxcfFXAF62vv7A7XY3aNmyZYWL/Zo3b06Xy3XkJZ26rRfCa/u1IyIrAJTfZ6mOy8/P/+Tdd99tffLJJx/x/2tJSQnefvvtIgBbPR5PVJ8Cp6enY/78+RCRPwwX2LJlC7766ivs3LkTbrcbxx13HILBINauXYvLL7+83H3NmTMHTZo0wdixYx3VqVHxer244oor/NOnT++1ZcuWBwFMFJFdqampP82aNavn2LFjAQDT/5+98wyPqtr6+H+f6TPpDRJSCJAQOtJUihQLijQRVERUQEQEkSuKYvcK6hUrCggI0hFFehNBEJDeJaSR3nuZPnPOWe+HSbiUhGRmMjPAe3/PwxdmdjmZc85ee+21/mvRIixduhRnz569Wg7Pe+fOnd6jRo3axRjrfgdoDreAE5uw/7dlGh2BMeal1+vb1OXN6d69O9RqtQrAFUO+uv7wGSJaTUQ/EtF6IkqsNlafUMi4c62CpWK/+3pjzZo1MJvNEEURu3btwn0978ZDbTRoHaqGKNqONaKDVRjYMQAfPR6t2vpaR+XouwND/jpwQJqZmenQNYmiiFWrVmH06NEsNzdXc/z4cd+ioiLljBkz+mg0mpOMsSCHOr49qcD/6rrfbhQIgqA/ffp0rR9evHgRBoPBCuBmJwYpRqNR1hjGpdVqxYkTJ+CnlkClVEAiaRxbKjIyUqJQKPre7Dtmszn+r7/+qvMijhw5YjWZTOcaZUK3DvsB3GmJZHc8BoNh3sKFC/nants5c+bwRqMxBcAFnU7n1IlQcHAwOI7D1evjwYMHsXbtWowcORI7d+7EypUrERISgp07d8JiqT0fKCEhAZcvX8ZTTz1VZ1WtmyGRSDB27Fg1x3Ev1shiVVRUvPrSSy8Ztm7dCovFgo8//hjr1q27RrsZAAYNGoRZs2Ypvb2937V74FuPBAB1FoioD2crXb3NGPNypo/bDI4xdtNMQqlUSqjn78oYC/ZSSrY39ZUvm/9crM/SF+Lk7zwcgO9nz4C3lwYqpQKvT34WI9vymNz/vyLhrzx0rRqEr1qKFweEce8NC8fiRT80+OijBkEQsHLlSvj6+mLx4sVXHkS1Wo333ntPOnLkSD+FQjHdrk5vY4joIhG94el5uArG2FDG2D2enkdjQkSixWL5fOrUqQaj8Vq7xWw2Y8qUKQZRFL+5WeICEYlyufyv8+fPO5yBSkTYt28fZs+ejezsbETFdoDZyuPs2bMwmUyOdnuFwMBAEFF96c4/79+/H7UZAXl5eZg/fz6v1+vnOz2ZWwgiGk1Ed2RpVsZYC8bYHRmeRESpJpNpTJ8+fQwTJ040bd26FatWrUKvXr10c+fOza2qqhoM4HxWVpZTqiYcx6FXr144cOAAACA7OxuHDh3CqVOnMGPGDLRv3x69evXCsmXLMHPmTBQXF9ca8/rHH39g6NChToXeeXt7Y/DgwUqVSvUxABDREZ1ON3js2LHpYWFhBi8vL3Tq1KnWtuPGjZNYLJbHHB78FoGI5hPRzUsE3gRnPayvAag1cPoORatSqTL27t1b64cXL15ERUWFgJskLjHGopQy7tzgzkEPrp/aTtM+3KYk0KOlD755Khx/vtURe2Z2wLJxUXikU+AVI5LjGMb0tJ3mVeh5rP67CB9uLcBnOwvgrZTgrUFNsXD+dzhw4ECdlXeuprCwEF9++aUpLS1NOHjwYK0JX9OnT1fI5fI78oX5/5RHAHSp91u3GVar9av4+Pjdbdq00S9YsIAOHjyIxYsXo23btubTp0+nmUymHMZYz5uJcRuNxi/37dunb8izUxu7d+9GdnY2Tp06hQsXLuDosRMoKChA586dsXTp0psKktvBTV07RKSzWCxj+/fvb/j222+puLgYVVVVWL16Nbp27Wrgef5jIkpsjIn8D7cQDeBpT0/CVQiCsNloNMasWrVq7vjx4w+9+uqru48cOfKSTqdrXR1vfkmv13OlpaVOjdOzZ0/k5OTg4sWLOHbsGKZPn16r1NW7774LPz8/pKamXvP/+fn5KC0tRYcOztfU6datG+N5vm9NCWYi2l9VVdWytLT0NW9vb0Nd7fz9/cHzvFPxvHcCziZd8bgDk66qhboHcAx3eyklfa0CdbLwog8DpNqqSnHSpEk4ceIEri6RqNPpMGHCBL0oil8QUa0xGoyxCKWMOzFpQFjg6Hub1HpWKJNyqO2uJCIYzCJOZ2gxZ3s+hg4dgjHPDkZpaSl+XDQfMosW3z8Tjc92/om//jqAfv36U7t27Vhg4H+NXpPJhMzMTBw5csRw6dIl8Dy/tn///qM0Go1vbXMJDw+HxWLxZ4yNBhAP4NKdLLHBGOsF4EkimubpubgIEU5o4N3C3CWYdcaCXJ35zTdnqlQKGalloFCNyNrHKlvqLfJvUgoMQl65ReOtlObxIm03WcV518m37dXpdIkHDhy4q7aKOjejsrISR44cQVpaGoKC/htBI5fLsWjRIvTu3Rv//POPw9JWAFBeXg7GWK1JY9WLXysAagB6rVb76vvvvz9q5syZfUVR5Ly8vE5VVFT8m4h2OzyBWxTG2CEAA4mozsX+NoYBcN49f4tSnTUfZjabc5lovSSTMD9vpWS4SPQQx7EkAKcYY+sOHz783LBhwxw21hQKBR599FGsXr0aTZo0wYMP1i6UYbFY0L9/f+Tm5qJVq/9WKE9KSkKrVq0aJbxHqVQiKipKkpOZetxbJRVBkHgrJWYiKrwUHy+vqKhAbUmTe/bsgbe3922/2WSMfQTgpKNeVmeTruou2XIbwhgLl0nYS0oZNyU8QMH1aOGtattMI2sdqkawtxxSCYNVECU/7C9CXGwrvDBxErp07YqkpCR89913vNFo3GcymT6to2+1Ss4dmtA3NKAuY/Vm6M0ihnx5ASq1Gnv3/4Vu3bpd+eyVV17Byy+9iFXHd2D5C62wN74cn27fIe7YscNIRDKVSmXheZ4zmUxyhUKRYjabl4iiuAJA2Llz557meb7WzMgTJ04gwEcla+GvXJRcYEC5jlf6qKQpRqu4nhdoEREV2nsdtzj+sAWF35EQ0RRPz6GxqK7+9rSXQvKuv0Ya+dQ9IYresX6SqCAlpJIrjshrnjMrLyK1yBi571L5xI0nS8Z5q6T/6EzCR0S0szqm/Mldu3ZdiIqK0rRs2bLBczl//jyGDRt2jbEKAMOHD8cbb7yBadOm4euvv3bKYM3KyhLNZvPB6msP5Bie91JKhlh46qSUcarwAIVJJeNAAIwWATnlOqUE0Ctl7HxVZcUWAMcdHvzWpjvu0GIYRLQXQO3Hebcp1RJNj3opJTMlHHo08ZEb20dopG3CNGqNQgKOASariOxSs/V8ls6QWmRQHT58WNqzZ89rHET2QEQ4f+YkukcpcbGoEnV5bFesWIG9e/eiR48eV/6vvLwcB//ajwH3N54aXMuWLWQt1eURz/QMAccYLLyI/ApLy+VHy+mtt97EwoU/XBMnW1VVhZkzZ+orKio+abRJeI6WAC472rgxZK1uexhjKqWM+0whZS8+3CmQPXl3iKJFSO2RDlKJBP8aGIrhXYzYcWwtzuxdCyUT0C1MpIOJxgfVcm4NY2zy9XFVKhn35b2tfEOe6dXUob+5IBJ4kTB5yivXGKuALU7nm3nfo1noL8gpM+PB9gFo4iOXvLIqRbBYxW5Wq5Vgy8zLNhgMfPU1yzmGAbzVIluyZAkmT558TZ8WiwUff/AuJvT0ZYPvCvIGAL1JQGK+oe2OcyVv7Ysvn+WllOzRm8WPiOhOKc9bDFvlnP9xC8MYi1IruHVNfOQdpz4YrrmnlQ8kXP2JEDIph7gwDeLCNLKJ/cJkBxIqeny3J+cXjUKy12ARJxBRGmNs+KJFi7aMHTtW3dAjQL1ej9atb6ygKQgCJBIJwsPDcX18rT0QEU6cOGEQBCHFSylZL5eyofe19qMB7fxVcaFqhPrJa7xV17TJK7coEvMN/fdeLOvxd0rlZxqFZIPBIs4logsOT+bWYyfuMKm2OxHGmEQmYVNVMu7dMH+5YkzPpt792/pBJZfU5TmVAfAVRcLXu3OwcsVyTHt1eq2ha/Vx4sRxlBVk4dsXY/DtnlzMnz8fDz98Y3XVqqoq5OTkYNy4cTAYDDh+7Bj+3LcHwT4KeHk1XqqOt7cPrKVSxDT9b4RSm2YadI32ZtN/3oCH7k/EK9NnIDw8HMeOHcMnn3xiqqysXAvg50abhOc4i5snwN4UZytdfQrgvdv5mJgxdq9Kzq3vHu0dOGtolNpf43iYiMEsYN6eHNPuC2UGk1UcS0Q7q8fo462U7N4wrb3aV+3YHsFkFTFqfjJ+//NQneLGY54ahebGkxjaxebp+WpXtnn72ZLf9WZh2NXfY4x1Vsu5X1uHqkOH3BWoWXigFM9NeBGTX56C0NBQHDlyBB++OwtyQzY+Hh5WqzGgNfLYfq5UXLI/zywS5pus4rtE1Hh1Yv9Ho1Md2pF0O28wpBI2TirhvhvXp6nimV5NpVd5Ux3CZBExf2+OedvZUqPJKo4hop2MsXvlcvmf7du1VQ56dPANntMaiAgZGRlYt2YlolvE4Njxa52YGzZswN13342VK1fi999/v1J22V4SEhKwYvlyrQRWyXN9QpVD7wri/DT2vUdKdVZsPVMirDpcYBEJP5is4ttEdMceN98JMMbaA+hKRCs8PRdnYIzFahTc+shAZcybgyM1cWH1V4C8GkEkvLcxCwVWPzw3fqJdiU+nTp3C9s0bsODZFmgRooLOJOD5pWmY9vrbeG3G61eO+UtKSnD//fejpKQYAT4apGdk4d4YX4zrHYwlh0oQcddAdO3a1a5518Vff/0FLvco3hjU7IbPTFYRe/4pw95kM6qMAjRyIDGr1CSI9IvJSq8QUVWjTOI2xVmDlQegqitm81ZHJuEmKWTs63eGNlcNaOffaP2eTtfivQ1pBr1Z+MbM03sqOZfxwWPNI/q1cW6MZ5dlYu3G3XU+OKOfGIFWljMYfJdtgTVZRYyad1FfrLWOIKI9jDFOIWXvcxybOeORCOWjnQMZYwwFFRasO1GGP+IroDOY0CLUD0M7aDC0SxDqMwhKtVbM3pJhOJ+lKzJYxGF3mPfmjoIxtg7AViJa5+m5OIJSxr3vrZK+OW9sjLquExBHOZepxetrU40GizBZJBT6ayS/DewUrN51vhyhoaGIahGD0LBmkMlkMBqNyM3JwuWkBAgWA0b3CMTKo2X4dfN29O17rfJUUVER2rdvj2effbbBNc2vxmw2Y/bs2RTlJ1q/HN1K7uiGt4ZSnRWfbMk0nMnUFhst4kgiOuVUh//DZTDGHgcwhohGeHoujiKTci9IOTZv8v1hilE9QjiuASchtcELhM935uJougmjnhqD2NjYm35fr9dj66YNyExNwpdPNUfLJv99X+SVm/Hv7YUoNTL0H/AASktLceCvg4gIVOLu5kq0DVOjY6QGNc6rhfvyUKBqj0cGPerQ3K/n1/Xr0MM3D0/eE9Kg7+tMAr7clWXaf6mi0mQV7yOi5EaZyG2IswarFoCvK2tTVydAtQPQVSXnesolrB1sAvwEQG+yimfMPB0DcBpASkPnIpdyr3orJZ8sGt9aHRHoeOm3uijTWfHST0n6wkrLH0Hesgc2TGvvZa9+Gy8QCistMFpFCIKItcdL0fHBsfh87pc3fFev1yMirCmWjW+OUL//7kC3ninBvD05B3Qm4QG1nFvVzF8x9MsxrTQhPnXqi9sNEWHX+TL6fEeWzmQVHyGivxutczfCGBsE4L47VGAfjLGVADYT0UY3jRcEoBMAP9iO+MwAigCcJ6LaaxbXgULGvRPoJXt7yYQ4dZC3a5Jl04uNmLQsyWC2imUfjogO79/WH2ariKOXK3Epz4jUEh5WnqBRMLRuIkPHCC90jvQCxzGcSK3Ch1vz8cabszD22eeg0Wjwyy+/4JOPP0JZeQVeeXU6QkIatkDVIIoili5dCoUxFwuebXhMbX0QEfbGl2POlkyDySqOIKLbsvIVY0wK4DgRNY7r6xaDMfYYgBFENNbF4yhhe067ahRcb6mEaw4iJRh4IlTpzcIxQcRJ2NbYXGqg0aCQcTO9FJIPFo5rrY4Kapw19u/kSny+Kw8qL190u7sXoqKi0KRJE0gkEmi1WmRnZyPh4nmcP38Bj3QKwMsDmkKtqD1lJDHPgMQ8PRQyDve28oVSxoFAUMmv/f5fCRVYcVbEiy+/2ijX8OV/5uDdRwLQKdK+MIOtZ0rEr3ZlV5msYm8iim+UybgZxti3sK1B+x1q74zB6koYYy2VMu4VIprgp5ZSu3AN1zHCSxMZqIRCZlPj0psFpBUb6UKWTncp18CZraLBKtA3vEhLiKi4rr4lHBvjo5IuXjYxTh3m71xJ05tRaeAxfkmCGNtUxT59slW91qogEo6mVOJ4ahUS8w24XGCEj0oCjUICXiTklJmhVKqw+/ff0adPnyvteJ7HuGefQUniQXw4LOyaPk0WEQM/P2fiGPu9VVPVg/PGxqivfyAbi2OXK/HW+jS9ySr2r67S0iAYYxyAWABdZRLWRS5lgYwxuSiSwWQVM0TCaQCnXV1+kTE2FraM42dcOc6dCmNMDmCEj0oyzipQF0Ekn+hgldFfI2UyCYOZF1FUZaWcUrNaIeMKARzVm4UfAOy/2SLIGBseoJGuWT6pjboxN1q1kVpoxAtLEzFvbAw6RNi3oFwuNOLnkxU4lFgOqyCCkYBxfZrARyXDwgPFGPv8BLRo0bCcPoPBgFWrVoLp8rHw2RZX3nmNyYUsHV5dnWI02k5G/rC3fXXiW1MAAQAUsG1ISomooJGnWtf4SgCVROS6l/gdDGPsLrWce80q0MgQH7mlY4RG2j7CSx3qK4dcyiCQzbuXnG8QzmfpdMkFBgXHWLbOLPwHwLqbKTPIpdxUX5X0P8smxqlDfBv3ma1ZJ/+4pMXxVC1VGcwMRPDRKBAbqkHXKCWGdA5EgFfdG9vqfAwk5htQXGWBmRdxucAIkYBRdwcjLlSDyEAFOI7BaBEw5OtLeO2NtxAQEODU3LOzs7HyxwXYNK1NvaeXtbH7Qil9ti2r3GQVuxJRRkPbVevldwbQVSFlLSUc0xDAW3kq50W6CNtmJJmIXKr6xBjbDmAREW1zqP2tZrAyxqK9FJKlItG9w7oGSUZ2D5E1C2jY+yghV4+fjxUZDySUM6mEbdCbxWlEVH5d/1EKGRf/44TWmquDnl1FUZUFzy1KwFdPt0KbZrXH7pTrrdh2phQbTxUjQCPFgHb+aBNmq3DlpbQZl7llZkxdmYzXB0Xg42356NW7NwYNHobi4mL8uHgRmqh5zB3VrNbd5ItLE3i9mWjJhNayunabjcWhpAq8+2tapZmn1jdTEahe7O7RKLgZZp4Geysl1rZhGmofofHyUUmZhAFWgZBfYcsWvVxoVHIcq+QF+sEq0A9ElN/Yc2eM9QfQgYjmNXbfdzKMsRC5lL3GgMkxTdXs8e7B3u3CNQj3t73wr4cXCGlFRpzN1NLPx4r0lQa+wmQV54qEJURkvK7vQKWMu/zt2Bg/ez0SjrL7QilWHirA8kltIJfe3FAs1VlxKLECCXm2xS+9yAiphEEqYdCaBMgkDK2bquGtkuBCrhldu3ZDvwEPwN+/9vAgnudx7tw5bNy4EfdEq/DB8EiXGKs1nMvUYfrqFL3JKvYgokv1fZ8xFifhMEajkPQzW8VOHMdkPiqpRSZhZBWIVRp4BQCTXMou6M3CfkHEGlcdYVYnmi0notGu6P9OhTHWTaPglkklXMun7glRDO0SJAm8iXFXgygSjqdWYe3RQt35LB3HgK/NPP2biK6RymOM3atRcPtWvdRW5UqHEGAzXl9fdxlRgUpMf/jmITdaI4+d50ux9Uwp8srNaNVEhdahaoT6K6CQMhy7XAWdSUCAlxSJeQZUGHj0jPHF492D8WdiFQok0Xh81JNOzXfFTz/i7qByPN/HcYGlVYcLhJ8O5v9jsIjdbmZgMsa8GfCMRin5l8kiRocHKPQdIjSK5kFKpVzKQSRAbxYoPlevS8jVsyqjIFPKuD91ZmEugAMN9aTbA2PsHQC7q0vX29/e0TlVe1NmE9FMhzq4sT9OyrEpEgk+e6FvmGLU3SESpYMv6koDj4X7cmuSn54loh3VYzCNgjv8TK+mPcbdF+o2hYS6FkAiws7zZfh+Tw56xdoejLqMWp1JwKGkCjzSKRB6s4A9/5ThcokAnudhsVpxNLkSQ7sG44V+odeMkVpoxOTlSVg5qS2a+rnWO1XDd3tyLJtOF/9pMIuDarvpGWOPaRTc5yq5JHT0vU1Uj3QM4G62GwZsf6vkAiM2nCgy7fmnjMkk3B86s/AqEaW57ELuMBhjLwI43BDDpIH9MQY8JZeyRY90CpQ/eU+IIjrYvthSIsL5LB1+OlhguJCtKzVaxCeI6IpSg0Yh2fRo58BHZgyKdJsXjYjw1vo0NA9WYvL9NyZG2Oasx8aTRTiSUoWesb7oEK5BXJgaMU3UUFaXP997sQxxYRoUVlqQmG/AqdRKnMsxQSQgOjIckdEtEdKkKTiOg1arRW52OhITkwAiDO/ii5fvD79hbFew8VSx+P2enESDRexUWwJt9dH7cC+lZCYI7Yd0CZR2ivSStQnTIMRHdo0EDxGhoNKCxDwDzmXqrNvPlfCMsfM6k/A5bPHTd5xut6tgjPUA0LKxYs4ZYwqljJvNMUx5/dFI5UPtA5ijSYt55WZ8ui3TEJ+jzzdYxFFEdLZ6DJVKziW/N6x5eGPmhdyMSgOPMQsuYfaoaHSO8r7h81KdFYv/zMOfl8pxb4wvRnQLQvtwrxs8nIl5evACoX31yUqFnseei2X47WQxQIQyI/Dc+InXaLTaw/nz57Fn2y9YMykWjto2gG3jMH5Jov5ykfF9Ky9+df3njLFAlZz7VBDpme7RPuKT94Ro7orygqyezXelgccfF8tozZHCGifC+yJhuSsMV0dxxmD1AlBIRPal/NXel7dGwe0M81Pc9fHIFprmwY0T73I6XYv3f0szGC3iOoNFnMQxjG0erPx+5aS2Gmezi+2BiPDmz6mIDVXjhX62I/viKgs+25aFoioL3hveHLGhznl7PJQfTgAAIABJREFUS3VWfL49C1mlJrw/vDnaNNOAFwgTlyZiaJcgPNbNMQ07RzBbRTw1P15fUGGZIBKtr/l/xliwRsH9qFFIHnhrSJT6npY+tXrg6kNnErDhZJGw/GCBmRdoFi/S966Mo75TYIztBvAtEe1qhL4CNQpujY9K2nvOqBaatnVstOxhX3w5PtmaaRREWmyyim8A6Oavkf6x6dUOmhoj0F2Uaq0YvSAeq15qiyZXHWkWVlrw6dZM5JSbMbJ7MAZ1DoSPquF73+IqC347UYSNp0oQ4itHWKAXGAMC1BzahCqRnG9AQaUFc0e3dKhmuSMQEV5clmRIyjPMMfPiNVqPjLF2ajn3S5i/IvK5Pk29+sX51bvwXY2FF/HnpXKsOFSgK6y0pBlsG5Kk+lv+D8bYBAC9iGh8I/QVoZJz+ztHeoW+O6y5OrAR4sBrche+2Jll4gV628yL36jkkm97tPB+4fPRrVx/fHkVfyVU4Ls/crD6pbZXNoxEhD8uluPr3dkY1CkQT/dsgoZ4kq+HiHA6XYs5WzNRaZbglWnTEBoaalcfGRkZWLbkB3z1VNQVg9gZsktNGPtDgsFkFTsS0ZXSXIyxYUoZt/yRTgGqcfeFKhwJoSIinM3U4fPtWfpireWM3iyOIaJspyfdCDhjsPoAuEhEkU5NgDE/tZw7PKCdf8tZQ6KUDdFTtAe9WcCrq1L0aUXGPwC6a+7omKiu0TfuwlxNdqkJLy5NwuZ/dUB+hQWvrkrGo52DMO6+pg1aAHiBoDMJuJmcDRFh78VyfLUrG7OGRqHSwGPXhVLMfy7WbYtfDRdz9JiyPKnCzFMIEVkZY/crZNxvw7sGqSbf30zuzA6zhswSE97bkKbPLTdf0tu8uSXO9Fct+9SKiD52enK3IIyxTQC+I6I/newnTCXn/n60c2DYtIfC5fUdm9tDud6Kd35NMyTmGf4GSP9Cv2ZDn+7ZxL3WajVf7MyCj1KKFweEgYiw7WwpFuzNxZP3hGBsr6b1xqCV663wVkpr/Z7BLGDBvlz8lVCBNwdHondrP+SVmzFucQJWT26LYBfH6l5PXrkZo+fHG808tSKivGrdzFkSjr09bWC44rGuQZwz7xBRJGw4WSwu2JtrFkT6yCrQXGc3mYyxQABbiKi3M/3cqjDGngdwFxE5le3DGGuhlHHHJvQN9X+mVxNpY68F+RVmTFqWZKg08D8AeHnzvzoo7ZGHtPIi0opNuFxohN4sgBcIcilDkLcMcaEaNPGVNWj9emPdZdzd0gcje4TAZBHx8eYMpBUb8d7w5mjIhlpvFsCAOpO0rLyI9zak42i6EaNHj25QIRAiwvFjR7Fz+xZ8OCwCPWNrLSrpEAv25vK/nij6yWAWXmSMKTQKbqVKLhk8e2S0ujZPs73wAmHFoXx+5eECs4WncSLRr872yRhbAeB7e3JcrmnvSW8vY0yllnOHB3YMaDfz0UiFq4wqs1XEKyuTTRklJunuNzpKOa7+9c/KiziTqUNCrh6J+QakFBhQZRRgFQhyCYOvWorYUDXiQtVo20yDTpE3HjFcz/RVKegW7Y2fjxXhpfvDrshPNYSUAgM+3JiBNS+3rfe7l3L1eH3tZajkHGYMikTPGPseEq2RR3KBESVaKyy8CKmEQaOQoFUTVY1IeYP6Gbc4QZuQZ3gBgFkl59Z9MbqVqrE3C4JImP9HjmXT6ZI8o0Xs6UxsK2PsFQCtiWhqI07xjoIxFqKScaef7dO0qavCaqy8iHd+TTOdTNPKN0xrzzXUG1Rp4PFPts6WTJFnQF65GRaeAAaoZBwig5SIC1UjLkyN9uGaG7KBrye9yIipK5Px27T2+Hp3Di7l6vHBiGi0atKwsIeR8y7iq6dbIfImGdKn07X4eHMGHusWhCojD5GAVwc2TP7KYBZssbN5eiTkGZBTZoaZF8EAKGQcIgOViAuzvaPahGlQn5d6zpYM0+4LZZ9bBZqjlnMbIoOU938yqkWjJqbmlJkxa32qPrfcvMtgEZ92RhKxuiTtWbrDKi42JoyxUKWMOzPlgWYho+4OcdnGr1RrxfNLEqwh3jIsndim3ge2TGfFtrMl+CuhAmnFJoT6yRHTVA0flQQSxmAVCAWVZiTmGSAS0C5cg8GdA9GntV+d6+zpdC2+2JmFJRNaY8baVIT5yfHWkKgGx4Av2JsLtZzD8/fd3Hu69UwJvvo9HxEREbj/gQcRGxuL620KQRBw6dIlHNz/B2CqwAfDwut8b4giOXTSWFRpwcjvLhotPEWp5dymu5p7d5k9MlrV2EnVSfkGvLIy2WAwizOsgviDM30xxg4DmEVEhxxq70mDVS2XLOzewvu5z55sqXJUn62hGMwCJvyYiDE9m9zUUCystGDz6WJsPVOCMD8F2kdoriRA+amlkElsD1Opzoqk6oXxfJYO5XorhncLxtC7glDXArv7fCnm7szC9IERGNKl4cYqYJPg+GRrBla+VL/BCtgS0KasSMa3z8SgQz3JKkSEU+la7DhXios5epTprGjVRIWmvnJIJRxEIlQZeaQUGGHhRcSFadA3zg8DOwZAc5Mkrn3x5fh4c0YKYwhf+Hysyl7BaHv48UCedc2RwmyjRexORGWO9MEYewRAEyJa3rizuzNgjEnVcu7MqB4hcZMfaOYabalqeIHw2poURAQq8cajdR/i1MTA/nayGEdTqtC2mdpWzSpUjYhAhW2xIsBgEZFebERingGX8vTILDbhoY4BeLxbMKJvouk6aWki5DIOogh8PrrlTe/36xnxzT+Y92wswutJGi2usmDaqhQUVVnw04ttEFmPzF5yvgEbTxVjX3w5ooOVV643Kkh5JTbOaBWRUX29CXkGZJeZMLBDAEZ0D0ZdccYpBQa88GNiuYRjJztEePX+/KmWalckfJksImasu2xIyNXvNVjEEY7GtTLGAgDMIaLJ9X75/yHVORuHRvYIuXvy/c1cnrNRUGHBuMUJmDu6ZZ3H3ikFBqz6uxBHUyrRv60/HukYgLgwdZ2bRyJCsdaK0+labD5dgrxyMx7rFoyn7gm5wRNKRHji+3ioZBzaNdPgjUcj7TIEv9uTA1+1FM/2rn//k5xvwMSlifD1UkNvtiIyPAx+/oEgElFeWoqs3Hw0D1HjiW7+eLB9gC0J08jj2OWq6gRNvc2jbBIgECDlGAK8pGhd7QRrF65Bt2ifep1gr65K1l3I0uf3jPUN/2hEtMpVoY45ZWa88GOiocrIvyyIjheyYIz9G8AaR8OCnAkJ8AUwk4jecbD9fd5KyS5nqj/ZS0qBAa+sTMHKSW1wvdSGwSxgwd5c7PmnDAM7BmBEPQvZ9STlG7DxZDH+vFSO4V2D8EK/sBt2du9vSINaIcFbQ6Lsnnu53opzmTr0b9vwQPZ98eVYvD8PKye1qXWXabaK2Hy6GBtPFkMq4fBYtyB0be6NyCBlnaUuS7RWXMrVY+f5UpxJ1+KhDgEYfW8T1KbkkFduxugF8fj2mZhag+EbEyLCl7uyLbvOlx7Sm8UHb6VA8VsFxthrsGngOZSoppBy77Rpppn1w7hYjTtCTHQmAWMWxOPd4c3RvYXPDZ9fytXjs22ZMFtFjOgegkGdAuDdwHjSwkoLtpwuwZYzxYhtqsbMwZHX6BfXMGlpIniRMP+51vV6KK9nzz9l6BXjC42yfiO3VGfF+CUJGNurKUb2qF2vNaPYhP9sz0ReubnezfH1XL0Rj2mqxsxHI1Gb53TwF+etEYFKcd7YGIU9sar2YraKmLoi2ZBaZFyuNwtTXDbQbQxjbAAAP0d1k6US9mJkoPKrVS+5L2dj78UyLDmQf8OaY+VFLD9UgI0nizG2d1MMuSuwwc/q1aQUGLDmSCEuZOnwzrDmuP7EbtLSRPhppPj0iZZ2ey0vZuugkHFoqHpQSoEBU1ck47MnW8JoEVGis4IBCPGRIy5MjRq7JinfgN9OFmP/pXJ0ivRCu3AN2oSpEdNUDV+VFBLOtkEvqrJWnw7pcTZTh8JKC4Z3DcLQLkF1hgjNXHcZBosofvNMDOfq3zi92IjxSxKNRovYqybJzt04Y7CGAzhGRHansjLGVCoZl/rR49Gh98X5OTS+oyw9kIeLOXp8/UzMlf87na7FnC0Z6BzlhekPR9iVRHE9pTor5u7IssVXXhU7czCxAvN+z8GqyW3qPYpsTN7+JRWhfgq88tC1P1N8jh4fb85AeIACY3s3RccIjd1xrkWVFmw6XYxNp0owvm8oRnYPvvKSICL8a81ltA1T48UBN2ZauwIrL2L0gkv63DLzZJFolVsGvY1gjB0DMP3qLHw72rZTybiTa6e0VdVm2LmKIymV+Hx7Fta83PaKd9PCi1h6IB9bz5Zg+sAIPNTB3+EYbSsvYt3RIqw9WohJA8IwvGvQlb5OpVXhw40ZWDulrVPvhIaSXWrCCz8mYvGEOFwttC6IhHVHC7H670K80C8Uw7sGO6ThCNiud/3xIqz+uxAT+4fhsa5BV57ZoymVmLM1E+untGuQke0sVUYeI+ddNFYZhUcdFRK/k2GMvQ6gKRG97kDbCIWMS1j2QpymZQNDWBqLt39JRZi/AlMftK05GcUmvLchDSE+crw5JBKNoaV8JLkSn27LRN84P7w6MBwyKYcTqVX4eHMG1k1pd0UO0tX8drIYO86VYPH4uBueyRKtFf/ZnonkfIPdG0zAZhBvPFWMfRfLMaJ7MMb3vVYB6EKWDm+tT8Wal9vCmZLy9rDjXAl9uTM71WAR210vZ+YOnNlCMwB1ivPXw9NtwzXe7jZWAeC5PqHIKDHhUq4eALDmSCE+3JiO1x6JwPuPRTu9MAV6yfDpEy0w/r5QzFh7GVvPlMDCi5i7IwvvDIty2Fg1W0WU6ewP93p9UCR2nS9FWpFN3lIQCQv35uKNdZcxoV8o5o5uiU6RXg4t+CG+ckwa0AyLx7fGHxfLMGVFMgoqbPfw9rOlKNNZMb5vWD29NB4yKYfZI1to5FK2gDFm98CMsZcYYzNcMbdbhCrYxN3txkshWTzlwWYKdxqrANAzxhddmntj1WGbFn2pzooXfkxERokJqye3xcCOAU4lFMqkHJ7t0xQLno/FtjMleGt9GsxWEXqzgDlbM/H2sCiH3wlFVRYIYsMdAhGBSkzoF4bZmzOutNOZBExdkYyjKVVYNjEOI3uEOGysArbrfaZXUywcF4ud50rxxrpUmCwidCYBn23LxPvDm7vFWAUAH5UUHzwWrVLJuHXVqjN2wRiLZoztccXcbhHMsD2zdqOUcW+O6Bokd7exCgAzHonEltMlqDLySMzTY8qKJIzoHowvnm7ZKMYqAPSM9cXal9uioNKCmT+nokxnxSdbM/H20CiHjdVKAw+D2b7olMe6BkElk2Dd0f9KjtvUE0oxduEltGqiwoZp7THuvlC7jFUAiGmqxpuDo7B2SjukFhkxbnECEvNsdovJKmLOlgy8/mik24xVABjUKZC1a6YJU0jZB460Z4xtZYzFOTq+22NYbXE1kuQ5o6Jb3dOq8TLm7GHl4QJklZgQ5q/A7/+UYd7YmGukaxqLrBITpq1KQZfmXijRWjHv2ZvXP64NQbTFy55J12L98SL858mWCPSW1XlkXxs/HshDqc6K1x6OwAcb01FlFPDR49EOSXzcbJ6r/y7AplMl+PLpVpi+OgVzR7eEK+NW6+Kb3dnWzadLlhgt9h01MsbeA6AgonddNLXbEsZYrEbBndv1RidVYyoCNJTMEhMm/5SEH1+Iw6urUvBIp0CMu69poytfWHkRH23KQIWBR3SwEgaLiPeGN3e4v4c/P4+1L7e9acWd6xFFwpQVybi/nT8e6hCAqSuS0THSC689HOFQYsbN4AXCnC0ZyK+woGWICgKRQ+FKzvL+b2mmg4mVC40W4TV72jHG2gDYREQOL4B3IowxtVzKin6e0k7jauH+uvhwYzoCNDLsulCKtwZHoW8b1zineIHw703puFxkRHSQEnOesL+EsdEioEzHY+G+XEQFKfFYt2AEekkb/H7JLbOpe2z6VwcoZRy+2JmF85k6vP9Y80Zb/4gIuy+UYd7vOZj+cAS0Jh5HL1fhy6cd04R1hqIqC0bOu2iy8BRORKX2tGWMxQN4koguOjK2JwzWewK9ZHu3vdZB4+pEq7oo11sx4puLCPSWYdG41nbvfOwhr9yMCUsSMbBjQL2VOACb4XckpRKn07VIzDMgucAAlZwDA4PWxMNbKYXBIqBVExXiwjTo0twLvWPrzpwEbEkdo+fHo0tzW7zP7FEt6q3g4yjbz5Zg/h+5aOonx08vtnHJGPWRX2HGk9/HGyw2SS19Q9tV1+6WENEGF07vtkMll8wf2SN44tQHw923lb+Ol5YlobDKFtP1XB/7NBDtQRAJH21Kx99JlVj9ctta41pro6i6OEBingEphQboTAIuZOvQIdwLIT4yWzJFdXJUfR7Mf7J1+KB6wW8frsGrA8NdJksnioQ5WzNw4FIFVr3UFmENrCrYmOSWmfH0gnhdtQSesf4WNqrD0l4hojddOL3bDsbY+G7R3t9+/1yse0rD1cLxy5WY9Usa3hwciYEdA106Fi8Q/rU6Bb5qKWaPqr/0cZWRx96L5biYo0NCtZpIgJcMWqOtMh0AiES25zVMjT6t/dCumfqmz+Bb61PRLdobl3L1yK+w4IvRrVxyUpFaaMT01SkACB88Fo1utcT2u4N3fk0zHEgo/5AXaK497Rhjn8OmBZ7ryLgOn39XS4q8SEQf2dNOLmVPDO8a5HJVgJtRpuPBccC8sTEuNVYBIMxfge+ei8XUFcl48p6QOhfAMp0VW8+UYPPpEgR5y3BfnB/G9w1F61Bb8HZxlQWXC424N8YXWiNvUyjIN2DtkUJ8uTMbw7oGYVgdwdnBPnIE+8ihMwn4ZmyMy4xVABh8VxBMVhErDxfAZBHtTlRpDEL9FOgU6SWeTNM+BWBpQ9sR0SYXTsvjMMY+ALCEiPLsaCORS9lzI7oFe8xYBQCOA7pFe7vUWAUACcfw/vBoTFqWiD/jyzGmV90ZwyariH3x5dh4shg5ZSa0aWYzSB/pFAhflRSn0rXoEKFGmY5HYr4BBxIqkFpkxL2tfDGiezC6NK89FKd9uAZWXkSwt8ylxioAcBzDrCHNkVeejN3/lGF8X9f+fWujWYAC7cO9cDpD+ySA5Q1tR0Q5AO5YY5UxNgSAWFOpsaH4qCTjRnQL9pixCgB7LpZjQFt/lxurACCVMMx5ogXGLLiE0+naGxKxakjMM+C3k0U4kFCBe1r5oFu0D568pwlaBCshk3I4n6WDn1qKqCAliqtsG9BLuXp88FsavJRSPN49GA+1D6h1TXu8ezA+3JiO8AAFvn0m1mXrXssmKnz/XCxe+DERBovnisY9dU+I+mhK5WuMsS/t0VN2tjKqM0lXHQCsI6L29rTzVUtPfTQiuuu9dmqDNhY11Z+GdAnCCDdWf1pxKB+n0rWYNzbmhlKGm0+XYNGfebgvzg8jugUjLsy+IiE1wdl/xpdjfN8wjOoRfM3x4YUsHWb+nIqfp7S7aeGBxuT939IQoJE1yKvsCg4mVmD2loxTlQa+u0cmcAvCGEsEMJyIEu1o0y7IW3Z0+4yO7q+2Uc3BxAp8+3s2Vk9u67aExdwyM8YvScCi8XG4vvIeL9iSoNYcKURcmBqPdw9GzxjfBoXp6EwCdp0vxW8ni8EYMH1gBO5uda2X5FymFrN+ScPPU9rBXQoqRZUWPLcoAfOejWlwlnRj8ndyJT7YmB6vNfJ2rSd3Moyx2QBMRDTbjjZMIWWVv7zS3tsVYW4N4UhKJebuyMLqyW3tkoFzlsNJFfh6943vCZ1JwLe/Z+PY5SqM6hGCwXcF2h2mczy1ChtOFCO92FirOsHRlEr8e1MGfnmlnUPqB/byT7YOb/6citWT7Qs5aiyICI99c1FbUGl5yJEkXkdxZhsgAiiwpwFjjBktYlt7DbLGZP2xQngrJXisq306qM4ypmdT6E0Cdpz7b8hHfoUZr6xMwbYzJVj4fCzeHhplt7EK/Dc4e8kLcdgXX4aXlycjp8yWW2Oyipi9JQMzB0e6zVgFbIH3ey+W41ym1m1jXk2HCA2MFrEds8M9xRibxRib5Mp5eZhSAPZmdnZr20ztseMQnUnA3B1ZeHdYc7eqazQLUGBi/zDM2ZKBqzf16cVGvLgsEcdTq7B4fGt880wM+rT2a3BMuZdSglF3h2DdlLaY+mA4Pt2WiU+3ZkJvsnlLzFYRs7dkYtaQKLcZq4AtgXLKg83w8VUJX+6kewtvmCxia8ZYg7OEGGOdGGNbXDkvD6OH/UlXzSQck4X4eOZARBQJ3+zOxpuDI91qrAJA79Z+aNdMg5+PFV35v2OXKzFmQTykEoafp7bDs32a2m3gcRzDvTG++HJMK8wYFIkPN6Zj7o6sKwlaNQmLHz4e7RZjFQA6RHhhUOdAfLEzyy3jXQ9jDF2jvaUAutnZ7m/GmMOyQQ4brEQUT0QP2NksSiXjyJ1ZbVdj84wUYdrACLeXKpVKGKY+FI5VfxeCiJCUb8DEH5PQvYU3Fk+Is0vztS4iA5VYOK41+rbxw8SlibiYo8cvx4vQMkSFAXbotzYGvmopXhsUga92ZcPdcdIA4K+RQa2QiADsicIPAuAxT6KrIaJe9mqwKmXcvZ0ivdyfOVfNjnMl6BTphbuau/9nGdEtGFqTgDMZOgDAznOlmPyTraTyvLExN61gVR+MMfSK9cWaybZCIGMWXkJakRF/XipHmJ8cnlBQebRzICQcw7HLDiWmO4VcyiHUX24A0MmOZmoATVw0JY9DRJ8S0Tw7m3Vs1URldvf6VsOpdC0UUg53t/RMbOUzvZpi86liCCJhw4kizNmSiXeHN8ebg6MaxYDuFeuLNS+3hbZavaPSwOOHfbm4N8bX7dc8sV8YUotM+Cuhwq3j1tAhXKPyUkrus7NZc9gUphzCfVt4G74apYR385hXOJhYgYhARYNLKzY2d0V5QcKALWdsIQBvPBrZ6IakhGMYfW8TRAYq8fray+AY8NUY92cSAkC/OD8s3JuLizl6dKij8okriQtVC8dTq7oAuNzAJqcAOFQl605FIWWtQv0UHln9iAi/nSzG20Obe2J4cBzD492DsfGk7Shw1eECLBwXW2elKEfQKCWYNTQKuy+UYuqKZPhrpJjkJt3i62HMdr2/nSxCr0ased5QOkZ4ybJLzd0ANPSIsQTANhdO6XbEz0/tASmPan47WYwR3YPd7hCqITZUjWAfOT7fnolT6VosGt+61gIZzuCjkuKjEc0xf28uJv+UhKIqC36d5v5IFoWMw0sDwrD2aKHLVBhuRvVpcA87m/0KQOfomA7f2IyxVoyxt+1sJpNyzGMViGoeJk/BGMOD7f3x3Z4cvDGo8Y3Vq+kV64t3h0XBaBXhrXT3vsQGxzGM6B6M3046KtfrHMHeMhmAgIZ+n4jWEdHvLpySR2GMfckYs/emUyk8tP6dStdCJuHQKdJjDl480ikQf6dUYOXhAiwc17pRjdWrebhjIN54NBK55ZZ6S7m6kgfaB+BSrgG5ZQ7J9TpFi2ClSiFjrRv6fSJKIaI5rpyTJ2GMPc0Ys/cUUya1R/OwEak08DidrsXADg1+5bqE1qEqHEyqxPfPxTa6sVoDYwxTHmiGbi28oVZIPLbG9mnth7xyM1IKDG4fO0AjAy+QXTtbIppORA67hJ1ZiUIBPGJnG5NVII88TCaLiIs5OtzX2v07kRqICKczdHji7hAMaOf6I/rerf0w/r5QzN6SAdEDcWmA7ZjxUFIF9HYKMjcGcinHAfDc6n/rMQb2/z0ET8Q0AsDhpEoM7OBcYQBnKdZawDGGb8fGuGzxq6F/W39Me6gZPvgtHVa+wYm3jYpSxqFvnB8OJ1e6fWyFjIOUY3dsSI4D9ARgr8asycKLHrl5EvMNiGmqgtrNsatXU6q1Yl98Bb58ulWDJekchTGG6QMjEBGoxNojhfU3cAFSCcPwrkHY6AGnkEzKIBK5Nb7TGYPVCiDfzjaF5Xqr0hMxjSmFBjQPVl1T39jdbD9biiojjwlurP70dM8m4AXymJfTRyVFdLAKyfnu3wFWv7gb7CpijH3OGHvahVPyNPmwPbcNhoAKT2w2ANsC2KaZ5xI0BZEwe3MmJj/QzGWe1et5rFswQnzkWHHYrnzWRqVNMzWS8hssX9xoEAFEaPDNxhi7jzG21pVz8jCV1f/sobCg0uKRHWZint6hpOHGgojwn+2ZGNYl8EpJdFfDcQzvDovC2qOFV6pJupsB7fxxIs39cedWnsAxZleIJ2MsgTHHN6XOJF0dI6In7GxTzDGmy/HAcVNCngFtPPgwVRl5zN+bi/eGN3eqtKK9SDiGd4c3v1LtyhPEhaqRkOd+g7VYa7XCvpjUIACOZ9Lc4hDRXfZWJtGahONJ+Qa33ziCSEgpMKB1qOee2Z+PFUEp4/C4G+XvGGN4c0gkfjtR7JFjPgBoE6ZBogeeV6NVBC+SPSuvBoB7s0ndCBG9Q0Sr7Gx2NrPEpOYF99usiXkGxIV6LnznUFIlcsrMmNDPfQ4hwKb7PWlAGD7f7pmM/chAJcr1PKqM7k0PKjfwkEqYvRuqSNgUphzC7e5GmZSdSfSAty292IgWjZCJ7yg7zpWiR0sfj2gcRgUp0beNP7adKXH72IAtONsTv3livkEC4IwdTQ4BaLBG6f8HiHDqfJbO7T9eQYUFPiopfNwkE3M9ZquI1YcLMHNwZKOXRK2PEB85nu3TFCs95GVtEaxEdpkZ7jZ6EvP0egtvV8nGXAA7XTWf2xEiqpRJWGlmicntY5fqrGji67n6Ir8cL8K4+0JdWhinLoZ2CUJBpQVJHljnJBxDTFO128dOzDOA2RKV7WE57Dzluxpnkq46M8Zm2NtOZxL2nUyrcruL1WQR3a4LV4MoEjaeLMbjHkz4GtEtGJuq5T7cTaifAiUe4aZkAAAgAElEQVRae+U/naNcb4XBLHAAUhvahoh+IqIjLpyWR2GM/cgYszew63RqkVHlbuNFbxY89rwCwJ+XyhEbqkaUE9JVzjC4cyCOXa5Cqdb9pyIyKQeZhMFsdW8oZHyOXoQdCyARXSCi71w4JY/CGHuRMdbb3nYcY0fPZmrd/qK38ASZxDMhd+nFRqQVGdHPA9nygM1oHN41yGOhdy1ClMgodu8mJT5XZ9KahIP2tCGiKUTksDHgzN0VCaCfvY1Ewuo9/5STwc1xcQIBnqoGeyZDC4WMQ8cIzx2XxIXZ5D6Oprg/mUIuZTBb3fv+/CdbD5WciydPBEzfujxrbwMiKpFLuZQjbr5vRCJ4aO0DAI9vML1VUgxo64+tZz1zKiLlGHg3bm4NZgGlOl4J4JLbBr316QubbqVd6MzCgnVHi3TufvVJJcwjDhEA2HK6BEO6BEHmOUUvDOkShP2Xyj2SYKyWS2B08wbzdLrOCvs9rE7hzK9rhO1Ixi6IKEcqYQd//6fMrXe2Qup+j0ENZzJ06B3r69FsZwDoHet7RQTdnQgiuTVuFwB+PVGkqzIKP9jThjG2hDE21FVzugVIBxqe1FKDziT8Z+2RQreWLJNLOVh4zyx+JVorMktM6Omh8tE1PNIpEPsvlXtkbDMvwp1yZn8lVkAl544TUYNdyoyxYYyxxa6cl4cphv1JVwDwZ7neqr2Q7d7EOS+FxO1xlDWcydCijwe0g68m0EuG6GClR+K/OQa3FuhJyNWjTGc1o+GayWCMcYwxexP1r8GZpKs/iOglR9rqTMLc5YcK9O6Ubgn1UyDbA8leAJDg4ezJGmyxpO7P/tWaBKjk7lv88srNOJ+l4wCss7NpINxfTMNtEFFrInJkRfk1IU+PrFL3HTkFe8tQVGXxiMemJtvZ3Zus64kLUyOzxASLmyWuynRWyKUcFDL3Xf/qvwu1WpMw185mGtzZlemmE5HdhRGISDRZxS9+OujeoMaWTVRILnB/pryFF5FZYkIrD+SHXE9cmAYJee5fY01W924w1x8vMloF+pqI7HGASGBLbHYYT/nP92mN/MllB/PdFqDlqcQfIrLJ84R5LhyghrhQDZLyDW7XZE0pMLq1utgvx4usHGPLicjeH3wPALtKl/5/gIhMBMz9bFum3l27eG+VFAEaGbI8kDySmG9A3C3wvCplHMIDlEgtdK8RkJhvU2dw14lQYp4BeeVmM4Addja9DNsz+z+uQyT8cD5LV7ov3n0eek85RFILjQgPUELpQcnKGuJC1R7xsGaXml2uE11Dqc6K/ZfKGS/Sjw40t+vU83qcSbrqxRib4khbIiKDRXxm7ZFCs7sy22w3kt7tde2NFhEGs4AQH89lT9bgp5FCyjFUGNx7bJOYp0ecm+SJEvMM2HSq2GyyinZXwCGiH4jonCvm5WkYYzLG2EpH21t4+iwxz5Cz7Wyp2x6g1h7aZKYUGBHT1HOKIlfTOlSFFHcbrHnukxMjIny1K0tvFekzO701IKITRPSTq+bmaRhjrzPGujrSloiMRov4xCdbM4zlevf4hdqEaXAhy/1rbE65GVFBt0Z9mMggJdwt21njFHPHKS4R4ZOtmQbG2A9EVGRnWysRveLM+M5sSVoCuMfRxkSUZ+Zpysx1l/kKvesNqDI9DyIgPse9O0AzL0Ip4zwev1qDUsbB7MYjRiJCQp57PFZWXsS7G9L0Fp5eJqI8lw94eyEDMMrRxkRkNVjEJ77elW3NK3fPC9lbKcHfHqi4pDcLHpPTuh4flRQ6k3uTOE6na9HBTQmim06XiJcLjRm8QN+6ZcDbi/6wVZR0CCI6xgu0+q31aeSO8LvMEiN4gXAq3a3h7jBbxVvCuwq4f30FgIJKCyScLYzK1eyLL8eZDG2JySrOcvlgteDMr6yFA0lXNTDGmFLG9RAI9OrqZFS60OuXUWzCy8uT0L2FNzaecq/sBMOtYajWQHDvnM5k6KCQcmjmL3fpOESEb/fkWMr11mMErHakD8bYb4yxfo08tVsFBuc1ZgMFIkxdkYwSF8stxefo8cfFMhy9XOnSd0NtCCJB6ilJkeuQuDlbP6PYhLRio1sSzvLKzfju9xyzwSI+4UhsNWPsecbYV66Y2y1CHgCHSxgxxgIYw/1aE4/3XVzu91RaFd79NR3Du7lf2ulWcQYBtnXI3a+OIymV6BTp5fK/Q26ZGZ9uyzQabc+r3bFajDF/xthlZ+bgTNLVJiJ6y4mxn/BVS59fM7mNrFu0Dyb/lIRcF7jSz2fpMGVFEvxUEkQEKHEw0b0LoELGYLKKbj8mqQuTRXRzMkUBirUWl8f1LNmfjx3nSgv0ZvEJJ6Ss/OG5uG6XQkR6IrrL0faMsSYKGdv89ZgY+dAuQZj8UxLyK1zjaT2bocX01SnwUUnRO9YP28/ZVZzLaeRSDlbBM4oi12PhRSik7nte1x8vJJEIRotrr79MZ8VLPyWZBaK3iMhRKSs17uzKdBOJyC6dyxoYY0yj4NYO6hwUvmxiHBNEwhs/p7oki39ffDne+TUNjAF94/xwJkOLggr36W4rZRwMLr5fG4q7k5+ICGv+LoTRIro0N6Ww0oKJyxIFk1V8nYiOO9iNFIBTO2GPLM62xY9b/MmoFhpftQxTH2yGYV2DMX5JAn49XtQof3iTVcS833Mwa30qZg2JwvfPt8aOc6XoGu2F1X+7r4KMSi6Bj0qKvHL3CufXRonWCjDAT+2e486iKgvOZ+nwzrDmeG3NZZx2wVGRIBK+/yMH286WoFqQ2JlBNgPIbqSp3TEwxphawS1/vHuIsmu0N56/LxQje4RgwpJENGZSBy8QVhzKx6xf0jB7VDSaBymhlDH8erwI7tRtDvSSorDSM2WMr6eoyooAL/fEv5dqrdh9voz1i/PD1BXJLivlXFRlweTlyag08BILT3860dVFAPsba153Egx4ykcl7T19YLhcKZPgkydaIipQiTELLuFwUkWjjFFpsHluF+7LxbdjYzD94XDM3ZGFkd1DMHdnltucNJGBCqS7WTS/LtKKTG4tNnI+yxbiqDPxmL0lwyWKIhnFJkxalgQZxwkcYwFOdGUG4JQMnTNJVw8zxiY40laj4H56vHuwsl24pqYvPHlPCBaNj8Oef8owZUUyTqdrHbrheYHwf+xdZ3hUVddd506fSSW9kgRIQuhNBFFAsSBVEBUR7EpRsTd87V3sCoIFFRAV6U0UEFCkd9IT0uukT6bduffu78cEXkra1PDyfOuPz2PuKXe455x99l577e2ptbj7qzRUNPBYPjsFw5ICEOqnxJzro1BYZcWmY9VIK/Eel7WjFAouREap0WvZv0SEdzcU4NYrQnBDr05449Z4vLzqND7dWgSLm/RwC6osmPldJtJKjFg6szuSI3URSjl70YU5f0ZE2W6Z3CUGxlgAY8yZrE4w4DZ/jfzqh0dGnuV13H5lKN6/owsW7yjBvF9Pu2zc5FaY8dC3GTiUZ8CSh5IxuIs/XhjXGbsy6pEYrsGCbU6zjxxGUlOC5qUAbyUsEhFeW5OHsf2C8Py4zhjePQAPfJOBI/nuvWQeyG3Ag99kYEyfIDx+UwynVXIrGWNO3aCJaDcRrXTrBC8hMMZeZ4x1d6JdmFLOFr01JUF3pkypXMbwxOgYvDY5Hh//XoSXV+Uhx8lkPjMvYu1hPaYtSEMnnRzLZqYgOVKHcf2C0clHAcbsHrktx2uc6t9RxIdoUFHPd4hg/4XIKPOehCURYfGOEkwdEobPZyTBxEu4d3G62/YuSSL8sq8CD3+XgQdGROCr+xKVco69yBjr4eR8G4honitzcsXDmgigr6ONGGN95Rwbfu7hdwZxIWp8dV8SbuzVCfM3F+KOL9Pwy75KFNdYWzVeBZGQU2HG13+VYuLHJ/HbgUrMuT4ab01JQKDuv96JMX2D4K+V47oegXhjbb7XCgkkhGhwqtj7gv0XIqPUe/Ja21JrUVxjxf3DIwEAAxP8sHxWD1QZbJjxVRr+zap32pPeaBHx49/lePi7TNzYuxO+mJGIAJ0Cr94Sr5Mx9hxjrJc73+UygRbAGEcbMcY4jZL76KUJcTrVBYkNPWN88MPMFEQEKnHHF6l4fU0eThW3P0tYlAh/Z9bh8WXZmPNDFsb3D8Zn07shIsCe8Rvqr8S910RAIsKujDqPeOibQ1KEDie9nJzZHGoabTBaJUR38nwG9NaTNSistuLRG6LBGMMDIyLx1OgYvLIqD/M3F8LMu2YMGK0i3ttQgLfW5eOF8Z0x4+pwTBwQzCWGa2NcuWRe5hgFwGGPllLOnr65T5AyJerivb5/nC+WzUpBXLAaTyzLxszvMrH1RE2bJYBtgoS0EiM+3lKECR+fxJ6serxzewIevykG6iaNbcYYnh/bGSv3V+Lp0TH4/I9ij9D8LoRcxhDiq0DWJeAUyvRSgjEArDlUBbNNwi0DQ6BWcnjntgRMHxaOJ5bnYOG2EpeojxmlRsz6Pgs70urw9QPJGNsvGBEBKjx2Y7TKlUumq2DOuu0ZY9MBRBHRu46006lkP0wbGnbn/SMiW31hIsKxgkasOVyFYwUGmHkJSRFaxAapoVIwEAEmXkJepRk5FWYE+ypwRRc/TBoYgi6taH7uSK3Fr/srIOMYfNRyvH1bAmQeZEnbBAn3f5OBerOINXN7guugZA4iwtQv0/Dk6Bhc0cXPo2NV1vOY/lUaPprWDWe86OdiZ3otvttVBpNVwi2DQjC6d6c2w55EhKxyM9Yc0mN7ai2u6OKH2ddFIeqCw3zl/kpa9FfpXwazcJ2j82aM7QTwFBEddrTtpQ7GWBiAn4jIod+FMXZ9dKBq9crHevi05pmvNwnYeLQKqw/pIZdx6BmtQ3KEFkkRWvhr7XJqvCihymBDRqkJGaUmnChqRKifEpMHheDaHoHNZvoaLSImfnISD18bia93lmLJg909rje4P6ceT6/IxcanesPfS/SZ5rC9aa9adF+yR8fJLjdh5pJMfD6jG1KifM77W4NZwCe/F2F/bgPG9w/GxAEhCPNvfwJlaa0Vaw9XYcPRKgxL9MfcG2Pgo5ad/Xt5HY/bvjhl5gWKIiKH+CWMsScABBDRK460+18BY2w5gPeJ6LgDbVQqOdP/ODPFt63QtCDaL4wbjlbhVJERKgWHpAgtIgKUUCk4CCLBaBWRVW5Gvt6M6E4qDEsMwMSBwWcvlc3hxV9z0a+zD3am16Gg2oolDyYjxM9zSbdbT1Tj3Q0FGNc/GE+OjvXYOG2htNaKexanY/2TvT2uWlBex+PuRWlYeG8SEkLPt3eqDDYs3F6C3Rl1uDopAJMGhSAlUtum7WHhJexIq8Wqg3pUN9owbWgYJg0KOc8+IiLcuzijMaPMNJOIljsyZ8ZYAoA1RNTHkXbn9eHNZCDGmL9SzsrWzO2lCXJQgqGm0YaMMhNKaqzgBQmMMagUHOKC1UiK0J63CbYGQSSM/fA4RIkQGahCtzAtXhjf2SNGKy9IeO6XXGSXmeCvlWPujZ43FlvCicJGPL4sG69PjsewpACPjVNrtOGexRmYNDAYd1/dsiILESG12IhVB/XYlVEHf60cyRFaJEZo4auWQS5j4AVCRb09YSuzzAQ/jQxj+wVjfP9gBLfw/Vh4CaM/OG4x26RkIipwZO6MsaMA7ieiIw699GUMX438jzmjokbdMjCkXQtEkgiZZSZklNmN0uxyExqtImwiQSnnEKiVIylCi+RILVKidO3ie729rgAHTjegc7AKxTU8vri7W6sHpis4WdSIx5Zmo0eUFlclBmDqkDCPjNMe3P91OopqrFjyYPeLLmbuQm6FGTOXZGLigGDMuT66xefy9GasOVSF309Uo0eUDr1ifM5eSAJ1cnAcgyQRaoyC/d+/1IgTRUZklBoxuk8QJg0MQWwL/9Yv/JJr3JVR9x9Roo8dmTtjbB4AHRH9v4e2CYyxO3vF6L76+v5khyqAERHK6nikl5pQZeBhtRFkMgatkkOXUA0Sw7VnPalt4XCeAS+vOo0QXyWuTvLH5uPV+Gx6oke+4fVH9PhoSzGmXhmKVYf0WPdEL2iU7bMF3I3P/ijCgZwGLJ2V4lHancEs4IFvMzG+XxCmXRXe4nN1RgEbj1Vh/ZEq1DQKZ/fdqEAVlHK7089oFZFTYUZGmQlF1Rb0i/PF5EEhGNrNv0WbaGd6Ld5cV3DSYBZ6OzJvxlgSgA1ElOjQC58Db7sPbu4T6yM4aqwCQCcfhVukVuykZIaXJsRhUIIvnvslF8//kot54+MQoHPfz1HZwOP1NflQyBhsIuGqbv5YdVDfYQbrT/9W4NqUQLy1rgDPjSOM6B7o9jEq63nM+SELBrOAsf1ar8DGGEPPGB/0jPGBJBGKaqx2A6fChMoGHoJIUMk5BPkqcNdVYUiO1J5H72gJaiWHsf2C2LojVXMAPOvgKywDUOFgm8sWjDGtnGMjbuzVqd27L8cxdI/SoXsz4Uhn0WgV0D1Si7dvS8Cv+/WY+V0m3r6tS7Pee1ewI60W728sROcgNUL9lFh9UI/bB4d2SFQkX29BUbUVD4yIxMwlmXjntgT0jPFpu6EDOHi6AS+vyoNE1GahgPgQDZ4cHYOZ10Zib04D0kuN+GlvBbLKTGiwiJBxgCgBfhoZkiK06B6pw4QBwXjv9i5tGjp3DAnT7c9teJox9ikROcLT2g+7vvD/owm+atkDtw4KdbhcLWMMkYEqt0QvJCIIIuHju7oiUKdAoE6BB77JwJzrozCmb5BbjDm7578YxwsNuPvqMGxPrUPvGB/8cbIWEwa4VP3TKfCChPVHqhHmp8CHm4vw5OgYj+wb9SYBc37IgiBKuHNo65fpAJ0cd10VjruuCket0YbMMhPSS03IKjfBapMg4xjUSg49onWYPMgemVa2Q+FgWGIAZKygC2Osr4OFdmoBfOvA8xfBFUrAFABKR9zCagX32X3DIx65++qIDhNOm7+pEI1WEa9Oigdg/9C+2l6KrSer8dTNsbg2xTVDjoiw+XgNvvijGJMGheDeayKwLbUGP+wuQ4NFxDu3dUHvWPcePG0hs8yER3/MwspHe6KsjsczK3IwqmcgHh4Z1e5bc1vYkVaLDzcXYuqQMNSbBBTVWPHObQkdopGXV2nGvV9n1Jh5Mcjrg1+iYIxFAXiRiNpdnY4xNiQ2SPX7r4/27JhbFuz6jq+vzcfyWSnwbRLz35FWi/mbCjG2XzDuHx6BC7m1jqLOKODDLYXIKDXhlUnxCPdX4q4FqfDXyTH7umgM7+65iERLeGNtHoJ9lJg1Kgq70uvw3sYCjO4ThIdGRrr8viariC/+LME/WXWYNyEOOpUMz67IwbJZKU4pElCTgSKXMafWOxHhlk9OGcrr+RuIaJ/DHVymYIx9AuBjRyJFGqWs9qfZKQHeKtN5IYxWEXctTMMzY2LPczBll5vwxtp8BPsq8PTNsU4bxkSEvzPr8cGmQgxPDsDsUVHQKDk8syIXvmoZjhY0YvmsFOjaGXF1F77ZWYrUYiPeuDUBT/2UgyAfOZ4b29mtlKLschNeXpWHKxL8sDenDg9fG43rerjf8dQeLNpRIq7YW/mlmRfnenNcV3a+FABJjjRQKbhrkiN1HWasHiswYFdGHZ4cHXP2/ynlHB67MRpv39YFC7eV4KnlOTiQ2+BwQpAoEf7JrMOjP2Zjxd4KfDq9Gx4cGQm5jOHGXp0QE6RGn1gfvLku321Z8u2BTZDw8qrTeOKmGHvYPVKLpTNTUGWwYfpXaThW4FoiS3WjDS+tPI2vtpfgndu64K6rwvHAiEgUVFmw7ZT36lifi7gQNQDoGGNOV4m5DOEP4FoH2wzoFeMlTaVmYLFJeGt9AZ4f2/mssQoA16YEYumsFBRUWXD3onRsOV7tVAJlg1nAz3srcNfCNAT5KLB0Zgp6RusQ7KvA3JtiYBMkfLil0CPala3h0OkGHMg14K6r7B6U4d0DsGxWCsrqrJixKB2/n6h2Sr7GwktYf6QKdy1Mg1WQsHxWCgZ38UPPaB1u7huEDzY5J0XEGINC7nw1P8YYBsb7KgBc4VQHly9uANBu7wZjLByAOiLAs0VaWsPC7SUYEOd7UTS0W7gW3z3YHT2jfXDP4nQ8syIH+3Lan3hrMAv4ZV8F7vgiFYt2lOLVSfF4ekwstCoZGGN4bmws9uU0IDlSg8//LPbEq7WI3Aozft1fiefGdoaPWoZPp3dDiK8Sdy1Mw+4M1yXEBJHw3a4yPPpjNu4cEobHb4rGyxPj8eHmQtR4SHquLfTr7CtTyr1fZMcVD+tMwF5/vb1tlHLOvPGp3uqOSmR4cnk2RnYPxLj+zYcMLDYJvx+vxm8H9eAFCWP7BqNnjA5J4dpmb2wGs52vdbywERuOVqOTjxyTBobgxl6doLjAtX6GkD0gzhfh/krMvSnmov48gS//LEZupRkf3tn1ogPlr7RafLq1GMG+CnviS0pgu7w3RIRTTfzTfzLrMWFAMB4cGXke0Ty12IhnVuRgzeO9XPYIOYOHvs2oP1FkvIuINra3DWPsJIBbiMilahyXIhhjXQG8S0S3treNr1r+0+xRUVMnDQrx4MxaxsajVdieWouP7+rW7N+JCP9mN2Dl/kpklpkwpl8Qrurmj8QILXSq5j0stUZ70teOtFrsTK/DkG7+uOPKUFyYUU1EePDbTPhr5PDXyvDyLfFuf7/mYLKKmLogDc+NicXQxIspUHuy6vHLvgpkV5gxrl8whiX6t8ovNFpFZJWZsCujDluOV6NnjA+mXhmKgQnnO82tNgnTv0rDU6NjMbir9x3qaw/r8cWfJasMZqHd3ydj7HUADUQ034NT6zAwxtYAeJKI8tr5/E09onQ/f/tgsufLlDWDOqOAKZ+fwm+P9WzVs2jmRfxxsharDlaiymBD90gdkiO1SAzXwlcjg4xjsAkSypvyFzLKTMjTm3FVN39MGhSKPrG6Zi9Hy/+tQFpJI04VGfHi+DivfMc2QcI9i9MxZXAoJg44f588mm/AW+sK0DlYjSmDQ3BFgp9DNAELL+GPUzX4ZV8FQnyVeGF85/MSHj/bWgyjVcQL4zu77X3ai3qTgLEfnrDaRNK2l8bDGOsP4HMiusrZcZ22HB0xVAGAMSYDoPbTdAwhuqTGirQSE96+rUuLz6gVHCYODMGEAcE4UWQvDblgWx1yKswI9VMgQCuHQmavglPTKKDGaEO3cC26R2rxzm0JrfL2IgNV6BPrg8QIDZbtqUBMkBqeNgQ2HavChqPVWNYCCXxkSiCuSQ7Anqx6rD6ox8e/F6FHlH3zSI7QIcxfCaWcQZQIDWYRWeX2RJrUJg3bSQNDznpuL0SPaB0Sw7XYkVaL0X28H5nvE+ujSy02DgLQboMVdm9Gx4v5eQBNRni7jQEAkHEIdSev21GsOqjHgyMiW/w7YwxXJfrjqkR/FFZbsP5IFb7cVoKcCjPC/BSICVJDreCaFEVE5OktaLSISIrQ4oouvvjlkR4thsAZY5g8KAQbj1YhNduI9UeqML6Fi667IEqEV1fnYWC8b7PGKoDz3nfd4Sp8tKUIeXozogJV6BysPntpNNsk5OktqKjnkRCqxqAEPyx5qGWFBZWCw7Sh4fjtYGWHGKzJEToAGORgMw2AjtcL9BCI6BYHmwQG+XixzNIFWH+0CsOTA9oMg2uUMkwYEIzx/YPOM0rXH62CySpCaErQDPKRIylSh2t7BLboNDoXY/sG4fvdZbjjylC88GsuFt2XhG7hntNEFSXCvJWnEeqnxIRm9oZ+TRJiW0/WYMG2Esy3FmF0n07oEa1DcoTuopwZIkJpLY+MMhOOFhjwx8ka9I7xwSPXR+PKrn4XneHTrgrDHV+kYs71UfDTeHef9tfK4aOW2WqNQjcAme1spoCLnHNvvqWCYxCbDFevY/UhPW7uE9QuuQnGGPrE+qBPE9dUEAkFVRYYLAJsIkEh4+CvlSE2SO2QusDEAcF4fU0+rkkKwJLdZWAMuGWgZ4zWTceq8P7GQjwzJrbFjHrAXqv8muQAXJMcAH0Dj7QSEzLKjFh72C5twQt2bppOJUPXMA0GxvvirqvC0CVU0+ZtcdKgEPz4T3mHGKwhfkq5SsFFOdhsEQD3lIG5PKCSd5AMW1qJEfUmod3GU2yQGo80ZbkLIiFPb0ZpLQ+rIIEx+2U0LliNqEBVu70c16YE4sPNhfDXyvHV9hJoFByu7+VKoZeWIUqEN9bmI7PMhF8f7dnm87FBajx6g/19bYKE3EoLimsssAoEBrsBGhukQnyIBnJZ+973hl6B+HJbMcrqrB5TYWgJwb4KCCI5ShbeCdcq211uUMo9qdHYCkSJsOaQHm9PSWh3G8YYIgJUiAhQYaSLuSOA3Yjq19kHK/ZVYuqQMDy+LBsf3tnNI0L+gkh4ZXUejuQbsPrxXi3SYdRK7qxxnlpsxI60Ovz4Tzkyy0zQqexVMBUyBl6QoG+wQaXgziqofN/KBRMAgnwUGNLVD5uPVeOODlAzCfJRiLVGIRTtN1jLAPzgyphOG6xNVa5qiWh1O5vwEkFGRB2SiLMnq/5sopWjkMtYq9qu7UVGqQnxIRr8Z2IcSmqtmLs0216t6Vr3JT9ZbRK+2VmKP07W4KFrI7F0TwWu79mpXWH5ED8lhvsp3ZZkclWiPz7cXIjcCrNbfj9HoJQxcIw5NKijmsL/S2iSFHmYiJ50oJlFEL0ne3cu/smsx/U9OzklNyeXMXQL17rsXSmstgBgWHBPEhrMAp5cnoMao4DbBoe4dQ8zWkW8vb4AVQYevCAhu8KMng4oICjk9kPO1YNZo5Thpt5B2Hi0Gg+ObNmz7QkoZAyiRA55X4hok6fmcymAMfY9gGeISN/OJlabKHkvQeIc5OktkDcphHQU7BdVCx67IRrj+wcjMVyLJ5Zlu1WdALDT+95aVwCVgqFPZx8s21OB2aNa942cq4oD2OX/yurs1bnskn8MnXQKOJU8VAsAACAASURBVKqgNGlQCN7fWNghBqtSzgCg3TdbIioE8KUrY7piJfUEENfeh4lIUsqZsbrRuwkMgJ0XVl7Po6uXjaZzkV1uwsoDerw+OR4cxxATpMaSh7qjptGe/HS80PXIVmqxEfcstms3fvdQd0wbGo4uoWp8vbPUDW/gOGQcw8AEP5ws8n7UTpAIEtGlUWD60kAQgCsdaWATqVjfRgUcTyGjzIjuUd4pcdgcBNHu8Zx7YzTC/JXoFq7FgnsSseV4NeYuzUZ5He+WcQ6dbsBdC9OgVXL4bHoinrw5Fm96sQrfhRjcxQ8nOmi9cgzePxwubdwAwJEMKn1FPd8hN8yMUmOHGqsA8P3fZYjupMK4fvaI3vDuAfhsRjf8ur8ST/2Ug8oG19YsEWH1QT3u+zoDg7v64f07uuLF8XHYcLQKqQ5WxuM4hqhOKiRGaNEjWodu4VqHjVUA6BXjg/J6HgYvJ4YCgM3uzHDPRthOuGKwFsPu4m031AoutSNqdGeVm5EQom53aMzdICK8s74Ac0ZFIfQc0rS/Vo7Xb03AnOujMO/X03j6pxzszXasZKkkEQ7kNuC5n3PxzIoc3Dc8Au/cloCgJm7eM2NiseV4NbLLO6ZsXXKEFhkdUDKv1ihIvECVjrRhjJUxxrwv4ucdmACkOtSAl/aeLGr0+oIlImSUms7wGjsEP++rQJCPAmP7/ZfOEhOkxjcPJKN/nC/uWZyOpf+UO13+MF9vwVvr8vH62nw8MyYW8ybEQaXgMKpHIOJD1Pjxn3J3vYpDSI7QIrPU5JRagCuoNwmQy5hDljJj7EvG2MOemtMlgBMAHKlteixfb9E4W/LaFdjXa8ddMAuqLPjtgB4vjO98nie1W7gW3z6QjJQoHe5akIb3NxYip8LsUN8mq4i1h/SY/lU6Nh6rwsJ7EjFjWDjkMoYgHwWevCkGb6/Pd7rUuCuQcQxdwzTI7IAztt4kcLBrq7YLjLFrGWNbXRnTlaSrDx1tY+alXemlpoHDkgK8SgzPrTSjqwfJ123heKERRqt43uF3LkZ0D8TgLn7481QtFm4vwfzNhbg6KeBs8lNs0H95d5JEKK61ni1v+U9WHRQyDrdeEYKXb4m7KDs6UKfA7VeG4df9lZg3Ic7Tr3oRkiO12His2uvjnihqbBQkhytWaXD5Jl0dA/Cgg80OnSo2et3VV2+yh8nC/DtGUUsQCb/sq8RH0y5W1pDLGO65JgLXJAdg6T/luPWzU7gmOQA39wlC90gttC2oEwBAtcGGIwUGrDtchTy9Pcv/XH1ZwB46nDUqCg9/m4kZw8K9rrAR5KuASsGhrI73eAncc5FZZoKMY0cdbKYE0DGcFS+AiG5y8PlqnUpWX1htDWmS9vMacivNHaJXfAYrD1Ri0sAQhDZTAlYh5/DAiEhM6B+MdUeq8MSybEQEKDEw3q/pjNUixE9xdq1bbJK9+lOpEWkl9jO2X2dfPHZDNAbG+17EgR/VMxBL95Tj4GlDxyQsRuqQUWa6SPnDkzDzImoaBTWADAeaKQC45DX0amqZTaS9B3IbGh8cGenVf1WTVYRPKweJp7H6YCUmDWqd96ZRyjC+fzDG9QtCWokJh/MM2J1Rh0U7SlHZwEPGMciYvR57iK/y7EJ7aUIcesU0L/NxBuP6BeG2z1Px6A2C17MJowJVqKj3atQAAJBZZpIDOORgs/kAHLt+X944VWWwqeqMglurwLUFEy9C16Sv2BH4J6vOXra5lUtuQqgGr0yKR63Rho1Hq/Hln8U4rbcg3F+JxAgtfFQcZByDRPaqdxml9uoyKdH2ClAjkgMukr47g9ggNRIjOk5hIypQhfJ67xqsqSVGvsEs7nKw2SbYI33/jybIOLb/eGHjWG8brCZe6rAz1mQV8ceJGiybldLqcyF+SjwwIhL3XB2B/bkNOFXciNWH9MgsNaHBLIDjGBgAiYCEUDWSI3XoGa3Dw9dGnicndSEYY5g0KASrOkhhIypQhaJq77LfssrN0Ci50w1mwRHO2GkA7S401RxcSbp6AkA6Ef3uQLPfs8pNXHGNFdEeqo3dHCQiyDpI7KPWaMPenAY8Mya2Xc8zxtAjWnde2UmbICG91IggHyVC/RQtHnQtIVCnwFWJ/h2STaiSc17n41XW87DYJAKQ70g7InrTMzPqeDDGBsKuMTuvvW2IiPdRy9ZtOFo1efqwcK+tIFHquPUKAKsP6jG5nZJzgToFpg8Lx/Rh4RBEwulKM7IrzKiotydURAQoMSDOF0/cFIOIAGW7jfDJg0KwdE/HKGwo5czra/ZInsEC4LAjbYhorYemc0mgSYd1GhG1O95rsIiLftlXMXzCgGCHy7O6AkmiDilhDAB/nKxBvzjf8+h2rUEu+68c3hlYbBKO5hvQP84XSrnjFdtu7NUJC7aVoKKeb9W49QRUCgarE4VEXMGp4kYSJdrrSBsiygaQ7cq4rhwLvQE4lEpKRGbG2LerDlZ61eWmlHPghY6JHB0raETvGJ/zwn6OQiHn0DvWF1GdVA4bq2cwPDkAB/O8rwBjvyx4dyNbf7RKkHPsN/I2Ee/SRhiAfo42MlqlD1fsrTB7k5+l6sD1ahMknCgyYlgLOqitQS5jSIzQYkzfINw3PAKP3hCNW68IxbU9AhEZqHLoEBzS1Q9ZZSaYee8zVCSCV9dsXqUZJXU8Afjba4P+b+BGOB5C3VJWx1vSS7xLPbefsR2TKHgwz4ARLtIR1AoOQ7r5Q6VwrmKbRinDoHg/HMnvgDNW8u56JSL8dkBvNPHSj14btAmuGKy5ABzODLDYpM/XHq6SvJnVFuqnRGmdI9x19yGjzOQWHbiMprCFs0iO1KIjEt6MVgkaN0l2tQeCSPh1fyVv4qWPHG3LGBMYYx2nlO9Z1MHBpKsmHLAKUskuN5QYbC8CdHI0mAVYeO8fgKf1FkQEKFvlorYHlfU8CqucD9Mp5BwSQjXIKvc+Q8VkFb26Zn89UGkhogVE5JAjgzG2gjF2h6fmdQlgP+CYcgIRiTaRPvnxH+9+OKF+CpS5STnDUWS6IeHLJkg4VuCaOkZSpLZDkp+MXl6vRwsa0WAWamHXQW43GGOTGWO/uTK2029JRG8S0WYn2uUC+Gn+5iKvLajkCC0ySjsmSz6z1IQkN2RPfrq1yOHsxnMR7q+EIBL0Lkp7OIqcChMSQr0nJ7Yrow4SIZuITjjSjtmv1TJcvklXe4joGSfakdEqzXp3Q4HJW5dMpZxD52A1siu8v2bdle28M6MOvx5wSKTiInTEviVKdi3L+BDvrFmDWcDm4zXgBVrgRHM5LtP1CgBENJKIHPa0CBJ9vjenofHf7HpPTKtZJEdq4W2vLgA0mO0VJ2ODXePs1ptFzFuZ61IfHWVn5FSYkeCl9QoAy/ZUmMy89IETEUw5AJe8EB3CFDPz0tzdGXUNe7K8s6AiA5Uw2yRUd4CmZEG1BfFuIMDbdQqdd/szxhAfokFhtXc9zRll3pM7abSIeH9ToanRIj7lZBev/j+N4GIQ0Q5epJ/nb/HeJTOpgzb/wmr3GGuiRJC5mDQWH6ppKl7gPRRUWRDkq4BPG2Uw3YX5W4rMHMNPRORM8tRKOBc1uKxBRAaLTZr62uo8r10ykyN0OOGgFqk7UFRtRYyDFSebg+ji+QrYEzELXIiqOAt3RXHbgz1Z9TiabzBIhCVONE8F8Ksr4zttsDLGXmGMDXemLRE1mm3S1FdX55nKvBCqN1klKDgmHTjd4PGxLoTVJkGrdH3zf3ZMLLq5WPhArfR+AlR6iQlJXlpM8zcXWnhB+oWItjvalux4zRPzuhTAGBvJGHvJ2fZmXpq7K72uYeuJaq8Y9PoGm7A7s87r3jOrzT0Ulut6BOK2waEu9aFReH+9ppUYvXbB/DerHrvS6wxmXnrcmfZE9CsRpbl7XpcCGGMyxtgfzrYnou28SD+/tibfLHqBfy4SIbvchEaLd5esxSZB64b1GqiT493bu7jUh0bBeT35yWgRUVHPeyUiYjALeH1Nnslsk6YSkcP8CSI6RUQdQwkA0Bf26jlOgYj+stqklx7+LtPkagWK1mC0inh0aZax0Sr+tWJvhdcZ0e7aKrqFa6Fz0evB4F3RwjqjgGOFBgyI83zC6l9ptdhpP/zmOtOe2XG58lcBe4Jk67ovrYCIGi02adQ7GwoNu9I9y2ddsrtMOFZoKD1e2MhXelkSzV3rI9RPiSgXlVAY877I6KZj1RjSzfPSPPoGHq+tyTNZ7IefU/tyk1HXManpnoccwAhXOjDz0pwj+Ybjb63Lt3jSaE0vMeKlladNChnbv+W4dy607oZSzp2nzOMUGODt+Ny21Fr0idV5vCiSJBHeWldg4UVaQUR/OdMHY4xjjLl0u3ClcTqA9tY4bhZWQfq43iS8ec+idFOe3v3RxupGGx7+LtOYp7estok0urDaKni74pNKzsHiBi/JyaJGGK2u3V4tNgkquff29/VH9JJEELI8/JsfyG3Aa2vyGy02abSzhx8AH9gTky5X6AG45I0iolMWm3Tdy6vzGjYcrXI7e0KUCJ//Ucz/+Hd5qcVGQ2UcW7b6kN6rNQfdtV5LaqwuayPa16v3WFtF1RaklRil1GKj1ZPMmDqjgIeXZJrMNuldItrhQlebYc+kv1zhqC7teSAii4mXbtiZXndi3srTZk8kMR7IbcDsH7JMJl6aarRKzy3/t8LoTVaVSs7csl7NvOhy0pXFJnm10AcRYdmecj63wmxxtuJee8f5YHOh9cDphjQzLz3mQlf3Afjalbm4knT1IhG5LENisUnv1JuFR+5dnGFatqdcdMdNkIjwx8kamvLZKXNxjfUzMy/dTUQ2UaJPv9lZ5tXsyehOKhS4gYf27oZClNY6T58gIhRUWRDdyTuC0qJE+HlfpdnMS3OeWZFr3O2hLPNd6XV49udco8UmjSEih3QcL8Blm3AFAET0hzt0ZonokNUmDfloS1Hh0ytyTTWN7uGF5+nNuHtRunHNIf1Rs00aSEQlZl76ZOWBSpsnN+MLEd1J5Rbe6NaTNdh83LUKb/lVFq/qVa88oOcBLPr9RM3pdzYUWAXR/YZHRT2Pe79ON9U0Cl/wgsvf42W7ZonISkTXu6Efg4mXRhw43bB5yhenjK4aZWdg5kW8t7HA+uzPubVmXhpDROsB7G4wC/rdGd5L9orupEZhtcXlsqjl9Tze2VDgUh8FVRbEeHG9nio2oqrRVttoERfe93W6RyLVgkh4c12BZeuJmmwTL13niCZwM5DBQdWLC9GB8tz/hSDSEotN6rVkd9nRexalG4/mG5yuZZ1dbsKTy3NM72woKDTx0nAzL754xhVkE+mDA6cbaj1lPDUHd2VP2sXUnfeO6g02SASvlbv8eV+FaBWkNCJabLFJI19elVfzzoYCq8lFL/EZmKwi3l5fYHlldV61xSaNJKLdLnZpBfC6O+Z2uYOI0sy8lHw4z7BoyuenzGsP652WoKo3CfhmZ6nYFGV51sRLQ4lIf2YcScIP720s9J6iSKQW6W5I9nJHEkdGqfeSKfL1Fqw7ohesAr1r4qUrt6fWHrh7Uboxr9I9Pz0RYfOxapr6Zaq5utH2qpkXn3ODi34JgBx3zO9yBhGZjRbxVn2D7a7Hl2XXvbexwFrupASVIBJ2pNXi1s9STVtP1Kyz2KQuRLSzaRwy8dK9b67LN3vrkhmgk8NXLUdxjWu5MKIEuBpVTy/1Xr6GKBHe31ho5AXpVYtAT1U3Cm/e8UWq+fcT1W6LfOVWmDH9qzTjzvTafU37squG00EALhX7YM6+HGPsYwDLicjR8pet9clxDPerFdzL/lp5wLShYbprUwJZJ5/WjaxGi4h/s+ux/N8KQ0GVRRAl+tgm0vvNSYIwxq72Vcu2/vZYT42/1vOUxT9P1WD9kSp8PiPRpX6yy02I6aSG2kmC+e6MOqw+pMcnd3VzaR7tQUGVBTMWpZusNql3k4wZGGOBWiX3lUbJjZ03Pk47pJufUwLNkkTYl9OAN9fnmyy8tN7ES7PcsJAuezDGJgDoSkQfurnfK3zUsvdFkQaP6x/MTegfrIwPUbda9UYQCWklRvx2oNK8M6OOU8jYBqNVepaI8prpX6dRcNmvTY6PuCbZ87XKeUHCqHeO4Y/n+0LtQnivsp4HAU5XvRFEwqh3j2HTU71d5q63BVEi3LM43ZhXaXnRJkqfAXZOt4zDTDnH5t9zdYTy9itD5c5q0xZVW/D+pkJTarGx3MRLtxLRUbe+wGUIxpg/gGVENM7N/QZpFNybItGMfp196Y4rQ3X94nxb/daJCGV1PDYfrxZW7q/kRUJOo0V8oSVZS61Ktuiqbv7T35yS4BWtpSeXZ+Om3kG4oVcnp/uw8BKKaiytlmNuCy//dhpXdPHD2H7BTvfRXiz9p1z8/u+yI0ardCURSYC9mqFWya3sE+sT+vTNsVpnOfRGq4if91YIP+6psAqi9IQo4ZtLRT3HFYP1LwCvO0vAbaNvBmCEj1r2jIUXR6gUHML8lPBRyzQyjoHIvogaraJVb7CJRqso0yplRwwWcT6ADUTUapxSo5R9nxiumfHF3YlM6WGO2Ce/F2Ht4Spsfrq3y2LkruCNtXnoHKTBjKvDPTqOTZBw/zcZxtOVlnk2Ufr0wr8zxsboVNxnWpUs9M4hYdoxfYM4v3ZUAWswC9h0rFr6aW+FyWQVK41W6VFndIBbQtM3p3RG9/B/AYyxOQB6ENFsD/Ufr1Jwj8gY7rIJUkCov8oWqJOr5TIms2cOESw2EmqNNmuVwabUKGWlZpu4UJTwLRFVtdH3cJ2K2/7NA8kyT2fDppcY8djSbLw4vjNGpgR6dKzWsC+nHgu3l+CHh53Ok2s3fvi7TPzxn/KjRqs0+MzhdwaMsTgflWyhINGIm/sEsSmDQ1Tt+TcQJTrjRGhsijB9YhXoDUeLA7QGxpgKgO3COV8OYIyFAThJRK5JTbTcvw8DpvqoZU8YrWLXTjo5H+ynUKjknJJj9jNWkEiqNwmWSgMvA8Ei49g6Ey99RETH2+hbp1ZwhXNGRXWa4qJSRlswWUXc/kUqekbr8I6LWf6uwGqTMP7jk/jugWSXky3bwulKM+5dnG6yCtTzwks+Y0ylVnAvE9FjPaJ1uHNImM+Qbv7tis7mVpjx64FK6+8nakjBsR2NVnEWERW6a95nkpqJyGn3uysuxmMAXCNptQ4RBJExBjnHEOyrQEq0jnxVMgbGwAsSTleamYmXJKNVVMCeNOML+zu1aLAyxkLUcnadxSbhpZWn8catCR4jSi/dU471R6rQP84HW0/W4JaB7atP3hwOnW5Az2gfpzysBrOAbadqMXuUZ8MVokSY99tpa3GN9V9Bos+be4aINjHGNhut0tBvd5Y9/eWfJTeH+Mot3cK1ik46heaslAGz91fbaDPnVppteoOgVim4TUarOB/AXg/c+KIB7G367+WIEniWAiQSkQDGwDgGX7UMKZFaBPspoZAx2ERCZT3PThQ1Uq1RIIlILeOYvyhRq6RqxhjTKLn7ArVyce7SbNln0xMR5wZd4+aQUWrCIz9mYWT3AKw+pHfJYM3Tm6GUcU4fXsv2VECU7J5WT2YA/36impbsLq+32KQpzRl+RJQPYDRjLGbjsapZm49Xz1LKmbJbmEaICFD6KGQcR2hSMyCC0SrZ8qvMpsIqq0ap4HIaLeK7AFYSkScEKncDmAtgnwf67mgIcDHpqg1IZOf/SoyBqRQcYoPUSAjRQCnnQEQwWEWWWmxEvVmQLLykYAx+AHwYY6yN/Xc4QNrv/y6DWsFhXH/PeBxNVhGPLc2Gn0aGg6cNqG60IaiNaGxLaDALyNNb0CfWx6n2O9JqIUkET2tWlNZaMfv7LLNNpJnNRaSaHC7zGGNvHslvvC2r3PwcL0hd4oLV5vgQjU6j5M6z+aw2SSqv5425lWYZL5BVkmgBL9JCC1GJB6b/BOwlwp92tgOnPayeALO7uaZrVdzrPmp50LShYbrregSytj5CCy/hYF4DVuytaEwtNoIx9rXFJr1KROcJrzLGmFbJbR3fP3jEnFFRildW56HeJOL1yfEI8nUft9MmSPhqRyl2ZdTBapMwcUAw/kqvw9KZ3Z0KgwPAhI9OYNF9yQgPcDzEuGJvBXak1qKsjsfD10Z6ZAOxCRJeXp2HfTkNkpmX+rVVaYoxpgQw0Vctm80L0kCOY8quYRqE+Sk5pZwxm0hU3WiTcirMZOYl0ii5kwazuJiAn4jI7QrVjLE4ALuIqLO7+76cwRjr56OWzRdFGjq2XxCbMCBEldAGJUCSCLmVZqw+pLduOV5Dco7tbrSKTxPRyQuf5RibFhGoXLR0Zorur7RaLNxegtcnJ2BAvHul0vZk1ePNtfmID1EjxE+BA6cNWHRvktMVdD75vQghfkpMGxrmcNvyOh5TF6SiR5QOvmoZXpscD09EgjYdq8L8TUXgRekDQaRn23qeMTZAq+QeYww3WG0UEt1JJcaFqGUaJceIQI0WkXIrzWJlAy/XKmWFvEhrrDbpi+YOVneAMXYIwCwiOuiJ/i9HMMYCNArudYnovt6xPjT1yjCf/vGtUwIAoNpgwx+naqSf/q0wm3ixwmSV/kPAigsNV8ZYhErOsj6fkejjr5Vj7tJsjO8fjLuvDndrvfuyOivmrTwNf40c2eUmDEzwQ1ywGvdcE+FUfyeLGvHp1mJ880CyU+2nLUhFYrgWRwsa8dn0bi5X3moOhVUWzP4+CwaLUGgVKIGIWk0IYYyFKWTsIbWCm2bmpS4BOrnYNUwj89fImYwDs9gkKq6xCvl6i0wuY/WMsd1Gq/g5gJ2eoAAwxp4HEEhEzzndx6VisDLGYnQqbnmIr7L/0zfH6AbE+zpl3BXXWPHNzlLLzvQ6g8Um3UlE2878jWPszohA5eKf5/TQKeUcBJHwzc5SrDtShbk3RuPGXp2cNijPIKPUhDfW5iPcX4l5Ezojv8qCl387DR+1HA+OjMR1PZzz2oydfwLfPZSMUD/HDNZGi4jbvziF927vAj+NHHOXZWNoN3/MGRXlNopCvt6CN9bmI9hXgX6dfaTFf5WmmXipb3MLijEWqJSzZxhjs7uEqrnbB4f59u3sg1A/RYu/fU2jDaeKjVh1UN94tMDAyRj70WyT3iaiIre8gH1eAQBuJ6JF7urzcgZjTKmSs1dkHHti9qgo9Zi+QUzjRIEMk1XEhqNV9NX2UotI9B4v0NtnKD2MsQiVgstcdG+ib3KkXSPx3+x6vLO+AMO7B2DOqCg4M+a5MJgFfLK1GEfyDXh5YhySIrSY/lUaUqJ0kAh4a0qCU/1+tKUIUYFK3H6l4wbry6tOI0inwKxRUXhlVR4q6nn8Z2Ic4t1U4tjMi1iwrQR/Z9bjhXGd8dwvuWaLTepPRBkXPttElbnTRy2bp5CxzrcPDlVdlegviw/RtOj5tfASsitM+PNULb/hSJUkk7H9jRbxNXfTxxhjdwPYSkTl7uz3cgVjbLRawS0b1TNQe981EerIQMe9/5JEOJhnwAebCo21Rtt+o1WaTkSlTf0zrYr7c8qg0GtmjYpSAHZViNfW5MFqI/xnYpzL0REiwtrDVVi0oxR3DgnD9GFhmL+5CBX1PNJLjVg+qwcCdI4Hjo8VGLBgWwkW3++4wfpvVj3e31SI3x7riS3Hq7FgWwnm3uQeewI4o3pUi0+2FuHhkZFYf7TKmF1ufp0XpPebe54x1k+nkr0iiNKN1/UIpDF9gzXJES3ruEsSoajGigO5DfTT3gpjvUmos9ik9yTCorbolY6AMXYlABUROR05cIXDugTAe81tco5CzrHb5TL27d1XR6hmDAuXuyMEtje7Hq+tyTfxgrTCxEszAXRSKbicr+5J9O0edb5AcHqJEW+szUeYv90j4oyxXFhlwcoDldh2qhaP3hCN0X3++7HO31yIkhorsspNWDYrBYE6x7252eUmxAWroXDQ0/LKqjxolByeH2d3HJ45oI/mG/Di+M4YmOC8SLggEn7eV4Fleyrw4MhI3DLA7rl96LtMY1aZ6S2rIL1z7vOMsTFqBffjyO4B2unDwtUJThzA5XU8fj1QaVt1oJK3ifS4RPj2UiGEX8pgjE0H4EvkVM32C/vqolVyv/eI0kW+fEucNsTBS1RzKK/j8eqaPGNWmanIxEs3EVGBj1q2dfKg0JGzmw6/M6g3Cfjk9yKcKGrEjGERuKFXoMOGa6NFxJbj1Vi6pxzDEgMw5/oo6JoucIfzDHhl1WmolTLMHhWFa52gBlTU85DLmMMhyn+z6vHuxgL8PKcHtCrZRQf0nUPDXKIIHMk34K11+egT64O5N8bAXyvHyv2V0sLtJakme2Tk7CWTMRanVXErwv2VvWZdF6Ub2k4u3Lmw8BL+PFWDBdtLTFabtMbES3OIyHu6R/+jYIxFA/iYiKa4oS+lVsl9o1Zwk1+fHK91Zc8/A5sg4bvdZbaf/q2w8iJNlyRayzE2LaqTatGK2Sm6c88pSSKsPqTH13+VYlz/YEwaGAJHjWVJIuzLbcCyPeUw8xL+MzEOZ84Pk1XEXQvTkBihhULG8Matjl8yjVYR+gabwwa1wSzg9i9T8cY5UZ/0EiPeXJePyEAVnhvbGcEuRG+rDDa8v7EAxTVW/GdiHLpH6VBSY8W0hWlmi03qR0SZZ55t4q++wTE88sDISNW4vkGcbzvyQ84FEeFYQSMW/1VqzCwzlZp4aUpbnGVvwhWD9RiAe13N+FTIuNlaFTf/ixmJmkQ3lwRstIh4ZkWOKbPM9DcvSEdH9wl6Yt6EuGZXCi9IWH+kCqsP6iESMHFAMPp19kWX0OaNRCJCcY0Vp4qN2Hy8GjkVZozrF4zbBode9IGarCJu+eQkRnQPhMEi4K0pCW65ebWFf7Pr8e6GAqyY0+PsYXz2b023wlA/BSYPCsXIlIB2hx2rDTasO1KFtYf1iAtW4/lxnc/bgEprrZj6gv82UgAAIABJREFUZarZKlA3IiphjGm1Sm6JRikb+/rkeK07wrk5FWa8tPK0UW/gjxqt0q1EVOFKf4wxGQC1J+gGlwIYY/MA6IjoRRf76aFWcLtnj4oKmHJFCOfO75iI8NPeCvHrv8pqLTbpPl+17OdNT/fWtvRdHjrdgF/2V+J4YSNG9w7CsCR/JEVo0VISX51RQEaZEbvS67AttRZXJPjh9itD0bsZ3trLq/Lgr5FhR1qt05dMR3Hm8Ht9UvxFF8myOive21CI3EozbhkYgvH9g9t9ENoECX+l12HVQT3K66x4ekwsrk76r+KCJBEe+DbDmF5iekpqijDIZewBhYz75N5rwlXThrruRDBaRHz8e5FlW2qtsYkv67K3lTGmA2C+TJOukmBPIHZJXoYxptEquc29YnyuePu2BO2F54CrSCsxYu7SbLPRKj6iVnCvfTC1a3RL+3tZnRW/7KvEluPV6Bnjg9G9OyElSoeIAGWz56HVJiGnwozD+QasO6yHj1qOWweFYHSfoIsubTvTa/HjP+VotIiYeZ1zl0xn8J/fTsNXLcOzY89nkvGChO92leG3A3pckxyAyYNCkBKlbde5T0RIKzFh1UE9dmfUYfIVIbh/eMR55/Mv+yqlr/8qPWCwCEMAgDHWS6vk1veJ9Ql9aUKc1lWKIxFh07Fq+nBLkUWS6H2rQK+56hhqSpIkVxIvXTFYFwL4gIhOOzu4XMbu9VXLv/j2gWSnJRjagk2Q8OzPueZjhY3yxfcmKbq1YRSfuWGsP1qF4wWNqGzgEahTQC5jZ8uuERHqTALUCg7dI3W4sXcgruvRqVWD75PfiwAi/Jlai4kDQvDgyEiH3mNPVj0Gd/Frt3clp8KMWUsy8e7tXVrk+wki4Z8s+0GWU2FG/86+SIrUokuo+iynicBAEqHOLCC73Iz0UiMySk24NiUQkwaFIKmF3/Od9QWWLcerP+RFmq9VcjuGdPPvPm98Z7U7lRIEkbBoR4nttwP6SrNNGupKRiNjrBfsnKyebpvgJQTG2J0AFET0gwt9dFUruAPPj4sNuKl3kMduXOuPVEkfbSmyjesfJHtqdGybLoLyOh5rD+uxN6cBeZVmaFQcNApZU/6efdGaeAkWm4QuoWpc1S0AEweGtGrwnSxqxGtr8pEYrkFRtRWL709yyIubWWaCn0aGiID27WsWm4TZ32eiR5QOT90c2+Jz2eUmrD6ox7bUWiRFaNE9UoukCC0CdXIwxiA1bVJmXkK+3oKMMhOO5BvQJVSDSYNCcE1SQLN7yOE8A55ZkVNo4qU4lZy94a+VP/HpXd207qIhnMH+nAY8/2uu2WKTpksSrXKlL8ZYJoAJ7ojyXWpgjMUC+A8RPehCHwqtktsyKMFv6FtTEjSeSt4rrLLggW8zrBwDbXmmj7oto+yM1/3PUzXIKDOBFyQEaBX2UsRkL0ksiIQaow0R/kr07eyDWwaGtmrwCSJh8qcnMXVIGBb/VYov707EhZHU1lDdaENprRW9YtqfdPXjP2VYfbAKP81OaZFeV2cUsOFoFdYc0kOj5NArxgfJkVpEB6rAMXvmGwNgk4CyOguyysw4UWSEiRcxaWAIxvYLavaybBMkjJ5/wtxoEQcCCFAruN+fHRPrM7pPJ7dWK65s4PHUTzmm4hrrOjMvTW+LN9saGGNvwX7BdLpgSIdxWJs0w3Yveai7prMHCMrnghckzP4+C1d29cMDI1o2FM+92exMr0VskPrsoRAeoIRCxkEigsEsIqvchIwyEzJKTQjzV2LyoBDc0LNTi1n8hVUW3Pd1OqKDVDCYRYzvH4wZw8Lb7Wm99u2j2PBk+zQZs8tNmPV9Fq5J8sfLt8S3+bzFJmHVgUr8m1OPomoraowC/NQyKJrKuFptBKNVRKifAvEhGlzf026gt7YB5laYcd/X6XUyjhXe0KtT0rNjYlWtJeK4guX/lotf/1VWabFJA4iozJk+GGN9AXxPRH3dPL3LAowxtUbJpc8ZFRV76xWhHi84smxPOTYfr8aPD6e0+p1VGWxYf8R+ICjlDEkRWiRHaBEfqoG2ycC02CQUVFmQXmpEZpkJBouI8f2CMXFgcIsGJRFhyuenUG8S0C/OFwaziPlTu7ZbE/WNtfnoG+vTrgRHMy/iyeU5KKy2YP2TvdsMuxMR9mY3YMPRKpyuNKO8nodSzkGj5MAxBpsoocEswk8jQ0wnNQbE++K2waFoTXeaiDDxk1ONNY22laF+itsX35+sdTbjui1klpkw+/tMs9Eq3U5EG5zthzGWA2A0EWW7cXqXDTRK2fzukdpZn89I1Hq61nxuhRkzl2RiwT2JrWqZ8oKEv9Lq8NvBShToLUiKtK/XxAgt/DVyyDgGQSRUNvBN69WMPL0ZQ7r5Y/KgEPTr7NPimbloRwnWHa7CFV18sT/XgPlTu6JHdPuM1n+z6vHrgcp265T/9G8FFv9VisX3JiIxsu0xSmqtWPZPOdJLTSits8Jqk+CrkUPBMYhEaLSIYAyIDFAhMUKLO64MbVMTduH2EmHF3oqNHGPXv3t7gu7Krv7tmrujMPMiHluabcqpMK9pMlqdMhoZY+8CqCeid9p8uAV4Xjm/GTDGVFolt/K5sbFqTxurAKCUc3j7tgTc/VU6hicHNPshnChsxMe/F6HeJGDSoBA8dkOvVsnb1zYlT53h1qw6qMcXfxbjjivDMGNY+EWHbHk9D4WMw2fTE2G1SZi7NBultTweuzH6onB9cxAlAtcOM2F7ai3mby7E1CvDsPpQJeqMQovvUVRtwepDemw+Vo0e0TqM7B6I5EgduoZpLsoaNVpEZJabkF5qwm8H9FiwrQQTBoRgwoDgZnl6XcI08FHLfPrH+XZ/dkyswlPGKgBMGxous/BS8PJ/K7Yxxvo6SRQvAzDf3XO7XKBWcG/0jfUJnTwoxCvV8aYNDcPhPAN+/Kcc9w2/OPO31mjD538U4+/MeozqEYiPpnVtdYO/KvG/m3m+3oI1h/S4e1E6BsT54ombYhB6gcC/ySqh0SLhtckJGNzFD/M3F2Lmkky8Njke7eFeSxK1qpRwBoVVFry6Og8xQSpUNvD4O7MOI7o3H840Wu2829UH9RAlwo29gzC+fzCSI7UXeWEEkZCnNyOzzIQDpw249bNTuCY5ALcOCmnW88QYw8B4X92+nPoZi+5LlnnKWAWApAgtPp+RqJn9fdbPjLHBRHTKya7eBqB359wuFzDGrtCpuNlvetCzei66hGnwyPVReGNtPr57sPtF558oEVbur8QP/5Sja5gG04aGYVhi897+Mzhz2Wu0iNh8vBrvbSyEjAFzb4zB4K4X83BLaq0YlOCLVycl4O/MOjz9Uw4euzEaN/VuO/lJJIKsHc4jCy/hy23F2J/bgJHdA7BsbyVen9y8U4iIsL/JNjhe2IhRPTph2tAwJJ3xsJ6zPxARKhtsyCg14WRxI55cnoPoTipMGhiCkSmBzf5O13YPkP+8t2LiG7fGw1PGKgBolDJ8elc37UPfZU4srLK8APu6cwabALgkb+cKJWANgEeJqNjRthql7L1+nX0e+WhaV603uJxnsPFoFX7dX3negrLYJCzaUYI/Ttbg8ZticF1KYLsOmuZQUmPF/M2FqG604aUJcTjDyTVaRUxbkIbnx8We/bAaLSI++b0Ih/MNmNeO5KecCjNakwuqNdowf1MRcipM+M/EOPSM8cGnW4tQZbBdREK3CRK+/7scqw7qMa5fEG5xggSfVWb3RO/KqMPM6yIxoX/weZvC9tRaLNhWgp9mp3hM5/ZcEBFmfZ9lTCsxfmC1Sa95fMD/MTDGHgXQ4AwlgDE2SKvkdq18tKfGnfJvbaGynsfdi9Lx2Yxu5xmjO1Jr8eGWQtzUOwj3XhMBHycrQZl5ET/9W4GVB/SYMyoKY/sFnf2G391QAEEivDQhDoD9+1p3uApf7SjF1CGhmDb04kvpuaio56FWcC16NUWJ8Mu+SvzwdxnuHxGJWweF4GSxEfN+PY3ls1MuaneGj54SpcOUK0LRP65lT1NzqDXasOFoNX47UInBXfzw2A3/x955h0dVZn/8e+7UzKRQEiCh14QiiBQVFesiig0rYkNFXV2x/Na6trWXVbEgdlQEXRAERUGxAUrvUhISIAVI78n0uff8/piJixggM5N733BzP8/j8+zNzr3vF8g797zve873dMHBBRmV9QFcM30nXp7Y9F2pWFm4sUx54/v9u9x+ZXAsZuJ6hIgGAHiIma+P4l67wyplPXRh925jjmun2QuWmXHv7N0Y3DX+T4vMgnIvnvkqDxIRHrygW9RuF8yMldk1eHnxvj9+hxtOPNbursXzi/Ix+/YBf/yswbEnra31qMVPLq+MGk/wiO/BLfn1eOarPAzo7MQ/z+sKm1nCte/sxJS/dcHp/f/cha+42o/nvs5DeV0AV53UMeLC0KDMWJFVjblrS+HyyXjskv/FEw1/Fw/P3Yu0NlbcdW7XJj83FoqqfZj41k6PJ6CMYOYdmgx6CLEErHkAzozUY4+IOljNlL/g7uPsWr78gNA/8p2f5OD849tj3PHtUVjlw72zd6Nvxzjcd363qOwwGhvj2y0VmPbDAUw+IxWXj+yAVxYXwBtQ8Ej45XcwDVXB/dOcuGxECob3TIgoYC6q9mHhhnJ8vbkc5w9pj1vOTPtjd9QbUHD9OzsxZUyXP4ossotCk7hDohUPXtgtYpusQ9lT4sHTC/OQGGfCIxf3QMckKyrrA7j27Z146eo+GKTRyw8I+1e+tcPjCSgjI921ISILgLhDvXv1AhG9DKCEmf8T6b0JcebVd4/pcuKFJyRrt7oMM29dKVbvrsUrE/sgKDOeX5SPbfvq8dglPSLKNzsSOcWhOZHaxoYnL+uJnQdceGpB3p9efg0UVfvw3Nf5qKgL4IoTO+Dc49pFZA/n9StYur0SX6wtRbw9NGe6HJS//9p3+1DrCf6RylPvlfH69/uwfm8dHrm4O0bEWOHt8smYtnQ/VuaELK1O7psEZsa/5u5Fl3Y2/ONv2vXNYGb8/aNsV2ah60VfQHk60vvDVnS1Oi26OhHAm8w8MtJ7JaJbT+iR8Oq0G/o6tdwQAkILtWvf3ol5dw1CksOMpdsq8criAtx0ehquGJkS9WbQwdR7ZbyxdD/W7Ql9L3RpZ8NV03bg4Qu7/2Xn1R9U8NGKIizYUI5xx4c2Z7pEUC/DzNiUV48vw7uk94/r9qfgdEt+PR79Yi/mTAkVNjMzvtpUjnd+KsSEkzrg2lOOvLBtyviLt1Zi2tL9uHRECm4cnQqzifDDtkrMWFGEj2/tr8mGUAMLNpQpby6NbpEZLpIMcgzdJGO1tXqQmUsjuc9qlh49Z1Dbfz0xvqcmfYYP5ddd1fh4RTGeGN8Dd87MxnWndIIa7eMOVPpw16fZGDu4PeauLcWcOwei3WGO2dw+GUu3VWLe+rJQL/OB7ZCRFsqdTUm0gpnxy85qnDmgDXwB/iN/du2eWmzbV4/zh4QmYmPpFT/vqMIX60rx9o3pWLO7Bv/+Mg9TxnTB+UOaxyMOCK0GZ/5WjAUbyvDatX2xYEMZzCbCPWO1WfkdzPz1pfz2j4Wr67zBUyK5j4hGA3iWmU9TSZpQiGgygApmXhDhfelOm7R5yf1D4tRuY9wYXr+Ci6f+jvduTse0Hw5AUULeqNF0fDsSgaCCp7/KR1ltaGf0zAFtcdFh8k+ZGev31uHLDWXYlFuHswa2xeBwMUX3ZDtMEmHbvnp0TLSifYIFBRXe0FHfPhd+2lGJ47rG47IRKTixd+JfXuAub8hRZPYdAyBJhLs/zUH/NAfuGdu1SalDTWXdnlo8vTAPk0anokey/Y/dKS1ffkBoAXDVtB1ef5C7MXNEx/tEVAJgiB59WIkoA8BkZo6oKxARkdMm7X7+yt69RvaO3b4qGp6Yn4uMNAccVhM+WFaIqdf2RZ+Ozf+6/+73Crz+/f7Q6cQ+F1677vD5p/srfViwoQzfbqlARpoDJ/dJRHqqE/06xcFhM6G42o/y+gAGdXGioi6ArCI3Mgtd+HF7FYiAy0ak4LzB7RvNYX94zh4M75WI8cOSMfW7fdiUV4+nLuuJ3s34Zy6t9ePphXmwmiU8Ob4HJry1Ey9M6K3phhAQ+u6b/MEu144DrinM/FEk9xLRWwAymXlatONrWnRFRKY4i1T89o3pyRlp6rYJPRyywrhk6jYwgFvPTDvsS6k5KKv145YPd6Gd04wZt/Y/6ucbcl6+2lSOnGI3aj0yAjIjKCsIyIDNTDCbCA6rCWltrBjZOxFXn9wBDtvhd4aDMmP8a9tw0+hUvPdLIV6Y0Dvq9nNHY+m2Srz23T74Ago+v3NgzLu30eALKDjvP1u94RVgk4sxiOhMAE8w8xnqqTv2iLOa3rp8ZMotd/6ti7bHIQcxdUkBNubWIa2tDc9d2Vu1VqWKwnjmqzwsz6rGwnuOQ1M8DIurfZi1sgS/76tHeV0AvqCCoMzwBxkmKZQ/b7NIaOs0Y2BnJ64Z1RE9Uo78InvpmwLEWQm/ZdfinIFtMfmMVFVs8A5U+jBlZjYS7CZceEIyLh+pbt/3w/HE/FzPTzuqngnISkS5cURUDqB/pIGuniGiUSmJlu+/uue4eDXrBo7E7wX1eHjOHkgSYfqkfujaXr06lWWZVXhqQR7+8bfOuGzE0X9/vQEFC9aX4dfsGuyv9MLjVxCQFQSCDAWAzSzBaibE203o3SEO5w9pjzP6tzni/NuwtxavLC7A8d0TsKfUg1cm9mnSd0ekBGXGUwtykVfuhcMm4Z0bo+vKFSurcmrw+Lzc7HqfnBFJARYRvQNgKzO/He3YWhddnZqcaLGJClaBkIWEzSLh/CHtVQ1WASAl0Yrpk/rhpvezkFXoQsYRqglzit34ckMZftpehQGdnfjboHbISHWib6c42CyEi17dhoX3Hoeiaj+yCkM7rEu2VmJZZnXIoeAwR5NmE+GUfkmY9uN+TL2mb6Oek83FmOPaQVEYLy/eB7OgL0ubRcL44SnS/PVldwGYEsGtOQBeV0nWMQkRkc1M140fliIsWAVCLxFQyBBczQISSSL866IeKKvNwWerS3DbWZ0P+9ka9//sauwWE4Z2D+2wpqc6kJJgxZMLcjHmuHYY2NmJXcUhN5Ed+124/aNsjBva/ojm6RedkIx/fLILV4zsELH9XSR0bmfDtBv64cb3MhHfzP6ckTDhpA5xK7Kq7yGiFyO0zXkYQJ1auo5FHFZp8pUjOzhEBasA0MZhgsuv4IPJGaoGqwBwRv+2qHIFMW9dKS4amnzYxjpBmfHrrpCF455SD0b0SsSp/ZJCpyLt7ViRVY2NeXW469wuyC31IqsoZN/4/Nf5WLqtEpeNSDlsQ6FhPRNQ65Gx84AL029Mb9aTkIMxmwiPj++JB/67G16/uCyYk3onwmqmzvBhBIB1Edw6BzEWScaSErAMwCXMXB3BPfddOjzl2Qcu6Kb91luYOWtK8cP2Srx7U3qz9jY+Et+FO+p8dGv/v3i1ltb68dI3BdhV5MYlw5Jx0QnJOLRzkKIw8iu86HnIzkxDm7z560Lm6bef0/kvxU+BoIJr38nEdad0xAVD1Q3QG3hz6X4UVvnw3JXaNEg4lMIqHyZM2+Hyy5yox/y2aCCiRwFkMfO8CO7pEW837fjxoeOFrTALKry45YMsvD85A91Ufvk1UF4XwHVv78TUa/v8ZZHZkP7y+eoSnJaehEtHdMDARvwhS2r8iLeb/vLyKqgIORQs3lKB0Rlt/lL8BADv/1KIrQX1ePP6vprMn+37XXjg892YdfuAw6Ytqc1V03bU5Zd7xzPzT0IEtDDCOaw3MfNtkdyXGGfe9crEPv3U3Jg4EorCuP3jbJye0QYTR0XeljgamBn3fb4H6Z0cuPWsvy7w1u6uxQvf5CMl4fBNcuo8QXgCyl9OBQ925jCb6E/F1A1kFboxZWY2PrtjwF/e3WrgDSi44d2duPXMzlG3eo+Vmb8WKx//WjTT5ZNv1HLcWBKWjkdow7LJJNhNpw/s4hQWrB6o9GHG8kI8fkkPzYJVADh3cDt0bmvDRyv+ZxHKzPhmczlueCcTGWkOfHn3INx8Rlqjv/CSRH8JVht+fmLvRLx0dR9Mn5SOhRvKcfenOSiu/l8jiY9/LUbntlaMO769On+4Rrj1zDTklnnx444qzcY8mLS2toZco6aZ6iFktRYu4tArPQBE+u02LD3VIax6m5nx3Ff5uHF0qmbBKgAkJ1hw17ld8PTCPATl/y3oc4rduPmDLPy+rx6zbh+Ax8f3xKAuzkaDyo5J1kZ3Wrq1t+Puc7viy3uOg9lEuObtnViV879OpbuKQo0BnhjfQ7PF3qAuTpx/fHu8vDjqvhsxM6pvol0inBzJPUTUoVld0lsWbRCas02GiKxuv9yz31H8O9Xki3WhDbQJJ2mXXkJEeOiCbliwoQy7itx//NzllfH81/l4flE+HrygG967OQPnDm68wU9CnLnRFDanzYTLR3bA7DsG4KqTOuLuT3Pw/i+FCARD+yCBoIKnF+bh3rFdNAlWAcBukfDYxT3wyuICVNZH4+AYO8N6JkgmiUZHcg8RJRFRTF/ksQSs3wKIqNpLYR6W0cztVyNh9qoSXDoiBd008H49GCLCfeO6Yf66Mri8MoIy4+mF+ZizphSvX9cXk89IO+xRRlPp3TEOH0zOwLCeCbjxvUxsya/Dvgov5q8vw8MXdtd0p9NmkfDYJT0wdck+ePxRN8aIiYw0hwJgWAS3nAvgU5XktAS2AIioK51ZomFDusWL2aoBsCmvHrWeoCpFkUdj7OB2SLCbsSwztOj6cXslpszMweUjUjD1mj7omBTby8lpM+HBC7rjsUt64KVvCvD+L4VQFAUvLsrHlDHavfwauOWMNOwp9WJVds3RP6wCA9Kclni76fQIb9sPQNgGiMqUA/gtwnsGpCRYPc1dkNhUAkEFM38rwgPjujWLG0AkpCRaMWl0Kmb+Gqq/K6zy4Yb3MsEAZt0+IGafUiLCuOPb45O/90dWoRt3fJKNOk8Q/11Tik5trDhviHYbQgBCrW2HtMf0Hw9oOm4DfTrGwe1XukYYgE4HcHks40adw8rM10R6TyDISU3tf93cuLwyftxeidl3DBAyfodEK0b0TsS3W8qxpaAebr+C92/OaNZqZ7OJcMNpqchIc+KhOXtxQo/4RlMMtGBgFycGdnFi6bYqXDxMm1SEgxnSLT5+U27dSQA+a+ItJgBiomsNiKYy02aRuqQkaFw2fhDz15fispEdND0NaYCIcMWJHTBvXSkCMmP6jwfw5iF+sM3BiF6JmHFLBu6ZlYP9lV7UeIIYO7hds47RFGwWCZNO64S5a0sxqp96JuSHIz3NgYDMQyO8Tbdzlpk3AtgY4W0pyQkWYSlQy7Kq0SM5rlmr4yPhguPb44NlhdhWUI9H5+3FtSo4AHVItOI/V/fG69/vxx0fZ6PaHcArE7VJ3TmU60/thMvf2I47j9AcSC1sFgmdkqyeA1W+wWh6HmvM81XTl5HMMFs06LrRGEt+r8DwXglCgrcGLhuRgo9+LYbLJ+OlCb2b3ZqngRN7J+LZK3phVU4tjuuqre3FwVw2IgXz15dCRPvfTklWslmko/el/R9bAURdvahHJEKcqPlaWuvHhr11QoK3Bk7PaIM9JR68uXT/X5oXNCft4i2YdkM/ZBa60SP58M1B1OasAW2xq8iNfRUxNaOJik5JVngDSqQpK3dApwFrlNgsTWnXpBLz15XhshEpooaH027CaelJeGDOHlx/WqpqJzOSRLhnbBec0CMBARmN2klqQZLDjNMz2mDR5nIh46e2sTKAv7YhPDwfAlgfy5hRR0xE9DsRRVQOJxHkgKx98AIAyzKrNd+2P5SK+gBsZgkvXNW70Tya5mRYzwT866LumPbDAfgCYhbdI3slwu1TkFnoPvqHmxmrWQIBTV7qM/NeZv5eTU0iIaKXiei8SO5hhlfUfP1tVw1OTU9SreK2KdR6glAYeHFC70ZzyJuTxDgzpk9KR2ahG9v3u1Qd63DYLBLGDW2PhRu1fwFaTARmSJG8U5j53Wj7mrd0iOhcIno1wtv8ouZrtSuInBI3RmeIKwNgZpTU+DFmUFvVA2eiUNA6sIsTHy4vOvoNKnHpiBQs2FAmZFPIZpEIQJOjdWb+gZl3xzJmVFFTONH9OAARRUIWE9VVubSv4WBm7CpyY2AjPbS1orI+gKlL9uGZK3pF1KItFsYc1w69O9jx/rJCTcY7FEkinNw3EZvztHeekRUGA/6jfzIEETmISEzJpTb0ABBRPqovqJRWuYJC3oCZhS4MEDhfmRn/+bYAFw9LbraOWkcjOcGCe8/rimcW5glbZJ7SNwlb8us1H1cJ/ZYxmvhOoRDqeX6Jpy2Aw/uqNU5llSsoZIc1q8iF9FSHqrZzR+PH7VWodAVxxznadGsjIvzrou74ZnM5dh4Qs8jsn+aAx6+grFb74qugzAygyQMTUXtRRVcEYG6kq1uzibYcXMWnFQeq/IizSsIsW4BQy8Xzj2+veWeK+87vhsVbKpBVKGZCZaQ5kSXg37zOI0NWEIlNweUA3lBLTwtgNYCIysADMq//fV+99tELQtXyR/ItVptlmdXILfPiVhV9UBvjnIFt0TPFjo9/FbNr0y/VgT2lnj+5I2hBnUeGxUTeCN4pcQD2qKlJMPsBrIrwnh0lNX6HiMVOVqEbGani5muNO4hXv9uHRy/uoWm3tvbxFtw7tiueXpgHWdF+bU9ESE9zCDnFrPUEgch8kGcCOCeWMaP6l2VmhZmvivS+Oq+8fOcBl+ZLgZxiN0RafRRW+bBmTy1uPj2SdI/moV28Bded2gmfrY6og26zkZHqQJaAybSz0OV1+eS1Edyi2wIOAGDmV5g5kr8PANiYWejWvOhKVhh5ZV5V2jk2lVkrS3DHOZ01b1VKRJgypgt4EqlIAAAgAElEQVTmry8T4rDhtJnQMdGC3DKPpuNmF7sRZ5Wa3JkO+p+vvzFzRI1MmNljt0r79pRq+28HADklHvRLFTdfv9lcjhN7J2KgxhtCAHDOoLZwWE1/sqfTkoxUB7KKtN2QYmbklnntAH6P4DYzgJiO2DX9NmbG+o15dZrPJpdPRqIKrdKaylcby3H+kPaapQIcyrjj22N1To0Qz7buyXYcqPJpnmOzbZ/Lh8iqbFcCmKGSnGOVPW6fLJXWNjmzolnwBhSYJIJdkEFBVqELFfUBnCKgWh4I+Qgf1zUeP2wX42PcLdmO/ZURORbGTFaRm/1BjsTGyYdQ0ZXBQTDz6m376jXf6qv3invHKgpjwYZyXC6o4IuIwgXGYjoEd0+244DG87Wwyg8CXMxcEsFtrwHYHsu40eawxhPRpihuXb6vwqv56j0os7DcmkBQwdeby3HpcHHVk4lxZpzRX0w1odlEIIKmR4zegIIDlT4nQt6jTYKZs5l5hYqyhEJEHxLRaZHcw8yKxURfLNpUrmniucj5CgDz15dh/PBkIXZaDVwu8AVot0jwBbU9Vt6cV+fyBpTVTf08M/uZeaaamkRCRFcS0TOR3ufyKR9/sbbMpfUGQVBmYe241+2thdNuErK72sDZA9tiV6Fb84UeECqW9GlcarDzgAsWMzX5/QoAzLyEmffHMm60WxgWAL0ivYmZ/Qy8/cXaMk3/Va1m6Y/OFFqzq8iNlASL5s0KDuWcge2wZnetmMEZmvrU/byjCnartJaZm3xOQkSJRCTOQ0l9uiOCis4G3H5l6ty1pX4tFxw2swS/oPkKAGt21+LsgWJ/FU7snYgDlT5UaZ9BBWZA0nC+1riD2JhXZwHQZJcOIjITUVcVZYmmHYBodjl+qnQFarft0/aI2GomiHIoWJ1Ti7MHthXihdqAzSLh1PQkrN0j4B3LgNZ/9IUby+trPfKsSO4holQissUybrQBaxDA3Ghu9Ad5+uKtFVzn0W7Tpn28BSUaH2s2kFnoRn+BxSMNZKQ5sKvIDUXjxHBfQAERabpjNntVcV29V34xwtsmAXhSBTkthV8ARGwXwcxbZMae5VnVKkhqHJsl9PtS49beUaSiPgBfQEHntmIbKEkSIV1Q/rfHr2iajrFoc7liMdEiZo5kS7kDmm5YfiyyB0CkOedgZsUbUF6ZtbJY01+c9vEWlNSIecdmFbnRP01cjUoDGWlOIcXNbr+s6Xw9UOnD9v31APDfCG+dD2B4LGNHW3RVx8y3RnlvgUSY/fLifZrlBaSnignWgNBkymgBkynJYUYbhxn7ND6yyC3zoHtyTIuqiMg84EJhld8HYHGEt+q9iONZZt4Rzb31Xvmel77Jd2u1yCQi9OskJljbVehGv1SH0N2aBjLSHEIcNvaWeTQzQw/KjM9Xl3hcPuWVCG/V+3z9gZmjyqlXGO+v31tXv1rDIiBRv6uywsgpdiNdYMv3BkQVGOeWeTVtXvDFutIAEc1g5khjuGOr01UDbr9yz4qs6lqt+la3cZrhtJmwv0r7/JLcMg96dxBXPXkwvTvGYa/GFaRZhW6ka2R3EpQZz3yV7/LLyuPMHOnE+B7AbDV0Hesw889+mee8vES7RWb/NCcyBexW7C3zCHUnOJg+HeOQq/F8rXEHUeMOoms7bRaZs1cVB70BZRsi302sAnCPCpKOeZi5zhNQJj65IM9d79Umps9IdWDHfu0d8Epq/Ii3m4QWVTfQu2Mc9pZ5NC8wDlmKaROw7y31YMHGcr83EPECEwCeAiCkcUAaEUVS0fknmLneE1Am/ntBrluLCmRZYZgkkjcJMLD3+hWh3XoOxmE1wauxR18oJUKbyTR7VXGwuMa3Q1bwbqT3MvNOZo6pbVxLhoi+JKLjo73f41fuXp5ZXffTjkpNvo19QYVXZtdofiTiaeXzNbTAdGjSHja31IOPVhT7XD7l6kg9vZm5npnnqaVNNER0CxE9FO39zPyTP6h88czCPK8WJ4vxcSYUVPg0T+PxBlrOfLVbJIABWcMpqygc9qxW/x0blBmPfrHXFZSV+5g5Ik9vAGDmb5k5psrvaHdYbQBiaifBzD97A8rTt83Y5VbTbklRGM9+le+tqAvk/Hd1qeZLQFlAQvThkOiPjjKaEAgqWJFVjRG9ElUfK7vIjRnLi/wunzKBmSP+yiCidq2g6CrqbQhmrvMGlLFPLcx3rdmt7snIiqxqLNpUVp9d7PZobdeiMLeY+UoU+v7QkqXbKzGiV4Lq43gDCh75Yq8rICv3M3NepPcTkZWIuqsgraXQHqFuV1Hj9it3rNtbu/M/iwt8au76ldcFMOWTHLfFRL8v2lyu6QpLVhgtZLoCCOWea9lAYENuHTokWtDWqX5TpBkrioIltf7fo9kQAgAi6kZEMRUHRBuwugF8EcvAAOALKC9UuYJTb3wv012ownG9P6jgsfm5nmWZVTv8Mp9UUuv3aN1CzWYmoRXPB+MPMqxm7ab3sqxq+GVWqlVedRdUePGPT7Ld/iBPYubcKB8zBfo+YvwWQEw+Scy82RdQxj44Z2/9zzvU8Qj9/vcKfmx+bp1fxjlE9OH89WWaVnLYzJKwtqiH4g8qsGpYrFjvlfHj9kourw/41QxwAkEFD3y+211c4/9BVvBOlI/pA+C75tTVwtgBYEMsD2Bmt9uvnP3975XZTy3M86rh9LGvwotJ72a6azzB591+5bbPV5d4tKwVsVkk+MV0j/4LssIIytq+Y2evKgnUeII+tVM/Fm4sUz5bVVLp8ilXRHoachDfAegbi45oi65KmPn+WAZuwOOXH61yBR+5ZvpOz9ebymP4u/gzWYUuXP3WTtea3TXL3H5lNDPXBII89aMV2maGp7axaV7odDj2VXqR2ka7AqjZq0rqXD75uXtm5bjVsvvYXeLB5PezPJ6AcrfCHMsiSu9FHI8zc34zPGelL6CMfmphXtlTC/K8zfVFWesJ4rF5ez3PLyoo9QWUU5h5nTegvLFgY5mi5TFjahubEC/FxthX6UNaW+3m6+KtFWw20Q9Ltlbuf+37/aoErV6/gntm73ZvP+D6ze1XrozhC1/v83URx/Z91vCcardfGbUiq3rlxOk7XDnNZB6gKIz560uV69/J9NR4gvf5AsozANZ6A0rhMg0dRTokWFFW528Rm0IHKn3omGTVrGCztMaPzfl1QZdPnj35gyyXWhZ4c9aUyK99t7/KG1BOZeYDMTzq2Cy6OhRfUHnNE1BOfO27fdl3fJztiqXQoNYTxFs/7A/c9lF2XWGV7zaXTxnHzG4ACCr8xoa9dTUrNJxQIWsa7YtHDsUXUJBfrl27y193VSOvzOsC8JQ3oIx94PPddW/9sD/QXF8sisKYu7ZUmfxBlqfOK98UCCofxPjIeQC+bA5teoeZN3sDSp9fMqvmXv7GdveyzKqoj8GCMuOnHVW4/I3t7t+ya2Z5A0ofZt4WHmc3Mz556dsCzSqPRFU7N4aWxRQ17iDe/6XQ4/IpT3j8yohFm8p33jZjl6uouvmC9+37XZjw1g5X5gHXN26fcgEzx/KG3Q/gwebSpmeYud7lU/62v9J31+QPd9W/9/OBYCyOH3tKPLjto12ut344kOkJKMP8QeXt8Djs8im3PP91vkerRabdKqFLO5vmxcSNobUj0OtL93tMEr3vDfDk4hr/tAnTdribs5C9xh3Ew3P2eN75qbDQG1CGM3MkrZMb4z5EYa14MBTNApeI+gN4nZnHxDJ4I8+1WM30ABHd36dDnDRxVMeE0elJsJiPHFczMzIL3ZizptTzS2aVZDHRIpdPuYuZixoZ47QEu+n7eXcNiktyqF9ZuDqnBjOWF+H9yRmqj3Ukdh5w4bmv8zHr9gGqj1XrCeLyN7a7az3yOGZeBoRMg502aWYbh+XkJy/r6RwUQ1eSgnIv/r0g15Vf7t3j8ilXMvOu5tKuV4hoOYAbmXlvMz/3b06b6XWrmbpNOKmD/cKhyaZ28UfPp6qoC+CrTeXynLWlPlnmvfU+eUrD78ohz3fGWaScJy/rmTo6o01zSm8URWGc/fwWLLz3OGjx/XAkLpm6DW9c3xfd2qtvWfPoF3s9K3NqPnX75NuAkDG/1UQPmiR65M4xXWwXn5AsReul7PbJ+GBZoX/+hnKvL6DcCmBusx2l6RQiegCAh5nfbObndnHapDeDMo89Z1A7vvLEDnH9OsUddVfQH1SwPLMas1aV1OWXe4Oyws8HZJ7KzH+JTB0207un9E267pkremmyO/LE/FwM7R6PSwR2kwSAN77fjySHCTeclqr6WCuyqvHE/NwiT0Dp29Agh4jOslukz0/PaJNw79iucW2c0X1/MTN+yazG81/ne4IKf+zxK/c1bPqJJtqAdSiAGcw8tPklhRLqAYyPt5se9PqVQV3a2TyDu8Vb01MddodVgkShvND8Cm9wa0G9K6fYYydCdSDIbwQV/oCZS4/0fIfNNH1gZ+cNr13b16G2of2SLRV4/pt8fPvPwUgQaL3xwbJCVLmCuH9cN1XHYWY8Ni/XszKnZpbbJ//Jq5eIiICJNov0Rrf2Nss1ozolnDmgDaxHWZAAofygNbtrMXtVcf32/S5ixlMBmV+Jwr6qUYioI4AAM1c2x/NaGkS0B8AYZt6j0vOHO23S//kCfGkbp9k/oLODBneNd7Z1WshiCnXBqawP8NaC+vrMQhfVeGSL1URz3X5lKjNvPsqzR8fbTEtm3JrhUDt4K6v145q3dyr/PK+bdO5gcTV4uWUe/OPjbHzzz8GqV+yHX37FntDu9p+Og4hooNNm+sQkof8VIztYxw9PMScnNK3AI7fUg7nrSn1LtlbCJNF3Lp9869G+m5sKETkAdIimYOtYgIheAFDNzC+o9PxOZhPdapHoTiIk9O3k8A3pFh/ftZ3NZDVLUJjh8snIKnJ7t+1z+Q5U+px2q7Q53JDl6yPtjhOR026Rcv7vvK4dLzohWdVTXEVh3PR+lj/JYTK9fl0/YXYBzIyJ03figXHdMLSHukWLNe4gLn9ju6fOK489tJ04EcU7rNIrQYVvOD2jjTzhpI6OAZ2b5itd5wnimy0VyuerS9z1XrnE7VcmMXPUblCHQkR9AOTHcrISbcDaA8D1zPxUtANHMFY8gOMBDHPapOEmiRIAmBWGy+OTs2TGegAbG9tNPcIzLQ6r9P2IXoknPXtFrzi1gtbVOTV4eO7eeknCptvOTDvtqpM6CiloDMqM81/eiutO6cjXnZqqqoYPlxUGZq0qKfD4leOZuVFXBiIyA7gw3m56ICjz0H6d4nxDusU7+3d2mpLjLbCaCUGZUe0OIqvIrWzJr6/PLHRbCMiv98kvApgThWnxESGiqQD2MfOrzfnclgIRvQjgP7HaijRhHDOADADD7BbpRKuZOgKwM8PjDyrFviCvBbARwK5IFhtmE92aFGee+sHkDIdaeZ0VdQHc8mGWu6TW/1Wfjo4LPrmtv/rl8ofhhUX5ytaCemX27QPMagasOw+48I9Pst0evzKGmVce7nNENMRhle4NKnxlWhtbYHA3p3Vg53h792QbbGYJCgMev4w9pV5s21fv2r7fpVS5AszAdH+QpzPzvubUTUSjALzMzKOa87ktBSK6AkA9My9ReRwCkAZgmEnCCIfVlE4EJ4CArHCty6dsQGi+bomw1XU/u0Va+6+LuieNOa6dKr/AisJ44ZsC3w/bKjJlRvq8KYPiOiSJ6VC3Jb8O//xsN392+wDqqGKdiNsn49YZu1wHqnzvuH3yfYf7HBElmyTcbDVL/+ewSo4BnZ0Y0i3e2beTg5w2E8wS4AsyCqt82HHA5d9aUO/JL/PaLWbpO5dPfhnAyuY+BSGiQgAjYsmDjSpg1QNEFOewSt8O6uI88dkrejmac/eTmbHk90p+6ZuCem9AOReAuWOSdfHCewbFi+igszyzGk8vzNsdkJWOj1zcI16NLxBmxozlRYFPV5aUhfNdmrSAIKJUAMNNEkbE20yjGejADBsR/ARUe/zKSr/MawFsiMEBoCk63gCwh5lfV2sMg9iwmqU7HVbppdev6xuX0cztjvPKvJgyM9td6wm+7AvyM3aLVPrB5Iw2IpoIuH0yzn/5dx/Amaf0a5P+5KU9VVlUb9tXj7s/zfG4/cpVzLyoKfcQkRPAUADDEuym0UTox4w4IigEuAMyb3H7lZUIBTnbYsxTPZKO0QCeZebT1Hi+QewQ0XF2i7T89rM7J115YorUnO8+j1/GM1/le1fn1Ox0+5Wz4qzSKxNO6nDDbWd1FnKM+eB/97h+y67+uZ3Tcva7N6Wrsqiu8wQx5dMcV0G5d4Hbr1zflICSiCQA/RHaQBhlM9MIBuKZYSaCB0B+rUdejtB83cjMqhX4EFEJgCHMXBz1M1prwAqEdlrjrNJ0q4kmPjG+p2NUv6SYn1lRH8BzX+W7N+XXlXv8yoXM/DsRkcMqZf/rou59zhmk7TFjUGZc/+7O+r2l3tsAbLNbpGVXjExJuOXMNEtTjuKbQq0niJe+KfCszKkp9PiV0yLZ7W4phNNc6pshsdxARSSiK6xmmnH1yR1tN5+eajlafvvRkBXGZ6tK5A+WF/pkBfcGgsp7AGCzSP8+sXfiA/+5uo/mEetHK4qCs1eW/Fzvk8c7rNLiXh3ihj91WU9nc70EFYWxYGOZ8ubSA15vQLlc7V08NSCiFACDmPkX0VoMDg8R9XFYpUX9Ojm6/fvSno5ObWLfAd2UV4fH5uW6PQF5idunXM/M7lDqirR+/l3HRZ27GS25pR5Mei/T5QtyF4uJbrBbpOf+fWlPxynNEE80kHnAhX99sddV5QrO8gaUO6LxGhcNEV0EYCkze6N+RpQpAScBeJCZx0c7cEuiIVn5lH5JiTeOTrVHs6vi8slYsrWC3/7pgFdW8LY3oDxy8D8MEZ0cbzP99MVdA+O0MPlt4JNfi4Izfyve6PIpo5hZIaJO4eKnUc9e0dMZ607VyuwaPLkg1x2Q+TOPX7n3cGkABmIhom0AzmLmmLxYWwJElOa0SZ+2dVpOvGtMF+eovkmIdAdSURjr99Zh2g/7XYXVvp3hhhN/FKQRUVycVcp+7JIeXc4aEJN/e0Tklnpw4/tZLm9AGcTMeeHipwdMEj1655gutkuHJ8e0U1VY5cMTX+a69pR48sK2UjubUb5BM0FEzyOUNvOxaC2xQkRmq5n+ZSJ66KbTU20XnZAsRVPQWFDuxacri30/bK9yewPKJGb++uD/32E1vTmyd+LNL07ordkiMygzrn9npyu/wntfUOZ3AICIzoyzSJ+flp6UeP+4bnGxnN76gwre/6UwMHddmdcfUP7OwOetuWAx2oD1TACPM/OZzS9JDEQUbzXRg5JEd/ZItpuuPrljwrCeCThSgYEvoCCn2I1FWyp83/1eyRYTLav3yo8xc6OGz3FW0+sjeyVMfunqPpp4Xxz68mv4ebj46Wqrmd45oUcCXX1yx/jhPROaXNwRCCpYnlWN2atK6vLKvC5PQJl4rO90EFEXAG4dF11VAOjHzBWitTQH4dy7y+PtpsdNEvW66sQOtjMHtDF1a2+H6TC/x4rC2F/lw2+7apTPVpd4PH6l2O2Tn2FgZmM7FkQ0Kt5m+lGrRWb4NMSVX+69Pyjz24doGeCwSnPbOs3drxnVyXnu4HYUSUvKrEIX/rum1PPLziow4xm/zC81VuF9rEBEiQDa6bjo6l0Am5g5qq5CLZHwLuhTQZnPP6N/W75kWHJc/zQn7NbDn5JUu4LYlF+HOWtK6rMK3Qyi930B5dnGvqeJyBFnkbIfG9+js1aLzI9XFAU/XVm83uVTTjk4kCSi+Dir9BqAay4ZlixdNqKDtUu7pp+QVNQH8PWmcnnOmlJfQFZ+c/mUG2I5Sm8JENFAAFmxFEpHG7BmABjLzK9FO3BLhYgsAC5KsJvu9gWVE6xmidJTHcGu7WzWOKtkCgSZazzBwM4Dbrm4xu+Is0r7fAHls4DMbx8tmTi8a7P9ptGp3a47tZOq5xZVrgAmvZflLq/z3xOU+f3D6IknYKLDJj3osJo6nN6/jXVgZ6c1I9WBru3tf+xa+YMK9pZ6kVXkwvb9Lu/yzGoGsLMuVDG6UK08NS0hoo8A/MrMM0RrUQMiehXAoy3FnqQ5IaKhDpt0DxjnBmRu1yPF7u7XyWF12kxmAuD2y8HdJR7/3lJPnCRRrURY5vIprwJYc7TdCofV9GrPDvZbp09Kd9ot6hU8MzOe+zrf+9POqvVun3J6Y7rCQfpZ8TbTAwGFR5/SN0kZ0i3ekZHmQN+OcYizSiAiKAqjqNqPrCI3dh5wBVdm13hKavy+gMxTm+KicixAROMA/IOZzxetRQ2I6FoAec1Zpd1SIKIOZokmx1mlSW6/3CMlweod0NkhtXVaLFYzkTegKIVVfn9WoUty+xUpziptq/XIbwKYd7TjZCIa5bBKP7x7U7qjbyd194VWZdfg4bl76nxBPo4P05SFiHrbLdKdzDy5XyeHMrJ3Ynz/NIeUkeZE+3gziAjMjFqPjF1FbmQVuXljbp1rc36d2WKi+S6f8iozb1L1D6IRROQGkBJJ8d5fntGKd5ePSvgF0R3AMACdAdgBBADUAfgdwO+R5mMQURe7Rdp421lp7a8+uaMqNhyV9QH8/aNdrpLawDSvX36oCZoIwEgAoxPspjNkhYe5/UoHicAAwAw4bab9IKyr98orAPzIzJlqaBcFEc0E8BMzfyJai0H0EFFbhIqCMgDEASAAHgC7ESoqiMglgYgkh1Wam57qOG/qNX0dR9oNihZFYby6ZJ9/8daK3W6/chIz1zVBVxcA5zms0ikmiU52+eRezDBJEhRZgclukaqtZtrq8snLZAUrAfzcXBZwLYFwPtwtzHyhaC0G0RO2sBwEYAiAJABWAF4AJQgVAu2NNF9TIroi3m76ZPqkfnFqBa1rdtfgoTl73d6AcjYzrzna54koDsB5FhOdFGeVTvcGlIH+IDtNEmRZgckskc9hk3b5g/ybN6CsBvCNmgVQIiAiH4AkzXNYDWKDiLrbLdKqS4Ylp9xxTudmK34CgOwiN+77fLe71iO/Hs6jjeofOBzE2gAoCPmT6voXhYiGAyhl5gLRWgxaFkRkdlil2V3a2ca9cFXvZit+AkKVv88tyveu3V27y+1XzmTmqlh0ArAA8B2LRRmREHYX6cHMq0VrMWh5SERXxVmlGU9d1tNxanrzNRxhZizYWM5vfL+/3htQxjLzqmifRUQmhAL0wLGcntNUiOhiAIti+W6KNiVgLIBrmfnaaAdu7RBRB6dN+jQpznzKM1f0cg7oHFvxUyCoYMaKosBnq0t8gSDfKSvGTqHB/wh74PVgZr9oLcciRCSFc9wfmzKms238sBQpVn/UVTk1ePLLXLdf5jkevzIllqMyA30RzmFdyszzRWs5ViGi0XEWae6p4eKnxBitK4ur/fj3l7mu7GL3PrdfucwoWNSeaAPWSwFcpxeXAFGEi58mWC3Su2cPaGu5+uQO9kiPMLwBBT9ur8SM5UX1NZ7gepdPuS4WY97WChH1AlCpt2OYBohIBmDV07GwCBqKn7q2t3efdFqn+NPS20TkUMDM2JJfj1mrStwbc+tc3oByNTP/pKJkXUJE7RA6XlTNm1kkRDQHwJfMPEe0lmOZcPHTVLNEE68/tZP9wqHJUqS2V8XVfszfUBqYt7YsKDO/4A/yc61hR7Q5CZ/YHs9H6Wp41OdEGbAeD2A4M38Qy+AGIYgoxWKiO0wS3dW1nc1y8bDkhIGdnejdMa7RtqXldQFkFbqwdk+t/5vNFYrJROvCLfOW6P3oXi2IaAGAT5n5S9Fa1ICI3gRwl/H7ETvho/crE+ymB4jQ99LhKbahPRJMGakONGbX4/HLyC72YPv+ep63rsxV4w7WeAPKfxTGjKbkqxr8FSK6BsA4Zp4oWosaENFNADbH+oI3CEGh1tEPBGS+6PSMNvLpGW0cGWlOdG5r/UvbUllhFJR7kVnoxnfbKuq35NdLJqKZnoDyquHTHR1EZANQx8wxGfEaOawtiD9altpME0A40etXOndItLidNpPVJBEFZMVbUhuw+IMK2y3Sdo9f+TEg84yD/SMNooOIvgbwwaHefgYGR4KIhtjMdKPNIp3h9isZCXZTsJ3TrJhN5FQYdbXuoFReH7A7rKZcWeHVbr8yE8AvxsIhNojoegDnMPP1orUYHDsQUbJEuCHebhoXCPIQBXCmtrF6rSaKJ4LH41fkomq/w2KiSrOJNtR65IUIeZ8a6ToxQEQOAOXMHFMVnBGwtlCI6HYABQCKANwKoA2ANwAUAsg3XnjNCxGNBFBwrHvdGYghfOp0MYDPEXIpmArgKgA1CHkPGrnDzQgRdQWQbOxAGkQLEX0K4HEAnQDMBPASgK0AsvWaGiaK8Gbc32LtqhdtSsBEAKcx8+2xDG5weIjoQwCrmfmDsDcsGS89g2gIr273MHOqaC16hYjOA3A3M48N9++2MbNHtC6DYxMi+gLA+8y8VLQWvUJE9QA6MXM9EdkB+PXurnGsE62fUhxClkcG6nEAQDkAMHPACFbVhYj6h7vn6BEzgHjRInROPYA9AMDMihGsqgsRdSSiHqJ1qEgSQh7CBuqxE0AQAJjZawSr6kFEFiI6IdbnRBuw7gDwfayDGxweZn6cmRcCIXsOIjpVtCadMx2hBhF6JADgTdEi9Awz/8rM/wBClnVEdLNoTTrncgAPiBahIgsA5IkWoWeYeWSDiT0R3UZE7UVr0jHtAHwX60OiCliZeY1ht6Ep5wI4Q7QInWMGoEvLJ2b2MPO/ROtoRXQGMEW0CJ1jQnh3TI8w89vMvEu0jlbE/QgFVQbq0CzzVb3m2AYxQUQPHrSraoJOg6kWxH0AtokWYXBsQkSnEtGD4UtjvqrPVwDeFi3C4NiEiCQi+uqgHxlzVl0qAcRsQRdt0dU/AHRnZj0fyQiFiHg+cYEAACAASURBVOYjZKcxL9zCDYbpu0E0EFEagOXM3Fe0Fr1CRFcDuJiZJ4RNsk2GubhBtBDRzwAeZua1orXokXAhs4eZzeFrMwDZcN9p2US7w2pH6AjVQD1yEVqVgJllI1hVFyI6noj0WphkgVEkqTbV+F/RFRvBqroQUVci6i5ah4ro9buopUAA1jdcMHPQCFbVg4jiiCjmGpFoA9YNAH6OdXCDw8PM9zHzz0DIMifsE2qgHh8D0OsOZB2M41NVYeYlzPwIABBRdyKaJFiS3rkewG2iRajIpwh5bhuoADP7mfnkhmsiuo+InCI16ZxuCHlUx0S0RVfLmfmbWAc3aDIXADACVnXRbREHM1cy8/OidbQiegGYJFqEztHtfAUAZn6TmfeJ1tGKeBghu04DdTCKrvQMET13kG+ZkRCuPrcglIZhYBAx4VOQu8KXxnxVn08ROhUxMIgYIkogorkH/ciYs+pSAOCmWB8SbdHVo+F7n45VgEHjhJPun2Xmn8LXZOTYGEQDEQ0AMJuZh4rWolfCrZSHMPPfw9fGfDWIGiLaCmACM2eK1qJHiCgFQCYzJ4evCQjlnwsVZnBEot1htcHowqE2WQgVcgAwJpLaENFJRKTXIyELQjsIBupRinDRFWDMV7Uhot5E1E20DhWJg7HjpyYygHUNF+FCSWPOqgQRJYosuloB4NdYBzc4PMx8BzNvBAAiuoyIjhetSef8F0An0SJUogzA+6JF6Blmns/M/wEAIsogomtEa9I5twO4SrQIFXkXQIVoEXolnNd/fsM1ET0RtrYyUIf+aIbC32iLrn5g5l9iHdygyVwKYKBoETrHDJ0WcTBzITMbrVm1oz+AK0SL0Dm6na8AwMyvMLMRsGrH46IF6Jxmma9G0VULhYimEVH6QT/S7ZdzC+FyhI51DQwihoiuIqJbGi4B+EXqaQW8CeAL0SIMjk2IKJWIPg3/bwKgwEjBUJPtAO6I9SFRBaxENJWI7ol1cIMjMgph82hmvoaZ5wjWo2uYeQ0z+0TrUAMiOoWIfhOtQ+f0Cv8HZv6Sma8UrEfXMPMeZt4vWodaEFEhEaWK1qFj4gGcDPyRv2oxcljVg5lrmHlLrM+JdofVjNCKxEA9tgKoFS2itUBEZ4Tb9ekRY76qTyEMWzTNIKIBRNRZtA4VscDY8VMTHw4qujJQFyJqf5BNZ9REG7AuAWD0OFYRZr6RmXMAgIhuIKL+ojXpnIUA9NrpJB/AR6JF6Blm/oSZ3wMAIjqBiIwcVnX5PwDniRahIi8DqBctQq8wcwEzTwQAIrIS0VOiNemc4QBibl4TbdHVYmY2AlbtuArh40YD1TBDpzsazJzHzEbAqh1DAIwTLULn6Ha+AgAzv8jMbtE6Wgk2hBZABuohruiKiKQGo10DdSCiWUTUJXwpAwiI1NMKOBuALl8QFMIosFQRIrr1ICsrBuAVqacV8AyAb0WLUAvjHasuRNSPiBqs/iQALpF6WgGrANwX60OifYnNAHBDrIMbHJFRCK38wMwXMvNSwXp0DTOvZWa97tiMA7BItAid0wdAFwBg5o8bOl4ZqAMz72ZmPbt6uAHYRYvQMUkAhgJ/FAR1FKxH1zBzVXN0bYs2YDX67qrPWhirPs0I94LX646GMV/VZy+APNEiWgtENFTnVfTGnFWXehhFV5oRthGLuTV4tAHrfAAxWxQYHB5mvpqZiwGAiP5BRD1Fa9Ir4UB1sWgdKpIFYJZoEXqGmd9psJ4jotOI6GLRmnTOIwBOES1CRZ6A4b2tGsycycx3AAARJRHRY6I16ZzTATwY60OiLbpayMzbYh3coMlcAyBNtAgdYwKg6NWHj5l3MfNc0TpaESMAnCFahM7Re9HVc8xsWNFpQyKA20SL0DnNMl+jLbqyGEUc6kJE3xBR2/ClF0bnHDVRAJwkWoRaEJHJ6JOtLkR0HxGND18GodMCvhbE/QCWiRahBuEiSZtoHXqGiIYT0esNlwBqROppBXyHZmh/G+1LbB5Cvo4LYxVgcFhGIbygYOazBGvRNeGdjPWidajI1Qh5Vl5ztA8aRE0/AHUAwMxvCNaiexo8qnWKDaEAygha1aMdgAFAyJMVwECxcvQNM5cDKI/1OUbRVctlGULdOAxUJnxioGcTchOMfDi1yQSwT7SI1gIRnUxEKaJ1qITxflWfSgAbRItoLRBRDyIaEutzog1YP0HoC9pAJZj5UmauBwAiepCIjBxW9YgH8JloESqyCYCRw6oizDyVmRcDABGN1fkCqCXwNEINGvRIAMCjokXoGWbewMwPA39UsD8kWpPOGQvg9lgfEm3R1RfMvDvWwQ2azA0A2h71UwbRovcCjm3MrFuT9RbIKAAjRYvQObqds8zsZ+ZXRetoRaTASJdSG6FFV3FEZIp1cIPDQ0QricgSvqyHUXSlJtUAzhQtQi3CvbKNfDgVIaJniGhM+NIPw0NZbW6BTo90w12unKJ16BkiOouIXghfMkIpAgbqMQfAC0f91FGgaJx8iGg5gCeYeVmsAgwah4gUAGbD2sQgVohoCoB0Zr5TtBa9QkRzAcxv8GI1MIgWIuoEYKvRfUk9iOhKAFcw8xWitRg0HaPoqgUSNrJfYgSr2kBETiIaK1qHihjzVX22ADggWkRrgYjOPMj2T28Y81V9SgBsFC2itUBE6UQ0KNbnRBuwTkOoFaGBCnCIcQ3XRPSsjr+cWwIdAbwtWoSK/ArDgk5VwkbvvwEAEV1ORIYVnbq8DKC3aBEqUQvg36JF6BlmXs7MLwAAEfUmon+K1qRzLgVwbawPibbo6r/MbOwmaMeNAOJEi9Axut7RYOaNzPyLaB2tiNEAYt5NMDgiurVqY+Y6Zn5PtI5WRGcARitldWmWd2y0RVcJRtGVehCRg4h+O+hHVdDpl3MLoQDA+aJFqAUR2YnILlqHniGiN4moobe9G6FCSQP1uBzALtEi1CDcmS5RtA49Q0SXEFFD5yUZQIVIPa2AdwHE3FAl2k5XvyFktbQlVgEGjWIBMLjhgpmNLhwqwsw+ANmidajI/Qh1zTG8HdUjHSE/XzCz4emoMjq3VeyLUApPhmghOqYDgK4AwMwrAawUK0ffMHNZczzHKLpqmcgAFokW0VogorZEdK5oHSpizFf1WYNQIYeBBhDROCJKEK1DJYz5qj4FADaLFtFaIKLBRNQ/5udEaWt1LUJV7MY2ugYQ0ZsA7mdmr2gteoSITgDwITMPFa1FDYjoJIRq+daK1tIaIKJJALKZeZVoLXqFiPYC+Bsz7xGtpbkhomQA5zLzbNFaWgNENBjAaGaeJlqLXiGiFwFUNRS6RUu0RVezjGBVUyYDINEidIyudzSYeY0RrGrKWQD6iBahc3Q7Z5m53AhWNaUHgDFH+5BBTAgtumpvFF2pBxF1JKIfD/pRMXT65dxC2I5QEYcuIaJ4InKI1qFniGgWETX0tq+BUXSlNmdBp763RGQxbAzVhYhuIKL7wpd+AM2SY2lwWF4C8GGsD4k2h3UTwgnLBqpgB9Cv4YKZezKz0ZpVJZjZw8x5onWoyJMA7hAtQuekI1TYBmaewsxfCtaja5h5DzMHROtQieEAlogWoXM6hv8DM3/HzDcL1qNrmLmUmWNuf2sUXbVMPAC+ES2itRDe0f6baB0qYsxX9fkFhjWOZoSbM+jVqs2Yr+qTDWCraBGtBSI6kYj6xvycKIuubkCob7Zx7KUy4dSLd5l5smgteiXclegxZj5TtBY1IKJTAdQy8++itbQGiGgKgJXMvEm0Fr1CRJUA+uqxloKIUgGcxMwLRGtpDRDRyQCOM5o1qAcRvQdgIzO/G8tzoi26+sQIVjXDDOA60SJ0jq53NJj5NyNY1ZQxALqIFqFzdDtnmbnICFY1pS+AU0WL0DlCi646EVG06QQGR4GI+hJRgw+rBCBXpJ5WwEoA14sWoRZE1MYoulIXIvqWiHqFL8thFF2pzfEAakWLUINwZ7p2onXoGSK6m4huD196AJSK1NMKeAjA3FgfEm3QmQ1Ar6bNLQEHgO7AHwVBRscTFWFmNzMXitahIq8AmChahM7ph3DnQGa+kZl/FqxH1zBzLjMronWoxJkAPhMtQud0AtAWAJj5C2a+7yifN4gBZi5j5pgXmEbRVcukBsBi0SJaC0TUjYjOFq1DRYz5qj6LodMdv5ZI2JZIr6d8xnxVn20AdogW0VogotMPOoGK/jlRFl3dCOBTZg7GKsDgyBBRIoAXmNmwJVIJIroUwHXMPF60FjUgotMBFDFztmgtrQEiegTAl8ycKVqLXiEiBYCFmXUX2BFRNwD9mfl70VpaA2GHmFRmnilai14horkIFerPieU50RZdfWQEq5rhAHCZaBE6xwRAt7/PzLzcCFY15TwAyaJF6BUiIoQ6/+kyJYCZC4xgVVMGIOR9a6AeQouuuoW/NAxUgIiGE9F/w5cMQHf9slsYi6FjY30iSjGKrtSFiFYTUUr4shCAS6SeVkAPjuZ48BiAiJxEZCx4VISIniCiBvedWgAlIvW0Av6OZmiGYY70hnDeUD4zGwGrejgBpAEAM5cAGCVWjr5hZhf0HWC8g1ARx3zRQnRMH4R2/cDMVwrWomvCgWq+aB0qchGAiwFMEC1Ex3RCuB0rM38kWIvuYeZmaX0bzQ6rkRCuPqUAvhMtorVAROnhPE+9YoYxZ9XmC4TscQxUhogs4ToKvWK8Y9VnHYAs0SJaC0R0HhF1jfk5kZ6qhDsvXcvMn8Q6uMHRIaJOAB5k5ntFa9ErRHQzgFOY+SbRWtQg7ICQw8wForW0BojoRQDTmVnPu4DCCBeiHmBmXVorElFvAJ2ZeYVoLa2BcNGtmZlj9gk1aBwiWgrglVhzsyPeYWVm2QhWNaUNgPNFi9A5ut7RYOafjGBVU8YBSBQtQsfofb7uMYJVTRkMYKBoETpHTNEVEZmIqEesAxscHiI6m4g+DF8GAOSI1NMKmAXgftEi1IKI0ogoTrQOPUNEu4jIHr7Mg5EeoCY1AHTbTIWIkg4q4DNQASJ6jYgabAwrYHS6UpsrAfwa60OiyWFNArA51oENjkgCwrY44dX2BYL16Jpwp6tq0TpUZBaMwj216YWwzRIzX8DMuwXr0S3MrDBzsWgdKjIRwNOiReicjgDiAICZ32TmtwTr0TXMXMHMvlifYxRdtUz2AfhBtIjWAhENIaJTROtQEWPOqs9M6NjLtyVBRPFEdL1oHSpizFf1WQHDLlIziOjScD1ObM+JoujKDuACZp4X6+AGR4eI+gC4hZkfFK1FrxDRPxEqcvg/0VrUgIjGANgatkgzUBkiehvA481l5WLwZ4ioO4BfmbmbaC1qQETpABKZeb1oLa0BIroBQBUzfy1ai14honUApjDz2lieE03RldcIVjUlGYCeLZdaArre0WDmpUawqikXArAf9VMG0aL3+brLCFY1ZSiA3qJF6BxhRVcWIuoV68AGh4eILiOiN8KXXgBGW011mQbgSdEi1IKIehxUEGTQzBCRjYgOPl7cBSDmfC2Dw5IPHbfSJKL2RtGVuhDRJ0R0TviyGEbRldqcA2BrrA+JJoe1K4CfYh3Y4IgkIGyLw8xb/r+98w6zpKq697uGmSHMkBlyTpJzUoISFCSIqICIIAKKgIACnwKKKFEkqAgfwQ9EBEQUlIxIDoIgWXIGyTkNDDCs3x/nNHPhx4TuvqfP7br7fR4eqrqrq9Z031N1ap+99rbd5Hyt6mTT1Ru1dRTkbKJsS0mGkjrnAGB7bdvxACxELq34Ym0dBdkBaGR6UgcxMzAMwPbPbZ9WWU+jsf2y7Xf7e54wXXUmDwBX1BbRLUhaRdLKtXUUJMZsWd4HTpzoUUFbkDSTpC1r6yhIjNfyXAxEbeoBQtJWkmbo93n6YLqaGljd9oX9vXgwcSQtC2xs+6e1tTQVSQcA79rev7aWEkhaD7ih4aW7OgZJp5OMkm/W1tJEJC0N/MH2UrW1lEDS4sAQ23fW1tINSNoFuNd2VOYphKT7SWb9fqU39sV09XpMVgeUWYFVaotoOI2OaNi+OCarA8oX6NvqVTBpNH283hWT1QFleWDO2iIazlAqma6mzKWWgkJI2k7SwXn3dcJ0VZoDgCNriyiFpIUlTV5bR1ORNIuk1gnG7URN1pLcToMrp0iaNUxXZZF0bksa2ONAlKAryzKkDoD9oi9RgEWBM/t74WCCTA1MBWD7Wtu7VtbTaGy/ZbvJrTQvJiIIJRkGTN+zY3vVhn+eqpJNV6/V1lGQ3YFta4toOKPI8x/bP7F9fmU9jcb2a7YHPsJKm0K7wQS5gzb03Q0mDUlrSlquto6CNHoJtQMYDZxcW0S3IGlOSV+traMgMV7LczapnFUwAEj6jqQR/T5PH0xXMwBL2w4X+wAgaXVgNduH1NbSVCT9BnjA9lETPXgQIunzwFW2R9fW0nQkCTjP9oa1tTQVSZ8GDrC9Rm0tJZC0FPCW7Qdqa+kGJO1Duj9eV1tLU5H0PLB4f8v9De3tD9h+iSi5NJDMCSxdW0TDaXREw/ZFtTV0EZMB69UW0XCaPl7vqK2hy1gJuKe2iIZTrdPV1GG6KoukPfNbH8BLhOmqNN8HTqgtohSSlpTU65fTYNLIprYb8u4Q4IYJHR/0m6uA9WuLKIWkuSTNVFtHk5H0T0mL5t0HgSY3ougE5iLNZfpFX3JYV6LBD/cOYSQwHMD2323/pLKeRmN7TDu6cHQwVwHT1hbRYIaTxiy237G9WmU9jSabrppsatsX+HJtEQ1nBsAAtve0fXVlPY3G9pvubf7px9DXTldRsqUsNxJRmgFD0oaSlqitoyAxZsvyEvCH2iK6BUkLSvpKbR0FifFanj/QhohfMGnkVeN+r/L1xXQ1MzC/7ZhQDQCS1gcWtv2r2lqaiqRTgb/bbuSkI3+GLrEdD8HC5E6AJ9uOCFkhJG0MbGf7C7W1lEDSMsBLtqN16AAg6VDgDNu31tbSVCS9C4yw/U5/ztMX09VzQL+cXkGvmAdYpLaIhtN0E0d0phs4pqDBRe07hKaP19tqa+gyVgbCmFqWaqar6cN0VRZJB+f+xgDPAlHepCxbAX+qLaIUklbM5ZaCAkhaWdJleXcscH1NPV3A34DNa4soRU55mKG2jiYj6T5Js+fdu4BoXV2WKYH3+3uSvuQUrAVsCXypvxcPxssIxnXhOLuylsbTBUvlN5LecPud9B58LMOByeGDsn8b1ZXTbGy/D/RrabHDORg4iwa/RHcA05EnULZ3rqyl8dge047zhOmqM7kCuLm2iG5B0maSFq6towSShgDOD/mgDE8Cp9cW0S3kMm0b19ZRkHjGludY4I3aIroBSUMl7dWWc/XBdDUbMGskKA8MuQXhtLaPr62lqUg6Hzje9nm1tbSbPGH9nO2La2vpBvL98XDbW9bW0lQkfQNYy/Y3amspQW4T/ZTtaB06AEg6HjjCdtQ7L0Buyfq87an6e66+mK6eBp7u74WDSWY+ooZmaRpr4siR1ZisDhwjSSaOoBxDaeh4BbB9S20NXcYqQL8nU8F4advztS+mq1nCdFUWScfnKALAE6ROHEE5NqChLlFJk0mKCVRBJK0r6Zy8+zZhuirNScD2tUWUQtJikqavraPJSHoxR/4gpd+9VlNPw3mdlDPcb/qSw7ohsM9Ejwr6w5Q9G7ZPtf1/NcU0Hdvvt6MLR4cyEvhHbRENZzgpioDtJ2xvVVlPo3GiyTnZvwJWrC2i4YwkR/1sb2v74cp6Gkser3UirERC+EBwPnBnbRHdgqRvSpq3to5CxHgtz4PAmbVFdAu5jNjna+soyFBizJbmMKDJ7bg7BklTS9qzLefqg+lqTmBq2/e0Q0AwYSR9G3irqV2YOgFJ1wJ7276mtpZ2I2k48CnbV9bW0g3kahM/tL1dbS1NRdL3gPls71ZbSwkkLQ88kkukBYWR9Cdgd9tP1tbSRCTNAdxke/aJHjwR+mK6+m9/Lxr0igWInselEQ01ceRWeFfW1tFFTAssXVtEwxENjkDajpKGA8sqwLDaIhrMZLQpmt0X09WckhZox8WDj0fSXyRtkncfAh6pqafp2F7V9j9r6yiBpCnCdFUWSV+V1LMC8jpwQ009Tcf2L23vUVtHKSQtK2ma2jqaiqRhkt5q+dJ1wOhaepqO7cdtz9OOc/Ulh3VzIDpDlGU447pwnGA78uOCvjIL8OfaIhrOB9EZ2/fa/m5NMcGg5wRgkdoiGsxQUpQeANtfs/1cRT3BJNKXCWskhJfnTOC+2iK6BUm75ILvTSTGa3n+Q+pvHwwAktaStE5tHQWJMVuWsaT2t8EAIGnmnHfe/3P1wXQ1NzDM9kPtEBBMGEk/AB60fXZtLU1F0n+ALWw3rjKDpCmBpWz/q7aWbkDSCsA2EWUth6Sfkp5d+9XWUoL8GbrP9uu1tXQDki4BNrX9am0tTUTSEsCfbC/e33P1xXT1eH8vGkwYSWqpC7ogEAOpLGNpaETD9ltATFYL8pHxOj2xnFsa0+CSRLb/XVtDk5EkSPVB85dWpiVFIGg7Q0gNVdpyol4haQFJ87Xj4sF4uVzSWnn7LuCxmmKaju2lm1qmTdLIMF0VZydJ/5u3XyJMV0Wx/TPbB9XWUQpJn2zpwhS0n1FAa87qpcA7lbQ0Htt32F6+HefqSw7rN4Et23HxYLx80HvX9q9tRy/4oK8sQDJxBOVoHa832/5xZT3B4OYUoN81K4Px8qFmKra/bDuqBAwC+mq6amTNyg7iJODR2iK6BUl7N7h3d4zX8vwLuKC2iG5B0kaS1qitoyAxZsvyBvCL2iK6BUnzSGpLZam+mK7mBd6LBgIDg6SDgats/722lqYi6b/AKk38TEsaCSxo+7baWroBSWsD69r+QW0tTUXSr4DHbP+ytpYSZNPVf2y3Je8vmDCSbgJWtv1+bS1NRNJqwKG2V+3vufpiunq0vxcNJoykocDYnBS+IHB7ZUlN522aa7p6A4jJakEkTQZgeywwIxA5/mV5jzaZODqRMF2VJZuuJrP9Xt5egWTkC8rxRjtO0hfT1aKS2tK1IBgvNwHL5u2bgcZF/joJ2wvafqa2jhJImiFMV8XZCzggbz9NGr9BIWzvafvY2jpKIWlNSZPX1tFgFgbuzttDgHPc26XmYJKxfa3tddtxrr7ksO4MbNSOiwfjpdXEcajt6yrrCQYvSxH5WqVpHa/X2I7fd9AfzgSiNWs5WsfrWNtfrKwnmETCdNWZHEWK1AQDgKRDJE1RW0chPuSIDYpwBfCP2iK6BUlfbfiqQTxjy/I8cGRtEd1CXpX/dlvO1UfT1Vu2n22HgGDCSDoG+KPta2traSqS3gRmyfmejULStMBstu+traUbkLQJsLTtn9bW0lQknUwyov6utpYSSFoeuN12vGgWJptSL7H9qdpamoqkDYEdbW/Q33OF6aoDye00x2TX4kJAFJEuyxs0NKKR2w1Gp7SCSBoOvJ8nGKOAOSpLajpjaLbp6ubaGppMNkkOy1UYhgGLVpbUdN4HXmvHifpiulpW0pztuHgwXu4G5s3b/wQaaQjqFGzPkluYNg5Js0pasbaOhnMQsHvefoxklAwKYXsH23+sraMUktbvqTwRFGEF4Oq8PRY4p6KWxmP7QttbtONcfclh3QP4TDsuHoyXD/IObf/UdpS1CvrKKsCPaotoOK0mjr/bPq6ynmBwcw59ezYHk0breH3N9jZ15QSTSpiuOpODgZdri+gGJA2RdFRtHQWJ8Vqe84CraovoFiRtL2np2joKEmO2LI8Bv6ktoluQtIKkbdpyrj6YruYDXrX9UjsEBBNG0unAL6JTURkkDQNG2x5WW0sJcsvZ6W0/XFtLN5BvzDNHaatySDoHOMl2I5dyJS1n+5baOroBSbMDp9hep7aWpiLp68B6tr/e33P1xXT1SH8vGkwYSVMDb7R0uooi0uWYDHiutohS2H6ZiNYXJZskx9p+B5iZZLwKyvEGzTZdxWS1IDlIMcz2aGAKYIHKkprOe7TJ+NsX09UnJc3WjosH4+UJYLq8fSnwQkUtjcb227Yb6+qWNHcukxOU4yhgm7x9H3BrPSnNx/aWtv9eW0cJJA2V9IXaOhrOWsDf8vZo4NyKWhqP7TNs79yOc/Ulh/XHQDwAy9KaFL6P7Ycq6wkGL2sDu9QW0XBax+s5tk+vrCcYvEwJxOenLK3j9Rnbu1XWE0wifZmwfvDHDoqxFw1e8uokJI2QdERtHQWJ8VqeM4AbaovoFiR9X9InausoRIzX8twDHF9bRLcg6dOS2lLWqq+mqxdsv94OAcGEkXQxsHNEWcsgaRRwt+1G5h1KmgGYyvZ/a2vpBiR9D3jX9jG1tTQVSVcBP7HduMoMuf7qIrbvqq2lG5C0CHCY7Y1qa2kqkr5L+kx/t7/nCtNVByJpRuClbLpagPTWHZRBwFO1RZQiV/OIih4FySbJd2yPIZmu3qwsqem8QkNXoGyPBWKyWhBJkwPDc9BtKiAaIZXlbdpk/O2L6WrNHJUKCiBJfNhkdR7RWrMYtp+z3diajpIWlrRsbR0N53dAj1HmduDOiloaj+2Nbf+rto4SSJpSUkT7yvJF4MS8/TJwfkUtjcf2/9netx3n6ksO64FAU/OHOoEhgHN0Fdu72362sqZg8LIB8I3aIhpOq4njT7bDdRz0lZmAY2uLaDit4/WRdk2mgvL01XT1XruFBB9goN+5HsGkIWmUpENq6yhImDjK81sgGnsMEJJ+Imme2joKEeO1PDeTVkWCAUDSBpK+1JZz9cF0NS/wrO232iEgmDCSbgQ2tN3Y4vY1kbQQcKHthWprKUHOhx4aUfqBQdLPgIdt/762lqYi6XZga9u319bSbiQNB+a2/WBtLd2ApJWB3W1vXltLU5G0LylnuN+R7L6Yrh7t70WD8SNpCDBTywR1vpp6ugADT9YWUQrbL9bW0HRyJYY3W0xX8XJQlueBMbVFlCB3GrFwvgAAIABJREFUS4vJakEkTUV6iX8NGEEas0E53iCZm/tNX0xXG0iabuJHBn1kGuCBlv0/kbpxBAWw/aDtz9TWUQpJS0paqraOhnMmsEbevoFU5zEohO11bN9bW0cJJE0raYPaOhrOVsDheftZ4KKKWhqP7V/aPrId5+p1hBU4DNiUVFokaD8fymFqR+2yoKv5CimKfEdtIQ2m1cQRqQBBf5gbOBS4oLaQBvOBDyfXu40yYoOE6HTVeYwGvl9bRLcgad6cd9hUYryW50igkRG/TkTS4ZKauowb47U8VxPtbwcMSV+VtH5bztUH09U8wNM51yYojKQHgSVsN7JQdm0krQQcbXul2lpKkE1Xzg0EgsJIOgq40vbZtbU0FUmPA6vbfqy2lnaTi9rPYvvx2lq6AUmfA75qe9vaWpqKpCOBJ233uwV6ryOsth+LyWo5JA2VNFvLl+Yh3rhL8i7wRG0RpbD9YkxWyyJpluzuBhgFTF5TTxfwBNDIZ5DtMTFZLYukaSRNm3dHAuHJKcvLVOx0tamkEe24ePCxzEkybvRwEjFhLYbtW21/ubaOUkhaSdJitXU0nAuAnm5pVxAu76LYXtX207V1lEDSzO1aPg3Gy07APnn7MeAfFbU0HtsH2D6pHefqSw7rr4k3kpJ81HS1g+33K+oJBjdfBz5bW0TDaTVdnWD7psp6gsHLJxg3mQrK0Dpeb7YdncUGCWG66jyeB35YW0S3IGkJSU1+QMR4Lc/PSJGaYACQdIKkkbV1FCLGa3kuAM6qLaJbkLS9pLXacq4+mK7mAp6yHYOqMDkB/37bTW1DWB1J6wJ72P5cbS0lyEXt38tFsoPCSPoDcIrtWGYshKTXgLlsv1pbS7uRNAUwne1namvpBiRtDnzK9m61tTQVSScB19k+sb/n6ovp6omYrJZD0uSS5si7Q4GZaurpAt4CGmtysP1STFbLImkuScPy7kz0rb51MOk8RDJLNg7bb8dktSySZmhpfjQy/xeU4zkqmq62brk5B+1nMcYVjR4LnFBRS+OxfbXt7WvrKIWkNSQtXFtHw7kCmDdvX0ikBxTF9rK2G9n9L7/8rFtbR8P5AbBj3r6PNH6DQtjeq11l/vqSw3ocMHyiRwV9pTUh/G3b0UQg6A/bA5+sLaLhtI7Z39i+u7KeYPCyDLBLbRENp3W8Xmv71Mp6gkkkTFedx6PAvrVFdAuSVpG0R20dBYnxWp49SMteQWGUOFOSamspxFBy29CgGGcQrW8HDEm7S2pL0KQvpqvZSZ2ueveDQa+RNAtwhe2oo1mInHT/Zdub1dZSAknTA2OauoTaaUi6ADjA9g0TPTjoNZImA9613ZdgS8cjaUpgKtsv1tbSDUj6DsnA96PaWpqKpLOB02z3uzJDX0xXT8VktRySppI0Z94dBkw7oeODfvM6Dc45tP1yTFbLIml+ST1GqxmApkb/OoEhwJ21RZTC9lsxWS1Lbs7Q81wdAUxRU08X8F/glXacqFcTVklDJO3QjgsH42VloCen5k3g/ypqaTy2L7T9P7V1lELSepLmq62j4dzIuGYqfwEa2YWpE7D9ru2lJ37k4ETSwpLWrq2j4ewPfC1v3wZcXVFL47G9q+3L2nGu3kZYhwJHt+PCwXhpTQh/2fZ+lfUEg5udgKVqi2g4Qxk3Zo+w/WhdOcEgZhVgm9oiGk7rM/Yy2+dU1hNMIr2dsIaBozz/AQ6qLaJbkPRZSTvX1lGQGLPl2Z60GhIURtIISU12dcd4Lc9vgbZE/IKJI+mnktqyKtIr01V2Zo6yHY7YAUDSJ4BTba9YW0tTySkuy9v+dm0tJcgFst+yPaa2lm5A0g3AtlHaqgySZgQesD1DbS0lyKar4U3s4tWJSNobwPYhtbU0FUmXAwfavry/5+pVhNWJmKwWRNI0uf0tpHq3U9bU0wW8RLNNV6/EZLUskhZrKbM0HRCm1HK8D9xeW0QpsukqJqsFkTTnR0xX0ZmuLI8Abem22FvT1ZSSGtsVqENYG/hN3n4ROLmelOZj+8+2G5uCIelLLVUngjLcSVrKhTRew+VdiJzXv2ZtHaWQtJSkT9fW0XAOB9bP2/8Erq+opfHY3s72v9txrt7msE4DHNyOCwfjpTUh/Cnbh1fWEwxuvgcsUFtEU8mR1SGMG7M/j1WooB98BvhKbRENp/UZe6HtSyvrCSaRMF11HjeQ3gCDAUDSJpK2ra2jIDFmy7NZ1KYeGCTNIunE2joKEuO1PEeSIqvBACDpl5IWasu5emm6GgJMY7stRWCDCSNpZeDnTV4Cq42kHwIz2v5BbS0lkDQNyXT1bm0t3YCkh4DVbT9VW0sTkbQA8A/b89fWUgJJUwBDotnHwCDpMOAx21GusxCSbgW2s31Lf8/VW9PV+zFZLYukGT5iuhpWU08X8CzweG0RpbD9WkxWy5GbqSzZ8qWRJGNQUIZ3ScXeG4ntt2OyWpbcmW7qvDsl0ZmuNPcAb7TjRL01XU0v6ZvtuHAwXjYGDsjb/2Vc16ugALZPbvLbtaSvS5q5to4GM4IPmzaOpk035+D/x/bjtr9UW0cpJK0sadXaOhrOsUDP7/hSoC2GoODjsf012/e341y9zWGdGdi7HRcOxktrQvgjto+rrCcY3PwAmLW2iAbzoZxD2wfYjglr0FfWBdarLaLhtD5j/2Y7qgQMEsJ01XlcSrS/HTAkbSNpi9o6CvJB29CgCKMZ15c8KIykBST9b20dBYlnbHn2o8FpJZ2GpJMkzdGOc/W2YO69wArtuHDw8eQ+5I8CSFoP2Nn2RjU1NZxFgCYX6l4ReLu2iKZi+x3ggp59Sa8Ac9iOVq1lmA5YpbaIgkTZyMLYvq5nO1ecuMr2KRUlNZ01gcnbcaK+mK7iRlyQXLalx3Q1jEgIL80T+b9GYvtN2xGxKYSk4ZKWavnSlESErCSjaXB0zPaY6ExXFkmLSBqZdycnTJKluQ14qx0n6q3pajZJW7fjwsF4+Rqwe95+ADijopbGY/sY2401tkn6TksbwqD9zAJc2LL/c5KTPSiA7XtsN7ZusqQ1Ja1UW0fD+T2wRN4+h9SpLiiE7U1sP92Oc/U2h3Vu4LvtuHAwXj7IObR9b5MnU8GA8CMgJqzlmAx4r2fH9n4R0Q76wUbA6rVFNJwPxmxuzX17ZT3BJBKmq87jbKDJnVw6Ckm7Stq4to6CxJgty3NAYyN+nYakZSQdWVtHQWK8lud7pNXLYACQdHa7Vvl6a7q6gdTrOCiE7Yd6trN7fSPb4UIux2I0ewl3fiBy4gqRi7xfDiBpGPCm7eF1VTWaGYGla4soyJ5AtPktiO1re7YlnQOcZPucipKazpqkF7F+0xfTVTz8CiJpLklz5t2hREJ4aR4EnqwtohS5c048AAshaSpJPROoGK/leY1mm67etf3exI8M+oqkpSVNmXej7F95rgfeaceJemu6mk9SRPvK8i1gu7x9B3BWRS2Nx/bhts+traMUkvbI/cmDMiwAnJa3xzKuS11QANs32d6jto5SSNpA0jK1dTScM4F58vbpwH0VtTQe2+u3q5lKb3NYFwa2aceFg/HSmhB+u+2/VtYTDG5+CsQSdTlax+s7tg+qrCcY3HwFWK62iIbTOmZPsx35rIOEMF11HicDfwSQNERSb/9GQS+QtK+kz9bWUZAYs2V5GNgRQIm25GoFH4+kNSQdWFtHQWK8luebwFMAkiaTFLXOCyLpckm99Ut9LL2dDF0EbNiOCwcfj+0HbD+cd3ck2rSWZnFgptoiCjI1qdh6UADbr7X0Ih8FPFNTTxcwM7BobREF2Qb4Q20RTcb2NdksCXAFsEZNPV3AmrQpt7+3pitHjcGySJpf0ux5N962y3M30Jaixp2I7bFhuiqHpGlbOl3FeC3PC6Tc/kaSjc1h3CuIpJUl9aRJxZgtSI5eX96uz3RvTVeLSdq0HRcOxsv3gJ7f8fXAeRW1NB7b+9u+sraOEuQl6v1iyasoSwPH5O03iF7wRbF9pe2f1dZRCkmbSVqsto6Gcy4wQ94+AXi0npRmk4Oca7frfL1NCViSlBQelKM1Ifwm25dU1hMMXoYA+0WEtSit4/V120dV1hMMbrYAFqktouG0jtnf2/5vZT3BJBKmq87jKFJ/YyQNa1eycvDxSDpc0qdq6yjEh9qGBkW4DdgdPjBJTl5ZT6PJZZ9+VFtHQYYQz9jSbAK8CiBp8jA2lyPXqb60XefrbQ7r6cCW7bp48P9j+76WN74fAPvX1NMFLAG0pW1cp2H7HSAmUAWx/bLtW/PugsB/aurpAmYjdW9rJLY3Ji1ZB4XIpque7oY3kVaOgzIMB1Zs18l6/WYRy4tlkbSopNnybkS0y3MLqR98I4nxWhZJM33EdBUR7bI8BdxZW0RJYsyWRdKnW8rPxTO2LGOBf7TrZOrN2JC0IjCH7b+1S0DwYSSdAlxm+/eSViX9ja6d2M8FwUfJ7Qd3j2L25ZD0BeBbtjeSNBPwBdsn1dYVDE4kfRO42vZDtbU0FUljgGlsj5G0HXCO7Rdq6womTm8jrMsDny8hJABJI4AZgVGShti+LiarQT+YCmhsG8va5CjN3MCUkqay/UJMVoN+sjXj2oYGbSRXTZkNGApMD2D7xJisDh76kmz8TttVdCmSVpe0v6S58sPvCVLx8V2A8yRNIWlFSdtLamzeVk0k/Z+kpWvrKMQQ4O3aIpqCpDkl7SGpp3nK70gvBFMDL0iaLtdl3VPS6vWUNhdJW0j6fm0dBXmfSCtpC3mCur2kH+YvbUxKJ7kSuEfS1pJGSvqapC/ngFHQRiTNIun8dp2vt6ar42zv0q6LdwOSZpW0Wt5eStLNkg7P314AECltaSwwk+2VbM9DcjIeQOp8sgY5cVnSgZJ+L2m+vB+DrH8sAUxZW0QJbD9ve/aJHxn0IGmEpJUkzZj3z5XUY6SalhT96umSs43t+WyvDEwHfAK4nBR13TT//GfzOTbJ+1NFXdx+MQcwZ20RpbC9tu2ra+sYLOTWqov01K6VtJukByWtlnOBl2LceD0XGJXrgs5AaoF+N8ks+Q3SSskwSZdJOiifb3hLk4Gg90xJG01tUTKpTeQl/Pfz5HRT4A+2/00qUfWapHWBx4CdgbsAbJ/ceo7WbhC238lR14dtH9Fy2BnAKsDr+cH3gKRXgcVIkZ5PAzfbfqrQP7VpXA+8VFtEMLC0jNdZSGPyKdvHAf8DfAHYCXgROIQ0brF9F7BrzznGM17fsb3ruCtxG3AK44x9hwJbStrY9jX5vvAkcHd0OJokHiHGa9eRn3VDbI+V9B1Sw46dgGWAM4HjSZPPi4G/Aw8CtI7Fj4xXA+/mMXuS7f3zdYaSxvys+dDVgPMl/cr2PtlgOQ1wq+03S/6bG8LbQNvKWvXWdPVpYITtC9slYDAiaUHSQDmb9MZ/BXCH7S9J+ixpMJ1l+5F+XuczwOu2b57AMZMBc9p+LOs6BnjU9g6SPk/q43uG7Vv6oyUYfEiagWQIOrS2lprk38MKwIO2H5b0Z2A9Ul/6kaRJ6BW2L+/ndeYAVrd9xkSOmwV4w/abkk4AVgWWBUaQJrRX5hKCQZchaRfgr91czF7SMGA5YHLbV2dj1M+B3WyfLml34HXgd7b7lT4h6dvA6bbfmMAxU5FMWs9I2gr4LnCw7XMk7Utywh9t+7X+aAkmTm8nrHsB09v+4UQPHuTkN7qpbb8maRVgR+Ds/CH9PWl58BvAm8BCwAP9HTztRtKSpGjRNXngn0lartzS9kP5bfEB229VFRoUIb/A/N32ArW1DASSRpKW/0YABwFv2f5hfshsCxxk+1JJCwHP2H69otz/D0nTAl8n3ZePlrQ1sA9wSK4aMj/pBfb5qkKDYki6FdiuGwIMOZo5eX5x2xFYl2Q6m5wUJf2b7f3zC94Q0pjtqJJfOdVnVWBfYBhwL3CJ7W0kTUfypDwUqyftobemq7E01MQhaUlJ38rGiSlIS3gX5G+PBq4l1/+z/Q3bX7T9qu33bN9TYrKa8+n6XPjd9p22D2rJidqetOT5dI7MnkgaYEiaIxtK2lbkdzAg6SxJTZ3QifRC1TgkzazUd32lvH8yaczOTRqvj5HHr+0/2F7T9qV5/4ESk9Wc/zZ1X38+30+OsX10/tIfSelFPZVCtgXub8mJ3yWbRSb7mNM1Ekk7Stqhto6CvAO8O9GjBhk513RdSZvn/S8DrwHb5UNeAE4D3su598v1LNPbftb20yUmq5Km709Oue2/2t4zB31eB1YGjs3fXoZUg/SIfK11JW2ncXXWG4+khfKKVlvorenqMNv7teviA42koZLmydtLS/qbpJ4cly+S3pRG2n4bWJxkdsL2HbZ/a/vhAZb8K1IUty3Yfs2py8do22Ntr8i4rjHDSA/7zwAouSvPkbRG3p+2oWaRJUj/9saRJ2ZLTfzIzkSJ2SVNmbd/J+ms/O1FSX3XZ877e5BWRB7Nn+0jKphX1gLOmuhRk4jtd/NL50N5/8cks8g/8yHTApsB70uaW9JVkvaAD8xdTfxcz8G4v3njsL2y7UHbGEHJdT9z3v62pCskLQIY+B7JawEp13SU7aMAbP85/zf6Y09cjsdJ3o9+48QTtv+V96+0PS+wZz5kGMlj0jMHOU3SyZKmzve3JnZcHAEs3K6TNdp0JWkZYB1STtBDpIT950j1ZHve6Ho+XAe0/qztTuh+VLwLh1N1Amw/CuzW8q0LgVcYZxb5A7CqpOVtPyppM+AuJyPKYOYy0pt+UBmlwvubAM/aPpf0wvY1YH3bN0m6hHFmiquAq3p+1vaLFSR/lIEYryY9/LF9YM/XJT1PqirS42jeHDhG0h62j1Uqs2WSIXMwpwDdS0NX+QYjkr4EzGf7iBzcuJiUb7o/yQR1MPBEXhL/oIZ7BxmWBvIZez7QWuLpcNJc5E1gJuARSdfaXi8H1pYEbrL9bEl9hXmN9IxtC73NYV2f5IJtm+urv+S8tUVs/zvneJ0I3Gl715y7tgJwjO37JU1ue0xVwb1A0trA07bvrq0FPjCLvEAa5H8g5TN/TtLipLfnc22fJ0mdlmvUjUiaE9jM9pG1tfSQ89YWAp63/YKkE4GVSEbF+YEfkzrP/DWnw7wzWD5LkuYFlrR9XmUpwAdmkeG2X1EyqmwObG/7TknHkdImDiUHh2pqDRKS9gFOcIcUs8+rarORVh7vz3nV3wd+aPsSSUcDz5MmqJMxrkTjoCDnzv62REpfH7RMRoo6PyPpU8B+wKW2D5P0LdJ983gn/0lXPmN7O2H9OfCq7UPKSRrvtWXbOWr6ZeBC29dLug6YgrScPxT4FHD7IH8rGVTknJwvAc/Z/rOkn5EejrvZ/ruS+esZh1lkQJG0AukGt3yFa/eM12mBbwFv5kjfXqRc6p3yA+/TwDPA/d14A65BnoRsASxg+4B8T70YONn2XkrVDkaQqiqEWWQAkfQosKb7WWGmj9fuGbObA58kLWXPR0pBOcnJwLgIqazTnYM8Uj+oUDJ+rwP8yfYDkm4n5eqvRcp5Xo4u+Jv01nT1LlD8FyJpNknrSZo8J0XfQrqhQsrhMuOWcVezvbztt22/YfuSpkxWc97oFLV1TIycEH+M7Z7k6gNJuXW35f3vk+rFzi5piKSfSdqoitiPIOnynpyrhlI83UHJHPhJZfOapP8ldX6amdS5Zy7SpBTgUNsL2r4E0tK+7fuaMFlV6kzX8XloOdfu9JY0qNtJUe5T8/4awCWk8j1I+rqSWWT6gVf7YSTtLWnL2joKMprCpqt8D14sR/GQ9CVJDwM9ZrY5Sbmdw0gpODM7Vwayfa/tG5swMcp5o4Pi3m/7BtsH2n4gf2kNoMfsNTNwAjndQNKySgbqJSrJ/QBJy0s6pW3nq/mc6Fmiz7/YHYCrbP9F0knAvKQSL0+Tujzd1UF5LwOCkrvuz7bPrK2lv+SoDqTOF3uRDDLfzxG2/Uk19U5WKgXypu0BccpKehZY2vYzEz24y8nL+SYt/f0EGJajLpuS/qYH2z5LqevMS6Rc1EE/EZ1UcmTqy7Y3q62lHbRE3LYk1a3dm9Tk4BpSfuIOpEnNlLZfHSBNxwD3eFwlhWA85HvuMKemFlsA65OqxLxGCiZcZHs3SbOSDHwPDqbl/P6Sl+Dftd2XFvUdx0dWobcDrrX9J0kHk8xu+9i+W9Io4IWBuDcr1ZL/me1Pt+N8A/aHkjSfpM0ljcpvePcyLgL3PskQdS+A7W1tr2X7qRwNuLHbJquZyWhIX2mPY7Ttn9ju6Qd+Kykie1/e/x7wqnK/dklfkLSayjmez2cAVg0GG5KmyascPWWjDic96JZg3ErLNfCBw3d522fl/bttd1zNxAGguIFjIOn5+9k+zfZWTsXsRVox+Uf+/lLAk0oNEJC0jKQNlVvbFuBW4IGJHtVl5GjhJ5XKRSFpLVKnth/lQ94jNbh5K9+DF7a9G0Aeq/d102Q109TxepvtXWz/KX/rBJLBvCcv+grgGaWqSSPzCsonCsl6gdSuui30Nod1U1Iu4jXj+b5IScPPKRUt/wkpn/QISXuTIqV7275PyaDwRBcOkklG0nrAfTXymWqiZBax7beUejqvk/97DzgZuNr2McrtNStK7WgkLQx8bkLRKKW6oe/l3/VhwPy2v5wnqoeQIt+n5ijM61364jhJKDUkmMcdZEodCHKkahrbL0v6AqnV7W/zatl+wFTA4bafjzE7YSQdAhw4vnGWVzmmsf2SUqWWrwP72b5VqYrGA7Z3VjIjj2hKelwJ8ud2W9u/ra1loJE0o+0XlfLVjwRetL1TnnNsApzq1Dq6o8ZrbyesxwG3OfXc7rlBr0layr9P0m2k8gzz5P9vAFxv+562Kw+6DklTksxdI20fn6MJR5PyIn+V3xLfJzqLACBpHWAv2+vk/WlIpWVGO1Vz2Je0lL+xUweorUgpOJd1YXQ0KICkNUlG2KNJUflngBtsr5/TfxYimUWiVBUg6RVgXtuv5P21SJUnfq3UmfAG0svAbkqlymYmtRV+qZ7qoClkH8J6pFbz10j6I6kBwqa2/6Nk/nrQlapY9HbCeiLpH3OT7S/mvJjPAUflN7xpHP1024ZSXco34mb+8eSI/pzAZE61YXcn9YXfyfaFSkXUXyb1iv7Y32F+yfqkG2Ai+Cj5YXcaaaKwPCnSdRRwnlO+8EjSEmGscrQBSSNIOYOv1NbSqSiZ0ua1fbuk5UllCP9le4cc3VmM1JLzY5u0SDqUFAT528CpHjgkvUTK/z7U9m+Vyka9C/yAlD8+eaxytAdJQ4BZbD9dW0unklPxFgUeIhkCLyKN0XlJbWd3JgUsP7bWan4GbWp7x3bo6W0O64+Bf5OiMABvALOQOiT1CGxEAnOHcAawem0RnUrOiX3CqekBto906ixyUT5kNMlN+Z5SK8/b85IbSt1FpiMNxkZGE21fDvwFeDR/6UXSeO3pPPIeDe3yVYmvA7+oLaKTcWo/e3vevtn2MsB38rdHkx6EswNIOkOp296ovD8n6QV15IALHzh2I+Xo9gR+3iNFUWdxqhXa6GY/A8z0wGBvfFMUp257d9h+Mz9v1yOlPb3PuPnj0gCStlXqtrd+3p+VVNWpba1o+1UlIEcAVwWedCrcfyywJSlv7oacz/QEKY+165doe4ukK4AD8sQj6Af5TXE5Uo7mH3P09VDSy9d87oDC0aXJv4NPkfLM/yJpXeCvwBG291Wq2zo18G/br9fUOhiRtBNp+bYt0YRuR6kRzNqkF3eAZ0kvl5+zfV01YQOIUsWNFUnj9E1SSsUztpfMz9/VSSue/60oc1CiVNLqLtujamtpAkpl71YjNYW5QdL1pHJ5v7X9nQn/9CReo92papJmIOXIvZ2do8sBK5NyWn9O6txwmtSdnRp6g6QNgFtiyaJvSFqWVFj5L6S6gk8Dj9leWdLcpOLYt9u+t6LMqih1kxqZE/C3Br4N7O9U1P8QUjrBkbbfiDE7YfLkYvpumUy1mxxJ3Rh4Kqf0HENqQPLZnHL2deC/tq+sqbMmOQ1qVttPS1oUOAy41/aeSm1SVwNOsX1bjNcJ0+OJsH1abS2DlewjmTPnWK8DnEsKsh2iVNJqalLKQFtSRQesDmteft0MGGv7REk7kGrC/dTJhfwJUorBUzHIgkkl52EulB9oCwPHkfql/4+kbUkJ40fZflDSFJEPPOlI2pj0srkfqU7jvaT812/m6M4spCoWjY9OB+0hu9wXJHXFe0nS70hLissDnyDVej3b9jlKTVPGxPNg0lDqKPh5kgnrJkkXkfIMv2j7v0qVP+62/UZVocGgoecFiVRx4kFJ3wR2AfawfYWk40lztp/lFTyXfB5UaxyQS0osRIrGPq5U3PZbwAa2b5T0A1JU7MxuTSdQann6ku0xtbXUpidaIGk54IukidNNkv6dD/kkqSnBKkRr3iLknKTZ8svBOsCxpMnFD/PkdjbgXNtPVRVaiWwoGmL75dpaatMyXqcnFTF/NZuIfgJ8A/iW7cuzKeMp0otPTEzbSDYBLkXynQwh1U0eaXuxnG6xGXC57RsryqxGnmDNbPvJ2lo6gZYxuwUpDWVPksHqSuAE2/tIWpyUQ36HKxiVqxmkbI91avP2eN7fh5RcflOe1Q8D1rX9vqRPSPpnzjvsKWre8S1L28B5pBtOVyFpVknrKhU2HqXUmvfc/O2ZSMXLe6IEK9peISeHv+YGtebtNJwKjN+aty+1vRCpLBakAtwrkqKuZLPM6ZKmVGoUMlMd1QPKjoz7fXQNSq15V1GqrU1OBXs+r6pBMuU+n7cPsL1AT16+7cvzcyAmq20mG2Wuz/fGMbZXAhbP3xYp+roCgKTvSbpQ0qp5fyY130A9N7n5STchaTJJiyqVqELSZkqtebfNh8xD6mg3jGRKG5XnZ9i+y/a/akxWoXJr1kkl55qsRCpfdLmkbwO/BnbNb+3rkB6YN9T6RZZA0q3Adrbhw6hVAAAJIElEQVRvqa2lBJKGO7UNXArYnlT/8xxJp5Kcwl8lOdtXICXHx1LWICH/TZcm9aefndTK81rbGyg1FVmSNF4bk58t6UekpbN9amspQV7Ofx8YTuqgJNs/zrml3yflPp+Tl6ZfIJmDOv8BE/Ss5q1Mipw9LOlikkFzYZLZbWvgVtt3VJTZVnIK2QX5xbtx9AT+8jN2S2BdYHdSmbSbSathu0uaHZiGVF+1o9O7BsWE9ePIEdZhtl/PkdcvAdsADwK/B+4HDuo5fjDeOCVtBFznBhSFljQfKQJ3Kalkyz2kdJCl8+Tms8DFtqPMSAPJ0Zrps7lrdVIk8kKnjmW7AvMBv8kPy47qrjKp5M/xZD1R6MFMTm9YhdRz/GZJvyIZ8lYg5TL/mJQrfkFFmUFB8qrIi8AIkjdgCttfUaomsjPwF9sXDOLxOi2wphtQ0zdPTlcmlT87R6kCzOnAL20fKOlrpJS5P7fLAFWDQTthHR/5wfgVYG7bh0taGbiAcTkY85IKqHdj7+Si5EEzo+0X8tvrj0glkn6j1KJxGeB/cvL2/CTHfvwNupw8Rj8DnGH7MUn3A6+QSuYNIRly7ogIe/vJpsX3nKq6HAHMYfurklYD9ieVpPljjsK86iha3/Xkz8JGpHKW5+fPzcakvOQrlBpCPA08PRgDRZ1MXuUYafsVSV8FtiA9Z+8CLid1jdtVqavhVMCzTfobNG7C+nFks8g0tu/Py1f7Ab/I6QTfAiYjPSw7qkONpHlIg/6d2lo+jjwp/TQpcf8hSXeSlhbmJRlwPk+KEHdt2aig92SzyCI5sjcPqSzZM7Y3ynlXnwHOt/2fmjo/So5Ije1U01WOKK0LvG77Ikk/I1VqWd/2lUoO4CdsX1pVaDCoUDJQL0yaHL0k6RTSvX8hUlm8n5K6k507/rMMPHmVdpTtJ2prGR+S1ibdC4/JLwLXAMfa3kOp7fGMpFS6jrzntJumJ1UDH5hF7s/bp+aclf/L3x5NWqoenk0+t2TTANkwMmMd1QBcRkqArkb+nSyixOyS/paXByEtGa7KuM4zq5PaLtr2U7ZPjMlq0FuyWeTmvP2Y7RWBL+RvjyGZRRYAkPQrSRfklyfyZ7TWfW1PxnVtqkLLOJ0h7x8m6UYlR/T0pLzwnkLphwNTO9c1tf27mKwGvcXJQH1PT+qa7a1J7vtXSPnOPc9YJK0v6TpJW+X9mZRqQddgcaB6OoBS18V58vYmkq6U9Pn87c2BBfNLwR2kCfYeALavsP2XbpmsQpdEWCeV/KBbEpgrL3V8ErgYOM32TpJWJLUGvNb28xM6V5v0PAKs7fH01S50zU+QJgfX2r5e0lXAHKQGEO8B65OaGQyYpiAYH0rtOlcArgZeBv5Dijr0GAk2IfWqv3sAtBxG6vIyYO1ZJU1FagnrvGK0Gym/dAfbZ+dctpdJ+aaRfhNURdLUpBSfV53K4x1EMuxtZPsySZsBz5GeP0UNQDkV6TdO1RMGDKXWpauSUm5GAfeR5hjfViobNTupe1lHrfh2AjFhnQh5Ejsim7s2JtWKPS5PaH9BKv1wYDaTDG3nIMvXu6zduXv5prEEcCspyn4RyQD1+bzMsDFpAN3U7n9TEJRG0jS2X8tRi4NI6QR7Svoi6bN9ou1rC4zX5YC32z05znlrC5PG6KNKNau3ANYglYs6FrjS9u/zsWOblLcWNJu8NG/bY5S6660KrENauTuR1B3zmBxlfL9dn+2cwrO87b+343wt5xVp0jlrTmtal9QG/Lf53/FDUn7pEcDrpNrN8TI5CcSEtR9IWoNUsP43+UvPkdqQbZAHw8JUNIsouzclLQB8k1SW5Cyl7jJLApsCjwJrkspGRf3SoLFko99ngdts/0vSX0njYEPb9yq1EryfSmaR/KBT3t2TlHf/Y6WWmz8HDsqT0mVIy6wPxYMuaCo5l31DYHLbp+Q0gl8C++WJ35KkcpbVDNQtz9gNSHm7PyfNAx4meTu2zia1OUjP2NE1dDaFmLC2ESXH7Vy278nLDUeTlja+n6M7i5A6d03ScrqkhYBHJiUKJGlm0rL9zaQaiNeR3vDmV+o5vTmpjFBXdjUJgo+So5ELAY8A7wAXknLGFwPmAnYiRXcmKa8zmzvHTEpOmVJt6WXy8bdI+jGpRuKGtv8p6UBSj/hT+/BPC4LGkV/oZiVFJJ9U6ob5bVKt8qsk7UuaLJ5k+91JON8IUlWbxyfx+ouRPBoXKpk//0jq9LeHUneoWYFTByJdsFuJCesAkaOxXwT+mJfazyVVJ9iKlGM2H/CoW+rZSXoBWLR1AEgaYfvNnLu3FynK8ktJ+wBrAXvavi3nwjzqKEMTBL0mR0W2J9Uh/V9J3yUtwx9g++I8/l6w/XbLzxxHagt8bMvXhpEesGMk7U2K6G4JLAscT3q4HpvTF96OVY4g6BuSdiQ1K9mR9OJ5AWlCua9Si+Ahtl9sOX4dYG/ba7d8TcBU+Rn7eeBrwK9t/1vSFcAz+WsjSRPUh2OVY+CICWsl8gNqOVLL0alJDsBnba+Yl/DXIHXz+h6pNNR9ki4ldfwalX9mK1IE96Ya/4Yg6BaUSkItCzzu1NzgFFJKzZJOdYV3JNWmfAC4KE9qdyUtEX7N9t+USui9BPzVg7DQehAMFvKL4uKkKhjXKNUsPZ70wnl4noyuDGyQv34iqfLIjaSSeVvnFKH5SSuTz9T4dwQfJiasHYSkqWyPzjlqe5ISt18BjrB9XV5yfD7e6IKgPtks8g5ppeSXJCPjMFLaz69zVGdM5K0FQX2ygXq4U5OM7UgrJkNIPo7vksbyjLHK0bnEhDUIgiAIgiDoaLqicUAQBEEQBEEweIkJaxAEQRAEQdDRxIQ1CIIgCIIg6GhiwhoEQRAEQRB0NDFhDYIgCIIgCDqamLAGQRAEQRAEHU1MWIMgCIIgCIKOJiasQRAEQRAEQUcTE9YgCIIgCIKgo4kJaxAEQRAEQdDRxIQ1CIIgCIIg6GhiwhoEQRAEQRB0NDFhDYIgCIIgCDqamLAGQRAEQRAEHU1MWIMgCIIgCIKOJiasQRAEQRAEQUcTE9YgCIIgCIKgo4kJaxAEQRAEQdDRxIQ1CIIgCIIg6GhiwhoEQRAEQRB0NP8PhpKMf3gZp54AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 3)\n", + "labels = ['initial', 'middle', 'final']\n", + "for i in range(3):\n", + " ax[i].axis('off')\n", + " ax[i].set_title(labels[i])\n", + "ase.visualize.plot.plot_atoms(traj[0], ax[0], radii=0.8, rotation=(\"-75x, 45y, 10z\"))\n", + "ase.visualize.plot.plot_atoms(traj[50], ax[1], radii=0.8, rotation=(\"-75x, 45y, 10z\"))\n", + "ase.visualize.plot.plot_atoms(traj[-1], ax[2], radii=0.8, rotation=(\"-75x, 45y, 10z\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving a trajectory video\n", + "\n", + "More visualization resources can be found here: https://wiki.fysik.dtu.dk/ase/ase/visualize/visualize.html." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter imagemagick unavailable; trying to use instead.\n" + ] + }, + { + "data": { + "image/png": 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7+wcIQOBAggUNHkSYUKHBAwQAPIQooAAAihUtXsSYUeNGjh09fsQoAMBIkiVNnkSZUuVKli1dkpRwAMBMmjVt3sSZU+dOnj19+iRgAMBQokWNHkWaVOlSpk2dEpVwAMBUqv9VrV7FmlXrVq5dvW5FgAAAAQMAzJ5Fm1btWrZt3b6FG/esAgIA7N6NUADAXr59/f4FHFjwYMKFDe9dwAAAgAMAHD+GHFnyZMqVLV/GnPmyhAMAPH8GHVr0aNKlTZ9GndrzAgYAXL+GHVv2bNq1bd/GnVs3hQMAfP8GHlz4cOLFjR9Hnvw3AQAACACAHl36dOrVrV/Hnl379ugSCgAAH178ePLlzZ9Hn179+vUFKgCAH1/+fPr17d/Hn1///vgVCgAEIHAgwYIGDyJMqHAhw4YKCRAAUKACgIoWL2LMqHEjx44eP4K0CIEAgJImFRAAoHIly5YuX8KMKXMmzZoqGSz/AEBgAYCePn8CDSp0KNGiRo8iNVqhAICmTp9CjSp1KtWqVq9ibcpgAYCuXr+CDSt2LNmyZs+iTVuhAIC2bt/CjSt3Lt26du/ibYvgAIC+fv8CDix4MOHChg8jDswAAOPGAA4AiCx5MuXKli9jzqx5M+fKBSIACC16NOnSpk+jTq16NWvRBggAiC17Nu3atm/jzq17N2/eByQACC58OPHixo8jT658OXPhBggAiC59OvXq1q9jz659O/fsDBQAOEABAPny5s+jT69+Pfv27t+XJwBgPn0ABggAyK9/P//+/gECEDiQYEGDBxEmVKgwggAADyFGlDiRYkWLFzFm1LjR/wABAB9BhhQ5kmRJkydRplT5kYECAC9hxpQ5k2ZNmzdx5tQ58wAAnz8BRAAwlGhRo0eRJlW6lGlTp0cJIAAwlWpVq1exZtW6lWtXr1QHABA7lmxZs2fRplW7lm1btwgoAJA7l25du3fx5tW7l2/fuQMABBY8mHBhw4cRJ1a8mLFiBQUAIKAAgHJly5cxZ9a8mXNnz58rLwAwmjQACgBQp1a9mnVr169hx5Y9OzUEBQAIFACwm3dv37+BBxc+nHhx48QHAFC+nHlz58+hR5c+nXr15RAUANC+nXt379/Bhxc/nnx58wMApFe/nn179+/hx5c/n756AgDw59e/n39///8AAQgcSLCgwYMIEyo8WAGAw4cQI0qcSLGixYsYM2pUAAGAx48gQ4ocSbKkyZMoU34cAKCly5cwY8qcSbOmzZs4bRIAAEABBABAgwodSrSo0aNIkypdGlQCgKdQASgAQLWq1atYs2rdyrWr169VKSAAcEAAgLNo06pdy7at27dw48qFOwCA3bt48+rdy7ev37+AA9+lgACA4cOIEytezLix48eQI0seAKCy5cuYM2vezLmz58+gLQsoAKC06dOoU6tezbq169ewUy8AQLs2AAQAcuvezbu379/AgwsfTrw3AgEAkitfzry58+fQo0ufTj05AQMAsmvfzr279+/gw4v/H0++vIAIANKrX8++vfv38OPLn08/fQEDAPLr38+/v3+AAAQOJFjQ4EGECRUuRAihAAABDABMpFjR4kWMGTVu5NjRI8UCAESOJGAAwEmUKVWuZNnS5UuYMWWirHAAwE2cOXXu5NnT50+gQYUKLWAAwFGkSZUuZdrU6VOoUaUipVAAwFWsWbVu5drV61ewYcVuLQDA7FkCEQCsZdvW7Vu4ceXOpVvX7tsCBwDs5dvX71/AgQUPJlzY8N4CFQAsZtzY8WPIkSVPplzZ8uUFDABs5tzZ82fQoUWPJl3a9OYDFQCsZt3a9WvYsWXPpl3bNm0BBAAwYADA92/gwYUPJ17c//hx5Ml9ExAAwPlzAgwATKde3fp17Nm1b+fe3Tt1AwUAFCgAwPx59OnVr2ff3v17+PHdH6gAwP59/Pn17+ff3z9AAAIHEixo8CDCgQYIAGjo8CHEiBInUqxo8SJGjAckAOjo8SPIkCJHkixp8iRKjwcAsGzp8iXMmDJn0qxp8ybMAxEA8Ozp8yfQoEKHEi1q9ChSBgsAMG3q9CnUqFKnUq1q9SpTBBIAcO3q9SvYsGLHki1r9mxZAgAARFgA4C3cuHLn0q1r9y7evHrfHoAA4C9gAgcAEC5s+DDixIoXM27s+HHhAQQACEAA4DLmzJo3c+7s+TPo0KI/I5AA4DTq1P+qV7Nu7fo17NiyUQ8AYPs27ty6d/Pu7fs38ODCEVAAYPw48uTKlzNv7vw59OjHGQCobv069uzat3Pv7v07+OwFFAAob54AAgDq17Nv7/49/Pjy59Ov714AAgD69/Pv7x8gAIEDCRY0eBBhQoULDSqgAABiRIkTKVa0eBFjRo0bOUIQAABkSJEjSZY0eRJlSpUrQSqAAABmTJkzada0eRNnTp07c0oAAACCAgBDiRY1ehRpUqVLmTZ1OpRAAQBTqSKAAABrVq1buXb1+hVsWLFjsw4AcBZtWrVr2bZ1+xZuXLlzFUAAcBdvXr17+fb1+xdwYMF4DQAwfBhxYsWLGTf/dvwYcuTFBABUtnxgAQDNmzl39vwZdGjRo0mX9nyAAADVq1m3dv0admzZs2nXVi0AAgDdu3n39v0beHDhw4kXN05BAQDly5k3d/4cenTp06lXVy4gAgDt27l39/4dfHjx48mXHy8AAAAKCAC0d/8efnz58+nXt38ff/sDCgD09w/wgAAABAsaPIgwocKFDBs6fEiQgAEAABAQAIAxo8aNHDt6/AgypMiRIAVEAIAypcqVLFu6fAkzpsyZKAkYAIAzp86dPHv6/Ak0qNChRAVEAIA0qdKlTJs6fQo1qtSpSQ8AuIo1q9atXLt6/Qo2rNitCgQAOIs2rdq1bNu6fQs3/67cuRQOALiLN6/evXz7+v0LOLDguwsYADiMOLHixYwbO34MObLkyRIOALiMObPmzZw7e/4MOrToywIWADiNmkABAKxbu34NO7bs2bRr277NukAFAAAYFAAAPLjw4cSLGz+OPLny5cgXMAAAPbr06dSrW7+OPbv27dALVAAAPrz48eTLmz+PPr369ewXMAAAP778+fTr27+PP7/+/fAJMAAIQOBAggUNHkSYUOFChg0NHjgAQOLEAwcAXMSYUeNGjh09fgQZUuRGBgQAnESZUuVKli1dvoQZU+ZJBgsA3MSZU+dOnj19/gQaVOjQCgUAHEWaVOlSpk2dPoUaVepRBv8LAFzFmlXrVq5dvX4FG1bs1wIRAACoUADAWrZt3b6FG1fuXLp17a4tQADAXr4LFgAAHFjwYMKFDR9GnFjxYsAHJACAHFnyZMqVLV/GnFnzZs4MFgAAHVr0aNKlTZ9GnVr1atAHKACAHVv2bNq1bd/GnVv3bt4CFAAAHlz4cOLFjR9Hnlz5cuIHADyHHl36dOrVrV/Hnl079AgCAHwHH178ePLlzZ9Hn179egMEALyHH1/+fPr17d/Hn1//+wgCAAAEIHAgwYIGDyJMqHAhw4YJCSAAAMAAAQAWL2LMqHEjx44eP4IMaVHBAQAmTyo4AGAly5YuX8KMKXMmzZo2VyL/oAAAgAIAPn8CDSp0KNGiRo8iTXo0ggAATp9CjSp1KtWqVq9izeoUAQUAXr+CDSt2LNmyZs+iTas2ggAAbt/CjSt3Lt26du/izeuWwAEAfv8CDix4MOHChg8jTix4AQIAjh8TACB5MuXKli9jzqx5M+fOlw0ACC16NOnSpk+jTq16NWvREBQAiC17Nu3atm/jzq17N+/eAwAADy58OPHixo8jT658eXAICgBAj16AAIDq1q9jz659O/fu3r+Dr64AAgAAFACgT69+Pfv27t/Djy9/fnwICgDgz69/P//+/gECEDiQYEGDBxEmVChQAQQADyFGlDiRYkWLFzFm1LgR/4ICAB9BhhQ5kmRJkydRplT58YAAAC9hxpQ5k2ZNmzdx5tQ5EwEBAD+BIigAgGhRo0eRJlW6lGlTp0+RRgAwlWpVq1exZtW6lWtXr1QpIAAwlmxZs2fRplW7lm1bt28HAJA7l25du3fx5tW7l2/fuRQQABA8mHBhw4cRJ1a8mHFjxQgEAAAwAEBly5cxZ9a8mXNnz59BWz5AAEBp0xAOAFC9mnVr169hx5Y9m3Zt1QIiAABAAEBv37+BBxc+nHhx48eRG6eAAEBz58+hR5c+nXp169exNxcQAUB379/Bhxc/nnx58+fRp49wAEB79+/hx5c/n359+/fxxz8AgH9///8AAQgcSLCgwYMIEypcyNCghAMAIkqcSLGixYsYM2rcyJEjAQMAQoocSbKkyZMoU6pcyVKkhAMAYsqcSbOmzZs4c+rcyTNngQIACBgAQLSo0aNIkypdyrSp06dFGRQAQLWqAAIAsmrdyrWr169gw4odSzbrAgYAAAgAwLat27dw48qdS7eu3bt1JRwAwLev37+AAwseTLiw4cN8FzAAwLix48eQI0ueTLmy5cuYJRwAwLmz58+gQ4seTbq06dOcCxQAwLq169ewY8ueTbu27duwIxAAwLt3AQDAgwsfTry48ePIkytfTrwABQDQo0ufTr269evYs2vfHr1CAQDgw4v/H0++vPnz6NOrX7++QAUA8OPLn0+/vv37+PPr3x/fQAGAAAQOLADA4EGECRUuZNjQ4UOIEQ8yWACgAAQAGTVu5NjR40eQIUWOJCmyQgEAKVWuZNnS5UuYMWXOpJkywgIAOXXu5NnT50+gQYUOJVq0QgEASZUuZdrU6VOoUaVOpZpUAAIAWbVu5drV61ewYcWOJdsVAQC0aQEsIADA7Vu4ceXOpVvX7l28eeMWEADA71/AgQUPJlzY8GHEif8aIADA8WPIkSVPplzZ8mXMmTMfkADA82fQoUWPJl3a9GnUqT8PIADA9WvYsWXPpl3b9m3cuW0LOAAAgQQAwYUPJ17c//hx5MmVL2cuXAEA6NEBSCAAwPp17Nm1b+fe3ft38OGtQxAAAAABAOnVr2ff3v17+PHlz6cvfwABAPn17+ff3z9AAAIHEixo8CDChAoVQlAA4CHEiBInUqxo8SLGjBo3SgDg8SPIkCJHkixp8iTKlCMLAGjp8iXMmDJn0qxp8yZOlwMA8Ozp8yfQoEKHEi1q9ChSBBQAMG3q9CnUqFKnUq1q9WrTAQC2cu3q9SvYsGLHki1rluwBAgAUUADg9i3cuHLn0q1r9y7evG8pAOjrF8ACAIIHEy5s+DDixIoXM248mIICAAUUAKhs+TLmzJo3c+7s+TNozwMAkC5t+jTq1P+qV7Nu7fp1aQoIANCubfs27ty6d/Pu7fs38AEAhhMvbvw48uTKlzNv7pw4AgIAplOvbv069uzat3Pv7v16BADixwM4AOA8+vTq17Nv7/49/Pjy1yNgAOA+/vz69/Pv7x8gAIEDCRY0eBBhwoEEBgBw+BBiRIkTKVa0eBFjRo0CIADw+BFkSJEjSZY0eRJlSo8EDABw+RIAAQAzada0eRNnTp07efb0SZPCAQAKGAAwehRpUqVLmTZ1+hRqVKcEBgCwehVrVq1buXb1+hVs2KsSDgAwexZtWrVr2bZ1+xZu3LgEDACwexdvXr17+fb1+xdw4LsRCgAwfBhxYsWLGTf/dvwYcmTFBwBUtgyAAQDNmzl39vwZdGjRo0mX9nwAAQDVq1m3dv0admzZs2nXVl2gAgDdu3n39v0beHDhw4kXN74gAgDly5k3d/4cenTp06lXV16gAgDt27l39/4dfHjx48mXH7+AAIAFDAC0d/8efnz58+nXt38ff3sCCgD09w+QAAQABAsaPIgwocKFDBs6fFiwQgEABAgAuIgxo8aNHDt6/AgypMiPBSoAOIkypcqVLFu6fAkzpkyUFQoAuIkzp86dPHv6/Ak0qFChBSQAOIo0qdKlTJs6fQo1qlSkBAAAIEAAgNatXLt6/Qo2rNixZMtqLSABgNq1bNu6fQs3/67cuXTr2mXAAIDevXz7+v0LOLDgwYQL6z0gAYDixYwbO34MObLkyZQrTz4AAACDBQA6e/4MOrTo0aRLmz6NunOBCABauyagAIDs2bRr276NO7fu3bx7zzZAAAACBACKGz+OPLny5cybO38OvfkBCQCqW7+OPbv27dy7e/8O3roBAgDKmz+PPr369ezbu38PH/4BCQDq27+PP7/+/fz7+wcIQOBAggUNHhQAQOFChg0dPoQYUeJEihUdFhAAQONGAAcAfAQZUuRIkiVNnkSZUuXIBQoAvIQZU+ZMmjVt3sSZU+dLBBQA/AQaVOhQokWNHkWaVOnSCAIAPIUaVepUqv9VrV7FmlXrUwQUAHwFC4AAALJlzZ5Fm1btWrZt3b4tWwEAAAYKANzFm1fvXr59/f4FHFgwXgIADB9GQAHAYsaNHT+GHFnyZMqVLTMeAEDzZs6dPX8GHVr0aNKlTSOgAED1atatXb+GHVv2bNq1V0sAkFv3bt69ff8GHlz4cOK9CRQAkFx5AQEAnD+HHl36dOrVrV/Hnl06ggIAvH8HH178ePLlzZ9Hn967AggA3L+HH1/+fPr17d/Hn18/BAUA/AMEIHAgwYIGDyJMqHAhQ4YKIACIKHEixYoWL2LMqHEjR40MAACAoAAAyZImT6JMqXIly5YuX5IsgAAAzZoHFgD/yKlzJ8+ePn8CDSp0KFGdAwAAKEAAANOmTp9CjSp1KtWqVq9SVQABANeuXr+CDSt2LNmyZs92HQBgLdu2bt/CjSt3Lt26du8qiABgL9++fv8CDix4MOHChvkWAACAAIDGjh9Djix5MuXKli9jdqyAAYDOnj+DDi16NOnSpk+jTk0BAYDWrl/Dji17Nu3atm/jbi0gAoDevn8DDy58OPHixo8jN14AAAAKCABAjy59OvXq1q9jz659O3QFCwCAD1/gAIDy5s+jT69+Pfv27t/DL0/AAAAAAgoAyK9/P//+/gECEDiQYEGDBxEmVLjwoIAIACBGlDiRYkWLFzFm1LgR/yIBAwBAhhQ5kmRJkydRplS5kqWACABgxpQ5k2ZNmzdx5tS5M+YCAD+BBhU6lGhRo0eRJlU69AACAE+hFigAgGpVq1exZtW6lWtXr1+xRigAgGxZs2fRplW7lm1bt2/JLmAAgG5du3fx5tW7l29fv38BSzgAgHBhw4cRJ1a8mHFjx48JL2AAgHJly5cxZ9a8mXNnz585F6AAAACFAwBQp1a9mnVr169hx5Y9GzUBAgBw517AAEBv37+BBxc+nHhx48eR9y5QAUBz58+hR5c+nXp169exZ1/AAEB379/Bhxc/nnx58+fRdy9AAUB79+/hx5c/n359+/fxxydAAEB///8AFSgAQLCgwYMIEypcyLChw4cIFRAAQLGixYsYM2rcyLGjx48UGSwAQLKkyZMoU6pcybKly5cwKxQAQLOmzZs4c+rcybOnz580GSwAQLSo0aNIkypdyrSp06dMCSgAAKBCAQBYs2rdyrWr169gw4odixXBAQBo0ypQAKCt27dw48qdS7eu3bt42x6QAAAAAgCAAwseTLiw4cOIEytenJjBAgCQI0ueTLmy5cuYM2veDPmABACgQ4seTbq06dOoU6tezZrBAgCwY8ueTbu27du4c+veHbsAgN/AgwsfTry48ePIkysfzkABgOfQo0ufTr269evYs2vfboAAgO/gw4v/H0++vPnz6NOr/x5BAID38OPLn0+/vv37+PPrx08AAACABggAIFjQ4EGECRUuZNjQ4UOCDBQAoFjxQAEAGTVu5NjR40eQIUWOJJkRAQUAACIAYNnS5UuYMWXOpFnT5s2aEQQA4NnT50+gQYUOJVrU6FGeCigAYNrU6VOoUaVOpVrV6lWsEQQA4NrV61ewYcWOJVvW7FmuBQQAYNvW7Vu4ceXOpVvX7l24CgoA4Nv3QAEAgQUPJlzY8GHEiRUvZlwYAgDIkSVPplzZ8mXMmTVvjgxBAQDQoUWPJl3a9GnUqVWvZj0AwGvYsWXPpl3b9m3cuXXDpqAAwG/gwYUPJ17c//hx5MmVH0fAAACAAQCkT6de3fp17Nm1b+fefXoBAgDEj4egAMB59OnVr2ff3v17+PHlnxcAAcB9/Pn17+ff3z9AAAIHEixo8CDChAoLUlAA4CHEiBInUqxo8SLGjBofKmAA4CPIkCJHkixp8iTKlCpHEgDg8iWABQcA0Kxp8ybOnDp38uzp8ydOBQCGEi1q9CjSpEqXMm3qlCgFBACmUq1q9SrWrFq3cu3q9esAAGLHki1r9izatGrXsm07VgICAHLn0q1r9y7evHr38u2r98ABAAQGAChs+DDixIoXM27s+DFkwwIKAKhseUEBAJo3c+7s+TPo0KJHky6teUEEAP8AFABo7fo17NiyZ9Oubfs2btsSEADo7fs38ODChxMvbvw48t4LGABo7vw59OjSp1Ovbv069uwSDgDo7v07+PDix5Mvb/48+u4ECgBo7/49/Pjy59Ovb/8+/vgQCgDo7x8gAIEDCRY0eBBhQoULGTY8SMAAAIkTKVa0eBFjRo0bOXacWOEAAJEjSZY0eRJlSpUrWbZsWcAAAJkzada0eRNnTp07efacSaEAAKFDDwAwehRpUqVLmTZ1+hRq1KMMGAAgEAFAVq1buXb1+hVsWLFjyYqtcABAWrVr2bZ1+xZuXLlz6aZlsABAXr17+fb1+xdwYMGDCReuUABAYsWLGTf/dvwYcmTJkyknVoAAQGbNmwkIiBBBAADRo0mXNn0adWrVq1mTFgAAdmwACggAsH0bd27du3n39v0bePDcBBgAMH6cgQEIAhZQMCAAQHTp06lXt34de3bt26MbKAAAfHjx48mXN38efXr169cfqAAAPnwIFAgAsA+gQIUFAPj39w8QgMCBBAsaPIgwocKFAw0QAAAxosSJFCtavIgxo8aNGAUoAHBAAoCRABRIAIAyJQACBgoAeAkzpsyZNGvavIkz5wEAPHsCqEAAgNChRIsaPYo0qdKlTIkWECBAAYCpVAFEEAAgq1YAEhQA+Ar264IIAMqaPYs2rdq1bNu6fbvW/wABAHTr2r2LN6/evXz7+qVbgIIBCBEoGGAAIHFiBgIAOH5MYACAyZQpFzAAILPmzZw7e/4MOrTo0QQAmD4NAAIBAKxbu34NO7bs2bRr2wZQwIAAALwBFKBAAYDw4QAIHACAnIABAMybOx8AILr06dSrW7+OPbv27QMAeP8OPrz48eTLmz+P3nwFBQDauwdAYQGA+fQRUACAH8AAAPz79wdYwAAAggUNHkSYUOFChg0dDgAQUeJEihUtXsSYUeNGjAgkAAAZEmQBAwBMHigAAAEFAC0BUBAAQOZMmQwYAMCZU+dOnj19/gQaVGgEAEWNAmAAQOlSpk2dPoUaVepUqv8AIAgAkFWrVgkIAACgAGEBgwgHAJxFUIEAALZtCxgoAEDuXLp17d7Fm1fvXr54BwAAHFjwYMKFDR9GnFgxAAoIADyGDBlCBAkDDEigQEFChQEUEABgIKEAANIAEBgQAED1atatXb+GHVv2bNqxBwDAnVv3bt69ff8GHlw4AAgKABxHjlxChQgLBDyHvgBChQoIBBiQwIBBhQoKAHwHH178ePLlzZ9Hn/47BQDt3QMgAED+fPr17d/Hn1//fv4ABACkAGAgwYEEBiwQoFBhBAkCHgqIYCACAAULFiAAoHEjx44eP4IMKXIkSY4DAKBMqXIly5YuX8KMKfOlggEHAOD/zAkgggQBPn9GqCBg6NAFEigASKp0KdOmTp9CjSp16lMDAK5iBXAAANeuXr+CDSt2LFmyBBZAgLCAAIC2bQtEGFBBgoEDAO7eZWBggYC+fiNQECB48AIJFAAgTqx4MePGjh9Djix5MuIBAC5jzqx5M+fOnj97ZjAAggIFEQxEAKBawAAKDAQIiGBAggAFDAxUWCBgN+/evgUsqCAAAPHixo8jT658OfPmzp8DGABgOvXq1q9jz659e/YIEggACB+eAgQCFCowEKBe/QIIEgxIYCBgPv369uszGFAAAP/+/gECEDiQYEGDBxEmVLjwoAIADyECEACAYkWLFzFm1LiR/2PGAxUAhBQZUkIFCQsEpFSZsgIEAS9hxhQQAYIAmzdvUoAAgGdPnz+BBhU6lGhRoz4JGACwlGlTp0+hRpU6lWpTCAIAZNWaVYKEBQLAhgXLwMACAWfRphUAQYIAt28XRKBQYUAFCREEFACwl29fv38BBxY8mDBhAgYAJFa8mHFjx48hR5a8eAABAJcxA1BgYIEAz58/S6AggHRp06QhSBCwWsACCgMMSIAQgTYFCQYkKACwm3dv37+BBxc+nPhwBACQJydQAUBz58+hR5c+nXp16QMAZNcOgIABBgLAhxdvgIEA8+fRm1+wQICABRAGSGAggH59+gsgVJBQAEB///8AAQgcSLCgwYMIEypcmJCAAQAQI0qcSLGixYsYKxooAKCjxwUSBIgcSXLBgAUCUqpcyXJBBQMMBMicSVPmAggDBADYybOnz59AgwodSrToTgIUAChdyrSp06dQo0p9yoABgKtYDTAQwLWrVwYGBIgdS3YsAwYLDEhYIKCt27dwGRgQAKCu3bt48+rdy7evX7wFKgAYTLiw4cOIEytezLhwAQMFAEgGcMDAAgGYM2tmYECA58+gP1OgUEHCAgGoU6tejZqBAQQAYsueTbu27du4c+ueXaACgN/AgwsfTry48ePIhSswgACAcwESBEifTl0AAwMCsmvfrp1CBQMLBIj/H0++PPkIBggAWM++vfv38OPLn0+fPQEGAPLrJ7AAgH+AAAQOJFjQ4EGECRUaRCChQoQIBiAIoFjRooAFAxYI4NjRI8cIAxgIIFnS5EmUFCAciEABggAAMWXOpFnT5k2cOXXWLFABwE+gQYUOJVrU6FGkBwQIkBBBwFOoUZ8aYCDA6lWsVilIENDV61ewYQUwGFBhAQIFEAwIANDW7Vu4ceXOpVvX7tsCFQDs5dvX71/AgQUPJsyXQgQBiRUvTiwBggDIkSULWDCAgQDMmTVv5rzAwAIAoUMXqCAAwGnUqVWvZt3a9evVBRgAoF0bAAEAuXXv5t3b92/gwYXrhgBB/8Bx5MmPRzCwQMBz6NEjGIAgwPp17Nm1U4gAwPt3AAUMEABQ3vx59OnVr2ffHv0BCQDkz6df3/59/Pn179+/gAJAAQIHEhS4wAADAQoXMqRggIKAiBInUqS4YAABABo3aoSwAADIkCJHkixp8iTKkQckAGjpEkABADJn0qxp8ybOnDp3zkRQQQDQoEKDQjCwQADSpEkrSIAg4CnUqFKlRpAA4CpWrAgoAOjq9SvYsGLHki1rtusBCQDWsm3r9i3cuHLn0mVLYMACAXr38tW7oAIFAYIHDzbAQADixIoXM45AAQDkyJEPSABg+TLmzJo3c+7s+bPlAxIAkC5t+jTq1P+qV7NubRoCBAGyZ9OezWBABAG6d+s2wEAA8ODChxNnUAEA8uTJBUAA4Pw59OjSp1Ovbj06AQQAtnMnoAAA+PDix5Mvb/48+vTiDxhYIOA9/PjwGQyAIOA+fgEGKEQQ4B+gAIEDCRYUuMDAAQALGS6UoABARIkTKVa0eBFjRooIKADw+BFkSJEjSZY0eRIlAAgUBLR0+fIlAwMSFgiwaVNCBQgCePb0+RMoBAkAiBYFoKACAKVLmTZ1+hRqVKlOEVAAcBVrVq1buXb1+hVsWAAEDEQQcBZt2rQLJAygsEBAXAgGIAiwexdvXr0LJEg4AAAwgAUGCgAwfBhxYsWLGTf/dqyYwAEAkykfiAAAc2bNmzl39vwZdGjOCAwwEHAadWrVDCQMMEABAoQBCwTUtn0bd24BCyQMkAABggEIBQAUN34ceXLly5k3d34cAQUA06lXt34de3bt27ljR2AAwgIB48mXJ7+AggEBCyhIMBBBQHz58+nXj19BAAIFCggA8A8QgMCBBAsaPIgwocKFAxFEAAAxosSJFCtavIgxo8UDFSQsEAAypEgBDCpIIAAgJYAFFRYIeAkzpsyZDAwAuIkzp86dPHv6/AkUpwIIAIoaPYo0qdKlTJs6LYoAQoUKEA4AuIo1K4MBEiIsEAAW7AIIFQYIAIA2LQQDEAS4fQs3/27cBRUEALiLN6/evXz7+v0LGK8CCAAKGz6MOLHixYwbOyYgoYKAAgUWVKAAILPmzQAESBhgQIJoAwMMDIgAILVqABAWDGAgILbs2bRnU5AAILduBBEiMCgAILjw4cSLGz+OPPlwBAIAOH9eQAGA6dSrW7+OPbv27dQlMAAAPjwECgDKmz9v/gACARAGWJiQwQCA+fQBKChgwMACAfz7+wcoQOBAARAMEACQEACCChIWCGBggAIBABUtXsSYUeNGjh07KoAAQORIkiVNnkSZUqVIARQAvIT5UgICADVt3rSpwMADDAF8WhAAQOhQAAouNDDAQMBSpk2bLqBgoAAAqv8AEBhAAECrVgYVCAAAG1bsWLJlzZ5Fe1YABABt3b6FG1fuXLp121ZAAEDvXr0KKAAAHFgwAAIULmQIkDgxhgEKADx+rGAAhgAcBlBYIEDzZs4CGFSQUADA6NEGDgBAnRpAhAgAXL+GHVv2bNq1bQNAIADAbt4ECAAAHlz4cOLFjR9HDoDAAADNnTsnMADAdOrVERhokCDAdu4BMFyosEDBggoDGgRA78DCAAoMBLyHvyCChAECANzHr4ACAP79+QMkYAAAwYIGDyJMqHAhQwACIgCIKHEixYoWL2LMeLFAhAEAPoIMOcBABwUATqJEMGBCgJYuXyaY8GDmBAsTEgT/yBkAw4MBAypICFphQAUBAI4iPSCgggAATp8+paAAANWqVq9izap1K9cFEQCADUuAAICyZs+iTat2Ldu1CDoMeDCAAIC6duseMOBgggUDDAoAAIDAQIYAhg8jTozhwYDGFiYkCCA5gYMJDRpYqEAAAGfOCCgMuPDAAgIApk+fhsAAAOvWrl/Dji17Nm3ZAiIAyK17N+/evn8D743gggUOCQI8YABgOfPlEBoEiI7hwQAICAxkCKB9O/fuEwYwKAAAgAIJFhwESK8+QIIHEgAAILDAgIUGDgIEeCAAAP/+/QFKMGBhAQEABxEmVLiQYUOHDxUuiACAYkWLFzFm1Ljx/2IEAxMChAyAYcABACdRIjDgIEDLlg4eDNgQgGZNmzczGDgAgGdPEBYCBBUaNIEFBgoMPMgQgCnTCRUARJUatcCABBkeGFAAgGtXr1/BhhU7FmyBAgDQpj2AAEBbt2/hxpU7l67bAwYeOAiwl+8EAwsIAABAYMSADAEQJ0Y8wYAGBwEgR5YcWYMAAAAEEACwGYCBDAFAhwaNYcAFDAFQpw6QwIACAK9hA6DwIEDtDAYgEACwm3dv37+BBxfeewEDAMeRJ1e+nPnyAwsgVLBg4EKFDwwUFACwnTt3BQYmBBA/fjyGBwMqVBjwAEMA9+/hO3hwwUEA+/cDJMjQQIOBAf8AAQiUcACAQQALHgRYyDDAhAEbEgSYSJEiBgMCAGgEQACChQQBQgZI8MAAAgAoU6pcybKly5cpGTAAQLOmzZs4c9YUUGGAhQcNMmAYimFCAwsGDEQoAKBpUwEGMASYSrXqVAcZMjgIwLWr164JHlxwEKBsAAcNBlx4wKGBBABwKxwAQBfAAQMTEgTYG6DBgAwBAgseHBiDBQMeGFAY8CBBgMeQA2QwoACA5cuYARQQEMEDBAgeGCggAKC06dOoU6M+cACA69cCBACYTbu2bdsFIgzQMCFBgN/Ag//GsGHABwUAACgwgCGA8+fQo0ufDn3DBQcBMGgY8ABDgO8TJAD/GF8AgHnzBy5cGNAgQYAGAzAEmE+/vn0MDRpwcBCgv3+AAQQGwGBAAQCECQkwkDDgwoMGDTg0aLDBwgADHQQA4NjR40eQITsyYADA5EmUKVEyGLDBQQCYMWXOhJlgggUJCgZgCNDT50+gQYUCTfDAQoMBDRwEYMrUwQAAUaVKXfAgQAYLFxoMwBDA61ewYcWOBYvBAAIAaRFQGPAgg4MAceXKTeBggoUBEQoA4NvX71/AgRcIAFDY8GHEhQ9U0OAgwGPIkSVPTtBggIYEATRv5tzZ82fPGAZcwBDA9GnTGgQAAFAAwOvXBjIECJBAxIAHCQLs5t3b92/gvjMYIHCg/8KFBg4CLGfe3HkADA8GQCAAwPp17NmvMxAAwPt38OHFf2dgYEKCAOnVr2ffXj0GCxccBKBf3/59/PntTxjQIAHAAAIHDsRg4ACACgUAMIRgIQBEiA4sXHAQ4CLGjBo3ctTY4IKBCQkCkCxp8uTJBA8MKADg8iXMmC4jLABg8ybOnDptQrDgIADQoEKHEiWaoMEADAGWMmWawMGEDRosUNXwYAKGBAG2ct3aYACGAGLHkhU7YUAEAwUAKJBgwUGAuHITbDDgIADevHoDOMgw4e8EDAkCEC5smHAGAxYcBGjs+DHkyI4zGIBAAADmzJo3C1AA4DNoBAcAkC5t2jQEC/8OArBu7fo17NisGwzAEOD27QQTNAwYYOEBhwnCJzSwMGCAhQkJAjAP0GAAhgDSp1On7uDBgOwWJiQI4P379wYGHAQoXz5BhgYaDAy4YOG9BQMDLjzg4CAA/vwBJgyYEABgAIEDCRY0WDDBgwoEADR0+BBiRAARFgCweBHjRQ8WEgTw+BFkSJEjQTYY4CBAAAcNDFxo4CBBAJkzaTqYYGHABgcBJgzAEABoUKFDgyZIEABpUqVJG1xwECBAggYXDDzggCFBAK1bHUxoYGHAAwwByAaYMCBDALVr2bZ1+zZAggYXCACwexdvXr0RBADw+xewXwEXHAQwfBhxYsWLFTf/uJCgwQANGRIEsHwZc+YAGB4MeDAgQwDRo0mXNn3adAINDxw8GGBhQoIAs2nXru2gwYALEwJMGIAhQHDhw4kXNz68QQUCAJg3d85cwAEA06kXIAAAe3btAAgYwBAAfHjx48mXL5/ggoELGAK0d/8efnwHFgZkCHAff379+C04CAAwgMCBBAk6GDBAA4YADBs6fOgwwYQBGgZkCIAxo8aNHDtyfCABgMiRJEVCUAAgpcqVLFd2aBAgpsyZNGvatJmgwYAGCQL4/Ak0qNCfEwY8SBAgqdKlTAMMcBAgqtSpUx08GDAhgNatXLt61epAw4AJAcqaPYs2rdq0CSwsAAA3/65cABAUALiLlwCAvXz7AhBwIUGAwYQLGz6M+HCCBxcwBHgMObLkyZMdWLCQIIDmzZwDOJjQoMGABhMwJAiAOrVqBwY0OAgAO7bs2bRnTxjQIEGA3bx7+/4N3DeGAQUAGD+OPPlxCAIAOH8O3UCGANSrW7+OPTv2BA8uOAgAPrz48eTLB0igwUKCAOzbY2hgYcAACxoe2NdwYcCFBxMSBAAYQGAADAMaBECYUOFChg0DOLjwIEEAihUtXsSY8WIDCQA8fgQZ8iMEBQBMnjyJwEIAli1dvoQZE2aCBxcSBMCZU+dOnj1zJtBgIUGAAAkmWBjwYAKGBAGcPg3gIEMDC/8DNjgIEMDBgAYBvH4FG1bs2K8OLjwIkFbtWrZt3bJNYEEBALp16xYgAEDvXgEHAPwFDLgDhwCFDR9GnFhx4gYXHASAHFnyZMqVJyew8CBBgwEXOCQIEFr0aNIYHgzQgMFAgwCtXb+GHVs2bAcXGgTAnVv3bt69d0+QAED48OEUEABAnlz5cgAFBiQIEF36dOrVrVN3MABDAO7dvX8HHz68gwEXLmQIkF79evbsHTQYcCFBAPr17d/Hnx+/gwEYAgAMIHAgwYIGDw5MYKAAgIYOG1JAAGAixYoWASx4EGAjx44eP4L0mMBCgwAmT6JMqXLlygQNBmxIEGAmzZo2b87/xHDBgoMAPn8CDSp0qNAGFxIESKp0KdOmTpc2iABgKtWpCAoAyKqVwQEAXr96pTAhANmyZs+iTXu2wYUEAd7CjSt3Lt25CR5cwBBgL9++fv/6TdBgAIYAhg8jTqx4ceIEFhoEiCx5MuXKlic7GABgM+fOngFQQABgNOnRBjAESK16NevWrlcnGIAhAO3atm/jzo07gQYLDgIADy58OPHiASYMyBBgOfPmzp9Dd+5gAIYA1q9jz659O/YLBQCADy9+PAQEAM6jB0BgQIIA7t/Djy9/PnwOFgLgz69/P//+/AEm0GAhQQCDBxEmVLjw4IQBGAJElDiRYkWLFB88CLCR/2NHjx9BdnwgAEBJkwAgHACwkmVLlwgsBJA5k2ZNmzdrXpgQgGdPnz+BBgW64YKDAEeRJlW6lKnSCQMcBJA6lWpVq1epYhiQIEBXr1/BhhXrtUEEAGfRApBwAEBbt2/hCngQgG5du3fx5rWLYUCCAH8BBxY8mLDgDAMcBFC8mHFjx48fb7CQIEBly5cxZ9Z82QKHAJ9BhxY9mjToDBUApFYNIEIBAK9hIyAAgHZtAAIeBNC9m3dv3795P2gQgHhx48eRJz+ewMCEAM+hR5c+nXr1BBc4BNC+nXt379+5T7gQgHx58+fRpy/vwAAA9+/hx5dwAEB9+wAEPAiwn39///8AAwgcSLCgwAsZAihcyLChw4cNH2gIQLGixYsYM2qkiGGAgwAgQ4ocSbJkyAQDHARYybKly5cwVzoYAKCmzZs4JRwAwLMnAAEPAggdSrSo0aNDEwxwEKCp06dQo0p9imGAgwBYs2rdyrWr16wbNAQYS7as2bNoy17IEKCt27dw48ptm2AAgLt4ASwgAKCv3wMEAAgeDEDBgwCIEytezLhxYgwXAkieTLmy5cuVH2wIwLmz58+gQ4v27GCAgwCoU6tezbp16gcNAsieTbu27duyHQwAwLs3gAoFAAgfTrz4gQsBkitfzry5c+UNNASYTr269evYqzsY4CCA9+/gw4v/H08+/IMGAdKrX8++vXv1DTQEmE+/vv37+Oc7MACgv3+AACoUAFDQ4EGEAAY4CNDQ4UOIESU23LAhwEWMGTVu5JixgYUAIUWOJFnS5MmSGAYkCNDS5UuYMWW2xHAhwE2cOXXu5HkzQwUAQYUCIADA6FEAFQoAYNqUaYUMAaROpVrV6lWpDxoE4NrV61ewYb1emBDA7Fm0adWuZbv2woQAceXOpVvXblwMBgLs5dvX71/AextEAFDY8GHEFQoAYNyYsYcGASRPplzZ8mXJDxoE4NzZ82fQoTs7GJAgwGnUqVWvZt2adYMHAWTPpl3b9m3ZDgYE4N3b92/gwXk/EADA//hxAAUALGcOgAEBANGlR1dgIcB17Nm1b+d+/UGDAOHFjydf3rz4CRcCrGff3v17+PHjT7gQwP59/Pn177fvYADAAAIHEixo8KDACwUAMGwIwAABABInUqwo0QCGABo3cuzo8WOADRsCkCxp8iTKlCUbPAjg8iXMmDJn0qTpYECCADp38uzp82cADAYCEC1q9CjSpAEcDADg9KlTAwQAUK1q9SpVBg8CcO3q9SvYsAE4aAhg9izatGrXntXAIQDcuHLn0q1r964BDAH28u3r9y/gABMuBChs+DDixIoDNIgA4DHkxwIAUK4MAAIBAJo3byYwwEGA0KJHky5tGoOBAP+qV7Nu7fr16gsYAtCubfs27ty6d1uYEOA38ODChxMP0OBBgOTKlzNv7jyBgQIAplOvbh2AAQIAtnPvTqFBgPDix5Mvbz7BAAcB1rNv7/49/PUGMASob/8+/vz69/PXMAFgAIEDCRY0eDCAhgYBGDZ0+BBixAkSAFS0eBFjRQMEAHT0+BHCAAwBSJY0eRJlygsTArR0+RJmTJktBzgIcBNnTp07efb0+aBBAKFDiRY1ejSAAQwBmDZ1+hQq1AQWFACwevVqBQBbuXb1yvWAgQYXEgQwexZtWrVqHzwI8BZuXLlz6b41gCFAXr17+fb1+xewBg4BCBc2fBhxYgcDEgT/cPwYcmTJkhtIAHAZc+YBADh39vy5s4QJCSw0CHAadWrVq1djGJAgQGzZs2nXth3gAoYAu3n39v0beHDhGiYEMH4ceXLlyxtoCPAcenTp06djGFAAQHbt2ysA8P4dgAIA48mTP2AgQQAHAyYEcP8efnz58i1wCHAff379+/kHsABwQoCBBAsaPIgwocILGQI4fAgxokSJCQxkCIAxo8aNHDcmuHBhAYCRJEuaJDkAgMqVK0E0CAAzw4AJAWravIkzJ84JFwL4/Ak0qNChAR40CIA0qdKlTJs6dZpggIMAVKtavYoV64QLCQJ4/Qo2rNiwDy5kMAAgrdq1bNUOAAA3/y5cAgMcBLgbIMOACQH6+v0LOPDfBAMmBDiMOLHixYwnWAgAObLkyZQrW7aM4UKAzZw7e/4M2kKDAKRLmz6N+nSDCw4CWEAAILZs2QsA2L4NQAGA3bx3C3gQILjwDAMaJAiAPLny5cyTTzDgIID06dSrW7eOYUCA7dy7e/8OPnz4BhoCmD+PPr169RMMOAgAP778+fTjJ2hgwEGAAA0oAAAIQOBAgQMAHESYUCEACA0CPIQYwIGFCxgCXMSYUePGiwkuPAgQUuRIkiVLJhjgIMBKli1dvoQZE+aDBgFs3sSZU2dOBwMmBAAaVOhQokETPLjgIMDSBAMIAIAaFeoAAP9VrV7FCqBChgBdvXZN0GBAAwcBzJ5FmxZtggkGNAzIEEDuXLp17dp9sCHAXr59/f4FHPivgwEOAhxGnFjxYsUaHgSAHFnyZMqRMxjQ4CDA5s0aFAAAHRp0AQClTQMYAED1atUDEgSAHVu2Aw0DHmAIkFv3bt4OGhgYMCHAhAEOAhxHnlz5cuUYBiQIEF36dOrVrV+n3kBDAO7dvX8H/33CgAETEgRAn179+vUJHgyYEED+/AANIgDAn1//fgADAAAEIHDgAQsBDiJMiNDBhgEXGkxwEGAixQAJMDR4MEDDhQYBPjYw4CAAyZImT6I8aWFCgJYuX8KMKXPmywQXMgT/yKlzJ8+eOycMyJBhwIUGDgIgTap0aQAMDwZocBBgKtWpEyQAyKo1awEAXr8CgABgLFkACh4ESKt2LdsEEx5cGGDAgoYHGjRcGGBAQwMHGAYkCCA4QAMDDgIgTqx4MWPFEy4kCCB5MuXKli9jnjzhQoIAnj+DDi3684QBEwIEaGDBwoAHExwEiC1bdgIHEywMeIAhAO/evh0MACB8OAACBgAgT658uYIHAZ5Djy49egIMEzg0aDAhg4MA3gM8eBBgPPkGAzIESK9+Pfv26hNcaBBgPv369u/jzz8/gYEJAQAGEDiQYEGDAicMmBCAoYMBDhxsuDDAgIYGFy9usDBg/4CFBgkChBQ5UqQBAgBQpiRgAEBLly9hCngQgGZNmzdx5rR5YUIAnz8DTBjwwEEAo0eRJlVqFMMABwGgRpU6lWpVqwEeaAiwlWtXr1+3OngwIEMAs2Y1NAiwNgGGBg8eaHjwYMMEBwkC5NW7l28AAwUABBZMYAEAw4cJRACwmDEABQ8CRJY8mXJly5ITDHAQgHNnzg40GJgQgHRp06dRY7hgwEKCAK9hx5Y9mzbtDAMwBNC9m3dv3wEmDHjgIEBx4w0eBFC+nHlz58+XXygAgHp169YJGACwnTsABRoChBc/nnx58+IxGAiwnn37ABMGXOCQIEB9+/fxJ5hgYUCDBP8ALzQIQLCgwYMIEyJ0YMDCgAYOAkicSLGixAQZNAyYEKCjx44ZLgQYSbKkyZMoSRooAKCly5cvCRgAQLMmgAIXAujcybOnz587OWgIQLSoUaIJJlgY8GCCgwBQo0Z1kKHBgAsDJgQIgGEAhwBgw4odS7asWAcXHgTQcGGAhgwJAsidS1eugwYXDDRwEKCvX78OBiQIQLiw4cOIExM2QACA48cFIACYTLmyZQADHATYzLmz58+gN29oEKC06dOoMTy4MMCAhg0NYm/QYGDAhQcYMhhIEKA3hgEcAggfTry48ePCHVx4kCAAhgEOGhgYYGHDBAcOEjhwgKHBgwsDNEz/SBCgvPnz5S9gCMC+vfv38OMHSDAAgP37AApUAMC/v3+AAAQOrJAhwEGECRUuZHjwQYMAESVOpCgxAYYJGx5s3NAAQ4IAITU0CFCyJIYBDRIEYNnS5UuYMB1ceJAgwE0LEwIkcDDhgYUBQYNe0NAgg4MASZUuZWphQgCoUaVOpVo1QIYKALRuBVCAAgCwYQEgAFDWbNkIDQKsZdvW7Vu4ax80CFDX7l28efXaTTDAQQDAgTFcsOAgwGHEiRUvRpygwYAGCQJMDtBAQwDMmTVv5tw5s4UJAUSPJl3a9OkADUAAYN3a9esCFQDMpj1bgYUAuXXv5t3bd+4HDQIMJ17c//hx5MQdDEgQwPnzAAkaDGiQIMB17Nm1b3dg4QKGAOHFY7gQwPx59OnVrz+vYUIA+PHlz6dfP8ADAQD07+ff/wDACgAGEiRoAEOAhAoXMmzoMMCDBgEmUqxo8SJGihMsBOjo8SOGCxc4JAhg8iTKlCYxPBjQIEGAmDIDJBjgIADOnDp38uyJ08KEAEKHEi1q9GiCCwcAMG0KgIACAFKnEkAA4CpWrAweBOjq9SvYsGIDNHgQ4CzatGrXskW7YUOAuHLnBkgwwcKABxgSBOjr92+ABBMsDNjgIADixIovZAjg+DHkyJInO74wIQDmzJo3c+48oQKA0KJDH5AA4DTq1P+qTxcYkCAA7NiyZ9OuneFCgNy6d/Pu7Vu3hQkBhhMvXhzDgwEDLDyYkAED9AwNHlwYcGFCggDat3PX/qBBgPDix5Mvbz5AggEOArBv7/49/PgPBACob78+AgkA9vPv7x8gAIEdGgQweBBhQoULEwxIEABiRIkTKVaEaGFCAI0bOXbU6GDCBgsXBgy4cOFBgwwJArR0+RLmgwYBaNa0eRNnzgAYBgTw+RNoUKFDHQwAcBRp0gIAmDY9QAFAVKlTCwzAEABrVq1buXa9gCFAWLFjyZY1G/ZChgBr2bZ1+zZAggQB6Na1exfvhgYB+Pb1+xdw4AATLAQwfBhxYsWLN0T/APAYcmTJjxFIAHAZc2YACywkCPAZdGjRnxMkCHAa9ekHDQK0dv0admzZrS9kCHAbd27du3n39v2gQQDhw4kXN348wIMGAZg3d/4cOnQMBggAsH4dOwEA27kfYAAAfHjx4CU0CHAeffoEGDg8uDAAfnwLDSY4CBAgw4UEAfj39w8wgMCBBAsStDAhgMKFDBs6DIABQ4CJFCtavPigQYCNHDt6/AgywYUMAUqaPIkyJcoEFwQAeAkzJgIKAGravIkTZ4EBGQL4/BnAQYMBBjQ0mOAggdIEGCY8sDDgwoQEFyYEuIo1q9atXANo4BAgrNixZMsGeNAggNq1bNu61dAg/4DcuXTr2r2b4UKCAAEcTGigwcKFCxYsbJjgIEGAxYwbB2jQAYDkyZQBKIAAILPmzZw7IxiAIYDoABk0DHiAIYDq1axXJ5hgYYAFDQFq276NO7fuAA0eBPgNPLjw4QE0NAiAPLny5cwNYAgAPbr06dSra2iA4YGBARcecJiQIcOECRssDBigIUOCAOzbB8hggACA+fTrAzggAID+/QcEAAAIQOBAggMVGMgQwMGDAQ0cBIAYUeLEiBgeDMAQQONGjh09fpxwIcBIkiVNngyQAUMAli1dvnzpYECCADVt3sSZM6eDARYGPMCQIMBQokUTOGhg4EIDBwGcBsAwAAEAqv9VrV69qoACAK5dvX5FMODBgAcOApxFm1bt2gAPLiQIEFfuXLp16zoYkCDAXr59/f4FHBhwhgsBDB9GnFjx4gcDOCQIEFnyZMoBEkywMGBCgAAZDCgAEFr0aNKlFUAAkFr1atYAIAyYEED2bNq1bc9OcKFBAN69ff8GHtwAhgDFjR9Hnlz5cuUNHgSAHl36dOrUMwxwEED7du7dvWcwoGHCAAQAzJ9Hjx7BAgDt3b+HH789BAsOAtzHn1//fv0YBgB0EGAgwYIBEmDg8MDChYYWHjTIkCAAxQcPAmDMqHEjRw0TAoAMKXKkyAQXJgRImTKBAwwuHSQIIHMmTZoODEz/CKBzJ8+ePnUmeDBgAYCiRo8iFRABANOmTp9CBQDBQoIAVq9izap1a4MLCQKADRsgwQQLAwxoaDAhA9sJDR5cGHChgQMMAxIEyKt3L1++FiYECCx4MOHBGS4kSJChgYYLAwYYuGBgwAALDyY4CKB5M+cHGgKADi16NOnRGQwIAKB6NWvWCiIAiC2bQAEAtm/jvu3BQoIAvn8DDy58eIAEGiwkCKA8gIMNAy5McBBgOvXqARJk0DDgwYUGAb6DDy9e/IYMAc6jT68+vYYGDQxceNAAQ4IA9u07mNDAwgALEwAmCDCQYIMBDgIkVLiQYcOGGAwIADCRYkWLFwVAALCR/2PHjQouOAgwkmRJkydRkkxgwUKCAAkaDHiAIUBNmzdx1nTQYMCFBAGABhU6lGhRo0EdDBigIUOCAE+hRoXqoMEFAw0SBNA6YQCGAF/BhhU7lmwADAYUAFC7lm3btgIiAJA7ly4AAgYwBNC7l29fv3/7JtBgYcIFCw4CJFa8mHFjBwMaBJA8mXJly5cxT7ZgwUEAz59Bh/6cIIOFCxgCNBiAIUBr169hx5btGsOAAgBw59YNoMABAL+BF0AAgHhx4wAoNAiwnHlz58+hQ09wYUCDBAGwZ9e+nTt2DAMcBBA/nnz58Q0wBFC/nn179RMMJAgwn359+/cTNBhwYQCGAP8AAwgcSLCgwYMEQ1QAwLChQwALGACYSLGiRYoKLiQIwLGjx48gQ4JM8OCCgwAoU6pcyXJlAwsJAsicSVNmAgwNNjwYoKHBBAwJAggdSlSogwEZAihdyrSp06UOLlxwEKCq1atYs2rFmsDCAgBgw4pdwACA2bNo056tMCGA27dw48qdKzeBBgsOAujdy7evX78JLjQIQLhwYQwPLgwwoGFDAwMPNmgwMODCAwwBMmvOnMDCgwCgQ4seTXp0ggcXHARYzbq169ewXTsYUACA7du3CxQAwLu3AAYAggsXfuBCggDIkytfzrz58gQPLiQIQL269evYswfAMGBCgO/fE0z/uDBgQwYHAdIHcJAggHsHEx4MsDAhQYD7ARI8uJAggH+AAQQOJFjQIMEGBhwEYNjQ4UOIER82oADA4kWMGS0uYADA48ePEBoEIFnS5EmUKVE2uOAgwEuYMWXOpAkTw4AJAQIkaDDAwoQEAYQOJVo0AYcLAyYECJDgwQUHAaROpVrV6lWpDy4kCNDV61ewYcV+dTCAAAC0adWuBaBAAAC4ceESGOAgwF28efXu5avXwQAMAQQPJlzY8OHCGQY0cGDhAoYAkSVPplw5gwENDh5ccBDA82fQoUWP/pxAw4MAqVWvZt3aNesHDADMpj2bwQIAuXXv5g1AwIMAwYUPJ17c/zjxBBYaBGDe3Plz6NGjYxgwoEGCANm1b+fePbuDBwMuOAhQ3vx59OnVo3cwIEMA+PHlz6dfXz4GAwD079fPYAFAAAIHEiwIwEODAAoXMmzo8GHDBhcSBKho8SLGjBozJnhwAUOAkCJHkixZMsOABgFWsmzp8iXMlxMMJAhg8ybOnDp34rSAAADQoAAUIABg9CgCBACWMl1aIUOAqFKnUq1qdWqCARgCcO3q9SvYsGATPLjgIADatGrXsm0bwIGBBgHm0q1r9y7euxoeBOjr9y/gwIL/PlgA4DDixIoZLADg+LHjAQkCUK5s+TLmzJYnWAjg+TPo0KJHi07w4IKDAP+qV7Nu7fr1agcGGgSobfs27ty6cTsY4CAA8ODChxMvHnwCBQDKlzNvzmABgOjSARy4EOA69uzat3PXfmFCgPDix5Mvb758gwsOArBv7/49/PjvHQyYEOA+/vz69/PXrwFggwADCRY0eBAhQQwGADR0CEAAAgATKRIAcBHjRQQWAnT0+BFkSJEfMQxIEABlSpUrWbZciWEAhgAzada0eRMnzgwDHATw+RNoUKFDgU64kCBAUqVLmTZ1mjTBAAIAqFaNIABAVq1buSKwEABsWLFjyZYV++BBALVr2bZ1+7ZtggsNAtS1exdvXr17AzzQEABwYMGDCRcWnMBAhgCLGTf/dvwYMmMLBwBUthxBAADNmwsQAPAZNAAEFgKUNn0adWrVpy1MCPAadmzZs2nLbnAhQQDdu3n39v0beIAEBiYEMH4ceXLly5FveBAAenTp06lXj27hAADt27lzjyAAQHjxABBYCHAefXr169mjTzDAQQD58+nXt3+fvoMBGAL09w8wgMCBBAsaPFgww4AEARo6fAgxokSHEywEuIgxo8aNHDFaQAAgpMiRIyMIAIAyJYADFgK4fAkzpsyZLzEMCIAzp86dPHvubKAhgNChRIsaPYq06IUJAZo6fQo1qlSnDgYkCIA1q9atXLtitXAAgNixBwoAOIsWwQEAbNuyHZAg/4DcuXTr2r0rd4KFAHz7+v0LOLDfBAYyBDiMOLHixYwbK55gIYDkyZQrW75MeQCGAJw7e/4MOjTnCwUAmD4NQQGA1axbuwZgAUOA2bRr276Ne3aDBwF6+/4NPLjw3xMuJAiAPLny5cybO1+eYACGANSrW7+OPXt1CxMCeP8OPrz48QESDACAPj0ACAoAuH8PPz4ACA0C2L+PP7/+/fYbPAAYQOBAggUNHiSooUEAhg0dPoQYUWLEBw8CXMSYUeNGjhg1cAgQUuRIkiVNBsBQAcBKlgAOEAAQUyYDBABs3rS54EEAnj19/gQalOeGDQGMHkWaVOlSpAMwBIAaVepUqv9VrVadcCHAVq5dvX4Fy/VBgwBlzZ5Fm1ZtgAYQALyFG1cuBAUA7N61W8BAggB9/f4FHFhwgAYPAhxGnFjxYsaIHQxIEEDyZMqVLV/GfNnBgAQBPH8GHVr0aM8PGgRAnVr1atatAzwQAED2bNq1GSAAkFu3bgkTAvwGHlz4cOIBGjwIkFz5cubNnSufYCHAdOrVrV/Hnl27AQwBvH8HH178eO8aOARAn179evbtExg4AED+fAAUEADAn1//fvwKLAAMIHAgwYIGDwaYYCEAw4YOH0KM2LDBgwAWL2LMqHEjx44aOAQIKXIkyZImQ1qYEGAly5YuX8KcUAEAzZo0KSD/AKBzJ8+eOw1gCCB0KNGiRo86GJAgANOmTp9CjcpUQ4MAVq9izap1K9euDTYECCt2LNmyZgMkGOAgANu2bt/CjWtBAIC6dusuKABgL18FBQAADix4gYUEAQ4jTqx4MeMBGAJAjix5MuXKkDVMCKB5M+fOnj+DDt3gQYDSpk+jTq06gIMBCQLAji17Nm3aGAwAyK17N+/cFBAACC58OIAKEwIgT658OfPmFiYEiC59OvXq1qNbmBBgO/fu3r+DDy++wYMA5s+jT69+fYAJFgLAjy9/Pv36DxgAyK9/P//8FAAiADCQYEEABww4CLCQYUOHDx820BCAYkWLFzFmpKhh/0IAjx9BhhQ5kmTJBg8CpFS5kmVLlwE2bAgwk2ZNmzdvTjAAgGdPnwwKABA6lAAAo0eRHmVgIUEAp0+hRpUa1cEABwGwZtW6lWvXABo4BBA7lmxZs2fRpm3wIEBbt2/hxpWbwECGAHfx5tW7V68DAwgABBY8WMIBAIcRJ1a8WMKDBAEgR5Y8mfJkDQ0CZNa8mXNnzwEebAgwmnRp06dRp1b9oEEA169hx5Y9e8KFALdx59a9e/cDEACABxcOQMIBAMeRFwCwnHnz5gQqNEgQgHp169exW89gIEEA79/Bhxc/foKFAOfRp1e/nn179xcwBJA/n359+/ctNAiwn39///8AAwgcSDBACAMAEipcyJChhAMAIkqcOJFAhQcJAmjcyLGjx40JLkwIQLKkyZMoUzoYEKCly5cwY8qcOTPBgAQBcurcybNnTwwDEgQYSrSo0aNFJxgoAKCp06dQoUo4AKCq1atYCUiw4CCA169gw4r9mmGAgwBo06pdy5ZtggEOAsidS7eu3bt472a4EKCv37+AAwdOYGFDgMOIEytenJiDgQIAIkueLBkBgMuYAQggAKCz58+gOzMYwCFBgNOoU6teHcDBBQsJAsieTbu2bdsWJgTYzbu379/AgwNv8CCA8ePIkytXPuFCggDQo0ufTh16gg0GCgDYzr179woFAIj/H0++vPnyBSpYwBCgvfv38N8nmGDAQ4UJAfLr38+/f3+AEy4EIFjQ4EGECRUiTGAgQwCIESVOpDjRwQAMATRu5NjRo0YMFygQAFDS5EmUFQoAYNnS5UuYMRcYsMAhQQCcOXXqdNDAgAQEAA4YwBDA6FGkSZUmTTAAQwCoUaVOpVrV6tQJFwJs5drV61evCSwY0OAgwFm0adWqddDAgAIAceXOpRtXAQC8eQFQKADA71/AgQUDQNBhwIMGGRIEYNzYwYQGFgZEKADAMgAFBjAE4NzZ82fQnzc8CFDa9GnUqVWvRm2hQQDYsWXPpi07wQMJABgMeDAhQQDgwYUPx/Bg/wAEAgCUL2fe3HnzCgUATKde3fp16gUEeKgw4IIF8BYMDPjAQAEA9OnRKzCAIcB7+PHlz4/vYICDAPn17+ff3z/AAAIHEgyAYUCCAAoXMmzocGGCBxUAUAQgwMKFBhMcBOjosWOCDA0sGGBAAADKlCpXsmwpgQCAmDJn0qxpE0CBAwgOHCgA4CfQoEAVGJiQIADSpEqXMk36QEOAqFKnUq1q9WrUBBcaBOjq9SvYsF4TPJBAAADatAgiSBhw4QHcDQ8eWBhQAYICAHr38u3rV68BAgAGEy5s+DDixIoXDz5QQYODAJInU65sWbKDARMCcO7s+TPo0KIDNLCQIADq1P+qV7NGjcFCBwCyZ9OWXUDBgtwLBBwA4Ps38ODChRsgAOA48uTKlzNv7vx5cgYGOCQIYP069uzZM1yQMMBBgPDix5Mvb948hgETArBv7/49/AAJGgwQAOA+/vz69/Pv7x8gAIEQABQ0CEAAAIULGTZ0+BBiRIgHJBho4CBARo0bOSbgYMGAAgAMLCQIcBJlSpUrWap0cAGCgQcYAtS0eROnzQQTLEgoAABoUKFDiRY1ehQpUAMEADR1+hRqVKlTqVI9AGHAgwkYEgTw+tWrgwkPBnxQAAAtAAoPEgRw+xZuXLlz3yawEAEAgQgDLExIEABwYMGBHTQwIEEBAMWLGTf/dvwYcmTJjAcQAHAZc2bNmzl39vwZAAEBFAwMsPDgwYYHDywYGPCBQQEAs2kD+PAgQQDdu3n39v07gAMLEAAULy6ggoEHDSY4CPA8QAIMHB5YGADiAADt27l39/4dfHjwEQCUNw/gAAD169m3d/8efnz57gkgELAAvwAFBAD09w8QgMCBFCw4CIAwocKFDBlisOABgMSJEgsoiCBhgEYDAwYYoLAAAYCRJEuaPIkypcqVAAYAeAkzpsyZNGvavIkzp04GBiYE+Ak0qNChQBOEGLAAgNKlTJsSeAogqtSpVKtavYo1a9UBALp6BUAAgNixZMuaPYs2rdq1bM0esPDA/0GAuXTr2r2LwYKEAgD6+v0LOLDgwYQLGz7clwCAxYwBDAAAObLkyZQrW76MObNmywwGPMAQILTo0aRDZ3gwYAGA1axbu34NO7bs2bRr1x4AILfu3bx7+/4NPLjw4cAJMDBgYYKDAMybO0/goIEFAwsIALiOPbv27dy7e/8OPrx2BADKmwfAAID69ezbu38PP778+fTpK+gwwICGBhP69wfYwMIAAxQQAECYUOFChg0dPoQYUaLDAQAsXsSYUeNGjh09fgQZ0mMBBQwoVLhAAQIDBQQAvIQZU+ZMmjVt3sSZ8yaBAQB8/gQaVOhQokWNHkWa9KiAAgAEQAAQVepUqv9VrV7FmlXrVq5SBQAAGxYABQBlzZ5Fm1btWrZt3b6Fa1YCAgAECgDAm1fvXr59/f4FHFjwYMAEBgBAnFjxYsaNHT+GHFny5MQSDgDAnFnzZs6dPX8GHVr06NEEKgBAnVr1atatXb+GHVv27NQEANwmAED3bt69ff8GHlz4cOLFdROoAED5cubNnT+HHl36dOrVrQuIAED7du7dvX8HH178ePLltReoAED9evbt3b+HH1/+fPr15xcAAGABAwD9/QMEIHAgwYIGDyJMqHAhw4QEIACIKBGAAgAWL2LMqHEjx44eP4IMebFCAQAIEABIqXIly5YuX8KMKXMmzZgFDAD/yKlzJ8+ePn8CDSp0KFGdFQoASKp0KdOmTp9CjSp1KlWqBSoAyKp1K9euXr+CDSt2LFmtAggASKt2Ldu2bt/CjSt3Ll22BBYAyKsXwAEAfv8CDix4MOHChg8jTixYgAAAjh9Djix5MuXKli9jzuz4gAQAnj+DDi16NOnSpk+jTq2awQIArl/Dji17Nu3atm/jzu36gAQAvn8DDy58OPHixo8jT35cAgEACxYAiC59OvXq1q9jz659O3fpBACAD3+gAoDy5s+jT69+Pfv27t/DN2+AAID69u/jz69/P//+/gECEDiQYEGDBxEKPCABQEOHDyFGlDiRYkWLFzE6pEAA/0BHjx9BhhQ5kmRJkydRhiwAgGXLAgsAxJQ5k2ZNmzdx5tS5k2fNAwcABBU6lGhRo0eRJlW6lGlQBBQARJU6lWpVq1exZtW6lWvXCAIAhBU7lmxZs2fRplW7lm1YBBQAxJU7l25du3fx5tW7l6/eBQAARBAAgHBhw4cRJ1a8mHFjx48JF1AAgHLlAwwAZNa8mXNnz59BhxY9mrTmAQAAFCAAgHVr169hx5Y9m3Zt27dpI6AAgHdv37+BBxc+nHhx48d7DwCwnHlz58+hR5c+nXp169cRUACwnXt379/Bhxc/nnx589wLAFC/nn179+/hx5c/n3599wgiANC/n39///8AAQgcSLCgwYMIEypcyNAgBAUAIkqcSLGixYsYM2rcyDGiAggAQoocSbKkyZMoU6pcyVJlAQAAICgAQLOmzZs4c+rcybOnz580ETAAQLQoAQQAkipdyrSp06dQo0qdSlXpAAAABBwAwLWr169gw4odS7as2bNkFUAAwLat27dw48qdS7eu3bttBwDYy7ev37+AAwseTLiw4cMKIABYzLix48eQI0ueTLmyZcYLAGjezLmz58+gQ4seTbq05wMKAKheTeAAgNewY8ueTbu27du4c+uezeAAgN/AgwsfTry48ePIkyv/LSACgOfQo0ufTr269evYs2vfTgEBgO/gw4v/H0++vPnz6NOr/y4gAoD38OPLn0+/vv37+PPrv0+AAgCAACAgAFDQ4EGECRUuZNjQ4UOIBQkQAFDRooAIADRu5NjR40eQIUWOJFlSIwEDAFSuZNnS5UuYMWXOpFnTpoAIAHTu5NnT50+gQYUOJVpUJwEJAJQuZdrU6VOoUaVOpVrVKQECALRuRSAAwFewYcWOJVvW7Fm0adWORUAAwFu4ceXOpVvX7l28efW+XcAAwF/AgQUPJlzY8GHEiRUvlnAAwGPIkSVPplzZ8mXMmTU/XsAAwGfQoUWPJl3a9GnUqVWfJiAAAAAJBwDMpl3b9m3cuXXv5t3b9+wDCAAMJ65A/wEA5MmVL2fe3Plz6NGlT0deoAIAAAcIAODe3ft38OHFjydf3vx58gsYAGDf3v17+PHlz6df3/599gUqAODf3z9AAAIHEixo8CDChAoXMmxIcAEDABInUqxo8SLGjBo3cuw4sQCAkCJHkixp8iTKlCpXsiy5QACAmDJn0qxp8ybOnDp38uxZoQCAoEKHEi1q9CjSpEqXMg3KYAGAqFKnUq1q9SrWrFq3ctVKAACACgUAkC1r9izatGrXsm3r9i3ZBQIA0K1boACAvHr38u3r9y/gwIIHE857QAIAAAwIAGjs+DHkyJInU65s+TLmygwWAOjs+TPo0KJHky5t+jTqzv8HJABo7fo17NiyZ9Oubfs27twMFgDo7fs38ODChxMvbvw48t4FBABo7vw59OjSp1Ovbv069ugIDgDo7v1AAQDix5Mvb/48+vTq17Nvbz4CAQDy59Ovb/8+/vz69/PvLx9gBAEACBY0eBBhQoULGTZ0+BCiAQIAKFa0eBFjRo0bOXb0+JFiBAEASJY0eRJlSpUrWbZ0+ZLlAQYAABggAABnTp07efb0+RNoUKFDcRYgAABp0ggCADR1+hRqVKlTqVa1ehVrUwQUAHT1+hVsWLFjyZY1exZt2ggCALR1+xZuXLlz6da1exdvWwQQAPT1+xdwYMGDCRc2fBhxYAIAGDf/BiAAAQDJkylXtnwZc2bNmzl3tqwAQGjRo0mXNn0adWrVq1mLhqAAQGzZs2nXtn0bd27du3n3HgAAeHDhw4kXN34ceXLly4NDUAAAenTp06lXt34de3bt27EXQAAAwAAA48mXN38efXr169m3d09eQAEA8+kLOAAAf379+/n39w8QgMCBBAsaPIgwoUKBCiAAAKAAgMSJFCtavIgxo8aNHDtuhKAAgMiRJEuaPIkypcqVLFuKFAABgMyZNGvavIkzp86dPHv6pKAAgNChRIsaPYo0qdKlTJsKJVAAgNSpVKtavYo1q9atXLtajXAAgNixZMuaPYs2rdq1bNu6HQAg/67cuXTr2r2LN6/evXzlUkAAILDgwYQLGz6MOLHixYwbDwAAObLkyZQrW76MObPmzZEhHAAAOvQBAgBKmz6NOrXq1axbu34Nu7SACAAAQACAO7fu3bx7+/4NPLjw4cEpIACAPLny5cybO38OPbr06cgXRACAPbv27dy7e/8OPrz48eQlIACAPr369ezbu38PP778+egRKACAP7/+/fz7+wcIQOBAggUNHkSYUKFBBQQAPISIgAAAihUtXsSYUeNGjh09frxIIAIAkiVNnkSZUuVKli1dviwp4QAAmjVt3sSZU+dOnj19/vxJwAAAokWNHkWaVOlSpk2dPi1aoQAAqv9VrV7FmlXrVq5dvX7lqkABgAIGAJxFm1btWrZt3b6FG1cu2gMA7N4FQOEAAL59/f4FHFjwYMKFDR/my4ABAMaNHT+GHFnyZMqVLV/GXOEAAM6dPX8GHVr0aNKlTZ/mvGABANatXb+GHVv2bNq1bd+GTQDAbt4AIhQAEFz4cOLFjR9Hnlz5cubFDwCAHl36dOrVrV/Hnl379ugVCgAAH178ePLlzZ9Hn179+vUFKgCAH1/+fPr17d/Hn1///vgGCAAEIHAgwYIGDyJMqHAhw4YKDxQAcKACgIoWL2LMqHEjx44eP4K0yIAAgJImGRAAoHIly5YuX8KMKXMmzZoqIwj/AEBAAYCePn8CDSp0KNGiRo8iNWqgAICmTp9CjSp1KtWqVq9ibRpBAICuXr+CDSt2LNmyZs+iTWuAAIC2bt/CjSt3Lt26du/ibVugAIC+fv8CDix4MOHChg8jDgwBAOPGAAgAiCx5MuXKli9jzqx5M+fKBygACC16NOnSpk+jTq16NWvRAwDAji17Nu3atm/jzq17N28EFAAADy58OPHixo8jT658eXADAJ5DB3AAAPXq1q9jz659O/fu3r9Xh6AAwAEGAM6jT69+Pfv27t/Djy8f/gAA9u/jz69/P//+/gECEDiQYEGDBxEOhKAAQEOHDyFGlDiRYkWLFzFmHACA/2NHjx9BhhQ5kmRJkyc7CjgAgGVLly9hxpQ5k2ZNmzdhCgCwkycAAQCABhU6lGhRo0eRJlW6lGgBAQCgRpU6lWpVq1exZtW6NeoAAF/BhhU7lmxZs2fRplW7VgEEAG/hxpU7l25du3fx5tULdwAAv38BBxY8mHBhw4cRJz684AAABRAARJY8mXJly5cxZ9a8mbNkBABAhwZgAEBp06dRp1a9mnVr169hm6aAAAAAAgBw59a9m3dv37+BBxc+PPgAAMeRJ1e+nHlz58+hR5eOnAICANexZ9e+nXt379/Bhxc/XgIA8+fRp1e/nn179+/hx1dPoAAA+/fx59e/n39///8AAQgcSLCgwYMIBRIwAKChw4cQI0qcSLGixYsYMwqIAKCjx48gQ4ocSbKkyZMoOxIwAKCly5cwY8qcSbOmzZs4bSIgAEBABABAgwodSrSo0aNIkypdCpRABABQowJYAKCq1atYs2rdyrWr169grUo4AKAAAgBo06pdy7at27dw48qdC5eAAQB48+rdy7ev37+AAwsenFfCAQCIEytezLix48eQI0uePJmAAQCYM2vezLmz58+gQ4senRkBAQCoU6tezbq169ewY8uevZpABAC4cwMoAKC379/AgwsfTry48ePIgwtYAKC58+fQo0ufTr269evYmxeoAKC79+/gw4v/H0++vPnz6NMvYACgvfv38OPLn0+/vv37+NsXqACgv3+AAAoAIFjQ4EGECRUuZNjQ4cOCFQoAUCAAwEWMGTVu5NjR40eQIUV+LFABwEmUKVWuZNnS5UuYMWWirFAAwE2cOXXu5NnT50+gQYUKLVABwFGkSZUuZdrU6VOoUaUijUAAwFWsWbVu5drV61ewYcVqJYAAwFm0BAQAYNvW7Vu4ceXOpVvX7l24CBAA4NvX71/AgQUPJlzY8GG+ByQAYNzY8WPIkSVPplzZ8mXMDBYA4NzZ82fQoUWPJl3a9GnOByQAYN3a9WvYsWXPpl3b9u3aEQgAYLAAwG/gwYUPJ17c//hx5MmV/yaAAMBz6AUgAKBe3fp17Nm1b+fe3fv36gYIACBAAMB59OnVr2ff3v17+PHlvz8gAcB9/Pn17+ff3z9AAAIHEixo8CDChAQNEADg8CHEiBInUqxo8SLGjBkPQADg8SPIkCJHkixp8iTKlCIJEADg8iXMmDJn0qxp8ybOnC4RUADg8yfQoEKHEi1q9CjSpEojCADg9CnUqFKnUq1q9SrWrE4RUADg9SvYsGLHki1r9izatGcRAAAQQQCAuHLn0q1r9y7evHr38o17YAGAwIILKABg+DDixIoXM27s+DHkyIcHAACA4ACAzJo3c+7s+TPo0KJHkw6NgAKA1P+qV7Nu7fo17NiyZ9NWPQAA7ty6d/Pu7fs38ODChxNHQAEA8uTKlzNv7vw59OjSpycXAOA69uzat3Pv7v07+PDitx8QAOA8egAFALBv7/49/Pjy59Ovb/8+fAYIAPDv7x8gAIEDCRY0eBBhQoULGRZUAAFARIkTKVa0eBFjRo0bOXaEoABASJEjSZY0eRJlSpUrWYZUAAFATJkACACweRNnTp07efb0+RNo0JsGAACIgABAUqVLmTZ1+hRqVKlTqUZVAAFAVq1buXb1+hVsWLFjyWodAABtWrVr2bZ1+xZuXLlz6SqAAABvXr17+fb1+xdwYMGD81IAcBhxYsWLGTf/dvwYcmTJiwkUAHAZ8wEFADh39vwZdGjRo0mXNn0atIICAFi3dv0admzZs2nXtn2btYAIAHj39v0beHDhw4kXN34cOQUEAJg3d/4cenTp06lXt36duYAIALh39/4dfHjx48mXN3+ePAEGAABQQAAAfnz58+nXt38ff379++EXOAAQgMCBChYAOIgwocKFDBs6fAgxosSDBAwAAFAAgMaNHDt6/AgypMiRJEuOFBABgMqVLFu6fAkzpsyZNGuqLGAAgM6dPHv6/Ak0qNChRIsaFcAAgNKlTJs6fQo1qtSpVKsuJQAAAAEAXLt6/Qo2rNixZMuaPdt1wQIAbNu6fQs3/67cuXTr2r2LV8IBAHz7+v0LOLDgwYQLGz7MdwEDAIwbO34MObLkyZQrW75c+QAAABIOAPgMOrTo0aRLmz6NOrXqzwoEAHgN+8ABALRr276NO7fu3bx7+/5Nu0AFAAAEEACAPLny5cybO38OPbr06dAXMACAPbv27dy7e/8OPrz48dgPVACAPr369ezbu38PP778+fQZMACAP7/+/fz7+wcIQOBAggUNHkSYUKFAAgoAPIQYUeJEihUtXsSYUeNEBQgAfARJgAAAkiVNnkSZUuVKli1dvkQJgQAAmjVt3sSZU+dOnj19/qTJYAEAokWNHkWaVOlSpk2dPoVaoQAAqv9VrV7FmlXrVq5dvX6lGkEAALJlzZ5Fm1btWrZt3b5lewACAAASCADAm1fvXr59/f4FHFjwYMAMFgBAnFjxYsaNHT+GHFnyZMQIJADAnFnzZs6dPX8GHVr0aNIRFgBAnVr1atatXb+GHVv2bNQHIgDAnVv3bt69ff8GHlz4cN4FCABAnlwBAgDNnT+HHl36dOrVrV/HHl0AAO7dvX8HH178ePLlzZ/vHkEAAPbt3b+HH1/+fPr17d/Hb4AAAP79/QMEIHAgwYIGDyJMqHAhw4IQFACIKHEixYoWL2LMqHEjx4wFFAAAMAAAyZImT6JMqXIly5YuX5ZEUAAAzZoLEAD/yKlzJ8+ePn8CDSp0KNGcCiAAAHAAANOmTp9CjSp1KtWqVq9WhSAAANeuXr+CDSt2LNmyZs9yVQABANu2bt/CjSt3Lt26du/ijaAAAN++fv8CDix4MOHChg/3JQAAAAEAjh9Djix5MuXKli9jzvw4AgIAnj+DDi16NOnSpk+jTq16AIDWrl/Dji17Nu3atm/jdk0BAYDevn8DDy58OPHixo8jL06AAAAAAwBAjy59OvXq1q9jz659e3QGBwCAD4+gAIDy5s+jT69+Pfv27t/DLy8AAgAAEQDgz69/P//+/gECEDiQYEGDBxEmVLiQAgIADyFGlDiRYkWLFzFm1PhQ/0AEAB9BhhQ5kmRJkydRplS5kgICAC9hxpQ5k2ZNmzdx5tT58gACAD+BBhU6lGhRo0eRJlU6VEABAE+hHiAAgGpVq1exZtW6lWtXr1+xUgAwlmxZs2fRplW7lm1bt2QlHAAwl25du3fx5tW7l29fv34JGAAwmHBhw4cRJ1a8mHFjx4QlHAAwmXJly5cxZ9a8mXNnz5sFLABAoAIA06dRp1a9mnVr169hxz5NAEBt2wAkHACwm3dv37+BBxc+nHhx47sXMACwnHlz58+hR5c+nXp169clHACwnXt379/Bhxc/nnx589sFLACwnn179+/hx5c/n359++8LANC/H8CCAv8AAQgcSLCgwYMIEypcyLChQQUAIkqcSLGixYsYM2rcyFFihQIAQoocSbKkyZMoU6pcyZJlgQoAYsqcSbOmzZs4c+rcyVNmhQIAggodSrSo0aNIkypdyjQpggMAClQAQLWq1atYs2rdyrWr169VBRAAQLZshAIA0qpdy7at27dw48qdSzctgwUAABwAwLev37+AAwseTLiw4cOFKxQAwLix48eQI0ueTLmy5cuMGSwAwLmz58+gQ4seTbq06dOoKxQAwLq169ewY8ueTbu27dusCRAAwLu379/AgwsfTry48ePAKRAAwLy58+fQo0ufTr269evXD0gAwL279+/gw4v/H0++vPnz3Q0QAMC+vfv38OPLn0+/vv379AkAAHBAAgCAAAQOJFjQ4EGECRUuZNhQoAQCACRORADA4kWMGTVu5NjR40eQIS9GEACgwAIAKVWuZNnS5UuYMWXOpCnTAAEAOXXu5NnT50+gQYUOJZozggAASZUuZdrU6VOoUaVOpVrVAAEAWbVu5drV61ewYcWOJZtVwQEAadWuZdvW7Vu4ceXOpdt2AQC8eQEgANDX71/AgQUPJlzY8GHEgQswANDY8WPIkSVPplzZ8mXMjgcA4NzZ82fQoUWPJl3a9GnUCCgAYN3a9WvYsWXPpl3b9u3WAwDs5t3b92/gwYUPJ17c/zhxBggAIKAAwPlz6NGlT6de3fp17NmfFwDQ3TuAAQDEjydf3vx59OnVr2fffjwEBQDkz6df3/59/Pn17+ff3z/AAQAGEixo8CDChAoXMmzokGAEBAAmUqxo8SLGjBo3cuzo8WIBACJHAoAA4CTKlCpXsmzp8iXMmDJXEjgA4CbOnDp38uzp8yfQoEJxDgBg9CjSpEqXMm3q9CnUqFIVQABg9SrWrFq3cu3q9SvYsFcHAChr9izatGrXsm3r9i1ctwoKAFAAAQDevHr38u3r9y/gwIIH52UA4DBiABAAMG7s+DHkyJInU65s+XJjCggAEDgA4DPo0KJHky5t+jTq1P+qUQ8A4Po17NiyZ9Oubfs27tyvKSAA4Ps38ODChxMvbvw48uTKBwBo7vw59OjSp1Ovbv06ducFCADo7v07+PDix5Mvb/48evAEKABo7/49/Pjy59Ovb/8+/vwCIgDo7x8gAIEDCRY0eBBhQoULGSYkYABARIkTKVa0eBFjRo0bOWokAACAgAgASJY0eRJlSpUrWbZ0+ZIkAQkAaNYEgABATp07efb0+RNoUKFDieqUcAAAAgEAmDZ1+hRqVKlTqVa1erWqAQBbuXb1+hVsWLFjyZY1y1XCAQBr2bZ1+xZuXLlz6da1e9cAAL17+fb1+xdwYMGDCRfeu4AAAMWLGTf/dvwYcmTJkylXbkxAAADNmwEoAPAZdGjRo0mXNn0adWrVoxEoAPAadmzZs2nXtn0bd27drwtUAPAbeHDhw4kXN34ceXLlyxcwAPAcenTp06lXt34de3btzwtUAPAdfHjx48mXN38efXr16CEQALCAAQD58+nXt38ff379+/n3nw/wAICBBAFIAIAwocKFDBs6fAgxosSJCSsUAIAxo8aNHDt6/AgypMiRJCsAOIkypcqVLFu6fAkzpkwABAQYWFAAgM6dPHv6/Ak0qNChRIv2JAAgqVIAEQA4fQo1qtSpVKtavYqVKgEABSBEAHAAwoIICwoIgKAAgNq1bNu6fQs3/67cuXTrHpAAIK/evXz7+v0LOLDgwYIRCABAYAAFAAQUFAAAOTIDBgoUAFBggAEAAgUAeP4MOrTo0aRLmz6NWvQBCQBau34NO7bs2bRr274tmwEFAAAoRAAAPLhw4QwWADgOoEABAAgMQABQQEEBANSrW7+OPbv27dy7ewdQYAGA8eQBLACAPr369ezbu38PP376AgoIAKAwoAAAAQgA+AcIQOBAggUPFACQUOFCAAcgMABwIIICABUtXsSYUeNGjh09fswoAcBIkiVNnkSZUuXKlQoiHAAQgUIBAAQA3MSZU+dOnj1zEhAgAACCCgwAACAAQOlSpk2dPoUaVerUqf8SAFzFmlXrVq5dvX7FeqAAAAEGBABQIIAAALZt3b6FG1fuXLgFDgA4YIACAAIKCgAAHFjwYMKFDR9GbPgAAwCNHQMgAEDyZMqVLV/GnBlzAQYCAAioIAAAAQIATJ9GnVr1atURBACAHVv2bNq0C1CAAKBABAUAfP8GHlz4cOLFjQNHQAHAcubNnT+HHl26dAIHABSQQAFAAQYIAHwHH178ePLlxUcQAED9evbt3b9fT2DBAgAHKjAAkF//fv79/QMEIHAgwYIGDSKAAGAhQwAFAECMKHEixYoWKQpgAICAAQgAABwAIHIkyZImT6JMCWABAgAuX8KMKXMmzQMIABT/GEABAAAEBAAADSp0KNGiRo8iBUABANOmTp9CjRq1AAAAECoAABBhAYCuXr+CDSt2LNmyZs+iBUsAQAEKEgAQYKAAAN26du/izat3b14KAP4CDix4MOG/BxYQACChQgEACAoAiCx5MuXKli9jzqx5M+fOAAgsYACAgAQGAE6jTq16NevWqAsgACB7NgAEAG7jzq17t24BFBAAWBChAIDixo8jT658OfPmzAUcACB9OvXq1q9jz379gAIABAZIAAAAAQEA5s+jT69+/XkFEADAjy9/Pv35BBQcALBggAAABwAiIACAYEGDBxEmVLiQYUOCEBQAkDiRYkWLFzFm1Cix/wAAAhIqAACwAAEAkydRplSpUkAEAC9hxpQ5E0ABCAsAKKCgAEBPnz+BBhU6lGhRo0UpIACwlGlTp0+hRpU6NSoBBhAAAKDAAEBXr1/Bhu1KoAAAs2cBRACwli2AAwgAFDBAAUABAQUA5NW7l29fv38BBxY8mHBhw4cLIxAAAMAACQAAHAAwmXJly5crQwCwmQEEAAQkRAAAgAAA06dRp1a9mnVr169hx5Y9m3bt1QcAAJBgAACABQgABBc+nDjxAQMKAFigAEBz58+hR5c+nXp169exAzhAAEB379/Bhxc/nnx58+e7R6AAAAAEBgDgxwcgIAIA+/cBLIgAAIAECv8ACQAgAKCgwYMIEypcyLChw4cOKSAAQLGixYsYM2rcyLGjR4wKFgAAYIACAAAFFjAAwLKlSwAEEAAAIGFAAQAIDgDYybOnz59AgwodSrToTwkHAChdyrSp06dQo0qdSlUqAQUAABgYIACA169gwwIgAABAhAoFACwQQACA27dw48qdS7eu3bt4ASggAKCv37+AAwseTLiw4cOBCwggAEDCgAIAFCAAQLmy5cuYKyuIQAAABAgFAIgeTbq06dOoU6tezbq169ewY5NWAAEBAAYQCgAgAKC379/AgwsHXkABAQASDBwAcKAAgOfQo0ufTr269evYs2vfzr37dAIIDgD/EDBAAAAECggAWM++vfv38OPHJwAAQIQKBwAIEEAAgH+AAAQOJFjQ4EGECRUWpFAAwEOIESVOpFjR4kWMDwswWABAgQQFAAgAIFnS5EmUKVWuZElSAYQCACJAKADA5k2cOXXu5NnTJ84KBQAMJVrU6FGkSZUuPVrgAIACFSgAKLDgAACsWbVu5drV61ewYQEUEEAAAAUDBwAUIADA7Vu4ceXOpVt3LoQCAPTu5dvX71/AgQXrXRABAIEKEAAAKADA8WPIkSVPplzZ8uXLBQAAYGAAAQAFCggAIF3a9GnUqVWvZt3a9WvVBwAAoGCAAAAGAgDs5t3b92/gwYUPJ17c/7gCCAcALIhQAMBz6NGlT6de3fp17NmzI2BQAICECgUAHCAAwPx59OnVr2ff3v17+PHZHxBQAACECggAFCAAwD9AAAIHEixo8GDBBQQAMGzo8CHEiBIZEiAAQIAEBQAWMCgA4CPIkCJHkixp8iTKlCpXjjxAAACDAQoAIFBAAADOnDp38uyZ0wABAEKHEi1q9GhRAgIQAFhgQACAAwcAUK1q9SrWrFq3cu3q9SvYsFUVUFAAQACDAwDWsm3r9u1bAwQA0K1r9y5evAQAFIDAAAACCAgAEC5s+DDixIoXM27s+DHkyJAPLDgAgIEEBAAIAOjs+TPo0AQAkC5t+jRq0v8IFAAoMIACAAIKCgCobfs27ty6d/Pu7fs38ODCh+s+UADAggECABxAQAAA9OjSp1OvXj0CBQAAJDAA4P07+PDix5Mvb/48+vTq17NvH14BBQEAFDA4AOA+/vwADgDo7x8gAIECCygAAEDCAAIABCAA8BBiRIkTKVa0eBFjRo0bOXb0qPEAAwQAFkhQAABlSgADALR0CUABhAMAIFAoAIAAAJ07efb0+RNoUKFDiRY1ehRpUqU8CSAoAGDBgAUAChwYgKAAgAUGBABQIIAAALFjyZY1exZtWrVr2bZ1+xZuXLlwCQBQIKGCBAUACBAA8BdwYMGDCRc2fBhxYsWEAwIAIf8LTkVUU0NBUEUyLjADAQAAACxWAXcAygBuAof+/v7IgDORkZHn5+cNCQbX19cXFBHHx8fEfTGnp6e3t7eIiIh3d3dvRxxUNhe3dS5oZ2dIR0eKWCNXV1c3NzYoKCemaiooGgs2JBFFLBKaYidhPhh+UCBBPTlEQT1jYF5gXVp3TCB9UB+Af32gn58AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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IkSVPppx4QQQAmTVv5tzZ82fQoUWPJi06AQAAFBIAYN3a9WvYsWXPpl3b9m3WCQQA4N37QAIAwYUPJ17c//hx5MmVL2cevIABAAAUEABQ3fp17Nm1b+fe3ft38N0FRABQ3vx59OnVr2ff3v17+OULVABQ3/59/Pn17+ff3z9AAAIHEixo8CDCgQsYAGjo8CHEiBInUqxo8SJGhwoAcOzo8SPIkCJHkixp8iRIBQoAsGxJgACAmDJn0qxp8ybOnDp38qw5oQCAoEKHEi1q9CjSpEqXMg3KgAGAqFKnUq1q9SrWrFq3cu1a4QCAsGLHki1r9izatGrXsg3LYAGAuHIJEABg9y7evHr38u3r9y/gwHYLUAAAQEIBAIoXM27s+DHkyJInU64smQEDAJo3c+7s+TPo0KJHky6t+QAFAP+qV7Nu7fo17NiyZ9OubZvBAgC6d/Pu7fs38ODChxMvrrtABADKlzNv7vw59OjSp1Ov7rxAAQDatydIAOA7+PDix5Mvb/48+vTqxy8gAOA9/Pjy59Ovb/8+/vz630dYAAAgAIEDCRY0eBBhQoULGTZsaKAAAIkTKVa0eBFjRo0bOXaUGEEAAJEjSZY0eRJlSpUrWbZUWWABAAAGCACweRNnTp07efb0+RNoUJsHCgAwenSBAgBLmTZ1+hRqVKlTqVa1ujTBBAAACgDw+hVsWLFjyZY1exZt2rMRBABw+xZuXLlz6da1exdvXrcJJgDw+xdwYMGDCRc2fBhxYsUMFAD/cPwYcmTJkylXtnwZc2bJBQB09vwZdGjRo0mXNn0atecICgC0dv0admzZs2nXtn0bd+4BBAD09v0beHDhw4kXN34ceW8JCgA0d/4cenTp06lXt34de3UCBQAAGAAAfHjx48mXN38efXr168MvSAAAfvwEBQDUt38ff379+/n39w8QgMCBBAsaNKhAAgAACwA4fAgxosSJFCtavIgx40UJCgB4/AgypMiRJEuaPIkypUcFEgC4fAkzpsyZNGvavIkzp04JCgD4/Ak0qNChRIsaPYo0qc8CCQA4fQo1qtSpVKtavYo1q1QBBwB4/VqAAICxZMuaPYs2rdq1bNu6PVsB/4DcuXTr2r2LN6/evXz7zp2QAIDgwYQLGz6MOLHixYwbOx4AILLkyZQrW76MObPmzZwlT0gAILRoAgBKmz6NOrXq1axbu34N27QCBgAAVACAO7fu3bx7+/4NPLjw4cEnJACAPLny5cybO38OPbr06cgFRACAPbv27dy7e/8OPrz48eQnJACAPr369ezbu38PP778+egVLACAP7/+/fz7+wcIQOBAggUNHkSYUKHBAwQAPIQooAAAihUtXsSYUeNGjh09fsQoAMBIkiVNnkSZUuVKli1dkqRwAMBMmjVt3sSZU+dOnj19+iRgAMBQokWNHkWaVOlSpk2dEqVwAMBUqv9VrV7FmlXrVq5dvW5NkAAAAQMAzJ5Fm1btWrZt3b6FG/esAgIA7N6NUADAXr59/f4FHFjwYMKFDe9dwAAAgAMAHD+GHFnyZMqVLV/GnPkyhQMAPH8GHVr0aNKlTZ9GndrzAgYAXL+GHVv2bNq1bd/GnVv3hAMAfP8GHlz4cOLFjR9Hnvw3AQAACACAHl36dOrVrV/Hnl379ugUCgAAH178ePLlzZ9Hn179+vUFKgCAH1/+fPr17d/Hn1///vgVCgAEIHAgwYIGDyJMqHAhw4YKCRAAUKACgIoWL2LMqHEjx44eP4K0KIEAgJImFRAAoHIly5YuX8KMKXMmzZoqGSz/AEBgAYCePn8CDSp0KNGiRo8iNVqhAICmTp9CjSp1KtWqVq9ibcpgAYCuXr+CDSt2LNmyZs+iTVuhAIC2bt/CjSt3Lt26du/ibZvgAIC+fv8CDix4MOHChg8jDswAAOPGAA4AiCx5MuXKli9jzqx5M+fKBSIACC16NOnSpk+jTq16NWvRBggAiC17Nu3atm/jzq17N2/eBygACC58OPHixo8jT658OXPhBggAiC59OvXq1q9jz659O/fsDBQAODABAPny5s+jT69+Pfv27t+XJwBgPn0ABggAyK9/P//+/gECEDiQYEGDBxEmVKgwggAADyFGlDiRYkWLFzFm1LjR/wABAB9BhhQ5kmRJkydRplT5kYECAC9hxpQ5k2ZNmzdx5tQ58wAAnz8BRAAwlGhRo0eRJlW6lGlTp0cJJAAwlWpVq1exZtW6lWtXr1QHABA7lmxZs2fRplW7lm1btwkmAJA7l25du3fx5tW7l2/fuQMABBY8mHBhw4cRJ1a8mLFiBQUAJJgAgHJly5cxZ9a8mXNnz58rLwAwmjSACQBQp1a9mnVr169hx5Y9O7UEBQAIFACwm3dv37+BBxc+nHhx48QHAFC+nHlz58+hR5c+nXr15RIUANC+nXt379/Bhxc/nnx58wMApFe/nn179+/hx5c/n756AgDw59e/n39///8AAQgcSLCgwYMIEyo8WAGAw4cQI0qcSLGixYsYM2pUIAGAx48gQ4ocSbKkyZMoU34cAKCly5cwY8qcSbOmzZs4bRIAAECBBABAgwodSrSo0aNIkypdGpQCgKdQASgAQLWq1atYs2rdyrWr169VJyQAcEAAgLNo06pdy7at27dw48qFOwCA3bt48+rdy7ev37+AA9+dkACA4cOIEytezLix48eQI0seAKCy5cuYM2vezLmz58+gLQsoAKC06dOoU6tezbq169ewUy8AQLs2gAQAcuvezbu379/AgwsfTrx3AgEAkitfzry58+fQo0ufTj05AQMAsmvfzr279+/gw4v/H0++vIAIANKrX8++vfv38OPLn08/fQEDAPLr38+/v3+AAAQOJFjQ4EGECRUuRCihAAABDABMpFjR4kWMGTVu5NjRI8UCAESOJGAAwEmUKVWuZNnS5UuYMWWirHAAwE2cOXXu5NnT50+gQYUKLWAAwFGkSZUuZdrU6VOoUaUinVAAwFWsWbVu5drV61ewYcVuLQDA7FkCEQCsZdvW7Vu4ceXOpVvX7tsCBwDs5dvX71/AgQUPJlzY8N4CFQAsZtzY8WPIkSVPplzZ8uUFDABs5tzZ82fQoUWPJl3a9OYDFQCsZt3a9WvYsWXPpl3bNm0BBAAwYADA92/gwYUPJ17c//hx5Ml9ExAAwPlzAgwATKde3fp17Nm1b+fe3Tt1AwUAFCgAwPx59OnVr2ff3v17+PHdH6gAwP59/Pn17+ff3z9AAAIHEixo8CDCgQYIAGjo8CHEiBInUqxo8SJGjAcoAOjo8SPIkCJHkixp8iRKjwcAsGzp8iXMmDJn0qxp8ybMAxEA8Ozp8yfQoEKHEi1q9ChSBgsAMG3q9CnUqFKnUq1q9SrTBBQAcO3q9SvYsGLHki1r9mxZAgAARFgA4C3cuHLn0q1r9y7evHrfHpAA4C9gAgcAEC5s+DDixIoXM27s+HHhAQQACEgA4DLmzJo3c+7s+TPo0KI/J6AA4DTq1P+qV7Nu7fo17NiyUQ8AYPs27ty6d/Pu7fs38ODCE0wAYPw48uTKlzNv7vw59OjHGQCobv069uzat3Pv7v07+OwFFAAob55AAgDq17Nv7/49/Pjy59Ov715AAgD69/Pv7x8gAIEDCRY0eBBhQoULDSqYAABiRIkTKVa0eBFjRo0bOUoQAABkSJEjSZY0eRJlSpUrQSqQAABmTJkzada0eRNnTp07c1IAAECCAgBDiRY1ehRpUqVLmTZ1OpRAAQBTqSaQAABrVq1buXb1+hVsWLFjsw4AcBZtWrVr2bZ1+xZuXLlzFUgAcBdvXr17+fb1+xdwYMF4DQAwfBhxYsWLGTf/dvwYcuTFBABUtnxgAQDNmzl39vwZdGjRo0mX9nyAAADVq1m3dv0admzZs2nXVi1AAgDdu3n39v0beHDhw4kXNz5BAQDly5k3d/4cenTp06lXVy4gAgDt27l39/4dfHjx48mXHy8AAIAJCQC0d/8efnz58+nXt38ff/sDCgD09w/wgAAABAsaPIgwocKFDBs6fEiQgAEAABIQAIAxo8aNHDt6/AgypMiRIAVEAIAypcqVLFu6fAkzpsyZKAkYAIAzp86dPHv6/Ak0qNChRAVEAIA0qdKlTJs6fQo1qtSpSQ8AuIo1q9atXLt6/Qo2rNitCgQAOIs2rdq1bNu6fQs3/67cuRMOALiLN6/evXz7+v0LOLDguwsYADiMOLHixYwbO34MObLkyRQOALiMObPmzZw7e/4MOrToywIWADiNmkABAKxbu34NO7bs2bRr277NukAFAAAYFAAAPLjw4cSLGz+OPLny5cgXMAAAPbr06dSrW7+OPbv27dALVAAAPrz48eTLmz+PPr369ewXMAAAP778+fTr27+PP7/+/fAJMAAIQOBAggUNHkSYUOFChg0NHjgAQOLEAwcAXMSYUeNGjh09fgQZUuRGBgQAnESZUuVKli1dvoQZU+ZJBgsA3MSZU+dOnj19/gQaVOjQCgUAHEWaVOlSpk2dPoUaVepRBv8LAFzFmlXrVq5dvX4FG1bs1wIRAACoUADAWrZt3b6FG1fuXLp17a4tQADAXr4LFgAAHFjwYMKFDR9GnFjxYsAHKACAHFnyZMqVLV/GnFnzZs4MFgAAHVr0aNKlTZ9GnVr1atAHJgCAHVv2bNq1bd/GnVv3bt4CFAAAHlz4cOLFjR9Hnlz5cuIHADyHHl36dOrVrV/Hnl079AgCAHwHH178ePLlzZ9Hn179egMEALyHH1/+fPr17d/Hn1//+wgCAAAEIHAgwYIGDyJMqHAhw4YJCSQAAMAAAQAWL2LMqHEjx44eP4IMaVHBAQAmTyo4AGAly5YuX8KMKXMmzZo2Vyb/mAAAgAIAPn8CDSp0KNGiRo8iTXo0ggAATp9CjSp1KtWqVq9izeo0wQQAXr+CDSt2LNmyZs+iTas2ggAAbt/CjSt3Lt26du/izeuWwAEAfv8CDix4MOHChg8jTix4QQIAjh8TACB5MuXKli9jzqx5M+fOlw0ACC16NOnSpk+jTq16NWvREhQAiC17Nu3atm/jzq17N+/eAwAADy58OPHixo8jT658eXAJCgBAj16AAIDq1q9jz659O/fu3r+Dr65AAgAAEwCgT69+Pfv27t/Djy9/fnwJCgDgz69/P//+/gECEDiQYEGDBxEmVChQgQQADyFGlDiRYkWLFzFm1LhR/4ICAB9BhhQ5kmRJkydRplT58YAAAC9hxpQ5k2ZNmzdx5tQ5MwEBAD+BJigAgGhRo0eRJlW6lGlTp0+RRgAwlWpVq1exZtW6lWtXr1QnJAAwlmxZs2fRplW7lm1bt28HAJA7l25du3fx5tW7l2/fuRMSABA8mHBhw4cRJ1a8mHFjxQkEAAAwAEBly5cxZ9a8mXNnz59BWz5AAEBp0xIOAFC9mnVr169hx5Y9m3Zt1QIiAABAAEBv37+BBxc+nHhx48eRG5+QAEBz58+hR5c+nXp169exNxcQAUB379/Bhxc/nnx58+fRp49wAEB79+/hx5c/n359+/fxxz8AgH9///8AAQgcSLCgwYMIEypcyNAghQMAIkqcSLGixYsYM2rcyJEjAQMAQoocSbKkyZMoU6pcyVIkhQMAYsqcSbOmzZs4c+rcyTNngQIACBgAQLSo0aNIkypdyrSp06dFGRQAQLWqAAIAsmrdyrWr169gw4odSzbrAgYAAAgAwLat27dw48qdS7eu3bt1KRwAwLev37+AAwseTLiw4cN8FzAAwLix48eQI0ueTLmy5cuYKRwAwLmz58+gQ4seTbq06dOcCxQAwLq169ewY8ueTbu27duwIxAAwLt3AQDAgwsfTry48ePIkytfTrzABADQo0ufTr269evYs2vfHr1CAQDgw4v/H0++vPnz6NOrX7++QAUA8OPLn0+/vv37+PPr3x/fQAGAAAQOLADA4EGECRUuZNjQ4UOIEQ8yWACggAQAGTVu5NjR40eQIUWOJCmyQgEAKVWuZNnS5UuYMWXOpJkywgIAOXXu5NnT50+gQYUOJVq0QgEASZUuZdrU6VOoUaVOpZpUQAIAWbVu5drV61ewYcWOJds1AQC0aQEsIADA7Vu4ceXOpVvX7l28eeMWEADA71/AgQUPJlzY8GHEif8aIADA8WPIkSVPplzZ8mXMmTMfoADA82fQoUWPJl3a9GnUqT8PIADA9WvYsWXPpl3b9m3cuW0LOAAgAQUAwYUPJ17c//hx5MmVL2cuXAEA6NEBUCAAwPp17Nm1b+fe3ft38OGtSxAAAAABAOnVr2ff3v17+PHlz6cvfwABAPn17+ff3z9AAAIHEixo8CDChAoVSlAA4CHEiBInUqxo8SLGjBo3UgDg8SPIkCJHkixp8iTKlCMLAGjp8iXMmDJn0qxp8yZOlwMA8Ozp8yfQoEKHEi1q9CjSBBMAMG3q9CnUqFKnUq1q9WrTAQC2cu3q9SvYsGLHki1rluwBAgAUTADg9i3cuHLn0q1r9y7evG8nAOjrF8ACAIIHEy5s+DDixIoXM248eIICAAUUAKhs+TLmzJo3c+7s+TNozwMAkC5t+jTq1P+qV7Nu7fp16QkJANCubfs27ty6d/Pu7fs38AEAhhMvbvw48uTKlzNv7px4AgIAplOvbv069uzat3Pv7v16BADixwM4AOA8+vTq17Nv7/49/Pjy1ydgAOA+/vz69/Pv7x8gAIEDCRY0eBBhwoEEBgBw+BBiRIkTKVa0eBFjRo0CJADw+BFkSJEjSZY0eRJlSo8EDABw+RIAAQAzada0eRNnTp07efb0SXPCAQAKGAAwehRpUqVLmTZ1+hRqVKcEBgCwehVrVq1buXb1+hVs2KsUDgAwexZtWrVr2bZ1+xZu3LgEDACwexdvXr17+fb1+xdw4LsRCgAwfBhxYsWLGTf/dvwYcmTFBwBUtgyAAQDNmzl39vwZdGjRo0mX9nwgAQDVq1m3dv0admzZs2nXVl2gAgDdu3n39v0beHDhw4kXN74gAgDly5k3d/4cenTp06lXV16gAgDt27l39/4dfHjx48mXH7+AAIAFDAC0d/8efnz58+nXt38ff3sCCgD09w+QgAQABAsaPIgwocKFDBs6fFiwQgEABAgAuIgxo8aNHDt6/AgypMiPBSoAOIkypcqVLFu6fAkzpkyUFQoAuIkzp86dPHv6/Ak0qFChBSgAOIo0qdKlTJs6fQo1qlSkBAAAIEAAgNatXLt6/Qo2rNixZMtqLUABgNq1bNu6fQs3/67cuXTr2mXAAIDevXz7+v0LOLDgwYQL6z1AAYDixYwbO34MObLkyZQrTz4AAACDBQA6e/4MOrTo0aRLmz6NunOBCABauyagAIDs2bRr276NO7fu3bx7zzZAAECCBACKGz+OPLny5cybO38OvfkBCgCqW7+OPbv27dy7e/8O3roBAgDKmz+PPr369ezbu38PH/4BCgDq27+PP7/+/fz7+wcIQOBAggUNHhQAQOFChg0dPoQYUeJEihUdFhAAQONGAAcAfAQZUuRIkiVNnkSZUuXIBQoAvIQZU+ZMmjVt3sSZU+fLBBMA/AQaVOhQokWNHkWaVOnSCAIAPIUaVepUqv9VrV7FmlXr0wQTAHwFC4AAALJlzZ5Fm1btWrZt3b4tWwEAAAYKANzFm1fvXr59/f4FHFgwXgIADB9OMAHAYsaNHT+GHFnyZMqVLTMeAEDzZs6dPX8GHVr0aNKlTSeYAED1atatXb+GHVv2bNq1V1MAkFv3bt69ff8GHlz4cOK9CRQAkFx5AQEAnD+HHl36dOrVrV/Hnl16ggIAvH8HH178ePLlzZ9Hn967AgkA3L+HH1/+fPr17d/Hn1+/BAUA/AMEIHAgwYIGDyJMqHAhQ4YKJACIKHEixYoWL2LMqHEjR40MAACQoAAAyZImT6JMqXIly5YuX5IskAAAzZoHFgD/yKlzJ8+ePn8CDSp0KFGdAwAAKEAAANOmTp9CjSp1KtWqVq9SVSABANeuXr+CDSt2LNmyZs92HQBgLdu2bt/CjSt3Lt26du8qiABgL9++fv8CDix4MOHChvkWAACAAIDGjh9Djix5MuXKli9jdqyAAYDOnj+DDi16NOnSpk+jTj0hAYDWrl/Dji17Nu3atm/jbi0gAoDevn8DDy58OPHixo8jN14AAIAJCQBAjy59OvXq1q9jz659O3QFCwCAD1/gAIDy5s+jT69+Pfv27t/DL0/AAAAAAgoAyK9/P//+/gECEDiQYEGDBxEmVLjwoIAIACBGlDiRYkWLFzFm1LgR/yIBAwBAhhQ5kmRJkydRplS5kqWACABgxpQ5k2ZNmzdx5tS5M+YCAD+BBhU6lGhRo0eRJlU69EACAE+hFigAgGpVq1exZtW6lWtXr1+xRigAgGxZs2fRplW7lm1bt2/JLmAAgG5du3fx5tW7l29fv38BUzgAgHBhw4cRJ1a8mHFjx48JL2AAgHJly5cxZ9a8mXNnz585F5gAAMCEAwBQp1a9mnVr169hx5Y9GzUBAgBw517AAEBv37+BBxc+nHhx48eR9y5QAUBz58+hR5c+nXp169exZ1/AAEB379/Bhxc/nnx58+fRdy8wAUB79+/hx5c/n359+/fxxydAAEB///8AFSgAQLCgwYMIEypcyLChw4cIFRAAQLGixYsYM2rcyLGjx48UGSwAQLKkyZMoU6pcybKly5cwKxQAQLOmzZs4c+rcybOnz580GSwAQLSo0aNIkypdyrSp06dMCSgAAKBCAQBYs2rdyrWr169gw4odizXBAQBo0ypQAKCt27dw48qdS7eu3bt42x6gAABAAgCAAwseTLiw4cOIEytenJjBAgCQI0ueTLmy5cuYM2veDPkABQCgQ4seTbq06dOoU6tezZrBAgCwY8ueTbu27du4c+veHbsAgN/AgwsfTry48ePIkysfzkABgOfQo0ufTr269evYs2vfboAAgO/gw4v/H0++vPnz6NOr/x5BAID38OPLn0+/vv37+PPrx08AAACABggAIFjQ4EGECRUuZNjQ4UOCDBQAoFjxQAEAGTVu5NjR40eQIUWOJJkxwQQAACIAYNnS5UuYMWXOpFnT5s2aEQQA4NnT50+gQYUOJVrU6FGeCiYAYNrU6VOoUaVOpVrV6lWsEQQA4NrV61ewYcWOJVvW7FmuBQQAYNvW7Vu4ceXOpVvX7l24CgoA4Nv3QAEAgQUPJlzY8GHEiRUvZlxYAgDIkSVPplzZ8mXMmTVvjixBAQDQoUWPJl3a9GnUqVWvZj0AwGvYsWXPpl3b9m3cuXXDnqAAwG/gwYUPJ17c//hx5MmVH0/AAACAAQCkT6de3fp17Nm1b+fefXoBAgDEj5egAMB59OnVr2ff3v17+PHlnxcgAcB9/Pn17+ff3z9AAAIHEixo8CDChAoLTlAA4CHEiBInUqxo8SLGjBofKmAA4CPIkCJHkixp8iTKlCpHEgDg8iWABQcA0Kxp8ybOnDp38uzp8ydOBQCGEi1q9CjSpEqXMm3qlOiEBACmUq1q9SrWrFq3cu3q9esAAGLHki1r9izatGrXsm07lkICAHLn0q1r9y7evHr38u2r98ABAAQGAChs+DDixIoXM27s+DFkwwIKAKhseUEBAJo3c+7s+TPo0KJHky6teUEEAP8AFABo7fo17NiyZ9Oubfs2btsUEgDo7fs38ODChxMvbvw48t4LGABo7vw59OjSp1Ovbv069uwUDgDo7v07+PDix5Mvb/48+u4ECgBo7/49/Pjy59Ovb/8+/vgSCgDo7x8gAIEDCRY0eBBhQoULGTY8SMAAAIkTKVa0eBFjRo0bOXacWOEAAJEjSZY0eRJlSpUrWbZsWcAAAJkzada0eRNnTp07efacOaEAAKFDDwAwehRpUqVLmTZ1+hRq1KMMGAAgEAFAVq1buXb1+hVsWLFjyYqtcABAWrVr2bZ1+xZuXLlz6aZlsABAXr17+fb1+xdwYMGDCReuUABAYsWLGTf/dvwYcmTJkyknVpAAQGbNmwEIYMBAAQDRo0mXNn0adWrVq1mTFgAAdmwACggAsH0bd27du3n39v0bePDcBBgAMH6cgYEJCxZQMKAAQHTp06lXt34de3bt26MbKAAAfHjx48mXN38efXr169cfqAAAPvwIFAgAsA/gQAUBAPj39w8QgMCBBAsaPIgwocKFAw0QAAAxosSJFCtavIgxo8aNGAUoAHCAAoCRABRUAIAyJQACBgoAeAkzpsyZNGvavIkz5wEAPHsCqEAAgNChRIsaPYo0qdKlTIkSUCAgAYCpVAFEEAAgq1YAExQA+Ar2KwMGAMqaPYs2rdq1bNu6fbvW/wABAHTr2r2LN6/evXz7+qVbQIIBCRIoGFgAIHFiBgIAOH5MYACAyZQpFzAAILPmzZw7e/4MOrTo0QQAmD4NQAIBAKxbu34NO7bs2bRr2wZQwMACAgB6F5ggAYDw4QAIHACAvIABAMybOx8AILr06dSrW7+OPbv27QMAeP8OPrz48eTLmz+P3nwFAQDauwcwYQGA+fQTTACAn8AAAPz79wdYwAAAggUNHkSYUOFChg0dDgAQUeJEihUtXsSYUeNGjAkqAAAZEuQBAwBMHigAIMEEAC0BTFAAQOZMmQwYAMCZU+dOnj19/gQaVGgEAEWNAmAAQOlSpk2dPoUaVepUqv8AJAgAkFWrVgoJAACYEEHAAgYFAJxVUAHAWrYAChgoAEDuXLp17d7Fm1fvXr54BwAAHFjwYMKFDR9GnFgxgAkJADyGDFkCgwkDDFDATMHAAAkHAESgUADAaAAHKggAkFr1atatXb+GHVv2bNgDANzGnVv3bt69ff8GHhyABAUAjB8/TsGAhAUCnD9fIMEAhQMLDExYsICCAQUAvH8HH178ePLlzZ9H730CAPbtARAAEF/+fPr17d/Hn1//fgACJgAEIHCgQAIDFghImDACBQEOF0gwwACAAgYMEgDIqHEjx44eP4IMKXLkxgEATqJMqXIly5YuX8KM+dJAAQA2bwL/YEBBAM+eESoICBp0QQUJAI4iTap0KdOmTp9CjdrUAICqVgEcAKB1K9euXr+CDSt2rIAIDBQASKs2rQAKAwwcACBX7gIDCwTgzRthgoC+fhdUkABgMOHChg8jTqx4MePGjgcPACB5MuXKli9jzqw5MwMDEhYsmGBAAIDSABYMoBBhgYQBExQoWGCgAgMBtm/jzi1ggQEFAH4DDy58OPHixo8jT64cwAAAzp9Djy59OvXq1qlLmEAAAHcABSosAFCgQgUGAs4LWCChggEKERYIiC9/Pn35DAwQAKB/P//+/gECEDiQYEGDBxEmVHhQAQCHDwEIADCRYkWLFzFm1LgR/6MCCgBAhgRAwIAAAxIWCFC5UkAFCQJgxpQpIIIEATdx4pwQAUBPnz+BBhU6lGhRo0d/EjAAgGlTp0+hRpU6lWpVpxQUANC6VWuEAREEhBUblsGABQLQplUrQAIFAW/hvl0QYUCCAwDw5tW7l29fv38BBxaMl4ABAIcRJ1a8mHFjx48hIyYwAEBly5YNRBCwmTNnChMEhBY9OrQECgJQo14gocIAAwYqVBhQIUIBALdx59a9m3dv37+BA08AgHhxAhUAJFe+nHlz58+hR29ewAAA69etR6AggHt37wYiCBA/nrz4BQsEpF8wYQCFCAsExBewgMEEAxQOANC/n39///8AAQgcSLCgwYMIEyo0SMAAgIcQI0qcSLGixYsUBwDYyBFAAQMLBIgcOXLBgAUCUqpcyVJABAMUFgiYSbPmAgkGGADYybOnz59AgwodSrQoTwITAChdyrSp06dQo0p9OkEBgKtYI0wQwLWrVwYGBIgdS3YsAwYCJAyIIKCt27dvF1SgAKCu3bt48+rdy7ev37wFKgAYTLiw4cOIEytezLhwggoAIksesECA5cuYGRgQwLmz584TJkgwwECA6dOoUwtYQIECgNewY8ueTbu27du4ZReoAKC379/AgwsfTry4ceARKBQAwPxABQHQo0sXwMCAgOvYs2OfQGEAAwHgw4v/Hx9+AQUGANKrX8++vfv38OPLX0+AAYD7+AksAMC/v3+AAAQOJFjQ4EGECRMuMDCBAQMDFARMpFhRwIIBCwRs5NhxIwMDEgSMJFnSpMkFBg4QEMCAgQIAMWXOpFnT5k2cOXXWLFABwE+gQYUOJVrU6FGkChYsmCBBwFOoUZ8aYCDA6lWsViVUWCDA61ewYcVKMGBAwoIFFAwoANDW7Vu4ceXOpVvX7tsCFQDs5dvX71/AgQUPJsxXggQBiRUvTkxBggDIkSULWGAgggDMmTVv5ryAAgUCAEQDOFBBAADUqVWvZt3a9WvYrAswAFDbNgACAHTv5t3b92/gwYUP3y1B/4IA5MmVI49QQcBz6NEFMBggQcB17Nm1b5dAAcB38AAKGCgAwPx59OnVr2ff3n36AxQAzKdf3/59/Pn17+fPnwHACQIGEiw4cIEBBgIWMmw4wcAEARInUqxosUICABo3amTAAADIkCJHkixp8iTKkQcoAGjpEkABADJn0qxp8ybOnDp3zlRAQQDQoEKDSqiwQADSpEkrUJAg4CnUqFKlLjAA4CpWrAcqAOjq9SvYsGLHki1rtusBCgDWsm3r9i3cuHLn0mVbwMACAXr38tW7wIAEAYIHDzbAQADixIoXM2ZQAQDkyJEJGABg+TLmzJo3c+7s+bPlAxQAkC5t+jTq1P+qV7NubbpCBAGyZ9OezWBABAG6d+sewEAA8ODChxNfMAAA8uTJD1QA4Pw59OjSp1Ovbj06gQQAtnMnoAAA+PDix5Mvb/48+vTiBVAQ4P49fPgRBkQQYP++AAMSIgjo7x+gAIEDCRasoABAQoUJIzAA8BBiRIkTKVa0eFFiggkAOHb0+BFkSJEjSZY0CcBABAErWbZsGWHAhAUCaNI0UEGCAJ07efb0KaECAKFDARQwQABAUqVLmTZ1+hRqVKYJJgCwehVrVq1buXb1+hUsgAQGFggwexYt2gUUDEhYIAAuhQoSBNS1exdv3gUUKBQA8BfAAQMCABQ2fBhxYsWLGTf/TkzgAADJkw9EAHAZc2bNmzl39vwZ9GYJFBYIMH0adeoIFQZUmBBhQoUFAmjXtn0bt4AFFAZMYMCgQgUFAIgXN34ceXLly5k3N55gAgDp06lXt34de3bt269ToLBAQHjx48kvqEAhAoUKAxYIcP8efnz57icwUMBgQQIA+/n39w8QgMCBBAsaPIgwoUKBCSIAeAgxosSJFCtavIix4oQKDAR4/AjyIwMDEgCYBDBhgoCVLFu6fClgwQACAGravIkzp86dPHv6rKlAAoChRIsaPYo0qdKlTIcmkFChgoQDAKpatSrAwAQGArp69cqAggEFAMqWnTBggYC1bNu6fTtB/wKAuXTr2r2LN6/evXzpKpAAILDgwYQLGz6MOLFiAhQqCChQQECFCQAqW7ZMgIGBChMiMPgcYYKBARQIADiNWgIFCgJau34NGzYDAwQA2LadIEIEBgUA+P4NPLjw4cSLGweeQACA5cwLKAAAPbr06dSrW7+OPToFBgC6e48wAYD48eQBKGBAwYCBAQMsPMBgAID8+QAUHBggQYD+/fz77we4wIACAAUBJDBAYYEABgYmEAAQUeJEihUtXsSYMaMCCQA8fgQZUuRIkiVNehQwAcBKlispKAAQU+ZMmQwMPEAQQOcFAQB8/gSgwMAACQKMHkWaVMACAwwAPAWQwEACAP9VqzKoQADAVq5dvX4FG1bsWLECJABAm1btWrZt3b6Fi5ZCAgB17dZVQAHAXr59ARyooMFBAMKEMRhQAECx4gQDMmAYQGGBAMqVLVeOYGABAM6cDRwAEFo0gAgRAJxGnVr1atatXb8GkEAAANq1CRAAkFv3bt69ff8GHjz3AADFjR8fUADAcubMBRiAgCDAdOoBMFiosECBgAoDHgQI4ECDAQkLBJxHj54BBQMJALx/L2ACAPr16RMwAED/fv79/QMEIHAgwYIGDx4UEAEAw4YOH0KMKHEixYgEGBgYAGAjx44DDAyIUAAASZILDGAIoHIlSwQQGsCEcAECggA2IVz/GEBBAoMFPiNMqGBgAYCiAA4IAGFAAYCmTp1OSABgKtWqVq9izap164IIAL6CJUAAANmyZs+iTat2bdoEEwY0wDCgAIC6dusWMBDAwQYDFBQAALDAgIMAhg8jToyhwYDGFyAgCBAAw4MLBgZgHhAhAYDOBBYYsNDgwYUEAE6jRj1hAgEArl/Dji17Nu3atmcLiABgN+/evn8DDy7cN4EJFh44CBCgAQMAzp87j/AgAHUEEC5QWGDAQYDu3r+DhzCAQQEAABRQuOAgAPv27B9YIADggIQBDTAEyN9AAID+/gECEEjhwoAJBwAkVLiQYUOHDyFGZLggAgCLFzFm1LiR/2NHjAoMNEAQgGQADAMOAFC5MsEABwFgwkTwYEADBAFw5tSpM4OBAwCABpWgIUBRo0YbSGBg4IGDAE+fQqAAgGpVqgUMIHDwwEAEAF/BhhU7lmxZs2MLFACwlu2BBADgxpU7l25du3fjSrCQIUBfvwEgDFhAAAAAAgsMQAiwmPFiDBYsOAgwmXLlAAgyWBAAAIAAAgBAAzCAIUBp06UhDNDgIEBr1wEQGFAAgHZtABMeBNDtoIGBBACABxc+nHhx48eFL2AAgHlz58+hR39+QICECgOwYzcwYUECAgDAhwdPoQGCAOfRo8fQYECFCgMaYAgwn359BBsMOAiwnz8CCP8AG1gYYGEAgIMUDgBYCGBEgwAQIyJ4MABCgIsYM2IYIACARwAEJlxAEKBkSQgGGABYybKly5cwY8pkyYABgJs4c+rcyfNmgQgDLDR4kMEBggABEGCA0ODCgAkJAEiVSqEBggBYs2rF6iBDBgcBwoodO/bBAAcB0jp4YMHCgwwIIHgAQLfCAQB4ARwwoCEDggABEDSw4CCA4cOIDWO4YKADgwkDGiAIQLlyAAcXOgDYzLlzAQUMPlCoUIHChAUJCABYzbq169etDxwAQLu2AAEAcuvezZu3AgoGHjgIQLy48eIIHlwwsAAAAAoNEASYTr269evYqz8w4ADBgwEaMiD/CEAeggcA6AsAWL/+wIUHFixkQNDAgoMA+PPr34/hwQOAHBwEIFjQoIMLHQAsZHhAwgADGh5AyFAxA4QGFwYYiFAAwEeQIUWOJPmRAQMAKVWuZKmSwAcLEBAEoFnT5s2aGRoYkKABQQCgQYUOJVqUaAMLFi44CNDUqYMBAKROnSqiQQAEEAZYsOAgwFewYcWOJRvWwQUGANQKqGDhgYMAceXODYAAw4YBHhQA4NvX71/AgRcIAFDY8GHEhQUYeIAgwGPIkSVPDgBhgAYHATRv5tzZ8+fOCDYMeIAgwGnUpy8IAACgAADYsA1gCFDbwQUDGQLs5t3b92/gvh0YSKDA/4AGCAgCLGfe3HkABBAuVDgAwPp17NmvMxAAwPt38OHFf59gAUMA9OnVr2ev3kGDARgCzKdf3/59/PQRXLjgIADAAAIHDsxg4ACACgUAMJSgIQDEiBAGQAhg8SLGjBo3ZoQwwEKGACJHkixpEgEEAwwAsGzp8iXLCAsA0Kxp8yZOAAQoNEAQ4CfQoEKHEoUwIEOApEqTInAAYcMFAwOmGriwAYIDBAG2ckVwQQOCAGLHkhULYUAEAwUAKKhwAUGAuHIDYBgAIQDevHoRZHjwYMOGBw8yIAhg+DDiABAGNEAQ4DHkyJInQ3ZwoUIBAJo3c+4sQAGA0KITHABg+jRq1P8VGiAI4Po17NiyZ7uGMCBDgNy5HTwYMODCBggYMDjAgAHChgsDBmxwEOA5ggsaEASobv36dQwNBnC/AAFBgPDixWMYACEAevQIIGiwMMBCgwYbNjRoYGGAhQYQEATo7x/ggwEZAhQ0eBBhwoQIQhg4AABiRIkTKQKIsABARo0bNVJogCBASJEjSZY0ORLCAAwBEEDQMKABBgQBaNa0GQABhgYDLkBA0OACggBDiRY1ShQBggBLmTZlmmEAhgABHGwYYOEBBgQBuHbliiDDAwsDHjgIcPbBAAwB2LZ1+xZuXLYQDBwAcBdvXr17IwgA8Bdw4L8MNCAIcBhxYsWLGS//fmDBwQUDDxwEsHwZc2bLDh4YsDDAQQDRo0mXNn369AMLDjQMaIAhQGzZs2kHwNBgQAMHEAZgCPAbeHDhw4kHh2CgAADly5krF3AAQHTpBQgAsH4dO4ADBhwE8P4dfHjx48cjsDCgAYIA69m3d/8eQYMBHBAEsH8ff377FxwE8A8wgMCBBAUisDCggYMADBs6fPjQQYMBAzAEuIgxo8aNHDdCMAAgpMiRISUoAIAypcqVKi9ACAAzpsyZNGvWdKDBQoYAPHv6/AnUJwYLFxwEOIo0qdIAAxwEeAo1alQHGgxkCIA1q9atXLNCGNDAQYCxZMuaPYv2bIMIANq6fQtA/4ICAHTrEgCAN69eAAw0BPgLOLDgwYQJO7DQAEGAxYwbO378GEEDCw4CWL6M2UGGBw0aDGiwAQIGBAFKmzbtwEADBAFau34NOzZsBw0sOAiAO7fu3bx773ZgIAGA4cSLGycuQQCA5cybD3AQILr06dSrW6/uwEIDBAG6e/8OPrz4AAgaWHAQIH16BBA0GBhgocGD+Q82XBgw4MIDBwH69weIYcCDAAUNHkSYUGEABA8MOAgQUeJEihUtUoRgAMBGjh09cpSgAMBIkiQFNAiQUuVKli1dtnRgoQGCADVt3sSZU6dNBA0sOAgQwMEDAxYeYEAQQOnSpQ4gNBigIQOCAP8ODDwIkFXrVq5dvW59YMBBALJlzZ5Fm/ZsgwUA3L59W4AAALp1BRwAkFev3goQAvwFHFjwYMKDG2hAEEDxYsaNHT9ujEBDAwQNBmjIgCDAZs6dPTt4YMFCBgsPApxGnVr1atarN1hAEED2bNq1bd+mncECAN69e09IAED4cOLFARywgCDAcubNnT+H7hzCAAcBrF/Hnl379u0OBhjQ4CDAePLlzZtHAGGABQQB3L+HH1/+fPkILjwIkF//fv79/QMMIDAAAgsJACBMiHBCAgAOH0KMCCDCgwAWL2LMqHFjRgcGIAQIKXIkyZImTSJoMABCgJYuX8KM6dKBBgMZAuD/zKlzJ8+ePB0MwBBgKNGiRo8iLfpgAoCmTpsmKABgKlUGBwBgzYqVAoQAXr+CDSt2bNgGDQKgTat2Ldu2bR1cuOAgAN26du/ixQthAIQAfv8CDix4sOAHFhAESKx4MePGjhU7GEAAAOXKli9PSABgM+fNAxwECC16NOnSpkc7GOAgAOvWrl/Djg3bgYUGCALgzq17N+/eATIMgBBgOPHixo8jN47gwoMAzp9Djy59OvQLCQBgz659u4QEAL6DB1DAQoDy5s+jT68e/YYGAd7Djy9/Pv35CC40QBBgP//+/gEGEDiQYMEAGAZACLCQYUOHDyE6hGABQQCLFzFm1Ljx/2KDBQBAhgQg4QAAkydRplTQIEBLly9hxpT5EsEADAFw5tS5k2dPnho0IAgwlGhRo0eRGs0wAEMAp0+hRpU6FSoCAxkCZNW6lWtXr1ohTAAwliwACgcApFW7lq2ABgHgxpU7l25duRAsBNC7l29fv3/9QjDgIEBhw4cRJ1as+IEFBAEgR5Y8mXJlyQ80BNC8mXNnz583YzAAgHRpABEKAFC9OgEBAK9hA1jQIEBt27dx59Z9WwOHAL+BBxc+nLhwBwMyBFC+nHlz58+hI7jwIEB169exZ9d+3cEABwHAhxc/nnx58AgGAFC/nn17CgcAxJcPYEGDAPfx59e/n3/+Af8AMQQYSLCgwYMIDWpoEKChw4cQI0qc2NDBAAwBMmrcyLGjx40XIAQYSbKkyZMoRyIYAKCly5cwKRwAQLMmgAUNAujcybOnz587HQxAEKCo0aNIkyo9mmGAgwBQo0qdSrWq1agPLgTYyrWr169guzZ4EKCs2bNo06o1OwCA27cAFhAAQLfuAQIA8uoFIKBBgL+AAwseTBgwhAsBEitezLixY8YaHgSYTLmy5cuYM1dGMABDgM+gQ4seTRo0hAsBUqtezbq169QIBgCYTRtAhQIAcuvezfuAhQDAgwsfTrx48AcNAihfzry58+fMHQxwEKC69evYs2vfjr1BgwDgw4v/H0++fHgMAwKoX8++vfv36h0MAEC/PoAKBQDo38+/PwCAAxAEIFjQ4EGECQk2eBDA4UOIESVOhPigQQCMGTVu5NjRI0cMAxAEIFnS5EmUKUkiGOAgwEuYMWXOpBkgQwUAOXUCIADA508AFQoAIFqUaIUMAZQuZdrU6VOlDR4EoFrV6lWsWa0ayBDA61ewYcWOJTv2AoQAadWuZdvWrdoBDgLMpVvX7l28AR50ANDX71/AFQoAIFyYMIgHARQvZtzY8WPFDR4EoFzZ8mXMmSs7GIAgwGfQoUWPJl2a9IMGAVSvZt3a9evVAxwEoF3b9m3cuQM0EADA928ABQAMJw6A/wEBAMmVJ1dwIcBz6NGlT6f+vMGDANm1b+fe3bt2CBcCjCdf3vx59OnTZ7AQwP17+PHlz38/wEEA/Pn17+ffPwBACwcAECwIwAABAAoXMmyo0ACGABInUqxo8WKABg8CcOzo8SPIkB0fNAhg8iTKlCpXsmTpYACCADJn0qxp86bMAQ4C8Ozp8yfQoBgGAChqtKgBAgCWMm3qdCmDBgGmUq1q9SrWAA80BOjq9SvYsGK9XoAQ4CzatGrXsm3r1gKGAHLn0q1r924ABwMQBOjr9y/gwIIbRABg+LBhAQAWMwYggQCAyJIlExjgIADmzJo3c+6MwUCA0KJHky5tWrQFDP8BVrNu7fo17NiyL0AIYPs27ty6dweAcCEA8ODChxMvjmBAAQDKlzNvDsAAAQDSp1OX0CAA9uzat3PvjmCAgwDix5Mvb/68eAMYArBv7/49/Pjy52vgEOA+/vz69/MP8ABggwADCRY0eBAhBA8AGDZ0+JChAQIAKFa0CGJAhgAbOXb0+BGkhQwBSJY0eRJlSpIDHARw+RJmTJkzadZs8CBATp07efb0GeAChABDiRY1evQoAgsJADR16rQCAKlTqVadesDAAwMIAnT1+hVs2LANGgQwexZtWrVrzRrAEABuXLlz6da1e1cDhwB7+fb1+xcwggEYAhQ2fBhx4sQPJgD/cPwY8gAAkylXtkyZAoQAGhoE8PwZdGjRojEMQBAAdWrVq1m3DmABQwDZs2nXtn0bd24NEAL09v0beHDhECwEMH4ceXLlyjEMKAAAenTpFQBUtw5AAQDt27cfMIAggAMDDwKUN38effr0Fx4EcP8efnz58wNcgBAAf379+/n39w8wgMCBAy1kCIAwocKFDBtegBAgosSJFCtSRGDBwgIAHDt6/NhxAICRJEmCeBAgJYYBEAK4fAkzpsyYECwgCIAzp86dPHtuaBAgqNChRIsaPXoUwQAEAZo6fQo1alQMAxAEuIo1q9atWRE0uJDBAICxZMuaJTsAgNq1agkMcBAg/24ADAMeBLiLN6/evXkRGIAQILDgwYQLG4ZwIYDixYwbO34MGXIGCwEqW76MObPmBg0CeP4MOrRo0AgaWHAQ4EICAKxbt14AILZsAAoA2L5tW0CDALx7YzDQwEGA4cSLGz9OPMMABwGaO38OPXp0BwMQBLiOPbv27dy7c3/QIID48eTLmzefYYCDAOzbu38Pvz2CBhYcBAjwYAKA/fz5DwAIQOBAggUBSHgQQOHCAA4aDIAQQOJEihUtSsRgQEMAjh09fgQJEsEADAFMnkSZUuVKlis1PAgQU+ZMmjVpIjAAIcBOnj19/uTp4MIFBwGMIhhAAMBSpksHAIAaVepUAP8VMgTAmjUrBAMaMAQAG1bsWLEOHgx4YABCALZt3b6FC3dDgwB17d7Fm1fv3rwOBjgIEFjwYMKFCTe4EEDxYsaNHStGAGFAAwQBLFvWoADAZs6bCwAAHRrAAAClTZcegCDAatatHTQYcAECggC1bd++jaHBgAEYAmQYgCHAcOLFjR837mCAgwDNnT+HHl36dOgPGgTAnl37du7bIQwY0MBBAPLlzZ8/7+CChQwB3L8P8CACAPr17d8HMADAfv4ADgC8EGAgwYIEEUCwMKDBgwwIAkCEiAADhAYWBjy4wCEARwgDMAQIKXIkyZIkLzwIoHIly5YuX8JkiWAAhgA2b+L/zKkTZ4YBGRwMGNAAQ4CiRo8iRQDhwoANCAJAjQoVAgUAVq9aLQBgK1cAEgCADQtAQYMAZs+iTRsAw4MGFgYYsCDXwgADGh5AQIBhAIIAfgNwGIAhAOHChg8jNgzBAoIAjh9Djix5MuXHECwEyKx5M+fOmyEMgBAgwAMNDwZYeJABQYDWrls7gPDAgIUHDgLgzq3bwQAAvn8DIGAAAPHixo8raBBgOfPmzp0jwJBhOgYHAa5fb9AgAPfuEAY8QBBgPPny5s+PR3DhQYD27t/Djy9/fnsHAzIEyK9/P//++QEigDAgQwCDDgY4QAChgYUBFjQ0kNhAg4EBFhpkQBCA/2NHjx0NEAAwkiQBAwBQplS5UkCDAC9hxpQ5k2ZMCxAC5NQZAIOFCw4CBBU6lGjRoBkGYAiwlGlTp0+hRg2goUEAq1exZtVq1YEGCxgChA2r4UEAswEQZHiwdi0EDAgCxJU7l25cAwUA5NVLYAEAv38JRAAwmDAABQ0CJFa8mHFjx4oRDHAQgHJlyggeDHiAIEBnz59Bf0bwYMAFCwgCpFa9mnVr164hDIAQgHZt27dxI4AwoAGCAL+BP2gQgHhx48eRJy9uoQAA58+hQydgAEB16wAUaAiwnXt379/Bc8dgIEB58+cDYLgwoAGGAO/hx5fvoMGAARgQXGiAIEB///8AAwgcSLCgQYMYBjwYYAECggAQI0qcGAABBAsWMgTYyHFjBgsBQoocSbKkSZEGCgBYybJlSwIGAMicCaCAhQA4c+rcybNnTg4aAggdSnQohgYDLjzI4CCA06cBHGR4cGFAAwsQAgRwYOFBgK9gw4odSzasgwEPAjS4YGHABgwIAsidSxcBhgYDLkBAEKCvX78OBiAIQLiw4cOIExM2QACA48cFJACYTLmyZQADHATYzLmz58+gN294EKC06dOoEUBoYGGAgQsaYl8wMMBCAw4OMhhAEKC3AwMbEAQYTry48ePIA2Aw8CBAAAwDEGBoYGCAhQYcIGiHwKGBhQEGGmD/CEC+vPnyFjAEWM++vfv38AMgGACgvn0ABSoA2M+/v3+AACpkCFDQ4EGECRUWbPAgwEOIESVGRIABAgQOECBgQBDAYwANDwKMHOnAQgMEAVSuZNnSpcsMAx4EoBngAoQAOR1AeKDhgoULFzQ8gOAgwFGkSZUGuAAhwFOoUaVOpRogQwUAWbUCKDABwFewABIAIFuWbIQHAdSuZdvW7Vu1DR4EoFvX7l28eesiGOAgwF/ADjQYyBDA8GHEiRUfRtBgAIQAkSM/0BDA8mXMmTVvvnwBQgDQoUWPJl06wAMQAFSvZt26QAUAsWXHVnAhwG3cuXXv5n27wYMAwYUPJ17c/7hwBwMQBGDenDmEAQ0QBKBe3fp17BkMaHAQwPt3DBYCjCdf3vx59OQ1QAjQ3v17+PHlB2ggAMB9/Pn1H6gAwD9AAAIHGsAQ4CDChAoXMgzQ4EGAiBInUqxoUSKECwE2cuzoQMOADQ4CkCxp8mQABBAuDIAQ4CXMlwgGOAhg8ybOnDp32rwAIQDQoEKHEi2KwMIBAEqXAiCgAADUqAQSAKhq1SqDBgG2cu3q9SvYAA8aBChr9izatGrNbtgQ4C3cuG8xNBhwAYKDAHr38kWA4cEACxwQBChs+LCFDAEWM27s+DHkxRYgBKhs+TLmzJohVADg+bPnAxQAkC5t+jTpAv8DEARo7fo17NiyM1gIYPs27ty6d9++ACEA8ODChTt4cGGAAQ0POEBo/qCBhQEGGmAIYP069usNHgTo7v07+PDiAyAY4CAA+vTq17Nv30AAgPjy4yegAOA+/vz68X94EABgAIEDCRY0aBDBAAQBGDZ0+BBiRIYXIASweBFjRosIMEBooMGCBQ0NHkBwEABlSpUrAzR4EABmTJkzadYMgGFAAJ07efb0+dPBAABDiRYtAABp0gMTADR1+rTAAAwBqFa1ehVrVgsYAnT1+hVsWLFdLWQIcBZtWrVrAyBAEABuXLlz6W54EABvXr17+fYNAOFCAMGDCRc2fHhDBACLGTf/drw4AQUAkylXBrDgAoIAmzl39rwZAYIAo0mPbvAgQGrVq1m3dp3aQoYAs2nXtn0bd27dDR4E8P0beHDhwwM0eBAAeXLly5kzx2CAAADp06kTAHAd+wEGALh3986dwoMA48mXR4CBQwMLA9i3v/AAgoMAATJYQBAAf379+/n3DwDwAoQABAsaPIgwAAYMARo6fAgxYoMHASpavIgxo0YEFjIE+AgypMiRIhFYEAAgpcqVCSYAeAkzpkyZBQZkCIAzZwAHDwYY0PAAggMERBFggNDgwgALEBBYgBAgqtSpVKtaDaCBQ4CtXLt6/RqgwYMAZMuaPYtWw4MAbNu6fQs3/24GCwgCBHAA4YGGCxYsXLiwAYIDBAEKGz4c4MEHAIwbOwagQAKAyZQrW76cYACGAJwDZNAwoAGGAKRLmy6NAMKFARc0BHgNO7bs2bQDPGgQILfu3bx7B9DwIIDw4cSLGzeAIYDy5cybO3+u4QGGBgYGWGjAAUKGDBAgbLgwYICGDAgCmD8fIIMBAgDau38P4IAAAPTrHxAAIL/+/fsVGACYIYCDBgMeOAiQUOFChgoxNBiAIcBEihUtXsQIwUIAjh09fgQZIAOGACVNnkSJ0sEABAFcvoQZU6ZMBwMuDGiAAUEAnj19InDwwICFBw4CHA2AYUACAE2dPoUKVcEEAP9VrV7FmmBAgwENHAQAG1bsWLIBGlhAEEDtWrZt3bp1MABBALp17d7Fm1dv3gwWAvwFHFjwYMINBnBAEEDxYsaNAyCAcGEAhAABMhhQAEDzZs6dPSuQAED0aNKlAUgYACHAatatXb9mjcDCgwC1bd/GnVu3AQwBfP8GHlz4cOLDHzQIkFz5cubNm2cY4CDAdOrVrV/PYEADhAEJAHwHHz58ggUAzJ9Hn169eQkXHASAH1/+fPrzMQxwEED/fv4BEADEwKHBBQsGLzR4kAFBgIYNGgSIKHEixYoaIATIqHEjx40ILEAIIFIkAgcYTjpAEGAly5YtHRiAEGAmzZo2b87/RNBgwAIAPn8CDSogAoCiRo8iTQpAwgUEAZ5CjSp1KtUHFhAEyKo1AAIIFwYY0PAAQoayEB40sDDAwgMHGAYgCCB3Lt26dS9ACKB3L9++fDNYQIAgwwMNFgYMMGDBwIABFxpAcBBgMuXKDTQEyKx5M+fOnDMYEABgNOnSpRVEAKB6NYECAF7Djg27wwUEAW7jzq17N+8ACDRcQBBgeAAHGwZYgOAgAPPmzgMgyKBhQAMLDwJgz659+/YNGQKADy9+vHgNDx4YsNDgAQYEAd6/dwDhwYUBFyAgCKB//4MBDgAGEDiQYEGDBjEYEACAYUOHDyEKkACAYkWLFBVYcBCA/2NHjx9BhuyI4MIFBAEQPBjQAEMAly9hxnTp4MEACwgC5NS5k2dPnz91OhgwQEMGBAGQJlWa1MEDCwYeIAgwFcIADAGwZtW6lWvXABgMKAAwlmxZs2YFRACwlm1bAAQMYAgwl25du3fx2kWg4QIECxccBBA8mHBhww4GPAiwmHFjx48hR2Z84YKDAJcxZ9aMGUGGCxYwBHgwAEMA06dRp1a9+jSGAQUAxJY9G0CBAwBw5y6QAEBv378BTHgQgHhx48eRJ0+OwMKABwgCRJc+nXr16BgGOAiwnXt379wfYAgwnnx58+MhGEAQgH179+/hI3gwwMIADAHw59e/n3///f8AQ1QAQLCgQQALGABYyLChQ4YKLCAIQLGixYsYM2JE0MCCgwAgQ4ocSXLkgwsIAqhcyVIlAgwPNjQYoOEBBAwIAujcyVOngwEZAggdSrSo0aEOLFhwEKCp06dQo0qFiuDCAgBYs2pdwACA169gw36tACGA2bNo06pdqxaBhgsOAsidS7euXbsILDwIwLdvXwwNLAwwoGHDAwMNNmgwMMBCAwwBIkuOjOBCgwCYM2vezHkzggYWHAQYTbq06dOoTTsYUACA69evCxQAQLu2AAYAcuvWfcACggDAgwsfTrz4cAQNLCAIwLy58+fQowfAMABCgOvXEUCwMGBDBgcBwgf/cIAggHkHEBoMuAABQYD3ARA0sIAggP37+PPr3//AgAOAAQQOJFjQ4MGCDyYAYNjQ4UOGCxgAoFixooQHATRu5NjR40ePDyw4CFDS5EmUKVWaxDAAQoAACB4MuAABQQCcOXXuRMDBwgAIAQIgaGDBQQCkSZUuZdoUaQMLCAJMpVrV6lWsVR0MIADA61ewYQEoEADA7FmzBAY4CNDW7Vu4ceXCdTAAQwC8efXu5dt3b4YBDxxcsIAhwGHEiRUvzmBAg4MGFhwEoFzZ8mXMmSsj0NAgwGfQoUWPJi26AQMAqVWnZrAAwGvYsWUDENAgwG3cuXXv5q0bwYUHAYQPJ17c//jx4xgGDHiAIMBz6NGlT3/uoMEACw4CbOfe3ft38N4dDMgQwPx59OnVr0ePwQAA+PHhM1gAwP59/PkBdHgQwD/AAAIHEixo8KDABxYQBGjo8CHEiBIjImhgAUOAjBo3cuzYMcOABwFGkixp8iTKkxAMIAjg8iXMmDJnwryQAADOnAAUJADg82eCBACGEh1aIUOApEqXMm3qdCmCARgCUK1q9SrWrFgRNLDgIADYsGLHki0bwIGBBwHWsm3r9i3ctxoaBKhr9y7evHrvNlgA4C/gwIIZLABg+LDhAQgCMG7s+DHkyI4hXAhg+TLmzJo3a0bQwIKDAKJHky5t+vRoB/8GHgRo7fo17NiyYTsY4CAA7ty6d/PunRvCBADChxMvzmABgOTKARywEOA59OjSp1OXbgFCgOzat3Pv7r37AwsOApAvb/48+vTnHQyAEOA9/Pjy59OXr+FBgPz69/Pv7x9gAIEYDAAweBCAgAQAGDYkAABiRIgJLgSweBFjRo0bMWIYgCBASJEjSZY0SRLDAAwBWLZ0+RJmzJgZBjgIcBNnTp07eeaEYAFBAKFDiRY1elQoggEEADR1GkEAAKlTqVZNcCFAVq1buXb1urVBgwBjyZY1exatWQQWHgRw+xZuXLlz6QZooCFAXr17+fb1uxeBgQwBCBc2fBhx4sIXDgD/cPw4ggAAkykXIAAAc2YACS4E8PwZdGjRo0FfgBAAdWrVq1m3Xv3AAoIAs2nXtn0bd+4ACAxACPAbeHDhw4kH39AgQHLly5k3d678wgEA06lXrx5BAADt2wEkuBAAfHjx48mXD49ggIMA69m3d/8efnsHAzAEsH8ff379+/nfzwBwAIIABAsaPIgwYUEIFwI4fAgxosSJDy8kAIAxo0aNEQQA+AgSwIELAUqaPIkypUqTGAYEeAkzpsyZNGU+0BAgp86dPHv6/MnTAoQARIsaPYo0aVEHAxAEeAo1qtSpVJ9eOAAgq9YDBQB4/ZrgAICxZMcOQBAgrdq1bNu6TQvh/0KAuXTr2r2Lty4CAxkC+P0LOLDgwYQDQ7gQILHixYwbO148AEOAyZQrW76MebKFAgA6e5agAIDo0aRLA7iAIYDq1axbu36t+kGDALRr276NO7dtCBYQBPgNPLjw4cSLC0cwAEOA5cybO38OnfkFCAGqW7+OPbv2AAgGAPgOHoAEBQDKmz+PHoCEBwHau38PP7789g8aBLiPP7/+/fzzawD4IMBAggUNHkSYEGGDBgEcPoQYUeLEhxo4BMCYUeNGjh0DYKgAQORIAAcIAECZkkECAC1dtlzQIMBMmjVt3sQ5c8OGAD19/gQaVOjPARgCHEWaVOlSpk2ZQrAQQOpUqv9VrV6d2uBBAK5dvX4FGzbAAwkAzJ5Fm1aCAgBt3bYtYABBALp17d7FmzfAgwYB/P4FHFjw4L8OBiAIkFjxYsaNHT927GAAggCVLV/GnFlz5QYPAnwGHVr0aNIBGggAkFr1atYMEgCAHTs2BQgBbN/GnVv37gAPGgQAHlz4cOLFg0O4EED5cubNnT+HHt0AhgDVrV/Hnl17dQ0cAnwHH178ePIIDBwAkF49gAkJALyHH1/+ewUXAtzHn1//fv4BIAC8EGAgwYIGDyIk+KBBgIYOH0KMKHEiRQ0cAmDMqHEjx44YL0AIIHIkyZImT0KoAGAly5UTEgCIKXMmTZkGMAT/yKlzJ8+ePh0MQBBgKNGiRo8iHarhQYCmTp9CjSp1KtUHGwJgzap1K9euARAMcBBgLNmyZs+ivSAAANu2bBcUACB3roICAO7izbvgAoIAfv8CDix48AAMAQ4jTqx4MePDGiAEiCx5MuXKli9jftAgAOfOnj+DDh3AwQAEAU6jTq169WoMBgDAji17NuwJCQDgzq0bQAUIAX4DDy58OPELEAIgT658OfPmyC9ACCB9OvXq1q9jz/6gQYDu3r+DDy8+AIQLAc6jT69+PfsGDADAjy9/PvwJCQDgz68fwAEDDgAGEDiQYEGDBh9oCLCQYUOHDyEu1AAhQEWLFzFm1LiR/+ODBgFAhhQ5kmTJABs2BFC5kmVLly4hGAAwk2ZNBgUA5NRJAEBPnz99MriAIEBRo0eRJkXqYICDAE+hRpU6lWoADRwCZNW6lWtXr1/BPmgQgGxZs2fRpkVgIEMAt2/hxpUb14GBBADw5tVL4QAAv38BBxZMoQGCAIcRJ1a8WLGGBwEgR5Y8mXLlAA02BNC8mXNnz59Bh27wIEBp06dRp1YNwUIA169hx5YtuwEIALdx5wZA4QAA378LABA+nDhxAhUeIAiwnHlz58+bZzCAIEB169exZ9cO4UIA79/Bhxc/nnx5CxgCpFe/nn179xceBJA/n359+/VDGACwn39///8AAQgUSOEAgIMIEyYkUKEBggAQI0qcSDEiAgsQAmjcyLGjx48OBgQYSbKkyZMoU6ZEMABBgJcwY8qcORPDAAQBcurcybPnTggGCgAYSrSoUaMUDgBYyrSpUwIULjgIQLWq1atYq2YY4CCA169gw4oVi2CAgwBo06pdy7at27YZLASYS7eu3bt3EVzYEKCv37+AA//lYKAAgMOIEyNOAKCxYwACCACYTLmy5ckMBnBAEKCz58+gQwdwYOECggCoU6tezZr1BQgBYsueTbu27du2HzQIwLu379/AgUOwgCCA8ePIkys3jmCDgQIAokufPr1CAQDYs2vfzn17gQoXMAT/GE++vPnyCCAY6FABQoD38OPLnz8fgoUA+PPr38+/v3+AAQQORGAgQwCECRUuZLjQwQAMASROpFjRokQMFiYQANDR40eQFQoAIFnS5EmUKRcYuMABQQCYMWXKdPDAAIUEAA4YwBDA50+gQYUGRTAAQwCkSZUuZdrU6VIIFgJMpVrV6lWrCC4Y0OAgwFewYcWKdfDAgAIAadWuZZtWAQC4cQFMKADA7l28efUCSPBhQIMHGRAEIFzYAYQHFwZEKADAMQAFBjAEoFzZ8mXMlzc0CNDZ82fQoUWPBn3hQQDUqVWvZq0aQQMKABgMaAABQQDcuXXvxtBggAQCAIQPJ17c/3jxCgUALGfe3Plz5gUEdKgwwMIF7BcMDPDAQAEA8OHBKzCAIcB59OnVr0/vYICDAPHlz6df3/59+RgGIAjQ3z/AAAIHEiw4EEGDCgAWAhBwwcIDCA4CUKxIEUGGBxcMMCAA4CPIkCJHkqRAAADKlCpXsmwJoMCBBAcOFABg8ybOmwoMQEAQ4CfQoEKHAm2gIQDSpEqXMm3qFCkCCw8CUK1q9SrWqggaUCAA4CvYBBEoDLDQ4OyGBg0uDKggQQGAuHLn0q0b1wABAHr38u3r9y/gwIL1HqigwUGAxIoXM26c2MEACAEmU65s+TLmzAEeXEAQ4DPo0KJHf8Zw4QOA1P+qV6cuoGAB7AUCDgCobfs27ty5DRAA4Ps38ODChxMvbhw4AwMcEARo7vw5dOgZLFAY4CAA9uzat3Pv3h3DAAgBxpMvb/58AAQPBggA4P49/Pjy59Ov/14CgPz6AQgA4B8gAIEDCRY0eBBhwoIHKBh44CBARIkTKSLgcMGAAgAMLiAI8BFkSJEjSYp0YEGCgQYYArR0+RKmSwQQLlAoAABnTp07efb0+RMoTgMEABQ1ehRpUqVLmTI9IGFAAwgYEASwetWqAwgNBnhQAAAsgAkNEAQwexZtWrVrzyK4EAEAgQgDLkBAEABvXr15HTwwQEEBAMGDCRc2fBhxYsWEBxD/APAYcmTJkylXtnwZAAEBEwwMuNCgwYYGDS4YGOCBQQEAq1kD8NAAQQDZs2nXtn07gIMLEgD07i2ggoEGDyA4CHA8AAIMHBpcGADiAADp06lXt34de3bsEQB09w7gAADx48mXN38efXr15gkkELAAvgAFBADUt3+//oQLDgL09w8wgMCBBAsaxHChA4CFDBcWUBCBwoCJBgYMMDBhQQIAHDt6/AgypMiRJAEMAIAypcqVLFu6fAkzpsyZDAxACIAzp86dPHMiCDFgAYChRIsaJYAUgNKlTJs6fQo1qlSnAwBYvQqAAICtXLt6/Qo2rNixZMt+PXChgYMAbNu6fQsX/8MFCgUA2L2LN6/evXz7+v0L2C4BAIQLAxgAILHixYwbO34MObLkyY8ZDGiAIYDmzZw7a87QYMACAKRLmz6NOrXq1axbu3Y9AIDs2bRr276NO7fu3bxzE2Bg4AIEBwGKGz+OwMGDCwYWEAAAPbr06dSrW7+OPbv26QkAeP8OgAGA8eTLmz+PPr369ezbt1fwYYABDQ8g2Lf/4MIAAxMSAAAIQOBAggUNHkSYUOFChgcHAIAYUeJEihUtXsSYUeNGjAUUMJhQwcIECQwUEACQUuVKli1dvoQZU+bMmAQGAMCZU+dOnj19/gQaVOjQoAIKABAgAcBSpk2dPoUaVepUqv9VrTIVAEDrVgATAHwFG1bsWLJlzZ5Fm1YtWAoJABAoAEDuXLp17d7Fm1fvXr599RIYAEDwYMKFDR9GnFjxYsaNB1M4AEDyZMqVLV/GnFnzZs6dOxOoAED0aNKlTZ9GnVr1atatRxMAEJsAANq1bd/GnVv3bt69ff+mTaACAOLFjR9Hnlz5cubNnT+HLiACAOrVrV/Hnl37du7dvX+nXqACAPLlzZ9Hn179evbt3b9vXwAAgAUMANzHn1//fv79/QMEIHAgwYIGDyJMOJCABAAOHwJQAGAixYoWL2LMqHEjx44eKVYoACBBAgAmT6JMqXIly5YuX8KM6bKAAQA2b+L/zKlzJ8+ePn8CDXqzQgEARo8iTap0KdOmTp9CjRq1QAUAVq9izap1K9euXr+CDXtVAAEAZs+iTat2Ldu2bt/CjZuWwAIAdu8COABgL9++fv8CDix4MOHChv8KEABgMePGjh9Djix5MuXKlhcfoABgM+fOnj+DDi16NOnSpk8zWABgNevWrl/Dji17Nu3atlcfoABgN+/evn8DDy58OPHixolTIABgwQIAzp9Djy59OvXq1q9jz/6cAIDu3g9UACB+PPny5s+jT69+Pfv24w0QACB/Pv369u/jz69/P//+/QEeoACAYEGDBxEmVLiQYUOHDwtOIACAYkWLFzFm1LiR/2NHjx8xFgAwkmSBBQBQplS5kmVLly9hxpQ5k+WBAwBw5tS5k2dPnz+BBhU6FGeCCQCQJlW6lGlTp0+hRpU6lWoEAQCwZtW6lWtXr1/BhhU7FmuCCQDQplW7lm1bt2/hxpU7N+4CAAAiCACwl29fv38BBxY8mHBhw3sLKACwmPEBBgAgR5Y8mXJly5cxZ9a8OfIAAAAKEAAwmnRp06dRp1a9mnVr16sTTAAwm3Zt27dx59a9m3dv37QHABA+nHhx48eRJ1e+nHlz5wkmAJA+nXp169exZ9e+nXv36QUAhBc/nnx58+fRp1e/nn35BBEAxJc/n359+/fx59e/n39/Cf8AFQAYSLCgwYMIEypcyLChw4EKJACYSLGixYsYM2rcyLGjR44FAACQoACAyZMoU6pcybKly5cwY5pMwACAzZsEEgDYybOnz59AgwodSrSoUZ4DAAAQcACA06dQo0qdSrWq1atYs1pVIAGA169gw4odS7as2bNo034dAKCt27dw48qdS7eu3bt48yqQAKCv37+AAwseTLiw4cOI/S4AwLix48eQI0ueTLmy5cuQDygAwLkzgQMAQoseTbq06dOoU6tezbo0gwMAYsueTbu27du4c+vezTu2gAgAggsfTry48ePIkytfzrz5hAQAokufTr269evYs2vfzj26gAgAwov/H0++vPnz6NOrX88+PYEJAABISACgvv37+PPr38+/v3+AAAQOJFjQoEECBAAsZCggAgCIESVOpFjR4kWMGTVuhEjAAACQIUWOJFnS5EmUKVWuZCkgAgCYMWXOpFnT5k2cOXXuhEmAAgCgQYUOJVrU6FGkSZUuJUqAAACoURMIAFDV6lWsWbVu5drV61ewWRMQAFDW7Fm0adWuZdvW7Vu4ZRcwAFDX7l28efXu5dvX71/AgSkcAFDY8GHEiRUvZtzY8WPIhRcwAFDZ8mXMmTVv5tzZ82fQnQkIAACAwgEAqVWvZt3a9WvYsWXPpp36QAIAuXUrUADA92/gwYUPJ17c//hx5Ml9F6gAAMABAgCkT6de3fp17Nm1b+feXfsCBgDEjydf3vx59OnVr2ffXnyBCgDkz6df3/59/Pn17+ff3z/ABQwAECxo8CDChAoXMmzo8GHBAgAmUqxo8SLGjBo3cuzo8eICAQBGkixp8iTKlCpXsmzp8mWFAgBm0qxp8ybOnDp38uzpcyaDBQCGEi1q9CjSpEqXMm3qlCkBAAAqFABg9SrWrFq3cu3q9SvYsFYXCABg9myBAgDWsm3r9i3cuHLn0q1rd+0BCgAAMCAA4C/gwIIHEy5s+DDixIoPM1gA4DHkyJInU65s+TLmzJofH6AA4DPo0KJHky5t+jTq1P+qVzNYAOA17NiyZ9Oubfs27ty6XxcQAOA38ODChxMvbvw48uTKhyc4AOA59AMFAFCvbv069uzat3Pv7v079ggEAJAvb/48+vTq17Nv7/49+QgCANCvb/8+/vz69/Pv7x8gAIEDCRY0aNAAAQALGTZ0+BBiRIkTKVa0uDCCAAAbOXb0+BFkSJEjSZY0OfIAAwAADBAA8BJmTJkzada0eRNnTp0vCxAA8BNoBAEAiBY1ehRpUqVLmTZ1+pRoggkAqFa1ehVrVq1buXb1+hVsBAEAyJY1exZtWrVr2bZ1+5ZsAgkA6Na1exdvXr17+fb1+xcvAQCDCQMQkABAYsWLGTf/dvwYcmTJkyk3VgAAc2bNmzl39vwZdGjRozNLUAAAdWrVq1m3dv0admzZs2kPAHAbd27du3n39v0beHDhuCUoAHAceXLly5k3d/4cenTpzwskAABgAADt27l39/4dfHjx48mX3y6gAAD16wUcAPAefnz58+nXt38ff3797xVIAAAQgAIABAsaPIgwocKFDBs6fNhQggIAFCtavIgxo8aNHDt6/EhRgAQAJEuaPIkypcqVLFu6fAlzggIANGvavIkzp86dPHv6/EmTQAEARIsaPYo0qdKlTJs6fYo0wgEAVKtavYo1q9atXLt6/Qp2AICxZMuaPYs2rdq1bNu6JTsh/wGAuXTr2r2LN6/evXz7+v07AIDgwYQLGz6MOLHixYwbD5ZwAIDkyQcIALiMObPmzZw7e/4MOrToywIiAAAgAYDq1axbu34NO7bs2bRrz56QAIDu3bx7+/4NPLjw4cSL614QAYDy5cybO38OPbr06dSrW6eQAID27dy7e/8OPrz48eTLa0+gAID69ezbu38PP778+fTru1dAAID+/QkIAAAIQOBAggUNHkSYUOFChg0JEogAQOJEihUtXsSYUeNGjh0nUjgAQORIkiVNnkSZUuVKli1bEjAAQOZMmjVt3sSZU+dOnj1nVigAQOhQokWNHkWaVOlSpk2VKlAAoIABAP9VrV7FmlXrVq5dvX4Fa/UAALJlAUw4AEDtWrZt3b6FG1fuXLp11TJgAEDvXr59/f4FHFjwYMKFDVc4AEDxYsaNHT+GHFnyZMqVFS9YAEDzZs6dPX8GHVr0aNKlPRMAkFo1gAgFALyGHVv2bNq1bd/GnVv37AMAfP8GHlz4cOLFjR9Hnvx3hQIAnD+HHl36dOrVrV/Hnj17gQoAvH8HH178ePLlzZ9Hn/67AQIA3L+HH1/+fPr17d/Hn9/+gQIADgCsAGAgwYIGDyJMqHAhw4YOCTIgAGAiRQYEAGDMqHEjx44eP4IMKXIkxggCABBQAGAly5YuX8KMKXMmzZo2aRr/KABgJ8+ePn8CDSp0KNGiRndGEABgKdOmTp9CjSp1KtWqVq8aIABgK9euXr+CDSt2LNmyZrcWKABgLdu2bt/CjSt3Lt26dt9KAKB3LwACAP4CDix4MOHChg8jTqx48IEJAB5Djix5MuXKli9jzqwZ8gAAnj+DDi16NOnSpk+jTq06wQQArl/Dji17Nu3atm/jzv3aAIDevgEcACB8OPHixo8jT658OfPmwyUoAHCAAYDq1q9jz659O/fu3r+D9z4AAPny5s+jT69+Pfv27t+Xl6AAAP369u/jz69/P//+/gECEDiQYEGDBgcAULiQYUOHDyFGlDiRYsWFAg4A0LiR/2NHjx9BhhQ5kmRJjwIApFQJQAAAly9hxpQ5k2ZNmzdx5pRZQAAAnz+BBhU6lGhRo0eRJv05AEBTp0+hRpU6lWpVq1exZlUgAUBXr1/BhhU7lmxZs2fReh0AgG1bt2/hxpU7l25du3frLjgAQIEEAH8BBxY8mHBhw4cRJ1YMOAEAx48BGAAwmXJly5cxZ9a8mXNnz5QnJAAAgAAA06dRp1a9mnVr169hx349AEBt27dx59a9m3dv37+B256QAEBx48eRJ1e+nHlz58+hR6cAgHp169exZ9e+nXt379+xEygAgHx58+fRp1e/nn179+/JEzAAgH59+/fx59e/n39///8AAQgcSLCgQYMCIgBYyLChw4cQI0qcSLGixYUEDADYyLGjx48gQ4ocSbKkSZIJCAAQEAGAy5cwY8qcSbOmzZs4c7okEAGAz58AFgAYSrSo0aNIkypdyrSpU6IUDgAokACA1atYs2rdyrWr169gw3olYACA2bNo06pdy7at27dw456lcACA3bt48+rdy7ev37+AAwcmYACA4cOIEytezLix48eQIx9OQACA5cuYM2vezLmz58+gQ2cmEAGA6dMACgBYzbq169ewY8ueTbu27dcCFgDYzbu379/AgwsfTry48d0FKgBYzry58+fQo0ufTr269esLGADYzr279+/gw4v/H0++vPntBSoAWM8eQAEA8OPLn0+/vv37+PPr3x+/QgGAABQIAFDQ4EGECRUuZNjQ4UOIDQtUAFDR4kWMGTVu5NjR40eQFisUAFDS5EmUKVWuZNnS5UuYMAtUAFDT5k2cOXXu5NnT50+gNiMQAFDU6FGkSZUuZdrU6VOoSAkkAFDVKgEBALRu5drV61ewYcWOJVvWa4IEANSuZdvW7Vu4ceXOpVtX7QEKAPTu5dvX71/AgQUPJlzYMIMFABQvZtzY8WPIkSVPplxZ8QEKADRv5tzZ82fQoUWPJl16dAQCABgsANDa9WvYsWXPpl3b9m3crQkkANDbdwEJAIQPJ17c//hx5MmVL2fefLgBAgAIEABQ3fp17Nm1b+fe3ft38N0PUABQ3vx59OnVr2ff3v17+OYNEABQ3/59/Pn17+ff3z9AAAIHEixo8CBCgQckAGjo8CHEiBInUqxo8SLGiAQIAOjo8SPIkCJHkixp8iTKjgkmAGjp8iXMmDJn0qxp8ybOnBEEAOjp8yfQoEKHEi1q9CjSngkmAGjq9CnUqFKnUq1q9SpWqwkAAIggAADYsGLHki1r9izatGrXgj2wAADcuAUUAKhr9y7evHr38u3r9y9guwMAAEhwAADixIoXM27s+DHkyJInQ04wAQDmzJo3c+7s+TPo0KJHZx4A4DTq1P+qV7Nu7fo17NiyZyeYAOA27ty6d/Pu7fs38ODCcQsAYPw48uTKlzNv7vw59OjKDwgAYP06gAIAtnPv7v07+PDix5Mvb/47gwQA1rNv7/49/Pjy59Ovb3+9AgkA9vPv7x8gAIEDCRY0eBBhQoULGTKUoABARIkTKVa0eBFjRo0bOUZUIAFASJEACAAweRJlSpUrWbZ0+RJmzJMGAACIkABATp07efb0+RNoUKFDiQZVIAFAUqVLmTZ1+hRqVKlTqSodAABrVq1buXb1+hVsWLFjySqQAABtWrVr2bZ1+xZuXLlz004AcBdvXr17+fb1+xdwYMF7CRQAcBjxAQUAGDf/dvwYcmTJkylXtnwZsoICADh39vwZdGjRo0mXNn2as4AIAFi3dv0admzZs2nXtn0b94QEAHj39v0beHDhw4kXN36ct4AIAJg3d/4cenTp06lXt36dOgEGAABMSAAAfHjx48mXN38efXr168EXOAAAfnwFCwDUt38ff379+/n39w8QgMCBBAsaNEjAAAAABQA4fAgxosSJFCtavIgx40UBEQB4/AgypMiRJEuaPIkypccCBgC4fAkzpsyZNGvavIkzp04BDAD4/Ak0qNChRIsaPYo06U8CAAAQAAA1qtSpVKtavYo1q9atURcsAAA2rNixZMuaPYs2rdq1bCkcAAA3/67cuXTr2r2LN6/evXAXMAAAOLDgwYQLGz6MOLHixYkPAABA4QCAyZQrW76MObPmzZw7e56sQACA0aQPHACAOrXq1axbu34NO7bs2agLVAAAQAABALx7+/4NPLjw4cSLGz9OfAEDAMybO38OPbr06dSrW7/O/EAFANy7e/8OPrz48eTLmz+PngEDAOzbu38PP778+fTr27/PnoACAPz7+wcIQOBAggUNHkSYUOFChgkVJAAQUSIBAgAsXsSYUeNGjh09fgQZUqMEAgBMnkSZUuVKli1dvoQZ0ySDBQBs3sSZU+dOnj19/gQaVGiFAgCMHkWaVOlSpk2dPoUa1WgEAf8ArF7FmlXrVq5dvX4FG9brAQkAAFAgAEDtWrZt3b6FG1fuXLp15TJYAEDvXr59/f4FHFjwYMKF9SagAEDxYsaNHT+GHFnyZMqVLUdYAEDzZs6dPX8GHVr0aNKlNR+IAED1atatXb+GHVv2bNq1XRcgAED3bgUJAPwGHlz4cOLFjR9Hnlz5cAEAnD+HHl36dOrVrV/Hnv15BAEAvH8HH178ePLlzZ9Hn169AQIA3L+HH1/+fPr17d/Hn9+9BAUA/AMEIHAgwYIGDyJMqHAhw4YHCygAAGAAgIoWL2LMqHEjx44eP4K0mKAAgJImFyQAoHIly5YuX8KMKXMmzZoqFUj/AADgAICePn8CDSp0KNGiRo8iNSpBAICmTp9CjSp1KtWqVq9ibapAAoCuXr+CDSt2LNmyZs+iTRtBAYC2bt/CjSt3Lt26du/idUsAAAACAP4CDix4MOHChg8jTqwYcIQEAB5Djix5MuXKli9jzqx58wAAnj+DDi16NOnSpk+jTv15QgIArl/Dji17Nu3atm/jzm2bAAEAAAYACC58OPHixo8jT658OXPhDA4AiC49QQEA1q9jz659O/fu3r+DD29dgAQAACIASK9+Pfv27t/Djy9/Pn35ExIAyK9/P//+/gECEDiQYEGDBxEmVKhQQAQADyFGlDiRYkWLFzFm1Lhx/0ICAB9BhhQ5kmRJkydRplT58UACAC9hxpQ5k2ZNmzdx5tQ5U0ABAD+BHiAAgGhRo0eRJlW6lGlTp0+RTgAwlWpVq1exZtW6lWtXr1QpHAAwlmxZs2fRplW7lm1bt24JGAAwl25du3fx5tW7l29fv3QpHAAwmHBhw4cRJ1a8mHFjx4sFLABAoAIAy5cxZ9a8mXNnz59Bh75MAEBp0wAoHACwmnVr169hx5Y9m3Zt26sXMACwm3dv37+BBxc+nHhx48cpHACwnHlz58+hR5c+nXp168sFLACwnXt379/Bhxc/nnx5898LAFC/HsCCAgDgx5c/n359+/fx59e/n74CAP8AAQgcSLCgwYMIEypcyLChwAoFAEicSLGixYsYM2rcyLFjxwIVAIgcSbKkyZMoU6pcybLlyAoFAMicSbOmzZs4c+rcybOnzgQHABSoAKCo0aNIkypdyrSp06dQjQogAKCq1QgFAGjdyrWr169gw4odS7asVgYLAAA4AKCt27dw48qdS7eu3bt47VYoAKCv37+AAwseTLiw4cOI+zJYAKCx48eQI0ueTLmy5cuYM1coAKCz58+gQ4seTbq06dOoOxMgAKC169ewY8ueTbu27du4Y08gAKC379/AgwsfTry48ePIkR+gAKC58+fQo0ufTr269evYnRsgAKC79+/gw4v/H0++vPnz6MsTAADgAAUA8OPLn0+/vv37+PPr3x+fAgGAAAQOTADA4EGECRUuZNjQ4UOIEQ9GEACgwAIAGTVu5NjR40eQIUWOJCnSAAEAKVWuZNnS5UuYMWXOpJkyggAAOXXu5NnT50+gQYUOJVrUAAEASZUuZdrU6VOoUaVOpZpUwQEAWbVu5drV61ewYcWOJdt1AQC0aQEkANDW7Vu4ceXOpVvX7l28cQswANDX71/AgQUPJlzY8GHEfgcAYNzY8WPIkSVPplzZ8mXMCSYA4NzZ82fQoUWPJl3a9OnOAwCsZt3a9WvYsWXPpl3bNm0GCQAkmADA92/gwYUPJ17c//hx5Ml/FwDQ3DmAAQCkT6de3fp17Nm1b+fefboEBQDEjydf3vx59OnVr2ff3v0AAPHlz6df3/59/Pn17+cvPwLABAAGEixo8CDChAoXMmzo8GABABInApAA4CLGjBo3cuzo8SPIkCI3EjgA4CTKlCpXsmzp8iXMmDJRDgBg8ybOnDp38uzp8yfQoEIVSABg9CjSpEqXMm3q9CnUqEcHAKhq9SrWrFq3cu3q9StYrwoKAFAgAQDatGrXsm3r9i3cuHLnpmUA4C5eABIA8O3r9y/gwIIHEy5s+HDfCQkAEDgA4DHkyJInU65s+TLmzJoxDwDg+TPo0KJHky5t+jTq1P+fJyQA4Po17NiyZ9Oubfs27ty6BwDo7fs38ODChxMvbvw4ct8FCABo7vw59OjSp1Ovbv06dugEJgDo7v07+PDix5Mvb/48+vQCIgBo7/49/Pjy59Ovb/8+/vYEDADo7x8gAIEDCRY0eBBhQoULGTYsSAAAAAERAFS0eBFjRo0bOXb0+BFkRQIUAJQ0CSABAJUrWbZ0+RJmTJkzadZcSeEAgAQCAPT0+RNoUKFDiRY1ehSpUQMAmDZ1+hRqVKlTqVa1erUphQMAuHb1+hVsWLFjyZY1exatAQBr2bZ1+xZuXLlz6da1y3YBAQB7+fb1+xdwYMGDCRc27JeAAACLGQP/UAAAcmTJkylXtnwZc2bNmyknUAAAdGjRo0mXNn0adWrVq0EXqAAAdmzZs2nXtn0bd27du3kvYAAAeHDhw4kXN34ceXLly4EXqAAAenTp06lXt34de3bt27NLIABgAQMA48mXN38efXr169m3d0/+AAD58wFQAHAff379+/n39w8QgMCBBAsaPIgwIcEKBQA4fAgxosSJFCtavIgxo8YKADp6/AgypMiRJEuaPIkSAAEBBhYUAAAzpsyZNGvavIkzp86dMwkA+AkUQAQARIsaPYo0qdKlTJs6VUoAQAEJEQAckLAgwoICAiQoAAA2rNixZMuaPYs2rdq1BygAeAs3/67cuXTr2r2LNy/eBAIAEBgwAQABBQUAGD7MgIECBQAUGGAAgEABAJQrW76MObPmzZw7e8Z8gAKA0aRLmz6NOrXq1axbo2YwAQCACREA2L6NGzeDBQB6AyhQAEACAxIAFFBQAIDy5cybO38OPbr06dQBFFgAILt2AAsAeP8OPrz48eTLmz//vYACAgAmDCgAQEACAPTr279f/0ABAPz7+wcI4IAEBgAORFAAQOFChg0dPoQYUeJEig4pAMCYUeNGjh09fgQJUkGEAwAiTCgAgAAAli1dvoQZU6ZLAgIEAEhQgQEAAAQA/AQaVOhQokWNHkWKlAIApk2dPoUaVepUqv9NDxQAIMCAAAAKBBAAEFbsWLJlzZ5FW7bAAQAHDEwAQEBBAQB17d7Fm1fvXr599x5gAEDwYAAEABxGnFjxYsaNHTcuwEAAAAEVBAAgQADAZs6dPX8G/TmCAAClTZ9GnTp1gQkSABSIoADAbNq1bd/GnVv37toJJgAAHlz4cOLFjR8/TuAAgAIUJgAowCABAOrVrV/Hnl379QgCAHwHH178ePLgCSxYAOBABQYA3L+HH1/+fPr17SeQAED/fgAFAAAEIHAgwYIGDyIsKIABAAIGJAAAcAAAxYoWL2LMqHEjgAUJAIAMKXIkyZImDyQAUGDABAAAEhAAIHMmzZo2b+L/zKkTwAQAPn8CDSp06NACAABIqAAAQIQFAJ5CjSp1KtWqVq9izapVKgEABSZQAECAgQIAZs+iTat2Ldu2aycAiCt3Lt26duMeWEAAAIUKBQAkKABgMOHChg8jTqx4MePGjh8DILCAAQACFBgAyKx5M+fOnj9rLpAAAOnSABIASK16NevWrAVMSABgQYQCAG7jzq17N+/evn/7FnAAAPHixo8jT658efIDCgAQGEABAIAEBABgz659O/fu2RVIACB+PPny5ssTUHAAwIIBAgAcSEAAAP369u/jz69/P//+9AFKUACAYEGDBxEmVLiQIcECAAhQqAAAwIIEADBm1LiR/yNHAREAhBQ5kmRJAAUkLACgYIICAC9hxpQ5k2ZNmzdx3pyQAEBPnz+BBhU6lGjRoQQYSAAAYAIDAE+hRpU69SmBAgCwZgUQAUBXrwAOJABQwMAEAAUEFACwlm1bt2/hxpU7l25du3fx5r2bQAAAAAMoAABwAEBhw4cRJz4sAUBjBhIAEKAQAQAAAgAwZ9a8mXNnz59BhxY9mnRp06c7HwAAgIIBAAAWJAAwm3Zt27YHDCgAYIECAL+BBxc+nHhx48eRJ1cO4AABAM+hR5c+nXp169exZ38eYQIAABIYABA/HoCACADQpwewIAIAABQmEABAAEB9+/fx59e/n39///8AAQgcSLCgQYETEgBYyLChw4cQI0qcSLHiQwULAAAwMAEAgAILGAAYSbIkAAIJAACgMKAAgAQHAMicSbOmzZs4c+rcydMmhQMAggodSrSo0aNIkypdmpSAAgAADAwQAKCq1atYARAAACBChQIAFgggAKCs2bNo06pdy7at27cAFBAAQLeu3bt48+rdy7evX7wFBBAAQGFAAQAKEgBYzLix48eMFUQgAECChAIAMmvezLmz58+gQ4seTbq06dOoNyuQkAAAAwkFABAAQLu27du4c98uoIAAAAoGDgA4UACA8ePIkytfzry58+fQo0ufTl05gQQHAAgYIABAAgUEAIj/H0++vPnz6NETAAAgQoUDAAQIIACgvv37+PPr38+//36AEwoAIFjQ4EGECRUuZNiQYAEGCwAooKAAAAEAGTVu5NjR40eQITMqkFAAQAQJBQCsZNnS5UuYMWXObFmhAACcOXXu5NnT50+gPAscAFCgwgQABRYcANDU6VOoUaVOpVrVKoACAggAmGDgAIACBACMJVvW7Fm0adWilVAAwFu4ceXOpVvX7t23CyIAIFBBAgAABQAMJlzY8GHEiRUvZsy4AAAADAwkAKBAAQEAmTVv5tzZ82fQoUWPJv35AAAAEwwQAMBAAADYsWXPpl3b9m3cuXX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z58+gQ4veXOAAgNOoCygAwLq169ewY8ueTbu27duwFRwAwLu379/AgwsfTry48eO8FUQAwLy58+fQo0ufTr269evYIygAwL279+/gw4v/H0++vPnz3AVEAMC+vfv38OPLn0+/vv379R0AAPBAAQCAAAQOJFjQ4EGECRUuZNgQQIEDACRORMAAwEWMGTVu5NjR40eQIUVeHEAAAIABAFSuZNnS5UuYMWXOpFlzpoIIAHTu5NnT50+gQYUOJVpU5wADAJQuZdrU6VOoUaVOpVrVqgIGALRu5drV61ewYcWOJVvWawEAadWuZdvW7Vu4ceXOpatWgAMAefXu5dvX71/AgQUPJlz4AQIAiRUvZtzY8WPIkSVPppx4gQMAmTVv5tzZ82fQoUWPJi0aAQAAFBAAYN3a9WvYsWXPpl3b9m3WCAQA4N37AAIAwYUPJ17c//hx5MmVL2cevIABAAAUDABQ3fp17Nm1b+fe3ft38N0FOABQ3vx59OnVr2ff3v17+OULWABQ3/59/Pn17+ff3z9AAAIHEixo8CDCgQsYAGjo8CHEiBInUqxo8SJGhwoAcOzo8SPIkCJHkixp8iRIBQoAsGw5YACAmDJn0qxp8ybOnDp38qz5oACAoEKHEi1q9CjSpEqXMg3KgAGAqFKnUq1q9SrWrFq3cu1q4QCAsGLHki1r9izatGrXsg3LYAGAuHIHDABg9y7evHr38u3r9y/gwHYLUAAAIEIBAIoXM27s+DHkyJInU64smQEDAJo3c+7s+TPo0KJHky6t+QAFAP+qV7Nu7fo17NiyZ9OubZvBAgC6d/Pu7fs38ODChxMvrruAAwDKlzNv7vw59OjSp1Ov7rxAAQDatyNAAOA7+PDix5Mvb/48+vTqxy8YAOA9/Pjy59Ovb/8+/vz63ztYAAAgAIEDCRY0eBBhQoULGTZsaKAAAIkTKVa0eBFjRo0bOXaU6EAAAJEjSZY0eRJlSpUrWbZUWWABAAAGBgCweRNnTp07efb0+RNoUJsHCgAwenSBAgBLmTZ1+hRqVKlTqVa1uhTBAwAACgDw+hVsWLFjyZY1exZt2rMOBABw+xZuXLlz6da1exdvXrcIHgDw+xdwYMGDCRc2fBhxYsUMFAD/cPwYcmTJkylXtnwZc2bJBQB09vwZdGjRo0mXNn0atWcHCgC0dv0admzZs2nXtn0bd24CAwD09v0beHDhw4kXN34cee8ICgA0d/4cenTp06lXt34de/UBBQAAIAAAfHjx48mXN38efXr168MvQAAAfnwEBQDUt38ff379+/n39w8QgMCBBAsaNKggAgAACwA4fAgxosSJFCtavIgx48UICgB4/AgypMiRJEuaPIkypUcFEQC4fAkzpsyZNGvavIkzp84ICgD4/Ak0qNChRIsaPYo0qc8CCAA4fQo1qtSpVKtavYo1q1QBBwB4/VpgAICxZMuaPYs2rdq1bNu6PWsB/4DcuXTr2r2LN6/evXz7zn2AAIDgwYQLGz6MOLHixYwbOyYAILLkyZQrW76MObPmzZwlP0AAILToAQBKmz6NOrXq1axbu34N27QCBgAAWACAO7fu3bx7+/4NPLjw4cEfIACAPLny5cybO38OPbr06cgFOACAPbv27dy7e/8OPrz48eQfIACAPr369ezbu38PP778+egVLACAP7/+/fz7+wcIQOBAggUNHkSYUKHBAwMAPIQooAAAihUtXsSYUeNGjh09fsQoAMBIkiVNnkSZUuVKli1dkqRwAMBMmjVt3sSZU+dOnj19+hxgAMBQokWNHkWaVOlSpk2dEqVwAMBUqv9VrV7FmlXrVq5dvW5FgADAAAMAzJ5Fm1btWrZt3b6FG/esggEA7N51UADAXr59/f4FHFjwYMKFDe9dwAAAgAMAHD+GHFnyZMqVLV/GnPkyhQMAPH8GHVr0aNKlTZ9GndrzAgYAXL+GHVv2bNq1bd/GnVv3gwMAfP8GHlz4cOLFjR9Hnvz3AAAABgCAHl36dOrVrV/Hnl379ugUCgAAH178ePLlzZ9Hn179+vUFLACAH1/+fPr17d/Hn1///vgWCgAEIHAgwYIGDyJMqHAhw4YKBwwAUMACgIoWL2LMqHEjx44eP4K0GGEAgJImFQwAoHIly5YuX8KMKXMmzZoqGSz/ADBgAYCePn8CDSp0KNGiRo8iNWqhAICmTp9CjSp1KtWqVq9ibcpgAYCuXr+CDSt2LNmyZs+iTWuhAIC2bt/CjSt3Lt26du/ibYvgAIC+fv8CDix4MOHChg8jDswAAOPGAA4AiCx5MuXKli9jzqx5M+fKBRwACC16NOnSpk+jTq16NWvRBgYAiC17Nu3atm/jzq17N2/eBygACC58OPHixo8jT658OXPhBgYAiC59OvXq1q9jz659O/fsDBQAOPAAAPny5s+jT69+Pfv27t+XHwBgPn0ABgYAyK9/P//+/gECEDiQYEGDBxEmVKjQgQAADyFGlDiRYkWLFzFm1LjR/8AAAB9BhhQ5kmRJkydRplT5kYECAC9hxpQ5k2ZNmzdx5tQ58wAAnz8BOAAwlGhRo0eRJlW6lGlTp0cHIAAwlWpVq1exZtW6lWtXr1QJABA7lmxZs2fRplW7lm1btwgeAJA7l25du3fx5tW7l2/fuQQABBY8mHBhw4cRJ1a8mLFiBQUAIHgAgHJly5cxZ9a8mXNnz58rLwAwmjSABwBQp1a9mnVr169hx5Y9O3UEBQAGFACwm3dv37+BBxc+nHhx48QJAFC+nHlz58+hR5c+nXr15REUANC+nXt379/Bhxc/nnx58wQApFe/nn179+/hx5c/n776AQDw59e/n39///8AAQgcSLCgwYMIEyo8aAGAw4cQI0qcSLGixYsYM2pUEAGAx48gQ4ocSbKkyZMoU34kAKCly5cwY8qcSbOmzZs4bQ4AAEBBBABAgwodSrSo0aNIkypdGpQCgKdQASgAQLWq1atYs2rdyrWr169VHyAAcEAAgLNo06pdy7at27dw48qFSwCA3bt48+rdy7ev37+AA999gACA4cOIEytezLix48eQI0smAKCy5cuYM2vezLmz58+gLQsoAKC06dOoU6tezbq169ewUy8AQLs2AAQAcuvezbu379/AgwsfTrw3AgEAkitfzry58+fQo0ufTj35AAMAsmvfzr279+/gw4v/H0++vAAHANKrX8++vfv38OPLn08/fQEDAPLr38+/v3+AAAQOJFjQ4EGECRUuRBihAAABDABMpFjR4kWMGTVu5NjRI8UCAESOHGAAwEmUKVWuZNnS5UuYMWWitHAAwE2cOXXu5NnT50+gQYUKLWAAwFGkSZUuZdrU6VOoUaUifVAAwFWsWbVu5drV61ewYcVuLQDA7NkBDgCsZdvW7Vu4ceXOpVvX7tsCBwDs5dvX71/AgQUPJlzY8N4CFgAsZtzY8WPIkSVPplzZ8uUFDABs5tzZ82fQoUWPJl3a9OYDFgCsZt3a9WvYsWXPpl3bNm0BAwAwYADA92/gwYUPJ17c//hx5Ml9DxAAwPnzAQwATKde3fp17Nm1b+fe3Tt1AwUAFCgAwPx59OnVr2ff3v17+PHdH7AAwP59/Pn17+ff3z9AAAIHEixo8CDCgQYGAGjo8CHEiBInUqxo8SJGjAcoAOjo8SPIkCJHkixp8iRKjwcAsGzp8iXMmDJn0qxp8ybMAw4A8Ozp8yfQoEKHEi1q9ChSBgsAMG3q9CnUqFKnUq1q9SpTBBQAcO3q9SvYsGLHki1r9mzZAQAAOFgA4C3cuHLn0q1r9y7evHrfHogA4C/gAQcAEC5s+DDixIoXM27s+HFhAgMACEAA4DLmzJo3c+7s+TPo0KI/I6AA4DTq1P+qV7Nu7fo17NiyURMAYPs27ty6d/Pu7fs38ODCETwAYPw48uTKlzNv7vw59OjHGQCobv069uzat3Pv7v07+OwFFAAob34AAgDq17Nv7/49/Pjy59Ov714AAgD69/Pv7x8gAIEDCRY0eBBhQoULDSp4AABiRIkTKVa0eBFjRo0bOUYQAABkSJEjSZY0eRJlSpUrQSqIAABmTJkzada0eRNnTp07c1IAACCCAgBDiRY1ehRpUqVLmTZ1OnRAAQBTqSKIAABrVq1buXb1+hVsWLFjsxIAcBZtWrVr2bZ1+xZuXLlzFUQAcBdvXr17+fb1+xdwYMF4DQAwfBhxYsWLGTf/dvwYcuTFAwBUtnxgAQDNmzl39vwZdGjRo0mX9nxgAADVq1m3dv0admzZs2nXVi0gAgDdu3n39v0beHDhw4kXN/5AAQDly5k3d/4cenTp06lXVy7AAQDt27l39/4dfHjx48mXHy8AAIAHCAC0d/8efnz58+nXt38ff/sDCgD09w/wgAAABAsaPIgwocKFDBs6fEhwgAEAABAMAIAxo8aNHDt6/AgypMiRIAU4AIAypcqVLFu6fAkzpsyZKAcYAIAzp86dPHv6/Ak0qNChRAU4AIA0qdKlTJs6fQo1qtSpSQ8AuIo1q9atXLt6/Qo2rNitCgQAOIs2rdq1bNu6fQs3/67cuQ8OALiLN6/evXz7+v0LOLDguwsYADiMOLHixYwbO34MObLkyRQOALiMObPmzZw7e/4MOrToywIWADiNekABAKxbu34NO7bs2bRr277NuoAFAAAYFAAAPLjw4cSLGz+OPLny5cgXMAAAPbr06dSrW7+OPbv27dALWAAAPrz48eTLmz+PPr369ewXMAAAP778+fTr27+PP7/+/fAHMAAIQOBAggUNHkSYUOFChg0NHjgAQOLEAwcAXMSYUeNGjh09fgQZUuRGBgMAnESZUuVKli1dvoQZU+ZJBgsA3MSZU+dOnj19/gQaVOhQCwUAHEWaVOlSpk2dPoUaVepRBv8LAFzFmlXrVq5dvX4FG1bs1wIOAACwUADAWrZt3b6FG1fuXLp17a4tMADAXr4LFgAAHFjwYMKFDR9GnFjxYsAHKACAHFnyZMqVLV/GnFnzZs4MFgAAHVr0aNKlTZ9GnVr1atAHHgCAHVv2bNq1bd/GnVv3bt4CFAAAHlz4cOLFjR9Hnlz5cuIHADyHHl36dOrVrV/Hnl07dAcCAHwHH178ePLlzZ9Hn179egMDALyHH1/+fPr17d/Hn1//ewcCAAAEIHAgwYIGDyJMqHAhw4YJByAAAMDAAAAWL2LMqHEjx44eP4IMaVHBAQAmTyo4AGAly5YuX8KMKXMmzZo2VyL/eAAAgAIAPn8CDSp0KNGiRo8iTXrUgQAATp9CjSp1KtWqVq9izeoUwQMAXr+CDSt2LNmyZs+iTavWgQAAbt/CjSt3Lt26du/izet2wAEAfv8CDix4MOHChg8jTix4AQIAjh8PACB5MuXKli9jzqx5M+fOlw0ACC16NOnSpk+jTq16NWvRERQAiC17Nu3atm/jzq17N+/eBAAADy58OPHixo8jT658efAICgBAj15gAIDq1q9jz659O/fu3r+Dr64gAgAADwCgT69+Pfv27t/Djy9/fvwICgDgz69/P//+/gECEDiQYEGDBxEmVChQQQQADyFGlDiRYkWLFzFm1Lgx/4ICAB9BhhQ5kmRJkydRplT58YAAAC9hxpQ5k2ZNmzdx5tQ5E8EAAD+BIigAgGhRo0eRJlW6lGlTp0+ROgAwlWpVq1exZtW6lWtXr1QfIAAwlmxZs2fRplW7lm1bt28JAJA7l25du3fx5tW7l2/fuQ8QABA8mHBhw4cRJ1a8mHFjxQgEAABAAEBly5cxZ9a8mXNnz59BWz4wAEBp0xEOAFC9mnVr169hx5Y9m3Zt1QIcAAAwAEBv37+BBxc+nHhx48eRG3+AAEBz58+hR5c+nXp169exNxfgAEB379/Bhxc/nnx58+fRp3dwAEB79+/hx5c/n359+/fxxz8AgH9///8AAQgcSLCgwYMIEypcyNAghQMAIkqcSLGixYsYM2rcyJHjAAMAQoocSbKkyZMoU6pcyVIkhQMAYsqcSbOmzZs4c+rcyTNngQIABhgAQLSo0aNIkypdyrSp06dFGRQAQLWqgAEAsmrdyrWr169gw4odSzbrAgYAAAgAwLat27dw48qdS7eu3bt1KRwAwLev37+AAwseTLiw4cN8FzAAwLix48eQI0ueTLmy5cuYKRwAwLmz58+gQ4seTbq06dOcCxQAwLq169ewY8ueTbu27duwHQwAwLt3AQDAgwsfTry48ePIkytfTrzAAwDQo0ufTr269evYs2vfHt1CAQDgw4v/H0++vPnz6NOrX7++gAUA8OPLn0+/vv37+PPr3x/fQAGAAAQOLADA4EGECRUuZNjQ4UOIEQ8yWACgQAQAGTVu5NjR40eQIUWOJCnSQgEAKVWuZNnS5UuYMWXOpJnSwQIAOXXu5NnT50+gQYUOJVrUQgEASZUuZdrU6VOoUaVOpZpUAAIAWbVu5drV61ewYcWOJdsVAQC0aQEsGADA7Vu4ceXOpVvX7l28eeMWEADA71/AgQUPJlzY8GHEif8aGADA8WPIkSVPplzZ8mXMmTMfoADA82fQoUWPJl3a9GnUqT8TGADA9WvYsWXPpl3b9m3cuW0LOAAAAQUAwYUPJ17c//hx5MmVL2cuXAEA6NEBUBgAwPp17Nm1b+fe3ft38OGtRxAAAMAAAOnVr2ff3v17+PHlz6cvn8AAAPn17+ff3z9AAAIHEixo8CDChAoVRlAA4CHEiBInUqxo8SLGjBo3UgDg8SPIkCJHkixp8iTKlCMLAGjp8iXMmDJn0qxp8yZOlwQA8Ozp8yfQoEKHEi1q9ChSBA8AMG3q9CnUqFKnUq1q9WpTAgC2cu3q9SvYsGLHki1rluyBAQAUPADg9i3cuHLn0q1r9y7evG8fAOjrF8ACAIIHEy5s+DDixIoXM248+IECAAUUAKhs+TLmzJo3c+7s+TNozwQAkC5t+jTq1P+qV7Nu7fp16QcIANCubfs27ty6d/Pu7fs3cAIAhhMvbvw48uTKlzNv7pw4ggEAplOvbv069uzat3Pv7v26AwDixwM4AOA8+vTq17Nv7/49/Pjy1yNgAOA+/vz69/Pv7x8gAIEDCRY0eBBhwoEDCABw+BBiRIkTKVa0eBFjRo0CIgDw+BFkSJEjSZY0eRJlSo8DDABw+RLAAAAzada0eRNnTp07efb0SfPBAQAKGAAwehRpUqVLmTZ1+hRqVKcDCACwehVrVq1buXb1+hVs2KsUDgAwexZtWrVr2bZ1+xZu3LgDDACwexdvXr17+fb1+xdw4LsOCgAwfBhxYsWLGTf/dvwYcmTFBwBUtgyAAQDNmzl39vwZdGjRo0mX9nwAAQDVq1m3dv0admzZs2nXVl3AAgDdu3n39v0beHDhw4kXN77AAQDly5k3d/4cenTp06lXV17AAgDt27l39/4dfHjx48mXH79gAIAFDAC0d/8efnz58+nXt38ff/sBCgD09w9wQAQABAsaPIgwocKFDBs6fFjQQgEAAwYAuIgxo8aNHDt6/AgypMiPBSwAOIkypcqVLFu6fAkzpkyUFgoAuIkzp86dPHv6/Ak0qFChBSgAOIo0qdKlTJs6fQo1qlSkAwAAGDAAgNatXLt6/Qo2rNixZMtqLUABgNq1bNu6fQs3/67cuXTr2mXAAIDevXz7+v0LOLDgwYQL6z1AAYDixYwbO34MObLkyZQrTz4AAACDBQA6e/4MOrTo0aRLmz6NunMBBwBaux6gAIDs2bRr276NO7fu3bx7zzYwAAACBACKGz+OPLny5cybO38OvfkBCgCqW7+OPbv27dy7e/8O3rqBAQDKmz+PPr369ezbu38PH/4BCgDq27+PP7/+/fz7+wcIQOBAggUNHhQAQOFChg0dPoQYUeJEihUdFhAAQONGAAcAfAQZUuRIkiVNnkSZUuXIBQoAvIQZU+ZMmjVt3sSZU+dLBA8A/AQaVOhQokWNHkWaVOlSBwIAPIUaVepUqv9VrV7FmlXrUwQPAHwFC2AAALJlzZ5Fm1btWrZt3b4tawEAAAYKANzFm1fvXr59/f4FHFgw3gEADB9G8ADAYsaNHT+GHFnyZMqVLTMmAEDzZs6dPX8GHVr0aNKlTSN4AED1atatXb+GHVv2bNq1V1MAkFv3bt69ff8GHlz4cOK9BxQAkFx5AQEAnD+HHl36dOrVrV/Hnl06ggIAvH8HH178ePLlzZ9Hn967gggA3L+HH1/+fPr17d/Hn19/BAUA/AMEIHAgwYIGDyJMqHAhQ4YKIgCIKHEixYoWL2LMqHEjR40MAACIoAAAyZImT6JMqXIly5YuX5IsgAAAzZoHFgD/yKlzJ8+ePn8CDSp0KFGdBAAAKDAAANOmTp9CjSp1KtWqVq9SVRABANeuXr+CDSt2LNmyZs92JQBgLdu2bt/CjSt3Lt26du8qcABgL9++fv8CDix4MOHChvkWAABgAIDGjh9Djix5MuXKli9jdqyAAYDOnj+DDi16NOnSpk+jTv0AAYDWrl/Dji17Nu3atm/jbi3AAYDevn8DDy58OPHixo8jN14AAIAHCABAjy59OvXq1q9jz659O3QFCwCAD1/gAIDy5s+jT69+Pfv27t/DLz/AAAAAAgoAyK9/P//+/gECEDiQYEGDBxEmVLjwoAAHACBGlDiRYkWLFzFm1LgR/+IAAwBAhhQ5kmRJkydRplS5kqUABwBgxpQ5k2ZNmzdx5tS5M+YCAD+BBhU6lGhRo0eRJlU69AACAE+hFigAgGpVq1exZtW6lWtXr1+xOigAgGxZs2fRplW7lm1bt2/JLmAAgG5du3fx5tW7l29fv38BUzgAgHBhw4cRJ1a8mHFjx48JL2AAgHJly5cxZ9a8mXNnz585F3gAAMCDAwBQp1a9mnVr169hx5Y9G/WAAQBw517AAEBv37+BBxc+nHhx48eR9y5gAUBz58+hR5c+nXp169exZ1/AAEB379/Bhxc/nnx58+fRdy/wAEB79+/hx5c/n359+/fxxx8wAEB///8AFSgAQLCgwYMIEypcyLChw4cIFQwAQLGixYsYM2rcyLGjx48UGSwAQLKkyZMoU6pcybKly5cwLRQAQLOmzZs4c+rcybOnz580GSwAQLSo0aNIkypdyrSp06dMBygAAMBCAQBYs2rdyrWr169gw4odixXBAQBo0ypQAKCt27dw48qdS7eu3bt42x6gAAAAAgCAAwseTLiw4cOIEytenJjBAgCQI0ueTLmy5cuYM2veDPkABQCgQ4seTbq06dOoU6tezZrBAgCwY8ueTbu27du4c+veHbsAgN/AgwsfTry48ePIkysfzkABgOfQo0ufTr269evYs2vfbmAAgO/gw4v/H0++vPnz6NOr/+5AAID38OPLn0+/vv37+PPrxz8AAACABgYAIFjQ4EGECRUuZNjQ4UOCDBQAoFjxQAEAGTVu5NjR40eQIUWOJJkRwQMAABwAYNnS5UuYMWXOpFnT5s2aDgQA4NnT50+gQYUOJVrU6FGeCh4AYNrU6VOoUaVOpVrV6lWsDgQA4NrV61ewYcWOJVvW7FmuBQQAYNvW7Vu4ceXOpVvX7l24CgoA4Nv3QAEAgQUPJlzY8GHEiRUvZlw4AgDIkSVPplzZ8mXMmTVvjhxBAQDQoUWPJl3a9GnUqVWvZk0AwGvYsWXPpl3b9m3cuXXDfqAAwG/gwYUPJ17c//hx5MmVH0fAAAAAAgCkT6de3fp17Nm1b+fefXqBAQDEj4+gAMB59OnVr2ff3v17+PHlnxcQAcB9/Pn17+ff3z9AAAIHEixo8CDChAoLPlAA4CHEiBInUqxo8SLGjBofKmAA4CPIkCJHkixp8iTKlCpHDgDg8iWABQcA0Kxp8ybOnDp38uzp8ydOBQCGEi1q9CjSpEqXMm3qlOgDBACmUq1q9SrWrFq3cu3q9SsBAGLHki1r9izatGrXsm07lgICAHLn0q1r9y7evHr38u2r98ABAAMIAChs+DDixIoXM27s+DFkwwIKAKhseUEBAJo3c+7s+TPo0KJHky6teYEDAP8AFABo7fo17NiyZ9Oubfs2btsUEADo7fs38ODChxMvbvw48t4LGABo7vw59OjSp1Ovbv069uwUDgDo7v07+PDix5Mvb/48+u4DCgBo7/49/Pjy59Ovb/8+/vgRCgDo7x8gAIEDCRY0eBBhQoULGTY8OMAAAIkTKVa0eBFjRo0bOXacaOEAAJEjSZY0eRJlSpUrWbZsWcAAAJkzada0eRNnTp07efac+aAAAKFDDwAwehRpUqVLmTZ1+hRq1KMMGAAY4ABAVq1buXb1+hVsWLFjyYq1cABAWrVr2bZ1+xZuXLlz6aZlsABAXr17+fb1+xdwYMGDCRe2UABAYsWLGTf/dvwYcmTJkyknVoAAQGbNmzl39vwZdGjRo0l3FgAAdWoACgYAcP0admzZs2nXtn0bd+7YAxgA8P0beHDhw4kXN34cefLfBgoAcP4cenTp06lXt34de/bsBywA8P4dfHjx48mXN38effrvBgYAcP8efnz58+nXt38ff377AhQAOACQAoCBBAsaPIgwocKFDBs6JHgAgMSJACwMAIAxo8aNHDt6/AgypMiRGB0IAIAypcoBAwC4fAkzpsyZNGvavIkzp0sDAwD4/Ak0qNChRIsaPYo0qU8GAgA4fQqgAAMDVA0sGAAgq9atXLt6/Qo2rNixWwcAOIsWQIQBANq6fQs3/67cuXTr2r2LF+6AAwD6IjDAoAAAAAciWCgAILHixYwbO34MObLkyYoJALiMObPmzZw7e/4MOrTo0QgeAABQwMABAKxbC7AAILbs2bRr276NO7fu3bIJAPgNPLjw4cSLGz+OPLny3wocRGBQAIB0AAcKAEDwAAAABwwAeP/u/YECAOTLmz+PPr369ezbuyfvAID8+QAYALiPP7/+/fz7+wcIQOBAggUNHkQoUIGBBwIUMDDwYAAAABEUABiAAAAAAwMAfAT5EQEFACVNnkSZUuVKli1dvmxJAMBMmjVt3sSZU+dOnj1rCjBQAMDQoQ4oAAAQQQEApgAKGAAQVarUAf8GAFzFmlXrVq5dvX4FG/YrAQBlzZ5Fm1btWrZt3b41O4BAAQB17QKIwADAgQEA/AIoYADAYMKEBxgAkFjxYsaNHT+GHFny5MQPAFzGDGAAAM6dPX8GHVr0aNKlTXdeEAHAatarCxgAEBsAgggAABgoAED3bt0KHgAAHlz4cOLFjR9Hnlw5cAIAnD+HHl36dOrVrV/Hfp0CAgDdvXu3cADAeAURAABg4ADAevbrKSAAEF/+fPr17d/Hn1///vgGAAAEIHDgAQAGDyJMqHAhw4YOH0I8SOEAgIoWLT5A8AABAAQLAAAYYEABgJImHVAAoHIly5YuX8KMKXMmzZkEAOD/zKlzJ8+ePn8CDSo05wMFAI4iRWohggECTg08WFCggIEICAAMUECBwgAAXr+CDSt2LNmyZs+iPUsAANu2bt/CjSt3Lt26dtsKeABgL9+9CAw8cLBgMIMIDwxQUCCAAgECDxQAiCx5MuXKli9jzqx5M2UFAD6DBiAAAOnSpk+jTq16NevWrk0bQABgNm0AFCIIyC3AQQQBAhZEMBBhAIDixo8jT658OfPmzp8vH2AAAPXq1q9jz659O/fu3r0fMCAAAHkABShQWCBgvYAIFATAF7DggYEDAO7jz69/P//+/gECEDiQYEGDBxESHGAAQEOHDyFGlDiRYkWLFzEeeGAg/4IDCgYeLBAwcmQECgJQpnRg4AAAly9hxpQ5k2ZNmzdxzkQAgGfPARYABBU6lGhRo0eRJlW61KgAAg8cLBAwlaqABQsEZNUqwIGBAQDAhhU7lmxZs2fRplW7doABAG/hxpU7l25du3fx5p07wIADAX8BBxYs+EEEAIcRJ1a8mHFjx48hR5Y84AEAy5cxZ9a8mXNnz59Ba3ZAQUBp06cFMGAggHVr1gsMIAAwm3Zt27dx59a9m3fv2gUsABA+nHhx48eRJ1e+XLkCCgQIGHBQAEB169UHEFgggHt37wIePBAwnjz5CBQApFe/nn179+/hx5c/f30BCwDw59e/n39///8AAQgcSLCgwYMIBz6goAAAgAEMDCgAQLEigAUUBGjcyFHjgwcCQooUucBAAQAoU6pcybKly5cwY8pMOYABgJs4BywAwLOnz59AgwodSrRohAgAkiotYOAAgKdQKTgQQLWqVaoMHAjYyrXrgwUAwoodS7as2bNo06pdq7aABQBw48qdS7eu3bt48RYwAKCv374CHgAYTJjAAgGIEytezDhxhAcAIkueTLmy5cuYM2verLmABQCgQ4seTbq06dOoUTtYAKC1a9cGCgCYDaCAAQG4c+vezVs3AwMAggsfTry48ePIkytfLrwAAwDQowMYAKC69evYs2vfzr17dwoIAIj/Hz/+AQIA6AEcsCCgvfv37h9EEEC/vv0FBADo38+/v3+AAAQOJFjQ4EGECRUWPEABwEOIESVOpFjR4kWMFCkgANDRo8cHCACMHLDAggCUKVWmfPBAwEuYMRcQAFDT5k2cOXXu5NnT50+bBygAIFoUQAEASZUuZdrU6VOoUaM6YADA6tWrBgYAQPCAgAUDAsSOJTs2QgQBadWuXWAAwFu4ceXOpVvX7l28efEeoADA71/AgQUPJlzYsOECBgAsZrxYwIMCFCxEWLCAwAIBmTVv5txZswMKAESPJl3a9GnUqVWvZr36AAUAsWXPpl3b9m3cuXVHiADA9+8DBhgQiLBA/8BxAwwELGfe3Plz5g8YAKBevfoABQoQAODe3ft38OHFjydfnvsABADUrx+gAMB7+PHlz6df3/59/AAeUFAAAADAAgwMODDAQABChBQeCGjo8GFDBw4EUBSwgEGEBxoNCADg8SOAAREMPIhAwcACACpXsmzp8iXMmDJnIngA4CbOnDp38uzp8yfQnwoeECBggIEAAwwEMG3KgMACAVKnUhVAIYIAAQweEDBg4QFYChYIRDgA4GwBAwwGAGh7gEIEAHLn0q1r9y7evHr1IngA4C/gwIIHEy5s+DDixIAHGGAg4DHkxxYiCKhs+bIAChEWUCDwgIGA0KIFLIhggEIBAP8WBABo7RoAhQUAZtOubfs27ty6d+secAAA8OAHHAAobvw48uTKlzNv7lx5hAcCplOnzoDAAgHat3Nf4MAAhQUCxpMvL2BBBAIOLABo7779AQMA5tOvb/8+/vz69/O/jwDgAwADCRY0eBBhQoULGR4sYGCBAIkTKT6wsEBARo0aHRBwIABkSJEjGRgQAABlypQUEABw+RJmTJkzada0eTMmAgcAePb0+RNoUKFDifoU8OBBBAQAmDZl6uCBAKlTqQpYYIHCAgFbuQpgQMCBALFjyZYVawEBALVr10ZQAACBAAEHANS1exdvXr17+fa1qyACAMGDCRc2fBhxYsWKFRj/iKAAgQAKFgoAsHzZAAMBmzl33rzAgoUFAkiTXmDAQgQBq1m3dr2aggIAs2nTfuDAAgUHDixYQAAAeHDhw4kXN34cOQAFEQA0d/4cenTp06lXp67AQAEA27crMFAAQHgAAwwsEHAefXr0Cx4QiLBAQHwKFCw4EHAff3799yM8AAAQgMCBAAYQsHAAgEIACAwIAAAxosSJFCtavIgRgQAAHDsWUAAgpMiRJEuaPIkyJQADBwC4fAlgQQQANAEooCAgp86dPAUwsEDgAQMGBBY4YCAgqdKlTJMuIFAAgNSpABgYGAAgq9YCBgoA+Ao2rNixZMuaPUtWQQQAbNu6fQs3/67cuXQVPACAN29eAwMA+BXwQIDgwYQLD2ZAwQABCgIaO34MOXIEAwUAWLYswMACAJw7c2bAAIDo0aRLmz6NOrXq0wIiAHgNO7bs2bRr274dQQCA3bx5R1AAIPiCBwKKGz+OPLkBBgKaO38OPbqACAQeKEAgwIIFAgC6e/dewACA8eTLmz+PPr169QgEAHgPf8AAAPTr27+PP7/+/fwjKAAIQODAgREYAEAogIIAhg0dPny4gMACBhQcCMCYUePGjQssUHjgAEEBAwBMnkRJAMBKli1dvoQZU6ZMAQ4A3MSZU+dOnj19/uzpYAEAokWLUqhAoEKEBRYEPIUaVWrUBf8UCBgwQMACAwFdvX4FC/bBgwUPDBAgAEDt2rUDCACAG1fuXLp17d69u8ABAL59BwwAEFjwYMKFDR9GfPjAAwIGADyG/LiAgQQJLkyoQGCBAM6dPX/mzMBAhAMAAERwYMCBANatXb927cAABAkXEmBQAED3bt0LHgAAHlz4cOLFjR9HXlyAAwDNnT+HHl36dOrRD1ioMKEBhgUAvH8H4GFCAPLkDTAQkF79evYCFhhYAED+/AMGGAjAn1///vwOMAAMIDCABAsADiIEMMCAAQsKAECMKHEixYoWL2KcuMABgI4eP4IMKXIkSZAMDEhIECBAAwMOBgCIeaBDhgQBbt7/nEBBAM+ePn8KiEABANGiRBdQEKB0KdOmSx1gCCA1QAIIHgYAyApgAAUICSRU8DAAANmyZs+iTat27dkCBQDAjXsAAYC6du/izau3AAIFfhEUACB4MOHBByxkaBBg8eIGEAh0eGDBwIQEAS5jbkBggYDOnj+DtqAAAGnSAgYAGEBggYDWrl/Dbh0BQoDatRNAIABCgIAIBCYkCBAgwQQDAgAgT658OfPmzp8rX8AAAPXq1q9jz25dgQMKBAxggCAeQwUCFBwoAKB+vXoEBCQkCCB/vvwGEu4nCKB//34IFgAuEDCQYMGCBgYAUKiQwgEAACwwEDCRYkWLEylMCLCR/2OABhMgQJjQIEBJkxcqPACwkmVLlwNgDgAwk2ZNmzdxAmDAAEBPnz+BBhUKoAADAxgmSGgQgGnTAA0kTMBAwEEBAFcBKDCgIUBXr1/BhvWawEAEAWfRpkW7wEABAG/fWjgAAIAFBgLw5tW7V8ACAxcCBBY8mDDhBBk6AFC8ePEBAREsECBgwAABAhgiCDgAgHNnz59Bdz5wAEBp0wIEAFC9mnVr1gUeEIBwIUBt27dxX4BAoMMBAAgMXAgwnHhx48eNXyDgQEBz584XOKBAwIAAANevFwAAoACBBQLAhxc/XoCDCgkCpFe/nn37BBA6AJAvfwADAxUgTLiQIED/AP8AE1yYAAGDAQYDAChcyLChw4cAGDAAQLGixYsVFxCYkCCAx48gQ4JMMIGAAwIaAqhcybKly5caCERYIKBmzQURDBiY0ECCBQBAgwJ18ECA0aNIkxq1MCGA06dQo0p1miDDAwAADjwgAOFCgK9gw4a9AIHAgwMA0qpdy7Zt2wUCAMidS7cugAIUMFwIwLev37+A+TbAYOBCgMOIEytezDjABQMWFgiY7MAABgkJAgRIgAEEgM8ACgBQYGCBgNOoU6sW4MBAgwCwY8ueTTt2ggoCHBiY0CCA79/Ag/9uMMGAAwDIkytfnpyBAADQo0ufTh06AgITEgTYzr279+/dE0z/ICAhgPnz6NOrXx8gAQQCFBx0ICAhgP37DTBYEDBggAGADwwwEFDQ4EGEAhYYkBDA4UOIESVGnEAAQoMAGTVu5NgxQAMIBhAAIFnS5EkADhYAYNnS5UuYABQY0BDA5k2cOXXuDHCBwIQAQYUGTXBhAoQKBJQqrQBhwoUEAaRODdAAAgEMDQJs5bo1gQQMBMRSWCDA7Fm0aQUssAAhwFu4CS5ooHuhQQC8efXiTbCBgIQAgQUPJlyYsAQDDAAsZtzYsQAFACRPRnAAwGXMmTEjMHAhwGfQoUWPJg36AgEJAVSrvgCBQIUMEzQ0aJCgQQMJEzIYIADhQgDgwDUQEBHA//hx5MkzEGAgwPlz6NEXYMiQIECACxMgVCBgoML3CgQIYNggIUEA9OkTQKjQIMB7+PHlz6ffAMMHAPn17+ffHwBABwsAECxokOABAxoCMGzo8CHEiA8vEJAQIIGECgQmNAjg8SPIjxcgEMAgIUEADQQkBGjp8iXMlhIIPFgg4CbOnDcdEICQIIEEDAQyTNDQIABSpAkaSICAgQCECwGmJoBQoUGArFq3cu3qNWsDDCAAkC1r9ixaBwIAsG3rli0GCQHm0q1r9y5evBIIaKhQQUKCAIIHEy4sOIGEChgkEJAQ4DHkyJIjN8BgIMICAZo3C1jgwAIBDQkmEKggIUGA1P+qV6++AIEABg0BJlRoEOA27ty6d/PO3QADAwDChxMXLuAAgOTKCwwA4Pw5dAAMMgSobv069uzatyeoQGBCggDix5Mvbz7BBAIZEgRo7/49/PYYGgRIIAEDAQsPIjiI8ACgBQIVJlTYUAHDhQALGTZ02DDBBAIZCDQIcBFjRo0bOW5sYAABAJEjSQKIoABASpUrWao8YKBBAJkzada0efNmgwoYLgTw+RNoUKFAL1TA0CBAUqVLmQYg0CBA1AANJEzIgCEDBAkXAiSAQEBEggBjyZY1ezZAAwwENARw+xZuXLlz50owAABvXr0AIigA8BfwAACDCRcGYEFCAMWLGTf/dvz48QUDExIEsHwZc2bNmhNAqNAgQGjRoRNckAABQwUDBAxUyDBBQ4MAs2nPblABQ4MAu3n39v3btwQCEwIUN34ceXLlyiF8APAcenTp0CMIAHAdO3YEFRIE8P4dfHjx48U3IDAhQHr169m3d58+wYYKDQLUr38BAgECGCZIuADwgkANEyBUIFCBQ4IADAM0MAAhQYCJFCtavIixgYENCQJ4/AgypMiRIRsYQAAgpcqVLFNGUAAgpkyZDyYEuIkzp86dPHc2MDAhgNChRIsaPVoUQoUGARJIqEBgQ4MAVKtapZpAAgYCEC4EaGBgQoCxZMuaPYuWbIMKEwK4fQs3/67cuXIneACAN2/eAgMA+P0r4ACAwYQHDyDQIIDixYwbO37cOAEGCAEqW76MObPmzAkyQLhQoYKEBAFKmz6NuvQFCAQ2VNgQILbs2bRr26bdwMCEALx7+/4NPPjvBAQKADiO/PgDBACaO38OHQADCAGqW7+OPbv27BwqJAgAPrz48eTLl29AgACHBAHau38PP34DDAQuBLiPP7/+/fz3XwBI4EIAggUNHkSY8CAEBgAcPnT4AAEAihUtXgRAQUIAjh09fgQZ8mMDAhcCnESZUuVKliwvVMDQIMBMmjVt3qSZYAKBCQkC/AQaVOhQokInVEgQQOlSpk2dPmV6wQAAqv9VqSIoAEDrVgYHAHwF+5VAgwBlzZ5Fm1YtWgwbAryFG1fuXLp0LxCYkCDAXr59/f7926AChAQBDB9GnFjxYsQJKkwIEFnyZMqVLVPGgADAZs6dPT9AAED0aAAFDARAnVr1atatV18gkCDAbNq1bd/GffsCAQkBfP8GHlz4cN8NKkBIEED5cubNnT9nfoFAgwDVrV/Hnl37dQgLAHwHH158BAQAzJ8HoABCAPbt3b+HH/89hAkB7N/Hn1//fv0XCACUEGAgwYIGDyIs2KAChAAOH0KMKHFiRAgQAmDMqHEjx44aOUQAIHIkgAgHAKBMqXIlgwkBXsKMKXMmzZgNCDT/CKBzJ8+ePn/2TFBhQoCiRo8iTao0aQMDEgJAjSp1KtWqUi8QSBBgK9euXr+C5XqhAoCyZgFQOABgLdu2bh1MCCB3Lt26du/SnZAhAN++fv8CDgx4A4YEAQ4jTqx4MWPGGgg0CCB5MuXKli9TxiAhAOfOnj+DDt05AQEApk8DcFAAAOvWCAYAiC0bwIcJAW7jzq17N+/cFSQECC58OPHixolfINAgAPPmzp9Djy49AIQMAa5jz659O/fsEioECC9+PPny5scbGABgPfv27SkcACB/PgAHEwLgz69/P//++QEmINAgQEGDBxEmVHgwQQUOASBGlDiRYkWLEBMYkBCA/2NHjx9BhuyYgECDACdRplS5kiVKAwUAxJQ5cyaFAwBw5gTgYEIAnz+BBhU69OcFAwGQJlW6lGnTpRIqJAgwlWpVq1exZqUqoUKCAF/BhhU7lizYChoCpFW7lm1bt2oNFAAwl+6CAQDw5j0wAEBfvwAWQAgwmHBhw4cRE+aQIUBjx48hR5YMuYKEAJcxZ9a8mXPnzAkqSAgwmnRp06dRk4YwIUBr169hx5bt2sAAALdxWygAgHdv378RYAgwnHhx48eRE4cwIUBz58+hR5f+/AKBBAGwZ9e+nXt379snZAgwnnx58+fRk5+QIUB79+/hx5ffPgEBAPfxA7BQAEB///8AAQgcOHAAgQQBEipcyLChw4QZOASYSLGixYsYK0KYEKCjx48gQ4ocGbIBgQYBUqpcybKly5QXKgSYSbOmzZs4Z16oAKCnTwADAAgdCsBCAQBIkyI1cCGA06dQo0qd6jSDhABYs2rdyrVr1gQEGgQYS7as2bNo06KFMCGA27dw48qd6/YCgQB48+rdy7cvXgkPAAgeTLiwhQIAEitO/GBCgMeQI0ueTPkxBgkBMmvezLmzZ80XCAQYTbq06dOoU6eekCGA69ewY8ue7boBgQC4c+vezbs3bggLAAgfDqAAgOPIATAYAKC58+YKMASYTr269evYp2eQEKC79+/gw4v/9y4hQ4Dz6NOrX8++ffsLFQLIn0+/vv378hsQCMC/v3+AAQQOJFiwIAYEABQuBGBgAACIESVOhGjgQgCMGTVu5NgxAIQJAUSOJFnS5MmRECYEYNnS5UuYMWXKTECgQQCcOXXu5NkzwAUDAYQOJVrU6NEAFwgAYNqUqYEBAKROpVpV6ggIAbRu5drV69cAEyAEIFvW7Fm0actikBDA7Vu4ceXOpVu3goYAefXu5dvXbwANFQIMJlzY8GHEASA4ANDYcWMBACRPBhBhAADMmTMPINAgwGfQoUWPJq2hQgDUqVWvZt06tYELAWTPpl3b9m3cuTNICNDb92/gwYUHmAAh/8Bx5MmVL2eegEABANGlT6cOwMAAANm1b3eQIcB38OHFjyefgECCAOnVr2ff3n16AxcCzKdf3/59/Pn1Z+AQwD/AAAIHEixosCCECQEWMmzo8CFEDh0AUKxo8SJFAwMAcOzo0QEBCQFGkixp8iTKChcCsGzp8iXMmCwJNAhg8ybOnDp38uyZYUKAoEKHEi1qNEAFDQGWMm3q9OnTBgYQAKhq1aoFAFq3cu269YABCQQaBChr9izatGkhQAjg9i3cuHLnujVwIQDevHr38u3r928GCQEGEy5s+DDiBgQSBGjs+DHkyJEhRABg+TJmAgA2c+7smTMFCQEgZEgQ4DTq1P+qV6u+QCBBgNiyZ9OubTtAhQsBdvPu7fs38ODCMUgIYPw48uTKl0/AEOA59OjSp0+XQGAAgOzat1sA4P07AAUAxpMnf8BAggAJKkBIEOA9/Pjy58vHICEA/vz69/PvHwBgBg4BCBY0eBBhQoULCVwI8BBiRIkTJyaoICFARo0bOXbk2MBAhQUASJY0ebIkAQArWbIEMSFAzAYVICQIcBNnTp07c0qoEABoUKFDiRYNMAFCAKVLmTZ1+hQq1AYEEgSwehVrVq1aNRhIEABsWLFjyYpNUAGCBgMA2LZ1+7YtAQBz6c4dQKBBAL0BGlSAkCBAYMGDCRcWnMCAhACLGTf/dvwYsoQKAShXtnwZc2bNmiVgCPAZdGjRo0lnmBAAdWrVq1mrToAhQ4IAGBAAsH379gIAu3kDUAAAeHDgAiAEMH68AYYKGgI0d/4cenTnEgg0CHAde3bt27c3IJAgQHjx48mXN3/e/AQIAdi3d/8ePnwNBBoEsH8ff3799xtUyAAwQYAAEx4AOIgQIQEADBs6fAggwoQAFCsGSCCBAIQEATp6/AgyZIIJBDIEOIkypcqVLCtICAAzpsyZNGvarFlBQoCdPHv6/OmzgQEJAYoaPYo0qVEJBiAkCAA1AYEBAKparUoAgNatXLsCsKAhgNixYxtgMDChQYC1bNu6XZtg/4KBChcMSAiAN6/evXz5TsAQILDgwYQLGz5cWIOBBAEaO34MOTJkCAYaBLiMObPmzQEaQCAgIYDo0RkUADiN+nQBAKxbAyAAILbs2AQSBLiNO3cCCRkIQLiQIIDw4cSFX4BAoIKBBAE0ELgQILr06dSrU29AoEGA7dy7e/8OPrx3CBMCmD+PPr369BIIYCAwIUGA+fTr26efgIMBCA0C+AcYQGCACQ4AHESYUCEAAgAcPgRwAEMAihUtWmywgQABDBskXGgQ8oKECRgIEIBwAQOHAC0lELgQQOZMmjVt1oQAIcBOnj19/gQatGcDAg0CHEWaVOnSpBoISEhAoAIBCP8XAlzFmjXrBQgEMEgIEFasWAkUAJxFe7YAALZtAUQAEFcuAAUQAtzFm1dvgAQNJEDAYIDAYAMYIEi4kCDABQIJAjwOIIGAhgCVLV/GnPnyBQINAnwGHVr0aNKlQUPIEED1atatXbO+QEBCgAATIFyAQKACBA4XEgQAHiDBBQ4QKhCAcCHAcubNAzQgAED6dAADDADAnl37dgUQAnwHH178ePLhIUAIkF69BAITEgSAH1/+fPrxIWBIEED/fv79/QMMIHAgwYIBNBBoEGAhw4YOHzKUQEBCgIoNCDQIkEDChAwGCBAwYIAAAQMZJkhIEGAly5YtDQwAIHPmAAMAbuL/zKlTAIQAPn8CDSp0KNAKEgIgTRrgQgUMFwJAjSp1KlWoGghICKB1K9euXr+CTVBBQoCyZs+iTVs2AQQCEgLAhZthQoC6dhs0uHChQYMAfv8CDgzYQAEAhg8PWABgMeMBDgBAjgxAAYQAli9jzqx58+UEBBoECC06dIIJBCY0CKB6NevWrBtsILCBQIMAtm/jzq17924IBhoECC58OPHiATRUyNAgAPPmEyAEiC59OvXq1qVXKABgO/fu3QcYACB+PAAFGQKgT69+Pfv26S8YCCB/Pv0AFzAQgHAhAP/+/gEGEChQAwQCBhoE2FChQQCHDyFGlDgxogQCGAhMaBCA/2NHjx87XoBAQEKCACdRBtBQIUBLly9hxpTp0kABADdx5sw5wAAAnz8BFKgQgGhRo0eRJi3KIUMAp0+hPm2wgUCFDRIaJAiwdWuCCxIgVDAwoYKEAAESQMCQIEBbt2/hxpXrVgKBCwEyVCAA4UIAv38B/00goQKBDQ0CJFasuAGBBAEgR5Y8mXJlyAYGANC8uUAEAJ9BhxYNgECDAKdRp1a9mvXpDRMCxJY9m3YCCRswECBQAUPvCgQIYJggIYEGAwkCJE+QoUKDAM+hR5c+nXoACQQ0BAhwgcCFDQQMZJigoUGCAAkSXJAAAQMBDBISBJA/n778ChcC5Ne/n39///8AAyQgAKCgQQAFLABYyLChQwAWNASYSLGixYsYJ0KYEKCjx48gPyZooEGCSQ0NAqhUmWFCgJcvE0CocCGAzZs4c+rMmQACgQsBggbAICFAggsTIFQgwLQphgkSGgSYSrWqVQwSAmjdyrWr168BNFgAQLYsgAIPAKhdCwABgLdw3zqYEKCu3bt48+qtC2FCgL+AAwseTBhwAgINAihenEACgQ0JAkieTLmyZckaKmBoEKBz5wkZAogeHSCB6QCoU6tezRqDhACwY8ueTbt2gAkgAOjezbt3AQsAggsPrgBDgOPIkytfzvw4hAkBokufTr26dekNCCQIwL079wYYKmj/SBCgvPnz6M83gEBAQoIA8ONfqBCgvv37+PPrt59BQgCAAQQOJFjQ4EEIAgAsZNjQ4QELACROnGjgQgCMGTVu5NgxAIQJAUSOJFnS5MmREjAEYNnSZQIJBipMaBDA5k2cOS9AIJChQQCgQYEmINAgwFGkSZUuZXoUg4QAUaVOpVrVaoIKBwBs5QpggAIAYcUOQADA7NmzDCAEYNvW7Vu4cQNMgBDA7l28efXuvbthQwDAgQUDTiABAwEIHC4kCNDYcYAGEiZUIDChQQDMmTUHqKAhwGfQoUWPJv25goQAqVWvZt3atQQLAGTPln2AAgDcuXXvxl2AQIIAwYUPJ17c/7iGCgGUL2fe3Pnz5RgkBKBe3fr1BhMyGCBQIcP3DBgMEKgAQUKCAOnVr1cPYUIA+PHlz6dfP0ACAg0C7Off3z/AAAIHEhwIQQCAhAoTIqAA4CHEiBIhepgQ4CLGjBo3ckxAIEGAkCJHkixpMiQGCQFWsmzpsmUDCRIwZOAg4UKCADp38uypE8KEAEKHEi1q9GiACwQCMG3q9CnUqA0IAKhq9WoBAFq3HngA4CvYsAUIXAhg9izatGrXVrgQ4C3cuHLn0n1bQUOAvHr38u0bIEGCAIIHEy5seMOEAIoXM27s+HEACRgCUK5s+TLmzBscAOjs+TPozggoACht+jSABf8YEgRo7fo17NYJEgSobbs2hAkBdvPu7fs38N0VNAQobvw48uTKlzOHMCEA9OjSp1OvHgDChADat3Pv7t37BQMDAJAvb34AgPTqDzAA4P49fPcUJgSob/9+ggscIFQg4B8gAYEYJkhoECCAhgoJAjR0+BBiRIkBMEgIcBFjRo0bA1y4EABkSJEjSUKYEABlSpUrWbZMUEFDAJkzada0WTNBBQEAePb0ieABAKFDiRYtWoCAhgBLmQZoMIGAgQwTJDRIcDXBBQkQMBCoICFBBQkByJY1exZt2gAZOARw+xZuXLkBIEwIcBdvXr17M0wI8BdwYMGDCWuokCBAgAYSJmT/wFChAgYMGyQ0SBAAc2bNASZ4APAZdGgACiIAMH0adWrVCAhcCPA6gIYMBCBcCHAbd27cCSRgIIAhQwDhw4kXN348wAQIAZg3d/4ceoAMEwJUt34de3YDFwJ09/4dfHjxGSZcgGCAQAUIHCRo0CBBwgYMBAhk0JAgQH79ATQYGAAQgMCBBAEcEAAgocIDAgA4fAgRogIDGgI0gEBgQoMAHDt6/NjxAgQCFwKYPIkypcqVEioEeAkzpsyZATRcCIAzp86dOxsQSBAgqNChRIsWbUAAAwEIFxIEeAo1aoIGEwxUmNAggNYAFwggAAA2rNixYxU8AIA2rdq1CAhAIACh/0GAuXTr2r0bAEKFBAH6+v0LOHDgBgQSBDiMOLHixYwbM9ZQIYDkyZQrW74MgQCHBAE6e/4MOkACCRgISAgQQIMBBQBau34NO7aCCABq276NG0AEAhIC+P4NPLjw3wkqTAiAPLny5cybG7gQILr06dSrW79ufQKEANy7e/8OHrwGAg0CmD+PPr16DQYySCCAAID8+fTpI1gAIL/+/fz75wcYAUODAAUNHkSYEOEFAg0CPIQYMUCCCxwgYKiQEQOECRoSBAAJAUIAkiVNnkSZQUIAli1dvnSZoIKEADVrJmhwQWeDBAF8/gQKtIEBCQGMHkWaVKnRBBAILAAQVepUqv8CHADAmlXrVq4AImBIEEDsWLJlzZ6dUCFBALZtAySQgIGAgQwTJGjAK2EChAoEKkxocIFAggCFDR9GjBiDhACNHT+G/FhDhQQJNEzIUIEAAQMVDBAggAGChAYBTJ9GDSFDANatXb+G/VqDAQEAbN/GjVuBAwC9fQ8oAED4cOLDP2BIEED5cubNnT8PkCADhgQBrAdosIFABQkNAnwHHz5AAg0ZCECoMCHAevbt3bvfoCHAfPr17dfPMGGCgQoQJgC8kCAAQYINJEzAQACDhAQBHkKcQKBBgIoWL2LMmPGCAQEAPoIMKXKkgAgATqJMeVJBhQYBXsKMKXMmTZgJMGD/SBAgwQQCEC4ECCp0KNGgDSYQqJAgANOmTp9CjSq1aQMCBDJoSBBgK9euXBtMqGBgQoIAZiUQuBBgLdu2bt/CDXDBgAIAdu/izZtXgAMAfv8CBjDAwIUAhg8jTqx4ceIEGTBIqIChQYDKli9jztyAwIQAnj+DDi16NOnPGDA0CKB6NevWqxNowFDhQoAJBC4EyK17N+/evnVfIFAAAPHixgEUOABgOfMCCABAjy4dwIMJAa5jz659O3fuCSoQmJAgAPny5s+jJ3+BQIMA7t/Dj/9+woUA9u/jz29fgoEEAQAGEDiQYMGCCSYQqEDgQgCHDyFGlDgxYggLADBm1Ahg/wEDAB9BhhQJUkGFBAFQplS5kmVLlgkgVGgQgGZNmzdx3pyAIUEAnz+B+kxwYcIGCAQyTJBwIUEAp0+hOm1AQEMAq1exZtV6tUGFCg0ChBU7lmxZs2QTYFgAgG1btwsYAJA7l27duRYkBNC7l29fv3/9JsiAoUEAw4cRJ1asOEGFCQEgR458AUIFAgYybJhgAMKGDAYIVIBwIUBp06UTYIAQgHVr169hv04AoUKDALdx59a9m7fuBgQKABA+fHiBAgCQJxfAAEBz584PVEgQgHp169exZ7+eAEKFBAHAhxc/nnz5ABcISAiwfn0CCRUIbNDQIED9AA0SBNDfQAIEAv8AMUhIEKBggAQQKiQIwLChw4cQI04w0CCAxYsYM2rcmHHCAwAgQ4ocCXIBAwAoU6aMMCGAy5cwY8qcKXNChQYBcurcybOnT50XCEgIECDBBAIYJCQIwLSp06cJOFQgICFAgAQQKjQIwLWr169gw3KFUCFBgLNo06pdyzZtAwIDAMidS7cuAAUCAOjdq3cAgQYBAgseTLiwYcINCFwIwLix48eQIz/WQGBCAwwVLgTYzLmz588aDGRoAKFCgwCoU6tezbp16gQZIASYTbu27du4bUNgAKC3794MFgAYTry4cQACIARYzry58+fQnSfAMCGA9evYs2vfvv0CAQITEgT/GE++vPnz4xtAIFChQYD38OPLn09ffgMCGgLo38+/v3+AAQQOJBjgggEACRUmZLAAwEOIESUC+DAhwEWMGTVu5KhxQoUEAUSOJFnS5EmTCSBUuBDA5UuYMWXK1EBgQgCcOXXu5NmTpwQDCQIMJVrU6FGkRTEgANDUKQAFCABMpYoAAQCsWbFa0BDA61ewYcWOBZuAwIUAadWuZdvWbdsEECo0CFDX7l28efUGaGBgQgDAgQUPJlyYcAYIARQvZtzY8WPGEBYAoFzZ8mUGCwBs5ryZQIIAoUWPJl3a9GgJGAKsZt3a9WvYrxNAqNAgwG3cuXXv5o27gYEJAYQPJ17c//jx4g0INAjQ3Plz6NGlO5fwAMB17Nm1M1gAwPt3AAcqBCBf3vx59OnPV5AQwP17+PHlz5c/oUKDAPn17+ff3z/AAAIFNiAgIQDChAoXMmy4MMOEABInUqxo8eLECwYAcOwIQAACACJHDgBg8qRJBBgCsGzp8iXMmC4vEEgQ4CbOnDp38tR5gcCFAEKHEi1q9OhRDQQaBGjq9CnUqFKfSqiQIADWrFq3cu2KNQGBAQDGknUgAADatGrXIsAQ4C3cuHLn0o0LAUKAvHr38u3rl2+CChMCEC5s+DDixIoDQMgQ4DHkyJInU46cwICGAJo3c+7s+fNmDAcAkC7tQACA1P+qCwwA4Po1AAQYAtCubfs27ty2MUgI4Ps38ODChwefUCFBgOTKlzNv7vx5gAQGJASobv069uzar2+AEOA7+PDix5MHj+EAgPTq1693IAAA/PgAEGAIYP8+/vz6999PQABggwADCRY0eBBhwQYELgRw+BBiRIkTKT7UQCBBAI0bOXb0+HGjBAwBSJY0eRJlypIYEABw+RImTAcCANS0CeAAhgA7efb0+RMozwsEAhQ1ehRpUqVIJ2QI8BRqVKlTqVaVWkFCAK1buXb1+nVrAwIJApQ1exZtWrVlMRwA8BbugQIA6NZFcABAXr15CSQI8BdwYMGDCf+VgCFAYsWLGTf/drw4gQENAShXtnwZc2bNlyVgCPAZdGjRo0mHJnAhQGrVq1m3dp26QgEAs2lHUAAAd27duwFguBAAeHDhw4kXBz4BQgDly5k3d/6cuYQKCQJUt34de3bt27EnIHAhQHjx48mXNy8eg4QA69m3d/8efoAEBADUtw8gggIA+/n39w8QQIQJAQoaPIgwocKCEyAEeAgxosSJFCNmmBAgo8aNHDt6/OgRAoQAJEuaPIkyZckMHAK4fAkzpsyZAS5YAIAzJ4ADAwD4/MkAAYChRIcugBAgqdKlTJs6TbphQ4CpVKtavYq1KoELAbp6/Qo2rNixYiVUCIA2rdq1bNumhTAh/4DcuXTr2r0bYEIEAHz7+v0bQQGAwYQHFzCQIIDixYwbO34cYAKEAJQrW76MOXPlBgQSBPgMOrTo0aRLk25AIEGA1axbu34NezWECQFq276NO7fuABAEAPgNPLhwBggAGD9+nIKEAMybO38OPXqACRACWL+OPbv27dclYAgAPrz48eTLmz9v4EKA9ezbu38Pf30GDgHq27+PP7/+BAYOAAAIQODABwgAHESYUOFBBRgCPIQYUeJEigEkYAiQUeNGjh09apwAIcBIkiVNnkSZUmUGDgFcvoQZU+ZMlxgkBMCZU+dOnj0lWAAQVGjQBwgAHEWaVClSAxcCPIUaVepUqv8NCCQIkFXrVq5dvWbNMCHAWLJlzZ5Fm1bthA0B3L6FG1fu3AAJCDQIkFfvXr59/WIQAEDwYMELCgBAnFhBAQCNHT9egCFBAMqVLV/GnJnAhQCdPX8GHVp05wwSApxGnVr1atatXU+AEED2bNq1bd8O0IBAggC9ff8GHjz4BQMAjB9Hntz4AwQAnD+HDsCChADVrV/Hnl07BgkBvH8HH178eO8YJARAn179evbt3b+fACHAfPr17d/HH0AChgD9/QMMIHAgwYIGA0BgAGAhw4YOFz5AAGAixYoADhhoEGAjx44eP36ckCEAyZImT6JMSTKDhAAuX8KMKXMmzZoTIAT/yKlzJ8+ePgNs2BBgKNGiRo8elWAAANOmThkUACB16gAAVq9ivcoAQ4IAXr+CDSs2bAMCDQKgTat2Ldu2ATJwCCB3Lt26du/izTsBQoC+fv8CDiw4gQENAQ4jTqx4seIGBhAAiCx5MoUDAC5jzqx5MwUICQKADi16NOnRGSYESK16NevWrgNA2BBgNu3atm/jzq0bwoQAvn8DDy58uIQKAY4jT658+XIIIABAjy4dAIUDAK5jLwBgO/fu3QdYmJAgAPny5s+jN6/BQIIA7t/Djy9/vgQMAe7jz69/P//+/gFWuBCAYEGDBxEmxDAhQEOHDyFGhBjCAACLFzFmzEjh/wAAjx9BghxgAUKCACdRplS5EmWCChICxJQ5k2ZNmw0IBNC5k2dPnz+BAk1AIEEAo0eRJlWq9AKBBAGgRpU6lapUCQYKANC6lWvXrhQOABA7lmzZARQwNAiwlm1bt2/ZaiDQIEBdu3fx5s2bgECDAH8BBxY8mHBhwhoqBFC8mHFjx44TYNgQgHJly5cxW+ZgoAAAz59Bf0YAgHRpAAIGAFC9mnVr1QwIcEgQgHZt27dxB2hQAUOCAL+BBxc+fDgGCQGQJ1e+nHlz580nQAgwnXp169evS6iQIEB379/Bh++eYIOBAgDQp1ev3kIBAO/hx5c/X34BCxguBNC/n39//v8AE0gw8MGChAAIEypcyJChhAoBIkqcSLGixYsVExjQEKCjx48gQ4JsQOBCgJMoU6pcefJChQcDAMicSbOmhQIAcurcybOnzwUGMHBIEKCo0aNHG0wwQAEBgAMGLgSYSrWq1atWExC4EKCr169gw4odC1ZChQBo06pdy3ZtAgwGMjQIQLeu3bt3G0wwoACA37+AA/tVAKCwYQAPCgBYzLix48cAEHggAGGChgQBMmtuIGECBgIOCgAYDUCBgQsBUqtezbo16w0QAsieTbu27du4a2OYEKC379/Ag/9OAIECAAYEIEhIEKC58+fQL0AgEGEAgOvYs2vfrt1CAQDgw4v/H08+fAEBHywQqIChPQYDBDowUACgvv36CgxcCMC/v3+AAQQOJFiwAYEGARQuZNjQ4UOICy8QSBDA4kWMGTVeTADBAgCQAARgqDBBQoMAKVWmTKBhAgYDDAYAoFnT5k2cOSkMANDT50+gQYUCKHAAwYEDBQAsZdqUqQIDEhIEoFrV6lWsVSFkCNDV61ewYcWO7ZqgwoQAadWuZdtWbQIIFAYAoFsXgQMKBCpA4LsBAgQMBCxEUADA8GHEiRUbNjAAwGPIkSVPplzZ8uXHByxkaBDA82fQoUV7bkBAQgDUqVWvZt3adYAJGBIEoF3b9m3ctC9g8ADA92/gvgsoWFB8/4GAAwCUL2fe3LlzAwMATKde3fp17Nm1b6/OwACHBAHEjydfvryGChQINAjQ3v17+PHly79AQEIA/Pn17+cfIAHACQQEACho8CDChAoXMjQYAQDEiAAEAKho8SLGjBo3ctx4gIKBCQ0CkCxp8mQCDhgMKADAAEOCADJn0qxp82bNBhUiGIBwIQDQoEKHBk0gAQOFAgCWMm3q9CnUqFKnLjUwAADWrFq3cu3q9evXAxEIQJBwIUGAtGrTNpAAgUAHBQDmAngAIUGAvHr38u3rV28CDA4ADHBAAIOEBAEWM27MuMEEAxQUAKhs+TLmzJo3c+58mcAAAKJHky5t+jTq1P+qAQwQ8MAAAQwQIGyAAAGDAQIdGBQA4Ps3gA4QEgQobvw48uTKAzTAEAEAdOgCLBiAMEFCgwDaAyS4wAECBgIgDgAob/48+vTq17Nf7wAA/PgADgCob/8+/vz69/Pvnx/gAAQCFhQUoGAAAIULGSp8gKFBAIkTKVa0aPEChg8AOHbkWECBAwoESBogQMDAgwUIALR0+RJmTJkzadYEQABATp07efb0+RNoUKFDiTIwICFAUqVLmTZVmiAEgQUAqFa1enVAVgBbuXb1+hVsWLFjvxIAcBYtgAEA2LZ1+xZuXLlz6da1C/cABggNAvT1+xdw4AsYKBQAcBhxYsWLGTf/dvwYcuTDAwBUtgyAAADNmzl39vwZdGjRo0mDZkAAwoUAq1m3dr1aAwQCCwDUtn0bd27du3n39v37NwEAw4kXN34ceXLly5k3Vz6AgQEMEhoEsH4de4IGEzAYWDAAQHjx48mXN38efXr168kjAPAePgAGAOjXt38ff379+/n39w8QgMCBBAUq8EDAQIYJEho2nICBgIEHCABYvIgxo8aNHDt6/AiSIwEAJEuaPIkypcqVLFu6fMmygAIGDyxUeBCBgYIBAHr6/Ak0qNChRIsaPVp0AAEATJs6fQo1qtSpVKtavVpVQAEAAiIA+Ao2rNixZMuaPYs2rVqwAgC4fQvg/wGAuXTr2r2LN6/evXz7+qVLAQGAAQUAGD6MOLHixYwbO34MObLjAQQAWL6MObPmzZw7e/4MOvRlCgcAmD6NOrXq1axbu34NO3bsARYA2L6NO7fu3bx7+/4NPPjtAQCKDwCAPLny5cybO38OPbr06cgHWACAPbv27dy7e/8OPrz48eQFOACAPr369ezbu38PP778+egLWACAP7/+/fz7+wcIQOBAggUNHkSYUOHCAgAALGAAQOJEihUtXsSYUeNGjh0lDogAQORIAAoAnESZUuVKli1dvoQZUyZKCwUAIEAAQOdOnj19/gQaVOhQokWFFjAAQOlSpk2dPoUaVepUqv9Vl1ooAEDrVq5dvX4FG1bsWLJlyxawAEDtWrZt3b6FG1fuXLp11woYAEDvXr59/f4FHFjwYMKF+w5YAEDxYgAHADyGHFnyZMqVLV/GnFnzZAECAHwGHVr0aNKlTZ9GnVr15wMUALyGHVv2bNq1bd/GnVv3bgYLAPwGHlz4cOLFjR9Hnlz57wMUADyHHl36dOrVrV/Hnl07dgoDACxYAED8ePLlzZ9Hn179evbtxw8AEF/+AQsA7N/Hn1//fv79/QMEIHAgwYIGDyIcaGAAgIYOH0KMKHEixYoWL2LEeIACgI4eP4IMKXIkyZImT6L0+GAAgJYuX8KMKXMmzZo2b+L/jFkAAM+eBRYACCp0KNGiRo8iTap0KdOiBw4AiCp1KtWqVq9izap1K9eoCB4ACCt2LNmyZs+iTat2Ldu2DgQAiCt3Lt26du/izat3L9+4CB4ACCx4MOHChg8jTqx4MWPFCwAAcCAAAOXKli9jzqx5M+fOnj9TLqAAAOnSBxgASK16NevWrl/Dji17Nm3VBAAAKDAAAO/evn8DDy58OPHixo8TR/AAAPPmzp9Djy59OvXq1q83JwBgO/fu3r+DDy9+PPny5s8jeABgPfv27t/Djy9/Pv369tkXAKB/P//+/gECEDiQYEGDBxEmVLhQIQIHACBGlDiRYkWLFzFm1LiR/2MEBQBAhhQ5kmRJkydRplS5EqSCCABgxpQ5k2ZNmzdx5tS5M2cBAAAiKAAwlGhRo0eRJlW6lGlTp0MRMAAwleoABACwZtW6lWtXr1/BhhU7NisBAAAEHACwlm1bt2/hxpU7l25du3MVRACwl29fv38BBxY8mHBhw3wJAFC8mHFjx48hR5Y8mXJlywoiANC8mXNnz59BhxY9mnTpzQsApFa9mnVr169hx5Y9m3brAwoA5NY94AAA37+BBxc+nHhx48eRJxfO4AAA58+hR5c+nXp169exZ3cuwAEA79/Bhxc/nnx58+fRp1f/AAEA9+/hx5c/n359+/fx53cvwAEA//8AAQgcSLCgwYMIEypcyLDhwQEPAACIgACAxYsYM2rcyLGjx48gQ1ocMACAyZMCHABYybKly5cwY8qcSbOmzZUDDADYybOnz59AgwodSrSo0aMCHABYyrSp06dQo0qdSrWq1aUDKADYyrWr169gw4odS7as2a8DBgBYyxaBAABw48qdS7eu3bt48+rdSxfBAACAAwseTLiw4cOIEyteDHgBAwCQI0ueTLmy5cuYM2vezJnCAQCgQ4seTbq06dOoU6teDXoBAwCwY8ueTbu27du4c+vejXuAAAAAKBwAQLy48ePIkytfzry58+fEDyAAQL26AgUAsmvfzr279+/gw4v/H08+ewELAAAcGACgvfv38OPLn0+/vv37+OsvYACgv3+AAAQOJFjQ4EGECRUuZJiwgAUAESVOpFjR4kWMGTVu5NhxAQMAIUWOJFnS5EmUKVWuZCmyAACYMWXOpFnT5k2cOXXupLlAAACgQYUOJVrU6FGkSZUuZWqhAACoUaVOpVrV6lWsWbVuhcpgAQCwYcWOJVvW7Fm0adWuTTsAAAALBQDMpVvX7l28efXu5dvX79wFAgAMJlygAADEiRUvZtzY8WPIkSVPRnyAAgAADAYA4NzZ82fQoUWPJl3a9GnSDBYAYN3a9WvYsWXPpl3b9m3WBygA4N3b92/gwYUPJ17c//hx5AwWAGDe3Plz6NGlT6de3fp15gUEAODe3ft38OHFjydf3vx58AgOAGDf/kABAPHlz6df3/59/Pn17+df3wHAAQAGEixo8CDChAoXMmzocKADAQAmUqxo8SLGjBo3cuzo8aOBAQBGkixp8iTKlCpXsmzpcqQDAQBm0qxp8ybOnDp38uzpc+cBBgAAGBgA4CjSpEqXMm3q9CnUqFKPFhgA4CpWBwIAcO3q9SvYsGLHki1r9ixXBA8AsG3r9i3cuHLn0q1r9y5eBwIA8O3r9y/gwIIHEy5s+DBfBBEAMG7s+DHkyJInU65s+TLkAQA2cwYgAAGA0KJHky5t+jTq1P+qV7MurQAA7NiyZ9Oubfs27ty6d8eOoAAA8ODChxMvbvw48uTKlzMnAOA59OjSp1Ovbv069uzaoUdQAOA7+PDix5Mvb/48+vTqzxdAAAAAAQDy59Ovb/8+/vz69/PvPx+ggAIACBYUcABAQoULGTZ0+BBiRIkTKSZUEAEAAAUAOHb0+BFkSJEjSZY0ebJkBAUAWLZ0+RJmTJkzada0eZOlgAgAePb0+RNoUKFDiRY1ehTpAwUAmDZ1+hRqVKlTqVa1epXpgAIAuHb1+hVsWLFjyZY1exasgwMA2LZ1+xZuXLlz6da1excvAQB7+fb1+xdwYMGDCRc2zPcBAgCLGTf/dvwYcmTJkylXtnyZAADNmzl39vwZdGjRo0mX3hzhAADVqw8MAPAadmzZs2nXtn0bd27drwU4AAAgAgDhw4kXN34ceXLly5k3X/4AAQDp06lXt34de3bt27l3l77AAQDx48mXN38efXr169m3d08BAQD58+nXt38ff379+/n3lw8QgQIABAsaPIgwocKFDBs6fIhQwQAAFCsiGAAgo8aNHDt6/AgypMiRJDkOcAAgpcqVLFu6fAkzpsyZNFVSOAAgp86dPHv6/Ak0qNChRIkOMAAgqdKlTJs6fQo1qtSpVJVaKAAgq9atXLt6/Qo2rNixZMMqUACggAEAbNu6fQs3/67cuXTr2r3b9gCAvXwBPDgAILDgwYQLGz6MOLHixYwDM2AAILLkyZQrW76MObPmzZw7WzgAILTo0aRLmz6NOrXq1axDL1gAILbs2bRr276NO7fu3bxrDwAAPDgABwUAGD+OPLny5cybO38OPbryAwCqW7+OPbv27dy7e/8O3rqFAgDKmz+PPr369ezbu38PH34BCwDq27+PP7/+/fz7+wcIQOBAggUNHjQwAMBChg0dPoQYUeJEihUtTjxQAMABCwA8fgQZUuRIkiVNnkSZ8iODAQBcvmQwAMBMmjVt3sSZU+dOnj19znQgAMAABQCMHkWaVOlSpk2dPoUa9amBAv8ArF7FmlXrVq5dvX4FG9aqAwEAzJ5Fm1btWrZt3b6FG1eugQEA7N7Fm1fvXr59/f4FHNhugQIADB9GnFjxYsaNHT+GHFlxBACVLQMYAEDzZs6dPX8GHVr0aNKlPR94AED1atatXb+GHVv2bNq1VxMAkFv3bt69ff8GHlz4cOLFETwAkFz5cubNnT+HHl36dOrKDQDAnh3AAQDdvX8HH178ePLlzZ9H7z2CAgAHGACAH1/+fPr17d/Hn1///vwEAAAEIHAgwYIGDyJMqHAhw4YCIygAIHEixYoWL2LMqHEjx44eCQAIKXIkyZImT6JMqXIlS5ECDgCIKXMmzZo2b+L/zKlzJ8+aAgAADQpAAICiRo8iTap0KdOmTp9CTVpAAICqVq9izap1K9euXr+CtUoAANmyZs+iTat2Ldu2bt/CVRABAN26du/izat3L9++fv/WJQBgMOHChg8jTqx4MePGjhkvOABAQQQAli9jzqx5M+fOnj+DDn0ZAYDSpgEYAKB6NevWrl/Dji17Nu3aqx8gAABgAIDevn8DDy58OPHixo8jN04AAPPmzp9Djy59OvXq1q83f4AAAPfu3r+DDy9+PPny5s+jpwBgPfv27t/Djy9/Pv369t8PKABgP//+/gECEDiQYEGDBxEmVLiQ4QADACBGlDiRYkWLFzFm1LiR/6MABwBAhhQ5kmRJkydRplS5EuQAAwBgxpQ5k2ZNmzdx5tS5MyeCAQAEOAAwlGhRo0eRJlW6lGlTp0MHOAAwlSqABQCwZtW6lWtXr1/BhhU7NiuFAwAKIACwlm1bt2/hxpU7l25du3MHGACwl29fv38BBxY8mHBhw3wpHACwmHFjx48hR5Y8mXJly5YHGACwmXNnz59BhxY9mnRp05wRDACwmnVr169hx5Y9m3Zt264HOACwmzeAAgCABxc+nHhx48eRJ1e+nLiABQCgR5c+nXp169exZ9e+HXoBCwDAhxc/nnx58+fRp1e/nv0CBgDgx5c/n359+/fx59e/H34BC/8AAQgcCKAAgIMIEypcyLChw4cQI0pEaKEAAAUCAGjcyLGjx48gQ4ocSbKkyAIWAKhcybKly5cwY8qcSbPmSgsFAOjcybOnz59AgwodSrRo0QIWAChdyrSp06dQo0qdSrXqUgcDAGjdyrWr169gw4odS7Zs1wEIAKhdO0AAgLdw48qdS7eu3bt48+qdiwABgL+AAwseTLiw4cOIEyv+e4ACgMeQI0ueTLmy5cuYM2vezGABgM+gQ4seTbq06dOoU6v+fIACgNewY8ueTbu27du4c+vG7WAAAAYLAAgfTry48ePIkytfzry58AEIAEifXiACgOvYs2vfzr279+/gw4v/x25gAIABAwCoX8++vfv38OPLn0+/vvwDFADo38+/v3+AAAQOJFjQ4EGECRUuPGhgAACIESVOpFjR4kWMGTVu3HggAgCQIUWOJFnS5EmUKVWuJDlgAACYMWXOpFnT5k2cOXXuhIngAQCgQYUOJVrU6FGkSZUuZepAAACoUaVOpVrV6lWsWbVuhYrgAQCwYcWOJVvW7Fm0adWuTYsAAAAHAgDMpVvX7l28efXu5dvX79wDCwAMJlxAAQDEiRUvZtzY8WPIkSVPTkwAAAAEBwBs5tzZ82fQoUWPJl3a9GgEDwCsZt3a9WvYsWXPpl3bNmsCAHTv5t3b92/gwYUPJ17c/ziCBwCUL2fe3Plz6NGlT6defbkAANm1b+fe3ft38OHFjyff/YAAAOnVAygAwP17+PHlz6df3/59/PnlM0AAwD9AAAIHEixo8CDChAoXMmSoIAKAiBInUqxo8SLGjBo3cuwYQQGAkCJHkixp8iTKlCpXsgypIAKAmDIBDABg8ybOnDp38uzp8yfQoDcNAADgAAGApEqXMm3q9CnUqFKnUo2qIAKArFq3cu3q9SvYsGLHktVKAADatGrXsm3r9i3cuHLn0lUQAQDevHr38u3r9y/gwIIH530A4DDixIoXM27s+DHkyJIXDygA4DLmAwoAcO7s+TPo0KJHky5t+jRoBf8FALBu7fo17NiyZ9Oubfs2awEOAPDu7fs38ODChxMvbvw48gcIADBv7vw59OjSp1Ovbv06cwEOAHDv7v07+PDix5Mvb/48+QEMAAB4gAAA/Pjy59Ovb/8+/vz698MvcAAgAIEDFSwAcBBhQoULGTZ0+BBiRIkHBxgAAKAAAI0bOXb0+BFkSJEjSZYcKcABAJUrWbZ0+RJmTJkzadZUWcAAAJ07efb0+RNoUKFDiRY1KoABAKVLmTZ1+hRqVKlTqVZdOgAAgAEAuHb1+hVsWLFjyZY1e7brggUA2LZ1+xZuXLlz6da1excvhQMA+Pb1+xdwYMGDCRc2fJjvAgYAGDf/dvwYcmTJkylXtny58gEAACgcAPAZdGjRo0mXNn0adWrVnxUIAPAa9oEDAGjXtn0bd27du3n39v2bdgELAAAIGAAAeXLly5k3d/4cenTp06EvYAAAe3bt27l39/4dfHjx47EfsAAAfXr169m3d/8efnz58+kzYAAAf379+/n39w8QgMCBBAsaPIgwoUKBAxQAeAgxosSJFCtavIgxo8aJChAA+AhywAAAJEuaPIkypcqVLFu6fIkywgAANGvavIkzp86dPHv6/EmTwQIARIsaPYo0qdKlTJs6fQrVQgEAVKtavYo1q9atXLt6/UrVgQAAZMuaPYs2rdq1bNu6fcv2/0AEAAAoDACAN6/evXz7+v0LOLDgwYAZLACAOLHixYwbO34MObLkyYgRUACAObPmzZw7e/4MOrTo0aQdLACAOrXq1axbu34NO7bs2agPOACAO7fu3bx7+/4NPLjw4bwLDACAPLkCBACaO38OPbr06dSrW7+OPboAANy7e/8OPrz48eTLmz/f3YEAAOzbu38PP778+fTr27+P38AAAPz7+wcIQOBAggUNHkSYUOFChgUjKAAQUeJEihUtXsSYUeNGjhkLKAAAgAAAkiVNnkSZUuVKli1dviyJoAAAmjUXIACQU+dOnj19/gQaVOhQojkVRAAA4AAApk2dPoUaVepUqv9VrV6tGkEAAK5dvX4FG1bsWLJlzZ7lqiACALZt3b6FG1fuXLp17d7F60ABAL59/f4FHFjwYMKFDR/uOwAAgAEAHD+GHFnyZMqVLV/GnPmxAwQAPH8GHVr0aNKlTZ9GnVo1AQCtXb+GHVv2bNq1bd/G7foBAgC9ff8GHlz4cOLFjR9HXnzAAAAACACAHl36dOrVrV/Hnl379ugMDgAAHx5BAQDlzZ9Hn179evbt3b+HX15ABAAAHADAn1//fv79/QMEIHAgwYIGDyJMqHDhAwQAHkKMKHEixYoWL2LMqPGhAAcAPoIMKXIkyZImT6JMqXLlAwQAXsKMKXMmzZo2b+L/zKnz5QEEAH4CDSp0KNGiRo8iTap0qIACAJ5CPTAAANWqVq9izap1K9euXr9ifQBgLNmyZs+iTat2Ldu2bslSOABgLt26du/izat3L9++fv0OMABgMOHChg8jTqx4MePGjglTOABgMuXKli9jzqx5M+fOnjcLWABggAUApk+jTq16NevWrl/Djn16AIDatgFQOABgN+/evn8DDy58OPHixncvYABgOfPmzp9Djy59OvXq1q9TOABgO/fu3r+DDy9+PPny5rcLWABgPfv27t/Djy9/Pv369t8XAKB/P4AFBQACEDiQYEGDBxEmVLiQYUODCgBElDiRYkWLFzFm1LiR/6NECwUAhBQ5kmRJkydRplS5kiXLAhYAxJQ5k2ZNmzdx5tS5k6dMCwUABBU6lGhRo0eRJlW6lGlSBAcAFLAAgGpVq1exZtW6lWtXr1+rChgAgGxZBwUApFW7lm1bt2/hxpU7l25aBgsAADgAgG9fv38BBxY8mHBhw4cLWygAgHFjx48hR5Y8mXJly5cZM1gAgHNnz59BhxY9mnRp06dRWygAgHVr169hx5Y9m3Zt27dZDxgAgHdv37+BBxc+nHhx48eBPxgAgHlz58+hR5c+nXp169evH6AAgHt379/Bhxc/nnx58+e7GxgAgH179+/hx5c/n359+/fpDwAA4AAFAP8AAQgcSLCgwYMIEypcyLChQAoDAEiciACAxYsYM2rcyLGjx48gQ150IABAgQUAUqpcybKly5cwY8qcSVOmgQEAcurcybOnz59AgwodSjSnAwEAkipdyrSp06dQo0qdSrWqgQEAsmrdyrWr169gw4odSzarggMA0qpdy7at27dw48qdS7ftAgB48wJAAKCv37+AAwseTLiw4cOIAxdgAKCx48eQI0ueTLmy5cuYHRMAwLmz58+gQ4seTbq06dOoETwAwLq169ewY8ueTbu27dutCQDYzbu379/AgwsfTry4ceIMEABA8ACA8+fQo0ufTr269evYsz8vAKC7dwAEAIj/H0++vPnz6NOrX8++/fgICgDIn0+/vv37+PPr38+/v3+ABAAMJFjQ4EGECRUuZNjQIUEHCABMpFjR4kWMGTVu5NjR48UCAESOBBABwEmUKVWuZNnS5UuYMWWuHHAAwE2cOXXu5NnT50+gQYXiJADA6FGkSZUuZdrU6VOoUaUqiADA6lWsWbVu5drV61ewYa8SAFDW7Fm0adWuZdvW7Vu4bhUUAKAgAgC8efXu5dvX71/AgQUPzssAwGHEACIAYNzY8WPIkSVPplzZ8uXGDxAAGHAAwGfQoUWPJl3a9GnUqVWjJgDA9WvYsWXPpl3b9m3cuV8/QADA92/gwYUPJ17c//hx5MmVEwDQ3Plz6NGlT6de3fp17M4LDADQ3ft38OHFjydf3vx59OAHPADQ3v17+PHlz6df3/59/PkFOADQ3z9AAAIHEixo8CDChAoXMkw4wACAiBInUqxo8SLGjBo3ctQ4AAAAAQ4AkCxp8iTKlCpXsmzp8iXJARQA0KwJAAGAnDp38uzp8yfQoEKHEtVJ4QAABAIAMG3q9CnUqFKnUq1q9WpVAwC2cu3q9SvYsGLHki1rliuFAwDWsm3r9i3cuHLn0q1r964BAHr38u3r9y/gwIIHEy68d8EAAIoXM27s+DHkyJInU67ceIAAAJo3A1AA4DPo0KJHky5t+jTq1P+qRyNQAOA17NiyZ9Oubfs27ty6XxewAOA38ODChxMvbvw48uTKly9gAOA59OjSp1Ovbv069uzanxewAOA7+PDix5Mvb/48+vTq0UcYAGABAwDy59Ovb/8+/vz69/PvPx/gAQADCQKgAABhQoULGTZ0+BBiRIkTE1ooAABjRo0bOXb0+BFkSJEjSVoAcBJlSpUrWbZ0+RJmTJkABggwsKAAAJ07efb0+RNoUKFDiRbtOQBAUqUAHABw+hRqVKlTqVa1ehUr1QEACkRwAOBAhAUOFhQQEEEBALVr2bZ1+xZuXLlz6dY9QAFAXr17+fb1+xdwYMGDBSMQAGAAgQcABij/KAAAcmQGDBQoAKDAAAMAAwoA8PwZdGjRo0mXNn0ategDFAC0dv0admzZs2nXtn1bNoMHAAA8cAAAeHDhwhksAHAcQIECABAYiACggIICAKhXt34de3bt27l39w6gwAIA48kDWAAAfXr169m3d/8efvz0BRQMAPCAQAEAAhAA8A8QgMCBBAseKAAgocKFAA5EYADggAMFACpavIgxo8aNHDt6/JiRAoCRJEuaPIkypcqVKxU4OADAwYMCAAYAuIkzp86dPHvmHCBAAAAEFhgAADAAgNKlTJs6fQo1qtSpUykAuIo1q9atXLt6/Yr1QAEAAgwIAKBAwAAAbNu6fQs3/67cuXALHABwwMADAAMUFAAAOLDgwYQLGz6M2PABBgAaOwYwAIDkyZQrW76MOTPmAgwEABBgQQCAAQMAmD6NOrXq1aodCAAAO7bs2bRpF3gQAUABBwoA+P4NPLjw4cSLGweO4AGA5cybO38OPbp06QMOAChA4QGAAgwQAPgOPrz48eTLi3cgAID69ezbu3+/fsCCBQAOWGAAIL/+/fz7+wcIQOBAggUNGkQQAcBChgAKAIAYUeJEihUtUhTAAMAAAxEAADgAQORIkiVNnkSZEsACBABcvoQZU+ZMmgcQAChA4AEAAAgGAAAaVOhQokWNHkUK4AEApk2dPoUaNWoBAP8AIlgAAMDBAgBdvX4FG1bsWLJlzZ5FC3YAgAIPKAAYwEABALp17d7Fm1fv3rwPAPwFHFjwYMJ/DywYAICChQIAEBQAEFnyZMqVLV/GnFnzZs6dAQxYwADAAAoMAJxGnVr1atatURdAAED2bAAIANzGnVv3bt0CHiAAsMBBAQDFjR9Hnlz5cubNmQs4AED6dOrVrV/Hnv36AQUABhCgAAAAggEAzJ9Hn179+vMKIgCAH1/+fPrzByg4AGABAQEADgBEMAAAwYIGDyJMqHAhw4YEIygAIHEixYoWL2LMqFFiAQADKFgAAGABAgAmT6JMqVKlAAcAXsKMKXMmgAIRFgD/UPBAAYCePn8CDSp0KNGiRos+QABgKdOmTp9CjSp1atQBDCIAAPCAAYCuXr+CDdt1QAEAZs8CcABgLVsABxAAKGDgAYACAgoAyKt3L9++fv8CDix4MOHChg8XRiAAAAACFAAAOABgMuXKli9XjgBgM4MIAAZQcAAAwAAApk+jTq16NevWrl/Dji17Nu3aqw8AAEDBAAAACxAACC58OHHiBAgUALBAAYDmzp9Djy59OvXq1q9jB3BgAIDu3r+DDy9+PPny5s93d/AAAIAIDADAjw9AgAMA9u8DWOAAAAAKDwAOADAAQEGDBxEmVLiQYUOHDx0+QACAYkWLFzFm1LiR/2NHjxgVLAAAwMADAAAKLGAAgGVLlwAGIAAAgAKBAgAQHACwk2dPnz+BBhU6lGjRnxQOAFC6lGlTp0+hRpU6larUAQoAADBAQAAAr1/BhgUwAAAABxYKAFggYAAAt2/hxpU7l25du3fxAlAwAEBfv38BBxY8mHBhw4cDFxAwAAAFAgUAKEAAgHJly5cxV1bgYACACBEKABA9mnRp06dRp1a9mnVr169hxyatIAICAAwiFAAwAEBv37+BBxcOvICCAQAoGDgA4EABAM+hR5c+nXp169exZ9e+nXv36QMQHAAggIAAAAgUDACwnn179+/hx48/AAAABxYOABAgYAAA//8AAQgcSLCgwYMIEyos+KAAgIcQI0qcSLGixYsYHxZgsACAAgoKAAwAQLKkyZMoU6pcyZKkgggFADiIUACAzZs4c+rcybOnT5wWCgAYSrSo0aNIkypderTAAQAFLDwAUGDBAQBYs2rdyrWr169gwwIoIGAAgAcGDgAoMACA27dw48qdS7fu3AgFAOjdy7ev37+AAwvWu8ABgAEWIgAAUACA48eQI0ueTLmy5cuXCwAAwMAAAgAKFAwAQLq06dOoU6tezbq169eqDwAA8MDAAAAMBADYzbu379/AgwsfTry4cQU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4kf9jR48fARSIAIBkSZMnUaZUuZJlS5cvARhQAAAAgQM3AQBYkABAT58/gQYVOpRoUaNHixqgAIBpU6dPoUaVOpVqVatXsT5gAIBrV69fwYYVO5ZsWbNnuRqgAIBtW7dv4caVO5duXbt36yoAAIDBAgB/AQcWPJhwYcOHESdW/LfAAgCPIRcQAIByZcuXMWfWvJlzZ8+fKx8YAMCAAQCnUadWvZp1a9evYceW/doABQC3cefWvZt3b9+/gQcXjvvAAADHkSdXvpx5c+fPoUeXLt0ABQDXsWfXvp17d+/fwYcXjz0BAPPn0adXv559e/fv4cdXb4ABAPv3AQwAsJ9/f///AAEIHEiwoMGDCBMqXMiQIAMFACJKnEixosWLGDNq3MgxYgIJAEKKHEmypMmTKFOqXMmy5QMBAGLKnEmzps2bOHPq3MkzZoIIAIIKHVAAgNGjSJMqXcq0qdOnUKMeJQAAwIIEALJq3cq1q9evYMOKHUs2bAIJANKqXcu2rdu3cOPKnUtXLQEAePPq3cu3r9+/gAMLHkw4gQQAiBMrXsy4sePHkCNLnpw4AoDLmDNr3sy5s+fPoEOL3lzAAIDTqAsoAMC6tevXsGPLnk27tu3bsBUYAMC7t+/fwIMLH068uPHjvBVEAMC8ufPn0KNLn069uvXr2CMoAMC9u/fv4MOL/x9Pvrz589wFRADAvr379/Djy59Pv779+/UfAAAgQQEAgAAEDiRY0OBBhAkVLmTYEEABAwAkTkzAAMBFjBk1buTY0eNHkCFFXhxAAACAAQBUrmTZ0uVLmDFlzqRZc6aCCAB07uTZ0+dPoEGFDiVaVOeAAwCULmXa1OlTqFGlTqVa1aoCBgC0buXa1etXsGHFjiVb1msBAGnVrmXb1u1buHHlzqWrVsADAHn17uXb1+9fwIEFDyZcWEICAIkVL2bc2PFjyJElT6aceMEDAJk1b+bc2fNn0KFFjyYtOgEAABQSAGDd2vVr2LFlz6Zd2/Zt1gkEAODd20ACAMGFDyde3P/4ceTJlS9nHrzAAQAAFAwAUN36dezZtW/n3t37d/DdBTwAUN78efTp1a9n3979e/jlC0wAUN/+ffz59e/n398/QAACBxIsaPAgwoELGABo6PAhxIgSJ1KsaPEiRocKAHDs6PEjyJAiR5IsafIkSAUKALBsOWAAgJgyZ9KsafMmzpw6d/KsKaEAgKBChxItavQo0qRKlzINyoABgKhSp1KtavUq1qxat3LtOsEAgLBix5Ita/Ys2rRq17INy2ABgLhyBwwAYPcu3rx69/Lt6/cv4MB2C1AAACBCAQCKFzNu7Pgx5MiSJ1OuLJkBAwCaN3Pu7Pkz6NCiR5MurdkABQD/qlezbu36NezYsmfTrm2bwQIAunfz7u37N/DgwocTL667wAMAypczb+78OfTo0qdTr+68QAEA2rcnSADgO/jw4seTL2/+PPr06scvGADgPfz48ufTr2//Pv78+t8/WAAAIACBAwkWNHgQYUKFCxk2bHigAACJEylWtHgRY0aNGzl2lPhAAACRI0mWNHkSZUqVK1m2VFlgAQAABwYAsHkTZ06dO3n29PkTaFCbBgoAMHp0gQIAS5k2dfoUalSpU6lWtbo0gQQAAAoA8PoVbFixY8mWNXsWbdqzDwQAcPsWbly5c+nWtXsXb163CSQA8PsXcGDBgwkXNnwYcWLFDBQA/3D8GHJkyZMpV7Z8GXNmyQUAdPb8GXRo0aNJlzZ9GrXnBwoAtHb9GnZs2bNp17Z9G3duAgMA9Pb9G3hw4cOJFzd+HHnvCAoANHf+HHp06dOpV7d+HXv1AQUAACAAAHx48ePJlzd/Hn169evDL0gAAH78BAUA1Ld/H39+/fv59/cPEIDAgQQLGjSoIAIAAAsAOHwIMaLEiRQrWryIMePFCAoAePwIMqTIkSRLmjyJMqVHBREAuHwJM6bMmTRr2ryJM6fOCAoA+PwJNKjQoUSLGj2KNKnPAgkAOH0KNarUqVSrWr2KNatUAQYAeP1aYACAsWTLmj2LNq3atWzbuj07Af+A3Ll069q9izev3r18+86VkACA4MGECxs+jDix4sWMGzsmACCy5MmUK1u+jDmz5s2cJUtIACC06AEASps+jTq16tWsW7t+Ddu0AgYAAEwAgDu37t28e/v+DTy48OHBJSQAgDy58uXMmzt/Dj269OnIBTwAgD279u3cu3v/Dj68+PHkJSQAgD69+vXs27t/Dz++/PnoFSwAgD+//v38+/sHCEDgQIIFDR5EmFChQQMDADyEKKAAAIoVLV7EmFHjRo4dPX7EKADASJIlTZ5EmVLlSpYtXZKkYADATJo1bd7EmVPnTp49ffoccADAUKJFjR5FmlTpUqZNnRKlYADAVKr/Va1exZpV61auXb1uTZAAwIADAMyeRZtW7Vq2bd2+hRv3rIIBAOzefVAAwF6+ff3+BRxY8GDChQ3vXcAAAAADABw/hhxZ8mTKlS1fxpz5MgUDADx/Bh1a9GjSpU2fRp3a8wIGAFy/hh1b9mzatW3fxp1btwQDAHz/Bh5c+HDixY0fR5789wAAAAYAgB5d+nTq1a1fx55d+/boFAoAAB9e/Hjy5c2fR59e/fr1BSYAgB9f/nz69e3fx59f//74EwoABCBwIMGCBg8iTKhwIcOGCgcMAFBgAoCKFi9izKhxI8eOHj+CtBhhAICSJhUMAKByJcuWLl/CjClzJs2aKhks/wAwYAGAnj5/Ag0qdCjRokaPIjU6oQCApk6fQo0qdSrVqlavYm3KYAGArl6/gg0rdizZsmbPok07oQCAtm7fwo0rdy7dunbv4m2bwACAvn7/Ag4seDDhwoYPIw7MAADjxgAMAIgseTLlypYvY86seTPnygUeAAgtejTp0qZPo06tejVr0QcGAIgtezbt2rZv486tezdv3gYoAAgufDjx4saPI0+ufDlz4QcGAIgufTr16tavY8+ufTv37AwUADAgAQD58ubPo0+vfj379u7flx8AYD59AAcGAMivfz///v4BAhA4kGBBgwcRJlSo8IEAAA8hRpQ4kWJFixcxZtS48f/AAAAfQYYUOZJkSZMnUaZU+ZGBAgAvYcaUOZNmTZs3cebUOdMAAJ8/ATwAMJRoUaNHkSZVupRpU6dHByQAMJVqVatXsWbVupVrV69UCQAQO5ZsWbNn0aZVu5ZtW7cJJACQO5duXbt38ebVu5dv37kEAAQWPJhwYcOHESdWvJixYgUFACSQAIByZcuXMWfWvJlzZ8+fKy8AMJo0AAkAUKdWvZp1a9evYceWPTt1BAUABhQAsJt3b9+/gQcXPpx4cePECQBQvpx5c+fPoUeXPp169eURFADQvp17d+/fwYcXP558efMEAKRXv559e/fv4ceXP5+++gEA8OfXv59/f///AAEIHEiwoMGDCBMqPDgBgMOHECNKnEixosWLGDNqVBABgMePIEOKHEmypMmTKFN+JACgpcuXMGPKnEmzps2bOG0OAABAQQQAQIMKHUq0qNGjSJMqXRqUAoCnUAEoAEC1qtWrWLNq3cq1q9evVSUkAGBAAICzaNOqXcu2rdu3cOPKhUsAgN27ePPq3cu3r9+/gAPflZAAgOHDiBMrXsy4sePHkCNLJgCgsuXLmDNr3sy5s+fPoC0LKACgtOnTqFOrXs26tevXsFMvAEC7NoAEAHLr3s27t+/fwIMLH068dwIBAJIrX868ufPn0KNLn049+YADALJr3869u/fv4MOL/x9PvryABwDSq1/Pvr379/Djy59PP32BAwDy69/Pv79/gAAEDiRY0OBBhAkVLkQYoQAAAQwATKRY0eJFjBk1buTY0SPFAgBEjhxwAMBJlClVrmTZ0uVLmDFlopxgAMBNnDl17uTZ0+dPoEGFCi1wAMBRpEmVLmXa1OlTqFGlIpVQAMBVrFm1buXa1etXsGHFbi0AwOzZAQ8ArGXb1u1buHHlzqVb1+7bAgYA7OXb1+9fwIEFDyZc2PDeAhMALGbc2PFjyJElT6Zc2fLlBQwAbObc2fNn0KFFjyZd2vRmAxMArGbd2vVr2LFlz6Zd2zZtAQMAMGAAwPdv4MGFDyde3P/4ceTJfQ8QAMD58wEMAEynXt36dezZtW/n3t079QMFABQoAMD8efTp1a9n3979e/jx3RuYAMD+ffz59e/n398/QAACBxIsaPAgwoEHBgBo6PAhxIgSJ1KsaPEiRowGKADo6PEjyJAiR5IsafIkSo8GALBs6fIlzJgyZ9KsafMmTAMPAPDs6fMn0KBChxItavQoUgYLADBt6vQp1KhSp1KtavUq0wQUAHDt6vUr2LBix5Ita/Zs2QEAADxYAOAt3Lhy59Kta/cu3rx63xqIAOAv4AEGABAubPgw4sSKFzNu7PhxYQIDAAhIAOAy5syaN3Pu7Pkz6NCiPyegAOA06tT/qlezbu36NezYslETAGD7Nu7cunfz7u37N/DgwhNIAGD8OPLkypczb+78OfToxxkAqG79Ovbs2rdz7+79O/jsBRQAKG9+QAIA6tezb+/+Pfz48ufTr+9eQAIA+vfz7+8fIACBAwkWNHgQYUKFCw0qkAAAYkSJEylWtHgRY0aNGzlGEAAAZEiRI0mWNHkSZUqVK0EqiAAAZkyZM2nWtHkTZ06dO3NSAAAgggIAQ4kWNXoUaVKlS5k2dTp0QAEAU6kmiAAAa1atW7l29foVbFixY7MSAHAWbVq1a9m2dfsWbly5cxVEAHAXb169e/n29fsXcGDBeA8AMHwYcWLFixk3/3b8GHLkxQMAVLZsYAEAzZs5d/b8GXRo0aNJl/ZsYAAA1atZt3b9GnZs2bNp11YtIAIA3bt59/b9G3hw4cOJFzcuQQEA5cuZN3f+HHp06dOpV1cu4AEA7du5d/f+HXx48ePJlx8vAAAACQkAtHf/Hn58+fPp17d/H397AwoA9PcP0IAAAAQLGjyIMKHChQwbOnxIcMABAAASDACAMaPGjRw7evwIMqTIkSAFPACAMqXKlSxbunwJM6bMmSgHHACAM6fOnTx7+vwJNKjQoUQFPACANKnSpUybOn0KNarUqUkNALiKNavWrVy7ev0KNqzYrQoEADiLNq3atWzbun0LN/+u3LkSDAC4izev3r18+/r9Cziw4LsLGAA4jDix4sWMGzt+DDmy5MkUDAC4jDmz5s2cO3v+DDq06MsCFgA4jXpAAQCsW7t+DTu27Nm0a9u+zbrABAAAGBQAADy48OHEixs/jjy58uXIFzAAAD269OnUq1u/jj279u3QC0wAAD68+PHky5s/jz69+vXsFzAAAD++/Pn069u/jz+//v3wBzAACEDgQIIFDR5EmFDhQoYNDRowAEDiRAMGAFzEmFHjRo4dPX4EGVLkRgYDAJxEmVLlSpYtXb6EGVPmSQYLANzEmVPnTp49ff4EGlTo0AkFABxFmlTpUqZNnT6FGlXqUQb/CwBcxZpV61auXb1+BRtW7NcCDwAAmFAAwFq2bd2+hRtX7ly6de2uLTAAwF6+CxYAABxY8GDChQ0fRpxY8WLABigAgBxZ8mTKlS1fxpxZ82bODBYAAB1a9GjSpU2fRp1a9WrQBiQAgB1b9mzatW3fxp1b927eAhQAAB5c+HDixY0fR55c+XLiBgA8hx5d+nTq1a1fx55dO/QHAgB8Bx9e/Hjy5c2fR59e/foDAwC8hx9f/nz69e3fx59f//sHAgAABCBwIMGCBg8iTKhwIcOGCQckAADgwAAAFi9izKhxI8eOHj+CDGlRgQEAJk8qMABgJcuWLl/CjClzJs2aNlcm/5AAAIACAD5/Ag0qdCjRokaPIk169IEAAE6fQo0qdSrVqlavYs3qNIEEAF6/gg0rdizZsmbPok2r9oEAAG7fwo0rdy7dunbv4s3rdoABAH7/Ag4seDDhwoYPI04seEECAI4fDwAgeTLlypYvY86seTPnzpcPAAgtejTp0qZPo06tejVr0REUAIgtezbt2rZv486tezfv3gQAAA8ufDjx4saPI0+ufHnwCAoAQI9eYACA6tavY8+ufTv37t6/g6+uIAIAABIAoE+vfj379u7fw48vf378CAoA4M+vfz///v4BAhA4kGBBgwcRJlQoUEEEAA8hRpQ4kWJFixcxZtS4Mf+CAgAfQYYUOZJkSZMnUaZU+dGAAAAvYcaUOZNmTZs3cebUOTPBAAA/gSYoAIBoUaNHkSZVupRpU6dPkT4AMJVqVatXsWbVupVrV69UJSQAMJZsWbNn0aZVu5ZtW7dvCQCQO5duXbt38ebVu5dv37kSEgAQPJhwYcOHESdWvJhxY8UJBAAAQABAZcuXMWfWvJlzZ8+fQVs2MABAadMRDABQvZp1a9evYceWPZt2bdUCHgAAMABAb9+/gQcXPpx4cePHkRuXkABAc+fPoUeXPp16devXsTcX8ABAd+/fwYcXP558efPn0ad/YABAe/fv4ceXP59+ffv38cc3AIB/f///AAEIHEiwoMGDCBMqXMjQIAUDACJKnEixosWLGDNq3MiR44ADAEKKHEmypMmTKFOqXMlSJAUDAGLKnEmzps2bOHPq3MkzZ4ECAAYcAEC0qNGjSJMqXcq0qdOnRRkUAEC1qoABALJq3cq1q9evYMOKHUs26wIGAAAIAMC2rdu3cOPKnUu3rt27dSkYAMC3r9+/gAMLHky4sOHDfBcwAMC4sePHkCNLnky5suXLmCkYAMC5s+fPoEOLHk26tOnTnAsUAMC6tevXsGPLnk27tu3bsB8MAMC7dwEAwIMLH068uPHjyJMrX068gAQA0KNLn069uvXr2LNr3x59QgEA4MOL/x9Pvrz58+jTq1+/vsAEAPDjy59Pv779+/jz698f/0ABgAAEDiwAwOBBhAkVLmTY0OFDiBEPMlgAoEAEABk1buTY0eNHkCFFjiQpckIBAClVrmTZ0uVLmDFlzqSZ8sECADl17uTZ0+dPoEGFDiVadEIBAEmVLmXa1OlTqFGlTqWaVEACAFm1buXa1etXsGHFjiXbNQEAtGkBLBgAwO1buHHlzqVb1+5dvHnjFhAAwO9fwIEFDyZc2PBhxIn/HhgAwPFjyJElT6Zc2fJlzJkzG6AAwPNn0KFFjyZd2vRp1Kk/ExgAwPVr2LFlz6Zd2/Zt3LltCzAAIAEFAMGFDyde3P/4ceTJlS9nLlwBAOjRAVAYAMD6dezZtW/n3t37d/DhrUcQAADAAADp1a9n3979e/jx5c+nL5/AAAD59e/n398/QAACBxIsaPAgwoQKFUZQAOAhxIgSJ1KsaPEixowaN1IA4PEjyJAiR5IsafIkypQjCwBo6fIlzJgyZ9KsafMmTpcEAPDs6fMn0KBChxItavQo0gQSADBt6vQp1KhSp1KtavVqUwIAtnLt6vUr2LBix5Ita5asgQEAFEgA4PYt3Lhy59Kta/cu3rxvJQDo6xfAAgCCBxMubPgw4sSKFzNuPFiCAgAFFACobPky5syaN3Pu7PkzaM8EAJAubfo06tT/qlezbu36dWkJCQDQrm37Nu7cunfz7u37N3ACAIYTL278OPLkypczb+6ceIIBAKZTr279Ovbs2rdz7+79+gMA4scDMADgPPr06tezb+/+Pfz48tcnYADgPv78+vfz7+8fIACBAwkWNHgQYcKBAwgAcPgQYkSJEylWtHgRY0aNAiIA8PgRZEiRI0mWNHkSZUqPAw4AcPkSwAAAM2nWtHkTZ06dO3n29ElTggEAChgAMHoUaVKlS5k2dfoUalSnAwgAsHoVa1atW7l29foVbNirFAwAMHsWbVq1a9m2dfsWbty4Aw4AsHsXb169e/n29fsXcOC7DwoAMHwYcWLFixk3/3b8GHJkxQYAVLYMgAEAzZs5d/b8GXRo0aNJl/ZsIAEA1atZt3b9GnZs2bNp11ZdYAIA3bt59/b9G3hw4cOJFze+4AEA5cuZN3f+HHp06dOpV1deYAIA7du5d/f+HXx48ePJlx+/YACABQwAtHf/Hn58+fPp17d/H3/7AQoA9PcPcEAEAAQLGjyIMKHChQwbOnxYcEIBAAMGALiIMaPGjRw7evwIMqTIjwUmADiJMqXKlSxbunwJM6ZMlBMKALiJM6fOnTx7+vwJNKhQoQUoADiKNKnSpUybOn0KNapUpAMAABgwAIDWrVy7ev0KNqzYsWTLai1AAYDatWzbun0LN/+u3Ll069plwACA3r18+/r9Cziw4MGEC+s1QAGA4sWMGzt+DDmy5MmUK082AAAAgwUAOnv+DDq06NGkS5s+jbpzgQcAWrseoACA7Nm0a9u+jTu37t28e88+MABAggQAihs/jjy58uXMmzt/Dr25AQoAqlu/jj279u3cu3v/Dt76gQEAyps/jz69+vXs27t/Dx++AQoA6tu/jz+//v38+/sHCEDgQIIFDR4UAEDhQoYNHT6EGFHiRIoVHRYQAEDjRgAGAHwEGVLkSJIlTZ5EmVLlyAUKALyEGVPmTJo1bd7EmVPnywQSAPwEGlToUKJFjR5FmlTp0gcCADyFGlXqVKr/Va1exZpV69MEEgB8BQtgAACyZc2eRZtW7Vq2bd2+LTsBAAAGCgDcxZtX716+ff3+BRxYMN4BAAwfTiABwGLGjR0/hhxZ8mTKlS0zJgBA82bOnT1/Bh1a9GjSpU0nkABA9WrWrV2/hh1b9mzatVdTAJBb927evX3/Bh5c+HDivQcUAJBceQEBAJw/hx5d+nTq1a1fx55deoICALx/Bx9e/Hjy5c2fR5/eu4IIANy/hx9f/nz69e3fx59ffwQFAPwDBCBwIMGCBg8iTKhwIUOGCiIAiChxIsWKFi9izKhxI0eNDAAAiKAAAMmSJk+iTKlyJcuWLl+SLJAAAM2aBhYA/8ipcyfPnj5/Ag0qdChRnQQAACgwAADTpk6fQo0qdSrVqlavUlUQAQDXrl6/gg0rdizZsmbPdiUAYC3btm7fwo0rdy7dunbvKngAYC/fvn7/Ag4seDDhwob5FgAAYACAxo4fQ44seTLlypYvY3asgAGAzp4/gw4tejTp0qZPo04tIQGA1q5fw44tezbt2rZv424t4AGA3r5/Aw8ufDjx4saPIzdeAAAACQkAQI8ufTr16tavY8+ufTt0BQsAgA9fwACA8ubPo0+vfj379u7fwy8/4AAAAAIKAMivfz///v4BAhA4kGBBgwcRJlS48KCABwAgRpQ4kWJFixcxZtS4Ef/igAMAQIYUOZJkSZMnUaZUuZKlgAcAYMaUOZNmTZs3cebUuTPmAgA/gQYVOpRoUaNHkSZVOtRAAgBPoRYoAIBqVatXsWbVupVrV69fsT4oAIBsWbNn0aZVu5ZtW7dvyS5gAIBuXbt38ebVu5dvX79/AVMwAIBwYcOHESdWvJhxY8ePCS9gAIByZcuXMWfWvJlzZ8+fOReQAACABAMAUKdWvZp1a9evYceWPRv1gAEAcOdewABAb9+/gQcXPpx4cePHkfcuMAFAc+fPoUeXPp16devXsWdfwABAd+/fwYcXP558efPn0XcvIAFAe/fv4ceXP59+ffv38ccfMABAf///ABUoAECwoMGDCBMqXMiwocOHCBUMAECxosWLGDNq3Mixo8ePFBksAECypMmTKFOqXMmypcuXMCcUAECzps2bOHPq3Mmzp8+fNBksAEC0qNGjSJMqXcq0qdOnTAcoAABgQgEAWLNq3cq1q9evYMOKHYs1gQEAaNMqUACgrdu3cOPKnUu3rt27eNsaoAAAQAIAgAMLHky4sOHDiBMrXpyYwQIAkCNLnky5suXLmDNr3gzZAAUAoEOLHk26tOnTqFOrXs2awQIAsGPLnk27tu3buHPr3h27AIDfwIMLH068uPHjyJMrH85AAYDn0KNLn069uvXr2LNr335gAIDv4MOL/x9Pvrz58+jTq//+QACA9/Djy59Pv779+/jz68c/AAAAgAcGACBY0OBBhAkVLmTY0OFDggwUAKBY0UABABk1buTY0eNHkCFFjiSZMYEEAAAeAGDZ0uVLmDFlzqRZ0+bNmg8EAODZ0+dPoEGFDiVa1OhRngokAGDa1OlTqFGlTqVa1epVrA8EAODa1etXsGHFjiVb1uxZrgUEAGDb1u1buHHlzqVb1+5duAoKAODb10ABAIEFDyZc2PBhxIkVL2ZcOAIAyJElT6Zc2fJlzJk1b44cQQEA0KFFjyZd2vRp1KlVr2ZNAMBr2LFlz6Zd2/Zt3Ll1w5agAMBv4MGFDyde3P/4ceTJlR9PwAAAAAIApE+nXt36dezZtW/n3n16gQEAxI+PoADAefTp1a9n3979e/jx5Z8XEAHAffz59e/n398/QAACBxIsaPAgwoQKC0pQAOAhxIgSJ1KsaPEixowaHypgAOAjyJAiR5IsafIkypQqRw4A4PIlgAUGANCsafMmzpw6d/Ls6fMnTgUAhhItavQo0qRKlzJt6pSohAQAplKtavUq1qxat3Lt6vUrAQBix5Ita/Ys2rRq17JtO5ZCAgBy59Kta/cu3rx69/Ltq9eAAQADCAAobPgw4sSKFzNu7PgxZMMCCgCobHlBAQCaN3Pu7Pkz6NCiR5MurXnBAwD/ABQAaO36NezYsmfTrm37Nm7bFBIA6O37N/DgwocTL278OPLeCxgAaO78OfTo0qdTr279OvbsFAwA6O79O/jw4seTL2/+PPruAwoAaO/+Pfz48ufTr2//Pv74EQoA6O8fIACBAwkWNHgQYUKFCxk2PDjgAACJEylWtHgRY0aNGzl2nDjBAACRI0mWNHkSZUqVK1m2bFngAACZM2nWtHkTZ06dO3n2nCmhAAChQw0AMHoUaVKlS5k2dfoUatSjDBgAGPAAQFatW7l29foVbFixY8mKnWAAQFq1a9m2dfsWbly5c+mmZbAAQF69e/n29fsXcGDBgwkXnlAAQGLFixk3/3b8GHJkyZMpJ1aQAEBmzZs5d/b8GXRo0aNJdxYAAHVqAAoGAHD9GnZs2bNp17Z9G3fu2AMYAPD9G3hw4cOJFzd+HHny3wcKAHD+HHp06dOpV7d+HXv27AYmAPD+HXx48ePJlzd/Hn367wcGAHD/Hn58+fPp17d/H39++wIUADAAkAKAgQQLGjyIMKHChQwbOiRoAIDEiQAmDACAMaPGjRw7evwIMqTIkRgfCACAMqXKlSxbunwJM6bMmTQPDACAM6fOnTx7+vwJNKjQoTgZCACANKnSpUybOn0KNarUqUwHALiKFUCEAQC6ev0KNqzYsWTLmj2LFuwAAwDaun0LN/+u3Ll069q9i9ctAQB8+/r9Cziw4MGECxs+jDiBBACMGzt+DDmy5MmUK1u+3JgAgM2cO3v+DDq06NGkS5smbaAAgAQSALh+DTu27Nm0a9u+jTv36wcAevsGwACA8OHEixs/jjy58uXMmw+PoADAgAQAqlu/jj279u3cu3v/Dt47AQDky5s/jz69+vXs27t/Xz6CAgD069u/jz+//v38+/sHCEDgQIIFDRokAEDhQoYNHT6EGFHiRIoVFxoYAEDjRo4dPX4EGVLkSJIlPUoAkFIlgAEAXL6EGVPmTJo1bd7EmVNmgggAfP4EGlToUKJFjR5FmvQnAQBNnT6FGlXqVKr/Va1exZpVQQQAXb1+BRtW7FiyZc2eRev1AAC2bQEYABBX7ly6de3exZtX716+ciUkAJBgAQDChQ0fRpxY8WLGjR0/bkwAwGTKlS1fxpxZ82bOnT1TlpAAwGjSpU2fRp1a9WrWrV2/JgBA9mzatW3fxp1b927evWcvKABA+HDhCh48WDAAwHLmzZ0/hx5d+nTq1Z8rAJBdOwABALx/Bx9e/Hjy5c2fR59evAEFANwLOCBBgIAHByIAwJ9f/37+/f0DBCBwIMGCBg8iTAhgwAEADh9CjChxIsWKFi9izKhRwAMAAARMKABg5MgIEgCgTKlyJcuWLl/CjCkz5YADAG7i/8ypcyfPnj5/Ag0qFGcCBQoGAEgKgEEBAAIeABhwYACAqlYBSBAAYCvXrl6/gg0rdizZslwTAEirdsAEAG7fwo0rdy7dunbv4pW74ACFCBIIPBgAAAAFAwAOA2DwAADjxowNTAAgeTLlypYvY86seTNnzQMOAAgtejTp0qZPo06tejXpCBIMAIg9gMGBAgAoGACgG8AEAwB+Awd+oACA4saPI0+ufDnz5s6fMx8gAQD16tavY8+ufTv37t6tL5AAYDx5AAIoAEgPYEABABMKAIgvXz4FAwDu48+vfz///v4BAhA4kGBBgwcNFpgAgGFDhw8hRpQ4kWJFixYPFACwkf/jxgkGAIRcwAAAhQQAUKZMeaAAAJcvYcaUOZNmTZs3cQIoMAFAT58/gQYVOpRoUaNHjSagAIBp06YLHiQAAGABAwALIgDQulVrAgoAwIYVO5ZsWbNn0aZVC3YAAwBv4Q5YAIBuXbt38ebVu5dvX791FUQAMJgw4QQSJhQAYMAAgAEHDACQPBkABQUAMGfWvJlzZ8+fQYcWDbrABACnUadWvZp1a9evUQuYQIDAhAUDAOTWvZs3AAUSAAQXLlxBhAkFACRPnuCAAgDPARSg8ABAdevXsWfXvp17d+/fvReYAIB8efPn0adXv549gAETJCQAAMDAgwMGAOTXv5//gAP/AAcAGEhwoAQBAhQoNACgYQIKBx4woHBgAYCLGDNq3Mixo8ePIENmLMAAgMmTAAYAWMmypcuXMGPKnAlgwgIAOHMqODAAgM+fQINGYACgqFEABQgQOECBwoEDBCYwGADAgIAFCgBo3cq1q9evYMOKHUvWqwEKANKqXcu2rdu3cOO+VSABgN27dh8wAMC3r9+/Aw4IAECYcIEDFBYIWCxBwgIGFAhEGACgsuXLmDNr3sy5s+fPnA1QAEC6NIACAFKrXs26tevXsGNTSACgtu3aBQ4A2M2794ACBQYAAFBgwgQBCRRIIBBBgHPnESIImL6AwoEEALJr3869u/fv4MOL/x9PXrsBCgDSq1/Pvr379/DjEwBAv779AwMA6N8vQMIBgAQOHCBwQMKCAQkiHJgQYYEAiBElRnxwQAAAjBk1buTY0eNHkCFFjsRogAIAlClVrmTZ0uVLmAQAzKRZ88AAADkBPCBAIQKDBQIELFjwgAKBCAciCGDa1OlTpgwOKABQ1epVrFm1buXa1evXrAMSACBbdoACAGnVrmXb1u1buHEpJABQ127dAgcA7E1wgAIDAYEFD15wQIIAxIkVP3ggwPFjAQwOFABQ2fJlzJk1b+bc2fNnzAkkACBd2vRp1KlVr2atWoEEALFlx37AAACABQceCODd2zfvCAcWCCBe3P84hQgClC9XHoECAOjRpU+nXt36dezZtU9PIAHAd/DhxY8nX978efMTFgBg317BgQEABBxgIMD+ffz2FxBgIMA/QAECBwqkEEEAwoQIF0xQAOAhxIgSJ1KsaPEixowRBxgA4PGjgQcARpIsafIkypQqVwIYMEFCAgAADDw4YACAgQMMBPDs6bNnhAkChhItOnTBAgFKly6NQAEA1KhSp1KtavUq1qxatSaQAOAr2LBix5Ita/YsWAETCBCYsGAAAAATHgioa/fu3QkPBPDt6/cvYL4LDhQwIGAB4gQDADBu7Pgx5MiSJ1OubNlxggcANnPu7Pkz6NCiR5NmQEEA6tT/qlUvILBAAOzYsmEzWCDgNu7bCx4cIDCBgoTgEwhMEADgOPLkyAcUAOD8OfTo0qdTr27duYIIALZz7+79O/jw4seLTyDhwAEJCgCwB3CAgYD48ufPZ3BAAP78+vNTiCAAoACBAxkcOBBhgQCFCxc8oEBAAACJEyUakHCAwAQCEQwA8PgRZEiRI0mWLKkgAgCVK1m2dPkSZkyZLwdQoKBgwAAFFCYMAKBgggChQ4kWjUBBQFKlS5VOeCAAKtQFEghEWCAAa1atAhhMoDAAQFgABSgceMAgwoEICw4IAPAWbly5c+nWtVs3gQAAe/kWUAAAcGDBgwkXNnz4MAUGABg3/14wAcCDCAIoV7Z8OQIFAZs5d+b8gIEA0QIWUJiwQEBq1atXL5BwoAAAAAUOCABwG8CABxQKHEgAAHhw4cOJFzd+HPlwBREANHf+HHp06dOpT1cgAUB27dklCJjwQEB48ePJR6AgAH169evTL6AwYYEA+fPp15cf4cAAABMEAPAPEIBAAA8eKKAAIKHChQwbOnwIMSJDAREAWLyIMaPGjRw7cqSQAIDIkSITTCCwQIDKlSxbPpggIKbMmTRlRjiwQIDOnTx78pQQwcAEAESLEh1wYMCBAgCaOn0KNarUqVSjJhAAIKvWAQMAeP0KNqzYsWTLkiUAIK3atQQICHgLN/+uXAELCCwQgDevXrwUHggQsIAAAwGECxs+fHjBgQkLADh+/FiCAgkJAFi+jDmz5s2cO2sW8ACA6NGkS5s+jTq1atMEALh+DZsAAQG0a9u+LWABAQYCevvuvYDBggUTHggQIIGCgOXMmzt/LuDBAQUAqlu3/kCAhAQAunv/Dj68+PHkwy94ACC9+gEDALh/Dz++/Pn0688fcMAAgP389xcASIDAAgEFDR5EKICCBAENHT6YQGDCgQkTHghYQICBAI4dPX4EKWDBgQUATJ48GUHAgQEAXL6EGVPmTJo1bcIU8ADATp49ff4EGlToTwYEKkQAkFRp0gcbKjwQEFXqVKr/AhgQWCBA6wIJExQAAJuAwoQFESYIQJtW7Vq2aSlMABBXbtwBBxZMAJBX716+ff3+BRyY74IHAAwfRpxY8WLGjREbuKDBgYMDCgBcxpyAgAMIEgR8Bh1a9OcJEgScjjBhAADWrSNQoCBBwGzatW3fps2AQAIAvX0DYCCBwIUKBgAcR55c+XLmzZ0rL1AAwHTqBhIAwJ5d+3bu3QsoUCBAgAIDAMyfR3+ewQELAdxjOMCgAAAAAxgQyBDAwoEFAvwDFCBwIMGBCwgwELDggAEADh8KKEDgwAMBFi9izKjx4gICBBQACBmSwQECGQJYOPAAAMuWLl/CjClzpssFDADg/8ypcyfPnjoVPKBA4IAGCEYhXCBwIcKCAQCeQgUg4YKDAFatOoBAoEIFAhAwBAiA4AADAWbPok1rNsKBBQ8mAIgrFwAFAw8ILBCgdy/fvn75VmhQ4YIIAQ8IHGjgIABjBxAmDAAgeTLlyQMGFCgwAADnzp4/gw4NgAEDAKZPo06tejWAASMOXGhgwUGA2rYDIMDQAAIBCQkAAAdAAQKCAMaPG0eAAQOCAM6dN5iwQAD16tavU5dwQMIDAN6/A5hgQAGBBQLOo0+vfn36CxYQZIAAoQGGAPbvB0CwYcIAAP4BAhAIYEACERIOEDiw8ACBCREEGAAwkWJFixcpGjAAgP9jRwECAIQUOZLkyAERCEDAEIBlS5cvHTSocEEBgA4QEATQuZNnz50IKkQQMJRoUaNDF0wgEAFAU6cACgAQQGCBAKtXsWbVivVChgBfwYYV22DCAABnARjwQOACBAsYEASQGwABhgYQKhxYMABAX79/AQcWDIABAwCHESdWjFjAgQYOAkSWPJnyZAQWKly4gCBAZ8+fQYPGQICBANOnUaNmQIHABQgHAMSWLVsCAQYCcOfWvZu3bgIYAgQXPpw4AggUAABQMOFAAwcBoEeXLh0DBAIRCgDQvp17d+/eFwgAMJ58efMABkiogCFAe/fv4cdvjwACAQsB8OfXv5+/BQL/ABkIGEiwoIAFEQhAcBAgwAUFACJKLFCAwIEIAjJq3Mixo8YFBBAEGEmypMkACC4w6FDBAoIAMGPKnBnTQQMCDADo3Mmz504GAgAIHUq0qFGhBg40QBCgqdOnUKNCzXAAAoIAWLNqxeogQwYLGTI4CGCBQIQFAtKqTctgQgUMAeIGsHDAAIC7dw8cuACBgoC/gAMLHgz4wYUAiBMrXowYQQMCDRAEmEy5suXLARxcmGAAgOfPoEMDeLAAgOnTqFOrBmDggIUAsGPLnk27dgAHFSAgCMC7N4IMDTQcIFChwoUKFQgQuADhwAQGCwRIl/6AQAMEAbJrt0Dgg4EBBRgQ/4CAwAGBBQLSq1/Pvn36CQ0cyEcQoL79+/URaKiAIYB/gAEEDiRY0GAABBwICADQ0OFDiAIUAKBYMYEBABk1btRo4ICFACFFjiRZ0qRIBxUgIAjQ0kGDAxUgWMCAIMDNmwgwWIBQgQCBAxIeLBAQgUCGAEmVKnXQoAKBAxAgOAgQQIMEAVm1buWqdQGDCBQOECBb9oCGBhYcBGDb1sEFDQgCzKVb1+5dvBgOLADQ1+9fwIEBPFgAwPBhxIYHHLAQwPFjyJElT47soEKDAA4gENCQAUEA0KFFi8YAgUCFCgRUYwjQ2vVr2LAxEFggwPZt3LkXRDhwQEODDA4CDA+AAP+DBQgVCEDAEMA5ggoQEASgXt36dezZqTuosADAd/DhxY9/IADAefTpz3doEMD9e/jx5c+f74BAAwIQHATg398/wAACBw500IAABAIYAjBs6PAhxAANJiwQYPEiRosMKBDQYAFBgJAiR4p00IBABQsILmhAEOAlzJgyZ9KM6eCAAAA6d/LUKcAAgKBCCwwAYPQoUgACKiAI4PQp1KhSp051UOFAhgBat3Lt6lUrhgoVMAQoa/Ys2rIXHARoi6CChAUC5tKluyACgQ0OAvDt6/dvXwQWKhBGEOAw4sSKFzNejIFAAQCSJ1MGEEEBgMyaN3PWPOAAhgCiR5Mubfr0aQz/BCAgCOD6NezYsmEjaEDAQoDcunfzDkDAQYDgARxU6LBAAPLkAhhcqIAhAPTo0qdTD4BgA4EGCAJw7+79O/jw4BtQAGD+PHoAERQAaO9+AID48ucDkNAgAP78+vfz798fYAYCFgIUNHgQYUKFATIQsBAAYkSICDA0gFCBQEYCBy5ssODAwYUKDASULOmBQAMEAVi2dPkSpksMFS4gCHATZ06dO3nqRHBhAQChQ4kWHRpBAAClS5cOIOAgQFSpU6lWtVo1AwELAbh29foVbNiuGAhYCHA2AIIMGghU0NAggwO5DjBY2HCBwIEGDQhQYCBAQAQCGAIUNnwYcWLFCDRU/3AQAHJkyZMpV56MgUABAJs5d/a8OYICAKNJk2YAIUBq1atZt3bdGgMBCwFo17Z9G3fu2xgIZAiAoEGFAw0cBDB+HLlxBBk0ENAAgcCBCQQwBLB+HXt27dutI4BQAUEA8ePJlzd/vjwEBgDYt29fYAAA+fMFGABwHz/+AxgC9PcPMIDAgQQLGjSIoEKDAAwbOnwIMWJECwcsHLhgAUGAjRw7etzooAEBCBsIYAiAMqXKlSxbqkSg4QKCADRr2ryJM6dNDAcA+Pz5U0ICAESLGj0KQMGFAEybOn0KNSrUBhUQBLiKNavWrVy5IqhAwEKAsWTLmj0bwIEGAg0CuH0LN/+u3LlyEVxoECCv3r18+/rle0EBgMGEB0tIACCx4sWMATxoECCy5MmUK1umjIEAhgCcO3v+DDp0aAwHNDgIgDq16tWsVVsgAAFBgNm0a9u+jds2BgIYAvj+DTy48OHALUwAgDw58gQFADh/zsAAgOnUp0/IECC79u3cu3vnfqFBgPHky5s/jx59BgIcArh/Dz++/PkOLmhAECC//v38+/sHGECgwAYVEARAmFDhQoYNEyIgMADARIoVLUpIAEDjRo0EHAQAGVLkSJIlRWIggCDASpYtXb6E+TIDAQsBbN7EmVPnTpsILlxAEEDoUKJFjR4liuBCgwBNnT6FGlXq0wv/CQBcxZpVa4QEALx+BVCgQgCyZc2eRZv2LIQGAdy+hRtX7ly5GAhYCJBX716+ff3uRXBBA4IAhQ0fRpxY8WEMBBAEgBxZ8mTKlSNDWABA82YAEQwAAB1a9GgFEAKcRp1a9WrWqR0QcBBA9mzatW3fro2gQoMAvX3/Bh5ceHAEFRoEQJ5c+XLmzZdXsBBA+nTq1a1fn25BAgDu3QFQMABA/Hjy5QVACJBe/Xr27d2vb6AhwHz69e3fx38fwgUEAfwDDCBwIMGCBg8SxEAAQ4CGDh9CjCjxoYULAS5izKhxI0eMGA4ACCkSwIMCAE6iTDAAAMuWAARACCBzJs2aNm/S/7xgIQDPnj5/Ag36MwMBBwGOIk2qdCnTpgEaXEAQYCrVqlavYqWKgACGAF6/gg0rdqxXBwQAoE2rdi0FAwDewgUgAEKAunbv4s2r1y4CAg4CAA4seDDhwoIRVLAQYDHjxo4fQ468GEGFBgEuY86seTPnzBAaBAgtejTp0qZDIyAAYDXr1q4pGAAgezYABRAC4M6tezfv3rkdEEAQYDjx4saPIy9u4QKCAM6fQ48ufTr15xgIIAigfTv37t6/b+egIQD58ubPo09PHgEBAO7fA1gwAAD9+gYGAMivH0CCCwEABhA4kGBBgwcFWrgQgGFDhw8hRnxYwUIAixcxZtS4kf9jxgoWAoQUOZJkSZMiMRwIsJJlS5cvYa50QABATZsAJhQAsJNnT58DCCAIMJRoUaNHkQ5tACFAU6dPoUaV+hQDAQQBsGbVupVrV69bOVwIMJZsWbNn0ZJFQMBBALdv4caVOzcAhgMA8OYFMKEAAL9/AQcGcABDAMOHESdWvNgwhAYBIEeWPJlyZckQGgTQvJlzZ8+fQXtGQABDANOnUadWvfp0hQwBYMeWPZt27QAWJADQvRvAAAC/gQOYUABAcePFJVgIsJx5c+fPoS+H0CBAdevXsWfXbh0BAQcBwIcXP558efPlITQIsJ59e/fv4bO/YCFAffv38efXHwDCAgD/AAEIHEiQ4IQCABIqTLgAQoCHECNKnEjxIYQGATJq3Mixo0eNGAgEGEmypMmTKFOmtHAhgMuXMGPKnPlSg4UAOHPq3MmzZ4ALCQAIHQqgAICjSAEwGACgqdOmBQggCEC1qtWrWLMGgNAggNevYMOKHfvVwoUAaNOqXcu2rVu3GAgEmEu3rt27eOlqsBCgr9+/gAMLRkBgAIDDiAEcGACgsePHkBtLaBCgsuXLmDNrDtAAQoDPoEOLHk0aNIQGAVKrXs26tevXrxEQcBCgtu3buHPrrn3BQoDfwIMLH07cAgUAyJMjPzAAgPPn0KM7T3AhgPXr2LNr3x7AQoUA4MOL/x9Pvnz4CxYCqF/Pvr379/DjX7AQoL79+/jz669fwUIAgAEEDiRY0KDBCwoALGS4UAAAiBEBRBgAwOJFjAcyBODY0eNHkCEdEEAQwORJlClVrjRZAUMAmDFlzqRZ0+ZNDRwC7OTZ0+dPoAEQEHAQwOhRpEmVKsVwAMBTqFGlPj0wAMBVrFkFHEAQwOtXsGHFjj2AIcBZtGnVrmV79gCGAHHlzqVb1+5dvBAaBODb1+9fwIEDYDgQwPBhxIkVL4bAAMBjyJElPz4wAMBlzJkfHGgQwPNn0KFFj9bQIMBp1KlVr2Z9+gCGALFlz6Zd2/Zt3BAaBODd2/dv4MEDWNAQwP/4ceTJlSvHQGAAAOjRo08AUN36dezWDRzAQABDAPDhxY8nT95ChQDp1a9n3959+goYAsynX9/+ffz59WtoEMA/wAACBxIsaLAghAYBFjJs6PChQwQXBACoaPEiAQAaN3LsuJGChQAcKjgIYPIkypQqUyIggCEAzJgyZ9KsGaBChgA6d/Ls6fMn0KAXLAQoavQo0qRKERDAEOAp1KhSp0ptQAAA1qxaAUwA4PUrAAUAxpIla+AAggAIIFRAEOAt3Lhy58ptACEA3rx69/LtGwBCgwCCBxMubPgw4sQHMARo7Pgx5MiSLVQIYPky5syaM2MgcGEBgNCiR5MWTQAA6tT/qT00COAagYYLDgLQrm37Nm7bDgg4COD7N/Dgwoc3gBDgOPLkypczb97cAQEEAaZTr279OvYLFgJw7+79O3jvDghwyHAAAPr06tenJwDgPfz3Awg4CGA/AAIIFRwE6O8fYACBAwkWJAgBQgCFCxk2dPgwQ4UAEylWtHgRY8aMFioE8PgRZEiRIzEQQBAAZUqVK1mmdHCgQYAAFxIAsHnz5gIAO3kCUAAAaFCgAiAEMHoUwQYCFhAEcPoUalSpTh0QsBAAa1atW7lyRUAAQQCxY8mWNXsW7dkGEAK0dfsWbty4CC40CHAXb169e/FiINAgQOAGEgAUNmyYAADFixk3/wYQoUEAyZMlZ6hwwUEAzZs5d/aMoAEBAg4ClDZ9GnXq1BcsBHD9GnZs2bNpz65gIUBu3bt59+7doAKCAMOJFzd+PACCBgQaBHAeAAGBAQCoV6dOAEB27du5A5iQIUB48eIRQCAAwUEA9evZt1ePwEKFAxggaEAQAH9+/fv577cA8EKAgQQLGjyIMOHBDAcQBHgIMaLEiRIdEMgQIKPGjRw7BnBwoQKGACRLalAAIKXKlAUAuHwJgACAmTRnEkAQIKfOnQEwQCBwwQKCAESLGiWKAQKBCgcQBHBQYUOAqVSrWr1qFQEBDAG6ev0KNqzYsWAhNAiANq3atWzXIrhw4P+ABQQB6tq9i9eugwYEGiAIADhwgAYPABg+jDgxAAIAGjsGYOBCgMmUK1d20KACgQoQGmTAABqDhQYXCBCAgOEChwCsHVRogCCA7Nm0a9umvQFCgN28e/v+DTx4bwcEHAQ4jjy58uXJEUC4gIBABQIbHAS4jj07dgQZNBCAgCGA+PHjLVAAgD49+gIA2rsHEAGA/PkAFEAIgD+//v34HWQA2EBDhQMEDlS4AMECBgQBMBBAEEBiAAcVICAIkFHjRo4dNToggCHASJIlTZ5EmZIkBAgBXL6EGVMmTAQQKjgI0AACBggEKkDggAFBAKIBEGRooOHAgQYOAjyFGjWAAwL/AKxeBTDgAACuXb1+VQAhwFiyZc2eRVsWAoQAbd06uFABQwC6de3exVu3QQUEAfz+BRxY8GDCATIQcBBA8WLGjR0vRgChgoMAARwQcBDAgYUGGg4QAB26AoQGGBAEQJ1ateoDAwC8hj3gAADatW3fFgAhwG7evX3/Bt67goUAxY0HQNCAQAMEAZw/hx5dOoIGBBoEwJ5d+3bu3b0jOGAhwHjy5c2fJ4+hwgUHAdwH0NAgwHz6CBzcd4AgwH7+/f0DDCAwwIECAA4iHLAAAMOGAx4AiCgRgAIIAS5izKhxI0eMCAg4CCBy5EgMFypYQBBgJcuWLlcisFDhgAUCGALg/8ypcyfPnj0hEHAQYCjRokaPBkDQgEADBAGePm0AIQDVqlavYs1atUIBAF6/ggU74ACAsmYBKNAQYC3btm7fwmWL4UCAunbvBkBg4QKBDQ4CAA4sOLCDBgcOVEAQoMEBBwEeQ44seTJlyQ0IQCDQwEGAzp4/g/aMwEKFChgCoE4dIEOFAK5fw44te/brAwUA4M6tW/eAAwB+AwdQoEKA4saPI0+u3DgHDQGeQ48eHQMEAhU0NMjgAAF3BxgaQKhAQEOGChYCoN9QwUGA9u7fw48v370FAhgCaKhAQEMGBAEABhA4kKBABw0OVGiAIEBDhw0dEEAQgGJFixcxZqR4YP8AAI8fC0QAMJJkSZMACDgIsJJlS5cvYa7c0CBATZs3cQZAgKEBhAoEgAKtoKFBBgcBMhxAEIApAggVHASQOpVqVatXAzQggCFAAAwEHDQ4UAFCgwwIAqQNgMCBhQ0XCGjIgCBAXbt3A1TAEIBvX79/AQcOgIAAAMOHARSYAIBxY8ePAUzIEIByZcuXMWemDKFBAM+fQYcW7RlBANOnTWtoEIA1awQNCFgIMJt2bdu3bTuAQABDAN8BLlgIgCBDAwgVCBA4cIBA8wsbLDgIMJ169eoXLATQvp17d+/fA2SYAIB8eQAFJABQvx5AAgDv4b9/0CBAffv38efXXx9CgwD/AAMIHEiwoMGDAhEQcBCgocMAFghAcBCgosWLGDNWzHBAg4MAIEE20BCgpEkEDjBgcOAAQYCXMGPKhHnBQoCbOHPq3MkzQAMPAIIKHUq0wAQASJMiVXAhgNOnUKNKneoUQoMAWLNq3cq1a1YHBBAEGEt2rAMIBCwgCMC2rdu3bh1AIGAhgN27ATBUCMC3r9+/gAP31WAhgOHDiBMrXhwAggAAkCNLnmxgAoDLmDEfwBCgs+fPoEOLDgChQYDTqFOrXs0atYULAWLLnh3AQgUCDRwE2M27d28EFjQQgOAggPHjxhEQcBCgufPn0KNLb37BQoDr2LNr384dQQUDAMKL/wcwQAGA8+gHJADAvn17BhACyJ9Pv779+wEaQAjAv79/gAEEDiRY0OCGDQEULmS4EAMEAhoaZHAQwOJFBBgsQKhQoYGDACFFjgxQIUMAlClVrmTZEmUFCwFkzqRZ0+ZNCxMA7OS50wAFAEGFDiUatAABBAGULmXa1OnTDBUCTKVa1epVrFQvWAjQ1etXsA4aQKhA4MAFtBcqECBwoUEGBAHkzqU7F0KDAHn17uXb128ABAQcBCBc2PBhxIkhCADQ2HHjBBQATKZc2TLlDg0CbObc2fNn0AgIIAhQ2vRp1KlVl75gIcBr2LFlx0aAwcIFDRYsOAjQ2/dv4L4hNAhQ3P/4ceTJlQfAQCDAc+jRpU+n7oAAAOzZtRcA0N27AQkAxI8nX4AAhgDp1a9n3959BQwB5M+nX9/+ffkVMgTg398/wAACBxIUiABBgIQKFzJsuKFBgIgSJ1KsaDGAhQsBNnLs6PEjyA0PAJAsafIkyQQUALBs6RLAggsIAtCsafMmTQQIAvDsyRNCgwBChxItavSo0AoZAjBt6vQp1KhSp0JoEOAq1qxat3INAKFBgLBix5ItWxbDgQEA1rJtOwAA3LgGGACoa/duXQoNAvDt6xcBBg4QKhAobPhCAwsOAgTIUAFBgMiSJ1OubDnABQsBNnPu7PlzAAwYApAubfo0agj/DQKwbu36NezYCCpkCGD7Nu7cunMjqCAAAPDgwhNIAGD8OPLkyQsQyBDgOfQADhoQOKChgQUHCLYjwGABwgUCFSwgqGAhAPr06tezbx9AA4cA8ufTr28/AIQGAfbz7+8fYACBAzU0CHAQYUKFCxlmqIAgQAAHFhpouFChwoULGyw4QBAAZEiRARp0AHASZUoACiIAcPkSZkyZCQhgCHAzQAYNBCBgCPATaFCgCCxcIHBBQwClS5k2dfo0QAMIAahWtXoVawANDQJ09foVbNgDGAKUNXsWbVq1GhpggHCAQAUIHCxkyGDBwoYLBAhoyIAgQGDBATIcGAAAcWLFAAwI/wDwGLIBAQAoV7ZsWcGBDAEcQCDQwEEA0aNJlx6NAQIBDAFYt3b9GnZsCxUC1LZ9G3fuABkwBPD9G3jw4A4IIAhwHHly5cuXOyBwgQAEDAgCVLd+HYGDBgcqNHAQAHwADAQSADB/Hn369AokAHD/Hn78BAQgEIDgIEB+/fv59w8AEEIFBAEKGjyIMGFCBwQQBHgIMaLEiRQrUsxQIYDGjRw7evwIgQAHBAFKmjyJMgACCxcIWAgQIMMBBQBq2ryJM6eCCAB6+vwJFEAEAhYCGD2KNKnSowgqNAgANarUqVSrHsAQIKvWrVy7ev3qtQGEAGTLmj2LFm0GAg4CuH0LN/+u3AwHNFggkACA3r18+SZYACCw4MGECweOcMFBgMWMGzt+7BgDAQcBKlu+HAABBg4QLlT4fAFCgwwIApiGACGA6tWsW7vWYCGA7Nm0a9NGUMFCgN27ETjAANwBggDEixs37uCAhQDMmzt/Dp05AggEFgC4jj27dgEPAHj/Dj68eAARLiAIgD69+vXs2zeogCCA/PkBEFi4QOCAhgYWMvgHaKEBhAoEKjRwgIEAggANHT6ECPGChQAVLV7EeDFDBQQIMjTQUIEAgQMVDhAgcAGCBQcBXL6ECUFDAJo1bd7EeTPDAQEAfP4EClTBAwBFjQ4oAEDpUqZLP1xAEEDqVKr/Va1eDYBAwwUEAbwGcLCBQAULDgKcRZs2AIIMGghAqNAgwFy6de3a3ZAhwF6+ff321dCgwYEKEBpgQBBAsWIHFhpcIHDBAoIAlS03IOAgwGbOnT1//ozhgAAApU2fRp1aQAQArV2/bq2ggoMAtW3fxp1bt20EFy4gCICgAQEIGAIcR55c+XEHDQhUQBBA+nTq1a1fxz7dAQECGjIgCBBe/HjxDhpUONAAQQD2FghgCBBf/nz69e0HwHBAAQD+/f0DBCBwIEEBDwAgTKgQwIADGAJAjChxIsWKExFouGChwgUHAT6CDClypAMCDQKgTKlyJcuWLlNeuOAgAM2aNm/W/0SQ4UIFDAEaEMAQYCjRokaPIiWKgUABAE6fQgVQwACAqlYLJACgdStXABIaBAgrdizZsmbNIqhAoAGCAG7fwo0r1y0GAg4C4M2rd2/eBhgCAA4seDBgCwcQBEiseDHjxggaEKhAAEOAypYvY86sGXOICQA+gw4NYAEDAKZPo059WkEFBAFew44tezbt2QggVHAQYDfv3r5/+25wAUGA4saPF0eAocEGCAQ0NLCAAUGA6tavV3dAIEOA7t6/gw/v3UGFCg4CoE+vfj379usRXFgAYD79+gsYAMivfz9//RMAWggwkGBBgwcRHkSg4YKDAA8hRpQ4cSKCCg0CZNSoEf8DhAoEDmjY0OAAhA0aDhCoAAFDAJcvXSK4ACFATZs3cebEiQBCBQcBgAYVOpRo0aEOCBQAsJQp0wIFAESVKoABAKtXrxqogCBAV69fwYYVCxYBhAoIAqRVu5ZtW7cBMBCwEIAuXQQWKhDYkMFBAL8BHCAIMNiBBQgELlhAEIBxAAQQKiAIMJlyZcuXMTc44CBAZ8+fQYcWDbqBBACnUadWfXoBAwCvYcOO0CBAbdu3cefWnbtBBQcBgAcXPpx48eAYCFgIEABBAwIXLCAIMJ16desIOFQgYCFAAAQQKjgIMJ58efPn0Y+HUAFBAPfv4ceXPx++AwIDAOTXv58/AAX/AAUAGEhw4AACDgIoXMiwocOHDR0QwBCgosWLGDNqxJiBQAMHFypgCECypMmTKDMc0OAAQgUHAWLKnEmzpk2ZCDRACMCzp8+fQIP+hMAAgNGjRhksAMC0qdOnAARACEC1qtWrWLNeRXChQYCvYMOKHUuWLAYCBBogCMC2rdu3cNk6gECggoMAePPq3cu3714HBDIEGEy4sOHDiAtjOACgsePGDBYAmEy5smUAHxoE2My5s+fPoD03qIAggOnTqFOrXq0aAYQKGALInk27tm3bGQg0CMC7t+/fwIMDt3AAQYDjyJMrX848+YUEAKJLB6AgAYDr2BMkAMC9O/cJGQKI/x9Pvrz58+QREMAQoL379/Djy4+PAEIFBwHy69/Pv79/gAEcHGgQwOBBhAkVLlSoAUIAiBElTqRYUSKEBQA0buTYkcECACFFhiSAIMBJlClVrmSZ0sKFADFlzqRZ02ZNBBAqOAjQ0+dPoEGF+nRwoEEApEmVLmXadKkDAg4CTKVa1epVrFQtSADQ1etXsAwWACBbFoCBCgHUrmXb1u3bthUsBKBb1+5dvHnxNqjgIMBfwIEFDyYs2AEBCwEUL2bc2PHjxhoaBKBc2fJlzJkrYzgAwPNnAAISACBdegAA1KlRJ7gQwPVr2LFlz4aNgQCCALl17+bd2zdvDAQwBCBe3P/4ceTJk2cg4CDAc+jRpU+nHt1CBQQBtG/n3t37d+0ICAwAUN78AwEA1K9n3z7BhQDx5c+nX9/+fAgQAuzn398/wAACBxIsOBBBhQYBFjJs6PAhxIgBIGgIYPEixowaN2JEcCBDgJAiR5IsaVLkBQMAVrJ8IAAAzJgFBgCoaRNAggsBdvLs6fMn0J4XLAQoavQo0qRKkTaogCAA1KhSp1KtajUAggMWAnDt6vUr2LBeN0AIYPYs2rRq1569YAAA3Lhy5T4QAOAuXgAJLgTo6/cv4MCC/SIg4CAA4sSKFzNurNgBAQwBJlOubPky5syUMxBAEOAz6NCiR5MGbeFCgNT/qlezbu1a9YUEAGbTrl37gQAAuncDMHAhAPDgwocTLx4cA4EAypczb+78efMGGgJQr279Ovbs2q9XsBDgO/jw4seTB++AAIIA6tezb+/+vfoLBgDQr2+gAID8+hMYAOAfIACBAgkgCHAQYUKFCxketHAhQESJEylWtDgRwYEMATh29PgRZEiRHy1cCHASZUqVK1mmJIAhQEyZM2nWtBmzQgEAO3lGUAAAaFChQwFcwBAAaVKlS5k2RdoAQgCpU6lWtXqVqoUKCAJ09foVbFixY8EiIIAhQFq1a9m2dav2goUAc+nWtXsXbwAEBAD09QsgggIAgwkXNgwgQoMAixk3/3b8GPLiBhACVLZ8GXNmzZc1NAjwGXRo0aNJlyYNAUIA1atZt3b9erUGDgFo17Z9G3fuABgmAPD9G4CBAQCIF2eQAEBy5ckXQAjwHHp06dOpP9+wIUB27du5d/e+nQCGAOPJlzd/Hn169BYqBHD/Hn58+fPfQ2gQAH9+/fv59w8AsEEEAAQLGjwYQQGAhQwXFjiAIIDEiRQrWrwYoAGEABw7evwIMmRHBwQQBDiJMqXKlSxbsnRAAEGAmTRr2ryJcyaEBgF6+vwJNKjQABAEADiKNKlSBgkAOH36lIKFAFSrWr2KNWuABhACeP0KNqzYsV8tXAiANq3atWzbun17AP9DgLl069q9i3euBg4B+vr9CziwYAQHDAA4jBiAhAQAGjt+DLmxggsBKlu+jDmz5gAWLgT4DDq06NGkQTeAECC16tWsW7t+DVsDhwC0a9u+jTs37QsWAvj+DTy48OEWJgA4jvy4hAQAmjt/Dt35AQwBqlu/jj27dgcEEAT4Dj68+PHkv2toECC9+vXs27t/D7/BhgD069u/jz9/AAQEHAQAGEDgQIIFDRq8IADAQoYLFxQAEFGiggIALF7EuOACggAdPX4EGVIkAQwBTJ5EmVLlSpMaLASAGVPmTJo1bd5sACHATp49ff4EGsABAQQBjB5FmlSpUgwHADyFGlXqUwn/CQBcxZoVwAQLAbx+BRtW7NgLFgKcRZtW7Vq2Zy9YCBBX7ly6de3exdsAQgC+ff3+BRw4gIULAQwfRpxY8WIIDAA8hhxZ8mMJCQBcxpwZgIEDDgJ8Bh1a9OjRDTQEQJ1a9WrWrVFrsBBA9mzatW3fxp27AYQAvX3/Bh5ceIANGwIcR55c+fLlFg4AgB5dOoMCAKxfHwBA+3bu2xlcQBBA/Hjy5c2Xd0DAQQD27d2/hx8/gAYOAezfx59f/37+/RsAhBBgIMGCBg8iRHAgQ4CGDh9CjAjRwYEEAC5izEjBAICOHj+CDEkBAoIAJk+iTKkypYYGAV7CjClzJs0AEDYE/8ipcyfPnj5/AoXQIADRokaPIk1qoUKApk6fQo0aFYIHAFavYgVAwQCArl4LAAgrduzYARMaIAigdi3btm7ZZjiAIADdunbv4s1r4UKAvn7/Ag4seDDhChgCIE6seDHjxhcaBIgseTLlypRDHACgeTPnzp0pGAAgejRp0gMmQEAQYDXr1q5fs0ZQwUKA2rZv486t2wGBAL5/Aw8ufDhx4ggIIAigfDnz5s6dYyCAIAD16tavY7du4UABAN6/gw8fnoIBAObPo08/gMIFBwHew48vfz78DAQcBMivfz///v0BIiDgIEBBgwcRJlS4UGGGCgEgRpQ4kSJFBBc2BNC4kf9jR48cORwoAIBkSZMlEwBQuRKAgAEAYMaUORMmAwIcEATQuZNnT58BHFS4gCBAUaNHkSZNesFCAKdPoUaVOpXq1AYQAmTVupVr164WKiAIMJZsWbNnxyLYcKAAALdv4cKdUABAXbt38ebFW2DCBQwBAAcWPFgwAgsHPkywEIBxY8ePIUO2UCFAZcuXMWfWvDkzggMZAoQWPZp0adIOCGAIsJp1a9evV2OoIGEAANu3ceeeUABAb9+/gQcXvuDABQ4IAiRXvny5gwYHKCQAYOAAhgDXsWfXvl07AgIYAoQXP558efPnyVuoEIB9e/fv4b9HcOGABgcB8OfXv3+/gwb/AA8oAECwoMGDBBUAWMgQgIQCACJKnEixIoAEHQhAaJABQYCPIB1YaHCBwIMCAFICUHAAQ4CXMGPKnClzA4QAOHPq3Mmzp8+dFxoEGEq0qNGjRRFAoACAAQEIFhAEmEq1qlUMEAhEGACgq9evYMOCnVAAgNmzaNOqPVtAwIcJBCpcmHvhAAEQDBQA2Mt3r4IDGAIIHky4sGHCDgg4CMC4sePHkCNLboyBAIIAmDNr3sw5MwIIEwCIBiDgQoUGFhwEWM16NYIMDS4cYDAAgO3buHPr3k1hAIDfwIMLH04cQAEDCQwYKACgufPnzhUcsIAggPXr2LNrvw5BQ4Dv4MOL/x9Pvvx3BBUaBFjPvr379+wRQKAwAID9+wkeUCBQAYJ/gBsgQLhAYEIEBQAULmTY0KHCAwMATKRY0eJFjBk1bpxoYIIGBwFEjiRZ0qRIBwQsBGDZ0uVLmDFlBmhwAUEAnDl17uSJE8OFDgCEDiUqtICCBUkXCDAAwOlTqFGlSj0wAMBVrFm1buXa1evXrAwOcEAQwOxZtGnTZqhAgYCDAHHlzqVb165dDAQsBODb1+9fwAEQNCAgAMBhxIkVL2bc2DHiCAAkTwYgAMBlzJk1b+bc2XNnAxQONHAQwPRp1KkRcLhwQAEABhcQBKBd2/Zt3LlvO6gQ4QAEDAGEDydefP84AgsXKBQA0Nz5c+jRpU+nXr35gQEAtG/n3t37d/DhwxuIQACCBQwIAqxnv96BBQgEQCgAUB+ABAgIAuzn398/wAACBxIsGADBhQcABjwgcMECggASJ1Kc6KDBAQoKAHDs6PEjyJAiR5L0SGAAgJQqV7Js6fIlzJgABgiQcIDABQgQNkCAcOEAARAMCgAoahQACAgIAjBt6vQp1KgBHFyIAODqVQETDkBoYMFBgLABEGDgAOECAQ8GALBt6/Yt3Lhy58p9AOAuXgAGAPDt6/cv4MCCBxMGPCCBgAWKBSgYAOAx5MiPJVxwEOAy5syaN2/GcOEDgNCiQxdQ8IACgdT/BwgQOCBhQQIAsmfTrm37Nu7cugEQAOD7N/DgwocTL278OPLkDA5YCOD8OfTo0p8jCEFgAYDs2rdzH+AdAPjw4seTL2/+PHryBACwbw9gAID48ufTr2//Pv78+vfXN3ABIAQHAQgWNHgQIYYLFAoAcPgQYkSJEylWtHgRo8MBADh2BEAAQEiRI0mWNHkSZUqVK08yIAABQwCZM2nWlJkBAoEFAHj29PkTaFChQ4kWNWqUAAClS5k2dfoUalSpU6lGHcDgwAULDgJ09foVgYMGFw4sGAAAbVq1a9m2dfsWbly5axMAsHsXAAMAe/n29fsXcGDBgwkXLqygA4EDGhpY/3DsuMEFAgckJABwGXNmzZs5d/b8GXTozgQAlDZ9GnVq1atZt3b9GnbrAgoYSJhQQUIEBgoGAPD9G3hw4cOJFzd+HLnxAQQANHf+HHp06dOpV7d+Hbt1AQUACIgAAHx48ePJlzd/Hn169evDCwDwHj4ACQDo17d/H39+/fv59/cPEIDAgQQLAqCQAMCAAgAaOnwIMaLEiRQrWryIseIAAgA6evwIMqTIkSRLmjyJ0iMFAwBaunwJM6bMmTRr2ryJE+eACQB6+vwJNKjQoUSLGj2K1OcAAEwHAHgKNarUqVSrWr2KNavWpwMmAPgKNqzYsWTLmj2LNq3atQIeAHgLN/+u3Ll069q9izev3rcFJgD4Cziw4MGECxs+jDixYsQFAABYwACA5MmUK1u+jDmz5s2cO0seEAGA6NEAFAA4jTq16tWsW7t+DTu2bNQTCgBIkACA7t28e/v+DTy48OHEiwsvcACA8uXMmzt/Dj269OnUqy+fUACA9u3cu3v/Dj68+PHky5cvMAGA+vXs27t/Dz++/Pn0668XMACA/v38+/sHCEDgQIIFDR5EmFDhwoQDFgCAGBGAAQAVLV7EmFHjRo4dPX4EmVGAAAAlTZ5EmVLlSpYtXb6EWdIABQA1bd7EmVPnTp49ff4EGpTBAgBFjR5FmlTpUqZNnT6FWtQABQD/Va1exZpV61auXb1+BeuVwgAACxYAQJtW7Vq2bd2+hRtX7ty0AwDcxWtgAgC+ff3+BRxY8GDChQ0f7ntgAADGjR0/hhxZ8mTKlS1fvmyAAgDOnT1/Bh1a9GjSpU2f7ixhAADWrV2/hh1b9mzatW3fhl0AwG7eBRYAAB5c+HDixY0fR55c+XLiBgwAgB5d+nTq1a1fx55d+3boCSQAAB9e/Hjy5c2fR59e/Xr2DwQAgB9f/nz69e3fx59f/374CSQABCBwIMGCBg8iTKhwIcOGCxcAAPBAAICKFi9izKhxI8eOHj+CrFhAAYCSJg0wAKByJcuWLl/CjClzJs2aKwkA/wBQYACAnj5/Ag0qdCjRokaPIi2aQAKApk6fQo0qdSrVqlavYnVKAADXrl6/gg0rdizZsmbPok0gAQDbtm7fwo0rdy7dunbvti0AYC/fvn7/Ag4seDDhwob/JngAYDHjxo4fQ44seTLlypYvR1AAYDPnzp4/gw4tejTp0qY3K4gAYDXr1q5fw44tezbt2rZpFwAAIIICAL5/Aw8ufDjx4saPI0/uOwEDAM6fD0gAYDr16tavY8+ufTv37t6pEwAAQIABAObPo0+vfj379u7fw4/vXkEEAPbv48+vfz///v4BAhA4kGBBgwcRDiQAgGFDhw8hRpQ4kWJFixcxKogAgP9jR48fQYYUOZJkSZMnOy4AsJJlS5cvYcaUOZNmTZsvDSgAsJPnAAMAgAYVOpRoUaNHkSZVupQoAwMAoEaVOpVqVatXsWbVuhWqgAcAwIYVO5ZsWbNn0aZVu5athAQA4MaVO5duXbt38ebVuxeugAcAAAcWPJhwYcOHESdWvBjxAAkAAERIAIByZcuXMWfWvJlzZ8+fKQ8YAIB0aQEPAKRWvZp1a9evYceWPZt26gEHAOTWvZt3b9+/gQcXPpx4cQEPACRXvpx5c+fPoUeXPp168gEUAGTXvp17d+/fwYcXP5589wEDAKRXn0AAAPfv4ceXP59+ffv38eeXn2AAAP//AAEIHEiwoMGDCBMqXMiQ4QIGACJKnEixosWLGDNq3MixIwUDAEKKHEmypMmTKFOqXMky5AIGAGLKnEmzps2bOHPq3Mkz5wABAABQMACgqNGjSJMqXcq0qdOnUIsaSACgqlUFCgBo3cq1q9evYMOKHUu2rNYCEwAAMDAAgNu3cOPKnUu3rt27ePPaXcAAgN+/gAMLHky4sOHDiBP7LTABgOPHkCNLnky5suXLmDNrXsAAgOfPoEOLHk26tOnTqFN/LgCgtevXsGPLnk27tu3buGMvEACgt+/fwIMLH068uPHjyJNPKACgufPn0KNLn069uvXr2JszWACgu/fv4MOL/x9Pvrz58+jNDwAAYEIBAPDjy59Pv779+/jz698Pf4EAgAAEDixQAMBBhAkVLmTY0OFDiBElHjRAAQAABgMAbOTY0eNHkCFFjiRZ0uRIBgsArGTZ0uVLmDFlzqRZ0+ZKAxQA7OTZ0+dPoEGFDiVa1OhRBgsALGXa1OlTqFGlTqVa1erSAgIAbOXa1etXsGHFjiVb1uzXBAYArGVroAAAuHHlzqVb1+5dvHn17qX7YAAAwIEFDyZc2PBhxIkVLwb8QAAAyJElT6Zc2fJlzJk1b+Z8YAAA0KFFjyZd2vRp1KlVrwb9QAAA2LFlz6Zd2/Zt3Ll178ZtgAEAAAcGACBe3P/4ceTJlS9n3tz5c+IFBgCgXv2BAADZtW/n3t37d/DhxY8nnz2BBADp1a9n3979e/jx5c+nX/+BAAD59e/n398/QAACBxIsaPAgwoQKFSaIAOAhxIgSJ1KsaPEixowaJw4A4PEjAAEJAJAsafIkypQqV7Js6fIlSgUAZtKsafMmzpw6d/Ls6ZNmBAUAhhItavQo0qRKlzJt6vQpAQBSp1KtavUq1qxat3LtOjWCAgBix5Ita/Ys2rRq17Jtq7ZAAgAACACoa/cu3rx69/Lt6/cvYLsCCgAobFiAAQCKFzNu7Pgx5MiSJ1OurFhBBAAAFADo7Pkz6NCiR5Mubfo0atP/ERQAaO36NezYsmfTrm37Nu7WAiIA6O37N/DgwocTL278OPLkEhQAaO78OfTo0qdTr279OvbmAwoA6O79O/jw4seTL2/+PPrwDwwAaO/+Pfz48ufTr2//Pv78BADw7+8fIACBAwkWNHgQYUKFCxkalJAAQESJEylWtHgRY0aNGzl2JAAAZEiRI0mWNHkSZUqVK0NGMAAAZkwDAwDUtHkTZ06dO3n29PkTaE0BDwAAiAAAaVKlS5k2dfoUalSpU6NKSAAAa1atW7l29foVbFixY7EueAAAbVq1a9m2dfsWbly5c+lSSAAAb169e/n29fsXcGDBg/EmUAAAcWLFixk3/3b8GHJkyZMZKxgAAHPmBAMAdPb8GXRo0aNJlzZ9GjXoAQ8AtHb9GnZs2bNp17Z9G7drCgYA9Pb9G3hw4cOJFzd+HDnyAQcANHf+HHp06dOpV7d+HbvzCQUAdPf+HXx48ePJlzd/Hn15BQoAFDgAAH58+fPp17d/H39+/fvjGwAAEIDAgRIMADiIMKHChQwbOnwIMaLEgwwYALiIMaPGjRw7evwIMqTIkRMMADiJMqXKlSxbunwJM6bMkwsWALiJM6fOnTx7+vwJNKjQnQMAGD0K4EEBAEybOn0KNarUqVSrWr0K1QCArVy7ev0KNqzYsWTLmuU6oQCAtWzbun0LN/+u3Ll069q1W2ACgL18+/r9Cziw4MGECxvme2AAgMWMGzt+DDmy5MmUK1uebKAAAAMTAHj+DDq06NGkS5s+jTr1ZwYDALh+zWAAgNm0a9u+jTu37t28e/ue/UAAgAEKABg/jjy58uXMmzt/Dj368wMFAFi/jj279u3cu3v/Dj689QcCAJg/jz69+vXs27t/Dz++/AMDANi/jz+//v38+/sHCEDgQIIFDR5EKLBAAQANHT6EGFHiRIoVLV7EGDECAI4dAQwAEFLkSJIlTZ5EmVLlSpYlDUgAEFPmTJo1bd7EmVPnTp4yCQAAGlToUKJFjR5FmlTpUqYJJACAGlXqVKr/Va1exZpV69aoBwB8BQvAAACyZc2eRZtW7Vq2bd2+LRtBAQADDADcxZtX716+ff3+BRxYMGACAAwfRpxY8WLGjR0/hhz5cAQFACxfxpxZ82bOnT1/Bh1aNAEApU2fRp1a9WrWrV2/hm1agAEAtW3fxp1b927evX3/Bp5bAADixQEIAJBc+XLmzZ0/hx5d+nTqzQsIAJBd+3bu3b1/Bx9e/Hjy2gkAQJ9e/Xr27d2/hx9f/nz6CiIAwJ9f/37+/f0DBCBwIMGCBg8iTKhwIAEADh9CjChxIsWKFi9izHhxgQEACiIACClyJMmSJk+iTKlyJUuRCQDAjAngAICaNm/i/8ypcyfPnj5/ArUpIQEAAAMAIE2qdCnTpk6fQo0qdWpUAgCuYs2qdSvXrl6/gg0rFquEBADOok2rdi3btm7fwo0rdy4FAHbv4s2rdy/fvn7/Ag6sd0ABAIYPI06seDHjxo4fQ45seMABAJYvY86seTPnzp4/gw4tWsADAKZPo06tejXr1q5fw45tesABALZv486tezfv3r5/Aw/+O8EAAAIeAEiufDnz5s6fQ48ufTr15AMeAMiuHcACAN6/gw8vfjz58ubPo0//nYIBAAUSAIgvfz79+vbv48+vfz///AMAHgAwkGBBgwcRJlS4kGFDhwQpGAAwkWJFixcxZtS4kf9jR48eBxwAMJJkSZMnUaZUuZJlS5ckEwwAMJNmTZs3cebUuZNnT582BzwAMJQogAIAkCZVupRpU6dPoUaVOpWpgAUAsGbVupVrV69fwYYVOxZrgQkA0KZVu5ZtW7dv4caVO5fuAgYA8ObVu5dvX79/AQcWPBhvgQkAECcGUABAY8ePIUeWPJlyZcuXMTueUACAAgEAQIcWPZp0adOnUadWvRp1gQkAYMeWPZt2bdu3cefWvTv2hAIAgAcXPpx4cePHkSdXvnx5gQkAoEeXPp16devXsWfXvj36gwEAwIcXP558efPn0adXv378gAQA4McfIABAffv38efXv59/f///AAEIHEiwoMGDAxMkAMCwocOHECNKnEixosWLDA1QAMCxo8ePIEOKHEmypMmTKBksAMCypcuXMGPKnEmzps2bLA1QAMCzp8+fQIMKHUq0qNGjRR8MAMBgAYCnUKNKnUq1qtWrWLNqfTogAYCvYAtEAEC2rNmzaNOqXcu2rdu3ZQ8MADBgAIC7ePPq3cu3r9+/gAML/muAAoDDiBMrXsy4sePHkCNLRnxgAIDLmDNr3sy5s+fPoEOLFm0gAoDTqFOrXs26tevXsGPLXj1gAIDbuHPr3s27t+/fwIMLv51AAoDjyJMrX868ufPn0KNLn/5AAIDr2LNr3869u/fv4MOL/7+eQAKA8+jTq1/Pvr379/Djy4efAACABwIA6N/Pv79/gAAEDiRY0OBBhAkVLjRoYAEAiBELKABQ0eJFjBk1buTY0eNHkBYJAACQwAAAlClVrmTZ0uVLmDFlzoSZQAIAnDl17uTZ0+dPoEGFDs1JAMBRpEmVLmXa1OlTqFGlTk0gAcBVrFm1buXa1etXsGHFYhUAwOxZtGnVrmXb1u1buHHVGhAAwO5dAAUA7OXb1+9fwIEFDyZc2PBfBgkALGbc2PFjyJElT6Zc2fJiBREAbObc2fNn0KFFjyZd2vTpCAoArGbd2vVr2LFlz6Zd2/ZqBREA7OYNYAAA4MGFDyde3P/4ceTJlS8PfgAAgAcJAEynXt36dezZtW/n3t37dgURAIwnX978efTp1a9n3949eQIA5M+nX9/+ffz59e/n398/QAURABAsaPAgwoQKFzJs6PBhQQkAJlKsaPEixowaN3Ls6PHigAIARpI0oAAAypQqV7Js6fIlzJgyZ7JUUAAAzpw6d/Ls6fMn0KBCh+IU8AAA0qRKlzJt6vQp1KhSp1KVkAAA1qxat3Lt6vUr2LBix2IV8AAA2rRq17Jt6/Yt3Lhy58IdwAAAAAkJAPDt6/cv4MCCBxMubPgw3wIGADBurGABgMiSJ1OubPky5syaN3OOPOAAAAAFAJAubfo06tT/qlezbu36dWsBDwDQrm37Nu7cunfz7u37N+0CBwAQL278OPLkypczb+78OXQBDABQr279Ovbs2rdz7+79e/UBAAAMAGD+PPr06tezb+/+Pfz45xcsAGD/Pv78+vfz7+8fIACBAwkWNHgQYcKCFAwAcPgQYkSJEylWtHgRY0aHCxgA8PgRZEiRI0mWNHkSZcqTBgAAoGAAQEyZM2nWtHkTZ06dO3nGVCAAQFChBgwAMHoUaVKlS5k2dfoUalSjBSYAACBgAACtW7l29foVbFixY8mWFbuAAQC1a9m2dfsWbly5c+nWVWtgAgC9e/n29fsXcGDBgwkXNsyAAQDFixk3/3b8GHJkyZMpV1Y8QAEAzZs5d/b8GXRo0aNJl/asIAEA1asHDADwGnZs2bNp17Z9G3du3bMjDADwG3hw4cOJFzd+HHly5b8ZLADwHHp06dOpV7d+HXt27dsnFADwHXx48ePJlzd/Hn169d8fCADwHn58+fPp17d/H39+/fcNRAAAEACFAQAKGjyIMKHChQwbOnwIsSGDBQAqWryIMaPGjRw7evwIsmICCgBKmjyJMqXKlSxbunwJM+aDBQBq2ryJM6fOnTx7+vwJtKaBBwCKGj2KNKnSpUybOn0KNWmBAQCqWlWQAIDWrVy7ev0KNqzYsWTLehUAIK3atWzbun0LN/+u3Ll01T4QACCv3r18+/r9Cziw4MGECx8YACCx4sWMGzt+DDmy5MmUE0dQACCz5s2cO3v+DDq06NGkQxdQAAAAAQCsW7t+DTu27Nm0a9u+3TpBAQC8ey9IACC48OHEixs/jjy58uXMgyuIAACAAQDUq1u/jj279u3cu3v/3j2CAADky5s/jz69+vXs27t/T15BBAD069u/jz+//v38+/sHCEDgQIIFDRp8oADAQoYNHT6EGFHiRIoVLTIcAADAAAAdPX4EGVLkSJIlTZ5E6fFBAgAtXb6EGVPmTJo1bd7EmZMAAJ49ff4EGlToUKJFjR7tKSEBAKZNnT6FGlXqVKr/Va1epTpgAAAABAB8BRtW7FiyZc2eRZtWLVgGBgC8hZugAAC6de3exZtX716+ff3+pSsgAgAADwAcRpxY8WLGjR0/hhxZMmQJCQBcxpxZ82bOnT1/Bh1a9GUBDwCcRp1a9WrWrV2/hh1b9mwJCQDcxp1b927evX3/Bh5c+G0DCQAcR55c+XLmzZ0/hx5d+nIBBQBcx25gAADu3b1/Bx9e/Hjy5c2fBy8BwHr27d2/hx9f/nz69e2zp2AAwH7+/f0DBCBwIMGCBg8iTKhwIcOFAw4AiChxIsWKFi9izKhxI0eJFAwACClyJMmSJk+iTKlyJcuUAhYAGDABAM2aNm/i/8ypcyfPnj5/1hwAYChRABQMAEiqdCnTpk6fQo0qdSrVpAsYAMiqdSvXrl6/gg0rdizZshQMAEirdi3btm7fwo0rdy7dtAIWAMirdy/fvn7/Ag4seDDhvgUAIE4MYEEBAI4fQ44seTLlypYvY84sWQGAzp4/gw4tejTp0qZPo/Y8oQCA1q5fw44tezbt2rZv48ZdYAKA3r5/Aw8ufDjx4saPI/c9oQCA5s6fQ48ufTr16tavY6+ewACAAhMAgA8vfjz58ubPo0+vfn14AQMAwI//oACA+vbv48+vfz///v4BAhA4kGBBgwYZLAAAwAAAhw8hRpQ4kWJFixcxZrw4of8AAI8fQYYUOZJkSZMnUab0yGABAJcvYcaUOZNmTZs3cebUOaEAAJ8/gQYVOpRoUaNHkSb1OWAAAKdPoUaVOpVqVatXsWaVKmEAAK9fwYYVO5ZsWbNn0aZNa4ACALdv4caVO5duXbt38eZ9e2AAAL9/AQcWPJhwYcOHESc2PAAAAAMUAESWPJlyZcuXMWfWvJmzZAoDAIQWnQBAadOnUadWvZp1a9evYZt+IABAgQUAcOfWvZt3b9+/gQcXPjz4gQEAkCdXvpx5c+fPoUeXPh35AwEAsGfXvp17d+/fwYcXP578gQEA0KdXv559e/fv4ceXPx+9AgMA8OfXv59/f///AAEIHEiwoMGDCBMqNLgAgMOHABIAmEixosWLGDNq3Mixo8eLBRgAGEmypMmTKFOqXMmypUuSBADInEmzps2bOHPq3Mmzp88EEgAIHUq0qNGjSJMqXcq06VACAKJKnUq1qtWrWLNq3cpVK4MEABJIAEC2rNmzaNOqXcu2rdu3ZQsAmEsXAAEAePPq3cu3r9+/gAMLHpw3ggIAiBMrXsy4sePHkCNLnkyZAIDLmDNr3sy5s+fPoEOLxvwgAYDTqFOrXs26tevXsGPLXl0AgO3bACIA2M27t+/fwIMLH068uPHfAwwAWM68ufPn0KNLn069unXmBABo3869u/fv4MOL/x9Pvrx5BREAqF/Pvr379/Djy59Pv/56AgDy69/Pv79/gAAEDiRY0OBBhAkVLkSooAAABREATKRY0eJFjBk1buTY0SNFBgBEjgQQAcBJlClVrmTZ0uVLmDFlopSQAMAAAwB07uTZ0+dPoEGFDiVadCgBAEmVLmXa1OlTqFGlTqWqVEICAFm1buXa1etXsGHFjiVblgAAtGnVrmXb1u1buHHlzk1bYAAAvHn17uXb1+9fwIEFD947QAIAxIkVL2bc2PFjyJElT6Ys4AEAzJk1b+bc2fNn0KFFj8Y84AAA1KlVr2bd2vVr2LFlz449AAAAAQ8A7Obd2/dv4MGFDyde3P/47gEUACxnDiABAOjRpU+nXt36dezZtW+PTsEAgAQCAIwnX978efTp1a9n3949+wMA5M+nX9/+ffz59e/n338+QAoGABAsaPAgwoQKFzJs6PAhxAMAJlKsaPEixowaN3Ls6JHiggEARpIsafIkypQqV7Js6dLkAAEAZtIEoAAAzpw6d/Ls6fMn0KBCh/JMoAAA0qRKlzJt6vQp1KhSpyItMAEA1qxat3Lt6vUr2LBix5JdwAAA2rRq17Jt6/Yt3Lhy56ItMAEA3rx69/Lt6/cv4MCCBweOMADAAgYAFjNu7Pgx5MiSJ1OubJmxAQCaNwOgAOAz6NCiR5Mubfo06tT/qkFPKADgNezYsmfTrm37Nu7cundPAOD7N/DgwocTL278OPLkAAYIOLCgAIDo0qdTr279Ovbs2rdzpz4AAPjwAB4AKG/+PPr06tezb+/+/foBAApEeADAQIQFDxYUEBABoAIAAwkWNHgQYUKFCxk2dGiAAgCJEylWtHgRY0aNGzluTCAAwAACEgAMUFAAQEqVDBgoUABAwQEGAAYUAHATZ06dO3n29PkTaNCdBigAMHoUaVKlS5k2dfoU6lIGEgAAkPAAQFatW7cyWAAALIACBQAkOBABQAEFBQC0dfsWbly5c+nWtXsXQIEFAPj2BbAAQGDBgwkXNnwYcWLFggso/xgAQAKBAgAEJABwGXNmzZgNFADwGXRoAAYiMABg4IECAKtZt3b9GnZs2bNp135NAUBu3bt59/b9G3jw4AoeGADwQEIBAAMANHf+HHp06dOfDxAgAECCCQwAABgAAHx48ePJlzd/Hn369BQAtHf/Hn58+fPp13dvoAAAAQcEAFAAUMAAAAQLGjyIMKHChQgLGABg4IAEAAMUFACAMaPGjRw7evwI0qMBBgBKmgQwAIDKlSxbunwJMybMAgwEABAwQQCAAQMA+PwJNKjQoUIfCACANKnSpUyZFpAQAUCBBwoAWL2KNavWrVy7esWaQAKAsWTLmj2LNq1atQMMAChAQf8CgAIMEgC4izev3r18++p9IACA4MGECxs+PHjAggUADExgACCy5MmUK1u+jDlzgggAOnsGUACA6NGkS5s+jdq0AAYABhyIAACAAQC0a9u+jTu37t0AFiQAADy48OHEixs3kABAAQISAABIMACA9OnUq1u/jj27dgASAHj/Dj68+PHjCwAAEGECAAAPFgB4Dz++/Pn069u/jz+/fvkDABQAKIECgAEMFABAmFDhQoYNHT5sKAHARIoVLV7EONHAggEAKEwoACBBAQAlTZ5EmVLlSpYtXb6EGRPAgAUMAAygwADATp49ff4EGpRngQQAjB4FkADAUqZNnT51KkBCAgD/Cx4UAJBV61auXb1+BRsWrAADAMyeRZtW7Vq2bdcaUABgAAEKAAAkGABA716+ff3+3asgAgDChQ0fRnx4gAIDABYQEADAQIIBACxfxpxZ82bOnT1/thxBAQDSpU2fRp1a9WrWpAsAGEBhAgAACxIAwJ1b927evAU8ABBc+HDixQEUiLAAgAIJCgA8hx5d+nTq1a1fx35dQgIA3b1/Bx9e/Hjy5ccPYBABAAAJDAC8hx9f/vz3AwoAwJ8fwAMA/f0DBGAgAYACByQAKCCgAICGDh9CjChxIsWKFi9izKhxY8YEAgAAIEABAAADAE6iTKlyZcoIAF4yiABgAIUHAAAM/wCgcyfPnj5/Ag0qdCjRokaPIk360wAAABQOAACwIAGAqlavYsVKgEABAAsUAAgrdizZsmbPok2rdi1bAAYGAIgrdy7dunbv4s2rd2/cBxIAAIjAAADhwgAEPACgeDGABQ8AAKAgYQCAAQAuY86seTPnzp4/gw4NWkICAKZPo06tejXr1q5fw1atYAEAAAckAABQYAEDAL5/AwcwIAEAABQIFACQwACA5s6fQ48ufTr16tavR6dgAAD37t6/gw8vfjz58ubJD1AAAMABAgIAwI8vfz6AAQAAPJhQAMACAQMAAhA4kGBBgwcRJlS4kCEABQMARJQ4kWJFixcxZtS4sf9iAQEDAFAgUACAggQAUKZUuZJlSgUPBgCIEKEAAJs3cebUuZNnT58/gQYVOpR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A7N7Fm1fvXr58ByxYAMAABQYABhQAkFjxYsaNHT+GzHjAAQCVLV/GnFnzZs6dBwgQAKDAAQcAABQAkFr1atatXQOoYADAbNq1bd/GnXu2AQAAJFAAMMBBAgDFjR9Hnlz5cubNiw84AED6dOrVrV/Hnl179QIGABQ4UAEAgAQFAJxHn179evQMCgCAH1/+fPr15yNgAADAAQoAAABEMAAAwYIGDyJMqHChwgEJAECMCEAAgIoWL2LMqHEjx44eAQyAIAHAAAcCAKBMqXIly5YuX6YcAADAggoGADD/YDAAAM+ePn8CDSp0KNGfBSgASKp0KdOmTp9CjSp1KoABAhgAGEDBAQAAAwCADSt2LNmyZgsIMADAwQEEABAYACB3Lt26du/izatXbwEKAP4CDix4MOHChg8jTjy4AAIAAw5QAAAgQQEAli9bdjAAAOfOnj9zRgBBAAABEAwASK16NevWrl/Dji3btQEAtm8XqABgN+/evn8DDy58OPHivAcAGCCBAgAADgQAiA6AQgEA1q9jR4AAAAICDgAUSDAAAPny5s+jT69+Pfv27tEXoABgPv369u/jz69/P//++wEucAAAQAUHFAoAUKhwgAMHAAxUWACAYkWLFzFm1LiR/2NHjx8BFIAAgGRJkydRplS5kmVLly8BGEgAAACBAzcBAFiAAEBPnz+BBhU6lGhRo0eLGqgAgGlTp0+hRpU6lWpVq1exOmAAgGtXr1/BhhU7lmxZs2e5GqgAgG1bt2/hxpU7l25du3frJgAAgMECAH8BBxY8mHBhw4cRJ1b8t8ACAI8hFxAAgHJly5cxZ9a8mXNnz58rHxgAwIABAKdRp1a9mnVr169hx5b92kAFALdx59a9m3dv37+BBxeO+8AAAMeRJ1e+nHlz58+hR5cu3UAFANexZ9e+nXt379/BhxePHQEA8+fRp1e/nn179+/hx1dvgAEA+/cBDACwn39///8AAQgcSLCgwYMIEypcyJAggwQAIkqcSLGixYsYM2rcyDEiAgkAQoocSbKkyZMoU6pcybKlAwEAYsqcSbOmzZs4c+rcyTMmAggAggodUACA0aNIkypdyrSp06dQox4lAADAAgQAsmrdyrWr169gw4odSzYsAgkA0qpdy7at27dw48qdS1ctAQB48+rdy7ev37+AAwseTBiBBACIEytezLix48eQI0uenBgCgMuYM2vezLmz58+gQ4veXMAAgNOoCyQAwLq169ewY8ueTbu27duwExgAwLu379/AgwsfTry48eO8E0AAwLy58+fQo0ufTr269evYISQAwL279+/gw4v/H0++vPnz3AVAAMC+vfv38OPLn0+/vv379R0AACAhAQCAAAQOJFjQ4EGECRUuZNgQQAEDACRORMAAwEWMGTVu5NjR40eQIUVeHEAAAIABAFSuZNnS5UuYMWXOpFlzZgIIAHTu5NnT50+gQYUOJVpU54ADAJQuZdrU6VOoUaVOpVrVagIGALRu5drV61ewYcWOJVvWawEAadWuZdvW7Vu4ceXOpatWgAMAefXu5dvX71/AgQUPJlxYAgIAiRUvZtzY8WPIkSVPppx4gQMAmTVv5tzZ82fQoUWPJi0aAQAAFRAAYN3a9WvYsWXPpl3b9m3WCAQA4N3bAAIAwYUPJ17c//hx5MmVL2cevMABAAASDABQ3fp17Nm1b+fe3ft38N0FOABQ3vx59OnVr2ff3v17+OULUABQ3/59/Pn17+ff3z9AAAIHEixo8CDCgQsYAGjo8CHEiBInUqxo8SJGhwkAcOzo8SPIkCJHkixp8iTIBAkAsGw5YACAmDJn0qxp8ybOnDp38qwpoQCAoEKHEi1q9CjSpEqXMg3KgAGAqFKnUq1q9SrWrFq3cu1KwQCAsGLHki1r9izatGrXsg3LYAGAuHIHDABg9y7evHr38u3r9y/gwHYLVAAAAEIBAIoXM27s+DHkyJInU64smQEDAJo3c+7s+TPo0KJHky6t2UAFAP+qV7Nu7fo17NiyZ9OubZvBAgC6d/Pu7fs38ODChxMvrruAAwDKlzNv7vw59OjSp1Ov7rxAAQDatyNAAOA7+PDix5Mvb/48+vTqxy8YAOA9/Pjy59Ovb/8+/vz63ztYAAAgAIEDCRY0eBBhQoULGTZseKAAAIkTKVa0eBFjRo0bOXaU6EAAAJEjSZY0eRJlSpUrWbZUWWABAAAHBgCweRNnTp07efb0+RNoUJsGCgAwenRBAgBLmTZ1+hRqVKlTqVa1uhSBBAAACgDw+hVsWLFjyZY1exZt2rMOBABw+xZuXLlz6da1exdvXrcIJADw+xdwYMGDCRc2fBhxYsUMEgD/cPwYcmTJkylXtnwZc2bJBQB09vwZdGjRo0mXNn0atWcHCQC0dv0admzZs2nXtn0bd24CAwD09v0beHDhw4kXN34ceW8ICQA0d/4cenTp06lXt34de/UBBQAAIAAAfHjx48mXN38efXr168MvQAAAfnwEBQDUt38ff379+/n39w8QgMCBBAsaNJgAAgAACwA4fAgxosSJFCtavIgx40UICQB4/AgypMiRJEuaPIkypccEEAC4fAkzpsyZNGvavIkzp04ICQD4/Ak0qNChRIsaPYo0qc8CCAA4fQo1qtSpVKtavYo1q1QBBgB4/VpgAICxZMuaPYs2rdq1bNu6PUsB/4DcuXTr2r2LN6/evXz7zpWAAIDgwYQLGz6MOLHixYwbOyYAILLkyZQrW76MObPmzZwlS0AAILToAQBKmz6NOrXq1axbu34N23QCBgAAUACAO7fu3bx7+/4NPLjw4cElIACAPLny5cybO38OPbr06cgFOACAPbv27dy7e/8OPrz48eQlIACAPr369ezbu38PP778+egTLACAP7/+/fz7+wcIQOBAggUNHkSYUKFBAwMAPIQooAAAihUtXsSYUeNGjh09fsQoAMBIkiVNnkSZUuVKli1dkqxgAMBMmjVt3sSZU+dOnj19+hxwAMBQokWNHkWaVOlSpk2dEq1gAMBUqv9VrV7FmlXrVq5dvW5FgADAgAMAzJ5Fm1btWrZt3b6FG/dsggEA7N51UADAXr59/f4FHFjwYMKFDe9dwAAAAAMAHD+GHFnyZMqVLV/GnPlyBQMAPH8GHVr0aNKlTZ9GndrzAgYAXL+GHVv2bNq1bd/GnVu3BAMAfP8GHlz4cOLFjR9Hnvz3AAAABgCAHl36dOrVrV/Hnl379ugVCgAAH178ePLlzZ9Hn179+vUFKACAH1/+fPr17d/Hn1///vgUCgAEIHAgwYIGDyJMqHAhw4YKBwwAUIACgIoWL2LMqHEjx44eP4K0CGEAgJImEwwAoHIly5YuX8KMKXMmzZoqGSz/ADBgAYCePn8CDSp0KNGiRo8iNUqhAICmTp9CjSp1KtWqVq9ibcpgAYCuXr+CDSt2LNmyZs+iTUuhAIC2bt/CjSt3Lt26du/ibYvAAIC+fv8CDix4MOHChg8jDswAAOPGAAwAiCx5MuXKli9jzqx5M+fKBRwACC16NOnSpk+jTq16NWvRBwYAiC17Nu3atm/jzq17N2/eBioACC58OPHixo8jT658OXPhBwYAiC59OvXq1q9jz659O/fsDBIAMCABAPny5s+jT69+Pfv27t+XHwBgPn0ABwYAyK9/P//+/gECEDiQYEGDBxEmVKjQgQAADyFGlDiRYkWLFzFm1Ljx/8AAAB9BhhQ5kmRJkydRplT5kUECAC9hxpQ5k2ZNmzdx5tQ50wAAnz8BOAAwlGhRo0eRJlW6lGlTp0cHIAAwlWpVq1exZtW6lWtXr1QJABA7lmxZs2fRplW7lm1btwgkAJA7l25du3fx5tW7l2/fuQQABBY8mHBhw4cRJ1a8mLHiBAUAIJAAgHJly5cxZ9a8mXNnz58rLwAwmjQACQBQp1a9mnVr169hx5Y9OzWEBAAGFACwm3dv37+BBxc+nHhx48QJAFC+nHlz58+hR5c+nXr15RASANC+nXt379/Bhxc/nnx58wQApFe/nn179+/hx5c/n776AQDw59e/n39///8AAQgcSLCgwYMIEyo8SAGAw4cQI0qcSLGixYsYM2pMAAGAx48gQ4ocSbKkyZMoU34kAKCly5cwY8qcSbOmzZs4bQ4AACABBABAgwodSrSo0aNIkypdGrQCgKdQASQAQLWq1atYs2rdyrWr169VJSAAYEAAgLNo06pdy7at27dw48qFSwCA3bt48+rdy7ev37+AA9+VgACA4cOIEytezLix48eQI0smAKCy5cuYM2vezLmz58+gLQsoAKC06dOoU6tezbq169ewUy8AQLs2AAQAcuvezbu379/AgwsfTrw3AgEAkitfzry58+fQo0ufTj35gAMAsmvfzr279+/gw4v/H0++vAAHANKrX8++vfv38OPLn08/fYEDAPLr38+/v3+AAAQOJFjQ4EGECRUuRAihAAABDABMpFjR4kWMGTVu5NjRI8UCAESOHHAAwEmUKVWuZNnS5UuYMWWipGAAwE2cOXXu5NnT50+gQYUKLXAAwFGkSZUuZdrU6VOoUaUilVAAwFWsWbVu5drV61ewYcVuLQDA7NkBDgCsZdvW7Vu4ceXOpVvX7tsCBgDs5dvX71/AgQUPJlzY8N4CFAAsZtzY8WPIkSVPplzZ8uUFDABs5tzZ82fQoUWPJl3a9GYDFACsZt3a9WvYsWXPpl3bNm0BAwAwYADA92/gwYUPJ17c//hx5Ml9DxAAwPnzAQwATKde3fp17Nm1b+fe3Tv1AwUAFCgAwPx59OnVr2ff3v17+PHdG6AAwP59/Pn17+ff3z9AAAIHEixo8CDCgQcGAGjo8CHEiBInUqxo8SJGjAYqAOjo8SPIkCJHkixp8iRKjwYAsGzp8iXMmDJn0qxp8yZMAw4A8Ozp8yfQoEKHEi1q9ChSBgsAMG3q9CnUqFKnUq1q9SpTBBUAcO3q9SvYsGLHki1r9mzZAQAAOFgA4C3cuHLn0q1r9y7evHrfGoAA4C/gAQYAEC5s+DDixIoXM27s+HFhAgMACEAA4DLmzJo3c+7s+TPo0KI/I6gA4DTq1P+qV7Nu7fo17NiyURMAYPs27ty6d/Pu7fs38ODCEUgAYPw48uTKlzNv7vw59OjHGQCobv069uzat3Pv7v07+OwFEgAob34AAgDq17Nv7/49/Pjy59Ov714AAgD69/Pv7x8gAIEDCRY0eBBhQoULDSaQAABiRIkTKVa0eBFjRo0bOUIQAABkSJEjSZY0eRJlSpUrQSaAAABmTJkzada0eRNnTp07c1YAAABCAgBDiRY1ehRpUqVLmTZ1OnRAAQBTqSKAAABrVq1buXb1+hVsWLFjsxIAcBZtWrVr2bZ1+xZuXLlzE0AAcBdvXr17+fb1+xdwYMF4DwAwfBhxYsWLGTf/dvwYcuTFAwBUtmxgAQDNmzl39vwZdGjRo0mX9mxgAADVq1m3dv0admzZs2nXVi0AAgDdu3n39v0beHDhw4kXNy4hAQDly5k3d/4cenTp06lXVy7AAQDt27l39/4dfHjx48mXHy8AAAAJCAC0d/8efnz58+nXt38ff3sDCQD09w/QgAAABAsaPIgwocKFDBs6fEhwwAEAABAMAIAxo8aNHDt6/AgypMiRIAU4AIAypcqVLFu6fAkzpsyZKAccAIAzp86dPHv6/Ak0qNChRAU4AIA0qdKlTJs6fQo1qtSpSQ0AuIo1q9atXLt6/Qo2rNitCQQAOIs2rdq1bNu6fQs3/67cuRIMALiLN6/evXz7+v0LOLDguwsYADiMOLHixYwbO34MObLkyRUMALiMObPmzZw7e/4MOrToywIWADiNekABAKxbu34NO7bs2bRr277NugAFAAAYFAAAPLjw4cSLGz+OPLny5cgXMAAAPbr06dSrW7+OPbv27dALUAAAPrz48eTLmz+PPr369ewXMAAAP778+fTr27+PP7/+/fAHMAAIQOBAggUNHkSYUOFChg0NGjAAQOJEAwYAXMSYUeNGjh09fgQZUuRGBgMAnESZUuVKli1dvoQZU+ZJBgsA3MSZU+dOnj19/gQaVOhQCgUAHEWaVOlSpk2dPoUaVepRBv8LAFzFmlXrVq5dvX4FG1bs1wIOAACgUADAWrZt3b6FG1fuXLp17a4tMADAXr4LFgAAHFjwYMKFDR9GnFjxYsAGKgCAHFnyZMqVLV/GnFnzZs4MFgAAHVr0aNKlTZ9GnVr1atAGJACAHVv2bNq1bd/GnVv3bt4CEgAAHlz4cOLFjR9Hnlz5cuIGADyHHl36dOrVrV/Hnl07dAcCAHwHH178ePLlzZ9Hn179+gMDALyHH1/+fPr17d/Hn1//ewcCAAAEIHAgwYIGDyJMqHAhw4YJByAAAODAAAAWL2LMqHEjx44eP4IMaTGBAQAmTyYwAGAly5YuX8KMKXMmzZo2VyL/kAAAQAIAPn8CDSp0KNGiRo8iTXrUgQAATp9CjSp1KtWqVq9izeoUgQQAXr+CDSt2LNmyZs+iTavWgQAAbt/CjSt3Lt26du/izet2gAEAfv8CDix4MOHChg8jTix4AQIAjh8PACB5MuXKli9jzqx5M+fOlw8ACC16NOnSpk+jTq16NWvREBIAiC17Nu3atm/jzq17N+/eBAAADy58OPHixo8jT658eXAICQBAj15gAIDq1q9jz659O/fu3r+Dr54AAgAAEgCgT69+Pfv27t/Djy9/fnwICQDgz69/P//+/gECEDiQYEGDBxEmVCgwAQQADyFGlDiRYkWLFzFm1LgR/0ICAB9BhhQ5kmRJkydRplT50YAAAC9hxpQ5k2ZNmzdx5tQ5E8EAAD+BIigAgGhRo0eRJlW6lGlTp0+ROgAwlWpVq1exZtW6lWtXr1QlIAAwlmxZs2fRplW7lm1bt28JAJA7l25du3fx5tW7l2/fuRIQABA8mHBhw4cRJ1a8mHFjxQgEAABAAEBly5cxZ9a8mXNnz59BWzYwAEBp0xAMAFC9mnVr169hx5Y9m3Zt1QIcAAAwAEBv37+BBxc+nHhx48eRG5eAAEBz58+hR5c+nXp169exNxfgAEB379/Bhxc/nnx58+fRp3dgAEB79+/hx5c/n359+/fxxzcAgH9///8AAQgcSLCgwYMIEypcyNBgBQMAIkqcSLGixYsYM2rcyJHjgAMAQoocSbKkyZMoU6pcyVJkBQMAYsqcSbOmzZs4c+rcyTNngQIABhwAQLSo0aNIkypdyrSp06dFGRQAQLWqgAEAsmrdyrWr169gw4odSzbrAgYAAAgAwLat27dw48qdS7eu3bt1KxgAwLev37+AAwseTLiw4cN8FzAAwLix48eQI0ueTLmy5cuYKxgAwLmz58+gQ4seTbq06dOcCxQAwLq169ewY8ueTbu27duwHQwAwLt3AQDAgwsfTry48ePIkytfTryABADQo0ufTr269evYs2vfHp1CAQDgw4v/H0++vPnz6NOrX7++AAUA8OPLn0+/vv37+PPr3x//QAGAAAQOLADA4EGECRUuZNjQ4UOIEQ8yWACgAAQAGTVu5NjR40eQIUWOJCmSQgEAKVWuZNnS5UuYMWXOpJnSwQIAOXXu5NnT50+gQYUOJVqUQgEASZUuZdrU6VOoUaVOpZpUAAIAWbVu5drV61ewYcWOJdsVAQC0aQEsGADA7Vu4ceXOpVvX7l28eeMWEADA71/AgQUPJlzY8GHEif8eGADA8WPIkSVPplzZ8mXMmTMbqADA82fQoUWPJl3a9GnUqT8TGADA9WvYsWXPpl3b9m3cuW0LMAAAQQUAwYUPJ17c//hx5MmVL2cuPAEA6NEBVBgAwPp17Nm1b+fe3ft38OGtQxAAAMAAAOnVr2ff3v17+PHlz6cvn8AAAPn17+ff3z9AAAIHEixo8CDChAoVQkgA4CHEiBInUqxo8SLGjBo3VgDg8SPIkCJHkixp8iTKlCMLAGjp8iXMmDJn0qxp8yZOlwQA8Ozp8yfQoEKHEi1q9ChSBBIAMG3q9CnUqFKnUq1q9WpTAgC2cu3q9SvYsGLHki1rlqyBAQASSADg9i3cuHLn0q1r9y7evG8lAOjrF8ACAIIHEy5s+DDixIoXM248WEICAAUSAKhs+TLmzJo3c+7s+TNozwQAkC5t+jTq1P+qV7Nu7fp1aQkIANCubfs27ty6d/Pu7fs3cAIAhhMvbvw48uTKlzNv7pw4ggEAplOvbv069uzat3Pv7v26AwDixwMwAOA8+vTq17Nv7/49/Pjy1yNgAOA+/vz69/Pv7x8gAIEDCRY0eBBhwoEDCABw+BBiRIkTKVa0eBFjRo0CIADw+BFkSJEjSZY0eRJlSo8DDgBw+RLAAAAzada0eRNnTp07efb0SVOCAQAJGAAwehRpUqVLmTZ1+hRqVKcDCACwehVrVq1buXb1+hVs2KsVDAAwexZtWrVr2bZ1+xZu3LgDDgCwexdvXr17+fb1+xdw4LsOCgAwfBhxYsWLGTf/dvwYcmTFBgBUtgyAAQDNmzl39vwZdGjRo0mX9mwAAQDVq1m3dv0admzZs2nXVl2AAgDdu3n39v0beHDhw4kXN77AAQDly5k3d/4cenTp06lXV16AAgDt27l39/4dfHjx48mXH79gAIAFDAC0d/8efnz58+nXt38ff/sBCQD09w9wAAQABAsaPIgwocKFDBs6fFiQQgEAAwYAuIgxo8aNHDt6/AgypMiPBSgAOIkypcqVLFu6fAkzpkyUFAoAuIkzp86dPHv6/Ak0qFChBSoAOIo0qdKlTJs6fQo1qlSkAwAAGDAAgNatXLt6/Qo2rNixZMtqLVABgNq1bNu6fQs3/67cuXTr2mXAAIDevXz7+v0LOLDgwYQL6zVQAYDixYwbO34MObLkyZQrTzYAAACDBQA6e/4MOrTo0aRLmz6NunMBBwBaux6QAIDs2bRr276NO7fu3bx7zz4wAAACBACKGz+OPLny5cybO38OvbmBCgCqW7+OPbv27dy7e/8O3vqBAQDKmz+PPr369ezbu38PH76BCgDq27+PP7/+/fz7+wcIQOBAggUNHhQAQOFChg0dPoQYUeJEihUdFhAAQONGAAYAfAQZUuRIkiVNnkSZUuXIBQkAvIQZU+ZMmjVt3sSZU+dLBBIA/AQaVOhQokWNHkWaVOlSBwIAPIUaVepUqv9VrV7FmlXrUwQSAHwFC2AAALJlzZ5Fm1btWrZt3b4tSwEAAAYJANzFm1fvXr59/f4FHFgw3gEADB9GIAHAYsaNHT+GHFnyZMqVLTMmAEDzZs6dPX8GHVr0aNKlTSOQAED1atatXb+GHVv2bNq1V1cAkFv3bt69ff8GHlz4cOK9BxQAkFx5AQEAnD+HHl36dOrVrV/Hnl06ggIAvH8HH178ePLlzZ9Hn957AggA3L+HH1/+fPr17d/Hn18/hAQA/AMEIHAgwYIGDyJMqHAhQ4YJIACIKHEixYoWL2LMqHEjR40MAACAkAAAyZImT6JMqXIly5YuX5IsgAAAzZoGFgD/yKlzJ8+ePn8CDSp0KFGdBAAAKDAAANOmTp9CjSp1KtWqVq9STQABANeuXr+CDSt2LNmyZs92JQBgLdu2bt/CjSt3Lt26du8mcABgL9++fv8CDix4MOHChvkWAABgAIDGjh9Djix5MuXKli9jdpyAAYDOnj+DDi16NOnSpk+jTi0BAYDWrl/Dji17Nu3atm/jbi3AAYDevn8DDy58OPHixo8jN14AAAAJCABAjy59OvXq1q9jz659O/QECwCAD1/AAIDy5s+jT69+Pfv27t/DLz/gAAAAAgoAyK9/P//+/gECEDiQYEGDBxEmVLjwoAAHACBGlDiRYkWLFzFm1LgR/+KAAwBAhhQ5kmRJkydRplS5kqUABwBgxpQ5k2ZNmzdx5tS5M+YCAD+BBhU6lGhRo0eRJlU61AACAE+hFigAgGpVq1exZtW6lWtXr1+xOigAgGxZs2fRplW7lm1bt2/JLmAAgG5du3fx5tW7l29fv38BVzAAgHBhw4cRJ1a8mHFjx48JL2AAgHJly5cxZ9a8mXNnz585F5AAAIAEAwBQp1a9mnVr169hx5Y9G/WAAQBw517AAEBv37+BBxc+nHhx48eR9y5AAUBz58+hR5c+nXp169exZ1/AAEB379/Bhxc/nnx58+fRdy8gAUB79+/hx5c/n359+/fxxx8wAEB///8AEyQAQLCgwYMIEypcyLChw4cIEwwAQLGixYsYM2rcyLGjx48UGSwAQLKkyZMoU6pcybKly5cwKRQAQLOmzZs4c+rcybOnz580GSwAQLSo0aNIkypdyrSp06dMByQAAIBCAQBYs2rdyrWr169gw4odixWBAQBo0yZIAKCt27dw48qdS7eu3bt42xqoAAAAAgCAAwseTLiw4cOIEytenJjBAgCQI0ueTLmy5cuYM2veDNlABQCgQ4seTbq06dOoU6tezZrBAgCwY8ueTbu27du4c+veHbsAgN/AgwsfTry48ePIkysfziABgOfQo0ufTr269evYs2vffmAAgO/gw4v/H0++vPnz6NOr/+5AAID38OPLn0+/vv37+PPrxz8AAACABwYAIFjQ4EGECRUuZNjQ4UOCDBIAoFjRQAEAGTVu5NjR40eQIUWOJJkRgQQAABwAYNnS5UuYMWXOpFnT5s2aDgQA4NnT50+gQYUOJVrU6FGeCSQAYNrU6VOoUaVOpVrV6lWsDgQA4NrV61ewYcWOJVvW7FmuBQQAYNvW7Vu4ceXOpVvX7l24CQoA4NvXQAEAgQUPJlzY8GHEiRUvZlwYAgDIkSVPplzZ8mXMmTVvjgwhAQDQoUWPJl3a9GnUqVWvZk0AwGvYsWXPpl3b9m3cuXXDlpAAwG/gwYUPJ17c//hx5MmVH0fAAAAAAgCkT6de3fp17Nm1b+fefXqBAQDEj4eQAMB59OnVr2ff3v17+PHlnxcAAcB9/Pn17+ff3z9AAAIHEixo8CDChAoLSkgA4CHEiBInUqxo8SLGjBofJmAA4CPIkCJHkixp8iTKlCpHDgDg8iWABQYA0Kxp8ybOnDp38uzp8yfOBACGEi1q9CjSpEqXMm3qlKgEBACmUq1q9SrWrFq3cu3q9SsBAGLHki1r9izatGrXsm07tgICAHLn0q1r9y7evHr38u2r14ABAAMIAChs+DDixIoXM27s+DFkwwIKAKhseUEBAJo3c+7s+TPo0KJHky6teYEDAP8AEgBo7fo17NiyZ9Oubfs2btsVEADo7fs38ODChxMvbvw48t4LGABo7vw59OjSp1Ovbv069uwVDADo7v07+PDix5Mvb/48+u4DCgBo7/49/Pjy59Ovb/8+/vgQCgDo7x8gAIEDCRY0eBBhQoULGTY8OOAAAIkTKVa0eBFjRo0bOXacSMEAAJEjSZY0eRJlSpUrWbZsWeAAAJkzada0eRNnTp07efacKaEAAKFDDQAwehRpUqVLmTZ1+hRq1KMMGAAY4ABAVq1buXb1+hVsWLFjyYqlYABAWrVr2bZ1+xZuXLlz6aZlsABAXr17+fb1+xdwYMGDCRemUABAYsWLGTf/dvwYcmTJkyknToAAQGbNmzl39vwZdGjRo0l3FgAAdWoACQYAcP0admzZs2nXtn0bd+7YAxgA8P0beHDhw4kXN34cefLfBwoAcP4cenTp06lXt34de/bsBigA8P4dfHjx48mXN38effrvBwYAcP8efnz58+nXt38ff377AhIAMACwAoCBBAsaPIgwocKFDBs6JGgAgMSJACgMAIAxo8aNHDt6/AgypMiRGB0IAIAypcqVLFu6fAkzpsyZNA8MAIAzp86dPHv6/Ak0qNChOBkIAIA0qdKlTJs6fQo1qtSpTAcAuIoVAIQBALp6/Qo2rNixZMuaPYsW7AADANq6fQs3/67cuXTr2r2L1y0BAHz7+v0LOLDgwYQLGz6MGIEEAIwbO34MObLkyZQrW77cmACAzZw7e/4MOrTo0aRLmyZtoAAABBIAuH4NO7bs2bRr276NO/drBwB6+wbAAIDw4cSLGz+OPLny5cybD4eQAMAABACqW7+OPbv27dy7e/8O3jsBAOTLmz+PPr369ezbu39fHkICAPTr27+PP7/+/fz7+wcIQOBAggUNGiQAQOFChg0dPoQYUeJEihUXGhgAQONGjh09fgQZUuRIkiU9SgCQUiWAAQBcvoQZU+ZMmjVt3sSZUyYCCAB8/gQaVOhQokWNHkWa9CcBAE2dPoUaVepUqv9VrV7FmjUBBABdvX4FG1bsWLJlzZ5F6/UAALZtARgAEFfuXLp17d7Fm1fvXr5yJSAAgGABAMKFDR9GnFjxYsaNHT9uTADAZMqVLV/GnFnzZs6dPVOWgADAaNKlTZ9GnVr1atatXb8mAED2bNq1bd/GnVv3bt69Zy8oAED4cOLFjR9Hnlz5cubNjScAEF06AAEArF/Hnl37du7dvX8HH167gQQAzJ9Hn179evbt3b+HH9/8gAMA7N/Hn1//fv79/QMEIHAgwYIGDyJMWFCAAwAOH0KMKHEixYoWL2LM6HDAAQAeP4IMKXIkyZImT6JMeZJBAQACHACIKXMmzZo2b+L/zKlzJ0+ZCAAADTqAAoCiRo8iTap0KdOmTp9CNVrBAICqVq9izap1K9euXr+CBTvgAICyZs+iTat2Ldu2bt/CNVvBAIC6du8CEFDhwAEICAAADix4MOHChg8jTqz48AAJAB5Djix5MuXKli9jzqx58oACAD6DNnAAAoIBAwRQgABgNevWrl/Dji17Nu3aBSgAyK17N+/evn8DDy58OPHiCxgASJ58wIEEAJ5DhwABAPXq1q9jz659O/fu3QtQACB+PPny5s+jT69+Pfv26xEAALCAAYD69R0wAKB/v34KBgACEDiQYEGDBxEmVLhQ4QAGACBGHLAAQEWLFzFm1LiR/2NHjx8vGkiQoAAAkydNUigAwIABAC9fHhgAgGZNmgscANC5k2dPnz+BBhU6lKjPAhQAJFW6lGlTp0+hRk2KYMECBACwZtW6VWuCChQgQDggwQAAs2cpFACwli0AAxUAxJUrtwAFAHfx5tW7l29fv38BB95bgAIAw4cRJ1a8mHFjxwIoVGDAoAKFBAAwZ9a8GcACCggAhAYg4AACAKdPIwCwmvVqAxUAxJYtuwAFALdx59a9m3dv37+B/y7AAEBx4wAGAFC+nHlz58+hR4++gIIBANcBGKCwAEB379+/IzgwAEB58wYODACw3sACBxAqSEgwAACAAQcA5NevX4AEAP8AAQgcSLCgwYMIEypcmNBABQAQI0qcSLGixYsYKRo4MACAx48DDhQAQLKkyZISBABYyXIlhAUGIBCgUAECBAo4CVRIAACCAABAgwKtgACA0aNIkypdyrSp06dODVQAQLUqgAIAsmrdyrWr169gv0JYAKCs2bILHABYy7YtgAEICACYS5eugQMHJCwQwFcABAgCFjigQEHAgQIAEiteQAGA48eQI0ueTLmy5cuYJxuoAKCz58+gQ4seTXo0gQEAUqtOPYAAgNewXyOAcIDAgQMAcuvWPYDAAgHAgwsH7uCAhAMLBgAAYAAChQEAokufTr269evYs2vfbt1ABQDgw4v/H0++vPnz5gkAWM++PQEA8OMjoHAAAoMFDA4A2M+ffwGABwQMJFiw4AIKEiAQoHDgwIIBACROpFjR4kWMGTVu5DgAAQCQIQckAFDS5EmUKVWuZLmSAACYMWMOIADAJoABEA44WCDA54IDBgAMJTp0QQUBSZUKcOBAwFOoCyg4AFBgAACsWbVu5drV61ewYcVmRSABwFm0adWuZdvW7du1EgQAoFuXrgAIAPQOoFBhgQDAgSFIAFDYMIABBxgIYNxYQAUIAiRPFrDgAAIAmTVv5tzZ82fQoUWP5oxAAgDUqVWvZt3a9WvYrBFQAFDbNoABFAwAADCAggQBwYUHX0DB/wEA5MgHVJAgwPlz5xUgCKBenbqDAwC0b+fe3ft38OHFjyfffYABAOnVG3AAwP17+PHlz6dfv76DCgMA7AcwoAJABwAGSpAg4CDChAsqHGCAIIEDAhIWCKhoseKCBQI2cuRYYQGAkCJHkixp8iTKlCpXrkQgAQDMmDJn0qxp8yZOBgcgCFgggQADAEITHFgg4CjSpEcZSDhwQMICAVKnUq1q1QEFAAMQCFggAMEAAGLHkhWLYAEDAQDWsm3r9i3cuHLn0kXgAADevHr38u3r9y9gAAMWVCBAgQIBCAYAHGAg4DHkyJIPMBBg+TJmywwWCOjs2fOCAwcIUKggoQIFAv8HGAwA4Po1AAQUKDBgIOEAAwC6d/Pu7fs38ODCASSAAOA48uTKlzNv7vy58wIHHBQAYF3AAQcUBHDv7v37AgILBJAvb558BQgC1rMXsEACgQoMFgioX38BAwkEIAwA4B8ggAQHEAAwCGCABAgAGDZ0+BBiRIkTKSaAAABjRo0bOXb0+BHkRwoCAJQ0WYCABAErWbZ06YCCAJkzac6k4EBATp0MDlBYIABoUKECFlQ4gAAAgAEHCgBwCmBAgQEVBACwehVrVq1buXbtikAAALFjCyQAcBZtWrVr2bZ1+zaBBABz6c5dUEFAXr17+UKoIABwYMGBHTAQcPiwAwIQBDT/dvwYsoMDCQAsgAAAc4IKBCgQqHAAQGjRo0mXNn0adWrTCSAAcP0admzZs2nXti0hAQDdu3cTWCAAeHDhwiFUEHAceXLlyRkQcCAAenTp06EzOJCgAgIAACBUSAAAfIIDEACUN38efXr169m3Ty8AAgD58+nXt38ff379BwoA8A8QgMCBFBgIOIgwYUIIFQQ4fAgx4sMFBxwIuIgxo8aMDA4cKACAgQQAJEsCqLAAgMqVLFu6fAkz5ksEAgDYvDlgAICdPHv6/Ak0qNChFAoAOIoUKQUGApo6ffrUAQUBVKsKWCCBwgEKEio4EAC2QgUBZMuaPYtWAgEDAw4MAAA3/y6AAgcA2L2LN6/evXz77hXgAIDgwYQLGz6MOLFixBUWAHgM+XGBAwsEWL6MGfMCAgsEePYMgYADAwUMQCAgQYAABgQWCHgNO7bs2QsOSBAAAYDu3bslJAAAPLjw4cSLGz9OfIEDAMybDxgAILr06dSrW7+O3XoBBwQsHAAAPjx4BxIEmD+PPr2AAwwEuBcA4UABAPTpSyDgQICECgL6+wcoQOBAggUFQiAAQQAAhg0bMlgAQOJEihUtXsSYUSNFAQ4AfAQZUuRIkiVNjuxwIEIDBRkkAIAZUwCBBQJs3sSZU4CECgJ8LjhgAMBQogAQHFhAgIEApk2dPoXKdAEBAv8LAFzFipUBBABdvX4FG1bsWLJlvy5wAEDtWrZt3b6FG5ctggMPGgTAqyDDhQUFCiT4cIDAAgGFDR9GLGABgQUCBDioAEDy5MkUIBwQkFnzZs6dN1uwUAHAaNKkKRwAMQDAatatXb+GHVt26wIFANzGbQABAN69ff8GDrxAAgYOPDhgsADBAADNnT8H0OHAhADVrSvQ8ODAAQsTFGSAIED8ePLlxUuoIECAhAUA3L8HIGAAgwoUBNzHn1///vwSNgAkUAAAwYIAEFxQEOFAAgAOH0KMKHEixYoPFzAAoHEjx44eP24cMKICgQMZIqBE+cACgQsQEgCIKRNAhQcNAuD/zKlzp4YDCwQADSp0qIAFByAIkCAAANOmACoYYEBBgoCqVq9izXoVQoYQFgYACBu2wIEJAQJgOCAAANu2btkOQIAgQQIEBgDgzat3L9++DBgACCx4MOHChgEgkEDgwYQGAR5DhqwAQwQLBxYMAKCZwgMFAT6DDi06gIILEASgTq16NWoGBBxIgABgNm0AFAxIoCBBAO/evn8D9+3AQoAIBxgUGFDAAYEQAZ4HwHBAAIDq1gEYICHhAAELFh5ksHCBAIUOAgYASK9+Pfv26Q0YACB/vgABAO7jz68/vwEKFwBGaBCAYEGDBwNgeEDAwYAKDxQEkDiRYsWJGAgwELCR/2NHjxsdEIBAYAAAkycLFCAgQYIAly9hxpQJE0KGAAEwPDhA4MADDAGABsVwIAEAowAEULjwYAIGBQGgRlWgIcIDAhAMANC6lWtXr18BMGAAgGxZs2fLMjgwQUEAt2/hxo3b4AEBCwoC5NW7ly/fCBcWCBA8mDDhBRAuECBwQAIAx48BVJAAoYIAy5cxZ9aMWcKDAJ9BhxYdAAOBAgAcEMgwQUEA169hw24Q4QCFBABw59a9mzfvBQIABBc+nDgAAxYyNAiwnHlz58+ZTyDwQEEA69exZ8eu4AGFBQLAhxcPnsEBCxMUNHhAoIIBAO8RUKiwwMEBAffx59e/Pz+FCf8AAwgcSLCgwAgUDjxoEKChw4cQHyqYcADCAAAYM2rciJGBAAAgQ4ocSRIkggMTAqhcybKlS5cNMlxoEKCmTZsKMER4YOGCTwsZIlg44ECA0aNHF0ggMEFBgKcBMFggcIDCgQMEKlggQGCBgK9gw4od+3UBAQwB0qpdyzZAgwcEJgSYS7eu3btzFTw4kACA37+AAwNwsACA4cOIEysGkOCAhgCQI0ueTLlyAAUPLjQIwJmzggkWCBzIEGGChtMTIjy4QIDABQYCYstmcMBCgwC4c+NWMGHDgwcEHkxokAGCgOPIkytfLmABhAMaJmjAoCCA9evYA2A48KBBgO/gw4v/Hy9ewwEGANKrX89eQAIA8OMjMACgvv379hEcwBCgv3+AAQQOJFjQIEEFDy40CBCgwQMCFiY0CFDR4sUACjRkIHCggoMFCxwQiKAgwEmUKVNGaBAggIYDCwTMpFnT5swFDiRQIEDgws8LBwhceBABQwCkSTEQiBDA6VOoUaVODdDAggcAWbVu5doVgIMFAMSOJSu2AAENAdSuZdvW7du2Ch5kULCBwAMMAfTu5dtXb4MIBAQLnhDA8GHEiRMruABBwGPIkSUzqEDAQoQJDQJs5qxAQ4QMBCxMUBAgAAYCEQKsZt3a9WvYrBtYcADA9m3cuXU7EADA92/gvitECFDc//hx5MmVK1dw4ICFBgGkT6de3bqCBwQITAjQ3ft38OEDYCDAQMB59OnPO7BAIEKDAPHlz5+vgMMFAhEaHIgQwD/AAAIHEixo8KDABhcWAGjo8GFDAQYAUKxYYACAjBo3AlhgQUGAkCJHkixpsqSCCAREKAjg8iXMmDJfajiQoUGAnDp38gxgoUGAoAEiHFgg4ChSpAsqHJigIADUqFKnSsVggUCGAFq3cu3q9evXBgQMAChr9iwACAkAsG3r9m3bAgQwBKhr9y7evHrzKnhwoUGAwIIHEy5cWMGDCw0CMG7smLGCBhgIYGigIEAABQ8OLBDg+bMABwceNAhg+jTq1P+qAyiIQCCCggCyZ9Oubfu2bQ4UAPDu7RsAhAQAhhMfAOA48uQAJEQI4Pw59OjSp0tXkMFCgwDat3Pv7v17AAUPLjQIYP68AgwRHlwgQOAAgfgELDyY0CACAQgLBPBfIAHggQkBCBY0eBDhwQYWLjQI8BBiRIkTKUpUYIEBAI0bOXbcCEEAAJEjRxYgoCBASpUrWbZ0yVLBAwsKAtS0eRNnTp03H1xoEABogwgXDmSIoKFBAKVKG0zYYIFAhggHKDhYsKDChQYBuHb1+hVsWAUPLjQIcBZtWrVr2aptQKAAALlz6daVCyEBAL179zJ4EABwYMGDCRcmHOFCgwCLGTf/dvwYsmMFDzIEaPCAQIYJCgJ09vzZc4MIFw5kuEDgwIUGAVi3dv0admzWCjZcaBAAd27du3n33r3BAQDhw4cXGAAAeXIBBgA0d+78AIYA06lXt34du3UMBDAE8P4dfHjx48crOPCAwIMGAdi3d/++vQINFi5kuNAgQH79+/n39w8wgMAACh5cUBAgocKFDBs6XNiAAICJFClKQAAgo8aNHAEksBAgpMiRJEuaJKnAQoQALFu6fAkzpswGFw5gCIAzp86dPAMoiEDggYIARIsaPYo06VEFFiIEeAo1qtSpVKVmEAAgq9asEhAA+Ao2rFgADiIEOIs2rdq1bNVGuKAg/4DcuXTr2r17dwKBCAoC+P0LOLBgwA0sXGgQILHixYwbO2bcgACGAJQrW76MObPlCRQAeP7sGUEBAKRLMzAAILXq1BUmBHgNO7bs2bRjKyCAIYDu3bx7+/79ewIBDQGKGz+OPLlyBQ8ONAgAPbr06dSrT49wQUGA7dy7e/8OnrsCAgMAmD+PPr0EBADau29PoEGA+fTr27+Pvz4HCwH6+wcYQOBAggUNHgwwgQCGAA0dPoQYUaLDCAcaBMCYUeNGjh01KrAQIcBIkiVNnkRZ0gICAC1dvoQJAQEAmjUBFDgQQOdOnj19/ux5YUIAokWNHkWaFOkEAhgCPIUaVepUqv9SN1xoEEDrVq5dvX7lioGAggBlzZ5Fm1at2QcLALyFCwCCAQB17d7Fi8BCAL59/f4FHNgvBgIKAhxGnFjxYsaKGxCYEEDyZMqVLV+2rCDDgwCdPX8GHVo06AsTApxGnVr1ataoOUgAEFs2gAoGANzGnVt3ggcBfP8GHlz4cOAPNgRAnlz5cubNmT94EED6dOrVrV/H3oDAhADdvX8HH1789wkWApxHn179evboMVgAEF8+AAcFANzHj2AAAP79AQBM8CAAwYIGDyJMaPDChAAOH0KMKHFixAkEGgTIqHEjx44ePwaYcKBBgJImT6JMqdKkAgIYAsCMKXMmzZowGxz/AKBzJ8+eFQwACCoUQIIHAY4iTap0KVOkCgg0CCB1KtWqVq9SVUBgQoCuXr+CDSt2rNcHDwKgTat2Ldu2ah9ECCB3Lt26du/KbXAAAN++fv9WMABgMGEACCwESKx4MePGjhVjOBBgMuXKli9jthzBQoDOnj+DDi169OcGBBoESK16NevWrlVHeBBgNu3atm/jnt3gAIDevgEsGABgOHEDAwAgTw6gwIUAzp9Djy59+nMOGQJgz659O/fu2hVcmBBgPPny5s+jT28+Q4QA7t/Djy9//nsMFwLgz69/P//++AFiOACAYEEAFAoAULiQYUMABBoEkDiRYkWLFyVGeBCA/2NHjx9BhvSo4YCCACdRplS5kmVLlRoOKAgwk2ZNmzdxzlRAQEEAnz+BBhU6NIAGCgCQJgVAoQAAp0+hRgVQQUMAq1exZtW61eqGDQHAhhU7lmxZsRkiBFC7lm1bt2/hulVwYUIAu3fx5tW79+4FDAEABxY8mHDhABEcAFC8GMAAAI8hA6BQAEBly5UdRAiwmXNnz59Bb34QIUBp06dRp1ZtWgGBBgFgx5Y9m3Zt27UjPAiwm3dv37+B874wIUBx48eRJ1ce4IEAAM+hR5dOoQAA69etJ7AQgHt379/Bh+ceYUMA8+fRp1e//jwGAgHgx5c/n359+/YnXAiwn39///8AAwgcSLBgAAsTAihcyLChw4cBLhgAQLEigAIAMmoEwGAAgI8gQRJoEKCkyZMoU6oMEOFBgJcwY8qcSRPmBAsBcurcybOnz58/GxBQEKCo0aNIkyotamFCgKdQo0qdSrUBAQBYs2I9MACA169gw3p1sCGA2bNo06pdG2CChQBw48qdS7du3AcRAujdy7ev37+AAx/AEKCw4cOIEysufEFDgMeQI0ueTDkCBACYM2M+MACA58+gQ3sucEBBgNOoU6tezboBAQUBYsueTbu27dgWJgTYzbu379/AgwvPMCGA8ePIkytfbvwAhgDQo0ufTp26ggsGAGjfrl0AgO/gAUD/GACgvPnzHyIEWM++vfv38AMQwBCgvv37+PPrr39BQwCAAQQOJFjQ4EGECB9ECNDQ4UOIESUGaEBAQQCMGTVu5MhxAgUAIUWOJBnywAAAKVWuNECgQQCYMWXOpFnTwoQAOXXu5NnTZ84LGAIMJVrU6FGkSZU+iBDA6VOoUaVODaDhQgCsWbVu5drVggAAYcWOJRv2wAAAadWudXDBgoIAceXOpVu3boQHAfTu5dvX71+9FzAEIFzY8GHEiRUvfhAhwGPIkSVPphwgwoMAmTVv5ty584QLAESPJk0BwGnUqVWjNnCgwQUOAWTPpl3btu0GBBoE4N3b92/gwQNc0BDA//hx5MmVL2fe/EGEANGlT6de3XqADBECbOfe3ft37w0OIABQ3vx5AgDUr2fffn2FCQEwEMAQwP59/Pn1688QIQDAAAIHEixo8KCFCQEWMmzo8CHEiBIzTAhg8SLGjBo3NiDQIADIkCJHkhyZ4QCAlCpXAqAA4CVMAAkA0KxZ08ABBQECTCCAIQDQoEKHEh2q4YCCAEqXMm3q9OmDDQGmUq1q9SrWrFoPYAjg9SvYsGLHRsgQ4CzatGrXqp1wwMICAHLn0q07lwCAvHr1dogQ4G+ACAQwBChs+DDixIcVXJgQ4DHkyJInU55gIQDmzJo3c+7s2XMDAgoCkC5t+jRq1P8KDmgI4Po17NiyYU8ggEHDAQC6d/PuvZsAgODCgw8g0CAAcuQcCEwI4Pw59OjSoWsg0CAA9uzat3Pn3oBAgPDix5Mvb/78+QkXArBv7/49/PgTLigIYP8+/vz6708goAFggAAWEAAwePDgAgALGQJIAABiRIgCHgSweDHABAIPGgTw+BFkSJEfM1gIcBJlSpUrVyog0CBATJkzada0edNmhAcBePb0+RMoUAUHJgQwehRpUqVHJxDQEABqBAkAqFatSgBAVq1buQKAECFAWLFhGzw4MCFAWrVr2bZV8IAAgQkB6Na1excv3gcRAvT1+xdwYMGDAyu4MCFAYsWLGTf/bvwgQwDJkylXtiy5QYYDGAJ0DqCAwAAAo0mPJgAAdWrVqwFQ0BAAdmzZEwhcmKAgQG7du3kHaPCAgIUGGghgCHAceXLly5VrOKAgQHTp06lXt36duoYDCgJ09/4dfHjwGghgCHAefXr16wNMIPCgQQD58zMkAHAf//0CAPj3BwCQAICBBAcSUBAgocKFARRMuEBgAwYFASpavNhgggUCFi4E+DiBAIYAJEuaPIny5IUJAVq6fAkzpsyZMDNECIAzp86dPHc2OHCBQIQGAYoaPYq0qIIJFg5MCAA1KtQIDgBYvYo1KwACALp6BWDAQoCxZMuaxfDgAoELDyJwmDAh/0IECwQIWIjQwAKHAHwDcCCAIYDgwYQLGyYcwUKAxYwbO34MOXLjBgQaBLiMObPmzZkVXHiggEAGAhkmKAiAOrVq1A0iHLgQoUGA2bRpT6gAILfu3AUA+P4NAAKA4cQBJHgQILny5cyVN9AQ4UGG6RkeTMCgIEAADAQUBPj+fQKBCQoCmD+PPr168woITAgAP778+fTr24+f4UGA/fz7+wcYQOBAgg0sZFAQIMKDBhEuELDwYAKGBhUbaIjw4AKBDBoUBAAZUmSABgQAnEQJYMABAC1dvoSZ4EEAmjVt3sSZ0+aDBwF8/gyA4YKFBgGMHkWaVKnRCQQaBIAaVepUqv9VrQaYcKBBAK5dvX4F27XBhQwKAgRoQKBBgAANJkSwcIDAXAIXHkTAoCDAXr59/R4YAEDw4AEHABxGnFixgAcBHD+GHFnyZMgXJgTAnBmzggcEOCgIEFr0aNKkNRw48CDAatatXb+GHbsBgQkBbN/GnVv37QkEHigIEDxAhggBjB9Hnlz58uMHCgCAHn3AAgDVrQ9wAED7dgAJHgQAH178ePLlwysg0CDAevbtNVwgEKFBAPr17d9XEOECgQ0NCACcEGAgwYIGDyI8qCADAQwBHkKMKHFigAYZDmgIoHFjhAcBPoIMKXIkSZAXCgBIqXLlygEHAMCMCSBBhgA2b+L/zKlz500MBwIADSo0KIYHBDJwwKAgANOmARQ0mPCAgIUHFgIE0EBAQ4CuXr+CDSsW7IYLEQg8wBBgLdu2btk2iEDgQYMAdu8G0HAhAN++fv8CDtz3QAEAhg8jRjzgAIDGjgEUuBBgMuXKli9jpswhQ4DOnj+DbhAhwwECFx6gRm2BAAELETAouDAhAO0JBDAEyK17N+/evnMriHCgQYAHFwhYmKAgAPPmzptjeEAgg4YA1q9fb0BAQYDu3r+DDy+++4EBAM6jLwABAPv27t8DINAgAP369u/jz09/Q4QA/gEGEDiQYMEADSZEUKhwQgMFASBqOKAgQMUAEwhoCLCR/2NHjx9BKnhwoUGAABgINJhwgYCFBxMwKJCpoMGECBkOEIjQIEBPnz97XsAQgGhRo0eRJg2ggAAAp08BFKAAgGpVq1cBUNAQgGtXr1/BhuX6IEIAs2fRplW7Fm2GCAHgxp1AYIOCAHfx5tW7Ny8GCxcaBBAcwMKEAAEaTIhggUBjxxceTMCgIEBly5cvW5gQgHNnz59Bhw6ggQIA06cBFJAAgHVrAAgAxJYd20GEALdx59a9m/ftBxECBBc+nHhx48IVEGgQgHnzAA0sXMAQgHp169exB1AQgUAEBQHAg4+QIUB58+UVKAiwnn179+8tTAgwn359+/fxB4jQAUB///8AAQgcOLAABQAIEyJMYCGAw4cQI0qc6PBBhAAYM2rcyLFjxgYEFAQYSXKkAg4EHmAIwLKly5ctFUywcAFDgJs4A2C4EKCnz59Agwr1mWFCgKNIkypdyjTAAwEAokqdStUABQBYs2Y9gCGA169gw4odG+BBhABo06pdy7Zt2gkWAsidSzdAgwcELExQEKCv379+G0Q4cCGCggCIEyNWQKBBgMeQI0ueTPmxhQkBMmvezLmzZwUXDAAYTRrAgAQAUqsegACA69evGTwIQLu27du4cweI8CCA79/Agwsf/nvDhgDIkytPriDCBQIZIkxoEKB6AAUYJjywQCCDBgUBwov/Hx/ggoYA6NOrX8++PfoLEwLIn0+/vv37EygA2M9/vwGAFQAMJFjQ4MACBBQEYNjQ4UOIETVcCFDR4kWMGTVatDAhwEeQIUUqwMDhwQUCKVUSsBBhQoMAMWXOnPkgQgCcOXXu5NkzgAICDQIMJVrU6FGkDwQAYNqUKYIKAKROpVp1KogIAbRu5drV61cFBBQEIFvW7Fm0aclamBDA7Vu4ceMqaPBgQ4MGAfTu5duX74MIAQQPJlzY8OEAGAgEYNzY8WPIkRsQAFDZ8uUCADRvNiABwGfQoQsQwBDA9GnUqVWvvoAhwGvYsWXPpv36goYAuXXv5t07gAIFAYQPJ17c//iGCAGUL2fe3PnzABMsBKBe3fp17Nk3OADQ3ft38N0RVABQ3vx5AAssKAjQ3v17+O0VKAhQ3379BxEC7Off3z/AAAIHEix4QUOAhAoXMmzo8CHEBxECUKxo8SLGjAEeRAjg8SPIkCJFYjgwAADKlCoHAGjp0gADADJn0pRZIUKAnDp3KsDA4cEFAkKHWogwoUGAABouKAjg9CnUqFKnBrAwIQDWrFq3cg2AAUOAsGLHki37IEKAtGrXsm3rVsEFDQHm0q1r965dBRcEAOjr9y8CCQAGEy5s2HABAhoCMG4coEEEAgcyRJjQQAFmBRgmPLBA4MIEBRcmBCht+jTq1P+qA2TgEOA17NiyZwd4ECEA7ty6d/POECEA8ODChxMvruGCggABGkyIkMHChQsWLGyY0EBBgOzatweIAAIA+PDiASSAAOA8+vTq1yMggCEA/AAaMhB4gCEA/vz68yuYYAEgAQsZAhQ0eBBhQoUBIjwI8BBiRIkTA2SIEABjRo0bOR7AEABkSJEjSZbMEAHDgwMELjzgMEGDhgkTNlggQCCDBgUBePYMoOHAAABDiRYFYEAAAKVLDQgA8BRq1KgJDmgI0OABgQgNAnT1+hWsVwwPCGAIcBZtWrVr2U64EABuXLlz6QbQgCFAXr17+fJtQEBBAMGDCRc2bLgBAQsEHmD/UBAAcmTJChpEOHAhQoMAmwNgIIAAQGjRo0mTTiABQGrVq1kjIPCAwIMGAWjXtn0bd4AHFxQE8P0beHDhwhsQUBAAeXLly5k3d95cw4UA06lXt34d+wMCHBQE8P4dfPgACiZYIDAhQAANBxIAcP8efnz5CSAAsH8ff34AEAhMCAAwgMCBBAsaFKjgQoQADBs6fAgx4gEMASpavIgxo8aNGiM8CAAypMiRJElqINAggMqVLFu61HAgwwQCCADYvIkTJ4IFAHr6/Ak0aE8IFhoEOIo0qdKlSjEQaBAgqtSpARRg4PDAwoWtFh5E0KAggNgHDwKYPYs2rdoMEwK4fQs3/y5cBRcmBLh7V0EDDHwbKAgAOLBgwQ0OTAiAOLHixYwRK3hAYAGAyZQrWxbgAIDmzZw7ewYAwYKCAKRLmz6NOnWECwoCuH4dQMEECwQOZIgwQYPuCREeXCBwIUIDDAQUBDiOPLly5RYmBHgOPbr06BouKFCgIUKGCwQIHLhwgAABCw8mNAiAPr36BxkCuH8PP778+BoOCACAP79+/QkcAAAIQKDAAQUAHESYEKEHCwoCPIQYUeJEigEUZLCgIMDGAA02ELgwoUEAkiVNBlCgIQOBBxciBIAZU+bMmRs0BMCZU+dOnRkiRDhw4UEEDAoCHD3aYEIECwQsTFAQQOrUCP8EGgTAmlXrVq5cMRwQAEDsWLJlzQqAAEDtWrZqE1xoEEDuXLp17d6dq8CCBQUBFEQg8ABDAMKFDR8m3CACgQsKAjyGHFnyZMqVITcgQCCDBgUBPH8G/blBhAsHIigIkHoCAQwBXL+GHVv27AAYDiQAkFv3bt68BTgAEFz4cAADDmAIkFz5cubNnTNXkMHChAsWGgTAnl37du4NCEQIEF78ePLlzZ8Xb8FCgwDt3b+H716BBgsXMASIQABDAP79/QMMIHAgwYIGMRAoAGAhw4YAChgAIHFiAQQALmLMCEBChAAeP4IMKXLkSAUXCERQEGAly5YuX67EQKBBgJo2b+L/tBkBQ4CePn8C7TnhgIIARo8iTapUQQQCFwhgCCB1KtWqVq9WDUEBANeuXgEsYABgLNmyZskmuKAgANu2bt/CjQtXwYMLDQLgzat3L9+9ESwoCCB4MGHBCjBE2PCAQIYIEzAoCCB5MmXJDQhoCKB5M+fOnjc3uHChQYDSpk+jTq0atQILCwDAji17AQMAtm/jzn2bwoQAvn8DDy58uHAFGSw0CKB8OfPmzp0ruBAhAPXq1TE8uEDgQIYNEQ482JDhAIELDzAESK8+vQILDwLAjy9/Pv35Ch5caBBgP//+/gEGEDiQYMGBDQgUALCQIcMCBQBElCiAAQCLFy8auKAg/0BHjx9BhhQJUsGDCwoCpFS5kmVLlwEwEJgQgCZNBRMuENigoUEAnwEaKAgwtMGEBwQsTFAQgGkABQ8uKAgwlWpVq1exRjjQIEBXr1/BhhULNoIEAGfRplV7dgEDAG/hwoUQIUBdu3fx5tWbN8KFBgEABxY8mHDhwBgITAgQQEEEAhYmKAgwmXJlywo4XCAwIUAABQ8uNAgwmnRp06dRj35wQUEA169hx5Y9G3YDAgMA5Na9mzeABAIABBcefACBBgGQJ1e+nHnz5Q0IYAgwnXp169exW9dAIEIDCxcwBBA/nnx58xoOZGjw4EKDAO/hx5c/nz58BRkeBNC/n39///8AAwgcSFDgAwYAEipMyGABgIcQI0oEIOBBgIsYM2rcyFGjAgsRAogcSbKkyZMnMRAgEEFBgJcwY8qc+bLBAwIXGgTYybOnz59AfTYgoCGA0aNIkypdihTDAQBQo0JlsACA1atYswLwECGA169gw4odGzbCBQUB0qpdy7at27YKHlzAEKCu3bt48+bVQCBCgL+AAwseTHjwhAMKAihezLix48eMLSAAQLkygAQIAGjejAABgM+gP1PQEKC06dOoU6s+rYAAhgCwY8ueTbs2bQUPLjQIwLu379/AgwdocCBCgOPIkytfznx5hgcBokufTr269ekPFgDYzr27dwYLAIj/Hy+egIIA6NOrX8++vfoJFgLIn0+/vv379hU8uNAggH+AAQQOJFjQ4MEGByIEYNjQ4UOIER82INAgwEWMGTVu5IhxggQAIUWOJMlgAQCUKQEYuBDA5UuYMWXOjHlhQgCcOXXu5NmTZ4QLDQIMJVrU6FGkRhsQmBDA6VOoUaVOjZohQgCsWbVu5do1K4YDAMSOBSAAAQC0aQcAYNuWLQILAeTOpVvX7l26GAgoCNDX71/AgQUDxkAAQwDEiRUvZty4sQYCDQJMplzZ8mXMlSdcUBDA82fQoUWP9qyAwAAAqVU7EADA9WvYsRFYCFDb9m3cuXXffvAgwG/gwYUPJy5c/8GFCAGUL2fe3Plz6AEeZAhQ3fp17Nm1X1dwQEMA8OHFjydfPrwFAwDUr3cgAMB7+AUGAKBfHwACCwH07+ff3z/AAAIHEgxgYUKAhAoXMmzokGGECwoCUKxo8SLGjBoDKDgwIQDIkCJHkiwpcsODACpXsmzp8uVKCwYA0Kxp06YDAQB28gSAwEKAoEKHEi1qVKgCAg0CMG3q9CnUqE4bEMAQ4CrWrFq3cu2KVQMBBQHGki1r9ixashMsBGjr9i3cuHLdWkAA4C7evHkdCADg9y8AAxYCEC5s+DDixIUxEAjg+DHkyJInR46QIQDmzJo3c+7sefOFCQFGky5t+jRq0v8NCCgI4Po17NiyZ7u2YAAA7twGCgDo7RuBAQDChwsnoCAA8uTKlzNvjnyChQDSp1Ovbv06dQUHNATo7v07+PDix4OfYCEA+vTq17Nvr54AhgDy59Ovb/++/AsFAPDvDwFgAgADCRY0CMAChgALGTZ0+BDiwggPAlS0eBFjRo0XJ1xQEABkSJEjSZY0OVIBAQwBWLZ0+RJmzJYWJgSweRNnTp07AyggAABoUAAQEgAwehRpUgAQIgRw+hRqVKlTnUZ4EABrVq1buXbVmiFCALFjyZY1exbt2QcPArR1+xZuXLluM3AIcBdvXr17+QbAQAFAYMEADAwAcBgxAwQAGDf/ZrzgQQDJkylXtnxZ8oYNATh39vwZdGjPBDAEMH0adWrVq1mvnnAhQGzZs2nXti37QYQAu3n39v0beIAIEAAUN34cOYQEAJg3Z17ggIIA06lXt34de4AIDwJ09/4dfHjx3hsQUBAAfXr169m3d9++AQEFAejXt38ff376DyIE8A8wgMCBBAsaLPhAAICFDBs6ZIAAgMSJEytMCIAxo8aNHDsGiPAggMiRJEuaPDlygoUALFu6fAkzpsyZBzAEuIkzp86dPG9m4BAgqNChRIsaVXDAAIClTAFIQAAgqtSpVKMmsBAgq9atXLt6DTDBQoCxZMuaPYuWbIQHAdq6fQs3/67cuXQzcAiAN6/evXz74rUwIYDgwYQLGz48gQKAxYwXS0AAILLkyZQlH8AQILPmzZw7e25AQEGA0aRLmz6NenSGCAFau34NO7bs2bQjbAiAO7fu3bx7B1BAoEGA4cSLGz+O3IIAAMybM19QAID06QkKALiOPfsCCwoCeP8OPrz48QQwBDiPPr369ezPZ5gQIL78+fTr27+PP8KDAPz7+wcYQOBAggUJNiCgIMBChg0dPnyI4QAAihUtXqQoAQEAjh09AqAwIcBIkiVNnkRpYUIAli1dvoQZk6WFCQFs3sSZU+dOnj0jPAgQVOhQokWNBphgIcBSpk2dPoX6gAEAqv9VrV6lKgEBAK5dvQIwcKBBALJlzZ5FizZChgBt3b6FG1du2wwTAtzFm1fvXr59/UZ4EEDwYMKFDR8OsGFDAMaNHT+GDHnCAQCVLV9mUADAZs4DAHwGHRo0AwsKApxGnVr1atUNCDQIEFv2bNq1bQfIwCHAbt69ff8GHlx4hAcBjB9Hnlz5cgUHNASAHl36dOrTGxxAAED7du4VDAAAH178ePIVHigIkF79evbt2WeIEED+fPr17d8P8GBDAP79/QMMIHAgwYIGDx58ECEAw4YOH0KMOOFCgIoWL2LMmPFBBwAeP4IEUMEAgJImCwBIqXLlygEUIigIIHMmzZo2aWr/OKAgAM+ePn8CDTrBQoCiRo8iTap0KdMLGAJAjSp1KtWqFiIEyKp1K9euXEMcACB2LNmyZSsYAKB2LVu2Ayg8UBBgLt26du/SVXBhQoC+fv8CDiy4AYEAhg8jTqx4MWPGCggoCCB5MuXKli1jIKAgAOfOnj+D9jzhQAEApk+jTp26ggEArl/Djj2ggoUGAW7jzq17N24NBBoECC58OPHixRUQaBBgOfPmzp9Djw5dw4UA1q9jz65duwILGwKADy9+PHnxHA4UAKB+Pfv1CADAjw9AwAAA9u/jz2+fAQEOCgAGEDiQYEGDARpcsKAgQEOHDyFGjGhhQgCLFzFm1LiR/+PGCA8ChBQ5kmTJkhMuKAiwkmVLly9XKthwoAAAmzdx4qRQAEBPnz+BBgVagIIFDAGQJlW6VKmCCQc8UJgQgGpVq1exYp1wIUBXr1/BhhU7NqyCAxoCpFW7lm1btg0IYAgwl25du3fnYrggYQAAv38BB6ZQAEBhw4cRJ1a84IAFDgoCRJY8eXKDCAcqIABg4ACGAJ9BhxY9WrQCAhgCpFa9mnVr169ZT7gQgHZt27dx31Zg4UCGBgGABxc+fHiDCAcSAFC+nHlz5QkARJcOQEIBANexZ9e+HQACEAQeRNCgIEB58w0mRLBAwEEBAO8BJDiAIUB9+/fx58e/4UEA//8AAwgcSLCgwYMIBVqIEKChw4cQIz5U8KACAAYEHkxQEKCjx48gMTwgAGEAgJMoU6pcqZJCAQAwY8qcSTNmAQEeKBC4YKGnhQMEPjBIAKCo0aIJDmAIwLSp06dQnTYg0CCA1atYs2rdyvUqBgIKAogdS7as2bEKHlAAwBaAAAsXIkxoEKCu3boKNESwcIDBAACAAwseTLhwhQEAEitezLixYwAFDCAwYKAAgMuYM2NOcGCCggCgQ4seTTr0gwwBUqtezbq169epFVyIEKC27du4c9tW8KDCAADAgyNwUIHAhQfINzx4YIEABQgJAEifTr26dekHBgDYzr279+/gw4v/H7/dAIUMDQKoX8++vXv1DQhMCEC/vv37+PPrDxDBggKAAQQOJFjQoEAMFkAAYNjQIcMCCRZMXCDAAACMGTVu5MjxwAAAIUWOJFnS5EmUKUcyOMBBQQCYMWXOnKnhQgUCDQLs5NnT50+gQDEQmBDA6FGkSZUGUBCBgAAAUaVOpVrV6lWsUiEA4NoVgAAAYcWOJVvW7Fm0Zw1UOBChQQC4ceXOVcDBwoEEABhYUBDA71/AgQUPDtzgAoQDDzAEYNzY8ePGCiZYqFAAwGXMmTVv5tzZ8+fLBwYAIF3a9GnUqVWvXm0AAoEHEzAoCFDbdu0GEx4Q+JAAwG8AEh4oCFDc//hx5MmVG1dgwQGAAQ4IWJigIMB17NmxN4hwoEICAOHFjydf3vx59OnHExgAwP17+PHlz6df3z6AAQIkHCBg4QHABxsePLBwgMAHBgUAMGwI4MMDBQEmUqxo8SLGAA0sQADg0aMACgceRJjQIADKAAowcHhggUAHAwBm0qxp8ybOnDpzOgDg8ycAAwCGEi1q9CjSpEqXHh2AQMCCqAISDABg9SpWqxIsNAjg9SvYsGLFYrDgAQDatGgLJHBQgQDcAwQIHJCwAAGAvHr38u3r9y/gwAAIAChs+DDixIoXM27s+DFkBgcmBKhs+TLmzJYVhCCwAADo0KJHDygN4DTq1P+qV7Nu7fr1agIAZtMGMAAA7ty6d/Pu7fs38ODCeRuw8KBBgOTKlzNvjsFChQIAplOvbv069uzat3PvPn0AgPDiARAAYP48+vTq17Nv7/49fPYMCDzAEOA+/vz672t4QADgAgADCRY0eBBhQoULGTZsSABARIkTKVa0eBFjRo0bMQ5gcMDChAYBSJY0qaBBBAsHFgwA8BJmTJkzada0eRNnTpkIAPT0CYABAKFDiRY1ehRpUqVLmTJNAILAgQwRJlStGsECgQMSEADw+hVsWLFjyZY1exYtWQIA2LZ1+xZuXLlz6da1e5dugQQMJFC4IAECgwQDABQ2fBhxYsWLGTf/dvy48QACAChXtnwZc2bNmzl39vy5s4ACAARAAHAadWrVq1m3dv0admzZqAUAsH0bgAQAu3n39v0beHDhw4kXN867AgIAAwoAcP4cenTp06lXt34de3brAwgA8P4dfHjx48mXN38effrvFQwAcP8efnz58+nXt38ff/78AygA8A8QgMCBBAsaPIgwocKFDBsOAABxAICJFCtavIgxo8aNHDt6nDiAAoCRJEuaPIkypcqVLFu6fCnAAYCZNGvavIkzp86dPHv6nFmAAoChRIsaPYo0qdKlTJs6ZVoAAIAFDABYvYo1q9atXLt6/Qo2rNUBEACYPQsgAYC1bNu6fQs3/67cuXTr2mVLoQAABAgA+P0LOLDgwYQLGz6MOLHhAgcAOH4MObLkyZQrW76MOfNjCgUAeP4MOrTo0aRLmz6NOnXqAhQAuH4NO7bs2bRr276NO/drAQMA+P4NPLjw4cSLGz+OPHnwAQsAOH8OwACA6dSrW7+OPbv27dy7e78uQACA8eTLmz+PPr369ezbux9voAKA+fTr27+PP7/+/fz7+wcIQOBAggQZLACQUOFChg0dPoQYUeJEigkNVACQUeNGjh09fgQZUuRIkiIrDACwYAEAli1dvoQZU+ZMmjVt3mw5AMBOngYoAAAaVOhQokWNHkWaVOnSoAcGAIAaVepUqv9VrV7FmlXr1q0GKgAAG1bsWLJlzZ5Fm1bt2rASBgCAG1fuXLp17d7Fm1fvXroFAPwFXGABAMKFDR9GnFjxYsaNHT9GbMAAAMqVLV/GnFnzZs6dPX+mjEACANKlTZ9GnVr1atatXb+G7UAAANq1bd/GnVv3bt69ff+mjUACAOLFjR9Hnlz5cubNnT9vvgAAAAcCAFzHnl37du7dvX8HH1789QIJAJxHb4ABAPbt3b+HH1/+fPr17d9vTwAAgAIDAAAEIHAgwYIGDyJMqHAhw4YJEUgAIHEixYoWL2LMqHEjx44TCQAIKXIkyZImT6JMqXIly5YIJACIKXMmzZo2b+L/zKlzJ0+ZBQAADSp0KNGiRo8iTap0KVEEDgBAjSp1KtWqVq9izap1K1cICQCADSt2LNmyZs+iTat2LdgEEADAjSt3Lt26du/izat3b94CAABASABgMOHChg8jTqx4MePGjgcjYABgMuUBCABgzqx5M+fOnj+DDi16dGYCAAAIMABgNevWrl/Dji17Nu3atmcngABgN+/evn8DDy58OPHixnkTAKB8OfPmzp9Djy59OvXq1hNAAKB9O/fu3r+DDy9+PPny2xcASK9+Pfv27t/Djy9/Pv32BhIAyK9/gAEA/gECEDiQYEGDBxEmVLiQYcOBDAwAkDiRYkWLFzFm1LiR/2NHiQIcABA5kmRJkydRplS5kmVLlxIQAJA5k2ZNmzdx5tS5k2dPmQIcABA6lGhRo0eRJlW6lGlTpQMkAAAAAQEAq1exZtW6lWtXr1/BhrU6YAAAs2cFOACwlm1bt2/hxpU7l25du2sHHACwl29fv38BBxY8mHBhw4cFOACwmHFjx48hR5Y8mXJly4sHVACwmXNnz59BhxY9mnRp058HDACwmjUCAQBgx5Y9m3Zt27dx59a9mzaCAQCABxc+nHhx48eRJ1e+HPgCBgCgR5c+nXp169exZ9e+nXsFAwDAhxc/nnx58+fRp1e/HvwCBgDgx5c/n359+/fx59e/H/8AAf8AAQCoYACAwYMIEypcyLChw4cQIxo0gACAxYsJEgDYyLGjx48gQ4ocSbKkyY0FKAAAYGAAgJcwY8qcSbOmzZs4c+q8uYABgJ9AgwodSrSo0aNIkyr9WYACgKdQo0qdSrWq1atYs2rduoABgK9gw4odS7as2bNo06oFWwCA27dw48qdS7eu3bt488pdIACA37+AAwseTLiw4cOIEyumUACA48eQI0ueTLmy5cuYMztmsACA58+gQ4seTbq06dOoU58eAAAAhQIAYsueTbu27du4c+vezTv2AgEAggsvUACA8ePIkytfzry58+fQoxs3UAEAAAYDAGjfzr279+/gw4v/H0++vHgGCwCoX8++vfv38OPLn0+/vnoDFQDo38+/v3+AAAQOJFjQ4EGECRUuZGiQwQIAESVOpFjR4kWMGTVu5BixgAAAIUWOJFnS5EmUKVWuZFkSgQEAMWUaKADA5k2cOXXu5NnT50+gQXU6GADA6FGkSZUuZdrU6VOoUY06EADA6lWsWbVu5drV61ewYcUeGADA7Fm0adWuZdvW7Vu4cc06EADA7l28efXu5dvX71/Agf0aYAAAwIEBABQvZtzY8WPIkSVPplxZcYEBADRvdiAAwGfQoUWPJl3a9GnUqVV/RiABwGvYsWXPpl3b9m3cuXXvdiAAwG/gwYUPJ17c//hx5MmV/0YAAcBz6NGlT6de3fp17Nm1Tx8AwPt3AAIQACBf3vx59OnVr2ff3v179AkAzKdf3/59/Pn17+ff3z9AAAIhJABg8CDChAoXMmzo8CHEiBIJAKho8SLGjBo3cuzo8SNIixASAChp8iTKlCpXsmzp8iXMlgUQAABAAADOnDp38uzp8yfQoEKH5hRQAADSpAIMAGjq9CnUqFKnUq1q9SrWpgkgAACQAADYsGLHki1r9izatGrXpoWQAADcuHLn0q1r9y7evHr3whUAAQDgwIIHEy5s+DDixIoXM5aQAADkyJInU65s+TLmzJo3Qx5QAADo0KJHky5t+jTq1P+qV5N2YAAA7NiyZ9Oubfs27ty6d/MmAOA38ODChxMvbvw48uTKgUtAAOA59OjSp1Ovbv069uzatxMA4P07+PDix5Mvb/48+vTfIRgA4P69gQEA5tOvb/8+/vz69/Pv7x8gAAACHAAAAAFAQoULGTZ0+BBiRIkTKUqUgABARo0bOXb0+BFkSJEjSWZc4ABASpUrWbZ0+RJmTJkzadasgABATp07efb0+RNoUKFDieZEkABAUqVLmTZ1+hRqVKlTqTZNMABAVq0IBgDw+hVsWLFjyZY1exZt2rADHABw+xZuXLlz6da1exdv3rcVDADw+xdwYMGDCRc2fBhx4sQDDgD/cPwYcmTJkylXtnwZc+bHFAoA8PwZdGjRo0mXNn0adWrTCRIAKHAAQGzZs2nXtn0bd27du3nLNgAAeHAAEgwAMH4ceXLly5k3d/4cenTjDBgAsH4de3bt27l39/4dfHjxFAwAMH8efXr169m3d/8efnzzCxYAsH8ff379+/n39w8QgMCBBAsaPIjQ4AAADBsCcFAAgMSJFCtavIgxo8aNHDtaNAAgpMiRJEuaPIkypcqVLEVSKAAgpsyZNGvavIkzp86dPHkWoAAgqNChRIsaPYo0qdKlTIUeGAAgqtSpVKtavYo1q9atXLMaKADAAAUAZMuaPYs2rdq1bNu6fVuW/8EAAHTrMhgAIK/evXz7+v0LOLDgwYTzOhAAYEACAIwbO34MObLkyZQrW75c+UABAJw7e/4MOrTo0aRLmz7N2YEAAKxbu34NO7bs2bRr276N+8AAALx7+/4NPLjw4cSLGz/Ou0ABAMybO38OPbr06dSrW78OHQKA7dwBDAAAPrz48eTLmz+PPr369eQNSAAAP778+fTr27+PP7/+/fEJAAAIQOBAggUNHkSYUOFChg0bIpAAQOJEihUtXsSYUeNGjh0nHgAQUiQAAwBMnkSZUuVKli1dvoQZ8ySEBAAMMACQU+dOnj19/gQaVOhQokIJAECaVOlSpk2dPoUaVerUpP8QEgDAmlXrVq5dvX4FG1bsWLIEAJxFm1btWrZt3b6FG1cuWgEGANzFm1fvXr59/f4FHFjwXgEADB8GIADAYsaNHT+GHFnyZMqVLT8uIADAZs6dPX8GHVr0aNKlTXMmAED1atatXb+GHVv2bNq1bSeAAED3bt69ff8GHlz4cOLFdxMAkFz5cubNnT+HHl36dOrSFxgAkAACAO7dvX8HH178ePLlzZ/vjgDAevYADgCAH1/+fPr17d/Hn1///vgSEAAEAGAAgIIGDyJMqHAhw4YOH0J0SAAAxYoWL2LMqHEjx44eP1aUgAAAyZImT6JMqXIly5YuX8KsAGAmzZo2b+L/zKlzJ8+ePm8OKABgKNGiRo8iTap0KdOmTocOOABgKtWqVq9izap1K9euXr8KcABgLNmyZs+iTat2Ldu2bscOOABgLt26du/izat3L9++fvkiGABAgAMAhg8jTqx4MePGjh9Djmx4gAMAli8DWABgM+fOnj+DDi16NOnSpjlXMACgAAIArl/Dji17Nu3atm/jzm17wAEAvn8DDy58OPHixo8jT/67ggEAzp9Djy59OvXq1q9jz559wAEA3r+DDy9+PPny5s+jT/8dwQAA7t/Djy9/Pv369u/jzx9/gAMA/gECEAigAACDBxEmVLiQYUOHDyFGVChgAQCLFzFm1LiR/2NHjx9BhrRYgAIAkydRplS5kmVLly9hxpS5gAEAmzdx5tS5k2dPnz+BBrVZgAIAo0cBFACwlGlTp0+hRpU6lWpVq0wpFACQQAAAr1/BhhU7lmxZs2fRpjVbgAIAt2/hxpU7l25du3fx5n1LoQAAv38BBxY8mHBhw4cRJ05cgAIAx48hR5Y8mXJly5cxZ37sYAAAz59BhxY9mnRp06dRpw49AAEA168HCAAwm3Zt27dx59a9m3dv37cRIAAwnHhx48eRJ1e+nHlz58MNVAAwnXp169exZ9e+nXt3798ZLAAwnnx58+fRp1e/nn179+MNVAAwn359+/fx59e/n39///8AAQgcSBCAgwEAGCwAwLChw4cQI0qcSLGixYsMByAAwLFjAQgAQoocSbKkyZMoU6pcyVLkgQEABgwAQLOmzZs4c+rcybOnz588DVQAQLSo0aNIkypdyrSp06dFDwwAQLWq1atYs2rdyrWr169fDUAAQLas2bNo06pdy7at27doBwwAQLeu3bt48+rdy7ev3790EUgAQLiw4cOIEytezLix48eQHQgAQLmy5cuYM2vezLmz58+UEUgAQLq06dOoU6tezbq169etEQAA4EAAgNu4c+vezbu379/Agwu/bWABgOPICyQAwLy58+fQo0ufTr269evNCQAAgMAAgO/gw4v/H0++vPnz6NOrP49AAoD38OPLn0+/vv37+PPrh08AgH+AAAQOJFjQ4EGECRUuZNhwIQIJACROpFjR4kWMGTVu5NhxogAAIUWOJFnS5EmUKVWuZFnSgAAAMWUCKADA5k2cOXXu5NnT50+gQXUyQADA6FGkSZUuZdrU6VOoUY0mgADA6lWsWbVu5drV61ewYcVCSADA7Fm0adWuZdvW7Vu4cc0mgADA7l0AAwDs5dvX71/AgQUPJlzYMN8DAAA4QADA8WPIkSVPplzZ8mXMmS0ngADA82fQoUWPJl3a9GnUqT8TANDa9WvYsWXPpl3b9m3cuRNAANDb92/gwYUPJ17c//hx5L4lAGDe3Plz6NGlT6de3fp16AMKAODe3UACAOHFjydf3vx59OnVr2dfPkEBAPHlz6df3/59/Pn17+cfXwBABwAGEixo8CDChAoXMmzo8KEEBAAmUqxo8SLGjBo3cuzocaIABwBGkixp8iTKlCpXsmzpcuUABgAASEAA4CbOnDp38uzp8yfQoEJvFjAA4CjSBAsAMG3q9CnUqFKnUq1q9SrTAQcAACgA4CvYsGLHki1r9izatGrRCnAA4C3cuHLn0q1r9y7evHrfFjgA4C/gwIIHEy5s+DDixIoXC2AA4DHkyJInU65s+TLmzJohDwAAYACA0KJHky5t+jTq1P+qV7MWvWABgNiyZ9Oubfs27ty6d/PuXcEAgODChxMvbvw48uTKlzMPvoABgOjSp1Ovbv069uzat3PXbgAAgAoGAJAvb/48+vTq17Nv7/49+QQCANCvb8AAgPz69/Pv7x8gAIEDCRY0eBBhQoUKC1AAAEDAAAATKVa0eBFjRo0bOXb0uHEBAwAjSZY0eRJlSpUrWbZ0OdIABQAzada0eRNnTp07efb0+ZMBAwBDiRY1ehRpUqVLmTZ1OnRAAgBTqVa1ehVrVq1buXb1ejUBAgBjyQ4YAABtWrVr2bZ1+xZuXLlz2UIYAABvXr17+fb1+xdwYMGD8TJYAABxYsWLGTf/dvwYcmTJkylTKAAAc2bNmzl39vwZdGjRozE7EAAAdWrVq1m3dv0admzZs2EbgAAAQIUBAHj39v0beHDhw4kXN36cOIMFAJg3d/4cenTp06lXt36dOYIKALh39/4dfHjx48mXN38evYMFANi3d/8efnz58+nXt3+fvQEHAPj39w8QgMCBBAsaPIgwocKFDBMWGAAgosQECABYvIgxo8aNHDt6/AgypEYBAEqaPIkypcqVLFu6fAnTpAMBAGravIkzp86dPHv6/Ak06IEBAIoaPYo0qdKlTJs6fQq1KIQEAKpavYo1q9atXLt6/Qq2a4EEAAAQAIA2rdq1bNu6fQs3/67cuWkRFACAN+8CBAD6+v0LOLDgwYQLGz6MuG8CCAAAGAAAObLkyZQrW76MObPmzZkhCAAAOrTo0aRLmz6NOrXq1aATQAAAO7bs2bRr276NO7fu3bwdJAAAPLjw4cSLGz+OPLny5cEHAAAwAID06dSrW7+OPbv27dy7T3eAAID48eTLmz+PPr369ezbuycAIL78+fTr27+PP7/+/fzlSwCIAMBAggUNHkSYUOFChg0dLhwwAAAAAgAsXsSYUeNGjh09fgQZ8iIDAwBMnkRQAMBKli1dvoQZU+ZMmjVtrhQAAQAABwB8/gQaVOhQokWNHkWa9KgEBACcPoUaVepUqv9VrV7FmtWpAAcAvH4FG1bsWLJlzZ5Fm1atBAQA3L6FG1fuXLp17d7Fm9etAQQA/P4FHFjwYMKFDR9GnFiwgAIAHD82MADAZMqVLV/GnFnzZs6dPV+WAED0aNKlTZ9GnVr1atatR1cwAED2bNq1bd/GnVv3bt69ew84AED4cOLFjR9Hnlz5cubNh1cwAED6dOrVrV/Hnl37du7dtQtYAGAABQDlzZ9Hn179evbt3b+Hb34AAPr1AVQwAED/fv79/QMEIHAgwYIGDyJMqHChwQUMAECMKHEixYoWL2LMqHEjxwoGAIAMKXIkyZImT6JMqXIlSAELAMCMKXMmzZo2b+L/zKlzJ80CAH4CBbCgAICiRo8iTap0KdOmTp9CTZoAANWqVq9izap1K9euXr9WpVAAANmyZs+iTat2Ldu2bt++LUABAN26du/izat3L9++fv/WpVAAAOHChg8jTqx4MePGjh8zRmAAQAEKAC5jzqx5M+fOnj+DDi0as4ABAE6jdlAAAOvWrl/Dji17Nu3atm+zZrAAAAADAH4DDy58OPHixo8jT64cOYUCAJ5Djy59OvXq1q9jz679OYMFAL6DDy9+PPny5s+jT69+PYUCAN7Djy9/Pv369u/jz6///YABAAACEDiQYEGDBxEmVLiQYcOCEgYAkDiRYkWLFzFm1LiR/2PHjgYqABA5kmRJkydRplS5kmXLkQcGAJA5k2ZNmzdx5tS5k2dPnQMAADBQAUBRo0eRJlW6lGlTp0+hGq0wAEBVqwgAZNW6lWtXr1/BhhU7lqxWBwIAFFgAgG1bt2/hxpU7l25du3frHhgAgG9fv38BBxY8mHBhw4f5OhAAgHFjx48hR5Y8mXJly5cxHxgAgHNnz59BhxY9mnRp06c5JzAAgHVr169hx5Y9m3Zt27dhLwCwmzcABACABxc+nHhx48eRJ1e+nHgBBgCgR5c+nXp169exZ9e+PToBAN/Bhxc/nnx58+fRp1e/HoEEAO/hx5c/n359+/fx59cPnwAA//8AAQgcSLCgwYMIEypcyLAhQgYIACCQAKCixYsYM2rcyLGjx48gLRYAQLIkAAIAUqpcybKly5cwY8qcSVMlhAQAcurcybOnz59AgwodSrQoAQBIkypdyrSp06dQo0qdmtQBAgBYs2rdyrWr169gw4ody7UAgLNoAUAAwLat27dw48qdS7eu3btwBxgAwLev37+AAwseTLiw4cN9CQBYzLix48eQI0ueTLmy5csJIADYzLmz58+gQ4seTbq0ac4EAKhezbq169ewY8ueTbv27AQFACSAAKC379/AgwsfTry48ePIfTMAwLw5AAgAokufTr269evYs2vfzl26BAQABhj/AEC+vPnz6NOrX8++vfv37QkAmE+/vv37+PPr38+/v3+AAARKQADA4EGECRUuZNjQ4UOIESUSAFDR4kWMGTVu5NjR40eQFgsMAFDS5EmUKVWuZNnS5UuYKAdIAFDT5k2cOXXu5NnT50+gQQU4AFDU6FGkSZUuZdrU6VOoRQccAFDV6lWsWbVu5drV61ewXgcAACDAAQC0adWuZdvW7Vu4ceXORTugAgC8eQEgANDX71/AgQUPJlzY8GHEfisYAIBAAADIkSVPplzZ8mXMmTVvznwAwGfQoUWPJl3a9GnUqVWDrmAAwGvYsWXPpl3b9m3cuXXvPgDA92/gwYUPJ17c//hx5Ml/LxgAwPlz6NGlT6de3fp17NmjDxAAwPt3AAkAjCdf3vx59OnVr2ff3v15BAkAzKdf3/59/Pn17+ff3z9AAAAKUABg8CDChAoXMmzo8CHEiBIXMABg8SLGjBo3cuzo8SPIkBYLUABg8iTKlCpXsmzp8iXMmC8hDACwgAGAnDp38uzp8yfQoEKHEtVpAADSpAAqAGjq9CnUqFKnUq1q9SpWpxQKAOjq9SvYsGLHki1r9izatBQAsG3r9i3cuHLn0q1r9y6AAQIOLCgA4C/gwIIHEy5s+DDixIoFDwDg+DEABwAmU65s+TLmzJo3c+6ceQCAAhAcADAAYYGDBf8FBEBIAOA17NiyZ9Oubfs27ty6DVQA4Ps38ODChxMvbvw48uMIBAAYQEACgAEJCgCobp0BgwQJACQ4wADAgAIAxpMvb/48+vTq17Nvf95ABQDy59Ovb/8+/vz69/O/zwCgBAAAJDgAcBBhwoQMFgBwCKBAAQAIDkAAUCBBAQAbOXb0+BFkSJEjSZYEUGABAJUrASwA8BJmTJkzada0eRMnzAIJBgCQQKAAAAEIABQ1ehSpUQMFADR1+hSAAQgMABhwkABAVq1buXb1+hVsWLFju1YAcBZtWrVr2bZ1+/ZtAgcGADiQUADAAAB7+fb1+xdw4L4DBAgAgIACAwAABgD/cPwYcmTJkylXtnz5cgUAmzl39vwZdGjRozkbKABAwAEBABIIGAAAdmzZs2nXtn2bdgEDAAwckABgQIICAIgXN34ceXLly5krN8AAQHTpAAYAsH4de3bt27l3516AgQAAAigIADBgAAD169m3d//evQMBAOjXt38fP/4CEiAAKADQQQIABAsaPIgwocKFDA0ikAAgosSJFCtavIgR4wADAApUkACgAAMEAEqaPIkypcqVKB0IAAAzpsyZNGvGHLBgAQADFBgA+Ak0qNChRIsaPYoAAoClTAEUAAA1qtSpVKtapSqAAYABByAAAGAAgNixZMuaPYs2LYAFCAC4fQs3/67cuXQNIABQgIAEAAAQDAAAOLDgwYQLGz6MGIAEAIwbO34MOXLkAgAAQKAAAICDBQA6e/4MOrTo0aRLmz6NGvQAAAUkVAAwgEECALRr276NO7fu3bklAPgNPLjw4cR/G1gwAEAFCgUAICgAILr06dSrW7+OPbv27dy7AxiwgAGAARUYADiPPr369ezboy+AAID8+QAQALiPP7/+/foFSACIAMACBwUAHESYUOFChg0dPnQowAAAihUtXsSYUePGjAYSABhAoAIAAAgGAECZUuVKli1TJoAAQOZMmjVt1hyQwACABQQEADCAYAAAokWNHkWaVOlSpk2JQkgAQOpUqv9VrV7FmlWr1AIABlSgAADAAgQAzJ5Fm1atWgEOALyFG1fuXAAFICwAkEBCAgB9/f4FHFjwYMKFDReWgADAYsaNHT+GHFny5MgDGEAAAEACAwCdPX8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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "identifier = \"toy_c3h8_relax.extxyz\"\n", + "traj = ase.io.read(\"data/%s\" % identifier, index=\":\")\n", + "\n", + "ase.io.write(os.path.join(videos_dir, identifier + \".gif\"),\n", + " traj,\n", + " interval=1,\n", + " rotation=(\"-75x, 45y, 10z\"))\n", + "plt.close()\n", + "Image(open(os.path.join(videos_dir, identifier + \".gif\"),'rb').read())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data contents\n", + "Here we take a closer look at what information is contained within these trajectories." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Atoms(symbols='Cu27C3H8', pbc=True, cell=[7.65796644025031, 7.65796644025031, 33.266996999999996], energies=..., forces=..., tags=..., constraint=FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), calculator=SinglePointCalculator(...))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i_structure = traj[0]\n", + "i_structure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Atomic numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29\n", + " 29 29 29 6 6 6 1 1 1 1 1 1 1 1]\n" + ] + } + ], + "source": [ + "numbers = i_structure.get_atomic_numbers()\n", + "print(numbers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Atomic symbols" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu'\n", + " 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'C' 'C'\n", + " 'C' 'H' 'H' 'H' 'H' 'H' 'H' 'H' 'H']\n" + ] + } + ], + "source": [ + "symbols = np.array(i_structure.get_chemical_symbols())\n", + "print(symbols)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Unit cell\n", + "\n", + "The unit cell is the volume containing our system of interest. Express as a 3x3 array representing the directional vectors that make up the volume. Illustrated as the dashed box in the above visuals." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 7.65796644 0. 0. ]\n", + " [ 0. 7.65796644 0. ]\n", + " [ 0. 0. 33.266997 ]]\n" + ] + } + ], + "source": [ + "cell = np.array(i_structure.cell)\n", + "print(cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Periodic boundary conditions (PBC)\n", + "\n", + "x,y,z boolean representing whether a unit cell repeats in the corresponding directions. The OC20 dataset sets this to [True, True, True], with a large enough vacuum layer above the surface such that a unit cell does not see itself in the z direction." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True True True]\n" + ] + } + ], + "source": [ + "pbc = i_structure.pbc\n", + "print(pbc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Tags\n", + "\n", + "The OC20 dataset consists of systems with several different types of atoms. To help with identifying the index of certain atoms, we tag each atom according to where it is found in the system - sub-surface slab atoms, surface slab atoms, and adsorbate atoms.\n", + "\n", + "Tag:\n", + "\n", + "0 - Sub-surface slab atoms
\n", + "1 - Surface slab atoms
\n", + "2 - Adsorbate atoms" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2\n", + " 2]\n" + ] + } + ], + "source": [ + "tags = i_structure.get_tags()\n", + "print(tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fixed atoms constraint\n", + "\n", + "In reality, surfaces contain many, many more atoms beneath what we've illustrated as the surface. At an infinite depth, these subsurface atoms would look just like the bulk structure. We approximate a true surface by fixing the subsurface atoms into their “bulk” locations. This ensures that they cannot move at the “bottom” of the surface. If they could, this would throw off our calculations. Consistent with the above, we fix all atoms with tags=0, and denote them as \"fixed\". All other atoms are considered \"free\". " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]) \n", + "\n", + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17] \n", + "\n", + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n" + ] + } + ], + "source": [ + "cons = i_structure.constraints[0]\n", + "print(cons, '\\n')\n", + "\n", + "# indices of fixed atoms\n", + "indices = cons.index\n", + "print(indices, '\\n')\n", + "\n", + "# fixed atoms correspond to tags = 0\n", + "print(tags[indices])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Energy\n", + "\n", + "The energy of the system is one of the properties of interest in the OC20 dataset. It's important to note that absolute energies provide little value to researchers and must be referenced properly to be useful. The OC20 dataset references all it's energies to the bare slab + gas references to arrive at adsorption energies. Adsorption energies are important in studying catalysts and their corresponding reaction rates. In addition to the structure realxations of the OC20 dataset, bare slab and gas (N2, H2, H2O, CO) relaxations were carried out with DFT in order to calculate adsorption energies." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Relaxed absolute energy = 8.481898405400525 eV\n", + "Raw slab energy = 8.127167122751175 eV\n", + "Adsorbte reference energy = 3.4499999999999993 eV\n", + "\n", + "Adsorption energy: -3.095268717350649 eV\n" + ] + } + ], + "source": [ + "final_structure = traj[-1]\n", + "relaxed_energy = final_structure.get_potential_energy()\n", + "print(f'Relaxed absolute energy = {relaxed_energy} eV')\n", + "\n", + "# Corresponding raw slab used in original adslab (adsorbate+slab) system. \n", + "raw_slab = fcc100(\"Cu\", size=(3, 3, 3))\n", + "raw_slab.set_calculator(EMT())\n", + "raw_slab_energy = raw_slab.get_potential_energy()\n", + "print(f'Raw slab energy = {raw_slab_energy} eV')\n", + "\n", + "\n", + "adsorbate = Atoms(\"C3H8\").get_chemical_symbols()\n", + "# For clarity, we define arbitrary gas reference energies here.\n", + "# A more detailed discussion of these calculations can be found in the corresponding paper's SI. \n", + "gas_reference_energies = {'H': .3, 'O': .45, 'C': .35, 'N': .50}\n", + "\n", + "adsorbate_reference_energy = 0\n", + "for ads in adsorbate:\n", + " adsorbate_reference_energy += gas_reference_energies[ads]\n", + "\n", + "print(f'Adsorbte reference energy = {adsorbate_reference_energy} eV\\n')\n", + "\n", + "adsorption_energy = relaxed_energy - raw_slab_energy - adsorbate_reference_energy\n", + "print(f'Adsorption energy: {adsorption_energy} eV')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Energy, eV')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot energy profile of toy trajectory\n", + "energies = [image.get_potential_energy() - raw_slab_energy - adsorbate_reference_energy for image in traj]\n", + "\n", + "plt.figure(figsize=(7, 7))\n", + "plt.plot(range(len(energies)), energies, lw=3)\n", + "plt.xlabel(\"Step\", fontsize=24)\n", + "plt.ylabel(\"Energy, eV\", fontsize=24)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Forces\n", + "\n", + "Forces are another important property of the OC20 dataset. Unlike datasets like QM9 which contain only ground state properties, the OC20 dataset contains per-atom forces necessary to carry out atomistic simulations. Physically, forces are the negative gradient of energy w.r.t atomic positions: $F = -\\frac{dE}{dx}$. Maintaining this energy-force consistency is important for models that seek to make predictions on both.\n", + "\n", + "The \"apply_constraint\" argument controls whether to apply system constraints to the forces. In the OC20 dataset, this controls whether to return forces for fixed atoms (apply_constraint=False) or return 0s (apply_constraint=True)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.07900000e-05, -3.80000000e-06, 1.13560540e-01],\n", + " [ 0.00000000e+00, -4.29200000e-05, 1.13302410e-01],\n", + " [ 1.07900000e-05, -3.80000000e-06, 1.13560540e-01],\n", + " [-1.84600000e-05, -0.00000000e+00, 1.13543430e-01],\n", + " [-0.00000000e+00, -0.00000000e+00, 1.13047800e-01],\n", + " [ 1.84600000e-05, 0.00000000e+00, 1.13543430e-01],\n", + " [-1.07900000e-05, 3.80000000e-06, 1.13560540e-01],\n", + " [ 0.00000000e+00, 4.29200000e-05, 1.13302410e-01],\n", + " [ 1.07900000e-05, 3.80000000e-06, 1.13560540e-01],\n", + " [-1.10430500e-02, -2.53094000e-03, -4.84573700e-02],\n", + " [ 1.10430500e-02, -2.53094000e-03, -4.84573700e-02],\n", + " [-0.00000000e+00, -2.20890000e-04, -2.07827000e-03],\n", + " [-1.10430500e-02, 2.53094000e-03, -4.84573700e-02],\n", + " [ 1.10430500e-02, 2.53094000e-03, -4.84573700e-02],\n", + " [-0.00000000e+00, 2.20890000e-04, -2.07827000e-03],\n", + " [-3.49808000e-03, -0.00000000e+00, -7.85544000e-03],\n", + " [ 3.49808000e-03, -0.00000000e+00, -7.85544000e-03],\n", + " [-0.00000000e+00, -0.00000000e+00, -5.97640000e-04],\n", + " [-3.18144370e-01, -2.36420450e-01, -3.97089230e-01],\n", + " [ 0.00000000e+00, -2.18895316e+00, -2.74768262e+00],\n", + " [ 3.18144370e-01, -2.36420450e-01, -3.97089230e-01],\n", + " [-5.65980520e-01, 0.00000000e+00, -6.16046990e-01],\n", + " [ 0.00000000e+00, 0.00000000e+00, -4.47152822e+00],\n", + " [ 5.65980520e-01, -0.00000000e+00, -6.16046990e-01],\n", + " [-3.18144370e-01, 2.36420450e-01, -3.97089230e-01],\n", + " [ 0.00000000e+00, 2.18895316e+00, -2.74768262e+00],\n", + " [ 3.18144370e-01, 2.36420450e-01, -3.97089230e-01],\n", + " [-0.00000000e+00, -0.00000000e+00, -3.96835355e+00],\n", + " [-0.00000000e+00, -3.64190926e+00, 5.71458646e+00],\n", + " [-0.00000000e+00, 3.64190926e+00, 5.71458646e+00],\n", + " [-2.18178516e+00, 0.00000000e+00, 1.67589182e+00],\n", + " [ 2.18178516e+00, 0.00000000e+00, 1.67589182e+00],\n", + " [-0.00000000e+00, 2.46333681e+00, 1.78299828e+00],\n", + " [ 0.00000000e+00, -2.46333681e+00, 1.78299828e+00],\n", + " [ 6.18714050e+00, 2.26336330e-01, -5.99485570e-01],\n", + " [-6.18714050e+00, 2.26336330e-01, -5.99485570e-01],\n", + " [-6.18714050e+00, -2.26336330e-01, -5.99485570e-01],\n", + " [ 6.18714050e+00, -2.26336330e-01, -5.99485570e-01]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Returning forces for all atoms - regardless of whether \"fixed\" or \"free\"\n", + "i_structure.get_forces(apply_constraint=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [ 0. , 0. , 0. ],\n", + " [-0.31814437, -0.23642045, -0.39708923],\n", + " [ 0. , -2.18895316, -2.74768262],\n", + " [ 0.31814437, -0.23642045, -0.39708923],\n", + " [-0.56598052, 0. , -0.61604699],\n", + " [ 0. , 0. , -4.47152822],\n", + " [ 0.56598052, -0. , -0.61604699],\n", + " [-0.31814437, 0.23642045, -0.39708923],\n", + " [ 0. , 2.18895316, -2.74768262],\n", + " [ 0.31814437, 0.23642045, -0.39708923],\n", + " [-0. , -0. , -3.96835355],\n", + " [-0. , -3.64190926, 5.71458646],\n", + " [-0. , 3.64190926, 5.71458646],\n", + " [-2.18178516, 0. , 1.67589182],\n", + " [ 2.18178516, 0. , 1.67589182],\n", + " [-0. , 2.46333681, 1.78299828],\n", + " [ 0. , -2.46333681, 1.78299828],\n", + " [ 6.1871405 , 0.22633633, -0.59948557],\n", + " [-6.1871405 , 0.22633633, -0.59948557],\n", + " [-6.1871405 , -0.22633633, -0.59948557],\n", + " [ 6.1871405 , -0.22633633, -0.59948557]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Applying the fixed atoms constraint to the forces\n", + "i_structure.get_forces(apply_constraint=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resources\n", + "\n", + "More helpful resources, tutorials, and documentation can be found at ASE's webpage: https://wiki.fysik.dtu.dk/ase/index.html. We point to specific pages that may be of interest:\n", + "\n", + "- Interacting with Atoms Object: https://wiki.fysik.dtu.dk/ase/ase/atoms.html
\n", + "- Visualization: https://wiki.fysik.dtu.dk/ase/ase/visualize/visualize.html
\n", + "- Structure optimization: https://wiki.fysik.dtu.dk/ase/ase/optimize.html
\n", + "- Tutorials: https://wiki.fysik.dtu.dk/ase/tutorials/tutorials.html
\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/lmdb_dataset_creation.ipynb b/tutorials/lmdb_dataset_creation.ipynb new file mode 100644 index 0000000..56b0694 --- /dev/null +++ b/tutorials/lmdb_dataset_creation.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "controversial-lodge", + "metadata": {}, + "source": [ + "### OCP LMDB Dataset Tutorial\n", + "\n", + "This notebook provides an overview of how to create LMDB datasets to be used with the OCP repo. This tutorial is intended for those who wish to use OCP to train on their own datasets. Those interested in just using OCP data need not worry about these steps as they've been automated as part of the download script: https://github.com/Open-Catalyst-Project/ocp/blob/master/scripts/download_data.py." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cardiac-message", + "metadata": {}, + "outputs": [], + "source": [ + "from ocpmodels.preprocessing import AtomsToGraphs\n", + "from ocpmodels.datasets import SinglePointLmdbDataset, TrajectoryLmdbDataset\n", + "import ase.io\n", + "from ase.build import bulk\n", + "from ase.build import fcc100, add_adsorbate, molecule\n", + "from ase.constraints import FixAtoms\n", + "from ase.calculators.emt import EMT\n", + "from ase.optimize import BFGS\n", + "import matplotlib.pyplot as plt\n", + "import lmdb\n", + "import pickle\n", + "from tqdm import tqdm\n", + "import torch\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "municipal-passenger", + "metadata": {}, + "source": [ + "### Generate toy dataset: Relaxation of CO on Cu" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "amended-thread", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adslab = fcc100(\"Cu\", size=(2, 2, 3))\n", + "ads = molecule(\"CO\")\n", + "add_adsorbate(adslab, ads, 3, offset=(1, 1))\n", + "cons = FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 3)])\n", + "adslab.set_constraint(cons)\n", + "adslab.center(vacuum=13.0, axis=2)\n", + "adslab.set_pbc(True)\n", + "adslab.set_calculator(EMT())\n", + "dyn = BFGS(adslab, trajectory=\"CuCO_adslab.traj\", logfile=None)\n", + "dyn.run(fmax=0, steps=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "voluntary-hotel", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1001" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = ase.io.read(\"CuCO_adslab.traj\", \":\")\n", + "len(raw_data)" + ] + }, + { + "cell_type": "markdown", + "id": "quality-renewal", + "metadata": {}, + "source": [ + "### Initial Structure to Relaxed Energy/Structure (IS2RE/IS2RS) LMDBs\n", + "\n", + "IS2RE/IS2RS LMDBs utilize the SinglePointLmdb dataset. This dataset expects the data to be contained in a SINGLE LMDB file. In addition to the attributes defined by AtomsToGraph, the following attributes must be added for the IS2RE/IS2RS tasks:\n", + "\n", + "- pos_relaxed: Relaxed adslab positions\n", + "- sid: Unique system identifier, arbitrary\n", + "- y_init: Initial adslab energy, formerly Data.y\n", + "- y_relaxed: Relaxed adslab energy\n", + "- tags (optional): 0 - subsurface, 1 - surface, 2 - adsorbate\n", + "\n", + "\n", + "As a demo, we will use the above generated data to create an IS2R* LMDB file." + ] + }, + { + "cell_type": "markdown", + "id": "different-produce", + "metadata": {}, + "source": [ + "#### Initialize AtomsToGraph feature extractor" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "strange-acquisition", + "metadata": {}, + "outputs": [], + "source": [ + "a2g = AtomsToGraphs(\n", + " max_neigh=50,\n", + " radius=6,\n", + " r_energy=True, # False for test data\n", + " r_forces=True,\n", + " r_distances=False,\n", + " r_fixed=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "manual-seventh", + "metadata": {}, + "source": [ + "#### Initialize LMDB file" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "binding-grain", + "metadata": {}, + "outputs": [], + "source": [ + "db = lmdb.open(\n", + " \"sample_CuCO.lmdb\",\n", + " map_size=1099511627776 * 2,\n", + " subdir=False,\n", + " meminit=False,\n", + " map_async=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "alpha-haiti", + "metadata": {}, + "source": [ + "#### Write data to LMDB" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "sophisticated-verification", + "metadata": {}, + "outputs": [], + "source": [ + "def read_trajectory_extract_features(a2g, traj_path):\n", + " traj = ase.io.read(traj_path, \":\")\n", + " tags = traj[0].get_tags()\n", + " images = [traj[0], traj[-1]]\n", + " data_objects = a2g.convert_all(images, disable_tqdm=True)\n", + " data_objects[0].tags = torch.LongTensor(tags)\n", + " data_objects[1].tags = torch.LongTensor(tags)\n", + " return data_objects" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "useful-exposure", + "metadata": {}, + "outputs": [], + "source": [ + "system_paths = [\"CuCO_adslab.traj\"]\n", + "idx = 0\n", + "\n", + "for system in system_paths:\n", + " # Extract Data object\n", + " data_objects = read_trajectory_extract_features(a2g, system)\n", + " initial_struc = data_objects[0]\n", + " relaxed_struc = data_objects[1]\n", + " \n", + " initial_struc.y_init = initial_struc.y # subtract off reference energy, if applicable\n", + " del initial_struc.y\n", + " initial_struc.y_relaxed = relaxed_struc.y # subtract off reference energy, if applicable\n", + " initial_struc.pos_relaxed = relaxed_struc.pos\n", + " \n", + " # Filter data if necessary\n", + " # OCP filters adsorption energies > |10| eV\n", + " \n", + " initial_struc.sid = idx # arbitrary unique identifier \n", + " \n", + " # no neighbor edge case check\n", + " if initial_struc.edge_index.shape[1] == 0:\n", + " print(\"no neighbors\", traj_path)\n", + " continue\n", + " \n", + " # Write to LMDB\n", + " txn = db.begin(write=True)\n", + " txn.put(f\"{idx}\".encode(\"ascii\"), pickle.dumps(initial_struc, protocol=-1))\n", + " txn.commit()\n", + " db.sync()\n", + " idx += 1\n", + "\n", + "db.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "failing-election", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = SinglePointLmdbDataset({\"src\": \"sample_CuCO.lmdb\"})\n", + "len(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "synthetic-recipient", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(atomic_numbers=[14], cell=[1, 3, 3], cell_offsets=[636, 3], edge_index=[2, 636], fixed=[14], force=[14, 3], natoms=14, pos=[14, 3], pos_relaxed=[14, 3], sid=0, tags=[14], y_init=3.989314410668539, y_relaxed=3.9683558933957266)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[0]" + ] + }, + { + "cell_type": "markdown", + "id": "governing-liabilities", + "metadata": {}, + "source": [ + "### Structure to Energy and Forces (S2EF) LMDBs\n", + "\n", + "S2EF LMDBs utilize the TrajectoryLmdb dataset. This dataset expects a directory of LMDB files. In addition to the attributes defined by AtomsToGraph, the following attributes must be added for the S2EF task:\n", + "\n", + "- tags (optional): 0 - subsurface, 1 - surface, 2 - adsorbate\n", + "- fid: Frame index along the trajcetory\n", + "- sid- sid: Unique system identifier, arbitrary\n", + "\n", + "Additionally, a \"length\" key must be added to each LMDB file.\n", + "\n", + "As a demo, we will use the above generated data to create an S2EF LMDB dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "addressed-underground", + "metadata": {}, + "outputs": [], + "source": [ + "os.makedirs(\"s2ef\", exist_ok=True)\n", + "db = lmdb.open(\n", + " \"s2ef/sample_CuCO.lmdb\",\n", + " map_size=1099511627776 * 2,\n", + " subdir=False,\n", + " meminit=False,\n", + " map_async=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "adjustable-environment", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1001/1001 [00:06<00:00, 163.53it/s]\n" + ] + } + ], + "source": [ + "tags = raw_data[0].get_tags()\n", + "data_objects = a2g.convert_all(raw_data, disable_tqdm=True)\n", + "\n", + "\n", + "for fid, data in tqdm(enumerate(data_objects), total=len(data_objects)):\n", + " #assign sid\n", + " data.sid = torch.LongTensor([0])\n", + " \n", + " #assign fid\n", + " data.fid = torch.LongTensor([fid])\n", + " \n", + " #assign tags, if available\n", + " data.tags = torch.LongTensor(tags)\n", + " \n", + " # Filter data if necessary\n", + " # OCP filters adsorption energies > |10| eV and forces > |50| eV/A\n", + "\n", + " # no neighbor edge case check\n", + " if data.edge_index.shape[1] == 0:\n", + " print(\"no neighbors\", traj_path)\n", + " continue\n", + "\n", + " txn = db.begin(write=True)\n", + " txn.put(f\"{fid}\".encode(\"ascii\"), pickle.dumps(data, protocol=-1))\n", + " txn.commit()\n", + " \n", + "txn = db.begin(write=True)\n", + "txn.put(f\"length\".encode(\"ascii\"), pickle.dumps(len(data_objects), protocol=-1))\n", + "txn.commit()\n", + "\n", + "\n", + "db.sync()\n", + "db.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "infectious-approval", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1001" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = TrajectoryLmdbDataset({\"src\": \"s2ef/\"})\n", + "len(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "drawn-script", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(atomic_numbers=[14], cell=[1, 3, 3], cell_offsets=[636, 3], edge_index=[2, 636], fid=[1], fixed=[14], force=[14, 3], id=\"0_0\", natoms=14, pos=[14, 3], sid=[1], tags=[14], y=3.989314410668539)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[0]" + ] + }, + { + "cell_type": "markdown", + "id": "fiscal-mother", + "metadata": {}, + "source": [ + "#### Advanced usage\n", + "\n", + "TrajectoryLmdbDataset supports multiple LMDB files because the need to highly parallelize the dataset construction process. With OCP's largest split containing 135M+ frames, the need to parallelize the LMDB generation process for these was necessary. If you find yourself needing to deal with very large datasets we recommend parallelizing this process." + ] + }, + { + "cell_type": "markdown", + "id": "fuzzy-society", + "metadata": {}, + "source": [ + "### Interacting with the LMDBs\n", + "\n", + "Below we demonstrate how to interact with an LMDB to extract particular information." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "formed-cuisine", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = TrajectoryLmdbDataset({\"src\": \"s2ef/\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "sexual-atlas", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(atomic_numbers=[14], cell=[1, 3, 3], cell_offsets=[636, 3], edge_index=[2, 636], fid=[1], fixed=[14], force=[14, 3], id=\"0_0\", natoms=14, pos=[14, 3], sid=[1], tags=[14], y=3.989314410668539)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = dataset[0]\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "developmental-kruger", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([3.9893, 3.9835, 3.9784, ..., 3.9684, 3.9684, 3.9684])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energies = torch.tensor([data.y for data in dataset])\n", + "energies" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "detailed-flesh", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(energies, bins = 10)\n", + "plt.yscale(\"log\")\n", + "plt.xlabel(\"Energies\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ocp-models", + "language": "python", + "name": "ocp-models" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/train_s2ef_example.ipynb b/tutorials/train_s2ef_example.ipynb new file mode 100644 index 0000000..0e9c571 --- /dev/null +++ b/tutorials/train_s2ef_example.ipynb @@ -0,0 +1,666 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SchNet S2EF training example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The purpose of this notebook is to demonstrate some of the basics of the Open Catalyst Project's (OCP) codebase and data. In this example, we will train a schnet model for predicting the energy and forces of a given structure (S2EF task). First, ensure you have installed the OCP ocp repo and all the dependencies according to the [README](https://github.com/Open-Catalyst-Project/ocp/blob/master/README.md)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Disclaimer: This notebook is for tutorial purposes, it is unlikely it will be practical to train baseline models on our larger datasets using this format. As a next step, we recommend trying the command line examples. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "from ocpmodels.trainers import ForcesTrainer\n", + "from ocpmodels import models\n", + "from ocpmodels.common import logger\n", + "from ocpmodels.common.utils import setup_logging\n", + "setup_logging()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "# a simple sanity check that a GPU is available\n", + "if torch.cuda.is_available():\n", + " print(\"True\")\n", + "else:\n", + " print(\"False\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The essential steps for training an OCP model\n", + "\n", + "1) Download data\n", + "\n", + "2) Preprocess data (if necessary)\n", + "\n", + "3) Define or load a configuration (config), which includes the following\n", + " \n", + " - task\n", + " - model\n", + " - optimizer\n", + " - dataset\n", + " - trainer\n", + "\n", + "4) Train\n", + "\n", + "5) Depending on the model/task there might be intermediate relaxation step\n", + "\n", + "6) Predict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This examples uses the LMDB generated from the following [tutorial](http://laikapack.cheme.cmu.edu/notebook/open-catalyst-project/mshuaibi/notebooks/projects/ocp/docs/source/tutorials/lmdb_dataset_creation.ipynb). Please run that notebook before moving on. Alternatively, if you have other LMDBs available you may specify that instead." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# set the path to your local lmdb directory\n", + "train_src = \"s2ef\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this example, we will explicitly define the config; however, a set of default config files exists in the config folder of this repository. Default config yaml files can easily be loaded with the `build_config` util (found in `ocp/ocpmodels/common/utils.py`). Loading a yaml config is preferrable when launching jobs from the command line. We have included our best models' config files [here](https://github.com/Open-Catalyst-Project/ocp/tree/master/configs/s2ef)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Task** " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "task = {\n", + " 'dataset': 'trajectory_lmdb', # dataset used for the S2EF task\n", + " 'description': 'Regressing to energies and forces for DFT trajectories from OCP',\n", + " 'type': 'regression',\n", + " 'metric': 'mae',\n", + " 'labels': ['potential energy'],\n", + " 'grad_input': 'atomic forces',\n", + " 'train_on_free_atoms': True,\n", + " 'eval_on_free_atoms': True\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Model** - SchNet for this example" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "model = {\n", + " 'name': 'schnet',\n", + " 'hidden_channels': 1024, # if training is too slow for example purposes reduce the number of hidden channels\n", + " 'num_filters': 256,\n", + " 'num_interactions': 3,\n", + " 'num_gaussians': 200,\n", + " 'cutoff': 6.0\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Optimizer**" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = {\n", + " 'batch_size': 16, # if hitting GPU memory issues, lower this\n", + " 'eval_batch_size': 8,\n", + " 'num_workers': 8,\n", + " 'lr_initial': 0.0001,\n", + " 'scheduler': \"ReduceLROnPlateau\",\n", + " 'mode': \"min\",\n", + " 'factor': 0.8,\n", + " 'patience': 3,\n", + " 'max_epochs': 80,\n", + " 'max_epochs': 1, # used for demonstration purposes\n", + " 'force_coefficient': 100,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Dataset**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simplicity, `train_src` is used for all the train/val/test sets. Feel free to update with the actual S2EF val and test sets, but it does require additional downloads and preprocessing. If you desire to normalize your targets, `normalize_labels` must be set to `True` and corresponding `mean` and `stds` need to be specified. These values have been precomputed for you and can be found in any of the [`base.yml`](https://github.com/Open-Catalyst-Project/ocp/blob/master/configs/s2ef/20M/base.yml#L5-L9) config files." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = [\n", + "{'src': train_src, 'normalize_labels': False}, # train set \n", + "{'src': train_src}, # val set (optional)\n", + "{'src': train_src} # test set (optional - writes predictions to disk)\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Trainer**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the `ForcesTrainer` for the S2EF and IS2RS tasks, and the `EnergyTrainer` for the IS2RE task " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "amp: false\n", + "cmd:\n", + " checkpoint_dir: ./checkpoints/2021-09-04-08-51-28-SchNet-example\n", + " commit: 98a06d8\n", + " identifier: SchNet-example\n", + " logs_dir: ./logs/tensorboard/2021-09-04-08-51-28-SchNet-example\n", + " print_every: 5\n", + " results_dir: ./results/2021-09-04-08-51-28-SchNet-example\n", + " seed: 0\n", + " timestamp_id: 2021-09-04-08-51-28-SchNet-example\n", + "dataset:\n", + " normalize_labels: false\n", + " src: s2ef\n", + "gpus: 1\n", + "logger: tensorboard\n", + "model: schnet\n", + "model_attributes:\n", + " cutoff: 6.0\n", + " hidden_channels: 1024\n", + " num_filters: 256\n", + " num_gaussians: 200\n", + " num_interactions: 3\n", + "optim:\n", + " batch_size: 16\n", + " eval_batch_size: 8\n", + " factor: 0.8\n", + " force_coefficient: 100\n", + " lr_initial: 0.0001\n", + " max_epochs: 1\n", + " mode: min\n", + " num_workers: 8\n", + " patience: 3\n", + " scheduler: ReduceLROnPlateau\n", + "slurm: {}\n", + "task:\n", + " dataset: trajectory_lmdb\n", + " description: Regressing to energies and forces for DFT trajectories from OCP\n", + " eval_on_free_atoms: true\n", + " grad_input: atomic forces\n", + " labels:\n", + " - potential energy\n", + " metric: mae\n", + " train_on_free_atoms: true\n", + " type: regression\n", + "test_dataset:\n", + " src: s2ef\n", + "val_dataset:\n", + " src: s2ef\n", + "\n", + "2021-09-04 08:51:37 (INFO): Loading dataset: trajectory_lmdb\n", + "2021-09-04 08:51:37 (INFO): Loading model: schnet\n", + "2021-09-04 08:51:37 (INFO): Loaded SchNet with 5704193 parameters.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:37 (WARNING): Model gradient logging to tensorboard not yet supported.\n" + ] + } + ], + "source": [ + "trainer = ForcesTrainer(\n", + " task=task,\n", + " model=model,\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"SchNet-example\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=5,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + " amp=False, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check the model" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OCPDataParallel(\n", + " (module): SchNet(hidden_channels=1024, num_filters=256, num_interactions=3, num_gaussians=200, cutoff=6.0)\n", + ")\n" + ] + } + ], + "source": [ + "print(trainer.model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:43 (INFO): forcesx_mae: 6.12e-01, forcesy_mae: 7.54e-01, forcesz_mae: 7.98e-01, forces_mae: 7.21e-01, forces_cos: -8.32e-03, forces_magnitude: 1.34e+00, energy_mae: 3.14e+01, energy_force_within_threshold: 0.00e+00, loss: 1.04e+02, lr: 1.00e-04, epoch: 1.25e-01, step: 5.00e+00\n", + "2021-09-04 08:51:43 (INFO): forcesx_mae: 4.95e-01, forcesy_mae: 5.85e-01, forcesz_mae: 6.06e-01, forces_mae: 5.62e-01, forces_cos: -1.64e-03, forces_magnitude: 9.97e-01, energy_mae: 2.38e+01, energy_force_within_threshold: 0.00e+00, loss: 8.02e+01, lr: 1.00e-04, epoch: 2.50e-01, step: 1.00e+01\n", + "2021-09-04 08:51:44 (INFO): forcesx_mae: 4.35e-01, forcesy_mae: 5.44e-01, forcesz_mae: 5.30e-01, forces_mae: 5.03e-01, forces_cos: 2.57e-02, forces_magnitude: 9.14e-01, energy_mae: 2.09e+01, energy_force_within_threshold: 0.00e+00, loss: 7.11e+01, lr: 1.00e-04, epoch: 3.75e-01, step: 1.50e+01\n", + "2021-09-04 08:51:44 (INFO): forcesx_mae: 3.70e-01, forcesy_mae: 4.50e-01, forcesz_mae: 4.22e-01, forces_mae: 4.14e-01, forces_cos: 3.03e-03, forces_magnitude: 7.05e-01, energy_mae: 1.66e+01, energy_force_within_threshold: 0.00e+00, loss: 5.83e+01, lr: 1.00e-04, epoch: 5.00e-01, step: 2.00e+01\n", + "2021-09-04 08:51:45 (INFO): forcesx_mae: 3.61e-01, forcesy_mae: 4.58e-01, forcesz_mae: 4.42e-01, forces_mae: 4.20e-01, forces_cos: 3.09e-02, forces_magnitude: 7.07e-01, energy_mae: 1.40e+01, energy_force_within_threshold: 0.00e+00, loss: 5.58e+01, lr: 1.00e-04, epoch: 6.25e-01, step: 2.50e+01\n", + "2021-09-04 08:51:45 (INFO): forcesx_mae: 3.51e-01, forcesy_mae: 3.96e-01, forcesz_mae: 3.91e-01, forces_mae: 3.79e-01, forces_cos: 2.94e-02, forces_magnitude: 6.65e-01, energy_mae: 1.39e+01, energy_force_within_threshold: 0.00e+00, loss: 5.19e+01, lr: 1.00e-04, epoch: 7.50e-01, step: 3.00e+01\n", + "2021-09-04 08:51:46 (INFO): forcesx_mae: 3.13e-01, forcesy_mae: 3.46e-01, forcesz_mae: 3.38e-01, forces_mae: 3.32e-01, forces_cos: 2.50e-02, forces_magnitude: 5.61e-01, energy_mae: 9.40e+00, energy_force_within_threshold: 0.00e+00, loss: 4.23e+01, lr: 1.00e-04, epoch: 8.75e-01, step: 3.50e+01\n", + "2021-09-04 08:51:46 (INFO): forcesx_mae: 3.06e-01, forcesy_mae: 3.59e-01, forcesz_mae: 3.59e-01, forces_mae: 3.41e-01, forces_cos: 1.31e-02, forces_magnitude: 5.62e-01, energy_mae: 1.02e+01, energy_force_within_threshold: 0.00e+00, loss: 4.91e+01, lr: 1.00e-04, epoch: 1.00e+00, step: 4.00e+01\n", + "2021-09-04 08:51:46 (INFO): Evaluating on val.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "device 0: 100%|██████████| 79/79 [00:01<00:00, 39.87it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:48 (INFO): forcesx_mae: 0.2778, forcesy_mae: 0.3467, forcesz_mae: 0.3606, forces_mae: 0.3284, forces_cos: 0.0278, forces_magnitude: 0.5615, energy_mae: 12.4560, energy_force_within_threshold: 0.0000, loss: 44.8795, epoch: 1.0000\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:49 (INFO): Predicting on test.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "device 0: 100%|██████████| 79/79 [00:01<00:00, 41.47it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:51 (INFO): Writing results to ./results/2021-09-04-08-51-28-SchNet-example/s2ef_predictions.npz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Checkpoint\n", + "Once training has completed a `Trainer` class, by default, is loaded with the best checkpoint as determined by training or validation (if available) metrics. To load a `Trainer` class directly with a pretrained model, specify the `checkpoint_path` as defined by your previously trained model (`checkpoint_dir`):" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./checkpoints/2021-09-04-08-51-28-SchNet-example/checkpoint.pt'" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "checkpoint_path = os.path.join(trainer.config[\"cmd\"][\"checkpoint_dir\"], \"checkpoint.pt\")\n", + "checkpoint_path" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "amp: false\n", + "cmd:\n", + " checkpoint_dir: ./checkpoints/2021-09-04-08-51-28-SchNet-example\n", + " commit: 98a06d8\n", + " identifier: SchNet-example\n", + " logs_dir: ./logs/tensorboard/2021-09-04-08-51-28-SchNet-example\n", + " print_every: 10\n", + " results_dir: ./results/2021-09-04-08-51-28-SchNet-example\n", + " seed: 0\n", + " timestamp_id: 2021-09-04-08-51-28-SchNet-example\n", + "dataset:\n", + " normalize_labels: false\n", + " src: s2ef\n", + "gpus: 1\n", + "logger: tensorboard\n", + "model: schnet\n", + "model_attributes:\n", + " cutoff: 6.0\n", + " hidden_channels: 1024\n", + " num_filters: 256\n", + " num_gaussians: 200\n", + " num_interactions: 3\n", + "optim:\n", + " batch_size: 16\n", + " eval_batch_size: 8\n", + " factor: 0.8\n", + " force_coefficient: 100\n", + " lr_initial: 0.0001\n", + " max_epochs: 1\n", + " mode: min\n", + " num_workers: 8\n", + " patience: 3\n", + " scheduler: ReduceLROnPlateau\n", + "slurm: {}\n", + "task:\n", + " dataset: trajectory_lmdb\n", + " description: Regressing to energies and forces for DFT trajectories from OCP\n", + " eval_on_free_atoms: true\n", + " grad_input: atomic forces\n", + " labels:\n", + " - potential energy\n", + " metric: mae\n", + " train_on_free_atoms: true\n", + " type: regression\n", + "test_dataset:\n", + " src: s2ef\n", + "val_dataset:\n", + " src: s2ef\n", + "\n", + "2021-09-04 08:51:51 (INFO): Loading dataset: trajectory_lmdb\n", + "2021-09-04 08:51:51 (INFO): Loading model: schnet\n", + "2021-09-04 08:51:51 (INFO): Loaded SchNet with 5704193 parameters.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:51 (WARNING): Model gradient logging to tensorboard not yet supported.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:51 (INFO): Loading checkpoint from: ./checkpoints/2021-09-04-08-51-28-SchNet-example/checkpoint.pt\n" + ] + } + ], + "source": [ + "model = {\n", + " 'name': 'schnet',\n", + " 'hidden_channels': 1024, # if training is too slow for example purposes reduce the number of hidden channels\n", + " 'num_filters': 256,\n", + " 'num_interactions': 3,\n", + " 'num_gaussians': 200,\n", + " 'cutoff': 6.0\n", + "}\n", + "\n", + "pretrained_trainer = ForcesTrainer(\n", + " task=task,\n", + " model=model,\n", + " dataset=dataset,\n", + " optimizer=optimizer,\n", + " identifier=\"SchNet-example\",\n", + " run_dir=\"./\", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!\n", + " is_debug=False, # if True, do not save checkpoint, logs, or results\n", + " is_vis=False,\n", + " print_every=10,\n", + " seed=0, # random seed to use\n", + " logger=\"tensorboard\", # logger of choice (tensorboard and wandb supported)\n", + " local_rank=0,\n", + " amp=False, # use PyTorch Automatic Mixed Precision (faster training and less memory usage)\n", + ")\n", + "\n", + "pretrained_trainer.load_checkpoint(checkpoint_path=checkpoint_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If a test has been provided in your config, predictions are generated and written to disk automatically upon training completion. Otherwise, to make predictions on unseen data a `torch.utils.data` DataLoader object must be constructed. Here we reference our test set to make predictions on. Predictions are saved in `{results_file}.npz` in your `results_dir`." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:51 (INFO): Predicting on test.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "device 0: 100%|██████████| 79/79 [00:01<00:00, 44.68it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2021-09-04 08:51:53 (INFO): Writing results to ./results/2021-09-04-08-51-28-SchNet-example/s2ef_s2ef_results.npz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# make predictions on the existing test_loader\n", + "predictions = pretrained_trainer.predict(pretrained_trainer.test_loader, results_file=\"s2ef_results\", disable_tqdm=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "energies = predictions[\"energy\"]\n", + "forces = predictions[\"forces\"]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ocp-models", + "language": "python", + "name": "ocp-models" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}