diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..706919f --- /dev/null +++ b/.flake8 @@ -0,0 +1,26 @@ +[flake8] +extend-ignore = + B006 + B007 + B008 + B010 + B023 + B028 + B601 + C403 + C405 + C408 + C416 + C417 + C419 + E203 + E402 + E501 + E731 + W391 + W605 +exclude=build,notebooks,protobuf + +# ignore unused imports in __init__.py files +per-file-ignores = + __init__.py:F401 \ No newline at end of file diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 0000000..593cd3a --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,59 @@ +name: CI +on: push + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.9] + + steps: + - uses: actions/checkout@v2 + + - uses: mamba-org/setup-micromamba@v1.8.1 + with: + environment-file: environment.yml + cache-environment: true + + - name: Check formatting + shell: bash -l {0} + run: | + ufmt check flow_matching/ examples/ + + - name: flake8 lint + shell: bash -l {0} + run: | + flake8 flow_matching + + - name: Run tests + shell: bash -l {0} + run: | + coverage run --include='flow_matching/**/*.py' -m unittest discover tests -v + + - name: Docstring Lint + shell: bash -l {0} + run: | + pydoclint --style=google flow_matching + + - name: Build doc pages + shell: bash -l {0} + working-directory: docs + run: | + micromamba env update --file deps.yml + PYTHONPATH=../:. make html + + - name: coverage + shell: bash -l {0} + run: | + pip install coverage-badge + coverage html --include='flow_matching/**/*.py' -d docs/build/html/coverage + coverage-badge -o docs/build/html/coverage/coverage-badge.svg + rm docs/build/html/coverage/.gitignore + + - name: Deploy docs to GitHub Pages + uses: peaceiris/actions-gh-pages@v3 + if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/coverage' + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./docs/build/html diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..2fdc9fc --- /dev/null +++ b/.gitignore @@ -0,0 +1,73 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# 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 + +# VSCode +*.vscode + +# Others +examples/image_generation/data +examples/*/output_dir* +examples/image/scripts +examples/image/outputs +examples/image/data +examples/image/output_dir +examples/images/* +examples/imagenet/* +examples/image_generation/* +examples/*.ignore +examples/*/snapshots* +examples/*/outputs + +examples/imagenet/scripts +*.ipynb_checkpoints* + +make.bat +docs/output +docs/source/generated +docs/source/notebooks +docs/source/images +**/*.ipynb_checkpoints/ + +projects/image_latent/cache +projects/image_latent/vqvae_training/cache +projects/image_latent/outputs +*/assets/ + +outputs/ +output_dir/ + +*logs/ +*mixture_uniform_step=320001/ +*.out +*.err \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..504a6e0 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,17 @@ +repos: + - repo: https://github.com/omnilib/ufmt + rev: v2.3.0 + hooks: + - id: ufmt + additional_dependencies: + - black == 22.6.0 + - usort == 1.0.4 + - repo: https://github.com/pycqa/flake8 + rev: 7.0.0 + hooks: + - id: flake8 + - repo: https://github.com/jsh9/pydoclint + rev: 0.5.9 + hooks: + - id: pydoclint + args: [--style=google, flow_matching] diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..65faa80 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,5 @@ +# Change log + +## [0.1] - 2024-12-01 + +- Initial release. \ No newline at end of file diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..3232ed6 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,80 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or +advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic +address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a +professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +This Code of Conduct also applies outside the project spaces when there is a +reasonable belief that an individual's behavior may have a negative impact on +the project or its community. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..a8d06b6 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,28 @@ +# Contributing to flow_matching +We want to make contributing to this project as easy and transparent as +possible. + + +## Pull Requests +We actively welcome your pull requests. + +1. 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+ +# Flow Matching + +[![arXiv](assets/arXiv-2412.06264-red.svg)](https://arxiv.org/abs/2412.06264) +[![CI](https://github.com/facebookresearch/flow_matching/actions/workflows/ci.yaml/badge.svg)](https://github.com/facebookresearch/flow_matching/actions/workflows/ci.yaml) +[![Coverage](https://github.com/facebookresearch/flow_matching/raw/refs/heads/gh-pages/coverage/coverage-badge.svg)](https://stunning-potato-4k4z71e.pages.github.io/coverage/) +[![License: CC BY-NC 4.0](assets/License-CC_BY--NC_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/) + + +
+ +`flow_matching` is a PyTorch library for Flow Matching algorithms, featuring continuous and discrete implementations. It includes examples for both text and image modalities. This repository is part of [Flow Matching Guide and Codebase](https://arxiv.org/abs/2412.06264). + + +![](./assets/teaser.png) + +## Installation + +This repository requires Python 3.9 and Pytorch 2.1 or greater. To install the latest version run: +``` +pip install flow_matching +``` + +## Repository structure + +The core and example folders are structured in the following way: +```bash +. +├── flow_matching # Core library +│   ├── loss # Loss functions +│   │   └── ... +│   ├── path # Path and schedulers +│   │   ├── ... +│   │   └── scheduler # Schedulers and transformations +│   │   └── ... +│   ├── solver # Solvers for continuous and discrete flows +│   │   └── ... +│   └── utils +│   └── ... +└── examples # Synthetic, image, and text examples +    ├── ... +    ├── image +    │   └── ... +    └── text +       └── ... +``` + +## Development + +To create a conda environment with all required dependencies, run: +``` +conda env create -f environment.yml +conda activate flow_matching +``` + +Install pre-commit hook. This will ensure that all linting is done on each commit +``` +pre-commit install +``` + +Install the `flow_matching` package in an editable mode: +``` +pip install -e . +``` + +## FAQ + +#### I want to train a Flow Matching model, where can I find the training code? + +We provide [training examples](examples). Under this folder, you can find synthetic data for [continuous](examples/2d_flow_matching.ipynb), [discrete](examples/2d_discrete_flow_matching.ipynb), and [Riemannian](examples/2d_riemannian_flow_matching_flat_torus.ipynb) Flow Matching. We also provide full training [examples](examples/image) (continuous and discrete) on CIFAR10 and face-blurred ImageNet, and a scalable discrete Flow Matching example for [text modeling](examples/text). + +#### Do you release pre-trained models? + +In this version, we don't release pre-trained models. All models under [examples](examples) can be trained from scratch by a single running command. + +#### How to contribute to this codebase? +Please follow the [contribution guide](CONTRIBUTING.md). + +## License + +The code in this repository is CC BY-NC licensed. See the [LICENSE](LICENSE) for details. + +## Citation + +If you found this repository useful, please cite the following. + +``` +@misc{lipman2024flowmatchingguidecode, + title={Flow Matching Guide and Code}, + author={Yaron Lipman and Marton Havasi and Peter Holderrieth and Neta Shaul and Matt Le and Brian Karrer and Ricky T. Q. Chen and David Lopez-Paz and Heli Ben-Hamu and Itai Gat}, + year={2024}, + eprint={2412.06264}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2412.06264}, +} +``` diff --git a/RELEASE.md b/RELEASE.md new file mode 100644 index 0000000..33f6163 --- /dev/null +++ b/RELEASE.md @@ -0,0 +1,21 @@ +# Release Instructions + +Build a wheel: + +``` +pip wheel --no-deps . --wheel-dir dist +``` + +In your home directory, create `~/.pypirc` with the following: + +``` +[pypi] +username = __token__ +password = +``` + +Upload the wheel: + +``` +twine upload dist/* +``` diff --git a/assets/License-CC_BY--NC_4.0-lightgrey.svg b/assets/License-CC_BY--NC_4.0-lightgrey.svg new file mode 100644 index 0000000..fd9a9fb --- /dev/null +++ b/assets/License-CC_BY--NC_4.0-lightgrey.svg @@ -0,0 +1 @@ +License: CC BY-NC 4.0LicenseCC BY-NC 4.0 \ No newline at end of file diff --git a/assets/arXiv-2412.06264-red.svg b/assets/arXiv-2412.06264-red.svg new file mode 100644 index 0000000..aaf5e02 --- /dev/null +++ b/assets/arXiv-2412.06264-red.svg @@ -0,0 +1 @@ +arXiv: 2412.06264arXiv2412.06264 \ No newline at end of file diff --git a/assets/teaser.png b/assets/teaser.png new file mode 100644 index 0000000..be47878 Binary files /dev/null and b/assets/teaser.png differ diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..05c2bb7 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,37 @@ +# 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 + +ROOT_DIR:=$(shell dirname $(realpath $(firstword $(MAKEFILE_LIST)))) + +links: + mkdir -p source/notebooks && ln -sfn $(ROOT_DIR)/../examples/standalone_flow_matching.ipynb source/notebooks/standalone_flow_matching.ipynb + mkdir -p source/notebooks && ln -sfn $(ROOT_DIR)/../examples/2d_discrete_flow_matching.ipynb source/notebooks/2d_discrete_flow_matching.ipynb + mkdir -p source/notebooks && ln -sfn $(ROOT_DIR)/../examples/2d_riemannian_flow_matching_flat_torus.ipynb source/notebooks/2d_riemannian_flow_matching_flat_torus.ipynb + mkdir -p source/notebooks && ln -sfn $(ROOT_DIR)/../examples/2d_riemannian_flow_matching_sphere.ipynb source/notebooks/2d_riemannian_flow_matching_sphere.ipynb + ln -sfn $(ROOT_DIR)/../assets/teaser.png source/_images/teaser.png + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +%: export PYTHONPATH=../:./ + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile links + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +deploy: html + python deploy.py + +serve: + uvicorn server:app --reload --reload-include 'build/html/*.html' \ No newline at end of file diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..1e31a2e --- /dev/null +++ b/docs/README.md @@ -0,0 +1,33 @@ +## How to build docs + +Install `sphinx` + +``` +conda env update --file deps.yml +``` + +Build HTML + +``` +make html +``` + +Start server to view the html + +``` +cd build/html && python3 -m http.server +``` + +To run auto-update the server when files change (`pip install fastapi[standard]`): + +``` +make serve +``` + +## Adding to Papers + +The "/papers" page lists relevant papers. To add, insert a bibtex citation to `source/refs.bib`. The order in which citations are listed is the order that they will appear in the page. + +## Deploy + +To deploy the docs (in the current branch) to github pages, run `make deploy` diff --git a/docs/_templates/classtemplate.rst b/docs/_templates/classtemplate.rst new file mode 100644 index 0000000..4a1f19d --- /dev/null +++ b/docs/_templates/classtemplate.rst @@ -0,0 +1,14 @@ +.. role:: hidden + :class: hidden-section +.. currentmodule:: {{ module }} + + +{{ name | underline}} + +.. autoclass:: {{ name }} + :members: + + +.. + autogenerated from source/_templates/classtemplate.rst + note it does not have :inherited-members: \ No newline at end of file diff --git a/docs/custom_directives.py b/docs/custom_directives.py new file mode 100644 index 0000000..af6b9d1 --- /dev/null +++ b/docs/custom_directives.py @@ -0,0 +1,243 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +# This implementation is adapted from https://github.com/pytorch/audio/blob/fa44bdab1fe49bab58389e7b6a33061ffced9bc7/docs/source/custom_directives.py#L4 +# which is released under BSD license + +import hashlib +import os +from pathlib import Path +from typing import List +from urllib.parse import quote, urlencode + +import requests +from docutils import nodes +from docutils.parsers.rst import Directive, directives +from docutils.parsers.rst.directives.images import Image +from docutils.statemachine import StringList +from sphinx.util.docutils import SphinxDirective + + +_THIS_DIR = Path(__file__).parent + +# Color palette from PyTorch Developer Day 2021 Presentation Template +YELLOW = "F9DB78" +GREEN = "70AD47" +BLUE = "00B0F0" +PINK = "FF71DA" +ORANGE = "FF8300" +TEAL = "00E5D1" +GRAY = "7F7F7F" + + +def _get_cache_path(key, ext): + filename = f"{hashlib.sha256(key).hexdigest()}{ext}" + cache_dir = _THIS_DIR / "gen_images" + cache_dir.mkdir(parents=True, exist_ok=True) + return cache_dir / filename + + +def _download(url, path): + response = requests.get(url) + response.raise_for_status() + with open(path, "wb") as file: + file.write(response.content) + + +def _fetch_image(url): + path = _get_cache_path(url.encode("utf-8"), ext=".svg") + if not path.exists(): + _download(url, path) + return os.sep + str(path.relative_to(_THIS_DIR)) + + +def _get_relpath(target, base): + target = os.sep + target + base = os.sep + base + target_path, filename = os.path.split(target) + rel_path = os.path.relpath(target_path, os.path.dirname(base)) + return os.path.normpath(os.path.join(rel_path, filename)) + + +class BaseShield(Image, SphinxDirective): + def run(self, params, alt, section) -> List[nodes.Node]: + url = f"https://img.shields.io/static/v1?{urlencode(params, quote_via=quote)}" + path = _fetch_image(url) + self.arguments = [path] + self.options["alt"] = alt + if "class" not in self.options: + self.options["class"] = [] + self.options["class"].append("shield-badge") + target = _get_relpath("supported_features.html", self.env.docname) + self.options["target"] = f"{target}#{section}" + return super().run() + + +def _parse_devices(arg: str): + devices = sorted(arg.strip().split()) + + valid_values = {"CPU", "CUDA"} + if any(val not in valid_values for val in devices): + raise ValueError( + f"One or more device values are not valid. The valid values are {valid_values}. Given value: '{arg}'" + ) + return ", ".join(sorted(devices)) + + +def _parse_properties(arg: str): + properties = sorted(arg.strip().split()) + + valid_values = {"Autograd", "TorchScript"} + if any(val not in valid_values for val in properties): + raise ValueError( + "One or more property values are not valid. " + f"The valid values are {valid_values}. " + f"Given value: '{arg}'" + ) + return ", ".join(sorted(properties)) + + +class SupportedDevices(BaseShield): + """List the supported devices""" + + required_arguments = 1 + final_argument_whitespace = True + + def run(self) -> List[nodes.Node]: + devices = _parse_devices(self.arguments[0]) + alt = f"This feature supports the following devices: {devices}" + params = { + "label": "Devices", + "message": devices, + "labelColor": GRAY, + "color": BLUE, + "style": "flat-square", + } + return super().run(params, alt, "devices") + + +class SupportedProperties(BaseShield): + """List the supported properties""" + + required_arguments = 1 + final_argument_whitespace = True + + def run(self) -> List[nodes.Node]: + properties = _parse_properties(self.arguments[0]) + alt = f"This API supports the following properties: {properties}" + params = { + "label": "Properties", + "message": properties, + "labelColor": GRAY, + "color": GREEN, + "style": "flat-square", + } + return super().run(params, alt, "properties") + + +_CARDLIST_START = """ +.. raw:: html + +
+ + +
+ +
+
+
+""" + +_CARD_TEMPLATE = """ +.. raw:: html + +
+
+ +
+
+

{header}

+
+

{card_description}

+
{image}
+
+ +
+
+""" + +_CARDLIST_END = """ +.. raw:: html + +
+ +
+
+
+""" + + +class CustomCardStart(Directive): + def run(self): + para = nodes.paragraph() + self.state.nested_parse( + StringList(_CARDLIST_START.split("\n")), self.content_offset, para + ) + return [para] + + +class CustomCardItem(Directive): + option_spec = { + "header": directives.unchanged, + "image": directives.unchanged, + "link": directives.unchanged, + "card_description": directives.unchanged, + "tags": directives.unchanged, + } + + def run(self): + for key in ["header", "card_description", "link"]: + if key not in self.options: + raise ValueError(f"Key: `{key}` is missing") + + header = self.options["header"] + link = self.options["link"] + card_description = self.options["card_description"] + tags = self.options.get("tags", "") + + if "image" in self.options: + image = "" + else: + image = "_static/img/thumbnails/default.png" + + card_rst = _CARD_TEMPLATE.format( + header=header, + image=image, + link=link, + card_description=card_description, + tags=tags, + ) + card_list = StringList(card_rst.split("\n")) + card = nodes.paragraph() + self.state.nested_parse(card_list, self.content_offset, card) + return [card] + + +class CustomCardEnd(Directive): + def run(self): + para = nodes.paragraph() + self.state.nested_parse( + StringList(_CARDLIST_END.split("\n")), self.content_offset, para + ) + return [para] diff --git a/docs/deploy.py b/docs/deploy.py new file mode 100644 index 0000000..8de8fc0 --- /dev/null +++ b/docs/deploy.py @@ -0,0 +1,31 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import os +import shutil +from subprocess import check_call +from tempfile import TemporaryDirectory + +this_dir = os.path.dirname(os.path.realpath(__file__)) + +remote = "git@github.com:fairinternal/flow_matching.git" +branch = "gh-pages" + + +with TemporaryDirectory() as tdir: + local = os.path.join(tdir, "repo") + shutil.copytree(os.path.join(this_dir, "build/html"), local) + + with open(os.path.join(local, ".nojekyll"), "w") as fout: + print("", end="", file=fout) + + check_call(["git", "init", local]) + check_call(["git", "remote", "add", "origin", remote], cwd=local) + check_call(["git", "checkout", "-b", branch], cwd=local) + + check_call(["git", "add", "--all"], cwd=local) + check_call(["git", "commit", "-m", "Update github pages"], cwd=local) + + check_call(["git", "push", "--set-upstream", "origin", "gh-pages", "-f"], cwd=local) diff --git a/docs/deps.yml b/docs/deps.yml new file mode 100644 index 0000000..cf9dffb --- /dev/null +++ b/docs/deps.yml @@ -0,0 +1,8 @@ +dependencies: + - pandoc + - pip: + - sphinx + - sphinxcontrib-katex + - nbsphinx + - sphinxcontrib.bibtex + - pydata-sphinx-theme \ No newline at end of file diff --git a/docs/server.py b/docs/server.py new file mode 100644 index 0000000..3cc9e1c --- /dev/null +++ b/docs/server.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +from fastapi import FastAPI +from fastapi.staticfiles import StaticFiles + +app = FastAPI() + +app.mount("/", StaticFiles(directory="build/html", html=True), name="static") diff --git a/docs/source/_images/discrete.png b/docs/source/_images/discrete.png new file mode 100644 index 0000000..f449234 Binary files /dev/null and b/docs/source/_images/discrete.png differ diff --git a/docs/source/_images/riemannian_sphere.png b/docs/source/_images/riemannian_sphere.png new file mode 100644 index 0000000..015d0fe Binary files /dev/null and b/docs/source/_images/riemannian_sphere.png differ diff --git a/docs/source/_images/riemannian_torus.png b/docs/source/_images/riemannian_torus.png new file mode 100644 index 0000000..4333a89 Binary files /dev/null and b/docs/source/_images/riemannian_torus.png differ diff --git a/docs/source/_images/standalone.png b/docs/source/_images/standalone.png new file mode 100644 index 0000000..cedef4a Binary files /dev/null and b/docs/source/_images/standalone.png differ diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css new file mode 100644 index 0000000..d12cb9b --- /dev/null +++ b/docs/source/_static/css/custom.css @@ -0,0 +1,7 @@ +/* Copyright (c) Meta Platforms, Inc. and affiliates. +All rights reserved. + +This source code is licensed under the CC-by-NC license found in the +LICENSE file in the root directory of this source tree. */ + +div.math { justify-content: center } diff --git a/docs/source/_templates/classtemplate.rst b/docs/source/_templates/classtemplate.rst new file mode 100644 index 0000000..4a1f19d --- /dev/null +++ b/docs/source/_templates/classtemplate.rst @@ -0,0 +1,14 @@ +.. role:: hidden + :class: hidden-section +.. currentmodule:: {{ module }} + + +{{ name | underline}} + +.. autoclass:: {{ name }} + :members: + + +.. + autogenerated from source/_templates/classtemplate.rst + note it does not have :inherited-members: \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..c3f6359 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,80 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = "Flow Matching" +copyright = "2024 Meta Platforms, Inc" +author = "FAIR" + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "nbsphinx", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.doctest", + "sphinx.ext.intersphinx", + "sphinx.ext.todo", + "sphinx.ext.coverage", + "sphinx.ext.napoleon", + "sphinx.ext.viewcode", + "sphinxcontrib.katex", + "sphinx.ext.autosectionlabel", + "sphinxcontrib.bibtex", +] + +bibtex_bibfiles = ["refs.bib"] +bibtex_default_style = "unsrt" + +templates_path = ["_templates"] +exclude_patterns = ["_build", "**.ipynb_checkpoints"] + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output +html_theme = "pydata_sphinx_theme" +html_static_path = ["_static", "_images"] + +# katex config +katex_css_path = "https://cdn.jsdelivr.net/npm/katex@0.16.10/dist/katex.min.css" +katex_js_path = "katex.min.js" +katex_autorender_path = "auto-render.min.js" +katex_inline = [r"\(", r"\)"] +katex_display = [r"\[", r"\]"] +katex_prerender = False +katex_options = "" + +# autodoc config +autodoc_member_order = "bysource" +autosummary_generate = True # Turn on sphinx.ext.autosummary + +from custom_directives import ( + CustomCardEnd, + CustomCardItem, + CustomCardStart, + SupportedDevices, + SupportedProperties, +) + +# Register custom directives + +from docutils.parsers import rst + +rst.directives.register_directive("devices", SupportedDevices) +rst.directives.register_directive("properties", SupportedProperties) +rst.directives.register_directive("customcardstart", CustomCardStart) +rst.directives.register_directive("customcarditem", CustomCardItem) +rst.directives.register_directive("customcardend", CustomCardEnd) + + +def setup(app): + app.add_css_file("css/custom.css") # may also be an URL diff --git a/docs/source/dummy.rst b/docs/source/dummy.rst new file mode 100644 index 0000000..820ca98 --- /dev/null +++ b/docs/source/dummy.rst @@ -0,0 +1,9 @@ +.. toctree:: + :maxdepth: 0 + :hidden: + :titlesonly: + + notebooks/standalone_flow_matching + notebooks/2d_discrete_flow_matching + notebooks/2d_riemannian_flow_matching_flat_torus + notebooks/2d_riemannian_flow_matching_sphere diff --git a/docs/source/flow_matching.loss.rst b/docs/source/flow_matching.loss.rst new file mode 100644 index 0000000..aa027cb --- /dev/null +++ b/docs/source/flow_matching.loss.rst @@ -0,0 +1,16 @@ +``flow_matching.loss`` +============================= + +.. currentmodule:: flow_matching.loss + + +MixturePathGeneralizedKL +-------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + MixturePathGeneralizedKL + diff --git a/docs/source/flow_matching.path.rst b/docs/source/flow_matching.path.rst new file mode 100644 index 0000000..af8a97c --- /dev/null +++ b/docs/source/flow_matching.path.rst @@ -0,0 +1,34 @@ +``flow_matching.path`` +============================= + +.. currentmodule:: flow_matching.path + + +Probability Paths +-------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + ProbPath + AffineProbPath + CondOTProbPath + MixtureDiscreteProbPath + GeodesicProbPath + + +Path Sample +-------------------------------- + +Corresponds to an instance of a sample drawn from the probability path. + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + path_sample.PathSample + path_sample.DiscretePathSample + diff --git a/docs/source/flow_matching.path.scheduler.rst b/docs/source/flow_matching.path.scheduler.rst new file mode 100644 index 0000000..0ad9e55 --- /dev/null +++ b/docs/source/flow_matching.path.scheduler.rst @@ -0,0 +1,30 @@ +``flow_matching.path.scheduler`` +================================= + +.. currentmodule:: flow_matching.path.scheduler + +Scheduler +---------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + Scheduler + CondOTScheduler + CosineScheduler + VPScheduler + PolynomialConvexScheduler + +ScheduleTransformedModel +------------------------ + +ScheduleTransformedModel wraps a given model and converts its scheduler + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + ScheduleTransformedModel diff --git a/docs/source/flow_matching.solver.rst b/docs/source/flow_matching.solver.rst new file mode 100644 index 0000000..99b00e8 --- /dev/null +++ b/docs/source/flow_matching.solver.rst @@ -0,0 +1,18 @@ +``flow_matching.solver`` +============================= + +.. currentmodule:: flow_matching.solver + +Solvers +------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + Solver + ODESolver + MixtureDiscreteEulerSolver + RiemannianODESolver + diff --git a/docs/source/flow_matching.utils.manifolds.rst b/docs/source/flow_matching.utils.manifolds.rst new file mode 100644 index 0000000..fc6b862 --- /dev/null +++ b/docs/source/flow_matching.utils.manifolds.rst @@ -0,0 +1,29 @@ +``flow_matching.utils.manifolds`` +================================= + +.. currentmodule:: flow_matching.utils.manifolds + + +Manifold +----------------- + +Manifold classes for logarithmic and exponential map projections + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + Manifold + Sphere + FlatTorus + +Utility Functions +----------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + geodesic \ No newline at end of file diff --git a/docs/source/flow_matching.utils.model_wrapper.rst b/docs/source/flow_matching.utils.model_wrapper.rst new file mode 100644 index 0000000..4310c96 --- /dev/null +++ b/docs/source/flow_matching.utils.model_wrapper.rst @@ -0,0 +1,16 @@ +``flow_matching.utils.model_wrapper`` +============================= + +.. currentmodule:: flow_matching.utils.model_wrapper + + +ModelWrapper +-------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + ModelWrapper + diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..79c90bc --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,33 @@ +============= +Flow Matching +============= + +`flow_matching` is a PyTorch library for implementing flow matching algorithms, featuring state-of-the-art continuous and discrete implementations. It includes practical examples for both text and image modalities. This repository is part of `Flow Matching Guide and Codebase `_. + +.. image:: _images/teaser.png + :width: 800 + :align: center + + +Table of contents +----------------- + +.. toctree:: + :maxdepth: 1 + + modules + installation + notebooks + references + +Code index +================== + +* :ref:`genindex` +* :ref:`search` + +Legal +----------------- + +* `Terms of Use `_ +* `Privacy Policy `_ diff --git a/docs/source/installation.rst b/docs/source/installation.rst new file mode 100644 index 0000000..5d71c8b --- /dev/null +++ b/docs/source/installation.rst @@ -0,0 +1,33 @@ +Installation +============ + +This repository requires Python 3.9 and Pytorch 2.1 or greater. To install the latest version run: + +:: + + pip install flow-matching + +Development +----------------- + +To create a conda environment with all required dependencies, run: + +:: + + conda env create -f environment.yml + conda activate flow_matching + +Install pre-commit hook. This will ensure that all linting is done on each commit + +:: + + pre-commit install + conda activate flow_matching + + +Install the `flow_matching` package in an editable mode: + +:: + + pip install -e . + diff --git a/docs/source/modules.rst b/docs/source/modules.rst new file mode 100644 index 0000000..093361a --- /dev/null +++ b/docs/source/modules.rst @@ -0,0 +1,12 @@ +API Reference +=============================== + +.. toctree:: + :maxdepth: 2 + + flow_matching.loss + flow_matching.path + flow_matching.path.scheduler + flow_matching.solver + flow_matching.utils.model_wrapper + flow_matching.utils.manifolds diff --git a/docs/source/notebooks.rst b/docs/source/notebooks.rst new file mode 100644 index 0000000..1967e99 --- /dev/null +++ b/docs/source/notebooks.rst @@ -0,0 +1,32 @@ +Notebooks +=============== + + + +.. customcardstart:: + +.. customcarditem:: + :header: Simple Training/Sampling example + :card_description: Train and sample from a 2D Flow Matching model. + :image: _static/standalone.png + :link: notebooks/standalone_flow_matching.html + +.. customcarditem:: + :header: Discrete Flow Matching + :card_description: Train and sample from a 2D Discrete Flow Matching model. + :image: _static/discrete.png + :link: notebooks/2d_discrete_flow_matching.html + +.. customcarditem:: + :header: Riemannian Flow Matching (Sphere) + :card_description: 2D sphere riemannian flow matching example + :image: _static/riemannian_sphere.png + :link: notebooks/2d_riemannian_flow_matching_sphere.html + +.. customcarditem:: + :header: Riemannian Flow Matching (Flat Torus) + :card_description: 2D flat torus riemannian flow matching example + :image: _static/riemannian_torus.png + :link: notebooks/2d_riemannian_flow_matching_flat_torus.html + +.. customcardend:: diff --git a/docs/source/references.rst b/docs/source/references.rst new file mode 100644 index 0000000..b34f925 --- /dev/null +++ b/docs/source/references.rst @@ -0,0 +1,8 @@ +References +------ + + +.. bibliography:: + :list: enumerated + :all: + :notcited: diff --git a/docs/source/refs.bib b/docs/source/refs.bib new file mode 100644 index 0000000..d87dc9b --- /dev/null +++ b/docs/source/refs.bib @@ -0,0 +1,93 @@ +% Copyright (c) Meta Platforms, Inc. and affiliates. +% All rights reserved. +% +% This source code is licensed under the CC-by-NC license found in the +% LICENSE file in the root directory of this source tree. + +@misc{lipman2023flowmatchinggenerativemodeling, + title={Flow Matching for Generative Modeling}, + author={Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matt Le}, + year={2023}, + eprint={2210.02747}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2210.02747}, +} + +@misc{gat2024discreteflowmatching, + title={Discrete Flow Matching}, + author={Itai Gat and Tal Remez and Neta Shaul and Felix Kreuk and Ricky T. Q. Chen and Gabriel Synnaeve and Yossi Adi and Yaron Lipman}, + year={2024}, + eprint={2407.15595}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2407.15595}, +} + +@misc{chen2024flowmatchinggeneralgeometries, + title={Flow Matching on General Geometries}, + author={Ricky T. Q. Chen and Yaron Lipman}, + year={2024}, + eprint={2302.03660}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2302.03660}, +} + +@misc{holderrieth2024generator, + title={Generator Matching: Generative modeling with arbitrary Markov processes}, + author={Holderrieth, Peter and Havasi, Marton and Yim, Jason and Shaul, Neta and Gat, Itai and Jaakkola, Tommi and Karrer, Brian and Chen, Ricky TQ and Lipman, Yaron}, + eprint={2410.20587}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2410.20587}, + year={2024} +} + +@misc{shaul2024flow, + title={Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective}, + author={Neta Shaul and Itai Gat and Marton Havasi and Daniel Severo and Anuroop Sriram and Peter Holderrieth and Brian Karrer and Yaron Lipman and Ricky T. Q. Chen}, + eprint={2412.03487}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2412.03487}, + year={2024} +} + +@article{albergo2022building, + title={Building normalizing flows with stochastic interpolants}, + author={Albergo, Michael S and Vanden-Eijnden, Eric}, + journal={arXiv preprint arXiv:2209.15571}, + year={2022} +} + + + +@article{liu2022flow, + title={Flow straight and fast: Learning to generate and transfer data with rectified flow}, + author={Liu, Xingchao and Gong, Chengyue and Liu, Qiang}, + journal={arXiv preprint arXiv:2209.03003}, + year={2022} +} + +@article{tong2023improving, + title={Improving and generalizing flow-based generative models with minibatch optimal transport}, + author={Tong, Alexander and Malkin, Nikolay and Huguet, Guillaume and Zhang, Yanlei and Rector-Brooks, Jarrid and Fatras, Kilian and Wolf, Guy and Bengio, Yoshua}, + journal={arXiv preprint arXiv:2302.00482}, + year={2023} +} + +@article{benhamu2022cnfm, + author = {Ben-Hamu, Heli and Cohen, Samuel and Bose, Joey and Amos, Brandon and Nickel, Maximillian and Grover, Aditya and Chen, Ricky T. Q. and Lipman, Yaron}, + journal = {Proceedings of the 39th International Conference on Machine Learning}, + title = {Matching Normalizing Flows and Probability Paths on Manifolds}, + volume = {162}, + year = {2022} +} + +@article{campbell2024generative, + title={Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design}, + author={Campbell, Andrew and Yim, Jason and Barzilay, Regina and Rainforth, Tom and Jaakkola, Tommi}, + journal={arXiv preprint arXiv:2402.04997}, + year={2024} +} diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..6a0dd3e --- /dev/null +++ b/environment.yml @@ -0,0 +1,25 @@ +name: flow_matching +channels: + - pytorch + - conda-forge + - nvidia +dependencies: + - python=3.9 + - pytorch + - pytorch-cuda + - matplotlib + - jupyter + - numpy + - pip + - tqdm + - pip: + - pre-commit + - black==22.6.0 + - usort==1.0.4 + - ufmt==2.3.0 + - flake8==7.0.0 + - ipykernel + - torchdiffeq + - scikit-learn + - pydoclint + - coverage diff --git a/examples/2d_discrete_flow_matching.ipynb b/examples/2d_discrete_flow_matching.ipynb new file mode 100644 index 0000000..ca494cb --- /dev/null +++ b/examples/2d_discrete_flow_matching.ipynb @@ -0,0 +1,562 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A simple 2D Discrete Flow Matching model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook trains and evaluates a simple 2D discrete FM model with $\\kappa_t = t^2$ scheduler.\n", + "\n", + "Dataset: 2D discrete checkerboard\n", + "Model (probability denoiser): MLP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports and init device" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "rb5VSo4mNkVd" + }, + "outputs": [], + "source": [ + "import time\n", + "import torch\n", + "\n", + "from torch import nn, Tensor\n", + "\n", + "# flow_matching\n", + "from flow_matching.path import MixtureDiscreteProbPath\n", + "from flow_matching.path.scheduler import PolynomialConvexScheduler\n", + "from flow_matching.solver import MixtureDiscreteEulerSolver\n", + "from flow_matching.utils import ModelWrapper\n", + "from flow_matching.loss import MixturePathGeneralizedKL\n", + "\n", + "# visualization\n", + "import numpy as np\n", + "import matplotlib.cm as cm\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using gpu\n" + ] + } + ], + "source": [ + "if torch.cuda.is_available():\n", + " device = 'cuda:0'\n", + " print('Using gpu')\n", + "else:\n", + " device = 'cpu'\n", + " print('Using cpu.')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m2wy46WpLZs0" + }, + "source": [ + "## Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def inf_train_gen(n_grid_points: int = 128, batch_size: int = 200, device: str = \"cpu\") -> Tensor:\n", + " assert n_grid_points % 4 == 0, \"number of grid points has to be divisible by 4\"\n", + " \n", + " n_grid_points = n_grid_points // 4\n", + " \n", + " x1 = torch.randint(low=0, high=n_grid_points * 4, size=(batch_size,), device=device)\n", + " samples_x2 = torch.randint(low=0, high=n_grid_points, size=(batch_size,), device=device)\n", + " \n", + " x2 = (\n", + " samples_x2\n", + " + 2 * n_grid_points\n", + " - torch.randint(low=0, high=2, size=(batch_size,), device=device) * 2 * n_grid_points\n", + " + (torch.floor(x1 / n_grid_points) % 2) * n_grid_points\n", + " )\n", + " \n", + " x_end = 1.0 * torch.cat([x1[:, None], x2[:, None]], dim=1)\n", + "\n", + " return x_end.long()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Activation class\n", + "class Swish(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def forward(self, x: Tensor) -> Tensor: \n", + " return torch.sigmoid(x) * x\n", + "\n", + "# Model class\n", + "class MLP(nn.Module):\n", + " def __init__(\n", + " self, input_dim: int = 128, time_dim: int = 1, hidden_dim=128, length=2):\n", + " super().__init__()\n", + " self.input_dim = input_dim\n", + " self.time_dim = time_dim\n", + " self.hidden_dim = hidden_dim\n", + "\n", + " self.time_embedding = nn.Linear(1, time_dim)\n", + " self.token_embedding = torch.nn.Embedding(self.input_dim, hidden_dim)\n", + "\n", + " self.main = nn.Sequential(\n", + " Swish(),\n", + " nn.Linear(hidden_dim * length + time_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, self.input_dim * length),\n", + " )\n", + "\n", + " def forward(self, x, t):\n", + " t = self.time_embedding(t.unsqueeze(-1))\n", + " x = self.token_embedding(x)\n", + "\n", + " B, N, d = x.shape\n", + " x = x.reshape(B, N * d)\n", + " \n", + " h = torch.cat([x, t], dim=1)\n", + " h = self.main(h)\n", + "\n", + " h = h.reshape(B, N, self.input_dim)\n", + "\n", + " return h" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train Discrete Flow Matching model with a uniform source distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| iter 3000 | 3.68 ms/step | loss 5.697 \n", + "| iter 6000 | 3.49 ms/step | loss 5.539 \n", + "| iter 9000 | 3.31 ms/step | loss 5.296 \n", + "| iter 12000 | 3.39 ms/step | loss 5.520 \n", + "| iter 15000 | 3.56 ms/step | loss 5.714 \n", + "| iter 18000 | 3.49 ms/step | loss 5.556 \n", + "| iter 21000 | 3.58 ms/step | loss 5.392 \n", + "| iter 24000 | 3.49 ms/step | loss 5.354 \n", + "| iter 27000 | 3.30 ms/step | loss 6.423 \n", + "| iter 30000 | 3.30 ms/step | loss 5.445 \n" + ] + } + ], + "source": [ + "source_distribution = \"uniform\"\n", + "\n", + "# training arguments\n", + "lr = 0.001\n", + "batch_size = 4096\n", + "iterations = 30001\n", + "print_every = 3000\n", + "\n", + "vocab_size = 128\n", + "hidden_dim = 128\n", + "\n", + "epsilon = 1e-3\n", + "\n", + "if source_distribution == \"uniform\":\n", + " added_token = 0\n", + "elif source_distribution == \"mask\":\n", + " mask_token = vocab_size # tokens starting from zero\n", + " added_token = 1\n", + "else:\n", + " raise NotImplementedError\n", + " \n", + "# additional mask token\n", + "vocab_size += added_token\n", + "\n", + "# probability denoiser model init\n", + "probability_denoiser = MLP(input_dim=vocab_size, time_dim=1, hidden_dim=hidden_dim).to(device)\n", + "\n", + "# instantiate a convex path object\n", + "scheduler = PolynomialConvexScheduler(n=2.0)\n", + "path = MixtureDiscreteProbPath(scheduler=scheduler)\n", + "\n", + "# init optimizer\n", + "optim = torch.optim.Adam(probability_denoiser.parameters(), lr=lr) \n", + "\n", + "loss_fn = MixturePathGeneralizedKL(path=path)\n", + "\n", + "# train\n", + "start_time = time.time()\n", + "\n", + "steps = 0\n", + "losses = []\n", + "for i in range(iterations):\n", + " optim.zero_grad() \n", + "\n", + " # sample data (user's responsibility): in this case, (X_0,X_1) ~ pi(X_0,X_1)\n", + " x_1 = inf_train_gen(n_grid_points=vocab_size - added_token, batch_size=batch_size, device=device) # sample data\n", + " \n", + " if source_distribution == \"uniform\":\n", + " x_0 = torch.randint_like(x_1, high=vocab_size)\n", + " elif source_distribution == \"mask\":\n", + " x_0 = torch.zeros_like(x_1) + mask_token\n", + " else:\n", + " raise NotImplementedError\n", + "\n", + " # sample time (user's responsibility)\n", + " t = torch.rand(x_1.shape[0]).to(device) * (1 - epsilon)\n", + "\n", + " # sample probability path\n", + " path_sample = path.sample(t=t, x_0=x_0, x_1=x_1)\n", + "\n", + " # discrete flow matching generalized KL loss\n", + " logits = probability_denoiser(x=path_sample.x_t, t=path_sample.t)\n", + " loss = loss_fn(logits=logits, x_1=x_1, x_t=path_sample.x_t, t=path_sample.t)\n", + "\n", + " # optimizer step\n", + " loss.backward() # backward\n", + " optim.step() # update\n", + " \n", + " # log loss\n", + " if (i+1) % print_every == 0:\n", + " elapsed = time.time() - start_time\n", + " print('| iter {:6d} | {:5.2f} ms/step | loss {:8.3f} ' \n", + " .format(i+1, elapsed*1000/print_every, loss.item())) \n", + " start_time = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sample from trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class WrappedModel(ModelWrapper):\n", + " def forward(self, x: torch.Tensor, t: torch.Tensor, **extras):\n", + " return torch.softmax(self.model(x, t), dim=-1)\n", + "\n", + "wrapped_probability_denoiser = WrappedModel(probability_denoiser)\n", + "solver = MixtureDiscreteEulerSolver(model=wrapped_probability_denoiser, path=path, vocabulary_size=vocab_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NFE: 64: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 0.9990000128746033/0.9990000128746033 [00:08<00:00, 8.13s/it]\n" + ] + } + ], + "source": [ + "nfe = 64\n", + "step_size = 1 / nfe\n", + "\n", + "safe_sampling = True\n", + "n_samples = 1000000\n", + "dim = 2\n", + "\n", + "if source_distribution == \"uniform\":\n", + " x_init = torch.randint(size=(n_samples, dim), high=vocab_size, device=device)\n", + "elif source_distribution == \"mask\":\n", + " x_init = (torch.zeros(size=(n_samples, dim), device=device) + mask_token).long()\n", + "else:\n", + " raise NotImplementedError\n", + "\n", + "n_plots = 9\n", + "linspace_to_plot = torch.linspace(0, 1 - epsilon, n_plots)\n", + "\n", + "sol = solver.sample(x_init=x_init, \n", + " step_size=step_size, \n", + " verbose=True, \n", + " return_intermediates=True,\n", + " time_grid=linspace_to_plot)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = sol.cpu().numpy()\n", + "\n", + "fig, axs = plt.subplots(1, n_plots, figsize = (20, 20))\n", + "\n", + "if source_distribution == \"mask\":\n", + " mask_tensor = torch.tensor([mask_token, mask_token]).unsqueeze(0)\n", + "\n", + "for idx, step in enumerate(linspace_to_plot):\n", + " step = int(step.item() * nfe)\n", + " \n", + " if source_distribution == \"uniform\":\n", + " sol_step = sol[idx, ...]\n", + " elif source_distribution == \"mask\": \n", + " sol_step = sol[idx, ...]\n", + " sol_step = sol_step[torch.ne(torch.from_numpy(sol_step), mask_tensor).all(dim=1), ...]\n", + " \n", + " if sol_step.size == 0:\n", + " axs[idx].hist2d([], [], bins=10)\n", + " axs[idx].set_aspect('equal')\n", + " axs[idx].axis('off')\n", + " axs[idx].set_title('t= %.2f' % (step * step_size))\n", + " \n", + " continue\n", + " else:\n", + " raise NotImplementedError\n", + "\n", + " H = axs[idx].hist2d(sol_step[:, 0], sol_step[:, 1], bins=vocab_size)\n", + " \n", + " cmin = 0.0\n", + " cmax = torch.quantile(torch.from_numpy(H[0]), 0.95).item()\n", + " \n", + " norm = cm.colors.Normalize(vmax=cmax, vmin=cmin)\n", + " \n", + " _ = axs[idx].hist2d(sol_step[:, 0], sol_step[:, 1], bins=vocab_size, norm=norm)\n", + " \n", + " axs[idx].set_aspect('equal')\n", + " axs[idx].axis('off')\n", + " axs[idx].set_title(f't= {linspace_to_plot[idx].item():.2f}')\n", + " \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize ELBO" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "n_discretization = 1024 # Time discretization of integration interval\n", + "n_samples = 10 # Number of samples to approximate the expectation on X_t ~ p_t(\\cdot| x_1)\n", + "\n", + "# Generalized KL function (will use it to compute the elbo)\n", + "generalized_kl_fn = MixturePathGeneralizedKL(\n", + " path = path,\n", + " reduction ='none'\n", + ")\n", + "\n", + "# Grid of vocab_size X vocab_size\n", + "grid = torch.meshgrid(\n", + " torch.arange(0, vocab_size, device=device),\n", + " torch.arange(0, vocab_size, device=device),\n", + " indexing='ij'\n", + ")\n", + "x_1 = torch.stack(\n", + " [grid[0].reshape(-1), grid[1].reshape(-1)],\n", + " dim=1\n", + ")\n", + "\n", + "# Time discretization\n", + "discretization = (\n", + " torch.linspace(0, 1, n_discretization + 1, device=device)[:-1]\n", + " .view(-1, 1)\n", + " .repeat(1, x_1.shape[0])\n", + ")\n", + "\n", + "elbo = torch.zeros(size=(x_1.shape[0],), device=device)\n", + "\n", + "with torch.no_grad():\n", + " for _ in range(n_samples):\n", + " # Lower variance estimator for time discretization\n", + " discretization = discretization + torch.rand(\n", + " size=(1, x_1.shape[0]), device=device\n", + " )\n", + " discretization = discretization % 1\n", + " discretization = discretization * (1 - epsilon)\n", + " \n", + " for t in discretization:\n", + " # sample X_t ~ p_t(\\cdot| x_1)\n", + " if source_distribution == \"uniform\":\n", + " x_0 = torch.randint(size=x_1.shape, high=vocab_size, device=device)\n", + " elif source_distribution == \"mask\":\n", + " x_0 = (torch.zeros(size=x_1.shape, device=device) + mask_token).long()\n", + " else:\n", + " raise NotImplementedError\n", + " \n", + " x_t = path.sample(t=t, x_0=x_0, x_1=x_1).x_t\n", + " \n", + " logits = probability_denoiser(x_t, t)\n", + " \n", + " # compute ELBO\n", + " elbo += -generalized_kl_fn(\n", + " logits=logits, x_1=x_1, x_t=x_t, t=t\n", + " ).sum(dim=1)\n", + "\n", + " elbo /= n_discretization * n_samples\n", + "\n", + "# Remember that log_q(x_1) >= ELBO(x_1)\n", + "probability_lower_bound = torch.exp(elbo)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmin = 0.0\n", + "cmax = probability_lower_bound.max().item() / 1.5 \n", + "\n", + "norm = cm.colors.Normalize(vmax=cmax, vmin=cmin)\n", + "\n", + "plt.figure(figsize=(5, 5))\n", + "plt.imshow(\n", + " probability_lower_bound.reshape(vocab_size, vocab_size).cpu(), \n", + " origin='lower', cmap='viridis', norm=norm\n", + ")\n", + "plt.gca().axis(\"off\")\n", + "plt.colorbar(cm.ScalarMappable(norm=norm, cmap='viridis'), ax=plt.gca(), orientation='horizontal', label='density')\n", + "plt.title(\"ELBO Estimator\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "g8QtNgs1-PlE", + "wW3VMmrK2t2d", + "_7aH8D0H3IJT" + ], + "name": "scalable_CNF.ipynb", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/2d_flow_matching.ipynb b/examples/2d_flow_matching.ipynb new file mode 100644 index 0000000..f7ff170 --- /dev/null +++ b/examples/2d_flow_matching.ipynb @@ -0,0 +1,474 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A simple 2D Flow Matching model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook trains and evaluates a simple 2D FM model with CondOT (i.e., linear) scheduler.\n", + "\n", + "Dataset: 2D checkerboard\n", + "Model (velocity): MLP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports and init device" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "rb5VSo4mNkVd" + }, + "outputs": [], + "source": [ + "import time\n", + "import torch\n", + "\n", + "from torch import nn, Tensor\n", + "\n", + "# flow_matching\n", + "from flow_matching.path.scheduler import CondOTScheduler\n", + "from flow_matching.path import AffineProbPath\n", + "from flow_matching.solver import Solver, ODESolver\n", + "from flow_matching.utils import ModelWrapper\n", + "\n", + "# visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from matplotlib import cm\n", + "\n", + "\n", + "# To avoide meshgrid warning\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning, module='torch')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using gpu\n" + ] + } + ], + "source": [ + "if torch.cuda.is_available():\n", + " device = 'cuda:0'\n", + " print('Using gpu')\n", + "else:\n", + " device = 'cpu'\n", + " print('Using cpu.')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m2wy46WpLZs0" + }, + "source": [ + "## Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def inf_train_gen(batch_size: int = 200, device: str = \"cpu\"):\n", + " x1 = torch.rand(batch_size, device=device) * 4 - 2\n", + " x2_ = torch.rand(batch_size, device=device) - torch.randint(high=2, size=(batch_size, ), device=device) * 2\n", + " x2 = x2_ + (torch.floor(x1) % 2)\n", + "\n", + " data = 1.0 * torch.cat([x1[:, None], x2[:, None]], dim=1) / 0.45\n", + " \n", + " return data.float()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Activation class\n", + "class Swish(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def forward(self, x: Tensor) -> Tensor: \n", + " return torch.sigmoid(x) * x\n", + "\n", + "# Model class\n", + "class MLP(nn.Module):\n", + " def __init__(self, input_dim: int = 2, time_dim: int = 1, hidden_dim: int = 128):\n", + " super().__init__()\n", + " \n", + " self.input_dim = input_dim\n", + " self.time_dim = time_dim\n", + " self.hidden_dim = hidden_dim\n", + "\n", + " self.main = nn.Sequential(\n", + " nn.Linear(input_dim+time_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, input_dim),\n", + " )\n", + " \n", + "\n", + " def forward(self, x: Tensor, t: Tensor) -> Tensor:\n", + " sz = x.size()\n", + " x = x.reshape(-1, self.input_dim)\n", + " t = t.reshape(-1, self.time_dim).float()\n", + "\n", + " t = t.reshape(-1, 1).expand(x.shape[0], 1)\n", + " h = torch.cat([x, t], dim=1)\n", + " output = self.main(h)\n", + " \n", + " return output.reshape(*sz)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train Velocity Flow Matching model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| iter 2000 | 3.59 ms/step | loss 3.772 \n", + "| iter 4000 | 3.45 ms/step | loss 3.684 \n", + "| iter 6000 | 3.45 ms/step | loss 3.780 \n", + "| iter 8000 | 3.45 ms/step | loss 3.729 \n", + "| iter 10000 | 3.45 ms/step | loss 3.705 \n", + "| iter 12000 | 3.45 ms/step | loss 3.661 \n", + "| iter 14000 | 3.45 ms/step | loss 3.625 \n", + "| iter 16000 | 3.45 ms/step | loss 3.837 \n", + "| iter 18000 | 3.45 ms/step | loss 3.796 \n", + "| iter 20000 | 3.45 ms/step | loss 3.872 \n" + ] + } + ], + "source": [ + "# training arguments\n", + "lr = 0.001\n", + "batch_size = 4096\n", + "iterations = 20001\n", + "print_every = 2000 \n", + "hidden_dim = 512\n", + "\n", + "# velocity field model init\n", + "vf = MLP(input_dim=2, time_dim=1, hidden_dim=hidden_dim).to(device) \n", + "\n", + "# instantiate an affine path object\n", + "path = AffineProbPath(scheduler=CondOTScheduler())\n", + "\n", + "# init optimizer\n", + "optim = torch.optim.Adam(vf.parameters(), lr=lr) \n", + "\n", + "# train\n", + "start_time = time.time()\n", + "for i in range(iterations):\n", + " optim.zero_grad() \n", + "\n", + " # sample data (user's responsibility): in this case, (X_0,X_1) ~ pi(X_0,X_1) = N(X_0|0,I)q(X_1)\n", + " x_1 = inf_train_gen(batch_size=batch_size, device=device) # sample data\n", + " x_0 = torch.randn_like(x_1).to(device)\n", + "\n", + " # sample time (user's responsibility)\n", + " t = torch.rand(x_1.shape[0]).to(device) \n", + "\n", + " # sample probability path\n", + " path_sample = path.sample(t=t, x_0=x_0, x_1=x_1)\n", + "\n", + " # flow matching l2 loss\n", + " loss = torch.pow( vf(path_sample.x_t,path_sample.t) - path_sample.dx_t, 2).mean() \n", + "\n", + " # optimizer step\n", + " loss.backward() # backward\n", + " optim.step() # update\n", + " \n", + " # log loss\n", + " if (i+1) % print_every == 0:\n", + " elapsed = time.time() - start_time\n", + " print('| iter {:6d} | {:5.2f} ms/step | loss {:8.3f} ' \n", + " .format(i+1, elapsed*1000/print_every, loss.item())) \n", + " start_time = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sample from trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class WrappedModel(ModelWrapper):\n", + " def forward(self, x: torch.Tensor, t: torch.Tensor, **extras):\n", + " return self.model(x, t)\n", + "\n", + "wrapped_vf = WrappedModel(vf)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# step size for ode solver\n", + "step_size = 0.05\n", + "\n", + "norm = cm.colors.Normalize(vmax=50, vmin=0)\n", + "\n", + "batch_size = 50000 # batch size\n", + "eps_time = 1e-2\n", + "T = torch.linspace(0,1,10) # sample times\n", + "T = T.to(device=device)\n", + "\n", + "x_init = torch.randn((batch_size, 2), dtype=torch.float32, device=device)\n", + "solver = ODESolver(velocity_model=wrapped_vf) # create an ODESolver class\n", + "sol = solver.sample(time_grid=T, x_init=x_init, method='midpoint', step_size=step_size, return_intermediates=True) # sample from the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize the path" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = sol.cpu().numpy()\n", + "T = T.cpu()\n", + "\n", + "fig, axs = plt.subplots(1, 10,figsize=(20,20))\n", + "\n", + "for i in range(10):\n", + " H= axs[i].hist2d(sol[i,:,0], sol[i,:,1], 300, range=((-5,5), (-5,5)))\n", + " \n", + " cmin = 0.0\n", + " cmax = torch.quantile(torch.from_numpy(H[0]), 0.99).item()\n", + " \n", + " norm = cm.colors.Normalize(vmax=cmax, vmin=cmin)\n", + " \n", + " _ = axs[i].hist2d(sol[i,:,0], sol[i,:,1], 300, range=((-5,5), (-5,5)), norm=norm)\n", + " \n", + " axs[i].set_aspect('equal')\n", + " axs[i].axis('off')\n", + " axs[i].set_title('t= %.2f' % (T[i]))\n", + " \n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute and Visualize Model Log-likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.distributions.multivariate_normal import MultivariateNormal" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# sample with likelihood\n", + "\n", + "T = torch.tensor([1., 0.]) # sample times\n", + "T = T.to(device=device)\n", + "\n", + "grid_size = 200\n", + "x_1 = torch.meshgrid(torch.linspace(-5, 5, grid_size), torch.linspace(-5, 5, grid_size))\n", + "x_1 = torch.stack([x_1[0].flatten(), x_1[1].flatten()], dim=1).to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# source distribution is a gaussian\n", + "gaussian_log_density = MultivariateNormal(torch.zeros(2, device=device), torch.eye(2, device=device)).log_prob\n", + "\n", + "# compute log likelihood with unbiased hutchinson estimator, average over num_acc\n", + "num_acc = 10\n", + "log_p_acc = 0\n", + "\n", + "for i in range(num_acc):\n", + " _, log_p = solver.compute_likelihood(x_1=x_1, method='midpoint', step_size=step_size, exact_divergence=False, log_p0=gaussian_log_density)\n", + " log_p_acc += log_p\n", + "\n", + "log_p_acc /= num_acc\n", + "\n", + "# compute with exact divergence\n", + "_, exact_log_p = solver.compute_likelihood(x_1=x_1, method='midpoint', step_size=step_size, exact_divergence=True, log_p0=gaussian_log_density)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "likelihood = torch.exp(log_p_acc).cpu().reshape(grid_size, grid_size).detach().numpy()\n", + "exact_likelihood = torch.exp(exact_log_p).cpu().reshape(grid_size, grid_size).detach().numpy()\n", + "\n", + "fig, axs = plt.subplots(1, 2,figsize=(10,10))\n", + "\n", + "cmin = 0.0\n", + "cmax = 1/32 # 1/32 is the gt likelihood value\n", + "\n", + "norm = cm.colors.Normalize(vmax=cmax, vmin=cmin)\n", + "\n", + "axs[0].imshow(likelihood, extent=(-5, 5, -5, 5), origin='lower', cmap='viridis', norm=norm)\n", + "axs[0].set_title('Model Likelihood, Hutchinson Estimator, #acc=%d' % num_acc)\n", + "\n", + "axs[1].imshow(exact_likelihood, extent=(-5, 5, -5, 5), origin='lower', cmap='viridis', norm=norm)\n", + "axs[1].set_title('Exact Model Likelihood')\n", + "\n", + "fig.colorbar(cm.ScalarMappable(norm=norm, cmap='viridis'), ax=axs, orientation='horizontal', label='density')\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "g8QtNgs1-PlE", + "wW3VMmrK2t2d", + "_7aH8D0H3IJT" + ], + "name": "scalable_CNF.ipynb", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.19" + }, + "vscode": { + "interpreter": { + "hash": "a9223c1449c722e9a3173d1229627827aabf67ca877d945d23ebe719b18ba9c7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/2d_riemannian_flow_matching_flat_torus.ipynb b/examples/2d_riemannian_flow_matching_flat_torus.ipynb new file mode 100644 index 0000000..8a80135 --- /dev/null +++ b/examples/2d_riemannian_flow_matching_flat_torus.ipynb @@ -0,0 +1,465 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A simple 2D Riemannian Flow Matching model on sphere" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports and init device" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "rb5VSo4mNkVd" + }, + "outputs": [], + "source": [ + "import time\n", + "import torch\n", + "import math\n", + "import numpy as np\n", + "\n", + "from torch import nn, Tensor\n", + "\n", + "# flow_matching\n", + "from flow_matching.path import GeodesicProbPath\n", + "from flow_matching.path.scheduler import CondOTScheduler\n", + "from flow_matching.solver import ODESolver, RiemannianODESolver\n", + "from flow_matching.utils import ModelWrapper\n", + "from flow_matching.utils.manifolds import FlatTorus, Manifold\n", + "\n", + "# visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from matplotlib import cm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using gpu\n" + ] + } + ], + "source": [ + "if torch.cuda.is_available():\n", + " device = 'cuda:0'\n", + " print('Using gpu')\n", + "else:\n", + " device = 'cpu'\n", + " print('Using cpu.')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m2wy46WpLZs0" + }, + "source": [ + "## Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def inf_train_gen(batch_size: int = 200, device: str = \"cpu\"):\n", + " x1 = torch.rand(batch_size, device=device) * 4 - 2\n", + " x2_ = (torch.rand(batch_size, device=device) - torch.randint(high=2, size=(batch_size, ), device=device) * 2)\n", + " x2 = x2_ + (torch.floor(x1) % 2)\n", + "\n", + " data = torch.cat([x1[:, None], x2[:, None]], dim=1)\n", + "\n", + " return data.float()\n", + "\n", + "def wrap(manifold, samples):\n", + " center = torch.zeros_like(samples)\n", + "\n", + " return manifold.expmap(center, samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Activation class\n", + "class Swish(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def forward(self, x: Tensor) -> Tensor:\n", + " return torch.sigmoid(x) * x\n", + "\n", + "\n", + "# Model class\n", + "class MLP(nn.Module):\n", + " def __init__(\n", + " self,\n", + " input_dim: int = 2,\n", + " time_dim: int = 1,\n", + " hidden_dim: int = 128,\n", + " ):\n", + " super().__init__()\n", + "\n", + " self.input_dim = input_dim\n", + " self.time_dim = time_dim\n", + " self.hidden_dim = hidden_dim\n", + "\n", + " self.input_layer = nn.Sequential(\n", + " FourierFeatures(1),\n", + " nn.Linear((input_dim + time_dim) * 2, hidden_dim),\n", + " )\n", + "\n", + " self.main = nn.Sequential(\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, input_dim),\n", + " )\n", + "\n", + " def forward(self, x: Tensor, t: Tensor) -> Tensor:\n", + " sz = x.size()\n", + " x = x.reshape(-1, self.input_dim)\n", + " t = t.reshape(-1, self.time_dim).float()\n", + "\n", + " t = t.reshape(-1, 1).expand(x.shape[0], 1)\n", + " h = torch.cat([x, t], dim=1)\n", + " h = self.input_layer(h)\n", + " output = self.main(h)\n", + "\n", + " return output.reshape(*sz)\n", + "\n", + "\n", + "class FourierFeatures(nn.Module):\n", + " \"\"\"Assumes input is in [0, 2pi].\"\"\"\n", + "\n", + " def __init__(self, n_fourier_features: int):\n", + " super().__init__()\n", + " self.n_fourier_features = n_fourier_features\n", + "\n", + " def forward(self, x: Tensor) -> Tensor:\n", + " feature_vector = [\n", + " torch.sin((i + 1) * x) for i in range(self.n_fourier_features)\n", + " ]\n", + " feature_vector += [\n", + " torch.cos((i + 1) * x) for i in range(self.n_fourier_features)\n", + " ]\n", + " return torch.cat(feature_vector, dim=-1)\n", + "\n", + "\n", + "class ProjectToTangent(nn.Module):\n", + " \"\"\"Projects a vector field onto the tangent plane at the input.\"\"\"\n", + "\n", + " def __init__(self, vecfield: nn.Module, manifold: Manifold):\n", + " super().__init__()\n", + " self.vecfield = vecfield\n", + " self.manifold = manifold\n", + "\n", + " def forward(self, x: Tensor, t: Tensor) -> Tensor:\n", + " x = self.manifold.projx(x)\n", + " v = self.vecfield(x, t)\n", + " v = self.manifold.proju(x, v)\n", + " return v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train Velocity Flow Matching model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| iter 1000 | 5.01 ms/step | loss 1.602 \n", + "| iter 2000 | 4.71 ms/step | loss 1.610 \n", + "| iter 3000 | 4.73 ms/step | loss 1.645 \n", + "| iter 4000 | 4.73 ms/step | loss 1.589 \n", + "| iter 5000 | 4.77 ms/step | loss 1.647 \n" + ] + } + ], + "source": [ + "# training arguments\n", + "lr = 0.001\n", + "batch_size = 4096\n", + "iterations = 5001\n", + "print_every = 1000\n", + "manifold = FlatTorus()\n", + "dim = 2\n", + "hidden_dim = 512\n", + "\n", + "# velocity field model init\n", + "vf = ProjectToTangent( # Ensures we can just use Euclidean divergence.\n", + " MLP( # Vector field in the ambient space.\n", + " input_dim=dim,\n", + " hidden_dim=hidden_dim,\n", + " ),\n", + " manifold=manifold,\n", + ")\n", + "vf.to(device)\n", + "\n", + "# instantiate an affine path object\n", + "path = GeodesicProbPath(scheduler=CondOTScheduler(), manifold=manifold)\n", + "\n", + "# init optimizer\n", + "optim = torch.optim.Adam(vf.parameters(), lr=lr) \n", + "\n", + "# train\n", + "start_time = time.time()\n", + "for i in range(iterations):\n", + " optim.zero_grad() \n", + "\n", + " # sample data (user's responsibility): in this case, (X_0,X_1) ~ pi(X_0,X_1) = N(X_0|0,I)q(X_1)\n", + " x_1 = inf_train_gen(batch_size=batch_size, device=device) # sample data\n", + " x_0 = torch.randn_like(x_1).to(device)\n", + "\n", + " x_1 = wrap(manifold, x_1)\n", + " x_0 = wrap(manifold, x_0)\n", + "\n", + " # sample time (user's responsibility)\n", + " t = torch.rand(x_1.shape[0]).to(device) \n", + "\n", + " # sample probability path\n", + " path_sample = path.sample(t=t, x_0=x_0, x_1=x_1)\n", + "\n", + " # flow matching l2 loss\n", + " loss = torch.pow( vf(path_sample.x_t,path_sample.t) - path_sample.dx_t, 2).mean()\n", + "\n", + " # optimizer step\n", + " loss.backward() # backward\n", + " optim.step() # update\n", + " \n", + " # log loss\n", + " if (i+1) % print_every == 0:\n", + " elapsed = time.time() - start_time\n", + " print('| iter {:6d} | {:5.2f} ms/step | loss {:8.3f} ' \n", + " .format(i+1, elapsed*1000/print_every, loss.item())) \n", + " start_time = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sample from trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class WrappedModel(ModelWrapper):\n", + " def forward(self, x: torch.Tensor, t: torch.Tensor, **extras):\n", + " return self.model(x=x, t=t)\n", + "\n", + "wrapped_vf = WrappedModel(vf)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:02<00:00, 45.66it/s]\n" + ] + } + ], + "source": [ + "# step size for ode solver\n", + "step_size = 0.01\n", + "N = 6\n", + "\n", + "norm = cm.colors.Normalize(vmax=50, vmin=0)\n", + "\n", + "batch_size = 50000 # batch size\n", + "eps_time = 1e-2\n", + "T = torch.linspace(0, 1, N) # sample times\n", + "T = T.to(device=device)\n", + "\n", + "x_init = torch.randn((batch_size, 2), dtype=torch.float32, device=device)\n", + "x_init = wrap(manifold, x_init)\n", + "\n", + "solver = RiemannianODESolver(velocity_model=wrapped_vf, manifold=manifold) # create an ODESolver class\n", + "sol = solver.sample(\n", + " x_init=x_init,\n", + " step_size=step_size,\n", + " method=\"midpoint\",\n", + " return_intermediates=True,\n", + " time_grid=T,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize the path" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = sol.cpu()\n", + "T = T.cpu()\n", + "\n", + "gt_samples = inf_train_gen(batch_size=50000) # sample data\n", + "gt_samples = wrap(manifold, gt_samples)\n", + "\n", + "samples = torch.cat([sol, gt_samples[None]], dim=0).numpy()\n", + "\n", + "_, axs = plt.subplots(1, N + 1, figsize=(20, 3.2))\n", + "for i in range(N + 1):\n", + " H = axs[i].hist2d(\n", + " samples[i, :, 0],\n", + " samples[i, :, 1],\n", + " 300,\n", + " range=((0, 2 * math.pi), (0, 2 * math.pi)),\n", + " )\n", + " cmin = 0.0\n", + " cmax = torch.quantile(torch.from_numpy(H[0]), 0.99).item()\n", + " norm = cm.colors.Normalize(vmax=cmax, vmin=cmin)\n", + " _ = axs[i].hist2d(\n", + " samples[i, :, 0],\n", + " samples[i, :, 1],\n", + " 300,\n", + " range=((0, 2 * math.pi), (0, 2 * math.pi)),\n", + " norm=norm,\n", + " )\n", + " axs[i].set_aspect(\"equal\")\n", + " axs[i].set_xlim([0, 2 * math.pi])\n", + " axs[i].set_ylim([0, 2 * math.pi])\n", + " axs[i].axis(\"off\")\n", + "\n", + " if i < N:\n", + " axs[i].set_title(\"t= %.2f\" % (T[i]))\n", + " else:\n", + " axs[i].set_title(\"data\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "g8QtNgs1-PlE", + "wW3VMmrK2t2d", + "_7aH8D0H3IJT" + ], + "name": "scalable_CNF.ipynb", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.14" + }, + "vscode": { + "interpreter": { + "hash": "a9223c1449c722e9a3173d1229627827aabf67ca877d945d23ebe719b18ba9c7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/2d_riemannian_flow_matching_sphere.ipynb b/examples/2d_riemannian_flow_matching_sphere.ipynb new file mode 100644 index 0000000..c5d8a58 --- /dev/null +++ b/examples/2d_riemannian_flow_matching_sphere.ipynb @@ -0,0 +1,446 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A simple 2D Riemannian Flow Matching model on sphere" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports and init device" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "rb5VSo4mNkVd" + }, + "outputs": [], + "source": [ + "import time\n", + "import torch\n", + "import math\n", + "import numpy as np\n", + "\n", + "from torch import nn, Tensor\n", + "\n", + "# flow_matching\n", + "from flow_matching.path import GeodesicProbPath\n", + "from flow_matching.path.scheduler import CondOTScheduler\n", + "from flow_matching.solver import ODESolver, RiemannianODESolver\n", + "from flow_matching.utils import ModelWrapper\n", + "from flow_matching.utils.manifolds import Sphere, Manifold\n", + "\n", + "# visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from matplotlib import cm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using gpu\n" + ] + } + ], + "source": [ + "if torch.cuda.is_available():\n", + " device = 'cuda:0'\n", + " print('Using gpu')\n", + "else:\n", + " device = 'cpu'\n", + " print('Using cpu.')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m2wy46WpLZs0" + }, + "source": [ + "## Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def inf_train_gen(batch_size: int = 200, device: str = \"cpu\"):\n", + " x1 = torch.rand(batch_size, device=device) * 4 - 2\n", + " x2_ = (torch.rand(batch_size, device=device) - torch.randint(high=2, size=(batch_size, ), device=device) * 2)\n", + " x2 = x2_ + (torch.floor(x1) % 2)\n", + "\n", + " data = torch.cat([x1[:, None], x2[:, None]], dim=1)\n", + "\n", + " return data.float()\n", + "\n", + "def wrap(manifold, samples):\n", + " center = torch.cat([torch.zeros_like(samples), torch.ones_like(samples[..., 0:1])], dim=-1)\n", + " samples = torch.cat([samples, torch.zeros_like(samples[..., 0:1])], dim=-1) / 2\n", + "\n", + " return manifold.expmap(center, samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Activation class\n", + "class Swish(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def forward(self, x: Tensor) -> Tensor:\n", + " return torch.sigmoid(x) * x\n", + "\n", + "\n", + "# Model class\n", + "class MLP(nn.Module):\n", + " def __init__(\n", + " self,\n", + " input_dim: int = 2,\n", + " time_dim: int = 1,\n", + " hidden_dim: int = 128,\n", + " ):\n", + " super().__init__()\n", + "\n", + " self.input_dim = input_dim\n", + " self.time_dim = time_dim\n", + " self.hidden_dim = hidden_dim\n", + "\n", + " self.input_layer = nn.Linear(input_dim + time_dim, hidden_dim)\n", + "\n", + " self.main = nn.Sequential(\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " nn.Linear(hidden_dim, input_dim),\n", + " )\n", + "\n", + " def forward(self, x: Tensor, t: Tensor) -> Tensor:\n", + " sz = x.size()\n", + " x = x.reshape(-1, self.input_dim)\n", + " t = t.reshape(-1, self.time_dim).float()\n", + "\n", + " t = t.reshape(-1, 1).expand(x.shape[0], 1)\n", + " h = torch.cat([x, t], dim=1)\n", + " h = self.input_layer(h)\n", + " output = self.main(h)\n", + "\n", + " return output.reshape(*sz)\n", + "\n", + "\n", + "class ProjectToTangent(nn.Module):\n", + " \"\"\"Projects a vector field onto the tangent plane at the input.\"\"\"\n", + "\n", + " def __init__(self, vecfield: nn.Module, manifold: Manifold):\n", + " super().__init__()\n", + " self.vecfield = vecfield\n", + " self.manifold = manifold\n", + "\n", + " def forward(self, x: Tensor, t: Tensor) -> Tensor:\n", + " x = self.manifold.projx(x)\n", + " v = self.vecfield(x, t)\n", + " v = self.manifold.proju(x, v)\n", + " return v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train Velocity Flow Matching model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| iter 1000 | 6.37 ms/step | loss 0.281 \n", + "| iter 2000 | 5.97 ms/step | loss 0.278 \n", + "| iter 3000 | 6.10 ms/step | loss 0.277 \n", + "| iter 4000 | 6.12 ms/step | loss 0.272 \n", + "| iter 5000 | 6.01 ms/step | loss 0.286 \n" + ] + } + ], + "source": [ + "# training arguments\n", + "lr = 0.001\n", + "batch_size = 4096\n", + "iterations = 5001\n", + "print_every = 1000\n", + "manifold = Sphere()\n", + "dim = 3\n", + "hidden_dim = 512\n", + "\n", + "# velocity field model init\n", + "vf = ProjectToTangent( # Ensures we can just use Euclidean divergence.\n", + " MLP( # Vector field in the ambient space.\n", + " input_dim=dim,\n", + " hidden_dim=hidden_dim,\n", + " ),\n", + " manifold=manifold,\n", + ")\n", + "vf.to(device)\n", + "\n", + "# instantiate an affine path object\n", + "path = GeodesicProbPath(scheduler=CondOTScheduler(), manifold=manifold)\n", + "\n", + "# init optimizer\n", + "optim = torch.optim.Adam(vf.parameters(), lr=lr) \n", + "\n", + "# train\n", + "start_time = time.time()\n", + "for i in range(iterations):\n", + " optim.zero_grad() \n", + "\n", + " # sample data (user's responsibility): in this case, (X_0,X_1) ~ pi(X_0,X_1) = N(X_0|0,I)q(X_1)\n", + " x_1 = inf_train_gen(batch_size=batch_size, device=device) # sample data\n", + " x_0 = torch.randn_like(x_1).to(device)\n", + "\n", + " x_1 = wrap(manifold, x_1)\n", + " x_0 = wrap(manifold, x_0)\n", + "\n", + " # sample time (user's responsibility)\n", + " t = torch.rand(x_1.shape[0]).to(device) \n", + "\n", + " # sample probability path\n", + " path_sample = path.sample(t=t, x_0=x_0, x_1=x_1)\n", + "\n", + " # flow matching l2 loss\n", + " loss = torch.pow( vf(path_sample.x_t,path_sample.t) - path_sample.dx_t, 2).mean()\n", + "\n", + " # optimizer step\n", + " loss.backward() # backward\n", + " optim.step() # update\n", + " \n", + " # log loss\n", + " if (i+1) % print_every == 0:\n", + " elapsed = time.time() - start_time\n", + " print('| iter {:6d} | {:5.2f} ms/step | loss {:8.3f} ' \n", + " .format(i+1, elapsed*1000/print_every, loss.item())) \n", + " start_time = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sample from trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class WrappedModel(ModelWrapper):\n", + " def forward(self, x: torch.Tensor, t: torch.Tensor, **extras):\n", + " return self.model(x=x, t=t)\n", + "\n", + "wrapped_vf = WrappedModel(vf)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:02<00:00, 45.77it/s]\n" + ] + } + ], + "source": [ + "# step size for ode solver\n", + "step_size = 0.01\n", + "N = 6\n", + "\n", + "norm = cm.colors.Normalize(vmax=50, vmin=0)\n", + "\n", + "batch_size = 50000 # batch size\n", + "eps_time = 1e-2\n", + "T = torch.linspace(0, 1, N) # sample times\n", + "T = T.to(device=device)\n", + "\n", + "x_init = torch.randn((batch_size, 2), dtype=torch.float32, device=device)\n", + "x_init = wrap(manifold, x_init)\n", + "\n", + "solver = RiemannianODESolver(velocity_model=wrapped_vf, manifold=manifold) # create an ODESolver class\n", + "sol = solver.sample(\n", + " x_init=x_init,\n", + " step_size=step_size,\n", + " method=\"midpoint\",\n", + " return_intermediates=True,\n", + " time_grid=T,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize the path" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = sol.cpu()\n", + "T = T.cpu()\n", + "\n", + "gt_samples = inf_train_gen(batch_size=50000) # sample data\n", + "gt_samples = wrap(manifold, gt_samples)\n", + "\n", + "samples = torch.cat([sol, gt_samples[None]], dim=0).numpy()\n", + "\n", + "_, axs = plt.subplots(1, N + 1, figsize=(20, 3.2), subplot_kw={\"projection\": \"3d\"})\n", + "\n", + "for i in range(N + 1):\n", + " # Sphere parameters (theta: azimuth, phi: polar angle)\n", + " u = np.linspace(0, 2 * np.pi, 100)\n", + " v = np.linspace(0, np.pi, 100)\n", + "\n", + " # Parametric equations for the sphere\n", + " x = np.outer(np.cos(u), np.sin(v))\n", + " y = np.outer(np.sin(u), np.sin(v))\n", + " z = np.outer(np.ones(np.size(u)), np.cos(v))\n", + "\n", + " # Plot the surface of the sphere\n", + " axs[i].plot_surface(x, y, z, color=\"c\", alpha=0.3, rstride=5, cstride=5)\n", + "\n", + " # Plot only the visible points on the front side of the sphere\n", + " x_points, y_points, z_points = (\n", + " samples[i, :, 0],\n", + " samples[i, :, 1],\n", + " samples[i, :, 2],\n", + " )\n", + " axs[i].scatter(\n", + " x_points, y_points, z_points, color=\"r\", s=1, alpha=0.1\n", + " ) # Red points\n", + "\n", + " # Set labels\n", + " axs[i].set_xlabel(\"X\")\n", + " axs[i].set_ylabel(\"Y\")\n", + " axs[i].set_zlabel(\"Z\")\n", + "\n", + " # Set the aspect ratio to equal for better visualization of a sphere\n", + " axs[i].set_box_aspect([1, 1, 1])\n", + " axs[i].view_init(elev=90, azim=0)\n", + " axs[i].axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "g8QtNgs1-PlE", + "wW3VMmrK2t2d", + "_7aH8D0H3IJT" + ], + "name": "scalable_CNF.ipynb", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.14" + }, + "vscode": { + "interpreter": { + "hash": "a9223c1449c722e9a3173d1229627827aabf67ca877d945d23ebe719b18ba9c7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/image/README.md b/examples/image/README.md new file mode 100644 index 0000000..137b354 --- /dev/null +++ b/examples/image/README.md @@ -0,0 +1,77 @@ +# Image example + +## Training instructions + +1. Download and unpack blurred ImageNet from the [official website](https://image-net.org/download.php). + +``` +export IMAGENET_DIR=~/flow_matching/examples/image/data/ +export IMAGENET_RES=64 +tar -xf ~/Downloads/train_blurred.tar.gz -C $IMAGENET_DIR +``` + +2. Downsample Imagenet to the desired resolution. + +``` +cd ~/ +git clone git@github.com:PatrykChrabaszcz/Imagenet32_Scripts.git +python Imagenet32_Scripts/image_resizer_imagent.py -i ${IMAGENET_DIR}train_blurred -o ${IMAGENET_DIR}train_blurred_$IMAGENET_RES -s $IMAGENET_RES -a box -r -j 10 +``` + +3. Set up the virtual environment. First, set up the virtual environment by following the steps in the repository's `README.md`. Then, + +``` +conda activate flow_matching + +cd examples/image +pip install -r requirements.txt +``` + +4. [Optional] Test-run training locally. A test run executes one step of training followed by one step of evaluation. + +``` +python train.py --data_path=${IMAGENET_DIR}train_blurred_$IMAGENET_RES/box/ --test_run +``` + +5. Launch training on a SLURM cluster + +``` +python submitit_train.py --data_path=${IMAGENET_DIR}train_blurred_$IMAGENET_RES/box/ +``` + +6. Evaluate the model using the `--eval_only` flag. The evaluation script will generate snapshots under the `/snapshots` folder. Specify the `--compute_fid` flag to also compute the FID with respect to the training set. Make sure to specify your most recent checkpoint to resume from. The results are printed to `log.txt`. + +``` +python submitit_train.py --data_path=${IMAGENET_DIR}train_blurred_$IMAGENET_RES/box/ --resume=./output_dir/checkpoint-899.pth --compute_fid --eval_only +``` + + +## Results +| Data | Model type | Epochs | FID | Command | +|-----------------------|----------------------------------|-------|------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Cifar10 | Unconditional UNet | 1800 | 2.07 | `python submitit_train.py \`
`--dataset=cifar10 \`
`--batch_size=64 \`
`--nodes=1 \`
`--accum_iter=1 \`
`--eval_frequency=100 \`
`--epochs=3000 \`
`--class_drop_prob=1.0 \`
`--cfg_scale=0.0 \`
`--compute_fid \`
`--ode_method heun2 \`
`--ode_options '{"nfe": 50}' \`
`--use_ema \`
`--edm_schedule \`
`--skewed_timesteps` | +| ImageNet32 (Blurred) | Class conditional Unet | 900 | 1.14 | `export IMAGENET_RES=32 \`
`python submitit_train.py \`
`--data_path=${IMAGENET_DIR}train_blurred_$IMAGENET_RES/box/ \`
`--batch_size=32 \`
`--nodes=8 \`
`--accum_iter=1 \`
`--eval_frequency=100 \`
`--decay_lr \`
`--compute_fid \`
`--ode_method dopri5 \`
`--ode_options '{"atol": 1e-5, "rtol":1e-5}'` | +| ImageNet64 (Blurred) | Class conditional Unet | 900 | 1.64 | `export IMAGENET_RES=64 \`
`python submitit_train.py \`
`--data_path=${IMAGENET_DIR}train_blurred_$IMAGENET_RES/box/ \`
`--batch_size=32 \`
`--nodes=8 \`
`--accum_iter=1 \`
`--eval_frequency=100 \`
`--decay_lr \`
`--compute_fid \`
`--ode_method dopri5 \`
`--ode_options '{"atol": 1e-5, "rtol":1e-5}'` | +| Cifar10 (Discrete Flow) | Unconditional Unet | 2500 | 3.58 | `python submitit_train.py \`
`--dataset=cifar10 \`
`--nodes=1 \`
`--discrete_flow_matching \`
`--batch_size=32 \`
`--accum_iter=1 \`
`--cfg_scale=0.0 \`
`--use_ema \`
`--epochs=3000 \`
`--class_drop_prob=1.0 \`
`--compute_fid \`
`--sym_func` | + + + +## Acknowledgements + +This example partially use code from: +- [Guided diffusion](https://github.com/openai/guided-diffusion/) +- [ConvNext](https://github.com/facebookresearch/ConvNeXt) + +## License + +The majority of the code in this example is licensed under CC-BY-NC, however portions of the project are available under separate license terms: +- The UNet model is under MIT license. +- The distributed computing and the grad scaler code is under MIT license. + +## Citations + +Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database." 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009. + +Karras, Tero, et al. "Elucidating the design space of diffusion-based generative models." Advances in neural information processing systems 35 (2022): 26565-26577. + +Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer International Publishing, 2015. diff --git a/examples/image/load_model_checkpoint.ipynb b/examples/image/load_model_checkpoint.ipynb new file mode 100644 index 0000000..e487a1d --- /dev/null +++ b/examples/image/load_model_checkpoint.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading model checkpoints\n", + "\n", + "Once a model is trained, a corresponding model checkpoint (eg. `checkpoint-99.pth`) is saved in `output_dir` along with the `args.json` that contains the command line arguments for the training run.\n", + "\n", + "This notebook shows how to load a model checkpoint and generate a few snapshots." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import json\n", + "from models.model_configs import instantiate_model\n", + "import torch\n", + "from training.eval_loop import CFGScaledModel\n", + "from flow_matching.path import MixtureDiscreteProbPath\n", + "from flow_matching.path.scheduler import PolynomialConvexScheduler\n", + "from flow_matching.solver.ode_solver import ODESolver\n", + "from flow_matching.solver.discrete_solver import MixtureDiscreteEulerSolver\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Meta Only] Pretrained checkpoints\n", + "\n", + "\n", + "| Model | FID |\n", + "| -------- | ----|\n", + "| Cifar10, unconditional | 2.07 |\n", + "| Imagenet32, face-blurred, conditional | 1.14 |\n", + "| Imagenet64, face-blurred, conditional | 1.68 |\n", + "| Cifar10, discrete flow matching, unconditional | 3.58 |" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Substitute your pretrained checkpoint path\n", + "checkpoint_path = Path(\"/path/to/checkpoint.pth\")\n", + "args_filepath = checkpoint_path.parent / 'args.json'\n", + "with open(args_filepath, 'r') as f:\n", + " args_dict = json.load(f)\n", + "\n", + "model = instantiate_model(architechture=args_dict['dataset'], is_discrete='discrete_flow_matching' in args_dict and args_dict['discrete_flow_matching'],\n", + " use_ema=args_dict['use_ema'])\n", + "checkpoint = torch.load(checkpoint_path, map_location=\"cpu\")\n", + "model.load_state_dict(checkpoint[\"model\"])\n", + "model.train(False)\n", + "\n", + "device = 'cuda'\n", + "model.to(device=device)\n", + "\n", + "# Set the sampling resolution corresponding to the model\n", + "if 'train_blurred_64' in args_dict['data_path'] and args_dict['dataset'] == 'imagenet':\n", + " sample_resolution = 64\n", + "elif 'train_blurred_32' in args_dict['data_path'] or args_dict['dataset'] == 'cifar10':\n", + " sample_resolution = 32\n", + "\n", + "batch_size = args_dict['batch_size']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sampling" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate from classes 1,2,..,batch_size - 1\n", + "labels = torch.tensor(list(range(batch_size)), dtype=torch.int32, device=device)\n", + "\n", + "cfg_weighted_model = CFGScaledModel(model=model)\n", + "\n", + "if 'discrete_flow_matching' in args_dict and args_dict['discrete_flow_matching']:\n", + " if 'sym_func' in args_dict and args_dict['sym_func']:\n", + " sym = lambda t: 12.0 * torch.pow(t, 2.0) * torch.pow(1.0 - t, 0.25)\n", + " else:\n", + " sym = args_dict['sym']\n", + " path = MixtureDiscreteProbPath(scheduler=PolynomialConvexScheduler(n=3.0))\n", + " p = torch.zeros(size=[257], dtype=torch.float32, device=device)\n", + " p[256] = 1.0\n", + " solver = MixtureDiscreteEulerSolver(model=cfg_weighted_model, path=path, vocabulary_size=257, p=p)\n", + " x_0 = torch.zeros([batch_size, 3, sample_resolution, sample_resolution], dtype=torch.long, device=device) + 256\n", + " synthetic_samples = solver.sample(\n", + " x_init=x_0,\n", + " step_size=1.0 / args_dict['discrete_fm_steps'],\n", + " verbose=False,\n", + " div_free=sym,\n", + " dtype_categorical=torch.float32,\n", + " label=labels,\n", + " cfg_scale=args_dict['cfg_scale'],\n", + " )\n", + "else:\n", + " x_0 = torch.randn([batch_size, 3, sample_resolution, sample_resolution], dtype=torch.float32, device=device) \n", + " solver = ODESolver(velocity_model=cfg_weighted_model)\n", + " ode_opts = args_dict['ode_options']\n", + " ode_opts[\"method\"] = args_dict['ode_method']\n", + " synthetic_samples = solver.sample(\n", + " time_grid=torch.tensor([0.0, 1.0], device=device),\n", + " x_init=x_0,\n", + " method=args_dict['ode_method'],\n", + " atol=args_dict['ode_options']['atol'] if 'atol' in args_dict['ode_options'] else None,\n", + " rtol=args_dict['ode_options']['rtol'] if 'rtol' in args_dict['ode_options'] else None,\n", + " step_size=args_dict['ode_options']['step_size'] if 'step_size' in args_dict['ode_options'] else None,\n", + " label=labels,\n", + " cfg_scale=args_dict['cfg_scale'],\n", + " )\n", + "\n", + " # Scaling to [0, 1] from [-1, 1]\n", + " synthetic_samples = torch.clamp(\n", + " synthetic_samples * 0.5 + 0.5, min=0.0, max=1.0\n", + " )\n", + " synthetic_samples = torch.floor(synthetic_samples * 255) / 255.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting the samples" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cols = 10\n", + "rows = (batch_size + cols - 1) // cols\n", + "plt.figure(figsize=(cols * 3, rows * 3))\n", + "for i in range(batch_size):\n", + " image = synthetic_samples[i].cpu().permute(1, 2, 0).numpy()\n", + " plt.subplot(rows, cols, i + 1)\n", + " plt.imshow(image)\n", + " plt.axis('off')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "flow_matching", + "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": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/image/models/discrete_unet.py b/examples/image/models/discrete_unet.py new file mode 100644 index 0000000..9fca029 --- /dev/null +++ b/examples/image/models/discrete_unet.py @@ -0,0 +1,97 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +from dataclasses import dataclass +from typing import Mapping, Optional, Tuple + +import torch +import torch.nn as nn +from models.unet import UNetModel + + +class PixelEmbedding(nn.Module): + def __init__( + self, + n_tokens: int, + hidden_size: int, + ): + super().__init__() + self.embedding_table = nn.Embedding(n_tokens, hidden_size) + + def forward(self, x: torch.Tensor): + B, _, H, W = x.shape + emb = self.embedding_table(x) + result = emb.permute(0, 1, 4, 2, 3).reshape(B, -1, H, W) + return result + + +@dataclass(eq=False) +class DiscreteUNetModel(nn.Module): + vocab_size: int + in_channels: int = 3 + model_channels: int = 128 + out_channels: int = 3 + num_res_blocks: int = 2 + attention_resolutions: Tuple[int] = (1, 2, 2, 2) + dropout: float = 0.0 + channel_mult: Tuple[int] = (1, 2, 4, 8) + conv_resample: bool = True + dims: int = 2 + num_classes: Optional[int] = None + use_checkpoint: bool = False + num_heads: int = 1 + num_head_channels: int = -1 + num_heads_upsample: int = -1 + use_scale_shift_norm: bool = False + resblock_updown: bool = False + use_new_attention_order: bool = False + with_fourier_features: bool = False + + def __post_init__(self): + super().__init__() + assert ( + self.model_channels * self.channel_mult[0] % self.in_channels == 0 + ), f"Unet input dimensions must be divisible by the number of channels. Got {self.model_channels * self.channel_mult[0]} / {self.in_channels}" + self.embedding_dim = ( + self.model_channels * self.channel_mult[0] // self.in_channels + ) + + self.pixel_embedding = PixelEmbedding( + n_tokens=self.vocab_size, hidden_size=self.embedding_dim + ) + + self.unet = UNetModel( + in_channels=self.in_channels * self.embedding_dim, + model_channels=self.model_channels, + out_channels=self.out_channels * (self.vocab_size), + num_res_blocks=self.num_res_blocks, + attention_resolutions=self.attention_resolutions, + dropout=self.dropout, + channel_mult=self.channel_mult, + conv_resample=self.conv_resample, + dims=self.dims, + num_classes=self.num_classes, + use_checkpoint=self.use_checkpoint, + num_heads=self.num_heads, + num_head_channels=self.num_head_channels, + num_heads_upsample=self.num_heads_upsample, + use_scale_shift_norm=self.use_scale_shift_norm, + resblock_updown=self.resblock_updown, + use_new_attention_order=self.use_new_attention_order, + with_fourier_features=self.with_fourier_features, + ignore_time=True, + input_projection=False, + ) + + def forward( + self, x_t: torch.Tensor, t: torch.Tensor, extra: Mapping[str, torch.Tensor] + ) -> torch.Tensor: + B, C, H, W = x_t.shape + logits = ( + self.unet(self.pixel_embedding(x_t), t, extra) + .reshape(B, C, self.vocab_size, H, W) + .permute(0, 1, 3, 4, 2) + ) + return logits diff --git a/examples/image/models/ema.py b/examples/image/models/ema.py new file mode 100644 index 0000000..1a7d722 --- /dev/null +++ b/examples/image/models/ema.py @@ -0,0 +1,79 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import logging +from typing import List + +import torch +from torch.nn import Module, Parameter, ParameterList + +logger = logging.getLogger(__name__) + + +class EMA(Module): + def __init__(self, model: Module, decay: float = 0.999): + super().__init__() + self.model = model + self.decay = decay + + # Put this in a buffer so that it gets included in the state dict + self.register_buffer("num_updates", torch.tensor(0)) + + self.shadow_params: ParameterList = ParameterList( + [ + Parameter(p.clone().detach(), requires_grad=False) + for p in model.parameters() + if p.requires_grad + ] + ) + self.backup_params: List[torch.Tensor] = [] + + def train(self, mode: bool) -> None: + if self.training == mode: + super().train(mode) + return + + if not mode: + logger.info( + "EMA: Switching from train to eval, backing up parameters and copying EMA params" + ) + self.backup() + self.copy_to_model() + else: + logger.info("EMA: Switching from eval to train, restoring saved parameters") + self.restore_to_model() + + super().train(mode) + + def update_ema(self) -> None: + self.num_updates += 1 + num_updates = self.num_updates.item() + decay = min(self.decay, (1 + num_updates) / (10 + num_updates)) + with torch.no_grad(): + params = [p for p in self.model.parameters() if p.requires_grad] + for shadow, param in zip(self.shadow_params, params): + shadow.sub_((1 - decay) * (shadow - param)) + + def forward(self, *args, **kwargs) -> torch.Tensor: + return self.model(*args, **kwargs) + + def copy_to_model(self) -> None: + params = [p for p in self.model.parameters() if p.requires_grad] + for shadow, param in zip(self.shadow_params, params): + param.data.copy_(shadow.data) + + def backup(self) -> None: + assert ( + self.training + ), "Backup can only be created in train mode to avoid backing-up ema weights." + if len(self.backup_params) > 0: + for p, b in zip(self.model.parameters(), self.backup_params): + b.data.copy_(p.data) + else: + self.backup_params = [param.clone() for param in self.model.parameters()] + + def restore_to_model(self) -> None: + for param, backup in zip(self.model.parameters(), self.backup_params): + param.data.copy_(backup.data) diff --git a/examples/image/models/model_configs.py b/examples/image/models/model_configs.py new file mode 100644 index 0000000..06b1320 --- /dev/null +++ b/examples/image/models/model_configs.py @@ -0,0 +1,112 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +from typing import Union + +from models.discrete_unet import DiscreteUNetModel +from models.ema import EMA +from models.unet import UNetModel + +MODEL_CONFIGS = { + "imagenet": { + "in_channels": 3, + "model_channels": 192, + "out_channels": 3, + "num_res_blocks": 3, + "attention_resolutions": [2, 4, 8], + "dropout": 0.1, + "channel_mult": [1, 2, 3, 4], + "num_classes": 1000, + "use_checkpoint": False, + "num_heads": 4, + "num_head_channels": 64, + "use_scale_shift_norm": True, + "resblock_updown": True, + "use_new_attention_order": True, + "with_fourier_features": False, + }, + "imagenet_discrete": { + "in_channels": 3, + "model_channels": 192, + "out_channels": 3, + "num_res_blocks": 4, + "attention_resolutions": [2, 4, 8], + "dropout": 0.2, + "channel_mult": [2, 3, 4, 4], + "num_classes": 1000, + "use_checkpoint": False, + "num_heads": -1, + "num_head_channels": 64, + "use_scale_shift_norm": True, + "resblock_updown": True, + "use_new_attention_order": True, + "with_fourier_features": False, + }, + "cifar10": { + "in_channels": 3, + "model_channels": 128, + "out_channels": 3, + "num_res_blocks": 4, + "attention_resolutions": [2], + "dropout": 0.3, + "channel_mult": [2, 2, 2], + "conv_resample": False, + "dims": 2, + "num_classes": None, + "use_checkpoint": False, + "num_heads": 1, + "num_head_channels": -1, + "num_heads_upsample": -1, + "use_scale_shift_norm": True, + "resblock_updown": False, + "use_new_attention_order": True, + "with_fourier_features": False, + }, + "cifar10_discrete": { + "in_channels": 3, + "model_channels": 96, + "out_channels": 3, + "num_res_blocks": 5, + "attention_resolutions": [2], + "dropout": 0.4, + "channel_mult": [3, 4, 4], + "conv_resample": False, + "dims": 2, + "num_classes": None, + "use_checkpoint": False, + "num_heads": -1, + "num_head_channels": 64, + "num_heads_upsample": -1, + "use_scale_shift_norm": True, + "resblock_updown": False, + "use_new_attention_order": True, + "with_fourier_features": False, + }, +} + + +def instantiate_model( + architechture: str, is_discrete: bool, use_ema: bool +) -> Union[UNetModel, DiscreteUNetModel]: + assert ( + architechture in MODEL_CONFIGS + ), f"Model architecture {architechture} is missing its config." + + if is_discrete: + if architechture + "_discrete" in MODEL_CONFIGS: + config = MODEL_CONFIGS[architechture + "_discrete"] + else: + config = MODEL_CONFIGS[architechture] + model = DiscreteUNetModel( + vocab_size=257, + **config, + ) + else: + model = UNetModel(**MODEL_CONFIGS[architechture]) + + if use_ema: + return EMA(model=model) + else: + return model diff --git a/examples/image/models/nn.py b/examples/image/models/nn.py new file mode 100644 index 0000000..552b396 --- /dev/null +++ b/examples/image/models/nn.py @@ -0,0 +1,174 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +""" +Various utilities for neural networks. +Taken from https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/nn.py +""" + +import math + +import torch as th +import torch.nn as nn + + +# PyTorch 1.7 has SiLU, but we support PyTorch 1.5. +class SiLU(nn.Module): + def forward(self, x): + return x * th.sigmoid(x) + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def update_ema(target_params, source_params, rate=0.99): + """ + Update target parameters to be closer to those of source parameters using + an exponential moving average. + :param target_params: the target parameter sequence. + :param source_params: the source parameter sequence. + :param rate: the EMA rate (closer to 1 means slower). + """ + for targ, src in zip(target_params, source_params): + targ.detach().mul_(rate).add_(src, alpha=1 - rate) + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def normalization(channels): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNorm32(32, channels) + + +def timestep_embedding(timesteps, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + half = dim // 2 + freqs = th.exp( + -math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = th.cat([th.cos(args), th.sin(args)], dim=-1) + if dim % 2: + embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + # Use pytorch's activation checkpointing. This has support for fp16 autocast + return th.utils.checkpoint.checkpoint(func, *inputs) + # args = tuple(inputs) + tuple(params) + # return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(th.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + with th.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with th.enable_grad(): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = th.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads diff --git a/examples/image/models/unet.py b/examples/image/models/unet.py new file mode 100644 index 0000000..6f7fb35 --- /dev/null +++ b/examples/image/models/unet.py @@ -0,0 +1,727 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +""" +Modified from https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/unet.py +""" + +import math +from abc import abstractmethod +from dataclasses import dataclass +from typing import Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from models.nn import ( + avg_pool_nd, + checkpoint, + conv_nd, + linear, + normalization, + timestep_embedding, + zero_module, +) + + +class ConstantEmbedding(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + self.embedding_table = nn.Parameter(torch.empty((1, out_channels))) + nn.init.uniform_( + self.embedding_table, -(in_channels**0.5), in_channels**0.5 + ) + + def forward(self, emb): + return self.embedding_table.repeat(emb.shape[0], 1) + + +class AttentionPool2d(nn.Module): + """ + Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py + """ + + def __init__( + self, + spacial_dim: int, + embed_dim: int, + num_heads_channels: int, + output_dim: int = None, + ): + super().__init__() + self.positional_embedding = nn.Parameter( + torch.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5 + ) + self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) + self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) + self.num_heads = embed_dim // num_heads_channels + self.attention = QKVAttention(self.num_heads) + + def forward(self, x): + b, c, *_spatial = x.shape + x = x.reshape(b, c, -1) # NC(HW) + x = torch.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) + x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) + x = self.qkv_proj(x) + x = self.attention(x) + x = self.c_proj(x) + return x[:, :, 0] + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + else: + x = layer(x) + return x + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=1 + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + emb_off=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + if emb_off: + self.emb_layers = ConstantEmbedding( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ) + else: + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 2 * self.out_channels + if use_scale_shift_norm + else self.out_channels, + ), + ) + + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, + (x, emb), + self.parameters(), + self.use_checkpoint and self.training, + ) + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + use_checkpoint=False, + use_new_attention_order=False, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.use_checkpoint = use_checkpoint + self.norm = normalization(channels) + self.qkv = conv_nd(1, channels, channels * 3, 1) + if use_new_attention_order: + # split qkv before split heads + self.attention = QKVAttention(self.num_heads) + else: + # split heads before split qkv + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) + + def forward(self, x): + return checkpoint( + self._forward, + (x,), + self.parameters(), + self.use_checkpoint and self.training, + ) + + def _forward(self, x): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +def count_flops_attn(model, _x, y): + """ + A counter for the `thop` package to count the operations in an + attention operation. + Meant to be used like: + macs, params = thop.profile( + model, + inputs=(inputs, timestamps), + custom_ops={QKVAttention: QKVAttention.count_flops}, + ) + """ + b, c, *spatial = y[0].shape + num_spatial = int(np.prod(spatial)) + # We perform two matmuls with the same number of ops. + # The first computes the weight matrix, the second computes + # the combination of the value vectors. + matmul_ops = 2 * b * (num_spatial**2) * c + model.total_ops += torch.DoubleTensor([matmul_ops]) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + a = torch.einsum("bts,bcs->bct", weight, v) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +class QKVAttention(nn.Module): + """ + A module which performs QKV attention and splits in a different order. + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv): + """ + Apply QKV attention. + :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.chunk(3, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", + (q * scale).view(bs * self.n_heads, ch, length), + (k * scale).view(bs * self.n_heads, ch, length), + ) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + a = torch.einsum( + "bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length) + ) + return a.reshape(bs, -1, length) + + @staticmethod + def count_flops(model, _x, y): + return count_flops_attn(model, _x, y) + + +@dataclass(eq=False) +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + in_channels: int + model_channels: int = 128 + out_channels: int = 3 + num_res_blocks: int = 2 + attention_resolutions: Tuple[int] = (1, 2, 2, 2) + dropout: float = 0.0 + channel_mult: Tuple[int] = (1, 2, 4, 8) + conv_resample: bool = True + dims: int = 2 + num_classes: Optional[int] = None + use_checkpoint: bool = False + num_heads: int = 1 + num_head_channels: int = -1 + num_heads_upsample: int = -1 + use_scale_shift_norm: bool = False + resblock_updown: bool = False + use_new_attention_order: bool = False + with_fourier_features: bool = False + ignore_time: bool = False + input_projection: bool = True + + image_size: int = -1 # not used... + _target_: str = "lib.models.gd_unet.UNetModel" + + def __post_init__(self): + super().__init__() + + if self.with_fourier_features: + self.in_channels += 12 + + if self.num_heads_upsample == -1: + self.num_heads_upsample = self.num_heads + + self.time_embed_dim = self.model_channels * 4 + if self.ignore_time: + self.time_embed = lambda x: torch.zeros( + x.shape[0], self.time_embed_dim, device=x.device, dtype=x.dtype + ) + else: + self.time_embed = nn.Sequential( + linear(self.model_channels, self.time_embed_dim), + nn.SiLU(), + linear(self.time_embed_dim, self.time_embed_dim), + ) + + if self.num_classes is not None: + self.label_emb = nn.Embedding( + self.num_classes + 1, self.time_embed_dim, padding_idx=self.num_classes + ) + + ch = input_ch = int(self.channel_mult[0] * self.model_channels) + if self.input_projection: + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(self.dims, self.in_channels, ch, 3, padding=1) + ) + ] + ) + else: + self.input_blocks = nn.ModuleList( + [TimestepEmbedSequential(torch.nn.Identity())] + ) + self._feature_size = ch + input_block_chans = [ch] + ds = 1 + for level, mult in enumerate(self.channel_mult): + for _ in range(self.num_res_blocks): + layers = [ + ResBlock( + ch, + self.time_embed_dim, + self.dropout, + out_channels=int(mult * self.model_channels), + dims=self.dims, + use_checkpoint=self.use_checkpoint, + use_scale_shift_norm=self.use_scale_shift_norm, + emb_off=self.ignore_time and self.num_classes is None, + ) + ] + ch = int(mult * self.model_channels) + if ds in self.attention_resolutions: + layers.append( + AttentionBlock( + ch, + use_checkpoint=self.use_checkpoint, + num_heads=self.num_heads, + num_head_channels=self.num_head_channels, + use_new_attention_order=self.use_new_attention_order, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(self.channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + self.time_embed_dim, + self.dropout, + out_channels=out_ch, + dims=self.dims, + use_checkpoint=self.use_checkpoint, + use_scale_shift_norm=self.use_scale_shift_norm, + down=True, + emb_off=self.ignore_time and self.num_classes is None, + ) + if self.resblock_updown + else Downsample( + ch, self.conv_resample, dims=self.dims, out_channels=out_ch + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + self.time_embed_dim, + self.dropout, + dims=self.dims, + use_checkpoint=self.use_checkpoint, + use_scale_shift_norm=self.use_scale_shift_norm, + emb_off=self.ignore_time and self.num_classes is None, + ), + AttentionBlock( + ch, + use_checkpoint=self.use_checkpoint, + num_heads=self.num_heads, + num_head_channels=self.num_head_channels, + use_new_attention_order=self.use_new_attention_order, + ), + ResBlock( + ch, + self.time_embed_dim, + self.dropout, + dims=self.dims, + use_checkpoint=self.use_checkpoint, + use_scale_shift_norm=self.use_scale_shift_norm, + emb_off=self.ignore_time and self.num_classes is None, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(self.channel_mult))[::-1]: + for i in range(self.num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + self.time_embed_dim, + self.dropout, + out_channels=int(self.model_channels * mult), + dims=self.dims, + use_checkpoint=self.use_checkpoint, + use_scale_shift_norm=self.use_scale_shift_norm, + emb_off=self.ignore_time and self.num_classes is None, + ) + ] + ch = int(self.model_channels * mult) + if ds in self.attention_resolutions: + layers.append( + AttentionBlock( + ch, + use_checkpoint=self.use_checkpoint, + num_heads=self.num_heads_upsample, + num_head_channels=self.num_head_channels, + use_new_attention_order=self.use_new_attention_order, + ) + ) + if level and i == self.num_res_blocks: + out_ch = ch + layers.append( + ResBlock( + ch, + self.time_embed_dim, + self.dropout, + out_channels=out_ch, + dims=self.dims, + use_checkpoint=self.use_checkpoint, + use_scale_shift_norm=self.use_scale_shift_norm, + up=True, + emb_off=self.ignore_time and self.num_classes is None, + ) + if self.resblock_updown + else Upsample( + ch, self.conv_resample, dims=self.dims, out_channels=out_ch + ) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(self.dims, input_ch, self.out_channels, 3, padding=1)), + ) + + def forward(self, x, timesteps, extra): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + if self.with_fourier_features: + z_f = base2_fourier_features(x, start=6, stop=8, step=1) + x = torch.cat([x, z_f], dim=1) + + hs = [] + emb = self.time_embed(timestep_embedding(timesteps, self.model_channels).to(x)) + + if self.ignore_time: + emb = emb * 0.0 + + if self.num_classes and "label" not in extra: + # Hack to deal with ddp find_unused_parameters not working with activation checkpointing... + # self.num_classes corresponds to the pad index of the embedding table + extra["label"] = torch.full( + (x.size(0),), self.num_classes, dtype=torch.long, device=x.device + ) + + if self.num_classes is not None and "label" in extra: + y = extra["label"] + assert ( + y.shape == x.shape[:1] + ), f"Labels have shape {y.shape}, which does not match the batch dimension of the input {x.shape}" + emb = emb + self.label_emb(y) + + h = x + if "concat_conditioning" in extra: + h = torch.cat([x, extra["concat_conditioning"]], dim=1) + + for module in self.input_blocks: + h = module(h, emb) + hs.append(h) + h = self.middle_block(h, emb) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb) + h = h.type(x.dtype) + result = self.out(h) + return result + + +# Based on https://github.com/google-research/vdm/blob/main/model_vdm.py +def base2_fourier_features( + inputs: torch.Tensor, start: int = 0, stop: int = 8, step: int = 1 +) -> torch.Tensor: + freqs = torch.arange(start, stop, step, device=inputs.device, dtype=inputs.dtype) + + # Create Base 2 Fourier features + w = 2.0**freqs * 2 * np.pi + w = torch.tile(w[None, :], (1, inputs.size(1))) + + # Compute features + h = torch.repeat_interleave(inputs, len(freqs), dim=1) + h = w[:, :, None, None] * h + h = torch.cat([torch.sin(h), torch.cos(h)], dim=1) + return h diff --git a/examples/image/requirements.txt b/examples/image/requirements.txt new file mode 100644 index 0000000..c559889 --- /dev/null +++ b/examples/image/requirements.txt @@ -0,0 +1,3 @@ +submitit +torchmetrics[image] +torchvision diff --git a/examples/image/submitit_train.py b/examples/image/submitit_train.py new file mode 100644 index 0000000..04cdfe8 --- /dev/null +++ b/examples/image/submitit_train.py @@ -0,0 +1,188 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# A script to run multinode training with submitit. +# -------------------------------------------------------- + +import argparse +import logging +import os +import sys +import uuid +from pathlib import Path + +import submitit +import train + +logger = logging.getLogger(__name__) + + +def parse_args(): + trainer_parser = train.get_args_parser() + parser = argparse.ArgumentParser( + "Submitit for flow_matching training", parents=[trainer_parser] + ) + parser.add_argument( + "--ngpus", default=8, type=int, help="Number of gpus to request on each node" + ) + parser.add_argument( + "--nodes", default=8, type=int, help="Number of nodes to request" + ) + parser.add_argument("--timeout", default=4320, type=int, help="Duration of the job") + parser.add_argument( + "--job_dir", default="", type=str, help="Job dir. Leave empty for automatic." + ) + parser.add_argument( + "--shared_dir", + default="/checkpoint", + type=str, + help="Directory shared among the nodes. A directory named USER/experiments is created under shared_dir that is used to coordinate in distributed mode.", + ) + + parser.add_argument( + "--partition", default="learnlab", type=str, help="Partition where to submit" + ) + parser.add_argument( + "--constraint", + default="", + type=str, + help="Slurm constraint eg.: ampere80gb For using A100s or volta32gb for using V100s.", + ) + parser.add_argument( + "--comment", default="", type=str, help="Comment to pass to scheduler" + ) + parser.add_argument("--qos", default="", type=str, help="Slurm QOS") + parser.add_argument("--account", default="", type=str, help="Slurm account") + parser.add_argument( + "--exclude", + default="", + type=str, + help="Exclude certain nodes from the slurm job.", + ) + return parser.parse_args() + + +def get_shared_folder(shared_dir: str) -> Path: + user = os.getenv("USER") + if Path(shared_dir).is_dir(): + p = Path(shared_dir) / user / "experiments" + p.mkdir(exist_ok=True) + return p + raise RuntimeError("No shared folder available") + + +def get_init_file(shared_dir: str): + # Init file must not exist, but it's parent dir must exist. + os.makedirs(str(get_shared_folder(shared_dir)), exist_ok=True) + init_file = get_shared_folder(shared_dir) / f"{uuid.uuid4().hex}_init" + if init_file.exists(): + os.remove(str(init_file)) + return init_file + + +class Trainer(object): + def __init__(self, args): + self.args = args + + def __call__(self): + import train + + self._setup_gpu_args() + train.main(self.args) + + def checkpoint(self): + import os + + import submitit + + self.args.dist_url = get_init_file(self.args.shared_dir).as_uri() + checkpoint_file = os.path.join(self.args.output_dir, "checkpoint.pth") + if os.path.exists(checkpoint_file) and not self.args.eval_only: + self.args.resume = checkpoint_file + logger.info("Requeuing ", self.args) + empty_trainer = type(self)(self.args) + return submitit.helpers.DelayedSubmission(empty_trainer) + + def _setup_gpu_args(self): + + import submitit + + job_env = submitit.JobEnvironment() + self.args.output_dir = str(self.args.output_dir).replace( + "%j", str(job_env.job_id) + ) + self.args.log_dir = self.args.output_dir + self.args.gpu = job_env.local_rank + self.args.rank = job_env.global_rank + self.args.world_size = job_env.num_tasks + logger.info( + f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}" + ) + + +def main(): + args = parse_args() + if args.job_dir == "": + args.job_dir = get_shared_folder(args.shared_dir) / "%j" + + # Note that the folder will depend on the job_id, to easily track experiments + executor = submitit.AutoExecutor(folder=args.job_dir, slurm_max_num_timeout=30) + + num_gpus_per_node = args.ngpus + nodes = args.nodes + timeout_min = args.timeout + + partition = args.partition + exclude = args.exclude + kwargs = {} + if len(args.constraint): + kwargs["slurm_constraint"] = args.constraint + if args.comment: + kwargs["slurm_comment"] = args.comment + if args.qos: + kwargs["slurm_qos"] = args.qos + if args.account: + kwargs["slurm_account"] = args.account + + executor.update_parameters( + mem_gb=40 * num_gpus_per_node, + gpus_per_node=num_gpus_per_node, + tasks_per_node=num_gpus_per_node, # one task per GPU + cpus_per_task=10, + nodes=nodes, + timeout_min=timeout_min, # max is 60 * 72 + # Below are cluster dependent parameters + slurm_partition=partition, + slurm_signal_delay_s=120, + slurm_exclude=exclude, + **kwargs, + ) + + executor.update_parameters(name="flow_matching") + + args.dist_url = get_init_file(args.shared_dir).as_uri() + args.output_dir = args.job_dir + + trainer = Trainer(args) + job = executor.submit(trainer) + + # print("Submitted job_id:", job.job_id) + logger.info(f"Submitted job {job.job_id}") + + +if __name__ == "__main__": + logging.basicConfig( + level=logging.INFO, + stream=sys.stdout, + format="%(asctime)s %(levelname)-8s %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + main() diff --git a/examples/image/train.py b/examples/image/train.py new file mode 100644 index 0000000..fb3d040 --- /dev/null +++ b/examples/image/train.py @@ -0,0 +1,224 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Copyright (c) Meta Platforms, Inc. and affiliates. + +import datetime +import json +import logging +import os +import sys +import time +from pathlib import Path + +import numpy as np +import torch +import torch.backends.cudnn as cudnn +import torchvision.datasets as datasets +from models.model_configs import instantiate_model +from train_arg_parser import get_args_parser + +from training import distributed_mode +from training.data_transform import get_train_transform +from training.eval_loop import eval_model +from training.grad_scaler import NativeScalerWithGradNormCount as NativeScaler +from training.load_and_save import load_model, save_model +from training.train_loop import train_one_epoch + +logger = logging.getLogger(__name__) + + +def main(args): + logging.basicConfig( + level=logging.INFO, + stream=sys.stdout, + format="%(asctime)s %(levelname)-8s %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + distributed_mode.init_distributed_mode(args) + + logger.info("job dir: {}".format(os.path.dirname(os.path.realpath(__file__)))) + logger.info("{}".format(args).replace(", ", ",\n")) + if distributed_mode.is_main_process(): + args_filepath = Path(args.output_dir) / "args.json" + logger.info(f"Saving args to {args_filepath}") + with open(args_filepath, "w") as f: + json.dump(vars(args), f) + + device = torch.device(args.device) + + # fix the seed for reproducibility + seed = args.seed + distributed_mode.get_rank() + torch.manual_seed(seed) + np.random.seed(seed) + + cudnn.benchmark = True + + logger.info(f"Initializing Dataset: {args.dataset}") + transform_train = get_train_transform() + if args.dataset == "imagenet": + dataset_train = datasets.ImageFolder(args.data_path, transform=transform_train) + elif args.dataset == "cifar10": + dataset_train = datasets.CIFAR10( + root=args.data_path, + train=True, + download=True, + transform=transform_train, + ) + else: + raise NotImplementedError(f"Unsupported dataset {args.dataset}") + + logger.info(dataset_train) + + logger.info("Intializing DataLoader") + num_tasks = distributed_mode.get_world_size() + global_rank = distributed_mode.get_rank() + sampler_train = torch.utils.data.DistributedSampler( + dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + data_loader_train = torch.utils.data.DataLoader( + dataset_train, + sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + logger.info(str(sampler_train)) + + # define the model + logger.info("Initializing Model") + model = instantiate_model( + architechture=args.dataset, + is_discrete=args.discrete_flow_matching, + use_ema=args.use_ema, + ) + + model.to(device) + + model_without_ddp = model + logger.info(str(model_without_ddp)) + + eff_batch_size = ( + args.batch_size * args.accum_iter * distributed_mode.get_world_size() + ) + + logger.info(f"Learning rate: {args.lr:.2e}") + + logger.info(f"Accumulate grad iterations: {args.accum_iter}") + logger.info(f"Effective batch size: {eff_batch_size}") + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[args.gpu], find_unused_parameters=True + ) + model_without_ddp = model.module + + optimizer = torch.optim.AdamW( + model_without_ddp.parameters(), lr=args.lr, betas=args.optimizer_betas + ) + if args.decay_lr: + lr_schedule = torch.optim.lr_scheduler.LinearLR( + optimizer, + total_iters=args.epochs, + start_factor=1.0, + end_factor=1e-8 / args.lr, + ) + else: + lr_schedule = torch.optim.lr_scheduler.ConstantLR( + optimizer, total_iters=args.epochs, factor=1.0 + ) + + logger.info(f"Optimizer: {optimizer}") + logger.info(f"Learning-Rate Schedule: {lr_schedule}") + + loss_scaler = NativeScaler() + + load_model( + args=args, + model_without_ddp=model_without_ddp, + optimizer=optimizer, + loss_scaler=loss_scaler, + lr_schedule=lr_schedule, + ) + + logger.info(f"Start from {args.start_epoch} to {args.epochs} epochs") + start_time = time.time() + for epoch in range(args.start_epoch, args.epochs): + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + if not args.eval_only: + train_stats = train_one_epoch( + model=model, + data_loader=data_loader_train, + optimizer=optimizer, + lr_schedule=lr_schedule, + device=device, + epoch=epoch, + loss_scaler=loss_scaler, + args=args, + ) + log_stats = { + **{f"train_{k}": v for k, v in train_stats.items()}, + "epoch": epoch, + } + else: + log_stats = { + "epoch": epoch, + } + + if args.output_dir and ( + (args.eval_frequency > 0 and (epoch + 1) % args.eval_frequency == 0) + or args.eval_only + or args.test_run + ): + if not args.eval_only: + save_model( + args=args, + model=model, + model_without_ddp=model_without_ddp, + optimizer=optimizer, + lr_schedule=lr_schedule, + loss_scaler=loss_scaler, + epoch=epoch, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(0) + if distributed_mode.is_main_process(): + fid_samples = args.fid_samples - (num_tasks - 1) * ( + args.fid_samples // num_tasks + ) + else: + fid_samples = args.fid_samples // num_tasks + eval_stats = eval_model( + model, + data_loader_train, + device, + epoch=epoch, + fid_samples=fid_samples, + args=args, + ) + log_stats.update({f"eval_{k}": v for k, v in eval_stats.items()}) + + if args.output_dir and distributed_mode.is_main_process(): + with open( + os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8" + ) as f: + f.write(json.dumps(log_stats) + "\n") + + if args.test_run or args.eval_only: + break + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + logger.info(f"Training time {total_time_str}") + + +if __name__ == "__main__": + args = get_args_parser() + args = args.parse_args() + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + main(args) diff --git a/examples/image/train_arg_parser.py b/examples/image/train_arg_parser.py new file mode 100644 index 0000000..ea2c567 --- /dev/null +++ b/examples/image/train_arg_parser.py @@ -0,0 +1,206 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import argparse +import json +import logging + +from models.model_configs import MODEL_CONFIGS +from torchdiffeq._impl.odeint import SOLVERS + +logger = logging.getLogger(__name__) + + +def get_args_parser(): + parser = argparse.ArgumentParser("Image dataset training", add_help=False) + parser.add_argument( + "--batch_size", + default=32, + type=int, + help="Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus", + ) + parser.add_argument("--epochs", default=921, type=int) + parser.add_argument( + "--accum_iter", + default=1, + type=int, + help="Accumulate gradient iterations (for increasing the effective batch size under memory constraints)", + ) + + # Optimizer parameters + parser.add_argument( + "--lr", + type=float, + default=0.0001, + help="learning rate (absolute lr)", + ) + parser.add_argument( + "--optimizer_betas", + nargs="+", + type=float, + default=[0.9, 0.95], + help="learning rate (absolute lr)", + ) + parser.add_argument( + "--decay_lr", + action="store_true", + help="Adds a linear decay to the lr during training.", + ) + parser.add_argument( + "--class_drop_prob", + type=float, + default=0.2, + help="Probability to drop conditioning during training", + ) + parser.add_argument( + "--skewed_timesteps", + action="store_true", + help="Use skewed timestep sampling proposed in the EDM paper: https://arxiv.org/abs/2206.00364.", + ) + parser.add_argument( + "--edm_schedule", + action="store_true", + help="Use the alternative time discretization during sampling proposed in the EDM paper: https://arxiv.org/abs/2206.00364.", + ) + parser.add_argument( + "--use_ema", + action="store_true", + help="When evaluating, use the model Exponential Moving Average weights.", + ) + + # Dataset parameters + parser.add_argument( + "--dataset", + default=list(MODEL_CONFIGS.keys())[0], + type=str, + choices=list(MODEL_CONFIGS.keys()), + help="Dataset to use.", + ) + parser.add_argument( + "--data_path", + default="./data/image_generation", + type=str, + help="imagenet root folder with train, val and test subfolders", + ) + + parser.add_argument( + "--output_dir", + default="./output_dir", + help="path where to save, empty for no saving", + ) + parser.add_argument( + "--ode_method", + default="midpoint", + choices=list(SOLVERS.keys()) + ["edm_heun"], + help="ODE solver used to generate samples.", + ) + parser.add_argument( + "--ode_options", + default='{"step_size": 0.01}', + type=json.loads, + help="ODE solver options. Eg. the midpoint solver requires step-size, dopri5 has no options to set.", + ) + parser.add_argument( + "--sym", + default=0.0, + type=float, + help="Symmetric term for sampling the discrete flow.", + ) + parser.add_argument( + "--temp", + default=1.0, + type=float, + help="Temperature for sampling the discrete flow.", + ) + parser.add_argument( + "--sym_func", + action="store_true", + help="Use a fixed function for the symmetric term in the discrete flow.", + ) + parser.add_argument( + "--sampling_dtype", + default="float32", + choices=["float32", "float64"], + help="Solver dtype for sampling the discrete flow.", + ) + parser.add_argument( + "--cfg_scale", + default=0.2, + type=float, + help="Classifier-free guidance scale for generating samples.", + ) + parser.add_argument( + "--fid_samples", + default=50000, + type=int, + help="number of synthetic samples for FID evaluations", + ) + parser.add_argument( + "--device", default="cuda", help="device to use for training / testing" + ) + parser.add_argument("--seed", default=0, type=int) + parser.add_argument("--resume", default="", help="resume from checkpoint") + + parser.add_argument( + "--start_epoch", + default=0, + type=int, + metavar="N", + help="start epoch (used when resumed from checkpoint)", + ) + parser.add_argument( + "--eval_only", action="store_true", help="No training, only run evaluation" + ) + parser.add_argument( + "--eval_frequency", + default=50, + type=int, + help="Frequency (in number of epochs) for running FID evaluation. -1 to never run evaluation.", + ) + parser.add_argument( + "--compute_fid", + action="store_true", + help="Whether to compute FID in the evaluation loop. When disabled, the evaluation loop still runs and saves snapshots, but skips the FID computation.", + ) + parser.add_argument( + "--save_fid_samples", + action="store_true", + help="Save all samples generated for FID computation.", + ) + parser.add_argument("--num_workers", default=10, type=int) + parser.add_argument( + "--pin_mem", + action="store_true", + help="Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.", + ) + parser.add_argument("--no_pin_mem", action="store_false", dest="pin_mem") + parser.set_defaults(pin_mem=True) + # distributed training parameters + parser.add_argument( + "--world_size", default=1, type=int, help="number of distributed processes" + ) + parser.add_argument("--local_rank", default=-1, type=int) + parser.add_argument("--dist_on_itp", action="store_true") + parser.add_argument( + "--dist_url", default="env://", help="url used to set up distributed training" + ) + parser.add_argument( + "--test_run", + action="store_true", + help="Only run one batch of training and evaluation.", + ) + parser.add_argument( + "--discrete_flow_matching", + action="store_true", + help="Train discrete flow matching model.", + ) + parser.add_argument( + "--discrete_fm_steps", + default=1024, + type=int, + help="Number of sampling steps for discrete FM.", + ) + + return parser diff --git a/examples/image/training/data_transform.py b/examples/image/training/data_transform.py new file mode 100644 index 0000000..7a7901e --- /dev/null +++ b/examples/image/training/data_transform.py @@ -0,0 +1,16 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import torch +from torchvision.transforms.v2 import Compose, RandomHorizontalFlip, ToDtype, ToImage + + +def get_train_transform(): + transform_list = [ + ToImage(), + RandomHorizontalFlip(), + ToDtype(torch.float32, scale=True), + ] + return Compose(transform_list) diff --git a/examples/image/training/distributed_mode.py b/examples/image/training/distributed_mode.py new file mode 100644 index 0000000..7b539a7 --- /dev/null +++ b/examples/image/training/distributed_mode.py @@ -0,0 +1,81 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import os +from datetime import timedelta + +import torch +import torch.distributed as dist + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def init_distributed_mode(args): + if args.dist_on_itp: + args.rank = int(os.environ["OMPI_COMM_WORLD_RANK"]) + args.world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"]) + args.gpu = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) + args.dist_url = "tcp://%s:%s" % ( + os.environ["MASTER_ADDR"], + os.environ["MASTER_PORT"], + ) + os.environ["LOCAL_RANK"] = str(args.gpu) + os.environ["RANK"] = str(args.rank) + os.environ["WORLD_SIZE"] = str(args.world_size) + # ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"] + elif "RANK" in os.environ and "WORLD_SIZE" in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + args.gpu = int(os.environ["LOCAL_RANK"]) + elif ( + "SLURM_PROCID" in os.environ and os.environ["SLURM_JOB_NAME"] != "bash" + ): # Exclude interactive shells + args.rank = int(os.environ["SLURM_PROCID"]) + args.gpu = args.rank % torch.cuda.device_count() + else: + print("Not using distributed mode") + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = "nccl" + print( + "| distributed init (rank {}): {}, gpu {}".format( + args.rank, args.dist_url, args.gpu + ), + flush=True, + ) + torch.distributed.init_process_group( + backend=args.dist_backend, + init_method=args.dist_url, + world_size=args.world_size, + rank=args.rank, + timeout=timedelta(hours=1), + ) + torch.distributed.barrier() diff --git a/examples/image/training/edm_time_discretization.py b/examples/image/training/edm_time_discretization.py new file mode 100644 index 0000000..9b93712 --- /dev/null +++ b/examples/image/training/edm_time_discretization.py @@ -0,0 +1,21 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +"""This is an ad-hoc sampling schedule that was proposed in https://arxiv.org/abs/2206.00364 it works very well for cifar 10 so we added its implementation here. It did not yield an improvement on ImageNet.""" +import torch + + +def get_time_discretization(nfes: int, rho=7): + step_indices = torch.arange(nfes, dtype=torch.float64) + sigma_min = 0.002 + sigma_max = 80.0 + sigma_vec = ( + sigma_max ** (1 / rho) + + step_indices / (nfes - 1) * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho)) + ) ** rho + sigma_vec = torch.cat([sigma_vec, torch.zeros_like(sigma_vec[:1])]) + time_vec = (sigma_vec / (1 + sigma_vec)).squeeze() + t_samples = 1.0 - torch.clip(time_vec, min=0.0, max=1.0) + return t_samples diff --git a/examples/image/training/eval_loop.py b/examples/image/training/eval_loop.py new file mode 100644 index 0000000..527b62b --- /dev/null +++ b/examples/image/training/eval_loop.py @@ -0,0 +1,221 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import gc +import logging +import os +from argparse import Namespace +from pathlib import Path +from typing import Iterable + +import PIL.Image + +import torch +from flow_matching.path import MixtureDiscreteProbPath +from flow_matching.path.scheduler import PolynomialConvexScheduler +from flow_matching.solver import MixtureDiscreteEulerSolver +from flow_matching.solver.ode_solver import ODESolver +from flow_matching.utils import ModelWrapper +from models.discrete_unet import DiscreteUNetModel +from models.ema import EMA +from torch.nn.modules import Module +from torch.nn.parallel import DistributedDataParallel +from torchmetrics.image.fid import FrechetInceptionDistance +from torchvision.utils import save_image +from training import distributed_mode +from training.edm_time_discretization import get_time_discretization +from training.train_loop import MASK_TOKEN + +logger = logging.getLogger(__name__) + +PRINT_FREQUENCY = 50 + + +class CFGScaledModel(ModelWrapper): + def __init__(self, model: Module): + super().__init__(model) + self.nfe_counter = 0 + + def forward( + self, x: torch.Tensor, t: torch.Tensor, cfg_scale: float, label: torch.Tensor + ): + module = ( + self.model.module + if isinstance(self.model, DistributedDataParallel) + else self.model + ) + is_discrete = isinstance(module, DiscreteUNetModel) or ( + isinstance(module, EMA) and isinstance(module.model, DiscreteUNetModel) + ) + assert ( + cfg_scale == 0.0 or not is_discrete + ), f"Cfg scaling does not work for the logit outputs of discrete models. Got cfg weight={cfg_scale} and model {type(self.model)}." + t = torch.zeros(x.shape[0], device=x.device) + t + + if cfg_scale != 0.0: + with torch.cuda.amp.autocast(), torch.no_grad(): + conditional = self.model(x, t, extra={"label": label}) + condition_free = self.model(x, t, extra={}) + result = (1.0 + cfg_scale) * conditional - cfg_scale * condition_free + else: + # Model is fully conditional, no cfg weighting needed + with torch.cuda.amp.autocast(), torch.no_grad(): + result = self.model(x, t, extra={"label": label}) + + self.nfe_counter += 1 + if is_discrete: + return torch.softmax(result.to(dtype=torch.float32), dim=-1) + else: + return result.to(dtype=torch.float32) + + def reset_nfe_counter(self) -> None: + self.nfe_counter = 0 + + def get_nfe(self) -> int: + return self.nfe_counter + + +def eval_model( + model: DistributedDataParallel, + data_loader: Iterable, + device: torch.device, + epoch: int, + fid_samples: int, + args: Namespace, +): + gc.collect() + cfg_scaled_model = CFGScaledModel(model=model) + cfg_scaled_model.train(False) + + if args.discrete_flow_matching: + scheduler = PolynomialConvexScheduler(n=3.0) + path = MixtureDiscreteProbPath(scheduler=scheduler) + p = torch.zeros(size=[257], dtype=torch.float32, device=device) + p[256] = 1.0 + solver = MixtureDiscreteEulerSolver( + model=cfg_scaled_model, path=path, vocabulary_size=257, p=p + ) + else: + solver = ODESolver(velocity_model=cfg_scaled_model) + ode_opts = args.ode_options + + fid_metric = FrechetInceptionDistance(normalize=True).to( + device=device, non_blocking=True + ) + + num_synthetic = 0 + snapshots_saved = False + if args.output_dir: + (Path(args.output_dir) / "snapshots").mkdir(parents=True, exist_ok=True) + + for data_iter_step, (samples, labels) in enumerate(data_loader): + samples = samples.to(device, non_blocking=True) + labels = labels.to(device, non_blocking=True) + fid_metric.update(samples, real=True) + + if num_synthetic < fid_samples: + cfg_scaled_model.reset_nfe_counter() + if args.discrete_flow_matching: + # Discrete sampling + x_0 = ( + torch.zeros(samples.shape, dtype=torch.long, device=device) + + MASK_TOKEN + ) + if args.sym_func: + sym = lambda t: 12.0 * torch.pow(t, 2.0) * torch.pow(1.0 - t, 0.25) + else: + sym = args.sym + if args.sampling_dtype == "float32": + dtype = torch.float32 + elif args.sampling_dtype == "float64": + dtype = torch.float64 + + synthetic_samples = solver.sample( + x_init=x_0, + step_size=1.0 / args.discrete_fm_steps, + verbose=False, + div_free=sym, + dtype_categorical=dtype, + label=labels, + cfg_scale=args.cfg_scale, + ) + else: + # Continuous sampling + x_0 = torch.randn(samples.shape, dtype=torch.float32, device=device) + + if args.edm_schedule: + time_grid = get_time_discretization(nfes=ode_opts["nfe"]) + else: + time_grid = torch.tensor([0.0, 1.0], device=device) + + synthetic_samples = solver.sample( + time_grid=time_grid, + x_init=x_0, + method=args.ode_method, + return_intermediates=False, + atol=ode_opts["atol"] if "atol" in ode_opts else 1e-5, + rtol=ode_opts["rtol"] if "atol" in ode_opts else 1e-5, + step_size=ode_opts["step_size"] + if "step_size" in ode_opts + else None, + label=labels, + cfg_scale=args.cfg_scale, + ) + + # Scaling to [0, 1] from [-1, 1] + synthetic_samples = torch.clamp( + synthetic_samples * 0.5 + 0.5, min=0.0, max=1.0 + ) + synthetic_samples = torch.floor(synthetic_samples * 255) + synthetic_samples = synthetic_samples.to(torch.float32) / 255.0 + logger.info( + f"{samples.shape[0]} samples generated in {cfg_scaled_model.get_nfe()} evaluations." + ) + if num_synthetic + synthetic_samples.shape[0] > fid_samples: + synthetic_samples = synthetic_samples[: fid_samples - num_synthetic] + fid_metric.update(synthetic_samples, real=False) + num_synthetic += synthetic_samples.shape[0] + if not snapshots_saved and args.output_dir: + save_image( + synthetic_samples, + fp=Path(args.output_dir) + / "snapshots" + / f"{epoch}_{data_iter_step}.png", + ) + snapshots_saved = True + + if args.save_fid_samples and args.output_dir: + images_np = ( + (synthetic_samples * 255.0) + .clip(0, 255) + .to(torch.uint8) + .permute(0, 2, 3, 1) + .cpu() + .numpy() + ) + for batch_index, image_np in enumerate(images_np): + image_dir = Path(args.output_dir) / "fid_samples" + os.makedirs(image_dir, exist_ok=True) + image_path = ( + image_dir + / f"{distributed_mode.get_rank()}_{data_iter_step}_{batch_index}.png" + ) + PIL.Image.fromarray(image_np, "RGB").save(image_path) + + if not args.compute_fid: + return {} + + if data_iter_step % PRINT_FREQUENCY == 0: + # Sync fid metric to ensure that the processes dont deviate much. + gc.collect() + running_fid = fid_metric.compute() + logger.info( + f"Evaluating [{data_iter_step}/{len(data_loader)}] samples generated [{num_synthetic}/{fid_samples}] running fid {running_fid}" + ) + + if args.test_run: + break + + return {"fid": float(fid_metric.compute().detach().cpu())} diff --git a/examples/image/training/grad_scaler.py b/examples/image/training/grad_scaler.py new file mode 100644 index 0000000..9303df6 --- /dev/null +++ b/examples/image/training/grad_scaler.py @@ -0,0 +1,67 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import torch + +from torch import Tensor + + +def get_grad_norm_(parameters, norm_type: float = 2.0) -> Tensor: + if isinstance(parameters, Tensor): + parameters = [parameters] + parameters = [p for p in parameters if p.grad is not None] + norm_type = float(norm_type) + if len(parameters) == 0: + return Tensor(0.0) + device = parameters[0].grad.device + if norm_type == torch.inf: + total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters) + else: + total_norm = torch.norm( + torch.stack( + [torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters] + ), + norm_type, + ) + return total_norm + + +class NativeScalerWithGradNormCount: + state_dict_key = "amp_scaler" + + def __init__(self): + self._scaler = torch.cuda.amp.GradScaler() + + def __call__( + self, + loss, + optimizer, + clip_grad=None, + parameters=None, + create_graph=False, + update_grad=True, + ): + self._scaler.scale(loss).backward(create_graph=create_graph) + if update_grad: + if clip_grad is not None: + assert parameters is not None + self._scaler.unscale_( + optimizer + ) # unscale the gradients of optimizer's assigned params in-place + norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad) + else: + self._scaler.unscale_(optimizer) + norm = get_grad_norm_(parameters) + self._scaler.step(optimizer) + self._scaler.update() + else: + norm = None + return norm + + def state_dict(self): + return self._scaler.state_dict() + + def load_state_dict(self, state_dict): + self._scaler.load_state_dict(state_dict) diff --git a/examples/image/training/load_and_save.py b/examples/image/training/load_and_save.py new file mode 100644 index 0000000..b038e30 --- /dev/null +++ b/examples/image/training/load_and_save.py @@ -0,0 +1,67 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +from pathlib import Path + +import torch +from training.distributed_mode import is_main_process + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def save_model( + args, epoch, model, model_without_ddp, optimizer, lr_schedule, loss_scaler +): + output_dir = Path(args.output_dir) + epoch_name = str(epoch) + if loss_scaler is not None: + checkpoint_paths = [ + output_dir / ("checkpoint-%s.pth" % epoch_name), + output_dir / "checkpoint.pth", + ] + for checkpoint_path in checkpoint_paths: + to_save = { + "model": model_without_ddp.state_dict(), + "optimizer": optimizer.state_dict(), + "lr_schedule": lr_schedule.state_dict(), + "epoch": epoch, + "scaler": loss_scaler.state_dict(), + "args": args, + } + + save_on_master(to_save, checkpoint_path) + else: + client_state = {"epoch": epoch} + model.save_checkpoint( + save_dir=args.output_dir, + tag="checkpoint-%s" % epoch_name, + client_state=client_state, + ) + + +def load_model(args, model_without_ddp, optimizer, loss_scaler, lr_schedule): + if args.resume: + if args.resume.startswith("https"): + checkpoint = torch.hub.load_state_dict_from_url( + args.resume, map_location="cpu", check_hash=True + ) + else: + checkpoint = torch.load(args.resume, map_location="cpu") + model_without_ddp.load_state_dict(checkpoint["model"]) + print("Resume checkpoint %s" % args.resume) + if ( + "optimizer" in checkpoint + and "epoch" in checkpoint + and not (hasattr(args, "eval") and args.eval) + ): + optimizer.load_state_dict(checkpoint["optimizer"]) + lr_schedule.load_state_dict(checkpoint["lr_schedule"]) + args.start_epoch = checkpoint["epoch"] + 1 + if "scaler" in checkpoint: + loss_scaler.load_state_dict(checkpoint["scaler"]) + print("With optim & sched!") diff --git a/examples/image/training/train_loop.py b/examples/image/training/train_loop.py new file mode 100644 index 0000000..93597ce --- /dev/null +++ b/examples/image/training/train_loop.py @@ -0,0 +1,137 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import argparse +import gc +import logging +import math +from typing import Iterable + +import torch +from flow_matching.path import CondOTProbPath, MixtureDiscreteProbPath +from flow_matching.path.scheduler import PolynomialConvexScheduler +from models.ema import EMA +from torch.nn.parallel import DistributedDataParallel +from torchmetrics.aggregation import MeanMetric +from training.grad_scaler import NativeScalerWithGradNormCount + +logger = logging.getLogger(__name__) + +MASK_TOKEN = 256 +PRINT_FREQUENCY = 50 + + +def skewed_timestep_sample(num_samples: int, device: torch.device) -> torch.Tensor: + P_mean = -1.2 + P_std = 1.2 + rnd_normal = torch.randn((num_samples,), device=device) + sigma = (rnd_normal * P_std + P_mean).exp() + time = 1 / (1 + sigma) + time = torch.clip(time, min=0.0001, max=1.0) + return time + + +def train_one_epoch( + model: torch.nn.Module, + data_loader: Iterable, + optimizer: torch.optim.Optimizer, + lr_schedule: torch.torch.optim.lr_scheduler.LRScheduler, + device: torch.device, + epoch: int, + loss_scaler: NativeScalerWithGradNormCount, + args: argparse.Namespace, +): + gc.collect() + model.train(True) + batch_loss = MeanMetric().to(device, non_blocking=True) + epoch_loss = MeanMetric().to(device, non_blocking=True) + + accum_iter = args.accum_iter + if args.discrete_flow_matching: + scheduler = PolynomialConvexScheduler(n=3.0) + path = MixtureDiscreteProbPath(scheduler=scheduler) + else: + path = CondOTProbPath() + + for data_iter_step, (samples, labels) in enumerate(data_loader): + if data_iter_step % accum_iter == 0: + optimizer.zero_grad() + batch_loss.reset() + if data_iter_step > 0 and args.test_run: + break + + samples = samples.to(device, non_blocking=True) + labels = labels.to(device, non_blocking=True) + + if torch.rand(1) < args.class_drop_prob: + conditioning = {} + else: + conditioning = {"label": labels} + + if args.discrete_flow_matching: + samples = (samples * 255.0).to(torch.long) + t = torch.torch.rand(samples.shape[0]).to(device) + + # sample probability path + x_0 = ( + torch.zeros(samples.shape, dtype=torch.long, device=device) + MASK_TOKEN + ) + path_sample = path.sample(t=t, x_0=x_0, x_1=samples) + + # discrete flow matching loss + logits = model(path_sample.x_t, t=t, extra=conditioning) + loss = torch.nn.functional.cross_entropy( + logits.reshape([-1, 257]), samples.reshape([-1]) + ).mean() + else: + # Scaling to [-1, 1] from [0, 1] + samples = samples * 2.0 - 1.0 + noise = torch.randn_like(samples).to(device) + if args.skewed_timesteps: + t = skewed_timestep_sample(samples.shape[0], device=device) + else: + t = torch.torch.rand(samples.shape[0]).to(device) + path_sample = path.sample(t=t, x_0=noise, x_1=samples) + x_t = path_sample.x_t + u_t = path_sample.dx_t + + with torch.cuda.amp.autocast(): + loss = torch.pow(model(x_t, t, extra=conditioning) - u_t, 2).mean() + + loss_value = loss.item() + batch_loss.update(loss) + epoch_loss.update(loss) + + if not math.isfinite(loss_value): + raise ValueError(f"Loss is {loss_value}, stopping training") + + loss /= accum_iter + + # Loss scaler applies the optimizer when update_grad is set to true. + # Otherwise just updates the internal gradient scales + apply_update = (data_iter_step + 1) % accum_iter == 0 + loss_scaler( + loss, + optimizer, + parameters=model.parameters(), + update_grad=apply_update, + ) + if apply_update and isinstance(model, EMA): + model.update_ema() + elif ( + apply_update + and isinstance(model, DistributedDataParallel) + and isinstance(model.module, EMA) + ): + model.module.update_ema() + + lr = optimizer.param_groups[0]["lr"] + if data_iter_step % PRINT_FREQUENCY == 0: + logger.info( + f"Epoch {epoch} [{data_iter_step}/{len(data_loader)}]: loss = {batch_loss.compute()}, lr = {lr}" + ) + + lr_schedule.step() + return {"loss": float(epoch_loss.compute().detach().cpu())} diff --git a/examples/standalone_discrete_flow_matching.ipynb b/examples/standalone_discrete_flow_matching.ipynb new file mode 100644 index 0000000..1a4848c --- /dev/null +++ b/examples/standalone_discrete_flow_matching.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "rb5VSo4mNkVd" + }, + "outputs": [], + "source": [ + "import torch\n", + "import matplotlib.pyplot as plt\n", + "from torch import nn, Tensor\n", + "from sklearn.datasets import make_moons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class DiscreteFlow(nn.Module):\n", + " def __init__(self, dim: int = 2, h: int = 128, v: int = 128):\n", + " super().__init__()\n", + " self.v = v\n", + " self.embed = nn.Embedding(v, h)\n", + " self.net = nn.Sequential(\n", + " nn.Linear(dim * h + 1, h), nn.ELU(),\n", + " nn.Linear(h, h), nn.ELU(),\n", + " nn.Linear(h, h), nn.ELU(),\n", + " nn.Linear(h, dim * v))\n", + " \n", + " def forward(self, x_t: Tensor, t: Tensor) -> Tensor:\n", + " return self.net(torch.cat((t[:, None], self.embed(x_t).flatten(1, 2)), -1)).reshape(list(x_t.shape) + [self.v])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 256\n", + "vocab_size = 128\n", + "\n", + "model = DiscreteFlow(v=vocab_size)\n", + "optim = torch.optim.Adam(model.parameters(), lr=0.001) \n", + "\n", + "for _ in range(10000):\n", + " x_1 = Tensor(make_moons(batch_size, noise=0.05)[0])\n", + " x_1 = torch.round(torch.clip(x_1 * 35 + 50, min=0.0, max=vocab_size - 1)).long()\n", + " \n", + " x_0 = torch.randint(low=0, high=vocab_size, size=(batch_size, 2))\n", + "\n", + " t = torch.rand(batch_size)\n", + " x_t = torch.where(torch.rand(batch_size, 2) < t[:, None], x_1, x_0)\n", + "\n", + " logits = model(x_t, t)\n", + " loss = nn.functional.cross_entropy(logits.flatten(0, 1), x_1.flatten(0, 1)).mean()\n", + " optim.zero_grad()\n", + " loss.backward()\n", + " optim.step()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sampling" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_t = torch.randint(low=0, high=vocab_size, size=(200, 2))\n", + "t = 0.0\n", + "results = [(x_t, t)]\n", + "while t < 1.0 - 1e-3:\n", + " p1 = torch.softmax(model(x_t, torch.ones(200) * t), dim=-1)\n", + " h = min(0.1, 1.0 - t)\n", + " one_hot_x_t = nn.functional.one_hot(x_t, vocab_size).float()\n", + " u = (p1 - one_hot_x_t) / (1.0 - t)\n", + " x_t = torch.distributions.Categorical(probs=one_hot_x_t + h * u).sample()\n", + " t += h\n", + " results.append((x_t, t))\n", + "\n", + "fig, axes = plt.subplots(1, len(results), figsize=(15, 2), sharex=True, sharey=True)\n", + "\n", + "for (x_t, t), ax in zip(results, axes):\n", + " ax.scatter(x_t.detach()[:, 0], x_t.detach()[:, 1], s=10)\n", + " ax.set_title(f't={t:.1f}')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "g8QtNgs1-PlE", + "wW3VMmrK2t2d", + "_7aH8D0H3IJT" + ], + "name": "scalable_CNF.ipynb", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/standalone_flow_matching.ipynb b/examples/standalone_flow_matching.ipynb new file mode 100644 index 0000000..3cd7a04 --- /dev/null +++ b/examples/standalone_flow_matching.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch \n", + "from torch import nn, Tensor\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import make_moons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class Flow(nn.Module):\n", + " def __init__(self, dim: int = 2, h: int = 64):\n", + " super().__init__()\n", + " self.net = nn.Sequential(\n", + " nn.Linear(dim + 1, h), nn.ELU(),\n", + " nn.Linear(h, h), nn.ELU(),\n", + " nn.Linear(h, h), nn.ELU(),\n", + " nn.Linear(h, dim))\n", + " \n", + " def forward(self, t: Tensor, x_t: Tensor) -> Tensor:\n", + " return self.net(torch.cat((t, x_t), -1))\n", + " \n", + " def step(self, x_t: Tensor, t_start: Tensor, t_end: Tensor) -> Tensor:\n", + " t_start = t_start.view(1, 1).expand(x_t.shape[0], 1)\n", + " \n", + " return x_t + (t_end - t_start) * self(t=t_start + (t_end - t_start) / 2, x_t= x_t + self(x_t=x_t, t=t_start) * (t_end - t_start) / 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "flow = Flow()\n", + "\n", + "optimizer = torch.optim.Adam(flow.parameters(), 1e-2)\n", + "loss_fn = nn.MSELoss()\n", + "\n", + "for _ in range(10000):\n", + " x_1 = Tensor(make_moons(256, noise=0.05)[0])\n", + " x_0 = torch.randn_like(x_1)\n", + " t = torch.rand(len(x_1), 1)\n", + " \n", + " x_t = (1 - t) * x_0 + t * x_1\n", + " dx_t = x_1 - x_0\n", + " \n", + " optimizer.zero_grad()\n", + " loss_fn(flow(t=t, x_t=x_t), dx_t).backward()\n", + " optimizer.step()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sampling" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = torch.randn(300, 2)\n", + "n_steps = 8\n", + "fig, axes = plt.subplots(1, n_steps + 1, figsize=(30, 4), sharex=True, sharey=True)\n", + "time_steps = torch.linspace(0, 1.0, n_steps + 1)\n", + "\n", + "axes[0].scatter(x.detach()[:, 0], x.detach()[:, 1], s=10)\n", + "axes[0].set_title(f't = {time_steps[0]:.2f}')\n", + "axes[0].set_xlim(-3.0, 3.0)\n", + "axes[0].set_ylim(-3.0, 3.0)\n", + "\n", + "for i in range(n_steps):\n", + " x = flow.step(x_t=x, t_start=time_steps[i], t_end=time_steps[i + 1])\n", + " axes[i + 1].scatter(x.detach()[:, 0], x.detach()[:, 1], s=10)\n", + " axes[i + 1].set_title(f't = {time_steps[i + 1]:.2f}')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/text/README.md b/examples/text/README.md new file mode 100644 index 0000000..242dbfe --- /dev/null +++ b/examples/text/README.md @@ -0,0 +1,122 @@ +# Text example + +This example implements training of a discrete flow matching model on text data. This repository provides the necessary tools and scripts to train and evaluate these models. + +**Note:** this example was tested only using PyTorch 2.5 and on a single node of H100 (8 gpus). With this setup, we achieved approximately 380k training steps in 24 hours. + +## Installation + +To get started with this project, follow these steps to set up your environment: + +```bash +conda env create -f environment.yml +conda activate discrete_flow_matching +``` + +## Usage + +To train a discrete flow matching model on fine-web-edu, run: + +```bash +CACHE_DIR=... + +python run_train.py data.cache_dir=${CACHE_DIR} +``` + +To use `slurm`, modify the `slurm` config according to the cluster you are working on, and run: +```bash +CACHE_DIR=... +HYDRA_RUN_DIR=... + +python run_train.py data.cache_dir=${CACHE_DIR} hydra_dir=${HYDRA_RUN_DIR} -m & +``` + +## Results + +We trained models with linear scheduler (`PolynomialConvexScheduler(n=1.0)`) for one million steps on FineWeb-EDU. + +```bash +PYTHONPATH="." python scripts/run_eval.py --work_dir "/path/to/exp/folder" --ngpus 8 --eval_elbo --eval_perplexity +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SchedulerSource distributionLossGenerative perplexityELBO
LinearMaskCross-entropy
128.9
53.2
Generalized KL
132.2
47.9
UniformCross-entropy
90.9
71.7
Generalized KL
82.1
71.3
+ +## Folder structure + +```bash +. +├── configs # Train configs +│   └── ... +├── data # Data loading and preprocessing +│   └── ... +├── logic # Logic components, such as flow related classes +│   └── ... +├── model # Transformer implementation +│   └── ... +├── scripts # Evaluation script +│   └── ... +├── utils # Utility functions +│ └── ... +├── README.md +├── environment.yml +├── train.py +└── run_train.py # Run training script +``` + +## Implemented methods + +This repository implements the following papers: +- [Discrete Flow Matching](https://arxiv.org/abs/2407.15595) +- [Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective](https://arxiv.org/abs/2412.03487) +- [Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design](https://arxiv.org/abs/2402.04997) +- [Simplified and Generalized Masked Diffusion for Discrete Data](https://arxiv.org/abs/2406.04329) + + +## Acknowledgements + +This example partially use code from: +- [Flash attention](https://github.com/Dao-AILab/flash-attention) +- [Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution](https://github.com/louaaron/Score-Entropy-Discrete-Diffusion) +- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://github.com/openai/glide-text2im/) +- [TorchData](https://github.com/pytorch/data/tree/main) + +## License + +The majority of the code in this example is licensed under CC-BY-NC, however portions of the project are available under separate license terms: +- flash attention and TorchData are under BSD 3 license. +- Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution and GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models are under MIT license. \ No newline at end of file diff --git a/examples/text/configs/config.yaml b/examples/text/configs/config.yaml new file mode 100644 index 0000000..a6d1c82 --- /dev/null +++ b/examples/text/configs/config.yaml @@ -0,0 +1,83 @@ +defaults: + - _self_ + - override hydra/launcher: submitit_slurm + +compute: + ngpus: 8 + nodes: 1 + +logging: + log_freq: 100 + log_lr_every: ${logging.log_freq} + log_file_name: stdout.log + enable_wandb: True + entity: flows + project: flow_matching + group: null + +data: + train: fineweb-edu + valid: wikitext103 + cache_dir: /path/to/cache/dir + num_workers: 8 + +training: + batch_size: 512 + snapshot: 2000 + eval_freq: 20000 + perplexity_freq: 20000 + seed: 42 + +eval: + batch_size: 512 + sample_batch_size: 16 + perplexity: True + perplexity_batch_size: 16 + +optim: + weight_decay: 0.03 + optimizer: AdamW + lr: 3e-4 + beta1: 0.9 + beta2: 0.95 + eps: 1e-8 + warmup: 2500 + grad_clip: 1. + eta_min_ratio: 0.1 + fused: false + n_iters: 1000000 + log_lr_every: ${logging.log_lr_every} + +flow: + source_distribution: uniform # [uniform, mask] + loss_function: cross_entropy # [cross_entropy, generalized_kl] + exponent: 1. + scheduler_type: polynomial + sampling_steps: 1024 + +model: + hidden_size: 768 + cond_dim: 128 + length: 1024 + n_blocks: 12 + n_heads: 12 + dropout: 0.1 + compile: true + +hydra_dir: /path/to/hydra/dir + +hydra: + run: + dir: ${hydra_dir}/${now:%Y.%m.%d}/${now:%H%M%S} + sweep: + dir: ${hydra_dir}/${now:%Y.%m.%d}/${now:%H%M%S} + subdir: ${hydra.job.num} + launcher: + max_num_timeout: 100000 + timeout_min: 4320 + partition: learn + qos: # TODO: change it to your own qos + gpus_per_node: ${compute.ngpus} + mem_gb: 1760 + cpus_per_task: 32 + nodes: ${compute.nodes} diff --git a/examples/text/data/__init__.py b/examples/text/data/__init__.py new file mode 100644 index 0000000..033cc9e --- /dev/null +++ b/examples/text/data/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .data import DataState + +__all__ = ["DataState"] diff --git a/examples/text/data/data.py b/examples/text/data/data.py new file mode 100644 index 0000000..9315334 --- /dev/null +++ b/examples/text/data/data.py @@ -0,0 +1,197 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Part of this implementation is adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion +# which is released under MIT license + +from dataclasses import dataclass, field +from itertools import chain +from typing import Dict, Iterable, Tuple + +from datasets import DatasetDict, load_dataset +from omegaconf import OmegaConf + +from torch.utils.data import DataLoader +from transformers import GPT2TokenizerFast + +from data.tokenizer import wt_detokenizer +from data.utils import cycle_loader, StatefulDistributedSampler + + +def _get_hf_dataset( + name: str, + mode: str, + cache_dir: str = None, + block_size: int = 1024, + num_proc: int = 8, +) -> DatasetDict: + detokenizer = None + + if name == "wikitext103": + data = load_dataset( + "wikitext", name="wikitext-103-raw-v1", cache_dir=cache_dir + )[mode] + detokenizer = wt_detokenizer + elif name == "fineweb-edu": + data = load_dataset( + "HuggingFaceFW/fineweb-edu", name="CC-MAIN-2024-10", cache_dir=cache_dir + )[mode] + else: + data = load_dataset(name, cache_dir=cache_dir)[mode] + + def _apply_detokenizer(detokenizer): + def detok(text): + for i, t in enumerate(text, 0): + text[i] = detokenizer(t) + return text + + return detok + + tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") + EOS = tokenizer.encode(tokenizer.eos_token)[0] + + def preprocess_and_tokenize(example: Dict): + text = example["text"] + + if detokenizer is not None: + text = _apply_detokenizer(detokenizer)(text) + + tokens = tokenizer(text, return_attention_mask=False) + # add in EOS token following + # https://github.com/jcpeterson/openwebtext/blob/master/tokenize_text.py#L67 + for token in tokens["input_ids"]: + token.append(EOS) + + return tokens + + tokenized_dataset = data.map( + preprocess_and_tokenize, + batched=True, + num_proc=num_proc, + load_from_cache_file=True, + ) + + if name == "fineweb-edu": + features = tokenized_dataset.features.keys() + for k in features: + if k != "input_ids": + tokenized_dataset = tokenized_dataset.remove_columns(k) + else: + tokenized_dataset = tokenized_dataset.remove_columns("text") + + def group_texts(examples: Dict): + # Concatenate all texts. + concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} + total_length = len(concatenated_examples[list(examples.keys())[0]]) + # We drop the small remainder, and if the total_length < block_size we exclude this batch and return an empty dict. + # We could add padding if the model supported it instead of this drop, you can customize this part to your needs. + total_length = (total_length // block_size) * block_size + # Split by chunks of max_len. + result = { + k: [t[i : i + block_size] for i in range(0, total_length, block_size)] + for k, t in concatenated_examples.items() + } + + return result + + chunked_dataset = tokenized_dataset.map( + group_texts, batched=True, num_proc=num_proc, load_from_cache_file=True + ) + chunked_dataset = chunked_dataset.with_format("torch") + + return chunked_dataset + + +@dataclass +class Dataset: + dataset: DatasetDict = field(metadata={"help": "Huggingface dataset"}) + sampler: StatefulDistributedSampler = field( + metadata={"help": "Stateful sampler for `dataset`"} + ) + + +@dataclass +class DataState: + train: Dataset = field(metadata={"help": "Train dataset"}) + test: Dataset = field(metadata={"help": "Test dataset"}) + + +def _get_dataset( + name: str, + mode: str, + cache_dir: str, + block_size: int, + num_proc: int, + batch_size: int, + ngpus: int, +) -> Dataset: + assert ( + batch_size % ngpus == 0 + ), f"{mode} batch size must be divisible by number of gpus." + + dataset = _get_hf_dataset( + name=name, + mode=mode, + cache_dir=cache_dir, + block_size=block_size, + num_proc=num_proc, + ) + + sampler = StatefulDistributedSampler(dataset=dataset) + + return Dataset(dataset=dataset, sampler=sampler) + + +def get_data_state(config: OmegaConf) -> DataState: + train = _get_dataset( + name=config.data.train, + mode="train", + cache_dir=config.data.cache_dir, + block_size=config.model.length, + num_proc=config.data.num_workers, + batch_size=config.training.batch_size, + ngpus=config.compute.ngpus, + ) + test = _get_dataset( + name=config.data.valid, + mode="validation", + cache_dir=config.data.cache_dir, + block_size=config.model.length, + num_proc=config.data.num_workers, + batch_size=config.eval.batch_size, + ngpus=config.compute.ngpus, + ) + + return DataState(train=train, test=test) + + +def get_data_loaders( + config: OmegaConf, + data_state: DataState, +) -> Tuple[Iterable, Iterable]: + train_loader = cycle_loader( + DataLoader( + data_state.train.dataset, + batch_size=config.training.batch_size // config.compute.ngpus, + sampler=data_state.train.sampler, + num_workers=config.data.num_workers, + pin_memory=True, + shuffle=(data_state.train.sampler is None), + persistent_workers=True, + ) + ) + + valid_loader = cycle_loader( + DataLoader( + data_state.test.dataset, + batch_size=config.eval.batch_size // config.compute.ngpus, + sampler=data_state.test.sampler, + num_workers=config.data.num_workers, + pin_memory=True, + shuffle=(data_state.test.sampler is None), + ) + ) + + return iter(train_loader), iter(valid_loader) diff --git a/examples/text/data/tokenizer.py b/examples/text/data/tokenizer.py new file mode 100644 index 0000000..48b456e --- /dev/null +++ b/examples/text/data/tokenizer.py @@ -0,0 +1,42 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# This implementation is adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion +# which is released under MIT license + +import re + + +def wt_detokenizer(string): + # contractions + string = string.replace("s '", "s'") + string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string) + # number separators + string = string.replace(" @-@ ", "-") + string = string.replace(" @,@ ", ",") + string = string.replace(" @.@ ", ".") + # punctuation + string = string.replace(" : ", ": ") + string = string.replace(" ; ", "; ") + string = string.replace(" . ", ". ") + string = string.replace(" ! ", "! ") + string = string.replace(" ? ", "? ") + string = string.replace(" , ", ", ") + # double brackets + string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string) + string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string) + string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string) + string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string) + string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string) + # miscellaneous + string = string.replace("= = = =", "====") + string = string.replace("= = =", "===") + string = string.replace("= =", "==") + string = string.replace(" " + chr(176) + " ", chr(176)) + string = string.replace(" \n", "\n") + string = string.replace("\n ", "\n") + string = string.replace(" N ", " 1 ") + string = string.replace(" 's", "'s") + return string diff --git a/examples/text/data/utils.py b/examples/text/data/utils.py new file mode 100644 index 0000000..2c51aa9 --- /dev/null +++ b/examples/text/data/utils.py @@ -0,0 +1,64 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# This implementation is adapted from https://github.com/pytorch/data/blob/main/torchdata/stateful_dataloader/sampler.py#L132 +# which is released under BSD-3 license + +import itertools +from typing import Any, Dict, Optional + +import numpy as np +import torch +from torch import Tensor +from torch.utils.data import DataLoader, Dataset, Sampler + + +def cycle_loader(dataloader: DataLoader, sampler: Sampler = None) -> Tensor: + while 1: + if sampler is not None: + sampler.set_epoch(np.random.randint(0, 100000)) + for data in dataloader: + yield data + + +class StatefulDistributedSampler(torch.utils.data.distributed.DistributedSampler): + """ + From: https://github.com/pytorch/data/blob/main/torchdata/stateful_dataloader/sampler.py#L132 + """ + + _YIELDED = "yielded" + + def __init__( + self, + dataset: Dataset, + num_replicas: Optional[int] = None, + rank: Optional[int] = None, + shuffle: bool = True, + seed: int = 0, + drop_last: bool = False, + ) -> None: + super().__init__(dataset, num_replicas, rank, shuffle, seed, drop_last) + self.yielded = 0 + self.next_yielded = None + + def __iter__(self): + self.yielded = 0 + if self.next_yielded is not None: + self.yielded = self.next_yielded + self.next_yielded = None + it = super().__iter__() + for idx in itertools.islice(it, self.yielded, None): + self.yielded += 1 + yield idx + + def state_dict(self) -> Dict[str, Any]: + return {self._YIELDED: self.yielded} + + def load_state_dict(self, state_dict: Dict[str, Any]) -> None: + if self._YIELDED not in state_dict: + raise ValueError("Invalid state_dict") + if state_dict[self._YIELDED] < 0: + raise ValueError("Cannot load state_dict with negative yielded value") + self.next_yielded = state_dict[self._YIELDED] diff --git a/examples/text/environment.yml b/examples/text/environment.yml new file mode 100644 index 0000000..e01af9b --- /dev/null +++ b/examples/text/environment.yml @@ -0,0 +1,19 @@ +name: discrete_flow_matching +channels: + - pytorch + - conda-forge + - nvidia +dependencies: + - python=3.10 + - numpy + - pip + - tqdm + - pip: + - torch>=2.5.0 + - hydra-core + - hydra-submitit-launcher + - datasets + - transformers + - wandb + - einops + - flow_matching \ No newline at end of file diff --git a/examples/text/logic/__init__.py b/examples/text/logic/__init__.py new file mode 100644 index 0000000..36d7195 --- /dev/null +++ b/examples/text/logic/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. diff --git a/examples/text/logic/evaluate.py b/examples/text/logic/evaluate.py new file mode 100644 index 0000000..a164cf2 --- /dev/null +++ b/examples/text/logic/evaluate.py @@ -0,0 +1,124 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Part of this implementation is adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion +# which is released under MIT license + +import math +from collections import Counter +from typing import List + +import torch +import torch.nn.functional as F +from flow_matching.loss import MixturePathGeneralizedKL +from flow_matching.path import MixtureDiscreteProbPath, ProbPath +from flow_matching.path.scheduler import PolynomialConvexScheduler +from flow_matching.utils import ModelWrapper +from torch import nn, Tensor +from torch.utils.data import DataLoader +from tqdm import tqdm +from transformers import GPT2LMHeadModel + +from logic.flow import SourceDistribution + + +class WrappedModel(ModelWrapper): + def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor: + return self.model(x_t=x, time=t).float() + + +@torch.no_grad() +def compute_perplexity(samples: Tensor, perplexity_batch_size: int) -> Tensor: + eval_model = GPT2LMHeadModel.from_pretrained("gpt2-large").to(samples.device).eval() + batches = samples.shape[0] // perplexity_batch_size + total_perplexity = 0 + + for i in range(batches): + s = samples[i * perplexity_batch_size : (i + 1) * perplexity_batch_size] + _, logits = eval_model(s, labels=s)[:2] + logits = logits.transpose(-1, -2).detach() + + perplexity = F.cross_entropy(logits[..., :-1], s[..., 1:], reduction="none") + perplexity = perplexity.mean(dim=-1).exp().mean() + + total_perplexity += perplexity + + total_perplexity /= batches + + return total_perplexity + + +def _sample_entropy(sample: List) -> float: + histogram = Counter(sample) + total = sum(histogram.values()) + entropy = 0 + + for count in histogram.values(): + p = count / total + entropy -= p * math.log2(p) + + return entropy + + +def compute_entropy(samples: Tensor) -> Tensor: + entropies = [_sample_entropy(sample.tolist()) for sample in samples] + entropy = sum(entropies) / len(entropies) + + return torch.tensor(entropy, device=samples.device) + + +@torch.no_grad() +def estimate_likelihood( + model: nn.Module, + dataloader: DataLoader, + source_distribution: SourceDistribution, + path: ProbPath, + n_discretization: int, + device: torch.device, + batch_size: int = 32, + epsilon: float = 1e-3, +) -> Tensor: + model = WrappedModel(model) + + # Generalized KL function (will use it to compute the elbo) + linear_scheduler = PolynomialConvexScheduler(n=1.0) + linear_path = MixtureDiscreteProbPath(scheduler=linear_scheduler) + + generalized_kl_fn = MixturePathGeneralizedKL(path=linear_path, reduction="none") + + # Time discretization + discretization = ( + torch.linspace(0, 1, n_discretization + 1, device=device)[:-1] + .view(-1, 1) + .repeat(1, batch_size) + ) + + elbo = torch.zeros((1,), device=device) + n_elements = torch.zeros((1,), device=device) + + for x_1 in tqdm(dataloader, total=len(dataloader)): + x_1 = x_1["input_ids"].to(device) + + # Lower variance estimator for time discretization + discretization = discretization + torch.rand( + size=(1, batch_size), device=device + ) + discretization = discretization % 1 + discretization = discretization * (1 - epsilon) + + for k in discretization[:, : x_1.shape[0]]: + x_0 = source_distribution.sample_like(x_1) + x_t = linear_path.sample(t=k, x_0=x_0, x_1=x_1).x_t + + t = path.scheduler.kappa_inverse(k) + + logits = model(x=x_t, t=t) + + generalized_kl = generalized_kl_fn(logits=logits, x_1=x_1, x_t=x_t, t=k) + n_elements += generalized_kl.numel() + + elbo += generalized_kl.sum() + + return elbo, n_elements diff --git a/examples/text/logic/flow.py b/examples/text/logic/flow.py new file mode 100644 index 0000000..3e707a7 --- /dev/null +++ b/examples/text/logic/flow.py @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC +from typing import Optional, Tuple + +import torch +from flow_matching.loss import MixturePathGeneralizedKL +from flow_matching.path import MixtureDiscreteProbPath, ProbPath +from flow_matching.path.scheduler import PolynomialConvexScheduler +from torch import Tensor +from torch.nn.modules.loss import _Loss + + +class SourceDistribution(ABC): + def __init__( + self, + ) -> None: + ... + + def sample(self, tensor_size: Tuple[int, ...], device: torch.device) -> Tensor: + ... + + def sample_like(self, tensor_like: Tensor) -> Tensor: + ... + + +class MaskedSourceDistribution(SourceDistribution): + def __init__(self, mask_token: int) -> None: + self.mask_token = mask_token + + @property + def masked(self) -> bool: + return True + + def sample(self, tensor_size: Tuple[int, ...], device: torch.device) -> Tensor: + return torch.zeros(tensor_size, device=device).fill_(self.mask_token).long() + + def sample_like(self, tensor_like: Tensor) -> Tensor: + return torch.zeros_like(tensor_like).fill_(self.mask_token).long() + + +class UniformSourceDistribution(SourceDistribution): + def __init__(self, vocab_size: int) -> None: + self.vocab_size = vocab_size + + @property + def masked(self) -> bool: + return False + + def sample(self, tensor_size: Tuple[int, ...], device: torch.device) -> Tensor: + return torch.randint(size=tensor_size, high=self.vocab_size, device=device) + + def sample_like(self, tensor_like: Tensor) -> Tensor: + return torch.randint_like(tensor_like, high=self.vocab_size) + + +def get_path(scheduler_type: str, exponent: Optional[float] = None) -> ProbPath: + if scheduler_type == "polynomial": + scheduler = PolynomialConvexScheduler(n=exponent) + else: + raise ValueError(f"{scheduler_type} is not supported") + + return MixtureDiscreteProbPath(scheduler=scheduler) + + +def get_source_distribution( + source_distribution: str, vocab_size: int +) -> SourceDistribution: + if source_distribution == "mask": + return MaskedSourceDistribution(mask_token=vocab_size) + elif source_distribution == "uniform": + return UniformSourceDistribution(vocab_size=vocab_size) + else: + raise ValueError(f"{source_distribution} is not supported") + + +def get_loss_function(loss_function: str, path: Optional[ProbPath] = None) -> _Loss: + if loss_function == "cross_entropy": + return torch.nn.CrossEntropyLoss() + elif loss_function == "generalized_kl": + assert path is not None + + return MixturePathGeneralizedKL(path=path) + else: + raise ValueError(f"{loss_function} is not supported") diff --git a/examples/text/logic/generate.py b/examples/text/logic/generate.py new file mode 100644 index 0000000..64fe474 --- /dev/null +++ b/examples/text/logic/generate.py @@ -0,0 +1,73 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from pathlib import Path +from typing import Optional + +import torch +from flow_matching.path import ProbPath +from flow_matching.solver import MixtureDiscreteEulerSolver +from flow_matching.utils import ModelWrapper +from torch import nn, Tensor +from transformers.tokenization_utils import PreTrainedTokenizer + +from .flow import SourceDistribution + + +class WrappedModel(ModelWrapper): + def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor: + # Note: logit's precision is important. + return torch.softmax(self.model(x_t=x, time=t).float(), -1) + + +def generate_samples( + model: nn.Module, + step: int, + vocab_size: int, + tokenizer: PreTrainedTokenizer, + rank: int, + device: torch.device, + path: ProbPath, + source_distribution: SourceDistribution, + sample_batch_size: int, + sequence_length: int, + sampling_steps: int, + time_epsilon: float = 0.0, + sample_dir: Optional[Path] = None, + dtype_categorical: torch.dtype = torch.float64, +) -> Tensor: + wrapped_probability_denoiser = WrappedModel(model=model) + + add_token = 1 if source_distribution.masked else 0 + solver = MixtureDiscreteEulerSolver( + model=wrapped_probability_denoiser, + path=path, + vocabulary_size=vocab_size + add_token, + ) + + x_init = source_distribution.sample( + tensor_size=(sample_batch_size, sequence_length), device=device + ) + + sample = solver.sample( + x_init=x_init, + step_size=1 / sampling_steps, + verbose=True, + dtype_categorical=dtype_categorical, + time_grid=torch.tensor([0.0, 1.0 - time_epsilon]), + ) + + sentences = tokenizer.batch_decode(sample) + + if sample_dir is not None: + file_name = sample_dir / f"iter_{step}" / f"sample_{rank}.txt" + file_name.parents[0].mkdir(exist_ok=True, parents=True) + + with open(file_name, "w") as file: + for sentence in sentences: + file.write(f"{sentence}\n{'=' * 20} New sample {'=' * 20}\n") + + return sample diff --git a/examples/text/logic/state.py b/examples/text/logic/state.py new file mode 100644 index 0000000..742c298 --- /dev/null +++ b/examples/text/logic/state.py @@ -0,0 +1,90 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from pathlib import Path + +import torch +from data import DataState + +from torch import nn +from torch.optim import Optimizer + + +class TrainState: + def __init__( + self, + model: nn.Module, + optimizer: Optimizer, + step: int, + data_state: DataState, + ): + self._model = model + self._optimizer = optimizer + self._step = step + self._data_state = data_state + + @property + def step(self) -> int: + return self._step + + @step.setter + def step(self, value: int) -> None: + self._step = value + + @property + def optimizer(self) -> Optimizer: + return self._optimizer + + @property + def model(self) -> nn.Module: + return self._model + + @property + def data_state(self) -> DataState: + return self._data_state + + def compile_model(self) -> None: + self._model = torch.compile(self._model) + + def restore_checkpoint( + self, ckpt_dir: Path, device: torch.device, rank: int + ) -> None: + if ckpt_dir.exists(): + loaded_state = torch.load(ckpt_dir, map_location=device, weights_only=True) + + self.optimizer.load_state_dict(loaded_state["optimizer"]) + self.model.module.load_state_dict(loaded_state["model"]) + self.step = loaded_state["step"] + self._data_state.test.load_state_dict(loaded_state["test_sampler"]) + self._data_state.train.sampler.load_state_dict( + loaded_state["train_sampler"] + ) + else: + ckpt_dir.parent.mkdir(exist_ok=True, parents=True) + + if rank == 0: + logging.warning( + f"No checkpoint found at {ckpt_dir}. Returned the same state as input" + ) + + def save_checkpoint(self, ckpt_dir: str, rank: int) -> None: + saved_state = { + "optimizer": self.optimizer.state_dict(), + "model": self.model.module.state_dict(), + "step": self.step, + "train_sampler": self._data_state.train.sampler.state_dict(), + "test_sampler": self._data_state.test.sampler.state_dict(), + } + + if rank == 0: + torch.save(saved_state, ckpt_dir) + + def eval(self) -> None: + self.train(training=False) + + def train(self, training: bool = True) -> None: + self._model.train(mode=training) diff --git a/examples/text/logic/training.py b/examples/text/logic/training.py new file mode 100644 index 0000000..13d7f7d --- /dev/null +++ b/examples/text/logic/training.py @@ -0,0 +1,128 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import math +from contextlib import nullcontext +from typing import Optional + +import torch +from flow_matching.loss import MixturePathGeneralizedKL +from flow_matching.path import ProbPath +from omegaconf.dictconfig import DictConfig +from torch import nn, Tensor +from torch.cuda.amp import GradScaler + +from torch.utils.data import DataLoader +from utils.logging import TrainLogger + +from .flow import SourceDistribution +from .state import TrainState + + +def _get_lr(lr: float, step: int, warmup: int, n_iters: int, eta_min_ratio: float): + if step < warmup: + # Linear warmup + return lr * (step / warmup) + else: + # Cosine annealing + total_steps = n_iters + eta_min = eta_min_ratio * lr + cosine_decay = 0.5 * ( + 1 + math.cos(math.pi * (step - warmup) / (total_steps - warmup)) + ) + return eta_min + (lr - eta_min) * cosine_decay + + +def optimization_step( + state: TrainState, + scaler: GradScaler, + loss: Tensor, + optim_params: DictConfig, + logger: TrainLogger, +) -> None: + scaler.scale(loss).backward() + scaler.unscale_(state.optimizer) + + lr = _get_lr( + lr=optim_params.lr, + step=state.step, + warmup=optim_params.warmup, + n_iters=optim_params.n_iters, + eta_min_ratio=optim_params.eta_min_ratio, + ) + + # Update learning rate in optimizer + for g in state.optimizer.param_groups: + g["lr"] = lr + + if state.step % optim_params.log_lr_every == 0: + logger.log_lr(value=lr, step=state.step) + + if optim_params.grad_clip >= 0: + torch.nn.utils.clip_grad_norm_( + state.model.parameters(), max_norm=optim_params.grad_clip + ) + + scaler.step(state.optimizer) + scaler.update() + + state.optimizer.zero_grad() + + +def step( + state: TrainState, + loss_fn: nn.Module, + path: ProbPath, + scaler: GradScaler, + iterator: DataLoader, + device: torch.device, + source_distribution: SourceDistribution, + logger: TrainLogger, + training: bool, + optim_params: Optional[DictConfig] = None, + time_epsilon: float = 0.0, +) -> Tensor: + assert (training and (optim_params is not None)) or (not training) + + if training: + state.train() + else: + state.eval() + + x_1 = next(iterator)["input_ids"].to(device) + + # Sample from path + with torch.no_grad(): + x_0 = source_distribution.sample_like(x_1) + t = torch.rand(x_1.shape[0], device=x_1.device) * (1.0 - time_epsilon) + path_sample = path.sample(t=t, x_0=x_0, x_1=x_1) + + # Forward and compute loss + ctx = nullcontext() if training else torch.no_grad() + + with ctx: + logits = state.model(x_t=path_sample.x_t, time=path_sample.t) + + if isinstance(loss_fn, nn.CrossEntropyLoss): + loss = loss_fn(logits.flatten(0, 1), x_1.flatten(0, 1)).mean() + elif isinstance(loss_fn, MixturePathGeneralizedKL): + loss = loss_fn( + logits=logits, x_1=x_1, x_t=path_sample.x_t, t=path_sample.t + ).mean() + else: + raise ValueError("Invalid loss function") + + # Optimization step (only if training=true) + if training: + optimization_step( + state=state, + loss=loss, + scaler=scaler, + optim_params=optim_params, + logger=logger, + ) + + return loss.detach() diff --git a/examples/text/model/__init__.py b/examples/text/model/__init__.py new file mode 100644 index 0000000..2ad1255 --- /dev/null +++ b/examples/text/model/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .transformer import Transformer + +__all__ = [ + "Transformer", +] diff --git a/examples/text/model/rotary.py b/examples/text/model/rotary.py new file mode 100644 index 0000000..6d7f7ba --- /dev/null +++ b/examples/text/model/rotary.py @@ -0,0 +1,72 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Part of this implementation is adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py#L20 +# which is released under BSD-3 license +# Part of this implementation is adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion +# which is released under MIT license + +from typing import Tuple + +import torch +from einops import repeat +from torch import Tensor + + +class Rotary(torch.nn.Module): + """ + From: https://github.com/louaaron/Score-Entropy-Discrete-Diffusion + """ + + def __init__(self, dim: int, base: int = 10_000): + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq) + self.seq_len_cached = None + self.cos_cached = None + self.sin_cached = None + + def forward(self, x: Tensor, seq_dim: int = 1) -> Tuple[Tensor, Tensor]: + seq_len = x.shape[seq_dim] + if seq_len != self.seq_len_cached: + self.seq_len_cached = seq_len + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + freqs = torch.einsum("i,j->ij", t, self.inv_freq.clone()) + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + + # dims are: batch, seq_len, qkv, head, dim + self.cos_cached = emb.cos()[None, :, None, None, :].repeat(1, 1, 3, 1, 1) + self.sin_cached = emb.sin()[None, :, None, None, :].repeat(1, 1, 3, 1, 1) + + # This makes the transformation on v an identity. + self.cos_cached[:, :, 2, :, :].fill_(1.0) + self.sin_cached[:, :, 2, :, :].fill_(0.0) + + return self.cos_cached, self.sin_cached + + +def rotate_half(x: Tensor) -> Tensor: + x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] + + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_emb_torch(x, cos, sin, interleaved=False): + """ + From: https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py#L20 + """ + cos = cos[0, :, 0, 0, : cos.shape[-1] // 2] + sin = sin[0, :, 0, 0, : sin.shape[-1] // 2] + + ro_dim = cos.shape[-1] * 2 + assert ro_dim <= x.shape[-1] + cos = repeat( + cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)" + ) + sin = repeat( + sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)" + ) + + return x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim]) * sin diff --git a/examples/text/model/transformer.py b/examples/text/model/transformer.py new file mode 100644 index 0000000..f1ed6ba --- /dev/null +++ b/examples/text/model/transformer.py @@ -0,0 +1,257 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Part of this implementation is adapted from https://github.com/facebookresearch/DiT +# which is released under NonCommercial-4.0 license +# Part of this implementation is adapted from https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py +# which is released under MIT license +# Part of this implementation is adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion +# which is released under MIT license + +import math +from typing import Optional + +import torch +import torch.nn.functional as F + +from einops import rearrange +from omegaconf import OmegaConf +from omegaconf.dictconfig import DictConfig + +from torch import nn, Tensor + +from . import rotary + + +def bias_dropout_add_scale( + x: Tensor, scale: Tensor, residual: Optional[Tensor], prob: float, training: bool +) -> Tensor: + return residual + scale * F.dropout(x, p=prob, training=training) + + +def modulate(x: Tensor, shift: Tensor, scale: Tensor) -> Tensor: + return x * (1 + scale) + shift + + +class LayerNorm(nn.Module): + def __init__(self, dim: int): + super().__init__() + self.weight = nn.Parameter(torch.ones([dim])) + self.dim = dim + + def forward(self, x: Tensor) -> Tensor: + with torch.amp.autocast("cuda", enabled=False): + x = F.layer_norm(x.float(), [self.dim]) + + return x * self.weight[None, None, :] + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size: int, frequency_embedding_size: int = 256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(time: Tensor, dim: int, max_period: int = 10000) -> Tensor: + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=time.device) + args = time[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + def forward(self, time: Tensor) -> Tensor: + t_freq = self.timestep_embedding(time=time, dim=self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + +class DDiTBlock(nn.Module): + def __init__( + self, + dim: int, + n_heads: int, + cond_dim: int, + mlp_ratio: int = 4, + dropout: float = 0.1, + ): + super().__init__() + assert dim % n_heads == 0, "dim must be devisable by n_heads" + + self.n_heads = n_heads + self.dim = dim + self.dropout = dropout + + self.head_dim = self.dim // self.n_heads + + self.norm1 = LayerNorm(dim=dim) + + self.qw = nn.Linear(dim, dim, bias=False) + self.kw = nn.Linear(dim, dim, bias=False) + self.vw = nn.Linear(dim, dim, bias=False) + + self.attn_out = nn.Linear(dim, dim, bias=False) + self.dropout1 = nn.Dropout(dropout) + + self.norm2 = LayerNorm(dim=dim) + self.mlp = nn.Sequential( + nn.Linear(dim, mlp_ratio * dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_ratio * dim, dim, bias=True), + ) + + self.adaLN_modulation = nn.Linear(cond_dim, 6 * dim, bias=True) + self.adaLN_modulation.weight.data.zero_() + self.adaLN_modulation.bias.data.zero_() + + def forward(self, x: Tensor, rotary_cos_sin: Tensor, c: Tensor) -> Tensor: + batch_size, seq_len = x.shape[0], x.shape[1] + + ( + shift_msa, + scale_msa, + gate_msa, + shift_mlp, + scale_mlp, + gate_mlp, + ) = self.adaLN_modulation(c)[:, None].chunk(6, dim=2) + + x_skip = x + x = modulate(x=self.norm1(x), shift=shift_msa, scale=scale_msa) + + q = self.qw(x) + k = self.kw(x) + v = self.vw(x) + + q, k, v = ( + item.view(batch_size, seq_len, self.n_heads, self.head_dim) + for item in (q, k, v) + ) + + with torch.amp.autocast("cuda", enabled=False): + cos, sin = rotary_cos_sin + original_dtype = q.dtype + + q = rotary.apply_rotary_emb_torch( + x=q.float(), cos=cos.float(), sin=sin.float() + ).to(original_dtype) + k = rotary.apply_rotary_emb_torch( + x=k.float(), cos=cos.float(), sin=sin.float() + ).to(original_dtype) + + q, k, v = (item.transpose(1, 2) for item in (q, k, v)) + + x = F.scaled_dot_product_attention(query=q, key=k, value=v) + x = rearrange(x, "b h s d -> b s (h d)", b=batch_size) + x = bias_dropout_add_scale( + x=self.attn_out(x), + scale=gate_msa, + residual=x_skip, + prob=self.dropout, + training=self.training, + ) + x = bias_dropout_add_scale( + x=self.mlp(modulate(x=self.norm2(x), shift=shift_mlp, scale=scale_mlp)), + scale=gate_mlp, + residual=x, + prob=self.dropout, + training=self.training, + ) + + return x + + +class DDitFinalLayer(nn.Module): + def __init__(self, hidden_size: int, out_channels: int, cond_dim: int): + super().__init__() + self.norm_final = LayerNorm(hidden_size) + self.linear = nn.Linear(hidden_size, out_channels) + self.linear.weight.data.zero_() + self.linear.bias.data.zero_() + + self.adaLN_modulation = nn.Linear(cond_dim, 2 * hidden_size, bias=True) + self.adaLN_modulation.weight.data.zero_() + self.adaLN_modulation.bias.data.zero_() + + def forward(self, x: Tensor, c: Tensor) -> Tensor: + shift, scale = self.adaLN_modulation(c)[:, None].chunk(2, dim=2) + x = modulate(x=self.norm_final(x), shift=shift, scale=scale) + x = self.linear(x) + + return x + + +class Transformer(nn.Module): + def __init__(self, vocab_size: int, masked: bool, config: DictConfig): + super().__init__() + + if isinstance(config, dict): + config = OmegaConf.create(config) + + self.config = config + self.vocab_size = vocab_size + + add_token = 1 if masked else 0 + + self.vocab_embed = nn.Embedding(self.vocab_size + add_token, config.hidden_size) + + self.time_embedding = TimestepEmbedder(hidden_size=config.cond_dim) + self.rotary_emb = rotary.Rotary(dim=config.hidden_size // config.n_heads) + + self.blocks = nn.ModuleList( + [ + DDiTBlock( + dim=config.hidden_size, + n_heads=config.n_heads, + cond_dim=config.cond_dim, + dropout=config.dropout, + ) + for _ in range(config.n_blocks) + ] + ) + + self.output_layer = DDitFinalLayer( + hidden_size=config.hidden_size, + out_channels=vocab_size + add_token, + cond_dim=config.cond_dim, + ) + + def forward(self, x_t: Tensor, time: Tensor) -> Tensor: + x = self.vocab_embed(x_t) + c = F.silu(self.time_embedding(time=time)) + + rotary_cos_sin = self.rotary_emb(x=x) + + with torch.amp.autocast("cuda", dtype=torch.bfloat16): + for i in range(len(self.blocks)): + x = self.blocks[i](x=x, rotary_cos_sin=rotary_cos_sin, c=c) + + x = self.output_layer(x=x, c=c) + + return x diff --git a/examples/text/run_train.py b/examples/text/run_train.py new file mode 100644 index 0000000..cdec1a3 --- /dev/null +++ b/examples/text/run_train.py @@ -0,0 +1,55 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Part of this implementation is adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion +# which is released under MIT license + +import os + +import hydra +import torch.multiprocessing as mp + +from hydra.core.hydra_config import HydraConfig +from hydra.types import RunMode +from omegaconf import open_dict +from omegaconf.dictconfig import DictConfig +from train import run_mp_training + +from utils import checkpointing + + +@hydra.main(version_base=None, config_path="configs", config_name="config") +def main(cfg: DictConfig): + if "load_dir" in cfg: + work_dir = cfg.load_dir + cfg = checkpointing.load_hydra_config_from_run(cfg.load_dir) + else: + hydra_cfg = HydraConfig.get() + work_dir = ( + hydra_cfg.run.dir + if hydra_cfg.mode == RunMode.RUN + else os.path.join(hydra_cfg.sweep.dir, hydra_cfg.sweep.subdir) + ) + os.makedirs(work_dir, exist_ok=True) + + with open_dict(cfg): + cfg.work_dir = work_dir + + port = 12346 + + if cfg.compute.ngpus == 1: + run_mp_training(rank=0, world_size=1, cfg=cfg, port=port) + else: + mp.set_start_method("forkserver") + mp.spawn( + run_mp_training, + args=(cfg.compute.ngpus, cfg, port), + nprocs=cfg.compute.ngpus, + join=True, + ) + + +if __name__ == "__main__": + main() diff --git a/examples/text/scripts/eval.py b/examples/text/scripts/eval.py new file mode 100644 index 0000000..31cf32a --- /dev/null +++ b/examples/text/scripts/eval.py @@ -0,0 +1,194 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import datetime +import os + +import torch +import torch.distributed as dist + +from data import data +from flow_matching.loss import MixturePathGeneralizedKL + +from logic import evaluate, flow, generate + +from torch.utils.data import DataLoader +from transformers import GPT2TokenizerFast +from utils import checkpointing + + +def run_eval( + rank: int, + seed: int, + work_dir: str, + batch_size: int, + perplexity_n_samples: int, + sampling_steps: int, + eval_perplexity: bool, + eval_elbo: bool, + elbo_data: str, + world_size: int, + n_discretization: float = 1024, +) -> None: + torch.manual_seed(seed + rank) + + # Logging and configuration + work_dirs = checkpointing.get_work_dirs(work_dir=work_dir, rank=rank) + + device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu") + + cfg = checkpointing.load_cfg_from_path(work_dir=work_dirs.checkpoint) + + # Data + tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") + vocab_size = tokenizer.vocab_size + + # Flow matching + path = flow.get_path( + scheduler_type=cfg.flow.scheduler_type, exponent=cfg.flow.exponent + ) + loss_fn = flow.get_loss_function(loss_function=cfg.flow.loss_function, path=path) + # Elbo may have singularity at 1 + time_epsilon = 1e-3 if isinstance(loss_fn, MixturePathGeneralizedKL) else 0.0 + + source_distribution = flow.get_source_distribution( + source_distribution=cfg.flow.source_distribution, vocab_size=vocab_size + ) + + model = checkpointing.load_model_from_path( + work_dir=work_dirs.checkpoint, + device=device, + source_distribution=source_distribution, + cfg=cfg.model, + vocab_size=vocab_size, + ) + model.eval() + + if cfg.model.compile: + model = torch.compile(model) + torch.set_float32_matmul_precision("high") + + if eval_perplexity: + assert perplexity_n_samples // batch_size > 0 + + samples = [] + + for _ in range(perplexity_n_samples // batch_size): + samples.append( + generate.generate_samples( + model=model, + step=0, + sample_dir=work_dirs.samples, + vocab_size=vocab_size, + tokenizer=tokenizer, + rank=rank, + device=device, + path=path, + source_distribution=source_distribution, + sample_batch_size=batch_size, + sequence_length=cfg.model.length, + sampling_steps=sampling_steps, + time_epsilon=time_epsilon, + ) + ) + + dist.barrier() + + samples = torch.cat(samples, dim=0) + + perplexity = evaluate.compute_perplexity( + samples=samples, + perplexity_batch_size=cfg.eval.perplexity_batch_size, + ) + dist.all_reduce(perplexity, dist.ReduceOp.AVG) + + entropy = evaluate.compute_entropy(samples=samples) + dist.all_reduce(entropy, dist.ReduceOp.AVG) + + if rank == 0: + print(f"Perplexity: {perplexity:.2f}, Entropy: {entropy:.2f}") + + if eval_elbo: + data_state = data._get_dataset( + name=elbo_data, + mode="validation", + cache_dir=cfg.data.cache_dir, + block_size=cfg.model.length, + num_proc=cfg.data.num_workers, + batch_size=batch_size, + ngpus=world_size, + ) + + dataloader = DataLoader( + data_state.dataset, + batch_size=batch_size, + sampler=data_state.sampler, + num_workers=cfg.data.num_workers, + pin_memory=True, + shuffle=(data_state.sampler is None), + ) + + elbo, num_elements = evaluate.estimate_likelihood( + model=model, + dataloader=dataloader, + source_distribution=source_distribution, + n_discretization=n_discretization, + device=device, + batch_size=batch_size, + path=path, + ) + dist.barrier() + + dist.all_reduce(elbo, dist.ReduceOp.SUM) + dist.all_reduce(num_elements, dist.ReduceOp.SUM) + + if rank == 0: + print(f"ELBO: {torch.exp(elbo / num_elements).item():.2f}") + + +def setup(rank: int, world_size: int, port: int) -> None: + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(port) + + torch.cuda.set_device(rank) + + timeout = datetime.timedelta(minutes=30) + dist.init_process_group("nccl", rank=rank, world_size=world_size, timeout=timeout) + + +def cleanup() -> None: + dist.destroy_process_group() + + +def run_mp_eval( + rank: int, + world_size: int, + seed: int, + work_dir: str, + batch_size: int, + sampling_steps: int, + eval_elbo: bool, + eval_perplexity: bool, + elbo_data: str, + perplexity_n_samples: int, + port: int, +) -> None: + try: + setup(rank=rank, world_size=world_size, port=port) + run_eval( + rank=rank, + seed=seed, + work_dir=work_dir, + batch_size=batch_size, + sampling_steps=sampling_steps, + eval_elbo=eval_elbo, + eval_perplexity=eval_perplexity, + elbo_data=elbo_data, + world_size=world_size, + perplexity_n_samples=perplexity_n_samples, + ) + finally: + cleanup() diff --git a/examples/text/scripts/run_eval.py b/examples/text/scripts/run_eval.py new file mode 100644 index 0000000..c7d9066 --- /dev/null +++ b/examples/text/scripts/run_eval.py @@ -0,0 +1,78 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Part of this implementation is adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion +# which is released under MIT license + +import argparse + +import torch.multiprocessing as mp + +from eval import run_mp_eval + + +def main(args: argparse.Namespace): + port = 12346 + + assert args.perplexity_n_samples % args.ngpus == 0 + assert args.batch_size % args.ngpus == 0 + + if args.ngpus == 1: + run_mp_eval( + rank=0, + world_size=1, + seed=args.seed, + work_dir=args.work_dir, + batch_size=args.batch_size // args.ngpus, + sampling_steps=args.sampling_steps, + eval_elbo=args.eval_elbo, + eval_perplexity=args.eval_perplexity, + elbo_data=args.elbo_data, + perplexity_n_samples=args.perplexity_n_samples // args.ngpus, + port=port, + ) + else: + mp.set_start_method("forkserver") + + mp.spawn( + run_mp_eval, + args=( + args.ngpus, + args.seed, + args.work_dir, + args.batch_size // args.ngpus, + args.sampling_steps, + args.eval_elbo, + args.eval_perplexity, + args.elbo_data, + args.perplexity_n_samples // args.ngpus, + port, + ), + nprocs=args.ngpus, + join=True, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--work_dir", type=str, required=True) + + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--batch_size", type=int, default=256) + parser.add_argument("--ngpus", type=int, default=8) + + parser.add_argument("--eval_elbo", action="store_true") + parser.add_argument("--eval_perplexity", action="store_true") + + # Perplexity parameters + parser.add_argument("--sampling_steps", type=int, default=1024) + parser.add_argument("--perplexity_n_samples", type=int, default=1024) + + # ELBO parameters + parser.add_argument("--elbo_data", type=str, default="wikitext103") + + args = parser.parse_args() + main(args) diff --git a/examples/text/train.py b/examples/text/train.py new file mode 100644 index 0000000..7faaff7 --- /dev/null +++ b/examples/text/train.py @@ -0,0 +1,221 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import datetime +import os + +import torch +import torch.distributed as dist +from data import data +from flow_matching.loss import MixturePathGeneralizedKL + +from logic import evaluate, flow, generate, training +from logic.state import TrainState +from model import Transformer +from omegaconf import OmegaConf +from torch import optim +from torch.nn.parallel import DistributedDataParallel as DDP +from transformers import GPT2TokenizerFast +from utils import checkpointing, logging + + +def run_train(rank: int, cfg: OmegaConf) -> None: + torch.manual_seed(cfg.training.seed + rank) + + # Logging and configuration + work_dirs = checkpointing.get_work_dirs(work_dir=cfg.work_dir, rank=rank) + + logger = logging.TrainLogger(log_dir=work_dirs.root, rank=rank, cfg=cfg) + logger.info(work_dirs) + logger.info(cfg) + + device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu") + logger.log_devices(device=device, logger=logger) + + # Data + tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") + vocab_size = tokenizer.vocab_size + + source_distribution = flow.get_source_distribution( + source_distribution=cfg.flow.source_distribution, vocab_size=vocab_size + ) + + # Model initialization + model = Transformer( + config=cfg.model, vocab_size=vocab_size, masked=source_distribution.masked + ).to(device) + + num_parameters = sum(p.numel() for p in model.parameters()) + logger.info(f"Number of parameters in the model: {num_parameters}") + + model = DDP(model, device_ids=[rank], static_graph=True) + logger.info(model) + + # Optimizer initialization + optimizer = optim.AdamW( + model.parameters(), + lr=cfg.optim.lr, + betas=(cfg.optim.beta1, cfg.optim.beta2), + eps=cfg.optim.eps, + weight_decay=cfg.optim.weight_decay, + fused=cfg.optim.fused, + ) + logger.info(f"Optimizer: {optimizer}") + scaler = torch.amp.GradScaler("cuda") + logger.info(f"Scaler: {scaler}") + + data_state = data.get_data_state(config=cfg) + + # Train state + state = TrainState(model=model, optimizer=optimizer, step=1, data_state=data_state) + state.restore_checkpoint(ckpt_dir=work_dirs.checkpoint, device=device, rank=rank) + + train_iter, eval_iter = data.get_data_loaders(config=cfg, data_state=data_state) + + if cfg.model.compile: + state.compile_model() + torch.set_float32_matmul_precision("high") + + # Flow matching + path = flow.get_path( + scheduler_type=cfg.flow.scheduler_type, exponent=cfg.flow.exponent + ) + loss_fn = flow.get_loss_function(loss_function=cfg.flow.loss_function, path=path) + # Elbo may have singularity at 1 + time_epsilon = 1e-3 if isinstance(loss_fn, MixturePathGeneralizedKL) else 0.0 + + num_train_steps = cfg.optim.n_iters + logger.info(f"Starting training loop at step {state.step}.") + + train_loss_values = [] + + while state.step <= num_train_steps: + loss = training.step( + loss_fn=loss_fn, + path=path, + state=state, + scaler=scaler, + iterator=train_iter, + optim_params=cfg.optim, + device=device, + source_distribution=source_distribution, + logger=logger, + training=True, + time_epsilon=time_epsilon, + ) + + train_loss_values.append(loss) + + # Train logging + if state.step % cfg.logging.log_freq == 0: + agg_train_loss_values = torch.tensor( + train_loss_values, device=device + ).mean() + dist.all_reduce(agg_train_loss_values, dist.ReduceOp.AVG) + logger.log_metric( + value=agg_train_loss_values, name="Loss", stage="Train", step=state.step + ) + + train_loss_values = [] + + # Checkpoint + if state.step % cfg.training.snapshot == 0: + logger.info("Saving checkpoint...", step=state.step) + + state.save_checkpoint(ckpt_dir=work_dirs.checkpoint, rank=rank) + + # Evaluation loss + if state.step % cfg.training.eval_freq == 0: + logger.info("Evaluating loss...", step=state.step) + + eval_loss = training.step( + state=state, + loss_fn=loss_fn, + path=path, + scaler=scaler, + iterator=eval_iter, + device=device, + source_distribution=source_distribution, + logger=logger, + training=False, + time_epsilon=time_epsilon, + ) + + dist.all_reduce(eval_loss, dist.ReduceOp.AVG) + logger.log_metric( + value=eval_loss.item(), name="Loss", stage="Evaluation", step=state.step + ) + + # Generation + if state.step % cfg.training.perplexity_freq == 0: + state.eval() + + logger.info("Generating text...", step=state.step) + + samples = generate.generate_samples( + model=state.model, + step=state.step, + sample_dir=work_dirs.samples, + vocab_size=vocab_size, + tokenizer=tokenizer, + rank=rank, + device=device, + path=path, + source_distribution=source_distribution, + sample_batch_size=cfg.eval.sample_batch_size, + sequence_length=cfg.model.length, + sampling_steps=cfg.flow.sampling_steps, + time_epsilon=time_epsilon, + ) + + perplexity = evaluate.compute_perplexity( + samples=samples, + perplexity_batch_size=cfg.eval.perplexity_batch_size, + ) + dist.all_reduce(perplexity, dist.ReduceOp.AVG) + logger.log_metric( + value=perplexity, name="Perplexity", stage="Evaluation", step=state.step + ) + + entropy = evaluate.compute_entropy(samples=samples) + dist.all_reduce(entropy, dist.ReduceOp.AVG) + + logger.log_metric( + value=entropy, name="Entropy", stage="Evaluation", step=state.step + ) + + dist.barrier() + + state.step = state.step + 1 + + if (state.step == num_train_steps) and (rank == 0): + logger.info("Saving checkpoint...", step=state.step) + + state.save_checkpoint(ckpt_dir=work_dirs.checkpoint, rank=rank) + + logger.finish() + + +def setup(rank: int, world_size: int, port: int) -> None: + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = str(port) + + torch.cuda.set_device(rank) + + timeout = datetime.timedelta(minutes=30) + dist.init_process_group("nccl", rank=rank, world_size=world_size, timeout=timeout) + + +def cleanup() -> None: + dist.destroy_process_group() + + +def run_mp_training(rank: int, world_size: int, cfg: OmegaConf, port: int) -> None: + try: + setup(rank=rank, world_size=world_size, port=port) + run_train(rank=rank, cfg=cfg) + finally: + cleanup() diff --git a/examples/text/utils/__init__.py b/examples/text/utils/__init__.py new file mode 100644 index 0000000..36d7195 --- /dev/null +++ b/examples/text/utils/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. diff --git a/examples/text/utils/checkpointing.py b/examples/text/utils/checkpointing.py new file mode 100644 index 0000000..d50f6a0 --- /dev/null +++ b/examples/text/utils/checkpointing.py @@ -0,0 +1,76 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +# Part of this implementation is adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion +# which is released under MIT license + +from dataclasses import dataclass, field +from pathlib import Path + +import torch +from logic.flow import SourceDistribution +from model import Transformer +from omegaconf import OmegaConf +from torch import nn +from torch.nn.parallel import DistributedDataParallel as DDP + + +def load_cfg_from_path(work_dir: str) -> OmegaConf: + work_dir = Path(work_dir) + + root_dir = work_dir if work_dir.is_dir() else work_dir.parents[1] + + cfg_path = root_dir / ".hydra/config.yaml" + + return OmegaConf.load(cfg_path) + + +def load_model_from_path( + work_dir: str, + source_distribution: SourceDistribution, + device: torch.device, + vocab_size: int, + cfg: OmegaConf, +) -> nn.Module: + work_dir = Path(work_dir) + + if work_dir.is_dir(): + root_dir = work_dir + ckpt_dir = work_dir / "checkpoints" / "checkpoint.pth" + else: + root_dir = work_dir.parents[1] + ckpt_dir = work_dir + + model = Transformer( + config=cfg, vocab_size=vocab_size, masked=source_distribution.masked + ).to(device) + model = DDP(model, device_ids=[device]) + + ckpt_dir = root_dir / "checkpoints" / "checkpoint.pth" + loaded_state = torch.load(ckpt_dir, map_location=device, weights_only=True) + + model.module.load_state_dict(loaded_state["model"]) + + return model + + +@dataclass +class WorkDirectory: + root: Path = field(metadata={"help": "Root work directory"}) + checkpoint: Path = field(metadata={"help": "Checkpoint directory"}) + samples: Path = field(metadata={"help": "Samples directory"}) + + +def get_work_dirs(work_dir: str, rank: int) -> WorkDirectory: + work_dir = Path(work_dir) + + sample_dir = work_dir / "samples" + checkpoint_dir = work_dir / "checkpoints" / "checkpoint.pth" + + if rank == 0: + sample_dir.mkdir(exist_ok=True) + checkpoint_dir.parents[0].mkdir(exist_ok=True) + + return WorkDirectory(root=work_dir, checkpoint=checkpoint_dir, samples=sample_dir) diff --git a/examples/text/utils/logging.py b/examples/text/utils/logging.py new file mode 100644 index 0000000..7b73067 --- /dev/null +++ b/examples/text/utils/logging.py @@ -0,0 +1,122 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import os +from logging import Logger +from pathlib import Path +from typing import Optional + +import torch +import wandb +from omegaconf import OmegaConf + + +def get_logger(log_path: str, rank: int): + if rank != 0: + return logging.getLogger("dummy") + + logger = logging.getLogger() + default_level = logging.INFO + + if logger.hasHandlers(): + logger.handlers.clear() + + logger.setLevel(default_level) + + formatter = logging.Formatter( + "%(levelname)s | %(asctime)s | %(message)s", "%Y-%m-%d %H:%M:%S" + ) + + info_file_handler = logging.FileHandler(log_path, mode="a") + info_file_handler.setLevel(default_level) + info_file_handler.setFormatter(formatter) + logger.addHandler(info_file_handler) + + console_handler = logging.StreamHandler() + console_handler.setLevel(default_level) + console_handler.setFormatter(formatter) + logger.addHandler(console_handler) + + return logger + + +class TrainLogger: + def __init__(self, log_dir: Path, rank: int, cfg: bool = False): + self.log_dir = log_dir + self.cfg = cfg + + self._init_text_logger(rank=rank) + + self.enable_wandb = self.cfg.logging.enable_wandb and (rank == 0) + + if self.enable_wandb: + self._init_wandb() + + def _init_text_logger(self, rank: int): + log_path = self.log_dir / self.cfg.logging.log_file_name + self._logger = get_logger(log_path=log_path, rank=rank) + + def _init_wandb( + self, + ): + wandb_run_id_path = self.log_dir / "wandb_run.id" + + try: + wandb_run_id = wandb_run_id_path.read_text() + except FileNotFoundError: + wandb_run_id = wandb.util.generate_id() + wandb_run_id_path.write_text(wandb_run_id) + + self.wandb_logger = wandb.init( + id=wandb_run_id, + project=self.cfg.logging.project, + group=self.cfg.logging.group, + dir=self.log_dir, + entity=self.cfg.logging.entity, + resume="allow", + config=OmegaConf.to_container(self.cfg, resolve=True), + ) + + def log_metric(self, value: float, name: str, stage: bool, step: int) -> None: + self._logger.info(f"[{step}] {stage} {name}: {value:.3f}") + + if self.enable_wandb: + self.wandb_logger.log(data={f"{stage}/{name}": value}, step=step) + + def log_lr(self, value: float, step: int) -> None: + if self.enable_wandb: + self.wandb_logger.log(data={"Optimization/LR": value}, step=step) + + def info(self, msg: str, step: Optional[int] = None) -> None: + step_str = f"[{step}] " if step else "" + self._logger.info(f"{step_str}{msg}") + + def warning(self, msg: str) -> None: + self._logger.warning(msg) + + def finish(self) -> None: + for handler in self._logger.handlers: + if isinstance(handler, logging.FileHandler): + handler.close() + + if self.enable_wandb: + wandb.finish() + + @staticmethod + def log_devices(device: torch.device, logger: Logger) -> None: + if device.type == "cuda": + logger.info("Found {} CUDA devices.".format(torch.cuda.device_count())) + for i in range(torch.cuda.device_count()): + props = torch.cuda.get_device_properties(i) + logger.info( + "{} \t Memory: {:.2f}GB".format( + props.name, props.total_memory / (1024**3) + ) + ) + else: + logger.warning("WARNING: Using device {}".format(device)) + logger.info(f"Found {os.cpu_count()} total number of CPUs.") diff --git a/flow_matching/__init__.py b/flow_matching/__init__.py new file mode 100644 index 0000000..7975227 --- /dev/null +++ b/flow_matching/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +__version__ = "1.0.9" diff --git a/flow_matching/loss/__init__.py b/flow_matching/loss/__init__.py new file mode 100644 index 0000000..24ec1a9 --- /dev/null +++ b/flow_matching/loss/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .generalized_loss import MixturePathGeneralizedKL + +__all__ = [ + "MixturePathGeneralizedKL", +] diff --git a/flow_matching/loss/generalized_loss.py b/flow_matching/loss/generalized_loss.py new file mode 100644 index 0000000..cc1507e --- /dev/null +++ b/flow_matching/loss/generalized_loss.py @@ -0,0 +1,80 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor +from torch.nn.modules.loss import _Loss + +from flow_matching.path import MixtureDiscreteProbPath + + +class MixturePathGeneralizedKL(_Loss): + r"""A generalized KL loss for discrete flow matching. + A class that measures the generalized KL of a discrete flow model :math:`p_{1|t}` w.r.t. a probability path given by ``path``. Note: this class is assuming that the model is trained on the same path. + + For a model trained on a space :math:`\mathcal{S} = \mathcal{T}^d`, :math:`\mathcal{T} = [K] = \set{1,2,\ldots,K}`, the loss is given by + + .. math:: + \ell_i(x_1, x_t, t) = -\frac{\dot{\kappa}_t}{1-\kappa_t} \biggr[ p_{1|t}(x_t^i|x_t) -\delta_{x^i_1}(x_t^i) + (1-\delta_{x^i_1}(x_t^i))\left(\log p_{1|t}(x_1^i|x_t)\right)\biggr], + + where :math:`\kappa_t` is the scheduler associated with ``path``. + + Args: + path (MixtureDiscreteProbPath): Probability path (x-prediction training). + reduction (str, optional): Specify the reduction to apply to the output ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction is applied to the output, ``'mean'``: the output is reduced by mean over sequence elements, ``'sum'``: the output is reduced by sum over sequence elements. Defaults to 'mean'. + """ + + def __init__(self, path: MixtureDiscreteProbPath, reduction: str = "mean") -> None: + super().__init__(None, None, reduction) + self.path = path + + def forward(self, logits: Tensor, x_1: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Evaluates the generalized KL loss. + + Args: + logits (Tensor): posterior model output (i.e., softmax(``logits``) :math:`=p_{1|t}(x|x_t)`), shape (batch, d, K). + x_1 (Tensor): target data point :math:`x_1 \sim q`, shape (batch, d). + x_t (Tensor): conditional sample at :math:`x_t \sim p_t(\cdot|x_1)`, shape (batch, d). + t (Tensor): times in :math:`[0,1]`, shape (batch). + + Raises: + ValueError: reduction value must be one of ``'none'`` | ``'mean'`` | ``'sum'``. + + Returns: + Tensor: Generalized KL loss. + """ + x_1_shape = x_1.shape + + # extract x_1 value of log(p_{1|t}(x|x_t)). + log_p_1t = torch.log_softmax(logits, dim=-1) + log_p_1t_x1 = torch.gather(log_p_1t, dim=-1, index=x_1.unsqueeze(-1)) + log_p_1t_x1 = log_p_1t_x1.view(*x_1_shape) + + # extract x_t value of p_{1|t}(x|x_t). + p_1t = torch.exp(log_p_1t) + p_1t_xt = torch.gather(p_1t, dim=-1, index=x_t.unsqueeze(-1)) + p_1t_xt = p_1t_xt.view(*x_1_shape) + + scheduler_output = self.path.scheduler(t) + + jump_coefficient = ( + scheduler_output.d_alpha_t / (1 - scheduler_output.alpha_t) + )[(...,) + (None,) * (x_1.dim() - 1)] + jump_coefficient = jump_coefficient.repeat(1, *x_1_shape[1:]) + delta_x1_xt = (x_t == x_1).to(log_p_1t.dtype) + + loss = -jump_coefficient * ( + p_1t_xt - delta_x1_xt + (1 - delta_x1_xt) * log_p_1t_x1 + ) + + if self.reduction == "mean": + return torch.mean(loss) + elif self.reduction == "sum": + return torch.sum(loss) + elif self.reduction == "none": + return loss + else: + raise ValueError(f"{self.reduction} is not a valid value for reduction") diff --git a/flow_matching/path/__init__.py b/flow_matching/path/__init__.py new file mode 100644 index 0000000..88d29a2 --- /dev/null +++ b/flow_matching/path/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .affine import AffineProbPath, CondOTProbPath +from .geodesic import GeodesicProbPath +from .mixture import MixtureDiscreteProbPath +from .path import ProbPath +from .path_sample import DiscretePathSample, PathSample + + +__all__ = [ + "ProbPath", + "AffineProbPath", + "CondOTProbPath", + "MixtureDiscreteProbPath", + "GeodesicProbPath", + "PathSample", + "DiscretePathSample", +] diff --git a/flow_matching/path/affine.py b/flow_matching/path/affine.py new file mode 100644 index 0000000..7e4a18f --- /dev/null +++ b/flow_matching/path/affine.py @@ -0,0 +1,261 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from torch import Tensor + +from flow_matching.path.path import ProbPath +from flow_matching.path.path_sample import PathSample +from flow_matching.path.scheduler.scheduler import CondOTScheduler, Scheduler +from flow_matching.utils import expand_tensor_like + + +class AffineProbPath(ProbPath): + r"""The ``AffineProbPath`` class represents a specific type of probability path where the transformation between distributions is affine. + An affine transformation can be represented as: + + .. math:: + + X_t = \alpha_t X_1 + \sigma_t X_0, + + where :math:`X_t` is the transformed data point at time `t`. :math:`X_0` and :math:`X_1` are the source and target data points, respectively. :math:`\alpha_t` and :math:`\sigma_t` are the parameters of the affine transformation at time `t`. + + The scheduler is responsible for providing the time-dependent parameters :math:`\alpha_t` and :math:`\sigma_t`, as well as their derivatives, which define the affine transformation at any given time `t`. + + Using ``AffineProbPath`` in the flow matching framework: + + .. code-block:: python + + # Instantiates a probability path + my_path = AffineProbPath(...) + mse_loss = torch.nn.MSELoss() + + for x_1 in dataset: + # Sets x_0 to random noise + x_0 = torch.randn() + + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path X_t ~ p_t(X_t|X_0,X_1) + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Computes the MSE loss w.r.t. the velocity + loss = mse_loss(path_sample.dx_t, my_model(x_t, t)) + loss.backward() + + Args: + scheduler (Scheduler): An instance of a scheduler that provides the parameters :math:`\alpha_t`, :math:`\sigma_t`, and their derivatives over time. + + """ + + def __init__(self, scheduler: Scheduler): + self.scheduler = scheduler + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from the affine probability path: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`(\alpha_t,\sigma_t)`. + | return :math:`X_0, X_1, X_t = \alpha_t X_1 + \sigma_t X_0`, and the conditional velocity at :math:`X_t, \dot{X}_t = \dot{\alpha}_t X_1 + \dot{\sigma}_t X_0`. + + Args: + x_0 (Tensor): source data point, shape (Batch, ...). + x_1 (Tensor): target data point, shape (Batch, ...). + t (Tensor, optional): times in [0,1], shape (Batch). + + Returns: + PathSample: a conditional sample at :math:`X_t \sim p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + + scheduler_output = self.scheduler(t) + + if t.ndim == 1: + alpha_t = expand_tensor_like( + input_tensor=scheduler_output.alpha_t, expand_to=x_1 + ) + sigma_t = expand_tensor_like( + input_tensor=scheduler_output.sigma_t, expand_to=x_1 + ) + d_alpha_t = expand_tensor_like( + input_tensor=scheduler_output.d_alpha_t, expand_to=x_1 + ) + d_sigma_t = expand_tensor_like( + input_tensor=scheduler_output.d_sigma_t, expand_to=x_1 + ) + + # construct xt ~ p_t(x|x1). + x_t = sigma_t * x_0 + alpha_t * x_1 + dx_t = d_sigma_t * x_0 + d_alpha_t * x_1 + + return PathSample(x_t=x_t, dx_t=dx_t, x_1=x_1, x_0=x_0, t=t) + + def target_to_velocity(self, x_1: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from x_1 representation to velocity. + + | given :math:`X_1`. + | return :math:`\dot{X}_t`. + + Args: + x_1 (Tensor): target data point. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = d_sigma_t / sigma_t + b_t = (d_alpha_t * sigma_t - d_sigma_t * alpha_t) / sigma_t + + return a_t * x_t + b_t * x_1 + + def epsilon_to_velocity(self, epsilon: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from epsilon representation to velocity. + + | given :math:`\epsilon`. + | return :math:`\dot{X}_t`. + + Args: + epsilon (Tensor): noise in the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = d_alpha_t / alpha_t + b_t = (d_sigma_t * alpha_t - d_alpha_t * sigma_t) / alpha_t + + return a_t * x_t + b_t * epsilon + + def velocity_to_target(self, velocity: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from velocity to x_1 representation. + + | given :math:`\dot{X}_t`. + | return :math:`X_1`. + + Args: + velocity (Tensor): velocity at the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: target data point. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = -d_sigma_t / (d_alpha_t * sigma_t - d_sigma_t * alpha_t) + b_t = sigma_t / (d_alpha_t * sigma_t - d_sigma_t * alpha_t) + + return a_t * x_t + b_t * velocity + + def epsilon_to_target(self, epsilon: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from epsilon representation to x_1 representation. + + | given :math:`\epsilon`. + | return :math:`X_1`. + + Args: + epsilon (Tensor): noise in the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: target data point. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + sigma_t = scheduler_output.sigma_t + + a_t = 1 / alpha_t + b_t = -sigma_t / alpha_t + + return a_t * x_t + b_t * epsilon + + def velocity_to_epsilon(self, velocity: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from velocity to noise representation. + + | given :math:`\dot{X}_t`. + | return :math:`\epsilon`. + + Args: + velocity (Tensor): velocity at the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: noise in the path sample. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = -d_alpha_t / (d_sigma_t * alpha_t - d_alpha_t * sigma_t) + b_t = alpha_t / (d_sigma_t * alpha_t - d_alpha_t * sigma_t) + + return a_t * x_t + b_t * velocity + + def target_to_epsilon(self, x_1: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from x_1 representation to velocity. + + | given :math:`X_1`. + | return :math:`\epsilon`. + + Args: + x_1 (Tensor): target data point. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: noise in the path sample. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + sigma_t = scheduler_output.sigma_t + + a_t = 1 / sigma_t + b_t = -alpha_t / sigma_t + + return a_t * x_t + b_t * x_1 + + +class CondOTProbPath(AffineProbPath): + r"""The ``CondOTProbPath`` class represents a conditional optimal transport probability path. + + This class is a specialized version of the ``AffineProbPath`` that uses a conditional optimal transport scheduler to determine the parameters of the affine transformation. + + The parameters :math:`\alpha_t` and :math:`\sigma_t` for the conditional optimal transport path are defined as: + + .. math:: + + \alpha_t = t \quad \text{and} \quad \sigma_t = 1 - t. + """ + + def __init__(self): + self.scheduler = CondOTScheduler() diff --git a/flow_matching/path/geodesic.py b/flow_matching/path/geodesic.py new file mode 100644 index 0000000..5575bfb --- /dev/null +++ b/flow_matching/path/geodesic.py @@ -0,0 +1,102 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from torch import Tensor +from torch.func import jvp, vmap + +from flow_matching.path.path import ProbPath + +from flow_matching.path.path_sample import PathSample +from flow_matching.path.scheduler import ConvexScheduler +from flow_matching.utils import expand_tensor_like + +from flow_matching.utils.manifolds import geodesic, Manifold + + +class GeodesicProbPath(ProbPath): + r"""The ``GeodesicProbPath`` class represents a specific type of probability path where the transformation between distributions is defined through the geodesic path. + Mathematically, a geodesic path can be represented as: + + .. math:: + + X_t = \psi_t(X_0 | X_1) = \exp_{X_1}(\kappa_t \log_{X_1}(X_0)), + + where :math:`X_t` is the transformed data point at time `t`, :math:`X_0` and :math:`X_1` are the source and target data points, respectively, and :math:`\kappa_t` is a scheduler. + + The scheduler is responsible for providing the time-dependent :math:`\kappa_t` and must be differentiable. + + Using ``GeodesicProbPath`` in the flow matching framework: + + .. code-block:: python + # Instantiates a manifold + manifold = FlatTorus() + + # Instantiates a scheduler + scheduler = CondOTScheduler() + + # Instantiates a probability path + my_path = GeodesicProbPath(scheduler, manifold) + mse_loss = torch.nn.MSELoss() + + for x_1 in dataset: + # Sets x_0 to random noise + x_0 = torch.randn() + + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path :math:`X_t \sim p_t(X_t|X_0,X_1)` + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Computes the MSE loss w.r.t. the velocity + loss = mse_loss(path_sample.dx_t, my_model(x_t, t)) + loss.backward() + + Args: + scheduler (ConvexScheduler): The scheduler that provides :math:`\kappa_t`. + manifold (Manifold): The manifold on which the probability path is defined. + + """ + + def __init__(self, scheduler: ConvexScheduler, manifold: Manifold): + self.scheduler = scheduler + self.manifold = manifold + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from the Riemannian probability path with geodesic interpolation: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`\kappa_t`. + | return :math:`X_0, X_1, X_t = \exp_{X_1}(\kappa_t \log_{X_1}(X_0))`, and the conditional velocity at :math:`X_t, \dot{X}_t`. + + Args: + x_0 (Tensor): source data point, shape (Batch, ...). + x_1 (Tensor): target data point, shape (Batch, ...). + t (Tensor, optional): times in [0,1], shape (Batch). + + Returns: + PathSample: A conditional sample at :math:`X_t \sim p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + + if t.ndim <= 1: + t = expand_tensor_like(input_tensor=t, expand_to=x_1[..., 0:1]).clone() + + def cond_u(x_0, x_1, t): + path = geodesic(self.manifold, x_0, x_1) + x_t, dx_t = jvp( + lambda t: path(self.scheduler(t).alpha_t), + (t,), + (torch.ones_like(t).to(t),), + ) + return x_t, dx_t + + x_t, dx_t = vmap(cond_u)(x_0, x_1, t) + x_t = x_t.reshape_as(x_1) + dx_t = dx_t.reshape_as(x_1) + + return PathSample(x_t=x_t, dx_t=dx_t, x_1=x_1, x_0=x_0, t=t) diff --git a/flow_matching/path/mixture.py b/flow_matching/path/mixture.py new file mode 100644 index 0000000..277ef36 --- /dev/null +++ b/flow_matching/path/mixture.py @@ -0,0 +1,118 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F + +from torch import Tensor + +from flow_matching.path.path import ProbPath + +from flow_matching.path.path_sample import DiscretePathSample +from flow_matching.path.scheduler import ConvexScheduler +from flow_matching.utils import expand_tensor_like, unsqueeze_to_match + + +class MixtureDiscreteProbPath(ProbPath): + r"""The ``MixtureDiscreteProbPath`` class defines a factorized discrete probability path. + + This path remains constant at the source data point :math:`X_0` until a random time, determined by the scheduler, when it flips to the target data point :math:`X_1`. + The scheduler determines the flip probability using the parameter :math:`\sigma_t`, which is a function of time `t`. Specifically, :math:`\sigma_t` represents the probability of remaining at :math:`X_0`, while :math:`1 - \sigma_t` is the probability of flipping to :math:`X_1`: + + .. math:: + + P(X_t = X_0) = \sigma_t \quad \text{and} \quad P(X_t = X_1) = 1 - \sigma_t, + + where :math:`\sigma_t` is provided by the scheduler. + + Example: + + .. code-block:: python + + >>> x_0 = torch.zeros((1, 3, 3)) + >>> x_1 = torch.ones((1, 3, 3)) + + >>> path = MixtureDiscreteProbPath(PolynomialConvexScheduler(n=1.0)) + >>> result = path.sample(x_0, x_1, t=torch.tensor([0.1])).x_t + >>> result + tensor([[[0.0, 0.0, 0.0], + [0.0, 0.0, 1.0], + [0.0, 0.0, 0.0]]]) + + >>> result = path.sample(x_0, x_1, t=torch.tensor([0.5])).x_t + >>> result + tensor([[[1.0, 0.0, 1.0], + [0.0, 1.0, 0.0], + [0.0, 1.0, 0.0]]]) + + >>> result = path.sample(x_0, x_1, t=torch.tensor([1.0])).x_t + >>> result + tensor([[[1.0, 1.0, 1.0], + [1.0, 1.0, 1.0], + [1.0, 1.0, 1.0]]]) + + Args: + scheduler (ConvexScheduler): The scheduler that provides :math:`\sigma_t`. + """ + + def __init__(self, scheduler: ConvexScheduler): + assert isinstance( + scheduler, ConvexScheduler + ), "Scheduler for ConvexProbPath must be a ConvexScheduler." + + self.scheduler = scheduler + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> DiscretePathSample: + r"""Sample from the affine probability path: + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`(\alpha_t,\sigma_t)`. + | return :math:`X_0, X_1, t`, and :math:`X_t \sim p_t`. + Args: + x_0 (Tensor): source data point, shape (Batch, ...). + x_1 (Tensor): target data point, shape (Batch, ...). + t (Tensor): times in [0,1], shape (Batch). + + Returns: + DiscretePathSample: a conditional sample at :math:`X_t ~ p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + + sigma_t = self.scheduler(t).sigma_t + + if t.ndim == 1: + sigma_t = expand_tensor_like(input_tensor=sigma_t, expand_to=x_1) + + source_indices = torch.rand(size=x_1.shape, device=x_1.device) < sigma_t + x_t = torch.where(condition=source_indices, input=x_0, other=x_1) + + return DiscretePathSample(x_t=x_t, x_1=x_1, x_0=x_0, t=t) + + def posterior_to_velocity( + self, posterior_logits: Tensor, x_t: Tensor, t: Tensor + ) -> Tensor: + r"""Convert the factorized posterior to velocity. + + | given :math:`p(X_1|X_t)`. In the factorized case: :math:`\prod_i p(X_1^i | X_t)`. + | return :math:`u_t`. + + Args: + posterior_logits (Tensor): logits of the x_1 posterior conditional on x_t, shape (..., vocab size). + x_t (Tensor): path sample at time t, shape (...). + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + posterior = torch.softmax(posterior_logits, dim=-1) + vocabulary_size = posterior.shape[-1] + x_t = F.one_hot(x_t, num_classes=vocabulary_size) + t = unsqueeze_to_match(source=t, target=x_t) + + scheduler_output = self.scheduler(t) + + kappa_t = scheduler_output.alpha_t + d_kappa_t = scheduler_output.d_alpha_t + + return (d_kappa_t / (1 - kappa_t)) * (posterior - x_t) diff --git a/flow_matching/path/path.py b/flow_matching/path/path.py new file mode 100644 index 0000000..45afcbd --- /dev/null +++ b/flow_matching/path/path.py @@ -0,0 +1,58 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod + +from torch import Tensor + +from flow_matching.path.path_sample import PathSample + + +class ProbPath(ABC): + r"""Abstract class, representing a probability path. + + A probability path transforms the distribution :math:`p(X_0)` into :math:`p(X_1)` over :math:`t=0\rightarrow 1`. + + The ``ProbPath`` class is designed to support model training in the flow matching framework. It supports two key functionalities: (1) sampling the conditional probability path and (2) conversion between various training objectives. + Here is a high-level example + + .. code-block:: python + + # Instantiate a probability path + my_path = ProbPath(...) + + for x_0, x_1 in dataset: + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path X_t ~ p_t(X_t|X_0,X_1) + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Optimizes the model. The loss function varies, depending on model and path. + loss(path_sample, my_model(x_t, t)).backward() + + """ + + @abstractmethod + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from an abstract probability path: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)`. + | returns :math:`X_0, X_1, X_t \sim p_t(X_t)`, and a conditional target :math:`Y`, all objects are under ``PathSample``. + + Args: + x_0 (Tensor): source data point, shape (Batch, ...). + x_1 (Tensor): target data point, shape (Batch, ...). + t (Tensor, optional): times in [0,1], shape (Batch). + + Returns: + PathSample: a conditional sample. + """ + + def assert_sample_shape(self, x_0: Tensor, x_1: Tensor, t: Tensor): + assert ( + t.shape[0] == x_0.shape[0] == x_1.shape[0] + ), f"Time t dimension must match the batch size [{x_1.shape[0]}]. Got {t.shape}" diff --git a/flow_matching/path/path_sample.py b/flow_matching/path/path_sample.py new file mode 100644 index 0000000..3db21b6 --- /dev/null +++ b/flow_matching/path/path_sample.py @@ -0,0 +1,53 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +from torch import Tensor + + +@dataclass +class PathSample: + r"""Represents a sample of a conditional-flow generated probability path. + + Attributes: + x_1 (Tensor): the target sample :math:`X_1`. + x_0 (Tensor): the source sample :math:`X_0`. + t (Tensor): the time sample :math:`t`. + x_t (Tensor): samples :math:`X_t \sim p_t(X_t)`, shape (Batch, ...). + dx_t (Tensor): conditional target :math:`\frac{\partial X}{\partial t}`, shape: (Batch, ...). + + """ + + x_1: Tensor = field(metadata={"help": "target samples X_1 (Batch, ...)."}) + x_0: Tensor = field(metadata={"help": "source samples X_0 (Batch, ...)."}) + t: Tensor = field(metadata={"help": "time samples t (Batch, ...)."}) + x_t: Tensor = field( + metadata={"help": "samples x_t ~ p_t(X_t), shape (Batch, ...)."} + ) + dx_t: Tensor = field( + metadata={"help": "conditional target dX_t, shape: (Batch, ...)."} + ) + + +@dataclass +class DiscretePathSample: + """ + Represents a sample of a conditional-flow generated discrete probability path. + + Attributes: + x_1 (Tensor): the target sample :math:`X_1`. + x_0 (Tensor): the source sample :math:`X_0`. + t (Tensor): the time sample :math:`t`. + x_t (Tensor): the sample along the path :math:`X_t \sim p_t`. + """ + + x_1: Tensor = field(metadata={"help": "target samples X_1 (Batch, ...)."}) + x_0: Tensor = field(metadata={"help": "source samples X_0 (Batch, ...)."}) + t: Tensor = field(metadata={"help": "time samples t (Batch, ...)."}) + x_t: Tensor = field( + metadata={"help": "samples X_t ~ p_t(X_t), shape (Batch, ...)."} + ) diff --git a/flow_matching/path/scheduler/__init__.py b/flow_matching/path/scheduler/__init__.py new file mode 100644 index 0000000..f3b1a43 --- /dev/null +++ b/flow_matching/path/scheduler/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .schedule_transform import ScheduleTransformedModel +from .scheduler import ( + CondOTScheduler, + ConvexScheduler, + CosineScheduler, + LinearVPScheduler, + PolynomialConvexScheduler, + Scheduler, + SchedulerOutput, + VPScheduler, +) + +__all__ = [ + "CondOTScheduler", + "CosineScheduler", + "ConvexScheduler", + "PolynomialConvexScheduler", + "ScheduleTransformedModel", + "Scheduler", + "VPScheduler", + "LinearVPScheduler", + "SchedulerOutput", +] diff --git a/flow_matching/path/scheduler/schedule_transform.py b/flow_matching/path/scheduler/schedule_transform.py new file mode 100644 index 0000000..a366f19 --- /dev/null +++ b/flow_matching/path/scheduler/schedule_transform.py @@ -0,0 +1,148 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from torch import Tensor + +from flow_matching.path.scheduler.scheduler import Scheduler +from flow_matching.utils import ModelWrapper + + +class ScheduleTransformedModel(ModelWrapper): + """ + Change of scheduler for a velocity model. + + This class wraps a given velocity model and transforms its scheduling + to a new scheduler function. It modifies the time + dynamics of the model according to the new scheduler while maintaining + the original model's behavior. + + Example: + + .. code-block:: python + + import torch + from flow_matching.path.scheduler import CondOTScheduler, CosineScheduler, ScheduleTransformedModel + from flow_matching.solver import ODESolver + + # Initialize the model and schedulers + model = ... + + original_scheduler = CondOTScheduler() + new_scheduler = CosineScheduler() + + # Create the transformed model + transformed_model = ScheduleTransformedModel( + velocity_model=model, + original_scheduler=original_scheduler, + new_scheduler=new_scheduler + ) + + # Set up the solver + solver = ODESolver(velocity_model=transformed_model) + + x_0 = torch.randn([10, 2]) # Example initial condition + + x_1 = solver.sample( + time_steps=torch.tensor([0.0, 1.0]), + x_init=x_0, + step_size=1/1000 + )[1] + + Args: + velocity_model (ModelWrapper): The original velocity model to be transformed. + original_scheduler (Scheduler): The scheduler used by the original model. Must implement the snr_inverse function. + new_scheduler (Scheduler): The new scheduler to be applied to the model. + """ + + def __init__( + self, + velocity_model: ModelWrapper, + original_scheduler: Scheduler, + new_scheduler: Scheduler, + ): + super().__init__(model=velocity_model) + self.original_scheduler = original_scheduler + self.new_scheduler = new_scheduler + + assert hasattr(self.original_scheduler, "snr_inverse") and callable( + getattr(self.original_scheduler, "snr_inverse") + ), "The original scheduler must have a callable 'snr_inverse' method." + + def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor: + r""" + Compute the transformed marginal velocity field for a new scheduler. + This method implements a post-training velocity scheduler change for + affine conditional flows. It transforms a generating marginal velocity + field :math:`u_t(x)` based on an original scheduler to a new marginal velocity + field :math:`\bar{u}_r(x)` based on a different scheduler, while maintaining + the same data coupling. + The transformation is based on the scale-time (ST) transformation + between the two conditional flows, defined as: + + .. math:: + + \bar{X}_r = s_r X_{t_r}, + + where :math:`X_t` and :math:`\bar{X}_r` are defined by their respective schedulers. + The ST transformation is computed as: + + .. math:: + + t_r = \rho^{-1}(\bar{\rho}(r)) \quad \text{and} \quad s_r = \frac{\bar{\sigma}_r}{\sigma_{t_r}}. + + Here, :math:`\rho(t)` is the signal-to-noise ratio (SNR) defined as: + + .. math:: + + \rho(t) = \frac{\alpha_t}{\sigma_t}. + + :math:`\bar{\rho}(r)` is similarly defined for the new scheduler. + The marginal velocity for the new scheduler is then given by: + + .. math:: + + \bar{u}_r(x) = \left(\frac{\dot{s}_r}{s_r}\right) x + s_r \dot{t}_r u_{t_r}\left(\frac{x}{s_r}\right). + + Args: + x (Tensor): :math:`x_t`, the input tensor. + t (Tensor): The time tensor (denoted as :math:`r` above). + **extras: Additional arguments for the model. + Returns: + Tensor: The transformed velocity. + """ + r = t + + r_scheduler_output = self.new_scheduler(t=r) + + alpha_r = r_scheduler_output.alpha_t + sigma_r = r_scheduler_output.sigma_t + d_alpha_r = r_scheduler_output.d_alpha_t + d_sigma_r = r_scheduler_output.d_sigma_t + + t = self.original_scheduler.snr_inverse(alpha_r / sigma_r) + + t_scheduler_output = self.original_scheduler(t=t) + + alpha_t = t_scheduler_output.alpha_t + sigma_t = t_scheduler_output.sigma_t + d_alpha_t = t_scheduler_output.d_alpha_t + d_sigma_t = t_scheduler_output.d_sigma_t + + s_r = sigma_r / sigma_t + + dt_r = ( + sigma_t + * sigma_t + * (sigma_r * d_alpha_r - alpha_r * d_sigma_r) + / (sigma_r * sigma_r * (sigma_t * d_alpha_t - alpha_t * d_sigma_t)) + ) + + ds_r = (sigma_t * d_sigma_r - sigma_r * d_sigma_t * dt_r) / (sigma_t * sigma_t) + + u_t = self.model(x=x / s_r, t=t, **extras) + u_r = ds_r * x / s_r + dt_r * s_r * u_t + + return u_r diff --git a/flow_matching/path/scheduler/scheduler.py b/flow_matching/path/scheduler/scheduler.py new file mode 100644 index 0000000..719b34f --- /dev/null +++ b/flow_matching/path/scheduler/scheduler.py @@ -0,0 +1,199 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field + +from typing import Union + +import torch + +from torch import Tensor + + +@dataclass +class SchedulerOutput: + r"""Represents a sample of a conditional-flow generated probability path. + + Attributes: + alpha_t (Tensor): :math:`\alpha_t`, shape (...). + sigma_t (Tensor): :math:`\sigma_t`, shape (...). + d_alpha_t (Tensor): :math:`\frac{\partial}{\partial t}\alpha_t`, shape (...). + d_sigma_t (Tensor): :math:`\frac{\partial}{\partial t}\sigma_t`, shape (...). + + """ + + alpha_t: Tensor = field(metadata={"help": "alpha_t"}) + sigma_t: Tensor = field(metadata={"help": "sigma_t"}) + d_alpha_t: Tensor = field(metadata={"help": "Derivative of alpha_t."}) + d_sigma_t: Tensor = field(metadata={"help": "Derivative of sigma_t."}) + + +class Scheduler(ABC): + """Base Scheduler class.""" + + @abstractmethod + def __call__(self, t: Tensor) -> SchedulerOutput: + r""" + Args: + t (Tensor): times in [0,1], shape (...). + + Returns: + SchedulerOutput: :math:`\alpha_t,\sigma_t,\frac{\partial}{\partial t}\alpha_t,\frac{\partial}{\partial t}\sigma_t` + """ + ... + + @abstractmethod + def snr_inverse(self, snr: Tensor) -> Tensor: + r""" + Computes :math:`t` from the signal-to-noise ratio :math:`\frac{\alpha_t}{\sigma_t}`. + + Args: + snr (Tensor): The signal-to-noise, shape (...) + + Returns: + Tensor: t, shape (...) + """ + ... + + +class ConvexScheduler(Scheduler): + @abstractmethod + def __call__(self, t: Tensor) -> SchedulerOutput: + """Scheduler for convex paths. + + Args: + t (Tensor, optional): times in [0,1], shape (...). + + Returns: + SchedulerOutput: :math:`\alpha_t,\sigma_t,\frac{\partial}{\partial t}\alpha_t,\frac{\partial}{\partial t}\sigma_t` + """ + ... + + @abstractmethod + def kappa_inverse(self, kappa: Tensor) -> Tensor: + """ + Computes :math:`t` from :math:`\kappa_t`. + + Args: + kappa (Tensor): :math:`\kappa`, shape (...) + + Returns: + Tensor: t, shape (...) + """ + ... + + def snr_inverse(self, snr: Tensor) -> Tensor: + r""" + Computes :math:`t` from the signal-to-noise ratio :math:`\frac{\alpha_t}{\sigma_t}`. + + Args: + snr (Tensor): The signal-to-noise, shape (...) + + Returns: + Tensor: t, shape (...) + """ + kappa_t = snr / (1.0 + snr) + + return self.kappa_inverse(kappa=kappa_t) + + +class CondOTScheduler(ConvexScheduler): + """CondOT Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t, + sigma_t=1 - t, + d_alpha_t=torch.ones_like(t), + d_sigma_t=-torch.ones_like(t), + ) + + def kappa_inverse(self, kappa: Tensor) -> Tensor: + return kappa + + +class PolynomialConvexScheduler(ConvexScheduler): + """Polynomial Scheduler.""" + + def __init__(self, n: Union[float, int]) -> None: + assert isinstance( + n, (float, int) + ), f"`n` must be a float or int. Got {type(n)=}." + assert n > 0, f"`n` must be positive. Got {n=}." + + self.n = n + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t**self.n, + sigma_t=1 - t**self.n, + d_alpha_t=self.n * (t ** (self.n - 1)), + d_sigma_t=-self.n * (t ** (self.n - 1)), + ) + + def kappa_inverse(self, kappa: Tensor) -> Tensor: + return torch.pow(kappa, 1.0 / self.n) + + +class VPScheduler(Scheduler): + """Variance Preserving Scheduler.""" + + def __init__(self, beta_min: float = 0.1, beta_max: float = 20.0) -> None: + self.beta_min = beta_min + self.beta_max = beta_max + super().__init__() + + def __call__(self, t: Tensor) -> SchedulerOutput: + b = self.beta_min + B = self.beta_max + T = 0.5 * (1 - t) ** 2 * (B - b) + (1 - t) * b + dT = -(1 - t) * (B - b) - b + + return SchedulerOutput( + alpha_t=torch.exp(-0.5 * T), + sigma_t=torch.sqrt(1 - torch.exp(-T)), + d_alpha_t=-0.5 * dT * torch.exp(-0.5 * T), + d_sigma_t=0.5 * dT * torch.exp(-T) / torch.sqrt(1 - torch.exp(-T)), + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + T = -torch.log(snr**2 / (snr**2 + 1)) + b = self.beta_min + B = self.beta_max + t = 1 - ((-b + torch.sqrt(b**2 + 2 * (B - b) * T)) / (B - b)) + return t + + +class LinearVPScheduler(Scheduler): + """Linear Variance Preserving Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t, + sigma_t=(1 - t**2) ** 0.5, + d_alpha_t=torch.ones_like(t), + d_sigma_t=-t / (1 - t**2) ** 0.5, + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + return torch.sqrt(snr**2 / (1 + snr**2)) + + +class CosineScheduler(Scheduler): + """Cosine Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + pi = torch.pi + return SchedulerOutput( + alpha_t=torch.sin(pi / 2 * t), + sigma_t=torch.cos(pi / 2 * t), + d_alpha_t=pi / 2 * torch.cos(pi / 2 * t), + d_sigma_t=-pi / 2 * torch.sin(pi / 2 * t), + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + return 2.0 * torch.atan(snr) / torch.pi diff --git a/flow_matching/solver/__init__.py b/flow_matching/solver/__init__.py new file mode 100644 index 0000000..6bd7b01 --- /dev/null +++ b/flow_matching/solver/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .discrete_solver import MixtureDiscreteEulerSolver +from .ode_solver import ODESolver +from .riemannian_ode_solver import RiemannianODESolver +from .solver import Solver + +__all__ = [ + "ODESolver", + "Solver", + "ModelWrapper", + "MixtureDiscreteEulerSolver", + "RiemannianODESolver", +] diff --git a/flow_matching/solver/discrete_solver.py b/flow_matching/solver/discrete_solver.py new file mode 100644 index 0000000..282c2a0 --- /dev/null +++ b/flow_matching/solver/discrete_solver.py @@ -0,0 +1,247 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from contextlib import nullcontext +from math import ceil +from typing import Callable, Optional, Union + +import torch +from torch import Tensor + +from torch.nn import functional as F +from tqdm import tqdm + +from flow_matching.path import MixtureDiscreteProbPath + +from flow_matching.solver.solver import Solver +from flow_matching.utils import categorical, ModelWrapper +from .utils import get_nearest_times + + +class MixtureDiscreteEulerSolver(Solver): + r"""Solver that simulates the CTMC process :math:`(X_t)_{t_{\text{init}}\leq t\leq t_{\text{final}}}` defined by :math:`p_t` the marginal probability path of ``path``. + Given :math:`X_t \sim p_t`, the algorithm of solver step from :math:`t` to :math:`t+h` for the i-th coordinate is: + + .. math:: + + \begin{align*} + & X_1^i \sim p_{1|t}^i(\cdot|X_t)\\ + & \lambda^i \gets \sum_{x^i\ne X_t^i} u_t^i(x^i, X_t^i|X_1^i)\\ + & Z^i_{\text{change}} \sim U[0,1]\\ + & X_{t+h}^i \sim \begin{cases} + \frac{u_t^i(\cdot, X_t^i|X_1^i)}{\lambda^i}(1-\delta_{X_t^i}(\cdot)) \text{ if $Z^i_{\text{change}}\le 1-e^{-h\lambda^i}$}\\ + \delta_{X_t^i}(\cdot) \text{ else } + \end{cases} + \end{align*} + + Where :math:`p_{1|t}(\cdot|X_t)` is the output of ``model``, and the conditional probability velocity is of the mixture probability path is: + + .. math:: + + u_t^i(x^i, y^i|x_1^i) = \hat{u}_t^i(x^i, y^i|x_1^i) + c_{\text{div\_free}}\left[\hat{u}_t^i(x^i, y^i|x_1^i) - \check{u}_t^i(x^i, y^i|x_1^i) \right], + + where + + .. math:: + \hat{u}_t^i(x^i, y^i|x_1^i) = \frac{\dot{\kappa}_t}{1-\kappa_t} \left[ \delta_{x_1^i}(x^i) - \delta_{y^i}(x^i) \right], + + and + + .. math:: + + \check{u}_t^i(x^i, y^i|x_1^i) = \frac{\dot{\kappa}_t}{\kappa_t}\left[ \delta_{y^i}(x^i) - p(x^i) \right]. + + The source distribution :math:`p(x^i)` is given by ``p``. + + Args: + model (ModelWrapper): trained with x-prediction, outputting posterior probabilities (in the range :math:`[0,1]`), output must be [..., vocabulary_size]. + path (MixtureDiscreteProbPath): Probability path used for x-prediction training. + vocabulary_size (int): size of the discrete vocabulary. + source_distribution_p (Optional[Tensor], optional): Source distribution, must be of shape [vocabulary_size]. Required only when divergence-free term for the probability velocity is non-zero. Defaults to None. + """ + + def __init__( + self, + model: ModelWrapper, + path: MixtureDiscreteProbPath, + vocabulary_size: int, + source_distribution_p: Optional[Tensor] = None, + ): + super().__init__() + self.model = model + self.path = path + self.vocabulary_size = vocabulary_size + + if source_distribution_p is not None: + assert source_distribution_p.shape == torch.Size( + [vocabulary_size] + ), f"Source distribution p dimension must match the vocabulary size {vocabulary_size}. Got {source_distribution_p.shape}." + + self.source_distribution_p = source_distribution_p + + @torch.no_grad() + def sample( + self, + x_init: Tensor, + step_size: Optional[float], + div_free: Union[float, Callable[[float], float]] = 0.0, + dtype_categorical: torch.dtype = torch.float32, + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + verbose: bool = False, + **model_extras, + ) -> Tensor: + """ + Sample a sequence of discrete values from the given model. + + .. code-block:: python + + import torch + from flow_matching.utils import ModelWrapper + from flow_matching.solver import MixtureDiscreteEulerSolver + + class DummyModel(ModelWrapper): + def __init__(self): + super().__init__(None) + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return ... + + model = DummyModel() + solver = MixtureDiscreteEulerSolver(model=model) + + x_init = torch.LongTensor([122, 725]) + step_size = 0.001 + time_grid = torch.tensor([0.0, 1.0]) + + result = solver.sample(x_init=x_init, step_size=step_size, time_grid=time_grid) + + Args: + x_init (Tensor): The initial state. + step_size (Optional[float]): If float then time discretization is uniform with the given step size. If None then time discretization is set to be time_grid. + div_free (Union[float, Callable[[float], float]]): The coefficient of the divergence-free term in the probability velocity. Can be either a float or a time dependent function. Defaults to 0.0. + dtype_categorical (torch.dtype): Precision to use for categorical sampler. Defaults to torch.float32. + time_grid (Tensor): The CTMC process is solved in the interval [time_grid[0], time_grid[-1]] and if step_size is None then time discretization is set by the time grid. Defaults to torch.tensor([0.0,1.0]). + return_intermediates (bool): If True then return intermediate time steps according to time_grid. Defaults to False. + verbose (bool): Whether to print progress bars. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Tensor: The sampled sequence of discrete values. + """ + if not div_free == 0.0: + assert ( + self.source_distribution_p is not None + ), "Source distribution p must be specified in order to add a divergence-free term to the probability velocity." + + # Initialize the current state `x_t` with the initial state `X_0`. + time_grid = time_grid.to(device=x_init.device) + + if step_size is None: + # If step_size is None then set the t discretization to time_grid. + t_discretization = time_grid + n_steps = len(time_grid) - 1 + else: + # If step_size is float then t discretization is uniform with step size set by step_size. + t_init = time_grid[0].item() + t_final = time_grid[-1].item() + assert ( + t_final - t_init + ) > step_size, f"Time interval [time_grid[0], time_grid[-1]] must be larger than step_size. Got a time interval [{t_init}, {t_final}] and step_size {step_size}." + + n_steps = ceil((t_final - t_init) / step_size) + t_discretization = torch.tensor( + [t_init + step_size * i for i in range(n_steps)] + [t_final], + device=x_init.device, + ) + + if return_intermediates: + # get order of intermediate steps: + order = torch.argsort(time_grid) + # Compute intermediate steps to return via nearest points in t_discretization to time_grid. + time_grid = get_nearest_times( + time_grid=time_grid, t_discretization=t_discretization + ) + + x_t = x_init.clone() + steps_counter = 0 + res = [] + + if return_intermediates: + res = [x_init.clone()] + + if verbose: + ctx = tqdm(total=t_final, desc=f"NFE: {steps_counter}") + else: + ctx = nullcontext() + + with ctx: + for i in range(n_steps): + t = t_discretization[i : i + 1] + h = t_discretization[i + 1 : i + 2] - t_discretization[i : i + 1] + + # Sample x_1 ~ p_1|t( \cdot |x_t) + p_1t = self.model(x=x_t, t=t.repeat(x_t.shape[0]), **model_extras) + x_1 = categorical(p_1t.to(dtype=dtype_categorical)) + + # Checks if final step + if i == n_steps - 1: + x_t = x_1 + else: + # Compute u_t(x|x_t,x_1) + scheduler_output = self.path.scheduler(t=t) + + k_t = scheduler_output.alpha_t + d_k_t = scheduler_output.d_alpha_t + + delta_1 = F.one_hot(x_1, num_classes=self.vocabulary_size).to( + k_t.dtype + ) + u = d_k_t / (1 - k_t) * delta_1 + + # Add divergence-free part + div_free_t = div_free(t) if callable(div_free) else div_free + + if div_free_t > 0: + p_0 = self.source_distribution_p[(None,) * x_t.dim()] + u = u + div_free_t * d_k_t / (k_t * (1 - k_t)) * ( + (1 - k_t) * p_0 + k_t * delta_1 + ) + + # Set u_t(x_t|x_t,x_1) = 0 + delta_t = F.one_hot(x_t, num_classes=self.vocabulary_size) + u = torch.where( + delta_t.to(dtype=torch.bool), torch.zeros_like(u), u + ) + + # Sample x_t ~ u_t( \cdot |x_t,x_1) + intensity = u.sum(dim=-1) # Assuming u_t(xt|xt,x1) := 0 + mask_jump = torch.rand( + size=x_t.shape, device=x_t.device + ) < 1 - torch.exp(-h * intensity) + + if mask_jump.sum() > 0: + x_t[mask_jump] = categorical( + u[mask_jump].to(dtype=dtype_categorical) + ) + + steps_counter += 1 + t = t + h + + if return_intermediates and (t in time_grid): + res.append(x_t.clone()) + + if verbose: + ctx.n = t.item() + ctx.refresh() + ctx.set_description(f"NFE: {steps_counter}") + + if return_intermediates: + if step_size is None: + return torch.stack(res, dim=0) + else: + return torch.stack(res, dim=0)[order] + else: + return x_t diff --git a/flow_matching/solver/ode_solver.py b/flow_matching/solver/ode_solver.py new file mode 100644 index 0000000..d2c1040 --- /dev/null +++ b/flow_matching/solver/ode_solver.py @@ -0,0 +1,194 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Callable, Optional, Sequence, Tuple, Union + +import torch +from torch import Tensor +from torchdiffeq import odeint + +from flow_matching.solver.solver import Solver +from flow_matching.utils import gradient, ModelWrapper + + +class ODESolver(Solver): + """A class to solve ordinary differential equations (ODEs) using a specified velocity model. + + This class utilizes a velocity field model to solve ODEs over a given time grid using numerical ode solvers. + + Args: + velocity_model (Union[ModelWrapper, Callable]): a velocity field model receiving :math:`(x,t)` and returning :math:`u_t(x)` + """ + + def __init__(self, velocity_model: Union[ModelWrapper, Callable]): + super().__init__() + self.velocity_model = velocity_model + + @torch.no_grad() + def sample( + self, + x_init: Tensor, + step_size: Optional[float], + method: str = "euler", + atol: float = 1e-5, + rtol: float = 1e-5, + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + **model_extras, + ) -> Union[Tensor, Sequence[Tensor]]: + r"""Solve the ODE with the velocity field. + + Example: + + .. code-block:: python + + import torch + from flow_matching.utils import ModelWrapper + from flow_matching.solver import ODESolver + + class DummyModel(ModelWrapper): + def __init__(self): + super().__init__(None) + + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return torch.ones_like(x) * 3.0 * t**2 + + velocity_model = DummyModel() + solver = ODESolver(velocity_model=velocity_model) + x_init = torch.tensor([0.0, 0.0]) + step_size = 0.001 + time_grid = torch.tensor([0.0, 1.0]) + + result = solver.sample(x_init=x_init, step_size=step_size, time_grid=time_grid) + + Args: + x_init (Tensor): initial conditions (e.g., source samples :math:`X_0 \sim p`). Shape: [batch_size, ...]. + step_size (Optional[float]): The step size. Must be None for adaptive step solvers. + method (str): A method supported by torchdiffeq. Defaults to "euler". Other commonly used solvers are "dopri5", "midpoint" and "heun3". For a complete list, see torchdiffeq. + atol (float): Absolute tolerance, used for adaptive step solvers. + rtol (float): Relative tolerance, used for adaptive step solvers. + time_grid (Tensor): The process is solved in the interval [min(time_grid, max(time_grid)] and if step_size is None then time discretization is set by the time grid. May specify a descending time_grid to solve in the reverse direction. Defaults to torch.tensor([0.0, 1.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Union[Tensor, Sequence[Tensor]]: The last timestep when return_intermediates=False, otherwise all values specified in time_grid. + """ + + time_grid = time_grid.to(x_init.device) + + def ode_func(t, x): + return self.velocity_model(x=x, t=t, **model_extras) + + ode_opts = {"step_size": step_size} if step_size is not None else {} + + # Approximate ODE solution with numerical ODE solver + sol = odeint( + ode_func, + x_init, + time_grid, + method=method, + options=ode_opts, + atol=atol, + rtol=rtol, + ) + + if return_intermediates: + return sol + else: + return sol[-1] + + @torch.no_grad() + def compute_likelihood( + self, + x_1: Tensor, + log_p0: Callable[[Tensor], Tensor], + step_size: Optional[float], + method: str = "euler", + atol: float = 1e-5, + rtol: float = 1e-5, + time_grid: Tensor = torch.tensor([1.0, 0.0]), + return_intermediates: bool = False, + exact_divergence: bool = False, + **model_extras, + ) -> Union[Tuple[Tensor, Tensor], Tuple[Sequence[Tensor], Tensor]]: + r"""Solve for log likelihood given a target sample at :math:`t=0`. + + Works similarly to sample, but solves the ODE in reverse to compute the log-likelihood. The velocity model must be differentiable with respect to x. + The function assumes log_p0 is the log probability of the source distribution at :math:`t=0`. + + Args: + x_1 (Tensor): target sample (e.g., samples :math:`X_1 \sim p_1`). + log_p0 (Callable[[Tensor], Tensor]): Log probability function of the source distribution. + step_size (Optional[float]): The step size. Must be None for adaptive step solvers. + method (str): A method supported by torchdiffeq. Defaults to "euler". Other commonly used solvers are "dopri5", "midpoint" and "heun3". For a complete list, see torchdiffeq. + atol (float): Absolute tolerance, used for adaptive step solvers. + rtol (float): Relative tolerance, used for adaptive step solvers. + time_grid (Tensor): If step_size is None then time discretization is set by the time grid. Must start at 1.0 and end at 0.0, otherwise the likelihood computation is not valid. Defaults to torch.tensor([1.0, 0.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Otherwise only return the final sample. Defaults to False. + exact_divergence (bool): Whether to compute the exact divergence or use the Hutchinson estimator. + **model_extras: Additional input for the model. + + Returns: + Union[Tuple[Tensor, Tensor], Tuple[Sequence[Tensor], Tensor]]: Samples at time_grid and log likelihood values of given x_1. + """ + assert ( + time_grid[0] == 1.0 and time_grid[-1] == 0.0 + ), f"Time grid must start at 1.0 and end at 0.0. Got {time_grid}" + + # Fix the random projection for the Hutchinson divergence estimator + if not exact_divergence: + z = (torch.randn_like(x_1).to(x_1.device) < 0) * 2.0 - 1.0 + + def ode_func(x, t): + return self.velocity_model(x=x, t=t, **model_extras) + + def dynamics_func(t, states): + xt = states[0] + with torch.set_grad_enabled(True): + xt.requires_grad_() + ut = ode_func(xt, t) + + if exact_divergence: + # Compute exact divergence + div = 0 + for i in range(ut.flatten(1).shape[1]): + div += gradient(ut[:, i], xt, create_graph=True)[:, i] + else: + # Compute Hutchinson divergence estimator E[z^T D_x(ut) z] + ut_dot_z = torch.einsum( + "ij,ij->i", ut.flatten(start_dim=1), z.flatten(start_dim=1) + ) + grad_ut_dot_z = gradient(ut_dot_z, xt) + div = torch.einsum( + "ij,ij->i", + grad_ut_dot_z.flatten(start_dim=1), + z.flatten(start_dim=1), + ) + + return ut.detach(), div.detach() + + y_init = (x_1, torch.zeros(x_1.shape[0], device=x_1.device)) + ode_opts = {"step_size": step_size} if step_size is not None else {} + + with torch.no_grad(): + sol, log_det = odeint( + dynamics_func, + y_init, + time_grid, + method=method, + options=ode_opts, + atol=atol, + rtol=rtol, + ) + + x_source = sol[-1] + source_log_p = log_p0(x_source) + + if return_intermediates: + return sol, source_log_p + log_det[-1] + else: + return sol[-1], source_log_p + log_det[-1] diff --git a/flow_matching/solver/riemannian_ode_solver.py b/flow_matching/solver/riemannian_ode_solver.py new file mode 100644 index 0000000..6eb3e5e --- /dev/null +++ b/flow_matching/solver/riemannian_ode_solver.py @@ -0,0 +1,243 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Callable + +import torch +from torch import Tensor +from tqdm import tqdm + +from flow_matching.solver.solver import Solver +from flow_matching.utils import ModelWrapper +from flow_matching.utils.manifolds import geodesic, Manifold + + +class RiemannianODESolver(Solver): + r"""Riemannian ODE solver + Initialize the ``RiemannianODESolver``. + + Args: + manifold (Manifold): the manifold to solve on. + velocity_model (ModelWrapper): a velocity field model receiving :math:`(x,t)` + and returning :math:`u_t(x)` which is assumed to lie on the tangent plane at `x`. + """ + + def __init__(self, manifold: Manifold, velocity_model: ModelWrapper): + super().__init__() + self.manifold = manifold + self.velocity_model = velocity_model + + @torch.no_grad() + def sample( + self, + x_init: Tensor, + step_size: float, + projx: bool = True, + proju: bool = True, + method: str = "euler", + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + verbose: bool = False, + **model_extras, + ) -> Tensor: + r"""Solve the ODE with the `velocity_field` on the manifold. + + Args: + x_init (Tensor): initial conditions (e.g., source samples :math:`X_0 \sim p`). + step_size (float): The step size. + projx (bool): Whether to project the point onto the manifold at each step. Defaults to True. + proju (bool): Whether to project the vector field onto the tangent plane at each step. Defaults to True. + method (str): One of ["euler", "midpoint", "rk4"]. Defaults to "euler". + time_grid (Tensor, optional): The process is solved in the interval [min(time_grid, max(time_grid)] and if step_size is None then time discretization is set by the time grid. Defaults to torch.tensor([0.0,1.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Defaults to False. + verbose (bool, optional): Whether to print progress bars. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Tensor: The sampled sequence. Defaults to returning samples at :math:`t=1`. + """ + step_fns = { + "euler": _euler_step, + "midpoint": _midpoint_step, + "rk4": _rk4_step, + } + assert method in step_fns.keys(), f"Unknown method {method}" + step_fn = step_fns[method] + + # --- Factor this out. + time_grid = torch.sort(time_grid.to(device=x_init.device)).values + + if step_size is None: + # If step_size is None then set the t discretization to time_grid. + t_discretization = time_grid + n_steps = len(time_grid) - 1 + else: + # If step_size is float then t discretization is uniform with step size set by step_size. + t_init = time_grid[0].item() + t_final = time_grid[-1].item() + assert ( + t_final - t_init + ) > step_size, f"Time interval [min(time_grid), max(time_grid)] must be larger than step_size. Got a time interval [{t_init}, {t_final}] and step_size {step_size}." + + n_steps = math.ceil((t_final - t_init) / step_size) + t_discretization = torch.tensor( + [step_size * i for i in range(n_steps)] + [t_final], + device=x_init.device, + ) + # --- + t0s = t_discretization[:-1] + + if verbose: + t0s = tqdm(t0s) + + if return_intermediates: + xts = [] + i_ret = 0 + + xt = x_init + for t0, t1 in zip(t0s, t_discretization[1:]): + dt = t1 - t0 + xt_next = step_fn( + self.velocity_model, + xt, + t0, + dt, + manifold=self.manifold, + projx=projx, + proju=proju, + ) + if return_intermediates: + while ( + i_ret < len(time_grid) + and t0 <= time_grid[i_ret] + and time_grid[i_ret] <= t1 + ): + xts.append( + interp(self.manifold, xt, xt_next, t0, t1, time_grid[i_ret]) + ) + i_ret += 1 + xt = xt_next + + if return_intermediates: + return torch.stack(xts, dim=0) + else: + return xt + + +def interp(manifold, xt, xt_next, t, t_next, t_ret): + return geodesic(manifold, xt, xt_next)( + (t_ret - t) / (t_next - t).reshape(1) + ).reshape_as(xt) + + +def _euler_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform an Euler step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + vt = velocity_fn(xt, t0) + + xt = xt + dt * vt + + return projx_fn(xt) + + +def _midpoint_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform a midpoint step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + half_dt = 0.5 * dt + vt = velocity_fn(xt, t0) + x_mid = xt + half_dt * vt + x_mid = projx_fn(x_mid) + + xt = xt + dt * velocity_fn(x_mid, t0 + half_dt) + + return projx_fn(xt) + + +def _rk4_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform an RK4 step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + k1 = velocity_fn(xt, t0) + k2 = velocity_fn(projx_fn(xt + dt * k1 / 3), t0 + dt / 3) + k3 = velocity_fn(projx_fn(xt + dt * (k2 - k1 / 3)), t0 + dt * 2 / 3) + k4 = velocity_fn(projx_fn(xt + dt * (k1 - k2 + k3)), t0 + dt) + + return projx_fn(xt + (k1 + 3 * (k2 + k3) + k4) * dt * 0.125) diff --git a/flow_matching/solver/solver.py b/flow_matching/solver/solver.py new file mode 100644 index 0000000..4819e1c --- /dev/null +++ b/flow_matching/solver/solver.py @@ -0,0 +1,17 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod + +from torch import nn, Tensor + + +class Solver(ABC, nn.Module): + """Abstract base class for solvers.""" + + @abstractmethod + def sample(self, x_0: Tensor = None) -> Tensor: + ... diff --git a/flow_matching/solver/utils.py b/flow_matching/solver/utils.py new file mode 100644 index 0000000..f3a34ee --- /dev/null +++ b/flow_matching/solver/utils.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + + +def get_nearest_times(time_grid: Tensor, t_discretization: Tensor) -> Tensor: + distances = torch.cdist( + time_grid.unsqueeze(1), + t_discretization.unsqueeze(1), + compute_mode="donot_use_mm_for_euclid_dist", + ) + nearest_indices = distances.argmin(dim=1) + + return t_discretization[nearest_indices] diff --git a/flow_matching/utils/__init__.py b/flow_matching/utils/__init__.py new file mode 100644 index 0000000..0085c44 --- /dev/null +++ b/flow_matching/utils/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .categorical_sampler import categorical +from .model_wrapper import ModelWrapper +from .utils import expand_tensor_like, gradient, unsqueeze_to_match + +__all__ = [ + "unsqueeze_to_match", + "expand_tensor_like", + "gradient", + "categorical", + "ModelWrapper", +] diff --git a/flow_matching/utils/categorical_sampler.py b/flow_matching/utils/categorical_sampler.py new file mode 100644 index 0000000..70937af --- /dev/null +++ b/flow_matching/utils/categorical_sampler.py @@ -0,0 +1,23 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + + +def categorical(probs: Tensor) -> Tensor: + r"""Categorical sampler according to weights in the last dimension of ``probs`` using :func:`torch.multinomial`. + + Args: + probs (Tensor): probabilities. + + Returns: + Tensor: Samples. + """ + + return torch.multinomial(probs.flatten(0, -2), 1, replacement=True).view( + *probs.shape[:-1] + ) diff --git a/flow_matching/utils/manifolds/__init__.py b/flow_matching/utils/manifolds/__init__.py new file mode 100644 index 0000000..1148872 --- /dev/null +++ b/flow_matching/utils/manifolds/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .manifold import Euclidean, Manifold +from .sphere import Sphere +from .torus import FlatTorus +from .utils import geodesic + +__all__ = [ + "Euclidean", + "Manifold", + "Sphere", + "FlatTorus", + "geodesic", +] diff --git a/flow_matching/utils/manifolds/manifold.py b/flow_matching/utils/manifolds/manifold.py new file mode 100644 index 0000000..52a6a1b --- /dev/null +++ b/flow_matching/utils/manifolds/manifold.py @@ -0,0 +1,93 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import abc + +import torch.nn as nn +from torch import Tensor + + +class Manifold(nn.Module, metaclass=abc.ABCMeta): + """A manifold class that contains projection operations and logarithm and exponential maps.""" + + @abc.abstractmethod + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + r"""Computes exponential map :math:`\exp_x(u)`. + + Args: + x (Tensor): point on the manifold + u (Tensor): tangent vector at point :math:`x` + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: transported point + """ + raise NotImplementedError + + @abc.abstractmethod + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + r"""Computes logarithmic map :math:`\log_x(y)`. + + Args: + x (Tensor): point on the manifold + y (Tensor): point on the manifold + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: tangent vector at point :math:`x` + """ + raise NotImplementedError + + @abc.abstractmethod + def projx(self, x: Tensor) -> Tensor: + """Project point :math:`x` on the manifold. + + Args: + x (Tensor): point to be projected + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: projected point on the manifold + """ + raise NotImplementedError + + @abc.abstractmethod + def proju(self, x: Tensor, u: Tensor) -> Tensor: + """Project vector :math:`u` on a tangent space for :math:`x`. + + Args: + x (Tensor): point on the manifold + u (Tensor): vector to be projected + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: projected tangent vector + """ + raise NotImplementedError + + +class Euclidean(Manifold): + """The Euclidean manifold.""" + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + return x + u + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + return y - x + + def projx(self, x: Tensor) -> Tensor: + return x + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u diff --git a/flow_matching/utils/manifolds/sphere.py b/flow_matching/utils/manifolds/sphere.py new file mode 100644 index 0000000..76bf748 --- /dev/null +++ b/flow_matching/utils/manifolds/sphere.py @@ -0,0 +1,45 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + +from flow_matching.utils.manifolds import Manifold + + +class Sphere(Manifold): + """Represents a hyperpshere in :math:`R^D`. Isometric to the product of 1-D spheres.""" + + EPS = {torch.float32: 1e-4, torch.float64: 1e-7} + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + norm_u = u.norm(dim=-1, keepdim=True) + exp = x * torch.cos(norm_u) + u * torch.sin(norm_u) / norm_u + retr = self.projx(x + u) + cond = norm_u > self.EPS[norm_u.dtype] + + return torch.where(cond, exp, retr) + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + u = self.proju(x, y - x) + dist = self.dist(x, y, keepdim=True) + cond = dist.gt(self.EPS[x.dtype]) + result = torch.where( + cond, + u * dist / u.norm(dim=-1, keepdim=True).clamp_min(self.EPS[x.dtype]), + u, + ) + return result + + def projx(self, x: Tensor) -> Tensor: + return x / x.norm(dim=-1, keepdim=True) + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u - (x * u).sum(dim=-1, keepdim=True) * x + + def dist(self, x: Tensor, y: Tensor, *, keepdim=False) -> Tensor: + inner = (x * y).sum(-1, keepdim=keepdim) + return torch.acos(inner) diff --git a/flow_matching/utils/manifolds/torus.py b/flow_matching/utils/manifolds/torus.py new file mode 100644 index 0000000..3587ed7 --- /dev/null +++ b/flow_matching/utils/manifolds/torus.py @@ -0,0 +1,28 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +from torch import Tensor + +from flow_matching.utils.manifolds import Manifold + + +class FlatTorus(Manifold): + r"""Represents a flat torus on the :math:`[0, 2\pi]^D` subspace. Isometric to the product of 1-D spheres.""" + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + return (x + u) % (2 * math.pi) + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + return torch.atan2(torch.sin(y - x), torch.cos(y - x)) + + def projx(self, x: Tensor) -> Tensor: + return x % (2 * math.pi) + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u diff --git a/flow_matching/utils/manifolds/utils.py b/flow_matching/utils/manifolds/utils.py new file mode 100644 index 0000000..b83d2fa --- /dev/null +++ b/flow_matching/utils/manifolds/utils.py @@ -0,0 +1,45 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Callable + +import torch +from torch import Tensor + +from flow_matching.utils.manifolds import Manifold + + +def geodesic( + manifold: Manifold, start_point: Tensor, end_point: Tensor +) -> Callable[[Tensor], Tensor]: + """Generate parameterized function for geodesic curve. + + Args: + manifold (Manifold): the manifold to compute geodesic on. + start_point (Tensor): point on the manifold at :math:`t=0`. + end_point (Tensor): point on the manifold at :math:`t=1`. + + Returns: + Callable[[Tensor], Tensor]: a function that takes in :math:`t` and outputs the geodesic at time :math:`t`. + """ + + shooting_tangent_vec = manifold.logmap(start_point, end_point) + + def path(t: Tensor) -> Tensor: + """Generate parameterized function for geodesic curve. + + Args: + t (Tensor): Times at which to compute points of the geodesics. + + Returns: + Tensor: geodesic path evaluated at time t. + """ + tangent_vecs = torch.einsum("i,...k->...ik", t, shooting_tangent_vec) + points_at_time_t = manifold.expmap(start_point.unsqueeze(-2), tangent_vecs) + + return points_at_time_t + + return path diff --git a/flow_matching/utils/model_wrapper.py b/flow_matching/utils/model_wrapper.py new file mode 100644 index 0000000..22733ac --- /dev/null +++ b/flow_matching/utils/model_wrapper.py @@ -0,0 +1,43 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC + +from torch import nn, Tensor + + +class ModelWrapper(ABC, nn.Module): + """ + This class is used to wrap around another model, adding custom forward pass logic. + """ + + def __init__(self, model: nn.Module): + super().__init__() + self.model = model + + def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor: + r""" + This method defines how inputs should be passed through the wrapped model. + Here, we're assuming that the wrapped model takes both :math:`x` and :math:`t` as input, + along with any additional keyword arguments. + + Optional things to do here: + - check that t is in the dimensions that the model is expecting. + - add a custom forward pass logic. + - call the wrapped model. + + | given x, t + | returns the model output for input x at time t, with extra information `extra`. + + Args: + x (Tensor): input data to the model (Batch, ...). + t (Tensor): time (Batch). + **extras: additional information forwarded to the model, e.g., text condition. + + Returns: + Tensor: model output. + """ + return self.model(x=x, t=t, **extras) diff --git a/flow_matching/utils/utils.py b/flow_matching/utils/utils.py new file mode 100644 index 0000000..9b75521 --- /dev/null +++ b/flow_matching/utils/utils.py @@ -0,0 +1,87 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional + +import torch +from torch import Tensor + + +def unsqueeze_to_match(source: Tensor, target: Tensor, how: str = "suffix") -> Tensor: + """ + Unsqueeze the source tensor to match the dimensionality of the target tensor. + + Args: + source (Tensor): The source tensor to be unsqueezed. + target (Tensor): The target tensor to match the dimensionality of. + how (str, optional): Whether to unsqueeze the source tensor at the beginning + ("prefix") or end ("suffix"). Defaults to "suffix". + + Returns: + Tensor: The unsqueezed source tensor. + """ + assert ( + how == "prefix" or how == "suffix" + ), f"{how} is not supported, only 'prefix' and 'suffix' are supported." + + dim_diff = target.dim() - source.dim() + + for _ in range(dim_diff): + if how == "prefix": + source = source.unsqueeze(0) + elif how == "suffix": + source = source.unsqueeze(-1) + + return source + + +def expand_tensor_like(input_tensor: Tensor, expand_to: Tensor) -> Tensor: + """`input_tensor` is a 1d vector of length equal to the batch size of `expand_to`, + expand `input_tensor` to have the same shape as `expand_to` along all remaining dimensions. + + Args: + input_tensor (Tensor): (B,). + expand_to (Tensor): (B, ...). + + Returns: + Tensor: (B, ...). + """ + assert input_tensor.ndim == 1, "Input tensor must be a 1d vector." + + dim_diff = expand_to.ndim - input_tensor.ndim + + t_expanded = input_tensor.clone() + t_expanded = t_expanded.reshape(-1, *([1] * dim_diff)) + + return t_expanded.expand_as(expand_to) + + +def gradient( + output: Tensor, + x: Tensor, + grad_outputs: Optional[Tensor] = None, + create_graph: bool = False, +) -> Tensor: + """ + Compute the gradient of the inner product of output and grad_outputs w.r.t :math:`x`. + + Args: + output (Tensor): [N, D] Output of the function. + x (Tensor): [N, d_1, d_2, ... ] input + grad_outputs (Optional[Tensor]): [N, D] Gradient of outputs, if `None`, + then will use a tensor of ones + create_graph (bool): If True, graph of the derivative will be constructed, allowing + to compute higher order derivative products. Defaults to False. + Returns: + Tensor: [N, d_1, d_2, ... ]. the gradient w.r.t x. + """ + + if grad_outputs is None: + grad_outputs = torch.ones_like(output).detach() + grad = torch.autograd.grad( + output, x, grad_outputs=grad_outputs, create_graph=create_graph + )[0] + return grad diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..2d43be2 --- /dev/null +++ b/setup.py @@ -0,0 +1,75 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import os + +import setuptools + +NAME = "flow_matching" +DESCRIPTION = "Flow Matching for Generative Modeling" +URL = "https://github.com/facebookresearch/flow_matching" +EMAIL = "ylipman@meta.com" +# Alphabetical +AUTHOR = ",".join( + [ + "Brian Karrer", + "David Lopez-Paz", + "Heli Ben-Hamu", + "Itai Gat", + "Marton Havasi", + "Matthew Le", + "Neta Shaul", + "Peter Holderrieth", + "Ricky T.Q. Chen", + "Yaron Lipman", + ] +) +REQUIRES_PYTHON = ">=3.9.0" + +for line in open("flow_matching/__init__.py"): + line = line.strip() + if "__version__" in line: + context = {} + exec(line, context) + VERSION = context["__version__"] + +readme_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "README.md") + +try: + with open(readme_path) as f: + long_description = "\n" + f.read() +except FileNotFoundError: + long_description = DESCRIPTION + +setuptools.setup( + name=NAME, + version=VERSION, + description=DESCRIPTION, + long_description=long_description, + long_description_content_type="text/markdown", + author=AUTHOR, + author_email=EMAIL, + python_requires=REQUIRES_PYTHON, + url=URL, + packages=setuptools.find_packages(), + extras_require={ + "dev": [ + "pre-commit", + "black==22.6.0", + "usort==1.0.4", + "ufmt==2.3.0", + "flake8==7.0.0", + "pydoclint", + ], + }, + install_requires=["numpy", "torch", "torchdiffeq"], + license="CC-by-NC", + classifiers=[ + # Trove classifiers + # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "License :: OSI Approved :: MIT License", + ], +) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..36d7195 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. diff --git a/tests/path/__init__.py b/tests/path/__init__.py new file mode 100644 index 0000000..36d7195 --- /dev/null +++ b/tests/path/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. diff --git a/tests/path/test_path.py b/tests/path/test_path.py new file mode 100644 index 0000000..ba7fb4b --- /dev/null +++ b/tests/path/test_path.py @@ -0,0 +1,181 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import math +import unittest + +import torch +from flow_matching.path import ( + AffineProbPath, + CondOTProbPath, + GeodesicProbPath, + MixtureDiscreteProbPath, +) +from flow_matching.path.scheduler import CondOTScheduler +from flow_matching.utils.manifolds import FlatTorus, Sphere + + +class TestAffineProbPath(unittest.TestCase): + def test_affine_prob_path_sample(self): + scheduler = CondOTScheduler() + affine_prob_path = AffineProbPath(scheduler) + x_0 = torch.randn(10, 5) + x_1 = torch.randn(10, 5) + t = torch.randn(10) + sample = affine_prob_path.sample(x_0, x_1, t) + self.assertEqual(sample.x_t.shape, x_0.shape) + self.assertEqual(sample.dx_t.shape, x_0.shape) + self.assertTrue((sample.t == t).all()) + self.assertTrue((sample.x_0 == x_0).all()) + self.assertTrue((sample.x_1 == x_1).all()) + + def test_assert_sample_shape(self): + scheduler = CondOTScheduler() + path = AffineProbPath(scheduler) + x_0 = torch.randn(10, 5) + x_1 = torch.randn(10, 5) + t = torch.randn(10) + path.assert_sample_shape(x_0, x_1, t) + + x_0 = torch.randn(10, 5) + x_1 = torch.randn(10, 5) + t = torch.randn(5) + with self.assertRaises(AssertionError): + path.assert_sample_shape(x_0, x_1, t) + + def test_cond_ot_prob_path_sample(self): + cond_ot_prob_path = CondOTProbPath() + scheduler = CondOTScheduler() + affine_path = AffineProbPath(scheduler) + x_0 = torch.randn(10, 5) + x_1 = torch.randn(10, 5) + t = torch.randn(10) + sample1 = cond_ot_prob_path.sample(x_0, x_1, t) + sample2 = affine_path.sample(x_0, x_1, t) + self.assertTrue(torch.allclose(sample1.x_t, sample2.x_t)) + + def test_to_velocity(self): + path = CondOTProbPath() + x_1 = torch.randn(10, 5, dtype=torch.float64) + x_t = torch.randn(10, 5, dtype=torch.float64) + t = torch.randn(10, 5, dtype=torch.float64) + velocity = path.target_to_velocity(x_1, x_t, t) + target = path.velocity_to_target(velocity, x_t, t) + self.assertTrue(torch.allclose(target, x_1)) + + def test_to_epsilon(self): + path = CondOTProbPath() + x_1 = torch.randn(10, 5, dtype=torch.float64) + x_t = torch.randn(10, 5, dtype=torch.float64) + t = torch.randn(10, 5, dtype=torch.float64) + epsilon = path.target_to_epsilon(x_1, x_t, t) + target = path.epsilon_to_target(epsilon, x_t, t) + self.assertTrue(torch.allclose(target, x_1)) + + def test_epsilson_velocity(self): + path = CondOTProbPath() + velocity = torch.randn(10, 5, dtype=torch.float64) + x_t = torch.randn(10, 5, dtype=torch.float64) + t = torch.randn(10, 5, dtype=torch.float64) + + epsilon = path.velocity_to_epsilon(velocity, x_t, t) + v = path.epsilon_to_velocity(epsilon, x_t, t) + self.assertTrue(torch.allclose(v, velocity)) + + +class TestGeodesicProbPath(unittest.TestCase): + def test_sphere(self): + manifold = Sphere() + path = GeodesicProbPath(manifold=manifold, scheduler=CondOTScheduler()) + + def wrap(samples): + center = torch.cat( + [torch.zeros_like(samples), torch.ones_like(samples[..., 0:1])], dim=-1 + ) + samples = ( + torch.cat([samples, torch.zeros_like(samples[..., 0:1])], dim=-1) / 2 + ) + return manifold.expmap(center, samples) + + x1 = manifold.projx(torch.rand(5, 5, dtype=torch.float64)) + x0 = torch.randn_like(x1) + x0 = wrap(x0) + x1 = wrap(x1) + t = torch.rand(x0.size(0), dtype=torch.float64) + + sample = path.sample(t=t, x_0=x0, x_1=x1) + + # Check that x_t is on the sphere + self.assertTrue( + torch.allclose( + sample.x_t.norm(2, -1), torch.ones(x0.size(0), dtype=torch.float64) + ) + ) + + def test_torus(self): + manifold = FlatTorus() + path = GeodesicProbPath(manifold=manifold, scheduler=CondOTScheduler()) + + def wrap(samples): + center = torch.zeros_like(samples) + return manifold.expmap(center, samples) + + batch_size = 5 + coord1 = torch.rand(batch_size, dtype=torch.float64) * 4 - 2 + coord2_ = ( + torch.rand(batch_size, dtype=torch.float64) + - torch.randint(high=2, size=(batch_size,), dtype=torch.float64) * 2 + ) + coord2 = coord2_ + (torch.floor(coord1) % 2) + + x1 = torch.stack([coord1, coord2], dim=1) + x0 = torch.randn_like(x1) + x0 = wrap(x0) + x1 = wrap(x1) + t = torch.rand(x0.size(0), dtype=torch.float64) + + sample = path.sample(t=t, x_0=x0, x_1=x1) + + self.assertTrue((sample.x_t < 2 * math.pi).all()) + + +class TestMixtureDiscreteProbPath(unittest.TestCase): + def test_mixture_discrete_prob_path_sample(self): + scheduler = CondOTScheduler() + discrete_prob_path = MixtureDiscreteProbPath(scheduler) + x_0 = torch.randn(10, 5) + x_1 = torch.randn(10, 5) + t = torch.randn(10) + sample = discrete_prob_path.sample(x_0, x_1, t) + self.assertEqual(sample.x_t.shape, x_0.shape) + self.assertTrue((sample.t == t).all()) + self.assertTrue((sample.x_0 == x_0).all()) + self.assertTrue((sample.x_1 == x_1).all()) + + # Test at t=0 + t = torch.zeros(10) + sample = discrete_prob_path.sample(x_0, x_1, t) + self.assertTrue(torch.allclose(sample.x_t, x_0)) + # Test at t=1 + t = torch.ones(10) + sample = discrete_prob_path.sample(x_0, x_1, t) + self.assertTrue(torch.allclose(sample.x_t, x_1)) + + def test_posterior_to_velocity(self): + scheduler = CondOTScheduler() + discrete_prob_path = MixtureDiscreteProbPath(scheduler) + posterior_logits = torch.randn(10, 5) + x_t = torch.randint(0, 5, size=[10]) + t = torch.randn(10) + x_t_one_hot = torch.nn.functional.one_hot(x_t, num_classes=5) + velocity = discrete_prob_path.posterior_to_velocity(posterior_logits, x_t, t) + expected_velocity = (torch.softmax(posterior_logits, dim=-1) - x_t_one_hot) / ( + 1 - t + ).unsqueeze(-1) + self.assertTrue(torch.allclose(velocity, expected_velocity)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/path/test_schedule_transform.py b/tests/path/test_schedule_transform.py new file mode 100644 index 0000000..e6b4c77 --- /dev/null +++ b/tests/path/test_schedule_transform.py @@ -0,0 +1,65 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import unittest + +import torch +from flow_matching.path.scheduler import ( + CondOTScheduler, + CosineScheduler, + ScheduleTransformedModel, +) +from flow_matching.solver import ODESolver +from flow_matching.utils import ModelWrapper + + +class DummyModel(ModelWrapper): + def __init__(self): + super().__init__(None) + + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return x * t**2 + + +class TestScheduleTransformedModel(unittest.TestCase): + def setUp(self): + self.batch_size = 10 + self.data_dim = 2 + self.num_steps = 1000 + self.x_0 = torch.randn([self.batch_size, self.data_dim]) + self.model = DummyModel() + self.original_scheduler = CondOTScheduler() + self.new_scheduler = CosineScheduler() + + def test_schedule_transformation(self): + solver_original = ODESolver(velocity_model=self.model) + x_1_original = solver_original.sample( + time_steps=torch.tensor([0.0, 1.0]), + x_init=self.x_0, + step_size=1 / self.num_steps, + method="euler", + )[1] + transformed_model = ScheduleTransformedModel( + velocity_model=self.model, + original_scheduler=self.original_scheduler, + new_scheduler=self.new_scheduler, + ) + + solver_transformed = ODESolver(velocity_model=transformed_model) + x_1_transformed = solver_transformed.sample( + time_steps=torch.tensor([0.0, 1.0]), + x_init=self.x_0, + step_size=1 / self.num_steps, + method="euler", + )[1] + + self.assertTrue( + torch.allclose(x_1_original, x_1_transformed, atol=1e-2), + "The samples with and without the transformed scheduler should be approximately equal.", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/path/test_scheduler.py b/tests/path/test_scheduler.py new file mode 100644 index 0000000..508b48c --- /dev/null +++ b/tests/path/test_scheduler.py @@ -0,0 +1,93 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import unittest + +import torch + +from flow_matching.path.scheduler import ( + CondOTScheduler, + CosineScheduler, + LinearVPScheduler, + PolynomialConvexScheduler, + SchedulerOutput, + VPScheduler, +) +from torch import Tensor + + +class TestScheduler(unittest.TestCase): + def setUp(self): + self.t = torch.tensor([0.1, 0.5, 0.9]) + + def assert_output_shapes( + self, outputs: SchedulerOutput, expected_shape: torch.Size + ): + self.assertEqual(outputs.alpha_t.shape, expected_shape) + self.assertEqual(outputs.sigma_t.shape, expected_shape) + self.assertEqual(outputs.d_alpha_t.shape, expected_shape) + self.assertEqual(outputs.d_sigma_t.shape, expected_shape) + + def assert_recover_t_from_kappa(self, scheduler, t: Tensor): + scheduler_output = scheduler(t) + t_recovered = scheduler.kappa_inverse(scheduler_output.alpha_t) + + self.assertTrue( + torch.allclose(t, t_recovered, atol=1e-5), + f"Recovered t: {t_recovered}, Original t: {t}", + ) + + def assert_recover_t_from_snr(self, scheduler, t: Tensor): + scheduler_output = scheduler(t) + snr = scheduler_output.alpha_t / scheduler_output.sigma_t + + t_recovered = scheduler.snr_inverse(snr) + + self.assertTrue( + torch.allclose(t, t_recovered, atol=1e-5), + f"Recovered t: {t_recovered}, Original t: {t}", + ) + + def test_cond_ot_scheduler(self): + scheduler = CondOTScheduler() + outputs = scheduler(self.t) + + self.assert_output_shapes(outputs, self.t.shape) + + self.assert_recover_t_from_kappa(scheduler, self.t) + self.assert_recover_t_from_snr(scheduler, self.t) + + def test_cosine_scheduler(self): + scheduler = CosineScheduler() + outputs = scheduler(self.t) + self.assert_output_shapes(outputs, self.t.shape) + + self.assert_recover_t_from_snr(scheduler, self.t) + + def test_scheduler_vp(self): + scheduler = VPScheduler() + outputs = scheduler(self.t) + self.assert_output_shapes(outputs, self.t.shape) + + self.assert_recover_t_from_snr(scheduler, self.t) + + def test_scheduler_vp_linear(self): + scheduler = LinearVPScheduler() + outputs = scheduler(self.t) + self.assert_output_shapes(outputs, self.t.shape) + + self.assert_recover_t_from_snr(scheduler, self.t) + + def test_polynomial_convex_scheduler(self): + scheduler = PolynomialConvexScheduler(n=2) + outputs = scheduler(self.t) + self.assert_output_shapes(outputs, self.t.shape) + + self.assert_recover_t_from_kappa(scheduler, self.t) + self.assert_recover_t_from_snr(scheduler, self.t) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/solver/__init__.py b/tests/solver/__init__.py new file mode 100644 index 0000000..36d7195 --- /dev/null +++ b/tests/solver/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. diff --git a/tests/solver/test_discrete_solver.py b/tests/solver/test_discrete_solver.py new file mode 100644 index 0000000..6877eae --- /dev/null +++ b/tests/solver/test_discrete_solver.py @@ -0,0 +1,115 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import unittest + +import torch +from flow_matching.path import MixtureDiscreteProbPath +from flow_matching.path.scheduler import PolynomialConvexScheduler +from flow_matching.solver import MixtureDiscreteEulerSolver + + +class DummyModel(torch.nn.Module): + def forward(self, x, t, **extras): + return torch.stack( + [torch.zeros_like(x), torch.zeros_like(x), torch.ones_like(x)], dim=-1 + ) + + +class TestMixtureDiscreteEulerSolver(unittest.TestCase): + def setUp(self): + self.model = DummyModel() + self.path = MixtureDiscreteProbPath(scheduler=PolynomialConvexScheduler(n=1.0)) + self.vocabulary_size = 3 + self.source_distribution_p = torch.tensor([0.5, 0.5, 0.0]) + + def test_init(self): + solver = MixtureDiscreteEulerSolver( + model=self.model, + path=self.path, + vocabulary_size=self.vocabulary_size, + source_distribution_p=self.source_distribution_p, + ) + self.assertEqual(solver.model, self.model) + self.assertEqual(solver.path, self.path) + self.assertEqual(solver.vocabulary_size, self.vocabulary_size) + self.assertTrue( + torch.allclose(solver.source_distribution_p, self.source_distribution_p) + ) + + def test_sample(self): + solver = MixtureDiscreteEulerSolver( + model=self.model, + path=self.path, + vocabulary_size=self.vocabulary_size, + source_distribution_p=self.source_distribution_p, + ) + x_init = torch.tensor([[0]]) + step_size = 0.1 + time_grid = torch.tensor([0.0, 1.0]) + result = solver.sample(x_init, step_size, time_grid=time_grid) + self.assertEqual(result, torch.ones_like(result) * 2) + + def test_sample_with_sym_term(self): + solver = MixtureDiscreteEulerSolver( + model=self.model, + path=self.path, + vocabulary_size=self.vocabulary_size, + source_distribution_p=self.source_distribution_p, + ) + x_init = torch.tensor([[0]]) + step_size = 0.1 + time_grid = torch.tensor([0.0, 1.0]) + div_free = 1.0 + result = solver.sample( + x_init, step_size, time_grid=time_grid, div_free=div_free, verbose=True + ) + self.assertIsInstance(result, torch.Tensor) + result = solver.sample( + x_init, step_size, time_grid=time_grid, div_free=lambda t: 1.0, verbose=True + ) + self.assertIsInstance(result, torch.Tensor) + + def test_init_p_none(self): + solver = MixtureDiscreteEulerSolver( + model=self.model, + path=self.path, + vocabulary_size=self.vocabulary_size, + ) + self.assertIsNone(solver.source_distribution_p) + + def test_sample_time_grid(self): + solver = MixtureDiscreteEulerSolver( + model=self.model, + path=self.path, + vocabulary_size=self.vocabulary_size, + source_distribution_p=self.source_distribution_p, + ) + x_init = torch.tensor([0]) + time_grid = torch.linspace(0.0, 1.0, steps=11) + result = solver.sample( + x_init, step_size=None, time_grid=time_grid, return_intermediates=True + ) + self.assertEqual(result[-1], torch.ones_like(result[-1]) * 2) + self.assertEqual(result.shape, (11, 1)) + + def test_sample_return_intermediate(self): + solver = MixtureDiscreteEulerSolver( + model=self.model, + path=self.path, + vocabulary_size=self.vocabulary_size, + source_distribution_p=self.source_distribution_p, + ) + x_init = torch.tensor([0]) + time_grid = torch.linspace(0.0, 1.0, steps=3) + result = solver.sample( + x_init, step_size=0.1, time_grid=time_grid, return_intermediates=True + ) + self.assertEqual(result[-1], torch.ones_like(result[-1]) * 2) + self.assertEqual(result.shape, (3, 1)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/solver/test_ode_solver.py b/tests/solver/test_ode_solver.py new file mode 100644 index 0000000..fbbefd2 --- /dev/null +++ b/tests/solver/test_ode_solver.py @@ -0,0 +1,120 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import unittest +from unittest.mock import MagicMock + +import torch +from flow_matching.solver import ODESolver +from flow_matching.utils import ModelWrapper +from torch import Tensor + + +class DummyModel(ModelWrapper): + def __init__(self): + super().__init__(None) + + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return (x * 0.0 + 1.0) * 3.0 * t**2 + + +class ConstantVelocityModel(ModelWrapper): + def __init__(self): + super().__init__(None) + + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return x * 0.0 + 1.0 + + +class TestODESolver(unittest.TestCase): + def setUp(self): + self.mock_velocity_model = MagicMock(spec=ModelWrapper) + self.mock_velocity_model.return_value = torch.tensor([1.0, 1.0]) + + self.dummy_velocity_model = DummyModel() + self.constant_velocity_model = ConstantVelocityModel() + + # Initialize the ODESolver with the mock model + self.mock_solver = ODESolver(velocity_model=self.mock_velocity_model) + self.dummy_solver = ODESolver(velocity_model=self.dummy_velocity_model) + self.constant_velocity_solver = ODESolver( + velocity_model=self.constant_velocity_model + ) + + def test_sample(self): + x_init = torch.tensor([0.0, 0.0]) + step_size = 0.1 + time_grid = torch.tensor([0.0, 1.0]) + + result = self.mock_solver.sample( + x_init=x_init, step_size=step_size, time_grid=time_grid + ) + + self.assertIsInstance(result, Tensor) + self.mock_velocity_model.assert_called() + self.assertEqual(x_init.shape, result.shape) + + def test_sample_with_different_methods(self): + x_init = torch.tensor([1.0, 0.0]) + step_size = 0.001 + time_grid = torch.tensor([0.0, 1.0]) + + for method in ["euler", "dopri5", "midpoint", "heun3"]: + with self.subTest(method=method): + result = self.dummy_solver.sample( + x_init=x_init, + step_size=step_size if method != "dopri5" else None, + time_grid=time_grid, + method=method, + ) + self.assertIsInstance(result, Tensor) + self.assertTrue( + torch.allclose(torch.tensor([2.0, 1.0]), result, atol=1e-2), + "The solution to the velocity field 3t^3 from 0 to 1 is incorrect.", + ) + + def test_compute_likelihood(self): + x_1 = torch.tensor([[0.0, 0.0]]) + step_size = 0.1 + + # Define a dummy log probability function + def dummy_log_p(x: Tensor) -> Tensor: + return -0.5 * torch.sum(x**2, dim=1) + + _, log_likelihood = self.dummy_solver.compute_likelihood( + x_1=x_1, + log_p0=dummy_log_p, + step_size=step_size, + exact_divergence=False, + ) + self.assertIsInstance(log_likelihood, Tensor) + self.assertEqual(x_1.shape[0], log_likelihood.shape[0]) + + def test_compute_likelihood_exact_divergence(self): + x_1 = torch.tensor([[0.0, 0.0]]) + step_size = 0.1 + + # Define a dummy log probability function + def dummy_log_p(x: Tensor) -> Tensor: + return -0.5 * torch.sum(x**2) + + x_0, log_likelihood = self.constant_velocity_solver.compute_likelihood( + x_1=x_1, + log_p0=dummy_log_p, + step_size=step_size, + exact_divergence=True, + ) + self.assertIsInstance(log_likelihood, Tensor) + self.assertEqual(x_1.shape[0], log_likelihood.shape[0]) + self.assertTrue( + torch.allclose(dummy_log_p(x_1 - 1.0), log_likelihood, atol=1e-2), + ) + self.assertTrue( + torch.allclose(x_1 - 1.0, x_0, atol=1e-2), + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/solver/test_riemannian_ode_solver.py b/tests/solver/test_riemannian_ode_solver.py new file mode 100644 index 0000000..1acf838 --- /dev/null +++ b/tests/solver/test_riemannian_ode_solver.py @@ -0,0 +1,157 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import unittest + +import torch +from flow_matching.solver.riemannian_ode_solver import RiemannianODESolver +from flow_matching.utils.manifolds import Sphere + + +class HundredVelocityModel(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, t): + return torch.ones_like(x) * 100.0 + + +class ZeroVelocityModel(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, t): + return torch.zeros_like(x) + + +class TestRiemannianODESolver(unittest.TestCase): + def setUp(self): + self.manifold = Sphere() + self.velocity_model = HundredVelocityModel() + self.solver = RiemannianODESolver(self.manifold, self.velocity_model) + + def test_init(self): + self.assertEqual(self.solver.manifold, self.manifold) + self.assertEqual(self.solver.velocity_model, self.velocity_model) + + def test_sample_euler(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + step_size = 0.01 + time_grid = torch.tensor([0.0, 1.0]) + result = self.solver.sample( + x_init, step_size, method="euler", time_grid=time_grid + ) + self.assertTrue( + torch.allclose( + result, + torch.nn.functional.normalize( + torch.tensor([1.0, 1.0, 1.0]), dim=0, p=2.0 + ), + rtol=1e-3, + ) + ) + + def test_sample_midpoint(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + step_size = 0.01 + time_grid = torch.tensor([0.0, 1.0]) + result = self.solver.sample( + x_init, step_size, method="midpoint", time_grid=time_grid + ) + self.assertTrue( + torch.allclose( + result, + torch.nn.functional.normalize( + torch.tensor([1.0, 1.0, 1.0]), dim=0, p=2.0 + ), + rtol=1e-3, + ) + ) + + def test_sample_rk4(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + step_size = 0.01 + time_grid = torch.tensor([0.0, 1.0]) + result = self.solver.sample( + x_init, step_size, method="rk4", time_grid=time_grid + ) + self.assertTrue( + torch.allclose( + result, + torch.nn.functional.normalize( + torch.tensor([1.0, 1.0, 1.0]), dim=0, p=2.0 + ), + rtol=1e-3, + ) + ) + + def test_zero_velocity_euler(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + step_size = 0.01 + time_grid = torch.tensor([0.0, 1.0]) + zero_velocity_model = ZeroVelocityModel() + solver = RiemannianODESolver(self.manifold, zero_velocity_model) + result = solver.sample(x_init, step_size, method="euler", time_grid=time_grid) + self.assertTrue(torch.allclose(result, x_init)) + + def test_zero_velocity_midpoint(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + step_size = 0.01 + time_grid = torch.tensor([0.0, 1.0]) + zero_velocity_model = ZeroVelocityModel() + solver = RiemannianODESolver(self.manifold, zero_velocity_model) + result = solver.sample( + x_init, step_size, method="midpoint", time_grid=time_grid + ) + self.assertTrue(torch.allclose(result, x_init)) + + def test_zero_velocity_rk4(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + step_size = 0.01 + time_grid = torch.tensor([0.0, 1.0]) + zero_velocity_model = ZeroVelocityModel() + solver = RiemannianODESolver(self.manifold, zero_velocity_model) + result = solver.sample(x_init, step_size, method="rk4", time_grid=time_grid) + self.assertTrue(torch.allclose(result, x_init)) + + def test_sample_euler_step_size_none(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + time_grid = torch.linspace(0.0, 1.0, steps=100) + result = self.solver.sample(x_init, None, method="euler", time_grid=time_grid) + self.assertTrue( + torch.allclose( + result, + torch.nn.functional.normalize( + torch.tensor([1.0, 1.0, 1.0]), dim=0, p=2.0 + ), + rtol=1e-3, + ) + ) + + def test_sample_euler_verbose(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + step_size = 0.01 + time_grid = torch.tensor([0.0, 1.0]) + result = self.solver.sample( + x_init, step_size, method="euler", time_grid=time_grid, verbose=True + ) + self.assertTrue(isinstance(result, torch.Tensor)) + + def test_sample_return_intermediates_euler(self): + x_init = self.manifold.projx(torch.randn(1, 3)) + step_size = 0.01 + time_grid = torch.tensor([0.0, 0.5, 1.0]) + result = self.solver.sample( + x_init, + step_size, + method="euler", + time_grid=time_grid, + return_intermediates=True, + ) + self.assertEqual(result.shape, (3, 1, 3)) # Two intermediate points + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/utils/__init__.py b/tests/utils/__init__.py new file mode 100644 index 0000000..36d7195 --- /dev/null +++ b/tests/utils/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py new file mode 100644 index 0000000..cd2858e --- /dev/null +++ b/tests/utils/test_utils.py @@ -0,0 +1,40 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. +import unittest + +import torch +from flow_matching.utils import expand_tensor_like, gradient, unsqueeze_to_match + + +class TestUtils(unittest.TestCase): + def test_unsqueeze_to_match_suffix(self): + source = torch.randn(3) + target = torch.randn(3, 4, 5) + result = unsqueeze_to_match(source, target) + self.assertEqual(result.shape, (3, 1, 1)) + + def test_unsqueeze_to_match_prefix(self): + source = torch.randn(3) + target = torch.randn(4, 5, 3) + result = unsqueeze_to_match(source, target, how="prefix") + self.assertEqual(result.shape, (1, 1, 3)) + + def test_expand_tensor_like(self): + input_tensor = torch.randn(3) + expand_to = torch.randn(3, 4, 5) + result = expand_tensor_like(input_tensor, expand_to) + self.assertEqual(result.shape, (3, 4, 5)) + + def test_gradient(self): + x = torch.randn(3, requires_grad=True) + output = x**2 + grad_outputs = torch.ones_like(output) + result = gradient(output, x, grad_outputs=grad_outputs) + self.assertTrue(torch.allclose(result, 2 * x)) + + +if __name__ == "__main__": + unittest.main()