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0000000..4cb0392 --- /dev/null +++ b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/README.md @@ -0,0 +1,3 @@ +| Model | Scenario | Accuracy | Throughput | Latency (in ms) | +|---------------------|------------|-----------------------|--------------|-------------------| +| stable-diffusion-xl | offline | (15.18026, 235.62721) | 0.37 | - | \ No newline at end of file diff --git a/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/README.md b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/README.md new file mode 100644 index 0000000..3425c19 --- /dev/null +++ b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/README.md @@ -0,0 +1,95 @@ +This experiment is generated using the [MLCommons Collective Mind automation framework (CM)](https://github.com/mlcommons/cm4mlops). + +*Check [CM MLPerf docs](https://docs.mlcommons.org/inference) for more details.* + +## Host platform + +* OS version: Linux-6.2.0-39-generic-x86_64-with-glibc2.35 +* CPU version: x86_64 +* Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] +* MLCommons CM version: 3.0.1 + +## CM Run Command + +See [CM installation guide](https://docs.mlcommons.org/inference/install/). + +```bash +pip install -U cmind + +cm rm cache -f + +cm pull repo gateoverflow@cm4mlops --checkout=0179dec7444249383219e6fcd3c85910554a0b19 + +cm run script \ + --tags=app,mlperf,inference,generic,_reference,_sdxl,_pytorch,_cuda,_test,_r4.1-dev_default,_float16,_offline \ + --quiet=true \ + --env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes \ + --env.CM_QUIET=yes \ + --env.CM_MLPERF_IMPLEMENTATION=reference \ + --env.CM_MLPERF_MODEL=sdxl \ + --env.CM_MLPERF_RUN_STYLE=test \ + --env.CM_MLPERF_SKIP_SUBMISSION_GENERATION=False \ + --env.CM_MLPERF_BACKEND=pytorch \ + --env.CM_MLPERF_SUBMISSION_SYSTEM_TYPE=datacenter \ + --env.CM_MLPERF_CLEAN_ALL=True \ + --env.CM_MLPERF_DEVICE=cuda \ + --env.CM_MLPERF_USE_DOCKER=True \ + --env.CM_MLPERF_MODEL_PRECISION=float16 \ + --env.OUTPUT_BASE_DIR=/home/arjun/scc_gh_action_results \ + --env.CM_MLPERF_LOADGEN_SCENARIO=Offline \ + --env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/home/arjun/scc_gh_action_submissions \ + --env.CM_MLPERF_INFERENCE_VERSION=4.1-dev \ + --env.CM_RUN_MLPERF_INFERENCE_APP_DEFAULTS=r4.1-dev_default \ + --env.CM_MLPERF_SUBMISSION_GENERATION_STYLE=short \ + --env.CM_MLPERF_SUT_NAME_RUN_CONFIG_SUFFIX4=scc24-base \ + --env.CM_DOCKER_IMAGE_NAME=scc24-reference \ + --env.CM_MLPERF_LOADGEN_ALL_MODES=yes \ + --env.CM_MLPERF_LAST_RELEASE=v4.0 \ + --env.CM_TMP_CURRENT_PATH=/home/arjun/actions-runner/_work/cm4mlops/cm4mlops \ + --env.CM_TMP_PIP_VERSION_STRING= \ + --env.CM_MODEL=sdxl \ + --env.CM_MLPERF_LOADGEN_COMPLIANCE=no \ + --env.CM_MLPERF_CLEAN_SUBMISSION_DIR=yes \ + --env.CM_RERUN=yes \ + --env.CM_MLPERF_LOADGEN_EXTRA_OPTIONS= \ + --env.CM_MLPERF_LOADGEN_MODE=performance \ + --env.CM_MLPERF_LOADGEN_SCENARIOS,=Offline \ + --env.CM_MLPERF_LOADGEN_MODES,=performance,accuracy \ + --env.CM_OUTPUT_FOLDER_NAME=test_results \ + --add_deps_recursive.get-mlperf-inference-results-dir.tags=_version.r4_1-dev \ + --add_deps_recursive.get-mlperf-inference-submission-dir.tags=_version.r4_1-dev \ + --add_deps_recursive.mlperf-inference-nvidia-scratch-space.tags=_version.r4_1-dev \ + --add_deps_recursive.submission-checker.tags=_short-run \ + --add_deps_recursive.coco2014-preprocessed.tags=_size.50,_with-sample-ids \ + --add_deps_recursive.coco2014-dataset.tags=_size.50,_with-sample-ids \ + --add_deps_recursive.nvidia-preprocess-data.extra_cache_tags=scc24-base \ + --v=False \ + --print_env=False \ + --print_deps=False \ + --dump_version_info=True \ + --env.OUTPUT_BASE_DIR=/home/arjun/scc_gh_action_results \ + --env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/home/arjun/scc_gh_action_submissions \ + --env.SDXL_CHECKPOINT_PATH=/home/cmuser/CM/repos/local/cache/6be1f30ecbde4c4e/stable_diffusion_fp16 +``` +*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf (CM scripts), + you should simply reload gateoverflow@cm4mlops without checkout and clean CM cache as follows:* + +```bash +cm rm repo gateoverflow@cm4mlops +cm pull repo gateoverflow@cm4mlops +cm rm cache -f + +``` + +## Results + +Platform: ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124 + +Model Precision: fp32 + +### Accuracy Results +`CLIP_SCORE`: `15.18026`, Required accuracy for closed division `>= 31.68632` and `<= 31.81332` +`FID_SCORE`: `235.62721`, Required accuracy for closed division `>= 23.01086` and `<= 23.95008` + +### Performance Results +`Samples per second`: `0.369623` diff --git a/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/accuracy_console.out 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Value is mostly int64_t. +# Model maybe '*' as wildcard. In that case the value applies to all models. +# All times are in milli seconds + +# Set performance_sample_count for each model. +# User can optionally set this to higher values in user.conf. +resnet50.*.performance_sample_count_override = 1024 +ssd-mobilenet.*.performance_sample_count_override = 256 +retinanet.*.performance_sample_count_override = 64 +bert.*.performance_sample_count_override = 10833 +dlrm.*.performance_sample_count_override = 204800 +dlrm-v2.*.performance_sample_count_override = 204800 +rnnt.*.performance_sample_count_override = 2513 +gptj.*.performance_sample_count_override = 13368 +llama2-70b.*.performance_sample_count_override = 24576 +stable-diffusion-xl.*.performance_sample_count_override = 5000 +# set to 0 to let entire sample set to be performance sample +3d-unet.*.performance_sample_count_override = 0 + +# Set seeds. The seeds will be distributed two weeks before the submission. +*.*.qsl_rng_seed = 3066443479025735752 +*.*.sample_index_rng_seed = 10688027786191513374 +*.*.schedule_rng_seed = 14962580496156340209 +# Set seeds for TEST_05. The seeds will be distributed two weeks before the submission. +*.*.test05_qsl_rng_seed = 16799458546791641818 +*.*.test05_sample_index_rng_seed = 5453809927556429288 +*.*.test05_schedule_rng_seed = 5435552105434836064 + + +*.SingleStream.target_latency_percentile = 90 +*.SingleStream.min_duration = 600000 + +*.MultiStream.target_latency_percentile = 99 +*.MultiStream.samples_per_query = 8 +*.MultiStream.min_duration = 600000 +*.MultiStream.min_query_count = 662 +retinanet.MultiStream.target_latency = 528 + +# 3D-UNet uses equal issue mode because it has non-uniform inputs +3d-unet.*.sample_concatenate_permutation = 1 + +# LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario +gptj.*.sample_concatenate_permutation = 1 +llama2-70b.*.sample_concatenate_permutation = 1 +mixtral-8x7b.*.sample_concatenate_permutation = 1 + +*.Server.target_latency = 10 +*.Server.target_latency_percentile = 99 +*.Server.target_duration = 0 +*.Server.min_duration = 600000 +resnet50.Server.target_latency = 15 +retinanet.Server.target_latency = 100 +bert.Server.target_latency = 130 +dlrm.Server.target_latency = 60 +dlrm-v2.Server.target_latency = 60 +rnnt.Server.target_latency = 1000 +gptj.Server.target_latency = 20000 +stable-diffusion-xl.Server.target_latency = 20000 +# Llama2-70b benchmarks measures token latencies +llama2-70b.*.use_token_latencies = 1 +mixtral-8x7b.*.use_token_latencies = 1 +# gptj benchmark infers token latencies +gptj.*.infer_token_latencies = 1 +gptj.*.token_latency_scaling_factor = 69 +# Only ttft and tpot are tracked for the llama2-70b & mixtral-8x7B benchmark therefore target_latency = 0 +llama2-70b.Server.target_latency = 0 +llama2-70b.Server.ttft_latency = 2000 +llama2-70b.Server.tpot_latency = 200 + +mixtral-8x7b.Server.target_latency = 0 +mixtral-8x7b.Server.ttft_latency = 2000 +mixtral-8x7b.Server.tpot_latency = 200 + +*.Offline.target_latency_percentile = 90 +*.Offline.min_duration = 600000 + +# In Offline scenario, we always have one query. But LoadGen maps this to +# min_sample_count internally in Offline scenario. If the dataset size is larger +# than 24576 we limit the min_query_count to 24576 and otherwise we use +# the dataset size as the limit + +resnet50.Offline.min_query_count = 24576 +retinanet.Offline.min_query_count = 24576 +dlrm-v2.Offline.min_query_count = 24576 +bert.Offline.min_query_count = 10833 +gptj.Offline.min_query_count = 13368 +rnnt.Offline.min_query_count = 2513 +3d-unet.Offline.min_query_count = 43 +stable-diffusion-xl.Offline.min_query_count = 5000 +llama2-70b.Offline.min_query_count = 24576 +mixtral-8x7b.Offline.min_query_count = 15000 + +# These fields should be defined and overridden by user.conf. +*.SingleStream.target_latency = 10 +*.MultiStream.target_latency = 80 +*.Server.target_qps = 1.0 +*.Offline.target_qps = 1.0 diff --git a/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/os_info.json b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/os_info.json new file mode 100644 index 0000000..a145cc5 --- /dev/null +++ b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/os_info.json @@ -0,0 +1,30 @@ +{ + "CM_HOST_OS_TYPE": "linux", + "CM_HOST_OS_BITS": "64", + "CM_HOST_OS_FLAVOR": "ubuntu", + "CM_HOST_OS_FLAVOR_LIKE": "debian", + "CM_HOST_OS_VERSION": "22.04", + "CM_HOST_OS_KERNEL_VERSION": "6.2.0-39-generic", + "CM_HOST_OS_GLIBC_VERSION": "2.35", + "CM_HOST_OS_MACHINE": "x86_64", + "CM_HOST_OS_PACKAGE_MANAGER": "apt", + "CM_HOST_OS_PACKAGE_MANAGER_INSTALL_CMD": "DEBIAN_FRONTEND=noninteractive apt-get install -y", + "CM_HOST_OS_PACKAGE_MANAGER_UPDATE_CMD": "apt-get update -y", + "+CM_HOST_OS_DEFAULT_LIBRARY_PATH": [ + "/usr/local/lib/x86_64-linux-gnu", + "/lib/x86_64-linux-gnu", + "/usr/lib/x86_64-linux-gnu", + "/usr/lib/x86_64-linux-gnu64", + "/usr/local/lib64", + "/lib64", + "/usr/lib64", + "/usr/local/lib", + "/lib", + "/usr/lib", + "/usr/x86_64-linux-gnu/lib64", + "/usr/x86_64-linux-gnu/lib" + ], + "CM_HOST_PLATFORM_FLAVOR": "x86_64", + "CM_HOST_PYTHON_BITS": "64", + "CM_HOST_SYSTEM_NAME": "ba096989170c" +} \ No newline at end of file diff --git a/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/performance_console.out b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/performance_console.out new file mode 100644 index 0000000..e69de29 diff --git a/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/pip_freeze.json b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/pip_freeze.json new file mode 100644 index 0000000..c0c0a09 --- /dev/null +++ b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/pip_freeze.json @@ -0,0 +1,73 @@ +{ + "pip_freeze": { + "accelerate": "1.0.1", + "certifi": "2024.8.30", + "charset-normalizer": "3.3.2", + "cmind": "3.0.1", + "diffusers": "0.30.3", + "dmiparser": "5.1", + "filelock": "3.16.1", + "fsspec": "2024.9.0", + "ftfy": "6.3.0", + "giturlparse": "0.12.0", + "huggingface-hub": "0.26.0", + "idna": "3.10", + "importlib_metadata": "8.5.0", + "Jinja2": "3.1.4", + "lightning-utilities": "0.11.8", + "Mako": "1.3.5", + "MarkupSafe": "3.0.2", + "mpmath": "1.3.0", + "networkx": "3.4.1", + "numpy": "1.26.4", + "nvidia-cublas-cu12": "12.4.5.8", + "nvidia-cuda-cupti-cu12": "12.4.127", + "nvidia-cuda-nvrtc-cu12": "12.4.127", + "nvidia-cuda-runtime-cu12": "12.4.127", + "nvidia-cudnn-cu12": "9.1.0.70", + "nvidia-cufft-cu12": "11.2.1.3", + "nvidia-curand-cu12": "10.3.5.147", + "nvidia-cusolver-cu12": "11.6.1.9", + "nvidia-cusparse-cu12": "12.3.1.170", + "nvidia-nccl-cu12": "2.21.5", + "nvidia-nvjitlink-cu12": "12.4.127", + "nvidia-nvtx-cu12": "12.4.127", + "open_clip_torch": "2.27.1", + "opencv-python": "4.10.0.84", + "packaging": "24.1", + "pandas": "2.2.3", + "pillow": "11.0.0", + "pip": "22.0.2", + "platformdirs": "4.3.6", + "psutil": "6.1.0", + "pybind11": "2.13.6", + "pycuda": "2024.1.2", + "python-dateutil": "2.9.0.post0", + "pytools": "2024.1.14", + "pytz": "2024.2", + "PyYAML": "6.0.2", + "regex": "2024.9.11", + "requests": "2.32.3", + "safetensors": "0.4.5", + "scipy": "1.10.1", + "setuptools": "59.6.0", + "six": "1.16.0", + "sympy": "1.13.1", + "tabulate": "0.9.0", + "timm": "1.0.11", + "tokenizers": "0.20.1", + "torch": "2.5.0", + "torch-fidelity": "0.3.0", + "torchmetrics": "1.5.0", + "torchvision": "0.20.0", + "tqdm": "4.66.5", + "transformers": "4.45.2", + "triton": "3.1.0", + "typing_extensions": "4.12.2", + "tzdata": "2024.2", + "urllib3": "2.2.3", + "wcwidth": "0.2.13", + "wheel": "0.44.0", + "zipp": "3.20.2" + } +} \ No newline at end of file diff --git a/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/user.conf b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/user.conf new file mode 100644 index 0000000..21df3f1 --- /dev/null +++ b/open/MLCommons/measurements/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/user.conf @@ -0,0 +1,5 @@ +stable-diffusion-xl.Offline.target_qps = 0.05 +stable-diffusion-xl.Offline.max_query_count = 10 +stable-diffusion-xl.Offline.min_query_count = 10 +stable-diffusion-xl.Offline.min_duration = 0 +stable-diffusion-xl.Offline.sample_concatenate_permutation = 0 diff --git a/open/MLCommons/results/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/accuracy/accuracy.txt b/open/MLCommons/results/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/accuracy/accuracy.txt new file mode 100644 index 0000000..6f7c157 --- /dev/null +++ b/open/MLCommons/results/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/accuracy/accuracy.txt @@ -0,0 +1,2 @@ +Accuracy Results: {'FID_SCORE': 235.62720741859, 'CLIP_SCORE': 15.180260419845581} +hash=a76f2d38442369a171a07f3303e44abd0db3e186376e22857834073f1434ca7b diff --git a/open/MLCommons/results/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/accuracy/images/1.png b/open/MLCommons/results/ba096989170c-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/offline/accuracy/images/1.png new file mode 100644 index 0000000000000000000000000000000000000000..27f2c1c3dd389521b27c926dd5da40b1e755a3f8 GIT binary patch literal 2175761 zcmV)|KzzT6P)f6fQt?gAix5G1iPp%QF8B&{aTI_zws+OaY^FXiDO4Dk!;7Z 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