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Added torchao nightly workflow (#2273)
Summary: X-link: pytorch/pytorch#128152 Add torchao benchmark workflow, upload the artifacts to GHA. Pull Request resolved: #2273 Test Plan: ``` python run_benchmark.py torchao --ci ``` Reviewed By: jerryzh168 Differential Revision: D58140479 Pulled By: xuzhao9 fbshipit-source-id: b274edb417f880df9b149bd5afc1acbe95737433
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| name: Torchao nightly workflow (A100) | ||
| on: | ||
| workflow_dispatch: | ||
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| jobs: | ||
| run-benchmark: | ||
| environment: docker-s3-upload | ||
| env: | ||
| BASE_CONDA_ENV: "torchbench" | ||
| CONDA_ENV: "torchao-nightly" | ||
| PLATFORM_NAME: "gcp_a100" | ||
| SETUP_SCRIPT: "/workspace/setup_instance.sh" | ||
| TORCHBENCH_USERBENCHMARK_SCRIBE_GRAPHQL_ACCESS_TOKEN: ${{ secrets.TORCHBENCH_USERBENCHMARK_SCRIBE_GRAPHQL_ACCESS_TOKEN }} | ||
| AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} | ||
| AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} | ||
| IS_GHA: 1 | ||
| BUILD_ENVIRONMENT: benchmark-nightly | ||
| if: ${{ github.repository_owner == 'pytorch' }} | ||
| runs-on: [a100-runner] | ||
| timeout-minutes: 1440 # 24 hours | ||
| steps: | ||
| - name: Checkout TorchBench | ||
| uses: actions/checkout@v3 | ||
| with: | ||
| path: benchmark | ||
| - name: Tune Nvidia GPU | ||
| run: | | ||
| sudo nvidia-smi -pm 1 | ||
| sudo nvidia-smi -ac 1215,1410 | ||
| nvidia-smi | ||
| sudo ldconfig | ||
| - name: Clone and setup conda env | ||
| run: | | ||
| CONDA_ENV=${BASE_CONDA_ENV} . "${SETUP_SCRIPT}" | ||
| conda create --name "${CONDA_ENV}" --clone "${BASE_CONDA_ENV}" | ||
| - name: Run the torchao userbenchmark | ||
| env: | ||
| WORKFLOW_RUN_ID: ${{ github.event.workflow_run.id }} | ||
| WORKFLOW_RUN_ATTEMPT: ${{ github.event.workflow_run.run_attempt }} | ||
| run: | | ||
| . "${SETUP_SCRIPT}" | ||
| set -x | ||
| # remove old results if exists | ||
| if [ -d benchmark-output ]; then rm -Rf benchmark-output; fi | ||
| pushd benchmark | ||
| if [ -d .userbenchmark ]; then rm -Rf .userbenchmark; fi | ||
| # Install torchao | ||
| echo "Installing torchao" | ||
| pip uninstall -y torchao | ||
| python install.py --userbenchmark torchao | ||
| echo "Running the torchao userbenchmark" | ||
| python run_benchmark.py torchao --ci --dashboard | ||
| - name: Copy the benchmark logs to benchmark-output | ||
| if: always() | ||
| run: | | ||
| pushd benchmark | ||
| cp -r ./.userbenchmark/torchao ../benchmark-output | ||
| - name: Upload result to GH Actions Artifact | ||
| uses: actions/upload-artifact@v3 | ||
| if: always() | ||
| with: | ||
| name: Torchao nightly result | ||
| path: benchmark-output/ | ||
| - name: Copy artifact and upload to scribe and Amazon S3 | ||
| env: | ||
| WORKFLOW_RUN_ID: ${{ github.event.workflow_run.id }} | ||
| WORKFLOW_RUN_ATTEMPT: ${{ github.event.workflow_run.run_attempt }} | ||
| run: | | ||
| . "${SETUP_SCRIPT}" | ||
| pushd benchmark | ||
| # Upload the result json to Amazon S3 | ||
| python ./scripts/userbenchmark/upload_s3_csv.py --s3-prefix torchbench-csv --userbenchmark torchao \ | ||
| --upload-path ../benchmark-output --match-filename "^torchao_.*\.csv" | ||
| - name: Clean up Conda env | ||
| if: always() | ||
| run: | | ||
| . "${SETUP_SCRIPT}" | ||
| conda deactivate && conda deactivate | ||
| conda remove -n "${CONDA_ENV}" --all |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| import argparse | ||
| import sys | ||
| import os | ||
| import re | ||
| from pathlib import Path | ||
| from datetime import datetime | ||
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| REPO_ROOT = Path(__file__).parent.parent.parent.resolve() | ||
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| class add_path: | ||
| def __init__(self, path): | ||
| self.path = path | ||
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| def __enter__(self): | ||
| sys.path.insert(0, self.path) | ||
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| def __exit__(self, exc_type, exc_value, traceback): | ||
| try: | ||
| sys.path.remove(self.path) | ||
| except ValueError: | ||
| pass | ||
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| with add_path(str(REPO_ROOT)): | ||
| from utils.s3_utils import ( | ||
| S3Client, | ||
| USERBENCHMARK_S3_BUCKET, | ||
| ) | ||
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| def upload_s3(s3_object: str, | ||
| ub_name: str, | ||
| workflow_run_id: str, | ||
| workflow_run_attempt: str, | ||
| file_path: Path, | ||
| dryrun: bool): | ||
| """S3 path: | ||
| s3://ossci-metrics/<s3_object>/<ub_name>/<workflow_run_id>/<workflow_run_attempt>/file_name | ||
| """ | ||
| s3client = S3Client(USERBENCHMARK_S3_BUCKET, s3_object) | ||
| prefix = f"{ub_name}/{workflow_run_id}/{workflow_run_attempt}" | ||
| print(f"Uploading to prefix: {prefix}") | ||
| if not dryrun: | ||
| s3client.upload_file(prefix=prefix, file_path=file_path) | ||
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| def _get_files_to_upload(file_path: str, match_filename: str): | ||
| filename_regex = re.compile(match_filename) | ||
| return [ file_name for file_name in os.listdir(file_path) if filename_regex.match(file_name) ] | ||
|
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser(description=__doc__) | ||
| parser.add_argument( | ||
| "--s3-prefix", | ||
| required=True, | ||
| help="S3 path prefix", | ||
| ) | ||
| parser.add_argument( | ||
| "--userbenchmark", | ||
| required=True, | ||
| help="Name of the userbenchmark.", | ||
| ) | ||
| parser.add_argument( | ||
| "--upload-path", | ||
| required=True, | ||
| help="Local directory contains files to upload.", | ||
| ) | ||
| parser.add_argument( | ||
| "--match-filename", | ||
| required=True, | ||
| help="Filename regex matched to upload.", | ||
| ) | ||
| parser.add_argument( | ||
| "--dryrun", | ||
| action="store_true", | ||
| help="Dryrun the upload", | ||
| ) | ||
| args = parser.parse_args() | ||
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| files_to_upload = _get_files_to_upload(args.upload_path, args.match_filename) | ||
| workflow_run_id = os.environ.get("WORKFLOW_RUN_ID", 0) | ||
| workflow_run_attempt = os.environ.get("WORKFLOW_RUN_ATTEMPT", 0) | ||
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| for file in files_to_upload: | ||
| file_path = Path(args.upload_path).joinpath(file) | ||
| upload_s3(s3_object=args.s3_prefix, | ||
| ub_name=args.userbenchmark, | ||
| workflow_run_id=workflow_run_id, | ||
| workflow_run_attempt=workflow_run_attempt, | ||
| file_path=file_path, | ||
| dryrun=args.dryrun) |
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91
torchbenchmark/util/framework/huggingface/list_extended_configs.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| import torch | ||
| import os | ||
| from torchbenchmark import REPO_PATH | ||
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| from typing import List | ||
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| DYNAMOBENCH_PATH = REPO_PATH.joinpath("userbenchmark", "dynamo", "dynamobench") | ||
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| # These models contain the models present in huggingface_models_list. It is a | ||
| # combination of models supported by HF Fx parser and some manually supplied | ||
| # models. For these models, we already know the largest batch size that can fit | ||
| # on A100 GPUs - 40 GB. | ||
| BATCH_SIZE_KNOWN_MODELS = dict() | ||
|
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| # Get the list of models and their batch sizes | ||
| # Only load the extended models in OSS | ||
| if hasattr(torch.version, "git_version"): | ||
| MODELS_FILENAME = os.path.join(DYNAMOBENCH_PATH, "huggingface_models_list.txt") | ||
| else: | ||
| from libfb.py import parutil | ||
| MODELS_FILENAME = parutil.get_file_path("caffe2/benchmarks/dynamo/huggingface_models_list.txt") | ||
| assert os.path.exists(MODELS_FILENAME) | ||
| with open(MODELS_FILENAME, "r") as fh: | ||
| lines = fh.readlines() | ||
| lines = [line.rstrip() for line in lines] | ||
| for line in lines: | ||
| model_name, batch_size = line.split(",") | ||
| batch_size = int(batch_size) | ||
| BATCH_SIZE_KNOWN_MODELS[model_name] = batch_size | ||
| assert len(BATCH_SIZE_KNOWN_MODELS) | ||
|
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| def is_extended_huggingface_models(model_name: str) -> bool: | ||
| return model_name in BATCH_SIZE_KNOWN_MODELS | ||
|
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| def list_extended_huggingface_models() -> List[str]: | ||
| return list(BATCH_SIZE_KNOWN_MODELS.keys()) | ||
|
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| # TODO - Fails even after fake tensors | ||
| BATCH_SIZE_DIVISORS = { | ||
| "AlbertForMaskedLM": 2, | ||
| "AlbertForQuestionAnswering": 2, | ||
| "AllenaiLongformerBase": 2, | ||
| "BartForCausalLM": 2, | ||
| "BartForConditionalGeneration": 2, | ||
| "BertForMaskedLM": 2, | ||
| "BertForQuestionAnswering": 2, | ||
| "BlenderbotForCausalLM": 8, | ||
| # "BlenderbotForConditionalGeneration" : 16, | ||
| "BlenderbotSmallForCausalLM": 4, | ||
| "BlenderbotSmallForConditionalGeneration": 2, | ||
| "CamemBert": 2, | ||
| "DebertaForMaskedLM": 4, | ||
| "DebertaForQuestionAnswering": 2, | ||
| "DebertaV2ForMaskedLM": 4, | ||
| "DebertaV2ForQuestionAnswering": 8, | ||
| "DistilBertForMaskedLM": 2, | ||
| "DistilBertForQuestionAnswering": 2, | ||
| "DistillGPT2": 2, | ||
| "ElectraForCausalLM": 2, | ||
| "ElectraForQuestionAnswering": 2, | ||
| "GPT2ForSequenceClassification": 2, | ||
| # "GPTJForCausalLM" : 2, | ||
| # "GPTJForQuestionAnswering" : 2, | ||
| # "GPTNeoForCausalLM" : 32, | ||
| # "GPTNeoForSequenceClassification" : 2, | ||
| "GoogleFnet": 2, | ||
| "LayoutLMForMaskedLM": 2, | ||
| "LayoutLMForSequenceClassification": 2, | ||
| "M2M100ForConditionalGeneration": 4, | ||
| "MBartForCausalLM": 2, | ||
| "MBartForConditionalGeneration": 2, | ||
| "MT5ForConditionalGeneration": 2, | ||
| "MegatronBertForCausalLM": 4, | ||
| "MegatronBertForQuestionAnswering": 2, | ||
| "MobileBertForMaskedLM": 2, | ||
| "MobileBertForQuestionAnswering": 2, | ||
| "OPTForCausalLM": 2, | ||
| "PLBartForCausalLM": 2, | ||
| "PLBartForConditionalGeneration": 2, | ||
| "PegasusForCausalLM": 4, | ||
| "PegasusForConditionalGeneration": 2, | ||
| "RobertaForCausalLM": 2, | ||
| "RobertaForQuestionAnswering": 2, | ||
| "Speech2Text2ForCausalLM": 4, | ||
| "T5ForConditionalGeneration": 2, | ||
| "T5Small": 2, | ||
| "TrOCRForCausalLM": 2, | ||
| "XGLMForCausalLM": 4, | ||
| "XLNetLMHeadModel": 2, | ||
| "YituTechConvBert": 2, | ||
| } |
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