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https://github.com/saymrwulf/onnxruntime.git
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d834ec895a
### Description `lintrunner` is a linter runner successfully used by pytorch, onnx and onnx-script. It provides a uniform experience running linters locally and in CI. It supports all major dev systems: Windows, Linux and MacOs. The checks are enforced by the `Python format` workflow. This PR adopts `lintrunner` to onnxruntime and fixed ~2000 flake8 errors in Python code. `lintrunner` now runs all required python lints including `ruff`(replacing `flake8`), `black` and `isort`. Future lints like `clang-format` can be added. Most errors are auto-fixed by `ruff` and the fixes should be considered robust. Lints that are more complicated to fix are applied `# noqa` for now and should be fixed in follow up PRs. ### Notable changes 1. This PR **removed some suboptimal patterns**: - `not xxx in` -> `xxx not in` membership checks - bare excepts (`except:` -> `except Exception`) - unused imports The follow up PR will remove: - `import *` - mutable values as default in function definitions (`def func(a=[])`) - more unused imports - unused local variables 2. Use `ruff` to replace `flake8`. `ruff` is much (40x) faster than flake8 and is more robust. We are using it successfully in onnx and onnx-script. It also supports auto-fixing many flake8 errors. 3. Removed the legacy flake8 ci flow and updated docs. 4. The added workflow supports SARIF code scanning reports on github, example snapshot:  5. Removed `onnxruntime-python-checks-ci-pipeline` as redundant ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Unified linting experience in CI and local. Replacing https://github.com/microsoft/onnxruntime/pull/14306 --------- Signed-off-by: Justin Chu <justinchu@microsoft.com>
107 lines
3.6 KiB
Python
Executable file
107 lines
3.6 KiB
Python
Executable file
#!/usr/bin/env python3
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import argparse
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import os
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import subprocess
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import sys
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import tempfile
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from compare_results import Comparisons, compare_results_files
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SCRIPT_DIR = os.path.realpath(os.path.dirname(__file__))
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def parse_args():
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parser = argparse.ArgumentParser(description="Runs a BERT convergence test.")
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parser.add_argument("--binary_dir", required=True, help="Path to the ORT binary directory.")
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parser.add_argument("--training_data_root", required=True, help="Path to the training data root directory.")
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parser.add_argument("--model_root", required=True, help="Path to the model root directory.")
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parser.add_argument(
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"--gpu_sku",
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choices=["V100_16G", "MI100_32G"],
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default="V100_16G",
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required=False,
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help="GPU model (e.g. V100_16G, MI100_32G).",
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)
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return parser.parse_args()
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def main():
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args = parse_args()
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with tempfile.TemporaryDirectory() as output_dir:
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convergence_test_output_path = os.path.join(output_dir, "convergence_test_out.csv")
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# run BERT training
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subprocess.run(
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[
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os.path.join(args.binary_dir, "onnxruntime_training_bert"),
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"--model_name",
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os.path.join(
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args.model_root,
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"nv/bert-base/bert-base-uncased_L_12_H_768_A_12_V_30528_S_512_Dp_0.1_optimized_layer_norm_opset12",
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),
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"--train_data_dir",
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os.path.join(args.training_data_root, "128/books_wiki_en_corpus/train"),
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"--test_data_dir",
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os.path.join(args.training_data_root, "128/books_wiki_en_corpus/test"),
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"--train_batch_size",
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"64",
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"--mode",
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"train",
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"--num_train_steps",
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"800",
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"--display_loss_steps",
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"5",
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"--optimizer",
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"adam",
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"--learning_rate",
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"5e-4",
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"--warmup_ratio",
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"0.1",
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"--warmup_mode",
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"Linear",
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"--gradient_accumulation_steps",
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"16",
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"--max_predictions_per_seq=20",
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"--use_mixed_precision",
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"--use_deterministic_compute",
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"--allreduce_in_fp16",
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"--lambda",
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"0",
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"--use_nccl",
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"--convergence_test_output_file",
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convergence_test_output_path,
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"--seed",
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"42",
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"--enable_grad_norm_clip=false",
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]
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).check_returncode()
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# reference data
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if args.gpu_sku == "MI100_32G":
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reference_csv = "bert_base.convergence.baseline.mi100.csv"
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elif args.gpu_sku == "V100_16G":
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reference_csv = "bert_base.convergence.baseline.csv"
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else:
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raise ValueError(f"Unrecognized gpu_sku {args.gpu_sku}")
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# verify output
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comparison_result = compare_results_files(
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expected_results_path=os.path.join(SCRIPT_DIR, "results", reference_csv),
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actual_results_path=convergence_test_output_path,
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field_comparisons={
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"step": Comparisons.eq(),
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"total_loss": Comparisons.float_le(1e-3),
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"mlm_loss": Comparisons.float_le(1e-3),
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"nsp_loss": Comparisons.float_le(1e-3),
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},
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)
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return 0 if comparison_result else 1
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if __name__ == "__main__":
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sys.exit(main())
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