mirror of
https://github.com/saymrwulf/onnxruntime.git
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37445d1198
Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
#!/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 subprocess
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import sys
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import os
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from collections import namedtuple
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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 BERT performance tests.")
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parser.add_argument("--binary_dir", required=True,
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help="Path to the ORT binary directory.")
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parser.add_argument("--training_data_root", required=True,
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help="Path to the training data root directory.")
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parser.add_argument("--model_root", required=True,
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help="Path to the model root directory.")
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return parser.parse_args()
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# using the same params from "GitHub Master Merge Schedule" in OneNotes
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def main():
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args = parse_args()
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Config = namedtuple('Config', ['use_mixed_precision', 'max_seq_length', 'batch_size', 'max_predictions_per_seq'])
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configs = [
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Config(True, 128, 76, 20),
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Config(True, 512, 11, 80),
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Config(False, 128, 39, 20),
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Config(False, 512, 6, 80)
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]
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# run BERT training
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for c in configs:
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print("######## testing name - " + ('fp16-' if c.use_mixed_precision else 'fp32-') + str(c.max_seq_length) + " ##############")
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cmds = [
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os.path.join(args.binary_dir, "onnxruntime_training_bert"),
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"--model_name", os.path.join(
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args.model_root, "nv/bert-large/bert-large-uncased_L_24_H_1024_A_16_V_30528_S_512_Dp_0.1_optimized_layer_norm_opset12"),
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"--train_data_dir", os.path.join(
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args.training_data_root, str(c.max_seq_length), "books_wiki_en_corpus/train"),
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"--test_data_dir", os.path.join(
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args.training_data_root, str(c.max_seq_length), "books_wiki_en_corpus/test"),
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"--train_batch_size", str(c.batch_size),
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"--mode", "train",
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"--max_seq_length", str(c.max_seq_length),
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"--num_train_steps", "640",
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"--display_loss_steps", "5",
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"--optimizer", "Lamb",
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"--learning_rate", "3e-3",
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"--warmup_ratio", "0.2843",
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"--warmup_mode", "Poly",
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"--gradient_accumulation_steps", "1",
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"--max_predictions_per_seq", str(c.max_predictions_per_seq),
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"--lambda", "0",
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"--use_nccl",
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"--perf_output_dir", os.path.join(SCRIPT_DIR, "results"),
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]
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if c.use_mixed_precision:
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cmds.append("--use_mixed_precision"),
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cmds.append("--allreduce_in_fp16"),
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subprocess.run(cmds).check_returncode()
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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