42 строки
1.5 KiB
Python
42 строки
1.5 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT license.
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"""Model benchmark example for bert-large (24-layer, 1024-hidden, 16-heads, 340M parameters).
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Commands to run:
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python3 examples/benchmarks/pytorch_bert_large.py (Single GPU)
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python3 -m torch.distributed.launch --use_env --nproc_per_node=8 examples/benchmarks/pytorch_bert_large.py \
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--distributed (Distributed)
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"""
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import argparse
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from superbench.benchmarks import Platform, Framework, BenchmarkRegistry
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from superbench.common.utils import logger
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--distributed', action='store_true', default=False, help='Whether to enable distributed training.'
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)
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args = parser.parse_args()
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# Specify the model name and benchmark parameters.
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model_name = 'bert-large'
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parameters = '--batch_size 1 --duration 120 --seq_len 128 --precision float32 --run_count 2'
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if args.distributed:
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parameters += ' --distributed_impl ddp --distributed_backend nccl'
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# Create context for bert-large benchmark and run it for 120 * 2 seconds.
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context = BenchmarkRegistry.create_benchmark_context(
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model_name, platform=Platform.CUDA, parameters=parameters, framework=Framework.PYTORCH
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)
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benchmark = BenchmarkRegistry.launch_benchmark(context)
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if benchmark:
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logger.info(
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'benchmark: {}, return code: {}, result: {}'.format(
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benchmark.name, benchmark.return_code, benchmark.result
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)
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)
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