Operator level microbenchmarking #3154
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Summary: This change introduces microbenchmarking for PyTorch operators. Since we need to capture and measure each operator call (which is happening under the hood of PyTorch), we need to use torch.profiler.profile. Example operators are `aten:mm`, `aten::sigmoid`, `cudaLaunchKernel`, etc… Use `--benchmark_operators` to enable the operator-level benchmarking. Use `--limit_operator_results` argument to specify the number of top runtime operators to benchmark. Use `--target_operators` argument to list PyTorch operators to benchmark. Example output: ``` TrainPipelineSparseDist | Malloc retries (P50/P90/P100): 0.0 / 0.0 / 0.0 | Runtime (P90): 442.08 ms | Peak Memory alloc (P90): 24.23 GB | Peak Memory reserved (P90): 26.21 GB operator_aten::copy_ | Malloc retries (P50/P90/P100): -1.0 / -1.0 / -1.0 | Runtime (P90): 39.21 ms | Peak Memory alloc (P90): 0.00 GB | Peak Memory reserved (P90): -0.00 GB ... ``` Differential Revision: D77676673
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Summary:
This change introduces microbenchmarking for PyTorch operators.
Since we need to capture and measure each operator call (which is happening under the hood of PyTorch), we need to use torch.profiler.profile. Example operators are
aten:mm,aten::sigmoid,cudaLaunchKernel, etc…Use
--benchmark_operatorsto enable the operator-level benchmarking.Use
--limit_operator_resultsargument to specify the number of top runtime operators to benchmark.Use
--target_operatorsargument to list PyTorch operators to benchmark.Example output:
Differential Revision: D77676673