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08f5c05d60
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/19450 We want to make each operator benchmark as a separate binary. The previous way to run the benchmark is by collecting all operators into a single binary, it is unnecessary when we want to filter a specific operator. This diff aims to resolve that issue. Reviewed By: ilia-cher Differential Revision: D14808159 fbshipit-source-id: 43cd25b219c6e358d0cd2a61463b34596bf3bfac
26 lines
807 B
Python
26 lines
807 B
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from benchmarks.operator_benchmark import benchmark_core, benchmark_utils
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import torch
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"""PyTorch performance microbenchmarks.
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This module contains PyTorch-specific functionalities for performance
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microbenchmarks.
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"""
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def PyTorchOperatorTestCase(test_name, op_type, input_shapes, op_args, run_mode):
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"""Benchmark Tester function for Pytorch framework.
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"""
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inputs = [torch.from_numpy(benchmark_utils.numpy_random_fp32(*input)) for input in input_shapes]
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def benchmark_func(num_runs):
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op_type(*(inputs + [num_runs]))
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benchmark_core.add_benchmark_tester("PyTorch", test_name, input_shapes, op_args, run_mode, benchmark_func)
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