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https://github.com/pytorch/pytorch.git
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dd3a77bc96
Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/105928 Approved by: https://github.com/albanD
106 lines
2.5 KiB
Python
106 lines
2.5 KiB
Python
import torch
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import operator_benchmark as op_bench
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"""Microbenchmarks for binary operators."""
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# Benchmark ops performance with broadcast
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binary_ops_bcast_list = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[
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["add", torch.add],
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],
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)
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# Configs with broadcast
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binary_configs_broadcast = op_bench.config_list(
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attr_names=["in_one", "in_two"],
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attrs=[
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[[64, 1, 64], [1, 64, 1]],
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],
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cross_product_configs={
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"device": ["cpu"],
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"dtype": [torch.float],
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},
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tags=["short"],
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)
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class BinaryOpBcastBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, in_one, in_two, dtype, device, op_func):
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self.inputs = {
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"in_one": torch.randn(in_one, device=device).to(dtype=dtype),
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"in_two": torch.randn(in_two, device=device).to(dtype=dtype),
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}
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self.op_func = op_func
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def forward(self, in_one, in_two):
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return self.op_func(in_one, in_two)
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op_bench.generate_pt_tests_from_op_list(
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binary_ops_bcast_list, binary_configs_broadcast, BinaryOpBcastBenchmark
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)
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def copy(in1, in2):
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return in1.copy_(in2)
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# Benchmark ops performance without broadcast
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binary_ops_list = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[
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["add", torch.add],
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["copy_", copy],
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],
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)
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binary_short_configs = op_bench.config_list(
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attr_names=["M", "N", "K"],
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attrs=[
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[1, 1, 1],
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[64, 64, 64],
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[64, 64, 128],
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],
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cross_product_configs={
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"device": ["cpu", "cuda"],
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"dtype_one": [torch.int32],
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"dtype_two": [torch.int32],
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},
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tags=["short"],
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)
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binary_long_configs = op_bench.cross_product_configs(
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M=[8, 128],
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N=[32, 64],
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K=[256, 512],
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device=["cpu", "cuda"],
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dtype_one=[torch.int8, torch.int32],
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dtype_two=[torch.int8, torch.int32],
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tags=["long"],
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)
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class BinaryOpBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, device, dtype_one, dtype_two, op_func):
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self.inputs = {
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"input_one": torch.randn(M, N, K, device=device).to(dtype=dtype_one),
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"input_two": torch.randn(M, N, K, device=device).to(dtype=dtype_two),
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}
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self.op_func = op_func
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def forward(self, input_one, input_two):
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return self.op_func(input_one, input_two)
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op_bench.generate_pt_tests_from_op_list(
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binary_ops_list, binary_short_configs + binary_long_configs, BinaryOpBenchmark
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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