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f3ddc08ddc
The list of added operators: add_, addcmul, arange, baddbmm…, bmm, clamp, div, div_, gelu, index_add, logical_and, mul_, sub_, topk, where This pull request is the same as a previous one: https://github.com/pytorch/pytorch/pull/145121 which inadvertently got deleted while merging. Pull Request resolved: https://github.com/pytorch/pytorch/pull/145625 Approved by: https://github.com/jeffdaily
124 lines
2.9 KiB
Python
124 lines
2.9 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""
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Microbenchmarks for batch matrix mult with einsum and torch.bmm.
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"""
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batch_mm_configs_short = op_bench.config_list(
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attr_names=["B", "M", "N", "K"],
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attrs=[
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[4, 5, 3, 2],
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[32, 25, 20, 30],
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[128, 100, 120, 110],
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],
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cross_product_configs={
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"device": ["cpu", "cuda"],
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},
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tags=["short"],
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)
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batch_mm_configs_long = op_bench.config_list(
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attr_names=["B", "M", "N", "K"],
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attrs=[
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[128, 256, 128, 256],
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[512, 1024, 1024, 512],
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],
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cross_product_configs={
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"device": ["cpu", "cuda"],
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},
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tags=["long"],
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)
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batch_mm_op_list = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[
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["einsum_bmm", torch.einsum],
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# ["bmm", torch.bmm],
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],
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)
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class BatchMatrixMultBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, B, M, N, K, device, op_func):
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self.inputs = {
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"input_one": torch.rand(B, M, N, device=device),
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"input_two": torch.rand(B, N, K, device=device),
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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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if self.op_func.__name__ == "einsum":
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return torch.einsum("bij,bjk->bik", input_one, input_two)
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else:
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return torch.bmm(input_one, input_two)
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"""
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Microbenchmarks for element-wise matrix mult with einsum and torch.mul.
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"""
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batch_elementwise_configs_short = op_bench.config_list(
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attr_names=["B", "M", "N"],
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attrs=[
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[4, 5, 3],
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[32, 25, 20],
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[100, 90, 110],
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],
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cross_product_configs={
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"device": ["cpu", "cuda"],
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},
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tags=["short"],
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)
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batch_elementwise_configs_long = op_bench.cross_product_configs(
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B=[128, 512, 1024],
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M=[128, 512, 1024],
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N=[128, 512, 1024],
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device=["cpu", "cuda"],
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tags=["long"],
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)
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batch_elementwise_op_list = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[
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["einsum_elementwise", torch.einsum],
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["mul", torch.mul],
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],
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)
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class BatchElementWiseBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, B, M, N, device, op_func):
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self.inputs = {
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"input_one": torch.rand(B, M, N, device=device),
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"input_two": torch.rand(B, M, N, device=device),
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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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if self.op_func.__name__ == "einsum":
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return torch.einsum("bij,bij->bij", input_one, input_two)
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else:
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return torch.mul(input_one, input_two)
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op_bench.generate_pt_tests_from_op_list(
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batch_mm_op_list,
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batch_mm_configs_short + batch_mm_configs_long,
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BatchMatrixMultBenchmark,
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)
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op_bench.generate_pt_tests_from_op_list(
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batch_elementwise_op_list,
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batch_elementwise_configs_short + batch_elementwise_configs_long,
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BatchElementWiseBenchmark,
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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