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7763c83af6
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126 Approved by: https://github.com/kit1980 ghstack dependencies: #127122, #127123, #127124, #127125
150 lines
4.2 KiB
Python
150 lines
4.2 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for add_ operator. Supports both Caffe2/PyTorch."""
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# Configs for PT add operator
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add_long_configs = op_bench.cross_product_configs(
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M=[8, 128], N=[32, 64], K=[256, 512], device=["cpu", "cuda"], tags=["long"]
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)
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add_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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},
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tags=["short"],
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)
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class AddBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, device):
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self.inputs = {
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"input_one": torch.rand(
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M, N, K, device=device, requires_grad=self.auto_set()
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),
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"input_two": torch.rand(
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M, N, K, device=device, requires_grad=self.auto_set()
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),
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}
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self.set_module_name("add")
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def forward(self, input_one, input_two):
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return torch.add(input_one, input_two)
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# The generated test names based on add_short_configs will be in the following pattern:
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# add_M8_N16_K32_devicecpu
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# add_M8_N16_K32_devicecpu_bwdall
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# add_M8_N16_K32_devicecpu_bwd1
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# add_M8_N16_K32_devicecpu_bwd2
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# ...
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# Those names can be used to filter tests.
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op_bench.generate_pt_test(add_long_configs + add_short_configs, AddBenchmark)
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op_bench.generate_pt_gradient_test(add_long_configs + add_short_configs, AddBenchmark)
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"""Mircobenchmark for addmm operator."""
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class AddmmBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, device):
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self.inputs = {
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"input_one": torch.rand(M, K, device=device, requires_grad=self.auto_set()),
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"mat1": torch.rand(M, N, device=device, requires_grad=self.auto_set()),
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"mat2": torch.rand(N, K, device=device, requires_grad=self.auto_set()),
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}
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self.set_module_name("addmm")
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def forward(self, input_one, mat1, mat2):
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return torch.addmm(input_one, mat1, mat2)
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op_bench.generate_pt_test(add_long_configs + add_short_configs, AddmmBenchmark)
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op_bench.generate_pt_gradient_test(add_long_configs + add_short_configs, AddmmBenchmark)
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"""Mircobenchmark for addr operator."""
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class AddrBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, device, dtype):
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self.inputs = {
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"input_one": torch.rand(
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(M, N), device=device, requires_grad=self.auto_set(), dtype=dtype
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),
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"vec1": torch.rand(
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(M,), device=device, requires_grad=self.auto_set(), dtype=dtype
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),
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"vec2": torch.rand(
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(N,), device=device, requires_grad=self.auto_set(), dtype=dtype
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),
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}
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self.set_module_name("addr")
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def forward(self, input_one, vec1, vec2):
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return torch.addr(input_one, vec1, vec2)
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addr_configs = op_bench.cross_product_configs(
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M=[8, 256],
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N=[256, 16],
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device=["cpu", "cuda"],
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dtype=[torch.double, torch.half],
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tags=["addr"],
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)
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op_bench.generate_pt_test(addr_configs, AddrBenchmark)
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op_bench.generate_pt_gradient_test(addr_configs, AddrBenchmark)
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"""Mircobenchmark for addbmm operator."""
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class AddbmmBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, B, M, N, K, device):
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self.inputs = {
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"input_one": torch.rand(
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(M, N), device=device, requires_grad=self.auto_set()
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),
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"batch1": torch.rand(
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(B, M, K), device=device, requires_grad=self.auto_set()
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),
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"batch2": torch.rand(
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(
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B,
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K,
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N,
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),
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device=device,
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requires_grad=self.auto_set(),
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),
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}
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self.set_module_name("addbmm")
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def forward(self, input_one, batch1, batch2):
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return torch.addbmm(input_one, batch1, batch2)
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addbmm_configs = op_bench.cross_product_configs(
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B=[2, 100],
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M=[8, 256],
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N=[256, 16],
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K=[15, 16],
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device=["cpu", "cuda"],
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tags=["addbmm"],
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)
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op_bench.generate_pt_test(addbmm_configs, AddbmmBenchmark)
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op_bench.generate_pt_gradient_test(addbmm_configs, AddbmmBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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