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85fab6c9b0
There have been duplicate entries for addmm in dashboard. This PR fixes the duplicate entries issues Pull Request resolved: https://github.com/pytorch/pytorch/pull/166652 Approved by: https://github.com/yangw-dev
112 lines
3.0 KiB
Python
112 lines
3.0 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for add_(matmul) operator. Supports both Caffe2/PyTorch."""
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# Configs for PT add operator
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addmm_long_configs = op_bench.cross_product_configs(
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M=[256, 1024, 3000],
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N=[512, 4096],
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K=[512, 4096],
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device=["cuda"],
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tags=["long"],
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dtype=[torch.float16, torch.bfloat16, torch.float32],
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)
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addmm_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": [torch.float],
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},
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tags=["short"],
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)
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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, dtype):
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self.inputs = {
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"input_one": torch.rand(
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M, K, device=device, requires_grad=self.auto_set(), dtype=dtype
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),
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"mat1": 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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"mat2": torch.rand(
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N, K, 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("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(addmm_short_configs + addmm_long_configs, AddmmBenchmark)
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op_bench.generate_pt_gradient_test(addmm_long_configs, AddmmBenchmark)
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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, 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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"batch1": torch.rand(
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(B, M, K), device=device, requires_grad=self.auto_set(), dtype=dtype
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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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dtype=dtype,
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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_long_configs = op_bench.cross_product_configs(
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B=[8, 32],
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M=[256, 1024],
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N=[256, 1024],
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K=[64, 128],
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device=["cuda"],
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dtype=[torch.float16, torch.bfloat16, torch.float32],
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tags=["long"],
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)
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addbmm_short_configs = op_bench.cross_product_configs(
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B=[1, 8],
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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=[torch.float16, torch.bfloat16, torch.float32],
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tags=["short"],
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)
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op_bench.generate_pt_test(addbmm_long_configs + addbmm_short_configs, AddbmmBenchmark)
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op_bench.generate_pt_gradient_test(addbmm_long_configs, AddbmmBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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