mirror of
https://github.com/pytorch/pytorch.git
synced 2025-11-13 13:35:25 +08:00
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
|
|
|
|
import torch
|
|
|
|
|
|
"""Microbenchmarks for add_(matmul) operator. Supports both Caffe2/PyTorch."""
|
|
|
|
# Configs for PT add operator
|
|
addmm_long_configs = op_bench.cross_product_configs(
|
|
M=[256, 1024, 3000],
|
|
N=[512, 4096],
|
|
K=[512, 4096],
|
|
device=["cuda"],
|
|
tags=["long"],
|
|
dtype=[torch.float16, torch.bfloat16, torch.float32],
|
|
)
|
|
|
|
|
|
addmm_short_configs = op_bench.config_list(
|
|
attr_names=["M", "N", "K"],
|
|
attrs=[
|
|
[1, 1, 1],
|
|
[64, 64, 64],
|
|
[64, 64, 128],
|
|
],
|
|
cross_product_configs={
|
|
"device": ["cpu", "cuda"],
|
|
"dtype": [torch.float],
|
|
},
|
|
tags=["short"],
|
|
)
|
|
|
|
|
|
"""Mircobenchmark for addmm operator."""
|
|
|
|
|
|
class AddmmBenchmark(op_bench.TorchBenchmarkBase):
|
|
def init(self, M, N, K, device, dtype):
|
|
self.inputs = {
|
|
"input_one": torch.rand(
|
|
M, K, device=device, requires_grad=self.auto_set(), dtype=dtype
|
|
),
|
|
"mat1": torch.rand(
|
|
M, N, device=device, requires_grad=self.auto_set(), dtype=dtype
|
|
),
|
|
"mat2": torch.rand(
|
|
N, K, device=device, requires_grad=self.auto_set(), dtype=dtype
|
|
),
|
|
}
|
|
self.set_module_name("addmm")
|
|
|
|
def forward(self, input_one, mat1, mat2):
|
|
return torch.addmm(input_one, mat1, mat2)
|
|
|
|
|
|
op_bench.generate_pt_test(addmm_short_configs + addmm_long_configs, AddmmBenchmark)
|
|
op_bench.generate_pt_gradient_test(addmm_long_configs, AddmmBenchmark)
|
|
|
|
"""Mircobenchmark for addbmm operator."""
|
|
|
|
|
|
class AddbmmBenchmark(op_bench.TorchBenchmarkBase):
|
|
def init(self, B, M, N, K, device, dtype):
|
|
self.inputs = {
|
|
"input_one": torch.rand(
|
|
(M, N), device=device, requires_grad=self.auto_set(), dtype=dtype
|
|
),
|
|
"batch1": torch.rand(
|
|
(B, M, K), device=device, requires_grad=self.auto_set(), dtype=dtype
|
|
),
|
|
"batch2": torch.rand(
|
|
(
|
|
B,
|
|
K,
|
|
N,
|
|
),
|
|
device=device,
|
|
requires_grad=self.auto_set(),
|
|
dtype=dtype,
|
|
),
|
|
}
|
|
self.set_module_name("addbmm")
|
|
|
|
def forward(self, input_one, batch1, batch2):
|
|
return torch.addbmm(input_one, batch1, batch2)
|
|
|
|
|
|
addbmm_long_configs = op_bench.cross_product_configs(
|
|
B=[8, 32],
|
|
M=[256, 1024],
|
|
N=[256, 1024],
|
|
K=[64, 128],
|
|
device=["cuda"],
|
|
dtype=[torch.float16, torch.bfloat16, torch.float32],
|
|
tags=["long"],
|
|
)
|
|
addbmm_short_configs = op_bench.cross_product_configs(
|
|
B=[1, 8],
|
|
M=[8, 128],
|
|
N=[32, 64],
|
|
K=[256, 512],
|
|
device=["cpu", "cuda"],
|
|
dtype=[torch.float16, torch.bfloat16, torch.float32],
|
|
tags=["short"],
|
|
)
|
|
|
|
op_bench.generate_pt_test(addbmm_long_configs + addbmm_short_configs, AddbmmBenchmark)
|
|
op_bench.generate_pt_gradient_test(addbmm_long_configs, AddbmmBenchmark)
|
|
|
|
if __name__ == "__main__":
|
|
op_bench.benchmark_runner.main()
|