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import operator_benchmark as op_bench
import torch
"""Microbenchmarks for add_ operator. Supports both Caffe2/PyTorch."""
# Configs for PT add operator
add_long_configs = op_bench.cross_product_configs(
M=[8, 128], N=[32, 64], K=[256, 512], device=["cpu", "cuda"], tags=["long"]
)
add_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"],
},
tags=["short"],
)
class AddBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, K, device):
self.inputs = {
"input_one": torch.rand(
M, N, K, device=device, requires_grad=self.auto_set()
),
"input_two": torch.rand(
M, N, K, device=device, requires_grad=self.auto_set()
),
}
self.set_module_name("add")
def forward(self, input_one, input_two):
return torch.add(input_one, input_two)
# The generated test names based on add_short_configs will be in the following pattern:
# add_M8_N16_K32_devicecpu
# add_M8_N16_K32_devicecpu_bwdall
# add_M8_N16_K32_devicecpu_bwd1
# add_M8_N16_K32_devicecpu_bwd2
# ...
# Those names can be used to filter tests.
op_bench.generate_pt_test(add_long_configs + add_short_configs, AddBenchmark)
op_bench.generate_pt_gradient_test(add_long_configs + add_short_configs, AddBenchmark)
"""Mircobenchmark for addmm operator."""
class AddmmBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, K, device):
self.inputs = {
"input_one": torch.rand(M, K, device=device, requires_grad=self.auto_set()),
"mat1": torch.rand(M, N, device=device, requires_grad=self.auto_set()),
"mat2": torch.rand(N, K, device=device, requires_grad=self.auto_set()),
}
self.set_module_name("addmm")
def forward(self, input_one, mat1, mat2):
return torch.addmm(input_one, mat1, mat2)
op_bench.generate_pt_test(add_long_configs + add_short_configs, AddmmBenchmark)
op_bench.generate_pt_gradient_test(add_long_configs + add_short_configs, AddmmBenchmark)
"""Mircobenchmark for addr operator."""
class AddrBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, device, dtype):
self.inputs = {
"input_one": torch.rand(
(M, N), device=device, requires_grad=self.auto_set(), dtype=dtype
),
"vec1": torch.rand(
(M,), device=device, requires_grad=self.auto_set(), dtype=dtype
),
"vec2": torch.rand(
(N,), device=device, requires_grad=self.auto_set(), dtype=dtype
),
}
self.set_module_name("addr")
def forward(self, input_one, vec1, vec2):
return torch.addr(input_one, vec1, vec2)
addr_configs = op_bench.cross_product_configs(
M=[8, 256],
N=[256, 16],
device=["cpu", "cuda"],
dtype=[torch.double, torch.half],
tags=["addr"],
)
op_bench.generate_pt_test(addr_configs, AddrBenchmark)
op_bench.generate_pt_gradient_test(addr_configs, AddrBenchmark)
"""Mircobenchmark for addbmm operator."""
class AddbmmBenchmark(op_bench.TorchBenchmarkBase):
def init(self, B, M, N, K, device):
self.inputs = {
"input_one": torch.rand(
(M, N), device=device, requires_grad=self.auto_set()
),
"batch1": torch.rand(
(B, M, K), device=device, requires_grad=self.auto_set()
),
"batch2": torch.rand(
(
B,
K,
N,
),
device=device,
requires_grad=self.auto_set(),
),
}
self.set_module_name("addbmm")
def forward(self, input_one, batch1, batch2):
return torch.addbmm(input_one, batch1, batch2)
addbmm_configs = op_bench.cross_product_configs(
B=[2, 100],
M=[8, 256],
N=[256, 16],
K=[15, 16],
device=["cpu", "cuda"],
tags=["addbmm"],
)
op_bench.generate_pt_test(addbmm_configs, AddbmmBenchmark)
op_bench.generate_pt_gradient_test(addbmm_configs, AddbmmBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
|
