mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 12:54:11 +08:00
c0ed38e644
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129754 Approved by: https://github.com/ezyang
57 lines
1.2 KiB
Python
57 lines
1.2 KiB
Python
import operator_benchmark as op_bench
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
|
|
"""
|
|
Microbenchmarks for the hardswish operators.
|
|
"""
|
|
|
|
|
|
# Configs for hardswish ops
|
|
hardswish_configs_short = op_bench.config_list(
|
|
attr_names=["N", "C", "H", "W"],
|
|
attrs=[
|
|
[1, 3, 256, 256],
|
|
[4, 3, 256, 256],
|
|
],
|
|
cross_product_configs={
|
|
"device": ["cpu"],
|
|
},
|
|
tags=["short"],
|
|
)
|
|
|
|
|
|
hardswish_configs_long = op_bench.cross_product_configs(
|
|
N=[8, 16], C=[3], H=[256, 512], W=[256, 512], device=["cpu"], tags=["long"]
|
|
)
|
|
|
|
|
|
hardswish_ops_list = op_bench.op_list(
|
|
attr_names=["op_name", "op_func"],
|
|
attrs=[
|
|
["Hardswish", nn.Hardswish],
|
|
],
|
|
)
|
|
|
|
|
|
class HardswishBenchmark(op_bench.TorchBenchmarkBase):
|
|
def init(self, N, C, H, W, device, op_func):
|
|
self.inputs = {"input_one": torch.rand(N, C, H, W, device=device)}
|
|
self.op_func = op_func()
|
|
|
|
def forward(self, input_one):
|
|
return self.op_func(input_one)
|
|
|
|
|
|
op_bench.generate_pt_tests_from_op_list(
|
|
hardswish_ops_list,
|
|
hardswish_configs_short + hardswish_configs_long,
|
|
HardswishBenchmark,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
op_bench.benchmark_runner.main()
|