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import operator_benchmark as op_bench
import torch
"""Microbenchmarks for point-wise unary operator."""
# Configs for pointwise unary ops
unary_ops_configs_short = op_bench.config_list(
attr_names=['M', 'N'],
attrs=[
[512, 512],
],
cross_product_configs={
'device': ['cpu', 'cuda'],
},
tags=['short']
)
unary_ops_configs_long = op_bench.cross_product_configs(
M=[256, 1024],
N=[256, 1024],
device=['cpu', 'cuda'],
tags=['long']
)
class UnaryOpBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, device, op_func):
self.inputs = {
"input": torch.rand(M, N, device=device)
}
self.op_func = op_func
def forward(self, input):
return self.op_func(input)
def bernoulli_(input):
return input.bernoulli_()
def cauchy_(input):
return input.cauchy_()
def digamma_(input):
return input.digamma_()
def exponential_(input):
return input.exponential_()
def normal_(input):
return input.normal_()
def random_(input):
return input.random_()
def sign_(input):
return input.sign_()
def uniform_(input):
return input.uniform_()
def half_(input):
return input.half()
def long_(input):
return input.long()
unary_ops_list = op_bench.op_list(
attr_names=['op_name', 'op_func'],
attrs=[
['abs', torch.abs],
['abs_', torch.abs_],
['acos', torch.acos],
['acos_', torch.acos_],
['argsort', torch.argsort],
['asin', torch.asin],
['asin_', torch.asin_],
['atan', torch.atan],
['atan_', torch.atan_],
['ceil', torch.ceil],
['ceil_', torch.ceil_],
['clone', torch.clone],
['cos', torch.cos],
['cos_', torch.cos_],
['cosh', torch.cosh],
['digamma', torch.digamma],
['erf', torch.erf],
['erf_', torch.erf_],
['erfc', torch.erfc],
['erfc_', torch.erfc_],
['erfinv', torch.erfinv],
['exp', torch.exp],
['exp_', torch.exp_],
['expm1', torch.expm1],
['expm1_', torch.expm1_],
['floor', torch.floor],
['floor_', torch.floor_],
['frac', torch.frac],
['frac_', torch.frac_],
['hardshrink', torch.hardshrink],
['lgamma', torch.lgamma],
['log', torch.log],
['log10', torch.log10],
['log10_', torch.log10_],
['log1p', torch.log1p],
['log1p_', torch.log1p_],
['log2', torch.log2],
['log2_', torch.log2_],
['log_', torch.log_],
['logit', torch.logit],
['logit_', torch.logit_],
['neg', torch.neg],
['neg_', torch.neg_],
['reciprocal', torch.reciprocal],
['reciprocal_', torch.reciprocal_],
['relu', torch.relu],
['relu_', torch.relu_],
['round', torch.round],
['round_', torch.round_],
['rsqrt', torch.rsqrt],
['rsqrt_', torch.rsqrt_],
['sigmoid', torch.sigmoid],
['sigmoid_', torch.sigmoid_],
['sign', torch.sign],
['sgn', torch.sgn],
['sin', torch.sin],
['sin_', torch.sin_],
['sinh', torch.sinh],
['sqrt', torch.sqrt],
['sqrt_', torch.sqrt_],
['square', torch.square],
['square_', torch.square_],
['tan', torch.tan],
['tan_', torch.tan_],
['tanh', torch.tanh],
['tanh_', torch.tanh_],
['trunc', torch.trunc],
['trunc_', torch.trunc_],
['unique', torch.functional._return_output],
['zero_', torch.zero_],
['bernoulli_', bernoulli_],
['cauchy_', cauchy_],
['digamma_', digamma_],
['exponential_', exponential_],
['normal_', normal_],
['random_', random_],
['sign_', sign_],
['uniform_', uniform_],
['half', half_],
['long', long_],
],
)
op_bench.generate_pt_tests_from_op_list(unary_ops_list,
unary_ops_configs_short + unary_ops_configs_long,
UnaryOpBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
|
