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import operator_benchmark as op_bench
import torch
"""Microbenchmarks for Split operator"""
# Configs for PT Split operator
split_configs_short = op_bench.config_list(
attr_names=["M", "N", "parts"],
attrs=[
[8, 8, 2],
[256, 512, 2],
[512, 512, 2],
],
cross_product_configs={
'device': ['cpu', 'cuda'],
},
tags=["short"],
)
split_configs_long = op_bench.cross_product_configs(
M=[128, 1024],
N=[128, 1024],
parts=[2, 4],
device=['cpu', 'cuda'],
tags=['long']
)
class SplitBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, parts, device):
self.inputs = {
"input": torch.rand(M, N, device=device),
"split_size": int(M * N / parts)
}
self.set_module_name('split')
def forward(self, input, split_size: int):
return torch.split(input, split_size)
op_bench.generate_pt_test(split_configs_short + split_configs_long,
SplitBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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