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import operator_benchmark as op_bench
import torch
"""Microbenchmarks for ClipRanges operator."""
torch.ops.load_library("//caffe2/torch/fb/sparsenn:sparsenn_operators")
# Configs for C2 ClipRanges operator
clip_ranges_long_configs = op_bench.cross_product_configs(
LENGTH=range(1, 100),
M=[1],
N=[2],
MAX_LENGTH=range(1, 100),
device=["cpu", "cuda"],
dtype=[torch.int32],
tags=["long"],
)
clip_ranges_short_configs = op_bench.config_list(
attrs=[
[6, 1, 2, 1, torch.int32],
[7, 1, 2, 2, torch.int32],
[8, 1, 2, 3, torch.int32],
[9, 1, 2, 4, torch.int32],
[10, 1, 2, 5, torch.int32],
],
attr_names=["LENGTH", "M", "N", "MAX_LENGTH", "dtype"],
cross_product_configs={
"device": ["cpu", "cuda"],
},
tags=["short"],
)
class ClipRangesBenchmark(op_bench.TorchBenchmarkBase):
def init(self, LENGTH, M, N, MAX_LENGTH, device, dtype):
self.inputs = {
"input": torch.rand(LENGTH, M, N, device=device).type(dtype),
"max_length": MAX_LENGTH,
}
self.set_module_name("clip_ranges")
def forward(self, input, max_length: int):
return torch.ops.fb.clip_ranges(input, max_length)
op_bench.generate_pt_test(
clip_ranges_long_configs + clip_ranges_short_configs, ClipRangesBenchmark
)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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