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https://github.com/zebrajr/pytorch.git
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f3ddc08ddc
The list of added operators: add_, addcmul, arange, baddbmm…, bmm, clamp, div, div_, gelu, index_add, logical_and, mul_, sub_, topk, where This pull request is the same as a previous one: https://github.com/pytorch/pytorch/pull/145121 which inadvertently got deleted while merging. Pull Request resolved: https://github.com/pytorch/pytorch/pull/145625 Approved by: https://github.com/jeffdaily
58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for ternary operators."""
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ternary_ops = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[
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["addcmul", torch.addcmul],
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["addcdiv", torch.addcdiv],
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],
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)
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ternary_configs_short = op_bench.config_list(
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attr_names=["M", "N"],
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attrs=[
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[1, 2],
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[32, 64],
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],
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cross_product_configs={
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"device": ["cpu"],
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"dtype": [torch.float, torch.bfloat16],
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},
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tags=["short"],
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)
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ternary_configs_long = op_bench.cross_product_configs(
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M=[8, 128],
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N=[32, 64],
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device=["cpu", "cuda"],
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dtype=[torch.float, torch.bfloat16],
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tags=["long"],
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)
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class TernaryOpBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, device, dtype, op_func):
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self.inputs = {
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"input_": torch.rand((M, N), device=device).to(dtype=dtype),
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"tensor1": torch.rand((M, N), device=device).to(dtype=dtype),
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"tensor2": torch.rand((M, N), device=device).to(dtype=dtype),
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}
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self.op_func = op_func
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def forward(self, input_, tensor1, tensor2):
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return self.op_func(input_, tensor1, tensor2)
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op_bench.generate_pt_tests_from_op_list(
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ternary_ops, ternary_configs_short + ternary_configs_long, TernaryOpBenchmark
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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