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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
89 lines
2.3 KiB
Python
89 lines
2.3 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for batched operators."""
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# binary ops (two inputs in shape of batches)
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batched_binary_ops = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[
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["bmm", torch.bmm],
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],
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)
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batched_binary_configs_short = op_bench.config_list(
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attr_names=["B", "M", "N", "K"],
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attrs=[
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[2, 1, 8, 2],
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[128, 64, 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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batched_binary_configs_long = op_bench.cross_product_configs(
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B=[1, 128],
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M=[8, 128],
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N=[32, 64],
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K=[4, 256],
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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 BatchedBinaryOpBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, B, M, N, K, device, dtype, op_func):
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self.inputs = {
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"batch1": torch.rand((B, M, N), device=device).to(dtype=dtype),
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"batch2": torch.rand((B, N, K), 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, batch1, batch2):
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return self.op_func(batch1, batch2)
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op_bench.generate_pt_tests_from_op_list(
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batched_binary_ops,
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batched_binary_configs_short + batched_binary_configs_long,
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BatchedBinaryOpBenchmark,
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)
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# batched ternary ops
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batched_ternary_ops = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[["baddbmm", torch.baddbmm]],
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)
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class BatchedTernaryOpBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, B, M, N, K, device, dtype, op_func):
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self.inputs = {
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"input_": torch.rand((B, M, K), device=device).to(dtype=dtype),
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"batch1": torch.rand((B, M, N), device=device).to(dtype=dtype),
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"batch2": torch.rand((B, N, K), 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_, batch1, batch2):
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return self.op_func(input_, batch1, batch2)
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op_bench.generate_pt_tests_from_op_list(
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batched_ternary_ops,
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batched_binary_configs_short + batched_binary_configs_long,
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BatchedTernaryOpBenchmark,
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)
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# TODO: does it automatically register new scripts?
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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