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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
141 lines
3.2 KiB
Python
141 lines
3.2 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for inplace binary operators."""
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def add_(in1, in2):
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return in1.add_(in2)
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def sub_(in1, in2):
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return in1.sub_(in2)
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def div_(in1, in2):
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return in1.div_(in2)
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def mul_(in1, in2):
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return in1.mul_(in2)
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def copy_(in1, in2):
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return in1.copy_(in2)
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######
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# Benchmark ops performance for inplace add + sub + mul + copy
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######
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binary_ops_list = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[
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["add_", add_],
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["sub_", sub_],
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# ["div_", div_ ], # done separately below because of data type
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["mul_", mul_],
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["copy_", copy_],
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],
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)
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binary_short_configs = op_bench.config_list(
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attr_names=["M", "N", "K"],
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attrs=[
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[1, 1, 1],
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[64, 64, 64],
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[64, 64, 128],
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],
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cross_product_configs={
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"device": ["cpu", "cuda"],
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"dtype_one": [torch.int32],
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"dtype_two": [torch.int32],
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},
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tags=["short"],
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)
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binary_long_configs = op_bench.cross_product_configs(
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M=[8, 128],
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N=[32, 64],
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K=[256, 512],
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device=["cpu", "cuda"],
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dtype_one=[torch.int8, torch.int32],
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dtype_two=[torch.int8, torch.int32],
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tags=["long"],
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)
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class InpBinaryOpBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, device, dtype_one, dtype_two, op_func):
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self.inputs = {
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"input_one": torch.randn(M, N, K, device=device).to(dtype=dtype_one),
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"input_two": torch.randn(M, N, K, device=device).to(dtype=dtype_two),
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}
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self.op_func = op_func
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def forward(self, input_one, input_two):
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return self.op_func(input_one, input_two)
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op_bench.generate_pt_tests_from_op_list(
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binary_ops_list, binary_short_configs + binary_long_configs, InpBinaryOpBenchmark
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)
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######
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# Benchmark ops performance for inplace div
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######
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# Performing division inplace benchmarks separately, as data needs to be float
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binary_ops_list = op_bench.op_list(
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attr_names=["op_name", "op_func"],
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attrs=[
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["div_", div_],
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],
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)
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binary_short_configs = op_bench.config_list(
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attr_names=["M", "N", "K"],
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attrs=[
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[1, 1, 1],
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[64, 64, 64],
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[64, 64, 128],
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],
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cross_product_configs={
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"device": ["cpu", "cuda"],
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"dtype_one": [torch.float],
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"dtype_two": [torch.float],
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},
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tags=["short"],
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)
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binary_long_configs = op_bench.cross_product_configs(
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M=[8, 128],
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N=[32, 64],
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K=[256, 512],
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device=["cpu", "cuda"],
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dtype_one=[torch.float, torch.float],
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dtype_two=[torch.float, torch.float],
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tags=["long"],
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)
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class InpBinaryOpBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, device, dtype_one, dtype_two, op_func):
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self.inputs = {
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"input_one": torch.randn(M, N, K, device=device).to(dtype=dtype_one),
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"input_two": torch.randn(M, N, K, device=device).to(dtype=dtype_two),
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}
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self.op_func = op_func
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def forward(self, input_one, input_two):
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return self.op_func(input_one, input_two)
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op_bench.generate_pt_tests_from_op_list(
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binary_ops_list, binary_short_configs + binary_long_configs, InpBinaryOpBenchmark
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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