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https://github.com/zebrajr/pytorch.git
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9e7c19f72b
`E721` checks for object type comparisons using == and other comparison operators. This is useful because it is recommended to use `is` for type comparisons. Pull Request resolved: https://github.com/pytorch/pytorch/pull/165162 Approved by: https://github.com/Skylion007
95 lines
2.6 KiB
Python
95 lines
2.6 KiB
Python
import random
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import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for Stack operator"""
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# Configs for PT stack operator
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stack_configs_static_runtime = op_bench.config_list(
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attr_names=["sizes", "N"],
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attrs=[
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[(20, 40), 5],
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[(1, 40), 5],
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],
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cross_product_configs={"device": ["cpu", "cuda"], "dim": list(range(3))},
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tags=["static_runtime"],
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)
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stack_configs_short = op_bench.config_list(
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attr_names=["sizes", "N"],
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attrs=[
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[(1, 1, 1), 2], # noqa: E241
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[(512, 512, 2), 2], # noqa: E241
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[(128, 1024, 2), 2], # noqa: E241
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],
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cross_product_configs={"device": ["cpu", "cuda"], "dim": list(range(4))},
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tags=["short"],
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)
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stack_configs_long = op_bench.config_list(
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attr_names=["sizes", "N"],
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attrs=[
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[(2**10, 2**10, 2), 2], # noqa: E241
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[(2**10 + 1, 2**10 - 1, 2), 2], # noqa: E226,E241
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[(2**10, 2**10, 2), 2], # noqa: E241
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],
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cross_product_configs={"device": ["cpu", "cuda"], "dim": list(range(4))},
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tags=["long"],
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)
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# There is a different codepath on CUDA for >4 dimensions
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stack_configs_multidim = op_bench.config_list(
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attr_names=["sizes", "N"],
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attrs=[
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[(2**6, 2**5, 2**2, 2**4, 2**5), 2], # noqa: E241
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[(2**4, 2**5, 2**2, 2**4, 2**5), 8], # noqa: E241
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[
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(2**3 + 1, 2**5 - 1, 2**2 + 1, 2**4 - 1, 2**5 + 1),
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17,
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], # noqa: E226,E241
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],
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cross_product_configs={"device": ["cpu", "cuda"], "dim": list(range(6))},
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tags=["multidim"],
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)
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class StackBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, sizes, N, dim, device):
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random.seed(42)
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inputs = []
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gen_sizes = []
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if type(sizes) is list and N == -1:
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gen_sizes = sizes
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else:
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for i in range(N):
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gen_sizes.append(
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[
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old_size() if callable(old_size) else old_size
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for old_size in sizes
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]
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)
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for s in gen_sizes:
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inputs.append(torch.rand(s, device=device))
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result = torch.rand(gen_sizes[0], device=device)
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self.inputs = {"result": result, "inputs": inputs, "dim": dim}
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self.set_module_name("stack")
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def forward(self, result: torch.Tensor, inputs: list[torch.Tensor], dim: int):
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return torch.stack(inputs, dim=dim, out=result)
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op_bench.generate_pt_test(
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stack_configs_static_runtime
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+ stack_configs_short
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+ stack_configs_long
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+ stack_configs_multidim,
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StackBenchmark,
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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