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7763c83af6
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126 Approved by: https://github.com/kit1980 ghstack dependencies: #127122, #127123, #127124, #127125
161 lines
4.4 KiB
Python
161 lines
4.4 KiB
Python
import random
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from typing import List
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import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for Cat operator"""
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cross_product_configs = {
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"device": ["cpu", "cuda"],
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}
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# Configs for PT Cat operator
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cat_configs_short = op_bench.config_list(
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attr_names=["sizes", "N", "dim"],
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attrs=[
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[(1, 1, 1), 2, 0], # noqa: E241
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[(512, 512, 2), 2, 1], # noqa: E241
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[(128, 1024, 2), 2, 1], # noqa: E241
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],
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cross_product_configs=cross_product_configs,
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tags=["short"],
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)
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# Configs specific to static runtime feature - a fast path runtime for pared down models
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cat_configs_static_runtime = op_bench.config_list(
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attr_names=["sizes", "N", "dim"],
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attrs=[
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[[(1, 160), (1, 14)], -1, 1],
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[[(1, 20, 40), (1, 4, 40), (1, 5, 40)], -1, 1],
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[[(1, 580), (1, 174)], -1, 1],
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[[(20, 160), (20, 14)], -1, 1],
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[[(20, 20, 40), (20, 4, 40), (20, 5, 40)], -1, 1],
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[[(20, 580), (20, 174)], -1, 1],
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],
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cross_product_configs=cross_product_configs,
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tags=["static_runtime"],
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)
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cat_configs_long = op_bench.config_list(
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attr_names=["sizes", "N", "dim"],
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attrs=[
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[(2**10, 2**10, 2), 2, 0], # noqa: E241
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[(2**10 + 1, 2**10 - 1, 2), 2, 1], # noqa: E226,E241
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[(2**10, 2**10, 2), 2, 2], # noqa: E241
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[
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[
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lambda: random.randint(2**6, 2**7),
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2**7 - 17,
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2**6 + 1,
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], # noqa: E201,E226,E241
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5,
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0,
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],
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[
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[
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2**6 + 2**5,
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lambda: random.randint(2**6, 2**7),
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2**6,
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], # noqa: E201,E226,E241,E272
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5,
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1,
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],
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[
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[
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2**7,
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2**6,
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lambda: random.randint(2**6, 2**7),
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], # noqa: E201,E241,E272
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5,
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2,
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],
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[[lambda: random.randint(2**5, 2**6), 2**5, 2**6], 50, 0], # noqa: E241
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[
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[2**5, lambda: random.randint(2**5, 2**6), 2**6], # noqa: E241,E272
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50,
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1,
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],
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[
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[
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2**5 + 1,
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2**6 + 1,
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lambda: random.randint(2**5, 2**6),
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], # noqa: E226,E241,E272
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50,
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2,
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],
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],
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cross_product_configs=cross_product_configs,
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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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cat_configs_multidim = op_bench.config_list(
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attr_names=["sizes", "N", "dim"],
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attrs=[
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[(2**6, 2**5, 2**2, 2**4, 2**5), 2, 2], # noqa: E241
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[(2**4, 2**5, 2**2, 2**4, 2**5), 8, 2], # 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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4,
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], # noqa: E226,E241
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],
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cross_product_configs=cross_product_configs,
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tags=["multidim"],
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)
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cat_configs_manyinputs = op_bench.config_list(
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attr_names=["sizes", "N", "dim"],
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attrs=[
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[[lambda: random.randint(1, 10000)], 100, 0],
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[[lambda: random.randint(1, 1000)], 1000, 0],
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[[lambda: random.randint(1, 500)], 2000, 0],
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[[lambda: random.randint(1, 300)], 3000, 0],
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],
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cross_product_configs=cross_product_configs,
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tags=["manyinputs"],
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)
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class CatBenchmark(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) == 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.empty(0, device=device)
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self.inputs = {"result": result, "inputs": inputs, "dim": dim}
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self.set_module_name("cat")
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def forward(self, result: torch.Tensor, inputs: List[torch.Tensor], dim: int):
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return torch.cat(inputs, dim=dim, out=result)
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op_bench.generate_pt_test(
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cat_configs_short
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+ cat_configs_long
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+ cat_configs_multidim
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+ cat_configs_manyinputs
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+ cat_configs_static_runtime,
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CatBenchmark,
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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