mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-14 20:57:59 +00:00
e3900d2ba5
Summary:
As this diff shows, currently there are a couple hundred instances of raw `noqa` in the codebase, which just ignore all errors on a given line. That isn't great, so this PR changes all existing instances of that antipattern to qualify the `noqa` with respect to a specific error code, and adds a lint to prevent more of this from happening in the future.
Interestingly, some of the examples the `noqa` lint catches are genuine attempts to qualify the `noqa` with a specific error code, such as these two:
```
test/jit/test_misc.py:27: print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
test/jit/test_misc.py:28: print(f"format blank") # noqa F541
```
However, those are still wrong because they are [missing a colon](https://flake8.pycqa.org/en/3.9.1/user/violations.html#in-line-ignoring-errors), which actually causes the error code to be completely ignored:
- If you change them to anything else, the warnings will still be suppressed.
- If you add the necessary colons then it is revealed that `E261` was also being suppressed, unintentionally:
```
test/jit/test_misc.py:27:57: E261 at least two spaces before inline comment
test/jit/test_misc.py:28:35: E261 at least two spaces before inline comment
```
I did try using [flake8-noqa](https://pypi.org/project/flake8-noqa/) instead of a custom `git grep` lint, but it didn't seem to work. This PR is definitely missing some of the functionality that flake8-noqa is supposed to provide, though, so if someone can figure out how to use it, we should do that instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56272
Test Plan:
CI should pass on the tip of this PR, and we know that the lint works because the following CI run (before this PR was finished) failed:
- https://github.com/pytorch/pytorch/runs/2365189927
Reviewed By: janeyx99
Differential Revision: D27830127
Pulled By: samestep
fbshipit-source-id: d6dcf4f945ebd18cd76c46a07f3b408296864fcb
98 lines
2.7 KiB
Python
98 lines
2.7 KiB
Python
import operator_benchmark as op_bench
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import torch
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import random
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from typing import List
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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={
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'device': ['cpu', 'cuda'],
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'dim': list(range(3))
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},
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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={
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'device': ['cpu', 'cuda'],
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'dim': list(range(4))
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},
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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={
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'device': ['cpu', 'cuda'],
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'dim': list(range(4))
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},
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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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[(2**3+1, 2**5-1, 2**2+1, 2**4-1, 2**5+1), 17], # noqa: E226,E241
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],
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cross_product_configs={
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'device': ['cpu', 'cuda'],
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'dim': list(range(6))
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},
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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) == 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([old_size() if callable(old_size) else old_size for old_size in sizes])
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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 = {
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"result": result,
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"inputs": inputs,
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"dim": dim
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}
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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(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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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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