mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
07669ed960
This is one of a series of PRs to update us to PEP585 (changing Dict -> dict, List -> list, etc). Most of the PRs were completely automated with RUFF as follows:
Since RUFF UP006 is considered an "unsafe" fix first we need to enable unsafe fixes:
```
--- a/tools/linter/adapters/ruff_linter.py
+++ b/tools/linter/adapters/ruff_linter.py
@@ -313,6 +313,7 @@
"ruff",
"check",
"--fix-only",
+ "--unsafe-fixes",
"--exit-zero",
*([f"--config={config}"] if config else []),
"--stdin-filename",
```
Then we need to tell RUFF to allow UP006 (as a final PR once all of these have landed this will be made permanent):
```
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,7 +40,7 @@
[tool.ruff]
-target-version = "py38"
+target-version = "py39"
line-length = 88
src = ["caffe2", "torch", "torchgen", "functorch", "test"]
@@ -87,7 +87,6 @@
"SIM116", # Disable Use a dictionary instead of consecutive `if` statements
"SIM117",
"SIM118",
- "UP006", # keep-runtime-typing
"UP007", # keep-runtime-typing
]
select = [
```
Finally running `lintrunner -a --take RUFF` will fix up the deprecated uses.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145101
Approved by: https://github.com/bobrenjc93
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
import operator_benchmark as op_bench
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import torch
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import torch.ao.nn.quantized as nnq
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"""Microbenchmarks for quantized Cat operator"""
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# Configs for PT Cat operator
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qcat_configs_short = op_bench.config_list(
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attr_names=["M", "N", "K", "L", "dim"],
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attrs=[
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[256, 512, 1, 2, 0],
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[512, 512, 2, 1, 1],
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],
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cross_product_configs={
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"contig": ("all", "one", "none"),
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"dtype": (torch.quint8, torch.qint8, torch.qint32),
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},
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tags=["short"],
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)
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qcat_configs_long = op_bench.cross_product_configs(
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M=[128, 1024],
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N=[128, 1024],
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K=[1, 2],
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L=[5, 7],
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dim=[0, 1, 2],
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contig=["all", "one", "none"],
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dtype=[torch.quint8],
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tags=["long"],
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)
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class QCatBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, L, dim, contig, dtype):
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f_input = (torch.rand(M, N, K) - 0.5) * 256
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self.qf = nnq.QFunctional()
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scale = 1.0
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zero_point = 0
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self.qf.scale = scale
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self.qf.zero_point = zero_point
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assert contig in ("none", "one", "all")
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q_input = torch.quantize_per_tensor(f_input, scale, zero_point, dtype)
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permute_dims = tuple(range(q_input.ndim - 1, -1, -1))
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q_input_non_contig = q_input.permute(permute_dims).contiguous()
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q_input_non_contig = q_input_non_contig.permute(permute_dims)
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if contig == "all":
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self.input = (q_input, q_input)
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elif contig == "one":
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self.input = (q_input, q_input_non_contig)
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elif contig == "none":
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self.input = (q_input_non_contig, q_input_non_contig)
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self.inputs = {"input": self.input, "dim": dim}
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self.set_module_name("qcat")
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def forward(self, input: list[torch.Tensor], dim: int):
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return self.qf.cat(input, dim=dim)
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op_bench.generate_pt_test(qcat_configs_short + qcat_configs_long, QCatBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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