mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
b6bdb67f82
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
"""
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This script will generate default values of quantization configs.
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These are for use in the documentation.
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"""
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import os.path
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import torch
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from torch.ao.quantization.backend_config import get_native_backend_config_dict
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from torch.ao.quantization.backend_config.utils import (
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entry_to_pretty_str,
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remove_boolean_dispatch_from_name,
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)
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# Create a directory for the images, if it doesn't exist
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QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH = os.path.join(
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os.path.realpath(os.path.dirname(__file__)), "quantization_backend_configs"
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)
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if not os.path.exists(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH):
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os.mkdir(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH)
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output_path = os.path.join(
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QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH, "default_backend_config.txt"
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)
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with open(output_path, "w") as f:
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native_backend_config_dict = get_native_backend_config_dict()
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configs = native_backend_config_dict["configs"]
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def _sort_key_func(entry):
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pattern = entry["pattern"]
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while isinstance(pattern, tuple):
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pattern = pattern[-1]
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pattern = remove_boolean_dispatch_from_name(pattern)
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if not isinstance(pattern, str):
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# methods are already strings
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pattern = torch.typename(pattern)
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# we want
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#
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# torch.nn.modules.pooling.AdaptiveAvgPool1d
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#
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# and
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#
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# torch._VariableFunctionsClass.adaptive_avg_pool1d
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#
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# to be next to each other, so convert to all lower case
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# and remove the underscores, and compare the last part
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# of the string
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pattern_str_normalized = pattern.lower().replace("_", "")
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key = pattern_str_normalized.split(".")[-1]
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return key
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configs.sort(key=_sort_key_func)
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entries = []
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for entry in configs:
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entries.append(entry_to_pretty_str(entry))
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entries = ",\n".join(entries)
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f.write(entries)
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