mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
c15fca1137
Summary:
This improves the documentation page for backend_config_dict to render
the configurations in a human readable format, such as
```
{
'pattern': torch.nn.modules.pooling.AdaptiveAvgPool1d,
'dtype_configs': [
{
'input_dtype': torch.quint8,
'output_dtype': torch.quint8,
},
{
'input_dtype': torch.float16,
'weight_dtype': torch.float16,
'bias_dtype': torch.float16,
'output_dtype': torch.float16,
},
],
'observation_type': ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
},
```
The results are also now sorted alphabetically by the normalized name of
the root op in the pattern.
A couple of utility functions are created to help with this. If in the future
we convert backend_config_dict to use typed objects, we can move this logic
to the objects at that time.
Test plan:
```
cd docs
make html
cd build
python -m server.http
// renders correctly, example: https://gist.github.com/vkuzo/76adfc7c89e119c59813a733fa2cd56f
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77535
Approved by: https://github.com/andrewor14
63 lines
1.9 KiB
Python
63 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 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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import os.path
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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.join(__file__, "..")),
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"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(QUANTIZATION_BACKEND_CONFIG_IMAGE_PATH, "default_backend_config.txt")
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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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