mirror of
https://github.com/zebrajr/pytorch.git
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b7bda236d1
Summary:
xref gh-38010 and gh-38011.
After this PR, there should be only two warnings:
```
pytorch/docs/source/index.rst:65: WARNING: toctree contains reference to nonexisting \
document 'torchvision/index'
WARNING: autodoc: failed to import class 'tensorboard.writer.SummaryWriter' from module \
'torch.utils'; the following exception was raised:
No module named 'tensorboard'
```
If tensorboard and torchvision are prerequisites to building docs, they should be added to the `requirements.txt`.
As for breaking up quantization into smaller pieces: I split out the list of supported operations and the list of modules to separate documents. I think this makes the page flow better, makes it much "lighter" in terms of page cost, and also removes some warnings since the same class names appear in multiple sub-modules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41321
Reviewed By: ngimel
Differential Revision: D22753099
Pulled By: mruberry
fbshipit-source-id: d504787fcf1104a0b6e3d1c12747ec53450841da
69 lines
1.8 KiB
ReStructuredText
69 lines
1.8 KiB
ReStructuredText
.. _torch_quantization:
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torch.quantization
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------------------
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.. automodule:: torch.quantization
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This module implements the functions you call
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directly to convert your model from FP32 to quantized form. For
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example the :func:`~torch.quantization.prepare` is used in post training
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quantization to prepares your model for the calibration step and
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:func:`~torch.quantization.convert` actually converts the weights to int8 and
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replaces the operations with their quantized counterparts. There are
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other helper functions for things like quantizing the input to your
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model and performing critical fusions like conv+relu.
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Top-level quantization APIs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: quantize
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.. autofunction:: quantize_dynamic
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.. autofunction:: quantize_qat
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.. autofunction:: prepare
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.. autofunction:: prepare_qat
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.. autofunction:: convert
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.. autoclass:: QConfig
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.. autoclass:: QConfigDynamic
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.. FIXME: The following doesn't display correctly.
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.. autoattribute:: default_qconfig
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Preparing model for quantization
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: fuse_modules
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.. autoclass:: QuantStub
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.. autoclass:: DeQuantStub
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.. autoclass:: QuantWrapper
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.. autofunction:: add_quant_dequant
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Utility functions
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~~~~~~~~~~~~~~~~~
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.. autofunction:: add_observer_
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.. autofunction:: swap_module
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.. autofunction:: propagate_qconfig_
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.. autofunction:: default_eval_fn
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Observers
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~~~~~~~~~~~~~~~
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.. autoclass:: ObserverBase
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:members:
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.. autoclass:: MinMaxObserver
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.. autoclass:: MovingAverageMinMaxObserver
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.. autoclass:: PerChannelMinMaxObserver
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.. autoclass:: MovingAveragePerChannelMinMaxObserver
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.. autoclass:: HistogramObserver
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.. autoclass:: FakeQuantize
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.. autoclass:: NoopObserver
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Debugging utilities
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~~~~~~~~~~~~~~~~~~~
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.. autofunction:: get_observer_dict
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.. autoclass:: RecordingObserver
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.. currentmodule:: torch
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.. autosummary::
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:nosignatures:
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nn.intrinsic
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