FM / quantization

/usr/local/lib64/python3.6/site-packages/torch/ao/quantization/

/usr/local/lib64/python3.6/site-packages/torch/ao/quantization

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
fake_quantize.py	18537	0644	edit dl rm
fuser_method_mappings.py	5361	0644	edit dl rm
fuse_modules.py	5769	0644	edit dl rm
observer.py	52793	0644	edit dl rm
qconfig.py	11701	0644	edit dl rm
quantization_mappings.py	9430	0644	edit dl rm
quantize.py	24267	0644	edit dl rm
quantize_jit.py	9044	0644	edit dl rm
quant_type.py	451	0644	edit dl rm
stubs.py	1855	0644	edit dl rm
utils.py	8442	0644	edit dl rm
_correct_bias.py	4967	0644	edit dl rm
_equalize.py	6553	0644	edit dl rm
_learnable_fake_quantize.py	7246	0644	edit dl rm
__init__.py	163	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/ao/quantization/stubs.py (1855B)

from torch import nn

class QuantStub(nn.Module):
    r"""Quantize stub module, before calibration, this is same as an observer,
    it will be swapped as `nnq.Quantize` in `convert`.

Args:
        qconfig: quantization configuration for the tensor,
            if qconfig is not provided, we will get qconfig from parent modules
    """
    def __init__(self, qconfig=None):
        super(QuantStub, self).__init__()
        if qconfig:
            self.qconfig = qconfig

def forward(self, x):
        return x

class DeQuantStub(nn.Module):
    r"""Dequantize stub module, before calibration, this is same as identity,
    this will be swapped as `nnq.DeQuantize` in `convert`.
    """
    def __init__(self):
        super(DeQuantStub, self).__init__()

def forward(self, x):
        return x

class QuantWrapper(nn.Module):
    r"""A wrapper class that wraps the input module, adds QuantStub and
    DeQuantStub and surround the call to module with call to quant and dequant
    modules.

This is used by the `quantization` utility functions to add the quant and
    dequant modules, before `convert` function `QuantStub` will just be observer,
    it observes the input tensor, after `convert`, `QuantStub`
    will be swapped to `nnq.Quantize` which does actual quantization. Similarly
    for `DeQuantStub`.
    """
    quant: QuantStub
    dequant: DeQuantStub
    module: nn.Module

def __init__(self, module):
        super(QuantWrapper, self).__init__()
        qconfig = module.qconfig if hasattr(module, 'qconfig') else None
        self.add_module('quant', QuantStub(qconfig))
        self.add_module('dequant', DeQuantStub())
        self.add_module('module', module)
        self.train(module.training)

def forward(self, X):
        X = self.quant(X)
        X = self.module(X)
        return self.dequant(X)

from torch import nn class QuantStub(nn.Module): r"""Quantize stub module, before calibration, this is same as an observer, it will be swapped as `nnq.Quantize` in `convert`. Args: qconfig: quantization configuration for the tensor, if qconfig is not provided, we will get qconfig from parent modules """ def __init__(self, qconfig=None): super(QuantStub, self).__init__() if qconfig: self.qconfig = qconfig def forward(self, x): return x class DeQuantStub(nn.Module): r"""Dequantize stub module, before calibration, this is same as identity, this will be swapped as `nnq.DeQuantize` in `convert`. """ def __init__(self): super(DeQuantStub, self).__init__() def forward(self, x): return x class QuantWrapper(nn.Module): r"""A wrapper class that wraps the input module, adds QuantStub and DeQuantStub and surround the call to module with call to quant and dequant modules. This is used by the `quantization` utility functions to add the quant and dequant modules, before `convert` function `QuantStub` will just be observer, it observes the input tensor, after `convert`, `QuantStub` will be swapped to `nnq.Quantize` which does actual quantization. Similarly for `DeQuantStub`. """ quant: QuantStub dequant: DeQuantStub module: nn.Module def __init__(self, module): super(QuantWrapper, self).__init__() qconfig = module.qconfig if hasattr(module, 'qconfig') else None self.add_module('quant', QuantStub(qconfig)) self.add_module('dequant', DeQuantStub()) self.add_module('module', module) self.train(module.training) def forward(self, X): X = self.quant(X) X = self.module(X) return self.dequant(X)