`quark.torch.quantization.tensor_quantize`#

Module Contents#

Classes#

class quark.torch.quantization.tensor_quantize.FakeQuantizeBase#

Base fake quantize module.

Base fake quantize module Any fake quantize implementation should derive from this class.

Concrete fake quantize module should follow the same API. In forward, they will update the statistics of the observed Tensor and fake quantize the input. They should also provide a calculate_qparams function that computes the quantization parameters given the collected statistics.

class quark.torch.quantization.tensor_quantize.FakeQuantize(quant_spec: quark.torch.quantization.config.config.QuantizationSpec, **kwargs: Any)#

Base fake quantize module.

Base fake quantize module Any fake quantize implementation should derive from this class.

property block_sizes: int#: Return block_sizes for quantization.

extra_repr() → str#

Set the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

export_amax() → Optional[torch.Tensor]#: Adapter for GPU export

class quark.torch.quantization.tensor_quantize.FreezedFakeQuantize#

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.

class quark.torch.quantization.tensor_quantize.SequentialFakeQuantize(*args: torch.nn.modules.module.Module)#

class quark.torch.quantization.tensor_quantize.SequentialFakeQuantize(arg: OrderedDict[str, Module])

A sequential container.

Modules will be added to it in the order they are passed in the constructor. Alternatively, an OrderedDict of modules can be passed in. The forward() method of Sequential accepts any input and forwards it to the first module it contains. It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.

The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module, such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).

What’s the difference between a Sequential and a :class:`torch.nn.ModuleList`? A ModuleList is exactly what it sounds like–a list for storing Module s! On the other hand, the layers in a Sequential are connected in a cascading way.

Example:

# Using Sequential to create a small model. When `model` is run,
# input will first be passed to `Conv2d(1,20,5)`. The output of
# `Conv2d(1,20,5)` will be used as the input to the first
# `ReLU`; the output of the first `ReLU` will become the input
# for `Conv2d(20,64,5)`. Finally, the output of
# `Conv2d(20,64,5)` will be used as input to the second `ReLU`
model = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )

# Using Sequential with OrderedDict. This is functionally the
# same as the above code
model = nn.Sequential(OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())
        ]))

quark.torch.quantization.tensor_quantize

Contents

quark.torch.quantization.tensor_quantize#

Module Contents#

Classes#

`quark.torch.quantization.tensor_quantize`#