quark.torch.export.nn.modules.qparamslinear

quark.torch.export.nn.modules.qparamslinear#

Module Contents#

Classes#

class quark.torch.export.nn.modules.qparamslinear.QparamsOperator(*args: Any, **kwargs: Any)#

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) -> None:
        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.export.nn.modules.qparamslinear.QParamsLinear(linear: torch.nn.Linear, custom_mode: str, pack_method: str | None = 'reorder', quant_config: quark.torch.quantization.config.config.QuantizationConfig | None = None)#

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) -> None:
        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.

classmethod from_module(linear: torch.nn.Linear, custom_mode: str, pack_method: str | None = 'reorder', quant_config: quark.torch.quantization.config.config.QuantizationConfig | None = None) → QParamsLinear#

Build a QParamsLinear from a QuantLinear or nn.Linear. Initialize the shape and data type of weight and bias in importing. Initialize weight and bias in exporting.

forward(*args: Any, **kwargs: Any) → torch.Tensor#

Dequantizes quantized weight/bias, runs a linear in high precision and apply QDQ on the (input)activation/output if required.

pack_qinfo() → None#

Calls RealQuantizer.pack_zero_point` and RealQuantizer.maybe_transpose_scale to do scale, zero_point packing if required.

state_dict(*args: Any, destination: Any = None, prefix: str = '', keep_vars: bool = False) → Any#

Return a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Args:
destination (dict, optional): If provided, the state of module will

be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

prefix (str, optional): a prefix added to parameter and buffer

names to compose the keys in state_dict. Default: ''.

keep_vars (bool, optional): by default the :class:`~torch.Tensor` s

returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:
dict:

a dictionary containing a whole state of the module

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']
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