torch.nn.quantized

This module implements the quantized versions of the nn modules and functionals.

Functional interface

Functional interface (quantized).

torch.nn.quantized.functional.linear(input, weight, bias=None, scale=None, zero_point=None) [source]

Applies a linear transformation to the incoming quantized data: $y = xA^T + b$ . See Linear

Note

Current implementation packs weights on every call, which has penalty on performance. If you want to avoid the overhead, use Linear.

Parameters

• input (Tensor) – Quantized input of type torch.quint8
• weight (Tensor) – Quantized weight of type torch.qint8
• bias (Tensor) – None or fp32 bias of type torch.float
• scale (double) – output scale. If None, derived from the input scale
• zero_point (long) – output zero point. If None, derived from the input zero_point

Shape:

• Input: $(N, *, in\_features)$ where * means any number of additional dimensions
• Weight: $(out\_features, in\_features)$
• Bias: $(out\_features)$
• Output: $(N, *, out\_features)$

torch.nn.quantized.functional.conv1d(input, weight, bias, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', scale=1.0, zero_point=0, dtype=torch.quint8) [source]

Applies a 1D convolution over a quantized 1D input composed of several input planes.

See Conv1d for details and output shape.

Parameters

• input – quantized input tensor of shape $(\text{minibatch} , \text{in\_channels} , iW)$
• weight – quantized filters of shape $(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , iW)$
• bias – non-quantized bias tensor of shape $(\text{out\_channels})$ . The tensor type must be torch.float.
• stride – the stride of the convolving kernel. Can be a single number or a tuple (sW,). Default: 1
• padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padW,). Default: 0
• dilation – the spacing between kernel elements. Can be a single number or a tuple (dW,). Default: 1
• groups – split input into groups, $\text{in\_channels}$ should be divisible by the number of groups. Default: 1
• padding_mode – the padding mode to use. Only “zeros” is supported for quantized convolution at the moment. Default: “zeros”
• scale – quantization scale for the output. Default: 1.0
• zero_point – quantization zero_point for the output. Default: 0
• dtype – quantization data type to use. Default: torch.quint8

Examples:

>>> from torch.nn.quantized import functional as qF
>>> filters = torch.randn(33, 16, 3, dtype=torch.float)
>>> inputs = torch.randn(20, 16, 50, dtype=torch.float)
>>> bias = torch.randn(33, dtype=torch.float)
>>>
>>> scale, zero_point = 1.0, 0
>>> dtype_inputs = torch.quint8
>>> dtype_filters = torch.qint8
>>>
>>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
>>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
>>> qF.conv1d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)

torch.nn.quantized.functional.conv2d(input, weight, bias, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', scale=1.0, zero_point=0, dtype=torch.quint8) [source]

Applies a 2D convolution over a quantized 2D input composed of several input planes.

See Conv2d for details and output shape.

Parameters

• input – quantized input tensor of shape $(\text{minibatch} , \text{in\_channels} , iH , iW)$
• weight – quantized filters of shape $(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW)$
• bias – non-quantized bias tensor of shape $(\text{out\_channels})$ . The tensor type must be torch.float.
• stride – the stride of the convolving kernel. Can be a single number or a tuple (sH, sW). Default: 1
• padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0
• dilation – the spacing between kernel elements. Can be a single number or a tuple (dH, dW). Default: 1
• groups – split input into groups, $\text{in\_channels}$ should be divisible by the number of groups. Default: 1
• padding_mode – the padding mode to use. Only “zeros” is supported for quantized convolution at the moment. Default: “zeros”
• scale – quantization scale for the output. Default: 1.0
• zero_point – quantization zero_point for the output. Default: 0
• dtype – quantization data type to use. Default: torch.quint8

Examples:

>>> from torch.nn.quantized import functional as qF
>>> filters = torch.randn(8, 4, 3, 3, dtype=torch.float)
>>> inputs = torch.randn(1, 4, 5, 5, dtype=torch.float)
>>> bias = torch.randn(8, dtype=torch.float)
>>>
>>> scale, zero_point = 1.0, 0
>>> dtype_inputs = torch.quint8
>>> dtype_filters = torch.qint8
>>>
>>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
>>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
>>> qF.conv2d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)

torch.nn.quantized.functional.conv3d(input, weight, bias, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', scale=1.0, zero_point=0, dtype=torch.quint8) [source]

Applies a 3D convolution over a quantized 3D input composed of several input planes.

See Conv3d for details and output shape.

Parameters

• input – quantized input tensor of shape $(\text{minibatch} , \text{in\_channels} , iD , iH , iW)$
• weight – quantized filters of shape $(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kD , kH , kW)$
• bias – non-quantized bias tensor of shape $(\text{out\_channels})$ . The tensor type must be torch.float.
• stride – the stride of the convolving kernel. Can be a single number or a tuple (sD, sH, sW). Default: 1
• padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padD, padH, padW). Default: 0
• dilation – the spacing between kernel elements. Can be a single number or a tuple (dD, dH, dW). Default: 1
• groups – split input into groups, $\text{in\_channels}$ should be divisible by the number of groups. Default: 1
• padding_mode – the padding mode to use. Only “zeros” is supported for quantized convolution at the moment. Default: “zeros”
• scale – quantization scale for the output. Default: 1.0
• zero_point – quantization zero_point for the output. Default: 0
• dtype – quantization data type to use. Default: torch.quint8

Examples:

>>> from torch.nn.quantized import functional as qF
>>> filters = torch.randn(8, 4, 3, 3, 3, dtype=torch.float)
>>> inputs = torch.randn(1, 4, 5, 5, 5, dtype=torch.float)
>>> bias = torch.randn(8, dtype=torch.float)
>>>
>>> scale, zero_point = 1.0, 0
>>> dtype_inputs = torch.quint8
>>> dtype_filters = torch.qint8
>>>
>>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
>>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
>>> qF.conv3d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)

torch.nn.quantized.functional.max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False) [source]

Applies a 2D max pooling over a quantized input signal composed of several quantized input planes.

Note

The input quantization parameters are propagated to the output.

See MaxPool2d for details.

torch.nn.quantized.functional.adaptive_avg_pool2d(input, output_size) [source]

Applies a 2D adaptive average pooling over a quantized input signal composed of several quantized input planes.

Note

The input quantization parameters propagate to the output.

See AdaptiveAvgPool2d for details and output shape.

Parameters

output_size – the target output size (single integer or double-integer tuple)

torch.nn.quantized.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) [source]

Applies 2D average-pooling operation in $kH \times kW$ regions by step size $sH \times sW$ steps. The number of output features is equal to the number of input planes.

Note

The input quantization parameters propagate to the output.

See AvgPool2d for details and output shape.

Parameters

• input – quantized input tensor $(\text{minibatch} , \text{in\_channels} , iH , iW)$
• kernel_size – size of the pooling region. Can be a single number or a tuple (kH, kW)
• stride – stride of the pooling operation. Can be a single number or a tuple (sH, sW). Default: kernel_size
• padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0
• ceil_mode – when True, will use ceil instead of floor in the formula to compute the output shape. Default: False
• count_include_pad – when True, will include the zero-padding in the averaging calculation. Default: True
• divisor_override – if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None

torch.nn.quantized.functional.interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None) [source]

Down/up samples the input to either the given size or the given scale_factor

See torch.nn.functional.interpolate() for implementation details.

The input dimensions are interpreted in the form: mini-batch x channels x [optional depth] x [optional height] x width.

Note

The input quantization parameters propagate to the output.

Note

Only 2D/3D input is supported for quantized inputs

Note

Only the following modes are supported for the quantized inputs:

• bilinear
• nearest

Parameters

• input (Tensor) – the input tensor
• size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]) – output spatial size.
• scale_factor (float or Tuple[float]) – multiplier for spatial size. Has to match input size if it is a tuple.
• mode (str) – algorithm used for upsampling: 'nearest' | 'bilinear'
• align_corners (bool, optional) – Geometrically, we consider the pixels of the input and output as squares rather than points. If set to True, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to False, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same. This only has an effect when mode is 'bilinear'. Default: False

torch.nn.quantized.functional.hardswish(input, scale, zero_point) [source]

This is the quantized version of hardswish().

Parameters

• input – quantized input
• scale – quantization scale of the output tensor
• zero_point – quantization zero point of the output tensor

torch.nn.quantized.functional.upsample(input, size=None, scale_factor=None, mode='nearest', align_corners=None) [source]

Upsamples the input to either the given size or the given scale_factor

Warning

This function is deprecated in favor of torch.nn.quantized.functional.interpolate(). This is equivalent with nn.quantized.functional.interpolate(...).

See torch.nn.functional.interpolate() for implementation details.

The input dimensions are interpreted in the form: mini-batch x channels x [optional depth] x [optional height] x width.

Note

The input quantization parameters propagate to the output.

Note

Only 2D input is supported for quantized inputs

Note

Only the following modes are supported for the quantized inputs:

• bilinear
• nearest

Parameters

• input (Tensor) – quantized input tensor
• size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]) – output spatial size.
• scale_factor (float or Tuple[float]) – multiplier for spatial size. Has to be an integer.
• mode (string) – algorithm used for upsampling: 'nearest' | 'bilinear'
• align_corners (bool, optional) – Geometrically, we consider the pixels of the input and output as squares rather than points. If set to True, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to False, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when scale_factor is kept the same. This only has an effect when mode is 'bilinear'. Default: False

Warning

With align_corners = True, the linearly interpolating modes (bilinear) don’t proportionally align the output and input pixels, and thus the output values can depend on the input size. This was the default behavior for these modes up to version 0.3.1. Since then, the default behavior is align_corners = False. See Upsample for concrete examples on how this affects the outputs.

torch.nn.quantized.functional.upsample_bilinear(input, size=None, scale_factor=None) [source]

Upsamples the input, using bilinear upsampling.

Warning

This function is deprecated in favor of torch.nn.quantized.functional.interpolate(). This is equivalent with nn.quantized.functional.interpolate(..., mode='bilinear', align_corners=True).

Note

The input quantization parameters propagate to the output.

Note

Only 2D inputs are supported

Parameters

• input (Tensor) – quantized input
• size (int or Tuple[int, int]) – output spatial size.
• scale_factor (int or Tuple[int, int]) – multiplier for spatial size

torch.nn.quantized.functional.upsample_nearest(input, size=None, scale_factor=None) [source]

Upsamples the input, using nearest neighbours’ pixel values.

Warning

This function is deprecated in favor of torch.nn.quantized.functional.interpolate(). This is equivalent with nn.quantized.functional.interpolate(..., mode='nearest').

Note

The input quantization parameters propagate to the output.

Note

Only 2D inputs are supported

Parameters

• input (Tensor) – quantized input
• size (int or Tuple[int, int] or Tuple[int, int, int]) – output spatial size.
• scale_factor (int) – multiplier for spatial size. Has to be an integer.

ReLU6

class torch.nn.quantized.ReLU6(inplace=False) [source]

Applies the element-wise function:

$\text{ReLU6}(x) = \min(\max(x_0, x), q(6))$ , where $x_0$ is the zero_point, and $q(6)$ is the quantized representation of number 6.

Parameters

inplace – can optionally do the operation in-place. Default: False

Shape:

• Input: $(N, *)$ where * means, any number of additional dimensions
• Output: $(N, *)$ , same shape as the input

Examples:

>>> m = nn.quantized.ReLU6()
>>> input = torch.randn(2)
>>> input = torch.quantize_per_tensor(input, 1.0, 0, dtype=torch.qint32)
>>> output = m(input)

ELU

class torch.nn.quantized.ELU(scale, zero_point, alpha=1.0) [source]

This is the quantized equivalent of ELU.

Parameters

• scale – quantization scale of the output tensor
• zero_point – quantization zero point of the output tensor
• alpha – the alpha constant

Hardswish

class torch.nn.quantized.Hardswish(scale, zero_point) [source]

This is the quantized version of Hardswish.

Parameters

• scale – quantization scale of the output tensor
• zero_point – quantization zero point of the output tensor

Conv1d

class torch.nn.quantized.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros') [source]

Applies a 1D convolution over a quantized input signal composed of several quantized input planes.

For details on input arguments, parameters, and implementation see Conv1d.

Note

Only zeros is supported for the padding_mode argument.

Note

Only torch.quint8 is supported for the input data type.

Variables

• ~Conv1d.weight (Tensor) – packed tensor derived from the learnable weight parameter.
• ~Conv1d.scale (Tensor) – scalar for the output scale
• ~Conv1d.zero_point (Tensor) – scalar for the output zero point

See Conv1d for other attributes.

Examples:

>>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
>>> input = torch.randn(20, 16, 100)
>>> # quantize input to quint8
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
                                        dtype=torch.quint8)
>>> output = m(q_input)

classmethod from_float(mod) [source]

Creates a quantized module from a float module or qparams_dict.

Parameters

mod (Module) – a float module, either produced by torch.quantization utilities or provided by the user

Conv2d

class torch.nn.quantized.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros') [source]

Applies a 2D convolution over a quantized input signal composed of several quantized input planes.

For details on input arguments, parameters, and implementation see Conv2d.

Note

Only zeros is supported for the padding_mode argument.

Note

Only torch.quint8 is supported for the input data type.

Variables

• ~Conv2d.weight (Tensor) – packed tensor derived from the learnable weight parameter.
• ~Conv2d.scale (Tensor) – scalar for the output scale
• ~Conv2d.zero_point (Tensor) – scalar for the output zero point

See Conv2d for other attributes.

Examples:

>>> # With square kernels and equal stride
>>> m = nn.quantized.Conv2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
>>> input = torch.randn(20, 16, 50, 100)
>>> # quantize input to quint8
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
>>> output = m(q_input)

classmethod from_float(mod) [source]

Creates a quantized module from a float module or qparams_dict.

Parameters

mod (Module) – a float module, either produced by torch.quantization utilities or provided by the user

Conv3d

class torch.nn.quantized.Conv3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros') [source]

Applies a 3D convolution over a quantized input signal composed of several quantized input planes.

For details on input arguments, parameters, and implementation see Conv3d.

Note

Only zeros is supported for the padding_mode argument.

Note

Only torch.quint8 is supported for the input data type.

Variables

• ~Conv3d.weight (Tensor) – packed tensor derived from the learnable weight parameter.
• ~Conv3d.scale (Tensor) – scalar for the output scale
• ~Conv3d.zero_point (Tensor) – scalar for the output zero point

See Conv3d for other attributes.

Examples:

>>> # With square kernels and equal stride
>>> m = nn.quantized.Conv3d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
>>> input = torch.randn(20, 16, 56, 56, 56)
>>> # quantize input to quint8
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
>>> output = m(q_input)

classmethod from_float(mod) [source]

Creates a quantized module from a float module or qparams_dict.

Parameters

mod (Module) – a float module, either produced by torch.quantization utilities or provided by the user

FloatFunctional

class torch.nn.quantized.FloatFunctional [source]

State collector class for float operations.

The instance of this class can be used instead of the torch. prefix for some operations. See example usage below.

Note

This class does not provide a forward hook. Instead, you must use one of the underlying functions (e.g. add).

Examples:

>>> f_add = FloatFunctional()
>>> a = torch.tensor(3.0)
>>> b = torch.tensor(4.0)
>>> f_add.add(a, b)  # Equivalent to ``torch.add(a, b)``

Valid operation names:

• add
• cat
• mul
• add_relu
• add_scalar
• mul_scalar

QFunctional

class torch.nn.quantized.QFunctional [source]

Wrapper class for quantized operations.

The instance of this class can be used instead of the torch.ops.quantized prefix. See example usage below.

Note

This class does not provide a forward hook. Instead, you must use one of the underlying functions (e.g. add).

Examples:

>>> q_add = QFunctional()
>>> a = torch.quantize_per_tensor(torch.tensor(3.0), 1.0, 0, torch.qint32)
>>> b = torch.quantize_per_tensor(torch.tensor(4.0), 1.0, 0, torch.qint32)
>>> q_add.add(a, b)  # Equivalent to ``torch.ops.quantized.add(a, b, 1.0, 0)``

Valid operation names:

• add
• cat
• mul
• add_relu
• add_scalar
• mul_scalar

Quantize

class torch.nn.quantized.Quantize(scale, zero_point, dtype) [source]

Quantizes an incoming tensor

Parameters

• scale – scale of the output Quantized Tensor
• zero_point – zero_point of output Quantized Tensor
• dtype – data type of output Quantized Tensor

Variables

zero_point, dtype (`scale`,) –

Examples::

>>> t = torch.tensor([[1., -1.], [1., -1.]])
>>> scale, zero_point, dtype = 1.0, 2, torch.qint8
>>> qm = Quantize(scale, zero_point, dtype)
>>> qt = qm(t)
>>> print(qt)
tensor([[ 1., -1.],
        [ 1., -1.]], size=(2, 2), dtype=torch.qint8, scale=1.0, zero_point=2)

DeQuantize

class torch.nn.quantized.DeQuantize [source]

Dequantizes an incoming tensor

Examples::

>>> input = torch.tensor([[1., -1.], [1., -1.]])
>>> scale, zero_point, dtype = 1.0, 2, torch.qint8
>>> qm = Quantize(scale, zero_point, dtype)
>>> quantized_input = qm(input)
>>> dqm = DeQuantize()
>>> dequantized = dqm(quantized_input)
>>> print(dequantized)
tensor([[ 1., -1.],
        [ 1., -1.]], dtype=torch.float32)

Linear

class torch.nn.quantized.Linear(in_features, out_features, bias_=True, dtype=torch.qint8) [source]

A quantized linear module with quantized tensor as inputs and outputs. We adopt the same interface as torch.nn.Linear, please see https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.

Similar to Linear, attributes will be randomly initialized at module creation time and will be overwritten later

Variables

• ~Linear.weight (Tensor) – the non-learnable quantized weights of the module of shape $(\text{out\_features}, \text{in\_features})$ .
• ~Linear.bias (Tensor) – the non-learnable bias of the module of shape $(\text{out\_features})$ . If bias is True, the values are initialized to zero.
• ~Linear.scale – scale parameter of output Quantized Tensor, type: double
• ~Linear.zero_point – zero_point parameter for output Quantized Tensor, type: long

Examples:

>>> m = nn.quantized.Linear(20, 30)
>>> input = torch.randn(128, 20)
>>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])

classmethod from_float(mod) [source]

Create a quantized module from a float module or qparams_dict

Parameters

mod (Module) – a float module, either produced by torch.quantization utilities or provided by the user

BatchNorm2d

class torch.nn.quantized.BatchNorm2d(num_features, eps=1e-05, momentum=0.1) [source]

This is the quantized version of BatchNorm2d.

BatchNorm3d

class torch.nn.quantized.BatchNorm3d(num_features, eps=1e-05, momentum=0.1) [source]

This is the quantized version of BatchNorm3d.

LayerNorm

class torch.nn.quantized.LayerNorm(normalized_shape, weight, bias, scale, zero_point, eps=1e-05, elementwise_affine=True) [source]

This is the quantized version of LayerNorm.

Additional args:

• scale - quantization scale of the output, type: double.
• zero_point - quantization zero point of the output, type: long.

GroupNorm

class torch.nn.quantized.GroupNorm(num_groups, num_channels, weight, bias, scale, zero_point, eps=1e-05, affine=True) [source]

This is the quantized version of GroupNorm.

Additional args:

• scale - quantization scale of the output, type: double.
• zero_point - quantization zero point of the output, type: long.

InstanceNorm1d

class torch.nn.quantized.InstanceNorm1d(num_features, weight, bias, scale, zero_point, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) [source]

This is the quantized version of InstanceNorm1d.

Additional args:

• scale - quantization scale of the output, type: double.
• zero_point - quantization zero point of the output, type: long.

InstanceNorm2d

class torch.nn.quantized.InstanceNorm2d(num_features, weight, bias, scale, zero_point, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) [source]

This is the quantized version of InstanceNorm2d.

Additional args:

• scale - quantization scale of the output, type: double.
• zero_point - quantization zero point of the output, type: long.

InstanceNorm3d

class torch.nn.quantized.InstanceNorm3d(num_features, weight, bias, scale, zero_point, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) [source]

This is the quantized version of InstanceNorm3d.

Additional args:

• scale - quantization scale of the output, type: double.
• zero_point - quantization zero point of the output, type: long.

Embedding

class torch.nn.quantized.Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, dtype=torch.quint8) [source]

A quantized Embedding module with quantized packed weights as inputs. We adopt the same interface as torch.nn.Embedding, please see https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding for documentation.

Similar to Embedding, attributes will be randomly initialized at module creation time and will be overwritten later

Variables

~Embedding.weight (Tensor) – the non-learnable quantized weights of the module of shape $(\text{num\_embeddings}, \text{embedding\_dim})$ .

Examples::

>>> m = nn.quantized.Embedding(num_embeddings=10, embedding_dim=12)
>>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8])
>>> output = m(indices)
>>> print(output.size())
torch.Size([9, 12]

classmethod from_float(mod) [source]

Create a quantized embedding module from a float module

Parameters

mod (Module) – a float module, either produced by torch.quantization utilities or provided by user

EmbeddingBag

class torch.nn.quantized.EmbeddingBag(num_embeddings, embedding_dim, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, mode='sum', sparse=False, _weight=None, include_last_offset=False, dtype=torch.quint8) [source]

A quantized EmbeddingBag module with quantized packed weights as inputs. We adopt the same interface as torch.nn.EmbeddingBag, please see https://pytorch.org/docs/stable/nn.html#torch.nn.EmbeddingBag for documentation.

Similar to EmbeddingBag, attributes will be randomly initialized at module creation time and will be overwritten later

Variables

~EmbeddingBag.weight (Tensor) – the non-learnable quantized weights of the module of shape $(\text{num\_embeddings}, \text{embedding\_dim})$ .

Examples::

>>> m = nn.quantized.EmbeddingBag(num_embeddings=10, embedding_dim=12, include_last_offset=True, mode='sum')
>>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7, 0, 8, 4, 6, 5, 8, 2, 3])
>>> offsets = torch.tensor([0, 19, 20, 28, 28, 32])
>>> output = m(indices, offsets)
>>> print(output.size())
torch.Size([5, 12]

classmethod from_float(mod) [source]

Create a quantized embedding_bag module from a float module

Parameters

mod (Module) – a float module, either produced by torch.quantization utilities or provided by user