Quantization API Reference

torch.quantization

This module contains Eager mode quantization APIs.

Top level APIs

quantize	Quantize the input float model with post training static quantization.
quantize_dynamic	Converts a float model to dynamic (i.e.
quantize_qat	Do quantization aware training and output a quantized model
prepare	Prepares a copy of the model for quantization calibration or quantization-aware training.
prepare_qat	Prepares a copy of the model for quantization calibration or quantization-aware training and converts it to quantized version.
convert	Converts submodules in input module to a different module according to mapping by calling from_float method on the target module class.

Preparing model for quantization

fuse_modules	Fuses a list of modules into a single module
QuantStub	Quantize stub module, before calibration, this is same as an observer, it will be swapped as nnq.Quantize in convert.
DeQuantStub	Dequantize stub module, before calibration, this is same as identity, this will be swapped as nnq.DeQuantize in convert.
QuantWrapper	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.
add_quant_dequant	Wrap the leaf child module in QuantWrapper if it has a valid qconfig Note that this function will modify the children of module inplace and it can return a new module which wraps the input module as well.

Utility functions

add_observer_	Add observer for the leaf child of the module.
swap_module	Swaps the module if it has a quantized counterpart and it has an observer attached.
propagate_qconfig_	Propagate qconfig through the module hierarchy and assign qconfig attribute on each leaf module
default_eval_fn	Default evaluation function takes a torch.utils.data.Dataset or a list of input Tensors and run the model on the dataset
get_observer_dict	Traverse the modules and save all observers into dict.

torch.quantization.quantize_fx

This module contains FX graph mode quantization APIs (prototype).

prepare_fx	Prepare a model for post training static quantization
prepare_qat_fx	Prepare a model for quantization aware training
convert_fx	Convert a calibrated or trained model to a quantized model
fuse_fx	Fuse modules like conv+bn, conv+bn+relu etc, model must be in eval mode.

torch (quantization related functions)

This describes the quantization related functions of the torch namespace.

quantize_per_tensor	Converts a float tensor to a quantized tensor with given scale and zero point.
quantize_per_channel	Converts a float tensor to a per-channel quantized tensor with given scales and zero points.
dequantize	Returns an fp32 Tensor by dequantizing a quantized Tensor

torch.Tensor (quantization related methods)

Quantized Tensors support a limited subset of data manipulation methods of the regular full-precision tensor.

view	Returns a new tensor with the same data as the self tensor but of a different shape.
as_strided	See torch.as_strided()
expand	Returns a new view of the self tensor with singleton dimensions expanded to a larger size.
flatten	See torch.flatten()
select	See torch.select()
ne	See torch.ne().
eq	See torch.eq()
ge	See torch.ge().
le	See torch.le().
gt	See torch.gt().
lt	See torch.lt().
copy_	Copies the elements from src into self tensor and returns self.
clone	See torch.clone()
dequantize	Given a quantized Tensor, dequantize it and return the dequantized float Tensor.
equal	See torch.equal()
int_repr	Given a quantized Tensor, self.int_repr() returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.
max	See torch.max()
mean	See torch.mean()
min	See torch.min()
q_scale	Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().
q_zero_point	Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().
q_per_channel_scales	Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer.
q_per_channel_zero_points	Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer.
q_per_channel_axis	Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.
resize_	Resizes self tensor to the specified size.
sort	See torch.sort()
topk	See torch.topk()

torch.quantization.observer

This module contains observers which are used to collect statistics about the values observed during calibration (PTQ) or training (QAT).

ObserverBase	Base observer Module.
MinMaxObserver	Observer module for computing the quantization parameters based on the running min and max values.
MovingAverageMinMaxObserver	Observer module for computing the quantization parameters based on the moving average of the min and max values.
PerChannelMinMaxObserver	Observer module for computing the quantization parameters based on the running per channel min and max values.
MovingAveragePerChannelMinMaxObserver	Observer module for computing the quantization parameters based on the running per channel min and max values.
HistogramObserver	The module records the running histogram of tensor values along with min/max values.
PlaceholderObserver	Observer that doesn’t do anything and just passes its configuration to the quantized module’s .from_float().
RecordingObserver	The module is mainly for debug and records the tensor values during runtime.
NoopObserver	Observer that doesn’t do anything and just passes its configuration to the quantized module’s .from_float().
get_observer_state_dict	Returns the state dict corresponding to the observer stats.
load_observer_state_dict	Given input model and a state_dict containing model observer stats, load the stats back into the model.
default_observer
default_placeholder_observer	Default placeholder observer, usually used for quantization to torch.float16.
default_debug_observer	Default debug-only observer.
default_weight_observer
default_histogram_observer
default_per_channel_weight_observer
default_dynamic_quant_observer
default_float_qparams_observer

torch.quantization.fake_quantize

This module implements modules which are used to perform fake quantization during QAT.

FakeQuantizeBase	Base fake quantize module Any fake quantize implementation should derive from this class.
FakeQuantize	Simulate the quantize and dequantize operations in training time. The output of this module is given by::.
FixedQParamsFakeQuantize	Simulate quantize and dequantize with fixed quantization parameters in training time.
FusedMovingAvgObsFakeQuantize	Fused module that is used to observe the input tensor (compute min/max), compute scale/zero_point and fake_quantize the tensor.
default_fake_quant
default_weight_fake_quant
default_per_channel_weight_fake_quant
default_histogram_fake_quant
default_fused_act_fake_quant
default_fused_wt_fake_quant
default_fused_per_channel_wt_fake_quant
disable_fake_quant	Disable fake quantization for this module, if applicable. Example usage::.
enable_fake_quant	Enable fake quantization for this module, if applicable. Example usage::.
disable_observer	Disable observation for this module, if applicable. Example usage::.
enable_observer	Enable observation for this module, if applicable. Example usage::.

torch.quantization.qconfig

This module defines QConfig objects which are used to configure quantization settings for individual ops.

QConfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
default_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
default_debug_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
default_per_channel_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
default_dynamic_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
float16_dynamic_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
float16_static_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
per_channel_dynamic_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
float_qparams_weight_only_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
default_qat_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
default_weight_only_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
default_activation_only_qconfig	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.
default_qat_qconfig_v2	Describes how to quantize a layer or a part of the network by providing settings (observer classes) for activations and weights respectively.

torch.nn.intrinsic

This module implements the combined (fused) modules conv + relu which can then be quantized.

ConvReLU1d	This is a sequential container which calls the Conv1d and ReLU modules.
ConvReLU2d	This is a sequential container which calls the Conv2d and ReLU modules.
ConvReLU3d	This is a sequential container which calls the Conv3d and ReLU modules.
LinearReLU	This is a sequential container which calls the Linear and ReLU modules.
ConvBn1d	This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.
ConvBn2d	This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.
ConvBn3d	This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.
ConvBnReLU1d	This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.
ConvBnReLU2d	This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.
ConvBnReLU3d	This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.
BNReLU2d	This is a sequential container which calls the BatchNorm 2d and ReLU modules.
BNReLU3d	This is a sequential container which calls the BatchNorm 3d and ReLU modules.

torch.nn.intrinsic.qat

This module implements the versions of those fused operations needed for quantization aware training.

LinearReLU	A LinearReLU module fused from Linear and ReLU modules, attached with FakeQuantize modules for weight, used in quantization aware training.
ConvBn1d	A ConvBn1d module is a module fused from Conv1d and BatchNorm1d, attached with FakeQuantize modules for weight, used in quantization aware training.
ConvBnReLU1d	A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.
ConvBn2d	A ConvBn2d module is a module fused from Conv2d and BatchNorm2d, attached with FakeQuantize modules for weight, used in quantization aware training.
ConvBnReLU2d	A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.
ConvReLU2d	A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with FakeQuantize modules for weight for quantization aware training.
ConvBn3d	A ConvBn3d module is a module fused from Conv3d and BatchNorm3d, attached with FakeQuantize modules for weight, used in quantization aware training.
ConvBnReLU3d	A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU, attached with FakeQuantize modules for weight, used in quantization aware training.
ConvReLU3d	A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with FakeQuantize modules for weight for quantization aware training.
update_bn_stats
freeze_bn_stats

torch.nn.intrinsic.quantized

This module implements the quantized implementations of fused operations like conv + relu. No BatchNorm variants as it’s usually folded into convolution for inference.

BNReLU2d	A BNReLU2d module is a fused module of BatchNorm2d and ReLU
BNReLU3d	A BNReLU3d module is a fused module of BatchNorm3d and ReLU
ConvReLU1d	A ConvReLU1d module is a fused module of Conv1d and ReLU
ConvReLU2d	A ConvReLU2d module is a fused module of Conv2d and ReLU
ConvReLU3d	A ConvReLU3d module is a fused module of Conv3d and ReLU
LinearReLU	A LinearReLU module fused from Linear and ReLU modules

torch.nn.intrinsic.quantized.dynamic

This module implements the quantized dynamic implementations of fused operations like linear + relu.

LinearReLU

A LinearReLU module fused from Linear and ReLU modules that can be used for dynamic quantization.

torch.nn.qat

This module implements versions of the key nn modules Conv2d() and Linear() which run in FP32 but with rounding applied to simulate the effect of INT8 quantization.

Conv2d	A Conv2d module attached with FakeQuantize modules for weight, used for quantization aware training.
Conv3d	A Conv3d module attached with FakeQuantize modules for weight, used for quantization aware training.
Linear	A linear module attached with FakeQuantize modules for weight, used for quantization aware training.

torch.nn.qat.dynamic

This module implements versions of the key nn modules such as Linear() which run in FP32 but with rounding applied to simulate the effect of INT8 quantization and will be dynamically quantized during inference.

Linear

A linear module attached with FakeQuantize modules for weight, used for dynamic quantization aware training.

torch.nn.quantized

This module implements the quantized versions of the nn layers such as ~`torch.nn.Conv2d` and torch.nn.ReLU.

ReLU6	Applies the element-wise function:
Hardswish	This is the quantized version of Hardswish.
ELU	This is the quantized equivalent of ELU.
LeakyReLU	This is the quantized equivalent of LeakyReLU.
Sigmoid	This is the quantized equivalent of Sigmoid.
BatchNorm2d	This is the quantized version of BatchNorm2d.
BatchNorm3d	This is the quantized version of BatchNorm3d.
Conv1d	Applies a 1D convolution over a quantized input signal composed of several quantized input planes.
Conv2d	Applies a 2D convolution over a quantized input signal composed of several quantized input planes.
Conv3d	Applies a 3D convolution over a quantized input signal composed of several quantized input planes.
ConvTranspose1d	Applies a 1D transposed convolution operator over an input image composed of several input planes.
ConvTranspose2d	Applies a 2D transposed convolution operator over an input image composed of several input planes.
ConvTranspose3d	Applies a 3D transposed convolution operator over an input image composed of several input planes.
Embedding	A quantized Embedding module with quantized packed weights as inputs.
EmbeddingBag	A quantized EmbeddingBag module with quantized packed weights as inputs.
FloatFunctional	State collector class for float operations.
FXFloatFunctional	module to replace FloatFunctional module before FX graph mode quantization, since activation_post_process will be inserted in top level module directly
QFunctional	Wrapper class for quantized operations.
Linear	A quantized linear module with quantized tensor as inputs and outputs.
LayerNorm	This is the quantized version of LayerNorm.
GroupNorm	This is the quantized version of GroupNorm.
InstanceNorm1d	This is the quantized version of InstanceNorm1d.
InstanceNorm2d	This is the quantized version of InstanceNorm2d.
InstanceNorm3d	This is the quantized version of InstanceNorm3d.

torch.nn.quantized.functional

Functional interface (quantized).

This module implements the quantized versions of the functional layers such as ~`torch.nn.functional.conv2d` and torch.nn.functional.relu. Note: relu() supports quantized inputs.

avg_pool2d	Applies 2D average-pooling operation in $kH \times kW$ regions by step size $sH \times sW$ steps.
avg_pool3d	Applies 3D average-pooling operation in $kD \ times kH \times kW$ regions by step size $sD \times sH \times sW$ steps.
adaptive_avg_pool2d	Applies a 2D adaptive average pooling over a quantized input signal composed of several quantized input planes.
adaptive_avg_pool3d	Applies a 3D adaptive average pooling over a quantized input signal composed of several quantized input planes.
conv1d	Applies a 1D convolution over a quantized 1D input composed of several input planes.
conv2d	Applies a 2D convolution over a quantized 2D input composed of several input planes.
conv3d	Applies a 3D convolution over a quantized 3D input composed of several input planes.
interpolate	Down/up samples the input to either the given size or the given scale_factor
linear	Applies a linear transformation to the incoming quantized data: $y = xA^T + b$.
max_pool1d	Applies a 1D max pooling over a quantized input signal composed of several quantized input planes.
max_pool2d	Applies a 2D max pooling over a quantized input signal composed of several quantized input planes.
celu	Applies the quantized CELU function element-wise.
leaky_relu	Applies element-wise, $\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)$
hardtanh	This is the quantized version of hardtanh().
hardswish	This is the quantized version of hardswish().
threshold	Applies the quantized version of the threshold function element-wise:
elu	This is the quantized version of elu().
hardsigmoid	This is the quantized version of hardsigmoid().
clamp	float(input, min_, max_) -> Tensor
upsample	Upsamples the input to either the given size or the given scale_factor
upsample_bilinear	Upsamples the input, using bilinear upsampling.
upsample_nearest	Upsamples the input, using nearest neighbours’ pixel values.

torch.nn.quantized.dynamic

Dynamically quantized Linear, LSTM, LSTMCell, GRUCell, and RNNCell.

Linear	A dynamic quantized linear module with floating point tensor as inputs and outputs.
LSTM	A dynamic quantized LSTM module with floating point tensor as inputs and outputs.
GRU	Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
RNNCell	An Elman RNN cell with tanh or ReLU non-linearity.
LSTMCell	A long short-term memory (LSTM) cell.
GRUCell	A gated recurrent unit (GRU) cell

Quantized dtypes and quantization schemes

Note that operator implementations currently only support per channel quantization for weights of the conv and linear operators. Furthermore, the input data is mapped linearly to the the quantized data and vice versa as follows:

$$\begin{aligned} \text{Quantization:}&\\ &Q_\text{out} = \text{clamp}(x_\text{input}/s+z, Q_\text{min}, Q_\text{max})\\ \text{Dequantization:}&\\ &x_\text{out} = (Q_\text{input}-z)*s \end{aligned}$$

where $\text{clamp}(.)$ is the same as clamp() while the scale $s$ and zero point $z$ are then computed as decribed in MinMaxObserver, specifically:

$$\begin{aligned} \text{if Symmetric:}&\\ &s = 2 \max(|x_\text{min}|, x_\text{max}) / \left( Q_\text{max} - Q_\text{min} \right) \\ &z = \begin{cases} 0 & \text{if dtype is qint8} \\ 128 & \text{otherwise} \end{cases}\\ \text{Otherwise:}&\\ &s = \left( x_\text{max} - x_\text{min} \right ) / \left( Q_\text{max} - Q_\text{min} \right ) \\ &z = Q_\text{min} - \text{round}(x_\text{min} / s) \end{aligned}$$

where $[x_\text{min}, x_\text{max}]$ denotes the range of the input data while $Q_\text{min}$ and $Q_\text{max}$ are respectively the minimum and maximum values of the quantized dtype.

Note that the choice of $s$ and $z$ implies that zero is represented with no quantization error whenever zero is within the range of the input data or symmetric quantization is being used.

Additional data types and quantization schemes can be implemented through the custom operator mechanism.

• torch.qscheme — Type to describe the quantization scheme of a tensor. Supported types:

  • torch.per_tensor_affine — per tensor, asymmetric

  • torch.per_channel_affine — per channel, asymmetric

  • torch.per_tensor_symmetric — per tensor, symmetric

  • torch.per_channel_symmetric — per channel, symmetric

• torch.dtype — Type to describe the data. Supported types:

  • torch.quint8 — 8-bit unsigned integer

  • torch.qint8 — 8-bit signed integer

  • torch.qint32 — 32-bit signed integer