# Quantization¶

This file is in the process of migration to torch/ao/quantization, and is kept here for compatibility while the migration process is ongoing. If you are adding a new entry/functionality, please, add it to the appropriate files under torch/ao/quantization/fx/, while adding an import statement here.

Warning

Quantization is in beta and subject to change.

## Introduction to Quantization¶

Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision. A quantized model executes some or all of the operations on tensors with reduced precision rather than full precision (floating point) values. This allows for a more compact model representation and the use of high performance vectorized operations on many hardware platforms. PyTorch supports INT8 quantization compared to typical FP32 models allowing for a 4x reduction in the model size and a 4x reduction in memory bandwidth requirements. Hardware support for INT8 computations is typically 2 to 4 times faster compared to FP32 compute. Quantization is primarily a technique to speed up inference and only the forward pass is supported for quantized operators.

PyTorch supports multiple approaches to quantizing a deep learning model. In most cases the model is trained in FP32 and then the model is converted to INT8. In addition, PyTorch also supports quantization aware training, which models quantization errors in both the forward and backward passes using fake-quantization modules. Note that the entire computation is carried out in floating point. At the end of quantization aware training, PyTorch provides conversion functions to convert the trained model into lower precision.

At lower level, PyTorch provides a way to represent quantized tensors and perform operations with them. They can be used to directly construct models that perform all or part of the computation in lower precision. Higher-level APIs are provided that incorporate typical workflows of converting FP32 model to lower precision with minimal accuracy loss.

## Quantization API Summary¶

PyTorch provides two different modes of quantization: Eager Mode Quantization and FX Graph Mode Quantization.

Eager Mode Quantization is a beta feature. User needs to do fusion and specify where quantization and dequantization happens manually, also it only supports modules and not functionals.

FX Graph Mode Quantization is a new automated quantization framework in PyTorch, and currently it’s a prototype feature. It improves upon Eager Mode Quantization by adding support for functionals and automating the quantization process, although people might need to refactor the model to make the model compatible with FX Graph Mode Quantization (symbolically traceable with torch.fx). Note that FX Graph Mode Quantization is not expected to work on arbitrary models since the model might not be symbolically traceable, we will integrate it into domain libraries like torchvision and users will be able to quantize models similar to the ones in supported domain libraries with FX Graph Mode Quantization. For arbitrary models we’ll provide general guidelines, but to actually make it work, users might need to be familiar with torch.fx, especially on how to make a model symbolically traceable.

New users of quantization are encouraged to try out FX Graph Mode Quantization first, if it does not work, user may try to follow the guideline of using FX Graph Mode Quantization or fall back to eager mode quantization.

The following table compares the differences between Eager Mode Quantization and FX Graph Mode Quantization:

 Eager Mode Quantization FX Graph Mode Quantization Release Status beta prototype Operator Fusion Manual Automatic Quant/DeQuant Placement Manual Automatic Quantizing Modules Supported Supported Quantizing Functionals/Torch Ops Manual Automatic Support for Customization Limited Support Fully Supported Quantization Mode Support Post Training Quantization: Static, Dynamic, Weight Only Quantization Aware Training: Static Post Training Quantization: Static, Dynamic, Weight Only Quantization Aware Training: Static Input/Output Model Type torch.nn.Module torch.nn.Module (May need some refactors to make the model compatible with FX Graph Mode Quantization)

There are three types of quantization supported:

1. dynamic quantization (weights quantized with activations read/stored in floating point and quantized for compute)

2. static quantization (weights quantized, activations quantized, calibration required post training)

3. static quantization aware training (weights quantized, activations quantized, quantization numerics modeled during training)

Please see our Introduction to Quantization on Pytorch blog post for a more comprehensive overview of the tradeoffs between these quantization types.

Operator coverage varies between dynamic and static quantization and is captured in the table below. Note that for FX quantization, the corresponding functionals are also supported.

 Static Quantization Dynamic Quantization nn.Linear nn.Conv1d/2d/3d Y Y Y N nn.LSTM nn.GRU Y (through custom modules) N Y Y nn.RNNCell nn.GRUCell nn.LSTMCell N N N Y Y Y nn.EmbeddingBag Y (activations are in fp32) Y nn.Embedding Y N nn.MultiheadAttention Y (through custom modules) Not supported Activations Broadly supported Un-changed, computations stay in fp32

### Eager Mode Quantization¶

For a general introduction to the quantization flow, including different types of quantization, please take a look at General Quantization Flow.

#### Post Training Dynamic Quantization¶

This is the simplest to apply form of quantization where the weights are quantized ahead of time but the activations are dynamically quantized during inference. This is used for situations where the model execution time is dominated by loading weights from memory rather than computing the matrix multiplications. This is true for LSTM and Transformer type models with small batch size.

Diagram:

# original model
# all tensors and computations are in floating point
previous_layer_fp32 -- linear_fp32 -- activation_fp32 -- next_layer_fp32
/
linear_weight_fp32

# dynamically quantized model
# linear and LSTM weights are in int8
previous_layer_fp32 -- linear_int8_w_fp32_inp -- activation_fp32 -- next_layer_fp32
/
linear_weight_int8


PTDQ API Example:

import torch

# define a floating point model
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.fc = torch.nn.Linear(4, 4)

def forward(self, x):
x = self.fc(x)
return x

# create a model instance
model_fp32 = M()
# create a quantized model instance
model_int8 = torch.quantization.quantize_dynamic(
model_fp32,  # the original model
{torch.nn.Linear},  # a set of layers to dynamically quantize
dtype=torch.qint8)  # the target dtype for quantized weights

# run the model
input_fp32 = torch.randn(4, 4, 4, 4)
res = model_int8(input_fp32)


#### Post Training Static Quantization¶

Post Training Static Quantization (PTQ static) quantizes the weights and activations of the model. It fuses activations into preceding layers where possible. It requires calibration with a representative dataset to determine optimal quantization parameters for activations. Post Training Static Quantization is typically used when both memory bandwidth and compute savings are important with CNNs being a typical use case.

We may need to modify the model before applying post training static quantization. Please see Model Preparation for Eager Mode Static Quantization.

Diagram:

# original model
# all tensors and computations are in floating point
previous_layer_fp32 -- linear_fp32 -- activation_fp32 -- next_layer_fp32
/
linear_weight_fp32

# statically quantized model
# weights and activations are in int8
previous_layer_int8 -- linear_with_activation_int8 -- next_layer_int8
/
linear_weight_int8


PTSQ API Example:

import torch

# define a floating point model where some layers could be statically quantized
class M(torch.nn.Module):
def __init__(self):
super().__init__()
# QuantStub converts tensors from floating point to quantized
self.quant = torch.quantization.QuantStub()
self.conv = torch.nn.Conv2d(1, 1, 1)
self.relu = torch.nn.ReLU()
# DeQuantStub converts tensors from quantized to floating point
self.dequant = torch.quantization.DeQuantStub()

def forward(self, x):
# manually specify where tensors will be converted from floating
# point to quantized in the quantized model
x = self.quant(x)
x = self.conv(x)
x = self.relu(x)
# manually specify where tensors will be converted from quantized
# to floating point in the quantized model
x = self.dequant(x)
return x

# create a model instance
model_fp32 = M()

# model must be set to eval mode for static quantization logic to work
model_fp32.eval()

# attach a global qconfig, which contains information about what kind
# of observers to attach. Use 'fbgemm' for server inference and
# 'qnnpack' for mobile inference. Other quantization configurations such
# as selecting symmetric or assymetric quantization and MinMax or L2Norm
# calibration techniques can be specified here.
model_fp32.qconfig = torch.quantization.get_default_qconfig('fbgemm')

# Fuse the activations to preceding layers, where applicable.
# This needs to be done manually depending on the model architecture.
# Common fusions include conv + relu and conv + batchnorm + relu
model_fp32_fused = torch.quantization.fuse_modules(model_fp32, [['conv', 'relu']])

# Prepare the model for static quantization. This inserts observers in
# the model that will observe activation tensors during calibration.
model_fp32_prepared = torch.quantization.prepare(model_fp32_fused)

# calibrate the prepared model to determine quantization parameters for activations
# in a real world setting, the calibration would be done with a representative dataset
input_fp32 = torch.randn(4, 1, 4, 4)
model_fp32_prepared(input_fp32)

# Convert the observed model to a quantized model. This does several things:
# quantizes the weights, computes and stores the scale and bias value to be
# used with each activation tensor, and replaces key operators with quantized
# implementations.
model_int8 = torch.quantization.convert(model_fp32_prepared)

# run the model, relevant calculations will happen in int8
res = model_int8(input_fp32)


#### Quantization Aware Training for Static Quantization¶

Quantization Aware Training (QAT) models the effects of quantization during training allowing for higher accuracy compared to other quantization methods. We can do QAT for static, dynamic or weight only quantization. During training, all calculations are done in floating point, with fake_quant modules modeling the effects of quantization by clamping and rounding to simulate the effects of INT8. After model conversion, weights and activations are quantized, and activations are fused into the preceding layer where possible. It is commonly used with CNNs and yields a higher accuracy compared to static quantization.

We may need to modify the model before applying post training static quantization. Please see Model Preparation for Eager Mode Static Quantization.

Diagram:

# original model
# all tensors and computations are in floating point
previous_layer_fp32 -- linear_fp32 -- activation_fp32 -- next_layer_fp32
/
linear_weight_fp32

# model with fake_quants for modeling quantization numerics during training
previous_layer_fp32 -- fq -- linear_fp32 -- activation_fp32 -- fq -- next_layer_fp32
/
linear_weight_fp32 -- fq

# quantized model
# weights and activations are in int8
previous_layer_int8 -- linear_with_activation_int8 -- next_layer_int8
/
linear_weight_int8


QAT API Example:

import torch

# define a floating point model where some layers could benefit from QAT
class M(torch.nn.Module):
def __init__(self):
super().__init__()
# QuantStub converts tensors from floating point to quantized
self.quant = torch.quantization.QuantStub()
self.conv = torch.nn.Conv2d(1, 1, 1)
self.bn = torch.nn.BatchNorm2d(1)
self.relu = torch.nn.ReLU()
# DeQuantStub converts tensors from quantized to floating point
self.dequant = torch.quantization.DeQuantStub()

def forward(self, x):
x = self.quant(x)
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
x = self.dequant(x)
return x

# create a model instance
model_fp32 = M()

# model must be set to eval for fusion to work
model_fp32.eval()

# attach a global qconfig, which contains information about what kind
# of observers to attach. Use 'fbgemm' for server inference and
# 'qnnpack' for mobile inference. Other quantization configurations such
# as selecting symmetric or assymetric quantization and MinMax or L2Norm
# calibration techniques can be specified here.
model_fp32.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')

# fuse the activations to preceding layers, where applicable
# this needs to be done manually depending on the model architecture
model_fp32_fused = torch.quantization.fuse_modules(model_fp32,
[['conv', 'bn', 'relu']])

# Prepare the model for QAT. This inserts observers and fake_quants in
# the model needs to be set to train for QAT logic to work
# the model that will observe weight and activation tensors during calibration.
model_fp32_prepared = torch.quantization.prepare_qat(model_fp32_fused.train())

# run the training loop (not shown)
training_loop(model_fp32_prepared)

# Convert the observed model to a quantized model. This does several things:
# quantizes the weights, computes and stores the scale and bias value to be
# used with each activation tensor, fuses modules where appropriate,
# and replaces key operators with quantized implementations.
model_fp32_prepared.eval()
model_int8 = torch.quantization.convert(model_fp32_prepared)

# run the model, relevant calculations will happen in int8
res = model_int8(input_fp32)


#### Model Preparation for Eager Mode Static Quantization¶

It is necessary to currently make some modifications to the model definition prior to Eager mode quantization. This is because currently quantization works on a module by module basis. Specifically, for all quantization techniques, the user needs to:

1. Convert any operations that require output requantization (and thus have additional parameters) from functionals to module form (for example, using torch.nn.ReLU instead of torch.nn.functional.relu).

2. Specify which parts of the model need to be quantized either by assigning .qconfig attributes on submodules or by specifying qconfig_mapping. For example, setting model.conv1.qconfig = None means that the model.conv layer will not be quantized, and setting model.linear1.qconfig = custom_qconfig means that the quantization settings for model.linear1 will be using custom_qconfig instead of the global qconfig.

For static quantization techniques which quantize activations, the user needs to do the following in addition:

1. Specify where activations are quantized and de-quantized. This is done using QuantStub and DeQuantStub modules.

2. Use FloatFunctional to wrap tensor operations that require special handling for quantization into modules. Examples are operations like add and cat which require special handling to determine output quantization parameters.

3. Fuse modules: combine operations/modules into a single module to obtain higher accuracy and performance. This is done using the fuse_modules() API, which takes in lists of modules to be fused. We currently support the following fusions: [Conv, Relu], [Conv, BatchNorm], [Conv, BatchNorm, Relu], [Linear, Relu]

### (Prototype) FX Graph Mode Quantization¶

There are multiple quantization types in post training quantization (weight only, dynamic and static) and the configuration is done through qconfig_mapping (an argument of the prepare_fx function).

FXPTQ API Example:

import torch
from torch.ao.quantization import QConfigMapping
import torch.quantization.quantize_fx as quantize_fx
import copy

model_fp = UserModel()

#
# post training dynamic/weight_only quantization
#

# we need to deepcopy if we still want to keep model_fp unchanged after quantization since quantization apis change the input model
model_to_quantize = copy.deepcopy(model_fp)
model_to_quantize.eval()
qconfig_mapping = QConfigMapping().set_global(torch.quantization.default_dynamic_qconfig)
# a tuple of one or more example inputs are needed to trace the model
example_inputs = (input_fp32)
# prepare
model_prepared = quantize_fx.prepare_fx(model_to_quantize, qconfig_mapping, example_inputs)
# no calibration needed when we only have dynamic/weight_only quantization
# quantize
model_quantized = quantize_fx.convert_fx(model_prepared)

#
# post training static quantization
#

model_to_quantize = copy.deepcopy(model_fp)
qconfig_mapping = QConfigMapping().set_global(torch.quantization.get_default_qconfig('qnnpack'))
model_to_quantize.eval()
# prepare
model_prepared = quantize_fx.prepare_fx(model_to_quantize, qconfig_mapping, example_inputs)
# calibrate (not shown)
# quantize
model_quantized = quantize_fx.convert_fx(model_prepared)

#
# quantization aware training for static quantization
#

model_to_quantize = copy.deepcopy(model_fp)
qconfig_mapping = QConfigMapping().set_global(torch.quantization.get_default_qat_qconfig('qnnpack'))
model_to_quantize.train()
# prepare
model_prepared = quantize_fx.prepare_qat_fx(model_to_quantize, qconfig_mapping, example_inputs)
# training loop (not shown)
# quantize
model_quantized = quantize_fx.convert_fx(model_prepared)

#
# fusion
#
model_to_quantize = copy.deepcopy(model_fp)
model_fused = quantize_fx.fuse_fx(model_to_quantize)


## Quantization Stack¶

Quantization is the process to convert a floating point model to a quantized model. So at high level the quantization stack can be split into two parts: 1). The building blocks or abstractions for a quantized model 2). The building blocks or abstractions for the quantization flow that converts a floating point model to a quantized model

### Quantized Model¶

#### Quantized Tensor¶

In order to do quantization in PyTorch, we need to be able to represent quantized data in Tensors. A Quantized Tensor allows for storing quantized data (represented as int8/uint8/int32) along with quantization parameters like scale and zero_point. Quantized Tensors allow for many useful operations making quantized arithmetic easy, in addition to allowing for serialization of data in a quantized format.

PyTorch supports both per tensor and per channel symmetric and asymmetric quantization. Per tensor means that all the values within the tensor are quantized the same way with the same quantization parameters. Per channel means that for each dimension, typically the channel dimension of a tensor, the values in the tensor are quantized with different quantization parameters. This allows for less error in converting tensors to quantized values since outlier values would only impact the channel it was in, instead of the entire Tensor.

The mapping is performed by converting the floating point tensors using

Note that, we ensure that zero in floating point is represented with no error after quantization, thereby ensuring that operations like padding do not cause additional quantization error.

Here are a few key attributes for quantized Tensor:

• QScheme (torch.qscheme): a enum that specifies the way we quantize the Tensor

• torch.per_tensor_affine

• torch.per_tensor_symmetric

• torch.per_channel_affine

• torch.per_channel_symmetric

• dtype (torch.dtype): data type of the quantized Tensor

• torch.quint8

• torch.qint8

• torch.qint32

• torch.float16

• quantization parameters (varies based on QScheme): parameters for the chosen way of quantization

• torch.per_tensor_affine would have quantization parameters of

• scale (float)

• zero_point (int)

• torch.per_channel_affine would have quantization parameters of

• per_channel_scales (list of float)

• per_channel_zero_points (list of int)

• axis (int)

#### Quantize and Dequantize¶

The input and output of a model are floating point Tensors, but activations in the quantized model are quantized, so we need operators to convert between floating point and quantized Tensors.

• Quantize (float -> quantized)

• torch.quantize_per_tensor(x, scale, zero_point, dtype)

• torch.quantize_per_channel(x, scales, zero_points, axis, dtype)

• torch.quantize_per_tensor_dynamic(x, dtype, reduce_range)

• to(torch.float16)

• Dequantize (quantized -> float)

• quantized_tensor.dequantize() - calling dequantize on a torch.float16 Tensor will convert the Tensor back to torch.float

• torch.dequantize(x)

#### Quantized Operators/Modules¶

• Quantized Operator are the operators that takes quantized Tensor as inputs, and outputs a quantized Tensor.

• Quantized Modules are PyTorch Modules that performs quantized operations. They are typically defined for weighted operations like linear and conv.

#### Quantized Engine¶

When a quantized model is executed, the qengine (torch.backends.quantized.engine) specifies which backend is to be used for execution. It is important to ensure that the qengine is compatible with the quantized model in terms of value range of quantized activation and weights.

### Quantization Flow¶

#### Observer and FakeQuantize¶

• Observer are PyTorch Modules used to:

• collect tensor statistics like min value and max value of the Tensor passing through the observer

• and calculate quantization parameters based on the collected tensor statistics

• FakeQuantize are PyTorch Modules used to:

• simulate quantization (performing quantize/dequantize) for a Tensor in the network

• it can calculate quantization parameters based on the collected statistics from observer, or it can learn the quantization parameters as well

#### QConfig¶

• QConfig is a namedtuple of Observer or FakeQuantize Module class that can are configurable with qscheme, dtype etc. it is used to configure how an operator should be observed

• Quantization configuration for an operator/module

• different types of Observer/FakeQuantize

• dtype

• qscheme

• quant_min/quant_max: can be used to simulate lower precision Tensors

• Currently supports configuration for activation and weight

• We insert input/weight/output observer based on the qconfig that is configured for a given operator or module

#### General Quantization Flow¶

In general, the flow is the following

• prepare

• insert Observer/FakeQuantize modules based on user specified qconfig

• calibrate/train (depending on post training quantization or quantization aware training)

• allow Observers to collect statistics or FakeQuantize modules to learn the quantization parameters

• convert

• convert a calibrated/trained model to a quantized model

There are different modes of quantization, they can be classified in two ways:

In terms of where we apply the quantization flow, we have:

1. Post Training Quantization (apply quantization after training, quantization parameters are calculated based on sample calibration data)

2. Quantization Aware Training (simulate quantization during training so that the quantization parameters can be learned together with the model using training data)

And in terms of how we quantize the operators, we can have:

• Weight Only Quantization (only weight is statically quantized)

• Dynamic Quantization (weight is statically quantized, activation is dynamically quantized)

• Static Quantization (both weight and activations are statically quantized)

We can mix different ways of quantizing operators in the same quantization flow. For example, we can have post training quantization that has both statically and dynamically quantized operators.

## Quantization Support Matrix¶

### Quantization Mode Support¶

 Quantization Mode Dataset Requirement Works Best For Accuracy Notes Post Training Quantization Dynamic/Weight Only Quantization activation dynamically quantized (fp16, int8) or not quantized, weight statically quantized (fp16, int8, in4) None LSTM, MLP, Embedding, Transformer good Easy to use, close to static quantization when performance is compute or memory bound due to weights Static Quantization activation and weights statically quantized (int8) calibration dataset CNN good Provides best perf, may have big impact on accuracy, good for hardwares that only support int8 computation Quantization Aware Training Dynamic Quantization activation and weight are fake quantized fine-tuning dataset MLP, Embedding best Limited support for now Static Quantization activation and weight are fake quantized fine-tuning dataset CNN, MLP, Embedding best Typically used when static quantization leads to bad accuracy, and used to close the accuracy gap

Please see our Introduction to Quantization on Pytorch blog post for a more comprehensive overview of the tradeoffs between these quantization types.

### Quantization Flow Support¶

PyTorch provides two modes of quantization: Eager Mode Quantization and FX Graph Mode Quantization.

Eager Mode Quantization is a beta feature. User needs to do fusion and specify where quantization and dequantization happens manually, also it only supports modules and not functionals.

FX Graph Mode Quantization is an automated quantization framework in PyTorch, and currently it’s a prototype feature. It improves upon Eager Mode Quantization by adding support for functionals and automating the quantization process, although people might need to refactor the model to make the model compatible with FX Graph Mode Quantization (symbolically traceable with torch.fx). Note that FX Graph Mode Quantization is not expected to work on arbitrary models since the model might not be symbolically traceable, we will integrate it into domain libraries like torchvision and users will be able to quantize models similar to the ones in supported domain libraries with FX Graph Mode Quantization. For arbitrary models we’ll provide general guidelines, but to actually make it work, users might need to be familiar with torch.fx, especially on how to make a model symbolically traceable.

New users of quantization are encouraged to try out FX Graph Mode Quantization first, if it does not work, user may try to follow the guideline of using FX Graph Mode Quantization or fall back to eager mode quantization.

The following table compares the differences between Eager Mode Quantization and FX Graph Mode Quantization:

 Eager Mode Quantization FX Graph Mode Quantization Release Status beta prototype Operator Fusion Manual Automatic Quant/DeQuant Placement Manual Automatic Quantizing Modules Supported Supported Quantizing Functionals/Torch Ops Manual Automatic Support for Customization Limited Support Fully Supported Quantization Mode Support Post Training Quantization: Static, Dynamic, Weight Only Quantization Aware Training: Static Post Training Quantization: Static, Dynamic, Weight Only Quantization Aware Training: Static Input/Output Model Type torch.nn.Module torch.nn.Module (May need some refactors to make the model compatible with FX Graph Mode Quantization)

### Backend/Hardware Support¶

 Hardware Kernel Library Eager Mode Quantization FX Graph Mode Quantization Quantization Mode Support server CPU fbgemm Supported All Supported mobile CPU qnnpack/xnnpack server GPU TensorRT (early prototype) Not support this it requires a graph Supported Static Quantization

Today, PyTorch supports the following backends for running quantized operators efficiently:

• x86 CPUs with AVX2 support or higher (without AVX2 some operations have inefficient implementations), via fbgemm

• ARM CPUs (typically found in mobile/embedded devices), via qnnpack

• (early prototype) support for NVidia GPU via TensorRT through fx2trt (to be open sourced)

#### Note for native CPU backends¶

We expose both fbgemm and qnnpack with the same native pytorch quantized operators, so we need additional flag to distinguish between them. The corresponding implementation of fbgemm and qnnpack is chosen automatically based on the PyTorch build mode, though users have the option to override this by setting torch.backends.quantization.engine to fbgemm or qnnpack.

When preparing a quantized model, it is necessary to ensure that qconfig and the engine used for quantized computations match the backend on which the model will be executed. The qconfig controls the type of observers used during the quantization passes. The qengine controls whether fbgemm or qnnpack specific packing function is used when packing weights for linear and convolution functions and modules. For example:

Default settings for fbgemm:

# set the qconfig for PTQ
qconfig = torch.quantization.get_default_qconfig('fbgemm')
# or, set the qconfig for QAT
qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
# set the qengine to control weight packing
torch.backends.quantized.engine = 'fbgemm'


Default settings for qnnpack:

# set the qconfig for PTQ
qconfig = torch.quantization.get_default_qconfig('qnnpack')
# or, set the qconfig for QAT
qconfig = torch.quantization.get_default_qat_qconfig('qnnpack')
# set the qengine to control weight packing
torch.backends.quantized.engine = 'qnnpack'


### Operator Support¶

Operator coverage varies between dynamic and static quantization and is captured in the table below. Note that for FX Graph Mode Quantization, the corresponding functionals are also supported.

 Static Quantization Dynamic Quantization nn.Linear nn.Conv1d/2d/3d Y Y Y N nn.LSTM nn.GRU N N Y Y nn.RNNCell nn.GRUCell nn.LSTMCell N N N Y Y Y nn.EmbeddingBag Y (activations are in fp32) Y nn.Embedding Y N nn.MultiheadAttention Not Supported Not supported Activations Broadly supported Un-changed, computations stay in fp32

Note: this will be updated with some information generated from native backend_config_dict soon.

## Quantization API Reference¶

The Quantization API Reference contains documentation of quantization APIs, such as quantization passes, quantized tensor operations, and supported quantized modules and functions.

## Quantization Backend Configuration¶

The Quantization Backend Configuration contains documentation on how to configure the quantization workflows for various backends.

## Quantization Accuracy Debugging¶

The Quantization Accuracy Debugging contains documentation on how to debug quantization accuracy.

## Quantization Customizations¶

While default implementations of observers to select the scale factor and bias based on observed tensor data are provided, developers can provide their own quantization functions. Quantization can be applied selectively to different parts of the model or configured differently for different parts of the model.

We also provide support for per channel quantization for conv1d(), conv2d(), conv3d() and linear().

Quantization workflows work by adding (e.g. adding observers as .observer submodule) or replacing (e.g. converting nn.Conv2d to nn.quantized.Conv2d) submodules in the model’s module hierarchy. It means that the model stays a regular nn.Module-based instance throughout the process and thus can work with the rest of PyTorch APIs.

### Quantization Custom Module API¶

Both Eager mode and FX graph mode quantization APIs provide a hook for the user to specify module quantized in a custom way, with user defined logic for observation and quantization. The user needs to specify:

1. The Python type of the source fp32 module (existing in the model)

2. The Python type of the observed module (provided by user). This module needs to define a from_float function which defines how the observed module is created from the original fp32 module.

3. The Python type of the quantized module (provided by user). This module needs to define a from_observed function which defines how the quantized module is created from the observed module.

4. A configuration describing (1), (2), (3) above, passed to the quantization APIs.

The framework will then do the following:

1. during the prepare module swaps, it will convert every module of type specified in (1) to the type specified in (2), using the from_float function of the class in (2).

2. during the convert module swaps, it will convert every module of type specified in (2) to the type specified in (3), using the from_observed function of the class in (3).

Currently, there is a requirement that ObservedCustomModule will have a single Tensor output, and an observer will be added by the framework (not by the user) on that output. The observer will be stored under the activation_post_process key as an attribute of the custom module instance. Relaxing these restrictions may be done at a future time.

Custom API Example:

import torch
import torch.nn.quantized as nnq
from torch.ao.quantization import QConfigMapping
import torch.ao.quantization.quantize_fx

# original fp32 module to replace
class CustomModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(3, 3)

def forward(self, x):
return self.linear(x)

# custom observed module, provided by user
class ObservedCustomModule(torch.nn.Module):
def __init__(self, linear):
super().__init__()
self.linear = linear

def forward(self, x):
return self.linear(x)

@classmethod
def from_float(cls, float_module):
assert hasattr(float_module, 'qconfig')
observed = cls(float_module.linear)
observed.qconfig = float_module.qconfig
return observed

# custom quantized module, provided by user
class StaticQuantCustomModule(torch.nn.Module):
def __init__(self, linear):
super().__init__()
self.linear = linear

def forward(self, x):
return self.linear(x)

@classmethod
def from_observed(cls, observed_module):
assert hasattr(observed_module, 'qconfig')
assert hasattr(observed_module, 'activation_post_process')
observed_module.linear.activation_post_process = \
observed_module.activation_post_process
quantized = cls(nnq.Linear.from_float(observed_module.linear))
return quantized

#
# example API call (Eager mode quantization)
#

m = torch.nn.Sequential(CustomModule()).eval()
prepare_custom_config_dict = {
"float_to_observed_custom_module_class": {
CustomModule: ObservedCustomModule
}
}
convert_custom_config_dict = {
"observed_to_quantized_custom_module_class": {
ObservedCustomModule: StaticQuantCustomModule
}
}
m.qconfig = torch.ao.quantization.default_qconfig
mp = torch.ao.quantization.prepare(
m, prepare_custom_config_dict=prepare_custom_config_dict)
# calibration (not shown)
mq = torch.ao.quantization.convert(
mp, convert_custom_config_dict=convert_custom_config_dict)
#
# example API call (FX graph mode quantization)
#
m = torch.nn.Sequential(CustomModule()).eval()
qconfig_mapping = QConfigMapping().set_global(torch.ao.quantization.default_qconfig)
prepare_custom_config_dict = {
"float_to_observed_custom_module_class": {
"static": {
CustomModule: ObservedCustomModule,
}
}
}
convert_custom_config_dict = {
"observed_to_quantized_custom_module_class": {
"static": {
ObservedCustomModule: StaticQuantCustomModule,
}
}
}
mp = torch.ao.quantization.quantize_fx.prepare_fx(
m, qconfig_mapping, torch.randn(3,3), prepare_custom_config=prepare_custom_config_dict)
# calibration (not shown)
mq = torch.ao.quantization.quantize_fx.convert_fx(
mp, convert_custom_config=convert_custom_config_dict)


## Best Practices¶

1. If you are using the fbgemm backend, we need to use 7 bits instead of 8 bits. Make sure you reduce the range for the quant\_min, quant\_max, e.g. if dtype is torch.quint8, make sure to set a custom quant_min to be 0 and quant_max to be 127 (255 / 2) if dtype is torch.qint8, make sure to set a custom quant_min to be -64 (-128 / 2) and quant_max to be 63 (127 / 2), we already set this correctly if you call the torch.ao.quantization.get_default_qconfig(backend) or torch.ao.quantization.get_default_qat_qconfig(backend) function to get the default qconfig for fbgemm or qnnpack backend

## Common Errors¶

### Passing a non-quantized Tensor into a quantized kernel¶

If you see an error similar to:

RuntimeError: Could not run 'quantized::some_operator' with arguments from the 'CPU' backend...


This means that you are trying to pass a non-quantized Tensor to a quantized kernel. A common workaround is to use torch.quantization.QuantStub to quantize the tensor. This needs to be done manually in Eager mode quantization. An e2e example:

class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant = torch.quantization.QuantStub()
self.conv = torch.nn.Conv2d(1, 1, 1)

def forward(self, x):
# during the convert step, this will be replaced with a
# quantize_per_tensor call
x = self.quant(x)
x = self.conv(x)
return x


### Passing a quantized Tensor into a non-quantized kernel¶

If you see an error similar to:

RuntimeError: Could not run 'aten::thnn_conv2d_forward' with arguments from the 'QuantizedCPU' backend.


This means that you are trying to pass a quantized Tensor to a non-quantized kernel. A common workaround is to use torch.quantization.DeQuantStub to dequantize the tensor. This needs to be done manually in Eager mode quantization. An e2e example:

class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.quant = torch.quantization.QuantStub()
self.conv1 = torch.nn.Conv2d(1, 1, 1)
# this module will not be quantized (see qconfig = None logic below)
self.conv2 = torch.nn.Conv2d(1, 1, 1)
self.dequant = torch.quantization.DeQuantStub()

def forward(self, x):
# during the convert step, this will be replaced with a
# quantize_per_tensor call
x = self.quant(x)
x = self.conv1(x)
# during the convert step, this will be replaced with a
# dequantize call
x = self.dequant(x)
x = self.conv2(x)
return x

m = M()
m.qconfig = some_qconfig
# turn off quantization for conv2
m.conv2.qconfig = None


When calling torch.load on a quantized model, if you see an error like:

AttributeError: 'LinearPackedParams' object has no attribute '_modules'


This is because directly saving and loading a quantized model using torch.save and torch.load is not supported. To save/load quantized models, the following ways can be used:

An example:

class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(5, 5)
self.relu = nn.ReLU()

def forward(self, x):
x = self.linear(x)
x = self.relu(x)
return x

m = M().eval()
prepare_orig = prepare_fx(m, {'' : default_qconfig})
prepare_orig(torch.rand(5, 5))
quantized_orig = convert_fx(prepare_orig)

# Save/load using state_dict
b = io.BytesIO()
torch.save(quantized_orig.state_dict(), b)

m2 = M().eval()
prepared = prepare_fx(m2, {'' : default_qconfig})
quantized = convert_fx(prepared)
b.seek(0)

1. Saving/Loading scripted quantized models using torch.jit.save and torch.jit.load

An example:

# Note: using the same model M from previous example
m = M().eval()
prepare_orig = prepare_fx(m, {'' : default_qconfig})
prepare_orig(torch.rand(5, 5))
quantized_orig = convert_fx(prepare_orig)

# save/load using scripted model
scripted = torch.jit.script(quantized_orig)
b = io.BytesIO()
torch.jit.save(scripted, b)
b.seek(0)

torch.fx.proxy.TraceError: symbolically traced variables cannot be used as inputs to control flow