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Quantization in PyTorch 2.0 Export Tutorial

Author: Leslie Fang, Weiwen Xia, Jiong Gong, Kimish Patel, Jerry Zhang


Quantization in PyTorch 2.0 export is still a work in progress.

Today we have FX Graph Mode Quantization which uses symbolic_trace to capture the model into a graph, and then perform quantization transformations on top of the captured model. In a similar way, for Quantization 2.0 flow, we will now use the PT2 Export workflow to capture the model into a graph, and perform quantization transformations on top of the ATen dialect graph. This approach is expected to have significantly higher model coverage, better programmability, and a simplified UX.


Previously in FX Graph Mode Quantization we were using QConfigMapping for users to specify how the model to be quantized and BackendConfig to specify the supported ways of quantization in their backend. This API covers most use cases relatively well, but the main problem is that this API is not fully extensible without involvement of the quantization team:

  • This API has limitation to support advanced quantization intention and complicated quantization operator patterns as in the discussion of Issue-96288 to support conv add fusion.

  • This API uses QConfigMapping and BackendConfig as separate object in quantization configuration which may cause confusion about incompatibilities between these two objects. Also these quantization configurations require too much quantization details users need to know which can be hidden from user interface to make it simpler.

To address these issues, Quantizer is introduced for quantization in PyTorch 2.0 export. Quantizer is a class that users can use to programmatically set the quantization specifications for input and output of each node in the model graph. It adds flexibility to the quantization API and allows modeling users and backend developers to configure quantization programmatically. This will allow users to express how they want an operator pattern to be observed in a more explicit way by annotating the appropriate nodes.

Imagine a backend developer who wishes to integrate a third-party backend with PyTorch’s quantization 2.0 flow. To accomplish this, they would only need to define the backend specific quantizer. A backend specific quantizer inherited from base quantizer. The main method that need to be implemented for the backend specific quantizer is the annotate method which is used to annotate nodes in the graph with QuantizationAnnotation objects to convey the desired way of quantization.

The high level architecture of quantization 2.0 with quantizer could look like this:

float_model(Python)                               Input
    \                                              /
     \                                            /
|                    Dynamo Export                     |
                    FX Graph in ATen     QNNPackQuantizer,
                            |            or X86InductorQuantizer,
                            |            or <Other Backend Quantizer>
                            |                /
|                 prepare_pt2e_quantizer                |
|                      convert_pt2e                     |
                Reference Quantized Model
|                        Lowering                       |
        Executorch, or Inductor, or <Other Backends>

Note: prepare_pt2e_quantizer will be updated to prepare_pt2e soon.

An existing quantizer object defined for QNNPack/XNNPack is in QNNPackQuantizer. Taking QNNPackQuantizer as an example, the overall Quantization 2.0 flow could be:

import torch
import torch._dynamo as torchdynamo
from torch.ao.quantization._quantize_pt2e import convert_pt2e, prepare_pt2e
import torch.ao.quantization._pt2e.quantizer.qnnpack_quantizer as qq

class M(torch.nn.Module):
    def __init__(self):
        self.linear = torch.nn.Linear(5, 10)

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

example_inputs = (torch.randn(1, 5),)
model = M().eval()

# Step 1: Trace the model into an FX graph of flattened ATen operators
exported_graph_module, guards = torchdynamo.export(

# Step 2: Insert observers or fake quantize modules
quantizer = qq.QNNPackQuantizer()
operator_config = qq.get_symmetric_quantization_config(is_per_channel=True)
prepared_graph_module = prepare_pt2e_quantizer(exported_graph_module, quantizer)

# Step 3: Quantize the model
convered_graph_module = convert_pt2e(prepared_graph_module)

# Step 4: Lower Reference Quantized Model into the backend

Annotation API:

Quantizer uses annotation API to convey quantization intent for different operators/patterns. Annotation API mainly consists of QuantizationSpec and QuantizationAnnotation.

QuantizationSpec is used to convey intent of how a tensor will be quantized, e.g. dtype, bitwidth, min, max values, symmetric vs. asymmetric etc. Furthermore, QuantizationSpec also allows quantizer to specify how a tensor value should be observed, e.g. MinMaxObserver, or HistogramObserver , or some customized observer.

QuantizationAnnotation composed of QuantizationSpec objects is used to annotate input tensors and output tensor of a pattern. Annotating input tensors is equivalent of annotating input edges, while annotating output tensor is equivalent of annotating node. QuantizationAnnotation is a dataclass with several fields:

  • input_qspec_map field is of class Dict to map each input tensor (as input edge) to a QuantizationSpec.

  • output_qspec field expresses the QuantizationSpec used to annotate the output tensor;

  • _annotated field indicates if this node has already been annotated by quantizer.

To conclude, annotation API requires quantizer to annotate edges (input tensors) or nodes (output tensor) of the graph. Now, we will have a step-by-step tutorial for how to use the annotation API with different types of QuantizationSpec.

1. Annotate common operator patterns

In order to use the quantized pattern/operators, e.g. quantized add, backend developers will have intent to quantize (as expressed by QuantizationSpec) inputs, output of the pattern. Following is an example flow (take add operator as example) of how this intent is conveyed in the quantization workflow with annotation API.

  • Step 1: Identify the original floating point pattern in the FX graph. There are several ways to identify this pattern: Quantizer may use a pattern matcher to match the operator pattern; Quantizer may go through the nodes from start to the end and compare the node’s target type to match the operator pattern. In this example, we can use the get_source_partitions to match this pattern. The original floating point add pattern only contain a single add node.

add_partitions = get_source_partitions(gm.graph, [operator.add, torch.add])
add_partitions = list(itertools.chain(*add_partitions.values()))
for add_partition in add_partitions:
    add_node = add_partition.output_nodes[0]
  • Step 2: Define the QuantizationSpec for inputs and output of the pattern. QuantizationSpec defines the data type, qscheme, and other quantization parameters about users’ intent of how to observe or fake quantize a tensor.

act_quantization_spec = QuantizationSpec(

input_act_qspec = act_quantization_spec
output_act_qspec = act_quantization_spec
  • Step 3: Annotate the inputs and output of the pattern with QuantizationAnnotation. In this example, we will create the QuantizationAnnotation object with the QuantizationSpec created in above step 2 for two inputs and one output of the add node.

input_qspec_map = {}
input_act0 = add_node.args[0]
input_qspec_map[input_act0] = input_act_qspec

input_act1 = add_node.args[1]
input_qspec_map[input_act1] = input_act_qspec

add_node.meta["quantization_annotation"] = QuantizationAnnotation(

After we annotate the add node like this, in the following up quantization flow, HistogramObserver will be inserted at its two input nodes and one output node in prepare phase. And HistogramObserver will be substituted with quantize node and dequantize node in the convert phase.

2. Annotate sharing qparams operators

It is natural that users want to annotate a quantized model where quantization parameters can be shared among some tensors explicitly. Two typical use cases are:

  • Example 1: One example is for add where having both inputs sharing quantization parameters makes operator implementation much easier. Without using of SharedQuantizationSpec, we must annotate add as example in above section 1, in which two inputs of add has different quantization parameters.

  • Example 2: Another example is that of sharing quantization parameters between inputs and output. This typically results from operators such as maxpool, average_pool, concat etc.

SharedQuantizationSpec is designed for this use case to annotate tensors whose quantization parameters are shared with other tensors. Input of SharedQuantizationSpec is an EdgeOrNode object which can be an input edge or an output value.

  • Input edge is the connection between input node and the node consuming the input, so it’s a Tuple[Node, Node].

  • Output value is an FX Node.

Now, if we want to rewrite add annotation example with SharedQuantizationSpec to indicate two input tensors as sharing quantization parameters. We can define its QuantizationAnnotation as this:

  • Step 1: Identify the original floating point pattern in the FX graph. We can use the same methods introduced in QuantizationSpec example to identify the add pattern.

  • Step 2: Annotate input_act0 of add with QuantizationSpec.

  • Step 3: Create a SharedQuantizationSpec object with input edge defined as (input_act0, add_node) which means to share the observer used for this edge. Then, user can annotate input_act1 with this SharedQuantizationSpec object.

input_qspec_map = {}
share_qparams_with_input_act0_qspec = SharedQuantizationSpec((input_act0, add_node))
input_qspec_map = {input_act0: act_quantization_spec, input_act1: share_qparams_with_input_act0_qspec}

add_node.meta["quantization_annotation"] = QuantizationAnnotation(

3. Annotate fixed qparams operators

Another typical use case to annotate a quantized model is for tensors whose quantization parameters are known beforehand. For example, operator like sigmoid, which has predefined and fixed scale/zero_point at input and output tensors. FixedQParamsQuantizationSpec is designed for this use case. To use FixedQParamsQuantizationSpec, users need to pass in parameters of scale and zero_point explicitly.

  • Step 1: Identify the original floating point pattern in the FX graph. We can use the same methods introduced in QuantizationSpec example to identify the sigmoid pattern.

  • Step 2: Create FixedQParamsQuantizationSpec object with inputs of fixed scale, zero_point value. These values will be used to create the quantize node and dequantize node in the convert phase.

  • Step 3: Annotate inputs and output to use this FixedQParamsQuantizationSpec object.

act_qspec = FixedQParamsQuantizationSpec(
    scale=1.0 / 256.0,
sigmoid_node.meta["quantization_annotation"] = QuantizationAnnotation(
    input_qspec_map={input_act: act_qspec},

4. Annotate tensor with derived quantization parameters

Another use case is to define the constraint for tensors whose quantization parameters are derived from other tensors. For example, if we want to annotate a convolution node, and define the scale of its bias input tensor as product of the activation tensor’s scale and weight tensor’s scale. We can use DerivedQuantizationSpec to annotate this conv node.

  • Step 1: Identify the original floating point pattern in the FX graph. We can use the same methods introduced in QuantizationSpec example to identify the convolution pattern.

  • Step 2: Define derive_qparams_fn function, it accepts list of ObserverOrFakeQuantize ( ObserverBase or FakeQuantizeBase) as input. From each ObserverOrFakeQuantize object, user can get the scale, zero point value. User can define its heuristic about how to derive new scale, zero point value based on the quantization parameters calculated from the observer or fake quant instances.

  • Step 3: Define DerivedQuantizationSpec obejct, it accepts inputs of: list of EdgeOrNode objects. The observer corresponding to each EdgeOrNode object will be passed into the derive_qparams_fn function; derive_qparams_fn function; several other quantization parameters such as dtype, qscheme.

  • Step 4: Annotate the inputs and output of this conv node with QuantizationAnnotation.

def derive_qparams_fn(obs_or_fqs: List[ObserverOrFakeQuantize]) -> Tuple[Tensor, Tensor]:
    assert len(obs_or_fqs) == 2, \
        "Expecting two obs/fqs, one for activation and one for weight, got: {}".format(len(obs_or_fq))
    act_obs_or_fq = obs_or_fqs[0]
    weight_obs_or_fq = obs_or_fqs[1]
    act_scale, act_zp = act_obs_or_fq.calculate_qparams()
    weight_scale, weight_zp = weight_obs_or_fq.calculate_qparams()
    return torch.tensor([act_scale * weight_scale]).to(torch.float32), torch.tensor([0]).to(torch.int32)

bias_qspec = DerivedQuantizationSpec(
    derived_from=[(input_act, node), (weight, node)],
    quant_max=2**31 - 1,
input_qspec_map = {input_act: act_quantization_spec, weight: weight_quantization_spec, bias: bias_qspec}
node.meta["quantization_annotation"] = QuantizationAnnotation(

5. A Toy Example with Resnet18

After above annotation methods defined with QuantizationAnnotation API, we can now put them together to construct a BackendQuantizer and run a toy example with Torchvision Resnet18. To better understand the final example, here are the classes and utility functions that are used in the example:


With this tutorial, we introduce the new quantization path in PyTorch 2.0. Users can learn about how to define a BackendQuantizer with the QuantizationAnnotation API and integrate it into the quantization 2.0 flow. Examples of QuantizationSpec, SharedQuantizationSpec, FixedQParamsQuantizationSpec, and DerivedQuantizationSpec are given for specific annotation use case.


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