PT2E Quantization

Created On: Jan 29, 2026 | Last Updated On: Jan 29, 2026

Quick Start

PyTorch 2 Export Quantization is a full graph quantization workflow mostly for static quantization. It targets hardwares that requires both input and output activation and weight to be quantized and relies of recognizing an operator pattern to make quantization decisions (such as linear - relu). PT2E quantization produces a pattern with quantize and dequantize ops inserted around the operators and during lowering quantized operator patterns will be fused into real quantized ops. Currently there are two typical lowering paths, 1. torch.compile through inductor lowering 2. ExecuTorch through delegation

Here we show an example with X86InductorQuantizer

API Example:

import torch
from torchao.quantization.pt2e.quantize_pt2e import prepare_pt2e
from torch.export import export
from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import (
    X86InductorQuantizer,
    get_default_x86_inductor_quantization_config,
)

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

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

# initialize a floating point model
float_model = M().eval()

# define calibration function
def calibrate(model, data_loader):
    model.eval()
    with torch.no_grad():
        for image, target in data_loader:
            model(image)

# Step 1. program capture
m = export(m, *example_inputs).module()
# we get a model with aten ops

# Step 2. quantization
# backend developer will write their own Quantizer and expose methods to allow
# users to express how they
# want the model to be quantized
quantizer = X86InductorQuantizer()
quantizer.set_global(xiq.get_default_x86_inductor_quantization_config())

# or prepare_qat_pt2e for Quantization Aware Training
m = prepare_pt2e(m, quantizer)

# run calibration
# calibrate(m, sample_inference_data)
m = convert_pt2e(m)

# Step 3. lowering
# lower to target backend

# Optional: using the C++ wrapper instead of default Python wrapper
import torch._inductor.config as config
config.cpp_wrapper = True

with torch.no_grad():
    optimized_model = torch.compile(converted_model)

    # Running some benchmark
    optimized_model(*example_inputs)

Please follow these tutorials to get started on PyTorch 2 Export Quantization:

Modeling Users:

• PyTorch 2 Export Post Training Quantization

• PyTorch 2 Export Quantization Aware Training

• PyTorch 2 Export Post Training Quantization with X86 Backend through Inductor

• PyTorch 2 Export Post Training Quantization with XPU Backend through Inductor

• PyTorch 2 Export Quantization for OpenVINO torch.compile Backend

Backend Developers (please check out all Modeling Users docs as well):

• How to Write a Quantizer for PyTorch 2 Export Quantization

• PyTorch 2 Export Post Training Quantization
• PyTorch 2 Export Quantization-Aware Training (QAT)
• PyTorch 2 Export Quantization with X86 Backend through Inductor
• PyTorch 2 Export Quantization with Intel GPU Backend through Inductor
• PyTorch 2 Export Quantization for OpenVINO torch.compile Backend
• How to Write a Quantizer for PyTorch 2 Export Quantization