(原型) PyTorch 2 导出 量化感知训练 (QAT)

创建日期：2023 年 10 月 02 日 | 最后更新：2024 年 10 月 23 日 | 最后验证：2024 年 11 月 05 日

作者：Andrew Or

本教程介绍如何基于 torch.export.export 在图模式下执行量化感知训练 (QAT)。关于 PyTorch 2 导出量化的更多详细信息，请参阅训练后量化教程。

PyTorch 2 导出 QAT 流程如下所示——它与训练后量化 (PTQ) 流程大部分相似

import torch
from torch._export import capture_pre_autograd_graph
from torch.ao.quantization.quantize_pt2e import (
  prepare_qat_pt2e,
  convert_pt2e,
)
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
  XNNPACKQuantizer,
  get_symmetric_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)


example_inputs = (torch.randn(1, 5),)
m = M()

# Step 1. program capture
# This is available for pytorch 2.5+, for more details on lower pytorch versions
# please check `Export the model with torch.export` section
m = torch.export.export_for_training(m, example_inputs).module()
# we get a model with aten ops

# Step 2. quantization-aware training
# backend developer will write their own Quantizer and expose methods to allow
# users to express how they want the model to be quantized
quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
m = prepare_qat_pt2e(m, quantizer)

# train omitted

m = convert_pt2e(m)
# we have a model with aten ops doing integer computations when possible

# move the quantized model to eval mode, equivalent to `m.eval()`
torch.ao.quantization.move_exported_model_to_eval(m)

请注意，在程序捕获后不允许调用 model.eval() 或 model.train()，因为这些方法无法再正确改变某些操作（如 dropout 和 batch normalization）的行为。请改用 torch.ao.quantization.move_exported_model_to_eval() 和 torch.ao.quantization.move_exported_model_to_train()（即将推出）。

定义辅助函数并准备数据集

要使用整个 ImageNet 数据集运行本教程中的代码，请首先按照 ImageNet 数据 中的说明下载 ImageNet。将下载的文件解压到 data_path 文件夹中。

接下来，下载 torchvision resnet18 模型 并将其重命名为 data/resnet18_pretrained_float.pth。

我们将首先进行必要的导入，定义一些辅助函数并准备数据。这些步骤与 Eager 模式静态训练后量化教程中定义的步骤非常相似。

import os
import sys
import time
import numpy as np

import torch
import torch.nn as nn
from torch.utils.data import DataLoader

import torchvision
from torchvision import datasets
from torchvision.models.resnet import resnet18
import torchvision.transforms as transforms

# Set up warnings
import warnings
warnings.filterwarnings(
    action='ignore',
    category=DeprecationWarning,
    module=r'.*'
)
warnings.filterwarnings(
    action='default',
    module=r'torch.ao.quantization'
)

# Specify random seed for repeatable results
_ = torch.manual_seed(191009)

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)

def accuracy(output, target, topk=(1,)):
    """
    Computes the accuracy over the k top predictions for the specified
    values of k.
    """
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

def evaluate(model, criterion, data_loader, device):
    torch.ao.quantization.move_exported_model_to_eval(model)
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    cnt = 0
    with torch.no_grad():
        for image, target in data_loader:
            image = image.to(device)
            target = target.to(device)
            output = model(image)
            loss = criterion(output, target)
            cnt += 1
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            top1.update(acc1[0], image.size(0))
            top5.update(acc5[0], image.size(0))
    print('')

    return top1, top5

def load_model(model_file):
    model = resnet18(pretrained=False)
    state_dict = torch.load(model_file, weights_only=True)
    model.load_state_dict(state_dict)
    return model

def print_size_of_model(model):
    if isinstance(model, torch.jit.RecursiveScriptModule):
        torch.jit.save(model, "temp.p")
    else:
        torch.jit.save(torch.jit.script(model), "temp.p")
    print("Size (MB):", os.path.getsize("temp.p")/1e6)
    os.remove("temp.p")

def prepare_data_loaders(data_path):
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    dataset = torchvision.datasets.ImageNet(
        data_path, split="train", transform=transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    dataset_test = torchvision.datasets.ImageNet(
        data_path, split="val", transform=transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ]))

    train_sampler = torch.utils.data.RandomSampler(dataset)
    test_sampler = torch.utils.data.SequentialSampler(dataset_test)

    data_loader = torch.utils.data.DataLoader(
        dataset, batch_size=train_batch_size,
        sampler=train_sampler)

    data_loader_test = torch.utils.data.DataLoader(
        dataset_test, batch_size=eval_batch_size,
        sampler=test_sampler)

    return data_loader, data_loader_test

def train_one_epoch(model, criterion, optimizer, data_loader, device, ntrain_batches):
    # Note: do not call model.train() here, since this doesn't work on an exported model.
    # Instead, call `torch.ao.quantization.move_exported_model_to_train(model)`, which will
    # be added in the near future
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    avgloss = AverageMeter('Loss', '1.5f')

    cnt = 0
    for image, target in data_loader:
        start_time = time.time()
        print('.', end = '')
        cnt += 1
        image, target = image.to(device), target.to(device)
        output = model(image)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        top1.update(acc1[0], image.size(0))
        top5.update(acc5[0], image.size(0))
        avgloss.update(loss, image.size(0))
        if cnt >= ntrain_batches:
            print('Loss', avgloss.avg)

            print('Training: * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
                  .format(top1=top1, top5=top5))
            return

    print('Full imagenet train set:  * Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f}'
          .format(top1=top1, top5=top5))
    return

data_path = '~/.data/imagenet'
saved_model_dir = 'data/'
float_model_file = 'resnet18_pretrained_float.pth'

train_batch_size = 32
eval_batch_size = 32

data_loader, data_loader_test = prepare_data_loaders(data_path)
example_inputs = (next(iter(data_loader))[0])
criterion = nn.CrossEntropyLoss()
float_model = load_model(saved_model_dir + float_model_file).to("cuda")

使用 torch.export 导出模型

以下是使用 torch.export 导出模型的方法

from torch._export import capture_pre_autograd_graph

example_inputs = (torch.rand(2, 3, 224, 224),)
# for pytorch 2.5+
exported_model = torch.export.export_for_training(float_model, example_inputs).module()
# for pytorch 2.4 and before
# from torch._export import capture_pre_autograd_graph
# exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs)

# or, to capture with dynamic dimensions:

# for pytorch 2.5+
dynamic_shapes = tuple(
  {0: torch.export.Dim("dim")} if i == 0 else None
  for i in range(len(example_inputs))
)
exported_model = torch.export.export_for_training(float_model, example_inputs, dynamic_shapes=dynamic_shapes).module()

# for pytorch 2.4 and before
# dynamic_shape API may vary as well
# from torch._export import dynamic_dim

# example_inputs = (torch.rand(2, 3, 224, 224),)
# exported_model = capture_pre_autograd_graph(
#     float_model,
#     example_inputs,
#     constraints=[dynamic_dim(example_inputs[0], 0)],
# )

导入后端特定的量化器并配置模型量化方式

以下代码片段描述了如何量化模型

from torch.ao.quantization.quantizer.xnnpack_quantizer import (
    XNNPACKQuantizer,
    get_symmetric_quantization_config,
)
quantizer = XNNPACKQuantizer()
quantizer.set_global(get_symmetric_quantization_config(is_qat=True))

Quantizer 是后端特定的，每个 Quantizer 都将提供自己的方式来允许用户配置其模型。

注意

查看我们的教程，该教程描述了如何编写新的 Quantizer。

为量化感知训练准备模型

prepare_qat_pt2e 在模型适当位置插入 fake quantizes，并执行适当的 QAT“融合”，例如 Conv2d + BatchNorm2d，以获得更好的训练精度。融合后的操作在准备好的图中表示为 ATen ops 的子图。

prepared_model = prepare_qat_pt2e(exported_model, quantizer)
print(prepared_model)

注意

如果你的模型包含批归一化，导出模型时图中的实际 ATen ops 取决于模型的设备。如果模型在 CPU 上，你将得到 torch.ops.aten._native_batch_norm_legit。如果模型在 CUDA 上，你将得到 torch.ops.aten.cudnn_batch_norm。但这并非根本性的，将来可能会有所改变。

在这两个操作中，torch.ops.aten.cudnn_batch_norm 已被证明在 MobileNetV2 等模型上提供更好的数值结果。要获取此操作，要么在导出前调用 model.cuda()，要么在准备后运行以下代码手动交换操作

for n in prepared_model.graph.nodes:
    if n.target == torch.ops.aten._native_batch_norm_legit.default:
        n.target = torch.ops.aten.cudnn_batch_norm.default
prepared_model.recompile()

未来，我们计划整合批归一化操作，使其不再需要上述步骤。

训练循环

训练循环与之前版本的 QAT 类似。为了获得更好的精度，你可以选择在一定数量的 epoch 后禁用观察者和更新批归一化统计信息，或每隔 N 个 epoch 评估当前的 QAT 模型或已训练的量化模型。

num_epochs = 10
num_train_batches = 20
num_eval_batches = 20
num_observer_update_epochs = 4
num_batch_norm_update_epochs = 3
num_epochs_between_evals = 2

# QAT takes time and one needs to train over a few epochs.
# Train and check accuracy after each epoch
for nepoch in range(num_epochs):
    train_one_epoch(prepared_model, criterion, optimizer, data_loader, "cuda", num_train_batches)

    # Optionally disable observer/batchnorm stats after certain number of epochs
    if epoch >= num_observer_update_epochs:
        print("Disabling observer for subseq epochs, epoch = ", epoch)
        prepared_model.apply(torch.ao.quantization.disable_observer)
    if epoch >= num_batch_norm_update_epochs:
        print("Freezing BN for subseq epochs, epoch = ", epoch)
        for n in prepared_model.graph.nodes:
            # Args: input, weight, bias, running_mean, running_var, training, momentum, eps
            # We set the `training` flag to False here to freeze BN stats
            if n.target in [
                torch.ops.aten._native_batch_norm_legit.default,
                torch.ops.aten.cudnn_batch_norm.default,
            ]:
                new_args = list(n.args)
                new_args[5] = False
                n.args = new_args
        prepared_model.recompile()

    # Check the quantized accuracy every N epochs
    # Note: If you wish to just evaluate the QAT model (not the quantized model),
    # then you can just call `torch.ao.quantization.move_exported_model_to_eval/train`.
    # However, the latter API is not ready yet and will be available in the near future.
    if (nepoch + 1) % num_epochs_between_evals == 0:
        prepared_model_copy = copy.deepcopy(prepared_model)
        quantized_model = convert_pt2e(prepared_model_copy)
        top1, top5 = evaluate(quantized_model, criterion, data_loader_test, neval_batches=num_eval_batches)
        print('Epoch %d: Evaluation accuracy on %d images, %2.2f' % (nepoch, num_eval_batches * eval_batch_size, top1.avg))

保存和加载模型检查点

PyTorch 2 导出 QAT 流程的模型检查点与其他训练流程中的检查点相同。它们对于暂停训练并在以后恢复、从失败的训练运行中恢复以及以后在不同机器上执行推理非常有用。你可以在训练期间或之后保存模型检查点，如下所示

checkpoint_path = "/path/to/my/checkpoint_%s.pth" % nepoch
torch.save(prepared_model.state_dict(), "checkpoint_path")

要加载检查点，必须以与最初导出和准备模型完全相同的方式导出和准备模型。例如

from torch._export import capture_pre_autograd_graph
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
    XNNPACKQuantizer,
    get_symmetric_quantization_config,
)
from torchvision.models.resnet import resnet18

example_inputs = (torch.rand(2, 3, 224, 224),)
float_model = resnet18(pretrained=False)
exported_model = capture_pre_autograd_graph(float_model, example_inputs)
quantizer = XNNPACKQuantizer()
quantizer.set_global(get_symmetric_quantization_config(is_qat=True))
prepared_model = prepare_qat_pt2e(exported_model, quantizer)
prepared_model.load_state_dict(torch.load(checkpoint_path))

# resume training or perform inference

将训练好的模型转换为量化模型

convert_pt2e 接受一个校准过的模型并生成一个量化模型。请注意，在推理之前，你必须先调用 torch.ao.quantization.move_exported_model_to_eval() 以确保某些操作（如 dropout）在评估图中行为正确。否则，例如，在推理期间的前向传播中，我们将继续错误地应用 dropout。

quantized_model = convert_pt2e(prepared_model)

# move certain ops like dropout to eval mode, equivalent to `m.eval()`
torch.ao.quantization.move_exported_model_to_eval(m)

print(quantized_model)

top1, top5 = evaluate(quantized_model, criterion, data_loader_test, neval_batches=num_eval_batches)
print('Final evaluation accuracy on %d images, %2.2f' % (num_eval_batches * eval_batch_size, top1.avg))

结论

在本教程中，我们演示了如何在 PyTorch 2 导出量化中运行量化感知训练 (QAT) 流程。转换后，其余流程与训练后量化 (PTQ) 相同；用户可以序列化/反序列化模型，并进一步将其下层到支持 XNNPACK 后端推理的后端。有关更多详细信息，请参阅 PTQ 教程。