（prototype）PyTorch 2 导出量化感知训练 (QAT)

创建于：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

本教程展示了如何基于 torch.export.export 在图模式下执行量化感知训练 (QAT)。有关 PyTorch 2 导出量化的一般详细信息，请参阅训练后量化教程。

PyTorch 2 导出 QAT 流程如下所示——它在很大程度上类似于训练后量化 (PTQ) 流程

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，程序捕获后不允许调用 model.eval() 或 model.train()，因为这些方法不再能正确更改某些操作（如 dropout 和批量归一化）的行为。相反，请分别使用 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

要使用完整的 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。将下载的文件解压缩到 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。

我们将从必要的导入开始，定义一些辅助函数并准备数据。这些步骤与 静态 eager 模式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

以下是如何使用 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如何量化模型

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 将提供他们自己的方式来允许用户配置他们的模型。

注意

查看我们的 教程，其中描述了如何编写新的 Quantizer。

准备模型以进行量化感知训练

prepare_qat_pt2e 在模型中的适当位置插入伪量化，并执行适当的 QAT “融合”，例如 Conv2d + BatchNorm2d，以获得更好的训练精度。融合操作在准备好的图中表示为 ATen 操作的子图。

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

注意

如果您的模型包含批量归一化，则您在图中获得的实际 ATen 操作取决于导出模型时模型的设备。如果模型在 CPU 上，那么您将获得 torch.ops.aten._native_batch_norm_legit。如果模型在 CUDA 上，那么您将获得 torch.ops.aten.cudnn_batch_norm。但是，这不是根本性的，并且将来可能会发生变化。

在这两个操作之间，已经表明 torch.ops.aten.cudnn_batch_norm 在像 MobileNetV2 这样的模型上提供更好的数值。要获得此操作，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()

将来，我们计划整合批量归一化操作，以便不再需要上述操作。

训练循环

训练循环与以前版本的 QAT 中的循环类似。为了获得更好的精度，您可以选择在一定数量的 epoch 后禁用观察者并更新批量归一化统计信息，或者每 N 个 epoch 评估 QAT 或迄今为止训练的量化模型。

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任何其他训练流程中的模型检查点相同。它们对于暂停训练并在以后恢复训练、从失败的训练运行中恢复以及稍后在不同机器上执行推理非常有用。您可以在训练期间或训练后按如下方式保存模型检查点

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

要加载检查点，您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

将训练后的模型转换为量化模型

convert_pt2e 接受经过校准的模型并生成量化模型。请注意，在推理之前，您必须首先调用 torch.ao.quantization.move_exported_model_to_eval() 以确保某些操作（如 dropout）在评估图中表现正确。否则，例如，我们将继续在推理期间的前向传播中错误地应用 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，并将其进一步降低到支持使用 XNNPACK 后端进行推理的后端。有关更多详细信息，请关注 PTQ 教程。