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使用 Core API 和 DTensor 进行分布式训练

简介

此笔记本使用 TensorFlow Core 低级 API 和 DTensor 来演示数据并行分布式训练示例。访问 Core API 概述以详细了解 TensorFlow Core 及其预期用例。请参阅 DTensor 概述指南和使用 DTensor 进行分布式训练教程以详细了解 DTensor。

本示例使用多层感知器教程中显示的相同模型和优化器。首先请参阅本教程，以熟悉使用 Core API 编写端到端机器学习工作流。

注：DTensor 仍然是一个实验性 TensorFlow API，这意味着它的功能可用于测试，并且仅用于测试环境。

使用 DTensor 进行数据并行训练的概述

在构建支持分布的 MLP 之前，花点时间探索 DTensor 用于数据并行训练的基础知识。

DTensor 允许您跨设备运行分布式训练，以提高效率、可靠性和可扩缩性。DTensor 通过称为单程序多数据 (SPMD) 扩展的过程根据分片指令分布程序和张量。DTensor 感知层的变量被创建为 dtensor.DVariable，除了通常的层参数外，DTensor 感知层对象的构造函数还接受额外的 Layout 输入。

数据并行训练的主要思路如下：

分别在 N 台设备上复制模型变量。
将全局批次拆分成 N 个按副本批次。
每个按副本批次都在副本设备上进行训练。
在对所有副本共同执行数据加权之前进行梯度归约。
数据并行训练能够根据设备数量提供近乎线性的速度。

安装

DTensor 是 TensorFlow 2.9.0 版本的一部分。

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#!pip install --quiet --upgrade --pre tensorflow

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import matplotlib
from matplotlib import pyplot as plt
# Preset Matplotlib figure sizes.
matplotlib.rcParams['figure.figsize'] = [9, 6]

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import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.experimental import dtensor
print(tf.__version__)
# Set random seed for reproducible results 
tf.random.set_seed(22)

为本实验配置 8 个虚拟 CPU。DTensor 也可以与 GPU 或 TPU 设备一起使用。鉴于此笔记本使用虚拟设备，分布式训练所获得的加速效果并不明显。

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def configure_virtual_cpus(ncpu):
  phy_devices = tf.config.list_physical_devices('CPU')
  tf.config.set_logical_device_configuration(phy_devices[0], [
        tf.config.LogicalDeviceConfiguration(),
    ] * ncpu)

configure_virtual_cpus(8)

DEVICES = [f'CPU:{i}' for i in range(8)]
devices = tf.config.list_logical_devices('CPU')
device_names = [d.name for d in devices]
device_names

MNIST 数据集

可从 TensorFlow Datasets 获得该数据集。将数据拆分为训练集和测试集。仅使用 5000 个样本进行训练和测试以节省时间。

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train_data, test_data = tfds.load("mnist", split=['train[:5000]', 'test[:5000]'], batch_size=128, as_supervised=True)

预处理数据

通过将数据的形状改变为二维并重新缩放数据以适合单位区间 [0,1] 来预处理数据。

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def preprocess(x, y):
  # Reshaping the data
  x = tf.reshape(x, shape=[-1, 784])
  # Rescaling the data
  x = x/255
  return x, y

train_data, test_data = train_data.map(preprocess), test_data.map(preprocess)

构建 MLP

使用 DTensor 感知层构建 MLP 模型。

密集层

首先创建一个支持 DTensor 的密集层模块。dtensor.call_with_layout 函数可用于调用接受 DTensor 输入并产生 DTensor 输出的函数。这对于使用 TensorFlow 支持的函数初始化 DTensor 变量 dtensor.DVariable 十分有用。

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class DenseLayer(tf.Module):

  def __init__(self, in_dim, out_dim, weight_layout, activation=tf.identity):
    super().__init__()
    # Initialize dimensions and the activation function
    self.in_dim, self.out_dim = in_dim, out_dim
    self.activation = activation

    # Initialize the DTensor weights using the Xavier scheme
    uniform_initializer = tf.function(tf.random.stateless_uniform)
    xavier_lim = tf.sqrt(6.)/tf.sqrt(tf.cast(self.in_dim + self.out_dim, tf.float32))
    self.w = dtensor.DVariable(
      dtensor.call_with_layout(
          uniform_initializer, weight_layout,
          shape=(self.in_dim, self.out_dim), seed=(22, 23),
          minval=-xavier_lim, maxval=xavier_lim))
        
    # Initialize the bias with the zeros
    bias_layout = weight_layout.delete([0])
    self.b = dtensor.DVariable(
      dtensor.call_with_layout(tf.zeros, bias_layout, shape=[out_dim]))

  def __call__(self, x):
    # Compute the forward pass
    z = tf.add(tf.matmul(x, self.w), self.b)
    return self.activation(z)

MLP 序贯模型

现在，创建一个按顺序执行密集层的 MLP 模块。

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class MLP(tf.Module):

  def __init__(self, layers):
    self.layers = layers
   
  def __call__(self, x, preds=False): 
    # Execute the model's layers sequentially
    for layer in self.layers:
      x = layer(x)
    return x

使用 DTensor 执行“数据并行”训练等效于 tf.distribute.MirroredStrategy。为此，每个设备将在数据批次的一个分片上运行相同的模型。因此，您将需要以下各项：

具有单个 "batch" 维度的 dtensor.Mesh
用于在网格中复制（对每个轴使用 dtensor.UNSHARDED）的所有权重的 dtensor.Layout
用于在网格中拆分批次维度的数据的 dtensor.Layout

创建一个包含单个批次维度的 DTensor 网格，其中每个设备都成为从全局批次接收分片的副本。使用此网格实例化具有以下架构的 MLP 模式：

前向传递：ReLU(784 x 700) x ReLU(700 x 500) x Softmax(500 x 10)

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mesh = dtensor.create_mesh([("batch", 8)], devices=DEVICES)
weight_layout = dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh)

input_size = 784
hidden_layer_1_size = 700
hidden_layer_2_size = 500
hidden_layer_2_size = 10

mlp_model = MLP([
    DenseLayer(in_dim=input_size, out_dim=hidden_layer_1_size, 
               weight_layout=weight_layout,
               activation=tf.nn.relu),
    DenseLayer(in_dim=hidden_layer_1_size , out_dim=hidden_layer_2_size,
               weight_layout=weight_layout,
               activation=tf.nn.relu),
    DenseLayer(in_dim=hidden_layer_2_size, out_dim=hidden_layer_2_size, 
               weight_layout=weight_layout)])

训练指标

使用交叉熵损失函数和准确率指标进行训练。

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def cross_entropy_loss(y_pred, y):
  # Compute cross entropy loss with a sparse operation
  sparse_ce = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=y_pred)
  return tf.reduce_mean(sparse_ce)

def accuracy(y_pred, y):
  # Compute accuracy after extracting class predictions
  class_preds = tf.argmax(y_pred, axis=1)
  is_equal = tf.equal(y, class_preds)
  return tf.reduce_mean(tf.cast(is_equal, tf.float32))

优化器

与标准梯度下降相比，使用优化器可以显著加快收敛速度。Adam 优化器在下面实现，并已配置为与 DTensor 兼容。要将 Keras 优化器与 DTensor 一起使用，请参阅实验性 tf.keras.dtensor.experimental.optimizers 模块。

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class Adam(tf.Module):

    def __init__(self, model_vars, learning_rate=1e-3, beta_1=0.9, beta_2=0.999, ep=1e-7):
      # Initialize optimizer parameters and variable slots
      self.model_vars = model_vars
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.learning_rate = learning_rate
      self.ep = ep
      self.t = 1.
      self.v_dvar, self.s_dvar = [], []
      # Initialize optimizer variable slots
      for var in model_vars:
        v = dtensor.DVariable(dtensor.call_with_layout(tf.zeros, var.layout, shape=var.shape))
        s = dtensor.DVariable(dtensor.call_with_layout(tf.zeros, var.layout, shape=var.shape))
        self.v_dvar.append(v)
        self.s_dvar.append(s)

    def apply_gradients(self, grads):
      # Update the model variables given their gradients
      for i, (d_var, var) in enumerate(zip(grads, self.model_vars)):
        self.v_dvar[i].assign(self.beta_1*self.v_dvar[i] + (1-self.beta_1)*d_var)
        self.s_dvar[i].assign(self.beta_2*self.s_dvar[i] + (1-self.beta_2)*tf.square(d_var))
        v_dvar_bc = self.v_dvar[i]/(1-(self.beta_1**self.t))
        s_dvar_bc = self.s_dvar[i]/(1-(self.beta_2**self.t))
        var.assign_sub(self.learning_rate*(v_dvar_bc/(tf.sqrt(s_dvar_bc) + self.ep)))
      self.t += 1.
      return

数据打包

首先，编写一个用于将数据传输到设备的辅助函数。此函数应使用 dtensor.pack 将用于副本的全局批次的分片发送（并且仅发送）到支持副本的设备。为简单起见，假设采用一个单客户端应用。

接下来，编写一个函数，它使用此辅助函数将训练数据批次打包到沿批次（第一个）轴分片的 DTensor 中。这可确保 DTensor 将训练数据均匀分布到“批次”网格维度。请注意，在 DTensor 中，批次大小始终指全局批次大小；因此，批次大小的选择应使其可以被批次网格维度的大小均匀拆分。计划使用其他 DTensor API 来简化 tf.data 集成，敬请期待。

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def repack_local_tensor(x, layout):
  # Repacks a local Tensor-like to a DTensor with layout
  # This function assumes a single-client application
  x = tf.convert_to_tensor(x)
  sharded_dims = []

  # For every sharded dimension, use tf.split to split the along the dimension.
  # The result is a nested list of split-tensors in queue[0].
  queue = [x]
  for axis, dim in enumerate(layout.sharding_specs):
    if dim == dtensor.UNSHARDED:
      continue
    num_splits = layout.shape[axis]
    queue = tf.nest.map_structure(lambda x: tf.split(x, num_splits, axis=axis), queue)
    sharded_dims.append(dim)

  # Now you can build the list of component tensors by looking up the location in
  # the nested list of split-tensors created in queue[0].
  components = []
  for locations in layout.mesh.local_device_locations():
    t = queue[0]
    for dim in sharded_dims:
      split_index = locations[dim]  # Only valid on single-client mesh.
      t = t[split_index]
    components.append(t)

  return dtensor.pack(components, layout)

def repack_batch(x, y, mesh):
  # Pack training data batches into DTensors along the batch axis
  x = repack_local_tensor(x, layout=dtensor.Layout(['batch', dtensor.UNSHARDED], mesh))
  y = repack_local_tensor(y, layout=dtensor.Layout(['batch'], mesh))
  return x, y

训练

编写一个可跟踪的函数，在给定一批数据的情况下执行单个训练步骤。此函数不需要任何特殊的 DTensor 注解。此外，还要编写一个执行测试步骤并返回适当性能指标的函数。

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@tf.function
def train_step(model, x_batch, y_batch, loss, metric, optimizer):
  # Execute a single training step
  with tf.GradientTape() as tape:
    y_pred = model(x_batch)
    batch_loss = loss(y_pred, y_batch)
  # Compute gradients and update the model's parameters
  grads = tape.gradient(batch_loss, model.trainable_variables)
  optimizer.apply_gradients(grads)
  # Return batch loss and accuracy
  batch_acc = metric(y_pred, y_batch)
  return batch_loss, batch_acc

@tf.function
def test_step(model, x_batch, y_batch, loss, metric):
  # Execute a single testing step
  y_pred = model(x_batch)
  batch_loss = loss(y_pred, y_batch)
  batch_acc = metric(y_pred, y_batch)
  return batch_loss, batch_acc

现在，以 128 为批次大小对 MLP 模型训练 3 个周期。

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# Initialize the training loop parameters and structures
epochs = 3
batch_size = 128
train_losses, test_losses = [], []
train_accs, test_accs = [], []
optimizer = Adam(mlp_model.trainable_variables)

# Format training loop
for epoch in range(epochs):
  batch_losses_train, batch_accs_train = [], []
  batch_losses_test, batch_accs_test = [], []

  # Iterate through training data
  for x_batch, y_batch in train_data:
    x_batch, y_batch = repack_batch(x_batch, y_batch, mesh)
    batch_loss, batch_acc = train_step(mlp_model, x_batch, y_batch, cross_entropy_loss, accuracy, optimizer)
   # Keep track of batch-level training performance
    batch_losses_train.append(batch_loss)
    batch_accs_train.append(batch_acc)

  # Iterate through testing data
  for x_batch, y_batch in test_data:
    x_batch, y_batch = repack_batch(x_batch, y_batch, mesh)
    batch_loss, batch_acc = test_step(mlp_model, x_batch, y_batch, cross_entropy_loss, accuracy)
    # Keep track of batch-level testing
    batch_losses_test.append(batch_loss)
    batch_accs_test.append(batch_acc)

# Keep track of epoch-level model performance
  train_loss, train_acc = tf.reduce_mean(batch_losses_train), tf.reduce_mean(batch_accs_train)
  test_loss, test_acc = tf.reduce_mean(batch_losses_test), tf.reduce_mean(batch_accs_test)
  train_losses.append(train_loss)
  train_accs.append(train_acc)
  test_losses.append(test_loss)
  test_accs.append(test_acc)
  print(f"Epoch: {epoch}")
  print(f"Training loss: {train_loss.numpy():.3f}, Training accuracy: {train_acc.numpy():.3f}")
  print(f"Testing loss: {test_loss.numpy():.3f}, Testing accuracy: {test_acc.numpy():.3f}")

性能评估

首先，编写一个绘图函数来呈现模型在训练期间的损失和准确率。

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def plot_metrics(train_metric, test_metric, metric_type):
  # Visualize metrics vs training Epochs
  plt.figure()
  plt.plot(range(len(train_metric)), train_metric, label = f"Training {metric_type}")
  plt.plot(range(len(test_metric)), test_metric, label = f"Testing {metric_type}")
  plt.xlabel("Epochs")
  plt.ylabel(metric_type)
  plt.legend()
  plt.title(f"{metric_type} vs Training Epochs");

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plot_metrics(train_losses, test_losses, "Cross entropy loss")

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plot_metrics(train_accs, test_accs, "Accuracy")

保存模型

tf.saved_model 和 DTensor 的集成仍在开发中。从 TensorFlow 2.9.0 开始，tf.saved_model 仅接受具有完全复制变量的 DTensor 模型。作为替代方法，您可以通过重新加载检查点将 DTensor 模型转换为完全复制的模型。但是，保存模型后，所有 DTensor 注解都会丢失，保存的签名只能与常规张量一起使用。本教程将在固化后进行更新以展示集成。

结论

此笔记本概括介绍了如何使用 DTensor 和 TensorFlow Core API 进行分布式训练。下面是一些可能有所帮助的提示：

TensorFlow Core API 可用于构建具有高度可配置性的机器学习工作流，并支持分布式训练。
DTensor 概念指南和使用 DTensor 进行分布式训练教程包含有关 DTensor 及其集成的最新信息。

有关使用 TensorFlow Core API 的更多示例，请查阅指南。如果您想详细了解如何加载和准备数据，请参阅有关图像数据加载或 CSV 数据加载的教程。