学习笔记TF041:分布式并行
学习笔记TF041:分布式并行
利炳根 发表于4个月前
学习笔记TF041:分布式并行
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TensorFlow分布式并行基于gRPC通信框架,一个master负责创建Session,多个worker负责执行计算图任务。

先创建TensorFlow Cluster对象,包含一组task(每个task一台独立机器),分布式执行TensorFlow计算图。一个Cluster切分多个job,一个job是一类特定任务(parameter server ps,worker),每个job可以包含多个task。每个task创建一个server,连接到Cluster,每个task执行在不同机器。也可以一台机器执行多个task(不同GPU)。tf.train.ClusterSpec初始化Cluster对象,初始化信息是Python dict,tf.train.ClusterSpec({"ps":["192.168.233.201:2222"],"worker":["192.168.233.202:2222","192.168.233.203:2222"]}),代表一个parameter server和两个worker,分别在三个不同机器上。每个task,定义自己身份,如server=tf.train.Server(cluster,job_name="ps",task_index=0),机器job定义ps第0台机器。程序中with tf.device("/job:worker/task:7"),限定Variable存放在哪个task或机器。

TensorFlow分布式模式:In-graph replication模型并行,模型计算图不同部分放在不同机器执行;Between-graph replication数据并行,每台机器相同计算图,计算不同batch数据。异步并行,每台机器独立计算梯度,计算完更新到parameter server,不等其他机器。同步并行,等所有机器都完成梯度计算,多个梯度合成统一更新模型参数。同步并行训练,loss下降速度更快,可达到最大精度更高,同步并行速度取决最慢机器,设备速度一致,效率较高。

TensorFlow实现包含1个paramter server、2个worker分布式并行训练程序,MNIST手写数据识别任务示例。写一个完整Python文件,在不同机器不同task执行。载入依赖库。

tf.app.flags定义标记,命令行执行TensorFlow设置参数。命令行指定参数被TensorFlow读取,转flags。设定数据储存目录data_dir默认/tmp/mnist-data,隐藏节点数默认100,训练最大步数train_steps默认1000000,batch_size默认100,学习速率默认0.01。

设定是否使用同步并行标记sync_replicas默认False,命令行执行时可设True开户同步并行。设定需要累计梯度个数更新模型值默认None,代表同步并行积累多少个batch梯度再进行参数更新,设None 为worker数量,所有worker完成一个batch训练后再更新模型参数。

定义ps地址,默认192.168.233.201:2222,根据集群实际情况配置。worker地址设置192.168.233.202:2222和192.168.233.203:2222.设置job_name和task_index FLAG。

flags.FLAGS直接命名FLAGS,简化使用。设置图片尺寸IMAGE_PIXELS 28。

编写程序主函数main,input_data.read_data_sets下载读取MNIST数据集,设置one_hot编码格式。检测命令行输入参数,确保job_name和task_index两个必备参数。显示job_name和task_index,ps、worker所有地址解析成列表ps_spec、worker_spec。

计算总共worker数量,tf.train.ClusterSpec生成TensorFlow Cluster对象,传入参数ps地址信息和worker地址信息。tf.train.Server创建当前机器server,连接Cluster。如当前节点是parameter server,不进行后续操作,server.join等待worker工作。

判断当前机器是否主节点,task_index是否0。定义当前机器worker_device,格式"job:worker/task:0/gpu:0"。多台机器,每台机器1块GPU,总共需要机器数量worker。如一台机器多GPU,一个task管理多个GPU或多个task分别管理。tf.train.replica_device_setter()设置worker资源,worker_device计算资源,ps_device存储模型参数资源。replica_device_setter将模型参数部署在独立ps服务器"/job:ps/cpu:0",训练操作部署在"/job:worker/task:0/gpu:0",本机GPU。创建记录全局训练步数变量global_step。

定义神经网络模型,tf.truncated_normal初始化权重,tf.zeros初始化偏置,创建输入 placeholder,tf.nn.xw_plus_b输入矩阵乘法、偏置操作,ReLU激活函数处理,得到第一个隐层输出hid。tf.nn.xw_plus_b、tf.nn.softmax对第一层输出hid处理,得到网络最终输出y。最后计算损失cross_entropy,定义优化器Adam。

判断是否设置同步训练模式sync_replicas,如果同步模型,先获取同步更新模型参数需要副本数replicas_to_aggregate;如果没有单独设置,worker数作默认值。tf.train.SyncReplicasOptimizer创建同步训练优化器,对原有优化器扩展。传入原有优化器及其他参数(replicas_to_aggregate、total_num_replicas、replica_id),原有优化器改造为同步分布式训练版本。用普通(异步)或同步优化器优化损失cross_entropy。

同步训练模式,主节点,opt.get_chief_queue_runner创建队列执行器,opt.get_init_tokens_op创建全局参数初始化器。

生成本地参数初始化操作init_op,创建临时训练目录,tf.train_Supervisor创建分布式训练监督器,传入参数is_chief、train_dir、init_op。Supervisor管理task参与到分布式训练。

设置Session参数,allow_soft_placement设True,代表操作在指定device不能执行时转到其他device执行。

如果主节点,显示初始化Session,其他节点显示等待主节点初始化操作。执行sv.prepate_or_wait_for_session()。

如果处于同步模型主节点,sv.start_queue_runners执行队列化执行器chief_queue_runner,执行全局参数初始化器init_tokens_op。

训练过程,记录worker执行训练启动时间,初始化本地训练步数local_step,进入训练循环。每步训练,从nnist.train.next_batch读取一个batch数据,生成feed_dict,调train_step执行训练。当全局训练步数达到预设最大值,停止训练。

训练结束,展示总训练时间,在验证数据上计算预测结果损失cross_entropy,展示。

在主程序执行tf.app.run()启动main函数,全部代码保存到distributed.py文件。3台不同机器分别执行distributed.py启动3个task,每次执行distributed.py,传入job_name、task_index指定worker身份。

分别在三台机器192.168.233.201、192.168.233.202、192.168.233.203执行python distributed.py。

同步模式,加上--sync_replicas=True。global_step,异步时,全局步数是所有worker训练步数和,同步时是多少轮并行训练。

#from __future__ import absolute_import
#from __future__ import division
#from __future__ import print_function
import math
#import sys
import tempfile
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
flags = tf.app.flags
flags.DEFINE_string("data_dir", "/tmp/mnist-data",
                    "Directory for storing mnist data")
#flags.DEFINE_boolean("download_only", False,
#                     "Only perform downloading of data; Do not proceed to "
#                     "session preparation, model definition or training")
flags.DEFINE_integer("task_index", None,
                     "Worker task index, should be >= 0. task_index=0 is "
                     "the master worker task the performs the variable "
                     "initialization ")
#flags.DEFINE_integer("num_gpus", 2,
#                     "Total number of gpus for each machine."
#                     "If you don't use GPU, please set it to '0'")
flags.DEFINE_integer("replicas_to_aggregate", None,
                     "Number of replicas to aggregate before parameter update"
                     "is applied (For sync_replicas mode only; default: "
                     "num_workers)")
flags.DEFINE_integer("hidden_units", 100,
                     "Number of units in the hidden layer of the NN")
flags.DEFINE_integer("train_steps", 1000000,
                     "Number of (global) training steps to perform")
flags.DEFINE_integer("batch_size", 100, "Training batch size")
flags.DEFINE_float("learning_rate", 0.01, "Learning rate")
flags.DEFINE_boolean("sync_replicas", False,
                     "Use the sync_replicas (synchronized replicas) mode, "
                     "wherein the parameter updates from workers are aggregated "
                     "before applied to avoid stale gradients")
#flags.DEFINE_boolean(
#    "existing_servers", False, "Whether servers already exists. If True, "
#    "will use the worker hosts via their GRPC URLs (one client process "
#    "per worker host). Otherwise, will create an in-process TensorFlow "
#    "server.")
flags.DEFINE_string("ps_hosts","192.168.233.201:2222",
                    "Comma-separated list of hostname:port pairs")
flags.DEFINE_string("worker_hosts", "192.168.233.202:2223,192.168.233.203:2224",
                    "Comma-separated list of hostname:port pairs")
flags.DEFINE_string("job_name", None,"job name: worker or ps")
FLAGS = flags.FLAGS
IMAGE_PIXELS = 28
def main(unused_argv):
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
#  if FLAGS.download_only:
#    sys.exit(0)
  if FLAGS.job_name is None or FLAGS.job_name == "":
    raise ValueError("Must specify an explicit `job_name`")
  if FLAGS.task_index is None or FLAGS.task_index =="":
    raise ValueError("Must specify an explicit `task_index`")
  print("job name = %s" % FLAGS.job_name)
  print("task index = %d" % FLAGS.task_index)
  #Construct the cluster and start the server
  ps_spec = FLAGS.ps_hosts.split(",")
  worker_spec = FLAGS.worker_hosts.split(",")
  # Get the number of workers.
  num_workers = len(worker_spec)
  cluster = tf.train.ClusterSpec({
      "ps": ps_spec,
      "worker": worker_spec})
  #if not FLAGS.existing_servers:
    # Not using existing servers. Create an in-process server.
  server = tf.train.Server(
      cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index)
  if FLAGS.job_name == "ps":
    server.join()
  is_chief = (FLAGS.task_index == 0)

#  if FLAGS.num_gpus > 0:
#    if FLAGS.num_gpus < num_workers:
#      raise ValueError("number of gpus is less than number of workers")
#    # Avoid gpu allocation conflict: now allocate task_num -> #gpu 
#    # for each worker in the corresponding machine
#    gpu = (FLAGS.task_index % FLAGS.num_gpus)
#    worker_device = "/job:worker/task:%d/gpu:%d" % (FLAGS.task_index, gpu)
#  elif FLAGS.num_gpus == 0:
#    # Just allocate the CPU to worker server
#    cpu = 0
#    worker_device = "/job:worker/task:%d/cpu:%d" % (FLAGS.task_index, cpu)
#  # The device setter will automatically place Variables ops on separate
#  # parameter servers (ps). The non-Variable ops will be placed on the workers.
#  # The ps use CPU and workers use corresponding GPU

  worker_device = "/job:worker/task:%d/gpu:0" % FLAGS.task_index
  with tf.device(
      tf.train.replica_device_setter(
          worker_device=worker_device,
          ps_device="/job:ps/cpu:0",
          cluster=cluster)):
    global_step = tf.Variable(0, name="global_step", trainable=False)
    # Variables of the hidden layer
    hid_w = tf.Variable(
        tf.truncated_normal(
            [IMAGE_PIXELS * IMAGE_PIXELS, FLAGS.hidden_units],
            stddev=1.0 / IMAGE_PIXELS),
        name="hid_w")
    hid_b = tf.Variable(tf.zeros([FLAGS.hidden_units]), name="hid_b")
    # Variables of the softmax layer
    sm_w = tf.Variable(
        tf.truncated_normal(
            [FLAGS.hidden_units, 10],
            stddev=1.0 / math.sqrt(FLAGS.hidden_units)),
        name="sm_w")
    sm_b = tf.Variable(tf.zeros([10]), name="sm_b")
    # Ops: located on the worker specified with FLAGS.task_index
    x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS])
    y_ = tf.placeholder(tf.float32, [None, 10])
    hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)
    hid = tf.nn.relu(hid_lin)
    y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))
    cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
    opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
    if FLAGS.sync_replicas:
      if FLAGS.replicas_to_aggregate is None:
        replicas_to_aggregate = num_workers
      else:
        replicas_to_aggregate = FLAGS.replicas_to_aggregate
      opt = tf.train.SyncReplicasOptimizer(
          opt,
          replicas_to_aggregate=replicas_to_aggregate,
          total_num_replicas=num_workers,
          replica_id=FLAGS.task_index,
          name="mnist_sync_replicas")
    train_step = opt.minimize(cross_entropy, global_step=global_step)
    if FLAGS.sync_replicas and is_chief:
      # Initial token and chief queue runners required by the sync_replicas mode
      chief_queue_runner = opt.get_chief_queue_runner()
      init_tokens_op = opt.get_init_tokens_op()
    init_op = tf.global_variables_initializer()
    train_dir = tempfile.mkdtemp()
    sv = tf.train.Supervisor(
        is_chief=is_chief,
        logdir=train_dir,
        init_op=init_op,
        recovery_wait_secs=1,
        global_step=global_step)
    sess_config = tf.ConfigProto(
        allow_soft_placement=True,
        log_device_placement=False,
        device_filters=["/job:ps", "/job:worker/task:%d" % FLAGS.task_index])
    # The chief worker (task_index==0) session will prepare the session,
    # while the remaining workers will wait for the preparation to complete.
    if is_chief:
      print("Worker %d: Initializing session..." % FLAGS.task_index)
    else:
      print("Worker %d: Waiting for session to be initialized..." %
        FLAGS.task_index)
#    if FLAGS.existing_servers:
#      server_grpc_url = "grpc://" + worker_spec[FLAGS.task_index]
#      print("Using existing server at: %s" % server_grpc_url)
#
#      sess = sv.prepare_or_wait_for_session(server_grpc_url, config=sess_config)
#    else:
    sess = sv.prepare_or_wait_for_session(server.target,
                                        config=sess_config)
    print("Worker %d: Session initialization complete." % FLAGS.task_index)
    if FLAGS.sync_replicas and is_chief:
      # Chief worker will start the chief queue runner and call the init op
      print("Starting chief queue runner and running init_tokens_op")
      sv.start_queue_runners(sess, [chief_queue_runner])
      sess.run(init_tokens_op)
    # Perform training
    time_begin = time.time()
    print("Training begins @ %f" % time_begin)
    local_step = 0
    while True:
      # Training feed
      batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
      train_feed = {x: batch_xs, y_: batch_ys}
      _, step = sess.run([train_step, global_step], feed_dict=train_feed)
      local_step += 1
      now = time.time()
      print("%f: Worker %d: training step %d done (global step: %d)" %
            (now, FLAGS.task_index, local_step, step))
      if step >= FLAGS.train_steps:
        break
    time_end = time.time()
    print("Training ends @ %f" % time_end)
    training_time = time_end - time_begin
    print("Training elapsed time: %f s" % training_time)
    # Validation feed
    val_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
    val_xent = sess.run(cross_entropy, feed_dict=val_feed)
    print("After %d training step(s), validation cross entropy = %g" %
          (FLAGS.train_steps, val_xent))
if __name__ == "__main__":
  tf.app.run()

参考资料: 《TensorFlow实战》

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