【6.5.2】tensorflow--多GPU并行

前两天,刚刚让tensorflow的程序跨节点运行,本来以为速度会有个线性增长,今天一看,原来我一个节点上就只用了一个GPU呀,尴尬。。。

多GPU并行可分为模型并行和数据并行两大类,下图展示的是数据并行,这也是我们经常用到的方法,而其中数据并行又可分为同步方式和异步方式两种,由于我们一般都会配置同样的显卡,因此这儿也选择了同步方式,也就是把数据分给不同的卡,等所有的GPU都计算完梯度后进行平均,最后再更新梯度。

初步认知多个GPU的例子,见: https://gist.github.com/j-min/69aae99be6f6acfadf2073817c2f61b0

整个流程说白了就3个过程:

  1. 将数据分别分配到不同的GPU
  2. 不同的GPU分开求loss, 然后对这个Loss求平均值,更新梯度 (train_op)
  3. batch数据的时候,考虑GPU的个数

代码:

import tensorflow as tf
import numpy as np
from tensorflow.contrib import slim
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/mnist/", one_hot=True)

num_gpus = 2   # gpu个数
num_steps = 1000
learning_rate = 0.001
batch_size = 1000
display_step = 10

num_input = 784
num_classes = 10

def conv_net_with_layers(x,is_training,dropout = 0.75):
    with tf.variable_scope("ConvNet", reuse=tf.AUTO_REUSE):
        x = tf.reshape(x, [-1, 28, 28, 1])
        x = tf.layers.conv2d(x, 12, 5, activation=tf.nn.relu)
        x = tf.layers.max_pooling2d(x, 2, 2)
        x = tf.layers.conv2d(x, 24, 3, activation=tf.nn.relu)
        x = tf.layers.max_pooling2d(x, 2, 2)
        x = tf.layers.flatten(x)
        x = tf.layers.dense(x, 100)
        x = tf.layers.dropout(x, rate=dropout, training=is_training)
        out = tf.layers.dense(x, 10)
        out = tf.nn.softmax(out) if not is_training else out
    return out

def conv_net(x,is_training):
    # "updates_collections": None is very import ,without will only get 0.10
    batch_norm_params = {"is_training": is_training, "decay": 0.9, "updates_collections": None}
    #,'variables_collections': [ tf.GraphKeys.TRAINABLE_VARIABLES ]
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        activation_fn=tf.nn.relu,
                        weights_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01),
                        weights_regularizer=slim.l2_regularizer(0.0005),
                        normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params):
        with tf.variable_scope("ConvNet",reuse=tf.AUTO_REUSE):
            x = tf.reshape(x, [-1, 28, 28, 1])
            net = slim.conv2d(x, 6, [5,5], scope="conv_1")
            net = slim.max_pool2d(net, [2, 2],scope="pool_1")
            net = slim.conv2d(net, 12, [5,5], scope="conv_2")
            net = slim.max_pool2d(net, [2, 2], scope="pool_2")
            net = slim.flatten(net, scope="flatten")
            net = slim.fully_connected(net, 100, scope="fc")
            net = slim.dropout(net,is_training=is_training)
            net = slim.fully_connected(net, num_classes, scope="prob", activation_fn=None,normalizer_fn=None)
            return net

 #tower_grads里面保存的形式是(第一个GPU上的梯度,第二个GPU上的梯度,...第N-1个GPU上的梯度),这里有一点需要注意的是zip(*),它的作用上把上面的那个列表转换成((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) 的形式,也就是以列访问的方式,取到的就是某个变量在不同GPU上的值。
def average_gradients(tower_grads):
    average_grads = []
    for grad_and_vars in zip(*tower_grads):
        grads = []
        for g, _ in grad_and_vars:
            expend_g = tf.expand_dims(g, 0)
            grads.append(expend_g)
        grad = tf.concat(grads, 0)
        grad = tf.reduce_mean(grad, 0)
        v = grad_and_vars[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads


def train():
    with tf.device("/cpu:0"):
        global_step=tf.train.get_or_create_global_step()
        tower_grads = []
        X = tf.placeholder(tf.float32, [None, num_input])
        Y = tf.placeholder(tf.float32, [None, num_classes])

        opt = tf.train.AdamOptimizer(learning_rate) # 定义learning_rate

        with tf.variable_scope(tf.get_variable_scope()):  # 由于我们多个GPU上共享同样的图,为了防止名字混乱,最好使用name_scope进行区分,
            for i in range(num_gpus):
                with tf.device("/gpu:%d" % i):
                    with tf.name_scope("tower_%d" % i):
                            _x = X[i * batch_size:(i + 1) * batch_size]  ## 将输入数据分别分配到不同gpu中
                            _y = Y[i * batch_size:(i + 1) * batch_size]
                            logits = conv_net(_x, True)

                            tf.get_variable_scope().reuse_variables() ##沿用已有的变量

                            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=_y, logits=logits))  ## 定义损失函数
                            grads = opt.compute_gradients(loss)  #使用当前gpu计算所有变量的梯度
                            tower_grads.append(grads)  # 我们需要有个列表tower_grads存储所有GPU上的梯度

                            if i == 0:
                                logits_test = conv_net(_x, False)
                                correct_prediction = tf.equal(tf.argmax(logits_test, 1), tf.argmax(_y, 1))
                                accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        grads = average_gradients(tower_grads)  #计算变量的平均梯度
        train_op = opt.apply_gradients(grads)  # 最后就是更新梯度了


        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            for step in range(1, num_steps + 1):
                batch_x, batch_y = mnist.train.next_batch(batch_size * num_gpus) # 注意,这里batch_size 乘以了num_gpus,因为输入的数据会被拆分到不同gpu中
                sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
                if step % 10 == 0 or step == 1:
                    loss_value, acc = sess.run([loss, accuracy], feed_dict={X: batch_x, Y: batch_y})
                    print("Step:" + str(step) + ":" + str(loss_value) + " " + str(acc))
            print("Done")
            print("Testing Accuracy:",
                  np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i + batch_size],
                                                         Y: mnist.test.labels[i:i + batch_size]}) for i in
                           range(0, len(mnist.test.images), batch_size)]))



def train_single():
    X = tf.placeholder(tf.float32, [None, num_input])
    Y = tf.placeholder(tf.float32, [None, num_classes])
    logits=conv_net(X,True)
    loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y,logits=logits))

    opt=tf.train.AdamOptimizer(learning_rate)
    train_op=opt.minimize(loss)  #反向传播,调参数

    logits_test=conv_net(X,False)
    correct_prediction = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for step in range(1,num_steps+1):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            sess.run(train_op,feed_dict={X:batch_x,Y:batch_y})

            if step%display_step==0 or step==1:
                loss_value,acc=sess.run([loss,accuracy],feed_dict={X:batch_x,Y:batch_y})
                print("Step:" + str(step) + ":" + str(loss_value) + " " + str(acc))
        print("Done")
        print("Testing Accuracy:",np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i + batch_size],
              Y: mnist.test.labels[i:i + batch_size]}) for i in
              range(0, len(mnist.test.images), batch_size)]))

if __name__ == "__main__":
    #train_single()
    train()

二、讨论

1. 如何查看GPU是否被调用

nvidia-smi

参考资料

个人公众号,比较懒,很少更新,可以在上面提问题,如果回复不及时,可发邮件给我: tiehan@sina.cn

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