TensorFlow多GPU并行

发表于 更新于 分类于 Waline：

概述

TensorFlow作为深度学习时代的“C语言”，值得好好学习一下。本文介绍TensorFlow的单机多卡数据并行加速，以mnist为例进行说明。

CNN模型

一个简单的识别mnist数据集的CNN模型，非常简单，如下：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
def get_weight_varible(name,shape):
    return tf.get_variable(name, shape=shape,
                       initializer=tf.contrib.layers.xavier_initializer())

def get_bias_varible(name,shape):
    return tf.get_variable(name, shape=shape,
                       initializer=tf.contrib.layers.xavier_initializer())

#filter_shape: [f_h, f_w, f_ic, f_oc]
def conv2d(layer_name, x, filter_shape):
    with tf.variable_scope(layer_name):
        w = get_weight_varible('w', filter_shape)
        b = get_bias_varible('b', filter_shape[-1])
        y = tf.nn.bias_add(tf.nn.conv2d(input=x, filter=w, strides=[1, 1, 1, 1], padding='SAME'), b)
        return y

def pool2d(layer_name, x):
    with tf.variable_scope(layer_name):
        y = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
        return y

#inp_shape: [N, L]
#out_shape: [N, L]
def fc(layer_name, x, inp_shape, out_shape):
    with tf.variable_scope(layer_name):
        inp_dim = inp_shape[-1]
        out_dim = out_shape[-1]
        y = tf.reshape(x, shape=inp_shape)
        w = get_weight_varible('w', [inp_dim, out_dim])
        b = get_bias_varible('b', [out_dim])
        y = tf.add(tf.matmul(y, w), b)
        return y

def build_model(x):
    y = tf.reshape(x,shape=[-1, 28, 28, 1])
    #layer 1
    y = conv2d('conv_1', y, [3, 3, 1, 8])
    y = pool2d('pool_1', y)
    #layer 2
    y = conv2d('conv_2', y, [3, 3, 8, 16])
    y = pool2d('pool_2', y)
    #layer fc
    y = fc('fc', y, [-1, 7*7*16], [-1, 10])
    return y

单GPU运行

非常简单，不多说，直接上代码：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
def single_gpu():
    batch_size = 128
    mnist = input_data.read_data_sets('/tmp/data/mnist',one_hot=True)

    tf.reset_default_graph()
    with tf.Session() as sess:
            print('build model...')
			x = tf.placeholder(tf.float32, [None, 784])
			y = tf.placeholder(tf.float32, [None, 10])
			pred = build_model(x)
			loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
			learning_rate = tf.placeholder(tf.float32, shape=[])
			train_op = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)            

            all_y = tf.reshape(y, [-1,10])
            all_pred = tf.reshape(pred, [-1,10])
            correct_pred = tf.equal(tf.argmax(all_y, 1), tf.argmax(all_pred, 1))
            accuracy = tf.reduce_mean(tf.cast(correct_pred, 'float'))

            print('run train op...')
            sess.run(tf.global_variables_initializer())
            lr = 0.01
            for epoch in range(2):
                start_time = time.time()
                total_batch = int(mnist.train.num_examples/batch_size)
                avg_loss = 0.0
                print('\n---------------------')
                print('Epoch:%d, lr:%.4f' % (epoch,lr))
                for batch_idx in range(total_batch):
                    batch_x,batch_y = mnist.train.next_batch(batch_size)
                    inp_dict = {}
                    inp_dict[learning_rate] = lr
                    inp_dict[x] = batch_x
                    inp_dict[y] = batch_y
                    _, _loss = sess.run([train_op, loss], inp_dict)
                    avg_loss += _loss
                avg_loss /= total_batch
                print('Train loss:%.4f' % (avg_loss))
                lr = max(lr * 0.7,0.00001)

                total_batch = int(mnist.validation.num_examples / batch_size)
                preds = None
                ys = None
                for batch_idx in range(total_batch):
                    batch_x,batch_y = mnist.validation.next_batch(batch_size)
                    inp_dict = {}
                    inp_dict[x] = batch_x
                    inp_dict[y] = batch_y
                    batch_pred,batch_y = sess.run([all_pred,all_y], inp_dict)
                    if preds is None:
                        preds = batch_pred
                    else:
                        preds = np.concatenate((preds, batch_pred), 0)
                    if ys is None:
                        ys = batch_y
                    else:
                        ys = np.concatenate((ys,batch_y),0)
                val_accuracy = sess.run([accuracy], {all_y:ys, all_pred:preds})[0]
                print('Val Accuracy: %0.4f%%' % (100.0 * val_accuracy))

                stop_time = time.time()
                elapsed_time = stop_time - start_time
                print('Cost time: ' + str(elapsed_time) + ' sec.')
            print('training done.')

多GPU运行

与单GPU相比，多GPU模型需要手动指定OP的device，各个device之间的变量共享，最后在CPU上综合loss与梯度、准确率等，CPU上应用梯度更新权值。
需要对TensorFlow的图计算依赖有一定理解，即需要获取目标数据时，TensorFlow只会计算该数据所以来的相关操作。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
def average_losses(loss):
    tf.add_to_collection('losses', loss)

    # Assemble all of the losses for the current tower only.
    losses = tf.get_collection('losses')

    # Calculate the total loss for the current tower.
    regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
    total_loss = tf.add_n(losses + regularization_losses, name='total_loss')

    # Compute the moving average of all individual losses and the total loss.
    loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
    loss_averages_op = loss_averages.apply(losses + [total_loss])

    with tf.control_dependencies([loss_averages_op]):
        total_loss = tf.identity(total_loss)
    return total_loss

def average_gradients(tower_grads):
    average_grads = []
    for grad_and_vars in zip(*tower_grads):
        # Note that each grad_and_vars looks like the following:
        #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
        grads = [g for g, _ in grad_and_vars]
        # Average over the 'tower' dimension.
        grad = tf.stack(grads, 0)
        grad = tf.reduce_mean(grad, 0)

        # Keep in mind that the Variables are redundant because they are shared
        # across towers. So .. we will just return the first tower's pointer to
        # the Variable.
        v = grad_and_vars[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads

def feed_all_gpu(inp_dict, models, payload_per_gpu, batch_x, batch_y):
    for i in range(len(models)):
        x, y, _, _, _ = models[i]
        start_pos = i * payload_per_gpu
        stop_pos = (i + 1) * payload_per_gpu
        inp_dict[x] = batch_x[start_pos:stop_pos]
        inp_dict[y] = batch_y[start_pos:stop_pos]
    return inp_dict
	
def multi_gpu(num_gpu):
    batch_size = 128 * num_gpu
    mnist = input_data.read_data_sets('/tmp/data/mnist',one_hot=True)

    tf.reset_default_graph()
    with tf.Session() as sess:
        with tf.device('/cpu:0'):
            learning_rate = tf.placeholder(tf.float32, shape=[])
            opt = tf.train.AdamOptimizer(learning_rate=learning_rate)

            print('build model...')
            print('build model on gpu tower...')
            models = []
            for gpu_id in range(num_gpu):
                with tf.device('/gpu:%d' % gpu_id):
                    print('tower:%d...'% gpu_id)
                    with tf.name_scope('tower_%d' % gpu_id):
                        with tf.variable_scope('cpu_variables', reuse=gpu_id>0):
                            x = tf.placeholder(tf.float32, [None, 784])
                            y = tf.placeholder(tf.float32, [None, 10])
                            pred = build_model(x)
                            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
                            grads = opt.compute_gradients(loss)
                            models.append((x,y,pred,loss,grads))
            print('build model on gpu tower done.')

            print('reduce model on cpu...')
            tower_x, tower_y, tower_preds, tower_losses, tower_grads = zip(*models)
            aver_loss_op = tf.reduce_mean(tower_losses)
            apply_gradient_op = opt.apply_gradients(average_gradients(tower_grads))

            all_y = tf.reshape(tf.stack(tower_y, 0), [-1,10])
            all_pred = tf.reshape(tf.stack(tower_preds, 0), [-1,10])
            correct_pred = tf.equal(tf.argmax(all_y, 1), tf.argmax(all_pred, 1))
            accuracy = tf.reduce_mean(tf.cast(correct_pred, 'float'))
            print('reduce model on cpu done.')

            print('run train op...')
            sess.run(tf.global_variables_initializer())
            lr = 0.01
            for epoch in range(2):
                start_time = time.time()
                payload_per_gpu = batch_size/num_gpu
                total_batch = int(mnist.train.num_examples/batch_size)
                avg_loss = 0.0
                print('\n---------------------')
                print('Epoch:%d, lr:%.4f' % (epoch,lr))
                for batch_idx in range(total_batch):
                    batch_x,batch_y = mnist.train.next_batch(batch_size)
                    inp_dict = {}
                    inp_dict[learning_rate] = lr
                    inp_dict = feed_all_gpu(inp_dict, models, payload_per_gpu, batch_x, batch_y)
                    _, _loss = sess.run([apply_gradient_op, aver_loss_op], inp_dict)
                    avg_loss += _loss
                avg_loss /= total_batch
                print('Train loss:%.4f' % (avg_loss))
                lr = max(lr * 0.7,0.00001)

                val_payload_per_gpu = batch_size / num_gpu
                total_batch = int(mnist.validation.num_examples / batch_size)
                preds = None
                ys = None
                for batch_idx in range(total_batch):
                    batch_x,batch_y = mnist.validation.next_batch(batch_size)
                    inp_dict = feed_all_gpu({}, models, val_payload_per_gpu, batch_x, batch_y)
                    batch_pred,batch_y = sess.run([all_pred,all_y], inp_dict)
                    if preds is None:
                        preds = batch_pred
                    else:
                        preds = np.concatenate((preds, batch_pred), 0)
                    if ys is None:
                        ys = batch_y
                    else:
                        ys = np.concatenate((ys,batch_y),0)
                val_accuracy = sess.run([accuracy], {all_y:ys, all_pred:preds})[0]
                print('Val Accuracy: %0.4f%%' % (100.0 * val_accuracy))

                stop_time = time.time()
                elapsed_time = stop_time-start_time
                print('Cost time: ' + str(elapsed_time) + ' sec.')
            print('training done.')

本文全部代码见：https://github.com/xylcbd/blogs_code/blob/master/tensorflow-mnist-multi-gpu/cnn.py