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tensorflow - 将 tf.map_fn 与多个 GPU 一起使用

问题描述

我正在尝试将我的单 GPU TensorFlow 代码扩展到多 GPU。我必须在 3 个自由度上工作，不幸的是我需要使用 tf.map_fn 来并行化第三个自由度。我尝试使用官方文档中显示的设备放置，但似乎无法使用tf.map_fn. 有没有办法tf.map_fn在多个 GPU 上运行？

这里的错误输出：

InvalidArgumentError (see above for traceback): Cannot assign a device for operation 'map_1/TensorArray_1': Could not satisfy explicit device specification '' because the node was colocated with a group of nodes that required incompatible device '/device:GPU:1'
Colocation Debug Info:
Colocation group had the following types and devices: 
TensorArrayGatherV3: GPU CPU 
Range: GPU CPU 
TensorArrayWriteV3: GPU CPU 
TensorArraySizeV3: GPU CPU 
MatMul: GPU CPU 
Enter: GPU CPU 
TensorArrayV3: GPU CPU 
Const: GPU CPU 

Colocation members and user-requested devices:
  map_1/TensorArrayStack/range/delta (Const) 
  map_1/TensorArrayStack/range/start (Const) 
  map_1/TensorArray_1 (TensorArrayV3) 
  map_1/while/TensorArrayWrite/TensorArrayWriteV3/Enter (Enter) /device:GPU:1
  map_1/TensorArrayStack/TensorArraySizeV3 (TensorArraySizeV3) 
  map_1/TensorArrayStack/range (Range) 
  map_1/TensorArrayStack/TensorArrayGatherV3 (TensorArrayGatherV3) 
  map_1/while/MatMul (MatMul) /device:GPU:1
  map_1/while/TensorArrayWrite/TensorArrayWriteV3 (TensorArrayWriteV3) /device:GPU:1

         [[Node: map_1/TensorArray_1 = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=true, tensor_array_name=""](map_1/TensorArray_1/size)]]

这里有一个简单的代码示例来重现它：

import tensorflow as tf
import numpy

rc = 1000

sess = tf.Session()

for deviceName in ['/cpu:0', '/device:GPU:0', '/device:GPU:1']:
        with tf.device(deviceName):
                matrices = tf.random_uniform([rc,rc,4],minval = 0, maxval = 1, dtype = tf.float32)

                def mult(i):
                        product = tf.matmul(matrices[:,:,i],matrices[:,:,i+1])
                        return product

                mul = tf.zeros([rc,rc,3], dtype = tf.float32)
                mul = tf.map_fn(mult, numpy.array([0,1,2]), dtype = tf.float32, parallel_iterations = 10)

m = sess.run(mul)

标签： tensorflowgpgpumulti-gpu

解决方案

您正在尝试做的事情可以通过批处理 matmul 来完成。考虑以下变化：

import tensorflow as tf
import numpy
import time
import numpy as np

rc = 1000

sess = tf.Session()

#compute on cpu for comparison later
vals = np.random.uniform(size=[rc,rc,4]).astype(np.float32)
mat1 = tf.identity(vals)
mat2 = tf.transpose(vals, [2, 0, 1])

#store mul in array so all are fetched in run call
muls = []
#I only have one GPU.
for deviceName in ['/cpu:0', '/device:GPU:0']:
    with tf.device(deviceName):

        def mult(i):
                product = tf.matmul(mat1[:,:,i],mat1[:,:,i+1])
                return product

        mul = tf.zeros([rc,rc,3], dtype = tf.float32)
    mul = tf.map_fn(mult, numpy.array([0,1,2]), dtype = tf.float32, parallel_iterations = 10)
    muls.append(mul)

#use transposed mat with a shift to matmul in one go
mul = tf.matmul(mat2[:-1], mat2[1:])

print(muls)
print(mul)

start = time.time()
m1 = sess.run(muls)
end = time.time()

print("muls:", end - start)

start = time.time()
m2 = sess.run(mul)
end = time.time()

print("mul:", end - start)

print(np.allclose(m1[0],m1[1]))
print(np.allclose(m1[0],m2))
print(np.allclose(m1[1],m2))

我的电脑上的结果是：

[<tf.Tensor 'map/TensorArrayStack/TensorArrayGatherV3:0' shape=(3, 1000, 1000) dtype=float32>, <tf.Tensor 'map_1/TensorArrayStack/TensorArrayGatherV3:0' shape=(3, 1000, 1000) dtype=float32>]
Tensor("MatMul:0", shape=(3, 1000, 1000), dtype=float32)
muls: 0.4262731075286865
mul: 0.3794088363647461
True
True
True

您很少希望将 CPU 与 GPU 同步使用，因为这将成为瓶颈。GPU 将等待 CPU 完成。如果你用 CPU 做任何事情，它应该与 GPU 异步，这样它们就可以全速运行。

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