Tensorflow: the bug of using multiple GPUs, related to tf.Variable pinned to CPU

Created on 9 May 2016  ·  3Comments  ·  Source: tensorflow/tensorflow

Environment info

Operating System: Ubuntu 14.04

Installed version of CUDA and cuDNN: 7.5 and 4.0.7
(please attach the output of ls -l /path/to/cuda/lib/libcud*):

If installed from sources, provide the commit hash: 4a4f2461533847dde239851ecebe5056088a828c

Steps to reproduce

Run the following code

import tensorflow as tf

def main():
    a = tf.Variable(1)
    init_a = tf.initialize_all_variables()
    with tf.Session() as sess:
        sess.run(init_a)

    with tf.device("/gpu:0"):
        b = tf.constant(2)
        init_b = tf.initialize_all_variables()
    with tf.Session() as sess:
        sess.run(init_b)

    with tf.device("/cpu:0"):
        c = tf.Variable(2)
        init_c = tf.initialize_all_variables()
    with tf.Session() as sess:
        sess.run(init_c)

    with tf.device("/gpu:0"):
        d = tf.Variable(2)
        init_d = tf.initialize_all_variables()
    with tf.Session() as sess:
        sess.run(init_d)

if __name__ == '__main__':
    main()

Logs or other output that would be helpful

(If logs are large, please upload as attachment).

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: 
name: GeForce GTX TITAN X
major: 5 minor: 2 memoryClockRate (GHz) 1.266
pciBusID 0000:05:00.0
Total memory: 12.00GiB
Free memory: 11.02GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 1 with properties: 
name: GeForce GTX 980
major: 5 minor: 2 memoryClockRate (GHz) 1.2785
pciBusID 0000:09:00.0
Total memory: 4.00GiB
Free memory: 3.91GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:59] cannot enable peer access from device ordinal 0 to device ordinal 1
I tensorflow/core/common_runtime/gpu/gpu_init.cc:59] cannot enable peer access from device ordinal 1 to device ordinal 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 1 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y N 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 1:   N Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:756] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:05:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:756] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 980, pci bus id: 0000:09:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:756] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:05:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:756] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 980, pci bus id: 0000:09:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:756] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:05:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:756] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 980, pci bus id: 0000:09:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:756] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:05:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:756] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 980, pci bus id: 0000:09:00.0)
Traceback (most recent call last):
  File "test_multi_gpu.py", line 30, in <module>
    main()
  File "test_multi_gpu.py", line 26, in main
    sess.run(init_d)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 332, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 572, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 652, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 672, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.InvalidArgumentError: Cannot assign a device to node 'Variable_2': Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available
     [[Node: Variable_2 = Variable[container="", dtype=DT_INT32, shape=[], shared_name="", _device="/device:GPU:0"]()]]
Caused by op u'Variable_2', defined at:
  File "test_multi_gpu.py", line 30, in <module>
    main()
  File "test_multi_gpu.py", line 23, in main
    d = tf.Variable(2)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 211, in __init__
    dtype=dtype)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 292, in _init_from_args
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/state_ops.py", line 139, in variable_op
    container=container, shared_name=shared_name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_state_ops.py", line 351, in _variable
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 693, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2177, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1161, in __init__
    self._traceback = _extract_stack()

I also noticed that the documentation for Using GPUs doesn't mentioned about tf.Variable, it only involves the tf.constant and tf.matmul.

OK, I found the documentation from [Convolutional Neural Networks](https://www.tensorflow.org/versions/r0.8/tutorials/deep_cnn/index.html),
quotes:

All variables are pinned to the CPU and accessed via tf.get_variable() in order to share them in a multi-GPU version. See how-to on Sharing Variables.

I want ask that since tf.Variables is pinned to CPU by tensorflow, could we fix this error? Do we need to looking very carefully to exclude the tf.Variable declaration outside the with tf.device('/gpu:xx') scope, or use netsted with tf.device(None) to handle it?

picture myme5261314
👍2

Most helpful comment

The high-level problem should be fixed by @vrv's ongoing work to improve device placement. (Making tf.Variable ignore tf.device() will not work, because many of our users, especially in distributed settings, use this to configure parameter servers.) In the short term, try using soft placement in your session constructor:

config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
    # ...
picture mrry on 9 May 2016
👍23

All 3 comments

So, there are some ops that are not valid for tf.device(), such as tf.nn.local_response_normalization(),
See the code below:

    with tf.device("/gpu:0"):
        d = tf.placeholder("float", shape=[100, 100, 100, 10])
        with tf.device(None):
            lrn1 = tf.nn.local_response_normalization(d, depth_radius=5, bias=1.0, alpha=1e-4, beta=0.75)
        lrn2 = tf.nn.local_response_normalization(d, depth_radius=5, bias=1.0, alpha=1e-4, beta=0.75)
        init_d = tf.initialize_all_variables()
    with tf.Session() as sess:
        sess.run(init_d)
        r = np.random.randn(100, 100, 100, 10)
        sess.run(lrn1, feed_dict={d: r}) #Run ok
        sess.run(lrn2, feed_dict={d: r}) # Error

The output is below:

Traceback (most recent call last):
  File "test_multi_gpu.py", line 44, in <module>
    main()
  File "test_multi_gpu.py", line 40, in main
    sess.run(lrn2, feed_dict={d: r})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 332, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 572, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 652, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 672, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.InvalidArgumentError: Cannot assign a device to node 'LRN_1': Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available
     [[Node: LRN_1 = LRN[alpha=0.0001, beta=0.75, bias=1, depth_radius=5, _device="/device:GPU:0"](Placeholder)]]
Caused by op u'LRN_1', defined at:
  File "test_multi_gpu.py", line 44, in <module>
    main()
  File "test_multi_gpu.py", line 34, in main
    lrn2 = tf.nn.local_response_normalization(d, depth_radius=5, bias=1.0, alpha=1e-4, beta=0.75)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 737, in lrn
    bias=bias, alpha=alpha, beta=beta, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 693, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2177, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1161, in __init__
    self._traceback = _extract_stack()

The reason of this error might be clear enough I think. There're some internal tf.Variable in the tf.nn.local_response_normalization which we couldn't use outside code to remain the computation node to specified gpu while excluding all the internal variables.

For now, I think tensorflow should do either of two things below:

  1. Make tf.Variable not influenced by the tf.device(). (This might be preferred.)
  2. List the ops out which needs to use tf.device(None) to help user finish their code, right?
myme5261314 picture myme5261314 on 9 May 2016
👍2

The high-level problem should be fixed by @vrv's ongoing work to improve device placement. (Making tf.Variable ignore tf.device() will not work, because many of our users, especially in distributed settings, use this to configure parameter servers.) In the short term, try using soft placement in your session constructor:

config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
    # ...
mrry picture mrry on 9 May 2016
👍23

Thanks for your suggestion, it seems using allow_soft_placement=True will fix the issue. As stated in #2292 , it's better to improve the corresponding document for user to know this.

myme5261314 picture myme5261314 on 11 May 2016
👍9
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