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TensorFlow：内部错误：Blas SGEMM 启动失败

tensorflowblas
76

当我运行sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})时，我收到了InternalError: Blas SGEMM launch failed的错误。以下是完整的错误和堆栈跟踪：

InternalErrorTraceback (most recent call last)
<ipython-input-9-a3261a02bdce> in <module>()
      1 batch_xs, batch_ys = mnist.train.next_batch(100)
----> 2 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    338     try:
    339       result = self._run(None, fetches, feed_dict, options_ptr,
--> 340                          run_metadata_ptr)
    341       if run_metadata:
    342         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
    562     try:
    563       results = self._do_run(handle, target_list, unique_fetches,
--> 564                              feed_dict_string, options, run_metadata)
    565     finally:
    566       # The movers are no longer used. Delete them.

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
    635     if handle is None:
    636       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
--> 637                            target_list, options, run_metadata)
    638     else:
    639       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
    657       # pylint: disable=protected-access
    658       raise errors._make_specific_exception(node_def, op, error_message,
--> 659                                             e.code)
    660       # pylint: enable=protected-access
    661 

InternalError: Blas SGEMM launch failed : a.shape=(100, 784), b.shape=(784, 10), m=100, n=10, k=784
     [[Node: MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_recv_Placeholder_0/_4, Variable/read)]]
Caused by op u'MatMul', defined at:
  File "/usr/lib/python2.7/runpy.py", line 162, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py", line 3, in <module>
    app.launch_new_instance()
  File "/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py", line 596, in launch_instance
    app.start()
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 442, in start
    ioloop.IOLoop.instance().start()
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 162, in start
    super(ZMQIOLoop, self).start()
  File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 883, in start
    handler_func(fd_obj, events)
  File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 276, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
    handler(stream, idents, msg)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 391, in execute_request
    user_expressions, allow_stdin)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py", line 199, in do_execute
    shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2723, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2825, in run_ast_nodes
    if self.run_code(code, result):
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2885, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-4-d7414c4b6213>", line 4, in <module>
    y = tf.nn.softmax(tf.matmul(x, W) + b)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1036, in matmul
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 911, in _mat_mul
    transpose_b=transpose_b, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2154, 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 1154, in __init__
    self._traceback = _extract_stack()

技术栈: EC2 g2.8xlarge 机器, Ubuntu 14.04

我怀疑这是GPU内存问题。我运行了从TensorFlow页面获取的mnist_softmax.py。在我的GTX950 PC（2GB vram）上，我遇到了这个错误。但在我的Quadro M2000M笔记本电脑（4GB vram）上，它可以正常运行。两个系统都使用Anaconda与Python 3.5和TensorFlow 1.0。 - mark jay
16个回答
108

虽然这个问题比较老，但可能对其他人有用。
尝试关闭在其他进程中处于活动状态的交互式会话（如果使用IPython笔记本，请重新启动内核）。这对我很有帮助！

此外，在实验期间，我使用以下代码来关闭此内核中的本地会话：

if 'session' in locals() and session is not None:
    print('Close interactive session')
    session.close()
请问您需要导入哪个库来使用“session”？ - Desperate Morty
10

我遇到了这个问题并通过设置allow_soft_placement=True和gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.3)来解决它，它们特别定义了GPU内存使用的比例。 我猜这有助于避免两个TensorFlow进程争夺GPU内存。

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
sess = tf.Session(config=tf.ConfigProto(
  allow_soft_placement=True, log_device_placement=True))
4
我的环境是Python 3.5、Tensorflow 0.12和Windows 10（没有Docker）。我在CPU和GPU上都训练神经网络。每当在GPU上训练时，我遇到相同的错误InternalError: Blas SGEMM launch failed。
我找不到这个错误发生的原因，但是我成功地通过避免tensorflow函数tensorflow.contrib.slim.one_hot_encoding()来在GPU上运行我的代码。相反，我在numpy中进行了one-hot编码操作（输入和输出变量）。
以下代码重现了错误和修复过程。它是一个最小化设置，用于使用梯度下降学习y = x ** 2函数。
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim

def test_one_hot_encoding_using_tf():

    # This function raises the "InternalError: Blas SGEMM launch failed" when run in the GPU

    # Initialize
    tf.reset_default_graph()
    input_size = 10
    output_size = 100
    input_holder = tf.placeholder(shape=[1], dtype=tf.int32, name='input')
    output_holder = tf.placeholder(shape=[1], dtype=tf.int32, name='output')

    # Define network
    input_oh = slim.one_hot_encoding(input_holder, input_size)
    output_oh = slim.one_hot_encoding(output_holder, output_size)
    W1 = tf.Variable(tf.random_uniform([input_size, output_size], 0, 0.01))
    output_v = tf.matmul(input_oh, W1)
    output_v = tf.reshape(output_v, [-1])

    # Define updates
    loss = tf.reduce_sum(tf.square(output_oh - output_v))
    trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
    update_model = trainer.minimize(loss)

    # Optimize
    init = tf.initialize_all_variables()
    steps = 1000

    # Force CPU/GPU
    config = tf.ConfigProto(
        # device_count={'GPU': 0}  # uncomment this line to force CPU
    )

    # Launch the tensorflow graph
    with tf.Session(config=config) as sess:
        sess.run(init)

        for step_i in range(steps):

            # Get sample
            x = np.random.randint(0, 10)
            y = np.power(x, 2).astype('int32')

            # Update
            _, l = sess.run([update_model, loss], feed_dict={input_holder: [x], output_holder: [y]})

        # Check model
        print('Final loss: %f' % l)

def test_one_hot_encoding_no_tf():

    # This function does not raise the "InternalError: Blas SGEMM launch failed" when run in the GPU

    def oh_encoding(label, num_classes):
        return np.identity(num_classes)[label:label + 1].astype('int32')

    # Initialize
    tf.reset_default_graph()
    input_size = 10
    output_size = 100
    input_holder = tf.placeholder(shape=[1, input_size], dtype=tf.float32, name='input')
    output_holder = tf.placeholder(shape=[1, output_size], dtype=tf.float32, name='output')

    # Define network
    W1 = tf.Variable(tf.random_uniform([input_size, output_size], 0, 0.01))
    output_v = tf.matmul(input_holder, W1)
    output_v = tf.reshape(output_v, [-1])

    # Define updates
    loss = tf.reduce_sum(tf.square(output_holder - output_v))
    trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
    update_model = trainer.minimize(loss)

    # Optimize
    init = tf.initialize_all_variables()
    steps = 1000

    # Force CPU/GPU
    config = tf.ConfigProto(
        # device_count={'GPU': 0}  # uncomment this line to force CPU
    )

    # Launch the tensorflow graph
    with tf.Session(config=config) as sess:
        sess.run(init)

        for step_i in range(steps):

            # Get sample
            x = np.random.randint(0, 10)
            y = np.power(x, 2).astype('int32')

            # One hot encoding
            x = oh_encoding(x, 10)
            y = oh_encoding(y, 100)

            # Update
            _, l = sess.run([update_model, loss], feed_dict={input_holder: x, output_holder: y})

        # Check model
        print('Final loss: %f' % l)
4

在运行Tensorflow分布式代码时，我遇到了这个错误。你是否检查过其中任何一个工作节点是否报告了CUDA_OUT_OF_MEMORY错误？如果是这种情况，则可能与您放置权重和偏差变量的位置有关。例如：

with tf.device("/job:paramserver/task:0/cpu:0"):
   W = weight_variable([input_units, num_hidden_units])       
   b = bias_variable([num_hidden_units])
3
也许你没有正确地释放GPU。如果你正在使用Linux系统，请尝试运行命令“ps -ef | grep python”来查看哪些任务正在使用GPU，然后结束这些任务。
2
在我的情况下，我打开了两个使用keras/tensorflow的python控制台。 当我关闭旧的控制台（前一天忘记关闭），一切都开始正常工作。 因此，最好检查一下，是否有多个控制台/进程占用了GPU。
2

我关闭了所有其他正在运行的Jupyter会话，问题得到了解决。我认为这是GPU内存问题。

1
在我的情况下，
首先，我运行
conda clean --all
清理tarballs和未使用的包。
然后，我重新启动IDE（在这种情况下是Pycharm），它可以正常工作。环境：anaconda python 3.6，windows 10 64位。我通过anaconda网站提供的命令安装了tensorflow-gpu。
1
对我来说，当我尝试运行多个tensorflow进程（例如2个），并且它们都需要访问GPU资源时，就会遇到这个问题。
一个简单的解决方案是确保每次只有一个tensorflow进程在运行。
更多细节请参见这里。
引用： 要明确的是，tensorflow将尝试（默认情况下）消耗所有可用的GPU。它不能与其他程序同时运行。如果这实际上是另一个问题，请随时重新打开。
1

2.0兼容性答案: 为了社区的利益，提供erko答案的2.0代码。

session = tf.compat.v1.Session()

if 'session' in locals() and session is not None:
    print('Close interactive session')
    session.close()
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