Created by: Z-TAO

多GPU训练的时候提示错误：

F0221 14:00:17.320631 17204 hl_cuda_device.cc:566] Check failed: cudaSuccess == cudaStat (0 vs. 77) Cuda Error: an illegal memory access was encountered
F0221 14:00:17.320631 17206 hl_cuda_device.cc:566] Check failed: cudaSuccess == cudaStat (0 vs. 77) Cuda Error: an illegal memory access was encountered
*** Check failure stack trace: ***
    @           0x9ea800  google::LogMessage::Fail()
    @           0x9ea800  google::LogMessage::Fail()
    @           0x9ea75c  google::LogMessage::SendToLog()
    @           0x9ea75c  google::LogMessage::SendToLog()
    @           0x9ea0e0  google::LogMessage::Flush()
    @           0x9ea0e0  google::LogMessage::Flush()
    @           0x9ed187  google::LogMessageFatal::~LogMessageFatal()
    @           0x9ed187  google::LogMessageFatal::~LogMessageFatal()
    @           0x9b4437  hl_stream_synchronize()
    @           0x9b4437  hl_stream_synchronize()
    @           0x9c1007  hl_matrix_csr_mul_dense()
    @           0x61c79b  paddle::TrainerThread::valueDispatchThread()
    @           0x7c6cc7  paddle::GpuMatrix::mul()
    @           0x7ca5af  paddle::GpuMatrix::mul()
    @           0x6fa234  paddle::FullyConnectedLayer::forward()
    @           0x6411a4  paddle::NeuralNetwork::forward()
    @     0x7f60239508a0  execute_native_thread_routine
    @           0x61d161  paddle::TrainerThread::forward()
    @           0x61e35c  paddle::TrainerThread::computeThread()
    @     0x7f60248371c3  start_thread
    @     0x7f60239508a0  execute_native_thread_routine
    @     0x7f60230c112d  __clone
    @     0x7f60248371c3  start_thread
    @              (nil)  (unknown)

问题的具体表现形式是：

1. 多卡训练的时候，指定大batch，不太容易出现以上的错误，小batch很容易出现
2. 单卡的时候不出现这个问题

神经网络很简单，就是一个600000 * 256的fc layer。

trainer_config:

unit_word = data_layer(name='unit_words', size=word_dict_len)
rec_word = data_layer(name='recword', size=word_dict_len)
labels = data_layer(name='label', size=2)

unit_word_embedding = fc_layer(input = unit_word,
        size = 256,
        act = IdentityActivation(),
        bias_attr = False,
        param_attr = ParamAttr(name='_source_language_embedding', initial_mean=0, initial_std=0.01,sparse_update=True))

rec_word_embedding = fc_layer(input = rec_word,
        size = 256,
        act = IdentityActivation(),
        bias_attr = False,
        param_attr=ParamAttr(name='_source_language_embedding', initial_mean=0,initial_std=0.01,sparse_update=True))

output_embedding = fc_layer(input = [unit_word_embedding, rec_word_embedding],
        size = 256,
        act = TanhActivation(),
        bias_attr = True)
output_embedding2 = fc_layer(input = output_embedding,
        size = 64, 
        act = TanhActivation(),
        bias_attr = True)

final_output = fc_layer(input = output_embedding2,
        size = 2,
        act = SoftmaxActivation(),
        bias_attr = True)

cost = cross_entropy(input=final_output,
                             label=labels)

dataprovider:

def hook(settings, word_dict, **kwargs):
    settings.word_dict = word_dict
    settings.line_idx = 0
    #all inputs are integral and sequential type
    settings.slots = [
        sparse_vector(len(word_dict)),
        sparse_binary_vector(len(word_dict)),
        integer_value(2)
    ]