以下是我使用cuda-gdb检查过的内容：
下面是代码的相关部分：wavenet_server.cc m分配源，将数据从文件复制到源，然后调用make_wavenet。 wavenet_infer.cu调用MyWaveNet的构造函数并调用setEmbeddings。
wavenet_server.cc：
#include "wavenet_infer.h"
void readArrayFromBinary(void* array, size_t len, size_t num_bytes_per_elem, const char* file_name) {
FILE* file = fopen(file_name, "rb");
fread(array, num_bytes_per_elem, len, file);
fclose(file);
}
void setEmbeddingCurr(const char* fileName, size_t len) {
this->embedding_curr = (float*)malloc(sizeof(float) * len);
readArrayFromBinary((void*)this->embedding_curr, len, sizeof(float), fileName);
}
void setWavenet(void) {
this->wavenet = make_wavenet(this->num_samples,
this->batch_size,
this->embedding_prev,
this->embedding_curr,
this->num_layers,
this->max_dilation,
this->dilate_weights_prev,
this->dilate_weights_curr,
this->dilate_biases,
this->res_weights,
this->res_biases,
this->skip_weights,
this->skip_biases,
this->conv_out,
this->conv_end,
this->is_using_embed_tanh,
this->implementation);
}
wavenet_infer.cu：
#include "nv_wavenet.cuh"
typedef nvWavenetInfer<float,float, R, S, A> MyWaveNet;
void* make_wavenet(int sample_count,
int batch_size,
float* embedding_prev,
float* embedding_curr,
int num_layers,
int max_dilation,
float** in_layer_weights_prev,
float** in_layer_weights_curr,
float** in_layer_biases,
float** res_layer_weights,
float** res_layer_biases,
float** skip_layer_weights,
float** skip_layer_biases,
float* conv_out_weight,
float* conv_end_weight,
bool use_embed_tanh,
int implementation
) {
MyWaveNet* wavenet = new MyWaveNet(num_layers, max_dilation, batch_size, sample_count,
implementation, use_embed_tanh);
wavenet->setEmbeddings(embedding_prev, embedding_curr);
// We didn't use biases on our outputs
std::vector<float> dummy_bias_first(S, 0);
std::vector<float> dummy_bias_second(A, 0);
wavenet->setOutWeights(conv_out_weight,
dummy_bias_first.data(),
conv_end_weight,
dummy_bias_second.data());
for (int l = 0; l < num_layers; l++) {
wavenet->setLayerWeights(l, in_layer_weights_prev[l],
in_layer_weights_curr[l],
in_layer_biases[l],
res_layer_weights[l],
res_layer_biases[l],
skip_layer_weights[l],
skip_layer_biases[l]);
}
return (void*)wavenet;
}
nv_wavenet.cuh：
nvWavenetInfer (int numLayers, int maxDilation, int batchSize, int numSamples, int impl=0, bool tanhEmbed=true) : m_numLayers(numLayers), m_maxBatch(batchSize), m_maxSamples(numSamples), m_implementation((nvWavenetInfer::Implementation)impl), m_tanhEmbed(tanhEmbed) {
m_maxDilation = maxDilation;
/*
gpuErrChk(cudaMalloc(&m_yOut, numSamples*batchSize*sizeof(int))); // one-hot vector represented as single value indicating which value is set
gpuErrChk(cudaMemset(m_yOut, 0, numSamples*batchSize*sizeof(int)));
*/
gpuErrChk(cudaMalloc(&m_outputSelectors, numSamples*batchSize*sizeof(float)));
gpuErrChk(cudaMalloc(&m_embedPrev, A*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_embedCur, A*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Wprev, numLayers*2*R*R*sizeof(T_weight)));
gpuErrChk(cudaMalloc(&m_Wcur, numLayers*2*R*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Bh, numLayers*2*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Lh, numSamples*numLayers*batchSize*2*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Wres, numLayers*R*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Bres, numLayers*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Wskip, numLayers*S*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Bskip, numLayers*S*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_XtOut, numLayers*R*batchSize*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_skipOut, numLayers*S*batchSize*sizeof(T_data)));
// For now, just burn memory as though all layers had the maximum dilation value
gpuErrChk(cudaMalloc(&m_XtIn, (m_maxDilation+1)*(numLayers+1)*R*batchSize*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_hOut, numLayers*batchSize*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_aPrev, numLayers*batchSize*2*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_skipIn, numLayers*S*batchSize*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_skipOutFinalAccumulate, A*batchSize*S/R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_outAccumulate, A*batchSize*A/R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_yInPrev, batchSize*sizeof(int))); // one-hot vector represented as single value indicating which value is set
gpuErrChk(cudaMalloc(&m_yInCur, batchSize*sizeof(int))); // one-hot vector represented as single value indicating which value is set
gpuErrChk(cudaMalloc(&m_WskipOut, A*S*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_BskipOut, A*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Wout, A*A*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_Bout, A*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_skipOutFinal, A*batchSize*S/R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_out, A*batchSize*A/R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_p, A*batchSize*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_h, numLayers*batchSize*R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_hSample, numLayers*batchSize*sizeof(int)));
gpuErrChk(cudaMalloc(&m_ySample, batchSize*sizeof(int)));
if (impl == PERSISTENT) {
gpuErrChk(cudaMalloc(&m_skipOutFinalAccumulate, A*batchSize*S/R*sizeof(T_data)));
gpuErrChk(cudaMalloc(&m_outAccumulate, A*batchSize*A/R*sizeof(T_data)));
}
}
virtual void setEmbeddings (float* embedPrev, float* embedCur) {
setActivation(m_embedPrev, embedPrev, A*R);
setActivation(m_embedCur, embedCur, A*R);
}
void setActivation(float* dst, float* src, size_t size) {
gpuErrChk(cudaMemcpy(dst, src, size*sizeof(float), cudaMemcpyHostToDevice));
}
答案 0 :(得分：0)
原来不是cudaMemcpy。使用cuda-gdb检查设备全局内存时，无法执行：x/10fw float_array。它将给出不正确的值。要查看，请尝试以下操作：p ((@global float*) float_array)[0]@10