void ConvertToMKL(unsigned long long tensor_)
{
long long * tensor = (long long *)tensor_;
if (tensor[CPUPtr] == 0 )
{
printf("error to conver to MKL tensor!\n");
return;
}
if (tensor[MKLLayout] == 0) return;//do not need convert
if (!dnnLayoutCompare_F32((dnnLayout_t)tensor[MKLLayout],
(dnnLayout_t)tensor[CPULayout]))
{
dnnError_t err; dnnPrimitive_t cv;
CHECK_ERR( dnnConversionCreate_F32(&cv, (dnnLayout_t)tensor[CPULayout],
(dnnLayout_t)tensor[MKLLayout]), err );
CHECK_ERR( dnnConversionExecute_F32(cv, (float *)tensor[CPUPtr], (float *)tensor[MKLPtr]), err );
}
else
{
memcpy((void*)tensor[MKLPtr], (void*)tensor[CPUPtr], dnnLayoutGetMemorySize_F32((dnnLayout_t)tensor[MKLLayout]));
}
ERR_RETURN:
return;
}
//convert tensor in MKL layout back to Numpy NCHW layout
//if layout diff, do conversion, else, copy memory directly
void ConvertBack(unsigned long long tensor_, int N, int C, int H, int W)
{
long long * tensor = (long long *)tensor_;
if (tensor[CPUPtr] == 0 )
{
printf("error to converback tensor!\n");
return;
}
if (tensor[MKLLayout] == 0) return;//do not need convert
dnnError_t err;
size_t inSize[DIM4] = { W, H, C, N};
size_t inStride[DIM4] = { 1, W, W*H, W*H*C};
dnnLayout_t lt_NCHW = NULL, lt_CHWN = NULL;
float* newPtr = NULL;
CHECK_ERR( dnnLayoutCreate_F32(<_NCHW, DIM4, inSize, inStride), err );
if (!dnnLayoutCompare_F32((dnnLayout_t)tensor[MKLLayout], (dnnLayout_t)tensor[CPULayout]))
{
float* cpuPtr = (float *)tensor[CPUPtr];
float* mklPtr = (float *)tensor[MKLPtr];
if (!dnnLayoutCompare_F32((dnnLayout_t)tensor[MKLLayout], lt_NCHW))
{
dnnPrimitive_t cv;
CHECK_ERR( dnnConversionCreate_F32(&cv, (dnnLayout_t)tensor[MKLLayout],lt_NCHW), err );
newPtr = (float*)malloc(N*C*H*W*sizeof(float));
CHECK_ERR( dnnConversionExecute_F32(cv, mklPtr, newPtr), err );
mklPtr = newPtr;
}
mkl_somatcopy('r', 't', N, C*H*W, 1.0, mklPtr, C*H*W, cpuPtr, N);
}
else
{
long long grad_in_len = (long long)dnnLayoutGetMemorySize_F32((dnnLayout_t)tensor[MKLLayout]) ;
float * destPtr = (float*)tensor[CPUPtr];
float * srcPtr = (float*)tensor[MKLPtr];
#pragma omp parallel for
for (long long i = 0; i < grad_in_len/4; ++i)
{
destPtr[i] = srcPtr[i];
}
}
ERR_RETURN:
if (newPtr!=NULL)
{
free(newPtr);
}
}
void MaxPooling_bprop(
unsigned long long gradOutput, //input, N*outC*outH*outW
unsigned long long gradInput, //output result
unsigned long long dnnprimitives,
int initOK, const float beta)
{
dnnError_t err;
long long* primitives = (long long*)dnnprimitives;
if (initOK == 0)
{
Init_b((long long *)gradInput, (long long *)gradOutput, primitives);
}
//get resource
float* resPool[dnnResourceNumber] = {0};
float* OutPtr= GetPtr(gradOutput);
resPool[dnnResourceDiffSrc] = (float*)primitives[BUFFER_POOLING_BACKWARD_INPUT];
resPool[dnnResourceDiffDst] = OutPtr;
resPool[dnnResourceWorkspace] = (float*)primitives[BUFFER_POOLING_FORWARD_WORKSPACE];
//make conversion for gradeOut if necessary
dnnPrimitive_t cv_out_b = (dnnPrimitive_t)(primitives[CV_POOLING_BACKWARD_OUTPUT]);
if (cv_out_b)
{
float* buf_out_b = (float*)primitives[BUFFER_POOLING_BACKWARD_OUTPUT];
CHECK_ERR( dnnConversionExecute_F32(cv_out_b, OutPtr, buf_out_b), err );
resPool[dnnResourceDiffDst] = buf_out_b;
}
long long grad_in_len = (long long)dnnLayoutGetMemorySize_F32((dnnLayout_t)primitives[POOL_L_B_I]) ;
float * tempPtr = (float*)primitives[BUFFER_POOLING_BACKWARD_INPUT];
#pragma omp parallel for
for (long long i = 0; i < grad_in_len/4; ++i)
{
tempPtr[i] = 0;
}
CHECK_ERR( dnnExecute_F32((dnnPrimitive_t)primitives[POOLING_BACKWARD], (void**)resPool), err );
if(beta != 0.0)
{
//require to add previous delta
long long* ptr_gradInput = (long long*)gradInput;
float* pFirstBuf = GetPtr(gradInput);
dnnLayout_t layout_pre_delta = (dnnLayout_t)ptr_gradInput[MKLLayout];
if(layout_pre_delta == NULL) layout_pre_delta = (dnnLayout_t)primitives[POOL_L_I];
dnnLayout_t layout_add_delta = (dnnLayout_t)primitives[POOL_L_B_I];
float* temp_memory = NULL;
if (!dnnLayoutCompare_F32(layout_add_delta, layout_pre_delta))
{
CHECK_ERR( dnnAllocateBuffer_F32((void**)&temp_memory, layout_add_delta) , err );
dnnPrimitive_t cv = NULL;
CHECK_ERR( dnnConversionCreate_F32(&cv, layout_pre_delta, layout_add_delta), err );
CHECK_ERR( dnnConversionExecute_F32(cv, pFirstBuf, temp_memory), err );
pFirstBuf = temp_memory;
}
long len = (long long)dnnLayoutGetMemorySize_F32(layout_add_delta) / 4 ;
cblas_saxpy(len, 1.0, pFirstBuf, 1, (float*)primitives[BUFFER_POOLING_BACKWARD_INPUT], 1);
if (temp_memory != NULL)
dnnReleaseBuffer_F32(temp_memory);
}
((long long *)gradInput)[MKLLayout] = primitives[POOL_L_B_I];
((long long *)gradInput)[MKLPtr] = primitives[BUFFER_POOLING_BACKWARD_INPUT];
ERR_RETURN:
return;
}
static void THNN_(BatchNormalization_MKLDNN_init_forward)(
THLongTensor *primitives,
int N,
int inC,
int inH,
int inW,
double eps)
{
dnnError_t err;
dnnPrimitive_t bn_forward = NULL;
dnnPrimitive_t bn_backward = NULL;
dnnPrimitive_t bn_bwd_scaleshift = NULL;
size_t inputSize[dimension] = {inW,inH,inC,N};
size_t inputStrides[dimension] = { 1, inW, inH * inW, inC * inH * inW };
dnnLayout_t lt_user_input = NULL;
if(primitives->storage->data[BN_LAYOUT_INPUT] == 0)
{
CHECK_ERR( dnnLayoutCreate_F32(<_user_input, dimension, inputSize, inputStrides) , err );
#if CONVERSION_LOG
fprintf(stderr ,"MKLDNN BN get input layout FAIL......\n");
#endif
}
else{
lt_user_input = (dnnLayout_t)primitives->storage->data[BN_LAYOUT_INPUT];
#if CONVERSION_LOG
fprintf(stderr ,"MKLDNN BN get input layout OK\n");
#endif
}
CHECK_ERR( dnnBatchNormalizationCreateForward_F32(&bn_forward,NULL,lt_user_input,eps), err );
CHECK_ERR( dnnBatchNormalizationCreateBackwardData_F32(&bn_backward,NULL,lt_user_input,eps), err );
CHECK_ERR( dnnBatchNormalizationCreateBackwardScaleShift_F32(&bn_bwd_scaleshift,NULL,lt_user_input,eps), err );
dnnLayout_t lt_bn_forward_workspace,lt_bn_forward_scaleshift,lt_bn_forward_output,lt_bn_backward_input;
real * buffer_forward_workspace = NULL; real * buffer_forward_scaleshift = NULL;
real * buffer_forward_output = NULL; real * buffer_backward_input = NULL;
dnnLayoutCreateFromPrimitive_F32(<_bn_forward_workspace, bn_forward, dnnResourceWorkspace);
dnnLayoutCreateFromPrimitive_F32(<_bn_forward_output, bn_forward, dnnResourceDst);
dnnLayoutCreateFromPrimitive_F32(<_bn_forward_scaleshift, bn_forward, dnnResourceScaleShift);
dnnLayoutCreateFromPrimitive_F32(<_bn_backward_input, bn_backward, dnnResourceDiffSrc);
CHECK_ERR( dnnAllocateBuffer_F32((void**)(&buffer_forward_workspace), lt_bn_forward_workspace), err );
CHECK_ERR( dnnAllocateBuffer_F32((void**)(&buffer_forward_scaleshift), lt_bn_forward_scaleshift), err );
//CHECK_ERR( dnnAllocateBuffer_F32((void**)(&buffer_forward_output), lt_bn_forward_output), err );
//CHECK_ERR( dnnAllocateBuffer_F32((void**)(&buffer_backward_input), lt_bn_backward_input), err );
int size1 = dnnLayoutGetMemorySize_F32(lt_bn_forward_output);
int size2 = inW*inH*inC*N*4;
if(size1 == size2)
{
#if CONVERSION_LOG
fprintf(stderr ,"MKLDNN BN forward ouput layout match OK\n");
#endif
}
else
{
CHECK_ERR( dnnAllocateBuffer_F32((void**)(&buffer_forward_output), lt_bn_forward_output), err );
fprintf(stderr ,"MKLDNN BN forward ouput layout match FAIL, size1 = %d, size2 = %d \n", size1, size2);
}
size1 = dnnLayoutGetMemorySize_F32(lt_bn_backward_input);
if(size1 == size2)
{
#if CONVERSION_LOG
fprintf(stderr ,"MKLDNN MaxPooling bwddata input layout match OK\n");
#endif
}
else
{
CHECK_ERR( dnnAllocateBuffer_F32((void**)(&buffer_backward_input), lt_bn_backward_input), err );
fprintf(stderr ,"MKLDNN MaxPooling bwddata input layout match FAIL, size1 = %d, size2 = %d \n", size1, size2);
}
//save the dnnPrimitive to THTensor(long int array)
primitives->storage->data[BN_LAYOUT_FORWARD_OUTPUT] = (long long)lt_bn_forward_output;
primitives->storage->data[BN_LAYOUT_BACKWARD_INPUT] = (long long)lt_bn_backward_input;
primitives->storage->data[BN_FORWARD] = (long long)bn_forward;
primitives->storage->data[BN_BACKWARD] = (long long)bn_backward;
primitives->storage->data[BN_SCALESHIFT] = (long long)bn_bwd_scaleshift;
primitives->storage->data[BUFFER_BN_FORWARD_WORKSPACE] = (long long)buffer_forward_workspace;
primitives->storage->data[BUFFER_BN_FORWARD_SCALESHIFT] = (long long)buffer_forward_scaleshift;
primitives->storage->data[BUFFER_BN_FORWARD_OUTPUT] = (long long)buffer_forward_output;
primitives->storage->data[BUFFER_BN_BACKWARD_INPUT] = (long long)buffer_backward_input;
primitives->storage->data[BUFFER_BN_BACKWARD_WORKSPACE] = (long long)buffer_forward_workspace;
}
