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Merge pull request #3163 from NHZlX/fix_conv_1x1

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ignore im2col if not necessary in conv 1 * 1
cblas_new
Zhaolong Xing 8 years ago committed by GitHub
parent 28db149187 fa10677aed
commit 94cee3d6a7
2 changed files with 90 additions and 48 deletions
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7
paddle/function/ConvOp.h
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@ -109,6 +109,13 @@ protected:
return filter[filter.ndims() - 1]; return filter[filter.ndims() - 1];
} }
// determine whether im2col needs to be performed
inline bool isNeedIm2col(const TensorShape& filter) const {
return !(getFilterHeight(filter) == 1 && getFilterWidth(filter) == 1 &&
strideH() == 1 && strideW() == 1 && paddingH() == 0 &&
paddingW() == 0);
}
std::vector<size_t> strides_; std::vector<size_t> strides_;
std::vector<size_t> paddings_; std::vector<size_t> paddings_;

131
paddle/function/GemmConvOp.cpp
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@ -66,16 +66,23 @@ public:
real* inputData = inputs[0].data<real>(); real* inputData = inputs[0].data<real>();
real* filterData = inputs[1].data<real>(); real* filterData = inputs[1].data<real>();
real* outputData = outputs[0].data<real>(); real* outputData = outputs[0].data<real>();
bool needIm2col = isNeedIm2col(filter);
TensorShape imShape = TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth}); TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements()); TensorShape colShape;
real* colData = reinterpret_cast<real*>(memory_->getBuf()); real* colData = NULL;
if (needIm2col) {
colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
colData = reinterpret_cast<real*>(memory_->getBuf());
}
Im2ColFunctor<kCFO, Device, real> im2col; Im2ColFunctor<kCFO, Device, real> im2col;
GemmFunctor<Device, real> gemm; GemmFunctor<Device, real> gemm;
@ -86,15 +93,18 @@ public:
for (size_t i = 0; i < batchSize; i++) { for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) { for (size_t g = 0; g < groups_; g++) {
im2col(inputData + g * inputOffset, if (needIm2col) {
imShape, im2col(inputData + g * inputOffset,
colData, imShape,
colShape, colData,
strideH(), colShape,
strideW(), strideH(),
paddingH(), strideW(),
paddingW()); paddingH(),
paddingW());
} else {
colData = inputData + g * inputOffset;
}
int M = outputChannels / groups_; int M = outputChannels / groups_;
int N = outputHeight * outputWidth; int N = outputHeight * outputWidth;
int K = inputChannels / groups_ * filterHeight * filterWidth; int K = inputChannels / groups_ * filterHeight * filterWidth;
@ -159,19 +169,27 @@ public:
real* outputGrad = inputs[0].data<real>(); real* outputGrad = inputs[0].data<real>();
real* filterData = inputs[1].data<real>(); real* filterData = inputs[1].data<real>();
real* inputGrad = outputs[0].data<real>(); real* inputGrad = outputs[0].data<real>();
bool needIm2col = isNeedIm2col(filter);
TensorShape imShape = TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth}); TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements()); TensorShape colShape;
real* colData = reinterpret_cast<real*>(memory_->getBuf()); real* colData = NULL;
if (needIm2col) {
colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
colData = reinterpret_cast<real*>(memory_->getBuf());
}
Col2ImFunctor<kCFO, Device, real> col2im; Col2ImFunctor<kCFO, Device, real> col2im;
GemmFunctor<Device, real> gemm; GemmFunctor<Device, real> gemm;
size_t inputOffset = imShape.getElements(); size_t inputOffset = imShape.getElements();
size_t outputOffset = size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth; (outputChannels / groups_) * outputHeight * outputWidth;
@ -182,6 +200,11 @@ public:
int K = outputChannels / groups_; int K = outputChannels / groups_;
int N = outputHeight * outputWidth; int N = outputHeight * outputWidth;
int M = inputChannels / groups_ * filterHeight * filterWidth; int M = inputChannels / groups_ * filterHeight * filterWidth;
real scale = 0.0f;
if (!needIm2col) {
colData = inputGrad + g * inputOffset;
scale = 1.0f;
}
gemm(CblasTrans, gemm(CblasTrans,
CblasNoTrans, CblasNoTrans,
M, M,
@ -192,17 +215,19 @@ public:
M, M,
outputGrad + g * outputOffset, outputGrad + g * outputOffset,
N, N,
0.0f, scale,
colData, colData,
N); N);
col2im(inputGrad + g * inputOffset, if (needIm2col) {
imShape, col2im(inputGrad + g * inputOffset,
colData, imShape,
colShape, colData,
strideH(), colShape,
strideW(), strideH(),
paddingH(), strideW(),
paddingW()); paddingH(),
paddingW());
}
} }
inputGrad += inputChannels * inputHeight * inputWidth; inputGrad += inputChannels * inputHeight * inputWidth;
outputGrad += outputChannels * outputHeight * outputWidth; outputGrad += outputChannels * outputHeight * outputWidth;
@ -255,16 +280,23 @@ public:
real* outputGrad = inputs[0].data<real>(); real* outputGrad = inputs[0].data<real>();
real* inputData = inputs[1].data<real>(); real* inputData = inputs[1].data<real>();
real* filterGrad = outputs[0].data<real>(); real* filterGrad = outputs[0].data<real>();
bool needIm2col = isNeedIm2col(filter);
TensorShape imShape = TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth}); TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements()); TensorShape colShape;
real* colData = reinterpret_cast<real*>(memory_->getBuf()); real* colData = NULL;
if (needIm2col) {
colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
colData = reinterpret_cast<real*>(memory_->getBuf());
}
Im2ColFunctor<kCFO, Device, real> im2col; Im2ColFunctor<kCFO, Device, real> im2col;
GemmFunctor<Device, real> gemm; GemmFunctor<Device, real> gemm;
@ -274,15 +306,18 @@ public:
size_t filterOffset = filter.getElements() / groups_; size_t filterOffset = filter.getElements() / groups_;
for (size_t i = 0; i < batchSize; i++) { for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) { for (size_t g = 0; g < groups_; g++) {
im2col(inputData + g * inputOffset, if (needIm2col) {
imShape, im2col(inputData + g * inputOffset,
colData, imShape,
colShape, colData,
strideH(), colShape,
strideW(), strideH(),
paddingH(), strideW(),
paddingW()); paddingH(),
paddingW());
} else {
colData = inputData + g * inputOffset;
}
int M = outputChannels / groups_; int M = outputChannels / groups_;
int K = outputHeight * outputWidth; int K = outputHeight * outputWidth;
int N = inputChannels / groups_ * filterHeight * filterWidth; int N = inputChannels / groups_ * filterHeight * filterWidth;

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