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Commit b57e889e authored by Olivier BICHLER's avatar Olivier BICHLER
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Added OpenMP

parent 84e1e2ad
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2 merge requests!166Update 0.5.0 -> 0.6.0,!158Added OpenMP
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with 83 additions and 32 deletions
CMakeLists.txt
+ 6
− 0
View file @ b57e889e
......@@ -64,6 +64,8 @@ if(NOT $ENV{AIDGE_INSTALL} STREQUAL "")
endif()
find_package(aidge_core REQUIRED)
find_package(OpenMP)
find_package(OpenSSL QUIET)
if(OpenSSL_FOUND)
message(STATUS "OpenSSL found: ${OPENSSL_VERSION}")
......@@ -86,6 +88,10 @@ target_link_libraries(${module_name}
_aidge_core # _ is added because we link the exported target and not the project
)
if(OpenMP_CXX_FOUND)
target_link_libraries(${module_name} PUBLIC OpenMP::OpenMP_CXX)
endif()
# Add definition _USE_MATH_DEFINES to enable math constant definitions from math.h/cmath.
if (WIN32)
target_compile_definitions(${module_name} PRIVATE _USE_MATH_DEFINES)
......
include/aidge/backend/cpu/operator/AvgPoolingImpl_kernels.hpp
+ 3
− 0
View file @ b57e889e
......@@ -76,6 +76,9 @@ void AvgPoolingImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideD
using signedsize = std::make_signed<std::size_t>::type;
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (dims[0] * dims[1] > 32)
#endif
for (std::size_t batch = 0; batch < dims[0]; ++batch) {
for (std::size_t ch = 0; ch < dims[1]; ++ch) {
const std::size_t oIndex = (ch + batch * dims[1]) * oxSize * oySize;
......
include/aidge/backend/cpu/operator/BatchNormImpl_kernels.hpp
+ 3
− 0
View file @ b57e889e
......@@ -53,6 +53,9 @@ void BatchNormImpl2D_cpu_forward_kernel(float epsilon, float momentum, const std
const DimSize_t featureMapSize = (dims.size() > 2) ? std::accumulate(dims.begin() + 2, dims.end(), 1, std::multiplies<DimSize_t>()) : 1;
if ((freeze == true) || (momentum == 0.0f)) {
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (nbBatch * nbChannels > 32)
#endif
for (std::size_t batch = 0; batch < nbBatch; ++batch) {
for (std::size_t ch = 0; ch < nbChannels; ++ch) {
const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
......
include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp
+ 22
− 13
View file @ b57e889e
......@@ -65,6 +65,9 @@ void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& stri
// weight (outCh, ch, kernelX, kernelY)
// does not take Dilation attribute into account
using signedsize = std::make_signed<std::size_t>::type;
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (inputDims[0] * inputDims[1] > 32)
#endif
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
const std::size_t oIndex = (ch + batch*inputDims[1]) * oxSize;
......@@ -152,16 +155,19 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
const std::size_t outChannels_s = oxSize * oySize;
if (dilated_kernel_x ==3 && dilated_kernel_y == 3) {
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (inputDims[0] * inputDims[1] > 32)
#endif
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
B biasVal = (biases != nullptr) ? biases[ch] : B(0);
std::size_t oIndex = (ch + batch*inputDims[1]) * outChannels_s;
std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = ch * 9;
if (strideDims[0] == 1 && strideDims[1]==1) {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex-=inputDims[3]) {
for (std::size_t ox = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex-=inputDims[3]) {
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] = biasVal + weights[wIndex+0]*input[iIndex+oy]+weights[wIndex+1]*input[iIndex+oy+1]+weights[wIndex+2]*input[iIndex+oy+2];
}
......@@ -175,7 +181,7 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
}
}
} else {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex+=(strideDims[0]-2)*inputDims[3]) {
for (std::size_t ox = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex+=(strideDims[0]-2)*inputDims[3]) {
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] = biasVal + weights[wIndex+0]*input[iIndex+oy*strideDims[1]]+weights[wIndex+1]*input[iIndex+oy*strideDims[1]+1]+weights[wIndex+2]*input[iIndex+oy*strideDims[1]+2];
}
......@@ -189,24 +195,25 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
}
}
}
output += outChannels_s;
}
}
} else if (dilated_kernel_x == 1 && dilated_kernel_y == 1) {
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (inputDims[0] * inputDims[1] > 32)
#endif
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
B biasVal = (biases != nullptr) ? biases[ch] : B(0);
std::size_t oIndex = (ch + batch*inputDims[1]) * outChannels_s;
std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = ch;
if (strideDims[0] == 1 && strideDims[1] == 1) {
for (std::size_t i = iIndex; i < iIndex + oxSize*oySize; ++i) {
output[i] = biasVal + weights[wIndex] * input[i];
output[oIndex + i] = biasVal + weights[wIndex] * input[i];
}
} else {
std::size_t oIndex = (ch + batch*inputDims[1]) * oxSize * oySize;
for (std::size_t ox = 0; ox < oxSize; ++ox, oIndex+=oySize, iIndex+=strideDims[0]*inputDims[3]) {
for (std::size_t oy = 0, iy = 0; oy < oySize; ++oy, iy+=strideDims[1]) {
output[oIndex + oy] = biasVal + weights[wIndex]*input[iIndex+iy];
......@@ -216,19 +223,22 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
}
}
} else {
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (inputDims[0] * inputDims[1] > 32)
#endif
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t ch = 0; ch < inputDims[1]; ++ch) {
B biasVal = (biases != nullptr) ? biases[ch] : B(0);
std::fill(output, output+outChannels_s, biasVal);
const std::size_t oIndex = (ch + batch*inputDims[1]) * outChannels_s;
const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = ch * kernelDims[0] * kernelDims[1];
B biasVal = (biases != nullptr) ? biases[ch] : B(0);
std::fill(output + oIndex, output + oIndex + outChannels_s, biasVal);
for (std::size_t ox = 0; ox < oxSize; ++ox) {
for (std::size_t oy = 0; oy < oySize; ++oy) {
const std::size_t oIndexFull = ox*oySize + oy;
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
const std::size_t ix = ox * strideDims[0];
const std::size_t iy = oy * strideDims[1];
......@@ -240,7 +250,6 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
}
}
}
output += outChannels_s;
}
}
}
......
include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
+ 30
− 13
View file @ b57e889e
......@@ -59,6 +59,9 @@ void ConvImpl1D_cpu_forward_kernel(const array<DimSize_t, 1> &strideDim,
const DimSize_t dilated_kernel_x = dilationDim[0] * (kernelDim[0] - 1) + 1;
using signedsize = std::make_signed<std::size_t>::type;
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (inputDims[0] * outChannels > 32)
#endif
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
const std::size_t oIndex = (outCh + batch * outChannels) * oxSize;
......@@ -478,18 +481,24 @@ void ConvImpl2D_cpu_forward_kernel(const array<DimSize_t, 2> &strideDims,
const std::size_t outChannels_s = oxSize * oySize;
if (dilated_kernel_x == 3 && dilated_kernel_y == 3) {
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (inputDims[0] * outChannels > 32)
#endif
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
std::size_t oIndex = (outCh + batch*inputDims[1]) * outChannels_s;
// If bias = nullptr, set B(0)
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output, output + outChannels_s, biasVal);
std::fill(output + oIndex, output + oIndex + outChannels_s, biasVal);
for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
oIndex = (outCh + batch*inputDims[1]) * outChannels_s;
std::size_t iIndex = (inCh + batch * inputDims[1]) *
inputDims[2] * inputDims[3];
const std::size_t wIndex =
(inCh + outCh * inputDims[1]) * 9;
if (strideDims[0] == 1 && strideDims[1] == 1) {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize;
for (std::size_t ox = 0; ox < oxSize;
++ox, oIndex += oySize, iIndex -= inputDims[3]) {
for (std::size_t oy = 0; oy < oySize; ++oy) {
output[oIndex + oy] +=
......@@ -519,7 +528,7 @@ void ConvImpl2D_cpu_forward_kernel(const array<DimSize_t, 2> &strideDims,
}
}
} else {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize; ++ox,
for (std::size_t ox = 0; ox < oxSize; ++ox,
oIndex += oySize,
iIndex += (strideDims[0] -
2) * inputDims[3]) {
......@@ -558,26 +567,30 @@ void ConvImpl2D_cpu_forward_kernel(const array<DimSize_t, 2> &strideDims,
}
}
}
output += outChannels_s;
}
}
} else if (dilated_kernel_x == 1 && dilated_kernel_y == 1) {
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (inputDims[0] * outChannels > 32)
#endif
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
std::size_t oIndex = (outCh + batch*inputDims[1]) * outChannels_s;
// If bias = nullptr, set B(0)
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output, output + outChannels_s, biasVal);
std::fill(output + oIndex, output + oIndex + outChannels_s, biasVal);
for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
oIndex = (outCh + batch*inputDims[1]) * outChannels_s;
std::size_t iIndex = (inCh + batch * inputDims[1]) *
inputDims[2] * inputDims[3];
const std::size_t wIndex = (inCh + outCh * inputDims[1]);
if (strideDims[0] == 1 && strideDims[1] == 1) {
for (std::size_t oIndex = 0; oIndex < oxSize * oySize;
++oIndex, ++iIndex) {
output[oIndex] += weights[wIndex] * input[iIndex];
for (std::size_t i = 0; i < outChannels_s; ++i) {
output[oIndex + i] += weights[wIndex] * input[iIndex + i];
}
} else {
for (std::size_t ox = 0, oIndex = 0; ox < oxSize;
for (std::size_t ox = 0; ox < oxSize;
++ox,
oIndex += oySize,
iIndex +=
......@@ -590,16 +603,21 @@ void ConvImpl2D_cpu_forward_kernel(const array<DimSize_t, 2> &strideDims,
}
}
}
output += outChannels_s;
}
}
} else {
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (inputDims[0] * outChannels > 32)
#endif
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
for (std::size_t outCh = 0; outCh < outChannels; ++outCh) {
std::size_t oIndex = (outCh + batch*inputDims[1]) * outChannels_s;
// If bias = nullptr, set B(0)
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output, output + outChannels_s, biasVal);
std::fill(output + oIndex, output + oIndex + outChannels_s, biasVal);
for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
oIndex = (outCh + batch*inputDims[1]) * outChannels_s;
std::size_t iIndex_channel =
(inCh + batch * inputDims[1]) * inputDims[2] *
inputDims[3];
......@@ -607,7 +625,7 @@ void ConvImpl2D_cpu_forward_kernel(const array<DimSize_t, 2> &strideDims,
kernelDims[0] * kernelDims[1];
// loop over each ouput line
for (std::size_t ox = 0, oIndex = 0; ox < oxSize;
for (std::size_t ox = 0; ox < oxSize;
++ox,
oIndex += oySize,
iIndex_channel +=
......@@ -633,7 +651,6 @@ void ConvImpl2D_cpu_forward_kernel(const array<DimSize_t, 2> &strideDims,
}
}
}
output += outChannels_s;
}
}
}
......
include/aidge/backend/cpu/operator/GlobalAveragePoolingImpl_kernels.hpp
+ 13
− 6
View file @ b57e889e
......@@ -63,18 +63,25 @@ void GlobalAveragePoolingImpl_cpu_forward_kernel(const std::shared_ptr<Tensor>&
using O = cpptype_t<DT_O>;
const I *input = static_cast<const I *>(inputTensor->getImpl()->rawPtr());
O *output = static_cast<O *>(output_);
const auto& dims = inputTensor->dims();
const DimSize_t strides_channels = inputTensor->strides()[1];
const auto& dims = inputTensor->dims();
DimSize_t nb_elems = std::accumulate(dims.begin(), dims.end(), std::size_t(1),
std::multiplies<std::size_t>());
const DimSize_t in_batch_nb_elems{nb_elems / dims[0]};
const DimSize_t in_channel_nb_elems{in_batch_nb_elems / dims[1]};
const DimSize_t out_batch_nb_elems{dims[1]};
// parse channel by channel and fill each output with the average of the
// values in the channel
std::size_t input_idx = 0;
std::size_t output_idx = 0;
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (dims[0] * dims[1] > 32)
#endif
for (DimSize_t batch = 0; batch < dims[0]; ++batch) {
for (DimSize_t channel = 0; channel < dims[1]; ++channel) {
output[output_idx++] = castFromFloat<O>(stableMean<I>(input + input_idx, strides_channels));
input_idx += strides_channels;
const I *filter_start = std::next(
input, (batch * in_batch_nb_elems) + (channel * in_channel_nb_elems));
output[batch * out_batch_nb_elems + channel] = castFromFloat<O>(stableMean<I>(filter_start, in_channel_nb_elems));
}
}
}
......
include/aidge/backend/cpu/operator/MaxPoolingImpl_kernels.hpp
+ 3
− 0
View file @ b57e889e
......@@ -66,6 +66,9 @@ void MaxPoolingImpl2D_cpu_forward_kernel(
using signedsize = std::make_signed<std::size_t>::type;
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (dims[0] * dims[1] > 32)
#endif
for (std::size_t batch = 0; batch < dims[0]; ++batch){
for (std::size_t channel = 0; channel < dims[1]; ++channel){
auto batchChannelIndex = (channel + batch * dims[1]);
......
include/aidge/backend/cpu/operator/SoftmaxImpl_kernels.hpp
+ 3
− 0
View file @ b57e889e
......@@ -37,6 +37,9 @@ void SoftmaxImpl_cpu_forward_kernel(std::size_t axisIdx, const std::vector<DimSi
preAxisElems *= inputDims[i];
}
#ifdef _OPENMP
#pragma omp parallel for collapse(2) if (preAxisElems * postAxisElems > 32)
#endif
for (std::size_t i = 0; i < preAxisElems; ++i) {
for (std::size_t j = 0; j < postAxisElems; ++j) {
I maxVal = input[i * inputDims[axisIdx] * postAxisElems + j];
......
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