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Commit e7eec22f authored by Maxence Naud's avatar Maxence Naud Committed by Maxence Naud
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improve conv kernel speed

parent 2a745b7d
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1 merge request!101Upd 2D Conv[DepthWise] kernels
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include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
+ 23
− 28
View file @ e7eec22f
......@@ -157,57 +157,52 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
// input (batch, inCh, Xin, Yin)
// weight (outCh, inCh, kernelX, kernelY)
// does not take Dilation attribute into account
using signedsize = std::make_signed<std::size_t>::type;
const std::size_t outChannels_s = oxSize * oySize;
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 * oySize;
// If bias = nullptr, set B(0)
B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal);
std::fill(output, output+outChannels_s, biasVal);
for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0] * kernelDims[1];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
// const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]);
// const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
// const std::size_t sxMax = (static_cast<signedsize>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
const std::size_t sxMin = 0;
const std::size_t sxMax = dilated_kernel_x;
for (std::size_t oy = 0; oy < oySize; ++oy) {
// const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]);
// const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
// const std::size_t syMax = (static_cast<signedsize>(inputDims[3]) + dify) < 0 ? 0 : ((inputDims[3] + dify) > kernelDims[1] ? kernelDims[1] : inputDims[3] + dify);
const std::size_t syMin = 0;
const std::size_t syMax = dilated_kernel_y;
const std::size_t oIndexFull = oIndex + ox*oySize + oy;
const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
const signedsize iy = static_cast<signedsize>(oy * strideDims[1]);
if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
output[oIndexFull] += (weights[wIndex + 0*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
weights[wIndex + 0*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 0*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+0)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + 1*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
weights[wIndex + 1*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 1*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + 2*kernelDims[1] + 0] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+0)] +
const std::size_t oIndexFull = ox*oySize + oy;
const size_t ix = ox * strideDims[0];
const size_t iy = oy * strideDims[1];
if (kernelDims[0] == 3 && kernelDims[1] == 3 && dilationDims[0] == 1 && dilationDims[1] == 1) {
output[oIndexFull] += (weights[wIndex] * input[iIndex + static_cast<std::size_t>(ix)*inputDims[3] + static_cast<std::size_t>(iy)] +
weights[wIndex + 1] * input[iIndex + static_cast<std::size_t>(ix)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 2] * input[iIndex + static_cast<std::size_t>(ix)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + kernelDims[1]] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy)] +
weights[wIndex + kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+1)*inputDims[3] + static_cast<std::size_t>(iy+2)] +
weights[wIndex + 2*kernelDims[1]] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy)] +
weights[wIndex + 2*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
weights[wIndex + 2*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+2)]);
} else {
for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
for (std::size_t sx = 0; sx*dilationDims[0] < dilated_kernel_x; ++sx) {
for (std::size_t sy = 0; sy*dilationDims[1] < dilated_kernel_y; ++sy) {
output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx*dilationDims[0]))*inputDims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(sy*dilationDims[1]))];
input[iIndex + (ix + (sx*dilationDims[0]))*inputDims[3] + (iy + (sy*dilationDims[1]))];
}
}
}
}
}
}
output += outChannels_s;
}
}
}
// Kernels registration to implementation entry point
REGISTRAR(ConvImpl2D_cpu,
{{DataType::Any, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}},
......
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