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Commit bf0cff39 authored by Maxence Naud's avatar Maxence Naud
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Fix - v0.3.1 

Merge branch 'fix' into 'main'

See merge request eclipse/aidge/aidge_backend_cpu!98
parents f1a9e9d3 e46aaa1b
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aidge_backend_cpu/unit_tests/test_scheduler.py
+ 1
− 1
View file @ bf0cff39
......@@ -17,12 +17,12 @@ class test_scheduler(unittest.TestCase):
input_node = aidge_core.Producer(aidge_core.Tensor(values), "Input")
relu = aidge_core.ReLU()
input_node.add_child(relu)
gv = aidge_core.GraphView()
gv.add(relu)
gv.add(input_node)
input_node.add_child(relu)
gv.set_datatype(aidge_core.dtype.int32)
gv.set_backend("cpu")
......
include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp
+ 26
− 16
View file @ bf0cff39
......@@ -38,7 +38,7 @@ namespace Aidge {
*/
template <class I, class W, class B, class O>
void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
const std::array<DimSize_t, 1>& /*dilationDims*/,
const std::array<DimSize_t, 1>& dilationDims,
const std::array<DimSize_t, 1>& kernelDims,
const std::array<DimSize_t, 3>& inputDims,
const void *input_,
......@@ -56,6 +56,8 @@ void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& stri
const std::size_t oxSize =
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - kernelDims[0] + strideDims[0]) /
static_cast<float>(strideDims[0])));
const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
......@@ -71,15 +73,17 @@ void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& stri
const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2];
const std::size_t wIndex = ch * kernelDims[0];
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 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;
const std::size_t oIndexFull = oIndex + ox;
const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
output[oIndexFull] += weights[wIndex + sx] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))];
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx*dilationDims[0]))];
}
}
}
......@@ -113,7 +117,7 @@ REGISTRAR(ConvDepthWiseImpl1D_cpu,
*/
template <class I, class W, class B, class O>
void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
const std::array<DimSize_t, 2>& /*dilationDims*/,
const std::array<DimSize_t, 2>& dilationDims,
const std::array<DimSize_t, 2>& kernelDims,
const std::array<DimSize_t, 4>& inputDims,
const void *input_,
......@@ -132,10 +136,12 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
const std::size_t oxSize =
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - kernelDims[0] + strideDims[0]) /
static_cast<float>(strideDims[0])));
const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
// output W size
const std::size_t oySize =
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - kernelDims[1] + strideDims[1]) /
static_cast<float>(strideDims[1])));
const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
......@@ -151,13 +157,17 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = ch * 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 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 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]);
......@@ -173,10 +183,10 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
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 < sxMax; ++sx) {
for (std::size_t sy = syMin; sy < syMax; ++sy) {
for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))*inputDims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(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]))];
}
}
}
......
include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
+ 29
− 19
View file @ bf0cff39
......@@ -40,7 +40,7 @@ namespace Aidge {
*/
template <class I, class W, class B, class O>
void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
const std::array<DimSize_t, 1>& /*dilationDims*/,
const std::array<DimSize_t, 1>& dilationDims,
const std::array<DimSize_t, 1>& kernelDims,
const std::array<DimSize_t, 3>& inputDims,
DimSize_t outChannels,
......@@ -57,8 +57,9 @@ void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
// output H size
const std::size_t oxSize =
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - kernelDims[0] + strideDims[0]) /
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - dilationDims[0]*(kernelDims[0] - 1) - 1 + strideDims[0]) /
static_cast<float>(strideDims[0])));
const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
......@@ -76,15 +77,17 @@ void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2];
const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0];
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 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;
const std::size_t oIndexFull = oIndex + ox;
const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
output[oIndexFull] += weights[wIndex + sx] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))];
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx*dilationDims[0]))];
}
}
}
......@@ -122,7 +125,7 @@ REGISTRAR(ConvImpl1D_cpu,
*/
template <class I, class W, class B, class O>
void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
const std::array<DimSize_t, 2>& /*dilationDims*/,
const std::array<DimSize_t, 2>& dilationDims,
const std::array<DimSize_t, 2>& kernelDims,
const std::array<DimSize_t, 4> &inputDims,
DimSize_t outChannels,
......@@ -139,12 +142,15 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
// output H size
const std::size_t oxSize =
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - kernelDims[0] + strideDims[0]) /
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - dilationDims[0]*(kernelDims[0] - 1) - 1 + strideDims[0]) /
static_cast<float>(strideDims[0])));
const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
// output W size
const std::size_t oySize =
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - kernelDims[1] + strideDims[1]) /
static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - dilationDims[1]*(kernelDims[1] - 1) - 1 + strideDims[1]) /
static_cast<float>(strideDims[1])));
const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
// TODO: kernel computation
// output (batch, outCh, Xout, Yout)
......@@ -162,13 +168,17 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
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 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 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]);
......@@ -184,10 +194,10 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
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 < sxMax; ++sx) {
for (std::size_t sy = syMin; sy < syMax; ++sy) {
for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] *
input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))*inputDims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(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]))];
}
}
}
......
unit_tests/data/Test_TensorImpl.cpp
+ 4
− 0
View file @ bf0cff39
......@@ -154,6 +154,10 @@ TEST_CASE("Test addition of Tensors","[TensorImpl][Add]") {
Tensor T4(T1->dims());
T4.setDataType(DataType::Float64);
REQUIRE_THROWS(*T0 + T4);
delete[] array0;
delete[] array1;
delete[] result;
}
}
......
unit_tests/operator/Test_MatMulImpl.cpp
+ 12
− 0
View file @ bf0cff39
......@@ -101,6 +101,10 @@ TEST_CASE("[cpu/operator] MatMul(forward)", "[MatMul][CPU]") {
duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
REQUIRE(approxEq<float>(*(op->getOutput(0)), *Tres));
delete[] bigArray1;
delete[] bigArray2;
delete[] res;
}
std::cout << "multiplications over time spent: " << totalComputation/duration.count() << std::endl;
std::cout << "total time: " << duration.count() << std::endl;
......@@ -165,6 +169,10 @@ TEST_CASE("[cpu/operator] MatMul(forward)", "[MatMul][CPU]") {
duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
REQUIRE(approxEq<float>(*(op->getOutput(0)), *Tres));
delete[] bigArray1;
delete[] bigArray2;
delete[] res;
}
std::cout << "multiplications over time spent: " << totalComputation/duration.count() << std::endl;
std::cout << "total time: " << duration.count() << std::endl;
......@@ -231,6 +239,10 @@ TEST_CASE("[cpu/operator] MatMul(forward)", "[MatMul][CPU]") {
end = std::chrono::system_clock::now();
duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
REQUIRE(approxEq<float>(*(op->getOutput(0)), *Tres));
delete[] bigArray1;
delete[] bigArray2;
delete[] res;
}
std::cout << "multiplications over time spent: " << totalComputation/duration.count() << std::endl;
std::cout << "total time: " << duration.count() << std::endl;
......
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