diff --git a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
index 720e331cafec570af73c355dda73ed85d435fa6c..9537e34a85aaf70597d04e0065807d4d69dedd22 100644
--- a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_forward_kernels.hpp
@@ -64,7 +64,7 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const ConvDepthWise_Op<2>::Attrs &at
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 * oySize;
- B biasVal = ((!std::get<3>(attrs)) && biases != nullptr) ? biases[ch] : B(0);
+ B biasVal = (biases != nullptr) ? biases[ch] : B(0);
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal);
const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
const std::size_t wIndex = ch * std::get<2>(attrs)[0] * std::get<2>(attrs)[1];
diff --git a/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
index 0f171d79ae425bb83b5f3b8f0b67d69434a85355..c2e5e4ca345bf145b0d02f1643dd48139f563c66 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl_forward_kernels.hpp
@@ -106,8 +106,8 @@ void ConvImpl2D_cpu_forward_kernel(const Conv_Op<2>::Attrs &attrs, const std::ar
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 NoBias or bias = nullptr, set B(0)
- B biasVal = ((!std::get<3>(attrs)) && biases != nullptr) ? biases[outCh] : B(0);
+ // If bias = nullptr, set B(0)
+ B biasVal = (biases != nullptr) ? biases[outCh] : B(0);
std::fill(output + oIndex, output+(oIndex+oxSize*oySize), biasVal);
for (std::size_t inCh = 0; inCh < inputDims[1]; ++inCh) {
const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
diff --git a/include/aidge/backend/cpu/operator/FCImpl.hpp b/include/aidge/backend/cpu/operator/FCImpl.hpp
index db5f76834411925f3356a42bfb4bfda7da600e8e..f9f97ffd097276704a97edf2de85ffd3a4b19697 100644
--- a/include/aidge/backend/cpu/operator/FCImpl.hpp
+++ b/include/aidge/backend/cpu/operator/FCImpl.hpp
@@ -30,8 +30,8 @@ class FCImplForward_cpu : public Registrable<FCImplForward_cpu,
DataType,
DataType,
DataType>,
- void(const FC_Op::Attrs&,
- const DimSize_t,
+ void(
+ const DimSize_t,
const DimSize_t,
const DimSize_t,
const void *,
@@ -43,8 +43,8 @@ class FCImplBackward_cpu : public Registrable<FCImplBackward_cpu,
DataType,
DataType,
DataType>,
- void(const FC_Op::Attrs&,
- const DimSize_t,
+ void(
+ const DimSize_t,
const DimSize_t,
const DimSize_t,
const void *,
diff --git a/include/aidge/backend/cpu/operator/FCImpl_backward_kernels.hpp b/include/aidge/backend/cpu/operator/FCImpl_backward_kernels.hpp
index 9dd91eb883902db907f5e5004dd0cf4db59bd6a2..c93a44d922dce2dc18df94bf903134ddadf5256f 100644
--- a/include/aidge/backend/cpu/operator/FCImpl_backward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/FCImpl_backward_kernels.hpp
@@ -19,8 +19,7 @@
namespace Aidge {
template <class I, class O, class W, class B>
-void FCImpl_cpu_backward_kernel(const FC_Op::Attrs& attrs,
- const DimSize_t batchSize,
+void FCImpl_cpu_backward_kernel(const DimSize_t batchSize,
const DimSize_t inputFeatureSize,
const DimSize_t outputFeatureSize,
const void* input_,
@@ -40,7 +39,7 @@ void FCImpl_cpu_backward_kernel(const FC_Op::Attrs& attrs,
// bias grad
- if (std::get<0>(attrs)) { // no bias
+ if (biasesGrad == nullptr) { // no bias
std::fill(biasesGrad, biasesGrad + outputFeatureSize, B(0));
} else {
for (std::size_t o = 0; o < outputFeatureSize; ++o) { // nb outputs
diff --git a/include/aidge/backend/cpu/operator/FCImpl_forward_kernels.hpp b/include/aidge/backend/cpu/operator/FCImpl_forward_kernels.hpp
index 2a1a86bac51106e23593a8dc5aa13d72511582b6..a82e8850e1575b52a786df2935efbd2cd93cf8f1 100644
--- a/include/aidge/backend/cpu/operator/FCImpl_forward_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/FCImpl_forward_kernels.hpp
@@ -83,7 +83,7 @@ namespace Aidge {
// }
template <class I, class W, class B, class O>
-void FCImpl_cpu_forward_kernel(const FC_Op::Attrs& attrs, const DimSize_t batchSize, const DimSize_t inputFeatureSize,
+void FCImpl_cpu_forward_kernel(const DimSize_t batchSize, const DimSize_t inputFeatureSize,
const DimSize_t outputFeatureSize,
const void* input_, const void* weights_, const void* biases_, void* output_) {
// FIXME: missing FC attributes as arguments
@@ -92,7 +92,7 @@ void FCImpl_cpu_forward_kernel(const FC_Op::Attrs& attrs, const DimSize_t batchS
const B* biases = static_cast<const B*>(biases_);
O* output = static_cast<O*>(output_);
- if (std::get<0>(attrs)) {
+ if (biases == nullptr) {
std::fill(output, output+(batchSize*outputFeatureSize), B(0));
}
else {
diff --git a/src/operator/ConvDepthWiseImpl.cpp b/src/operator/ConvDepthWiseImpl.cpp
index 5c8d2fe307c70bd7ee3f64e14735417f7ffb0c67..51677f05081f12dd48708f1537943e1799238cd7 100644
--- a/src/operator/ConvDepthWiseImpl.cpp
+++ b/src/operator/ConvDepthWiseImpl.cpp
@@ -28,23 +28,48 @@ Aidge::Elts_t Aidge::ConvDepthWiseImpl2D_cpu::getNbRequiredProtected(IOIndex_t /
}
void Aidge::ConvDepthWiseImpl2D_cpu::forward() {
+ const auto& opTensor = static_cast<const OperatorTensor&>(mOp);
+
assert(mOp.getRawInput(0) && "missing input #0");
assert(mOp.getRawInput(1) && "missing input #1");
assert(mOp.getRawInput(2) && "missing input #2");
- assert((std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->nbDims() == 4) && "support for 4-dimensions tensors only");
+ assert((std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->nbDims() == 4) && "support for 3-dimensions tensors only");
// Find the correct kernel type
- auto kernelFunc =
- Registrar<ConvDepthWiseImpl2DForward_cpu>::create({std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawInput(2))->dataType(),
- std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+ const auto outputDataType = opTensor.getOutput(0)->dataType();
+ const Registrar<ConvDepthWiseImpl2DForward_cpu>::registrar_key registrarKey = {
+ opTensor.getInput(0)->dataType(),
+ opTensor.getInput(1)->dataType(),
+ ((opTensor.getInput(2)) ? opTensor.getInput(2)->dataType() : opTensor.getInput(1)->dataType()),
+ outputDataType};
+
+ Registrar<ConvDepthWiseImpl2DForward_cpu>::registrar_type kernelFunc;
+ if (Registrar<ConvDepthWiseImpl2DForward_cpu>::exists(registrarKey)) {
+ // One exists with the right inputs/output types
+ kernelFunc = Registrar<ConvDepthWiseImpl2DForward_cpu>::create(registrarKey);
+ }
+ else {
+ // Otherwise, fallback to the kernel with all types matching output type
+ kernelFunc = Registrar<ConvDepthWiseImpl2DForward_cpu>::create({
+ outputDataType, outputDataType, outputDataType, outputDataType});
+ }
+
+ // Convert input data (no overhead if not needed!)
+ // TODO: right now, if needed, memory will be allocated/deallocated at each
+ // call to forward(). We might put the following shared_ptr as members of
+ // this class to avoid that.
+ std::shared_ptr<Tensor> input0Fallback, input1Fallback, input2Fallback;
+ const auto& input0 = opTensor.getInput(0)->refCastFrom(input0Fallback, *opTensor.getOutput(0));
+ const auto& input1 = opTensor.getInput(1)->refCastFrom(input1Fallback, *opTensor.getOutput(0));
+ const auto& input2 = (opTensor.getInput(2)) ? opTensor.getInput(2)->refCastFrom(input2Fallback, *opTensor.getOutput(0)) : Tensor();
// Call kernel
- kernelFunc(dynamic_cast<const ConvDepthWise_Op<2>&>(mOp).getStaticAttributes(), std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->template dims<4>(),
- getCPUPtr(mOp.getRawInput(0)),
- getCPUPtr(mOp.getRawInput(1)),
- getCPUPtr(mOp.getRawInput(2)),
- getCPUPtr(mOp.getRawOutput(0)));
+ kernelFunc(dynamic_cast<const ConvDepthWise_Op<2>&>(mOp).getStaticAttributes(), // Conv attributes
+ opTensor.getInput(0)->template dims<4>(), // input dimensions
+ input0.getImpl()->rawPtr(), // input
+ input1.getImpl()->rawPtr(), // weight
+ (opTensor.getInput(2)) ? input2.getImpl()->rawPtr() : nullptr, // bias
+ getCPUPtr(mOp.getRawOutput(0)) // output
+ );
}
diff --git a/src/operator/ConvImpl.cpp b/src/operator/ConvImpl.cpp
index 27e2882c8ee7ddcc60d3d8521802debdcf4b9eb4..b69bbc0725eeaac1e7aef4099d2bcc798bd7eb5d 100644
--- a/src/operator/ConvImpl.cpp
+++ b/src/operator/ConvImpl.cpp
@@ -40,7 +40,7 @@ void Aidge::ConvImpl2D_cpu::forward() {
const Registrar<ConvImpl2DForward_cpu>::registrar_key registrarKey = {
opTensor.getInput(0)->dataType(),
opTensor.getInput(1)->dataType(),
- opTensor.getInput(2)->dataType(),
+ ((opTensor.getInput(2)) ? opTensor.getInput(2)->dataType() : opTensor.getInput(1)->dataType()),
outputDataType};
Registrar<ConvImpl2DForward_cpu>::registrar_type kernelFunc;
@@ -61,7 +61,7 @@ void Aidge::ConvImpl2D_cpu::forward() {
std::shared_ptr<Tensor> input0Fallback, input1Fallback, input2Fallback;
const auto& input0 = opTensor.getInput(0)->refCastFrom(input0Fallback, *opTensor.getOutput(0));
const auto& input1 = opTensor.getInput(1)->refCastFrom(input1Fallback, *opTensor.getOutput(0));
- const auto& input2 = opTensor.getInput(2)->refCastFrom(input2Fallback, *opTensor.getOutput(0));
+ const auto& input2 = (opTensor.getInput(2)) ? opTensor.getInput(2)->refCastFrom(input2Fallback, *opTensor.getOutput(0)) : Tensor();
// Call kernel
kernelFunc(dynamic_cast<const Conv_Op<2>&>(mOp).getStaticAttributes(), // Conv attributes
@@ -69,7 +69,7 @@ void Aidge::ConvImpl2D_cpu::forward() {
dynamic_cast<const Conv_Op<2>&>(mOp).outChannels(), // outChannels
input0.getImpl()->rawPtr(), // input
input1.getImpl()->rawPtr(), // weight
- input2.getImpl()->rawPtr(), // bias
+ (opTensor.getInput(2)) ? input2.getImpl()->rawPtr() : nullptr, // bias
getCPUPtr(mOp.getRawOutput(0)) // output
);
}
diff --git a/src/operator/FCImpl.cpp b/src/operator/FCImpl.cpp
index 9ade584146cd14b22dfb7a0e31147f136dd5fc8a..ad3727f6ffd3c380f26a5b260f3b3be9ef3f574a 100644
--- a/src/operator/FCImpl.cpp
+++ b/src/operator/FCImpl.cpp
@@ -34,9 +34,9 @@ void Aidge::FCImpl_cpu::forward()
// Find the correct kernel type
const auto outputDataType = op_.getOutput(0)->dataType();
const Registrar<FCImplForward_cpu>::registrar_key registrarKey = {
- outputDataType,
- outputDataType,
- outputDataType,
+ op_.getInput(0)->dataType(),
+ op_.getInput(1)->dataType(),
+ ((op_.getInput(2)) ? op_.getInput(2)->dataType() : op_.getInput(1)->dataType()),
outputDataType};
Registrar<FCImplForward_cpu>::registrar_type kernelFunc;
@@ -57,15 +57,16 @@ void Aidge::FCImpl_cpu::forward()
std::shared_ptr<Tensor> input0Fallback, input1Fallback, input2Fallback;
const auto& input0 = op_.getInput(0)->refCastFrom(input0Fallback, *(op_.getOutput(0)));
const auto& input1 = op_.getInput(1)->refCastFrom(input1Fallback, *(op_.getOutput(0)));
- const auto& input2 = op_.getInput(2)->refCastFrom(input2Fallback, *(op_.getOutput(0)));
+ const auto& input2 = (op_.getInput(2)) ? op_.getInput(2)->refCastFrom(input2Fallback, *(op_.getOutput(0))) : Tensor();
// Call kernel
const auto batchSize = (input0.dims().size() > 1) ? input0.dims()[0] : 1;
- kernelFunc(dynamic_cast<const FC_Op&>(mOp).getStaticAttributes(),
- batchSize,
+ kernelFunc(batchSize,
input1.dims()[1], // nb input features
input1.dims()[0], // nb output features
- input0.getImpl()->rawPtr(), input1.getImpl()->rawPtr(), input2.getImpl()->rawPtr(),
+ input0.getImpl()->rawPtr(),
+ input1.getImpl()->rawPtr(),
+ (op_.getInput(2)) ? input2.getImpl()->rawPtr() : nullptr,
getCPUPtr(mOp.getRawOutput(0)));
}
@@ -81,9 +82,9 @@ void Aidge::FCImpl_cpu::backward()
// Find the correct kernel type
const Registrar<FCImplBackward_cpu>::registrar_key registrarKey = {
fc_grad->dataType(),
- op_.getInput(0)->grad()->dataType(),
op_.getInput(1)->grad()->dataType(),
- op_.getInput(2)->grad()->dataType()};
+ (op_.getInput(2)) ? op_.getInput(2)->grad()->dataType() : op_.getInput(1)->grad()->dataType(),
+ op_.getInput(0)->grad()->dataType()};
Registrar<FCImplBackward_cpu>::registrar_type kernelFunc;
if (Registrar<FCImplBackward_cpu>::exists(registrarKey)) {
@@ -103,12 +104,11 @@ void Aidge::FCImpl_cpu::backward()
std::shared_ptr<Tensor> input0gradFallback, input1gradFallback, input2gradFallback;
const auto& input0grad = op_.getInput(0)->grad()->refCastFrom(input0gradFallback, *(op_.getOutput(0)));
const auto& input1grad = op_.getInput(1)->grad()->refCastFrom(input1gradFallback, *(op_.getOutput(0)));
- const auto& input2grad = op_.getInput(2)->grad()->refCastFrom(input2gradFallback, *(op_.getOutput(0)));
+ const auto& input2grad = (op_.getInput(2)) ? op_.getInput(2)->grad()->refCastFrom(input2gradFallback, *(op_.getOutput(0))) : Tensor();
// Call kernel
const auto batchSize = (input0grad.dims().size() > 1) ? input0grad.dims()[0] : 1;
- kernelFunc(dynamic_cast<const FC_Op&>(mOp).getStaticAttributes(),
- batchSize,
+ kernelFunc(batchSize,
input1grad.dims()[1], // nb input features
input1grad.dims()[0], // nb output features
getCPUPtr(fc_grad),
@@ -116,5 +116,5 @@ void Aidge::FCImpl_cpu::backward()
getCPUPtr(mOp.getRawInput(1)),
input0grad.getImpl()->rawPtr(),
input1grad.getImpl()->rawPtr(),
- input2grad.getImpl()->rawPtr());
+ (op_.getInput(2)) ? input2grad.getImpl()->rawPtr() : nullptr);
}