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Commit ac8554fd authored by Maxence Naud's avatar Maxence Naud
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Upd FC, Pow, Sqrt implementation arguments

parent 1d32b0be
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2 merge requests!50version 0.2.0,!39Scheduler backprop
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include/aidge/backend/cpu/operator/FCImpl.hpp
+ 22
− 6
View file @ ac8554fd
......@@ -26,13 +26,29 @@ namespace Aidge {
// compute kernel registry for forward and backward
class FCImplForward_cpu : public Registrable<FCImplForward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
void(const FC_Op::Attrs &, const DimSize_t, const DimSize_t,
const void *, const void *, const void *, void *)> {};
std::tuple<DataType,
DataType,
DataType,
DataType>,
void(const FC_Op::Attrs&,
const DimSize_t,
const DimSize_t,
const void *,
const void *,
const void *,
void *)> {};
class FCImplBackward_cpu : public Registrable<FCImplBackward_cpu,
std::tuple<DataType, DataType, DataType, DataType>,
void(const FC_Op::Attrs &, const DimSize_t, const DimSize_t,
const void *, const void *, const void *, void *)> {};
std::tuple<DataType,
DataType,
DataType,
DataType>,
void(const FC_Op::Attrs&,
const DimSize_t,
const DimSize_t,
const void *,
const void *,
const void *,
void *)> {};
class FCImpl_cpu : public OperatorImpl {
public:
......
include/aidge/backend/cpu/operator/PowImpl.hpp
+ 1
− 0
View file @ ac8554fd
......@@ -41,6 +41,7 @@ public:
NbElts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
void forward() override;
void backward() override;
};
namespace {
......
src/operator/FCImpl.cpp
+ 53
− 10
View file @ ac8554fd
......@@ -24,16 +24,17 @@
void Aidge::FCImpl_cpu::forward()
{
assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(0)) && "missing input #0");
assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(1)) && "missing input #1");
assert(std::static_pointer_cast<Tensor>(mOp.getRawInput(2)) && "missing input #2");
const FC_Op& op_ = dynamic_cast<const FC_Op&>(mOp);
assert((op_.getInput(0)) && "missing input #0");
assert((op_.getInput(1)) && "missing input #1");
assert((op_.getInput(2)) && "missing input #2");
// Find the correct kernel type
const auto outputDataType = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType();
const auto outputDataType = op_.getOutput(0)->dataType();
const Registrar<FCImplForward_cpu>::registrar_key registrarKey = {
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(),
op_.getInput(0)->dataType(),
op_.getInput(1)->dataType(),
op_.getInput(2)->dataType(),
outputDataType};
Registrar<FCImplForward_cpu>::registrar_type kernelFunc;
......@@ -52,9 +53,9 @@ void Aidge::FCImpl_cpu::forward()
// 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 = std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->refCastFrom(input0Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
const auto& input1 = std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->refCastFrom(input1Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
const auto& input2 = std::static_pointer_cast<Tensor>(mOp.getRawInput(2))->refCastFrom(input2Fallback, *std::static_pointer_cast<Tensor>(mOp.getRawOutput(0)));
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)));
// Call kernel
const auto batchSize = (input0.dims().size() > 1) ? input0.dims()[0] : 1;
......@@ -64,3 +65,45 @@ void Aidge::FCImpl_cpu::forward()
input0.getImpl()->rawPtr(), input1.getImpl()->rawPtr(), input2.getImpl()->rawPtr(),
getCPUPtr(mOp.getRawOutput(0)));
}
// void Aidge::FCImpl_cpu::backward()
// {
// const FC_Op& op_ = dynamic_cast<const FC_Op&>(mOp);
// const auto& fc_grad = op_.getOutput(0)->grad();
// assert(fc_grad && "missing ouput #0 gradient");
// // Find the correct kernel type
// const Registrar<FCImplBackward_cpu>::registrar_key registrarKey = {
// op_.getInput(0)->grad()->dataType(),
// op_.getInput(1)->grad()->dataType(),
// op_.getInput(2)->grad()->dataType(),
// fc_grad->dataType()};
// Registrar<FCImplBackward_cpu>::registrar_type kernelFunc;
// if (Registrar<FCImplBackward_cpu>::exists(registrarKey)) {
// // One exists with the right inputs/output types
// kernelFunc = Registrar<FCImplBackward_cpu>::create(registrarKey);
// }
// else {
// // Otherwise, fallback to the kernel with all types matching output type
// kernelFunc = Registrar<FCImplBackward_cpu>::create({
// fc_grad->dataType(), fc_grad->dataType(), fc_grad->dataType(), fc_grad->dataType()});
// }
// // 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> 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)));
// // Call kernel
// const auto batchSize = (input0.dims().size() > 1) ? input0.dims()[0] : 1;
// kernelFunc(dynamic_cast<const FC_Op&>(mOp).getStaticAttributes(),
// batchSize,
// input0.size() / batchSize,
// input0.getImpl()->rawPtr(), input1.getImpl()->rawPtr(), input2.getImpl()->rawPtr(),
// getCPUPtr(mOp.getRawOutput(0)));
// }
src/operator/PowImpl.cpp
+ 22
− 0
View file @ ac8554fd
......@@ -48,3 +48,25 @@ void Aidge::PowImpl_cpu::forward() {
getCPUPtr(mOp.getRawInput(1)),
getCPUPtr(mOp.getRawOutput(0)));
}
void Aidge::PowImpl_cpu::backward() {
// Find the correct kernel type
const Pow_Op& op_ = dynamic_cast<const Pow_Op&>(mOp);
auto kernelFunc = Registrar<PowImplForward_cpu>::create({
op_.getOutput(0)->grad()->dataType(),
op_.getInput(0)->grad()->dataType(),
op_.getInput(1)->grad()->dataType()});
const std::vector<std::size_t> input0gradDims = getBroadcastedDims(op_.getInput(0)->grad()->dims(),
op_.getOutput(0)->grad()->dims());
const std::vector<std::size_t> input1gradDims = getBroadcastedDims(op_.getInput(1)->grad()->dims(),
op_.getOutput(0)->grad()->dims());
// Call kernel
kernelFunc(op_.getOutput(0)->grad()->dims(),
input0gradDims,
input1gradDims,
getCPUPtr(mOp.getRawOutput(0)),
getCPUPtr(mOp.getRawInput(0)),
getCPUPtr(mOp.getRawInput(1)));
}
\ No newline at end of file
src/operator/SqrtImpl.cpp
+ 9
− 8
View file @ ac8554fd
......@@ -45,17 +45,18 @@ void Aidge::SqrtImpl_cpu::forward() {
void Aidge::SqrtImpl_cpu::backward() {
// reversing in and out Data for backprop
std::shared_ptr<Tensor> in0 = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0));
std::shared_ptr<Tensor> out0 = std::static_pointer_cast<Tensor>(mOp.getRawInput(0));
AIDGE_ASSERT(out0, "missing output #0");
const Sqrt_Op& op_ = dynamic_cast<const Sqrt_Op&>(mOp);
std::shared_ptr<Tensor> out0grad = op_.getOutput(0)->grad();
std::shared_ptr<Tensor> in0grad = op_.getInput(0)->grad();
AIDGE_ASSERT(out0grad, "missing output #0");
// Find the correct kernel type
auto kernelFunc = Registrar<SqrtImplForward_cpu>::create({
in0->dataType(),
out0->dataType()});
out0grad->dataType(),
in0grad->dataType()});
// Call kernel
kernelFunc(in0->size(),
getCPUPtr(in0),
getCPUPtr(out0));
kernelFunc(out0grad->size(),
getCPUPtr(out0grad),
getCPUPtr(in0grad));
}
\ No newline at end of file
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