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Commit 1ab9eb04 authored by Olivier Antoni's avatar Olivier Antoni
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Add backward functions for ReLU, Sigmoid and Tanh

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with 39 additions and 47 deletions
include/aidge/backend/cpu/operator/ReLUImpl.hpp
+ 1
− 1
View file @ 1ab9eb04
......@@ -30,7 +30,7 @@ class ReLUImplForward_cpu
: public Registrable<ReLUImplForward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
};
class ReLUImplBackward_cpu
: public Registrable<ReLUImplBackward_cpu, std::tuple<DataType, DataType, DataType, DataType>, void(const std::size_t, const void*, const void*, const void*, void*)> {
: public Registrable<ReLUImplBackward_cpu, std::tuple<DataType, DataType, DataType>, void(const std::size_t, const void*, const void*, void*)> {
};
class ReLUImpl_cpu : public OperatorImpl {
......
include/aidge/backend/cpu/operator/ReLUImpl_backward_kernels.hpp
+ 9
− 10
View file @ 1ab9eb04
......@@ -18,12 +18,11 @@
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
template <class I, class O, class GI, class GO>
template <class I, class GI, class GO>
void ReLUImpl_cpu_backward_kernel(const std::size_t inputLenght,
const void* input_, const void* output_, const void* grad_output_,
void* grad_input_) {
const void* input_, const void* grad_output_,
void* grad_input_) {
const I* input = static_cast<const I*>(input_);
//const O* output = static_cast<const O*>(output_);
const GO* grad_output = static_cast<const GO*>(grad_output_);
GI* grad_input = static_cast<GI*>(grad_input_);
for (std::size_t i = 0; i < inputLenght; ++i) {
......@@ -33,14 +32,14 @@ void ReLUImpl_cpu_backward_kernel(const std::size_t inputLenght,
namespace {
static Registrar<ReLUImplBackward_cpu> registrarReLUImplBackward_cpu_Float32(
{DataType::Float32, DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::ReLUImpl_cpu_backward_kernel<float, float, float, float>);
{DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::ReLUImpl_cpu_backward_kernel<float, float, float>);
static Registrar<ReLUImplBackward_cpu> registrarReLUImplBackward_cpu_Int32(
{DataType::Int32, DataType::Int32, DataType::Int32, DataType::Int32},
Aidge::ReLUImpl_cpu_backward_kernel<int, int, int, int>);
{DataType::Int32, DataType::Int32, DataType::Int32},
Aidge::ReLUImpl_cpu_backward_kernel<int, int, int>);
static Registrar<ReLUImplBackward_cpu> registrarReLUImplBackward_cpu_Float64(
{DataType::Float64, DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::ReLUImpl_cpu_backward_kernel<double, double, double, double>);
{DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::ReLUImpl_cpu_backward_kernel<double, double, double>);
} // namespace
} // namespace Aidge
......
include/aidge/backend/cpu/operator/SigmoidImpl.hpp
+ 1
− 1
View file @ 1ab9eb04
......@@ -28,7 +28,7 @@ class SigmoidImplForward_cpu
: public Registrable<SigmoidImplForward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
};
class SigmoidImplBackward_cpu
: public Registrable<SigmoidImplBackward_cpu, std::tuple<DataType, DataType, DataType, DataType>, void(const std::size_t, const void*, const void*, const void*, void*)> {
: public Registrable<SigmoidImplBackward_cpu, std::tuple<DataType, DataType, DataType>, void(const std::size_t, const void*, const void*, void*)> {
};
class SigmoidImpl_cpu : public OperatorImpl {
......
include/aidge/backend/cpu/operator/SigmoidImpl_backward_kernels.hpp
+ 7
− 8
View file @ 1ab9eb04
......@@ -18,11 +18,10 @@
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
template <class I, class O, class GI, class GO>
template <class O, class GI, class GO>
void SigmoidImpl_cpu_backward_kernel(const std::size_t inputLenght,
const void* input_, const void* output_, const void* grad_output_,
void* grad_input_) {
//const I* input = static_cast<const I*>(input_);
const void* output_, const void* grad_output_,
void* grad_input_) {
const O* output = static_cast<const O*>(output_);
const GO* grad_output = static_cast<const GO*>(grad_output_);
GI* grad_input = static_cast<GI*>(grad_input_);
......@@ -33,11 +32,11 @@ void SigmoidImpl_cpu_backward_kernel(const std::size_t inputLenght,
namespace {
static Registrar<SigmoidImplBackward_cpu> registrarSigmoidImplBackward_cpu_Float32(
{DataType::Float32, DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::SigmoidImpl_cpu_backward_kernel<float, float, float, float>);
{DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::SigmoidImpl_cpu_backward_kernel<float, float, float>);
static Registrar<SigmoidImplBackward_cpu> registrarSigmoidImplBackward_cpu_Float64(
{DataType::Float64, DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::SigmoidImpl_cpu_backward_kernel<double, double, double, double>);
{DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::SigmoidImpl_cpu_backward_kernel<double, double, double>);
} // namespace
} // namespace Aidge
......
include/aidge/backend/cpu/operator/TanhImpl.hpp
+ 1
− 1
View file @ 1ab9eb04
......@@ -28,7 +28,7 @@ class TanhImplForward_cpu
: public Registrable<TanhImplForward_cpu, std::tuple<DataType, DataType>, void(const std::size_t, const void*, void*)> {
};
class TanhImplBackward_cpu
: public Registrable<TanhImplBackward_cpu, std::tuple<DataType, DataType, DataType, DataType>, void(const std::size_t, const void*, const void*, const void*, void*)> {
: public Registrable<TanhImplBackward_cpu, std::tuple<DataType, DataType, DataType>, void(const std::size_t, const void*, const void*, void*)> {
};
class TanhImpl_cpu : public OperatorImpl {
......
include/aidge/backend/cpu/operator/TanhImpl_backward_kernels.hpp
+ 7
− 8
View file @ 1ab9eb04
......@@ -18,11 +18,10 @@
#include "aidge/utils/Registrar.hpp"
namespace Aidge {
template <class I, class O, class GI, class GO>
template <class O, class GI, class GO>
void TanhImpl_cpu_backward_kernel(const std::size_t inputLenght,
const void* input_, const void* output_, const void* grad_output_,
void* grad_input_) {
//const I* input = static_cast<const I*>(input_);
const void* output_, const void* grad_output_,
void* grad_input_) {
const O* output = static_cast<const O*>(output_);
const GO* grad_output = static_cast<const GO*>(grad_output_);
GI* grad_input = static_cast<GI*>(grad_input_);
......@@ -33,11 +32,11 @@ void TanhImpl_cpu_backward_kernel(const std::size_t inputLenght,
namespace {
static Registrar<TanhImplBackward_cpu> registrarTanhImplBackward_cpu_Float32(
{DataType::Float32, DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::TanhImpl_cpu_backward_kernel<float, float, float, float>);
{DataType::Float32, DataType::Float32, DataType::Float32},
Aidge::TanhImpl_cpu_backward_kernel<float, float, float>);
static Registrar<TanhImplBackward_cpu> registrarTanhImplBackward_cpu_Float64(
{DataType::Float64, DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::TanhImpl_cpu_backward_kernel<double, double, double, double>);
{DataType::Float64, DataType::Float64, DataType::Float64},
Aidge::TanhImpl_cpu_backward_kernel<double, double, double>);
} // namespace
} // namespace Aidge
......
src/operator/ReLUImpl.cpp
+ 5
− 6
View file @ 1ab9eb04
......@@ -46,20 +46,19 @@ void Aidge::ReLUImpl_cpu::forward() {
void Aidge::ReLUImpl_cpu::backward() {
const ReLU_Op& op_ = dynamic_cast<const ReLU_Op&>(mOp);
std::shared_ptr<Tensor> in0 = op_.getInput(0);
std::shared_ptr<Tensor> in0 = op_.getInput(0);
std::shared_ptr<Tensor> out0 = op_.getOutput(0);
std::shared_ptr<Tensor> gra_int0 = op_.getInput(0)->grad();
std::shared_ptr<Tensor> gra_out0 = op_.getOutput(0)->grad();
std::shared_ptr<Tensor> gra_out0 = op_.getOutput(0)->grad();
AIDGE_ASSERT(out0, "missing output #0 for current {} operator", op_.type());
// Find the correct kernel type
auto kernelFunc = Registrar<ReLUImplBackward_cpu>::create({
in0->dataType(),
out0->dataType(),
in0->dataType(),
gra_int0->dataType(),
gra_out0->dataType()
gra_out0->dataType()
});
// Call kernel
kernelFunc(gra_int0->size(), getCPUPtr(in0), getCPUPtr(out0), getCPUPtr(gra_out0), getCPUPtr(gra_int0));
kernelFunc(gra_int0->size(), getCPUPtr(in0), getCPUPtr(gra_out0), getCPUPtr(gra_int0));
}
src/operator/SigmoidImpl.cpp
+ 5
− 7
View file @ 1ab9eb04
......@@ -47,20 +47,18 @@ void Aidge::SigmoidImpl_cpu::forward() {
void Aidge::SigmoidImpl_cpu::backward() {
const Sigmoid_Op& op_ = dynamic_cast<const Sigmoid_Op&>(mOp);
std::shared_ptr<Tensor> in0 = op_.getInput(0);
std::shared_ptr<Tensor> out0 = op_.getOutput(0);
std::shared_ptr<Tensor> gra_int0 = op_.getInput(0)->grad();
std::shared_ptr<Tensor> gra_out0 = op_.getOutput(0)->grad();
std::shared_ptr<Tensor> gra_int0 = op_.getInput(0)->grad();
std::shared_ptr<Tensor> gra_out0 = op_.getOutput(0)->grad();
AIDGE_ASSERT(out0, "missing output #0 for current {} operator", op_.type());
// Find the correct kernel type
auto kernelFunc = Registrar<SigmoidImplBackward_cpu>::create({
in0->dataType(),
out0->dataType(),
gra_int0->dataType(),
gra_int0->dataType(),
gra_out0->dataType()
});
// Call kernel
kernelFunc(gra_int0->size(), getCPUPtr(in0), getCPUPtr(out0), getCPUPtr(gra_out0), getCPUPtr(gra_int0));
}
\ No newline at end of file
kernelFunc(gra_int0->size(), getCPUPtr(out0), getCPUPtr(gra_out0), getCPUPtr(gra_int0));
}
src/operator/TanhImpl.cpp
+ 3
− 5
View file @ 1ab9eb04
......@@ -47,21 +47,19 @@ void Aidge::TanhImpl_cpu::forward() {
void Aidge::TanhImpl_cpu::backward() {
const Tanh_Op& op_ = dynamic_cast<const Tanh_Op&>(mOp);
std::shared_ptr<Tensor> in0 = op_.getInput(0);
std::shared_ptr<Tensor> out0 = op_.getOutput(0);
std::shared_ptr<Tensor> gra_int0 = op_.getInput(0)->grad();
std::shared_ptr<Tensor> gra_out0 = op_.getOutput(0)->grad();
std::shared_ptr<Tensor> gra_out0 = op_.getOutput(0)->grad();
AIDGE_ASSERT(out0, "missing output #0 for current {} operator", op_.type());
// Find the correct kernel type
auto kernelFunc = Registrar<TanhImplBackward_cpu>::create({
in0->dataType(),
out0->dataType(),
gra_int0->dataType(),
gra_int0->dataType(),
gra_out0->dataType()
});
// Call kernel
kernelFunc(gra_int0->size(), getCPUPtr(in0), getCPUPtr(out0), getCPUPtr(gra_out0), getCPUPtr(gra_int0));
kernelFunc(gra_int0->size(), getCPUPtr(out0), getCPUPtr(gra_out0), getCPUPtr(gra_int0));
}
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