Fix/PowBackwardKernel

Context

Fixes Pow Backward Kernel

Fixes #22 (closed)

Modified files

• PowImpl_backward_kernels.hpp, PowImpl.hpp, PowImpl.cpp and Test_PowImpl.cpp, add Pow Backward kernel and unit tests;

TODO

• Fix Pow Backward kernel

include/aidge/backend/cpu/operator/PowImpl_backward_kernels.hpp

-/********************************************************************************
- * Copyright (c) 2023 CEA-List
- *
- * This program and the accompanying materials are made available under the
- * terms of the Eclipse Public License 2.0 which is available at
- * http://www.eclipse.org/legal/epl-2.0.
- *
- * SPDX-License-Identifier: EPL-2.0
- *
- ********************************************************************************/
-
-#ifndef AIDGE_CPU_OPERATOR_POWIMPL_BACKWARD_KERNEL_H_
-#define AIDGE_CPU_OPERATOR_POWIMPL_BACKWARD_KERNEL_H_
-
-#include <cmath>
-#include <numeric>
-#include <vector>
-
-#include "aidge/backend/cpu/data/Broadcasting.hpp"
-#include "aidge/backend/cpu/operator/PowImpl.hpp"
-#include "aidge/utils/Registrar.hpp"
-
-
-namespace Aidge {
-template <class I1, class I2, class O>
-void PowImpl_cpu_backward_kernel(const std::vector<std::size_t>& input0Dims,
-                                const std::vector<std::size_t>& input1Dims,
-                                const std::vector<std::size_t>& outputDims,
-                                const void* input0_,
-                                const void* input1_,
-                                const void* gradOutput_,
-                                void* gradientInput0_,
-                                void* gradientInput1_) {
-	const I1* input0 = static_cast<const I1*>(input0_);
-	I1* grad0 = static_cast<I1*>(gradientInput0_);
-    const I2* input1 = static_cast<const I2*>(input1_);
-    I2* grad1 = static_cast<I2*>(gradientInput1_);
-    const O* gradOut = static_cast<const O*>(gradOutput_);
-
-    // Fill input grads with zeros
-	auto input0Elements = std::accumulate(input0Dims.cbegin(), input0Dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
-	std::fill(grad0, grad0 + input0Elements, I1(0));
-	auto input1Elements = std::accumulate(input1Dims.cbegin(), input1Dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
-	std::fill(grad1, grad1 + input1Elements, I1(0));
-
-	auto totalElements = std::accumulate(outputDims.cbegin(), outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
-    for (size_t i = 0; i < totalElements; ++i)
-    {
-        // Compute indexes in inputs 0 and 1 to support broadcasting
-        std::vector<std::size_t> indexes = getMultiDimIndices(outputDims, i);
-        std::size_t idx0 = getFlattenedIndex(input0Dims, indexes);
-        std::size_t idx1 = getFlattenedIndex(input1Dims, indexes);
-
-        // grad0 = grad_output * (input1 * pow(input0, (input1 -1)))
-        grad0[idx0] += gradOut[i]*input1[idx1]* std::pow(input0[idx0], input1[idx1]-1);
-
-        // grad1 = grad_output * (output * ln(input0))
-        grad1[idx1] += gradOut[i] * std::pow(input0[idx0], input1[idx1]) * std::log(input0[idx0]);
-    }
-}
-
-namespace {
-static Registrar<PowImplBackward_cpu> registrarPowImplBackward_cpu_Float32(
-        {DataType::Float32, DataType::Float32, DataType::Float32},
-        Aidge::PowImpl_cpu_backward_kernel<float, float, float>);
-static Registrar<PowImplBackward_cpu> registrarPowImplBackward_cpu_Int32(
-        {DataType::Int32, DataType::Int32, DataType::Int32},
-        Aidge::PowImpl_cpu_backward_kernel<int, int, int>);
-static Registrar<PowImplBackward_cpu> registrarPowImplBackward_cpu_Float64(
-        {DataType::Float64, DataType::Float64, DataType::Float64},
-        Aidge::PowImpl_cpu_backward_kernel<double, double, double>);
-}  // namespace
-}  // namespace Aidge
-
-#endif /* AIDGE_CPU_OPERATOR_POWIMPL_BACKWARD_KERNEL_H_ */