From 3008390391bcf1149ecd64b3acc15f9415134b59 Mon Sep 17 00:00:00 2001
From: hrouis <houssemeddine.rouis92@gmail.com>
Date: Wed, 25 Sep 2024 11:30:04 +0200
Subject: [PATCH] delete unused header
---
.../cpu/operator/PowImpl_backward_kernels.hpp | 75 -------------------
1 file changed, 75 deletions(-)
delete mode 100644 include/aidge/backend/cpu/operator/PowImpl_backward_kernels.hpp
diff --git a/include/aidge/backend/cpu/operator/PowImpl_backward_kernels.hpp b/include/aidge/backend/cpu/operator/PowImpl_backward_kernels.hpp
deleted file mode 100644
index 01b985ff..00000000
--- a/include/aidge/backend/cpu/operator/PowImpl_backward_kernels.hpp
+++ /dev/null
@@ -1,75 +0,0 @@
-/********************************************************************************
- * 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_ */
--
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