include/aidge/backend/cpu/data/Broadcasting.hpp

+/********************************************************************************
+ * Copyright (c) 2024 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_DATA_BROADCASTING_H_
+#define AIDGE_CPU_DATA_BROADCASTING_H_
+
+#include <vector>
+
+namespace Aidge {
+
+// Function to broadCast an input dims vector into the same size as an outputDims vector
+
+    /**
+     * @brief  Broadcast an input dims vector into the same size as an outputDims vector
+     * @details The missing dimensions would be completed by 1
+     * @param outputDims The vector of dimensions to follow 
+     * @param dimsToBroadcast The vecotr of dimensions to braodcast
+     * @return std::vector<std::size_t> a broadcasted vector by addding 1 on the missing dimensions.
+     */
+    std::vector<std::size_t> getBroadcastedDims(const std::vector<std::size_t>& outputDims, const std::vector<std::size_t>& dimsToBroadcast);
+
+    /**
+     * @brief Get a vector of indexes along the dimensions vector from a flattened index
+     * @param dimensions The vector of dimensions we want the indexes on
+     * @param idx The flattened index
+     * @return std::vector<std::size_t> vector of indexes along dimensions.
+     */
+    std::vector<std::size_t> getMultiDimIndices(const std::vector<std::size_t>& dimensions, std::size_t idx);
+
+    // Function to get a flattened index from multi-dimensional indices
+    /**
+     * @brief Get a flattened index the dimensions vector from a given vector of indices on a broadcasted vector
+     * @param dimensions The vector of dimensions we want the flattened index on
+     * @param indices The vector of indices we want to flatten
+     * @return std::size_t The flattened index on the dimensions vector
+     */
+    std::size_t getFlattenedIndex(const std::vector<std::size_t>& dimensions, const std::vector<std::size_t>& indices);
+
+} // namespace Aidge
+
+#endif // AIDGE_CPU_DATA_BROADCASTING_H_
\ No newline at end of file

include/aidge/backend/cpu/data/Interpolation.hpp

+/********************************************************************************
+ * Copyright (c) 2024 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_DATA_INTERPOLATION_H_
+#define AIDGE_CPU_DATA_INTERPOLATION_H_
+
+#include <vector>
+
+#include <aidge/data/Interpolation.hpp>
+#include <aidge/utils/Types.h>
+
+namespace Aidge {
+class InterpolationCPU : public Interpolation {
+  public:
+    /*
+     * @brief Interpolates values given via input in given mode.
+     *
+     * Values are contiguously arranged in a "square" shape around the point to
+     * interpolate. Depending on interpolation mode.
+     * The point that will be interpolated is located right in the
+     * middle of all points.
+     * Immediate neighbours :
+     * 1D interp :     2D interp :
+     *                 . . . . . .
+     * . . 1 2 . .     . . . . . .
+     *                 . . 1 2 . .
+     *                 . . 3 4 . .
+     *                 . . . . . .
+     *                 . . . . . .
+     *
+     * 2 neighbours :
+     * 1D interp :         2D interp :
+     *                   .  .  .  .  .  .  . .
+     *                   .  .  .  .  .  .  . .
+     * . . 1 2 3 4 . .   .  .  1  2  3  4  . .
+     *                   .  .  5  6  7  8  . .
+     *                   .  .  9 10 11 12  . .
+     *                   .  . 13 14 15 16  . .
+     *                   .  .  .  .  .  .  . .
+     *                   .  .  .  .  .  .  . .
+     *
+     * @param[in] originalCoords: coord of the point to interpolate in the
+     * original picture. These coords are generated with
+     * Interpolation::untransformCoords(coordsInInterpolatedTensor)
+     * @param[in] points : points to interpolate, arranged in a vector of a
+     * pairs ((point_coord), value) :
+     * [[[X1, X2, ..., XN], Xval], ...., [[A1, A2, ..., AN],Aval]].
+     * With :
+     * - N: the number of dimensions.
+     * - A: the number of points of the grid to interpolate.
+     * - All coordinates expressed in originalTensor frame.
+     * @param[in] interpMode: interpolation mode
+     * @return interpolated value
+     */
+    template <typename T>
+    static T interpolate(const std::vector<float> &coordsToInterpolate,
+                         const std::set<Point<T>> &points,
+                         const Mode interpMode = Interpolation::Mode::Linear);
+
+    /**
+     * @brief performs linear interpolation on given points.
+     * @param[in] values: values to interpolate, since we only do an average of
+     * all values, their indexes isn't useful.
+     * @return interpolated value
+     */
+    template <typename T>
+    static T linear(const std::vector<float> &originalCoords,
+                    const std::set<Point<T>> &points);
+
+    /**
+     * @brief performs nearest interpolation on given points.
+     * @note it is a wrapper for linearRecurse() private method
+     * @param[in] coordsToInterpolate: coordinates to interpolate
+     * @param[in] points: points to interpolate
+     * @param[in] interpMode: interpolation method, must be a Nearest...
+     * otherwise function will throw an error.
+     * @return interpolated value
+     */
+    template <typename T>
+    static T nearest(const std::vector<float> &coordsToInterpolate,
+                     const std::set<Point<T>> &points,
+                     const Interpolation::Mode nearestMode);
+
+  private:
+    /**
+     * @brief actual linear interpolation function.
+     * will :
+     * - Split all points along each dimension depending of if their coords at
+     * idx alongDim are above or under coordsToInterpolate until they are
+     * 1-to-1.
+     * - Perform interpolation in 2 leftover points and return interpolated
+     * point to parent call with a set of size 1.
+     * - repeat until all dimensions have been interpolated.
+     * @param[in] coordsToInterpolate: coordinates to interpolate
+     * @param[in] points: points to interpolate
+     * @param[in] alongDim: discriminant on along which dimension are being
+     * segregated.
+     * @return
+     */
+    template <typename T>
+    static std::set<Interpolation::Point<T>>
+    linearRecurse(const std::vector<float> &coordsToInterpolate,
+                  const std::set<Point<T>> &points,
+                  const DimIdx_t alongDim = 0);
+};
+
+} // namespace Aidge
+
+#endif // AIDGE_CPU_DATA_INTERPOLATION_H_

include/aidge/aidge_backend_cpu.hpp → include/aidge/backend/cpu/operator/AbsImpl.hpp

@@ -9,19 +9,23 @@
  *
  ********************************************************************************/
 
-#ifndef AIDGE_CPU_IMPORTS_H_
-#define AIDGE_CPU_IMPORTS_H_
+#ifndef AIDGE_CPU_OPERATOR_ABSIMPL_H_
+#define AIDGE_CPU_OPERATOR_ABSIMPL_H_
 
-#include "aidge/data/TensorImpl.hpp"
-#include "aidge/operator/AddImpl.hpp"
-#include "aidge/operator/AvgPoolingImpl.hpp"
-#include "aidge/operator/BatchNormImpl.hpp"
-#include "aidge/operator/ConvDepthWiseImpl.hpp"
-#include "aidge/operator/ConvImpl.hpp"
-#include "aidge/operator/FCImpl.hpp"
-#include "aidge/operator/LeakyReLUImpl.hpp"
-#include "aidge/operator/ProducerImpl.hpp"
-#include "aidge/operator/ReLUImpl.hpp"
-#include "aidge/operator/SoftmaxImpl.hpp"
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/Abs.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include <memory>
+#include <vector>
 
-#endif /* AIDGE_CPU_IMPORTS_H_ */
\ No newline at end of file
+namespace Aidge {
+// Operator implementation entry point for the backend
+using AbsImpl_cpu = OperatorImpl_cpu<Abs_Op,
+    void(const std::size_t, const void*, void*)>;
+
+// Implementation entry point registration to Operator
+REGISTRAR(Abs_Op, "cpu", Aidge::AbsImpl_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ABSIMPL_H_ */

include/aidge/operator/ReLUImpl_forward_kernels.hpp → include/aidge/backend/cpu/operator/AbsImpl_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_RELUIMPL_FORWARD_KERNEL_H_
-#define AIDGE_CPU_OPERATOR_RELUIMPL_FORWARD_KERNEL_H_
-
-#include "aidge/utils/Registrar.hpp"
-
-#include "aidge/operator/ReLUImpl.hpp"
-
-namespace Aidge {
-template <class I, class O>
-void ReLUImpl_cpu_forward_kernel(std::size_t inputLenght,
-                                     const void* input_,
-                                     void* output_) {
-
-    const I* input = static_cast<const I*>(input_);
-    O* output = static_cast<O*>(output_);
-
-    for (std::size_t i = 0; i < inputLenght; ++i) {
-        output[i] = input[i] > 0 ? input[i] : 0;
-    }
-}
-
-namespace {
-static Registrar<ReLUImplForward_cpu> registrarReLUImplForward_cpu_Float32(
-        {DataType::Float32, DataType::Float32}, Aidge::ReLUImpl_cpu_forward_kernel<float, float>);
-static Registrar<ReLUImplForward_cpu> registrarReLUImplForward_cpu_Int32(
-        {DataType::Int32, DataType::Int32}, Aidge::ReLUImpl_cpu_forward_kernel<int, int>);
-static Registrar<ReLUImplForward_cpu> registrarReLUImplForward_cpu_Float64(
-        {DataType::Float64, DataType::Float64}, Aidge::ReLUImpl_cpu_forward_kernel<double, double>);
-}  // namespace
-}  // namespace Aidge
-
-#endif /* AIDGE_CPU_OPERATOR_RELUIMPL_FORWARD_KERNEL_H_ */
+/********************************************************************************
+ * 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_ABSIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_ABSIMPL_KERNELS_H_
+
+#include <cmath>
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/AbsImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void AbsImpl_cpu_forward_kernel(std::size_t inputLenght,
+                                     const void* input_,
+                                     void* output_) {
+
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+    for (std::size_t i = 0; i < inputLenght; ++i) {
+        output[i] = std::abs(input[i]);
+    }
+}
+
+// Kernels registration to implementation entry point
+REGISTRAR(AbsImpl_cpu,
+    {DataType::Float32},
+    {ProdConso::inPlaceModel, Aidge::AbsImpl_cpu_forward_kernel<float, float>, nullptr});
+REGISTRAR(AbsImpl_cpu,
+    {DataType::Float64},
+    {ProdConso::inPlaceModel, Aidge::AbsImpl_cpu_forward_kernel<double, double>, nullptr});
+REGISTRAR(AbsImpl_cpu,
+    {DataType::Int32},
+    {ProdConso::inPlaceModel, Aidge::AbsImpl_cpu_forward_kernel<std::int32_t, std::int32_t>, nullptr});
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ABSIMPL_KERNELS_H_ */

include/aidge/backend/cpu/operator/AddImpl.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_ADDIMPL_H_
+#define AIDGE_CPU_OPERATOR_ADDIMPL_H_
+
+#include <cstddef>  // std::size_t
+#include <memory>   // std::unique_ptr, std::make_unique
+#include <string>
+#include <vector>
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/Add.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+using AddImpl_cpu = OperatorImpl_cpu<Add_Op,
+    void(std::vector<std::size_t>, std::vector<std::size_t>, const std::vector<std::size_t>&, const void*, const void*, void*)>;
+
+// Implementation entry point registration to Operator
+REGISTRAR(Add_Op, "cpu", Aidge::AddImpl_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ADDIMPL_H_ */

include/aidge/backend/cpu/operator/AddImpl_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_ADDIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_ADDIMPL_KERNELS_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include <cstddef>  // std::size_t
+
+#include "aidge/backend/cpu/data/Broadcasting.hpp"
+#include "aidge/backend/cpu/operator/AddImpl.hpp"
+
+namespace Aidge {
+
+namespace {
+// suppose values are contiguous in memory
+template <class I, class O>
+void add_contiguous_arrays(const std::size_t input1size,
+                            const std::size_t input2size,
+                            const std::size_t output1size,
+                            const I* input1,
+                            const I* input2,
+                            O* output)
+{
+    for (std::size_t i = 0; i < output1size; ++i)
+    {
+        const std::size_t in1_id = (input1size != 1) ? i : 0;
+        const std::size_t in2_id = (input2size != 1) ? i : 0;
+        output[i] = static_cast<O>(input1[in1_id] + input2[in2_id]);
+    }
+}
+}
+
+template <class I, class O>
+void AddImpl_cpu_forward_kernel(std::vector<std::size_t> dims0,
+                                std::vector<std::size_t> dims1,
+                                const std::vector<std::size_t>& outputDims,
+                                const void* input0_,
+                                const void* input1_,
+                                void* output_) {
+
+    const I* input_0 = static_cast<const I*>(input0_);
+    const I* input_1 = static_cast<const I*>(input1_);
+    O* output = static_cast<O*>(output_);
+
+    // [5,2,1,7] & [2,6,7]
+    // 1. Same number of dimensions -> [5,2,1,7] & [1,2,6,7]
+    // 2. Find the highest equal dimension -> 3
+    //    Exception: if the first diverging dimension is the last one, then -> 4 (dims.size())
+    // 3. Compute the highest number of contiguous data -> 7
+    // 4. Compute stride and offset step for the broadcast mechanism
+    // 5. Call a simple kernel
+
+    // special case for equal dimensions, the kernel is called with the entire arrays at once
+    if (dims0 == dims1) {
+        const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin(), dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+        for (std::size_t i = 0; i < input0_contiguous_size; ++i)
+        {
+            output[i] = static_cast<O>(input_0[i] + input_1[i]);
+        }
+        return;
+    }
+
+    // set dimensions to be of equal size by filling the smallest one with ones.
+    if (dims0.size() > dims1.size()) {
+        dims1.insert(dims1.cbegin(), dims0.size() - dims1.size(), std::size_t(1));
+    }
+    else if (dims1.size() > dims0.size()) {
+        dims0.insert(dims0.cbegin(), dims1.size() - dims0.size(), std::size_t(1));
+    }
+
+    const std::size_t nbDims = dims0.size();
+
+    // Find the highest equal dimension
+    // std::size_t contiguousIdx = nbDims - 1;
+    std::size_t contiguousIdx = nbDims;
+    while (contiguousIdx-- > 0) {
+    // for (; contiguousIdx+1 > 0; --contiguousIdx) {
+        if (dims0[contiguousIdx] != dims1[contiguousIdx]) {
+            if (contiguousIdx == (nbDims -1)) { // last dimensions of one of the input Tensor are of size 1
+                const std::vector<std::size_t>& dims = (dims0[contiguousIdx] == 1) ? dims0 : dims1;
+                while ((contiguousIdx+1 > 0) && (dims[contiguousIdx] == 1)) {
+                    --contiguousIdx;
+                }
+            }
+            break;
+        }
+    }
+    ++contiguousIdx;
+
+    // Compute the highest number of contiguous data for each Tensor
+    const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin()+contiguousIdx, dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    const std::size_t input1_contiguous_size = std::accumulate(dims1.cbegin()+contiguousIdx, dims1.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    const std::size_t output_contiguous_size = std::accumulate(outputDims.cbegin()+contiguousIdx, outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+
+    // initialize strides to iterate through data because of broadcasting
+    std::unique_ptr<std::int32_t[]> stride_post0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_post1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_step0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_step1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    if (contiguousIdx > 0) {
+        stride_post0[contiguousIdx - 1] = 1;
+        stride_post1[contiguousIdx - 1] = 1;
+        for (std::size_t i = contiguousIdx - 2; i != static_cast<std::size_t>(-1); --i) {
+            stride_post0[i] = stride_post0[i+1]*static_cast<std::int32_t>(dims0[i+1]);
+            stride_post1[i] = stride_post1[i+1]*static_cast<std::int32_t>(dims1[i+1]);
+        }
+        for (std::size_t i = 0; i != contiguousIdx; ++i) {
+            stride_step0[i] = (dims0[i] == 1) ? 1 - stride_post0[i] : 1;
+            stride_step1[i] = (dims1[i] == 1) ? 1 - stride_post1[i] : 1;
+        }
+    }
+
+    // variables for arrays offsets
+    std::size_t offsetIn0 = 0;
+    std::size_t offsetIn1 = 0;
+    std::size_t offsetOut = 0;
+
+
+    std::size_t dim = contiguousIdx - 1;
+    const std::size_t nbStacks = std::accumulate(outputDims.cbegin(), outputDims.cbegin() + contiguousIdx, std::size_t(1), std::multiplies<std::size_t>());
+    for (std::size_t stack = 0; stack < nbStacks;) {
+        add_contiguous_arrays<I,O>(input0_contiguous_size, input1_contiguous_size, output_contiguous_size,
+                    input_0 + offsetIn0*input0_contiguous_size,
+                    input_1 + offsetIn1*input1_contiguous_size,
+                    output + offsetOut*output_contiguous_size);
+        if (++stack < nbStacks) {
+            std::size_t tmp_stack = stack;
+            while(tmp_stack % outputDims[dim] == 0) {
+                tmp_stack /= outputDims[dim];
+                dim--;
+            }
+            offsetIn0 += stride_step0[dim];
+            offsetIn1 += stride_step1[dim];
+            ++offsetOut;
+            dim = contiguousIdx - 1;
+        }
+    }
+}
+
+// Kernels registration to implementation entry point
+REGISTRAR(AddImpl_cpu,
+    {ImplSpec::IOSpec{DataType::Any}, ImplSpec::IOSpec{DataType::Float32}},
+    {ProdConso::inPlaceModel, Aidge::AddImpl_cpu_forward_kernel<float, float>, nullptr});
+REGISTRAR(AddImpl_cpu,
+    {ImplSpec::IOSpec{DataType::Any}, ImplSpec::IOSpec{DataType::Float64}},
+    {ProdConso::inPlaceModel, Aidge::AddImpl_cpu_forward_kernel<double, double>, nullptr});
+REGISTRAR(AddImpl_cpu,
+    {ImplSpec::IOSpec{DataType::Any}, ImplSpec::IOSpec{DataType::Int8}},
+    {ProdConso::inPlaceModel, Aidge::AddImpl_cpu_forward_kernel<std::int8_t, std::int8_t>, nullptr});
+REGISTRAR(AddImpl_cpu,
+    {ImplSpec::IOSpec{DataType::Any}, ImplSpec::IOSpec{DataType::UInt8}},
+    {ProdConso::inPlaceModel, Aidge::AddImpl_cpu_forward_kernel<std::uint8_t, std::uint8_t>, nullptr});
+REGISTRAR(AddImpl_cpu,
+    {ImplSpec::IOSpec{DataType::Any}, ImplSpec::IOSpec{DataType::Int32}},
+    {ProdConso::inPlaceModel, Aidge::AddImpl_cpu_forward_kernel<std::int32_t, std::int32_t>, nullptr});
+REGISTRAR(AddImpl_cpu,
+    {ImplSpec::IOSpec{DataType::Any}, ImplSpec::IOSpec{DataType::Int64}},
+    {ProdConso::inPlaceModel, Aidge::AddImpl_cpu_forward_kernel<std::int64_t, std::int64_t>, nullptr});
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ADDIMPL_CPU_KERNELS_H_ */
\ No newline at end of file

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

+/********************************************************************************
+ * Copyright (c) 2024 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_ANDIMPL_H_
+#define AIDGE_CPU_OPERATOR_ANDIMPL_H_
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/And.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+using AndImpl_cpu = OperatorImpl_cpu<And_Op,
+    void(std::vector<std::size_t>, std::vector<std::size_t>, const std::vector<std::size_t>&, const void*, const void*, void*)>;
+
+// Implementation entry point registration to Operator
+REGISTRAR(And_Op, "cpu", Aidge::AndImpl_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ANDIMPL_H_ */

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

+/********************************************************************************
+ * Copyright (c) 2024 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_ANDIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_ANDIMPL_KERNELS_H_
+
+#include "aidge/backend/cpu/operator/AndImpl.hpp"
+#include "aidge/utils/Registrar.hpp"
+
+namespace Aidge {
+
+namespace {
+// suppose values are contiguous in memory
+template <class I, class O>
+void equal_contiguous_arrays(const std::size_t input1size,
+                            const std::size_t input2size,
+                            const std::size_t output1size,
+                            const I* input1,
+                            const I* input2,
+                            O* output)
+{
+    for (std::size_t i = 0; i < output1size; ++i)
+    {
+        const std::size_t in1_id = (input1size != 1) ? i : 0;
+        const std::size_t in2_id = (input2size != 1) ? i : 0;
+        output[i] = static_cast<O>(input1[in1_id] == input2[in2_id]);
+    }
+}
+}
+
+
+template <class I, class O>
+void EqualImpl_cpu_forward_kernel(std::vector<std::size_t> dims0,
+                                std::vector<std::size_t> dims1,
+                                const std::vector<std::size_t>& outputDims,
+                                const void* input0_,
+                                const void* input1_,
+                                void* output_) {
+
+    const I* input_0 = static_cast<const I*>(input0_);
+    const I* input_1 = static_cast<const I*>(input1_);
+    O* output = static_cast<O*>(output_);
+
+    // [5,2,1,7] & [2,6,7]
+    // 1. Same number of dimensions -> [5,2,1,7] & [1,2,6,7]
+    // 2. Find the highest equal dimension -> 3
+    //    Exception: if the first diverging dimension is the last one, then -> 4 (dims.size())
+    // 3. Compute the highest number of contiguous data -> 7
+    // 4. Compute stride and offset step for the broadcast mechanism
+    // 5. Call a simple kernel
+
+    // special case for equal dimensions, the kernel is called with the entire arrays at once
+    if (dims0 == dims1) {
+        const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin(), dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+        for (std::size_t i = 0; i < input0_contiguous_size; ++i)
+        {
+            output[i] = static_cast<O>(input_0[i] == input_1[i]);
+        }
+        return;
+    }
+
+    // set dimensions to be of equal size by filling the smallest one with ones.
+    if (dims0.size() > dims1.size()) {
+        dims1.insert(dims1.cbegin(), dims0.size() - dims1.size(), std::size_t(1));
+    }
+    else if (dims1.size() > dims0.size()) {
+        dims0.insert(dims0.cbegin(), dims1.size() - dims0.size(), std::size_t(1));
+    }
+
+    const std::size_t nbDims = dims0.size();
+
+    // Find the highest equal dimension
+    // std::size_t contiguousIdx = nbDims - 1;
+    std::size_t contiguousIdx = nbDims;
+    while (contiguousIdx-- > 0) {
+    // for (; contiguousIdx+1 > 0; --contiguousIdx) {
+        if (dims0[contiguousIdx] != dims1[contiguousIdx]) {
+            if (contiguousIdx == (nbDims -1)) { // last dimensions of one of the input Tensor are of size 1
+                const std::vector<std::size_t>& dims = (dims0[contiguousIdx] == 1) ? dims0 : dims1;
+                while ((contiguousIdx+1 > 0) && (dims[contiguousIdx] == 1)) {
+                    --contiguousIdx;
+                }
+            }
+            break;
+        }
+    }
+    ++contiguousIdx;
+
+    // Compute the highest number of contiguous data for each Tensor
+    const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin()+contiguousIdx, dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    const std::size_t input1_contiguous_size = std::accumulate(dims1.cbegin()+contiguousIdx, dims1.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    const std::size_t output_contiguous_size = std::accumulate(outputDims.cbegin()+contiguousIdx, outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+
+    // initialize strides to iterate through data because of broadcasting
+    std::unique_ptr<std::int32_t[]> stride_post0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_post1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_step0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_step1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    if (contiguousIdx > 0) {
+        stride_post0[contiguousIdx - 1] = 1;
+        stride_post1[contiguousIdx - 1] = 1;
+        for (std::size_t i = contiguousIdx - 2; i != static_cast<std::size_t>(-1); --i) {
+            stride_post0[i] = stride_post0[i+1]*static_cast<std::int32_t>(dims0[i+1]);
+            stride_post1[i] = stride_post1[i+1]*static_cast<std::int32_t>(dims1[i+1]);
+        }
+        for (std::size_t i = 0; i != contiguousIdx; ++i) {
+            stride_step0[i] = (dims0[i] == 1) ? 1 - stride_post0[i] : 1;
+            stride_step1[i] = (dims1[i] == 1) ? 1 - stride_post1[i] : 1;
+        }
+    }
+
+    // variables for arrays offsets
+    std::size_t offsetIn0 = 0;
+    std::size_t offsetIn1 = 0;
+    std::size_t offsetOut = 0;
+
+
+    std::size_t dim = contiguousIdx - 1;
+    const std::size_t nbStacks = std::accumulate(outputDims.cbegin(), outputDims.cbegin() + contiguousIdx, std::size_t(1), std::multiplies<std::size_t>());
+    for (std::size_t stack = 0; stack < nbStacks;) {
+        equal_contiguous_arrays<I,O>(input0_contiguous_size, input1_contiguous_size, output_contiguous_size,
+                    input_0 + offsetIn0*input0_contiguous_size,
+                    input_1 + offsetIn1*input1_contiguous_size,
+                    output + offsetOut*output_contiguous_size);
+        if (++stack < nbStacks) {
+            std::size_t tmp_stack = stack;
+            while(tmp_stack % outputDims[dim] == 0) {
+                tmp_stack /= outputDims[dim];
+                dim--;
+            }
+            offsetIn0 += stride_step0[dim];
+            offsetIn1 += stride_step1[dim];
+            ++offsetOut;
+            dim = contiguousIdx - 1;
+        }
+    }
+}
+
+// Kernels registration to implementation entry point
+REGISTRAR(AndImpl_cpu,
+    {DataType::Float32},
+    {ProdConso::inPlaceModel, Aidge::EqualImpl_cpu_forward_kernel<float, float>, nullptr});
+REGISTRAR(AndImpl_cpu,
+    {DataType::Float64},
+    {ProdConso::inPlaceModel, Aidge::EqualImpl_cpu_forward_kernel<double, double>, nullptr});
+REGISTRAR(AndImpl_cpu,
+    {DataType::Int32},
+    {ProdConso::inPlaceModel, Aidge::EqualImpl_cpu_forward_kernel<std::int32_t, std::int32_t>, nullptr});
+REGISTRAR(AndImpl_cpu,
+    {DataType::Int64},
+    {ProdConso::inPlaceModel, Aidge::EqualImpl_cpu_forward_kernel<std::int64_t, std::int64_t>, nullptr});
+
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ANDIMPL_KERNELS_H_ */

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

+/********************************************************************************
+ * Copyright (c) 2024 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_ARGMAXIMPL_H_
+#define AIDGE_CPU_OPERATOR_ARGMAXIMPL_H_
+
+#include <array>
+#include <memory>
+#include <tuple>
+#include <vector>
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/ArgMax.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+using ArgMaxImpl_cpu = OperatorImpl_cpu<ArgMax_Op,
+    void(std::int32_t,
+        DimSize_t,
+        const std::vector<DimSize_t>&,
+        const void *,
+        void *)>;
+
+// Implementation entry point registration to Operator
+REGISTRAR(ArgMax_Op, "cpu", Aidge::ArgMaxImpl_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ARGMAXIMPL_H_ */

include/aidge/backend/cpu/operator/ArgMaxImpl_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_ARGMAXIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_ARGMAXIMPL_KERNELS_H_
+
+#include <algorithm>   // std::for_each
+#include <cstddef>     // std::size_t
+#include <cstdint>     // std::int32_t
+#include <functional>  //std::multiplies
+#include <numeric>     //std::accumulate
+#include <vector>
+#include <limits>
+
+#include "aidge/backend/cpu/operator/ArgMaxImpl.hpp"
+#include "aidge/data/Data.hpp"
+#include "aidge/operator/ArgMax.hpp"
+#include "aidge/utils/Registrar.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void ArgMaxImpl_cpu_forward_kernel(std::int32_t axis_,
+                                    DimSize_t select_last_index,
+                                    const std::vector<DimSize_t>& inputDims,
+                                    const void* input_,
+                                    void* output_) {
+
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+    const std::size_t axis = static_cast<std::size_t>(axis_);
+
+    std::size_t stride_post = 1;
+    for (std::size_t i = axis + 1; i < inputDims.size(); ++i) {
+        stride_post *= inputDims[i];
+    }
+    std::size_t stride_pre = 1;
+    for (std::size_t i = 0; i < axis; ++i) {
+        stride_pre *= inputDims[i];
+    }
+    const std::size_t dim_i = inputDims[axis];
+    for (std::size_t pre = 0; pre < stride_pre; ++pre) {
+        for (std::size_t post = 0; post < stride_post; ++post) {
+            const std::size_t idx_i = pre * dim_i * stride_post + post;
+            const std::size_t idx_o = pre * stride_post + post;
+            I max = std::numeric_limits<I>::min();
+            for (std::size_t i = 0; i < dim_i; ++i) {
+                I curr_value = input[idx_i + i*stride_post];
+                if (select_last_index) {
+                    if (curr_value>=max) {
+                        output[idx_o] = i;
+                        max = curr_value;
+                    }
+                }
+                else {
+                    if (curr_value > max) {
+                        output[idx_o] = i;
+                        max = curr_value;
+                    }
+                }
+            }
+        }
+    }
+
+}
+
+// Kernels registration to implementation entry point
+REGISTRAR(ArgMaxImpl_cpu,
+    {DataType::Float32},
+    {ProdConso::defaultModel, Aidge::ArgMaxImpl_cpu_forward_kernel<float, float>, nullptr});
+REGISTRAR(ArgMaxImpl_cpu,
+    {DataType::Float64},
+    {ProdConso::defaultModel, Aidge::ArgMaxImpl_cpu_forward_kernel<double, double>, nullptr});
+REGISTRAR(ArgMaxImpl_cpu,
+    {DataType::Int32},
+    {ProdConso::defaultModel, Aidge::ArgMaxImpl_cpu_forward_kernel<std::int32_t, std::int32_t>, nullptr});
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ARGMAXIMPL_KERNELS_H_ */

include/aidge/backend/cpu/operator/AtanImpl.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_ATAN_H_
+#define AIDGE_CPU_OPERATOR_ATAN_H_
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/Atan.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+using AtanImpl_cpu = OperatorImpl_cpu<Atan_Op,
+    void(const std::size_t, const void*, void*),
+    void(const std::size_t, const void*, const void*, void*)>;
+
+// Implementation entry point registration to Operator
+REGISTRAR(Atan_Op, "cpu", Aidge::AtanImpl_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ATAN_H_ */

include/aidge/backend/cpu/operator/AtanImpl_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_ATANIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_ATANIMPL_KERNELS_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/AtanImpl.hpp"
+#include <cmath>  // For atan()
+
+
+namespace Aidge {
+template <class I, class O>
+void AtanImpl_cpu_forward_kernel(std::size_t inputLenght,
+                                    const void* input_,
+                                    void* output_) {
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+    for (size_t i = 0; i < inputLenght; ++i) {
+        output[i] = static_cast<O>(atan(input[i]));
+    }
+
+}
+
+template <class O, class GI, class GO>
+void AtanImpl_cpu_backward_kernel(const std::size_t inputLenght,
+                                     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_);
+
+    // Apply the derivative of atan for each element in the input array
+    for (size_t i = 0; i < inputLenght; ++i) {
+        // dx = dy * (1 / (1 + x^2))
+        grad_input[i] = grad_output[i] * static_cast<O>(1.0 / (1.0 + output[i] * output[i]));
+    }
+}
+
+
+// Kernels registration to implementation entry point
+REGISTRAR(AtanImpl_cpu,
+    {DataType::Float32},
+    {ProdConso::inPlaceModel, Aidge::AtanImpl_cpu_forward_kernel<float, float>, Aidge::AtanImpl_cpu_backward_kernel<float, float, float>});
+REGISTRAR(AtanImpl_cpu,
+    {DataType::Float64},
+    {ProdConso::inPlaceModel, Aidge::AtanImpl_cpu_forward_kernel<double, double>, Aidge::AtanImpl_cpu_backward_kernel<double, double, double>});
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ATANIMPL_KERNELS_H_ */

include/aidge/backend/cpu/operator/AvgPoolingImpl.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_AVGPOOLINGIMPL_H_
+#define AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_H_
+
+#include <array>
+#include <memory>
+#include <tuple>
+#include <vector>
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/AvgPooling.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+using AvgPooling2D_Op = AvgPooling_Op<2>;
+using AvgPoolingImpl2D_cpu = OperatorImpl_cpu<AvgPooling_Op<2>,
+    void(const std::array<DimSize_t, 2>&,
+        const std::array<DimSize_t, 2>&,
+        const std::array<DimSize_t, 4>&,
+        const void *,
+        void *)>;
+
+// Implementation entry point registration to Operator
+REGISTRAR(AvgPooling2D_Op, "cpu", Aidge::AvgPoolingImpl2D_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_H_ */

include/aidge/operator/AvgPoolingImpl_forward_kernels.hpp → include/aidge/backend/cpu/operator/AvgPoolingImpl_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_AVGPOOLINGIMPL_FORWARD_KERNEL_H_
-#define AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_FORWARD_KERNEL_H_
-
-#include "aidge/utils/Registrar.hpp"
-
-#include "aidge/operator/AvgPoolingImpl.hpp"
-#include "aidge/utils/Types.h"
-#include "aidge/data/Data.hpp"
-#include <array>
-#include <tuple>
-#include <cmath>
-
-namespace Aidge {
-/**
- * @brief Forward kernel for 2D AvgPoolingolution on CPU backend.
- * @tparam I Input data type.
- * @tparam O Output data type.
- * @param params tuple of Parameters from the Operator
- * @param dims Array of input dimensions.
- * @param input_ const input Tensor.
- * @param output_ Output Tensor.
- */
-template <class I, class O>
-void AvgPoolingImpl2D_cpu_forward_kernel(const AvgPooling_Op<2>::Parameters &params,
-                                             const std::array<DimSize_t, 4> &dims,
-                                             const void *input_,
-                                             void *output_) {
-    // FIXME: missing convolution parameters as arguments
-    const I *input = static_cast<const I *>(input_);
-    O *output = static_cast<O *>(output_);
-
-
-    // output H size
-    const std::size_t oxSize =
-            static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<2>(params)[0] + std::get<2>(params)[2] - std::get<1>(params)[0] + std::get<0>(params)[0]) /
-                                static_cast<float>(std::get<0>(params)[0])));
-    // output W size
-    const std::size_t oySize =
-            static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<2>(params)[1] + std::get<2>(params)[3] - std::get<1>(params)[1] + std::get<0>(params)[1]) /
-                                static_cast<float>(std::get<0>(params)[1])));
-
-    // TODO: kernel computation
-    // output (batch, outCh, Xout, Yout)
-    // input  (batch, ch, Xin, Yin)
-    // weight (outCh, ch, kernelX, kernelY)
-    // does not take Dilation parameter into account
-    using signedsize = std::make_signed<std::size_t>::type;
-    for (std::size_t batch = 0; batch < dims[0]; ++batch) {
-        for (std::size_t ch = 0; ch < dims[1]; ++ch) {
-            const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
-            const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
-            for (std::size_t ox = 0; ox < oxSize; ++ox) {
-                const signedsize difx = static_cast<signedsize>(std::get<2>(params)[0] - ox * std::get<0>(params)[0]);
-                const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
-                const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<1>(params)[0] ? std::get<1>(params)[0] : dims[2] + difx);
-                for (std::size_t oy = 0; oy < oySize; ++oy) {
-                    const signedsize dify = static_cast<signedsize>(std::get<2>(params)[1] - oy * std::get<0>(params)[1]);
-                    const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
-                    const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<1>(params)[1] ? std::get<1>(params)[1] : dims[3] + dify);
-                    const std::size_t oIndexFull = oIndex + ox*oySize + oy;
-                    const std::size_t ix = ox * std::get<0>(params)[0];
-                    const std::size_t iy = oy * std::get<0>(params)[1];
-
-                    if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
-                        output[oIndexFull] += static_cast<O>(
-                                               input[iIndex + (ix+0)*dims[3] + (iy+0)] +
-                                               input[iIndex + (ix+0)*dims[3] + (iy+1)] +
-                                               input[iIndex + (ix+0)*dims[3] + (iy+2)] +
-                                               input[iIndex + (ix+1)*dims[3] + (iy+0)] +
-                                               input[iIndex + (ix+1)*dims[3] + (iy+1)] +
-                                               input[iIndex + (ix+1)*dims[3] + (iy+2)] +
-                                               input[iIndex + (ix+2)*dims[3] + (iy+0)] +
-                                               input[iIndex + (ix+2)*dims[3] + (iy+1)] +
-                                               input[iIndex + (ix+2)*dims[3] + (iy+2)]) / O(9);
-                    } else {
-                        for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
-                            for (std::size_t sy = syMin; sy < syMax; ++sy) {
-                                output[oIndexFull] += input[iIndex + (ix+sx)*dims[3] + (iy+sy)];
-                            }
-                        }
-                        // padding not used
-                        output[oIndexFull] /= (sxMax - sxMin) * (syMax - syMin);
-                    }
-                }
-            }
-        }
-    }
-}
-
-namespace {
-static Registrar<AvgPoolingImpl2DForward_cpu> registrarAvgPoolingImpl2DForward_cpu_Float32(
-        std::tuple<DataType, DataType>({DataType::Float32, DataType::Float32}),
-        Aidge::AvgPoolingImpl2D_cpu_forward_kernel<float, float>);
-static Registrar<AvgPoolingImpl2DForward_cpu> registrarAvgPoolingImpl2DForward_cpu_Int32(
-        {DataType::Int32, DataType::Int32},
-        Aidge::AvgPoolingImpl2D_cpu_forward_kernel<int, int>);
-static Registrar<AvgPoolingImpl2DForward_cpu> registrarAvgPoolingImpl2DForward_cpu_Float64(
-        {DataType::Float64, DataType::Float64},
-        Aidge::AvgPoolingImpl2D_cpu_forward_kernel<double, double>);
-}  // namespace
-}  // namespace Aidge
-
-#endif /* AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_FORWARD_KERNEL_H_ */
+/********************************************************************************
+ * 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_AVGPOOLINGIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_KERNELS_H_
+
+#include <array>
+#include <tuple>
+#include <cmath>
+
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include "aidge/backend/cpu/operator/AvgPoolingImpl.hpp"
+#include "aidge/data/Data.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+
+namespace Aidge {
+/**
+ * @brief Forward kernel for 2D AvgPoolingolution on CPU backend.
+ * @tparam I Input data type.
+ * @tparam O Output data type.
+ * @param params tuple of Attributes from the Operator
+ * @param dims Array of input dimensions.
+ * @param input_ const input Tensor.
+ * @param output_ Output Tensor.
+ */
+template <class I, class O>
+void AvgPoolingImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
+                                        const std::array<DimSize_t, 2>& kernelDims,
+                                        const std::array<DimSize_t, 4> &dims,
+                                        const void *input_,
+                                        void *output_) {
+    // FIXME: missing convolution attributes as arguments
+    const I *input = static_cast<const I *>(input_);
+    O *output = static_cast<O *>(output_);
+
+
+    // output H size
+    const std::size_t oxSize =
+            static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] - kernelDims[0] + strideDims[0]) /
+                                static_cast<float>(strideDims[0])));
+    // output W size
+    const std::size_t oySize =
+            static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] - kernelDims[1] + strideDims[1]) /
+                                static_cast<float>(strideDims[1])));
+
+    // TODO: kernel computation
+    // output (batch, outCh, Xout, Yout)
+    // input  (batch, ch, Xin, Yin)
+    // weight (outCh, ch, kernelX, kernelY)
+    // does not take Dilation attribute into account
+    using signedsize = std::make_signed<std::size_t>::type;
+    for (std::size_t batch = 0; batch < dims[0]; ++batch) {
+        for (std::size_t ch = 0; ch < dims[1]; ++ch) {
+            const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
+            const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
+            std::fill(output + oIndex, output+(oIndex+oxSize*oySize), 0);
+            for (std::size_t ox = 0; ox < oxSize; ++ox) {
+                const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]);
+                const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
+                const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > kernelDims[0] ? kernelDims[0] : dims[2] + difx);
+                for (std::size_t oy = 0; oy < oySize; ++oy) {
+                    const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]);
+                    const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
+                    const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > kernelDims[1] ? kernelDims[1] : dims[3] + dify);
+                    const std::size_t oIndexFull = oIndex + ox*oySize + oy;
+                    const std::size_t ix = ox * strideDims[0];
+                    const std::size_t iy = oy * strideDims[1];
+
+                    if (sxMin == 0 && syMin == 0 && sxMax == 3 && syMax == 3) {
+                        output[oIndexFull] += static_cast<O>(
+                                               input[iIndex + (ix+0)*dims[3] + (iy+0)] +
+                                               input[iIndex + (ix+0)*dims[3] + (iy+1)] +
+                                               input[iIndex + (ix+0)*dims[3] + (iy+2)] +
+                                               input[iIndex + (ix+1)*dims[3] + (iy+0)] +
+                                               input[iIndex + (ix+1)*dims[3] + (iy+1)] +
+                                               input[iIndex + (ix+1)*dims[3] + (iy+2)] +
+                                               input[iIndex + (ix+2)*dims[3] + (iy+0)] +
+                                               input[iIndex + (ix+2)*dims[3] + (iy+1)] +
+                                               input[iIndex + (ix+2)*dims[3] + (iy+2)]) / O(9);
+                    } else {
+                        for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
+                            for (std::size_t sy = syMin; sy < syMax; ++sy) {
+                                output[oIndexFull] += input[iIndex + (ix+sx)*dims[3] + (iy+sy)];
+                            }
+                        }
+                        // padding not used
+                        output[oIndexFull] /= (sxMax - sxMin) * (syMax - syMin);
+                    }
+                }
+            }
+        }
+    }
+}
+
+// Kernels registration to implementation entry point
+REGISTRAR(AvgPoolingImpl2D_cpu,
+    {{DataType::Float32, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}},
+    {ProdConso::inPlaceModel, Aidge::AvgPoolingImpl2D_cpu_forward_kernel<float, float>, nullptr});
+REGISTRAR(AvgPoolingImpl2D_cpu,
+    {{DataType::Int32, DataFormat::NCHW}, {DataType::Int32, DataFormat::NCHW}},
+    {ProdConso::inPlaceModel, Aidge::AvgPoolingImpl2D_cpu_forward_kernel<std::int32_t, std::int32_t>, nullptr});
+REGISTRAR(AvgPoolingImpl2D_cpu,
+    {{DataType::Float64, DataFormat::NCHW}, {DataType::Float64, DataFormat::NCHW}},
+    {ProdConso::inPlaceModel, Aidge::AvgPoolingImpl2D_cpu_forward_kernel<double, double>, nullptr});
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_AVGPOOLINGIMPL_KERNELS_H_ */

include/aidge/backend/cpu/operator/BatchNormImpl.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_BATCHNORMIMPL_H_
+#define AIDGE_CPU_OPERATOR_BATCHNORMIMPL_H_
+
+#include <array>
+#include <memory>
+#include <tuple>
+#include <vector>
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/BatchNorm.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+using BatchNorm2D_Op = BatchNorm_Op<2>;
+using BatchNormImpl2D_cpu = OperatorImpl_cpu<BatchNorm_Op<2>,
+    void(float,
+        float,
+        const std::vector<DimSize_t> &,
+        const void *,
+        const void *,
+        const void *,
+        void *,
+        void *,
+        void *,
+        const bool)>;
+
+// Implementation entry point registration to Operator
+REGISTRAR(BatchNorm2D_Op, "cpu", Aidge::BatchNormImpl2D_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_BATCHNORMIMPL_H_ */

include/aidge/operator/BatchNormImpl_forward_kernels.hpp → include/aidge/backend/cpu/operator/BatchNormImpl_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_BATCHNORMIMPL_FORWARD_KERNEL_H_
-#define AIDGE_CPU_OPERATOR_BATCHNORMIMPL_FORWARD_KERNEL_H_
-
-#include "aidge/utils/Registrar.hpp"
-
-#include "aidge/operator/BatchNormImpl.hpp"
-#include "aidge/utils/Types.h"
-#include <array>
-#include <cmath>
-#include <algorithm>
-
-namespace Aidge {
-/**
- * @brief Forward kernel for 2D BatchNormolution on CPU backend.
- * @tparam I Input data type.
- * @tparam W Weight data type.
- * @tparam B Bias data type.
- * @tparam O Output data type.
- * @param params tuple of Parameters from the Operator
- * @param dims Array of input dimensions.
- * @param input_ const input Tensor.
- * @param scale_ const scale Tensor.
- * @param shift_ const shift Tensor.
- * @param batchMean_ const mean Tensor.
- * @param batchVar_ const variance Tensor.
- * @param output_ Output Tensor.
- */
-template <class I, class P, class O>
-void BatchNormImpl2D_cpu_forward_kernel(const BatchNorm_Op<2>::Parameters &params, const std::array<DimSize_t, 4> &dims,
-                                       const void *input_, const void *scale_, const void *shift_, void *batchMean_, void *batchVar_, void *output_, const bool freeze) {
-    // FIXME: missing convolution parameters as arguments
-    const I *input = static_cast<const I *>(input_);
-    const P *scale = static_cast<const P *>(scale_);
-    const P *shift = static_cast<const P *>(shift_);
-    P *batchMean = static_cast<P *>(batchMean_);
-    P *batchVar = static_cast<P *>(batchVar_);
-    O *output = static_cast<O *>(output_);
-
-    const DimSize_t nbBatch = dims[0];
-    const DimSize_t nbChannels = dims[1];
-    const DimSize_t featureMapSize = dims[2]*dims[3];
-
-
-    if ((freeze == true) || (std::get<1>(params) == 0.0f)) {
-        for (std::size_t batch = 0; batch < nbBatch; ++batch) {
-            for (std::size_t ch = 0; ch < nbChannels; ++ch) {
-                const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
-                std::fill(output + ioIndex, output + ioIndex + featureMapSize, shift[ch]);
-                const P var = std::sqrt(batchVar[ch] + static_cast<P>(std::get<0>(params)));
-
-                for (std::size_t feature = 0; feature<featureMapSize; ++feature) {
-                    output[ioIndex + feature] += scale[ch] * (input[ioIndex + feature]-batchMean[ch]) / var;
-                }
-            }
-        }
-    } else {
-        const std::size_t nbDataPerChannel = nbBatch * featureMapSize;
-        for (std::size_t ch = 0; ch < nbChannels; ++ch) {
-            I sum = I(0);
-            I sumSquare = I(0);
-            for (std::size_t batch = 0; batch < nbBatch; ++batch) {
-                const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
-                std::fill(output + ioIndex, output + ioIndex + featureMapSize, shift[ch]);
-
-                for (std::size_t feature = 0; feature<featureMapSize; ++feature) {
-                    sum += input[ioIndex + feature];
-                    sumSquare += input[ioIndex + feature] * input[ioIndex + feature];
-                }
-            }
-            const I inputMean = sum / static_cast<I>(nbDataPerChannel);
-            const I inputVar = sumSquare / static_cast<I>(nbDataPerChannel)  - inputMean*inputMean;
-
-            batchMean[ch] = batchMean[ch]*(1-std::get<1>(params)) + inputMean*std::get<1>(params);
-            batchVar[ch] = batchVar[ch]*(1-std::get<1>(params)) + inputVar*(static_cast<I>(nbDataPerChannel)/static_cast<I>(nbDataPerChannel-1))*std::get<1>(params);
-
-            const P var = std::sqrt(inputVar + static_cast<P>(std::get<0>(params)));
-            for (std::size_t batch = 0; batch < nbBatch; ++batch) {
-                const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
-                for (std::size_t feature = 0; feature<featureMapSize; ++feature) {
-                    output[ioIndex + feature] += scale[ch] * (input[ioIndex + feature]-inputMean) / var;
-                }
-            }
-        }
-    }
-}
-
-
-
-
-
-namespace {
-static Registrar<BatchNormImpl2DForward_cpu> registrarBatchNormImpl2DForward_cpu_Float32(
-        {DataType::Float32, DataType::Float32, DataType::Float32},
-        Aidge::BatchNormImpl2D_cpu_forward_kernel<float, float, float>);
-}  // namespace
-}  // namespace Aidge
-
-#endif /* AIDGE_CPU_OPERATOR_BATCHNORMIMPL_FORWARD_KERNEL_H_ */
+/********************************************************************************
+ * 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_BATCHNORMIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_BATCHNORMIMPL_KERNELS_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/BatchNormImpl.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <array>
+#include <cmath>
+#include <algorithm>
+
+namespace Aidge {
+/**
+ * @brief Forward kernel for 2D BatchNormolution on CPU backend.
+ * @tparam I Input data type.
+ * @tparam W Weight data type.
+ * @tparam B Bias data type.
+ * @tparam O Output data type.
+ * @param params tuple of Attributes from the Operator
+ * @param dims Array of input dimensions.
+ * @param input_ const input Tensor.
+ * @param scale_ const scale Tensor.
+ * @param shift_ const shift Tensor.
+ * @param batchMean_ const mean Tensor.
+ * @param batchVar_ const variance Tensor.
+ * @param output_ Output Tensor.
+ */
+template <class I, class P, class O>
+void BatchNormImpl2D_cpu_forward_kernel(float epsilon, float momentum, const std::vector<DimSize_t> &dims,
+                                       const void *input_, const void *scale_, const void *shift_, void *batchMean_, void *batchVar_, void *output_, const bool freeze) {
+    // FIXME: missing convolution attributes as arguments
+    const I *input = static_cast<const I *>(input_);
+    const P *scale = static_cast<const P *>(scale_);
+    const P *shift = static_cast<const P *>(shift_);
+    P *batchMean = static_cast<P *>(batchMean_);
+    P *batchVar = static_cast<P *>(batchVar_);
+    O *output = static_cast<O *>(output_);
+
+    const DimSize_t nbBatch = dims[0];
+    const DimSize_t nbChannels = (dims.size() > 1) ? dims[1] : 1;
+    const DimSize_t featureMapSize = (dims.size() > 2) ? std::accumulate(dims.begin() + 2, dims.end(), 1, std::multiplies<DimSize_t>()) : 1;
+
+    if ((freeze == true) || (momentum == 0.0f)) {
+        for (std::size_t batch = 0; batch < nbBatch; ++batch) {
+            for (std::size_t ch = 0; ch < nbChannels; ++ch) {
+                const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
+                std::fill(output + ioIndex, output + ioIndex + featureMapSize, shift[ch]);
+                const P var = std::sqrt(batchVar[ch] + static_cast<P>(epsilon));
+
+                for (std::size_t feature = 0; feature<featureMapSize; ++feature) {
+                    output[ioIndex + feature] += scale[ch] * (input[ioIndex + feature]-batchMean[ch]) / var;
+                }
+            }
+        }
+    } else {
+        const std::size_t nbDataPerChannel = nbBatch * featureMapSize;
+        for (std::size_t ch = 0; ch < nbChannels; ++ch) {
+            I sum = I(0);
+            I sumSquare = I(0);
+            for (std::size_t batch = 0; batch < nbBatch; ++batch) {
+                const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
+                std::fill(output + ioIndex, output + ioIndex + featureMapSize, shift[ch]);
+
+                for (std::size_t feature = 0; feature<featureMapSize; ++feature) {
+                    sum += input[ioIndex + feature];
+                    sumSquare += input[ioIndex + feature] * input[ioIndex + feature];
+                }
+            }
+            const I inputMean = sum / static_cast<I>(nbDataPerChannel);
+            const I inputVar = sumSquare / static_cast<I>(nbDataPerChannel)  - inputMean*inputMean;
+
+            batchMean[ch] = batchMean[ch]*(1-momentum) + inputMean*momentum;
+            batchVar[ch] = batchVar[ch]*(1-momentum) + inputVar*(static_cast<I>(nbDataPerChannel)/static_cast<I>(nbDataPerChannel-1))*momentum;
+
+            const P var = std::sqrt(inputVar + static_cast<P>(epsilon));
+            for (std::size_t batch = 0; batch < nbBatch; ++batch) {
+                const std::size_t ioIndex = (ch + batch*nbChannels) * featureMapSize;
+                for (std::size_t feature = 0; feature<featureMapSize; ++feature) {
+                    output[ioIndex + feature] += scale[ch] * (input[ioIndex + feature]-inputMean) / var;
+                }
+            }
+        }
+    }
+}
+
+// Kernels registration to implementation entry point
+REGISTRAR(BatchNormImpl2D_cpu,
+    {{DataType::Float32, DataFormat::NCHW}, {DataType::Float32, DataFormat::NCHW}},
+    {ProdConso::inPlaceModel, Aidge::BatchNormImpl2D_cpu_forward_kernel<float, float, float>, nullptr});
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_BATCHNORMIMPL_KERNELS_H_ */

include/aidge/backend/cpu/operator/BitShiftImpl.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_BITSHIFTIMPL_H_
+#define AIDGE_CPU_OPERATOR_BITSHIFTIMPL_H_
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/BitShift.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include <memory>
+#include <vector>
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+using BitShiftImpl_cpu = OperatorImpl_cpu<BitShift_Op,
+    void(const BitShift_Op::BitShiftDirection,
+    std::vector<std::size_t>,
+    std::vector<std::size_t>,
+    const std::vector<std::size_t>&,
+    const void*,
+    const void*,
+    void*)>;
+
+    // Implementation entry point registration to Operator
+    REGISTRAR(BitShift_Op,"cpu",Aidge::BitShiftImpl_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_BITSHIFTIMPL_H_ */

include/aidge/backend/cpu/operator/BitShiftImpl_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_BITSHIFTIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_BITSHIFTIMPL_KERNELS_H_
+
+
+#include <cstdint>  // std::int32_t, std::int64_t
+#include <cstddef>  // std::size_t
+
+#include "aidge/backend/cpu/data/Broadcasting.hpp"
+#include "aidge/backend/cpu/operator/BitShiftImpl.hpp"
+#include "aidge/operator/BitShift.hpp"
+#include "aidge/utils/Registrar.hpp"
+
+
+namespace {
+// suppose values are contiguous in memory
+template <class I1, class I2, class O>
+void bitshift_contiguous_arrays(
+    const Aidge::BitShift_Op::BitShiftDirection direction,
+    const std::size_t input1size,
+    const std::size_t input2size,
+    const std::size_t output1size,
+    const I1* input_1,
+    const I2* input_2,
+    O* output)
+{
+    if(direction == Aidge::BitShift_Op::BitShiftDirection::right) {
+        for (std::size_t i = 0; i < output1size; ++i) {
+            const std::size_t idx1 = (input1size != 1) ? i : 0;
+            const std::size_t idx2 = (input2size != 1) ? i : 0;
+            output[i]= input_1[idx1] >> input_2[idx2];
+        }
+
+    } else {
+        for (std::size_t i = 0; i < output1size; ++i) {
+            const std::size_t idx1 = (input1size != 1) ? i : 0;
+            const std::size_t idx2 = (input2size != 1) ? i : 0;
+            output[i] = input_1[idx1] << input_2[idx2];
+        }
+    }
+}
+}
+
+namespace Aidge {
+template <class I1, class I2, class O>
+void BitShiftImpl_cpu_forward_kernel(
+                                const BitShift_Op::BitShiftDirection direction,
+                                std::vector<std::size_t> dims0,
+                                std::vector<std::size_t> dims1,
+                                const std::vector<std::size_t>& outputDims,
+                                const void* input0_,
+                                const void* input1_,
+                                void* output_
+                                ) {
+
+    const I1* input_0 = static_cast<const I1*>(input0_);
+    const I2* input_1 = static_cast<const I2*>(input1_);
+    O* output = static_cast<O*>(output_);
+
+    // [5,2,1,7] & [2,6,7]
+    // 1. Same number of dimensions -> [5,2,1,7] & [1,2,6,7]
+    // 2. Find the highest equal dimension -> 3
+    //    Exception: if the first diverging dimension is the last one, then -> 4 (dims.size())
+    // 3. Compute the highest number of contiguous data -> 7
+    // 4. Compute stride and offset step for the broadcast mechanism
+    // 5. Call a simple kernel
+
+    // ## Compute compatible input dimensions
+    // special case for equal dimensions, the kernel is called with the entire arrays at once
+    if (dims0 == dims1) {
+        const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin(), dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+        bitshift_contiguous_arrays(direction, input0_contiguous_size, input0_contiguous_size, input0_contiguous_size, input_0, input_1, output);
+        return;
+    }
+
+    // set dimensions to be of equal size by filling the smallest one with ones.
+    if (dims0.size() > dims1.size()) {
+        dims1.insert(dims1.cbegin(), dims0.size() - dims1.size(), std::size_t(1));
+    }
+    else if (dims1.size() > dims0.size()) {
+        dims0.insert(dims0.cbegin(), dims1.size() - dims0.size(), std::size_t(1));
+    }
+
+    const std::size_t nbDims = dims0.size();
+
+    // Find the highest equal dimension
+    // std::size_t contiguousIdx = nbDims - 1;
+    std::size_t contiguousIdx = nbDims;
+    while (contiguousIdx-- > 0) {
+    // for (; contiguousIdx+1 > 0; --contiguousIdx) {
+        if (dims0[contiguousIdx] != dims1[contiguousIdx]) {
+            if (contiguousIdx == (nbDims -1)) { // last dimensions of one of the input Tensor are of size 1
+                const std::vector<std::size_t>& dims = (dims0[contiguousIdx] == 1) ? dims0 : dims1;
+                while ((contiguousIdx+1 > 0) && (dims[contiguousIdx] == 1)) {
+                    --contiguousIdx;
+                }
+            }
+            break;
+        }
+    }
+    ++contiguousIdx;
+
+    // Compute the highest number of contiguous data for each Tensor
+    const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin()+contiguousIdx, dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    const std::size_t input1_contiguous_size = std::accumulate(dims1.cbegin()+contiguousIdx, dims1.cend(), std::size_t(1), std::multiplies<std::size_t>());
+    const std::size_t output_contiguous_size = std::accumulate(outputDims.cbegin()+contiguousIdx, outputDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+
+    // initialize strides to iterate through data because of broadcasting
+    std::unique_ptr<std::int32_t[]> stride_post0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_post1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_step0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_step1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    if (contiguousIdx > 0) {
+        stride_post0[contiguousIdx - 1] = 1;
+        stride_post1[contiguousIdx - 1] = 1;
+        for (std::size_t i = contiguousIdx - 2; i != static_cast<std::size_t>(-1); --i) {
+            stride_post0[i] = stride_post0[i+1]*static_cast<std::int32_t>(dims0[i+1]);
+            stride_post1[i] = stride_post1[i+1]*static_cast<std::int32_t>(dims1[i+1]);
+        }
+        for (std::size_t i = 0; i != contiguousIdx; ++i) {
+            stride_step0[i] = (dims0[i] == 1) ? 1 - stride_post0[i] : 1;
+            stride_step1[i] = (dims1[i] == 1) ? 1 - stride_post1[i] : 1;
+        }
+    }
+
+    // variables for arrays offsets
+    std::size_t offsetIn0 = 0;
+    std::size_t offsetIn1 = 0;
+    std::size_t offsetOut = 0;
+
+
+    std::size_t dim = contiguousIdx - 1;
+    const std::size_t nbStacks = std::accumulate(outputDims.cbegin(), outputDims.cbegin() + contiguousIdx, std::size_t(1), std::multiplies<std::size_t>());
+    for (std::size_t stack = 0; stack < nbStacks;) {
+        bitshift_contiguous_arrays<I1,I2,O>(direction, input0_contiguous_size, input1_contiguous_size, output_contiguous_size,
+                    input_0 + offsetIn0*input0_contiguous_size,
+                    input_1 + offsetIn1*input1_contiguous_size,
+                    output + offsetOut*output_contiguous_size);
+        if (++stack < nbStacks) {
+            std::size_t tmp_stack = stack;
+            while(tmp_stack % outputDims[dim] == 0) {
+                tmp_stack /= outputDims[dim];
+                dim--;
+            }
+            offsetIn0 += stride_step0[dim];
+            offsetIn1 += stride_step1[dim];
+            ++offsetOut;
+            dim = contiguousIdx - 1;
+        }
+    }
+}
+
+REGISTRAR(BitShiftImpl_cpu,
+{DataType::Int32},
+{ProdConso::inPlaceModel,Aidge::BitShiftImpl_cpu_forward_kernel<std::int32_t, std::int32_t, std::int32_t>,nullptr});
+REGISTRAR(BitShiftImpl_cpu,
+{DataType::Int64},
+{ProdConso::inPlaceModel,Aidge::BitShiftImpl_cpu_forward_kernel<std::int64_t, std::int64_t, std::int64_t>,nullptr});
+
+
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_BitShiftIMPL_KERNELS_H_ */
\ No newline at end of file

include/aidge/backend/cpu/operator/ClipImpl.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_CLIPIMPL_H_
+#define AIDGE_CPU_OPERATOR_CLIPIMPL_H_
+
+#include <cstddef>  // std::size_t
+#include <memory>
+#include <tuple>    // std::tuple
+#include <vector>
+#include <algorithm>
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/Clip.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+    using ClipImpl_cpu = OperatorImpl_cpu<Clip_Op,
+    void(float, //Forward Types
+    float, 
+    const void*,
+    const std::size_t, 
+    void*),
+    void(float,//Backward Types
+    float, 
+    const std::size_t,
+    const void*, 
+    const void*,
+    void*)>;
+
+    REGISTRAR(Clip_Op,"cpu",Aidge::ClipImpl_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_CLIPIMPL_H_ */

include/aidge/backend/cpu/operator/ClipImpl_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_CLIPIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_CLIPIMPL_KERNELS_H_
+
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/backend/cpu/operator/ClipImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void ClipImpl_cpu_forward_kernel(
+        float min_,
+        float max_,
+        const void* input_,
+        const std::size_t length,
+        void* output_) 
+{
+    const I* input = static_cast<const I*>(input_);
+    O* output = static_cast<O*>(output_);
+
+    for (std::size_t i = 0; i < length; ++i) {
+        output[i] = std::min(std::max(static_cast<float>(input[i]), min_), max_);
+    }
+}
+
+template <class I, class GI, class GO>
+void ClipImpl_cpu_backward_kernel(
+        float min_,
+        float max_,
+        const std::size_t length,
+        const void* input_, 
+        const void* grad_output_,
+		void* grad_input_)           
+{
+    const I* input = static_cast<const I*>(input_);
+    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 < length; ++i) {
+        grad_input[i] = ((input[i] > min_) && (input[i] < max_)) ? grad_output[i] : 0;
+    }
+}
+
+REGISTRAR(ClipImpl_cpu,
+{DataType::Float32},
+{ProdConso::inPlaceModel,
+Aidge::ClipImpl_cpu_forward_kernel<float,float>,
+Aidge::ClipImpl_cpu_backward_kernel<float,float,float>});
+REGISTRAR(ClipImpl_cpu,
+{DataType::Float64},
+{ProdConso::inPlaceModel,
+Aidge::ClipImpl_cpu_forward_kernel<double,double>,
+Aidge::ClipImpl_cpu_backward_kernel<double,double,double>});
+REGISTRAR(ClipImpl_cpu,
+{DataType::Int32},
+{ProdConso::inPlaceModel,
+Aidge::ClipImpl_cpu_forward_kernel<std::int32_t,std::int32_t>,
+Aidge::ClipImpl_cpu_backward_kernel<std::int32_t,std::int32_t,std::int32_t>});
+REGISTRAR(ClipImpl_cpu,
+{DataType::Int64},
+{ProdConso::inPlaceModel,
+Aidge::ClipImpl_cpu_forward_kernel<std::int64_t,std::int64_t>,
+Aidge::ClipImpl_cpu_backward_kernel<std::int64_t,std::int64_t,std::int64_t>});
+
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_CLIPIMPL_KERNELS_H_ */