Ajout du Backend CPU pour l'opérateur BitShift (Ainsi que les tests unitaire de bitshift)

include/aidge/backend/cpu.hpp

@@ -16,6 +16,7 @@
 #include "aidge/backend/cpu/operator/AvgPoolingImpl.hpp"
 #include "aidge/backend/cpu/operator/MaxPoolingImpl.hpp"
 #include "aidge/backend/cpu/operator/BatchNormImpl.hpp"
+#include "aidge/backend/cpu/operator/BitShiftImpl.hpp"
 #include "aidge/backend/cpu/operator/ConvDepthWiseImpl.hpp"
 #include "aidge/backend/cpu/operator/ConvImpl.hpp"
 #include "aidge/backend/cpu/operator/DivImpl.hpp"

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/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 {
+// class BitShift_Op;
+
+// compute kernel registry for forward and backward
+class BitShiftImplForward_cpu
+    : public Registrable<BitShiftImplForward_cpu, std::tuple<DataType, DataType, DataType>, void(const Direction,const std::vector<std::size_t>&, const std::vector<std::size_t>&, const std::vector<std::size_t>&, const void*, const void*,void*)> {
+};
+class BitShiftImplBackward_cpu
+    : public Registrable<BitShiftImplBackward_cpu, std::tuple<DataType, DataType, DataType>, void(const Direction,const std::vector<std::size_t>&, const std::vector<std::size_t>&, const std::vector<std::size_t>&, const void*, const void*, void*)> {
+};
+
+class BitShiftImpl_cpu : public OperatorImpl {
+public:
+    BitShiftImpl_cpu(const BitShift_Op& op) : OperatorImpl(op, "cpu") {}
+
+    static std::unique_ptr<BitShiftImpl_cpu> create(const BitShift_Op& op) {
+        return std::make_unique<BitShiftImpl_cpu>(op);
+    }
+
+    Elts_t getNbRequiredProtected(const IOIndex_t inputIdx) const override final;
+    void forward() override;
+};
+
+namespace {
+static Registrar<BitShift_Op> registrarBitShiftImpl_cpu("cpu", Aidge::BitShiftImpl_cpu::create);
+}
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_BITSHIFTIMPL_H_ */

include/aidge/backend/cpu/operator/BitShiftImpl_forward_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_FORWARD_KERNEL_H_
+#define AIDGE_CPU_OPERATOR_BITSHIFTIMPL_FORWARD_KERNEL_H_
+
+#include "aidge/utils/Registrar.hpp"
+
+#include <cstdint>     // std::int32_t, std::int64_t
+#include "aidge/operator/BitShift.hpp"
+
+#include "aidge/backend/cpu/data/Broadcasting.hpp"
+#include "aidge/backend/cpu/operator/BitShiftImpl.hpp"
+
+
+
+namespace Aidge {
+template <class I1, class I2, class O>
+void BitShiftImpl_cpu_forward_kernel(
+                                const Direction direction,
+                                const std::vector<std::size_t>& input1Dims,
+                                const std::vector<std::size_t>& input2Dims,
+                                const std::vector<std::size_t>& outputDims,
+                                const void* input1_,
+                                const void* input2_,
+                                void* output_
+                                ) {
+
+    //Cast des entrées en classes I / O                                    
+    const I1* input_1 = static_cast<const I1*>(input1_);
+    const I2* input_2 = static_cast<const I2*>(input2_);
+    O* output = static_cast<O*>(output_);
+
+    size_t totalElements = 1;
+    for (size_t dimSize : outputDims) {
+        totalElements *= dimSize;
+    }
+
+    for (std::size_t oIndex = 0; oIndex < totalElements; ++oIndex)
+    {
+        std::vector<size_t> indexes = getMultiDimIndices(outputDims, oIndex);
+        std::size_t idx1 = getFlattenedIndex(input1Dims, indexes);
+        std::size_t idx2 = getFlattenedIndex(input2Dims, indexes);
+        if(direction == Direction::right)
+        {
+                //BitShift ne fonctionne pas sur les types à virgule flottante
+                output[oIndex]= input_1[idx1] >> input_2[idx2];
+        }
+        else if(direction == Direction::left)
+        {
+                output[oIndex] = input_1[idx1] << input_2[idx2];
+        }
+    }
+}
+
+namespace {
+static Registrar<BitShiftImplForward_cpu> registrarBitShiftImplForward_cpu_Int32(
+        {DataType::Int32, DataType::Int32, DataType::Int32},
+        Aidge::BitShiftImpl_cpu_forward_kernel<std::int32_t, std::int32_t, std::int32_t>);
+static Registrar<BitShiftImplForward_cpu> registrarBitShiftImplForward_cpu_Int64(
+        {DataType::Int64, DataType::Int64, DataType::Int64},
+        Aidge::BitShiftImpl_cpu_forward_kernel<std::int64_t, std::int64_t, std::int64_t>);
+}  // namespace
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_BitShiftIMPL_FORWARD_KERNEL_H_ */

src/operator/BitShiftImpl.cpp

+/********************************************************************************
+ * 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
+ *
+ ********************************************************************************/
+
+#include <cassert>
+#include <chrono>  // std::chrono::milliseconds
+#include <numeric>
+#include <thread>  // std::this_thread::sleep_for
+#include <vector>
+
+
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/data/Broadcasting.hpp"
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+
+#include "aidge/backend/cpu/operator/BitShiftImpl.hpp"
+#include "aidge/backend/cpu/operator/BitShiftImpl_forward_kernels.hpp"
+
+Aidge::Elts_t Aidge::BitShiftImpl_cpu::getNbRequiredProtected(const Aidge::IOIndex_t /*inputIdx*/) const {
+    // this implementation can be in-place
+    return Elts_t::DataElts(0);
+}
+
+void Aidge::BitShiftImpl_cpu::forward() {
+
+    const auto& op_ = dynamic_cast<const BitShift_Op&>(mOp);
+
+    auto kernelFunc = Registrar<BitShiftImplForward_cpu>::create({
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dataType(),
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()});
+
+    const std::vector<std::size_t> inputDims0 = getBroadcastedDims(std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims(),
+                                                                   std::static_pointer_cast<Tensor>(mOp.getRawInput(0))->dims());
+    const std::vector<std::size_t> inputDims1 = getBroadcastedDims(std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims(),
+                                                                   std::static_pointer_cast<Tensor>(mOp.getRawInput(1))->dims());
+
+    Direction direction = op_.direction();
+
+    // Call kernel
+    kernelFunc(
+        direction,
+        inputDims0,
+        inputDims1,
+        std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dims(),
+        getCPUPtr(mOp.getRawInput(0)),
+        getCPUPtr(mOp.getRawInput(1)),
+        getCPUPtr(mOp.getRawOutput(0)));
+        
+}

unit_tests/operator/Test_BitShift.cpp

+/********************************************************************************
+ * 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
+ *
+ ********************************************************************************/
+
+#include <catch2/catch_test_macros.hpp>
+#include <cstddef>   // std::size_t
+#include <cstdint>   // std::uint16_t
+#include <chrono>
+#include <iostream>
+#include <memory>
+#include <numeric>   
+#include <random>    // std::random_device, std::mt19937, std::uniform_real_distribution
+#include <iomanip>
+#include "aidge/data/Tensor.hpp"
+#include "aidge/operator/BitShift.hpp"
+#include "aidge/utils/TensorUtils.hpp"
+
+namespace Aidge {
+
+TEST_CASE("[cpu/operator] BitShift_TEST", "[BitShift][CPU]") {
+    constexpr std::uint16_t NBTRIALS = 15;
+    // Create a random number generator
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_int_distribution<int> valueDist(-15, 15); // Random int distribution between -15 and 15 
+    std::uniform_int_distribution<std::size_t> dimSizeDist(std::size_t(2), std::size_t(5));
+    std::uniform_int_distribution<std::size_t> nbDimsDist(std::size_t(1), std::size_t(3));
+    std::uniform_int_distribution<int> boolDist(0,1);
+
+    Direction direction = Direction::left;
+
+    if(valueDist(gen) % 2 == 0)
+    {
+        direction = Direction::right;
+    }
+
+    // Create BitShift Operator
+    std::shared_ptr<Node> myBitShift = BitShift(direction); // Left opérator to start
+    auto op = std::static_pointer_cast<OperatorTensor>(myBitShift-> getOperator());
+    op->setDataType(DataType::Int32);
+    op->setBackend("cpu");
+
+    // Create 2 input Tensors
+    std::shared_ptr<Tensor> T0 = std::make_shared<Tensor>();
+    op->associateInput(0,T0);
+    T0->setDataType(DataType::Int32);
+    T0->setBackend("cpu");
+    std::shared_ptr<Tensor> T1 = std::make_shared<Tensor>();
+    op -> associateInput(1,T1);
+    T1->setDataType(DataType::Int32);
+    T1->setBackend("cpu");
+
+    // Create results Tensor
+    std::shared_ptr<Tensor> Tres = std::make_shared<Tensor>();
+    Tres->setDataType(DataType::Int32);
+    Tres->setBackend("cpu");
+
+    // To measure execution time of 'BitShift_Op::forward()' member function call
+    std::chrono::time_point<std::chrono::system_clock> start;
+
+    std::chrono::time_point<std::chrono::system_clock> end;
+    std::chrono::duration<double, std::micro> duration{};
+
+    SECTION("BitShiftImpl_cpu::forward()") {
+        SECTION("+1-D Tensor / +1-D Tensor - same dimensions") {
+            std::size_t number_of_operation = 0;
+
+            for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
+                // generate 2 random Tensors
+                const std::size_t nbDims = nbDimsDist(gen);
+                std::vector<std::size_t> dims;
+                for (std::size_t i = 0; i < nbDims; ++i) {
+                    dims.push_back(dimSizeDist(gen));
+                }
+                const std::size_t nb_elements = std::accumulate(dims.cbegin(), dims.cend(), std::size_t(1), std::multiplies<std::size_t>());
+                number_of_operation += nb_elements;
+
+                // without broadcasting
+                int* array0 = new int[nb_elements];
+                int* array1 = new int[nb_elements];
+                int* result = new int[nb_elements];
+
+                for (std::size_t i = 0; i < nb_elements; ++i) {
+                    array0[i] = valueDist(gen);
+                    array1[i] = std::abs(valueDist(gen)); // bitshift is impossible with negative value
+                    if(direction == Direction::left)
+                    {
+                        result[i] = array0[i] << array1[i];
+                    }
+                    else
+                    {
+                        result[i] = array0[i] >> array1[i];
+                    }
+                }
+
+                // input0
+                T0->resize(dims);
+                T0 -> getImpl() -> setRawPtr(array0, nb_elements);
+
+                // input1
+                T1->resize(dims);
+                T1 -> getImpl() -> setRawPtr(array1, nb_elements);
+
+                // results
+                Tres->resize(dims);
+                Tres -> getImpl() -> setRawPtr(result, nb_elements);
+
+                op->forwardDims();
+                start = std::chrono::system_clock::now();
+                myBitShift->forward();
+                end = std::chrono::system_clock::now();
+                duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+
+                bool is_eq = approxEq<int>(*(op->getOutput(0)), *Tres);
+
+                auto Output = *(op->getOutput(0));
+                auto prt = Output.getImpl()->rawPtr();
+
+                REQUIRE(is_eq);
+
+                delete[] array0;
+                delete[] array1;
+                delete[] result;
+
+
+            }
+            std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
+            std::cout << "total time: " << duration.count() << "μs" << std::endl;
+        }
+        SECTION("+1-D Tensor / +1-D Tensor - broadcasting") {
+            std::size_t number_of_operation = 0;
+
+            for (std::uint16_t trial = 0; trial < NBTRIALS; ++trial) {
+                // generate 2 random Tensors
+                // handle dimensions, replace some dimensions with '1' to get broadcasting
+                constexpr std::size_t nbDims = 4;
+                std::vector<std::size_t> dims;
+                for (std::size_t i = 0; i < nbDims; ++i) {
+                    dims.push_back(dimSizeDist(gen));
+                }
+                std::vector<std::size_t> dims0 = dims;
+                std::vector<std::size_t> dims1 = dims;
+                std::vector<std::size_t> dimsOut = dims;
+                for (std::size_t i = 0; i < nbDims; ++i) {
+                    if (boolDist(gen)) {
+                        dims0[i] = 1;
+                    }
+                    if (boolDist(gen)) {
+                        dims1[i] = 1;
+                    }
+                    dimsOut[i] = (dims0[i] == 1) ? dims1[i] : dims0[i];
+                }
+
+                // create arrays and fill them with random values
+                int* array0 = new int[dims0[0]*dims0[1]*dims0[2]*dims0[3]];
+                int* array1 = new int[dims1[0]*dims1[1]*dims1[2]*dims1[3]];
+                int* result = new int[dimsOut[0]*dimsOut[1]*dimsOut[2]*dimsOut[3]];
+
+                for (std::size_t i = 0; i < dims0[0]*dims0[1]*dims0[2]*dims0[3]; ++i) {
+                    array0[i] = valueDist(gen);
+                }
+                for (std::size_t i = 0; i < dims1[0]*dims1[1]*dims1[2]*dims1[3]; ++i) {
+                    array1[i] = std::abs(valueDist(gen));
+                }
+
+                //True result with broadcast
+                const std::size_t strides0[nbDims] = {dims0[1]*dims0[2]*dims0[3], dims0[2]*dims0[3], dims0[3], 1};
+                const std::size_t strides1[nbDims] = {dims1[1]*dims1[2]*dims1[3], dims1[2]*dims1[3], dims1[3], 1};
+                for (std::size_t a = 0; a < dimsOut[0]; ++a) {
+                    for (std::size_t b = 0; b < dimsOut[1]; ++b) {
+                        const std::size_t idx0_0 = strides0[0] * ((dims0[0] > 1) ? a : 0)
+                                                    + strides0[1] * ((dims0[1] > 1) ? b : 0);
+                        const std::size_t idx1_0 = strides1[0] * ((dims1[0] > 1) ? a : 0)
+                                                    + strides1[1] * ((dims1[1] > 1) ? b : 0);
+                        for (std::size_t c = 0; c < dimsOut[2]; ++c) {
+                            const std::size_t idx_out = dimsOut[3] * (c + dimsOut[2] * (b + dimsOut[1] * a));
+                            for (std::size_t d = 0; d < dimsOut[3]; ++d) {
+                                std::size_t idx0 = idx0_0
+                                                    + strides0[2] * ((dims0[2] > 1) ? c : 0)
+                                                    + ((dims0[3] > 1) ? d : 0);
+                                std::size_t idx1 = idx1_0
+                                                    + strides1[2] * ((dims1[2] > 1) ? c : 0)
+                                                    + ((dims1[3] > 1) ? d : 0);
+                                if(direction == Direction::left)
+                                {
+                                    result[idx_out + d] = array0[idx0] << array1[idx1];
+                                }
+                                else
+                                {
+                                    result[idx_out + d] = array0[idx0] >> array1[idx1];                               
+                                }
+                            }
+                        }
+                    }
+                }
+
+                // conversion to Aidge::Tensors
+                // input0
+                T0->resize(dims0);
+                T0 -> getImpl() -> setRawPtr(array0, dims0[0]*dims0[1]*dims0[2]*dims0[3]);
+
+                // input1
+                T1->resize(dims1);
+                T1 -> getImpl() -> setRawPtr(array1, dims1[0]*dims1[1]*dims1[2]*dims1[3]);
+
+                // results
+                Tres->resize(dimsOut);
+                Tres -> getImpl() -> setRawPtr(result, dimsOut[0]*dimsOut[1]*dimsOut[2]*dimsOut[3]);
+
+                // compute result
+                op->forwardDims();
+                start = std::chrono::system_clock::now();
+                myBitShift->forward();
+                end = std::chrono::system_clock::now();
+                duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+
+                // comparison between truth and computed result
+                bool equiv = (approxEq<int>(*(op->getOutput(0)), *Tres));
+                if(equiv == false)
+                {
+                    std::cout << "Problem\n";
+                }
+                REQUIRE(equiv);
+
+                delete[] array0;
+                delete[] array1;
+                delete[] result;
+
+                const std::size_t nb_elements = std::accumulate(dimsOut.cbegin(), dimsOut.cend(), std::size_t(1), std::multiplies<std::size_t>());
+                number_of_operation += nb_elements;
+            }
+            std::cout << "number of elements over time spent: " << (number_of_operation / duration.count())<< std::endl;
+            std::cout << "total time: " << duration.count() << "μs" << std::endl;
+        }
+
+}
+} // namespace Aidge
+}
\ No newline at end of file