Merge branch 'feat_operator_round' into 'dev'

[Add] Round Operator

See merge request !94

include/aidge/backend/cpu.hpp

@@ -40,6 +40,7 @@
 #include "aidge/backend/cpu/operator/ReduceMeanImpl.hpp"
 #include "aidge/backend/cpu/operator/ReduceSumImpl.hpp"
 #include "aidge/backend/cpu/operator/ReLUImpl.hpp"
+#include "aidge/backend/cpu/operator/RoundImpl.hpp"
 #include "aidge/backend/cpu/operator/ScalingImpl.hpp"
 #include "aidge/backend/cpu/operator/SigmoidImpl.hpp"
 #include "aidge/backend/cpu/operator/SqrtImpl.hpp"

include/aidge/backend/cpu/operator/RoundImpl.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_ROUNDIMPL_H_
+#define AIDGE_CPU_OPERATOR_ROUNDIMPL_H_
+
+#include <cstddef>  // std::size_t
+#include <memory>
+#include <tuple>
+#include <vector>
+
+#include "aidge/backend/cpu/operator/OperatorImpl.hpp"
+#include "aidge/operator/Round.hpp"
+#include "aidge/utils/Registrar.hpp"
+#include "aidge/utils/Types.h"
+
+namespace Aidge {
+// Operator implementation entry point for the backend
+using RoundImpl_cpu = OperatorImpl_cpu<Round_Op,
+    void(const std::size_t, const void*, void*)>;
+
+// Implementation entry point registration to Operator
+REGISTRAR(Round_Op, "cpu", Aidge::RoundImpl_cpu::create);
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ROUNDIMPL_H_ */

include/aidge/backend/cpu/operator/RoundImpl_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_ROUNDIMPL_KERNELS_H_
+#define AIDGE_CPU_OPERATOR_ROUNDIMPL_KERNELS_H_
+
+#include <cmath>   //std::round 
+#include <cstddef>  // std::size_t
+
+#include "aidge/utils/Registrar.hpp"
+
+#include "aidge/backend/cpu/operator/RoundImpl.hpp"
+
+namespace Aidge {
+template <class I, class O>
+void RoundImpl_cpu_forward_kernel(const 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) {
+        //std::round would not work since it doesn't follow the halves rules (See ONNX Round)
+        output[i] = static_cast<O>(std::nearbyint(static_cast<float>(input[i])));
+    }
+}
+
+
+REGISTRAR(RoundImpl_cpu,
+    {DataType::Float32},
+    {ProdConso::inPlaceModel, Aidge::RoundImpl_cpu_forward_kernel<float, float>,nullptr});
+REGISTRAR(RoundImpl_cpu,
+    {DataType::Float64},
+    {ProdConso::inPlaceModel, Aidge::RoundImpl_cpu_forward_kernel<double, double>,nullptr});
+}  // namespace Aidge
+
+#endif /* AIDGE_CPU_OPERATOR_ROUNDIMPL_KERNELS_H_ */

src/operator/RoundImpl.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 <memory>
+#include <vector>
+
+#include "aidge/backend/cpu/data/GetCPUPtr.h"
+#include "aidge/data/Tensor.hpp"
+#include "aidge/operator/Round.hpp"
+#include "aidge/utils/ErrorHandling.hpp"
+#include "aidge/utils/Types.h"
+#include "aidge/backend/cpu/operator/RoundImpl.hpp"
+#include "aidge/backend/cpu/operator/RoundImpl_kernels.hpp"
+
+template <>
+void Aidge::RoundImpl_cpu::forward() {
+    std::shared_ptr<Tensor> in0 = std::static_pointer_cast<Tensor>(mOp.getRawInput(0));
+    std::shared_ptr<Tensor> out0 = std::static_pointer_cast<Tensor>(mOp.getRawOutput(0));
+    AIDGE_ASSERT(in0, "missing input #0");
+
+    // Find the correct kernel type
+    const auto impl = Registrar<RoundImpl_cpu>::create(getBestMatch(getRequiredSpec()));
+
+    // Call kernel
+    impl.forward(in0->size(),
+        getCPUPtr(mOp.getRawInput(0)),
+        getCPUPtr(mOp.getRawOutput(0)));
+}
+template <>
+void Aidge::RoundImpl_cpu::backward() {
+    AIDGE_THROW_OR_ABORT(std::runtime_error, "Backward not yet implemented for Round_Op on backend cpu");
+}
\ No newline at end of file

unit_tests/operator/Test_RoundImpl.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/Round.hpp"
+#include "aidge/utils/TensorUtils.hpp"
+
+namespace Aidge {
+
+TEST_CASE("[cpu/operator] Round_Test", "[Round][CPU]") {
+    constexpr std::uint16_t NBTRIALS = 15;
+    // Create a random number generator
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_real_distribution<float> valueDist(-15, 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));
+
+    // Create BitShift Operator
+    std::shared_ptr<Node> myRound = Round();
+    auto op = std::static_pointer_cast<OperatorTensor>(myRound-> getOperator());
+    op->setDataType(DataType::Float32);
+    op->setBackend("cpu");
+
+    // Create 2 input Tensors
+    std::shared_ptr<Tensor> T0 = std::make_shared<Tensor>();
+    op->associateInput(0,T0);
+    T0->setDataType(DataType::Float32);
+    T0->setBackend("cpu");
+    // Create results Tensor
+    std::shared_ptr<Tensor> Tres = std::make_shared<Tensor>();
+    Tres->setDataType(DataType::Float32);
+    Tres->setBackend("cpu");
+
+    // To measure execution time of 'Round_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("Round [Forward]") {
+        SECTION("Test Forward Kernel") {
+            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
+                float* array0 = new float[nb_elements];
+                float* result = new float[nb_elements];
+                
+                for (std::size_t i = 0; i < nb_elements; ++i) {
+                    array0[i] = valueDist(gen);
+                    result[i] = std::nearbyint(array0[i]);
+
+                }
+
+                // input0
+                T0->resize(dims);
+                T0 -> getImpl() -> setRawPtr(array0, nb_elements);
+
+                // results
+                Tres->resize(dims);
+                Tres -> getImpl() -> setRawPtr(result, nb_elements);
+                
+                op->forwardDims();
+                start = std::chrono::system_clock::now();
+                myRound->forward();
+                end = std::chrono::system_clock::now();
+                duration += std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+
+                bool is_eq = approxEq<float>(*(op->getOutput(0)), *Tres);
+
+                auto Output = *(op->getOutput(0));
+                
+                auto prt = Output.getImpl()->rawPtr();
+
+                REQUIRE(is_eq);
+                
+
+                delete[] array0;
+                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;
+        }
+    }
+} // namespace Aidge
+}
\ No newline at end of file