Basic Supervised Learning

Context

Required changes to add supervised learning to AIDGE features.

Modified files

• *Impl.hpp to add "cpu" string to constructor
• minor changes to clean some *Impl.cpp files

Detailed major modifications

• Add unit-test for operator+,-,*,/ of Tensor

TODO

unit_tests/data/Test_TensorImpl.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>   // std::accumulate
+#include <random>    // std::random_device, std::mt19937, std::uniform_real_distribution
+
+#include "aidge/data/Tensor.hpp"
+#include "aidge/backend/cpu/data/TensorImpl.hpp"
+#include "aidge/operator/Add.hpp"
+#include "aidge/backend/cpu/operator/AddImpl.hpp"
+
+namespace Aidge {
+
+TEST_CASE("Test addition of Tensors","[TensorImpl][Add]") {
+    constexpr std::uint16_t NBTRIALS = 10;
+    // Create a random number generator
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_real_distribution<float> valueDist(0.1f, 1.1f); // Random float distribution between 0 and 1
+    std::uniform_int_distribution<std::size_t> dimSizeDist(std::size_t(2), std::size_t(10));
+    std::uniform_int_distribution<int> boolDist(0,1);
+
+    // Create MatMul Operator
+    std::shared_ptr<Node> mySub = Add(2);
+    auto op = std::static_pointer_cast<OperatorTensor>(mySub-> 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");
+    std::shared_ptr<Tensor> T1 = std::make_shared<Tensor>();
+    op -> associateInput(1,T1);
+    T1->setDataType(DataType::Float32);
+    T1->setBackend("cpu");
+
+    // Create results Tensor
+    Tensor Tres{};
+    Tres.setDataType(DataType::Float32);
+    Tres.setBackend("cpu");
+
+    // To measure execution time of 'MatMul_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{};
+
+    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
+        float* array0 = new float[dims0[0]*dims0[1]*dims0[2]*dims0[3]];
+        float* array1 = new float[dims1[0]*dims1[1]*dims1[2]*dims1[3]];
+        float* result = new float[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] = valueDist(gen);
+        }
+
+        // compute true result
+        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);
+                        result[idx_out + d] = array0[idx0] + array1[idx1];
+                        // std::cout << "(" << idx0 << ", " << idx1 << ") -> " << array0[idx0] << " - " << array1[idx1] << " -> " << idx_out + d << std::endl;
+                    }
+                }
+            }
+        }
+
+        // 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]);
+
+        Tensor T2 = *T0 + *T1;
+        REQUIRE(T2 == Tres);
+
+    // no implementation
+        Tensor T3(T1->dims());
+        REQUIRE_THROWS(*T0 + T3);
+
+        // // wrong backend
+        // static Registrar<Add_Op> registrarAddImpl_custom("custom", [](const Add_Op& op) { return std::make_unique<AddImpl_cpu>(op); } );
+        // static Registrar<Tensor> registrarTensorImpl_custom_Int32({"custom", DataType::Int32},
+        //             [] (DeviceIdx_t device, std::vector<DimSize_t> dims) {
+        //                 return std::make_shared<TensorImpl_cpu<int>>(device, dims);
+        //             }
+        //         );
+        // T1.setBackend("custom");
+        // REQUIRE_THROWS(T0 + T1);
+
+    // wrong datatype
+        Tensor T4(T1->dims());
+        T4.setDataType(DataType::Float64);
+        REQUIRE_THROWS(*T0 + T4);
+    }
+}
+
+TEST_CASE("Test substraction of Tensors","[TensorImpl][Sub]") {
+    Tensor T0 = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+    Tensor T1 = Array3D<int, 2, 2, 2>{{{{7, 1}, {3, 7}}, {{54, 0}, {7, 12}}}};
+    Tensor T2 = T0 - T1;
+    T2.print();
+    REQUIRE(T2 == Tensor(Array3D<int, 2, 2, 2>{{{{-6,1},{0,-3}},{{-49,6},{0,-4}}}}));
+
+    Tensor T3(T1.dims());
+    REQUIRE_THROWS(T0 - T3);
+}
+
+TEST_CASE("Test multiplication of Tensors","[TensorImpl][Mul]") {
+    Tensor T0 = Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}};
+    Tensor T1 = Array3D<int, 2, 2, 2>{{{{7, 2}, {3, 7}}, {{5, 6}, {7, 8}}}};
+    Tensor T2 = T0 * T1;
+    T2.print();
+    REQUIRE(T2 == Tensor(Array3D<int, 2, 2, 2>{{{{7,4},{9,28}},{{25,36},{49,64}}}}));
+
+    Tensor T3(T1.dims());
+    REQUIRE_THROWS(T0 * T3);
+}
+
+TEST_CASE("Test division of Tensors","[TensorImpl][Div]") {
+    Tensor T0 = Array3D<int, 2, 2, 2>{{{{7,4},{9,28}},{{25,36},{49,64}}}};
+    Tensor T1 = Array3D<int, 2, 2, 2>{{{{7, 2}, {3, 7}}, {{5, 6}, {7, 8}}}};
+    Tensor T2 = T0 / T1;
+    T2.print();
+    REQUIRE(T2 == Tensor(Array3D<int, 2, 2, 2>{{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}}}));
+
+    Tensor T3(T1.dims());
+    REQUIRE_THROWS(T0 / T3);
+}
+} // namespace Aidge