Work

src/tensor/cpu/tensor.tpp

@@ -8,10 +8,7 @@
 // ===== CONSTRUCTORS =====
 template <typename T, int Dim>
 Tensor<T, Dim>::Tensor(const std::array<size_t, Dim> &shape) : ITensor(shape) {
-  size_t size = 1;
+  data_.resize(getSize());
-  for (size_t dim : shape)
-    size *= dim;
-  data_.resize(size);
 }
 template <typename T, int Dim>
 Tensor<T, Dim>::Tensor(const std::array<size_t, Dim> &shape, T value)

src/tensor/opencl/opencl.cpp

@@ -77,7 +77,8 @@ void OpenCL::initializeDevice() {
 
   device = devices[0];
   context = cl::Context(device);
-  queue = cl::CommandQueue(context, device);
+  queue =
+      cl::CommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE);
 
   std::cout << "Using device: " << device.getInfo<CL_DEVICE_NAME>()
             << "\nPlatform: " << platforms[0].getInfo<CL_PLATFORM_NAME>()

src/tensor/opencl/tensor.hpp

@@ -3,3 +3,110 @@
 #include "opencl.hpp"
 
 #include "../tensor.hpp"
+
+template <typename T, int Dim> class Tensor : public ITensor<T, Dim> {
+private:
+  cl::Buffer *data_ = nullptr;
+  cl::Event event_ = cl::Event();
+
+  template <typename... Events> std::vector<cl::Event> all(Events &&...events) {
+    return {std::forward<Events>(events)...};
+  }
+
+  void createBuf(size_t size) {
+    if (data_ != nullptr)
+      throw std::runtime_error("Tensor buffer already exists");
+    data_ = new cl::Buffer(openCL.getContext(), CL_MEM_READ_WRITE,
+                           size * sizeof(T));
+  }
+
+  void fillBuf(const std::vector<T> &data) {
+    createBuf(data.size());
+    openCL.getQueue().enqueueWriteBuffer(*data_, CL_FALSE, 0,
+                                         data.size() * sizeof(T), data.data(),
+                                         all(event_), &event_);
+  }
+  void fillBuf(size_t size, cl::Buffer *data) {
+    createBuf(size);
+    openCL.getQueue().enqueueWriteBuffer(*data_, CL_FALSE, 0,
+                                         data.size() * sizeof(T), other..data(),
+                                         all(event_), &event_);
+  }
+
+public:
+  typedef class ITensor<T, Dim> ITensor;
+
+  using ITensor::axes_;
+  using ITensor::checkAxisInDim;
+  using ITensor::checkItHasSameShape;
+  using ITensor::computeIndex;
+  using ITensor::getSize;
+  using ITensor::shape_;
+
+  Tensor() = delete;
+  Tensor(const std::array<size_t, Dim> &shape) : ITensor(shape) {
+    createBuf(getSize());
+  };
+  Tensor(const std::array<size_t, Dim> &shape, T value) : ITensor(shape) {
+    std::vector<T> data(getSize());
+    std::fill(data.begin(), data.end(), value);
+    fillBuf(data);
+  }
+  Tensor(const std::array<size_t, Dim> &shape, const std::vector<T> &data)
+      : ITensor(shape) {
+    fillBuf(data);
+  }
+  Tensor(const std::array<size_t, Dim> &shape, T min, T max) {
+    static std::random_device rd;
+    static std::mt19937 gen(rd());
+    std::vector<T> data(getSize());
+    if constexpr (std::is_integral_v<T>) {
+      std::uniform_int_distribution<T> dis(min, max);
+      for (T &e : data_)
+        e = dis(gen);
+    } else if constexpr (std::is_floating_point_v<T>) {
+      std::uniform_real_distribution<T> dis(min, max);
+      for (T &e : data_)
+        e = dis(gen);
+    } else
+      throw std::invalid_argument("Invalid randomized type");
+    fillBuf(data);
+  }
+
+  Tensor(const Tensor &other) : ITensor(other.shape) {
+    createBuf(other.getSize());
+    q.enqueueCopyBuffer(*other.buffer, *buffer, 0, 0,
+                        other.getSize() * sizeof(float));
+  }
+  Tensor &operator=(const Tensor &other);
+  Tensor(Tensor &&other) noexcept;
+  Tensor &operator=(Tensor &&other) noexcept;
+  ~Tensor() = default;
+
+  T &operator[](size_t i);
+  const T &operator[](size_t i) const;
+  template <typename... Indices> T &operator()(Indices... indices);
+  template <typename... Indices> const T &operator()(Indices... indices) const;
+
+  using ITensor::operator+;
+  using ITensor::operator-;
+
+  Tensor operator+() const override;
+  Tensor operator-() const override;
+
+  Tensor &operator+=(const T &scalar) override;
+
+  Tensor &operator*=(const T &scalar) override;
+
+  Tensor &operator+=(const Tensor &other) override;
+
+  Tensor &operator*=(const Tensor &other) override;
+
+  Tensor<T, Dim == 1 ? 0 : 2> operator%(const Tensor &other) const;
+
+  std::string toString() const override;
+};
+
+#include "tensor.tpp"
+
+#include "../fabric.hpp"