Rename files to .cu and fix IDX2C usage

src/CMakeLists.txt

@@ -1,6 +1,6 @@
-set(LAYER_SOURCES layers/dense.cpp)
+set(LAYER_SOURCES layers/dense.cu)
 
 add_library(CUDANet
-    utils/cuda_helper.cpp
+    utils/cuda_helper.cu
     ${LAYER_SOURCES}
 )

src/layers/dense.cu

@@ -4,7 +4,7 @@
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
 #include <cstdio>
-#include <random>
+#include <iostream>
 
 Layers::Dense::Dense(int inputSize, int outputSize, cublasHandle_t cublasHandle)
     : inputSize(inputSize), outputSize(outputSize), cublasHandle(cublasHandle) {
@@ -16,9 +16,12 @@ Layers::Dense::Dense(int inputSize, int outputSize, cublasHandle_t cublasHandle)
     initializeWeights();
     initializeBiases();
 
+    d_weights = nullptr;
+    d_biases = nullptr;
+
     // Allocate GPU memory for weights and biases
     CUDA_CHECK(cudaMalloc((void**)&d_weights, sizeof(float) * inputSize * outputSize));
-    CUDA_CHECK(cudaMalloc((void**)&d_biases, sizeof(float) * biases.size()));
+    CUDA_CHECK(cudaMalloc((void**)&d_biases, sizeof(float) * outputSize));
 
     toCuda();
 }
@@ -30,43 +33,43 @@ Layers::Dense::~Dense() {
 }
 
 void Layers::Dense::initializeWeights() {
-    int numWeights = inputSize * outputSize;
 
-    std::random_device rd;
-    std::mt19937 gen(rd());
-    std::normal_distribution<float> dist(0.0f, 0.01f); // Xavier initialization
-
-    for (int i = 0; i < outputSize; ++i) {
-        for (int j = 0; j < inputSize; ++j) {
-            int idx = IDX2C(i, j, inputSize);
-            weights[idx] = dist(gen);
+    for (int j = 0; j < inputSize; ++j) {
+        for (int i = 0; i < outputSize; ++i) {
+            int idx = IDX2C(i, j, outputSize);
+            weights[idx] = 0.0f;
         }
     }
 }
 
 void Layers::Dense::initializeBiases() {
-    std::fill(biases.begin(), biases.end(), 0.1f);
+    std::fill(biases.begin(), biases.end(), 0.0f);
 }
 
 void Layers::Dense::forward(const float* d_input, float* d_output) {
     const float alpha = 1.0f;
     const float beta = 1.0f;
 
-    cublasSgemv(cublasHandle, CUBLAS_OP_N, inputSize, outputSize, &alpha, d_weights, inputSize, d_input, 1, &beta, d_output, 1);
-    cublasSaxpy(cublasHandle, outputSize, &alpha, d_biases, 1, d_output, 1);
+    CUBLAS_CHECK(cublasSgemv(cublasHandle, CUBLAS_OP_N, inputSize, outputSize, &alpha, d_weights, inputSize, d_input, 1, &beta, d_output, 1));
+    CUBLAS_CHECK(cublasSaxpy(cublasHandle, outputSize, &alpha, d_biases, 1, d_output, 1));
 }
 
 void Layers::Dense::toCuda() {
-    CUBLAS_CHECK(cublasSetMatrix(outputSize, inputSize, sizeof(float), weights.data(), inputSize, d_weights, outputSize));
+    CUBLAS_CHECK(cublasSetMatrix(outputSize, inputSize, sizeof(float), weights.data(), outputSize, d_weights, outputSize));
     CUBLAS_CHECK(cublasSetVector(biases.size(), sizeof(float), biases.data(), 1, d_biases, 1));
 }
 
 void Layers::Dense::setWeights(const std::vector<std::vector<float>>& weights_input) {
     int numWeights = inputSize * outputSize;
 
-    for (int i = 0; i < outputSize; ++i) {
-        for (int j = 0; j < inputSize; ++j) {
-            int idx = IDX2C(i, j, inputSize);
+    if (weights.size() != numWeights) {
+        std::cerr << "Invalid number of weights" << std::endl;
+        exit(EXIT_FAILURE);
+    }
+
+    for (int j = 0; j < inputSize; ++j) {
+        for (int i = 0; i < outputSize; ++i) {
+            int idx = IDX2C(i, j, outputSize);
             weights[idx] = weights_input[i][j];
         }
     }