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Format source code using clang-format

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This commit is contained in:
Matúš Námešný
2024-02-27 18:51:22 +01:00
parent fb454de053
commit 48ba09b28d
12 changed files with 229 additions and 138 deletions
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72
src/layers/dense.cu
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@@ -1,16 +1,25 @@
#include "dense.cuh"
#include "cuda_helper.cuh"
#include "activations.cuh"
#include <cstdlib>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include <cstdio>
#include <iostream>
#include <cstdlib>
#include <functional>
#include <iostream>
Layers::Dense::Dense(int inputSize, int outputSize, std::string activation, cublasHandle_t cublasHandle)
: inputSize(inputSize), outputSize(outputSize), cublasHandle(cublasHandle), activation(activation) {
#include "activations.cuh"
#include "cuda_helper.cuh"
#include "dense.cuh"
Layers::Dense::Dense(
int inputSize,
int outputSize,
std::string activation,
cublasHandle_t cublasHandle
)
: inputSize(inputSize),
outputSize(outputSize),
cublasHandle(cublasHandle),
activation(activation) {
// Allocate memory for weights and biases
weights.resize(outputSize * inputSize);
biases.resize(outputSize);
@@ -19,10 +28,12 @@ Layers::Dense::Dense(int inputSize, int outputSize, std::string activation, cubl
initializeBiases();
d_weights = nullptr;
d_biases = 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_weights, sizeof(float) * inputSize * outputSize)
);
CUDA_CHECK(cudaMalloc((void**)&d_biases, sizeof(float) * outputSize));
toCuda();
@@ -44,30 +55,47 @@ void Layers::Dense::initializeBiases() {
void Layers::Dense::forward(const float* d_input, float* d_output) {
const float alpha = 1.0f;
const float beta = 1.0f;
const float beta = 1.0f;
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));
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)
);
int threadsPerBlock = 256;
int blocksPerGrid = (outputSize + threadsPerBlock - 1) / threadsPerBlock;
int blocksPerGrid = (outputSize + threadsPerBlock - 1) / threadsPerBlock;
if (activation == "sigmoid") {
sigmoid_kernel<<<blocksPerGrid, threadsPerBlock>>>(d_output, d_output, outputSize);
sigmoid_kernel<<<blocksPerGrid, threadsPerBlock>>>(
d_output, d_output, outputSize
);
} else if (activation == "relu") {
relu_kernel<<<blocksPerGrid, threadsPerBlock>>>(d_output, d_output, outputSize);
relu_kernel<<<blocksPerGrid, threadsPerBlock>>>(
d_output, d_output, outputSize
);
} else {
linear_kernel<<<blocksPerGrid, threadsPerBlock>>>(d_output, d_output, outputSize);
linear_kernel<<<blocksPerGrid, threadsPerBlock>>>(
d_output, d_output, outputSize
);
}
}
void Layers::Dense::toCuda() {
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));
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) {
void Layers::Dense::setWeights(
const std::vector<std::vector<float>>& weights_input
) {
int numWeights = inputSize * outputSize;
if (weights.size() != numWeights) {
@@ -77,7 +105,7 @@ void Layers::Dense::setWeights(const std::vector<std::vector<float>>& weights_in
for (int j = 0; j < inputSize; ++j) {
for (int i = 0; i < outputSize; ++i) {
int idx = IDX2C(i, j, outputSize);
int idx = IDX2C(i, j, outputSize);
weights[idx] = weights_input[i][j];
}
}