Use tiling shmem for mat vec mul kernel

This commit is contained in:
2024-03-15 23:33:09 +01:00
parent 88f7fff217
commit dc86cddeb7
4 changed files with 54 additions and 24 deletions

View File

@@ -10,7 +10,11 @@
#include "dense.cuh"
#include "matmul.cuh"
Layers::Dense::Dense(int inputSize, int outputSize, Layers::Activation activation)
Layers::Dense::Dense(
int inputSize,
int outputSize,
Layers::Activation activation
)
: inputSize(inputSize), outputSize(outputSize), activation(activation) {
// Allocate memory for weights and biases
weights.resize(outputSize * inputSize);
@@ -31,8 +35,12 @@ Layers::Dense::Dense(int inputSize, int outputSize, Layers::Activation activatio
cudaMalloc((void**)&d_weights, sizeof(float) * inputSize * outputSize)
);
CUDA_CHECK(cudaMalloc((void**)&d_biases, sizeof(float) * outputSize));
toCuda();
// Calculate block and grid sizes
forwardGridSize =
(std::max(inputSize, outputSize) + BLOCK_SIZE - 1) / BLOCK_SIZE;
biasGridSize = (outputSize + BLOCK_SIZE - 1) / BLOCK_SIZE;
}
Layers::Dense::~Dense() {
@@ -51,21 +59,25 @@ void Layers::Dense::initializeBiases() {
}
float* Layers::Dense::forward(const float* d_input) {
Kernels::mat_vec_mul<<<1, std::max(inputSize, outputSize), sizeof(float) * inputSize>>>(
Kernels::mat_vec_mul<<<forwardGridSize, BLOCK_SIZE>>>(
d_weights, d_input, d_output, inputSize, outputSize
);
Kernels::vec_vec_add<<<1, outputSize>>>(
Kernels::vec_vec_add<<<biasGridSize, BLOCK_SIZE>>>(
d_biases, d_output, d_output, outputSize
);
switch (activation) {
case SIGMOID:
Kernels::sigmoid<<<1, outputSize>>>(d_output, d_output, outputSize);
Kernels::sigmoid<<<biasGridSize, BLOCK_SIZE>>>(
d_output, d_output, outputSize
);
break;
case RELU:
Kernels::relu<<<1, outputSize>>>(d_output, d_output, outputSize);
Kernels::relu<<<biasGridSize, BLOCK_SIZE>>>(
d_output, d_output, outputSize
);
break;
default: