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Add Kernels namespace

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This commit is contained in:
Matúš Námešný
2024-03-11 21:04:23 +01:00
parent e0178e2d5c
commit d2ab78fbc7
18 changed files with 188 additions and 186 deletions
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4
src/kernels/activations.cu
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@@ -2,7 +2,7 @@
#include "activations.cuh"
__global__ void sigmoid_kernel(
__global__ void Kernels::sigmoid(
const float* __restrict__ src,
float* __restrict__ dst,
int len
@@ -16,7 +16,7 @@ __global__ void sigmoid_kernel(
}
__global__ void
relu_kernel(const float* __restrict__ src, float* __restrict__ dst, int len) {
Kernels::relu(const float* __restrict__ src, float* __restrict__ dst, int len) {
int stride = gridDim.x * blockDim.x;
int tid = blockDim.x * blockIdx.x + threadIdx.x;

79
src/kernels/convolution.cu
View File

@@ -1,7 +1,84 @@
#include "convolution.cuh"
#include <iostream>
__global__ void convolution_kernel(
/*
Pads matrix width x height x n_channels to width + 2 * padding x height + 2 *
padding x n_channels Matrix is represented as a pointer to a vector
For example:
w = 2
h = 3
n = 2
p = 1
Channel 0:
0 1
2 3
4 5
Channel 1:
6 7
8 9
10 11
Is represented as:
0 1 2 3 4 5 6 7 8 9 10 11
Padded result (as a continuous vector):
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f,
0.0f, 2.0f, 3.0f, 0.0f,
0.0f, 4.0f, 5.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 6.0f, 7.0f, 0.0f,
0.0f, 8.0f, 9.0f, 0.0f,
9.0f, 10.0f, 11.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f
Args:
d_input: Pointer to input vector representing matrix
d_padded: Pointer to output vector representing padded matrix (needs to be
pre-allocated)
w: Width of input matrix
h: Height of input matrix
n: Number of channels in input matrix
p: Padding
*/
__global__ void Kernels::padding(
const float* d_input,
float* d_padded,
int w,
int h,
int n,
int p
) {
int tid = blockDim.x * blockIdx.x + threadIdx.x;
if (tid >= (w + 2 * p) * (h + 2 * p) * n) {
return;
}
int idx = tid;
// unravel index into padded matrix
int i_n = idx / ((w + 2 * p) * (h + 2 * p));
int i_h = idx % ((w + 2 * p) * (h + 2 * p)) / (w + 2 * p);
int i_w = idx % (w + 2 * p);
// if i is in the padding region
if (i_w < p || i_w >= (w + p) || i_h < p || i_h >= (h + p)) {
d_padded[tid] = 0.0f;
} else {
// Get index into input vector
int i_orig = i_n * w * h + (i_h - p) * w + (i_w - p);
d_padded[tid] = d_input[i_orig];
}
}
__global__ void Kernels::convolution(
const float* d_input,
const float* d_kernel,
float* d_output,

6
src/kernels/matrix_math.cu → src/kernels/matmul.cu
View File

@@ -1,6 +1,6 @@
#include "matrix_math.cuh"
#include "matmul.cuh"
__global__ void mat_vec_mul_kernel(
__global__ void Kernels::mat_vec_mul(
const float* d_matrix,
const float* d_vector,
float* d_output,
@@ -22,7 +22,7 @@ __global__ void mat_vec_mul_kernel(
}
__global__ void vec_vec_add_kernel(
__global__ void Kernels::vec_vec_add(
const float* d_vector1,
const float* d_vector2,
float* d_output,

78
src/kernels/padding.cu
View File

@@ -1,78 +0,0 @@
#include <vector>
/*
Pads matrix width x height x n_channels to width + 2 * padding x height + 2 *
padding x n_channels Matrix is represented as a pointer to a vector
For example:
w = 2
h = 3
n = 2
p = 1
Channel 0:
0 1
2 3
4 5
Channel 1:
6 7
8 9
10 11
Is represented as:
0 1 2 3 4 5 6 7 8 9 10 11
Padded result (as a continuous vector):
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f,
0.0f, 2.0f, 3.0f, 0.0f,
0.0f, 4.0f, 5.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 6.0f, 7.0f, 0.0f,
0.0f, 8.0f, 9.0f, 0.0f,
9.0f, 10.0f, 11.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f
Args:
d_input: Pointer to input vector representing matrix
d_padded: Pointer to output vector representing padded matrix (needs to be
pre-allocated)
w: Width of input matrix
h: Height of input matrix
n: Number of channels in input matrix
p: Padding
*/
__global__ void pad_matrix_kernel(
const float* d_input,
float* d_padded,
int w,
int h,
int n,
int p
) {
int tid = blockDim.x * blockIdx.x + threadIdx.x;
if (tid >= (w + 2 * p) * (h + 2 * p) * n) {
return;
}
int idx = tid;
// unravel index into padded matrix
int i_n = idx / ((w + 2 * p) * (h + 2 * p));
int i_h = idx % ((w + 2 * p) * (h + 2 * p)) / (w + 2 * p);
int i_w = idx % (w + 2 * p);
// if i is in the padding region
if (i_w < p || i_w >= (w + p) || i_h < p || i_h >= (h + p)) {
d_padded[tid] = 0.0f;
} else {
// Get index into input vector
int i_orig = i_n * w * h + (i_h - p) * w + (i_w - p);
d_padded[tid] = d_input[i_orig];
}
}

60
src/layers/conv2d.cu
View File

@@ -5,17 +5,16 @@
#include "conv2d.cuh"
#include "convolution.cuh"
#include "cuda_helper.cuh"
#include "matrix_math.cuh"
#include "padding.cuh"
#include "matmul.cuh"
Layers::Conv2d::Conv2d(
int inputSize,
int inputChannels,
int kernelSize,
int stride,
Padding padding,
int numFilters,
Activation activation
int inputSize,
int inputChannels,
int kernelSize,
int stride,
Layers::Padding padding,
int numFilters,
Layers::Activation activation
)
: inputSize(inputSize),
inputChannels(inputChannels),
@@ -23,21 +22,19 @@ Layers::Conv2d::Conv2d(
stride(stride),
numFilters(numFilters),
activation(activation) {
switch (padding) {
case SAME:
outputSize = inputSize;
paddingSize = ((stride - 1) * inputSize - stride + kernelSize) / 2;
break;
switch (padding)
{
case SAME:
outputSize = inputSize;
paddingSize = ((stride - 1) * inputSize - stride + kernelSize) / 2;
break;
case VALID:
paddingSize = 0;
outputSize = (inputSize - kernelSize) / stride + 1;
break;
case VALID:
paddingSize = 0;
outputSize = (inputSize - kernelSize) / stride + 1;
break;
default:
break;
default:
break;
}
weights.resize(kernelSize * kernelSize * inputChannels * numFilters);
@@ -109,19 +106,19 @@ void Layers::Conv2d::forward(const float* d_input, float* d_output) {
int THREADS_PER_BLOCK = (inputSize + 2 * paddingSize) *
(inputSize + 2 * paddingSize) * inputChannels;
pad_matrix_kernel<<<1, THREADS_PER_BLOCK>>>(
Kernels::padding<<<1, THREADS_PER_BLOCK>>>(
d_input, d_padded, inputSize, inputSize, inputChannels, paddingSize
);
// Convolve
THREADS_PER_BLOCK = outputSize * outputSize * numFilters;
convolution_kernel<<<1, THREADS_PER_BLOCK>>>(
Kernels::convolution<<<1, THREADS_PER_BLOCK>>>(
d_padded, d_weights, d_output, inputSize + (2 * paddingSize),
inputChannels, kernelSize, stride, numFilters, outputSize
);
// Add bias
vec_vec_add_kernel<<<1, biases.size()>>>(
Kernels::vec_vec_add<<<1, biases.size()>>>(
d_biases, d_output, d_output, biases.size()
);
@@ -138,8 +135,7 @@ outputSize x numFilters
*/
void Layers::Conv2d::host_conv(const float* input, float* output) {
// Iterate over output matrix
for (int tid = 0; tid < outputSize * outputSize * numFilters; tid++)
{
for (int tid = 0; tid < outputSize * outputSize * numFilters; tid++) {
// Get output index
int f = tid / (outputSize * outputSize);
int i = tid % (outputSize * outputSize) / outputSize;
@@ -153,19 +149,17 @@ void Layers::Conv2d::host_conv(const float* input, float* output) {
for (int c = 0; c < inputChannels; c++) {
int kernelIndex =
f * kernelSize * kernelSize * inputChannels +
c * kernelSize * kernelSize + k * kernelSize +
l;
c * kernelSize * kernelSize + k * kernelSize + l;
int inputIndex = c * inputSize * inputSize +
(i * stride + k) * inputSize +
(j * stride + l);
(i * stride + k) * inputSize +
(j * stride + l);
sum += weights[kernelIndex] * input[inputIndex];
}
}
}
int outputIndex =
f * outputSize * outputSize + i * outputSize + j;
int outputIndex = f * outputSize * outputSize + i * outputSize + j;
output[outputIndex] = sum;
}

12
src/layers/dense.cu
View File

@@ -8,9 +8,9 @@
#include "activations.cuh"
#include "cuda_helper.cuh"
#include "dense.cuh"
#include "matrix_math.cuh"
#include "matmul.cuh"
Layers::Dense::Dense(int inputSize, int outputSize, 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);
@@ -46,21 +46,21 @@ void Layers::Dense::initializeBiases() {
}
void Layers::Dense::forward(const float* d_input, float* d_output) {
mat_vec_mul_kernel<<<1, outputSize>>>(
Kernels::mat_vec_mul<<<1, outputSize>>>(
d_weights, d_input, d_output, inputSize, outputSize
);
vec_vec_add_kernel<<<1, outputSize>>>(
Kernels::vec_vec_add<<<1, outputSize>>>(
d_biases, d_output, d_output, outputSize
);
switch (activation) {
case SIGMOID:
sigmoid_kernel<<<1, outputSize>>>(d_output, d_output, outputSize);
Kernels::sigmoid<<<1, outputSize>>>(d_output, d_output, outputSize);
break;
case RELU:
relu_kernel<<<1, outputSize>>>(d_output, d_output, outputSize);
Kernels::relu<<<1, outputSize>>>(d_output, d_output, outputSize);
break;
default: