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Migrate conv2d layer to Tensor

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This commit is contained in:
Matúš Námešný
2025-11-19 20:20:46 +01:00
parent 10c84d75fc
commit dfdfa19022
10 changed files with 226 additions and 290 deletions
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48
src/backends/cuda/kernels/convolution.cu
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@@ -9,52 +9,50 @@ __global__ void Kernels::convolution(
const float* __restrict__ d_kernel,
const float* __restrict__ d_bias,
float* __restrict__ d_output,
const shape2d inputSize,
const int nChannels,
const shape2d paddingSize,
const shape2d kernelSize,
const shape2d stride,
const int nFilters,
const shape2d outputSize
const Shape input_shape,
const Shape padding_shape,
const Shape kernel_shape,
const Shape stride_shape,
const Shape output_shape
) {
int j = blockDim.x * blockIdx.x + threadIdx.x;
int i = blockDim.y * blockIdx.y + threadIdx.y;
int f = blockDim.z * blockIdx.z + threadIdx.z;
if (i >= outputSize.first || j >= outputSize.second || f >= nFilters) {
if (i >= output_shape[0] || j >= output_shape[1] || f >= output_shape[2]) {
return;
}
float sum = 0.0f;
// Iterate over kernel and input matrix
for (int c = 0; c < nChannels; c++) {
for (int k = 0; k < kernelSize.first; k++) {
for (int l = 0; l < kernelSize.second; l++) {
for (int c = 0; c < input_shape[2]; c++) {
for (int k = 0; k < kernel_shape[0]; k++) {
for (int l = 0; l < kernel_shape[1]; l++) {
// if i, j is in the padding region
if (i * stride.first + k < paddingSize.first ||
i * stride.first + k >=
(inputSize.first + paddingSize.first) ||
j * stride.second + l < paddingSize.second ||
j * stride.second + l >=
(inputSize.second + paddingSize.second)) {
if (i * stride_shape[0] + k < padding_shape[0] ||
i * stride_shape[0] + k >=
(input_shape[0] + padding_shape[0]) ||
j * stride_shape[1] + l < padding_shape[1] ||
j * stride_shape[1] + l >=
(input_shape[1] + padding_shape[1])) {
continue;
}
int kernelIndex =
f * kernelSize.first * kernelSize.second * nChannels +
c * kernelSize.first * kernelSize.second +
k * kernelSize.second + l;
int inputIndex = c * inputSize.first * inputSize.second +
(i * stride.first + k - paddingSize.first) *
inputSize.second +
(j * stride.second + l - paddingSize.second);
f * kernel_shape[0] * kernel_shape[1] * input_shape[2] +
c * kernel_shape[0] * kernel_shape[1] +
k * kernel_shape[1] + l;
int inputIndex = c * input_shape[0] * input_shape[1] +
(i * stride_shape[0] + k - padding_shape[0]) *
input_shape[1] +
(j * stride_shape[1] + l - padding_shape[1]);
sum += d_kernel[kernelIndex] * d_input[inputIndex];
}
}
}
d_output[f * outputSize.first * outputSize.second + i * outputSize.second + j] =
d_output[f * output_shape[0] * output_shape[1] + i * output_shape[1] + j] =
sum + d_bias[f];
}

32
src/backends/cuda/layer_ops.cu
View File

@@ -1,5 +1,6 @@
#include "backend/cuda.cuh"
#include "kernels/activation_functions.cuh"
#include "kernels/convolution.cuh"
#include "kernels/matmul.cuh"
#include "utils/cuda_helper.cuh"
@@ -57,7 +58,7 @@ CUDANet::Tensor& CUDA::dense(
const CUDANet::Tensor& weights,
const CUDANet::Tensor& biases,
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
CUDANet::Tensor& output,
const size_t input_size,
const size_t output_size
) {
@@ -78,5 +79,34 @@ CUDANet::Tensor& CUDA::dense(
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaDeviceSynchronize());
return output;
}
CUDANet::Tensor& CUDA::conv2d(
const CUDANet::Tensor& weights,
const CUDANet::Tensor& biases,
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
const CUDANet::Shape in_shape,
const CUDANet::Shape padding_shape,
const CUDANet::Shape kernel_shape,
const CUDANet::Shape stride_shape,
const CUDANet::Shape out_shape
) {
dim3 block(8, 8, 8);
dim3 grid(
(out_shape[0] + block.x - 1) / block.x,
(out_shape[1] + block.y - 1) / block.y,
(out_shape[3] + block.z - 1) / block.z
);
Kernels::convolution<<<grid, block>>>(
input.data<float>(), weights.data<float>(), biases.data<float>(),
output.data<float>(), in_shape, padding_shape, kernel_shape,
stride_shape, out_shape
);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaDeviceSynchronize());
return output;
}

20
src/backends/cuda/layers/conv2d.cu
View File

@@ -49,25 +49,5 @@ void Conv2d::toCuda() {
float* Conv2d::forwardCUDA(const float* d_input) {
// Convolve
dim3 block(8, 8, 8);
dim3 grid(
(outputSize.first + block.x - 1) / block.x,
(outputSize.second + block.y - 1) / block.y,
(numFilters + block.z - 1) / block.z
);
CUDANet::Utils::clear(d_output, outputSize.first * outputSize.second * numFilters);
Kernels::convolution<<<grid, block>>>(
d_input, d_weights, d_biases, d_output, inputSize, inputChannels,
paddingSize, kernelSize, stride, numFilters, outputSize
);
CUDA_CHECK(cudaGetLastError());
// Apply activation
activation->activate(d_output);
CUDA_CHECK(cudaDeviceSynchronize());
return d_output;
}

185
src/layers/conv2d.cpp
View File

@@ -1,111 +1,136 @@
#include <stdexcept>
#include <vector>
#include "activation.hpp"
#include "conv2d.hpp"
#include <format>
#include <stdexcept>
#include "layer.hpp"
#include "tensor.hpp"
using namespace CUDANet::Layers;
Conv2d::Conv2d(
shape2d inputSize,
int inputChannels,
shape2d kernelSize,
shape2d stride,
int numFilters,
shape2d paddingSize,
ActivationType activationType
CUDANet::Shape input_shape,
CUDANet::Shape kernel_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Backend* backend
)
: inputSize(inputSize),
inputChannels(inputChannels),
kernelSize(kernelSize),
stride(stride),
numFilters(numFilters),
paddingSize(paddingSize) {
outputSize = {
(inputSize.first - kernelSize.first + 2 * paddingSize.first) /
stride.first +
1,
(inputSize.second - kernelSize.second + 2 * paddingSize.second) /
stride.second +
1
};
: in_shape(input_shape),
kernel_shape(kernel_shape),
stride_shape(stride_shape),
padding_shape(padding_shape),
backend(backend) {
if (in_shape.size() != 3) {
throw std::runtime_error(
std::format(
"Invalid input shape. Expected 3 dims, got {}", in_shape
)
);
}
activation = new Activation(
activationType, outputSize.first * outputSize.second * numFilters
if (kernel_shape.size() != 3) {
throw std::runtime_error(
std::format(
"Invalid kernel shape. Expected 3 dims, got {}", kernel_shape
)
);
}
if (stride_shape.size() != 2) {
throw std::runtime_error(
std::format(
"Invalid stride shape. Expected 2 dims, got {}", stride_shape
)
);
}
if (padding_shape.size() != 2) {
throw std::runtime_error(
std::format(
"Invalid padding shape. Expected 2 dims, got {}", padding_shape
)
);
}
size_t out_h = (in_shape[0] - kernel_shape[0] + 2 * padding_shape[0]) /
stride_shape[0] +
1;
size_t out_w = (in_shape[1] - kernel_shape[1] + 2 * padding_shape[1]) /
stride_shape[1] +
1;
out_shape.resize(3);
out_shape[0] = out_h;
out_shape[1] = out_w;
out_shape[2] = kernel_shape[2];
output = CUDANet::Tensor(
Shape{out_shape[0] * out_shape[1] * out_shape[3]},
CUDANet::DType::FLOAT32, backend
);
weights.resize(
kernelSize.first * kernelSize.second * inputChannels * numFilters
weights = CUDANet::Tensor(
Shape{
kernel_shape[0] * kernel_shape[1] * kernel_shape[2] * in_shape[2]
},
CUDANet::DType::FLOAT32, backend
);
biases = CUDANet::Tensor(
Shape{kernel_shape[2]}, CUDANet::DType::FLOAT32, backend
);
initializeWeights();
biases.resize(numFilters);
initializeBiases();
#ifdef USE_CUDA
initCUDA();
toCuda();
#endif
weights.zero();
biases.zero();
}
Conv2d::~Conv2d() {
#ifdef USE_CUDA
delCUDA();
#endif
delete activation;
Conv2d::~Conv2d() {}
CUDANet::Tensor& Conv2d::forward(const CUDANet::Tensor& input) {
output.zero();
backend->conv2d(
weights,
biases,
input,
output,
in_shape,
padding_shape,
kernel_shape,
stride_shape,
out_shape
);
return output;
}
void Conv2d::initializeWeights() {
std::fill(weights.begin(), weights.end(), 0.0f);
CUDANet::Shape Conv2d::input_shape() {
return in_shape;
}
void Conv2d::initializeBiases() {
std::fill(biases.begin(), biases.end(), 0.0f);
CUDANet::Shape Conv2d::output_shape() {
return out_shape;
}
void Conv2d::setWeights(const float* weights_input) {
std::copy(weights_input, weights_input + weights.size(), weights.begin());
#ifdef USE_CUDA
toCuda();
#endif
size_t Conv2d::input_size() {
return sizeof(float) * in_shape[0] * in_shape[1] * in_shape[2];
}
std::vector<float> Conv2d::getWeights() {
size_t Conv2d::output_size() {
return sizeof(float) * out_shape[0] * out_shape[1] * out_shape[2];
}
void Conv2d::set_weights(void* input) {
weights.set_data<float>(static_cast<float*>(input));
}
CUDANet::Tensor& Conv2d::get_weights() {
return weights;
}
void Conv2d::setBiases(const float* biases_input) {
std::copy(biases_input, biases_input + biases.size(), biases.begin());
#ifdef USE_CUDA
toCuda();
#endif
void Conv2d::set_biases(void* input) {
biases.set_data<float>(static_cast<float*>(input));
}
std::vector<float> Conv2d::getBiases() {
CUDANet::Tensor& Conv2d::get_biases() {
return biases;
}
float* Conv2d::forwardCPU(const float* input) {
throw std::logic_error("Not implemented");
}
float* Conv2d::forward(const float* input) {
#ifdef USE_CUDA
return forwardCUDA(input);
#else
return forwardCPU(input);
#endif
}
int Conv2d::getOutputSize() {
return outputSize.first * outputSize.second * numFilters;
}
int Conv2d::getInputSize() {
return inputSize.first * inputSize.second * inputChannels;
}
shape2d Conv2d::getOutputDims() {
return outputSize;
CUDANet::Shape Conv2d::get_padding_shape() {
return padding_shape;
}

20
src/layers/dense.cpp
View File

@@ -5,34 +5,30 @@
using namespace CUDANet::Layers;
Dense::Dense(CUDANet::Backend* backend, CUDANet::Shape in, CUDANet::Shape out)
Dense::Dense(CUDANet::Shape in, CUDANet::Shape out, CUDANet::Backend* backend)
: backend(backend),
in_shape(in),
out_shape(out),
weights(
CUDANet::Tensor(Shape{in[0] * out[0]}, CUDANet::DType::FLOAT32, backend)
),
biases(CUDANet::Tensor(Shape{out[0]}, CUDANet::DType::FLOAT32, backend)),
output(CUDANet::Tensor(Shape{out[0]}, CUDANet::DType::FLOAT32, backend)) {
// Allocate memory for weights and biases
out_shape(out) {
if (in.size() != 1) {
throw std::runtime_error(
std::format("Invalid shape. Expected [1], got {}", in)
std::format("Invalid shape. Expected [1], got {}", in_shape)
);
}
if (out.size() != 1) {
throw std::runtime_error(
std::format("Invalid shape. Expected [1], got {}", out)
std::format("Invalid shape. Expected [1], got {}", out_shape)
);
}
auto input_len = in[0];
auto output_len = out[0];
weights = CUDANet::Tensor(Shape{in[0] * out[0]}, CUDANet::DType::FLOAT32, backend);
biases = CUDANet::Tensor(Shape{out[0]}, CUDANet::DType::FLOAT32, backend);
output = CUDANet::Tensor(Shape{out[0]}, CUDANet::DType::FLOAT32, backend);
weights.zero();
biases.zero();
output.zero();
}
Dense::~Dense() {}