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Refactor size calculations in layers and backend

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This commit is contained in:
Matúš Námešný
2025-11-27 22:01:09 +01:00
parent c855ae89ec
commit e79667671a
13 changed files with 58 additions and 64 deletions
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1
include/backend.hpp
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@@ -28,6 +28,7 @@ class Backend {
std::optional<DType> default_dtype;
public:
// Dtypes
virtual bool supports_dtype(DType dtype) const = 0;
virtual void set_default_dtype(DType dtype) = 0;
virtual DType get_default_dtype() const = 0;

4
include/module.hpp
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@@ -15,10 +15,6 @@ class Module {
CUDANet::Shape output_shape();
size_t input_size();
size_t output_size();
void register_layer(const std::string& name, Layer* layer);
void register_module(Module& module);

13
include/tensor.hpp
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@@ -16,6 +16,19 @@ enum class DType
// INT32, // Not implemented yet
};
size_t dtype_size(DType dtype) {
switch (dtype)
{
case DType::FLOAT32:
return 4;
break;
default:
throw std::runtime_error("Unknown DType");
break;
}
}
class Tensor
{
public:

3
src/backend_factory.cpp
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@@ -16,7 +16,7 @@ std::unique_ptr<Backend> BackendFactory::create(BackendType backend_type, const
#ifdef USE_CUDA
if (!CUDANet::Backends::CUDA::is_cuda_available()) {
throw std::runtime_error("No CUDA devices found")
throw std::runtime_error("No CUDA devices found");
}
auto cuda = std::make_unique<CUDANet::Backends::CUDA>(config);
@@ -31,6 +31,7 @@ std::unique_ptr<Backend> BackendFactory::create(BackendType backend_type, const
break;
default:
throw std::runtime_error("Invalid backend");
break;
}

1
src/backends/cuda/cuda.cu
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@@ -65,7 +65,6 @@ CUDANet::DType CUDA::get_default_dtype() const {
return DType::FLOAT32;
}
void* CUDA::allocate(size_t bytes) {
void* d_ptr = nullptr;
CUDA_CHECK(cudaMalloc(&d_ptr, bytes));

2
src/backends/cuda/kernels/convolution.cu
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@@ -36,7 +36,7 @@ __global__ void Kernels::convolution(
return;
}
T sum = static_cast<t>(0);
T sum = static_cast<T>(0);
// Iterate over kernel and input matrix
for (int c = 0; c < input_shape[2]; c++) {

50
src/backends/cuda/layer_ops.cu
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@@ -24,7 +24,7 @@ template <typename T>
void CUDA::relu_impl(Tensor& tensor) {
int gridSize = (tensor.numel() + BLOCK_SIZE - 1) / BLOCK_SIZE;
Kernels::relu<<<gridSize, BLOCK_SIZE>>>(
tensor.data<T>(), tensor.data<T>(), tensor.numel()
static_cast<T*>(tensor.device_ptr()), static_cast<T*>(tensor.device_ptr()), tensor.numel()
);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaDeviceSynchronize());
@@ -48,7 +48,7 @@ template <typename T>
void CUDA::sigmoid_impl(CUDANet::Tensor& tensor) {
int gridSize = (tensor.numel() + BLOCK_SIZE - 1) / BLOCK_SIZE;
Kernels::sigmoid<<<gridSize, BLOCK_SIZE>>>(
tensor.data<T>(), tensor.data<T>(), tensor.numel()
static_cast<T*>(tensor.device_ptr()), static_cast<T*>(tensor.device_ptr()), tensor.numel()
);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaDeviceSynchronize());
@@ -78,13 +78,13 @@ void CUDA::softmax_impl(Tensor& tensor, Tensor& temp_max, Tensor& temp_sum) {
// Subtract max value to improve numerical stability
Kernels::vec_scalar_sub<<<gridSize, BLOCK_SIZE>>>(
tensor.data<T>(), tensor.data<T>(), temp_max.data<T>(), tensor.numel()
static_cast<T*>(tensor.device_ptr()), static_cast<T*>(tensor.device_ptr()), static_cast<T*>(temp_max.device_ptr()), tensor.numel()
);
CUDA_CHECK(cudaGetLastError());
// Compute exponentials
Kernels::vec_exp<<<gridSize, BLOCK_SIZE>>>(
tensor.data<T>(), tensor.data<T>(), tensor.numel()
static_cast<T*>(tensor.device_ptr()), static_cast<T*>(tensor.device_ptr()), tensor.numel()
);
CUDA_CHECK(cudaGetLastError());
@@ -92,7 +92,7 @@ void CUDA::softmax_impl(Tensor& tensor, Tensor& temp_max, Tensor& temp_sum) {
sum(tensor, temp_sum);
Kernels::vec_scalar_div<<<gridSize, BLOCK_SIZE>>>(
tensor.data<T>(), tensor.data<T>(), temp_sum.data<T>(), tensor.numel()
static_cast<T*>(tensor.device_ptr()), static_cast<T*>(tensor.device_ptr()), static_cast<T*>(temp_sum.device_ptr()), tensor.numel()
);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaDeviceSynchronize());
@@ -142,13 +142,13 @@ CUDANet::Tensor& CUDA::dense_impl(
auto biasGridSize = (output_size + BLOCK_SIZE - 1) / BLOCK_SIZE;
Kernels::mat_vec_mul<<<forwardGridSize, BLOCK_SIZE>>>(
weights.data<T>(), input.data<T>(), output.data<T>(), input_size,
static_cast<T*>(weights.device_ptr()), static_cast<T*>(input.device_ptr()), static_cast<T*>(output.device_ptr()), input_size,
output_size
);
CUDA_CHECK(cudaGetLastError());
Kernels::vec_vec_add<<<biasGridSize, BLOCK_SIZE>>>(
biases.data<T>(), output.data<T>(), output.data<T>(), output_size
static_cast<T*>(biases.device_ptr()), static_cast<T*>(output.device_ptr()), static_cast<T*>(output.device_ptr()), output_size
);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaDeviceSynchronize());
@@ -213,7 +213,7 @@ CUDANet::Tensor& CUDA::conv2d_impl(
);
Kernels::convolution<<<grid, block>>>(
input.data<T>(), weights.data<T>(), biases.data<T>(), output.data<T>(),
static_cast<T*>(input.device_ptr())(), static_cast<T*>(weights.device_ptr())(), static_cast<T*>(biases.device_ptr())(), static_cast<T*>(output.device_ptr())(),
in_shape, padding_shape, kernel_shape, stride_shape, out_shape
);
CUDA_CHECK(cudaGetLastError());
@@ -273,7 +273,7 @@ CUDANet::Tensor& CUDA::max_pool2d_impl(
);
Kernels::max_pool<<<grid, block>>>(
input.data<T>(), output.data<T>(), input_shape, output_shape,
static_cast<T*>(input.device_ptr())(), static_cast<T*>(output.device_ptr())(), input_shape, output_shape,
pool_shape, stride_shape, padding_shape
);
CUDA_CHECK(cudaGetLastError());
@@ -333,7 +333,7 @@ CUDANet::Tensor& CUDA::avg_pool2d_impl(
);
Kernels::avg_pool<<<grid, block>>>(
input.data<T>(), output.data<T>(), input_shape, output_shape,
static_cast<T*>(input.device_ptr())(), static_cast<T*>(output.device_ptr())(), input_shape, output_shape,
pool_shape, stride_shape, padding_shape
);
CUDA_CHECK(cudaGetLastError());
@@ -394,34 +394,34 @@ CUDANet::Tensor& CUDA::batch_norm_impl(
for (int i = 0; i < input_shape[2]; i++) {
// Subtract mean from input
Kernels::vec_scalar_sub<<<gridSize, BLOCK_SIZE>>>(
input.data<T>() + i * input_shape[0] * input_shape[1],
output.data<T>() + i * input_shape[0] * input_shape[1],
&running_mean.data<T>()[i], input_shape[0] * input_shape[1]
static_cast<T*>(input.device_ptr())() + i * input_shape[0] * input_shape[1],
static_cast<T*>(output.device_ptr())() + i * input_shape[0] * input_shape[1],
&static_cast<T*>(running_mean.device_ptr())()[i], input_shape[0] * input_shape[1]
);
CUDA_CHECK(cudaGetLastError());
// Divide by sqrt(running_var + epsilon)
Kernels::vec_scale<<<gridSize, BLOCK_SIZE>>>(
output.data<T>() + i * input_shape[0] * input_shape[1],
output.data<T>() + i * input_shape[0] * input_shape[1],
&running_var.data<T>()[i], epsilon.data<T>(),
static_cast<T*>(output.device_ptr())() + i * input_shape[0] * input_shape[1],
static_cast<T*>(output.device_ptr())() + i * input_shape[0] * input_shape[1],
&static_cast<T*>(running_var.device_ptr())()[i], static_cast<T*>(epsilon.device_ptr())(),
input_shape[0] * input_shape[1]
);
CUDA_CHECK(cudaGetLastError());
// Multiply by weights
Kernels::vec_scalar_mul<<<gridSize, BLOCK_SIZE>>>(
output.data<T>() + i * input_shape[0] * input_shape[1],
output.data<T>() + i * input_shape[0] * input_shape[1],
&weights.data<T>()[i], input_shape[0] * input_shape[1]
static_cast<T*>(output.device_ptr())() + i * input_shape[0] * input_shape[1],
static_cast<T*>(output.device_ptr())() + i * input_shape[0] * input_shape[1],
&static_cast<T*>(weights.device_ptr())()[i], input_shape[0] * input_shape[1]
);
CUDA_CHECK(cudaGetLastError());
// Add biases
Kernels::vec_scalar_add<<<gridSize, BLOCK_SIZE>>>(
output.data<T>() + i * input_shape[0] * input_shape[1],
output.data<T>() + i * input_shape[0] * input_shape[1],
&biases.data<T>()[i], input_shape[0] * input_shape[1]
static_cast<T*>(output.device_ptr())() + i * input_shape[0] * input_shape[1],
static_cast<T*>(output.device_ptr())() + i * input_shape[0] * input_shape[1],
&static_cast<T*>(biases.device_ptr())()[i], input_shape[0] * input_shape[1]
);
CUDA_CHECK(cudaGetLastError());
}
@@ -460,12 +460,12 @@ CUDANet::Tensor& CUDA::concat_impl(
CUDANet::Tensor& output
) {
CUDA_CHECK(cudaMemcpy(
output.data<T>(), input_a.data<T>(), input_a.size(),
static_cast<T*>(output.device_ptr())(), static_cast<T*>(input_a.device_ptr())(), input_a.size(),
cudaMemcpyDeviceToDevice
));
CUDA_CHECK(cudaMemcpy(
output.data<T>() + input_a.numel(), input_b.data<T>(), input_b.size(),
static_cast<T*>(output.device_ptr())() + input_a.numel(), static_cast<T*>(input_b.device_ptr())(), input_b.size(),
cudaMemcpyDeviceToDevice
));
@@ -508,7 +508,7 @@ CUDANet::Tensor& CUDA::add_impl(
auto gridSize = (input_a.numel() + BLOCK_SIZE - 1) / BLOCK_SIZE;
Kernels::vec_vec_add<<<gridSize, BLOCK_SIZE>>>(
input_a.data<T>(), input_b.data<T>(), output.data<T>(), input_a.numel()
static_cast<T*>(input_a.device_ptr())(), static_cast<T*>(input_b.device_ptr())(), static_cast<T*>(output.device_ptr())(), input_a.numel()
);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaDeviceSynchronize());

16
src/backends/cuda/tensor_ops.cu
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@@ -26,7 +26,7 @@ void CUDA::print_impl(const CUDANet::Tensor &input) {
std::vector<T> h_vec(input.numel());
CUDA_CHECK(cudaMemcpy(
h_vec.data(), input.data<T>(), sizeof(T) * length, cudaMemcpyDeviceToHost
h_vec.data(), static_cast<T*>(input.device_ptr())(), sizeof(T) * length, cudaMemcpyDeviceToHost
));
for (int i = 0; i < length; ++i) {
@@ -56,7 +56,7 @@ template void CUDA::fill_impl<float>(CUDANet::Tensor &input, int value);
template <typename T>
void CUDA::fill_impl(CUDANet::Tensor &input, int value) {
CUDA_CHECK(cudaMemset(input.data<T>(), value, sizeof(T) * input.numel()));
CUDA_CHECK(cudaMemset(static_cast<T*>(input.device_ptr())(), value, sizeof(T) * input.numel()));
}
void CUDA::copy_to_device(CUDANet::Tensor &tensor, void *data, size_t size) {
@@ -75,7 +75,7 @@ template void CUDA::copy_to_device_impl<float>(CUDANet::Tensor &tensor, void *da
template <typename T>
void CUDA::copy_to_device_impl(CUDANet::Tensor &tensor, void *data, size_t size) {
CUDA_CHECK(cudaMemcpy(tensor.data<T>(), data, size, cudaMemcpyHostToDevice));
CUDA_CHECK(cudaMemcpy(static_cast<T*>(tensor.device_ptr())(), data, size, cudaMemcpyHostToDevice));
}
void CUDA::sum(const CUDANet::Tensor &input, CUDANet::Tensor &sum) {
@@ -95,17 +95,17 @@ template void CUDA::sum_impl<float>(const CUDANet::Tensor &input, CUDANet::Tenso
template <typename T>
void CUDA::sum_impl(const CUDANet::Tensor &input, CUDANet::Tensor &sum) {
auto length = input.numel();
const int gridSize = ( + BLOCK_SIZE - 1) / BLOCK_SIZE;
const int gridSize = (length + BLOCK_SIZE - 1) / BLOCK_SIZE;
CUDANet::Kernels::sum_reduce<<<gridSize, BLOCK_SIZE>>>(
input.data<T>(), sum.data<T>(), length
static_cast<T*>(input.device_ptr())(), static_cast<T*>(sum.device_ptr())(), length
);
CUDA_CHECK(cudaGetLastError());
int remaining = gridSize;
while (remaining > 1) {
int blocks_needed = (remaining + BLOCK_SIZE - 1) / BLOCK_SIZE;
CUDANet::Kernels::sum_reduce<<<blocks_needed, BLOCK_SIZE>>>(sum.data<T>(), sum.data<T>(), remaining);
CUDANet::Kernels::sum_reduce<<<blocks_needed, BLOCK_SIZE>>>(static_cast<T*>(sum.device_ptr())(), static_cast<T*>(sum.device_ptr())(), remaining);
CUDA_CHECK(cudaGetLastError());
remaining = blocks_needed;
@@ -131,14 +131,14 @@ void CUDA::max_impl(const CUDANet::Tensor &input, CUDANet::Tensor &max) {
auto length = input.numel();
const int grid_size = (length + BLOCK_SIZE - 1) / BLOCK_SIZE;
Kernels::max_reduce<<<grid_size, BLOCK_SIZE>>>(input.data<T>(), max.data<T>(), length);
Kernels::max_reduce<<<grid_size, BLOCK_SIZE>>>(static_cast<T*>(input.device_ptr())(), static_cast<T*>(max.device_ptr())(), length);
CUDA_CHECK(cudaGetLastError());
int remaining = grid_size;
while (remaining > 1) {
int blocks_needed = (remaining + BLOCK_SIZE - 1) / BLOCK_SIZE;
CUDANet::Kernels::max_reduce<<<blocks_needed, BLOCK_SIZE>>>(max.data<T>(), max.data<T>(), remaining);
CUDANet::Kernels::max_reduce<<<blocks_needed, BLOCK_SIZE>>>(static_cast<T*>(max.device_ptr())(), static_cast<T*>(max.device_ptr())(), remaining);
CUDA_CHECK(cudaGetLastError());
remaining = blocks_needed;

4
src/layers/avg_pooling.cpp
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@@ -84,11 +84,11 @@ CUDANet::Shape AvgPool2d::output_shape() {
}
size_t AvgPool2d::input_size() {
return sizeof(float) * in_shape[0] * in_shape[1] * in_shape[2];
return dtype_size(dtype) * in_shape[0] * in_shape[1] * in_shape[2];
}
size_t AvgPool2d::output_size() {
return sizeof(float) * out_shape[0] * out_shape[1] * out_shape[2];
return dtype_size(dtype) * out_shape[0] * out_shape[1] * out_shape[2];
}
void AvgPool2d::set_weights(void* input) {}

4
src/layers/batch_norm.cpp
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@@ -73,11 +73,11 @@ CUDANet::Shape BatchNorm2d::output_shape() {
}
size_t BatchNorm2d::input_size() {
return sizeof(float) * in_shape[0] * in_shape[1] * in_shape[2];
return dtype_size(dtype) * in_shape[0] * in_shape[1] * in_shape[2];
}
size_t BatchNorm2d::output_size() {
return sizeof(float) * in_shape[0] * in_shape[1] * in_shape[2];
return dtype_size(dtype) * in_shape[0] * in_shape[1] * in_shape[2];
}
void BatchNorm2d::set_weights(void* input) {

4
src/layers/conv2d.cpp
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@@ -97,11 +97,11 @@ CUDANet::Shape Conv2d::output_shape() {
}
size_t Conv2d::input_size() {
return sizeof(float) * in_shape[0] * in_shape[1] * in_shape[2];
return dtype_size(dtype) * in_shape[0] * in_shape[1] * in_shape[2];
}
size_t Conv2d::output_size() {
return sizeof(float) * out_shape[0] * out_shape[1] * out_shape[2];
return dtype_size(dtype) * out_shape[0] * out_shape[1] * out_shape[2];
}
void Conv2d::set_weights(void* input) {

4
src/layers/max_pool.cpp
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@@ -78,11 +78,11 @@ CUDANet::Shape MaxPool2d::output_shape() {
}
size_t MaxPool2d::input_size() {
return sizeof(float) * in_shape[0] * in_shape[1] * in_shape[2];
return dtype_size(dtype) * in_shape[0] * in_shape[1] * in_shape[2];
}
size_t MaxPool2d::output_size() {
return sizeof(float) * out_shape[0] * out_shape[1] * out_shape[2];
return dtype_size(dtype) * out_shape[0] * out_shape[1] * out_shape[2];
}
void MaxPool2d::set_weights(void* input) {}

16
src/module.cpp
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@@ -12,22 +12,6 @@ CUDANet::Shape Module::output_shape() {
return out_shape;
}
size_t Module::input_size() {
size_t count = 1;
for (const auto& dim : in_shape) {
count *= dim;
}
return sizeof(float) * count;
}
size_t Module::output_size() {
size_t count = 1;
for (const auto& dim : out_shape) {
count *= dim;
}
return sizeof(float) * count;
}
void Module::register_layer(const std::string& name, Layer* layer) {
layers.push_back({name, layer});
}