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Refactor CUDA kernels and tensor operations for type generality

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This commit is contained in:
Matúš Námešný
2025-11-26 20:47:55 +01:00
parent 13d3d38b68
commit 9ff214d759
14 changed files with 818 additions and 297 deletions
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208
include/backend/cuda/cuda.cuh
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@@ -8,53 +8,60 @@
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 128
#endif // BLOCK_SIZE
#endif // BLOCK_SIZE
/**
* @brief CUDA error checking macro
*
*
*/
#define CUDA_CHECK(call) \
do { \
cudaError_t result = call; \
if (result != cudaSuccess) { \
fprintf(stderr, "CUDA error at %s:%d code=%d(%s) \"%s\" \n", \
__FILE__, __LINE__, static_cast<unsigned int>(result), \
cudaGetErrorString(result), #call); \
exit(EXIT_FAILURE); \
} \
} while (0)
#define CUDA_CHECK(call) \
do { \
cudaError_t result = call; \
if (result != cudaSuccess) { \
fprintf( \
stderr, "CUDA error at %s:%d code=%d(%s) \"%s\" \n", __FILE__, \
__LINE__, static_cast<unsigned int>(result), \
cudaGetErrorString(result), #call \
); \
exit(EXIT_FAILURE); \
} \
} while (0)
namespace CUDANet::Backends {
template <DType dtype>
struct cuda_dtype_map;
template <>
struct cuda_dtype_map<DType::FLOAT32> {
using type = float;
};
class CUDA : public Backend {
private:
int device_id;
std::set<DType> supported_dtypes;
public:
CUDA(const BackendConfig& config);
bool supports_dtype(DType dtype) const override;
void set_default_dtype(DType dtype) override;
bool supports_dtype(DType dtype) const override;
void set_default_dtype(DType dtype) override;
DType get_default_dtype() const override;
static bool is_cuda_available();
void initialize();
void initialize();
// Memory management
void* allocate(size_t bytes) override;
void deallocate(void* ptr) override;
// Tensor ops
// Tensor ops dispatchers
void print(const CUDANet::Tensor& input) override;
void zero(CUDANet::Tensor& input) override;
void fill(CUDANet::Tensor &input, int value) override;
void fill(CUDANet::Tensor& input, int value) override;
void
copy_to_device(CUDANet::Tensor& tensor, void* data, size_t size) override;
void sum(const CUDANet::Tensor& input, CUDANet::Tensor& sum) override;
void max(const CUDANet::Tensor& input, CUDANet::Tensor& max) override;
// Layer ops
// Layer ops dispatchers
void relu(CUDANet::Tensor& tensor) override;
void sigmoid(CUDANet::Tensor& tensor) override;
void softmax(
@@ -67,7 +74,7 @@ class CUDA : public Backend {
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
) override;
@@ -76,43 +83,43 @@ class CUDA : public Backend {
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
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
) override;
CUDANet::Tensor& max_pool2d(
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Shape output_shape
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Shape output_shape
) override;
CUDANet::Tensor& avg_pool2d(
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Shape output_shape
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Shape output_shape
) override;
CUDANet::Tensor& batch_norm(
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Tensor& weights,
CUDANet::Tensor& biases,
CUDANet::Tensor& running_mean,
CUDANet::Tensor& running_var,
CUDANet::Tensor& epsilon
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Tensor& weights,
CUDANet::Tensor& biases,
CUDANet::Tensor& running_mean,
CUDANet::Tensor& running_var,
CUDANet::Tensor& epsilon
) override;
CUDANet::Tensor& concat(
@@ -126,6 +133,111 @@ class CUDA : public Backend {
CUDANet::Tensor& input_b,
CUDANet::Tensor& output
) override;
private:
int device_id;
std::set<DType> supported_dtypes;
// Tensor ops template impls
template <typename T>
void print_impl(const CUDANet::Tensor& input);
template <typename T>
void fill_impl(CUDANet::Tensor& input, int value);
template <typename T>
void copy_to_device_impl(CUDANet::Tensor& tensor, void* data, size_t size);
template <typename T>
void sum_impl(const CUDANet::Tensor& input, CUDANet::Tensor& sum);
template <typename T>
void max_impl(const CUDANet::Tensor& input, CUDANet::Tensor& max);
// Layer ops template impls
template <typename T>
void relu_impl(CUDANet::Tensor& tensor);
template <typename T>
void sigmoid_impl(CUDANet::Tensor& tensor);
template <typename T>
void softmax_impl(
CUDANet::Tensor& tensor,
CUDANet::Tensor& temp_max,
CUDANet::Tensor& temp_sum
);
template <typename T>
CUDANet::Tensor& dense_impl(
const CUDANet::Tensor& weights,
const CUDANet::Tensor& biases,
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
const size_t input_size,
const size_t output_size
);
template <typename T>
CUDANet::Tensor& conv2d_impl(
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
);
template <typename T>
CUDANet::Tensor& max_pool2d_impl(
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Shape output_shape
);
template <typename T>
CUDANet::Tensor& avg_pool2d_impl(
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Shape output_shape
);
template <typename T>
CUDANet::Tensor& batch_norm_impl(
const CUDANet::Tensor& input,
CUDANet::Tensor& output,
CUDANet::Shape input_shape,
CUDANet::Tensor& weights,
CUDANet::Tensor& biases,
CUDANet::Tensor& running_mean,
CUDANet::Tensor& running_var,
CUDANet::Tensor& epsilon
);
template <typename T>
CUDANet::Tensor& concat_impl(
CUDANet::Tensor& input_a,
CUDANet::Tensor& input_b,
CUDANet::Tensor& output
);
template <typename T>
CUDANet::Tensor& add_impl(
CUDANet::Tensor& input_a,
CUDANet::Tensor& input_b,
CUDANet::Tensor& output
);
};
} // namespace CUDANet::Backend
} // namespace CUDANet::Backends