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63 lines
2.7 KiB
C
63 lines
2.7 KiB
C
#pragma once
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#include "ggml.h"
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#include "ggml-backend.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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#ifdef GGML_USE_HIP
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#define GGML_CUDA_NAME "ROCm"
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#define GGML_CUBLAS_NAME "hipBLAS"
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#elif defined(GGML_USE_MUSA)
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#define GGML_CUDA_NAME "MUSA"
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#define GGML_CUBLAS_NAME "muBLAS"
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#else
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#define GGML_CUDA_NAME "CUDA"
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#define GGML_CUBLAS_NAME "cuBLAS"
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#endif
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#define GGML_CUDA_MAX_DEVICES 16
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// backend API
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GGML_BACKEND_API ggml_backend_t ggml_backend_cuda_init(int device);
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GGML_BACKEND_API bool ggml_backend_is_cuda(ggml_backend_t backend);
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GGML_BACKEND_API void ggml_backend_cuda_trim_pools(ggml_backend_t backend);
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// Sets the CUDA scheduling priority used for this backend instance's lazily
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// created streams (lower = higher priority, 0 = default). Scoped to the
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// instance; already-created streams keep their priority.
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GGML_BACKEND_API void ggml_backend_cuda_set_stream_priority(ggml_backend_t backend, int priority);
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// Opts this backend instance into folding the bf16-reference rounding
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// (ggml_round_bf16) that directly follows RMS_NORM*MUL, ADD or SwiGLU into the
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// producing kernel. Off by default; results are bit-identical either way.
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GGML_BACKEND_API void ggml_backend_cuda_set_round_bf16_fusion(ggml_backend_t backend, bool enabled);
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// Returns the backend's current compute CUDA stream (cudaStream_t) so host
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// code can enqueue its own kernels/copies ordered with graph computes.
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GGML_BACKEND_API void * ggml_backend_cuda_get_stream(ggml_backend_t backend);
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GGML_BACKEND_API void ggml_backend_cuda_clear_graph(ggml_backend_t backend, const struct ggml_cgraph * graph);
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// device buffer
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GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
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// conduct allreduce operation between devices
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GGML_BACKEND_API bool ggml_backend_cuda_allreduce_tensor(ggml_backend_t * backends, struct ggml_tensor ** tensors, size_t n_backends);
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// split tensor buffer that splits matrices by rows across multiple devices
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GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split);
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// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
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GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
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GGML_BACKEND_API int ggml_backend_cuda_get_device_count(void);
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GGML_BACKEND_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
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GGML_BACKEND_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
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GGML_BACKEND_API bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
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GGML_BACKEND_API void ggml_backend_cuda_unregister_host_buffer(void * buffer);
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GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cuda_reg(void);
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#ifdef __cplusplus
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}
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#endif
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