mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-30 21:34:36 +00:00
fe680e3d10
* sync : ggml (part 1)
* sync : ggml (part 2, CUDA)
* sync : ggml (part 3, Metal)
* ggml : build fixes
ggml-ci
* cuda : restore lost changes
* cuda : restore lost changes (StableLM rope)
* cmake : enable separable compilation for CUDA
ggml-ci
* ggml-cuda : remove device side dequantize
* Revert "cmake : enable separable compilation for CUDA"
This reverts commit 09e35d04b1.
* cuda : remove assert for rope
* tests : add test-backend-ops
* ggml : fix bug in ggml_concat
* ggml : restore `ggml_get_n_tasks()` logic in `ggml_graph_plan()`
* ci : try to fix macOS
* ggml-backend : remove backend self-registration
* ci : disable Metal for macOS cmake build
ggml-ci
* metal : fix "supports family" call
* metal : fix assert
* metal : print resource path
ggml-ci
---------
Co-authored-by: slaren <slarengh@gmail.com>
65 lines
2.4 KiB
C
65 lines
2.4 KiB
C
#pragma once
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#include "ggml.h"
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#include "ggml-backend.h"
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#ifdef GGML_USE_HIPBLAS
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#define GGML_CUDA_NAME "ROCm"
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#define GGML_CUBLAS_NAME "hipBLAS"
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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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#ifdef __cplusplus
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extern "C" {
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#endif
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#define GGML_CUDA_MAX_DEVICES 16
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// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
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GGML_API void ggml_init_cublas(void);
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// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
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GGML_API bool ggml_cublas_loaded(void);
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GGML_API void * ggml_cuda_host_malloc(size_t size);
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GGML_API void ggml_cuda_host_free(void * ptr);
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GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
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GGML_API void ggml_cuda_set_tensor_split(const float * tensor_split);
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GGML_API void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
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GGML_API void ggml_cuda_free_data(struct ggml_tensor * tensor);
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GGML_API void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
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GGML_API void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
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GGML_API void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
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GGML_API void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor);
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GGML_API void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
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GGML_API void ggml_cuda_copy_to_device(struct ggml_tensor * tensor);
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GGML_API void ggml_cuda_set_main_device(int main_device);
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GGML_API void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
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GGML_API void ggml_cuda_set_scratch_size(size_t scratch_size);
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GGML_API void ggml_cuda_free_scratch(void);
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GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
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GGML_API int ggml_cuda_get_device_count(void);
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GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
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// backend API
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GGML_API ggml_backend_t ggml_backend_cuda_init(int device);
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GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
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GGML_API int ggml_backend_cuda_get_device(ggml_backend_t backend);
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GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
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// pinned host buffer for use with CPU backend for faster copies between CPU and GPU
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GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
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#ifdef __cplusplus
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}
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#endif
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