353 lines
19 KiB
C
353 lines
19 KiB
C
#pragma once
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#include "ggml.h"
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#include "ggml-alloc.h"
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#ifdef GGML_BACKEND_SHARED
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# if defined(_WIN32) && !defined(__MINGW32__)
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# ifdef GGML_BACKEND_BUILD
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# define GGML_BACKEND_API __declspec(dllexport) extern
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# else
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# define GGML_BACKEND_API __declspec(dllimport) extern
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# endif
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# else
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# define GGML_BACKEND_API __attribute__ ((visibility ("default"))) extern
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# endif
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#else
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# define GGML_BACKEND_API extern
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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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typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
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typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
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typedef struct ggml_backend_event * ggml_backend_event_t;
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typedef struct ggml_backend * ggml_backend_t;
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typedef void * ggml_backend_graph_plan_t;
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typedef struct ggml_backend_reg * ggml_backend_reg_t;
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typedef struct ggml_backend_device * ggml_backend_dev_t;
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//
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// Backend buffer type
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//
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GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft);
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GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size);
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GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
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GGML_API size_t ggml_backend_buft_get_max_size (ggml_backend_buffer_type_t buft);
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GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
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GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft);
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GGML_API ggml_backend_dev_t ggml_backend_buft_get_device (ggml_backend_buffer_type_t buft);
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//
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// Backend buffer
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//
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enum ggml_backend_buffer_usage {
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GGML_BACKEND_BUFFER_USAGE_ANY = 0,
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GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
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GGML_BACKEND_BUFFER_USAGE_COMPUTE = 2,
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};
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GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer);
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GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
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GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
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GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_max_size (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value);
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GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer);
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GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
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GGML_API enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage (ggml_backend_buffer_t buffer);
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GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer);
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GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer);
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// tensor copy between different backends
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GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
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//
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// Backend (stream)
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//
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GGML_API ggml_guid_t ggml_backend_guid(ggml_backend_t backend);
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GGML_API const char * ggml_backend_name(ggml_backend_t backend);
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GGML_API void ggml_backend_free(ggml_backend_t backend);
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GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
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GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
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GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
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GGML_API size_t ggml_backend_get_max_size(ggml_backend_t backend);
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GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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// "offset" refers to the offset in tensor->data for setting/getting data
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GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_memset( struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
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GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
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GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
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GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
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GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
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// NOTE: will be removed, use device version instead
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GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
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GGML_API bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
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GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
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// asynchronous copy
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// the copy is performed after all the currently queued operations in backend_src
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// backend_dst will wait for the copy to complete before performing other operations
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// automatic fallback to sync copy if async is not supported
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GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
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GGML_API ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend);
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//
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// Events
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//
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GGML_API ggml_backend_event_t ggml_backend_event_new(ggml_backend_dev_t device);
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GGML_API void ggml_backend_event_free(ggml_backend_event_t event);
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GGML_API void ggml_backend_event_record(ggml_backend_event_t event, ggml_backend_t backend);
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GGML_API void ggml_backend_event_synchronize(ggml_backend_event_t event);
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GGML_API void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event);
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//
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// Backend device
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//
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enum ggml_backend_dev_type {
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// CPU device using system memory
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GGML_BACKEND_DEVICE_TYPE_CPU,
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// GPU device using dedicated memory
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GGML_BACKEND_DEVICE_TYPE_GPU,
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// accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX)
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GGML_BACKEND_DEVICE_TYPE_ACCEL
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};
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// functionality supported by the device
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struct ggml_backend_dev_caps {
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// asynchronous operations
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bool async;
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// pinned host buffer
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bool host_buffer;
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// creating buffers from host ptr
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bool buffer_from_host_ptr;
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// event synchronization
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bool events;
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};
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// all the device properties
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struct ggml_backend_dev_props {
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const char * name;
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const char * description;
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size_t memory_free;
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size_t memory_total;
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enum ggml_backend_dev_type type;
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struct ggml_backend_dev_caps caps;
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};
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GGML_API const char * ggml_backend_dev_name(ggml_backend_dev_t device);
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GGML_API const char * ggml_backend_dev_description(ggml_backend_dev_t device);
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GGML_API void ggml_backend_dev_memory(ggml_backend_dev_t device, size_t * free, size_t * total);
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GGML_API enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device);
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GGML_API void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_dev_props * props);
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GGML_API ggml_backend_reg_t ggml_backend_dev_backend_reg(ggml_backend_dev_t device);
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GGML_API ggml_backend_t ggml_backend_dev_init(ggml_backend_dev_t device, const char * params);
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GGML_API ggml_backend_buffer_type_t ggml_backend_dev_buffer_type(ggml_backend_dev_t device);
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GGML_API ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device);
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GGML_API ggml_backend_buffer_t ggml_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device, void * ptr, size_t size, size_t max_tensor_size);
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GGML_API bool ggml_backend_dev_supports_op(ggml_backend_dev_t device, const struct ggml_tensor * op);
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GGML_API bool ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buffer_type_t buft);
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GGML_API bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op);
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//
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// Backend (reg)
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//
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GGML_API const char * ggml_backend_reg_name(ggml_backend_reg_t reg);
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GGML_API size_t ggml_backend_reg_dev_count(ggml_backend_reg_t reg);
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GGML_API ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index);
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GGML_API void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name);
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// Common functions that may be obtained using ggml_backend_reg_get_proc_address
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// Split buffer type for tensor parallelism
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typedef ggml_backend_buffer_type_t (*ggml_backend_split_buffer_type_t)(int main_device, const float * tensor_split);
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// Set the number of threads for the backend
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typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads);
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// Get additional buffer types provided by the device (returns a NULL-terminated array)
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typedef ggml_backend_buffer_type_t * (*ggml_backend_dev_get_extra_bufts_t)(ggml_backend_dev_t device);
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// Set the abort callback for the backend
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typedef void (*ggml_backend_set_abort_callback_t)(ggml_backend_t backend, ggml_abort_callback abort_callback, void * abort_callback_data);
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// Get a list of feature flags supported by the backend (returns a NULL-terminated array)
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struct ggml_backend_feature {
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const char * name;
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const char * value;
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};
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typedef struct ggml_backend_feature * (*ggml_backend_get_features_t)(ggml_backend_reg_t reg);
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//
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// Backend registry
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//
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// Backend (reg) enumeration
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GGML_API size_t ggml_backend_reg_count(void);
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GGML_API ggml_backend_reg_t ggml_backend_reg_get(size_t index);
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GGML_API ggml_backend_reg_t ggml_backend_reg_by_name(const char * name);
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// Device enumeration
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GGML_API size_t ggml_backend_dev_count(void);
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GGML_API ggml_backend_dev_t ggml_backend_dev_get(size_t index);
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GGML_API ggml_backend_dev_t ggml_backend_dev_by_name(const char * name);
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GGML_API ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type);
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// Direct backend (stream) initialization
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// = ggml_backend_dev_init(ggml_backend_dev_by_name(name), params)
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GGML_API ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params);
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// = ggml_backend_dev_init(ggml_backend_dev_by_type(type), params)
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GGML_API ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params);
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// = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU) OR ggml_backend_dev_by_type(CPU), NULL)
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GGML_API ggml_backend_t ggml_backend_init_best(void);
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// Load a backend from a dynamic library and register it
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GGML_API ggml_backend_reg_t ggml_backend_load(const char * path);
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// Unload a backend if loaded dynamically and unregister it
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GGML_API void ggml_backend_unload(ggml_backend_reg_t reg);
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// Load all known backends from dynamic libraries
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GGML_API void ggml_backend_load_all(void);
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GGML_API void ggml_backend_load_all_from_path(const char * dir_path);
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//
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// Backend scheduler
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//
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// The backend scheduler allows for multiple backend devices to be used together
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// Handles compute buffer allocation, assignment of tensors to backends, and copying of tensors between backends
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// The backends are selected based on:
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// - the backend that supports the operation
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// - the location of the pre-allocated tensors (e.g. the weights)
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/*
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Example usage:
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// operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned
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// preferrably to run on the same backend as the buffer
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ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
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sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
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// initialize buffers from a max size graph (optional)
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reserve_graph = build_graph(sched, max_batch_size);
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// manually assign nodes to a backend (optional, should not be needed in most cases)
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struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
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ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
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ggml_backend_sched_reserve(sched, reserve_graph);
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// compute
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graph = build_graph(sched); // the graph and its tensors are single-use in terms of allocation, multi-use in terms of computation
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for (int i = 0; i < 10; ++i) {
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ggml_backend_sched_graph_compute(sched, graph); // on the first iteration the graph is allocated automatically
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}
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// if there are graph inputs:
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graph = build_graph(sched); // get a new graph that is not allocated (the metadata for the old graph is freed once ggml_free is called)
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ggml_backend_sched_reset(sched); // clear the allocation of the previous graph
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ggml_backend_sched_alloc_graph(sched, graph); // explicitly allocate the new graph but do not execute it
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ggml_backend_tensor_set(input_tensor, ...); // copy data to the newly allocated graph tensors
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ggml_backend_sched_graph_compute(sched, graph); // execute the graph
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// as an alternative to the above it is also possible to assign the inputs to a dedicated context and
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// allocate them statically via ggml_backend_alloc_ctx_tensors
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}
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*/
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typedef struct ggml_backend_sched * ggml_backend_sched_t;
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// Evaluation callback for each node in the graph (set with ggml_backend_sched_set_eval_callback)
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// when ask == true, the scheduler wants to know if the user wants to observe this node
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// this allows the scheduler to batch nodes together in order to evaluate them in a single call
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//
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// when ask == false, the scheduler is passing the node tensor to the user for observation
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// if the user returns false, the scheduler will cancel the graph compute
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//
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typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
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// Initialize a backend scheduler, backends with low index are given priority over backends with high index
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GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
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GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
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// Initialize backend buffers from a measure graph
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GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success
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GGML_API int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
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GGML_API ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i);
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// Get the number of splits of the last graph
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GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
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GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
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GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
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GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
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GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
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// Allocate and compute graph on the backend scheduler
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GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // returns success
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GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
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GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
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GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
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// Reset all assignments and allocators - must be called before changing the node backends or allocating a new graph.
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// This in effect deallocates all tensors that were previously allocated and leaves them with dangling pointers.
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// The correct way to use this API is to discard the deallocated tensors and create new ones.
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GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched);
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// Set a callback to be called for each resulting node during graph compute
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GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
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//
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// Utils
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//
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struct ggml_backend_graph_copy {
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ggml_backend_buffer_t buffer;
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struct ggml_context * ctx_allocated;
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struct ggml_context * ctx_unallocated;
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struct ggml_cgraph * graph;
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};
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// Copy a graph to a different backend
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GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
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GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
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typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
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// Compare the output of two backends
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GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
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// Tensor initialization
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GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
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GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
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// CPU buffer types are always available
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GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
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GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
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#ifdef __cplusplus
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}
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#endif
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