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				https://github.com/ggml-org/llama.cpp.git
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	* llama : add pipeline parallelism support for batch processing with multiple CUDA GPUs ggml-ci * server : add -ub, --ubatch-size parameter * fix server embedding test * llama : fix Mamba inference for pipeline parallelism Tested to work correctly with both `main` and `parallel` examples. * llama : limit max batch size to n_batch * add LLAMA_SCHED_MAX_COPIES to configure the number of input copies for pipeline parallelism default increase to 4 (from 2) changing this value may improve performance for some systems, but increases memory usage * fix hip build * fix sycl build (disable cpy_tensor_async) * fix hip build * llama : limit n_batch and n_ubatch to n_ctx during context creation * llama : fix norm backend * batched-bench : sync after decode * swiftui : sync after decode * ggml : allow ggml_get_rows to use multiple threads if they are available * check n_ubatch >= n_tokens with non-casual attention * llama : do not limit n_batch to n_ctx with non-casual attn * server : construct batch with size of llama_n_batch * ggml_backend_cpu_graph_compute : fix return value when alloc fails * llama : better n_batch and n_ubatch comment * fix merge * small fix * reduce default n_batch to 2048 --------- Co-authored-by: Francis Couture-Harpin <git@compilade.net> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
		
			
				
	
	
		
			137 lines
		
	
	
		
			6.5 KiB
		
	
	
	
		
			C
		
	
	
	
	
	
			
		
		
	
	
			137 lines
		
	
	
		
			6.5 KiB
		
	
	
	
		
			C
		
	
	
	
	
	
#pragma once
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// ggml-backend internal header
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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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    //
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    // Backend buffer
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    //
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    // buffer type
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    typedef void * ggml_backend_buffer_type_context_t;
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    struct ggml_backend_buffer_type_i {
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        const char *          (*GGML_CALL get_name)        (ggml_backend_buffer_type_t buft);
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        ggml_backend_buffer_t (*GGML_CALL alloc_buffer)    (ggml_backend_buffer_type_t buft, size_t size);
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        size_t                (*GGML_CALL get_alignment)   (ggml_backend_buffer_type_t buft); // tensor alignment
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        size_t                (*GGML_CALL get_max_size)    (ggml_backend_buffer_type_t buft); // allocation max size
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        size_t                (*GGML_CALL get_alloc_size)  (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
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        bool                  (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
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        // check if tensor data is in host memory
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        // should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
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        bool                  (*GGML_CALL is_host)         (ggml_backend_buffer_type_t buft);
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    };
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    struct ggml_backend_buffer_type {
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        struct ggml_backend_buffer_type_i  iface;
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        ggml_backend_buffer_type_context_t context;
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    };
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    // buffer
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    typedef void * ggml_backend_buffer_context_t;
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    struct ggml_backend_buffer_i {
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        const char * (*GGML_CALL get_name)   (ggml_backend_buffer_t buffer);
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        void         (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
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        void *       (*GGML_CALL get_base)   (ggml_backend_buffer_t buffer);
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        void         (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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        void         (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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        void         (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
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        bool         (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
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        void         (*GGML_CALL clear)      (ggml_backend_buffer_t buffer, uint8_t value);
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        void         (*GGML_CALL reset)      (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
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    };
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    struct ggml_backend_buffer {
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        struct ggml_backend_buffer_i  iface;
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        ggml_backend_buffer_type_t    buft;
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        ggml_backend_buffer_context_t context;
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        size_t size;
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        enum ggml_backend_buffer_usage usage;
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    };
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    GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
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                   ggml_backend_buffer_type_t      buft,
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            struct ggml_backend_buffer_i           iface,
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                   ggml_backend_buffer_context_t   context,
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                   size_t                          size);
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    // do not use directly, use ggml_backend_tensor_copy instead
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    bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst);
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    // buffer that contains a collection of buffers
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    GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers);
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    GGML_CALL bool                  ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer);
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    GGML_CALL void                  ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
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    //
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    // Backend
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    //
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    typedef void * ggml_backend_context_t;
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    struct ggml_backend_i {
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        const char * (*GGML_CALL get_name)(ggml_backend_t backend);
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        void (*GGML_CALL free)(ggml_backend_t backend);
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        // buffer allocation
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        ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend);
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        // (optional) asynchronous tensor data access
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        void (*GGML_CALL set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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        void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
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        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
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        // (optional) complete all pending operations
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        void (*GGML_CALL synchronize)(ggml_backend_t backend);
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        // compute graph with a plan (not used currently)
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        ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
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        void                      (*GGML_CALL graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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        // compute graph with a plan
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        enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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        // compute graph without a plan (async)
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        enum ggml_status (*GGML_CALL graph_compute)     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
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        // check if the backend supports an operation
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        bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
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        // (optional) event synchronization
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        ggml_backend_event_t (*GGML_CALL event_new)         (ggml_backend_t backend);
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        void                 (*GGML_CALL event_free)        (ggml_backend_event_t event);
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        void                 (*GGML_CALL event_record)      (ggml_backend_event_t event);
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        void                 (*GGML_CALL event_wait)        (ggml_backend_t backend, ggml_backend_event_t event);
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        void                 (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
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    };
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    struct ggml_backend {
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        ggml_guid_t guid;
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        struct ggml_backend_i iface;
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        ggml_backend_context_t context;
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    };
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    struct ggml_backend_event {
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        ggml_backend_t backend;
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        void * context;
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    };
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    //
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    // Backend registry
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    //
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    typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data);
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    GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
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#ifdef  __cplusplus
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}
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#endif
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