mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
4760e7cc0b
* sync : ggml (backend v2) (wip) * sync : migrate examples and llama.cpp to dynamic graphs (wip) * sync : update tests + fix max op params to 64 ggml-ci * sync : ggml-cuda ggml-ci * llama : fix save/load state context size ggml-ci * sync : try to fix build on tvOS * sync : pass custom graph sizes in training examples * sync : update graph copies to new ggml API * sync : update sync-ggml.sh with new files * scripts : fix header in sync script * train : fix context size calculations * llama : increase inference graph size up to 4096 nodes * train : allocate grads for backward graphs * train : allocate grads for gb_tmp
88 lines
3.2 KiB
C
88 lines
3.2 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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typedef void * ggml_backend_buffer_context_t;
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struct ggml_backend_buffer_i {
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void (*free_buffer) (ggml_backend_buffer_t buffer);
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void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer
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size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
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void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
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void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
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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_t backend;
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ggml_backend_buffer_context_t context;
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size_t size;
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};
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GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
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struct ggml_backend * backend,
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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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//
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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 * (*get_name)(ggml_backend_t backend);
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void (*free)(ggml_backend_t backend);
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// buffer allocation
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ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
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// get buffer alignment
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size_t (*get_alignment)(ggml_backend_t backend);
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// tensor data access
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// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
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void (*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 (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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void (*synchronize) (ggml_backend_t backend);
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// (optional) copy tensor between different backends, allow for single-copy tranfers
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void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
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// compute graph with a plan
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ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
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void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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// compute graph without a plan
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void (*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 (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
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};
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struct ggml_backend {
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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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#ifdef __cplusplus
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}
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#endif
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