Merge remote-tracking branch 'origin/ggml-backends' into ggml-backends-metal
This commit is contained in:
Georgi Gerganov
commit
f38433ef5d
10 changed files with 656 additions and 394 deletions
128
ggml-backend.h
128
ggml-backend.h
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@ -5,12 +5,45 @@
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#ifdef __cplusplus
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extern "C" {
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#endif
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typedef void * ggml_graph_plan_t;
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typedef void * ggml_backend_context_t;
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typedef void * ggml_backend_buffer_t;
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struct ggml_backend;
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// backend buffers
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typedef void * ggml_buffer_context_t;
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struct ggml_backend_buffer;
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struct ggml_backend_buffer_interface {
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// allocator functions
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void (*free_buffer) (struct ggml_backend_buffer * alloc);
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void (*alloc_tensor) (struct ggml_backend_buffer * alloc, struct ggml_tensor * tensor);
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void (*free_tensor) (struct ggml_backend_buffer * alloc, struct ggml_tensor * tensor);
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void (*reset) (struct ggml_backend_buffer * alloc);
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// functions overriden by the backend
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size_t (*get_alloc_size)(struct ggml_backend_buffer * alloc, struct ggml_tensor * tensor); // pre-allocation callback
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void (*init_tensor) (struct ggml_backend_buffer * alloc, struct ggml_tensor * tensor); // post-allocation callback
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void (*free_data) (struct ggml_backend_buffer * alloc); // free backend-specific data // TODO: better name
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};
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struct ggml_backend_buffer {
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struct ggml_backend_buffer_interface interface;
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ggml_buffer_context_t context;
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void * backend_data;
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};
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// backend buffer helper functions
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GGML_API void ggml_backend_buffer_free(struct ggml_backend_buffer * alloc);
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static inline void ggml_backend_buffer_tensor_alloc(struct ggml_backend_buffer * alloc, struct ggml_tensor * tensor) { alloc->interface.alloc_tensor(alloc, tensor); }
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static inline void ggml_backend_buffer_free_tensor(struct ggml_backend_buffer * alloc, struct ggml_tensor * tensor) { alloc->interface.free_tensor(alloc, tensor); }
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static inline void ggml_backend_buffer_reset(struct ggml_backend_buffer * alloc) { alloc->interface.reset(alloc); }
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// default buffer allocators
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// simple buffer allocator: cannot free tensors, good for weights and small contexts
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// default buffer allocator: can free tensors, good for compute contexts
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GGML_API struct ggml_backend_buffer * ggml_allocator_simple_init(void * data, size_t size, size_t alignment);
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GGML_API struct ggml_backend_buffer * ggml_allocator_default_init(void * data, size_t size, size_t alignment, int max_free_blocks);
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// buffer
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// buffers have space for the tensor structs in host memory, and tensor data in backend-specific memory
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struct ggml_buffer {
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// host memory
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@ -19,87 +52,72 @@ extern "C" {
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// tensor data
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struct ggml_backend * backend;
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ggml_backend_buffer_t backend_buffer; // backend-specific data
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struct ggml_backend_buffer * backend_buffer;
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};
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GGML_API struct ggml_buffer * ggml_buffer_alloc(struct ggml_backend * backend, size_t size, size_t max_tensors);
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GGML_API void ggml_buffer_free(struct ggml_buffer * buffer);
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// backend
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typedef void * ggml_backend_context_t;
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typedef void * ggml_graph_plan_t;
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struct ggml_backend_interface {
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const char * (*get_name)(ggml_backend_context_t ctx);
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const char * (*get_name)(struct ggml_backend * backend);
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void (*free_context)(ggml_backend_context_t ctx);
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void (*free)(struct ggml_backend * backend);
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// buffers
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ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_context_t ctx, size_t size);
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void (*free_buffer) (ggml_backend_context_t ctx, ggml_backend_buffer_t buffer);
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void (*reset_buffer)(ggml_backend_context_t ctx, ggml_backend_buffer_t buffer);
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void (*alloc_tensor)(ggml_backend_context_t ctx, ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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// TODO: pinned buffers for faster transfers between host and device
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// buffer allocation
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struct ggml_backend_buffer * (*alloc_buffer)(struct ggml_backend * backend, size_t size);
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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_context_t ctx, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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void (*get_tensor_async)(ggml_backend_context_t ctx, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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void (*synchronize)(ggml_backend_context_t ctx);
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void (*set_tensor_async)(struct ggml_backend * backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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void (*get_tensor_async)(struct ggml_backend * backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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void (*synchronize) (struct ggml_backend * 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_context_t ctx, struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*cpy_tensor_to) (ggml_backend_context_t ctx, struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*cpy_tensor_from)(struct ggml_backend * backend, struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*cpy_tensor_to) (struct ggml_backend * backend, struct ggml_tensor * src, struct ggml_tensor * dst);
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// compute graph with a plan
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ggml_graph_plan_t (*graph_plan_create) (ggml_backend_context_t ctx, struct ggml_cgraph * cgraph);
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void (*graph_plan_free) (ggml_backend_context_t ctx, ggml_graph_plan_t plan);
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void (*graph_plan_compute)(ggml_backend_context_t ctx, ggml_graph_plan_t plan);
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ggml_graph_plan_t (*graph_plan_create) (struct ggml_backend * backend, struct ggml_cgraph * cgraph);
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void (*graph_plan_free) (struct ggml_backend * backend, ggml_graph_plan_t plan);
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void (*graph_plan_compute)(struct ggml_backend * backend, ggml_graph_plan_t plan);
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// compute graph without a plan
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void (*graph_compute) (ggml_backend_context_t ctx, struct ggml_cgraph * cgraph);
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void (*graph_compute) (struct ggml_backend * backend, struct ggml_cgraph * cgraph);
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// check if a backend supports a given operation
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// this could be used to fallback automatically to the CPU backend if a backend doesn't support an operation
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// bool (*supports_op)(ggml_backend_context_t ctx, struct ggml_tensor * op);
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// bool (*supports_op)(struct ggml_backend * backend, struct ggml_tensor * op);
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};
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struct ggml_backend {
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struct ggml_backend_interface * interface;
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struct ggml_backend_interface interface;
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ggml_backend_context_t context;
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bool is_ram_shared;
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};
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// backend helper functions
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static inline const char * ggml_backend_name(struct ggml_backend * backend) { return backend->interface->get_name(backend->context); }
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static inline void ggml_backend_free_context(struct ggml_backend * backend) { backend->interface->free_context(backend->context); }
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static inline void ggml_backend_set_tensor_async(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { tensor->backend->interface->set_tensor_async(tensor->backend->context, tensor, data, offset, size); }
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static inline void ggml_backend_get_tensor_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { tensor->backend->interface->get_tensor_async(tensor->backend->context, tensor, data, offset, size); }
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static inline void ggml_backend_set_tensor(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { tensor->backend->interface->set_tensor_async(tensor->backend->context, tensor, data, offset, size); tensor->backend->interface->synchronize(tensor->backend->context); }
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static inline void ggml_backend_get_tensor(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { tensor->backend->interface->get_tensor_async(tensor->backend->context, tensor, data, offset, size); tensor->backend->interface->synchronize(tensor->backend->context); }
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static inline void ggml_backend_synchronize(struct ggml_backend * backend) { backend->interface->synchronize(backend->context); }
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static inline ggml_graph_plan_t ggml_backend_graph_plan_create(struct ggml_backend * backend, struct ggml_cgraph * cgraph) { return backend->interface->graph_plan_create(backend->context, cgraph); }
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static inline void ggml_backend_graph_plan_free(struct ggml_backend * backend, ggml_graph_plan_t plan) { backend->interface->graph_plan_free(backend->context, plan); }
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static inline void ggml_backend_graph_plan_compute(struct ggml_backend * backend, ggml_graph_plan_t plan) { backend->interface->graph_plan_compute(backend->context, plan); }
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static inline void ggml_backend_graph_compute(struct ggml_backend * backend, struct ggml_cgraph * cgraph) { backend->interface->graph_compute(backend->context, cgraph); }
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// buffer and tensor allocation
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// TODO:
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// - return "struct ggml_buffer *"
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// - fix namings:
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// - ggml_backend_alloc_buffer -> ggml_backend_buffer_alloc
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// - ggml_backend_free_buffer -> ggml_backend_buffer_free
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// - ggml_backend_reset_buffer -> ggml_backend_buffer_reset
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// - ggml_backend_alloc_tensor -> ggml_backend_tensor_alloc
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// - ggml_backend_tensor_cpy -> ggml_backend_tensor_copy
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//
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GGML_API struct ggml_buffer ggml_backend_alloc_buffer(struct ggml_backend * backend, size_t size, size_t max_tensors);
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GGML_API void ggml_backend_free_buffer(struct ggml_buffer * buffer);
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static inline void ggml_backend_reset_buffer(struct ggml_buffer * buffer) { buffer->backend->interface->reset_buffer(buffer->backend->context, buffer->backend_buffer); }
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static inline void ggml_backend_alloc_tensor(struct ggml_buffer * buffer, struct ggml_tensor * tensor) { buffer->backend->interface->alloc_tensor(buffer->backend->context, buffer->backend_buffer, tensor); }
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static inline const char * ggml_backend_name(struct ggml_backend * backend) { return backend->interface.get_name(backend); }
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static inline void ggml_backend_free(struct ggml_backend * backend) { backend->interface.free(backend); }
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static inline void ggml_backend_tensor_set_async(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { tensor->backend->interface.set_tensor_async(tensor->backend, tensor, data, offset, size); }
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static inline void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { tensor->backend->interface.get_tensor_async(tensor->backend, tensor, data, offset, size); }
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static inline void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { tensor->backend->interface.set_tensor_async(tensor->backend, tensor, data, offset, size); tensor->backend->interface.synchronize(tensor->backend); }
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static inline void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { tensor->backend->interface.get_tensor_async(tensor->backend, tensor, data, offset, size); tensor->backend->interface.synchronize(tensor->backend); }
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static inline void ggml_backend_synchronize(struct ggml_backend * backend) { backend->interface.synchronize(backend); }
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static inline ggml_graph_plan_t ggml_backend_graph_plan_create(struct ggml_backend * backend, struct ggml_cgraph * cgraph) { return backend->interface.graph_plan_create(backend, cgraph); }
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static inline void ggml_backend_graph_plan_free(struct ggml_backend * backend, ggml_graph_plan_t plan) { backend->interface.graph_plan_free(backend, plan); }
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static inline void ggml_backend_graph_plan_compute(struct ggml_backend * backend, ggml_graph_plan_t plan) { backend->interface.graph_plan_compute(backend, plan); }
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static inline void ggml_backend_graph_compute(struct ggml_backend * backend, struct ggml_cgraph * cgraph) { backend->interface.graph_compute(backend, cgraph); }
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// tensor copy between different backends
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GGML_API void ggml_backend_cpy_tensor(struct ggml_tensor * dst, struct ggml_tensor * src);
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GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
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// CPU backend
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GGML_API struct ggml_backend ggml_backend_cpu_init(void);
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GGML_API struct ggml_backend * ggml_backend_cpu_init(void);
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GGML_API void ggml_backend_cpu_set_n_threads(struct ggml_backend * backend_cpu, int n_threads);
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///////////////////////////
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