mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-30 21:34:36 +00:00
2bf8d0f7c4
* backend : offload large batches to GPU * fix hip * code cleanup * fix CUDA split buffers * Update ggml-backend-impl.h Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * cuda : fix memset without set_device * imatrix : remove sched affix from weight names * sched : add a new split if the current one has too many inputs reduce max inputs per split more cleanup * update backends ggml-ci --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
142 lines
6.8 KiB
C
142 lines
6.8 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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// check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
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// these should be expensive operations with large batch sizes that may benefit from running on this backend
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// even if the weight has to be copied from the CPU temporarily
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bool (*GGML_CALL offload_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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