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author | Kawrakow <48489457+ikawrakow@users.noreply.github.com> | 2024-07-27 07:55:01 +0200 |
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committer | GitHub <noreply@github.com> | 2024-07-27 07:55:01 +0200 |
commit | 154e0d75fccf1784fe9ff6fd76a630b66563da3d (patch) | |
tree | 81ce6dbb5b1900c1aa78a879f0593c694cab9d27 /ggml/src/ggml-backend-impl.h | |
parent | 0684c3e9c70d49323b4fc517128cbe222cab7f96 (diff) |
Merge mainline llama.cpp (#3)
* Merging mainline - WIP
* Merging mainline - WIP
AVX2 and CUDA appear to work.
CUDA performance seems slightly (~1-2%) lower as it is so often
the case with llama.cpp/ggml after some "improvements" have been made.
* Merging mainline - fix Metal
* Remove check
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'ggml/src/ggml-backend-impl.h')
-rw-r--r-- | ggml/src/ggml-backend-impl.h | 153 |
1 files changed, 153 insertions, 0 deletions
diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h new file mode 100644 index 00000000..36ca3708 --- /dev/null +++ b/ggml/src/ggml-backend-impl.h @@ -0,0 +1,153 @@ +#pragma once + +// ggml-backend internal header + +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + + // + // Backend buffer + // + + // buffer type + typedef void * ggml_backend_buffer_type_context_t; + + struct ggml_backend_buffer_type_i { + const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft); + // allocate a buffer of this type + ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); + // tensor alignment + size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); + // max buffer size that can be allocated + size_t (*GGML_CALL get_max_size) (ggml_backend_buffer_type_t buft); + // data size needed to allocate the tensor, including padding + size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); + // check if tensor data is in host memory + bool (*GGML_CALL is_host) (ggml_backend_buffer_type_t buft); + }; + + struct ggml_backend_buffer_type { + struct ggml_backend_buffer_type_i iface; + ggml_backend_buffer_type_context_t context; + }; + + // buffer + typedef void * ggml_backend_buffer_context_t; + + struct ggml_backend_buffer_i { + const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer); + void (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer); + void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer); + void (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + 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 + void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value); + void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras + }; + + struct ggml_backend_buffer { + struct ggml_backend_buffer_i iface; + ggml_backend_buffer_type_t buft; + ggml_backend_buffer_context_t context; + size_t size; + enum ggml_backend_buffer_usage usage; + }; + + GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init( + ggml_backend_buffer_type_t buft, + struct ggml_backend_buffer_i iface, + ggml_backend_buffer_context_t context, + size_t size); + + // do not use directly, use ggml_backend_tensor_copy instead + bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst); + + // buffer that contains a collection of buffers + GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers); + GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer); + GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); + + // + // Backend + // + + typedef void * ggml_backend_context_t; + + struct ggml_backend_i { + const char * (*GGML_CALL get_name)(ggml_backend_t backend); + + void (*GGML_CALL free)(ggml_backend_t backend); + + // buffer allocation + ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend); + + // (optional) asynchronous tensor data access + void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + 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); + + // (optional) complete all pending operations + void (*GGML_CALL synchronize)(ggml_backend_t backend); + + // compute graph with a plan (not used currently) + // create a new plan for a graph + ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); + void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); + // update the plan with a new graph - this should be faster than creating a new plan when the graph has the same topology + void (*GGML_CALL graph_plan_update) (ggml_backend_t backend, ggml_backend_graph_plan_t plan, const struct ggml_cgraph * cgraph); + // compute the graph with the plan + enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + + // compute graph without a plan (async) + enum ggml_status (*GGML_CALL graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph); + + // check if the backend can compute an operation + bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); + + // check if the backend can use tensors allocated in a buffer type + bool (*GGML_CALL supports_buft)(ggml_backend_t backend, ggml_backend_buffer_type_t buft); + + // check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer + // these should be expensive operations with large batch sizes that may benefit from running on this backend + // even if the weight has to be copied from the CPU temporarily + bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op); + + // (optional) event synchronization + // create a new event that can record events on this backend instance + ggml_backend_event_t (*GGML_CALL event_new) (ggml_backend_t backend); + void (*GGML_CALL event_free) (ggml_backend_event_t event); + // record an event on the backend instance that created it + void (*GGML_CALL event_record) (ggml_backend_event_t event); + // wait for an event on on a different backend instance + void (*GGML_CALL event_wait) (ggml_backend_t backend, ggml_backend_event_t event); + // block until an event is recorded + void (*GGML_CALL event_synchronize) (ggml_backend_event_t event); + }; + + struct ggml_backend { + ggml_guid_t guid; + + struct ggml_backend_i iface; + ggml_backend_context_t context; + }; + + struct ggml_backend_event { + ggml_backend_t backend; + void * context; + }; + + // + // Backend registry + // + + typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data); + + 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); + +#ifdef __cplusplus +} +#endif |