f2890a4494
* fix(ext_server): Port llama.cpp sampling refactors to ext_server
This was a fairly large changeset. I closely followed the changes here:
df270ef745
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(server.cpp): Refactor server.cpp logging for llama.cpp overhaul
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Bump llama.cpp to the latest master with `granite` support
This does not yet have granite MoE support, but that can come in a
follow up PR
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(patches): Update all patches (except solar-pro) to work with bumped llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(solar): Update solar patch for llama.cpp bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump llama.cpp for granitemoe support
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump llama.cpp for granitemoe support
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(solar): Update the solar-pro patch for latest llama.cpp bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama.cpp): Bump to the latest master of llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(patches): Update all patches for latest bump
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama): Always run sync.sh from the right directory
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/patches): Update llama patches
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(llama)!: Rough sync with llama.cpp submodule
There are a number of changes that will need to be propagated to llama.go
before any of this works!
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/patches): Add a patch and update for missing ggml-impl.h include
This include is where the ggml_cgraph struct is defined. It is included in
many of the .c files to define the forward declartion in ggml.h. It seems
that with the subset of code included here, the import was somehow lost (or
out-of-order) when building, so adding this include to llama.cpp fixes the
missing definition.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama/sync): Add missing ggml-cpu-impl.h copy-over in sync.sh
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Add missing log.cpp
This was added as part of the logging overhaul done in llama.cpp
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Overhaul use of sampling module for llama.cpp changes
The changes here reflect the changes made in the big llama.cpp sampling PR
https://github.com/ggerganov/llama.cpp/pull/9294
The sampling functionality is now broken into the base interface
(llama_sampler) and the generation implementation (gpt_sampler). The
changes here reflect that. Since the sampling.h/sampling.cpp code uses c++
STL headers, the sampling_ext.[h|cpp] wrapper is maintained to allow go to
access a pure-C interface.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Fix the impl of SampleTokenGreedy for new sampling
I don't think this method is currently used, so it could probably just be
removed so that all sampling goes through the GPT interface, but in the
interest of doing no harm, this should keep the method working as expected.
Branch: IBMGraniteArchitectureSupport
* fix(llama): Remove unused SampleTokenGreedy
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(sync): Remove bash-specific change to sync.sh
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* chore(gofumpt): Format on llama.go to pass linting
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llm): Fix missing <thread> include in ext_server
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Remove TODO about grammar_first
This feature was not used/needed previously so should be fine without
plumbing it through now.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Better naming for sampling wrapper and args
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Fix patch 05 to use new wrapper api and re-sync
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* runner: Flush pending responses before returning
If there are any pending reponses (such as from potential stop
tokens) then we should send them back before ending the sequence.
Otherwise, we can be missing tokens at the end of a response.
Fixes #6707
* fix(llama/sampling): Use gpt_sampler with a forward declaration
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llama): Remove unnecessary patch for gguf impl header
This was caused by an earlier mistake in the embeddings patch that was
dereferencing the pointer instead of using the wrapper API.
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(llm): Remove use of deprecated --log-disable flag
Branch: IBMGraniteArchitectureSupport
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
267 lines
15 KiB
C
Vendored
267 lines
15 KiB
C
Vendored
/**
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* llama.cpp - commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555 - do not edit this file
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*
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* MIT License
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*
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* Copyright (c) 2023-2024 The ggml authors
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#pragma once
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#include "ggml.h"
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#include "ggml-alloc.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
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typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
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typedef struct ggml_backend_event * ggml_backend_event_t;
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typedef struct ggml_backend * ggml_backend_t;
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typedef void * ggml_backend_graph_plan_t;
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//
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// Backend buffer
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//
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// buffer type
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GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft);
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GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size);
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GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
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GGML_API size_t ggml_backend_buft_get_max_size (ggml_backend_buffer_type_t buft);
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GGML_API GGML_CALL size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
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GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft);
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// buffer
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enum ggml_backend_buffer_usage {
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GGML_BACKEND_BUFFER_USAGE_ANY = 0,
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GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
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GGML_BACKEND_BUFFER_USAGE_COMPUTE = 2,
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};
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GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer);
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GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
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GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
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GGML_API GGML_CALL void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_max_size (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value);
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GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer);
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GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
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GGML_API enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage (ggml_backend_buffer_t buffer);
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GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer);
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GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer);
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//
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// Backend
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//
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GGML_API ggml_guid_t ggml_backend_guid(ggml_backend_t backend);
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GGML_API const char * ggml_backend_name(ggml_backend_t backend);
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GGML_API void ggml_backend_free(ggml_backend_t backend);
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GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
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GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
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GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
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GGML_API size_t ggml_backend_get_max_size(ggml_backend_t backend);
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GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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// "offset" refers to the offset of the tensor data for setting/getting data
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GGML_API GGML_CALL void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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GGML_API GGML_CALL void ggml_backend_tensor_memset( struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
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GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
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GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
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GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
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GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
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GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
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GGML_API bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
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GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
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// tensor copy between different backends
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GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
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// asynchronous copy
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// the copy is performed after all the currently queued operations in backend_src
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// backend_dst will wait for the copy to complete before performing other operations
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// automatic fallback to sync copy if async is not supported
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GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
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// events
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GGML_API ggml_backend_event_t ggml_backend_event_new (ggml_backend_t backend);
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GGML_API void ggml_backend_event_free (ggml_backend_event_t event);
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GGML_API void ggml_backend_event_record (ggml_backend_event_t event);
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GGML_API void ggml_backend_event_synchronize(ggml_backend_event_t event);
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GGML_API void ggml_backend_event_wait (ggml_backend_t backend, ggml_backend_event_t event);
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//
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// CPU backend
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//
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GGML_API ggml_backend_t ggml_backend_cpu_init(void);
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GGML_API GGML_CALL bool ggml_backend_is_cpu (ggml_backend_t backend);
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GGML_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads);
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GGML_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
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GGML_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
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// Create a backend buffer from an existing pointer
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GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
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GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
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#ifdef GGML_USE_CPU_HBM
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GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
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#endif
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//
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// Backend registry
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//
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// The backend registry is a registry of all the available backends, and allows initializing backends in a generic way
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GGML_API size_t ggml_backend_reg_get_count(void);
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GGML_API size_t ggml_backend_reg_find_by_name(const char * name); // returns index of backend with name, or SIZE_MAX if not found
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GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is backend_name:params (params is optional)
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GGML_API const char * ggml_backend_reg_get_name(size_t i);
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GGML_API ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific
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GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i);
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GGML_API ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size);
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//
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// Backend scheduler
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//
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// The backend scheduler allows for multiple backends to be used together
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// Handles compute buffer allocation, assignment of tensors to backends, and copying of tensors between backends
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// The backends are selected based on:
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// - the backend that supports the operation
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// - the location of the pre-allocated tensors (e.g. the weights)
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/*
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Example usage:
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// operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned
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// preferrably to run on the same backend as the buffer
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ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
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sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
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// initialize buffers from a max size graph (optional)
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reserve_graph = build_graph(sched, max_batch_size);
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// manually assign nodes to a backend (optional, should not be needed in most cases)
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struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
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ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
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ggml_backend_sched_reserve(sched, reserve_graph);
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// compute
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graph = build_graph(sched);
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ggml_backend_sched_graph_compute(sched, graph);
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// if there are graph inputs:
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ggml_backend_sched_reset(sched);
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ggml_backend_sched_alloc_graph(sched, graph);
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ggml_backend_tensor_set(input_tensor, ...);
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ggml_backend_sched_graph_compute(sched, graph);
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}
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*/
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struct ggml_backend_sched;
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typedef struct ggml_backend_sched * ggml_backend_sched_t;
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// when ask == true, the scheduler wants to know if the user wants to observe this node
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// this allows the scheduler to batch nodes together in order to evaluate them in a single call
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//
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// when ask == false, the scheduler is passing the node tensor to the user for observation
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// if the user returns false, the scheduler will cancel the graph compute
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//
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typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
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// Initialize a backend scheduler
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GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
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GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
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// Initialize backend buffers from a measure graph
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GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
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GGML_API int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
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GGML_API ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i);
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// Get the number of splits of the last graph
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GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
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GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
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GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
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GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
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GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
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// Allocate and compute graph on the backend scheduler
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GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
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GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
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GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
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GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
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// Reset all assignments and allocators - must be called before changing the node backends
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GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched);
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// Set a callback to be called for each resulting node during graph compute
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GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
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//
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// Utils
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//
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struct ggml_backend_graph_copy {
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ggml_backend_buffer_t buffer;
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struct ggml_context * ctx_allocated;
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struct ggml_context * ctx_unallocated;
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struct ggml_cgraph * graph;
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};
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// Copy a graph to a different backend
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GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
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GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
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typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
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// Compare the output of two backends
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GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
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// Tensor initialization
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GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
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GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
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#ifdef __cplusplus
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}
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#endif
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