mirror of
https://github.com/ggerganov/llama.cpp.git
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42c76d1358
* Introduce ggml_compute_threadpool - OpenMP functional: check - Vanilla ggml functional: Check - ggml w/threadpool functional: Check - OpenMP no regression: No glaring problems - Vanilla ggml no regression: No glaring problems - ggml w/threadpool no regression: No glaring problems * Minor fixes * fixed use after release bug * fixed a harmless race condition * Fix Android bulid issue * fix more race conditions * fix deadlock for cases where cgraph.n_nodes == 1 and fix --poll case * threadpool: use cpu_get_num_math to set the default number of threadpool threads This way we avoid using E-Cores and Hyperthreaded siblings. * bench: create fresh threadpool for each test For benchmarking it's better to start a fresh pool for each test with the exact number of threads needed for that test. Having larger pools is suboptimal (causes more load, etc). * atomics: always use stdatomics with clang and use relaxed memory order when polling in ggml_barrier This also removes sched_yield() calls from ggml_barrier() to match OpenMP behavior. * threadpool: make polling the default to match openmp behavior All command line args now allow for setting poll to 0 (false). * threadpool: do not wakeup threads in already paused threadpool * fix potential race condition in check_for_work * threadpool: do not create two threadpools if their params are identical * threadpool: reduce pause/resume/wakeup overhead in common cases We now start threadpool in paused state only if we have two. The resume is now implicit (ie new work) which allows for reduced locking and context-switch overhead. * threadpool: add support for hybrid polling poll params (--poll, ...) now specify "polling level", i.e. how aggresively we poll before waiting on cond.var. poll=0 means no polling, 1 means poll for 128K rounds then wait, 2 for 256K rounds, ... The default value of 50 (ie 50x128K rounds) seems like a decent default across modern platforms. We can tune this further as things evolve. * threadpool: reduce the number of barrier required New work is now indicated with an atomic counter that is incremented for each new graph that needs to be computed. This removes the need for extra barrier for clearing the "new_work" and removes the special case for trivial graphs. * threadpool: remove special-casing for disposable threadpools With the efficient hybrid polling there is no need to make disposable pools any different. This simplifies the overall logic and reduces branching. Include n_threads in debug print for disposable threadpool. Declare pause and stop flags as atomic_bool This doesn't actually generate any memory barriers and simply informs the thread sanitizer that these flags can be written & read by different threads without locking. * threadpool: do not clear barrier counters between graphs computes (fixes race with small graphs) This fixes the race condition with very small graphs where the main thread happens to start a new graph while the workers are just about to exit from barriers. * threadpool: use relaxed order for chunk sync Full memory barrier is an overkill for this since each thread works on different chunk * threadpool: remove abort_callback from threadpool state * threadpool: better naming for thread/cpumask releated functions * threadpool: consistent use of int type for n_threads params * threadpool: add support for ggml_threadpool_params_default/init Also removes the need for explicit mask_specified param. all-zero cpumask means use default (usually inherited) cpu affinity mask. * threadpool: move typedef into ggml.h * threadpool: fix apply_priority() function name * threadpool: fix swift wrapper errors due to n_threads int type cleanup * threadpool: enable --cpu-mask and other threadpool related options only if threadpool is enabled * threadpool: replace checks for compute_thread ret code with proper status check * threadpool: simplify threadpool init logic and fix main thread affinity application Most of the init code is now exactly the same between threadpool and openmp. * threadpool: update threadpool resume/pause function names * threadpool: enable openmp by default for now * threadpool: don't forget to free workers state when omp is enabled * threadpool: avoid updating process priority on the platforms that do not require it On Windows we need to change overall process priority class in order to set thread priorities, but on Linux, Mac, etc we do not need to touch the overall process settings. * threadpool: update calling thread prio and affinity only at start/resume This avoids extra syscalls for each graph_compute() * llama-bench: turn threadpool params into vectors, add output headers, etc * llama-bench: add support for cool off between tests --delay This helps for long running tests on platforms that are thermally limited (phones, laptops, etc). --delay (disabled by default) introduces the sleep for N seconds before starting each test. * threadpool: move process priority setting into the apps (bench and cli) This avoids changing the overall process priority on Windows for the apps that use ggml/llama.cpp directy. * threadpool: move all pause/resume logic into ggml * threadpool: futher api cleanup and prep for future refactoring All threadpool related functions and structs use ggml_threadpool prefix. * threadpool: minor indent fixes * threadpool: improve setprioty error message * Update examples/llama-bench/llama-bench.cpp Co-authored-by: slaren <slarengh@gmail.com> * threadpool: fix indent in set_threadpool call * use int32_t for n_thread type in public llama.cpp API * threadpool: use _new and _free instead of _create and _release * fix two more public APIs to use int32_t for n_threads * build: set _GNU_SOURCE for Adroid --------- Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com> Co-authored-by: fmz <quic_fzaghlou@quic.com> Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
241 lines
14 KiB
C
241 lines
14 KiB
C
#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 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);
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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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