mirror of
https://github.com/ggerganov/whisper.cpp.git
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e990d1b791
* rename ggml-cpu-aarch64.c to .cpp * reformat extra cpu backend. - clean Q4_0_N_M and IQ4_0_N_M - remove from "file" tensor type - allow only with dynamic repack - extract cpu extra bufts and convert to C++ - hbm - "aarch64" - more generic use of extra buffer - generalise extra_supports_op - new API for "cpu-accel": - amx - aarch64 * clang-format * Clean Q4_0_N_M ref Enable restrict on C++ * add op GGML_OP_MUL_MAT_ID for Q4_0_N_M with runtime repack * added/corrected control on tensor size for Q4 repacking. * Update ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add debug logs on repacks. --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
136 lines
6.6 KiB
C
136 lines
6.6 KiB
C
#pragma once
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#include "ggml.h"
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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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// the compute plan that needs to be prepared for ggml_graph_compute()
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// since https://github.com/ggerganov/ggml/issues/287
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struct ggml_cplan {
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size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()`
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uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
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int n_threads;
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struct ggml_threadpool * threadpool;
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// abort ggml_graph_compute when true
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ggml_abort_callback abort_callback;
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void * abort_callback_data;
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};
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// numa strategies
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enum ggml_numa_strategy {
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GGML_NUMA_STRATEGY_DISABLED = 0,
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GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
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GGML_NUMA_STRATEGY_ISOLATE = 2,
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GGML_NUMA_STRATEGY_NUMACTL = 3,
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GGML_NUMA_STRATEGY_MIRROR = 4,
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GGML_NUMA_STRATEGY_COUNT
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};
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GGML_BACKEND_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems
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GGML_BACKEND_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node
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GGML_BACKEND_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
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GGML_BACKEND_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
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GGML_BACKEND_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
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GGML_BACKEND_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
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GGML_BACKEND_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
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GGML_BACKEND_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
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GGML_BACKEND_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
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GGML_BACKEND_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value);
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GGML_BACKEND_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
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GGML_BACKEND_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
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GGML_BACKEND_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
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GGML_BACKEND_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value);
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GGML_BACKEND_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params);
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GGML_BACKEND_API void ggml_threadpool_free (struct ggml_threadpool * threadpool);
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GGML_BACKEND_API int ggml_threadpool_get_n_threads (struct ggml_threadpool * threadpool);
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GGML_BACKEND_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool);
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GGML_BACKEND_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool);
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// ggml_graph_plan() has to be called before ggml_graph_compute()
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// when plan.work_size > 0, caller must allocate memory for plan.work_data
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GGML_BACKEND_API struct ggml_cplan ggml_graph_plan(
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const struct ggml_cgraph * cgraph,
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int n_threads, /* = GGML_DEFAULT_N_THREADS */
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struct ggml_threadpool * threadpool /* = NULL */ );
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GGML_BACKEND_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
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// same as ggml_graph_compute() but the work data is allocated as a part of the context
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// note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
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GGML_BACKEND_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
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//
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// system info
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//
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// x86
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GGML_BACKEND_API int ggml_cpu_has_sse3 (void);
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GGML_BACKEND_API int ggml_cpu_has_ssse3 (void);
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GGML_BACKEND_API int ggml_cpu_has_avx (void);
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GGML_BACKEND_API int ggml_cpu_has_avx_vnni (void);
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GGML_BACKEND_API int ggml_cpu_has_avx2 (void);
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GGML_BACKEND_API int ggml_cpu_has_f16c (void);
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GGML_BACKEND_API int ggml_cpu_has_fma (void);
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GGML_BACKEND_API int ggml_cpu_has_avx512 (void);
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GGML_BACKEND_API int ggml_cpu_has_avx512_vbmi(void);
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GGML_BACKEND_API int ggml_cpu_has_avx512_vnni(void);
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GGML_BACKEND_API int ggml_cpu_has_avx512_bf16(void);
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GGML_BACKEND_API int ggml_cpu_has_amx_int8 (void);
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// ARM
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GGML_BACKEND_API int ggml_cpu_has_neon (void);
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GGML_BACKEND_API int ggml_cpu_has_arm_fma (void);
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GGML_BACKEND_API int ggml_cpu_has_fp16_va (void);
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GGML_BACKEND_API int ggml_cpu_has_dotprod (void);
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GGML_BACKEND_API int ggml_cpu_has_matmul_int8(void);
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GGML_BACKEND_API int ggml_cpu_has_sve (void);
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GGML_BACKEND_API int ggml_cpu_get_sve_cnt (void); // sve vector length in bytes
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// other
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GGML_BACKEND_API int ggml_cpu_has_riscv_v (void);
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GGML_BACKEND_API int ggml_cpu_has_vsx (void);
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GGML_BACKEND_API int ggml_cpu_has_wasm_simd (void);
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GGML_BACKEND_API int ggml_cpu_has_llamafile (void);
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// Internal types and functions exposed for tests and benchmarks
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typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
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const void * GGML_RESTRICT y, size_t by, int nrc);
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struct ggml_type_traits_cpu {
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ggml_from_float_t from_float;
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ggml_vec_dot_t vec_dot;
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enum ggml_type vec_dot_type;
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int64_t nrows; // number of rows to process simultaneously
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};
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GGML_BACKEND_API const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type);
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GGML_BACKEND_API void ggml_cpu_init(void);
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//
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// CPU backend
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//
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GGML_BACKEND_API ggml_backend_t ggml_backend_cpu_init(void);
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GGML_BACKEND_API bool ggml_backend_is_cpu (ggml_backend_t backend);
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GGML_BACKEND_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads);
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GGML_BACKEND_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
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GGML_BACKEND_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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GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
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#ifdef __cplusplus
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}
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#endif
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