4343 lines
147 KiB
Python
4343 lines
147 KiB
Python
from __future__ import annotations
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import os
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import ctypes
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import pathlib
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from typing import (
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Callable,
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Union,
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NewType,
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Optional,
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TYPE_CHECKING,
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)
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from llama_cpp._ctypes_extensions import (
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load_shared_library,
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byref,
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ctypes_function_for_shared_library,
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)
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if TYPE_CHECKING:
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from llama_cpp._ctypes_extensions import (
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CtypesCData,
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CtypesArray,
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CtypesPointer,
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CtypesVoidPointer,
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CtypesRef,
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CtypesPointerOrRef,
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CtypesFuncPointer,
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)
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# Specify the base name of the shared library to load
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_lib_base_name = "llama"
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_override_base_path = os.environ.get("LLAMA_CPP_LIB_PATH")
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_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _override_base_path is None else pathlib.Path(_override_base_path)
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# Load the library
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_lib = load_shared_library(_lib_base_name, _base_path)
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ctypes_function = ctypes_function_for_shared_library(_lib)
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# from ggml.h
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# // NOTE: always add types at the end of the enum to keep backward compatibility
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# enum ggml_type {
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# GGML_TYPE_F32 = 0,
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# GGML_TYPE_F16 = 1,
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# GGML_TYPE_Q4_0 = 2,
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# GGML_TYPE_Q4_1 = 3,
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# // GGML_TYPE_Q4_2 = 4, support has been removed
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# // GGML_TYPE_Q4_3 = 5, support has been removed
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# GGML_TYPE_Q5_0 = 6,
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# GGML_TYPE_Q5_1 = 7,
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# GGML_TYPE_Q8_0 = 8,
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# GGML_TYPE_Q8_1 = 9,
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# GGML_TYPE_Q2_K = 10,
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# GGML_TYPE_Q3_K = 11,
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# GGML_TYPE_Q4_K = 12,
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# GGML_TYPE_Q5_K = 13,
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# GGML_TYPE_Q6_K = 14,
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# GGML_TYPE_Q8_K = 15,
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# GGML_TYPE_IQ2_XXS = 16,
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# GGML_TYPE_IQ2_XS = 17,
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# GGML_TYPE_IQ3_XXS = 18,
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# GGML_TYPE_IQ1_S = 19,
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# GGML_TYPE_IQ4_NL = 20,
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# GGML_TYPE_IQ3_S = 21,
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# GGML_TYPE_IQ2_S = 22,
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# GGML_TYPE_IQ4_XS = 23,
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# GGML_TYPE_I8 = 24,
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# GGML_TYPE_I16 = 25,
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# GGML_TYPE_I32 = 26,
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# GGML_TYPE_I64 = 27,
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# GGML_TYPE_F64 = 28,
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# GGML_TYPE_IQ1_M = 29,
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# GGML_TYPE_COUNT,
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# };
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GGML_TYPE_F32 = 0
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GGML_TYPE_F16 = 1
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GGML_TYPE_Q4_0 = 2
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GGML_TYPE_Q4_1 = 3
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GGML_TYPE_Q5_0 = 6
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GGML_TYPE_Q5_1 = 7
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GGML_TYPE_Q8_0 = 8
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GGML_TYPE_Q8_1 = 9
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GGML_TYPE_Q2_K = 10
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GGML_TYPE_Q3_K = 11
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GGML_TYPE_Q4_K = 12
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GGML_TYPE_Q5_K = 13
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GGML_TYPE_Q6_K = 14
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GGML_TYPE_Q8_K = 15
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GGML_TYPE_IQ2_XXS = 16
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GGML_TYPE_IQ2_XS = 17
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GGML_TYPE_IQ3_XXS = 18
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GGML_TYPE_IQ1_S = 19
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GGML_TYPE_IQ4_NL = 20
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GGML_TYPE_IQ3_S = 21
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GGML_TYPE_IQ2_S = 22
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GGML_TYPE_IQ4_XS = 23
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GGML_TYPE_I8 = 24
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GGML_TYPE_I16 = 25
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GGML_TYPE_I32 = 26
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GGML_TYPE_I64 = 27
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GGML_TYPE_F64 = 28
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GGML_TYPE_IQ1_M = 29
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GGML_TYPE_COUNT = 30
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# from ggml-backend.h
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# typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
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ggml_backend_sched_eval_callback = ctypes.CFUNCTYPE(
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ctypes.c_bool, ctypes.c_void_p, ctypes.c_bool, ctypes.c_void_p
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)
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# // Abort callback
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# // If not NULL, called before ggml computation
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# // If it returns true, the computation is aborted
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# typedef bool (*ggml_abort_callback)(void * data);
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ggml_abort_callback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_void_p)
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# llama.h bindings
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_lib.llama_max_devices.argtypes = []
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_lib.llama_max_devices.restype = ctypes.c_size_t
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LLAMA_MAX_DEVICES = _lib.llama_max_devices()
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# define LLAMA_DEFAULT_SEED 0xFFFFFFFF
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LLAMA_DEFAULT_SEED = 0xFFFFFFFF
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# define LLAMA_TOKEN_NULL -1
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LLAMA_TOKEN_NULL = -1
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# define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
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LLAMA_FILE_MAGIC_GGLA = 0x67676C61
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# define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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LLAMA_FILE_MAGIC_GGSN = 0x6767736E
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# define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
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LLAMA_FILE_MAGIC_GGSQ = 0x67677371
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# define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
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# define LLAMA_SESSION_VERSION 9
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LLAMA_SESSION_VERSION = 9
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# define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ
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LLAMA_STATE_SEQ_MAGIC = LLAMA_FILE_MAGIC_GGSQ
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# define LLAMA_STATE_SEQ_VERSION 2
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LLAMA_STATE_SEQ_VERSION = 2
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# struct llama_vocab;
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llama_vocab_p = NewType("llama_vocab_p", int)
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llama_vocab_p_ctypes = ctypes.c_void_p
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# struct llama_model;
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llama_model_p = NewType("llama_model_p", int)
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llama_model_p_ctypes = ctypes.c_void_p
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# struct llama_context;
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llama_context_p = NewType("llama_context_p", int)
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llama_context_p_ctypes = ctypes.c_void_p
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# typedef struct llama_memory_i * llama_memory_t;
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llama_memory_t = NewType("llama_memory_t", int)
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llama_memory_t_ctypes = ctypes.c_void_p
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# struct llama_kv_cache; (DEPRECATED)
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llama_kv_cache_p = NewType("llama_kv_cache_p", int)
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llama_kv_cache_p_ctypes = ctypes.c_void_p
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# typedef int32_t llama_pos;
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llama_pos = ctypes.c_int32
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# typedef int32_t llama_token;
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llama_token = ctypes.c_int32
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llama_token_p = ctypes.POINTER(llama_token)
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# typedef int32_t llama_seq_id;
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llama_seq_id = ctypes.c_int32
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# enum llama_vocab_type {
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# LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
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# LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
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# LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE
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# LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece
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# LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram
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# LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization
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# LLAMA_VOCAB_TYPE_PLAMO2 = 6, // PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming
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# };
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LLAMA_VOCAB_TYPE_NONE = 0
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"""For models without vocab"""
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LLAMA_VOCAB_TYPE_SPM = 1
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"""LLaMA tokenizer based on byte-level BPE with byte fallback"""
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LLAMA_VOCAB_TYPE_BPE = 2
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"""GPT-2 tokenizer based on byte-level BPE"""
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LLAMA_VOCAB_TYPE_WPM = 3
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"""BERT tokenizer based on WordPiece"""
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LLAMA_VOCAB_TYPE_UGM = 4
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"""T5 tokenizer based on Unigram"""
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LLAMA_VOCAB_TYPE_RWKV = 5
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"""RWKV tokenizer based on greedy tokenization"""
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LLAMA_VOCAB_TYPE_PLAMO2 = 6
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"""PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming"""
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# NOTE: Deprecated and will be removed in the future. (already gone in llama.cpp)
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# // pre-tokenization types
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# enum llama_vocab_pre_type {
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# LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0,
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# LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1,
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# LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2,
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# LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3,
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# LLAMA_VOCAB_PRE_TYPE_FALCON = 4,
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# LLAMA_VOCAB_PRE_TYPE_MPT = 5,
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# LLAMA_VOCAB_PRE_TYPE_STARCODER = 6,
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# LLAMA_VOCAB_PRE_TYPE_GPT2 = 7,
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# LLAMA_VOCAB_PRE_TYPE_REFACT = 8,
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# LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9,
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# LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10,
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# LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11,
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# LLAMA_VOCAB_PRE_TYPE_OLMO = 12,
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# LLAMA_VOCAB_PRE_TYPE_DBRX = 13,
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# LLAMA_VOCAB_PRE_TYPE_SMAUG = 14,
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# LLAMA_VOCAB_PRE_TYPE_PORO = 15,
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# LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16,
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# LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17,
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# LLAMA_VOCAB_PRE_TYPE_VIKING = 18,
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# LLAMA_VOCAB_PRE_TYPE_JAIS = 19,
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# LLAMA_VOCAB_PRE_TYPE_TEKKEN = 20,
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# LLAMA_VOCAB_PRE_TYPE_SMOLLM = 21,
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# LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22,
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# LLAMA_VOCAB_PRE_TYPE_BLOOM = 23,
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# LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24,
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# LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
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# LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26,
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# LLAMA_VOCAB_PRE_TYPE_MINERVA = 27,
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# LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28,
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# LLAMA_VOCAB_PRE_TYPE_GPT4O = 29,
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# LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30,
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# LLAMA_VOCAB_PRE_TYPE_TRILLION = 31,
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# LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32,
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# LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33,
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# LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34,
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# LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35,
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# };
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LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0
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LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1
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LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2
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LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3
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LLAMA_VOCAB_PRE_TYPE_FALCON = 4
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LLAMA_VOCAB_PRE_TYPE_MPT = 5
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LLAMA_VOCAB_PRE_TYPE_STARCODER = 6
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LLAMA_VOCAB_PRE_TYPE_GPT2 = 7
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LLAMA_VOCAB_PRE_TYPE_REFACT = 8
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LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9
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LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10
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LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11
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LLAMA_VOCAB_PRE_TYPE_OLMO = 12
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LLAMA_VOCAB_PRE_TYPE_DBRX = 13
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LLAMA_VOCAB_PRE_TYPE_SMAUG = 14
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LLAMA_VOCAB_PRE_TYPE_PORO = 15
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LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16
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LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17
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LLAMA_VOCAB_PRE_TYPE_VIKING = 18
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LLAMA_VOCAB_PRE_TYPE_JAIS = 19
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LLAMA_VOCAB_PRE_TYPE_TEKKEN = 20
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LLAMA_VOCAB_PRE_TYPE_SMOLLM = 21
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LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22
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LLAMA_VOCAB_PRE_TYPE_BLOOM = 23
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LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24
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LLAMA_VOCAB_PRE_TYPE_EXAONE = 25
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LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26
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LLAMA_VOCAB_PRE_TYPE_MINERVA = 27
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LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28
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LLAMA_VOCAB_PRE_TYPE_GPT4O = 29
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LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30
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LLAMA_VOCAB_PRE_TYPE_TRILLION = 31
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LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32
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LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33
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LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34
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LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35
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# // note: these values should be synchronized with ggml_rope
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# // TODO: maybe move this enum to ggml.h (ggml_rope_type)
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# enum llama_rope_type {
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# LLAMA_ROPE_TYPE_NONE = -1,
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# LLAMA_ROPE_TYPE_NORM = 0,
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# LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX,
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# LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE,
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# LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION,
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# };
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LLAMA_ROPE_TYPE_NONE = -1
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LLAMA_ROPE_TYPE_NORM = 0
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LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX = 2
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LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE = 8
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LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION = 24
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# enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
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# LLAMA_TOKEN_TYPE_UNDEFINED = 0,
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# LLAMA_TOKEN_TYPE_NORMAL = 1,
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# LLAMA_TOKEN_TYPE_UNKNOWN = 2,
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# LLAMA_TOKEN_TYPE_CONTROL = 3,
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# LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
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# LLAMA_TOKEN_TYPE_UNUSED = 5,
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# LLAMA_TOKEN_TYPE_BYTE = 6,
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# };
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LLAMA_TOKEN_TYPE_UNDEFINED = 0
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LLAMA_TOKEN_TYPE_NORMAL = 1
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LLAMA_TOKEN_TYPE_UNKNOWN = 2
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LLAMA_TOKEN_TYPE_CONTROL = 3
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LLAMA_TOKEN_TYPE_USER_DEFINED = 4
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LLAMA_TOKEN_TYPE_UNUSED = 5
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LLAMA_TOKEN_TYPE_BYTE = 6
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# enum llama_token_attr {
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# LLAMA_TOKEN_ATTR_UNDEFINED = 0,
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# LLAMA_TOKEN_ATTR_UNKNOWN = 1 << 0,
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||
# LLAMA_TOKEN_ATTR_UNUSED = 1 << 1,
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||
# LLAMA_TOKEN_ATTR_NORMAL = 1 << 2,
|
||
# LLAMA_TOKEN_ATTR_CONTROL = 1 << 3, // SPECIAL?
|
||
# LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4,
|
||
# LLAMA_TOKEN_ATTR_BYTE = 1 << 5,
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||
# LLAMA_TOKEN_ATTR_NORMALIZED = 1 << 6,
|
||
# LLAMA_TOKEN_ATTR_LSTRIP = 1 << 7,
|
||
# LLAMA_TOKEN_ATTR_RSTRIP = 1 << 8,
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||
# LLAMA_TOKEN_ATTR_SINGLE_WORD = 1 << 9,
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||
# };
|
||
LLAMA_TOKEN_ATTR_UNDEFINED = 0
|
||
LLAMA_TOKEN_ATTR_UNKNOWN = 1 << 0
|
||
LLAMA_TOKEN_ATTR_UNUSED = 1 << 1
|
||
LLAMA_TOKEN_ATTR_NORMAL = 1 << 2
|
||
LLAMA_TOKEN_ATTR_CONTROL = 1 << 3
|
||
LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4
|
||
LLAMA_TOKEN_ATTR_BYTE = 1 << 5
|
||
LLAMA_TOKEN_ATTR_NORMALIZED = 1 << 6
|
||
LLAMA_TOKEN_ATTR_LSTRIP = 1 << 7
|
||
LLAMA_TOKEN_ATTR_RSTRIP = 1 << 8
|
||
LLAMA_TOKEN_ATTR_SINGLE_WORD = 1 << 9
|
||
|
||
|
||
# // model file types
|
||
# enum llama_ftype {
|
||
# LLAMA_FTYPE_ALL_F32 = 0,
|
||
# LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
|
||
# // LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
|
||
# // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
|
||
# // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
|
||
# LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q3_K_S = 11, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q3_K_M = 12, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q3_K_L = 13, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q4_K_S = 14, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q4_K_M = 15, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_BF16 = 32, // except 1d tensors
|
||
# //LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33, // removed from gguf files, use Q4_0 and runtime repack
|
||
# //LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // removed from gguf files, use Q4_0 and runtime repack
|
||
# //LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // removed from gguf files, use Q4_0 and runtime repack
|
||
# LLAMA_FTYPE_MOSTLY_TQ1_0 = 36, // except 1d tensors
|
||
# LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors
|
||
#
|
||
# LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
|
||
# };
|
||
LLAMA_FTYPE_ALL_F32 = 0
|
||
LLAMA_FTYPE_MOSTLY_F16 = 1
|
||
LLAMA_FTYPE_MOSTLY_Q4_0 = 2
|
||
LLAMA_FTYPE_MOSTLY_Q4_1 = 3
|
||
LLAMA_FTYPE_MOSTLY_Q8_0 = 7
|
||
LLAMA_FTYPE_MOSTLY_Q5_0 = 8
|
||
LLAMA_FTYPE_MOSTLY_Q5_1 = 9
|
||
LLAMA_FTYPE_MOSTLY_Q2_K = 10
|
||
LLAMA_FTYPE_MOSTLY_Q3_K_S = 11
|
||
LLAMA_FTYPE_MOSTLY_Q3_K_M = 12
|
||
LLAMA_FTYPE_MOSTLY_Q3_K_L = 13
|
||
LLAMA_FTYPE_MOSTLY_Q4_K_S = 14
|
||
LLAMA_FTYPE_MOSTLY_Q4_K_M = 15
|
||
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16
|
||
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17
|
||
LLAMA_FTYPE_MOSTLY_Q6_K = 18
|
||
LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19
|
||
LLAMA_FTYPE_MOSTLY_IQ2_XS = 20
|
||
LLAMA_FTYPE_MOSTLY_Q2_K_S = 21
|
||
LLAMA_FTYPE_MOSTLY_IQ3_XS = 22
|
||
LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23
|
||
LLAMA_FTYPE_MOSTLY_IQ1_S = 24
|
||
LLAMA_FTYPE_MOSTLY_IQ4_NL = 25
|
||
LLAMA_FTYPE_MOSTLY_IQ3_S = 26
|
||
LLAMA_FTYPE_MOSTLY_IQ3_M = 27
|
||
LLAMA_FTYPE_MOSTLY_IQ2_S = 28
|
||
LLAMA_FTYPE_MOSTLY_IQ2_M = 29
|
||
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30
|
||
LLAMA_FTYPE_MOSTLY_IQ1_M = 31
|
||
LLAMA_FTYPE_MOSTLY_BF16 = 32
|
||
# LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33
|
||
# LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34
|
||
# LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35
|
||
LLAMA_FTYPE_MOSTLY_TQ1_0 = 36
|
||
LLAMA_FTYPE_MOSTLY_TQ2_0 = 37
|
||
LLAMA_FTYPE_GUESSED = 1024
|
||
|
||
# enum llama_rope_scaling_type {
|
||
# LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
|
||
# LLAMA_ROPE_SCALING_TYPE_NONE = 0,
|
||
# LLAMA_ROPE_SCALING_TYPE_LINEAR = 1,
|
||
# LLAMA_ROPE_SCALING_TYPE_YARN = 2,
|
||
# LLAMA_ROPE_SCALING_TYPE_LONGROPE = 3,
|
||
# LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_LONGROPE,
|
||
# };
|
||
LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1
|
||
LLAMA_ROPE_SCALING_TYPE_NONE = 0
|
||
LLAMA_ROPE_SCALING_TYPE_LINEAR = 1
|
||
LLAMA_ROPE_SCALING_TYPE_YARN = 2
|
||
LLAMA_ROPE_SCALING_TYPE_LONGROPE = 3
|
||
LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_LONGROPE
|
||
|
||
# enum llama_pooling_type {
|
||
# LLAMA_POOLING_TYPE_UNSPECIFIED = -1,
|
||
# LLAMA_POOLING_TYPE_NONE = 0,
|
||
# LLAMA_POOLING_TYPE_MEAN = 1,
|
||
# LLAMA_POOLING_TYPE_CLS = 2,
|
||
# LLAMA_POOLING_TYPE_LAST = 3,
|
||
# LLAMA_POOLING_TYPE_RANK = 4, // used by reranking models to attach the classification head to the graph
|
||
# };
|
||
LLAMA_POOLING_TYPE_UNSPECIFIED = -1
|
||
LLAMA_POOLING_TYPE_NONE = 0
|
||
LLAMA_POOLING_TYPE_MEAN = 1
|
||
LLAMA_POOLING_TYPE_CLS = 2
|
||
LLAMA_POOLING_TYPE_LAST = 3
|
||
LLAMA_POOLING_TYPE_RANK = 4
|
||
|
||
# enum llama_attention_type {
|
||
# LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1,
|
||
# LLAMA_ATTENTION_TYPE_CAUSAL = 0,
|
||
# LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1,
|
||
# };
|
||
LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1
|
||
LLAMA_ATTENTION_TYPE_CAUSAL = 0
|
||
LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1
|
||
|
||
|
||
# enum llama_split_mode {
|
||
# LLAMA_SPLIT_MODE_NONE = 0, // single GPU
|
||
# LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
|
||
# LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported
|
||
# };
|
||
LLAMA_SPLIT_MODE_NONE = 0
|
||
LLAMA_SPLIT_MODE_LAYER = 1
|
||
LLAMA_SPLIT_MODE_ROW = 2
|
||
|
||
|
||
# typedef struct llama_token_data {
|
||
# llama_token id; // token id
|
||
# float logit; // log-odds of the token
|
||
# float p; // probability of the token
|
||
# } llama_token_data;
|
||
class llama_token_data(ctypes.Structure):
|
||
"""Used to store token data
|
||
|
||
Attributes:
|
||
id (llama_token): token id
|
||
logit (float): log-odds of the token
|
||
p (float): probability of the token"""
|
||
|
||
if TYPE_CHECKING:
|
||
id: llama_token
|
||
logit: float
|
||
p: float
|
||
|
||
_fields_ = [
|
||
("id", llama_token),
|
||
("logit", ctypes.c_float),
|
||
("p", ctypes.c_float),
|
||
]
|
||
|
||
|
||
llama_token_data_p = ctypes.POINTER(llama_token_data)
|
||
|
||
|
||
# typedef struct llama_token_data_array {
|
||
# // TODO: consider SoA
|
||
# // NOTE: this pointer can be modified by the samplers
|
||
# llama_token_data * data;
|
||
# size_t size;
|
||
# int64_t selected; // this is the index in the data array (i.e. not the token id)
|
||
# bool sorted;
|
||
# } llama_token_data_array;
|
||
class llama_token_data_array(ctypes.Structure):
|
||
"""Used to sample tokens given logits
|
||
|
||
Attributes:
|
||
data (ctypes.Array[llama_token_data]): token data
|
||
size (int): size of the array
|
||
selected (int): index in the data array (i.e. not the token id)
|
||
sorted (bool): whether the array is sorted"""
|
||
|
||
if TYPE_CHECKING:
|
||
data: CtypesArray[llama_token_data]
|
||
size: int
|
||
selected: int
|
||
sorted: bool
|
||
|
||
_fields_ = [
|
||
("data", llama_token_data_p),
|
||
("size", ctypes.c_size_t),
|
||
("selected", ctypes.c_int64),
|
||
("sorted", ctypes.c_bool),
|
||
]
|
||
|
||
|
||
llama_token_data_array_p = ctypes.POINTER(llama_token_data_array)
|
||
|
||
# typedef bool (*llama_progress_callback)(float progress, void * user_data);
|
||
llama_progress_callback = ctypes.CFUNCTYPE(
|
||
ctypes.c_bool, ctypes.c_float, ctypes.c_void_p
|
||
)
|
||
|
||
|
||
# // Input data for llama_encode/llama_decode
|
||
# // A llama_batch object can contain input about one or many sequences
|
||
# // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
|
||
# //
|
||
# // - token : the token ids of the input (used when embd is NULL)
|
||
# // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
|
||
# // - pos : the positions of the respective token in the sequence
|
||
# // (if set to NULL, the token position will be tracked automatically by llama_encode/llama_decode)
|
||
# // - seq_id : the sequence to which the respective token belongs
|
||
# // (if set to NULL, the sequence ID will be assumed to be 0)
|
||
# // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
|
||
# // (if set to NULL:
|
||
# // - if embeddings: all tokens are output
|
||
# // - if not: only the last token is output
|
||
# // )
|
||
# //
|
||
# typedef struct llama_batch {
|
||
# int32_t n_tokens;
|
||
|
||
# llama_token * token;
|
||
# float * embd;
|
||
# llama_pos * pos;
|
||
# int32_t * n_seq_id;
|
||
# llama_seq_id ** seq_id;
|
||
# int8_t * logits; // TODO: rename this to "output"
|
||
# } llama_batch;
|
||
class llama_batch(ctypes.Structure):
|
||
"""Input data for llama_encode/llama_decode
|
||
|
||
A llama_batch object can contain input about one or many sequences
|
||
|
||
The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
|
||
|
||
Attributes:
|
||
n_tokens (int): number of tokens
|
||
token (ctypes.Array[llama_token]): the token ids of the input (used when embd is NULL)
|
||
embd (ctypes.Array[ctypes.ctypes.c_float]): token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
|
||
pos (ctypes.Array[ctypes.Array[llama_pos]]): the positions of the respective token in the sequence
|
||
seq_id (ctypes.Array[ctypes.Array[llama_seq_id]]): the sequence to which the respective token belongs
|
||
logits (ctypes.Array[ctypes.ctypes.c_int8]): if zero, the logits for the respective token will not be output
|
||
"""
|
||
|
||
if TYPE_CHECKING:
|
||
n_tokens: int
|
||
token: CtypesArray[llama_token]
|
||
embd: CtypesArray[ctypes.c_float]
|
||
pos: CtypesArray[CtypesArray[llama_pos]]
|
||
n_seq_id: CtypesArray[ctypes.c_int]
|
||
seq_id: CtypesArray[CtypesArray[llama_seq_id]]
|
||
logits: CtypesArray[ctypes.c_int8]
|
||
|
||
_fields_ = [
|
||
("n_tokens", ctypes.c_int32),
|
||
("token", ctypes.POINTER(llama_token)),
|
||
("embd", ctypes.POINTER(ctypes.c_float)),
|
||
("pos", ctypes.POINTER(llama_pos)),
|
||
("n_seq_id", ctypes.POINTER(ctypes.c_int32)),
|
||
("seq_id", ctypes.POINTER(ctypes.POINTER(llama_seq_id))),
|
||
("logits", ctypes.POINTER(ctypes.c_int8)),
|
||
]
|
||
|
||
|
||
# enum llama_model_kv_override_type {
|
||
# LLAMA_KV_OVERRIDE_TYPE_INT,
|
||
# LLAMA_KV_OVERRIDE_TYPE_FLOAT,
|
||
# LLAMA_KV_OVERRIDE_TYPE_BOOL,
|
||
# LLAMA_KV_OVERRIDE_TYPE_STR,
|
||
# };
|
||
LLAMA_KV_OVERRIDE_TYPE_INT = 0
|
||
LLAMA_KV_OVERRIDE_TYPE_FLOAT = 1
|
||
LLAMA_KV_OVERRIDE_TYPE_BOOL = 2
|
||
LLAMA_KV_OVERRIDE_TYPE_STR = 3
|
||
|
||
|
||
# struct llama_model_kv_override {
|
||
# enum llama_model_kv_override_type tag;
|
||
|
||
# char key[128];
|
||
|
||
|
||
# union {
|
||
# int64_t val_i64;
|
||
# double val_f64;
|
||
# bool val_bool;
|
||
# char val_str[128];
|
||
# };
|
||
# };
|
||
class llama_model_kv_override_value(ctypes.Union):
|
||
_fields_ = [
|
||
("val_i64", ctypes.c_int64),
|
||
("val_f64", ctypes.c_double),
|
||
("val_bool", ctypes.c_bool),
|
||
("val_str", ctypes.c_char * 128),
|
||
]
|
||
|
||
if TYPE_CHECKING:
|
||
val_i64: int
|
||
val_f64: float
|
||
val_bool: bool
|
||
val_str: bytes
|
||
|
||
|
||
class llama_model_kv_override(ctypes.Structure):
|
||
_fields_ = [
|
||
("tag", ctypes.c_int),
|
||
("key", ctypes.c_char * 128),
|
||
("value", llama_model_kv_override_value),
|
||
]
|
||
|
||
if TYPE_CHECKING:
|
||
tag: int
|
||
key: bytes
|
||
value: Union[int, float, bool, bytes]
|
||
|
||
|
||
# struct llama_model_tensor_buft_override {
|
||
# const char * pattern;
|
||
# ggml_backend_buffer_type_t buft;
|
||
# };
|
||
|
||
|
||
# struct llama_model_params {
|
||
# // NULL-terminated list of devices to use for offloading (if NULL, all available devices are used)
|
||
# ggml_backend_dev_t * devices;
|
||
|
||
# // NULL-terminated list of buffer types to use for tensors that match a pattern
|
||
# const struct llama_model_tensor_buft_override * tensor_buft_overrides;
|
||
|
||
# int32_t n_gpu_layers; // number of layers to store in VRAM
|
||
# enum llama_split_mode split_mode; // how to split the model across multiple GPUs
|
||
|
||
# // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
|
||
# int32_t main_gpu;
|
||
|
||
# // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
|
||
# const float * tensor_split;
|
||
|
||
# // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
|
||
# // If the provided progress_callback returns true, model loading continues.
|
||
# // If it returns false, model loading is immediately aborted.
|
||
# llama_progress_callback progress_callback;
|
||
|
||
# // context pointer passed to the progress callback
|
||
# void * progress_callback_user_data;
|
||
|
||
# // override key-value pairs of the model meta data
|
||
# const struct llama_model_kv_override * kv_overrides;
|
||
|
||
# // Keep the booleans together to avoid misalignment during copy-by-value.
|
||
# bool vocab_only; // only load the vocabulary, no weights
|
||
# bool use_mmap; // use mmap if possible
|
||
# bool use_mlock; // force system to keep model in RAM
|
||
# bool check_tensors; // validate model tensor data
|
||
# };
|
||
class llama_model_params(ctypes.Structure):
|
||
"""Parameters for llama_model
|
||
|
||
Attributes:
|
||
devices (ctypes.Array[ggml_backend_dev_t]): NULL-terminated list of devices to use for offloading (if NULL, all available devices are used)
|
||
tensor_buft_overrides (ctypes.Array[llama_model_tensor_buft_override]): NULL-terminated list of buffer types to use for tensors that match a pattern
|
||
n_gpu_layers (int): number of layers to store in VRAM
|
||
split_mode (int): how to split the model across multiple GPUs
|
||
main_gpu (int): the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
|
||
tensor_split (ctypes.Array[ctypes.ctypes.c_float]): proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
|
||
progress_callback (llama_progress_callback): called with a progress value between 0.0 and 1.0. Pass NULL to disable. If the provided progress_callback returns true, model loading continues. If it returns false, model loading is immediately aborted.
|
||
progress_callback_user_data (ctypes.ctypes.c_void_p): context pointer passed to the progress callback
|
||
kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data
|
||
vocab_only (bool): only load the vocabulary, no weights
|
||
use_mmap (bool): use mmap if possible
|
||
use_mlock (bool): force system to keep model in RAM
|
||
check_tensors (bool): validate model tensor data"""
|
||
|
||
if TYPE_CHECKING:
|
||
devices: CtypesArray[ctypes.c_void_p] # NOTE: unused
|
||
tensor_buft_overrides: CtypesArray[llama_model_tensor_buft_override] # NOTE: unused
|
||
n_gpu_layers: int
|
||
split_mode: int
|
||
main_gpu: int
|
||
tensor_split: CtypesArray[ctypes.c_float]
|
||
progress_callback: Callable[[float, ctypes.c_void_p], bool]
|
||
progress_callback_user_data: ctypes.c_void_p
|
||
kv_overrides: CtypesArray[llama_model_kv_override]
|
||
vocab_only: bool
|
||
use_mmap: bool
|
||
use_mlock: bool
|
||
check_tensors: bool
|
||
|
||
_fields_ = [
|
||
("devices", ctypes.c_void_p), # NOTE: unnused
|
||
("tensor_buft_overrides", ctypes.c_void_p), # NOTE: unused
|
||
("n_gpu_layers", ctypes.c_int32),
|
||
("split_mode", ctypes.c_int),
|
||
("main_gpu", ctypes.c_int32),
|
||
("tensor_split", ctypes.POINTER(ctypes.c_float)),
|
||
("progress_callback", llama_progress_callback),
|
||
("progress_callback_user_data", ctypes.c_void_p),
|
||
("kv_overrides", ctypes.POINTER(llama_model_kv_override)),
|
||
("vocab_only", ctypes.c_bool),
|
||
("use_mmap", ctypes.c_bool),
|
||
("use_mlock", ctypes.c_bool),
|
||
("check_tensors", ctypes.c_bool),
|
||
]
|
||
|
||
|
||
# // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
|
||
# // https://github.com/ggml-org/llama.cpp/pull/7544
|
||
# struct llama_context_params {
|
||
# uint32_t n_ctx; // text context, 0 = from model
|
||
# uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode
|
||
# uint32_t n_ubatch; // physical maximum batch size
|
||
# uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
|
||
# int32_t n_threads; // number of threads to use for generation
|
||
# int32_t n_threads_batch; // number of threads to use for batch processing
|
||
|
||
# enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
|
||
# enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id
|
||
# enum llama_attention_type attention_type; // attention type to use for embeddings
|
||
|
||
# // ref: https://github.com/ggml-org/llama.cpp/pull/2054
|
||
# float rope_freq_base; // RoPE base frequency, 0 = from model
|
||
# float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
|
||
# float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model
|
||
# float yarn_attn_factor; // YaRN magnitude scaling factor
|
||
# float yarn_beta_fast; // YaRN low correction dim
|
||
# float yarn_beta_slow; // YaRN high correction dim
|
||
# uint32_t yarn_orig_ctx; // YaRN original context size
|
||
# float defrag_thold; // defragment the KV cache if holes/size > thold, <= 0 disabled (default)
|
||
|
||
# ggml_backend_sched_eval_callback cb_eval;
|
||
# void * cb_eval_user_data;
|
||
|
||
# enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
|
||
# enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
|
||
|
||
# // Abort callback
|
||
# // if it returns true, execution of llama_decode() will be aborted
|
||
# // currently works only with CPU execution
|
||
# ggml_abort_callback abort_callback;
|
||
# void * abort_callback_data;
|
||
|
||
# // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
|
||
# bool embeddings; // if true, extract embeddings (together with logits)
|
||
# bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU
|
||
# bool flash_attn; // use flash attention [EXPERIMENTAL]
|
||
# bool no_perf; // measure performance timings
|
||
# bool op_offload; // offload host tensor operations to device
|
||
# bool swa_full; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
|
||
# // NOTE: setting to false when n_seq_max > 1 can cause bad performance in some cases
|
||
# // ref: https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573
|
||
# };
|
||
class llama_context_params(ctypes.Structure):
|
||
"""Parameters for llama_context
|
||
|
||
Attributes:
|
||
n_ctx (int): text context, 0 = from model
|
||
n_batch (int): logical maximum batch size that can be submitted to llama_decode
|
||
n_ubatch (int): physical maximum batch size
|
||
n_seq_max (int): max number of sequences (i.e. distinct states for recurrent models)
|
||
n_threads (int): number of threads to use for generation
|
||
n_threads_batch (int): number of threads to use for batch processing
|
||
rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type`
|
||
pooling_type (int): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
|
||
attention_type (int): attention type to use for embeddings
|
||
rope_freq_base (float): RoPE base frequency, 0 = from model
|
||
rope_freq_scale (float): RoPE frequency scaling factor, 0 = from model
|
||
yarn_ext_factor (float): YaRN extrapolation mix factor, negative = from model
|
||
yarn_attn_factor (float): YaRN magnitude scaling factor
|
||
yarn_beta_fast (float): YaRN low correction dim
|
||
yarn_beta_slow (float): YaRN high correction dim
|
||
yarn_orig_ctx (int): YaRN original context size
|
||
defrag_thold (float): defragment the KV cache if holes/size > thold, <= 0 disabled (default)
|
||
cb_eval (ggml_backend_sched_eval_callback): callback for scheduling eval
|
||
cb_eval_user_data (ctypes.ctypes.c_void_p): user data for cb_eval
|
||
type_k (int): data type for K cache
|
||
type_v (int): data type for V cache
|
||
abort_callback (ggml_abort_callback): abort callback if it returns true, execution of llama_decode() will be aborted
|
||
abort_callback_data (ctypes.ctypes.c_void_p): data for abort_callback
|
||
embeddings (bool): if true, extract embeddings (together with logits)
|
||
offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU
|
||
flash_attn (bool): whether to use flash attention
|
||
no_perf (bool): whether to measure performance timings
|
||
op_offload (bool): offload host tensor operations to device
|
||
swa_full (bool): use full-size SWA cache
|
||
"""
|
||
|
||
if TYPE_CHECKING:
|
||
n_ctx: int
|
||
n_batch: int
|
||
n_ubatch: int
|
||
n_seq_max: int
|
||
n_threads: int
|
||
n_threads_batch: int
|
||
rope_scaling_type: int
|
||
pooling_type: int
|
||
attention_type: int
|
||
rope_freq_base: float
|
||
rope_freq_scale: float
|
||
yarn_ext_factor: float
|
||
yarn_attn_factor: float
|
||
yarn_beta_fast: float
|
||
yarn_beta_slow: float
|
||
yarn_orig_ctx: int
|
||
defrag_thold: float
|
||
cb_eval: Callable[[ctypes.c_void_p, bool], bool]
|
||
cb_eval_user_data: ctypes.c_void_p
|
||
type_k: int
|
||
type_v: int
|
||
abort_callback: Callable[[ctypes.c_void_p], bool]
|
||
abort_callback_data: ctypes.c_void_p
|
||
embeddings: bool
|
||
offload_kqv: bool
|
||
flash_attn: bool
|
||
no_perf: bool
|
||
op_offload: bool
|
||
swa_full: bool
|
||
|
||
_fields_ = [
|
||
("n_ctx", ctypes.c_uint32),
|
||
("n_batch", ctypes.c_uint32),
|
||
("n_ubatch", ctypes.c_uint32),
|
||
("n_seq_max", ctypes.c_uint32),
|
||
("n_threads", ctypes.c_int32),
|
||
("n_threads_batch", ctypes.c_int32),
|
||
("rope_scaling_type", ctypes.c_int),
|
||
("pooling_type", ctypes.c_int),
|
||
("attention_type", ctypes.c_int),
|
||
("rope_freq_base", ctypes.c_float),
|
||
("rope_freq_scale", ctypes.c_float),
|
||
("yarn_ext_factor", ctypes.c_float),
|
||
("yarn_attn_factor", ctypes.c_float),
|
||
("yarn_beta_fast", ctypes.c_float),
|
||
("yarn_beta_slow", ctypes.c_float),
|
||
("yarn_orig_ctx", ctypes.c_uint32),
|
||
("defrag_thold", ctypes.c_float),
|
||
("cb_eval", ggml_backend_sched_eval_callback),
|
||
("cb_eval_user_data", ctypes.c_void_p),
|
||
("type_k", ctypes.c_int),
|
||
("type_v", ctypes.c_int),
|
||
("abort_callback", ggml_abort_callback),
|
||
("abort_callback_data", ctypes.c_void_p),
|
||
("embeddings", ctypes.c_bool),
|
||
("offload_kqv", ctypes.c_bool),
|
||
("flash_attn", ctypes.c_bool),
|
||
("no_perf", ctypes.c_bool),
|
||
("op_offload", ctypes.c_bool),
|
||
("swa_full", ctypes.c_bool),
|
||
]
|
||
|
||
|
||
# // Signature for logging events
|
||
# // Note that text includes the new line character at the end for most events.
|
||
# // If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
|
||
# // if it exists.
|
||
# // It might not exist for progress report where '.' is output repeatedly.
|
||
# typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
|
||
llama_log_callback = ctypes.CFUNCTYPE(
|
||
None, ctypes.c_int, ctypes.c_char_p, ctypes.c_void_p
|
||
)
|
||
"""Signature for logging events
|
||
Note that text includes the new line character at the end for most events.
|
||
If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
|
||
if it exists.
|
||
It might not exist for progress report where '.' is output repeatedly."""
|
||
|
||
|
||
# // model quantization parameters
|
||
# typedef struct llama_model_quantize_params {
|
||
# int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
|
||
# enum llama_ftype ftype; // quantize to this llama_ftype
|
||
# enum ggml_type output_tensor_type; // output tensor type
|
||
# enum ggml_type token_embedding_type; // token embeddings tensor type
|
||
# bool allow_requantize; // allow quantizing non-f32/f16 tensors
|
||
# bool quantize_output_tensor; // quantize output.weight
|
||
# bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
||
# bool pure; // quantize all tensors to the default type
|
||
# bool keep_split; // quantize to the same number of shards
|
||
# void * imatrix; // pointer to importance matrix data
|
||
# void * kv_overrides; // pointer to vector containing overrides
|
||
# void * tensor_types; // pointer to vector containing tensor types
|
||
# void * prune_layers; // pointer to vector containing layer indices to prune
|
||
# } llama_model_quantize_params;
|
||
class llama_model_quantize_params(ctypes.Structure):
|
||
"""Parameters for llama_model_quantize
|
||
|
||
Attributes:
|
||
nthread (int): number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
|
||
ftype (int): quantize to this llama_ftype
|
||
output_tensor_type (int): output tensor type
|
||
token_embedding_type (int): token embeddings tensor type
|
||
allow_requantize (bool): allow quantizing non-f32/f16 tensors
|
||
quantize_output_tensor (bool): quantize output.weight
|
||
only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
||
pure (bool): quantize all tensors to the default type
|
||
keep_split (bool): quantize to the same number of shards
|
||
imatrix (ctypes.c_void_p): pointer to importance matrix data
|
||
kv_overrides (ctypes.c_void_p): pointer to vector containing overrides
|
||
tensor_types (ctypes.c_void_p): pointer to vector containing tensor types
|
||
prune_layers (ctypes.c_void_p): pointer to vector containing layer indices to prune
|
||
"""
|
||
|
||
if TYPE_CHECKING:
|
||
nthread: int
|
||
ftype: int
|
||
output_tensor_type: int
|
||
token_embedding_type: int
|
||
allow_requantize: bool
|
||
quantize_output_tensor: bool
|
||
only_copy: bool
|
||
pure: bool
|
||
keep_split: bool
|
||
imatrix: ctypes.c_void_p
|
||
kv_overrides: ctypes.c_void_p
|
||
tensor_types: ctypes.c_void_p
|
||
prune_layers: ctypes.c_void_p
|
||
|
||
_fields_ = [
|
||
("nthread", ctypes.c_int32),
|
||
("ftype", ctypes.c_int),
|
||
("output_tensor_type", ctypes.c_int),
|
||
("token_embedding_type", ctypes.c_int),
|
||
("allow_requantize", ctypes.c_bool),
|
||
("quantize_output_tensor", ctypes.c_bool),
|
||
("only_copy", ctypes.c_bool),
|
||
("pure", ctypes.c_bool),
|
||
("keep_split", ctypes.c_bool),
|
||
("imatrix", ctypes.c_void_p),
|
||
("kv_overrides", ctypes.c_void_p),
|
||
("tensor_types", ctypes.c_void_p),
|
||
("prune_layers", ctypes.c_void_p),
|
||
]
|
||
|
||
|
||
# typedef struct llama_logit_bias {
|
||
# llama_token token;
|
||
# float bias;
|
||
# } llama_logit_bias;
|
||
class llama_logit_bias(ctypes.Structure):
|
||
"""Used to store logit bias
|
||
|
||
Attributes:
|
||
token (llama_token): token id
|
||
bias (float): bias"""
|
||
|
||
if TYPE_CHECKING:
|
||
token: llama_token
|
||
bias: float
|
||
|
||
_fields_ = [
|
||
("token", llama_token),
|
||
("bias", ctypes.c_float),
|
||
]
|
||
|
||
|
||
llama_logit_bias_p = ctypes.POINTER(llama_logit_bias)
|
||
|
||
|
||
# typedef struct llama_sampler_chain_params {
|
||
# bool no_perf; // whether to measure performance timings
|
||
# } llama_sampler_chain_params;
|
||
class llama_sampler_chain_params(ctypes.Structure):
|
||
"""Parameters for llama_sampler_chain
|
||
|
||
Attributes:
|
||
no_perf (bool): whether to measure performance timings"""
|
||
|
||
if TYPE_CHECKING:
|
||
no_perf: bool
|
||
|
||
_fields_ = [
|
||
("no_perf", ctypes.c_bool),
|
||
]
|
||
|
||
|
||
# // used in chat template
|
||
# typedef struct llama_chat_message {
|
||
# const char * role;
|
||
# const char * content;
|
||
# } llama_chat_message;
|
||
class llama_chat_message(ctypes.Structure):
|
||
_fields_ = [
|
||
("role", ctypes.c_char_p),
|
||
("content", ctypes.c_char_p),
|
||
]
|
||
|
||
|
||
# // lora adapter
|
||
# struct llama_adapter_lora;
|
||
llama_adapter_lora_p = ctypes.c_void_p
|
||
llama_adapter_lora_p_ctypes = ctypes.POINTER(ctypes.c_void_p)
|
||
|
||
|
||
# // Helpers for getting default parameters
|
||
# LLAMA_API struct llama_model_params llama_model_default_params(void);
|
||
@ctypes_function(
|
||
"llama_model_default_params",
|
||
[],
|
||
llama_model_params,
|
||
)
|
||
def llama_model_default_params() -> llama_model_params:
|
||
"""Get default parameters for llama_model"""
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_context_params llama_context_default_params(void);
|
||
@ctypes_function(
|
||
"llama_context_default_params",
|
||
[],
|
||
llama_context_params,
|
||
)
|
||
def llama_context_default_params() -> llama_context_params:
|
||
"""Get default parameters for llama_context"""
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_sampler_chain_params llama_sampler_chain_default_params(void);
|
||
@ctypes_function(
|
||
"llama_sampler_chain_default_params",
|
||
[],
|
||
llama_sampler_chain_params,
|
||
)
|
||
def llama_sampler_chain_default_params() -> llama_sampler_chain_params:
|
||
"""Get default parameters for llama_sampler_chain"""
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
|
||
@ctypes_function(
|
||
"llama_model_quantize_default_params",
|
||
[],
|
||
llama_model_quantize_params,
|
||
)
|
||
def llama_model_quantize_default_params() -> llama_model_quantize_params:
|
||
"""Get default parameters for llama_model_quantize"""
|
||
...
|
||
|
||
|
||
# // Initialize the llama + ggml backend
|
||
# // If numa is true, use NUMA optimizations
|
||
# // Call once at the start of the program
|
||
# LLAMA_API void llama_backend_init(void);
|
||
@ctypes_function(
|
||
"llama_backend_init",
|
||
[],
|
||
None,
|
||
)
|
||
def llama_backend_init():
|
||
"""Initialize the llama + ggml backend
|
||
Call once at the start of the program"""
|
||
...
|
||
|
||
|
||
# // numa strategies
|
||
# enum ggml_numa_strategy {
|
||
# GGML_NUMA_STRATEGY_DISABLED = 0,
|
||
# GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
|
||
# GGML_NUMA_STRATEGY_ISOLATE = 2,
|
||
# GGML_NUMA_STRATEGY_NUMACTL = 3,
|
||
# GGML_NUMA_STRATEGY_MIRROR = 4,
|
||
# GGML_NUMA_STRATEGY_COUNT
|
||
# };
|
||
GGML_NUMA_STRATEGY_DISABLED = 0
|
||
GGML_NUMA_STRATEGY_DISTRIBUTE = 1
|
||
GGML_NUMA_STRATEGY_ISOLATE = 2
|
||
GGML_NUMA_STRATEGY_NUMACTL = 3
|
||
GGML_NUMA_STRATEGY_MIRROR = 4
|
||
GGML_NUMA_STRATEGY_COUNT = 5
|
||
|
||
|
||
# // Call once at the end of the program - currently only used for MPI
|
||
# LLAMA_API void llama_backend_free(void);
|
||
@ctypes_function(
|
||
"llama_backend_free",
|
||
[],
|
||
None,
|
||
)
|
||
def llama_backend_free():
|
||
"""Call once at the end of the program - currently only used for MPI"""
|
||
...
|
||
|
||
|
||
# //optional:
|
||
# LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
|
||
@ctypes_function(
|
||
"llama_numa_init",
|
||
[ctypes.c_int],
|
||
None,
|
||
)
|
||
def llama_numa_init(numa: int, /):
|
||
...
|
||
|
||
|
||
# // Optional: an auto threadpool gets created in ggml if not passed explicitly
|
||
# LLAMA_API void llama_attach_threadpool(
|
||
# struct llama_context * ctx,
|
||
# ggml_threadpool_t threadpool,
|
||
# ggml_threadpool_t threadpool_batch);
|
||
# TODO: Add llama_attach_threadpool
|
||
|
||
|
||
# LLAMA_API void llama_detach_threadpool(struct llama_context * ctx);
|
||
# TODO: Add llama_detach_threadpool
|
||
|
||
|
||
# DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file(
|
||
# const char * path_model,
|
||
# struct llama_model_params params),
|
||
# "use llama_model_load_from_file instead");
|
||
@ctypes_function(
|
||
"llama_load_model_from_file",
|
||
[ctypes.c_char_p, llama_model_params],
|
||
llama_model_p_ctypes,
|
||
)
|
||
def llama_load_model_from_file(
|
||
path_model: bytes, params: llama_model_params, /
|
||
) -> Optional[llama_model_p]:
|
||
...
|
||
|
||
|
||
# // Load the model from a file
|
||
# // If the file is split into multiple parts, the file name must follow this pattern: <name>-%05d-of-%05d.gguf
|
||
# // If the split file name does not follow this pattern, use llama_model_load_from_splits
|
||
# LLAMA_API struct llama_model * llama_model_load_from_file(
|
||
# const char * path_model,
|
||
# struct llama_model_params params);
|
||
@ctypes_function(
|
||
"llama_model_load_from_file",
|
||
[ctypes.c_char_p, llama_model_params],
|
||
llama_model_p_ctypes,
|
||
)
|
||
def llama_model_load_from_file(
|
||
path_model: bytes, params: llama_model_params, /
|
||
) -> Optional[llama_model_p]:
|
||
"""Load the model from a file
|
||
|
||
If the file is split into multiple parts, the file name must follow this pattern: <name>-%05d-of-%05d.gguf
|
||
|
||
If the split file name does not follow this pattern, use llama_model_load_from_splits"""
|
||
...
|
||
|
||
|
||
# // Load the model from multiple splits (support custom naming scheme)
|
||
# // The paths must be in the correct order
|
||
# LLAMA_API struct llama_model * llama_model_load_from_splits(
|
||
# const char ** paths,
|
||
# size_t n_paths,
|
||
# struct llama_model_params params);
|
||
@ctypes_function(
|
||
"llama_model_load_from_splits",
|
||
[ctypes.POINTER(ctypes.c_char_p), ctypes.c_size_t, llama_model_params],
|
||
llama_model_p_ctypes,
|
||
)
|
||
def llama_model_load_from_splits(
|
||
paths: List[bytes], n_paths: int, params: llama_model_params, /
|
||
) -> Optional[llama_model_p]:
|
||
"""Load the model from multiple splits (support custom naming scheme)
|
||
|
||
The paths must be in the correct order"""
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_model_save_to_file(
|
||
# const struct llama_model * model,
|
||
# const char * path_model);
|
||
@ctypes_function(
|
||
"llama_model_save_to_file",
|
||
[llama_model_p_ctypes, ctypes.c_char_p],
|
||
None,
|
||
)
|
||
def llama_model_save_to_file(model: llama_model_p, path_model: bytes, /):
|
||
"""Save the model to a file"""
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
|
||
# "use llama_model_free instead");
|
||
@ctypes_function(
|
||
"llama_free_model",
|
||
[llama_model_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_free_model(model: llama_model_p, /):
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_model_free(struct llama_model * model);
|
||
@ctypes_function(
|
||
"llama_model_free",
|
||
[llama_model_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_model_free(model: llama_model_p, /):
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_context * llama_init_from_model(
|
||
# struct llama_model * model,
|
||
# struct llama_context_params params);
|
||
@ctypes_function(
|
||
"llama_init_from_model",
|
||
[llama_model_p_ctypes, llama_context_params],
|
||
llama_context_p_ctypes,
|
||
)
|
||
def llama_init_from_model(
|
||
model: llama_model_p, params: llama_context_params, /
|
||
) -> Optional[llama_context_p]:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API struct llama_context * llama_new_context_with_model(
|
||
# struct llama_model * model,
|
||
# struct llama_context_params params),
|
||
# "use llama_init_from_model instead");
|
||
@ctypes_function(
|
||
"llama_new_context_with_model",
|
||
[llama_model_p_ctypes, llama_context_params],
|
||
llama_context_p_ctypes,
|
||
)
|
||
def llama_new_context_with_model(
|
||
model: llama_model_p, params: llama_context_params, /
|
||
) -> Optional[llama_context_p]:
|
||
...
|
||
|
||
|
||
# // Frees all allocated memory
|
||
# LLAMA_API void llama_free(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_free",
|
||
[llama_context_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_free(ctx: llama_context_p, /):
|
||
"""Frees all allocated memory"""
|
||
...
|
||
|
||
|
||
# LLAMA_API int64_t llama_time_us(void);
|
||
@ctypes_function(
|
||
"llama_time_us",
|
||
[],
|
||
ctypes.c_int64,
|
||
)
|
||
def llama_time_us() -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API size_t llama_max_devices(void);
|
||
@ctypes_function("llama_max_devices", [], ctypes.c_size_t)
|
||
def llama_max_devices() -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API size_t llama_max_parallel_sequences(void);
|
||
@ctypes_function("llama_max_parallel_sequences", [], ctypes.c_size_t)
|
||
def llama_max_parallel_sequences() -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API bool llama_supports_mmap (void);
|
||
@ctypes_function("llama_supports_mmap", [], ctypes.c_bool)
|
||
def llama_supports_mmap() -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API bool llama_supports_mlock (void);
|
||
@ctypes_function("llama_supports_mlock", [], ctypes.c_bool)
|
||
def llama_supports_mlock() -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API bool llama_supports_gpu_offload(void);
|
||
@ctypes_function("llama_supports_gpu_offload", [], ctypes.c_bool)
|
||
def llama_supports_gpu_offload() -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API bool llama_supports_rpc (void);
|
||
@ctypes_function("llama_supports_rpc", [], ctypes.c_bool)
|
||
def llama_supports_rpc() -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
|
||
@ctypes_function("llama_n_ctx", [llama_context_p_ctypes], ctypes.c_uint32)
|
||
def llama_n_ctx(ctx: llama_context_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
|
||
@ctypes_function("llama_n_batch", [llama_context_p_ctypes], ctypes.c_uint32)
|
||
def llama_n_batch(ctx: llama_context_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx);
|
||
@ctypes_function("llama_n_ubatch", [llama_context_p_ctypes], ctypes.c_uint32)
|
||
def llama_n_ubatch(ctx: llama_context_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
|
||
@ctypes_function("llama_n_seq_max", [llama_context_p_ctypes], ctypes.c_uint32)
|
||
def llama_n_seq_max(ctx: llama_context_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead");
|
||
@ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_n_ctx_train(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API int32_t llama_n_embd (const struct llama_model * model), "use llama_model_n_embd instead");
|
||
@ctypes_function("llama_n_embd", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_n_embd(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API int32_t llama_n_layer (const struct llama_model * model), "use llama_model_n_layer instead");
|
||
@ctypes_function("llama_n_layer", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_n_layer(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API int32_t llama_n_head (const struct llama_model * model), "use llama_model_n_head instead");
|
||
@ctypes_function("llama_n_head", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_n_head(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API int32_t llama_n_vocab (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead");
|
||
@ctypes_function("llama_n_vocab", [llama_vocab_p_ctypes], ctypes.c_int32)
|
||
def llama_n_vocab(model: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API const struct llama_model * llama_get_model (const struct llama_context * ctx);
|
||
@ctypes_function("llama_get_model", [llama_context_p_ctypes], llama_model_p_ctypes)
|
||
def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]:
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_memory_t llama_get_memory (const struct llama_context * ctx);
|
||
@ctypes_function("llama_get_memory", [llama_context_p_ctypes], llama_memory_t_ctypes)
|
||
def llama_get_memory(ctx: llama_context_p, /) -> Optional[llama_memory_t]:
|
||
"""Get the memory for the context"""
|
||
...
|
||
|
||
|
||
# LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
|
||
@ctypes_function("llama_pooling_type", [llama_context_p_ctypes], ctypes.c_int)
|
||
def llama_pooling_type(ctx: llama_context_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API struct llama_kv_cache * llama_get_kv_self(struct llama_context * ctx), "use llama_get_memory instead");
|
||
@ctypes_function(
|
||
"llama_get_kv_self",
|
||
[llama_context_p_ctypes],
|
||
llama_kv_cache_p_ctypes,
|
||
)
|
||
def llama_get_kv_self(ctx: llama_context_p, /) -> Optional[llama_kv_cache_p]:
|
||
"""Get the KV cache for self-attention (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
|
||
@ctypes_function("llama_model_get_vocab", [llama_model_p_ctypes], llama_vocab_p_ctypes)
|
||
def llama_model_get_vocab(model: llama_model_p, /) -> Optional[llama_vocab_p]:
|
||
...
|
||
|
||
|
||
# LLAMA_API enum llama_rope_type llama_model_rope_type(const struct llama_model * model);
|
||
@ctypes_function("llama_model_rope_type", [llama_model_p_ctypes], ctypes.c_int)
|
||
def llama_model_rope_type(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_model_n_ctx_train(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API int32_t llama_model_n_embd (const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_embd", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_model_n_embd(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_layer", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_model_n_layer(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API int32_t llama_model_n_head (const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_head", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_model_n_head(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_head_kv", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_model_n_head_kv(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API int32_t llama_model_n_swa (const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_swa", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_model_n_swa(model: llama_model_p, /) -> int:
|
||
...
|
||
|
||
|
||
# // Get the model's RoPE frequency scaling factor
|
||
# LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model);
|
||
@ctypes_function("llama_model_rope_freq_scale_train", [llama_model_p_ctypes], ctypes.c_float)
|
||
def llama_model_rope_freq_scale_train(model: llama_model_p, /) -> float:
|
||
...
|
||
|
||
|
||
# // Returns the number of classifier outputs (only valid for classifier models)
|
||
# // Undefined behavior for non-classifier models
|
||
# LLAMA_API uint32_t llama_model_n_cls_out(const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_cls_out", [llama_model_p_ctypes], ctypes.c_uint32)
|
||
def llama_model_n_cls_out(model: llama_model_p, /) -> int:
|
||
"""Returns the number of classifier outputs (only valid for classifier models)"""
|
||
...
|
||
|
||
|
||
# // Returns label of classifier output by index (<n_cls_out). Returns nullptr if no label provided
|
||
# LLAMA_API const char * llama_model_cls_label(const struct llama_model * model, uint32_t i);
|
||
@ctypes_function("llama_model_cls_label", [llama_model_p_ctypes, ctypes.c_uint32], ctypes.c_char_p)
|
||
def llama_model_cls_label(model: llama_model_p, i: int, /) -> Optional[bytes]:
|
||
"""Returns label of classifier output by index. Returns None if no label provided"""
|
||
...
|
||
|
||
|
||
# LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model);
|
||
@ctypes_function("llama_vocab_type", [llama_vocab_p_ctypes], ctypes.c_int)
|
||
def llama_vocab_type(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API int32_t llama_vocab_n_tokens(const struct llama_vocab * vocab);
|
||
@ctypes_function("llama_vocab_n_tokens", [llama_vocab_p_ctypes], ctypes.c_int32)
|
||
def llama_vocab_n_tokens(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
|
||
# // Functions to access the model's GGUF metadata scalar values
|
||
# // - The functions return the length of the string on success, or -1 on failure
|
||
# // - The output string is always null-terminated and cleared on failure
|
||
# // - When retrieving a string, an extra byte must be allocated to account for the null terminator
|
||
# // - GGUF array values are not supported by these functions
|
||
|
||
|
||
# // Get metadata value as a string by key name
|
||
# LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
|
||
@ctypes_function(
|
||
"llama_model_meta_val_str",
|
||
[
|
||
llama_model_p_ctypes,
|
||
ctypes.c_char_p,
|
||
ctypes.c_char_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_meta_val_str(
|
||
model: llama_model_p,
|
||
key: Union[ctypes.c_char_p, bytes],
|
||
buf: bytes,
|
||
buf_size: int,
|
||
/,
|
||
) -> int:
|
||
"""Get metadata value as a string by key name"""
|
||
...
|
||
|
||
|
||
# // Get the number of metadata key/value pairs
|
||
# LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);
|
||
@ctypes_function("llama_model_meta_count", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_model_meta_count(model: llama_model_p, /) -> int:
|
||
"""Get the number of metadata key/value pairs"""
|
||
...
|
||
|
||
|
||
# // Get metadata key name by index
|
||
# LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
|
||
@ctypes_function(
|
||
"llama_model_meta_key_by_index",
|
||
[
|
||
llama_model_p_ctypes,
|
||
ctypes.c_int32,
|
||
ctypes.c_char_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_meta_key_by_index(
|
||
model: llama_model_p,
|
||
i: Union[ctypes.c_int, int],
|
||
buf: Union[bytes, CtypesArray[ctypes.c_char]],
|
||
buf_size: int,
|
||
/,
|
||
) -> int:
|
||
"""Get metadata key name by index"""
|
||
...
|
||
|
||
|
||
# // Get metadata value as a string by index
|
||
# LLAMA_API int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
|
||
@ctypes_function(
|
||
"llama_model_meta_val_str_by_index",
|
||
[
|
||
llama_model_p_ctypes,
|
||
ctypes.c_int32,
|
||
ctypes.c_char_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_meta_val_str_by_index(
|
||
model: llama_model_p,
|
||
i: Union[ctypes.c_int, int],
|
||
buf: Union[bytes, CtypesArray[ctypes.c_char]],
|
||
buf_size: int,
|
||
/,
|
||
) -> int:
|
||
"""Get metadata value as a string by index"""
|
||
...
|
||
|
||
|
||
# // Get a string describing the model type
|
||
# LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
|
||
@ctypes_function(
|
||
"llama_model_desc",
|
||
[llama_model_p_ctypes, ctypes.c_char_p, ctypes.c_size_t],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_desc(
|
||
model: llama_model_p,
|
||
buf: Union[bytes, CtypesArray[ctypes.c_char]],
|
||
buf_size: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> int:
|
||
"""Get a string describing the model type"""
|
||
...
|
||
|
||
|
||
# // Returns the total size of all the tensors in the model in bytes
|
||
# LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
|
||
@ctypes_function("llama_model_size", [llama_model_p_ctypes], ctypes.c_uint64)
|
||
def llama_model_size(model: llama_model_p, /) -> int:
|
||
"""Returns the total size of all the tensors in the model in bytes"""
|
||
...
|
||
|
||
|
||
# // Get the default chat template. Returns nullptr if not available
|
||
# // If name is NULL, returns the default chat template
|
||
# LLAMA_API const char * llama_model_chat_template(const struct llama_model * model, const char * name);
|
||
@ctypes_function("llama_model_chat_template", [llama_model_p_ctypes, ctypes.c_char_p], ctypes.c_char_p)
|
||
def llama_model_chat_template(model: llama_model_p, name: Optional[bytes], /) -> Optional[bytes]:
|
||
"""Get the default chat template. Returns None if not available
|
||
If name is None, returns the default chat template"""
|
||
...
|
||
|
||
|
||
# // Returns the total number of parameters in the model
|
||
# LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_params", [llama_model_p_ctypes], ctypes.c_uint64)
|
||
def llama_model_n_params(model: llama_model_p, /) -> int:
|
||
"""Returns the total number of parameters in the model"""
|
||
...
|
||
|
||
|
||
# // Returns true if the model contains an encoder that requires llama_encode() call
|
||
# LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
|
||
@ctypes_function("llama_model_has_encoder", [llama_model_p_ctypes], ctypes.c_bool)
|
||
def llama_model_has_encoder(model: llama_model_p, /) -> bool:
|
||
"""Returns true if the model contains an encoder that requires llama_encode() call"""
|
||
...
|
||
|
||
|
||
# // Returns true if the model contains a decoder that requires llama_decode() call
|
||
# LLAMA_API bool llama_model_has_decoder(const struct llama_model * model);
|
||
@ctypes_function("llama_model_has_decoder", [llama_model_p_ctypes], ctypes.c_bool)
|
||
def llama_model_has_decoder(model: llama_model_p, /) -> bool:
|
||
"""Returns true if the model contains a decoder that requires llama_decode() call"""
|
||
...
|
||
|
||
|
||
# // For encoder-decoder models, this function returns id of the token that must be provided
|
||
# // to the decoder to start generating output sequence. For other models, it returns -1.
|
||
# LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);
|
||
@ctypes_function(
|
||
"llama_model_decoder_start_token", [llama_model_p_ctypes], ctypes.c_int32
|
||
)
|
||
def llama_model_decoder_start_token(model: llama_model_p, /) -> int:
|
||
"""For encoder-decoder models, this function returns id of the token that must be provided
|
||
to the decoder to start generating output sequence. For other models, it returns -1.
|
||
"""
|
||
...
|
||
|
||
|
||
# // Returns true if the model is recurrent (like Mamba, RWKV, etc.)
|
||
# LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model);
|
||
@ctypes_function("llama_model_is_recurrent", [llama_model_p_ctypes], ctypes.c_bool)
|
||
def llama_model_is_recurrent(model: llama_model_p, /) -> bool:
|
||
"""Returns true if the model is recurrent (like Mamba, RWKV, etc.)"""
|
||
...
|
||
|
||
|
||
# // Returns 0 on success
|
||
# LLAMA_API uint32_t llama_model_quantize(
|
||
# const char * fname_inp,
|
||
# const char * fname_out,
|
||
# const llama_model_quantize_params * params);
|
||
@ctypes_function(
|
||
"llama_model_quantize",
|
||
[
|
||
ctypes.c_char_p,
|
||
ctypes.c_char_p,
|
||
ctypes.POINTER(llama_model_quantize_params),
|
||
],
|
||
ctypes.c_uint32,
|
||
)
|
||
def llama_model_quantize(
|
||
fname_inp: bytes,
|
||
fname_out: bytes,
|
||
params: CtypesPointerOrRef[llama_model_quantize_params],
|
||
/,
|
||
) -> int:
|
||
"""Returns 0 on success"""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Adapters
|
||
# //
|
||
|
||
# // Load a LoRA adapter from file
|
||
# LLAMA_API struct llama_adapter_lora * llama_adapter_lora_init(
|
||
# struct llama_model * model,
|
||
# const char * path_lora);
|
||
@ctypes_function(
|
||
"llama_adapter_lora_init",
|
||
[llama_model_p_ctypes, ctypes.c_char_p],
|
||
llama_adapter_lora_p_ctypes,
|
||
)
|
||
def llama_adapter_lora_init(
|
||
model: llama_model_p, path_lora: bytes, /
|
||
) -> Optional[llama_adapter_lora_p]:
|
||
...
|
||
|
||
|
||
# // Manually free a LoRA adapter
|
||
# // Note: loaded adapters will be free when the associated model is deleted
|
||
# LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter);
|
||
@ctypes_function(
|
||
"llama_adapter_lora_free",
|
||
[llama_adapter_lora_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_adapter_lora_free(adapter: llama_adapter_lora_p, /):
|
||
...
|
||
|
||
|
||
# // The following functions operate on a llama_context, hence the naming: llama_verb_...
|
||
|
||
|
||
# // Add a loaded LoRA adapter to given context
|
||
# // This will not modify model's weight
|
||
# LLAMA_API int32_t llama_set_adapter_lora(
|
||
# struct llama_context * ctx,
|
||
# struct llama_adapter_lora * adapter,
|
||
# float scale);
|
||
@ctypes_function(
|
||
"llama_set_adapter_lora",
|
||
[llama_context_p_ctypes, llama_adapter_lora_p_ctypes, ctypes.c_float],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_set_adapter_lora(
|
||
ctx: llama_context_p, adapter: llama_adapter_lora_p, scale: float, /
|
||
) -> int:
|
||
"""Add a loaded LoRA adapter to given context
|
||
This will not modify model's weight"""
|
||
...
|
||
|
||
|
||
# // Remove a specific LoRA adapter from given context
|
||
# // Return -1 if the adapter is not present in the context
|
||
# LLAMA_API int32_t llama_rm_adapter_lora(
|
||
# struct llama_context * ctx,
|
||
# struct llama_adapter_lora * adapter);
|
||
@ctypes_function(
|
||
"llama_rm_adapter_lora",
|
||
[llama_context_p_ctypes, llama_adapter_lora_p_ctypes],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_rm_adapter_lora(
|
||
ctx: llama_context_p, adapter: llama_adapter_lora_p, /
|
||
) -> int:
|
||
"""Remove a specific LoRA adapter from given context
|
||
Return -1 if the adapter is not present in the context"""
|
||
...
|
||
|
||
|
||
# // Remove all LoRA adapters from given context
|
||
# LLAMA_API void llama_clear_adapter_lora(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_clear_adapter_lora",
|
||
[llama_context_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_clear_adapter_lora(ctx: llama_context_p, /):
|
||
"""Remove all LoRA adapters from given context"""
|
||
...
|
||
|
||
|
||
# // Apply a loaded control vector to a llama_context, or if data is NULL, clear
|
||
# // the currently loaded vector.
|
||
# // n_embd should be the size of a single layer's control, and data should point
|
||
# // to an n_embd x n_layers buffer starting from layer 1.
|
||
# // il_start and il_end are the layer range the vector should apply to (both inclusive)
|
||
# // See llama_control_vector_load in common to load a control vector.
|
||
# LLAMA_API int32_t llama_apply_adapter_cvec(
|
||
# struct llama_context * ctx,
|
||
# const float * data,
|
||
# size_t len,
|
||
# int32_t n_embd,
|
||
# int32_t il_start,
|
||
# int32_t il_end);
|
||
@ctypes_function(
|
||
"llama_apply_adapter_cvec",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.POINTER(ctypes.c_float),
|
||
ctypes.c_size_t,
|
||
ctypes.c_int32,
|
||
ctypes.c_int32,
|
||
ctypes.c_int32,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_apply_adapter_cvec(
|
||
ctx: llama_context_p,
|
||
data: CtypesPointerOrRef[ctypes.c_float],
|
||
len: int,
|
||
n_embd: int,
|
||
il_start: int,
|
||
il_end: int,
|
||
/,
|
||
) -> int:
|
||
"""Apply a loaded control vector to a llama_context, or if data is NULL, clear
|
||
the currently loaded vector.
|
||
n_embd should be the size of a single layer's control, and data should point
|
||
to an n_embd x n_layers buffer starting from layer 1.
|
||
il_start and il_end are the layer range the vector should apply to (both inclusive)
|
||
See llama_control_vector_load in common to load a control vector."""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Memory
|
||
# //
|
||
|
||
# // Clear the memory contents
|
||
# // If data == true, the data buffers will also be cleared together with the metadata
|
||
# LLAMA_API void llama_memory_clear(
|
||
# llama_memory_t mem,
|
||
# bool data);
|
||
@ctypes_function(
|
||
"llama_memory_clear",
|
||
[llama_memory_t_ctypes, ctypes.c_bool],
|
||
None,
|
||
)
|
||
def llama_memory_clear(mem: llama_memory_t, data: bool, /):
|
||
"""Clear the memory contents
|
||
If data == true, the data buffers will also be cleared together with the metadata"""
|
||
...
|
||
|
||
|
||
# // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
|
||
# // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
|
||
# // seq_id < 0 : match any sequence
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# LLAMA_API bool llama_memory_seq_rm(
|
||
# llama_memory_t mem,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1);
|
||
@ctypes_function(
|
||
"llama_memory_seq_rm",
|
||
[
|
||
llama_memory_t_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_memory_seq_rm(
|
||
mem: llama_memory_t,
|
||
seq_id: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
/,
|
||
) -> bool:
|
||
"""Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
|
||
|
||
Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
|
||
|
||
seq_id < 0 : match any sequence
|
||
p0 < 0 : [0, p1]
|
||
p1 < 0 : [p0, inf)"""
|
||
...
|
||
|
||
|
||
# // Copy all tokens that belong to the specified sequence to another sequence
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# LLAMA_API void llama_memory_seq_cp(
|
||
# llama_memory_t mem,
|
||
# llama_seq_id seq_id_src,
|
||
# llama_seq_id seq_id_dst,
|
||
# llama_pos p0,
|
||
# llama_pos p1);
|
||
@ctypes_function(
|
||
"llama_memory_seq_cp",
|
||
[
|
||
llama_memory_t_ctypes,
|
||
llama_seq_id,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
None,
|
||
)
|
||
def llama_memory_seq_cp(
|
||
mem: llama_memory_t,
|
||
seq_id_src: Union[llama_seq_id, int],
|
||
seq_id_dst: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
/,
|
||
):
|
||
"""Copy all tokens that belong to the specified sequence to another sequence
|
||
p0 < 0 : [0, p1]
|
||
p1 < 0 : [p0, inf)"""
|
||
...
|
||
|
||
|
||
# // Removes all tokens that do not belong to the specified sequence
|
||
# LLAMA_API void llama_memory_seq_keep(
|
||
# llama_memory_t mem,
|
||
# llama_seq_id seq_id);
|
||
@ctypes_function(
|
||
"llama_memory_seq_keep", [llama_memory_t_ctypes, llama_seq_id], None
|
||
)
|
||
def llama_memory_seq_keep(mem: llama_memory_t, seq_id: Union[llama_seq_id, int], /):
|
||
"""Removes all tokens that do not belong to the specified sequence"""
|
||
...
|
||
|
||
|
||
# // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# LLAMA_API void llama_memory_seq_add(
|
||
# llama_memory_t mem,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1,
|
||
# llama_pos delta);
|
||
@ctypes_function(
|
||
"llama_memory_seq_add",
|
||
[
|
||
llama_memory_t_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
None,
|
||
)
|
||
def llama_memory_seq_add(
|
||
mem: llama_memory_t,
|
||
seq_id: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
delta: Union[llama_pos, int],
|
||
/,
|
||
):
|
||
"""Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
|
||
p0 < 0 : [0, p1]
|
||
p1 < 0 : [p0, inf)"""
|
||
...
|
||
|
||
|
||
# // Integer division of the positions by factor of `d > 1`
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# LLAMA_API void llama_memory_seq_div(
|
||
# llama_memory_t mem,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1,
|
||
# int d);
|
||
@ctypes_function(
|
||
"llama_memory_seq_div",
|
||
[
|
||
llama_memory_t_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
ctypes.c_int,
|
||
],
|
||
None,
|
||
)
|
||
def llama_memory_seq_div(
|
||
mem: llama_memory_t,
|
||
seq_id: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
d: Union[ctypes.c_int, int],
|
||
/,
|
||
):
|
||
"""Integer division of the positions by factor of `d > 1`
|
||
p0 < 0 : [0, p1]
|
||
p1 < 0 : [p0, inf)"""
|
||
...
|
||
|
||
|
||
# // Returns the smallest position present in the memory for the specified sequence
|
||
# // This is typically non-zero only for SWA caches
|
||
# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the memory
|
||
# // Return -1 if the sequence is empty
|
||
# LLAMA_API llama_pos llama_memory_seq_pos_min(
|
||
# llama_memory_t mem,
|
||
# llama_seq_id seq_id);
|
||
@ctypes_function(
|
||
"llama_memory_seq_pos_min", [llama_memory_t_ctypes, llama_seq_id], llama_pos
|
||
)
|
||
def llama_memory_seq_pos_min(
|
||
mem: llama_memory_t, seq_id: Union[llama_seq_id, int], /
|
||
) -> int:
|
||
"""Returns the smallest position present in the memory for the specified sequence
|
||
This is typically non-zero only for SWA caches
|
||
Return -1 if the sequence is empty"""
|
||
...
|
||
|
||
|
||
# // Returns the largest position present in the memory for the specified sequence
|
||
# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the memory
|
||
# // Return -1 if the sequence is empty
|
||
# LLAMA_API llama_pos llama_memory_seq_pos_max(
|
||
# llama_memory_t mem,
|
||
# llama_seq_id seq_id);
|
||
@ctypes_function(
|
||
"llama_memory_seq_pos_max", [llama_memory_t_ctypes, llama_seq_id], llama_pos
|
||
)
|
||
def llama_memory_seq_pos_max(
|
||
mem: llama_memory_t, seq_id: Union[llama_seq_id, int], /
|
||
) -> int:
|
||
"""Returns the largest position present in the memory for the specified sequence
|
||
Return -1 if the sequence is empty"""
|
||
...
|
||
|
||
|
||
# // Check if the memory supports shifting
|
||
# LLAMA_API bool llama_memory_can_shift(llama_memory_t mem);
|
||
@ctypes_function("llama_memory_can_shift", [llama_memory_t_ctypes], ctypes.c_bool)
|
||
def llama_memory_can_shift(mem: llama_memory_t, /) -> bool:
|
||
"""Check if the memory supports shifting"""
|
||
...
|
||
|
||
|
||
# //
|
||
# // KV cache for self-attention (TODO: deprecate in favor of llama_memory)
|
||
# //
|
||
|
||
# // Returns the number of tokens in the KV cache (slow, use only for debug)
|
||
# // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
|
||
# DEPRECATED(LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx),
|
||
# "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)");
|
||
@ctypes_function(
|
||
"llama_kv_self_n_tokens", [llama_context_p_ctypes], ctypes.c_int32
|
||
)
|
||
def llama_kv_self_n_tokens(ctx: llama_context_p, /) -> int:
|
||
"""Returns the number of tokens in the KV cache (slow, use only for debug) (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
|
||
# DEPRECATED(LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx),
|
||
# "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)");
|
||
@ctypes_function(
|
||
"llama_kv_self_used_cells", [llama_context_p_ctypes], ctypes.c_int32
|
||
)
|
||
def llama_kv_self_used_cells(ctx: llama_context_p, /) -> int:
|
||
"""Returns the number of used KV cells (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Clear the KV cache - both cell info is erased and KV data is zeroed
|
||
# DEPRECATED(LLAMA_API void llama_kv_self_clear(
|
||
# struct llama_context * ctx),
|
||
# "Use llama_memory_clear() instead");
|
||
@ctypes_function(
|
||
"llama_kv_self_clear", [llama_context_p_ctypes], None
|
||
)
|
||
def llama_kv_self_clear(ctx: llama_context_p, /):
|
||
"""Clear the KV cache (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
|
||
# // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
|
||
# // seq_id < 0 : match any sequence
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# DEPRECATED(LLAMA_API bool llama_kv_self_seq_rm(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1),
|
||
# "Use llama_memory_seq_rm() instead");
|
||
@ctypes_function(
|
||
"llama_kv_self_seq_rm",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_kv_self_seq_rm(
|
||
ctx: llama_context_p,
|
||
seq_id: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
/,
|
||
) -> bool:
|
||
"""Remove tokens from KV cache (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Copy all tokens that belong to the specified sequence to another sequence
|
||
# // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# DEPRECATED(LLAMA_API void llama_kv_self_seq_cp(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id_src,
|
||
# llama_seq_id seq_id_dst,
|
||
# llama_pos p0,
|
||
# llama_pos p1),
|
||
# "Use llama_memory_seq_cp() instead");
|
||
@ctypes_function(
|
||
"llama_kv_self_seq_cp",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_seq_id,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
None,
|
||
)
|
||
def llama_kv_self_seq_cp(
|
||
ctx: llama_context_p,
|
||
seq_id_src: Union[llama_seq_id, int],
|
||
seq_id_dst: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
/,
|
||
):
|
||
"""Copy tokens in KV cache (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Removes all tokens that do not belong to the specified sequence
|
||
# DEPRECATED(LLAMA_API void llama_kv_self_seq_keep(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id),
|
||
# "Use llama_memory_seq_keep() instead");
|
||
@ctypes_function(
|
||
"llama_kv_self_seq_keep", [llama_context_p_ctypes, llama_seq_id], None
|
||
)
|
||
def llama_kv_self_seq_keep(ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /):
|
||
"""Keep only specified sequence in KV cache (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
|
||
# // If the KV cache is RoPEd, the KV data is updated accordingly:
|
||
# // - lazily on next llama_decode()
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# DEPRECATED(LLAMA_API void llama_kv_self_seq_add(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1,
|
||
# llama_pos delta),
|
||
# "Use llama_memory_seq_add() instead");
|
||
@ctypes_function(
|
||
"llama_kv_self_seq_add",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
None,
|
||
)
|
||
def llama_kv_self_seq_add(
|
||
ctx: llama_context_p,
|
||
seq_id: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
delta: Union[llama_pos, int],
|
||
/,
|
||
):
|
||
"""Add delta to sequence positions in KV cache (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Integer division of the positions by factor of `d > 1`
|
||
# // If the KV cache is RoPEd, the KV data is updated accordingly:
|
||
# // - lazily on next llama_decode()
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# DEPRECATED(LLAMA_API void llama_kv_self_seq_div(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1,
|
||
# int d),
|
||
# "Use llama_memory_seq_div() instead");
|
||
@ctypes_function(
|
||
"llama_kv_self_seq_div",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
ctypes.c_int,
|
||
],
|
||
None,
|
||
)
|
||
def llama_kv_self_seq_div(
|
||
ctx: llama_context_p,
|
||
seq_id: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
d: Union[ctypes.c_int, int],
|
||
/,
|
||
):
|
||
"""Divide sequence positions in KV cache (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Returns the smallest position present in the KV cache for the specified sequence
|
||
# // This is typically non-zero only for SWA caches
|
||
# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the KV cache
|
||
# // Return -1 if the sequence is empty
|
||
# DEPRECATED(LLAMA_API llama_pos llama_kv_self_seq_pos_min(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id),
|
||
# "Use llama_memory_seq_pos_min() instead");
|
||
@ctypes_function(
|
||
"llama_kv_self_seq_pos_min", [llama_context_p_ctypes, llama_seq_id], llama_pos
|
||
)
|
||
def llama_kv_self_seq_pos_min(
|
||
ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /
|
||
) -> int:
|
||
"""Returns the smallest position in KV cache for sequence (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Returns the largest position present in the KV cache for the specified sequence
|
||
# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the KV cache
|
||
# // Return -1 if the sequence is empty
|
||
# DEPRECATED(LLAMA_API llama_pos llama_kv_self_seq_pos_max(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id),
|
||
# "Use llama_memory_seq_pos_max() instead");
|
||
@ctypes_function(
|
||
"llama_kv_self_seq_pos_max", [llama_context_p_ctypes, llama_seq_id], llama_pos
|
||
)
|
||
def llama_kv_self_seq_pos_max(
|
||
ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /
|
||
) -> int:
|
||
"""Returns the largest position in KV cache for sequence (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Defragment the KV cache
|
||
# // This will be applied:
|
||
# // - lazily on next llama_decode()
|
||
# DEPRECATED(LLAMA_API void llama_kv_self_defrag(struct llama_context * ctx),
|
||
# "simply remove this call, the context will automatically decide when to do a defragmentation based on 'defrag_thold'");
|
||
@ctypes_function("llama_kv_self_defrag", [llama_context_p_ctypes], None)
|
||
def llama_kv_self_defrag(ctx: llama_context_p, /):
|
||
"""Defragment the KV cache (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Check if the context supports KV cache shifting
|
||
# DEPRECATED(LLAMA_API bool llama_kv_self_can_shift(const struct llama_context * ctx),
|
||
# "use llama_memory_can_shift() instead");
|
||
@ctypes_function("llama_kv_self_can_shift", [llama_context_p_ctypes], ctypes.c_bool)
|
||
def llama_kv_self_can_shift(ctx: llama_context_p, /) -> bool:
|
||
"""Check if the context supports KV cache shifting (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
|
||
# DEPRECATED(LLAMA_API void llama_kv_self_update(struct llama_context * ctx),
|
||
# "simply remove this call, updates are applied lazily on the next llama_decode()");
|
||
@ctypes_function("llama_kv_self_update", [llama_context_p_ctypes], None)
|
||
def llama_kv_self_update(ctx: llama_context_p, /):
|
||
"""Apply the KV cache updates (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# //
|
||
# // State / sessions
|
||
# //
|
||
|
||
# // Returns the *actual* size in bytes of the state
|
||
# // (logits, embedding and memory)
|
||
# // Only use when saving the state, not when restoring it, otherwise the size may be too small.
|
||
# LLAMA_API size_t llama_state_get_size(struct llama_context * ctx);
|
||
@ctypes_function("llama_state_get_size", [llama_context_p_ctypes], ctypes.c_size_t)
|
||
def llama_state_get_size(ctx: llama_context_p, /) -> int:
|
||
"""Returns the *actual* size in bytes of the state (logits, embedding and memory)"""
|
||
...
|
||
|
||
|
||
# LLAMA_API DEPRECATED(size_t llama_get_state_size(struct llama_context * ctx),
|
||
# "use llama_state_get_size instead");
|
||
@ctypes_function("llama_get_state_size", [llama_context_p_ctypes], ctypes.c_size_t)
|
||
def llama_get_state_size(ctx: llama_context_p, /) -> int:
|
||
"""Returns the size in bytes of the state (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Copies the state to the specified destination address.
|
||
# // Destination needs to have allocated enough memory.
|
||
# // Returns the number of bytes copied
|
||
# LLAMA_API size_t llama_state_get_data(
|
||
# struct llama_context * ctx,
|
||
# uint8_t * dst,
|
||
# size_t size);
|
||
@ctypes_function(
|
||
"llama_state_get_data",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.POINTER(ctypes.c_uint8),
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_state_get_data(
|
||
ctx: llama_context_p,
|
||
dst: CtypesArray[ctypes.c_uint8],
|
||
size: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> int:
|
||
"""Copies the state to the specified destination address.
|
||
Destination needs to have allocated enough memory.
|
||
Returns the number of bytes copied"""
|
||
...
|
||
|
||
|
||
# LLAMA_API DEPRECATED(size_t llama_copy_state_data(
|
||
# struct llama_context * ctx,
|
||
# uint8_t * dst),
|
||
# "use llama_state_get_data instead");
|
||
@ctypes_function(
|
||
"llama_copy_state_data",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.POINTER(ctypes.c_uint8),
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_copy_state_data(
|
||
ctx: llama_context_p, dst: CtypesArray[ctypes.c_uint8], /
|
||
) -> int:
|
||
"""Copies the state to the specified destination address (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# // Set the state reading from the specified address
|
||
# // Returns the number of bytes read
|
||
# LLAMA_API size_t llama_state_set_data(
|
||
# struct llama_context * ctx,
|
||
# const uint8_t * src,
|
||
# size_t size);
|
||
@ctypes_function(
|
||
"llama_state_set_data",
|
||
[llama_context_p_ctypes, ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_state_set_data(
|
||
ctx: llama_context_p,
|
||
src: CtypesArray[ctypes.c_uint8],
|
||
size: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> int:
|
||
"""Set the state reading from the specified address
|
||
Returns the number of bytes read"""
|
||
...
|
||
|
||
|
||
# LLAMA_API DEPRECATED(size_t llama_set_state_data(
|
||
# struct llama_context * ctx,
|
||
# const uint8_t * src),
|
||
# "use llama_state_set_data instead");
|
||
@ctypes_function(
|
||
"llama_set_state_data",
|
||
[llama_context_p_ctypes, ctypes.POINTER(ctypes.c_uint8)],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_set_state_data(
|
||
ctx: llama_context_p, src: CtypesArray[ctypes.c_uint8], /
|
||
) -> int:
|
||
"""Set the state reading from the specified address (DEPRECATED)"""
|
||
...
|
||
|
||
|
||
# Save/load session file
|
||
# LLAMA_API bool llama_state_load_file(
|
||
# struct llama_context * ctx,
|
||
# const char * path_session,
|
||
# llama_token * tokens_out,
|
||
# size_t n_token_capacity,
|
||
# size_t * n_token_count_out);
|
||
@ctypes_function(
|
||
"llama_state_load_file",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_char_p,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
ctypes.POINTER(ctypes.c_size_t),
|
||
],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_state_load_file(
|
||
ctx: llama_context_p,
|
||
path_session: bytes,
|
||
tokens_out: CtypesArray[llama_token],
|
||
n_token_capacity: Union[ctypes.c_size_t, int],
|
||
n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
|
||
/,
|
||
) -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API DEPRECATED(bool llama_load_session_file(
|
||
# struct llama_context * ctx,
|
||
# const char * path_session,
|
||
# llama_token * tokens_out,
|
||
# size_t n_token_capacity,
|
||
# size_t * n_token_count_out),
|
||
# "use llama_state_load_file instead");
|
||
@ctypes_function(
|
||
"llama_load_session_file",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_char_p,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
ctypes.POINTER(ctypes.c_size_t),
|
||
],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_load_session_file(
|
||
ctx: llama_context_p,
|
||
path_session: bytes,
|
||
tokens_out: CtypesArray[llama_token],
|
||
n_token_capacity: Union[ctypes.c_size_t, int],
|
||
n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
|
||
/,
|
||
) -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API bool llama_state_save_file(
|
||
# struct llama_context * ctx,
|
||
# const char * path_session,
|
||
# const llama_token * tokens,
|
||
# size_t n_token_count);
|
||
@ctypes_function(
|
||
"llama_state_save_file",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_char_p,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_state_save_file(
|
||
ctx: llama_context_p,
|
||
path_session: bytes,
|
||
tokens: CtypesArray[llama_token],
|
||
n_token_count: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API DEPRECATED(bool llama_save_session_file(
|
||
# struct llama_context * ctx,
|
||
# const char * path_session,
|
||
# const llama_token * tokens,
|
||
# size_t n_token_count),
|
||
# "use llama_state_save_file instead");
|
||
@ctypes_function(
|
||
"llama_save_session_file",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_char_p,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_save_session_file(
|
||
ctx: llama_context_p,
|
||
path_session: bytes,
|
||
tokens: CtypesArray[llama_token],
|
||
n_token_count: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> bool:
|
||
...
|
||
|
||
|
||
# // Get the exact size needed to copy the state of a single sequence
|
||
# LLAMA_API size_t llama_state_seq_get_size(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id);
|
||
@ctypes_function(
|
||
"llama_state_seq_get_size",
|
||
[llama_context_p_ctypes, llama_seq_id],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_state_seq_get_size(ctx: llama_context_p, seq_id: llama_seq_id, /) -> int:
|
||
"""Get the exact size needed to copy the state of a single sequence"""
|
||
...
|
||
|
||
|
||
# // Copy the state of a single sequence into the specified buffer
|
||
# LLAMA_API size_t llama_state_seq_get_data(
|
||
# struct llama_context * ctx,
|
||
# uint8_t * dst,
|
||
# size_t size,
|
||
# llama_seq_id seq_id);
|
||
@ctypes_function(
|
||
"llama_state_seq_get_data",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.POINTER(ctypes.c_uint8),
|
||
ctypes.c_size_t,
|
||
llama_seq_id,
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_state_seq_get_data(
|
||
ctx: llama_context_p,
|
||
dst: CtypesArray[ctypes.c_uint8],
|
||
size: Union[ctypes.c_size_t, int],
|
||
seq_id: llama_seq_id,
|
||
/,
|
||
) -> int:
|
||
"""Copy the state of a single sequence into the specified buffer"""
|
||
...
|
||
|
||
|
||
# // Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence
|
||
# // Returns:
|
||
# // - Positive: Ok
|
||
# // - Zero: Failed to load
|
||
# LLAMA_API size_t llama_state_seq_set_data(
|
||
# struct llama_context * ctx,
|
||
# const uint8_t * src,
|
||
# size_t size,
|
||
# llama_seq_id dest_seq_id);
|
||
@ctypes_function(
|
||
"llama_state_seq_set_data",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.POINTER(ctypes.c_uint8),
|
||
ctypes.c_size_t,
|
||
llama_seq_id,
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_state_seq_set_data(
|
||
ctx: llama_context_p,
|
||
src: CtypesArray[ctypes.c_uint8],
|
||
size: Union[ctypes.c_size_t, int],
|
||
dest_seq_id: llama_seq_id,
|
||
/,
|
||
) -> int:
|
||
"""Copy the sequence data into the specified sequence"""
|
||
...
|
||
|
||
|
||
# LLAMA_API size_t llama_state_seq_save_file(
|
||
# struct llama_context * ctx,
|
||
# const char * filepath,
|
||
# llama_seq_id seq_id,
|
||
# const llama_token * tokens,
|
||
# size_t n_token_count);
|
||
@ctypes_function(
|
||
"llama_state_seq_save_file",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_char_p,
|
||
llama_seq_id,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_state_seq_save_file(
|
||
ctx: llama_context_p,
|
||
filepath: bytes,
|
||
seq_id: llama_seq_id,
|
||
tokens: CtypesArray[llama_token],
|
||
n_token_count: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> int:
|
||
...
|
||
|
||
|
||
# LLAMA_API size_t llama_state_seq_load_file(
|
||
# struct llama_context * ctx,
|
||
# const char * filepath,
|
||
# llama_seq_id dest_seq_id,
|
||
# llama_token * tokens_out,
|
||
# size_t n_token_capacity,
|
||
# size_t * n_token_count_out);
|
||
@ctypes_function(
|
||
"llama_state_seq_load_file",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_char_p,
|
||
llama_seq_id,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
ctypes.POINTER(ctypes.c_size_t),
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_state_seq_load_file(
|
||
ctx: llama_context_p,
|
||
filepath: bytes,
|
||
dest_seq_id: llama_seq_id,
|
||
tokens_out: CtypesArray[llama_token],
|
||
n_token_capacity: Union[ctypes.c_size_t, int],
|
||
n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
|
||
/,
|
||
) -> int:
|
||
...
|
||
|
||
|
||
# //
|
||
# // Decoding
|
||
# //
|
||
|
||
# // Return batch for single sequence of tokens
|
||
# // The sequence ID will be fixed to 0
|
||
# // The position of the tokens will be tracked automatically by llama_decode
|
||
# //
|
||
# // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
|
||
# //
|
||
# LLAMA_API struct llama_batch llama_batch_get_one(
|
||
# llama_token * tokens,
|
||
# int32_t n_tokens);
|
||
@ctypes_function(
|
||
"llama_batch_get_one",
|
||
[
|
||
llama_token_p,
|
||
ctypes.c_int32,
|
||
],
|
||
llama_batch,
|
||
)
|
||
def llama_batch_get_one(
|
||
tokens: CtypesArray[llama_token],
|
||
n_tokens: Union[ctypes.c_int, int],
|
||
/,
|
||
) -> llama_batch:
|
||
"""Return batch for single sequence of tokens
|
||
|
||
NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
|
||
"""
|
||
...
|
||
|
||
|
||
# // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
|
||
# // Each token can be assigned up to n_seq_max sequence ids
|
||
# // The batch has to be freed with llama_batch_free()
|
||
# // If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
|
||
# // Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
|
||
# // The rest of the llama_batch members are allocated with size n_tokens
|
||
# // All members are left uninitialized
|
||
# LLAMA_API struct llama_batch llama_batch_init(
|
||
# int32_t n_tokens,
|
||
# int32_t embd,
|
||
# int32_t n_seq_max);
|
||
@ctypes_function(
|
||
"llama_batch_init", [ctypes.c_int32, ctypes.c_int32, ctypes.c_int32], llama_batch
|
||
)
|
||
def llama_batch_init(
|
||
n_tokens: Union[ctypes.c_int32, int],
|
||
embd: Union[ctypes.c_int32, int],
|
||
n_seq_max: Union[ctypes.c_int32, int],
|
||
/,
|
||
) -> llama_batch:
|
||
"""Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
|
||
Each token can be assigned up to n_seq_max sequence ids
|
||
The batch has to be freed with llama_batch_free()
|
||
If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
|
||
Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
|
||
The rest of the llama_batch members are allocated with size n_tokens
|
||
All members are left uninitialized"""
|
||
...
|
||
|
||
|
||
# // Frees a batch of tokens allocated with llama_batch_init()
|
||
# LLAMA_API void llama_batch_free(struct llama_batch batch);
|
||
@ctypes_function("llama_batch_free", [llama_batch], None)
|
||
def llama_batch_free(batch: llama_batch, /):
|
||
"""Frees a batch of tokens allocated with llama_batch_init()"""
|
||
...
|
||
|
||
|
||
# // Process a batch of tokens.
|
||
# // In contrast to llama_decode() - this call does not use KV cache.
|
||
# // For encode-decoder contexts, processes the batch using the encoder.
|
||
# // Can store the encoder output internally for later use by the decoder's cross-attention layers.
|
||
# // 0 - success
|
||
# // < 0 - error. the memory state is restored to the state before this call
|
||
# LLAMA_API int32_t llama_encode(
|
||
# struct llama_context * ctx,
|
||
# struct llama_batch batch);
|
||
@ctypes_function("llama_encode", [llama_context_p_ctypes, llama_batch], ctypes.c_int32)
|
||
def llama_encode(ctx: llama_context_p, batch: llama_batch, /) -> int:
|
||
"""Process a batch of tokens using the encoder.
|
||
0 - success
|
||
< 0 - error"""
|
||
...
|
||
|
||
|
||
# // Process a batch of tokens.
|
||
# // Requires the context to have a memory.
|
||
# // For encode-decoder contexts, processes the batch using the decoder.
|
||
# // Positive return values does not mean a fatal error, but rather a warning.
|
||
# // Upon fatal-error or abort, the ubatches that managed to be been processed will remain in the memory state of the context
|
||
# // To handle this correctly, query the memory state using llama_memory_seq_pos_min() and llama_memory_seq_pos_max()
|
||
# // Upon other return values, the memory state is restored to the state before this call
|
||
# // 0 - success
|
||
# // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
|
||
# // 2 - aborted (processed ubatches will remain in the context's memory)
|
||
# // -1 - invalid input batch
|
||
# // < -1 - fatal error (processed ubatches will remain in the context's memory)
|
||
# LLAMA_API int32_t llama_decode(
|
||
# struct llama_context * ctx,
|
||
# struct llama_batch batch);
|
||
@ctypes_function("llama_decode", [llama_context_p_ctypes, llama_batch], ctypes.c_int32)
|
||
def llama_decode(ctx: llama_context_p, batch: llama_batch, /) -> int:
|
||
"""Process a batch of tokens.
|
||
0 - success
|
||
1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
|
||
2 - aborted (processed ubatches will remain in the context's memory)
|
||
-1 - invalid input batch
|
||
< -1 - fatal error (processed ubatches will remain in the context's memory)"""
|
||
...
|
||
|
||
|
||
# // Set the number of threads used for decoding
|
||
# // n_threads is the number of threads used for generation (single token)
|
||
# // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
|
||
# LLAMA_API void llama_set_n_threads(struct llama_context * ctx, int32_t n_threads, int32_t n_threads_batch);
|
||
@ctypes_function(
|
||
"llama_set_n_threads",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_int32,
|
||
ctypes.c_int32,
|
||
],
|
||
None,
|
||
)
|
||
def llama_set_n_threads(
|
||
ctx: llama_context_p,
|
||
n_threads: Union[ctypes.c_int32, int],
|
||
n_threads_batch: Union[ctypes.c_int32, int],
|
||
/,
|
||
):
|
||
"""Set the number of threads used for decoding
|
||
n_threads is the number of threads used for generation (single token)
|
||
n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
|
||
"""
|
||
...
|
||
|
||
|
||
# // Get the number of threads used for generation of a single token.
|
||
# LLAMA_API int32_t llama_n_threads(struct llama_context * ctx);
|
||
@ctypes_function("llama_n_threads", [llama_context_p_ctypes], ctypes.c_int32)
|
||
def llama_n_threads(ctx: llama_context_p, /) -> int:
|
||
"""Get the number of threads used for generation of a single token"""
|
||
...
|
||
|
||
|
||
# // Get the number of threads used for prompt and batch processing (multiple token).
|
||
# LLAMA_API int32_t llama_n_threads_batch(struct llama_context * ctx);
|
||
@ctypes_function("llama_n_threads_batch", [llama_context_p_ctypes], ctypes.c_int32)
|
||
def llama_n_threads_batch(ctx: llama_context_p, /) -> int:
|
||
"""Get the number of threads used for prompt and batch processing (multiple token)"""
|
||
...
|
||
|
||
|
||
# // Set whether the context outputs embeddings or not
|
||
# // TODO: rename to avoid confusion with llama_get_embeddings()
|
||
# LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
|
||
@ctypes_function("llama_set_embeddings", [llama_context_p_ctypes, ctypes.c_bool], None)
|
||
def llama_set_embeddings(ctx: llama_context_p, embeddings: bool, /):
|
||
"""Set whether the context outputs embeddings or not"""
|
||
...
|
||
|
||
|
||
# // Set whether to use causal attention or not
|
||
# // If set to true, the model will only attend to the past tokens
|
||
# LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
|
||
@ctypes_function("llama_set_causal_attn", [llama_context_p_ctypes, ctypes.c_bool], None)
|
||
def llama_set_causal_attn(ctx: llama_context_p, causal_attn: bool, /):
|
||
"""Set whether to use causal attention or not
|
||
If set to true, the model will only attend to the past tokens"""
|
||
...
|
||
|
||
|
||
# // Set whether the model is in warmup mode or not
|
||
# // If true, all model tensors are activated during llama_decode() to load and cache their weights.
|
||
# LLAMA_API void llama_set_warmup(struct llama_context * ctx, bool warmup);
|
||
@ctypes_function("llama_set_warmup", [llama_context_p_ctypes, ctypes.c_bool], None)
|
||
def llama_set_warmup(ctx: llama_context_p, warmup: bool, /):
|
||
"""Set whether the model is in warmup mode or not
|
||
If true, all model tensors are activated during llama_decode() to load and cache their weights."""
|
||
...
|
||
|
||
|
||
# // Set abort callback
|
||
# LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
|
||
@ctypes_function(
|
||
"llama_set_abort_callback",
|
||
[llama_context_p_ctypes, ggml_abort_callback, ctypes.c_void_p],
|
||
None,
|
||
)
|
||
def llama_set_abort_callback(
|
||
ctx: llama_context_p,
|
||
abort_callback: Callable[[ctypes.c_void_p], None],
|
||
abort_callback_data: ctypes.c_void_p,
|
||
/,
|
||
):
|
||
"""Set abort callback"""
|
||
...
|
||
|
||
|
||
# // Wait until all computations are finished
|
||
# // This is automatically done when using one of the functions below to obtain the computation results
|
||
# // and is not necessary to call it explicitly in most cases
|
||
# LLAMA_API void llama_synchronize(struct llama_context * ctx);
|
||
@ctypes_function("llama_synchronize", [llama_context_p_ctypes], None)
|
||
def llama_synchronize(ctx: llama_context_p, /):
|
||
"""Wait until all computations are finished
|
||
This is automatically done when using one of the functions below to obtain the computation results
|
||
and is not necessary to call it explicitly in most cases"""
|
||
...
|
||
|
||
|
||
# // Token logits obtained from the last call to llama_decode()
|
||
# // The logits for which llama_batch.logits[i] != 0 are stored contiguously
|
||
# // in the order they have appeared in the batch.
|
||
# // Rows: number of tokens for which llama_batch.logits[i] != 0
|
||
# // Cols: n_vocab
|
||
# LLAMA_API float * llama_get_logits(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_get_logits", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float)
|
||
)
|
||
def llama_get_logits(ctx: llama_context_p, /) -> CtypesArray[ctypes.c_float]:
|
||
"""Token logits obtained from the last call to llama_decode()
|
||
The logits for which llama_batch.logits[i] != 0 are stored contiguously
|
||
in the order they have appeared in the batch.
|
||
Rows: number of tokens for which llama_batch.logits[i] != 0
|
||
Cols: n_vocab
|
||
|
||
Returns:
|
||
Pointer to the logits buffer of shape (n_tokens, n_vocab)"""
|
||
...
|
||
|
||
|
||
# // Logits for the ith token. For positive indices, Equivalent to:
|
||
# // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab
|
||
# // Negative indicies can be used to access logits in reverse order, -1 is the last logit.
|
||
# // returns NULL for invalid ids.
|
||
# LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
|
||
@ctypes_function(
|
||
"llama_get_logits_ith",
|
||
[llama_context_p_ctypes, ctypes.c_int32],
|
||
ctypes.POINTER(ctypes.c_float),
|
||
)
|
||
def llama_get_logits_ith(
|
||
ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
|
||
) -> CtypesArray[ctypes.c_float]:
|
||
"""Logits for the ith token. Equivalent to:
|
||
llama_get_logits(ctx) + i*n_vocab"""
|
||
...
|
||
|
||
|
||
# // Get all output token embeddings.
|
||
# // when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model,
|
||
# // the embeddings for which llama_batch.logits[i] != 0 are stored contiguously
|
||
# // in the order they have appeared in the batch.
|
||
# // shape: [n_outputs*n_embd]
|
||
# // Otherwise, returns NULL.
|
||
# LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_get_embeddings", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float)
|
||
)
|
||
def llama_get_embeddings(ctx: llama_context_p, /) -> CtypesArray[ctypes.c_float]:
|
||
"""Get the embeddings for the input
|
||
shape: [n_embd] (1-dimensional)"""
|
||
...
|
||
|
||
|
||
# // Get the embeddings for the ith token. For positive indices, Equivalent to:
|
||
# // llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd
|
||
# // Negative indicies can be used to access embeddings in reverse order, -1 is the last embedding.
|
||
# // shape: [n_embd] (1-dimensional)
|
||
# // returns NULL for invalid ids.
|
||
# LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
|
||
@ctypes_function(
|
||
"llama_get_embeddings_ith",
|
||
[llama_context_p_ctypes, ctypes.c_int32],
|
||
ctypes.POINTER(ctypes.c_float),
|
||
)
|
||
def llama_get_embeddings_ith(
|
||
ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
|
||
) -> CtypesArray[ctypes.c_float]:
|
||
"""Get the embeddings for the ith sequence
|
||
llama_get_embeddings(ctx) + i*n_embd"""
|
||
...
|
||
|
||
|
||
# // Get the embeddings for a sequence id
|
||
# // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
|
||
# // when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[n_cls_out] with the rank(s) of the sequence
|
||
# // otherwise: float[n_embd] (1-dimensional)
|
||
# LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
|
||
@ctypes_function(
|
||
"llama_get_embeddings_seq",
|
||
[llama_context_p_ctypes, llama_seq_id],
|
||
ctypes.POINTER(ctypes.c_float),
|
||
)
|
||
def llama_get_embeddings_seq(
|
||
ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /
|
||
) -> CtypesArray[ctypes.c_float]:
|
||
"""Get the embeddings for a sequence id
|
||
Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
|
||
shape: [n_embd] (1-dimensional)"""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Vocab
|
||
# //
|
||
|
||
# LLAMA_API const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token);
|
||
@ctypes_function(
|
||
"llama_vocab_get_text", [llama_vocab_p_ctypes, llama_token], ctypes.c_char_p
|
||
)
|
||
def llama_vocab_get_text(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> bytes:
|
||
...
|
||
|
||
|
||
# LLAMA_API float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token);
|
||
@ctypes_function(
|
||
"llama_vocab_get_score", [llama_vocab_p_ctypes, llama_token], ctypes.c_float
|
||
)
|
||
def llama_vocab_get_score(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> float:
|
||
...
|
||
|
||
|
||
# LLAMA_API enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token);
|
||
@ctypes_function(
|
||
"llama_vocab_get_attr", [llama_vocab_p_ctypes, llama_token], ctypes.c_int
|
||
)
|
||
def llama_vocab_get_attr(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> int:
|
||
...
|
||
|
||
|
||
# // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
|
||
# LLAMA_API bool llama_vocab_is_eog(const struct llama_vocab * vocab, llama_token token);
|
||
@ctypes_function(
|
||
"llama_vocab_is_eog", [llama_vocab_p_ctypes, llama_token], ctypes.c_bool
|
||
)
|
||
def llama_vocab_is_eog(vocab: llama_vocab_p, token: Union[llama_token, int], /) -> bool:
|
||
"""Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)"""
|
||
...
|
||
|
||
|
||
# // Identify if Token Id is a control token or a render-able token
|
||
# LLAMA_API bool llama_vocab_is_control(const struct llama_vocab * vocab, llama_token token);
|
||
@ctypes_function(
|
||
"llama_vocab_is_control", [llama_vocab_p_ctypes, llama_token], ctypes.c_bool
|
||
)
|
||
def llama_vocab_is_control(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> bool:
|
||
"""Identify if Token Id is a control token or a render-able token"""
|
||
...
|
||
|
||
|
||
# // Special tokens
|
||
# LLAMA_API llama_token llama_vocab_bos(const struct llama_vocab * vocab); // beginning-of-sentence
|
||
@ctypes_function("llama_vocab_bos", [llama_vocab_p_ctypes], llama_token)
|
||
def llama_vocab_bos(vocab: llama_vocab_p, /) -> llama_token:
|
||
"""beginning-of-sentence"""
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_eos(const struct llama_vocab * vocab); // end-of-sentence
|
||
@ctypes_function("llama_vocab_eos", [llama_vocab_p_ctypes], llama_token)
|
||
def llama_vocab_eos(vocab: llama_vocab_p, /) -> llama_token:
|
||
"""end-of-sentence"""
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_eot(const struct llama_vocab * vocab); // end-of-turn
|
||
@ctypes_function("llama_vocab_eot", [llama_vocab_p_ctypes], llama_token)
|
||
def llama_vocab_eot(vocab: llama_vocab_p, /) -> llama_token:
|
||
"""end-of-turn"""
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_sep(const struct llama_vocab * vocab); // sentence separator
|
||
@ctypes_function("llama_vocab_sep", [llama_vocab_p_ctypes], llama_token)
|
||
def llama_vocab_sep(vocab: llama_vocab_p, /) -> llama_token:
|
||
"""sentence separator"""
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_nl (const struct llama_vocab * vocab); // next-line
|
||
@ctypes_function("llama_vocab_nl", [llama_vocab_p_ctypes], llama_token)
|
||
def llama_vocab_nl(vocab: llama_vocab_p, /) -> llama_token:
|
||
"""next-line"""
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_pad(const struct llama_vocab * vocab); // padding
|
||
@ctypes_function("llama_vocab_pad", [llama_vocab_p_ctypes], llama_token)
|
||
def llama_vocab_pad(vocab: llama_vocab_p, /) -> llama_token:
|
||
"""padding"""
|
||
...
|
||
|
||
|
||
# LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_get_add_bos",
|
||
[llama_vocab_p_ctypes],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_vocab_get_add_bos(vocab: llama_vocab_p, /) -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_get_add_eos",
|
||
[llama_vocab_p_ctypes],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_vocab_get_add_eos(vocab: llama_vocab_p, /) -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_get_add_sep",
|
||
[llama_vocab_p_ctypes],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_vocab_get_add_sep(vocab: llama_vocab_p, /) -> bool:
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_fim_pre",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_vocab_fim_pre(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_fim_suf",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_vocab_fim_suf(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_fim_mid",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_vocab_fim_mid(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_fim_pad",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_vocab_fim_pad(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_fim_rep",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_vocab_fim_rep(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_vocab_fim_sep",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_vocab_fim_sep(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
|
||
# DEPRECATED functions
|
||
# DEPRECATED(LLAMA_API const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_text instead");
|
||
@ctypes_function(
|
||
"llama_token_get_text",
|
||
[llama_vocab_p_ctypes, llama_token],
|
||
ctypes.c_char_p,
|
||
)
|
||
def llama_token_get_text(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> bytes:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead");
|
||
@ctypes_function(
|
||
"llama_token_get_score",
|
||
[llama_vocab_p_ctypes, llama_token],
|
||
ctypes.c_float,
|
||
)
|
||
def llama_token_get_score(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> float:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead");
|
||
@ctypes_function(
|
||
"llama_token_get_attr",
|
||
[llama_vocab_p_ctypes, llama_token],
|
||
ctypes.c_int,
|
||
)
|
||
def llama_token_get_attr(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> int:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead");
|
||
@ctypes_function(
|
||
"llama_token_is_eog",
|
||
[llama_vocab_p_ctypes, llama_token],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_token_is_eog(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> bool:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_control instead");
|
||
@ctypes_function(
|
||
"llama_token_is_control",
|
||
[llama_vocab_p_ctypes, llama_token],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_token_is_control(
|
||
vocab: llama_vocab_p, token: Union[llama_token, int], /
|
||
) -> bool:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_bos(const struct llama_vocab * vocab), "use llama_vocab_bos instead");
|
||
@ctypes_function(
|
||
"llama_token_bos",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_bos(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_eos(const struct llama_vocab * vocab), "use llama_vocab_eos instead");
|
||
@ctypes_function(
|
||
"llama_token_eos",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_eos(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_eot(const struct llama_vocab * vocab), "use llama_vocab_eot instead");
|
||
@ctypes_function(
|
||
"llama_token_eot",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_eot(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_cls(const struct llama_vocab * vocab), "use llama_vocab_cls instead");
|
||
@ctypes_function(
|
||
"llama_token_cls",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_cls(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_sep(const struct llama_vocab * vocab), "use llama_vocab_sep instead");
|
||
@ctypes_function(
|
||
"llama_token_sep",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_sep(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_nl (const struct llama_vocab * vocab), "use llama_vocab_nl instead");
|
||
@ctypes_function(
|
||
"llama_token_nl",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_nl(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_pad(const struct llama_vocab * vocab), "use llama_vocab_pad instead");
|
||
@ctypes_function(
|
||
"llama_token_pad",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_pad(vocab: llama_vocab_p, /) -> int:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API bool llama_add_bos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_bos instead");
|
||
@ctypes_function(
|
||
"llama_add_bos_token",
|
||
[llama_vocab_p_ctypes],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API bool llama_add_eos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_eos instead");
|
||
@ctypes_function(
|
||
"llama_add_eos_token",
|
||
[llama_vocab_p_ctypes],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_fim_pre(const struct llama_vocab * vocab), "use llama_vocab_fim_pre instead");
|
||
@ctypes_function(
|
||
"llama_token_fim_pre",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_fim_suf(const struct llama_vocab * vocab), "use llama_vocab_fim_suf instead");
|
||
@ctypes_function(
|
||
"llama_token_fim_suf",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_fim_mid(const struct llama_vocab * vocab), "use llama_vocab_fim_mid instead");
|
||
@ctypes_function(
|
||
"llama_token_fim_mid",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_fim_pad(const struct llama_vocab * vocab), "use llama_vocab_fim_pad instead");
|
||
@ctypes_function(
|
||
"llama_token_fim_pad",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead");
|
||
@ctypes_function(
|
||
"llama_token_fim_rep",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
# DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead");
|
||
@ctypes_function(
|
||
"llama_token_fim_sep",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
# // CLS is equivalent to BOS
|
||
# DEPRECATED(LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab), // classification
|
||
# "use llama_vocab_bos instead");
|
||
@ctypes_function(
|
||
"llama_vocab_cls",
|
||
[llama_vocab_p_ctypes],
|
||
llama_token,
|
||
)
|
||
def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token:
|
||
...
|
||
|
||
|
||
# //
|
||
# // Tokenization
|
||
# //
|
||
# // The API is thread-safe.
|
||
# //
|
||
|
||
# /// @details Convert the provided text into tokens.
|
||
# /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
|
||
# /// @return Returns the number of tokens on success, no more than n_tokens_max
|
||
# /// @return Returns a negative number on failure - the number of tokens that would have been returned
|
||
# /// @return Returns INT32_MIN on overflow (e.g., tokenization result size exceeds int32_t limit)
|
||
# /// @param add_special Allow to add BOS and EOS tokens if model is configured to do so.
|
||
# /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
|
||
# /// as plaintext. Does not insert a leading space.
|
||
# LLAMA_API int32_t llama_tokenize(
|
||
# const struct llama_vocab * vocab,
|
||
# const char * text,
|
||
# int32_t text_len,
|
||
# llama_token * tokens,
|
||
# int32_t n_tokens_max,
|
||
# bool add_special,
|
||
# bool parse_special);
|
||
@ctypes_function(
|
||
"llama_tokenize",
|
||
[
|
||
llama_vocab_p_ctypes,
|
||
ctypes.c_char_p,
|
||
ctypes.c_int32,
|
||
llama_token_p,
|
||
ctypes.c_int32,
|
||
ctypes.c_bool,
|
||
ctypes.c_bool,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_tokenize(
|
||
vocab: llama_vocab_p,
|
||
text: bytes,
|
||
text_len: Union[ctypes.c_int, int],
|
||
tokens: CtypesArray[llama_token],
|
||
n_tokens_max: Union[ctypes.c_int, int],
|
||
add_special: Union[ctypes.c_bool, bool],
|
||
parse_special: Union[ctypes.c_bool, bool],
|
||
/,
|
||
) -> int:
|
||
"""Convert the provided text into tokens.
|
||
|
||
Args:
|
||
vocab: The vocabulary to use for tokenization.
|
||
text: The text to tokenize.
|
||
text_len: The length of the text.
|
||
tokens: The tokens pointer must be large enough to hold the resulting tokens.
|
||
n_max_tokens: The maximum number of tokens to return.
|
||
add_special: Allow adding special tokens if the model is configured to do so.
|
||
parse_special: Allow parsing special tokens.
|
||
|
||
Returns:
|
||
Returns the number of tokens on success, no more than n_tokens_max
|
||
Returns a negative number on failure - the number of tokens that would have been returned
|
||
"""
|
||
...
|
||
|
||
|
||
# // Token Id -> Piece.
|
||
# // Uses the vocabulary in the provided context.
|
||
# // Does not write null terminator to the buffer.
|
||
# // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix')
|
||
# // @param special If true, special tokens are rendered in the output.
|
||
# LLAMA_API int32_t llama_token_to_piece(
|
||
# const struct llama_vocab * vocab,
|
||
# llama_token token,
|
||
# char * buf,
|
||
# int32_t length,
|
||
# int32_t lstrip,
|
||
# bool special);
|
||
@ctypes_function(
|
||
"llama_token_to_piece",
|
||
[
|
||
llama_vocab_p_ctypes,
|
||
llama_token,
|
||
ctypes.c_char_p,
|
||
ctypes.c_int32,
|
||
ctypes.c_int32,
|
||
ctypes.c_bool,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_token_to_piece(
|
||
vocab: llama_vocab_p,
|
||
token: Union[llama_token, int],
|
||
buf: Union[ctypes.c_char_p, bytes, CtypesArray[ctypes.c_char]],
|
||
length: Union[ctypes.c_int, int],
|
||
lstrip: Union[ctypes.c_int, int],
|
||
special: Union[ctypes.c_bool, bool],
|
||
/,
|
||
) -> int:
|
||
"""Token Id -> Piece.
|
||
Uses the vocabulary in the provided context.
|
||
Does not write null terminator to the buffer.
|
||
User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||
|
||
Args:
|
||
vocab: The vocabulary to use for tokenization.
|
||
token: The token to convert.
|
||
buf: The buffer to write the token to.
|
||
length: The length of the buffer.
|
||
lstrip: The number of leading spaces to skip.
|
||
special: If true, special tokens are rendered in the output."""
|
||
...
|
||
|
||
|
||
# /// @details Convert the provided tokens into text (inverse of llama_tokenize()).
|
||
# /// @param text The char pointer must be large enough to hold the resulting text.
|
||
# /// @return Returns the number of chars/bytes on success, no more than text_len_max.
|
||
# /// @return Returns a negative number on failure - the number of chars/bytes that would have been returned.
|
||
# /// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so.
|
||
# /// @param unparse_special If true, special tokens are rendered in the output.
|
||
# LLAMA_API int32_t llama_detokenize(
|
||
# const struct llama_vocab * vocab,
|
||
# const llama_token * tokens,
|
||
# int32_t n_tokens,
|
||
# char * text,
|
||
# int32_t text_len_max,
|
||
# bool remove_special,
|
||
# bool unparse_special);
|
||
@ctypes_function(
|
||
"llama_detokenize",
|
||
[
|
||
llama_vocab_p_ctypes,
|
||
ctypes.POINTER(llama_token),
|
||
ctypes.c_int32,
|
||
ctypes.c_char_p,
|
||
ctypes.c_int32,
|
||
ctypes.c_bool,
|
||
ctypes.c_bool,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_detokenize(
|
||
vocab: llama_vocab_p,
|
||
tokens: CtypesArray[llama_token],
|
||
n_tokens: Union[ctypes.c_int, int],
|
||
text: bytes,
|
||
text_len_max: Union[ctypes.c_int, int],
|
||
remove_special: Union[ctypes.c_bool, bool],
|
||
unparse_special: Union[ctypes.c_bool, bool],
|
||
/,
|
||
) -> int:
|
||
"""Convert the provided tokens into text (inverse of llama_tokenize()).
|
||
|
||
Args:
|
||
vocab: The vocabulary to use for tokenization.
|
||
tokens: The tokens to convert.
|
||
n_tokens: The number of tokens.
|
||
text: The buffer to write the text to.
|
||
text_len_max: The length of the buffer.
|
||
remove_special: Allow to remove BOS and EOS tokens if model is configured to do so.
|
||
unparse_special: If true, special tokens are rendered in the output."""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Chat templates
|
||
# //
|
||
|
||
# /// Apply chat template. Inspired by hf apply_chat_template() on python.
|
||
# /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
|
||
# /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
|
||
# /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model's default chat template will be used instead.
|
||
# /// @param chat Pointer to a list of multiple llama_chat_message
|
||
# /// @param n_msg Number of llama_chat_message in this chat
|
||
# /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
|
||
# /// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages)
|
||
# /// @param length The size of the allocated buffer
|
||
# /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
|
||
# LLAMA_API int32_t llama_chat_apply_template(
|
||
# const char * tmpl,
|
||
# const struct llama_chat_message * chat,
|
||
# size_t n_msg,
|
||
# bool add_ass,
|
||
# char * buf,
|
||
# int32_t length);
|
||
@ctypes_function(
|
||
"llama_chat_apply_template",
|
||
[
|
||
ctypes.c_char_p, # tmpl
|
||
ctypes.POINTER(llama_chat_message), # chat
|
||
ctypes.c_size_t, # n_msg
|
||
ctypes.c_bool, # add_ass (added)
|
||
ctypes.c_char_p, # buf
|
||
ctypes.c_int32, # length
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_chat_apply_template(
|
||
tmpl: bytes,
|
||
chat: CtypesArray[llama_chat_message],
|
||
n_msg: int,
|
||
add_ass: bool, # Added parameter
|
||
buf: bytes,
|
||
length: int,
|
||
/,
|
||
) -> int:
|
||
"""Apply chat template.
|
||
|
||
Args:
|
||
tmpl: Template to use. If None, uses model's default
|
||
chat: Array of chat messages
|
||
n_msg: Number of messages
|
||
add_ass: Whether to end prompt with assistant token
|
||
buf: Output buffer
|
||
length: Buffer length
|
||
|
||
Returns:
|
||
Number of bytes written, or needed if buffer too small
|
||
"""
|
||
...
|
||
|
||
|
||
# // Get list of built-in chat templates
|
||
# LLAMA_API int32_t llama_chat_builtin_templates(const char ** output, size_t len);
|
||
@ctypes_function(
|
||
"llama_chat_builtin_templates",
|
||
[
|
||
ctypes.POINTER(ctypes.c_char_p),
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_chat_builtin_templates(
|
||
output: CtypesArray[bytes],
|
||
len: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> int:
|
||
"""Get list of built-in chat templates.
|
||
|
||
Args:
|
||
output: Output buffer to store template names.
|
||
len: Length of the output buffer.
|
||
|
||
Returns:
|
||
Number of templates available.
|
||
Returns a negative number on error.
|
||
"""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Sampling API
|
||
# //
|
||
|
||
# typedef void * llama_sampler_context_t;
|
||
llama_sampler_context_t = ctypes.c_void_p
|
||
|
||
|
||
# // user code can implement the interface below in order to create custom llama_sampler
|
||
# struct llama_sampler_i {
|
||
# const char * (*name) (const struct llama_sampler * smpl); // can be NULL
|
||
# void (*accept)( struct llama_sampler * smpl, llama_token token); // can be NULL
|
||
# void (*apply) ( struct llama_sampler * smpl, llama_token_data_array * cur_p); // required
|
||
# void (*reset) ( struct llama_sampler * smpl); // can be NULL
|
||
# struct llama_sampler * (*clone) (const struct llama_sampler * smpl); // can be NULL if ctx is NULL
|
||
# void (*free) ( struct llama_sampler * smpl); // can be NULL if ctx is NULL
|
||
|
||
# // TODO: API for internal libllama usage for appending the sampling to an existing ggml_cgraph
|
||
# //void (*apply_ggml) (struct llama_sampler * smpl, ...);
|
||
# };
|
||
class llama_sampler_i(ctypes.Structure):
|
||
...
|
||
|
||
|
||
# struct llama_sampler {
|
||
# const struct llama_sampler_i * iface;
|
||
# llama_sampler_context_t ctx;
|
||
# };
|
||
class llama_sampler(ctypes.Structure):
|
||
_fields_ = [
|
||
("iface", ctypes.POINTER(llama_sampler_i)),
|
||
("ctx", llama_sampler_context_t),
|
||
]
|
||
|
||
|
||
if TYPE_CHECKING:
|
||
llama_sampler_p = CtypesPointer[llama_sampler]
|
||
|
||
llama_sampler_p_ctypes = ctypes.POINTER(llama_sampler)
|
||
|
||
llama_sampler_i_name = ctypes.CFUNCTYPE(ctypes.c_char_p, llama_sampler_p_ctypes)
|
||
llama_sampler_i_accept = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes, llama_token)
|
||
llama_sampler_i_apply = ctypes.CFUNCTYPE(
|
||
None, llama_sampler_p_ctypes, llama_token_data_array_p
|
||
)
|
||
llama_sampler_i_reset = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes)
|
||
llama_sampler_i_clone = ctypes.CFUNCTYPE(llama_sampler_p_ctypes, llama_sampler_p_ctypes)
|
||
llama_sampler_i_free = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes)
|
||
|
||
llama_sampler_i._fields_ = [
|
||
("name", llama_sampler_i_name),
|
||
("accept", llama_sampler_i_accept),
|
||
("apply", llama_sampler_i_apply),
|
||
("reset", llama_sampler_i_reset),
|
||
("clone", llama_sampler_i_clone),
|
||
("free", llama_sampler_i_free),
|
||
]
|
||
|
||
|
||
# // mirror of llama_sampler_i:
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
|
||
@ctypes_function(
|
||
"llama_sampler_init",
|
||
[ctypes.POINTER(llama_sampler_i), llama_sampler_context_t],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init(
|
||
iface: ctypes.POINTER(llama_sampler_i), ctx: llama_sampler_context_t, /
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# LLAMA_API const char * llama_sampler_name (const struct llama_sampler * smpl);
|
||
@ctypes_function(
|
||
"llama_sampler_name",
|
||
[llama_sampler_p_ctypes],
|
||
ctypes.c_char_p,
|
||
)
|
||
def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes:
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_sampler_accept( struct llama_sampler * smpl, llama_token token);
|
||
@ctypes_function(
|
||
"llama_sampler_accept",
|
||
[llama_sampler_p_ctypes, llama_token],
|
||
None,
|
||
)
|
||
def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], /):
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_sampler_apply ( struct llama_sampler * smpl, llama_token_data_array * cur_p);
|
||
@ctypes_function(
|
||
"llama_sampler_apply",
|
||
[llama_sampler_p_ctypes, llama_token_data_array_p],
|
||
None,
|
||
)
|
||
def llama_sampler_apply(
|
||
smpl: llama_sampler_p, cur_p: CtypesArray[llama_token_data_array], /
|
||
):
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_sampler_reset ( struct llama_sampler * smpl);
|
||
@ctypes_function(
|
||
"llama_sampler_reset",
|
||
[llama_sampler_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_sampler_reset(smpl: llama_sampler_p, /):
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl);
|
||
@ctypes_function(
|
||
"llama_sampler_clone",
|
||
[llama_sampler_p_ctypes],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add)
|
||
# LLAMA_API void llama_sampler_free ( struct llama_sampler * smpl);
|
||
@ctypes_function(
|
||
"llama_sampler_free",
|
||
[llama_sampler_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_sampler_free(smpl: llama_sampler_p, /):
|
||
...
|
||
|
||
|
||
# // llama_sampler_chain
|
||
# // a type of llama_sampler that can chain multiple samplers one after another
|
||
|
||
# LLAMA_API struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params);
|
||
@ctypes_function(
|
||
"llama_sampler_chain_init",
|
||
[llama_sampler_chain_params],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_chain_init(params: llama_sampler_chain_params, /) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# // important: takes ownership of the sampler object and will free it when llama_sampler_free is called
|
||
# LLAMA_API void llama_sampler_chain_add( struct llama_sampler * chain, struct llama_sampler * smpl);
|
||
@ctypes_function(
|
||
"llama_sampler_chain_add",
|
||
[llama_sampler_p_ctypes, llama_sampler_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /):
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i);
|
||
@ctypes_function(
|
||
"llama_sampler_chain_get",
|
||
[llama_sampler_p_ctypes, ctypes.c_int32],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_chain_get(
|
||
chain: llama_sampler_p, i: Union[ctypes.c_int32, int], /
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# LLAMA_API int llama_sampler_chain_n (const struct llama_sampler * chain);
|
||
@ctypes_function(
|
||
"llama_sampler_chain_n",
|
||
[llama_sampler_p_ctypes],
|
||
ctypes.c_int,
|
||
)
|
||
def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int:
|
||
...
|
||
|
||
|
||
# // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed
|
||
# LLAMA_API struct llama_sampler * llama_sampler_chain_remove( struct llama_sampler * chain, int32_t i);
|
||
@ctypes_function(
|
||
"llama_sampler_chain_remove",
|
||
[llama_sampler_p_ctypes, ctypes.c_int32],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_chain_remove(
|
||
chain: llama_sampler_p, i: Union[ctypes.c_int32, int], /
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# // available samplers:
|
||
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void);
|
||
@ctypes_function("llama_sampler_init_greedy", [], llama_sampler_p_ctypes)
|
||
def llama_sampler_init_greedy() -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_dist (uint32_t seed);
|
||
@ctypes_function("llama_sampler_init_dist", [ctypes.c_uint32], llama_sampler_p_ctypes)
|
||
def llama_sampler_init_dist(seed: int) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
|
||
# /// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first.
|
||
# DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax (void),
|
||
# "will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)");
|
||
@ctypes_function("llama_sampler_init_softmax", [], llama_sampler_p_ctypes)
|
||
def llama_sampler_init_softmax() -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||
# /// Setting k <= 0 makes this a noop
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k);
|
||
@ctypes_function("llama_sampler_init_top_k", [ctypes.c_int32], llama_sampler_p_ctypes)
|
||
def llama_sampler_init_top_k(k: int) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_top_p (float p, size_t min_keep);
|
||
@ctypes_function(
|
||
"llama_sampler_init_top_p",
|
||
[ctypes.c_float, ctypes.c_size_t],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_top_p(p: float, min_keep: int) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Minimum P sampling as described in https://github.com/ggml-org/llama.cpp/pull/3841
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_min_p (float p, size_t min_keep);
|
||
@ctypes_function(
|
||
"llama_sampler_init_min_p",
|
||
[ctypes.c_float, ctypes.c_size_t],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_typical (float p, size_t min_keep);
|
||
@ctypes_function(
|
||
"llama_sampler_init_typical",
|
||
[ctypes.c_float, ctypes.c_size_t],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_typical(p: float, min_keep: int) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// #details Updates the logits l_i` = l_i/t. When t <= 0.0f, the maximum logit is kept at it's original value, the rest are set to -inf
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_temp (float t);
|
||
@ctypes_function("llama_sampler_init_temp", [ctypes.c_float], llama_sampler_p_ctypes)
|
||
def llama_sampler_init_temp(t: float) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772.
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_temp_ext (float t, float delta, float exponent);
|
||
@ctypes_function(
|
||
"llama_sampler_init_temp_ext",
|
||
[ctypes.c_float, ctypes.c_float, ctypes.c_float],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_temp_ext(
|
||
t: float, delta: float, exponent: float
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_xtc (float p, float t, size_t min_keep, uint32_t seed);
|
||
@ctypes_function(
|
||
"llama_sampler_init_xtc",
|
||
[ctypes.c_float, ctypes.c_float, ctypes.c_size_t, ctypes.c_uint32],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_xtc(
|
||
p: float, t: float, min_keep: int, seed: int, /
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Top n sigma sampling as described in academic paper "Top-nσ: Not All Logits Are You Need" https://arxiv.org/pdf/2411.07641
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_top_n_sigma(float n);
|
||
@ctypes_function(
|
||
"llama_sampler_init_top_n_sigma",
|
||
[ctypes.c_float],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_top_n_sigma(n: float, /) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_mirostat(
|
||
# int32_t n_vocab,
|
||
# uint32_t seed,
|
||
# float tau,
|
||
# float eta,
|
||
# int32_t m);
|
||
@ctypes_function(
|
||
"llama_sampler_init_mirostat",
|
||
[ctypes.c_int32, ctypes.c_uint32, ctypes.c_float, ctypes.c_float, ctypes.c_int32],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_mirostat(
|
||
n_vocab: int, seed: int, tau: float, eta: float, m: int, /
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_mirostat_v2(
|
||
# uint32_t seed,
|
||
# float tau,
|
||
# float eta);
|
||
@ctypes_function(
|
||
"llama_sampler_init_mirostat_v2",
|
||
[ctypes.c_uint32, ctypes.c_float, ctypes.c_float],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_mirostat_v2(
|
||
seed: int, tau: float, eta: float, /
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Intializes a GBNF grammar, see grammars/README.md for details.
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_grammar(
|
||
# const struct llama_vocab * vocab,
|
||
# const char * grammar_str,
|
||
# const char * grammar_root);
|
||
@ctypes_function(
|
||
"llama_sampler_init_grammar",
|
||
[llama_vocab_p_ctypes, ctypes.c_char_p, ctypes.c_char_p],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_grammar(
|
||
vocab: llama_vocab_p, grammar_str: bytes, grammar_root: bytes, /
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy(
|
||
# const struct llama_vocab * vocab,
|
||
# const char * grammar_str,
|
||
# const char * grammar_root,
|
||
# const char ** trigger_words,
|
||
# size_t num_trigger_words,
|
||
# const llama_token * trigger_tokens,
|
||
# size_t num_trigger_tokens),
|
||
# "use llama_sampler_init_grammar_lazy_patterns instead");
|
||
@ctypes_function(
|
||
"llama_sampler_init_grammar_lazy",
|
||
[
|
||
llama_vocab_p_ctypes,
|
||
ctypes.c_char_p,
|
||
ctypes.c_char_p,
|
||
ctypes.POINTER(ctypes.c_char_p),
|
||
ctypes.c_size_t,
|
||
ctypes.POINTER(llama_token),
|
||
ctypes.c_size_t,
|
||
],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_grammar_lazy(
|
||
vocab: llama_vocab_p,
|
||
grammar_str: bytes,
|
||
grammar_root: bytes,
|
||
trigger_words: CtypesArray[bytes],
|
||
num_trigger_words: int,
|
||
trigger_tokens: CtypesArray[llama_token],
|
||
num_trigger_tokens: int,
|
||
/,
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details Lazy grammar sampler, introduced in https://github.com/ggml-org/llama.cpp/pull/9639
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy_patterns(
|
||
# const struct llama_vocab * vocab,
|
||
# const char * grammar_str,
|
||
# const char * grammar_root,
|
||
# const char ** trigger_patterns,
|
||
# size_t num_trigger_patterns,
|
||
# const llama_token * trigger_tokens,
|
||
# size_t num_trigger_tokens);
|
||
@ctypes_function(
|
||
"llama_sampler_init_grammar_lazy_patterns",
|
||
[
|
||
llama_vocab_p_ctypes,
|
||
ctypes.c_char_p,
|
||
ctypes.c_char_p,
|
||
ctypes.POINTER(ctypes.c_char_p),
|
||
ctypes.c_size_t,
|
||
ctypes.POINTER(llama_token),
|
||
ctypes.c_size_t,
|
||
],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_grammar_lazy_patterns(
|
||
vocab: llama_vocab_p,
|
||
grammar_str: bytes,
|
||
grammar_root: bytes,
|
||
trigger_patterns: CtypesArray[bytes],
|
||
num_trigger_patterns: int,
|
||
trigger_tokens: CtypesArray[llama_token],
|
||
num_trigger_tokens: int,
|
||
/,
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
|
||
# int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size)
|
||
# float penalty_repeat, // 1.0 = disabled
|
||
# float penalty_freq, // 0.0 = disabled
|
||
# float penalty_present); // 0.0 = disabled
|
||
@ctypes_function(
|
||
"llama_sampler_init_penalties",
|
||
[ctypes.c_int32, ctypes.c_float, ctypes.c_float, ctypes.c_float],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_penalties(
|
||
penalty_last_n: int,
|
||
penalty_repeat: float,
|
||
penalty_freq: float,
|
||
penalty_present: float,
|
||
/,
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# /// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_dry(
|
||
# const struct llama_vocab * vocab,
|
||
# int32_t n_ctx_train,
|
||
# float dry_multiplier,
|
||
# float dry_base,
|
||
# int32_t dry_allowed_length,
|
||
# int32_t dry_penalty_last_n,
|
||
# const char ** seq_breakers,
|
||
# size_t num_breakers);
|
||
@ctypes_function(
|
||
"llama_sampler_init_dry",
|
||
[
|
||
llama_vocab_p_ctypes,
|
||
ctypes.c_int32,
|
||
ctypes.c_float,
|
||
ctypes.c_float,
|
||
ctypes.c_int32,
|
||
ctypes.c_int32,
|
||
ctypes.POINTER(ctypes.c_char_p),
|
||
ctypes.c_size_t,
|
||
],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_dry(
|
||
vocab: llama_vocab_p,
|
||
n_ctx_train: int,
|
||
dry_multiplier: float,
|
||
dry_base: float,
|
||
dry_allowed_length: int,
|
||
dry_penalty_last_n: int,
|
||
seq_breakers,
|
||
num_breakers: int,
|
||
/,
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias(
|
||
# int32_t n_vocab,
|
||
# int32_t n_logit_bias,
|
||
# const llama_logit_bias * logit_bias);
|
||
@ctypes_function(
|
||
"llama_sampler_init_logit_bias",
|
||
[ctypes.c_int32, ctypes.c_int32, llama_logit_bias_p],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_logit_bias(
|
||
n_vocab: int, n_logit_bias: int, logit_bias: CtypesArray[llama_logit_bias], /
|
||
) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# // this sampler is meant to be used for fill-in-the-middle infilling
|
||
# LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab);
|
||
@ctypes_function(
|
||
"llama_sampler_init_infill",
|
||
[llama_vocab_p_ctypes],
|
||
llama_sampler_p_ctypes,
|
||
)
|
||
def llama_sampler_init_infill(vocab: llama_vocab_p, /) -> llama_sampler_p:
|
||
...
|
||
|
||
|
||
# // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
|
||
# LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
|
||
@ctypes_function(
|
||
"llama_sampler_get_seed",
|
||
[llama_sampler_p_ctypes],
|
||
ctypes.c_uint32,
|
||
)
|
||
def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int:
|
||
...
|
||
|
||
|
||
# /// @details Sample and accept a token from the idx-th output of the last evaluation
|
||
# LLAMA_API llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx);
|
||
@ctypes_function(
|
||
"llama_sampler_sample",
|
||
[llama_sampler_p_ctypes, llama_context_p_ctypes, ctypes.c_int32],
|
||
llama_token,
|
||
)
|
||
def llama_sampler_sample(
|
||
smpl: llama_sampler_p, ctx: llama_context_p, idx: int, /
|
||
) -> int:
|
||
...
|
||
|
||
|
||
# //
|
||
# // Model split
|
||
# //
|
||
|
||
# /// @details Build a split GGUF final path for this chunk.
|
||
# LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count);
|
||
@ctypes_function(
|
||
"llama_split_path",
|
||
[ctypes.c_char_p, ctypes.c_size_t, ctypes.c_char_p, ctypes.c_int, ctypes.c_int],
|
||
ctypes.c_int,
|
||
)
|
||
def llama_split_path(
|
||
split_path: bytes,
|
||
maxlen: Union[ctypes.c_size_t, int],
|
||
path_prefix: bytes,
|
||
split_no: Union[ctypes.c_int, int],
|
||
split_count: Union[ctypes.c_int, int],
|
||
/,
|
||
) -> int:
|
||
"""Build a split GGUF final path for this chunk."""
|
||
...
|
||
|
||
|
||
# /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match.
|
||
# LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count);
|
||
@ctypes_function(
|
||
"llama_split_prefix",
|
||
[ctypes.c_char_p, ctypes.c_size_t, ctypes.c_char_p, ctypes.c_int, ctypes.c_int],
|
||
ctypes.c_int,
|
||
)
|
||
def llama_split_prefix(
|
||
split_prefix: bytes,
|
||
maxlen: Union[ctypes.c_size_t, int],
|
||
split_path: bytes,
|
||
split_no: Union[ctypes.c_int, int],
|
||
split_count: Union[ctypes.c_int, int],
|
||
/,
|
||
) -> int:
|
||
"""Extract the path prefix from the split_path if and only if the split_no and split_count match."""
|
||
...
|
||
|
||
|
||
# // Print system information
|
||
# LLAMA_API const char * llama_print_system_info(void);
|
||
@ctypes_function("llama_print_system_info", [], ctypes.c_char_p)
|
||
def llama_print_system_info() -> bytes:
|
||
...
|
||
|
||
|
||
# // Set callback for all future logging events.
|
||
# // If this is not called, or NULL is supplied, everything is output on stderr.
|
||
# LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
|
||
@ctypes_function(
|
||
"llama_log_set",
|
||
[ctypes.c_void_p, ctypes.c_void_p],
|
||
None,
|
||
)
|
||
def llama_log_set(
|
||
log_callback: Optional[CtypesFuncPointer],
|
||
user_data: ctypes.c_void_p,
|
||
/,
|
||
):
|
||
"""Set callback for all future logging events.
|
||
|
||
If this is not called, or NULL is supplied, everything is output on stderr."""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Performance utils
|
||
# //
|
||
|
||
# struct llama_perf_context_data {
|
||
# double t_start_ms;
|
||
# double t_load_ms;
|
||
# double t_p_eval_ms;
|
||
# double t_eval_ms;
|
||
|
||
# int32_t n_p_eval;
|
||
# int32_t n_eval;
|
||
# };
|
||
class llama_perf_context_data(ctypes.Structure):
|
||
_fields_ = [
|
||
("t_start_ms", ctypes.c_double),
|
||
("t_load_ms", ctypes.c_double),
|
||
("t_p_eval_ms", ctypes.c_double),
|
||
("t_eval_ms", ctypes.c_double),
|
||
("n_p_eval", ctypes.c_int32),
|
||
("n_eval", ctypes.c_int32),
|
||
]
|
||
|
||
|
||
# struct llama_perf_sampler_data {
|
||
# double t_sample_ms;
|
||
|
||
# int32_t n_sample;
|
||
# };
|
||
class llama_perf_sampler_data(ctypes.Structure):
|
||
_fields_ = [
|
||
("t_sample_ms", ctypes.c_double),
|
||
("n_sample", ctypes.c_int32),
|
||
]
|
||
|
||
|
||
# LLAMA_API struct llama_perf_context_data llama_perf_context (const struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_perf_context",
|
||
[llama_context_p_ctypes],
|
||
llama_perf_context_data,
|
||
)
|
||
def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data:
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_perf_context_print(const struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_perf_context_print",
|
||
[llama_context_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_perf_context_print(ctx: llama_context_p, /):
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_perf_context_reset( struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_perf_context_reset",
|
||
[llama_context_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_perf_context_reset(ctx: llama_context_p, /):
|
||
...
|
||
|
||
|
||
# // NOTE: the following work only with samplers constructed via llama_sampler_chain_init
|
||
# LLAMA_API struct llama_perf_sampler_data llama_perf_sampler (const struct llama_sampler * chain);
|
||
@ctypes_function(
|
||
"llama_perf_sampler",
|
||
[llama_sampler_p_ctypes],
|
||
llama_perf_sampler_data,
|
||
)
|
||
def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data:
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain);
|
||
@ctypes_function(
|
||
"llama_perf_sampler_print",
|
||
[llama_sampler_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_perf_sampler_print(chain: llama_sampler_p, /):
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain);
|
||
@ctypes_function(
|
||
"llama_perf_sampler_reset",
|
||
[llama_sampler_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_perf_sampler_reset(chain: llama_sampler_p, /):
|
||
...
|
||
|
||
|
||
# //
|
||
# // training
|
||
# //
|
||
|
||
# // function that returns whether or not a given tensor contains trainable parameters
|
||
# typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata);
|
||
llama_opt_param_filter = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_void_p, ctypes.c_void_p)
|
||
|
||
# // always returns true
|
||
# LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata);
|
||
@ctypes_function(
|
||
"llama_opt_param_filter_all",
|
||
[ctypes.c_void_p, ctypes.c_void_p],
|
||
ctypes.c_bool,
|
||
)
|
||
def llama_opt_param_filter_all(tensor: ctypes.c_void_p, userdata: ctypes.c_void_p, /) -> bool:
|
||
...
|
||
|
||
|
||
# struct llama_opt_params {
|
||
# uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0
|
||
|
||
# llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters
|
||
# void * param_filter_ud; // userdata for determining which tensors contain trainable parameters
|
||
|
||
# ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
|
||
# void * get_opt_pars_ud; // userdata for calculating optimizer parameters
|
||
# };
|
||
class llama_opt_params(ctypes.Structure):
|
||
_fields_ = [
|
||
("n_ctx_train", ctypes.c_uint32),
|
||
("param_filter", llama_opt_param_filter),
|
||
("param_filter_ud", ctypes.c_void_p),
|
||
("get_opt_pars", ctypes.c_void_p), # ggml_opt_get_optimizer_params - not implemented here
|
||
("get_opt_pars_ud", ctypes.c_void_p),
|
||
]
|
||
|
||
|
||
# LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params);
|
||
@ctypes_function(
|
||
"llama_opt_init",
|
||
[llama_context_p_ctypes, llama_model_p_ctypes, llama_opt_params],
|
||
None,
|
||
)
|
||
def llama_opt_init(lctx: llama_context_p, model: llama_model_p, lopt_params: llama_opt_params, /):
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_opt_epoch(
|
||
# struct llama_context * lctx,
|
||
# ggml_opt_dataset_t dataset,
|
||
# ggml_opt_result_t result_train,
|
||
# ggml_opt_result_t result_eval,
|
||
# int64_t idata_split,
|
||
# ggml_opt_epoch_callback callback_train,
|
||
# ggml_opt_epoch_callback callback_eval);
|
||
@ctypes_function(
|
||
"llama_opt_epoch",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_void_p, # ggml_opt_dataset_t
|
||
ctypes.c_void_p, # ggml_opt_result_t
|
||
ctypes.c_void_p, # ggml_opt_result_t
|
||
ctypes.c_int64,
|
||
ctypes.c_void_p, # ggml_opt_epoch_callback
|
||
ctypes.c_void_p, # ggml_opt_epoch_callback
|
||
],
|
||
None,
|
||
)
|
||
def llama_opt_epoch(
|
||
lctx: llama_context_p,
|
||
dataset: ctypes.c_void_p,
|
||
result_train: ctypes.c_void_p,
|
||
result_eval: ctypes.c_void_p,
|
||
idata_split: int,
|
||
callback_train: ctypes.c_void_p,
|
||
callback_eval: ctypes.c_void_p,
|
||
/,
|
||
):
|
||
...
|