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from __future__ import annotations
import os
import ctypes
import pathlib
from typing import (
Callable,
Union,
NewType,
Optional,
TYPE_CHECKING,
)
from llama_cpp._ctypes_extensions import (
load_shared_library,
byref,
ctypes_function_for_shared_library,
)
if TYPE_CHECKING:
from llama_cpp._ctypes_extensions import (
CtypesCData,
CtypesArray,
CtypesPointer,
CtypesVoidPointer,
CtypesRef,
CtypesPointerOrRef,
CtypesFuncPointer,
)
# Specify the base name of the shared library to load
_lib_base_name = "llama"
_override_base_path = os.environ.get("LLAMA_CPP_LIB_PATH")
_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _override_base_path is None else pathlib.Path(_override_base_path)
# Load the library
_lib = load_shared_library(_lib_base_name, _base_path)
ctypes_function = ctypes_function_for_shared_library(_lib)
# from ggml.h
# // NOTE: always add types at the end of the enum to keep backward compatibility
# enum ggml_type {
# GGML_TYPE_F32 = 0,
# GGML_TYPE_F16 = 1,
# GGML_TYPE_Q4_0 = 2,
# GGML_TYPE_Q4_1 = 3,
# // GGML_TYPE_Q4_2 = 4, support has been removed
# // GGML_TYPE_Q4_3 = 5, support has been removed
# GGML_TYPE_Q5_0 = 6,
# GGML_TYPE_Q5_1 = 7,
# GGML_TYPE_Q8_0 = 8,
# GGML_TYPE_Q8_1 = 9,
# GGML_TYPE_Q2_K = 10,
# GGML_TYPE_Q3_K = 11,
# GGML_TYPE_Q4_K = 12,
# GGML_TYPE_Q5_K = 13,
# GGML_TYPE_Q6_K = 14,
# GGML_TYPE_Q8_K = 15,
# GGML_TYPE_IQ2_XXS = 16,
# GGML_TYPE_IQ2_XS = 17,
# GGML_TYPE_IQ3_XXS = 18,
# GGML_TYPE_IQ1_S = 19,
# GGML_TYPE_IQ4_NL = 20,
# GGML_TYPE_IQ3_S = 21,
# GGML_TYPE_IQ2_S = 22,
# GGML_TYPE_IQ4_XS = 23,
# GGML_TYPE_I8 = 24,
# GGML_TYPE_I16 = 25,
# GGML_TYPE_I32 = 26,
# GGML_TYPE_I64 = 27,
# GGML_TYPE_F64 = 28,
# GGML_TYPE_IQ1_M = 29,
# GGML_TYPE_COUNT,
# };
GGML_TYPE_F32 = 0
GGML_TYPE_F16 = 1
GGML_TYPE_Q4_0 = 2
GGML_TYPE_Q4_1 = 3
GGML_TYPE_Q5_0 = 6
GGML_TYPE_Q5_1 = 7
GGML_TYPE_Q8_0 = 8
GGML_TYPE_Q8_1 = 9
GGML_TYPE_Q2_K = 10
GGML_TYPE_Q3_K = 11
GGML_TYPE_Q4_K = 12
GGML_TYPE_Q5_K = 13
GGML_TYPE_Q6_K = 14
GGML_TYPE_Q8_K = 15
GGML_TYPE_IQ2_XXS = 16
GGML_TYPE_IQ2_XS = 17
GGML_TYPE_IQ3_XXS = 18
GGML_TYPE_IQ1_S = 19
GGML_TYPE_IQ4_NL = 20
GGML_TYPE_IQ3_S = 21
GGML_TYPE_IQ2_S = 22
GGML_TYPE_IQ4_XS = 23
GGML_TYPE_I8 = 24
GGML_TYPE_I16 = 25
GGML_TYPE_I32 = 26
GGML_TYPE_I64 = 27
GGML_TYPE_F64 = 28
GGML_TYPE_IQ1_M = 29
GGML_TYPE_COUNT = 30
# from ggml-backend.h
# typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
ggml_backend_sched_eval_callback = ctypes.CFUNCTYPE(
ctypes.c_bool, ctypes.c_void_p, ctypes.c_bool, ctypes.c_void_p
)
# // Abort callback
# // If not NULL, called before ggml computation
# // If it returns true, the computation is aborted
# typedef bool (*ggml_abort_callback)(void * data);
ggml_abort_callback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_void_p)
# llama.h bindings
_lib.llama_max_devices.argtypes = []
_lib.llama_max_devices.restype = ctypes.c_size_t
LLAMA_MAX_DEVICES = _lib.llama_max_devices()
# define LLAMA_DEFAULT_SEED 0xFFFFFFFF
LLAMA_DEFAULT_SEED = 0xFFFFFFFF
# define LLAMA_TOKEN_NULL -1
LLAMA_TOKEN_NULL = -1
# define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
LLAMA_FILE_MAGIC_GGLA = 0x67676C61
# define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
LLAMA_FILE_MAGIC_GGSN = 0x6767736E
# define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
LLAMA_FILE_MAGIC_GGSQ = 0x67677371
# define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
# define LLAMA_SESSION_VERSION 9
LLAMA_SESSION_VERSION = 9
# define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ
LLAMA_STATE_SEQ_MAGIC = LLAMA_FILE_MAGIC_GGSQ
# define LLAMA_STATE_SEQ_VERSION 2
LLAMA_STATE_SEQ_VERSION = 2
# struct llama_vocab;
llama_vocab_p = NewType("llama_vocab_p", int)
llama_vocab_p_ctypes = ctypes.c_void_p
# struct llama_model;
llama_model_p = NewType("llama_model_p", int)
llama_model_p_ctypes = ctypes.c_void_p
# struct llama_context;
llama_context_p = NewType("llama_context_p", int)
llama_context_p_ctypes = ctypes.c_void_p
# typedef struct llama_memory_i * llama_memory_t;
llama_memory_t = NewType("llama_memory_t", int)
llama_memory_t_ctypes = ctypes.c_void_p
# struct llama_kv_cache; (DEPRECATED)
llama_kv_cache_p = NewType("llama_kv_cache_p", int)
llama_kv_cache_p_ctypes = ctypes.c_void_p
# typedef int32_t llama_pos;
llama_pos = ctypes.c_int32
# typedef int32_t llama_token;
llama_token = ctypes.c_int32
llama_token_p = ctypes.POINTER(llama_token)
# typedef int32_t llama_seq_id;
llama_seq_id = ctypes.c_int32
# enum llama_vocab_type {
# LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
# LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
# LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE
# LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece
# LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram
# LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization
# };
LLAMA_VOCAB_TYPE_NONE = 0
"""For models without vocab"""
LLAMA_VOCAB_TYPE_SPM = 1
"""LLaMA tokenizer based on byte-level BPE with byte fallback"""
LLAMA_VOCAB_TYPE_BPE = 2
"""GPT-2 tokenizer based on byte-level BPE"""
LLAMA_VOCAB_TYPE_WPM = 3
"""BERT tokenizer based on WordPiece"""
LLAMA_VOCAB_TYPE_UGM = 4
"""T5 tokenizer based on Unigram"""
LLAMA_VOCAB_TYPE_RWKV = 5
"""RWKV tokenizer based on greedy tokenization"""
# // pre-tokenization types
# enum llama_vocab_pre_type {
# LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0,
# LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1,
# LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2,
# LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3,
# LLAMA_VOCAB_PRE_TYPE_FALCON = 4,
# LLAMA_VOCAB_PRE_TYPE_MPT = 5,
# LLAMA_VOCAB_PRE_TYPE_STARCODER = 6,
# LLAMA_VOCAB_PRE_TYPE_GPT2 = 7,
# LLAMA_VOCAB_PRE_TYPE_REFACT = 8,
# LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9,
# LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10,
# LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11,
# LLAMA_VOCAB_PRE_TYPE_OLMO = 12,
# LLAMA_VOCAB_PRE_TYPE_DBRX = 13,
# LLAMA_VOCAB_PRE_TYPE_SMAUG = 14,
# LLAMA_VOCAB_PRE_TYPE_PORO = 15,
# LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16,
# LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17,
# LLAMA_VOCAB_PRE_TYPE_VIKING = 18,
# LLAMA_VOCAB_PRE_TYPE_JAIS = 19,
# LLAMA_VOCAB_PRE_TYPE_TEKKEN = 20,
# LLAMA_VOCAB_PRE_TYPE_SMOLLM = 21,
# LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22,
# LLAMA_VOCAB_PRE_TYPE_BLOOM = 23,
# LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24,
# LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
# LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26,
# LLAMA_VOCAB_PRE_TYPE_MINERVA = 27,
# LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28,
# LLAMA_VOCAB_PRE_TYPE_GPT4O = 29,
# LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30,
# LLAMA_VOCAB_PRE_TYPE_TRILLION = 31,
# LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32,
# LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33,
# LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34,
# LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35,
# };
LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0
LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3
LLAMA_VOCAB_PRE_TYPE_FALCON = 4
LLAMA_VOCAB_PRE_TYPE_MPT = 5
LLAMA_VOCAB_PRE_TYPE_STARCODER = 6
LLAMA_VOCAB_PRE_TYPE_GPT2 = 7
LLAMA_VOCAB_PRE_TYPE_REFACT = 8
LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9
LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10
LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11
LLAMA_VOCAB_PRE_TYPE_OLMO = 12
LLAMA_VOCAB_PRE_TYPE_DBRX = 13
LLAMA_VOCAB_PRE_TYPE_SMAUG = 14
LLAMA_VOCAB_PRE_TYPE_PORO = 15
LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16
LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17
LLAMA_VOCAB_PRE_TYPE_VIKING = 18
LLAMA_VOCAB_PRE_TYPE_JAIS = 19
LLAMA_VOCAB_PRE_TYPE_TEKKEN = 20
LLAMA_VOCAB_PRE_TYPE_SMOLLM = 21
LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22
LLAMA_VOCAB_PRE_TYPE_BLOOM = 23
LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24
LLAMA_VOCAB_PRE_TYPE_EXAONE = 25
LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26
LLAMA_VOCAB_PRE_TYPE_MINERVA = 27
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28
LLAMA_VOCAB_PRE_TYPE_GPT4O = 29
LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30
LLAMA_VOCAB_PRE_TYPE_TRILLION = 31
LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32
LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33
LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34
LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35
# // note: these values should be synchronized with ggml_rope
# // TODO: maybe move this enum to ggml.h (ggml_rope_type)
# enum llama_rope_type {
# LLAMA_ROPE_TYPE_NONE = -1,
# LLAMA_ROPE_TYPE_NORM = 0,
# LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX,
# LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE,
# LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION,
# };
LLAMA_ROPE_TYPE_NONE = -1
LLAMA_ROPE_TYPE_NORM = 0
LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX = 2
LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE = 8
LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION = 24
# enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
# LLAMA_TOKEN_TYPE_UNDEFINED = 0,
# LLAMA_TOKEN_TYPE_NORMAL = 1,
# LLAMA_TOKEN_TYPE_UNKNOWN = 2,
# LLAMA_TOKEN_TYPE_CONTROL = 3,
# LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
# LLAMA_TOKEN_TYPE_UNUSED = 5,
# LLAMA_TOKEN_TYPE_BYTE = 6,
# };
LLAMA_TOKEN_TYPE_UNDEFINED = 0
LLAMA_TOKEN_TYPE_NORMAL = 1
LLAMA_TOKEN_TYPE_UNKNOWN = 2
LLAMA_TOKEN_TYPE_CONTROL = 3
LLAMA_TOKEN_TYPE_USER_DEFINED = 4
LLAMA_TOKEN_TYPE_UNUSED = 5
LLAMA_TOKEN_TYPE_BYTE = 6
# enum llama_token_attr {
# 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, // SPECIAL?
# 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,
# };
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"""