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from __future__ import annotations
import sys
import os
import ctypes
from ctypes import (
_Pointer, # type: ignore
Array,
)
import pathlib
from typing import (
List,
Union,
NewType,
Optional,
TYPE_CHECKING,
TypeVar,
Generic,
)
from typing_extensions import TypeAlias
# Load the library
def _load_shared_library(lib_base_name: str):
# Construct the paths to the possible shared library names
_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__)))
# Searching for the library in the current directory under the name "libllama" (default name
# for llamacpp) and "llama" (default name for this repo)
_lib_paths: List[pathlib.Path] = []
# Determine the file extension based on the platform
if sys.platform.startswith("linux"):
_lib_paths += [
_base_path / f"lib{lib_base_name}.so",
]
elif sys.platform == "darwin":
_lib_paths += [
_base_path / f"lib{lib_base_name}.so",
_base_path / f"lib{lib_base_name}.dylib",
]
elif sys.platform == "win32":
_lib_paths += [
_base_path / f"{lib_base_name}.dll",
_base_path / f"lib{lib_base_name}.dll",
]
else:
raise RuntimeError("Unsupported platform")
if "LLAMA_CPP_LIB" in os.environ:
lib_base_name = os.environ["LLAMA_CPP_LIB"]
_lib = pathlib.Path(lib_base_name)
_base_path = _lib.parent.resolve()
_lib_paths = [_lib.resolve()]
cdll_args = dict() # type: ignore
# Add the library directory to the DLL search path on Windows (if needed)
if sys.platform == "win32" and sys.version_info >= (3, 8):
os.add_dll_directory(str(_base_path))
if "CUDA_PATH" in os.environ:
os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "bin"))
os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "lib"))
if "HIP_PATH" in os.environ:
os.add_dll_directory(os.path.join(os.environ["HIP_PATH"], "bin"))
os.add_dll_directory(os.path.join(os.environ["HIP_PATH"], "lib"))
cdll_args["winmode"] = ctypes.RTLD_GLOBAL
# Try to load the shared library, handling potential errors
for _lib_path in _lib_paths:
if _lib_path.exists():
try:
return ctypes.CDLL(str(_lib_path), **cdll_args) # type: ignore
except Exception as e:
raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}")
raise FileNotFoundError(
f"Shared library with base name '{lib_base_name}' not found"
)
# Specify the base name of the shared library to load
_lib_base_name = "llama"
# Load the library
_lib = _load_shared_library(_lib_base_name)
# ctypes sane type hint helpers
#
# - Generic Pointer and Array types
# - PointerOrRef type with a type hinted byref function
#
# NOTE: Only use these for static type checking not for runtime checks
# no good will come of that
if TYPE_CHECKING:
CtypesCData = TypeVar("CtypesCData", bound=ctypes._CData) # type: ignore
CtypesArray: TypeAlias = ctypes.Array[CtypesCData] # type: ignore
CtypesPointer: TypeAlias = ctypes._Pointer[CtypesCData] # type: ignore
CtypesVoidPointer: TypeAlias = ctypes.c_void_p
class CtypesRef(Generic[CtypesCData]):
pass
CtypesPointerOrRef: TypeAlias = Union[
CtypesPointer[CtypesCData], CtypesRef[CtypesCData]
]
CtypesFuncPointer: TypeAlias = ctypes._FuncPointer # type: ignore
# 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
)
# 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_MAX_RNG_STATE (64*1024)
LLAMA_MAX_RNG_STATE = 64 * 1024
# 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_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
# define LLAMA_SESSION_VERSION 4
LLAMA_SESSION_VERSION = 4
# 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 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_SPM = 0, // SentencePiece
# LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
# LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece
# };
LLAMA_VOCAB_TYPE_SPM = 0
LLAMA_VOCAB_TYPE_BPE = 1
LLAMA_VOCAB_TYPE_WPM = 2
# enum llama_token_type {
# 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
# // 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_Q3_K_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_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_Q4_1_SOME_F16 = 4
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_Q3_K_XS = 22
LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23
LLAMA_FTYPE_MOSTLY_IQ1_S = 24
LLAMA_FTYPE_MOSTLY_IQ4_NL = 25
LLAMA_FTYPE_GUESSED = 1024
# enum llama_rope_scaling_type {
# LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
# LLAMA_ROPE_SCALING_NONE = 0,
# LLAMA_ROPE_SCALING_LINEAR = 1,
# LLAMA_ROPE_SCALING_YARN = 2,
# LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN,
# };
LLAMA_ROPE_SCALING_UNSPECIFIED = -1
LLAMA_ROPE_SCALING_NONE = 0
LLAMA_ROPE_SCALING_LINEAR = 1
LLAMA_ROPE_SCALING_YARN = 2
LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN
# enum llama_pooling_type {
# LLAMA_POOLING_NONE = 0,
# LLAMA_POOLING_MEAN = 1,
# LLAMA_POOLING_CLS = 2,
# };
LLAMA_POOLING_NONE = 0
LLAMA_POOLING_MEAN = 1
LLAMA_POOLING_CLS = 2
# enum llama_split_mode {
# LLAMA_SPLIT_NONE = 0, // single GPU
# LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs
# LLAMA_SPLIT_ROW = 2, // split rows across GPUs
# };
LLAMA_SPLIT_NONE = 0
LLAMA_SPLIT_LAYER = 1
LLAMA_SPLIT_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"""
_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 {
# llama_token_data * data;
# size_t size;
# 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
sorted (bool): whether the array is sorted"""
_fields_ = [
("data", llama_token_data_p),
("size", ctypes.c_size_t),
("sorted", ctypes.c_bool),
]
llama_token_data_array_p = ctypes.POINTER(llama_token_data_array)
# typedef bool (*llama_progress_callback)(float progress, void *ctx);
llama_progress_callback = ctypes.CFUNCTYPE(
ctypes.c_bool, ctypes.c_float, ctypes.c_void_p
)
# // Input data for 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
# // - seq_id : the sequence to which the respective token belongs
# // - logits : if zero, the logits for the respective token will not be 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;
# // NOTE: helpers for smooth API transition - can be deprecated in the future
# // for future-proof code, use the above fields instead and ignore everything below
# //
# // pos[i] = all_pos_0 + i*all_pos_1
# //
# llama_pos all_pos_0; // used if pos == NULL
# llama_pos all_pos_1; // used if pos == NULL
# llama_seq_id all_seq_id; // used if seq_id == NULL
# } llama_batch;
class llama_batch(ctypes.Structure):
"""Input data for 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:
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
"""
_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)),
("all_pos_0", llama_pos),
("all_pos_1", llama_pos),
("all_seq_id", llama_seq_id),
]
# enum llama_model_kv_override_type {
# LLAMA_KV_OVERRIDE_INT,
# LLAMA_KV_OVERRIDE_FLOAT,
# LLAMA_KV_OVERRIDE_BOOL,
# };
LLAMA_KV_OVERRIDE_INT = 0
LLAMA_KV_OVERRIDE_FLOAT = 1
LLAMA_KV_OVERRIDE_BOOL = 2
# struct llama_model_kv_override {
# char key[128];
# enum llama_model_kv_override_type tag;
# union {
# int64_t int_value;
# double float_value;
# bool bool_value;
# };
# };
class llama_model_kv_override_value(ctypes.Union):
_fields_ = [
("int_value", ctypes.c_int64),
("float_value", ctypes.c_double),
("bool_value", ctypes.c_bool),
]
class llama_model_kv_override(ctypes.Structure):
_fields_ = [
("key", ctypes.c_char * 128),
("tag", ctypes.c_int),
("value", llama_model_kv_override_value),
]
# struct llama_model_params {
# 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
# // main_gpu interpretation depends on split_mode:
# // LLAMA_SPLIT_NONE: the GPU that is used for the entire model
# // LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results
# // LLAMA_SPLIT_LAYER: ignored
# 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
# };
class llama_model_params(ctypes.Structure):
"""Parameters for llama_model
Attributes:
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. main_gpu interpretation depends on split_mode: LLAMA_SPLIT_NONE: the GPU that is used for the entire model LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results LLAMA_SPLIT_LAYER: ignored
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"""
_fields_ = [
("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),
]
# struct llama_context_params {
# uint32_t seed; // RNG seed, -1 for random
# uint32_t n_ctx; // text context, 0 = from model
# uint32_t n_batch; // prompt processing maximum batch size
# uint32_t n_threads; // number of threads to use for generation
# uint32_t n_threads_batch; // number of threads to use for batch processing
# int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
# // ref: https://github.com/ggerganov/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
# ggml_backend_sched_eval_callback cb_eval;
# void * cb_eval_user_data;
# enum ggml_type type_k; // data type for K cache
# enum ggml_type type_v; // data type for V cache
# // Keep the booleans together to avoid misalignment during copy-by-value.
# bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
# bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
# bool embedding; // embedding mode only
# bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
# bool do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
# };
class llama_context_params(ctypes.Structure):
"""Parameters for llama_context
Attributes:
seed (int): RNG seed, -1 for random
n_ctx (int): text context, 0 = from model
n_batch (int): prompt processing maximum batch size
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`
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
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
mul_mat_q (bool): if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
logits_all (bool): the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
embedding (bool): embedding mode only
offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU
do_pooling (bool): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
"""
_fields_ = [
("seed", ctypes.c_uint32),
("n_ctx", ctypes.c_uint32),
("n_batch", ctypes.c_uint32),
("n_threads", ctypes.c_uint32),
("n_threads_batch", ctypes.c_uint32),
("rope_scaling_type", ctypes.c_int32),
("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),
("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),
("mul_mat_q", ctypes.c_bool),
("logits_all", ctypes.c_bool),
("embedding", ctypes.c_bool),
("offload_kqv", ctypes.c_bool),
("do_pooling", 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
# 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; // disable k-quant mixtures and quantize all tensors to the same type
# void * imatrix; // pointer to importance matrix data
# } 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
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): disable k-quant mixtures and quantize all tensors to the same type
imatrix (ctypes.ctypes.c_void_p): pointer to importance matrix data
"""
_fields_ = [
("nthread", ctypes.c_int32),
("ftype", ctypes.c_int),
("allow_requantize", ctypes.c_bool),
("quantize_output_tensor", ctypes.c_bool),
("only_copy", ctypes.c_bool),
("pure", ctypes.c_bool),
("imatrix", ctypes.c_void_p),
]
# // grammar types
# struct llama_grammar;
llama_grammar_p = ctypes.c_void_p
# // grammar element type
# enum llama_gretype {
# // end of rule definition
# LLAMA_GRETYPE_END = 0,
# // start of alternate definition for rule
# LLAMA_GRETYPE_ALT = 1,
# // non-terminal element: reference to rule
# LLAMA_GRETYPE_RULE_REF = 2,
# // terminal element: character (code point)
# LLAMA_GRETYPE_CHAR = 3,
# // inverse char(s) ([^a], [^a-b] [^abc])
# LLAMA_GRETYPE_CHAR_NOT = 4,
# // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
# // be an inclusive range ([a-z])
# LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
# // modifies a preceding LLAMA_GRETYPE_CHAR or
# // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
# LLAMA_GRETYPE_CHAR_ALT = 6,
# };
LLAMA_GRETYPE_END = 0
LLAMA_GRETYPE_ALT = 1
LLAMA_GRETYPE_RULE_REF = 2
LLAMA_GRETYPE_CHAR = 3
LLAMA_GRETYPE_CHAR_NOT = 4
LLAMA_GRETYPE_CHAR_RNG_UPPER = 5
LLAMA_GRETYPE_CHAR_ALT = 6
# typedef struct llama_grammar_element {
# enum llama_gretype type;
# uint32_t value; // Unicode code point or rule ID
# } llama_grammar_element;
class llama_grammar_element(ctypes.Structure):
_fields_ = [
("type", ctypes.c_int),
("value", ctypes.c_uint32),
]
llama_grammar_element_p = ctypes.POINTER(llama_grammar_element)
# // performance timing information
# struct llama_timings {
# double t_start_ms;
# double t_end_ms;
# double t_load_ms;
# double t_sample_ms;
# double t_p_eval_ms;
# double t_eval_ms;
# int32_t n_sample;
# int32_t n_p_eval;
# int32_t n_eval;
# };
class llama_timings(ctypes.Structure):
_fields_ = [
("t_start_ms", ctypes.c_double),
("t_end_ms", ctypes.c_double),
("t_load_ms", ctypes.c_double),
("t_sample_ms", ctypes.c_double),
("t_p_eval_ms", ctypes.c_double),
("t_eval_ms", ctypes.c_double),
("n_sample", ctypes.c_int32),
("n_p_eval", ctypes.c_int32),
("n_eval", ctypes.c_int32),
]
# // 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),
]
# // Helpers for getting default parameters
# LLAMA_API struct llama_model_params llama_model_default_params(void);
def llama_model_default_params() -> llama_model_params:
"""Get default parameters for llama_model"""
...
llama_model_default_params = _lib.llama_model_default_params
llama_model_default_params.argtypes = []
llama_model_default_params.restype = llama_model_params
# LLAMA_API struct llama_context_params llama_context_default_params(void);
def llama_context_default_params() -> llama_context_params:
"""Get default parameters for llama_context"""
...
llama_context_default_params = _lib.llama_context_default_params
llama_context_default_params.argtypes = []
llama_context_default_params.restype = llama_context_params
# LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
def llama_model_quantize_default_params() -> llama_model_quantize_params:
"""Get default parameters for llama_model_quantize"""
...
llama_model_quantize_default_params = _lib.llama_model_quantize_default_params
llama_model_quantize_default_params.argtypes = []
llama_model_quantize_default_params.restype = llama_model_quantize_params
# // 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(bool numa);
# LLAMA_API void llama_backend_init(void);
def llama_backend_init():
"""Initialize the llama + ggml backend
If numa is true, use NUMA optimizations
Call once at the start of the program"""
...
llama_backend_init = _lib.llama_backend_init
llama_backend_init.argtypes = []
llama_backend_init.restype = None
# // 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
# //optional:
# LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
def llama_numa_init(numa: int, /):
...
llama_numa_init = _lib.llama_numa_init
llama_numa_init.argtypes = [ctypes.c_int]
llama_numa_init.restype = None
# // Call once at the end of the program - currently only used for MPI
# LLAMA_API void llama_backend_free(void);
def llama_backend_free():
"""Call once at the end of the program - currently only used for MPI"""
...
llama_backend_free = _lib.llama_backend_free
llama_backend_free.argtypes = []
llama_backend_free.restype = None
# LLAMA_API struct llama_model * llama_load_model_from_file(
# const char * path_model,
# struct llama_model_params params);
def llama_load_model_from_file(
path_model: bytes, params: llama_model_params, /
) -> Optional[llama_model_p]:
...
llama_load_model_from_file = _lib.llama_load_model_from_file
llama_load_model_from_file.argtypes = [ctypes.c_char_p, llama_model_params]
llama_load_model_from_file.restype = llama_model_p_ctypes
# LLAMA_API void llama_free_model(struct llama_model * model);
def llama_free_model(model: llama_model_p, /):
...
llama_free_model = _lib.llama_free_model
llama_free_model.argtypes = [llama_model_p_ctypes]
llama_free_model.restype = None
# LLAMA_API struct llama_context * llama_new_context_with_model(
# struct llama_model * model,
# struct llama_context_params params);
def llama_new_context_with_model(
model: llama_model_p, params: llama_context_params, /
) -> Optional[llama_context_p]:
...
llama_new_context_with_model = _lib.llama_new_context_with_model
llama_new_context_with_model.argtypes = [llama_model_p_ctypes, llama_context_params]
llama_new_context_with_model.restype = llama_context_p_ctypes
# // Frees all allocated memory
# LLAMA_API void llama_free(struct llama_context * ctx);
def llama_free(ctx: llama_context_p, /):
"""Frees all allocated memory"""
...
llama_free = _lib.llama_free
llama_free.argtypes = [llama_context_p_ctypes]
llama_free.restype = None
# LLAMA_API int64_t llama_time_us(void);
def llama_time_us() -> int:
...
llama_time_us = _lib.llama_time_us
llama_time_us.argtypes = []
llama_time_us.restype = ctypes.c_int64
# LLAMA_API size_t llama_max_devices(void);
def llama_max_devices() -> int:
...
llama_max_devices = _lib.llama_max_devices
llama_max_devices.argtypes = []
llama_max_devices.restype = ctypes.c_size_t
# LLAMA_API bool llama_supports_mmap (void);
def llama_supports_mmap() -> bool:
...
llama_supports_mmap = _lib.llama_supports_mmap
llama_supports_mmap.argtypes = []
llama_supports_mmap.restype = ctypes.c_bool
# LLAMA_API bool llama_supports_mlock (void);
def llama_supports_mlock() -> bool:
...
llama_supports_mlock = _lib.llama_supports_mlock
llama_supports_mlock.argtypes = []
llama_supports_mlock.restype = ctypes.c_bool
# LLAMA_API bool llama_supports_gpu_offload(void);
def llama_supports_gpu_offload() -> bool:
...
llama_supports_gpu_offload = _lib.llama_supports_gpu_offload
llama_supports_gpu_offload.argtypes = []
llama_supports_gpu_offload.restype = ctypes.c_bool
# LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead");
def llama_mmap_supported() -> bool:
...
llama_mmap_supported = _lib.llama_mmap_supported
llama_mmap_supported.argtypes = []
llama_mmap_supported.restype = ctypes.c_bool
# LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead");
def llama_mlock_supported() -> bool:
...
llama_mlock_supported = _lib.llama_mlock_supported
llama_mlock_supported.argtypes = []
llama_mlock_supported.restype = ctypes.c_bool
# LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]:
...
llama_get_model = _lib.llama_get_model
llama_get_model.argtypes = [llama_context_p_ctypes]
llama_get_model.restype = llama_model_p_ctypes
# LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
def llama_n_ctx(ctx: llama_context_p, /) -> int:
...
llama_n_ctx = _lib.llama_n_ctx
llama_n_ctx.argtypes = [llama_context_p_ctypes]
llama_n_ctx.restype = ctypes.c_uint32
# LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
def llama_n_batch(ctx: llama_context_p, /) -> int:
...
llama_n_batch = _lib.llama_n_batch
llama_n_batch.argtypes = [llama_context_p_ctypes]
llama_n_batch.restype = ctypes.c_uint32
# LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
def llama_vocab_type(model: llama_model_p, /) -> int:
...
llama_vocab_type = _lib.llama_vocab_type
llama_vocab_type.argtypes = [llama_model_p_ctypes]
llama_vocab_type.restype = ctypes.c_int
# LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
def llama_n_vocab(model: llama_model_p, /) -> int:
...
llama_n_vocab = _lib.llama_n_vocab
llama_n_vocab.argtypes = [llama_model_p_ctypes]
llama_n_vocab.restype = ctypes.c_int32
# LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
def llama_n_ctx_train(model: llama_model_p, /) -> int:
...
llama_n_ctx_train = _lib.llama_n_ctx_train
llama_n_ctx_train.argtypes = [llama_model_p_ctypes]
llama_n_ctx_train.restype = ctypes.c_int32
# LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
def llama_n_embd(model: llama_model_p, /) -> int:
...
llama_n_embd = _lib.llama_n_embd
llama_n_embd.argtypes = [llama_model_p_ctypes]
llama_n_embd.restype = ctypes.c_int32
# // Get the model's RoPE frequency scaling factor
# LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
def llama_rope_freq_scale_train(model: llama_model_p, /) -> float:
"""Get the model's RoPE frequency scaling factor"""
...
llama_rope_freq_scale_train = _lib.llama_rope_freq_scale_train
llama_rope_freq_scale_train.argtypes = [llama_model_p_ctypes]
llama_rope_freq_scale_train.restype = ctypes.c_float
# // Functions to access the model's GGUF metadata scalar values