import sys

import os

import ctypes

from ctypes import (

c_bool,

c_char_p,

c_int,

c_int8,

c_int32,

c_uint8,

c_uint32,

c_int64,

c_size_t,

c_float,

c_double,

c_void_p,

POINTER,

_Pointer, # type: ignore

Structure,

Union as CtypesUnion,

Array,

)

import pathlib

from typing import List, Union

# 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)

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)

# Misc

c_float_p = POINTER(c_float)

c_uint8_p = POINTER(c_uint8)

c_size_t_p = POINTER(c_size_t)

# 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(c_bool, c_void_p, c_bool, 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 = c_void_p

# struct llama_context;

llama_context_p = c_void_p

# typedef int32_t llama_pos;

llama_pos = c_int32

# typedef int32_t llama_token;

llama_token = c_int32

llama_token_p = POINTER(llama_token)

# typedef int32_t llama_seq_id;

llama_seq_id = 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_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_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(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", c_float),

("p", c_float),

]

llama_token_data_p = 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(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", c_size_t),

("sorted", c_bool),

]

llama_token_data_array_p = POINTER(llama_token_data_array)

# typedef bool (*llama_progress_callback)(float progress, void *ctx);

llama_progress_callback = ctypes.CFUNCTYPE(c_bool, c_float, 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(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.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", c_int32),

("token", POINTER(llama_token)),

("embd", c_float_p),

("pos", POINTER(llama_pos)),

("n_seq_id", POINTER(c_int32)),

("seq_id", POINTER(POINTER(llama_seq_id))),

("logits", POINTER(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(CtypesUnion):

_fields_ = [

("int_value", c_int64),

("float_value", c_double),

("bool_value", c_bool),

]

class llama_model_kv_override(Structure):

_fields_ = [

("key", ctypes.c_char * 128),

("tag", 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(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.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.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", c_int32),

("split_mode", c_int),

("main_gpu", c_int32),

("tensor_split", c_float_p),

("progress_callback", llama_progress_callback),

("progress_callback_user_data", c_void_p),

("kv_overrides", POINTER(llama_model_kv_override)),

("vocab_only", c_bool),

("use_mmap", c_bool),

("use_mlock", 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(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.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", c_uint32),

("n_ctx", c_uint32),

("n_batch", c_uint32),

("n_threads", c_uint32),

("n_threads_batch", c_uint32),

("rope_scaling_type", c_int32),

("rope_freq_base", c_float),

("rope_freq_scale", c_float),

("yarn_ext_factor", c_float),

("yarn_attn_factor", c_float),

("yarn_beta_fast", c_float),

("yarn_beta_slow", c_float),

("yarn_orig_ctx", c_uint32),

("cb_eval", ggml_backend_sched_eval_callback),

("cb_eval_user_data", c_void_p),

("type_k", c_int),

("type_v", c_int),

("mul_mat_q", c_bool),

("logits_all", c_bool),

("embedding", c_bool),

("offload_kqv", c_bool),

("do_pooling", 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, c_int, c_char_p, 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(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.c_void_p): pointer to importance matrix data

"""

_fields_ = [

("nthread", c_int32),

("ftype", c_int),

("allow_requantize", c_bool),

("quantize_output_tensor", c_bool),

("only_copy", c_bool),

("pure", c_bool),

("imatrix", c_void_p),

]

# // grammar types

# struct llama_grammar;

llama_grammar_p = 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(Structure):

_fields_ = [

("type", c_int),

("value", c_uint32),

]

llama_grammar_element_p = 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(Structure):

_fields_ = [

("t_start_ms", c_double),

("t_end_ms", c_double),

("t_load_ms", c_double),

("t_sample_ms", c_double),

("t_p_eval_ms", c_double),

("t_eval_ms", c_double),

("n_sample", c_int32),

("n_p_eval", c_int32),

("n_eval", c_int32),

]

# // used in chat template

# typedef struct llama_chat_message {

# const char * role;

# const char * content;

# } llama_chat_message;

class llama_chat_message(Structure):

_fields_ = [

("role", c_char_p),

("content", 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"""

return _lib.llama_model_default_params()

_lib.llama_model_default_params.argtypes = []

_lib.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"""

return _lib.llama_context_default_params()

_lib.llama_context_default_params.argtypes = []

_lib.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"""

return _lib.llama_model_quantize_default_params()

_lib.llama_model_quantize_default_params.argtypes = []

_lib.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"""

return _lib.llama_backend_init()

_lib.llama_backend_init.argtypes = []

_lib.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):

return _lib.llama_numa_init(numa)

_lib.llama_numa_init.argtypes = [c_int]

_lib.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"""

return _lib.llama_backend_free()

_lib.llama_backend_free.argtypes = []

_lib.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

) -> llama_model_p:

return _lib.llama_load_model_from_file(path_model, params)

_lib.llama_load_model_from_file.argtypes = [c_char_p, llama_model_params]

_lib.llama_load_model_from_file.restype = llama_model_p

# LLAMA_API void llama_free_model(struct llama_model * model);

def llama_free_model(model: llama_model_p):

return _lib.llama_free_model(model)

_lib.llama_free_model.argtypes = [llama_model_p]

_lib.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

) -> llama_context_p:

return _lib.llama_new_context_with_model(model, params)

_lib.llama_new_context_with_model.argtypes = [llama_model_p, llama_context_params]

_lib.llama_new_context_with_model.restype = llama_context_p

# // Frees all allocated memory

# LLAMA_API void llama_free(struct llama_context * ctx);

def llama_free(ctx: llama_context_p):

"""Frees all allocated memory"""

return _lib.llama_free(ctx)

_lib.llama_free.argtypes = [llama_context_p]

_lib.llama_free.restype = None

# LLAMA_API int64_t llama_time_us(void);

def llama_time_us() -> int:

return _lib.llama_time_us()

_lib.llama_time_us.argtypes = []

_lib.llama_time_us.restype = ctypes.c_int64

# LLAMA_API size_t llama_max_devices(void);

def llama_max_devices() -> int:

return _lib.llama_max_devices()

_lib.llama_max_devices.argtypes = []

_lib.llama_max_devices.restype = ctypes.c_size_t

# LLAMA_API bool llama_supports_mmap (void);

def llama_supports_mmap() -> bool:

return _lib.llama_supports_mmap()

_lib.llama_supports_mmap.argtypes = []

_lib.llama_supports_mmap.restype = c_bool

# LLAMA_API bool llama_supports_mlock (void);

def llama_supports_mlock() -> bool:

return _lib.llama_supports_mlock()

_lib.llama_supports_mlock.argtypes = []

_lib.llama_supports_mlock.restype = c_bool

# LLAMA_API bool llama_supports_gpu_offload(void);

def llama_supports_gpu_offload() -> bool:

return _lib.llama_supports_gpu_offload()

_lib.llama_supports_gpu_offload.argtypes = []

_lib.llama_supports_gpu_offload.restype = c_bool

# LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead");

def llama_mmap_supported() -> bool:

return _lib.llama_mmap_supported()

_lib.llama_mmap_supported.argtypes = []

_lib.llama_mmap_supported.restype = c_bool

# LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead");

def llama_mlock_supported() -> bool:

return _lib.llama_mlock_supported()

_lib.llama_mlock_supported.argtypes = []

_lib.llama_mlock_supported.restype = c_bool

# LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);

def llama_get_model(ctx: llama_context_p) -> llama_model_p:

return _lib.llama_get_model(ctx)

_lib.llama_get_model.argtypes = [llama_context_p]

_lib.llama_get_model.restype = llama_model_p

# LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);

def llama_n_ctx(ctx: llama_context_p) -> int:

return _lib.llama_n_ctx(ctx)

_lib.llama_n_ctx.argtypes = [llama_context_p]

_lib.llama_n_ctx.restype = c_uint32

# LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);

def llama_n_batch(ctx: llama_context_p) -> int:

return _lib.llama_n_batch(ctx)

_lib.llama_n_batch.argtypes = [llama_context_p]

_lib.llama_n_batch.restype = 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:

return _lib.llama_vocab_type(model)

_lib.llama_vocab_type.argtypes = [llama_model_p]

_lib.llama_vocab_type.restype = c_int

# LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);

def llama_n_vocab(model: llama_model_p) -> int:

return _lib.llama_n_vocab(model)

_lib.llama_n_vocab.argtypes = [llama_model_p]

_lib.llama_n_vocab.restype = 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:

return _lib.llama_n_ctx_train(model)

_lib.llama_n_ctx_train.argtypes = [llama_model_p]

_lib.llama_n_ctx_train.restype = c_int32

# LLAMA_API int32_t llama_n_embd (const struct llama_model * model);

def llama_n_embd(model: llama_model_p) -> int:

return _lib.llama_n_embd(model)

_lib.llama_n_embd.argtypes = [llama_model_p]

_lib.llama_n_embd.restype = 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"""

return _lib.llama_rope_freq_scale_train(model)

_lib.llama_rope_freq_scale_train.argtypes = [llama_model_p]

_lib.llama_rope_freq_scale_train.restype = c_float

# // 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

# // - 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);

def llama_model_meta_val_str(

model: llama_model_p, key: Union[c_char_p, bytes], buf: bytes, buf_size: int

) -> int:

"""Get metadata value as a string by key name"""

return _lib.llama_model_meta_val_str(model, key, buf, buf_size)

_lib.llama_model_meta_val_str.argtypes = [llama_model_p, c_char_p, c_char_p, c_size_t]

_lib.llama_model_meta_val_str.restype = c_int32

# // Get the number of metadata key/value pairs

# LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);

def llama_model_meta_count(model: llama_model_p) -> int:

"""Get the number of metadata key/value pairs"""

return _lib.llama_model_meta_count(model)

_lib.llama_model_meta_count.argtypes = [llama_model_p]

_lib.llama_model_meta_count.restype = c_int32

# // 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);

def llama_model_meta_key_by_index(

model: llama_model_p, i: Union[c_int, int], buf: bytes, buf_size: int

) -> int:

"""Get metadata key name by index"""

return _lib.llama_model_meta_key_by_index(model, i, buf, buf_size)

_lib.llama_model_meta_key_by_index.argtypes = [

llama_model_p,

c_int32,

c_char_p,

c_size_t,

]

_lib.llama_model_meta_key_by_index.restype = c_int32

# // 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);

def llama_model_meta_val_str_by_index(

model: llama_model_p, i: Union[c_int, int], buf: bytes, buf_size: int

) -> int:

"""Get metadata value as a string by index"""

return _lib.llama_model_meta_val_str_by_index(model, i, buf, buf_size)