llama-cpp-python/llama_cpp/llama_cpp.py at main · furyhawk/llama-cpp-python · GitHub

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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_size_t,

c_float,

c_double,

c_void_p,

POINTER,

_Pointer, # type: ignore

Structure,

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"))

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)

# llama.h bindings

GGML_USE_CUBLAS = hasattr(_lib, "ggml_init_cublas")

GGML_CUDA_MAX_DEVICES = 16

LLAMA_MAX_DEVICES = GGML_CUDA_MAX_DEVICES if GGML_USE_CUBLAS else 1

# 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_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 2

LLAMA_SESSION_VERSION = 2

# 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_SPM = 0

LLAMA_VOCAB_TYPE_BPE = 1

# 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_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_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

# 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):

_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):

_fields_ = [

("data", llama_token_data_p),

("size", c_size_t),

("sorted", c_bool),

]

llama_token_data_array_p = POINTER(llama_token_data_array)

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

llama_progress_callback = ctypes.CFUNCTYPE(None, 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):

_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),

]

# struct llama_model_params {

# int32_t n_gpu_layers; // number of layers to store in VRAM

# int32_t main_gpu; // the GPU that is used for scratch and small tensors

# const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)

# // called with a progress value between 0 and 1, pass NULL to disable

# llama_progress_callback progress_callback;

# // context pointer passed to the progress callback

# void * progress_callback_user_data;

# // 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):

_fields_ = [

("n_gpu_layers", c_int32),

("main_gpu", c_int32),

("tensor_split", c_float_p),

("progress_callback", llama_progress_callback),

("progress_callback_user_data", c_void_p),

("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

# int8_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, NaN = 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

# // 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 f16_kv; // use fp16 for KV cache, fp32 otherwise

# bool logits_all; // the llama_eval() call computes all logits, not just the last one

# bool embedding; // embedding mode only

# };

class llama_context_params(Structure):

_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_int8),

("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),

("mul_mat_q", c_bool),

("f16_kv", c_bool),

("logits_all", c_bool),

("embedding", 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)

# // model quantization parameters

# typedef struct llama_model_quantize_params {

# int 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

# } llama_model_quantize_params;

class llama_model_quantize_params(Structure):

_fields_ = [

("nthread", c_int),

("ftype", c_int),

("allow_requantize", c_bool),

("quantize_output_tensor", c_bool),

("only_copy", c_bool),

]

# // 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),

]

# // Helpers for getting default parameters

# LLAMA_API struct llama_model_params llama_model_default_params(void);

def llama_model_default_params() -> llama_model_params:

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:

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:

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);

def llama_backend_init(numa: Union[c_bool, bool]):

return _lib.llama_backend_init(numa)

_lib.llama_backend_init.argtypes = [c_bool]

_lib.llama_backend_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():

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):

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 int llama_max_devices (void);

def llama_max_devices() -> int:

return _lib.llama_max_devices()

_lib.llama_max_devices.argtypes = []

_lib.llama_max_devices.restype = c_int

# LLAMA_API bool llama_mmap_supported (void);

def llama_mmap_supported() -> bool:

return _lib.llama_mmap_supported()

_lib.llama_mmap_supported.argtypes = []

_lib.llama_mmap_supported.restype = c_bool

# LLAMA_API bool llama_mlock_supported(void);

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 int 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_int

# 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 int 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_int

# LLAMA_API int 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_int

# LLAMA_API int 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_int

# // 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:

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

# // Get a string describing the model type

# LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);

def llama_model_desc(

model: llama_model_p, buf: bytes, buf_size: Union[c_size_t, int]

) -> int:

return _lib.llama_model_desc(model, buf, buf_size)

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

_lib.llama_model_desc.restype = c_int

# // Returns the total size of all the tensors in the model in bytes

# LLAMA_API uint64_t llama_model_size(const struct llama_model * model);

def llama_model_size(model: llama_model_p) -> int:

return _lib.llama_model_size(model)

_lib.llama_model_size.argtypes = [llama_model_p]

_lib.llama_model_size.restype = ctypes.c_uint64

# // Returns the total number of parameters in the model

# LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);

def llama_model_n_params(model: llama_model_p) -> int:

return _lib.llama_model_n_params(model)

_lib.llama_model_n_params.argtypes = [llama_model_p]

_lib.llama_model_n_params.restype = ctypes.c_uint64

# // Get a llama model tensor

# LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);

def llama_get_model_tensor(

model: llama_model_p, name: Union[c_char_p, bytes]

) -> c_void_p:

return _lib.llama_get_model_tensor(model, name)

_lib.llama_get_model_tensor.argtypes = [llama_model_p, c_char_p]

_lib.llama_get_model_tensor.restype = c_void_p

# // Returns 0 on success

# LLAMA_API int llama_model_quantize(

# const char * fname_inp,

# const char * fname_out,

# const llama_model_quantize_params * params);

def llama_model_quantize(

fname_inp: bytes,

fname_out: bytes,

params, # type: POINTER(llama_model_quantize_params) # type: ignore

) -> int:

return _lib.llama_model_quantize(fname_inp, fname_out, params)

_lib.llama_model_quantize.argtypes = [

c_char_p,

POINTER(llama_model_quantize_params),

]

_lib.llama_model_quantize.restype = c_int

# // Apply a LoRA adapter to a loaded model

# // path_base_model is the path to a higher quality model to use as a base for

# // the layers modified by the adapter. Can be NULL to use the current loaded model.

# // The model needs to be reloaded before applying a new adapter, otherwise the adapter

# // will be applied on top of the previous one

# // Returns 0 on success

# LLAMA_API DEPRECATED(int llama_apply_lora_from_file(

# struct llama_context * ctx,

# const char * path_lora,

# float scale,

# const char * path_base_model,

# int n_threads),

# "use llama_model_apply_lora_from_file instead");

def llama_apply_lora_from_file(

ctx: llama_context_p,

path_lora: Union[c_char_p, bytes],

scale: Union[c_float, float],

path_base_model: Union[c_char_p, bytes],

n_threads: Union[c_int, int],

) -> int:

return _lib.llama_apply_lora_from_file(

ctx, path_lora, scale, path_base_model, n_threads

)

_lib.llama_apply_lora_from_file.argtypes = [

llama_context_p,

c_char_p,

c_float,

c_char_p,

c_int,

]

_lib.llama_apply_lora_from_file.restype = c_int

# LLAMA_API int llama_model_apply_lora_from_file(

# const struct llama_model * model,

# const char * path_lora,

# float scale,

# const char * path_base_model,

# int n_threads);

def llama_model_apply_lora_from_file(

model: llama_model_p,

path_lora: Union[c_char_p, bytes],

scale: Union[c_float, float],

path_base_model: Union[c_char_p, bytes],

n_threads: Union[c_int, int],

) -> int:

return _lib.llama_model_apply_lora_from_file(

model, path_lora, scale, path_base_model, n_threads

)

_lib.llama_model_apply_lora_from_file.argtypes = [

llama_model_p,

c_char_p,

c_float,

c_char_p,

c_int,

]

_lib.llama_model_apply_lora_from_file.restype = c_int

# //

# // KV cache

# //

# // Returns the number of tokens in the KV cache

# LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),

# "avoid using this, it will be removed in the future, instead - count the tokens in user code");

def llama_get_kv_cache_token_count(ctx: llama_context_p) -> int:

return _lib.llama_get_kv_cache_token_count(ctx)

_lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p]

_lib.llama_get_kv_cache_token_count.restype = c_int

# // Clear the KV cache

# LLAMA_API void llama_kv_cache_clear(

# struct llama_context * ctx);

def llama_kv_cache_clear(ctx: llama_context_p):

return _lib.llama_kv_cache_clear(ctx)

_lib.llama_kv_cache_clear.argtypes = [llama_context_p]

_lib.llama_kv_cache_clear.restype = None

# // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)

# // seq_id < 0 : match any sequence

# // p0 < 0 : [0, p1]

# // p1 < 0 : [p0, inf)

# LLAMA_API void llama_kv_cache_seq_rm(

# struct llama_context * ctx,

# llama_seq_id seq_id,

# llama_pos p0,

# llama_pos p1);

def llama_kv_cache_seq_rm(

ctx: llama_context_p,

seq_id: Union[llama_seq_id, int],

p0: Union[llama_pos, int],

p1: Union[llama_pos, int],

return _lib.llama_kv_cache_seq_rm(ctx, seq_id, p0, p1)

_lib.llama_kv_cache_seq_rm.argtypes = [

llama_context_p,

llama_seq_id,

llama_pos,

]

_lib.llama_kv_cache_seq_rm.restype = None

# // Copy all tokens that belong to the specified sequence to another sequence

# // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence

# // p0 < 0 : [0, p1]

# // p1 < 0 : [p0, inf)

# LLAMA_API void llama_kv_cache_seq_cp(

# struct llama_context * ctx,

# llama_seq_id seq_id_src,

# llama_seq_id seq_id_dst,

# llama_pos p0,

# llama_pos p1);

def llama_kv_cache_seq_cp(

ctx: llama_context_p,

seq_id_src: Union[llama_seq_id, int],

seq_id_dst: Union[llama_seq_id, int],

p0: Union[llama_pos, int],

p1: Union[llama_pos, int],

return _lib.llama_kv_cache_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1)

_lib.llama_kv_cache_seq_cp.argtypes = [

llama_context_p,

llama_seq_id,

llama_pos,

]

_lib.llama_kv_cache_seq_cp.restype = None

# // Removes all tokens that do not belong to the specified sequence

# LLAMA_API void llama_kv_cache_seq_keep(

# struct llama_context * ctx,

# llama_seq_id seq_id);

def llama_kv_cache_seq_keep(

ctx: llama_context_p,

seq_id: Union[llama_seq_id, int],

return _lib.llama_kv_cache_seq_keep(ctx, seq_id)

_lib.llama_kv_cache_seq_keep.argtypes = [llama_context_p, llama_seq_id]

_lib.llama_kv_cache_seq_keep.restype = None

# // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)

# // If the KV cache is RoPEd, the KV data is updated accordingly

# // p0 < 0 : [0, p1]

# // p1 < 0 : [p0, inf)

# LLAMA_API void llama_kv_cache_seq_shift(

# struct llama_context * ctx,

# llama_seq_id seq_id,

# llama_pos p0,

# llama_pos p1,

# llama_pos delta);

def llama_kv_cache_seq_shift(

ctx: llama_context_p,

seq_id: Union[llama_seq_id, int],

p0: Union[llama_pos, int],

p1: Union[llama_pos, int],

delta: Union[llama_pos, int],

return _lib.llama_kv_cache_seq_shift(ctx, seq_id, p0, p1, delta)

_lib.llama_kv_cache_seq_shift.argtypes = [

llama_context_p,

llama_seq_id,

llama_pos,

]

_lib.llama_kv_cache_seq_shift.restype = None

# //

# // State / sessions

# //

# Returns the maximum size in bytes of the state (rng, logits, embedding

# and kv_cache) - will often be smaller after compacting tokens

# LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);

def llama_get_state_size(ctx: llama_context_p) -> int:

return _lib.llama_get_state_size(ctx)

_lib.llama_get_state_size.argtypes = [llama_context_p]

_lib.llama_get_state_size.restype = c_size_t

# Copies the state to the specified destination address.

# Destination needs to have allocated enough memory.

# Returns the number of bytes copied

# LLAMA_API size_t llama_copy_state_data(

# struct llama_context * ctx,

# uint8_t * dst);

def llama_copy_state_data(

ctx: llama_context_p, dst # type: Array[c_uint8]

) -> int:

return _lib.llama_copy_state_data(ctx, dst)

_lib.llama_copy_state_data.argtypes = [llama_context_p, c_uint8_p]

_lib.llama_copy_state_data.restype = c_size_t

# Set the state reading from the specified address

# Returns the number of bytes read

# LLAMA_API size_t llama_set_state_data(

# struct llama_context * ctx,

# uint8_t * src);

def llama_set_state_data(

ctx: llama_context_p, src # type: Array[c_uint8]

) -> int:

return _lib.llama_set_state_data(ctx, src)

_lib.llama_set_state_data.argtypes = [llama_context_p, c_uint8_p]

_lib.llama_set_state_data.restype = c_size_t

# Save/load session file

# LLAMA_API bool llama_load_session_file(

# struct llama_context * ctx,

# const char * path_session,

# llama_token * tokens_out,

# size_t n_token_capacity,

# size_t * n_token_count_out);

def llama_load_session_file(

ctx: llama_context_p,

path_session: bytes,

tokens_out, # type: Array[llama_token]

n_token_capacity: Union[c_size_t, int],

n_token_count_out, # type: _Pointer[c_size_t]

) -> int:

return _lib.llama_load_session_file(

ctx, path_session, tokens_out, n_token_capacity, n_token_count_out

)

_lib.llama_load_session_file.argtypes = [

llama_context_p,

c_char_p,

llama_token_p,

c_size_t,

c_size_t_p,

]

_lib.llama_load_session_file.restype = c_size_t

# LLAMA_API bool llama_save_session_file(

# struct llama_context * ctx,

# const char * path_session,

# const llama_token * tokens,

# size_t n_token_count);

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