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1925 lines (1482 loc) · 61.6 KB
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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",
]
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
# 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;
# 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)),
("seq_id", 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
# // 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
# // Keep the booleans together to avoid misalignment during copy-by-value.
# bool mul_mat_q; // if true, use experimental mul_mat_q kernels
# 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_freq_base", c_float),
("rope_freq_scale", c_float),
("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
# } 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_context_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,
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
# // Remove all tokens data of cells in [c0, c1)
# // c0 < 0 : [0, c1]
# // c1 < 0 : [c0, inf)
# LLAMA_API void llama_kv_cache_tokens_rm(
# struct llama_context * ctx,
# int32_t c0,
# int32_t c1);
def llama_kv_cache_tokens_rm(
ctx: llama_context_p, c0: Union[c_int32, int], c1: Union[c_int32, int]
):
return _lib.llama_kv_cache_tokens_rm(ctx, c0, c1)
_lib.llama_kv_cache_tokens_rm.argtypes = [llama_context_p, c_int32, c_int32]
_lib.llama_kv_cache_tokens_rm.restype = None
# // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
# // p0 < 0 : [0, p1]
# // p1 < 0 : [p0, inf)
# LLAMA_API void llama_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: llama_seq_id,
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,
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: llama_seq_id,
seq_id_dst: llama_seq_id,
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_seq_id,
llama_pos,
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: llama_seq_id,
):
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: llama_seq_id,
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,
llama_pos,
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);
def llama_save_session_file(
ctx: llama_context_p,
path_session: bytes,
tokens, # type: Array[llama_token]
n_token_count: Union[c_size_t, int],
) -> int:
return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)
_lib.llama_save_session_file.argtypes = [
llama_context_p,
c_char_p,
llama_token_p,
c_size_t,
]
_lib.llama_save_session_file.restype = c_size_t
# //
# // Decoding
# //
# // Run the llama inference to obtain the logits and probabilities for the next token(s).