2869 lines
102 KiB
Python
2869 lines
102 KiB
Python
from __future__ import annotations
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import sys
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import os
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import ctypes
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import functools
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import pathlib
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from typing import (
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Any,
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Callable,
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List,
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Union,
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NewType,
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Optional,
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TYPE_CHECKING,
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TypeVar,
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Generic,
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)
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from typing_extensions import TypeAlias
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# Load the library
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def _load_shared_library(lib_base_name: str):
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# Construct the paths to the possible shared library names
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_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__)))
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# Searching for the library in the current directory under the name "libllama" (default name
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# for llamacpp) and "llama" (default name for this repo)
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_lib_paths: List[pathlib.Path] = []
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# Determine the file extension based on the platform
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if sys.platform.startswith("linux"):
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_lib_paths += [
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_base_path / f"lib{lib_base_name}.so",
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]
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elif sys.platform == "darwin":
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_lib_paths += [
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_base_path / f"lib{lib_base_name}.so",
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_base_path / f"lib{lib_base_name}.dylib",
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]
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elif sys.platform == "win32":
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_lib_paths += [
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_base_path / f"{lib_base_name}.dll",
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_base_path / f"lib{lib_base_name}.dll",
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]
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else:
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raise RuntimeError("Unsupported platform")
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if "LLAMA_CPP_LIB" in os.environ:
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lib_base_name = os.environ["LLAMA_CPP_LIB"]
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_lib = pathlib.Path(lib_base_name)
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_base_path = _lib.parent.resolve()
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_lib_paths = [_lib.resolve()]
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cdll_args = dict() # type: ignore
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# Add the library directory to the DLL search path on Windows (if needed)
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if sys.platform == "win32" and sys.version_info >= (3, 8):
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os.add_dll_directory(str(_base_path))
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if "CUDA_PATH" in os.environ:
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os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "bin"))
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os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "lib"))
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if "HIP_PATH" in os.environ:
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os.add_dll_directory(os.path.join(os.environ["HIP_PATH"], "bin"))
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os.add_dll_directory(os.path.join(os.environ["HIP_PATH"], "lib"))
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cdll_args["winmode"] = ctypes.RTLD_GLOBAL
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# Try to load the shared library, handling potential errors
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for _lib_path in _lib_paths:
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if _lib_path.exists():
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try:
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return ctypes.CDLL(str(_lib_path), **cdll_args) # type: ignore
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except Exception as e:
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raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}")
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raise FileNotFoundError(
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f"Shared library with base name '{lib_base_name}' not found"
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)
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# Specify the base name of the shared library to load
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_lib_base_name = "llama"
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# Load the library
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_lib = _load_shared_library(_lib_base_name)
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# ctypes sane type hint helpers
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#
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# - Generic Pointer and Array types
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# - PointerOrRef type with a type hinted byref function
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#
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# NOTE: Only use these for static type checking not for runtime checks
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# no good will come of that
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if TYPE_CHECKING:
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CtypesCData = TypeVar("CtypesCData", bound=ctypes._CData) # type: ignore
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CtypesArray: TypeAlias = ctypes.Array[CtypesCData] # type: ignore
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CtypesPointer: TypeAlias = ctypes._Pointer[CtypesCData] # type: ignore
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CtypesVoidPointer: TypeAlias = ctypes.c_void_p
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class CtypesRef(Generic[CtypesCData]):
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pass
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CtypesPointerOrRef: TypeAlias = Union[
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CtypesPointer[CtypesCData], CtypesRef[CtypesCData]
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]
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CtypesFuncPointer: TypeAlias = ctypes._FuncPointer # type: ignore
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F = TypeVar("F", bound=Callable[..., Any])
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def ctypes_function_for_shared_library(lib: ctypes.CDLL):
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def ctypes_function(
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name: str, argtypes: List[Any], restype: Any, enabled: bool = True
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):
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def decorator(f: F) -> F:
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if enabled:
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func = getattr(lib, name)
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func.argtypes = argtypes
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func.restype = restype
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functools.wraps(f)(func)
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return func
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else:
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return f
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return decorator
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return ctypes_function
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ctypes_function = ctypes_function_for_shared_library(_lib)
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def byref(obj: CtypesCData, offset: Optional[int] = None) -> CtypesRef[CtypesCData]:
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"""Type-annotated version of ctypes.byref"""
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...
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byref = ctypes.byref # type: ignore
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# from ggml-backend.h
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# typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
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ggml_backend_sched_eval_callback = ctypes.CFUNCTYPE(
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ctypes.c_bool, ctypes.c_void_p, ctypes.c_bool, ctypes.c_void_p
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)
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# // Abort callback
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# // If not NULL, called before ggml computation
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# // If it returns true, the computation is aborted
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# typedef bool (*ggml_abort_callback)(void * data);
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ggml_abort_callback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_void_p)
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# llama.h bindings
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_lib.llama_max_devices.argtypes = []
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_lib.llama_max_devices.restype = ctypes.c_size_t
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LLAMA_MAX_DEVICES = _lib.llama_max_devices()
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# define LLAMA_DEFAULT_SEED 0xFFFFFFFF
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LLAMA_DEFAULT_SEED = 0xFFFFFFFF
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# define LLAMA_MAX_RNG_STATE (64*1024)
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LLAMA_MAX_RNG_STATE = 64 * 1024
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# define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
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LLAMA_FILE_MAGIC_GGLA = 0x67676C61
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# define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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LLAMA_FILE_MAGIC_GGSN = 0x6767736E
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# define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
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LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
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# define LLAMA_SESSION_VERSION 4
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LLAMA_SESSION_VERSION = 4
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# struct llama_model;
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llama_model_p = NewType("llama_model_p", int)
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llama_model_p_ctypes = ctypes.c_void_p
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# struct llama_context;
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llama_context_p = NewType("llama_context_p", int)
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llama_context_p_ctypes = ctypes.c_void_p
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# typedef int32_t llama_pos;
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llama_pos = ctypes.c_int32
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# typedef int32_t llama_token;
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llama_token = ctypes.c_int32
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llama_token_p = ctypes.POINTER(llama_token)
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# typedef int32_t llama_seq_id;
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llama_seq_id = ctypes.c_int32
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# enum llama_vocab_type {
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# LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
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# LLAMA_VOCAB_TYPE_SPM = 1, // SentencePiece
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# LLAMA_VOCAB_TYPE_BPE = 2, // Byte Pair Encoding
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# LLAMA_VOCAB_TYPE_WPM = 3, // WordPiece
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# };
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LLAMA_VOCAB_TYPE_NONE = 0
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LLAMA_VOCAB_TYPE_SPM = 1
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LLAMA_VOCAB_TYPE_BPE = 2
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LLAMA_VOCAB_TYPE_WPM = 3
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# // note: these values should be synchronized with ggml_rope
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# // TODO: maybe move this enum to ggml.h (ggml_rope_type)
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# enum llama_rope_type {
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# LLAMA_ROPE_TYPE_NONE = -1,
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# LLAMA_ROPE_TYPE_NORM = 0,
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# LLAMA_ROPE_TYPE_NEOX = 2,
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# LLAMA_ROPE_TYPE_GLM = 4,
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# };
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LLAMA_ROPE_TYPE_NONE = -1
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LLAMA_ROPE_TYPE_NORM = 0
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LLAMA_ROPE_TYPE_NEOX = 2
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LLAMA_ROPE_TYPE_GLM = 4
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# enum llama_token_type {
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# LLAMA_TOKEN_TYPE_UNDEFINED = 0,
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# LLAMA_TOKEN_TYPE_NORMAL = 1,
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# LLAMA_TOKEN_TYPE_UNKNOWN = 2,
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# LLAMA_TOKEN_TYPE_CONTROL = 3,
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# LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
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# LLAMA_TOKEN_TYPE_UNUSED = 5,
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# LLAMA_TOKEN_TYPE_BYTE = 6,
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# };
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LLAMA_TOKEN_TYPE_UNDEFINED = 0
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LLAMA_TOKEN_TYPE_NORMAL = 1
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LLAMA_TOKEN_TYPE_UNKNOWN = 2
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LLAMA_TOKEN_TYPE_CONTROL = 3
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LLAMA_TOKEN_TYPE_USER_DEFINED = 4
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LLAMA_TOKEN_TYPE_UNUSED = 5
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LLAMA_TOKEN_TYPE_BYTE = 6
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# // model file types
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# enum llama_ftype {
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# LLAMA_FTYPE_ALL_F32 = 0,
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# LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
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# // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
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# // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
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# LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q3_K_S = 11, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q3_K_M = 12, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q3_K_L = 13, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q4_K_S = 14, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q4_K_M = 15, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors
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||
# LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
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# LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
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||
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# LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
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||
# };
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LLAMA_FTYPE_ALL_F32 = 0
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||
LLAMA_FTYPE_MOSTLY_F16 = 1
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||
LLAMA_FTYPE_MOSTLY_Q4_0 = 2
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||
LLAMA_FTYPE_MOSTLY_Q4_1 = 3
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||
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_IQ3_XS = 22
|
||
LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23
|
||
LLAMA_FTYPE_MOSTLY_IQ1_S = 24
|
||
LLAMA_FTYPE_MOSTLY_IQ4_NL = 25
|
||
LLAMA_FTYPE_MOSTLY_IQ3_S = 26
|
||
LLAMA_FTYPE_MOSTLY_IQ3_M = 27
|
||
LLAMA_FTYPE_MOSTLY_IQ2_S = 28
|
||
LLAMA_FTYPE_MOSTLY_IQ2_M = 29
|
||
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30
|
||
LLAMA_FTYPE_GUESSED = 1024
|
||
|
||
# enum llama_rope_scaling_type {
|
||
# LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
|
||
# LLAMA_ROPE_SCALING_TYPE_NONE = 0,
|
||
# LLAMA_ROPE_SCALING_TYPE_LINEAR = 1,
|
||
# LLAMA_ROPE_SCALING_TYPE_YARN = 2,
|
||
# LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN,
|
||
# };
|
||
LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1
|
||
LLAMA_ROPE_SCALING_TYPE_NONE = 0
|
||
LLAMA_ROPE_SCALING_TYPE_LINEAR = 1
|
||
LLAMA_ROPE_SCALING_TYPE_YARN = 2
|
||
LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN
|
||
|
||
# enum llama_pooling_type {
|
||
# LLAMA_POOLING_TYPE_UNSPECIFIED = -1,
|
||
# LLAMA_POOLING_TYPE_NONE = 0,
|
||
# LLAMA_POOLING_TYPE_MEAN = 1,
|
||
# LLAMA_POOLING_TYPE_CLS = 2,
|
||
# };
|
||
LLAMA_POOLING_TYPE_UNSPECIFIED = -1
|
||
LLAMA_POOLING_TYPE_NONE = 0
|
||
LLAMA_POOLING_TYPE_MEAN = 1
|
||
LLAMA_POOLING_TYPE_CLS = 2
|
||
|
||
# enum llama_split_mode {
|
||
# LLAMA_SPLIT_MODE_NONE = 0, // single GPU
|
||
# LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
|
||
# LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs
|
||
# };
|
||
LLAMA_SPLIT_MODE_NONE = 0
|
||
LLAMA_SPLIT_MODE_LAYER = 1
|
||
LLAMA_SPLIT_MODE_ROW = 2
|
||
|
||
|
||
# typedef struct llama_token_data {
|
||
# llama_token id; // token id
|
||
# float logit; // log-odds of the token
|
||
# float p; // probability of the token
|
||
# } llama_token_data;
|
||
class llama_token_data(ctypes.Structure):
|
||
"""Used to store token data
|
||
|
||
Attributes:
|
||
id (llama_token): token id
|
||
logit (float): log-odds of the token
|
||
p (float): probability of the token"""
|
||
|
||
_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 (and/or the embeddings) 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; // TODO: rename this to "output"
|
||
|
||
|
||
# // 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:
|
||
n_tokens (int): number of tokens
|
||
token (ctypes.Array[llama_token]): the token ids of the input (used when embd is NULL)
|
||
embd (ctypes.Array[ctypes.ctypes.c_float]): token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
|
||
pos (ctypes.Array[ctypes.Array[llama_pos]]): the positions of the respective token in the sequence
|
||
seq_id (ctypes.Array[ctypes.Array[llama_seq_id]]): the sequence to which the respective token belongs
|
||
logits (ctypes.Array[ctypes.ctypes.c_int8]): if zero, the logits for the respective token will not be output
|
||
"""
|
||
|
||
_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_TYPE_INT,
|
||
# LLAMA_KV_OVERRIDE_TYPE_FLOAT,
|
||
# LLAMA_KV_OVERRIDE_TYPE_BOOL,
|
||
# };
|
||
LLAMA_KV_OVERRIDE_TYPE_INT = 0
|
||
LLAMA_KV_OVERRIDE_TYPE_FLOAT = 1
|
||
LLAMA_KV_OVERRIDE_TYPE_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; // logical maximum batch size that can be submitted to llama_decode
|
||
# uint32_t n_ubatch; // physical maximum batch size
|
||
# uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
|
||
# uint32_t n_threads; // number of threads to use for generation
|
||
# uint32_t n_threads_batch; // number of threads to use for batch processing
|
||
|
||
# enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
|
||
# enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id
|
||
# // (ignored if no pooling layer)
|
||
|
||
# // 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
|
||
# float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default)
|
||
|
||
# ggml_backend_sched_eval_callback cb_eval;
|
||
# void * cb_eval_user_data;
|
||
|
||
# enum ggml_type type_k; // data type for K cache
|
||
# enum ggml_type type_v; // data type for V cache
|
||
|
||
# // Keep the booleans together to avoid misalignment during copy-by-value.
|
||
# bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
|
||
# bool embeddings; // if true, extract embeddings (together with logits)
|
||
# bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
|
||
|
||
|
||
# // Abort callback
|
||
# // if it returns true, execution of llama_decode() will be aborted
|
||
# // currently works only with CPU execution
|
||
# ggml_abort_callback abort_callback;
|
||
# void * abort_callback_data;
|
||
# };
|
||
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): logical maximum batch size that can be submitted to llama_decode
|
||
n_ubatch (int): physical maximum batch size
|
||
n_seq_max (int): max number of sequences (i.e. distinct states for recurrent models)
|
||
n_threads (int): number of threads to use for generation
|
||
n_threads_batch (int): number of threads to use for batch processing
|
||
rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type`
|
||
pooling_type (int): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
|
||
rope_freq_base (float): RoPE base frequency, 0 = from model
|
||
rope_freq_scale (float): RoPE frequency scaling factor, 0 = from model
|
||
yarn_ext_factor (float): YaRN extrapolation mix factor, negative = from model
|
||
yarn_attn_factor (float): YaRN magnitude scaling factor
|
||
yarn_beta_fast (float): YaRN low correction dim
|
||
yarn_beta_slow (float): YaRN high correction dim
|
||
yarn_orig_ctx (int): YaRN original context size
|
||
defrag_thold (float): defragment the KV cache if holes/size > thold, < 0 disabled (default)
|
||
cb_eval (ggml_backend_sched_eval_callback): callback for scheduling eval
|
||
cb_eval_user_data (ctypes.ctypes.c_void_p): user data for cb_eval
|
||
type_k (int): data type for K cache
|
||
type_v (int): data type for V cache
|
||
logits_all (bool): the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
|
||
embeddings (bool): if true, extract embeddings (together with logits)
|
||
offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU
|
||
abort_callback (ggml_abort_callback): abort callback if it returns true, execution of llama_decode() will be aborted
|
||
abort_callback_data (ctypes.ctypes.c_void_p): data for abort_callback
|
||
"""
|
||
|
||
_fields_ = [
|
||
("seed", ctypes.c_uint32),
|
||
("n_ctx", ctypes.c_uint32),
|
||
("n_batch", ctypes.c_uint32),
|
||
("n_ubatch", ctypes.c_uint32),
|
||
("n_seq_max", ctypes.c_uint32),
|
||
("n_threads", ctypes.c_uint32),
|
||
("n_threads_batch", ctypes.c_uint32),
|
||
("rope_scaling_type", ctypes.c_int),
|
||
("pooling_type", ctypes.c_int),
|
||
("rope_freq_base", ctypes.c_float),
|
||
("rope_freq_scale", ctypes.c_float),
|
||
("yarn_ext_factor", ctypes.c_float),
|
||
("yarn_attn_factor", ctypes.c_float),
|
||
("yarn_beta_fast", ctypes.c_float),
|
||
("yarn_beta_slow", ctypes.c_float),
|
||
("yarn_orig_ctx", ctypes.c_uint32),
|
||
("defrag_thold", ctypes.c_float),
|
||
("cb_eval", ggml_backend_sched_eval_callback),
|
||
("cb_eval_user_data", ctypes.c_void_p),
|
||
("type_k", ctypes.c_int),
|
||
("type_v", ctypes.c_int),
|
||
("logits_all", ctypes.c_bool),
|
||
("embeddings", ctypes.c_bool),
|
||
("offload_kqv", ctypes.c_bool),
|
||
("abort_callback", ggml_abort_callback),
|
||
("abort_callback_data", 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.
|
||
# typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
|
||
llama_log_callback = ctypes.CFUNCTYPE(
|
||
None, ctypes.c_int, ctypes.c_char_p, ctypes.c_void_p
|
||
)
|
||
"""Signature for logging events
|
||
Note that text includes the new line character at the end for most events.
|
||
If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
|
||
if it exists.
|
||
It might not exist for progress report where '.' is output repeatedly."""
|
||
|
||
|
||
# // model quantization parameters
|
||
# typedef struct llama_model_quantize_params {
|
||
# int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
|
||
# enum llama_ftype ftype; // quantize to this llama_ftype
|
||
# enum ggml_type output_tensor_type; // output tensor type
|
||
# enum ggml_type token_embedding_type; // itoken embeddings tensor type
|
||
# bool allow_requantize; // allow quantizing non-f32/f16 tensors
|
||
# bool quantize_output_tensor; // quantize output.weight
|
||
# bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
||
# bool pure; // quantize all tensors to the default type
|
||
# 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
|
||
output_tensor_type (int): output tensor type
|
||
token_embedding_type (int): itoken embeddings tensor type
|
||
allow_requantize (bool): allow quantizing non-f32/f16 tensors
|
||
quantize_output_tensor (bool): quantize output.weight
|
||
only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
||
pure (bool): quantize all tensors to the default type
|
||
imatrix (ctypes.c_void_p): pointer to importance matrix data
|
||
"""
|
||
|
||
_fields_ = [
|
||
("nthread", ctypes.c_int32),
|
||
("ftype", ctypes.c_int),
|
||
("output_tensor_type", ctypes.c_int),
|
||
("token_embedding_type", ctypes.c_int),
|
||
("allow_requantize", ctypes.c_bool),
|
||
("quantize_output_tensor", ctypes.c_bool),
|
||
("only_copy", ctypes.c_bool),
|
||
("pure", ctypes.c_bool),
|
||
("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);
|
||
@ctypes_function(
|
||
"llama_model_default_params",
|
||
[],
|
||
llama_model_params,
|
||
)
|
||
def llama_model_default_params() -> llama_model_params:
|
||
"""Get default parameters for llama_model"""
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_context_params llama_context_default_params(void);
|
||
@ctypes_function(
|
||
"llama_context_default_params",
|
||
[],
|
||
llama_context_params,
|
||
)
|
||
def llama_context_default_params() -> llama_context_params:
|
||
"""Get default parameters for llama_context"""
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
|
||
@ctypes_function(
|
||
"llama_model_quantize_default_params",
|
||
[],
|
||
llama_model_quantize_params,
|
||
)
|
||
def llama_model_quantize_default_params() -> llama_model_quantize_params:
|
||
"""Get default parameters for llama_model_quantize"""
|
||
...
|
||
|
||
|
||
# // 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);
|
||
@ctypes_function(
|
||
"llama_backend_init",
|
||
[],
|
||
None,
|
||
)
|
||
def llama_backend_init():
|
||
"""Initialize the llama + ggml backend
|
||
If numa is true, use NUMA optimizations
|
||
Call once at the start of the program"""
|
||
...
|
||
|
||
|
||
# // 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);
|
||
@ctypes_function(
|
||
"llama_numa_init",
|
||
[ctypes.c_int],
|
||
None,
|
||
)
|
||
def llama_numa_init(numa: int, /): ...
|
||
|
||
|
||
# // Call once at the end of the program - currently only used for MPI
|
||
# LLAMA_API void llama_backend_free(void);
|
||
@ctypes_function(
|
||
"llama_backend_free",
|
||
[],
|
||
None,
|
||
)
|
||
def llama_backend_free():
|
||
"""Call once at the end of the program - currently only used for MPI"""
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_model * llama_load_model_from_file(
|
||
# const char * path_model,
|
||
# struct llama_model_params params);
|
||
@ctypes_function(
|
||
"llama_load_model_from_file",
|
||
[ctypes.c_char_p, llama_model_params],
|
||
llama_model_p_ctypes,
|
||
)
|
||
def llama_load_model_from_file(
|
||
path_model: bytes, params: llama_model_params, /
|
||
) -> Optional[llama_model_p]: ...
|
||
|
||
|
||
# LLAMA_API void llama_free_model(struct llama_model * model);
|
||
@ctypes_function(
|
||
"llama_free_model",
|
||
[llama_model_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_free_model(model: llama_model_p, /): ...
|
||
|
||
|
||
# LLAMA_API struct llama_context * llama_new_context_with_model(
|
||
# struct llama_model * model,
|
||
# struct llama_context_params params);
|
||
@ctypes_function(
|
||
"llama_new_context_with_model",
|
||
[llama_model_p_ctypes, llama_context_params],
|
||
llama_context_p_ctypes,
|
||
)
|
||
def llama_new_context_with_model(
|
||
model: llama_model_p, params: llama_context_params, /
|
||
) -> Optional[llama_context_p]: ...
|
||
|
||
|
||
# // Frees all allocated memory
|
||
# LLAMA_API void llama_free(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_free",
|
||
[llama_context_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_free(ctx: llama_context_p, /):
|
||
"""Frees all allocated memory"""
|
||
...
|
||
|
||
|
||
# LLAMA_API int64_t llama_time_us(void);
|
||
@ctypes_function(
|
||
"llama_time_us",
|
||
[],
|
||
ctypes.c_int64,
|
||
)
|
||
def llama_time_us() -> int: ...
|
||
|
||
|
||
# LLAMA_API size_t llama_max_devices(void);
|
||
@ctypes_function("llama_max_devices", [], ctypes.c_size_t)
|
||
def llama_max_devices() -> int: ...
|
||
|
||
|
||
# LLAMA_API bool llama_supports_mmap (void);
|
||
@ctypes_function("llama_supports_mmap", [], ctypes.c_bool)
|
||
def llama_supports_mmap() -> bool: ...
|
||
|
||
|
||
# LLAMA_API bool llama_supports_mlock (void);
|
||
@ctypes_function("llama_supports_mlock", [], ctypes.c_bool)
|
||
def llama_supports_mlock() -> bool: ...
|
||
|
||
|
||
# LLAMA_API bool llama_supports_gpu_offload(void);
|
||
@ctypes_function("llama_supports_gpu_offload", [], ctypes.c_bool)
|
||
def llama_supports_gpu_offload() -> bool: ...
|
||
|
||
|
||
# LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
|
||
@ctypes_function("llama_get_model", [llama_context_p_ctypes], llama_model_p_ctypes)
|
||
def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: ...
|
||
|
||
|
||
# LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
|
||
@ctypes_function("llama_n_ctx", [llama_context_p_ctypes], ctypes.c_uint32)
|
||
def llama_n_ctx(ctx: llama_context_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
|
||
@ctypes_function("llama_n_batch", [llama_context_p_ctypes], ctypes.c_uint32)
|
||
def llama_n_batch(ctx: llama_context_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx);
|
||
@ctypes_function("llama_n_ubatch", [llama_context_p_ctypes], ctypes.c_uint32)
|
||
def llama_n_ubatch(ctx: llama_context_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
|
||
@ctypes_function("llama_n_seq_max", [llama_context_p_ctypes], ctypes.c_uint32)
|
||
def llama_n_seq_max(ctx: llama_context_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
|
||
@ctypes_function("llama_vocab_type", [llama_model_p_ctypes], ctypes.c_int)
|
||
def llama_vocab_type(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
|
||
@ctypes_function("llama_rope_type", [llama_model_p_ctypes], ctypes.c_int)
|
||
def llama_rope_type(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
|
||
@ctypes_function("llama_n_vocab", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_n_vocab(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
|
||
@ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_n_ctx_train(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
|
||
@ctypes_function("llama_n_embd", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_n_embd(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API int32_t llama_n_layer (const struct llama_model * model);
|
||
@ctypes_function("llama_n_layer", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_n_layer(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# // Get the model's RoPE frequency scaling factor
|
||
# LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
|
||
@ctypes_function("llama_rope_freq_scale_train", [llama_model_p_ctypes], ctypes.c_float)
|
||
def llama_rope_freq_scale_train(model: llama_model_p, /) -> float:
|
||
"""Get the model's RoPE frequency scaling factor"""
|
||
...
|
||
|
||
|
||
# // 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);
|
||
@ctypes_function(
|
||
"llama_model_meta_val_str",
|
||
[
|
||
llama_model_p_ctypes,
|
||
ctypes.c_char_p,
|
||
ctypes.c_char_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_meta_val_str(
|
||
model: llama_model_p,
|
||
key: Union[ctypes.c_char_p, bytes],
|
||
buf: bytes,
|
||
buf_size: int,
|
||
/,
|
||
) -> int:
|
||
"""Get metadata value as a string by key name"""
|
||
...
|
||
|
||
|
||
# // Get the number of metadata key/value pairs
|
||
# LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);
|
||
@ctypes_function("llama_model_meta_count", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_model_meta_count(model: llama_model_p, /) -> int:
|
||
"""Get the number of metadata key/value pairs"""
|
||
...
|
||
|
||
|
||
# // 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);
|
||
@ctypes_function(
|
||
"llama_model_meta_key_by_index",
|
||
[
|
||
llama_model_p_ctypes,
|
||
ctypes.c_int32,
|
||
ctypes.c_char_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_meta_key_by_index(
|
||
model: llama_model_p,
|
||
i: Union[ctypes.c_int, int],
|
||
buf: Union[bytes, CtypesArray[ctypes.c_char]],
|
||
buf_size: int,
|
||
/,
|
||
) -> int:
|
||
"""Get metadata key name by index"""
|
||
...
|
||
|
||
|
||
# // 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);
|
||
@ctypes_function(
|
||
"llama_model_meta_val_str_by_index",
|
||
[
|
||
llama_model_p_ctypes,
|
||
ctypes.c_int32,
|
||
ctypes.c_char_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_meta_val_str_by_index(
|
||
model: llama_model_p,
|
||
i: Union[ctypes.c_int, int],
|
||
buf: Union[bytes, CtypesArray[ctypes.c_char]],
|
||
buf_size: int,
|
||
/,
|
||
) -> int:
|
||
"""Get metadata value as a string by index"""
|
||
...
|
||
|
||
|
||
# // Get a string describing the model type
|
||
# LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
|
||
@ctypes_function(
|
||
"llama_model_desc",
|
||
[llama_model_p_ctypes, ctypes.c_char_p, ctypes.c_size_t],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_desc(
|
||
model: llama_model_p,
|
||
buf: Union[bytes, CtypesArray[ctypes.c_char]],
|
||
buf_size: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> int:
|
||
"""Get a string describing the model type"""
|
||
...
|
||
|
||
|
||
# // 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);
|
||
@ctypes_function("llama_model_size", [llama_model_p_ctypes], ctypes.c_uint64)
|
||
def llama_model_size(model: llama_model_p, /) -> int:
|
||
"""Returns the total size of all the tensors in the model in bytes"""
|
||
...
|
||
|
||
|
||
# // Returns the total number of parameters in the model
|
||
# LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
|
||
@ctypes_function("llama_model_n_params", [llama_model_p_ctypes], ctypes.c_uint64)
|
||
def llama_model_n_params(model: llama_model_p, /) -> int:
|
||
"""Returns the total number of parameters in the model"""
|
||
...
|
||
|
||
|
||
# // Get a llama model tensor
|
||
# LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
|
||
@ctypes_function(
|
||
"llama_get_model_tensor", [llama_model_p_ctypes, ctypes.c_char_p], ctypes.c_void_p
|
||
)
|
||
def llama_get_model_tensor(
|
||
model: llama_model_p, name: Union[ctypes.c_char_p, bytes], /
|
||
) -> ctypes.c_void_p:
|
||
"""Get a llama model tensor"""
|
||
...
|
||
|
||
|
||
# // Returns 0 on success
|
||
# LLAMA_API uint32_t llama_model_quantize(
|
||
# const char * fname_inp,
|
||
# const char * fname_out,
|
||
# const llama_model_quantize_params * params);
|
||
@ctypes_function(
|
||
"llama_model_quantize",
|
||
[
|
||
ctypes.c_char_p,
|
||
ctypes.c_char_p,
|
||
ctypes.POINTER(llama_model_quantize_params),
|
||
],
|
||
ctypes.c_uint32,
|
||
)
|
||
def llama_model_quantize(
|
||
fname_inp: bytes,
|
||
fname_out: bytes,
|
||
params: CtypesPointerOrRef[llama_model_quantize_params],
|
||
/,
|
||
) -> int:
|
||
"""Returns 0 on success"""
|
||
...
|
||
|
||
|
||
# // 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 int32_t llama_model_apply_lora_from_file(
|
||
# const struct llama_model * model,
|
||
# const char * path_lora,
|
||
# float scale,
|
||
# const char * path_base_model,
|
||
# int32_t n_threads);
|
||
@ctypes_function(
|
||
"llama_model_apply_lora_from_file",
|
||
[
|
||
llama_model_p_ctypes,
|
||
ctypes.c_char_p,
|
||
ctypes.c_float,
|
||
ctypes.c_char_p,
|
||
ctypes.c_int32,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_model_apply_lora_from_file(
|
||
model: llama_model_p,
|
||
path_lora: Union[ctypes.c_char_p, bytes],
|
||
scale: Union[ctypes.c_float, float],
|
||
path_base_model: Union[ctypes.c_char_p, bytes, None],
|
||
n_threads: Union[ctypes.c_int32, int],
|
||
/,
|
||
) -> 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"""
|
||
...
|
||
|
||
|
||
# // Apply a loaded control vector to a llama_context, or if data is NULL, clear
|
||
# // the currently loaded vector.
|
||
# // n_embd should be the size of a single layer's control, and data should point
|
||
# // to an n_embd x n_layers buffer starting from layer 1.
|
||
# // il_start and il_end are the layer range the vector should apply to (both inclusive)
|
||
# // See llama_control_vector_load in common to load a control vector.
|
||
# LLAMA_API int32_t llama_control_vector_apply(
|
||
# struct llama_context * lctx,
|
||
# const float * data,
|
||
# size_t len,
|
||
# int32_t n_embd,
|
||
# int32_t il_start,
|
||
# int32_t il_end);
|
||
@ctypes_function(
|
||
"llama_control_vector_apply",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.POINTER(ctypes.c_float),
|
||
ctypes.c_size_t,
|
||
ctypes.c_int32,
|
||
ctypes.c_int32,
|
||
ctypes.c_int32,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_control_vector_apply(
|
||
lctx: llama_context_p,
|
||
data: CtypesPointerOrRef[ctypes.c_float],
|
||
len: int,
|
||
n_embd: int,
|
||
il_start: int,
|
||
il_end: int,
|
||
/,
|
||
) -> int:
|
||
"""Apply a loaded control vector to a llama_context, or if data is NULL, clear
|
||
the currently loaded vector.
|
||
n_embd should be the size of a single layer's control, and data should point
|
||
to an n_embd x n_layers buffer starting from layer 1.
|
||
il_start and il_end are the layer range the vector should apply to (both inclusive)
|
||
See llama_control_vector_load in common to load a control vector."""
|
||
...
|
||
|
||
|
||
# //
|
||
# // KV cache
|
||
# //
|
||
|
||
|
||
# // Information associated with an individual cell in the KV cache view.
|
||
# struct llama_kv_cache_view_cell {
|
||
# // The position for this cell. Takes KV cache shifts into account.
|
||
# // May be negative if the cell is not populated.
|
||
# llama_pos pos;
|
||
# };
|
||
class llama_kv_cache_view_cell(ctypes.Structure):
|
||
"""Information associated with an individual cell in the KV cache view.
|
||
|
||
Attributes:
|
||
pos (llama_pos): The position for this cell. Takes KV cache shifts into account.
|
||
May be negative if the cell is not populated."""
|
||
|
||
_fields_ = [("pos", llama_pos)]
|
||
|
||
|
||
# // An updateable view of the KV cache.
|
||
# struct llama_kv_cache_view {
|
||
# // Number of KV cache cells. This will be the same as the context size.
|
||
# int32_t n_cells;
|
||
|
||
# // Maximum number of sequences that can exist in a cell. It's not an error
|
||
# // if there are more sequences in a cell than this value, however they will
|
||
# // not be visible in the view cells_sequences.
|
||
# int32_t n_seq_max;
|
||
|
||
# // Number of tokens in the cache. For example, if there are two populated
|
||
# // cells, the first with 1 sequence id in it and the second with 2 sequence
|
||
# // ids then you'll have 3 tokens.
|
||
# int32_t token_count;
|
||
|
||
# // Number of populated cache cells.
|
||
# int32_t used_cells;
|
||
|
||
# // Maximum contiguous empty slots in the cache.
|
||
# int32_t max_contiguous;
|
||
|
||
# // Index to the start of the max_contiguous slot range. Can be negative
|
||
# // when cache is full.
|
||
# int32_t max_contiguous_idx;
|
||
|
||
# // Information for an individual cell.
|
||
# struct llama_kv_cache_view_cell * cells;
|
||
|
||
|
||
# // The sequences for each cell. There will be n_seq_max items per cell.
|
||
# llama_seq_id * cells_sequences;
|
||
# };
|
||
class llama_kv_cache_view(ctypes.Structure):
|
||
_fields_ = [
|
||
("n_cells", ctypes.c_int32),
|
||
("n_max_seq", ctypes.c_int32),
|
||
("token_count", ctypes.c_int32),
|
||
("used_cells", ctypes.c_int32),
|
||
("max_contiguous", ctypes.c_int32),
|
||
("max_contiguous_idx", ctypes.c_int32),
|
||
("cells", ctypes.POINTER(llama_kv_cache_view_cell)),
|
||
("cells_sequences", ctypes.POINTER(llama_seq_id)),
|
||
]
|
||
|
||
|
||
llama_kv_cache_view_p = ctypes.POINTER(llama_kv_cache_view)
|
||
|
||
|
||
# // Create an empty KV cache view. (use only for debugging purposes)
|
||
# LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
|
||
@ctypes_function(
|
||
"llama_kv_cache_view_init",
|
||
[llama_context_p_ctypes, ctypes.c_int32],
|
||
llama_kv_cache_view,
|
||
)
|
||
def llama_kv_cache_view_init(
|
||
ctx: llama_context_p, n_seq_max: Union[ctypes.c_int32, int], /
|
||
) -> llama_kv_cache_view:
|
||
"""Create an empty KV cache view. (use only for debugging purposes)"""
|
||
...
|
||
|
||
|
||
# // Free a KV cache view. (use only for debugging purposes)
|
||
# LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
|
||
@ctypes_function("llama_kv_cache_view_free", [llama_kv_cache_view_p], None)
|
||
def llama_kv_cache_view_free(view: "ctypes.pointer[llama_kv_cache_view]", /): # type: ignore
|
||
"""Free a KV cache view. (use only for debugging purposes)"""
|
||
...
|
||
|
||
|
||
# // Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
|
||
# LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
|
||
@ctypes_function(
|
||
"llama_kv_cache_view_update", [llama_context_p_ctypes, llama_kv_cache_view_p], None
|
||
)
|
||
def llama_kv_cache_view_update(ctx: llama_context_p, view: CtypesPointerOrRef[llama_kv_cache_view], /): # type: ignore
|
||
"""Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)"""
|
||
...
|
||
|
||
|
||
# // Returns the number of tokens in the KV cache (slow, use only for debug)
|
||
# // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
|
||
# LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_get_kv_cache_token_count", [llama_context_p_ctypes], ctypes.c_int32
|
||
)
|
||
def llama_get_kv_cache_token_count(ctx: llama_context_p, /) -> int:
|
||
"""Returns the number of tokens in the KV cache (slow, use only for debug)
|
||
If a KV cell has multiple sequences assigned to it, it will be counted multiple times
|
||
"""
|
||
...
|
||
|
||
|
||
# // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
|
||
# LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_get_kv_cache_used_cells", [llama_context_p_ctypes], ctypes.c_int32
|
||
)
|
||
def llama_get_kv_cache_used_cells(ctx: llama_context_p, /) -> int:
|
||
"""Returns the number of used KV cells (i.e. have at least one sequence assigned to them)"""
|
||
...
|
||
|
||
|
||
# // Clear the KV cache
|
||
# LLAMA_API void llama_kv_cache_clear(
|
||
# struct llama_context * ctx);
|
||
@ctypes_function("llama_kv_cache_clear", [llama_context_p_ctypes], None)
|
||
def llama_kv_cache_clear(ctx: llama_context_p, /):
|
||
"""Clear the KV cache"""
|
||
...
|
||
|
||
|
||
# // 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 bool llama_kv_cache_seq_rm(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1);
|
||
@ctypes_function(
|
||
"llama_kv_cache_seq_rm",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
ctypes.c_bool,
|
||
)
|
||
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],
|
||
/,
|
||
) -> bool:
|
||
"""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)"""
|
||
...
|
||
|
||
|
||
# // 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);
|
||
@ctypes_function(
|
||
"llama_kv_cache_seq_cp",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_seq_id,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
None,
|
||
)
|
||
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],
|
||
/,
|
||
):
|
||
"""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)"""
|
||
...
|
||
|
||
|
||
# // 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);
|
||
@ctypes_function(
|
||
"llama_kv_cache_seq_keep", [llama_context_p_ctypes, llama_seq_id], None
|
||
)
|
||
def llama_kv_cache_seq_keep(ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /):
|
||
"""Removes all tokens that do not belong to the specified sequence"""
|
||
...
|
||
|
||
|
||
# // 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:
|
||
# // - lazily on next llama_decode()
|
||
# // - explicitly with llama_kv_cache_update()
|
||
# // p0 < 0 : [0, p1]
|
||
# // p1 < 0 : [p0, inf)
|
||
# LLAMA_API void llama_kv_cache_seq_add(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1,
|
||
# llama_pos delta);
|
||
@ctypes_function(
|
||
"llama_kv_cache_seq_add",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
llama_pos,
|
||
],
|
||
None,
|
||
)
|
||
def llama_kv_cache_seq_add(
|
||
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],
|
||
/,
|
||
):
|
||
"""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:
|
||
- lazily on next llama_decode()
|
||
- explicitly with llama_kv_cache_update()
|
||
p0 < 0 : [0, p1]
|
||
p1 < 0 : [p0, inf)"""
|
||
...
|
||
|
||
|
||
# // Integer division of the positions by factor of `d > 1`
|
||
# // 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_div(
|
||
# struct llama_context * ctx,
|
||
# llama_seq_id seq_id,
|
||
# llama_pos p0,
|
||
# llama_pos p1,
|
||
# int d);
|
||
@ctypes_function(
|
||
"llama_kv_cache_seq_div",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_seq_id,
|
||
llama_pos,
|
||
llama_pos,
|
||
ctypes.c_int,
|
||
],
|
||
None,
|
||
)
|
||
def llama_kv_cache_seq_div(
|
||
ctx: llama_context_p,
|
||
seq_id: Union[llama_seq_id, int],
|
||
p0: Union[llama_pos, int],
|
||
p1: Union[llama_pos, int],
|
||
d: Union[ctypes.c_int, int],
|
||
/,
|
||
):
|
||
"""Integer division of the positions by factor of `d > 1`
|
||
If the KV cache is RoPEd, the KV data is updated accordingly
|
||
p0 < 0 : [0, p1]
|
||
p1 < 0 : [p0, inf)"""
|
||
...
|
||
|
||
|
||
# // Defragment the KV cache
|
||
# // This will be applied:
|
||
# // - lazily on next llama_decode()
|
||
# // - explicitly with llama_kv_cache_update()
|
||
# LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
|
||
@ctypes_function("llama_kv_cache_defrag", [llama_context_p_ctypes], None)
|
||
def llama_kv_cache_defrag(ctx: llama_context_p, /):
|
||
"""Defragment the KV cache
|
||
This will be applied:
|
||
- lazily on next llama_decode()
|
||
- explicitly with llama_kv_cache_update()"""
|
||
...
|
||
|
||
|
||
# // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
|
||
# LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
|
||
@ctypes_function("llama_kv_cache_update", [llama_context_p_ctypes], None)
|
||
def llama_kv_cache_update(ctx: llama_context_p, /):
|
||
"""Apply the KV cache updates (such as K-shifts, defragmentation, etc.)"""
|
||
...
|
||
|
||
|
||
# //
|
||
# // 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);
|
||
@ctypes_function("llama_get_state_size", [llama_context_p_ctypes], ctypes.c_size_t)
|
||
def llama_get_state_size(ctx: llama_context_p, /) -> int:
|
||
"""Returns the maximum size in bytes of the state (rng, logits, embedding
|
||
and kv_cache) - will often be smaller after compacting tokens"""
|
||
...
|
||
|
||
|
||
# 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);
|
||
@ctypes_function(
|
||
"llama_copy_state_data",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.POINTER(ctypes.c_uint8),
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_copy_state_data(
|
||
ctx: llama_context_p, dst: CtypesArray[ctypes.c_uint8], /
|
||
) -> int:
|
||
"""Copies the state to the specified destination address.
|
||
Destination needs to have allocated enough memory.
|
||
Returns the number of bytes copied"""
|
||
...
|
||
|
||
|
||
# // 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,
|
||
# const uint8_t * src);
|
||
@ctypes_function(
|
||
"llama_set_state_data",
|
||
[llama_context_p_ctypes, ctypes.POINTER(ctypes.c_uint8)],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_set_state_data(
|
||
ctx: llama_context_p, src: CtypesArray[ctypes.c_uint8], /
|
||
) -> int:
|
||
"""Set the state reading from the specified address"""
|
||
...
|
||
|
||
|
||
# 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);
|
||
@ctypes_function(
|
||
"llama_load_session_file",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_char_p,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
ctypes.POINTER(ctypes.c_size_t),
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_load_session_file(
|
||
ctx: llama_context_p,
|
||
path_session: bytes,
|
||
tokens_out: CtypesArray[llama_token],
|
||
n_token_capacity: Union[ctypes.c_size_t, int],
|
||
n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
|
||
/,
|
||
) -> int: ...
|
||
|
||
|
||
# LLAMA_API bool llama_save_session_file(
|
||
# struct llama_context * ctx,
|
||
# const char * path_session,
|
||
# const llama_token * tokens,
|
||
# size_t n_token_count);
|
||
@ctypes_function(
|
||
"llama_save_session_file",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_char_p,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_size_t,
|
||
)
|
||
def llama_save_session_file(
|
||
ctx: llama_context_p,
|
||
path_session: bytes,
|
||
tokens: CtypesArray[llama_token],
|
||
n_token_count: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> int: ...
|
||
|
||
|
||
# //
|
||
# // Decoding
|
||
# //
|
||
|
||
|
||
# // Return batch for single sequence of tokens starting at pos_0
|
||
# //
|
||
# // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
|
||
# //
|
||
# LLAMA_API struct llama_batch llama_batch_get_one(
|
||
# llama_token * tokens,
|
||
# int32_t n_tokens,
|
||
# llama_pos pos_0,
|
||
# llama_seq_id seq_id);
|
||
@ctypes_function(
|
||
"llama_batch_get_one",
|
||
[
|
||
llama_token_p,
|
||
ctypes.c_int,
|
||
llama_pos,
|
||
llama_seq_id,
|
||
],
|
||
llama_batch,
|
||
)
|
||
def llama_batch_get_one(
|
||
tokens: CtypesArray[llama_token],
|
||
n_tokens: Union[ctypes.c_int, int],
|
||
pos_0: Union[llama_pos, int],
|
||
seq_id: llama_seq_id,
|
||
/,
|
||
) -> llama_batch:
|
||
"""Return batch for single sequence of tokens starting at pos_0
|
||
|
||
NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
|
||
"""
|
||
...
|
||
|
||
|
||
# // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
|
||
# // Each token can be assigned up to n_seq_max sequence ids
|
||
# // The batch has to be freed with llama_batch_free()
|
||
# // If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
|
||
# // Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
|
||
# // The rest of the llama_batch members are allocated with size n_tokens
|
||
# // All members are left uninitialized
|
||
# LLAMA_API struct llama_batch llama_batch_init(
|
||
# int32_t n_tokens,
|
||
# int32_t embd,
|
||
# int32_t n_seq_max);
|
||
@ctypes_function(
|
||
"llama_batch_init", [ctypes.c_int32, ctypes.c_int32, ctypes.c_int32], llama_batch
|
||
)
|
||
def llama_batch_init(
|
||
n_tokens: Union[ctypes.c_int32, int],
|
||
embd: Union[ctypes.c_int32, int],
|
||
n_seq_max: Union[ctypes.c_int32, int],
|
||
/,
|
||
) -> llama_batch:
|
||
"""Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
|
||
Each token can be assigned up to n_seq_max sequence ids
|
||
The batch has to be freed with llama_batch_free()
|
||
If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
|
||
Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
|
||
The rest of the llama_batch members are allocated with size n_tokens
|
||
All members are left uninitialized"""
|
||
...
|
||
|
||
|
||
# // Frees a batch of tokens allocated with llama_batch_init()
|
||
# LLAMA_API void llama_batch_free(struct llama_batch batch);
|
||
@ctypes_function("llama_batch_free", [llama_batch], None)
|
||
def llama_batch_free(batch: llama_batch, /):
|
||
"""Frees a batch of tokens allocated with llama_batch_init()"""
|
||
...
|
||
|
||
|
||
# // Positive return values does not mean a fatal error, but rather a warning.
|
||
# // 0 - success
|
||
# // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
|
||
# // < 0 - error
|
||
# LLAMA_API int32_t llama_decode(
|
||
# struct llama_context * ctx,
|
||
# struct llama_batch batch);
|
||
@ctypes_function("llama_decode", [llama_context_p_ctypes, llama_batch], ctypes.c_int32)
|
||
def llama_decode(ctx: llama_context_p, batch: llama_batch, /) -> int:
|
||
"""Positive return values does not mean a fatal error, but rather a warning.
|
||
0 - success
|
||
1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
|
||
< 0 - error"""
|
||
...
|
||
|
||
|
||
# // Set the number of threads used for decoding
|
||
# // n_threads is the number of threads used for generation (single token)
|
||
# // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
|
||
# LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
|
||
@ctypes_function(
|
||
"llama_set_n_threads",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.c_uint32,
|
||
ctypes.c_uint32,
|
||
],
|
||
None,
|
||
)
|
||
def llama_set_n_threads(
|
||
ctx: llama_context_p,
|
||
n_threads: Union[ctypes.c_uint32, int],
|
||
n_threads_batch: Union[ctypes.c_uint32, int],
|
||
/,
|
||
):
|
||
"""Set the number of threads used for decoding
|
||
n_threads is the number of threads used for generation (single token)
|
||
n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
|
||
"""
|
||
...
|
||
|
||
|
||
# // Set whether to use causal attention or not
|
||
# // If set to true, the model will only attend to the past tokens
|
||
# LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
|
||
@ctypes_function("llama_set_causal_attn", [llama_context_p_ctypes, ctypes.c_bool], None)
|
||
def llama_set_causal_attn(ctx: llama_context_p, causal_attn: bool, /):
|
||
"""Set whether to use causal attention or not
|
||
If set to true, the model will only attend to the past tokens"""
|
||
...
|
||
|
||
|
||
# // Set abort callback
|
||
# LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
|
||
@ctypes_function(
|
||
"llama_set_abort_callback",
|
||
[llama_context_p_ctypes, ggml_abort_callback, ctypes.c_void_p],
|
||
None,
|
||
)
|
||
def llama_set_abort_callback(
|
||
ctx: llama_context_p,
|
||
abort_callback: Callable[[ctypes.c_void_p], None],
|
||
abort_callback_data: ctypes.c_void_p,
|
||
/,
|
||
):
|
||
"""Set abort callback"""
|
||
...
|
||
|
||
|
||
# // Wait until all computations are finished
|
||
# // This is automatically done when using one of the functions below to obtain the computation results
|
||
# // and is not necessary to call it explicitly in most cases
|
||
# LLAMA_API void llama_synchronize(struct llama_context * ctx);
|
||
@ctypes_function("llama_synchronize", [llama_context_p_ctypes], None)
|
||
def llama_synchronize(ctx: llama_context_p, /):
|
||
"""Wait until all computations are finished
|
||
This is automatically done when using one of the functions below to obtain the computation results
|
||
and is not necessary to call it explicitly in most cases"""
|
||
...
|
||
|
||
|
||
# // Token logits obtained from the last call to llama_decode()
|
||
# // The logits for the last token are stored in the last row
|
||
# // Logits for which llama_batch.logits[i] == 0 are undefined
|
||
# // Rows: n_tokens provided with llama_batch
|
||
# // Cols: n_vocab
|
||
# LLAMA_API float * llama_get_logits(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_get_logits", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float)
|
||
)
|
||
def llama_get_logits(ctx: llama_context_p, /) -> CtypesArray[ctypes.c_float]:
|
||
"""Token logits obtained from the last call to llama_eval()
|
||
The logits for the last token are stored in the last row
|
||
Logits for which llama_batch.logits[i] == 0 are undefined
|
||
Rows: n_tokens provided with llama_batch
|
||
Cols: n_vocab
|
||
|
||
Returns:
|
||
Pointer to the logits buffer of shape (n_tokens, n_vocab)"""
|
||
...
|
||
|
||
|
||
# // Logits for the ith token. Equivalent to:
|
||
# // llama_get_logits(ctx) + i*n_vocab
|
||
# LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
|
||
@ctypes_function(
|
||
"llama_get_logits_ith",
|
||
[llama_context_p_ctypes, ctypes.c_int32],
|
||
ctypes.POINTER(ctypes.c_float),
|
||
)
|
||
def llama_get_logits_ith(
|
||
ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
|
||
) -> CtypesArray[ctypes.c_float]:
|
||
"""Logits for the ith token. Equivalent to:
|
||
llama_get_logits(ctx) + i*n_vocab"""
|
||
...
|
||
|
||
|
||
# // Get all output token embeddings
|
||
# // shape: [n_tokens*n_embd] (1-dimensional)
|
||
# LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_get_embeddings", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float)
|
||
)
|
||
def llama_get_embeddings(ctx: llama_context_p, /) -> CtypesArray[ctypes.c_float]:
|
||
"""Get the embeddings for the input
|
||
shape: [n_embd] (1-dimensional)"""
|
||
...
|
||
|
||
|
||
# // Get the embeddings for the ith token
|
||
# // llama_get_embeddings(ctx) + i*n_embd
|
||
# // shape: [n_embd] (1-dimensional)
|
||
# LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
|
||
@ctypes_function(
|
||
"llama_get_embeddings_ith",
|
||
[llama_context_p_ctypes, ctypes.c_int32],
|
||
ctypes.POINTER(ctypes.c_float),
|
||
)
|
||
def llama_get_embeddings_ith(
|
||
ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
|
||
) -> CtypesArray[ctypes.c_float]:
|
||
"""Get the embeddings for the ith sequence
|
||
llama_get_embeddings(ctx) + i*n_embd"""
|
||
...
|
||
|
||
|
||
# // Get the embeddings for a sequence id
|
||
# // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
|
||
# // shape: [n_embd] (1-dimensional)
|
||
# LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
|
||
@ctypes_function(
|
||
"llama_get_embeddings_seq",
|
||
[llama_context_p_ctypes, llama_seq_id],
|
||
ctypes.POINTER(ctypes.c_float),
|
||
)
|
||
def llama_get_embeddings_seq(
|
||
ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /
|
||
) -> CtypesArray[ctypes.c_float]:
|
||
"""Get the embeddings for a sequence id
|
||
Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
|
||
shape: [n_embd] (1-dimensional)"""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Vocab
|
||
# //
|
||
|
||
|
||
# LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
|
||
@ctypes_function(
|
||
"llama_token_get_text", [llama_model_p_ctypes, llama_token], ctypes.c_char_p
|
||
)
|
||
def llama_token_get_text(
|
||
model: llama_model_p, token: Union[llama_token, int], /
|
||
) -> bytes: ...
|
||
|
||
|
||
# LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
|
||
@ctypes_function(
|
||
"llama_token_get_score", [llama_model_p_ctypes, llama_token], ctypes.c_float
|
||
)
|
||
def llama_token_get_score(
|
||
model: llama_model_p, token: Union[llama_token, int], /
|
||
) -> float: ...
|
||
|
||
|
||
# LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
|
||
@ctypes_function(
|
||
"llama_token_get_type", [llama_model_p_ctypes, llama_token], ctypes.c_int
|
||
)
|
||
def llama_token_get_type(
|
||
model: llama_model_p, token: Union[llama_token, int], /
|
||
) -> int: ...
|
||
|
||
|
||
# // Special tokens
|
||
|
||
|
||
# LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
|
||
@ctypes_function("llama_token_bos", [llama_model_p_ctypes], llama_token)
|
||
def llama_token_bos(model: llama_model_p, /) -> int:
|
||
"""beginning-of-sentence"""
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
|
||
@ctypes_function("llama_token_eos", [llama_model_p_ctypes], llama_token)
|
||
def llama_token_eos(model: llama_model_p, /) -> int:
|
||
"""end-of-sentence"""
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
|
||
@ctypes_function("llama_token_nl", [llama_model_p_ctypes], llama_token)
|
||
def llama_token_nl(model: llama_model_p, /) -> int:
|
||
"""next-line"""
|
||
...
|
||
|
||
|
||
# // Returns -1 if unknown, 1 for true or 0 for false.
|
||
# LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model);
|
||
@ctypes_function("llama_add_bos_token", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_add_bos_token(model: llama_model_p, /) -> int:
|
||
"""Returns -1 if unknown, 1 for true or 0 for false."""
|
||
...
|
||
|
||
|
||
# // Returns -1 if unknown, 1 for true or 0 for false.
|
||
# LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);
|
||
@ctypes_function("llama_add_eos_token", [llama_model_p_ctypes], ctypes.c_int32)
|
||
def llama_add_eos_token(model: llama_model_p, /) -> int:
|
||
"""Returns -1 if unknown, 1 for true or 0 for false."""
|
||
...
|
||
|
||
|
||
# // codellama infill tokens
|
||
# LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
|
||
@ctypes_function("llama_token_prefix", [llama_model_p_ctypes], llama_token)
|
||
def llama_token_prefix(model: llama_model_p) -> int:
|
||
"""codellama infill tokens"""
|
||
...
|
||
|
||
|
||
# LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
|
||
@ctypes_function("llama_token_middle", [llama_model_p_ctypes], llama_token)
|
||
def llama_token_middle(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
|
||
@ctypes_function("llama_token_suffix", [llama_model_p_ctypes], llama_token)
|
||
def llama_token_suffix(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // End of infill middle
|
||
@ctypes_function("llama_token_eot", [llama_model_p_ctypes], llama_token)
|
||
def llama_token_eot(model: llama_model_p, /) -> int: ...
|
||
|
||
|
||
# //
|
||
# // Tokenization
|
||
# //
|
||
|
||
|
||
# /// @details Convert the provided text into tokens.
|
||
# /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
|
||
# /// @return Returns the number of tokens on success, no more than n_tokens_max
|
||
# /// @return Returns a negative number on failure - the number of tokens that would have been returned
|
||
# /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
|
||
# /// Does not insert a leading space.
|
||
# LLAMA_API int32_t llama_tokenize(
|
||
# const struct llama_model * model,
|
||
# const char * text,
|
||
# int32_t text_len,
|
||
# llama_token * tokens,
|
||
# int32_t n_tokens_max,
|
||
# bool add_bos,
|
||
# bool special);
|
||
@ctypes_function(
|
||
"llama_tokenize",
|
||
[
|
||
llama_model_p_ctypes,
|
||
ctypes.c_char_p,
|
||
ctypes.c_int32,
|
||
llama_token_p,
|
||
ctypes.c_int32,
|
||
ctypes.c_bool,
|
||
ctypes.c_bool,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_tokenize(
|
||
model: llama_model_p,
|
||
text: bytes,
|
||
text_len: Union[ctypes.c_int, int],
|
||
tokens: CtypesArray[llama_token],
|
||
n_tokens_max: Union[ctypes.c_int, int],
|
||
add_bos: Union[ctypes.c_bool, bool],
|
||
special: Union[ctypes.c_bool, bool],
|
||
/,
|
||
) -> int:
|
||
"""Convert the provided text into tokens.
|
||
|
||
Args:
|
||
model: The model to use for tokenization.
|
||
text: The text to tokenize.
|
||
text_len: The length of the text.
|
||
tokens: The tokens pointer must be large enough to hold the resulting tokens.
|
||
n_max_tokens: The maximum number of tokens to return.
|
||
add_bos: Whether to add a beginning-of-sentence token.
|
||
special: Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
|
||
Does not insert a leading space.
|
||
|
||
Returns:
|
||
Returns the number of tokens on success, no more than n_tokens_max
|
||
Returns a negative number on failure - the number of tokens that would have been returned
|
||
"""
|
||
...
|
||
|
||
|
||
# // Token Id -> Piece.
|
||
# // Uses the vocabulary in the provided context.
|
||
# // Does not write null terminator to the buffer.
|
||
# // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||
# LLAMA_API int32_t llama_token_to_piece(
|
||
# const struct llama_model * model,
|
||
# llama_token token,
|
||
# char * buf,
|
||
# int32_t length);
|
||
@ctypes_function(
|
||
"llama_token_to_piece",
|
||
[
|
||
llama_model_p_ctypes,
|
||
llama_token,
|
||
ctypes.c_char_p,
|
||
ctypes.c_int32,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_token_to_piece(
|
||
model: llama_model_p,
|
||
token: Union[llama_token, int],
|
||
buf: Union[ctypes.c_char_p, bytes, CtypesArray[ctypes.c_char]],
|
||
length: Union[ctypes.c_int, int],
|
||
/,
|
||
) -> int:
|
||
"""Token Id -> Piece.
|
||
Uses the vocabulary in the provided context.
|
||
Does not write null terminator to the buffer.
|
||
User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||
"""
|
||
...
|
||
|
||
|
||
# /// Apply chat template. Inspired by hf apply_chat_template() on python.
|
||
# /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
|
||
# /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
|
||
# /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
|
||
# /// @param chat Pointer to a list of multiple llama_chat_message
|
||
# /// @param n_msg Number of llama_chat_message in this chat
|
||
# /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
|
||
# /// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages)
|
||
# /// @param length The size of the allocated buffer
|
||
# /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
|
||
# LLAMA_API int32_t llama_chat_apply_template(
|
||
# const struct llama_model * model,
|
||
# const char * tmpl,
|
||
# const struct llama_chat_message * chat,
|
||
# size_t n_msg,
|
||
# bool add_ass,
|
||
# char * buf,
|
||
# int32_t length);
|
||
@ctypes_function(
|
||
"llama_chat_apply_template",
|
||
[
|
||
ctypes.c_void_p,
|
||
ctypes.c_char_p,
|
||
ctypes.POINTER(llama_chat_message),
|
||
ctypes.c_size_t,
|
||
],
|
||
ctypes.c_int32,
|
||
)
|
||
def llama_chat_apply_template(
|
||
model: llama_model_p,
|
||
tmpl: bytes,
|
||
chat: CtypesArray[llama_chat_message],
|
||
n_msg: int,
|
||
/,
|
||
) -> int: ...
|
||
|
||
|
||
# //
|
||
# // Grammar
|
||
# //
|
||
|
||
|
||
# LLAMA_API struct llama_grammar * llama_grammar_init(
|
||
# const llama_grammar_element ** rules,
|
||
# size_t n_rules,
|
||
# size_t start_rule_index);
|
||
@ctypes_function(
|
||
"llama_grammar_init",
|
||
[
|
||
ctypes.POINTER(llama_grammar_element_p),
|
||
ctypes.c_size_t,
|
||
ctypes.c_size_t,
|
||
],
|
||
llama_grammar_p,
|
||
)
|
||
def llama_grammar_init(
|
||
rules: CtypesArray[
|
||
CtypesPointer[llama_grammar_element]
|
||
], # NOTE: This might be wrong type sig
|
||
n_rules: Union[ctypes.c_size_t, int],
|
||
start_rule_index: Union[ctypes.c_size_t, int],
|
||
/,
|
||
) -> llama_grammar_p:
|
||
"""Initialize a grammar from a set of rules."""
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
|
||
@ctypes_function(
|
||
"llama_grammar_free",
|
||
[llama_grammar_p],
|
||
None,
|
||
)
|
||
def llama_grammar_free(grammar: llama_grammar_p, /):
|
||
"""Free a grammar."""
|
||
...
|
||
|
||
|
||
# LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
|
||
@ctypes_function(
|
||
"llama_grammar_copy",
|
||
[llama_grammar_p],
|
||
llama_grammar_p,
|
||
)
|
||
def llama_grammar_copy(grammar: llama_grammar_p, /) -> llama_grammar_p:
|
||
"""Copy a grammar."""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Sampling functions
|
||
# //
|
||
|
||
|
||
# // Sets the current rng seed.
|
||
# LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
|
||
@ctypes_function(
|
||
"llama_set_rng_seed",
|
||
[llama_context_p_ctypes, ctypes.c_uint32],
|
||
None,
|
||
)
|
||
def llama_set_rng_seed(ctx: llama_context_p, seed: Union[ctypes.c_uint32, int], /):
|
||
"""Sets the current rng seed."""
|
||
...
|
||
|
||
|
||
# /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||
# /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
|
||
# LLAMA_API void llama_sample_repetition_penalties(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# const llama_token * last_tokens,
|
||
# size_t penalty_last_n,
|
||
# float penalty_repeat,
|
||
# float penalty_freq,
|
||
# float penalty_present);
|
||
@ctypes_function(
|
||
"llama_sample_repetition_penalties",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_token_data_array_p,
|
||
llama_token_p,
|
||
ctypes.c_size_t,
|
||
ctypes.c_float,
|
||
ctypes.c_float,
|
||
ctypes.c_float,
|
||
],
|
||
None,
|
||
)
|
||
def llama_sample_repetition_penalties(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
last_tokens_data: CtypesArray[llama_token],
|
||
penalty_last_n: Union[ctypes.c_size_t, int],
|
||
penalty_repeat: Union[ctypes.c_float, float],
|
||
penalty_freq: Union[ctypes.c_float, float],
|
||
penalty_present: Union[ctypes.c_float, float],
|
||
/,
|
||
):
|
||
"""Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
||
Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
|
||
"""
|
||
...
|
||
|
||
|
||
# /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
|
||
# /// @param logits Logits extracted from the original generation context.
|
||
# /// @param logits_guidance Logits extracted from a separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
|
||
# /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
|
||
# LLAMA_API void llama_sample_apply_guidance(
|
||
# struct llama_context * ctx,
|
||
# float * logits,
|
||
# float * logits_guidance,
|
||
# float scale);
|
||
@ctypes_function(
|
||
"llama_sample_apply_guidance",
|
||
[
|
||
llama_context_p_ctypes,
|
||
ctypes.POINTER(ctypes.c_float),
|
||
ctypes.POINTER(ctypes.c_float),
|
||
ctypes.c_float,
|
||
],
|
||
None,
|
||
)
|
||
def llama_sample_apply_guidance(
|
||
ctx: llama_context_p,
|
||
logits: CtypesArray[ctypes.c_float],
|
||
logits_guidance: CtypesArray[ctypes.c_float],
|
||
scale: Union[ctypes.c_float, float],
|
||
/,
|
||
):
|
||
"""Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806"""
|
||
...
|
||
|
||
|
||
# /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
|
||
# LLAMA_API void llama_sample_softmax(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates);
|
||
@ctypes_function(
|
||
"llama_sample_softmax",
|
||
[llama_context_p_ctypes, llama_token_data_array_p],
|
||
None,
|
||
)
|
||
def llama_sample_softmax(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
/,
|
||
):
|
||
"""Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits."""
|
||
...
|
||
|
||
|
||
# /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||
# LLAMA_API void llama_sample_top_k(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# int32_t k,
|
||
# size_t min_keep);
|
||
@ctypes_function(
|
||
"llama_sample_top_k",
|
||
[llama_context_p_ctypes, llama_token_data_array_p, ctypes.c_int32, ctypes.c_size_t],
|
||
None,
|
||
)
|
||
def llama_sample_top_k(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
k: Union[ctypes.c_int, int],
|
||
min_keep: Union[ctypes.c_size_t, int],
|
||
/,
|
||
):
|
||
"""Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751"""
|
||
...
|
||
|
||
|
||
# /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||
# LLAMA_API void llama_sample_top_p(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# float p,
|
||
# size_t min_keep);
|
||
@ctypes_function(
|
||
"llama_sample_top_p",
|
||
[llama_context_p_ctypes, llama_token_data_array_p, ctypes.c_float, ctypes.c_size_t],
|
||
None,
|
||
)
|
||
def llama_sample_top_p(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
p: Union[ctypes.c_float, float],
|
||
min_keep: Union[ctypes.c_size_t, int],
|
||
/,
|
||
):
|
||
"""Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751"""
|
||
...
|
||
|
||
|
||
# /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
|
||
# LLAMA_API void llama_sample_min_p(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# float p,
|
||
# size_t min_keep);
|
||
@ctypes_function(
|
||
"llama_sample_min_p",
|
||
[llama_context_p_ctypes, llama_token_data_array_p, ctypes.c_float, ctypes.c_size_t],
|
||
None,
|
||
)
|
||
def llama_sample_min_p(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
p: Union[ctypes.c_float, float],
|
||
min_keep: Union[ctypes.c_size_t, int],
|
||
/,
|
||
):
|
||
"""Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841"""
|
||
...
|
||
|
||
|
||
# /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
|
||
# LLAMA_API void llama_sample_tail_free(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# float z,
|
||
# size_t min_keep);
|
||
@ctypes_function(
|
||
"llama_sample_tail_free",
|
||
[llama_context_p_ctypes, llama_token_data_array_p, ctypes.c_float, ctypes.c_size_t],
|
||
None,
|
||
)
|
||
def llama_sample_tail_free(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
z: Union[ctypes.c_float, float],
|
||
min_keep: Union[ctypes.c_size_t, int],
|
||
/,
|
||
):
|
||
"""Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/."""
|
||
...
|
||
|
||
|
||
# /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
|
||
# LLAMA_API void llama_sample_typical(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# float p,
|
||
# size_t min_keep);
|
||
@ctypes_function(
|
||
"llama_sample_typical",
|
||
[llama_context_p_ctypes, llama_token_data_array_p, ctypes.c_float, ctypes.c_size_t],
|
||
None,
|
||
)
|
||
def llama_sample_typical(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
p: Union[ctypes.c_float, float],
|
||
min_keep: Union[ctypes.c_size_t, int],
|
||
/,
|
||
):
|
||
"""Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666."""
|
||
...
|
||
|
||
|
||
# /// @details Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772.
|
||
# LLAMA_API void llama_sample_entropy(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates_p,
|
||
# float min_temp,
|
||
# float max_temp,
|
||
# float exponent_val);
|
||
@ctypes_function(
|
||
"llama_sample_entropy",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_token_data_array_p,
|
||
ctypes.c_float,
|
||
ctypes.c_float,
|
||
ctypes.c_float,
|
||
],
|
||
None,
|
||
)
|
||
def llama_sample_entropy(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
min_temp: Union[ctypes.c_float, float],
|
||
max_temp: Union[ctypes.c_float, float],
|
||
exponent_val: Union[ctypes.c_float, float],
|
||
/,
|
||
):
|
||
"""Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772."""
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_sample_temp(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# float temp);
|
||
@ctypes_function(
|
||
"llama_sample_temp",
|
||
[llama_context_p_ctypes, llama_token_data_array_p, ctypes.c_float],
|
||
None,
|
||
)
|
||
def llama_sample_temp(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
temp: Union[ctypes.c_float, float],
|
||
/,
|
||
):
|
||
"""Temperature sampling described in academic paper "Generating Long Sequences with Sparse Transformers" https://arxiv.org/abs/1904.10509
|
||
|
||
Parameters:
|
||
candidates: A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||
temp: The temperature value to use for the sampling. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||
"""
|
||
...
|
||
|
||
|
||
# /// @details Apply constraints from grammar
|
||
# LLAMA_API void llama_sample_grammar(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# const struct llama_grammar * grammar);
|
||
@ctypes_function(
|
||
"llama_sample_grammar",
|
||
[llama_context_p_ctypes, llama_token_data_array_p, llama_grammar_p],
|
||
None,
|
||
)
|
||
def llama_sample_grammar(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
grammar, # type: llama_grammar_p
|
||
/,
|
||
):
|
||
"""Apply constraints from grammar
|
||
|
||
Parameters:
|
||
candidates: A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||
grammar: A grammar object containing the rules and constraints to apply to the generated text.
|
||
"""
|
||
...
|
||
|
||
|
||
# /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||
# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||
# /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||
# /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||
# /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
|
||
# /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||
# LLAMA_API llama_token llama_sample_token_mirostat(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# float tau,
|
||
# float eta,
|
||
# int32_t m,
|
||
# float * mu);
|
||
@ctypes_function(
|
||
"llama_sample_token_mirostat",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_token_data_array_p,
|
||
ctypes.c_float,
|
||
ctypes.c_float,
|
||
ctypes.c_int32,
|
||
ctypes.POINTER(ctypes.c_float),
|
||
],
|
||
llama_token,
|
||
)
|
||
def llama_sample_token_mirostat(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
tau: Union[ctypes.c_float, float],
|
||
eta: Union[ctypes.c_float, float],
|
||
m: Union[ctypes.c_int, int],
|
||
mu: CtypesPointerOrRef[ctypes.c_float],
|
||
/,
|
||
) -> int:
|
||
"""Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||
|
||
Parameters:
|
||
candidates: A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||
tau: The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||
eta: The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||
m: The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
|
||
mu: Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||
"""
|
||
...
|
||
|
||
|
||
# /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||
# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||
# /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||
# /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||
# /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||
# LLAMA_API llama_token llama_sample_token_mirostat_v2(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates,
|
||
# float tau,
|
||
# float eta,
|
||
# float * mu);
|
||
@ctypes_function(
|
||
"llama_sample_token_mirostat_v2",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_token_data_array_p,
|
||
ctypes.c_float,
|
||
ctypes.c_float,
|
||
ctypes.POINTER(ctypes.c_float),
|
||
],
|
||
llama_token,
|
||
)
|
||
def llama_sample_token_mirostat_v2(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
tau: Union[ctypes.c_float, float],
|
||
eta: Union[ctypes.c_float, float],
|
||
mu: CtypesPointerOrRef[ctypes.c_float],
|
||
/,
|
||
) -> int:
|
||
"""Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||
|
||
Parameters:
|
||
candidates: A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
|
||
tau: The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
|
||
eta: The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
|
||
mu: Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
|
||
"""
|
||
...
|
||
|
||
|
||
# /// @details Selects the token with the highest probability.
|
||
# /// Does not compute the token probabilities. Use llama_sample_softmax() instead.
|
||
# LLAMA_API llama_token llama_sample_token_greedy(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates);
|
||
@ctypes_function(
|
||
"llama_sample_token_greedy",
|
||
[llama_context_p_ctypes, llama_token_data_array_p],
|
||
llama_token,
|
||
)
|
||
def llama_sample_token_greedy(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
/,
|
||
) -> int:
|
||
"""Selects the token with the highest probability."""
|
||
...
|
||
|
||
|
||
# /// @details Randomly selects a token from the candidates based on their probabilities.
|
||
# LLAMA_API llama_token llama_sample_token(
|
||
# struct llama_context * ctx,
|
||
# llama_token_data_array * candidates);
|
||
@ctypes_function(
|
||
"llama_sample_token",
|
||
[llama_context_p_ctypes, llama_token_data_array_p],
|
||
llama_token,
|
||
)
|
||
def llama_sample_token(
|
||
ctx: llama_context_p,
|
||
candidates: Union[
|
||
CtypesArray[llama_token_data_array], CtypesPointerOrRef[llama_token_data_array]
|
||
],
|
||
/,
|
||
) -> int:
|
||
"""Randomly selects a token from the candidates based on their probabilities."""
|
||
...
|
||
|
||
|
||
# /// @details Accepts the sampled token into the grammar
|
||
# LLAMA_API void llama_grammar_accept_token(
|
||
# struct llama_context * ctx,
|
||
# struct llama_grammar * grammar,
|
||
# llama_token token);
|
||
@ctypes_function(
|
||
"llama_grammar_accept_token",
|
||
[llama_context_p_ctypes, llama_grammar_p, llama_token],
|
||
None,
|
||
)
|
||
def llama_grammar_accept_token(
|
||
ctx: llama_context_p, grammar: llama_grammar_p, token: Union[llama_token, int], /
|
||
) -> None:
|
||
"""Accepts the sampled token into the grammar"""
|
||
...
|
||
|
||
|
||
# //
|
||
# // Beam search
|
||
# //
|
||
|
||
# struct llama_beam_view {
|
||
# const llama_token * tokens;
|
||
|
||
|
||
# size_t n_tokens;
|
||
# float p; // Cumulative beam probability (renormalized relative to all beams)
|
||
# bool eob; // Callback should set this to true when a beam is at end-of-beam.
|
||
# };
|
||
class llama_beam_view(ctypes.Structure):
|
||
_fields_ = [
|
||
("tokens", llama_token_p),
|
||
("n_tokens", ctypes.c_size_t),
|
||
("p", ctypes.c_float),
|
||
("eob", ctypes.c_bool),
|
||
]
|
||
|
||
|
||
# // Passed to beam_search_callback function.
|
||
# // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
|
||
# // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
|
||
# // These pointers are valid only during the synchronous callback, so should not be saved.
|
||
# struct llama_beams_state {
|
||
# struct llama_beam_view * beam_views;
|
||
# size_t n_beams; // Number of elements in beam_views[].
|
||
# size_t common_prefix_length; // Current max length of prefix tokens shared by all beams.
|
||
# bool last_call; // True iff this is the last callback invocation.
|
||
# };
|
||
class llama_beams_state(ctypes.Structure):
|
||
_fields_ = [
|
||
("beam_views", ctypes.POINTER(llama_beam_view)),
|
||
("n_beams", ctypes.c_size_t),
|
||
("common_prefix_length", ctypes.c_size_t),
|
||
("last_call", ctypes.c_bool),
|
||
]
|
||
|
||
|
||
# // Type of pointer to the beam_search_callback function.
|
||
# // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
|
||
# // passed back to beam_search_callback. This avoids having to use global variables in the callback.
|
||
# typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
|
||
llama_beam_search_callback_fn_t = ctypes.CFUNCTYPE(
|
||
None, ctypes.c_void_p, llama_beams_state
|
||
)
|
||
|
||
|
||
# /// @details Deterministically returns entire sentence constructed by a beam search.
|
||
# /// @param ctx Pointer to the llama_context.
|
||
# /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
|
||
# /// @param callback_data A pointer that is simply passed back to callback.
|
||
# /// @param n_beams Number of beams to use.
|
||
# /// @param n_past Number of tokens already evaluated.
|
||
# /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
|
||
# /// @param n_threads Number of threads as passed to llama_eval().
|
||
# LLAMA_API void llama_beam_search(
|
||
# struct llama_context * ctx,
|
||
# llama_beam_search_callback_fn_t callback,
|
||
# void * callback_data,
|
||
# size_t n_beams,
|
||
# int32_t n_past,
|
||
# int32_t n_predict);
|
||
@ctypes_function(
|
||
"llama_beam_search",
|
||
[
|
||
llama_context_p_ctypes,
|
||
llama_beam_search_callback_fn_t,
|
||
ctypes.c_void_p,
|
||
ctypes.c_size_t,
|
||
ctypes.c_int32,
|
||
ctypes.c_int32,
|
||
],
|
||
None,
|
||
)
|
||
def llama_beam_search(
|
||
ctx: llama_context_p,
|
||
callback: CtypesFuncPointer,
|
||
callback_data: ctypes.c_void_p,
|
||
n_beams: Union[ctypes.c_size_t, int],
|
||
n_past: Union[ctypes.c_int, int],
|
||
n_predict: Union[ctypes.c_int, int],
|
||
/,
|
||
): ...
|
||
|
||
|
||
# /// @details Build a split GGUF final path for this chunk.
|
||
# /// llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
|
||
# // Returns the split_path length.
|
||
# LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count);
|
||
@ctypes_function(
|
||
"llama_split_path",
|
||
[ctypes.c_char_p, ctypes.c_size_t, ctypes.c_char_p, ctypes.c_int, ctypes.c_int],
|
||
ctypes.c_int,
|
||
)
|
||
def llama_split_path(
|
||
split_path: bytes,
|
||
maxlen: Union[ctypes.c_size_t, int],
|
||
path_prefix: bytes,
|
||
split_no: Union[ctypes.c_int, int],
|
||
split_count: Union[ctypes.c_int, int],
|
||
/,
|
||
) -> int:
|
||
"""Build a split GGUF final path for this chunk."""
|
||
...
|
||
|
||
|
||
# /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match.
|
||
# /// llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0"
|
||
# // Returns the split_prefix length.
|
||
# LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count);
|
||
@ctypes_function(
|
||
"llama_split_prefix",
|
||
[ctypes.c_char_p, ctypes.c_size_t, ctypes.c_char_p, ctypes.c_int, ctypes.c_int],
|
||
ctypes.c_int,
|
||
)
|
||
def llama_split_prefix(
|
||
split_prefix: bytes,
|
||
maxlen: Union[ctypes.c_size_t, int],
|
||
split_path: bytes,
|
||
split_no: Union[ctypes.c_int, int],
|
||
split_count: Union[ctypes.c_int, int],
|
||
/,
|
||
) -> int:
|
||
"""Extract the path prefix from the split_path if and only if the split_no and split_count match."""
|
||
...
|
||
|
||
|
||
# Performance information
|
||
|
||
|
||
# LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_get_timings",
|
||
[llama_context_p_ctypes],
|
||
llama_timings,
|
||
)
|
||
def llama_get_timings(ctx: llama_context_p, /) -> llama_timings:
|
||
"""Get performance information"""
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_print_timings(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_print_timings",
|
||
[llama_context_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_print_timings(ctx: llama_context_p, /):
|
||
"""Print performance information"""
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_reset_timings(struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_reset_timings",
|
||
[llama_context_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_reset_timings(ctx: llama_context_p, /):
|
||
"""Reset performance information"""
|
||
...
|
||
|
||
|
||
# Print system information
|
||
# LLAMA_API const char * llama_print_system_info(void);
|
||
@ctypes_function(
|
||
"llama_print_system_info",
|
||
[],
|
||
ctypes.c_char_p,
|
||
)
|
||
def llama_print_system_info() -> bytes:
|
||
"""Print system information"""
|
||
...
|
||
|
||
|
||
# NOTE: THIS IS CURRENTLY BROKEN AS ggml_log_callback IS NOT EXPOSED IN LLAMA.H
|
||
# // Set callback for all future logging events.
|
||
# // If this is not called, or NULL is supplied, everything is output on stderr.
|
||
# LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
|
||
@ctypes_function(
|
||
"llama_log_set",
|
||
[ctypes.c_void_p, ctypes.c_void_p],
|
||
None,
|
||
)
|
||
def llama_log_set(
|
||
log_callback: Optional[CtypesFuncPointer],
|
||
user_data: ctypes.c_void_p,
|
||
/,
|
||
):
|
||
"""Set callback for all future logging events.
|
||
|
||
If this is not called, or NULL is supplied, everything is output on stderr."""
|
||
...
|
||
|
||
|
||
# LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
|
||
@ctypes_function(
|
||
"llama_dump_timing_info_yaml",
|
||
[ctypes.c_void_p, llama_context_p_ctypes],
|
||
None,
|
||
)
|
||
def llama_dump_timing_info_yaml(stream: ctypes.c_void_p, ctx: llama_context_p, /): ...
|