1535 lines
51 KiB
Python
1535 lines
51 KiB
Python
import sys
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import os
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import ctypes
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from ctypes import (
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c_double,
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c_int,
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c_float,
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c_char_p,
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c_int32,
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c_uint32,
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c_void_p,
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c_bool,
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POINTER,
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_Pointer, # type: ignore
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Structure,
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Array,
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c_uint8,
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c_size_t,
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)
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import pathlib
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from typing import List, Union
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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(__file__).parent.resolve()
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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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]
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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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cdll_args["winmode"] = 0
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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)
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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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# Misc
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c_float_p = POINTER(c_float)
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c_uint8_p = POINTER(c_uint8)
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c_size_t_p = POINTER(c_size_t)
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# llama.h bindings
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GGML_USE_CUBLAS = hasattr(_lib, "ggml_init_cublas")
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GGML_CUDA_MAX_DEVICES = ctypes.c_int(16)
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LLAMA_MAX_DEVICES = GGML_CUDA_MAX_DEVICES if GGML_USE_CUBLAS else ctypes.c_int(1)
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# define LLAMA_DEFAULT_SEED 0xFFFFFFFF
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LLAMA_DEFAULT_SEED = ctypes.c_int(0xFFFFFFFF)
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# define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
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LLAMA_FILE_MAGIC_GGSN = ctypes.c_uint(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 1
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LLAMA_SESSION_VERSION = ctypes.c_int(1)
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# struct llama_model;
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llama_model_p = c_void_p
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# struct llama_context;
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llama_context_p = c_void_p
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# typedef int llama_token;
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llama_token = c_int
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llama_token_p = POINTER(llama_token)
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# enum llama_log_level {
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# LLAMA_LOG_LEVEL_ERROR = 2,
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# LLAMA_LOG_LEVEL_WARN = 3,
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# LLAMA_LOG_LEVEL_INFO = 4
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# };
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LLAMA_LOG_LEVEL_ERROR = c_int(2)
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LLAMA_LOG_LEVEL_WARN = c_int(3)
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LLAMA_LOG_LEVEL_INFO = c_int(4)
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# enum llama_vocab_type {
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# LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
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# LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
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# };
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LLAMA_VOCAB_TYPE_SPM = c_int(0)
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LLAMA_VOCAB_TYPE_BPE = c_int(1)
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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 = c_int(0)
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LLAMA_TOKEN_TYPE_NORMAL = c_int(1)
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LLAMA_TOKEN_TYPE_UNKNOWN = c_int(2)
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LLAMA_TOKEN_TYPE_CONTROL = c_int(3)
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LLAMA_TOKEN_TYPE_USER_DEFINED = c_int(4)
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LLAMA_TOKEN_TYPE_UNUSED = c_int(5)
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LLAMA_TOKEN_TYPE_BYTE = c_int(6)
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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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#
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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 = c_int(0)
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LLAMA_FTYPE_MOSTLY_F16 = c_int(1)
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LLAMA_FTYPE_MOSTLY_Q4_0 = c_int(2)
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LLAMA_FTYPE_MOSTLY_Q4_1 = c_int(3)
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LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = c_int(4)
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LLAMA_FTYPE_MOSTLY_Q8_0 = c_int(7)
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LLAMA_FTYPE_MOSTLY_Q5_0 = c_int(8)
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LLAMA_FTYPE_MOSTLY_Q5_1 = c_int(9)
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LLAMA_FTYPE_MOSTLY_Q2_K = c_int(10)
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LLAMA_FTYPE_MOSTLY_Q3_K_S = c_int(11)
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LLAMA_FTYPE_MOSTLY_Q3_K_M = c_int(12)
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LLAMA_FTYPE_MOSTLY_Q3_K_L = c_int(13)
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LLAMA_FTYPE_MOSTLY_Q4_K_S = c_int(14)
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LLAMA_FTYPE_MOSTLY_Q4_K_M = c_int(15)
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LLAMA_FTYPE_MOSTLY_Q5_K_S = c_int(16)
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LLAMA_FTYPE_MOSTLY_Q5_K_M = c_int(17)
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LLAMA_FTYPE_MOSTLY_Q6_K = c_int(18)
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LLAMA_FTYPE_GUESSED = c_int(1024)
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# typedef struct llama_token_data {
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# llama_token id; // token id
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# float logit; // log-odds of the token
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# float p; // probability of the token
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# } llama_token_data;
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class llama_token_data(Structure):
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_fields_ = [
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("id", llama_token),
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("logit", c_float),
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("p", c_float),
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]
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llama_token_data_p = POINTER(llama_token_data)
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# typedef struct llama_token_data_array {
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# llama_token_data * data;
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# size_t size;
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# bool sorted;
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# } llama_token_data_array;
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class llama_token_data_array(Structure):
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_fields_ = [
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("data", llama_token_data_p),
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("size", c_size_t),
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("sorted", c_bool),
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]
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llama_token_data_array_p = POINTER(llama_token_data_array)
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# typedef void (*llama_progress_callback)(float progress, void *ctx);
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llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
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# struct llama_context_params {
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# uint32_t seed; // RNG seed, -1 for random
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# int32_t n_ctx; // text context
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# int32_t n_batch; // prompt processing batch size
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# int32_t n_gpu_layers; // number of layers to store in VRAM
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# int32_t main_gpu; // the GPU that is used for scratch and small tensors
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# const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
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# // ref: https://github.com/ggerganov/llama.cpp/pull/2054
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# float rope_freq_base; // RoPE base frequency
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# float rope_freq_scale; // RoPE frequency scaling factor
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# // called with a progress value between 0 and 1, pass NULL to disable
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# llama_progress_callback progress_callback;
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# // context pointer passed to the progress callback
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# void * progress_callback_user_data;
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# // Keep the booleans together to avoid misalignment during copy-by-value.
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# bool low_vram; // if true, reduce VRAM usage at the cost of performance
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# bool mul_mat_q; // if true, use experimental mul_mat_q kernels
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# bool f16_kv; // use fp16 for KV cache
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# bool logits_all; // the llama_eval() call computes all logits, not just the last one
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# bool vocab_only; // only load the vocabulary, no weights
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# bool use_mmap; // use mmap if possible
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# bool use_mlock; // force system to keep model in RAM
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# bool embedding; // embedding mode only
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# };
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class llama_context_params(Structure):
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_fields_ = [
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("seed", c_uint32),
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("n_ctx", c_int32),
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("n_batch", c_int32),
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("n_gpu_layers", c_int32),
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("main_gpu", c_int32),
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("tensor_split", c_float_p),
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("rope_freq_base", c_float),
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("rope_freq_scale", c_float),
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("progress_callback", llama_progress_callback),
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("progress_callback_user_data", c_void_p),
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("low_vram", c_bool),
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("mul_mat_q", c_bool),
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("f16_kv", c_bool),
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("logits_all", c_bool),
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("vocab_only", c_bool),
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("use_mmap", c_bool),
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("use_mlock", c_bool),
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("embedding", c_bool),
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]
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llama_context_params_p = POINTER(llama_context_params)
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# // Signature for logging events
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# // Note that text includes the new line character at the end for most events.
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# // If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
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# // if it exists.
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# // It might not exist for progress report where '.' is output repeatedly.
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# typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
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llama_log_callback = ctypes.CFUNCTYPE(None, c_int, c_char_p, c_void_p)
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# // model quantization parameters
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# typedef struct llama_model_quantize_params {
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# int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
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# enum llama_ftype ftype; // quantize to this llama_ftype
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# bool allow_requantize; // allow quantizing non-f32/f16 tensors
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# bool quantize_output_tensor; // quantize output.weight
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# bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
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# } llama_model_quantize_params;
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class llama_model_quantize_params(Structure):
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_fields_ = [
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("nthread", c_int),
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("ftype", c_int),
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("allow_requantize", c_bool),
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("quantize_output_tensor", c_bool),
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("only_copy", c_bool),
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]
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# // grammar types
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# struct llama_grammar;
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llama_grammar_p = c_void_p
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# // grammar element type
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# enum llama_gretype {
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# // end of rule definition
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# LLAMA_GRETYPE_END = 0,
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# // start of alternate definition for rule
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# LLAMA_GRETYPE_ALT = 1,
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# // non-terminal element: reference to rule
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# LLAMA_GRETYPE_RULE_REF = 2,
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# // terminal element: character (code point)
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# LLAMA_GRETYPE_CHAR = 3,
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# // inverse char(s) ([^a], [^a-b] [^abc])
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# LLAMA_GRETYPE_CHAR_NOT = 4,
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# // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
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# // be an inclusive range ([a-z])
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# LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
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# // modifies a preceding LLAMA_GRETYPE_CHAR or
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# // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
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# LLAMA_GRETYPE_CHAR_ALT = 6,
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# };
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LLAMA_GRETYPE_END = c_int(0)
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LLAMA_GRETYPE_ALT = c_int(1)
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LLAMA_GRETYPE_RULE_REF = c_int(2)
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LLAMA_GRETYPE_CHAR = c_int(3)
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LLAMA_GRETYPE_CHAR_NOT = c_int(4)
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LLAMA_GRETYPE_CHAR_RNG_UPPER = c_int(5)
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LLAMA_GRETYPE_CHAR_ALT = c_int(6)
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# typedef struct llama_grammar_element {
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# enum llama_gretype type;
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# uint32_t value; // Unicode code point or rule ID
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# } llama_grammar_element;
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class llama_grammar_element(Structure):
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_fields_ = [
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("type", c_int),
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("value", c_uint32),
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]
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llama_grammar_element_p = POINTER(llama_grammar_element)
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# // performance timing information
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# struct llama_timings {
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# double t_start_ms;
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# double t_end_ms;
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# double t_load_ms;
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# double t_sample_ms;
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# double t_p_eval_ms;
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# double t_eval_ms;
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# int32_t n_sample;
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# int32_t n_p_eval;
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# int32_t n_eval;
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# };
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class llama_timings(Structure):
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_fields_ = [
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("t_start_ms", c_double),
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("t_end_ms", c_double),
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("t_load_ms", c_double),
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("t_sample_ms", c_double),
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("t_p_eval_ms", c_double),
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("t_eval_ms", c_double),
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("n_sample", c_int32),
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("n_p_eval", c_int32),
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("n_eval", c_int32),
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]
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# LLAMA_API struct llama_context_params llama_context_default_params(void);
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def llama_context_default_params() -> llama_context_params:
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return _lib.llama_context_default_params()
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_lib.llama_context_default_params.argtypes = []
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_lib.llama_context_default_params.restype = llama_context_params
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# LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
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def llama_model_quantize_default_params() -> llama_model_quantize_params:
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return _lib.llama_model_quantize_default_params()
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_lib.llama_model_quantize_default_params.argtypes = []
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_lib.llama_model_quantize_default_params.restype = llama_model_quantize_params
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# // Initialize the llama + ggml backend
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# // If numa is true, use NUMA optimizations
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# // Call once at the start of the program
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# LLAMA_API void llama_backend_init(bool numa);
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def llama_backend_init(numa: c_bool):
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return _lib.llama_backend_init(numa)
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_lib.llama_backend_init.argtypes = [c_bool]
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_lib.llama_backend_init.restype = None
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# // Call once at the end of the program - currently only used for MPI
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# LLAMA_API void llama_backend_free(void);
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def llama_backend_free():
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return _lib.llama_backend_free()
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_lib.llama_backend_free.argtypes = []
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_lib.llama_backend_free.restype = None
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# LLAMA_API struct llama_model * llama_load_model_from_file(
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# const char * path_model,
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# struct llama_context_params params);
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def llama_load_model_from_file(
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path_model: bytes, params: llama_context_params
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) -> llama_model_p:
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return _lib.llama_load_model_from_file(path_model, params)
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_lib.llama_load_model_from_file.argtypes = [c_char_p, llama_context_params]
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_lib.llama_load_model_from_file.restype = llama_model_p
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# LLAMA_API void llama_free_model(struct llama_model * model);
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def llama_free_model(model: llama_model_p):
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return _lib.llama_free_model(model)
|
|
|
|
|
|
_lib.llama_free_model.argtypes = [llama_model_p]
|
|
_lib.llama_free_model.restype = None
|
|
|
|
|
|
# LLAMA_API struct llama_context * llama_new_context_with_model(
|
|
# struct llama_model * model,
|
|
# struct llama_context_params params);
|
|
def llama_new_context_with_model(
|
|
model: llama_model_p, params: llama_context_params
|
|
) -> llama_context_p:
|
|
return _lib.llama_new_context_with_model(model, params)
|
|
|
|
|
|
_lib.llama_new_context_with_model.argtypes = [llama_model_p, llama_context_params]
|
|
_lib.llama_new_context_with_model.restype = llama_context_p
|
|
|
|
|
|
# // Frees all allocated memory
|
|
# LLAMA_API void llama_free(struct llama_context * ctx);
|
|
def llama_free(ctx: llama_context_p):
|
|
return _lib.llama_free(ctx)
|
|
|
|
|
|
_lib.llama_free.argtypes = [llama_context_p]
|
|
_lib.llama_free.restype = None
|
|
|
|
|
|
# LLAMA_API int64_t llama_time_us(void);
|
|
def llama_time_us() -> int:
|
|
return _lib.llama_time_us()
|
|
|
|
|
|
_lib.llama_time_us.argtypes = []
|
|
_lib.llama_time_us.restype = ctypes.c_int64
|
|
|
|
|
|
# LLAMA_API int llama_max_devices (void);
|
|
def llama_max_devices() -> int:
|
|
return _lib.llama_max_devices()
|
|
|
|
|
|
_lib.llama_max_devices.argtypes = []
|
|
_lib.llama_max_devices.restype = c_int
|
|
|
|
|
|
# LLAMA_API bool llama_mmap_supported (void);
|
|
def llama_mmap_supported() -> bool:
|
|
return _lib.llama_mmap_supported()
|
|
|
|
|
|
_lib.llama_mmap_supported.argtypes = []
|
|
_lib.llama_mmap_supported.restype = c_bool
|
|
|
|
|
|
# LLAMA_API bool llama_mlock_supported(void);
|
|
def llama_mlock_supported() -> bool:
|
|
return _lib.llama_mlock_supported()
|
|
|
|
|
|
_lib.llama_mlock_supported.argtypes = []
|
|
_lib.llama_mlock_supported.restype = c_bool
|
|
|
|
|
|
# LLAMA_API int llama_n_vocab (const struct llama_context * ctx);
|
|
def llama_n_vocab(ctx: llama_context_p) -> int:
|
|
return _lib.llama_n_vocab(ctx)
|
|
|
|
|
|
_lib.llama_n_vocab.argtypes = [llama_context_p]
|
|
_lib.llama_n_vocab.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
|
|
def llama_n_ctx(ctx: llama_context_p) -> int:
|
|
return _lib.llama_n_ctx(ctx)
|
|
|
|
|
|
_lib.llama_n_ctx.argtypes = [llama_context_p]
|
|
_lib.llama_n_ctx.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_n_ctx_train(const struct llama_context * ctx);
|
|
def llama_n_ctx_train(ctx: llama_context_p) -> int:
|
|
return _lib.llama_n_ctx_train(ctx)
|
|
|
|
|
|
_lib.llama_n_ctx_train.argtypes = [llama_context_p]
|
|
_lib.llama_n_ctx_train.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_n_embd (const struct llama_context * ctx);
|
|
def llama_n_embd(ctx: llama_context_p) -> int:
|
|
return _lib.llama_n_embd(ctx)
|
|
|
|
|
|
_lib.llama_n_embd.argtypes = [llama_context_p]
|
|
_lib.llama_n_embd.restype = c_int
|
|
|
|
|
|
# LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_context * ctx);
|
|
def llama_vocab_type(ctx: llama_context_p) -> int:
|
|
return _lib.llama_vocab_type(ctx)
|
|
|
|
|
|
_lib.llama_vocab_type.argtypes = [llama_context_p]
|
|
_lib.llama_vocab_type.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_model_n_vocab (const struct llama_model * model);
|
|
def llama_model_n_vocab(model: llama_model_p) -> int:
|
|
return _lib.llama_model_n_vocab(model)
|
|
|
|
|
|
_lib.llama_model_n_vocab.argtypes = [llama_model_p]
|
|
_lib.llama_model_n_vocab.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_model_n_ctx (const struct llama_model * model);
|
|
def llama_model_n_ctx(model: llama_model_p) -> int:
|
|
return _lib.llama_model_n_ctx(model)
|
|
|
|
|
|
_lib.llama_model_n_ctx.argtypes = [llama_model_p]
|
|
_lib.llama_model_n_ctx.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_model_n_ctx_train(const struct llama_model * model);
|
|
def llama_model_n_ctx_train(model: llama_model_p) -> int:
|
|
return _lib.llama_model_n_ctx_train(model)
|
|
|
|
|
|
_lib.llama_model_n_ctx_train.argtypes = [llama_model_p]
|
|
_lib.llama_model_n_ctx_train.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_model_n_embd (const struct llama_model * model);
|
|
def llama_model_n_embd(model: llama_model_p) -> int:
|
|
return _lib.llama_model_n_embd(model)
|
|
|
|
|
|
_lib.llama_model_n_embd.argtypes = [llama_model_p]
|
|
_lib.llama_model_n_embd.restype = c_int
|
|
|
|
|
|
# // Get a string describing the model type
|
|
# LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
|
|
def llama_model_desc(model: llama_model_p, buf: bytes, buf_size: c_size_t) -> int:
|
|
return _lib.llama_model_desc(model, buf, buf_size)
|
|
|
|
|
|
_lib.llama_model_desc.argtypes = [llama_model_p, c_char_p, c_size_t]
|
|
_lib.llama_model_desc.restype = c_int
|
|
|
|
|
|
# // Returns the total size of all the tensors in the model in bytes
|
|
# LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
|
|
def llama_model_size(model: llama_model_p) -> int:
|
|
return _lib.llama_model_size(model)
|
|
|
|
|
|
_lib.llama_model_size.argtypes = [llama_model_p]
|
|
_lib.llama_model_size.restype = ctypes.c_uint64
|
|
|
|
|
|
# // Returns the total number of parameters in the model
|
|
# LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
|
|
def llama_model_n_params(model: llama_model_p) -> int:
|
|
return _lib.llama_model_n_params(model)
|
|
|
|
|
|
_lib.llama_model_n_params.argtypes = [llama_model_p]
|
|
_lib.llama_model_n_params.restype = ctypes.c_uint64
|
|
|
|
|
|
# // Returns 0 on success
|
|
# LLAMA_API int llama_model_quantize(
|
|
# const char * fname_inp,
|
|
# const char * fname_out,
|
|
# const llama_model_quantize_params * params);
|
|
def llama_model_quantize(
|
|
fname_inp: bytes,
|
|
fname_out: bytes,
|
|
params, # type: POINTER(llama_model_quantize_params) # type: ignore
|
|
) -> int:
|
|
return _lib.llama_model_quantize(fname_inp, fname_out, params)
|
|
|
|
|
|
_lib.llama_model_quantize.argtypes = [
|
|
c_char_p,
|
|
c_char_p,
|
|
POINTER(llama_model_quantize_params),
|
|
]
|
|
_lib.llama_model_quantize.restype = c_int
|
|
|
|
|
|
# Apply a LoRA adapter to a loaded model
|
|
# path_base_model is the path to a higher quality model to use as a base for
|
|
# the layers modified by the adapter. Can be NULL to use the current loaded model.
|
|
# The model needs to be reloaded before applying a new adapter, otherwise the adapter
|
|
# will be applied on top of the previous one
|
|
# Returns 0 on success
|
|
# LLAMA_API int llama_apply_lora_from_file(
|
|
# struct llama_context * ctx,
|
|
# const char * path_lora,
|
|
# const char * path_base_model,
|
|
# int n_threads);
|
|
def llama_apply_lora_from_file(
|
|
ctx: llama_context_p,
|
|
path_lora: c_char_p,
|
|
path_base_model: c_char_p,
|
|
n_threads: c_int,
|
|
) -> int:
|
|
return _lib.llama_apply_lora_from_file(ctx, path_lora, path_base_model, n_threads)
|
|
|
|
|
|
_lib.llama_apply_lora_from_file.argtypes = [llama_context_p, c_char_p, c_char_p, c_int]
|
|
_lib.llama_apply_lora_from_file.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_model_apply_lora_from_file(
|
|
# const struct llama_model * model,
|
|
# const char * path_lora,
|
|
# const char * path_base_model,
|
|
# int n_threads);
|
|
def llama_model_apply_lora_from_file(
|
|
model: llama_model_p,
|
|
path_lora: Union[c_char_p, bytes],
|
|
path_base_model: Union[c_char_p, bytes],
|
|
n_threads: c_int,
|
|
) -> int:
|
|
return _lib.llama_model_apply_lora_from_file(
|
|
model, path_lora, path_base_model, n_threads
|
|
)
|
|
|
|
|
|
_lib.llama_model_apply_lora_from_file.argtypes = [
|
|
llama_model_p,
|
|
c_char_p,
|
|
c_char_p,
|
|
c_int,
|
|
]
|
|
_lib.llama_model_apply_lora_from_file.restype = c_int
|
|
|
|
|
|
# Returns the number of tokens in the KV cache
|
|
# LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
|
|
def llama_get_kv_cache_token_count(ctx: llama_context_p) -> int:
|
|
return _lib.llama_get_kv_cache_token_count(ctx)
|
|
|
|
|
|
_lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p]
|
|
_lib.llama_get_kv_cache_token_count.restype = c_int
|
|
|
|
|
|
# Sets the current rng seed.
|
|
# LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
|
|
def llama_set_rng_seed(ctx: llama_context_p, seed: c_uint32):
|
|
return _lib.llama_set_rng_seed(ctx, seed)
|
|
|
|
|
|
_lib.llama_set_rng_seed.argtypes = [llama_context_p, c_int]
|
|
_lib.llama_set_rng_seed.restype = None
|
|
|
|
|
|
# Returns the maximum size in bytes of the state (rng, logits, embedding
|
|
# and kv_cache) - will often be smaller after compacting tokens
|
|
# LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
|
|
def llama_get_state_size(ctx: llama_context_p) -> int:
|
|
return _lib.llama_get_state_size(ctx)
|
|
|
|
|
|
_lib.llama_get_state_size.argtypes = [llama_context_p]
|
|
_lib.llama_get_state_size.restype = c_size_t
|
|
|
|
|
|
# Copies the state to the specified destination address.
|
|
# Destination needs to have allocated enough memory.
|
|
# Returns the number of bytes copied
|
|
# LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
|
|
def llama_copy_state_data(
|
|
ctx: llama_context_p, dst # type: Array[c_uint8]
|
|
) -> int:
|
|
return _lib.llama_copy_state_data(ctx, dst)
|
|
|
|
|
|
_lib.llama_copy_state_data.argtypes = [llama_context_p, c_uint8_p]
|
|
_lib.llama_copy_state_data.restype = c_size_t
|
|
|
|
|
|
# Set the state reading from the specified address
|
|
# Returns the number of bytes read
|
|
# LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
|
|
def llama_set_state_data(
|
|
ctx: llama_context_p, src # type: Array[c_uint8]
|
|
) -> int:
|
|
return _lib.llama_set_state_data(ctx, src)
|
|
|
|
|
|
_lib.llama_set_state_data.argtypes = [llama_context_p, c_uint8_p]
|
|
_lib.llama_set_state_data.restype = c_size_t
|
|
|
|
|
|
# Save/load session file
|
|
# LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
|
|
def llama_load_session_file(
|
|
ctx: llama_context_p,
|
|
path_session: bytes,
|
|
tokens_out, # type: Array[llama_token]
|
|
n_token_capacity: c_size_t,
|
|
n_token_count_out, # type: _Pointer[c_size_t]
|
|
) -> int:
|
|
return _lib.llama_load_session_file(
|
|
ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
|
|
)
|
|
|
|
|
|
_lib.llama_load_session_file.argtypes = [
|
|
llama_context_p,
|
|
c_char_p,
|
|
llama_token_p,
|
|
c_size_t,
|
|
c_size_t_p,
|
|
]
|
|
_lib.llama_load_session_file.restype = c_size_t
|
|
|
|
|
|
# LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
|
|
def llama_save_session_file(
|
|
ctx: llama_context_p,
|
|
path_session: bytes,
|
|
tokens, # type: Array[llama_token]
|
|
n_token_count: c_size_t,
|
|
) -> int:
|
|
return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)
|
|
|
|
|
|
_lib.llama_save_session_file.argtypes = [
|
|
llama_context_p,
|
|
c_char_p,
|
|
llama_token_p,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_save_session_file.restype = c_size_t
|
|
|
|
|
|
# Run the llama inference to obtain the logits and probabilities for the next token.
|
|
# tokens + n_tokens is the provided batch of new tokens to process
|
|
# n_past is the number of tokens to use from previous eval calls
|
|
# Returns 0 on success
|
|
# LLAMA_API int llama_eval(
|
|
# struct llama_context * ctx,
|
|
# const llama_token * tokens,
|
|
# int n_tokens,
|
|
# int n_past,
|
|
# int n_threads);
|
|
def llama_eval(
|
|
ctx: llama_context_p,
|
|
tokens, # type: Array[llama_token]
|
|
n_tokens: c_int,
|
|
n_past: c_int,
|
|
n_threads: c_int,
|
|
) -> int:
|
|
return _lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
|
|
|
|
|
|
_lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_int]
|
|
_lib.llama_eval.restype = c_int
|
|
|
|
|
|
# // Same as llama_eval, but use float matrix input directly.
|
|
# LLAMA_API int llama_eval_embd(
|
|
# struct llama_context * ctx,
|
|
# const float * embd,
|
|
# int n_tokens,
|
|
# int n_past,
|
|
# int n_threads);
|
|
def llama_eval_embd(
|
|
ctx: llama_context_p,
|
|
embd, # type: Array[c_float]
|
|
n_tokens: c_int,
|
|
n_past: c_int,
|
|
n_threads: c_int,
|
|
) -> int:
|
|
return _lib.llama_eval_embd(ctx, embd, n_tokens, n_past, n_threads)
|
|
|
|
|
|
_lib.llama_eval_embd.argtypes = [llama_context_p, c_float_p, c_int, c_int, c_int]
|
|
_lib.llama_eval_embd.restype = c_int
|
|
|
|
|
|
# // Export a static computation graph for context of 511 and batch size of 1
|
|
# // NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
|
|
# // parameters here to keep things simple
|
|
# // IMPORTANT: do not use for anything else other than debugging and testing!
|
|
# LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
|
|
def llama_eval_export(ctx: llama_context_p, fname: bytes) -> int:
|
|
return _lib.llama_eval_export(ctx, fname)
|
|
|
|
|
|
_lib.llama_eval_export.argtypes = [llama_context_p, c_char_p]
|
|
_lib.llama_eval_export.restype = c_int
|
|
|
|
|
|
# Token logits obtained from the last call to llama_eval()
|
|
# The logits for the last token are stored in the last row
|
|
# Can be mutated in order to change the probabilities of the next token
|
|
# Rows: n_tokens
|
|
# Cols: n_vocab
|
|
# LLAMA_API float * llama_get_logits(struct llama_context * ctx);
|
|
def llama_get_logits(
|
|
ctx: llama_context_p,
|
|
): # type: (...) -> Array[float] # type: ignore
|
|
return _lib.llama_get_logits(ctx)
|
|
|
|
|
|
_lib.llama_get_logits.argtypes = [llama_context_p]
|
|
_lib.llama_get_logits.restype = c_float_p
|
|
|
|
|
|
# Get the embeddings for the input
|
|
# shape: [n_embd] (1-dimensional)
|
|
# LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
|
|
def llama_get_embeddings(
|
|
ctx: llama_context_p,
|
|
): # type: (...) -> Array[float] # type: ignore
|
|
return _lib.llama_get_embeddings(ctx)
|
|
|
|
|
|
_lib.llama_get_embeddings.argtypes = [llama_context_p]
|
|
_lib.llama_get_embeddings.restype = c_float_p
|
|
|
|
|
|
# //
|
|
# // Vocab
|
|
# //
|
|
|
|
|
|
# LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token);
|
|
def llama_token_get_text(ctx: llama_context_p, token: llama_token) -> bytes:
|
|
return _lib.llama_token_get_text(ctx, token)
|
|
|
|
|
|
_lib.llama_token_get_text.argtypes = [llama_context_p, llama_token]
|
|
_lib.llama_token_get_text.restype = c_char_p
|
|
|
|
|
|
# LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
|
|
def llama_token_get_score(ctx: llama_context_p, token: llama_token) -> float:
|
|
return _lib.llama_token_get_score(ctx, token)
|
|
|
|
|
|
_lib.llama_token_get_score.argtypes = [llama_context_p, llama_token]
|
|
_lib.llama_token_get_score.restype = c_float
|
|
|
|
|
|
# LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
|
|
def llama_token_get_type(ctx: llama_context_p, token: llama_token) -> int:
|
|
return _lib.llama_token_get_type(ctx, token)
|
|
|
|
|
|
_lib.llama_token_get_type.argtypes = [llama_context_p, llama_token]
|
|
_lib.llama_token_get_type.restype = ctypes.c_int
|
|
|
|
|
|
# // Special tokens
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx); // beginning-of-sentence
|
|
def llama_token_bos(ctx: llama_context_p) -> llama_token:
|
|
return _lib.llama_token_bos(ctx)
|
|
|
|
|
|
_lib.llama_token_bos.argtypes = [llama_context_p]
|
|
_lib.llama_token_bos.restype = llama_token
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx); // end-of-sentence
|
|
def llama_token_eos(ctx: llama_context_p) -> llama_token:
|
|
return _lib.llama_token_eos(ctx)
|
|
|
|
|
|
_lib.llama_token_eos.argtypes = [llama_context_p]
|
|
_lib.llama_token_eos.restype = llama_token
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx); // next-line
|
|
def llama_token_nl(ctx: llama_context_p) -> llama_token:
|
|
return _lib.llama_token_nl(ctx)
|
|
|
|
|
|
_lib.llama_token_nl.argtypes = [llama_context_p]
|
|
_lib.llama_token_nl.restype = llama_token
|
|
|
|
|
|
# //
|
|
# // Tokenization
|
|
# //
|
|
|
|
|
|
# Convert the provided text into tokens.
|
|
# The tokens pointer must be large enough to hold the resulting tokens.
|
|
# Returns the number of tokens on success, no more than n_max_tokens
|
|
# Returns a negative number on failure - the number of tokens that would have been returned
|
|
# TODO: not sure if correct
|
|
# LLAMA_API int llama_tokenize(
|
|
# struct llama_context * ctx,
|
|
# const char * text,
|
|
# llama_token * tokens,
|
|
# int n_max_tokens,
|
|
# bool add_bos);
|
|
def llama_tokenize(
|
|
ctx: llama_context_p,
|
|
text: bytes,
|
|
tokens, # type: Array[llama_token]
|
|
n_max_tokens: c_int,
|
|
add_bos: c_bool,
|
|
) -> int:
|
|
return _lib.llama_tokenize(ctx, text, tokens, n_max_tokens, add_bos)
|
|
|
|
|
|
_lib.llama_tokenize.argtypes = [llama_context_p, c_char_p, llama_token_p, c_int, c_bool]
|
|
_lib.llama_tokenize.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_tokenize_with_model(
|
|
# const struct llama_model * model,
|
|
# const char * text,
|
|
# llama_token * tokens,
|
|
# int n_max_tokens,
|
|
# bool add_bos);
|
|
def llama_tokenize_with_model(
|
|
model: llama_model_p,
|
|
text: bytes,
|
|
tokens, # type: Array[llama_token]
|
|
n_max_tokens: c_int,
|
|
add_bos: c_bool,
|
|
) -> int:
|
|
return _lib.llama_tokenize_with_model(model, text, tokens, n_max_tokens, add_bos)
|
|
|
|
|
|
_lib.llama_tokenize_with_model.argtypes = [
|
|
llama_model_p,
|
|
c_char_p,
|
|
llama_token_p,
|
|
c_int,
|
|
c_bool,
|
|
]
|
|
_lib.llama_tokenize_with_model.restype = c_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.
|
|
# LLAMA_API int llama_token_to_piece(
|
|
# const struct llama_context * ctx,
|
|
# llama_token token,
|
|
# char * buf,
|
|
# int length);
|
|
def llama_token_to_piece(
|
|
ctx: llama_context_p, token: llama_token, buf: bytes, length: c_int
|
|
) -> int:
|
|
return _lib.llama_token_to_piece(ctx, token, buf, length)
|
|
|
|
|
|
_lib.llama_token_to_piece.argtypes = [llama_context_p, llama_token, c_char_p, c_int]
|
|
_lib.llama_token_to_piece.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_token_to_piece_with_model(
|
|
# const struct llama_model * model,
|
|
# llama_token token,
|
|
# char * buf,
|
|
# int length);
|
|
def llama_token_to_piece_with_model(
|
|
model: llama_model_p, token: llama_token, buf: bytes, length: c_int
|
|
) -> int:
|
|
return _lib.llama_token_to_piece_with_model(model, token, buf, length)
|
|
|
|
|
|
_lib.llama_token_to_piece_with_model.argtypes = [
|
|
llama_model_p,
|
|
llama_token,
|
|
c_char_p,
|
|
c_int,
|
|
]
|
|
_lib.llama_token_to_piece_with_model.restype = c_int
|
|
|
|
# //
|
|
# // Grammar
|
|
# //
|
|
|
|
|
|
# LLAMA_API struct llama_grammar * llama_grammar_init(
|
|
# const llama_grammar_element ** rules,
|
|
# size_t n_rules,
|
|
# size_t start_rule_index);
|
|
def llama_grammar_init(
|
|
rules, # type: Array[llama_grammar_element_p] # type: ignore
|
|
n_rules: c_size_t,
|
|
start_rule_index: c_size_t,
|
|
) -> llama_grammar_p:
|
|
return _lib.llama_grammar_init(rules, n_rules, start_rule_index)
|
|
|
|
|
|
_lib.llama_grammar_init.argtypes = [
|
|
POINTER(llama_grammar_element_p),
|
|
c_size_t,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_grammar_init.restype = llama_grammar_p
|
|
|
|
|
|
# LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
|
|
def llama_grammar_free(grammar: llama_grammar_p):
|
|
return _lib.llama_grammar_free(grammar)
|
|
|
|
|
|
_lib.llama_grammar_free.argtypes = [llama_grammar_p]
|
|
_lib.llama_grammar_free.restype = None
|
|
|
|
|
|
# LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
|
|
def llama_grammar_copy(grammar: llama_grammar_p) -> llama_grammar_p:
|
|
return _lib.llama_grammar_copy(grammar)
|
|
|
|
|
|
_lib.llama_grammar_copy.argtypes = [llama_grammar_p]
|
|
_lib.llama_grammar_copy.restype = llama_grammar_p
|
|
|
|
# //
|
|
# // Sampling functions
|
|
# //
|
|
|
|
|
|
# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
|
|
# LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
|
|
def llama_sample_repetition_penalty(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
last_tokens_data, # type: Array[llama_token]
|
|
last_tokens_size: c_int,
|
|
penalty: c_float,
|
|
):
|
|
return _lib.llama_sample_repetition_penalty(
|
|
ctx, candidates, last_tokens_data, last_tokens_size, penalty
|
|
)
|
|
|
|
|
|
_lib.llama_sample_repetition_penalty.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
llama_token_p,
|
|
c_int,
|
|
c_float,
|
|
]
|
|
_lib.llama_sample_repetition_penalty.restype = None
|
|
|
|
|
|
# @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
|
|
# LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
|
|
def llama_sample_frequency_and_presence_penalties(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
last_tokens_data, # type: Array[llama_token]
|
|
last_tokens_size: c_int,
|
|
alpha_frequency: c_float,
|
|
alpha_presence: c_float,
|
|
):
|
|
return _lib.llama_sample_frequency_and_presence_penalties(
|
|
ctx,
|
|
candidates,
|
|
last_tokens_data,
|
|
last_tokens_size,
|
|
alpha_frequency,
|
|
alpha_presence,
|
|
)
|
|
|
|
|
|
_lib.llama_sample_frequency_and_presence_penalties.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
llama_token_p,
|
|
c_int,
|
|
c_float,
|
|
c_float,
|
|
]
|
|
_lib.llama_sample_frequency_and_presence_penalties.restype = None
|
|
|
|
|
|
# /// @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 candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
|
|
# /// @params guidance_ctx 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.
|
|
# /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
|
|
# LLAMA_API void llama_sample_classifier_free_guidance(
|
|
# struct llama_context * ctx,
|
|
# llama_token_data_array * candidates,
|
|
# struct llama_context * guidance_ctx,
|
|
# float scale);
|
|
def llama_sample_classifier_free_guidance(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
guidance_ctx: llama_context_p,
|
|
scale: c_float,
|
|
):
|
|
return _lib.llama_sample_classifier_free_guidance(
|
|
ctx, candidates, guidance_ctx, scale
|
|
)
|
|
|
|
|
|
_lib.llama_sample_classifier_free_guidance.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
llama_context_p,
|
|
c_float,
|
|
]
|
|
_lib.llama_sample_classifier_free_guidance.restype = None
|
|
|
|
|
|
# @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);
|
|
def llama_sample_softmax(
|
|
ctx: llama_context_p, candidates # type: _Pointer[llama_token_data]
|
|
):
|
|
return _lib.llama_sample_softmax(ctx, candidates)
|
|
|
|
|
|
_lib.llama_sample_softmax.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
]
|
|
_lib.llama_sample_softmax.restype = None
|
|
|
|
|
|
# @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, int k, size_t min_keep);
|
|
def llama_sample_top_k(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
k: c_int,
|
|
min_keep: c_size_t,
|
|
):
|
|
return _lib.llama_sample_top_k(ctx, candidates, k, min_keep)
|
|
|
|
|
|
_lib.llama_sample_top_k.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_int,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_sample_top_k.restype = None
|
|
|
|
|
|
# @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);
|
|
def llama_sample_top_p(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
p: c_float,
|
|
min_keep: c_size_t,
|
|
):
|
|
return _lib.llama_sample_top_p(ctx, candidates, p, min_keep)
|
|
|
|
|
|
_lib.llama_sample_top_p.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_float,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_sample_top_p.restype = None
|
|
|
|
|
|
# @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);
|
|
def llama_sample_tail_free(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
z: c_float,
|
|
min_keep: c_size_t,
|
|
):
|
|
return _lib.llama_sample_tail_free(ctx, candidates, z, min_keep)
|
|
|
|
|
|
_lib.llama_sample_tail_free.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_float,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_sample_tail_free.restype = None
|
|
|
|
|
|
# @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);
|
|
def llama_sample_typical(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
p: c_float,
|
|
min_keep: c_size_t,
|
|
):
|
|
return _lib.llama_sample_typical(ctx, candidates, p, min_keep)
|
|
|
|
|
|
_lib.llama_sample_typical.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_float,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_sample_typical.restype = None
|
|
|
|
|
|
# LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
|
|
def llama_sample_temperature(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
temp: c_float,
|
|
):
|
|
return _lib.llama_sample_temperature(ctx, candidates, temp)
|
|
|
|
|
|
_lib.llama_sample_temperature.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_float,
|
|
]
|
|
_lib.llama_sample_temperature.restype = None
|
|
|
|
|
|
# LLAMA_API void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar);
|
|
def llama_sample_grammar(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
grammar, # type: llama_grammar_p
|
|
):
|
|
return _lib.llama_sample_grammar(ctx, candidates, grammar)
|
|
|
|
|
|
_lib.llama_sample_grammar.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
llama_grammar_p,
|
|
]
|
|
_lib.llama_sample_grammar.restype = None
|
|
|
|
|
|
# @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, int m, float * mu);
|
|
def llama_sample_token_mirostat(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
tau: c_float,
|
|
eta: c_float,
|
|
m: c_int,
|
|
mu, # type: _Pointer[c_float]
|
|
) -> int:
|
|
return _lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)
|
|
|
|
|
|
_lib.llama_sample_token_mirostat.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_float,
|
|
c_float,
|
|
c_int,
|
|
c_float_p,
|
|
]
|
|
_lib.llama_sample_token_mirostat.restype = llama_token
|
|
|
|
|
|
# @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);
|
|
def llama_sample_token_mirostat_v2(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
tau: c_float,
|
|
eta: c_float,
|
|
mu, # type: _Pointer[c_float]
|
|
) -> int:
|
|
return _lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)
|
|
|
|
|
|
_lib.llama_sample_token_mirostat_v2.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_float,
|
|
c_float,
|
|
c_float_p,
|
|
]
|
|
_lib.llama_sample_token_mirostat_v2.restype = llama_token
|
|
|
|
|
|
# @details Selects the token with the highest probability.
|
|
# LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
|
|
def llama_sample_token_greedy(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
) -> int:
|
|
return _lib.llama_sample_token_greedy(ctx, candidates)
|
|
|
|
|
|
_lib.llama_sample_token_greedy.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
]
|
|
_lib.llama_sample_token_greedy.restype = llama_token
|
|
|
|
|
|
# @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);
|
|
def llama_sample_token(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
) -> int:
|
|
return _lib.llama_sample_token(ctx, candidates)
|
|
|
|
|
|
_lib.llama_sample_token.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
]
|
|
_lib.llama_sample_token.restype = llama_token
|
|
|
|
|
|
# /// @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);
|
|
def llama_grammar_accept_token(
|
|
ctx: llama_context_p,
|
|
grammar: llama_grammar_p,
|
|
token: llama_token,
|
|
) -> None:
|
|
_lib.llama_grammar_accept_token(ctx, grammar, token)
|
|
|
|
|
|
_lib.llama_grammar_accept_token.argtypes = [
|
|
llama_context_p,
|
|
llama_grammar_p,
|
|
llama_token,
|
|
]
|
|
_lib.llama_grammar_accept_token.restype = None
|
|
# //
|
|
# // 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", c_size_t),
|
|
("p", c_float),
|
|
("eob", 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", POINTER(llama_beam_view)),
|
|
("n_beams", c_size_t),
|
|
("common_prefix_length", c_size_t),
|
|
("last_call", 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, 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, int n_past, int n_predict, int n_threads);
|
|
def llama_beam_search(
|
|
ctx: llama_context_p,
|
|
callback: "ctypes._CFuncPtr[None, c_void_p, llama_beams_state]", # type: ignore
|
|
callback_data: c_void_p,
|
|
n_beams: c_size_t,
|
|
n_past: c_int,
|
|
n_predict: c_int,
|
|
n_threads: c_int,
|
|
):
|
|
return _lib.llama_beam_search(
|
|
ctx, callback, callback_data, n_beams, n_past, n_predict, n_threads
|
|
)
|
|
|
|
|
|
# Performance information
|
|
|
|
|
|
# LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
|
|
def llama_get_timings(ctx: llama_context_p) -> llama_timings:
|
|
return _lib.llama_get_timings(ctx)
|
|
|
|
|
|
_lib.llama_get_timings.argtypes = [llama_context_p]
|
|
_lib.llama_get_timings.restype = llama_timings
|
|
|
|
|
|
# LLAMA_API void llama_print_timings(struct llama_context * ctx);
|
|
def llama_print_timings(ctx: llama_context_p):
|
|
_lib.llama_print_timings(ctx)
|
|
|
|
|
|
_lib.llama_print_timings.argtypes = [llama_context_p]
|
|
_lib.llama_print_timings.restype = None
|
|
|
|
|
|
# LLAMA_API void llama_reset_timings(struct llama_context * ctx);
|
|
def llama_reset_timings(ctx: llama_context_p):
|
|
_lib.llama_reset_timings(ctx)
|
|
|
|
|
|
_lib.llama_reset_timings.argtypes = [llama_context_p]
|
|
_lib.llama_reset_timings.restype = None
|
|
|
|
|
|
# Print system information
|
|
# LLAMA_API const char * llama_print_system_info(void);
|
|
def llama_print_system_info() -> bytes:
|
|
return _lib.llama_print_system_info()
|
|
|
|
|
|
_lib.llama_print_system_info.argtypes = []
|
|
_lib.llama_print_system_info.restype = c_char_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_log_set(llama_log_callback log_callback, void * user_data);
|
|
def llama_log_set(
|
|
log_callback: "ctypes._FuncPointer", user_data: c_void_p # type: ignore
|
|
):
|
|
return _lib.llama_log_set(log_callback, user_data)
|
|
|
|
|
|
_lib.llama_log_set.argtypes = [llama_log_callback, c_void_p]
|
|
_lib.llama_log_set.restype = None
|
|
|
|
|
|
# LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
|
|
def llama_dump_timing_info_yaml(stream: ctypes.c_void_p, ctx: llama_context_p):
|
|
return _lib.llama_dump_timing_info_yaml(stream, ctx)
|
|
|
|
|
|
_lib.llama_dump_timing_info_yaml.argtypes = [ctypes.c_void_p, llama_context_p]
|
|
_lib.llama_dump_timing_info_yaml.restype = None
|
|
|
|
|
|
###################################################################################################
|
|
|
|
|
|
_llama_initialized = False
|
|
|
|
if not _llama_initialized:
|
|
llama_backend_init(c_bool(False))
|
|
_llama_initialized = True
|