2400 lines
85 KiB
Python
2400 lines
85 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_bool,
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c_char_p,
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c_int,
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c_int8,
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c_int32,
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c_uint8,
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c_uint32,
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c_size_t,
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c_float,
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c_double,
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c_void_p,
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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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)
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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(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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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)
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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 = 16
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LLAMA_MAX_DEVICES = GGML_CUDA_MAX_DEVICES if GGML_USE_CUBLAS else 1
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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 3
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LLAMA_SESSION_VERSION = 3
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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 int32_t llama_pos;
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llama_pos = c_int32
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# typedef int32_t llama_token;
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llama_token = c_int32
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llama_token_p = POINTER(llama_token)
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# typedef int32_t llama_seq_id;
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llama_seq_id = c_int32
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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 = 0
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LLAMA_VOCAB_TYPE_BPE = 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 = 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_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
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LLAMA_FTYPE_MOSTLY_Q8_0 = 7
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LLAMA_FTYPE_MOSTLY_Q5_0 = 8
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LLAMA_FTYPE_MOSTLY_Q5_1 = 9
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LLAMA_FTYPE_MOSTLY_Q2_K = 10
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LLAMA_FTYPE_MOSTLY_Q3_K_S = 11
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LLAMA_FTYPE_MOSTLY_Q3_K_M = 12
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LLAMA_FTYPE_MOSTLY_Q3_K_L = 13
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LLAMA_FTYPE_MOSTLY_Q4_K_S = 14
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LLAMA_FTYPE_MOSTLY_Q4_K_M = 15
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LLAMA_FTYPE_MOSTLY_Q5_K_S = 16
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LLAMA_FTYPE_MOSTLY_Q5_K_M = 17
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LLAMA_FTYPE_MOSTLY_Q6_K = 18
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LLAMA_FTYPE_GUESSED = 1024
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# enum llama_rope_scaling_type {
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# LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
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# LLAMA_ROPE_SCALING_NONE = 0,
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# LLAMA_ROPE_SCALING_LINEAR = 1,
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# LLAMA_ROPE_SCALING_YARN = 2,
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# LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN,
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# };
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LLAMA_ROPE_SCALING_UNSPECIFIED = -1
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LLAMA_ROPE_SCALING_NONE = 0
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LLAMA_ROPE_SCALING_LINEAR = 1
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LLAMA_ROPE_SCALING_YARN = 2
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LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN
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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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"""Used to store token data
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Attributes:
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id (llama_token): token id
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logit (float): log-odds of the token
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p (float): probability of the token"""
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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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"""Used to sample tokens given logits
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Attributes:
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data (ctypes.Array[llama_token_data]): token data
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size (int): size of the array
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sorted (bool): whether the array is sorted"""
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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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# // Input data for llama_decode
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# // A llama_batch object can contain input about one or many sequences
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# // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
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# //
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# // - token : the token ids of the input (used when embd is NULL)
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# // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
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# // - pos : the positions of the respective token in the sequence
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# // - seq_id : the sequence to which the respective token belongs
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# // - logits : if zero, the logits for the respective token will not be output
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# //
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# typedef struct llama_batch {
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# int32_t n_tokens;
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# llama_token * token;
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# float * embd;
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# llama_pos * pos;
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# int32_t * n_seq_id;
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# llama_seq_id ** seq_id;
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# int8_t * logits;
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# // NOTE: helpers for smooth API transition - can be deprecated in the future
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# // for future-proof code, use the above fields instead and ignore everything below
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# //
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# // pos[i] = all_pos_0 + i*all_pos_1
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# //
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# llama_pos all_pos_0; // used if pos == NULL
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# llama_pos all_pos_1; // used if pos == NULL
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# llama_seq_id all_seq_id; // used if seq_id == NULL
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# } llama_batch;
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class llama_batch(Structure):
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"""Input data for llama_decode
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A llama_batch object can contain input about one or many sequences
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The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
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Attributes:
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token (ctypes.Array[llama_token]): the token ids of the input (used when embd is NULL)
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embd (ctypes.Array[ctypes.c_float]): token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
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pos (ctypes.Array[ctypes.Array[llama_pos]]): the positions of the respective token in the sequence
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seq_id (ctypes.Array[ctypes.Array[llama_seq_id]]): the sequence to which the respective token belongs"""
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_fields_ = [
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("n_tokens", c_int32),
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("token", POINTER(llama_token)),
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("embd", c_float_p),
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("pos", POINTER(llama_pos)),
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("n_seq_id", POINTER(c_int32)),
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("seq_id", POINTER(POINTER(llama_seq_id))),
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("logits", POINTER(c_int8)),
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("all_pos_0", llama_pos),
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("all_pos_1", llama_pos),
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("all_seq_id", llama_seq_id),
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]
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# enum llama_model_kv_override_type {
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# LLAMA_KV_OVERRIDE_INT,
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# LLAMA_KV_OVERRIDE_FLOAT,
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# LLAMA_KV_OVERRIDE_BOOL,
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# };
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class llama_model_kv_override_type(Structure):
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_fields_ = [
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("LLAMA_KV_OVERRIDE_INT", c_int),
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("LLAMA_KV_OVERRIDE_FLOAT", c_int),
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("LLAMA_KV_OVERRIDE_BOOL", c_int),
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]
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# struct llama_model_kv_override {
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# char key[128];
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# enum llama_model_kv_override_type tag;
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# union {
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# int64_t int_value;
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# double float_value;
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# bool bool_value;
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# };
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# };
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class llama_model_kv_override(Structure):
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_fields_ = [
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("key", ctypes.c_char * 128),
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("tag", llama_model_kv_override_type),
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("int_value", ctypes.c_int64),
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("float_value", c_double),
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("bool_value", c_bool),
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]
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# struct llama_model_params {
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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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# // 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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# // override key-value pairs of the model meta data
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# const struct llama_model_kv_override * kv_overrides;
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# // Keep the booleans together to avoid misalignment during copy-by-value.
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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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# };
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class llama_model_params(Structure):
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"""Parameters for llama_model
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Attributes:
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n_gpu_layers (int): number of layers to store in VRAM
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main_gpu (int): the GPU that is used for scratch and small tensors
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tensor_split (ctypes.Array[ctypes.c_float]): how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
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progress_callback (llama_progress_callback): called with a progress value between 0 and 1, pass NULL to disable
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progress_callback_user_data (ctypes.c_void_p): context pointer passed to the progress callback
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kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data
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vocab_only (bool): only load the vocabulary, no weights
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use_mmap (bool): use mmap if possible
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use_mlock (bool): force system to keep model in RAM"""
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_fields_ = [
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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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("progress_callback", llama_progress_callback),
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("progress_callback_user_data", c_void_p),
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("kv_overrides", POINTER(llama_model_kv_override)),
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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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]
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# struct llama_context_params {
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# uint32_t seed; // RNG seed, -1 for random
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# uint32_t n_ctx; // text context, 0 = from model
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# uint32_t n_batch; // prompt processing maximum batch size
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# uint32_t n_threads; // number of threads to use for generation
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# uint32_t n_threads_batch; // number of threads to use for batch processing
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# int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
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# // ref: https://github.com/ggerganov/llama.cpp/pull/2054
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# float rope_freq_base; // RoPE base frequency, 0 = from model
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# float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
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# float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model
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# float yarn_attn_factor; // YaRN magnitude scaling factor
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# float yarn_beta_fast; // YaRN low correction dim
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# float yarn_beta_slow; // YaRN high correction dim
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# uint32_t yarn_orig_ctx; // YaRN original context size
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# enum ggml_type type_k; // data type for K cache
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# enum ggml_type type_v; // data type for V cache
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# // Keep the booleans together to avoid misalignment during copy-by-value.
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# bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
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# bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
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# bool embedding; // embedding mode only
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# bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
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# };
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class llama_context_params(Structure):
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"""Parameters for llama_context
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Attributes:
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seed (int): RNG seed, -1 for random
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n_ctx (int): text context, 0 = from model
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n_batch (int): prompt processing maximum batch size
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n_threads (int): number of threads to use for generation
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n_threads_batch (int): number of threads to use for batch processing
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rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type`
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rope_freq_base (float): RoPE base frequency, 0 = from model
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rope_freq_scale (float): RoPE frequency scaling factor, 0 = from model
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yarn_ext_factor (float): YaRN extrapolation mix factor, negative = from model
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yarn_attn_factor (float): YaRN magnitude scaling factor
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yarn_beta_fast (float): YaRN low correction dim
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yarn_beta_slow (float): YaRN high correction dim
|
|
yarn_orig_ctx (int): YaRN original context size
|
|
type_k (int): data type for K cache
|
|
type_v (int): data type for V cache
|
|
mul_mat_q (bool): if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
|
|
f16_kv (bool): use fp16 for KV cache, fp32 otherwise
|
|
logits_all (bool): the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
|
|
embedding (bool): embedding mode only"""
|
|
_fields_ = [
|
|
("seed", c_uint32),
|
|
("n_ctx", c_uint32),
|
|
("n_batch", c_uint32),
|
|
("n_threads", c_uint32),
|
|
("n_threads_batch", c_uint32),
|
|
("rope_scaling_type", c_int8),
|
|
("rope_freq_base", c_float),
|
|
("rope_freq_scale", c_float),
|
|
("yarn_ext_factor", c_float),
|
|
("yarn_attn_factor", c_float),
|
|
("yarn_beta_fast", c_float),
|
|
("yarn_beta_slow", c_float),
|
|
("yarn_orig_ctx", c_uint32),
|
|
("type_k", c_int),
|
|
("type_v", c_int),
|
|
("mul_mat_q", c_bool),
|
|
("f16_kv", c_bool),
|
|
("logits_all", c_bool),
|
|
("embedding", c_bool),
|
|
]
|
|
|
|
|
|
# // Signature for logging events
|
|
# // Note that text includes the new line character at the end for most events.
|
|
# // If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
|
|
# // if it exists.
|
|
# // It might not exist for progress report where '.' is output repeatedly.
|
|
# typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
|
|
llama_log_callback = ctypes.CFUNCTYPE(None, c_int, c_char_p, c_void_p)
|
|
"""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 {
|
|
# int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
|
|
# enum llama_ftype ftype; // quantize to this llama_ftype
|
|
# bool allow_requantize; // allow quantizing non-f32/f16 tensors
|
|
# bool quantize_output_tensor; // quantize output.weight
|
|
# bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
|
# bool pure; // disable k-quant mixtures and quantize all tensors to the same type
|
|
# } llama_model_quantize_params;
|
|
class llama_model_quantize_params(Structure):
|
|
"""Parameters for llama_model_quantize
|
|
|
|
Attributes:
|
|
nthread (int): number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
|
|
ftype (int): quantize to this llama_ftype
|
|
allow_requantize (bool): allow quantizing non-f32/f16 tensors
|
|
quantize_output_tensor (bool): quantize output.weight
|
|
only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
|
|
pure (bool): disable k-quant mixtures and quantize all tensors to the same type"""
|
|
_fields_ = [
|
|
("nthread", c_int),
|
|
("ftype", c_int),
|
|
("allow_requantize", c_bool),
|
|
("quantize_output_tensor", c_bool),
|
|
("only_copy", c_bool),
|
|
]
|
|
|
|
|
|
# // grammar types
|
|
# struct llama_grammar;
|
|
llama_grammar_p = c_void_p
|
|
|
|
# // grammar element type
|
|
# enum llama_gretype {
|
|
# // end of rule definition
|
|
# LLAMA_GRETYPE_END = 0,
|
|
|
|
# // start of alternate definition for rule
|
|
# LLAMA_GRETYPE_ALT = 1,
|
|
|
|
# // non-terminal element: reference to rule
|
|
# LLAMA_GRETYPE_RULE_REF = 2,
|
|
|
|
# // terminal element: character (code point)
|
|
# LLAMA_GRETYPE_CHAR = 3,
|
|
|
|
# // inverse char(s) ([^a], [^a-b] [^abc])
|
|
# LLAMA_GRETYPE_CHAR_NOT = 4,
|
|
|
|
# // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
|
|
# // be an inclusive range ([a-z])
|
|
# LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
|
|
|
|
# // modifies a preceding LLAMA_GRETYPE_CHAR or
|
|
# // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
|
|
# LLAMA_GRETYPE_CHAR_ALT = 6,
|
|
# };
|
|
LLAMA_GRETYPE_END = 0
|
|
LLAMA_GRETYPE_ALT = 1
|
|
LLAMA_GRETYPE_RULE_REF = 2
|
|
LLAMA_GRETYPE_CHAR = 3
|
|
LLAMA_GRETYPE_CHAR_NOT = 4
|
|
LLAMA_GRETYPE_CHAR_RNG_UPPER = 5
|
|
LLAMA_GRETYPE_CHAR_ALT = 6
|
|
|
|
|
|
# typedef struct llama_grammar_element {
|
|
# enum llama_gretype type;
|
|
# uint32_t value; // Unicode code point or rule ID
|
|
# } llama_grammar_element;
|
|
class llama_grammar_element(Structure):
|
|
_fields_ = [
|
|
("type", c_int),
|
|
("value", c_uint32),
|
|
]
|
|
|
|
|
|
llama_grammar_element_p = POINTER(llama_grammar_element)
|
|
|
|
# // performance timing information
|
|
# struct llama_timings {
|
|
# double t_start_ms;
|
|
# double t_end_ms;
|
|
# double t_load_ms;
|
|
# double t_sample_ms;
|
|
# double t_p_eval_ms;
|
|
# double t_eval_ms;
|
|
|
|
|
|
# int32_t n_sample;
|
|
# int32_t n_p_eval;
|
|
# int32_t n_eval;
|
|
# };
|
|
class llama_timings(Structure):
|
|
_fields_ = [
|
|
("t_start_ms", c_double),
|
|
("t_end_ms", c_double),
|
|
("t_load_ms", c_double),
|
|
("t_sample_ms", c_double),
|
|
("t_p_eval_ms", c_double),
|
|
("t_eval_ms", c_double),
|
|
("n_sample", c_int32),
|
|
("n_p_eval", c_int32),
|
|
("n_eval", c_int32),
|
|
]
|
|
|
|
|
|
# // Helpers for getting default parameters
|
|
# LLAMA_API struct llama_model_params llama_model_default_params(void);
|
|
def llama_model_default_params() -> llama_model_params:
|
|
"""Get default parameters for llama_model"""
|
|
return _lib.llama_model_default_params()
|
|
|
|
|
|
_lib.llama_model_default_params.argtypes = []
|
|
_lib.llama_model_default_params.restype = llama_model_params
|
|
|
|
|
|
# LLAMA_API struct llama_context_params llama_context_default_params(void);
|
|
def llama_context_default_params() -> llama_context_params:
|
|
"""Get default parameters for llama_context"""
|
|
return _lib.llama_context_default_params()
|
|
|
|
|
|
_lib.llama_context_default_params.argtypes = []
|
|
_lib.llama_context_default_params.restype = llama_context_params
|
|
|
|
|
|
# LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
|
|
def llama_model_quantize_default_params() -> llama_model_quantize_params:
|
|
"""Get default parameters for llama_model_quantize"""
|
|
return _lib.llama_model_quantize_default_params()
|
|
|
|
|
|
_lib.llama_model_quantize_default_params.argtypes = []
|
|
_lib.llama_model_quantize_default_params.restype = llama_model_quantize_params
|
|
|
|
|
|
# // Initialize the llama + ggml backend
|
|
# // If numa is true, use NUMA optimizations
|
|
# // Call once at the start of the program
|
|
# LLAMA_API void llama_backend_init(bool numa);
|
|
def llama_backend_init(numa: Union[c_bool, bool]):
|
|
"""Initialize the llama + ggml backend
|
|
If numa is true, use NUMA optimizations
|
|
Call once at the start of the program"""
|
|
return _lib.llama_backend_init(numa)
|
|
|
|
|
|
_lib.llama_backend_init.argtypes = [c_bool]
|
|
_lib.llama_backend_init.restype = None
|
|
|
|
|
|
# // Call once at the end of the program - currently only used for MPI
|
|
# LLAMA_API void llama_backend_free(void);
|
|
def llama_backend_free():
|
|
"""Call once at the end of the program - currently only used for MPI"""
|
|
return _lib.llama_backend_free()
|
|
|
|
|
|
_lib.llama_backend_free.argtypes = []
|
|
_lib.llama_backend_free.restype = None
|
|
|
|
|
|
# LLAMA_API struct llama_model * llama_load_model_from_file(
|
|
# const char * path_model,
|
|
# struct llama_model_params params);
|
|
def llama_load_model_from_file(
|
|
path_model: bytes, params: llama_model_params
|
|
) -> llama_model_p:
|
|
return _lib.llama_load_model_from_file(path_model, params)
|
|
|
|
|
|
_lib.llama_load_model_from_file.argtypes = [c_char_p, llama_model_params]
|
|
_lib.llama_load_model_from_file.restype = llama_model_p
|
|
|
|
|
|
# LLAMA_API void llama_free_model(struct llama_model * model);
|
|
def llama_free_model(model: llama_model_p):
|
|
return _lib.llama_free_model(model)
|
|
|
|
|
|
_lib.llama_free_model.argtypes = [llama_model_p]
|
|
_lib.llama_free_model.restype = None
|
|
|
|
|
|
# LLAMA_API struct llama_context * llama_new_context_with_model(
|
|
# struct llama_model * model,
|
|
# struct llama_context_params params);
|
|
def llama_new_context_with_model(
|
|
model: llama_model_p, params: llama_context_params
|
|
) -> llama_context_p:
|
|
return _lib.llama_new_context_with_model(model, params)
|
|
|
|
|
|
_lib.llama_new_context_with_model.argtypes = [llama_model_p, llama_context_params]
|
|
_lib.llama_new_context_with_model.restype = llama_context_p
|
|
|
|
|
|
# // Frees all allocated memory
|
|
# LLAMA_API void llama_free(struct llama_context * ctx);
|
|
def llama_free(ctx: llama_context_p):
|
|
"""Frees all allocated memory"""
|
|
return _lib.llama_free(ctx)
|
|
|
|
|
|
_lib.llama_free.argtypes = [llama_context_p]
|
|
_lib.llama_free.restype = None
|
|
|
|
|
|
# LLAMA_API int64_t llama_time_us(void);
|
|
def llama_time_us() -> int:
|
|
return _lib.llama_time_us()
|
|
|
|
|
|
_lib.llama_time_us.argtypes = []
|
|
_lib.llama_time_us.restype = ctypes.c_int64
|
|
|
|
|
|
# LLAMA_API int llama_max_devices (void);
|
|
def llama_max_devices() -> int:
|
|
return _lib.llama_max_devices()
|
|
|
|
|
|
_lib.llama_max_devices.argtypes = []
|
|
_lib.llama_max_devices.restype = c_int
|
|
|
|
|
|
# LLAMA_API bool llama_mmap_supported (void);
|
|
def llama_mmap_supported() -> bool:
|
|
return _lib.llama_mmap_supported()
|
|
|
|
|
|
_lib.llama_mmap_supported.argtypes = []
|
|
_lib.llama_mmap_supported.restype = c_bool
|
|
|
|
|
|
# LLAMA_API bool llama_mlock_supported(void);
|
|
def llama_mlock_supported() -> bool:
|
|
return _lib.llama_mlock_supported()
|
|
|
|
|
|
_lib.llama_mlock_supported.argtypes = []
|
|
_lib.llama_mlock_supported.restype = c_bool
|
|
|
|
|
|
# LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
|
|
def llama_get_model(ctx: llama_context_p) -> llama_model_p:
|
|
return _lib.llama_get_model(ctx)
|
|
|
|
|
|
_lib.llama_get_model.argtypes = [llama_context_p]
|
|
_lib.llama_get_model.restype = llama_model_p
|
|
|
|
|
|
# LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
|
|
def llama_n_ctx(ctx: llama_context_p) -> int:
|
|
return _lib.llama_n_ctx(ctx)
|
|
|
|
|
|
_lib.llama_n_ctx.argtypes = [llama_context_p]
|
|
_lib.llama_n_ctx.restype = c_int
|
|
|
|
|
|
# LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
|
|
def llama_vocab_type(model: llama_model_p) -> int:
|
|
return _lib.llama_vocab_type(model)
|
|
|
|
|
|
_lib.llama_vocab_type.argtypes = [llama_model_p]
|
|
_lib.llama_vocab_type.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_n_vocab (const struct llama_model * model);
|
|
def llama_n_vocab(model: llama_model_p) -> int:
|
|
return _lib.llama_n_vocab(model)
|
|
|
|
|
|
_lib.llama_n_vocab.argtypes = [llama_model_p]
|
|
_lib.llama_n_vocab.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
|
|
def llama_n_ctx_train(model: llama_model_p) -> int:
|
|
return _lib.llama_n_ctx_train(model)
|
|
|
|
|
|
_lib.llama_n_ctx_train.argtypes = [llama_model_p]
|
|
_lib.llama_n_ctx_train.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_n_embd (const struct llama_model * model);
|
|
def llama_n_embd(model: llama_model_p) -> int:
|
|
return _lib.llama_n_embd(model)
|
|
|
|
|
|
_lib.llama_n_embd.argtypes = [llama_model_p]
|
|
_lib.llama_n_embd.restype = c_int
|
|
|
|
|
|
# // Get the model's RoPE frequency scaling factor
|
|
# LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
|
|
def llama_rope_freq_scale_train(model: llama_model_p) -> float:
|
|
"""Get the model's RoPE frequency scaling factor"""
|
|
return _lib.llama_rope_freq_scale_train(model)
|
|
|
|
|
|
_lib.llama_rope_freq_scale_train.argtypes = [llama_model_p]
|
|
_lib.llama_rope_freq_scale_train.restype = c_float
|
|
|
|
# // Functions to access the model's GGUF metadata scalar values
|
|
# // - The functions return the length of the string on success, or -1 on failure
|
|
# // - The output string is always null-terminated and cleared on failure
|
|
# // - GGUF array values are not supported by these functions
|
|
|
|
|
|
# // Get metadata value as a string by key name
|
|
# LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
|
|
def llama_model_meta_val_str(
|
|
model: llama_model_p, key: Union[c_char_p, bytes], buf: bytes, buf_size: int
|
|
) -> int:
|
|
"""Get metadata value as a string by key name"""
|
|
return _lib.llama_model_meta_val_str(model, key, buf, buf_size)
|
|
|
|
|
|
_lib.llama_model_meta_val_str.argtypes = [llama_model_p, c_char_p, c_char_p, c_size_t]
|
|
_lib.llama_model_meta_val_str.restype = c_int
|
|
|
|
|
|
# // Get the number of metadata key/value pairs
|
|
# LLAMA_API int llama_model_meta_count(const struct llama_model * model);
|
|
def llama_model_meta_count(model: llama_model_p) -> int:
|
|
"""Get the number of metadata key/value pairs"""
|
|
return _lib.llama_model_meta_count(model)
|
|
|
|
|
|
_lib.llama_model_meta_count.argtypes = [llama_model_p]
|
|
_lib.llama_model_meta_count.restype = c_int
|
|
|
|
|
|
# // Get metadata key name by index
|
|
# LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
|
|
def llama_model_meta_key_by_index(
|
|
model: llama_model_p, i: Union[c_int, int], buf: bytes, buf_size: int
|
|
) -> int:
|
|
"""Get metadata key name by index"""
|
|
return _lib.llama_model_meta_key_by_index(model, i, buf, buf_size)
|
|
|
|
|
|
_lib.llama_model_meta_key_by_index.argtypes = [llama_model_p, c_int, c_char_p, c_size_t]
|
|
_lib.llama_model_meta_key_by_index.restype = c_int
|
|
|
|
|
|
# // Get metadata value as a string by index
|
|
# LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
|
|
def llama_model_meta_val_str_by_index(
|
|
model: llama_model_p, i: Union[c_int, int], buf: bytes, buf_size: int
|
|
) -> int:
|
|
"""Get metadata value as a string by index"""
|
|
return _lib.llama_model_meta_val_str_by_index(model, i, buf, buf_size)
|
|
|
|
|
|
_lib.llama_model_meta_val_str_by_index.argtypes = [
|
|
llama_model_p,
|
|
c_int,
|
|
c_char_p,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_model_meta_val_str_by_index.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: Union[c_size_t, int]
|
|
) -> int:
|
|
"""Get a string describing the model type"""
|
|
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:
|
|
"""Returns the total size of all the tensors in the model in bytes"""
|
|
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:
|
|
"""Returns the total number of parameters in the model"""
|
|
return _lib.llama_model_n_params(model)
|
|
|
|
|
|
_lib.llama_model_n_params.argtypes = [llama_model_p]
|
|
_lib.llama_model_n_params.restype = ctypes.c_uint64
|
|
|
|
|
|
# // Get a llama model tensor
|
|
# LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
|
|
def llama_get_model_tensor(
|
|
model: llama_model_p, name: Union[c_char_p, bytes]
|
|
) -> c_void_p:
|
|
"""Get a llama model tensor"""
|
|
return _lib.llama_get_model_tensor(model, name)
|
|
|
|
|
|
_lib.llama_get_model_tensor.argtypes = [llama_model_p, c_char_p]
|
|
_lib.llama_get_model_tensor.restype = c_void_p
|
|
|
|
|
|
# // Returns 0 on success
|
|
# LLAMA_API int llama_model_quantize(
|
|
# const char * fname_inp,
|
|
# const char * fname_out,
|
|
# const llama_model_quantize_params * params);
|
|
def llama_model_quantize(
|
|
fname_inp: bytes,
|
|
fname_out: bytes,
|
|
params, # type: POINTER(llama_model_quantize_params) # type: ignore
|
|
) -> int:
|
|
"""Returns 0 on success"""
|
|
return _lib.llama_model_quantize(fname_inp, fname_out, params)
|
|
|
|
|
|
_lib.llama_model_quantize.argtypes = [
|
|
c_char_p,
|
|
c_char_p,
|
|
POINTER(llama_model_quantize_params),
|
|
]
|
|
_lib.llama_model_quantize.restype = c_int
|
|
|
|
|
|
# // Apply a LoRA adapter to a loaded model
|
|
# // path_base_model is the path to a higher quality model to use as a base for
|
|
# // the layers modified by the adapter. Can be NULL to use the current loaded model.
|
|
# // The model needs to be reloaded before applying a new adapter, otherwise the adapter
|
|
# // will be applied on top of the previous one
|
|
# // Returns 0 on success
|
|
# LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
|
|
# struct llama_context * ctx,
|
|
# const char * path_lora,
|
|
# float scale,
|
|
# const char * path_base_model,
|
|
# int n_threads),
|
|
# "use llama_model_apply_lora_from_file instead");
|
|
def llama_apply_lora_from_file(
|
|
ctx: llama_context_p,
|
|
path_lora: Union[c_char_p, bytes],
|
|
scale: Union[c_float, float],
|
|
path_base_model: Union[c_char_p, bytes],
|
|
n_threads: Union[c_int, int],
|
|
) -> int:
|
|
"""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"""
|
|
return _lib.llama_apply_lora_from_file(
|
|
ctx, path_lora, scale, path_base_model, n_threads
|
|
)
|
|
|
|
|
|
_lib.llama_apply_lora_from_file.argtypes = [
|
|
llama_context_p,
|
|
c_char_p,
|
|
c_float,
|
|
c_char_p,
|
|
c_int,
|
|
]
|
|
_lib.llama_apply_lora_from_file.restype = c_int
|
|
|
|
|
|
# LLAMA_API int llama_model_apply_lora_from_file(
|
|
# const struct llama_model * model,
|
|
# const char * path_lora,
|
|
# float scale,
|
|
# const char * path_base_model,
|
|
# int n_threads);
|
|
def llama_model_apply_lora_from_file(
|
|
model: llama_model_p,
|
|
path_lora: Union[c_char_p, bytes],
|
|
scale: Union[c_float, float],
|
|
path_base_model: Union[c_char_p, bytes],
|
|
n_threads: Union[c_int, int],
|
|
) -> int:
|
|
return _lib.llama_model_apply_lora_from_file(
|
|
model, path_lora, scale, path_base_model, n_threads
|
|
)
|
|
|
|
|
|
_lib.llama_model_apply_lora_from_file.argtypes = [
|
|
llama_model_p,
|
|
c_char_p,
|
|
c_float,
|
|
c_char_p,
|
|
c_int,
|
|
]
|
|
_lib.llama_model_apply_lora_from_file.restype = c_int
|
|
|
|
# //
|
|
# // KV cache
|
|
# //
|
|
|
|
|
|
# // 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(Structure):
|
|
_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_max_seq;
|
|
|
|
# // 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_max_seq items per cell.
|
|
# llama_seq_id * cells_sequences;
|
|
# };
|
|
class llama_kv_cache_view(Structure):
|
|
_fields_ = [
|
|
("n_cells", c_int32),
|
|
("n_max_seq", c_int32),
|
|
("token_count", c_int32),
|
|
("used_cells", c_int32),
|
|
("max_contiguous", c_int32),
|
|
("max_contiguous_idx", c_int32),
|
|
("cells", POINTER(llama_kv_cache_view_cell)),
|
|
("cells_sequences", POINTER(llama_seq_id)),
|
|
]
|
|
|
|
|
|
# // 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_max_seq);
|
|
def llama_kv_cache_view_init(
|
|
ctx: llama_context_p, n_max_seq: Union[c_int32, int]
|
|
) -> llama_kv_cache_view:
|
|
"""Create an empty KV cache view. (use only for debugging purposes)"""
|
|
return _lib.llama_kv_cache_view_init(ctx, n_max_seq)
|
|
|
|
|
|
_lib.llama_kv_cache_view_init.argtypes = [llama_context_p, c_int32]
|
|
_lib.llama_kv_cache_view_init.restype = llama_kv_cache_view
|
|
|
|
|
|
# // Free a KV cache view. (use only for debugging purposes)
|
|
# LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
|
|
def llama_kv_cache_view_free(view: llama_kv_cache_view):
|
|
"""Free a KV cache view. (use only for debugging purposes)"""
|
|
return _lib.llama_kv_cache_view_free(view)
|
|
|
|
|
|
_lib.llama_kv_cache_view_free.argtypes = [llama_kv_cache_view]
|
|
_lib.llama_kv_cache_view_free.restype = None
|
|
|
|
|
|
# // 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);
|
|
def llama_kv_cache_view_update(ctx: llama_context_p, view: llama_kv_cache_view):
|
|
"""Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)"""
|
|
return _lib.llama_kv_cache_view_update(ctx, view)
|
|
|
|
|
|
_lib.llama_kv_cache_view_update.argtypes = [llama_context_p, llama_kv_cache_view]
|
|
_lib.llama_kv_cache_view_update.restype = None
|
|
|
|
|
|
# // 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 int llama_get_kv_cache_token_count(const struct llama_context * ctx);
|
|
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
|
|
"""
|
|
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
|
|
|
|
|
|
# // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
|
|
# LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context * ctx);
|
|
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)"""
|
|
return _lib.llama_get_kv_cache_used_cells(ctx)
|
|
|
|
|
|
_lib.llama_get_kv_cache_used_cells.argtypes = [llama_context_p]
|
|
_lib.llama_get_kv_cache_used_cells.restype = c_int
|
|
|
|
|
|
# // Clear the KV cache
|
|
# LLAMA_API void llama_kv_cache_clear(
|
|
# struct llama_context * ctx);
|
|
def llama_kv_cache_clear(ctx: llama_context_p):
|
|
"""Clear the KV cache"""
|
|
return _lib.llama_kv_cache_clear(ctx)
|
|
|
|
|
|
_lib.llama_kv_cache_clear.argtypes = [llama_context_p]
|
|
_lib.llama_kv_cache_clear.restype = None
|
|
|
|
|
|
# // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
|
|
# // seq_id < 0 : match any sequence
|
|
# // p0 < 0 : [0, p1]
|
|
# // p1 < 0 : [p0, inf)
|
|
# LLAMA_API void llama_kv_cache_seq_rm(
|
|
# struct llama_context * ctx,
|
|
# llama_seq_id seq_id,
|
|
# llama_pos p0,
|
|
# llama_pos p1);
|
|
def llama_kv_cache_seq_rm(
|
|
ctx: llama_context_p,
|
|
seq_id: Union[llama_seq_id, int],
|
|
p0: Union[llama_pos, int],
|
|
p1: Union[llama_pos, int],
|
|
):
|
|
"""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)"""
|
|
return _lib.llama_kv_cache_seq_rm(ctx, seq_id, p0, p1)
|
|
|
|
|
|
_lib.llama_kv_cache_seq_rm.argtypes = [
|
|
llama_context_p,
|
|
llama_seq_id,
|
|
llama_pos,
|
|
llama_pos,
|
|
]
|
|
_lib.llama_kv_cache_seq_rm.restype = None
|
|
|
|
|
|
# // Copy all tokens that belong to the specified sequence to another sequence
|
|
# // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
|
|
# // p0 < 0 : [0, p1]
|
|
# // p1 < 0 : [p0, inf)
|
|
# LLAMA_API void llama_kv_cache_seq_cp(
|
|
# struct llama_context * ctx,
|
|
# llama_seq_id seq_id_src,
|
|
# llama_seq_id seq_id_dst,
|
|
# llama_pos p0,
|
|
# llama_pos p1);
|
|
def llama_kv_cache_seq_cp(
|
|
ctx: llama_context_p,
|
|
seq_id_src: 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)"""
|
|
return _lib.llama_kv_cache_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1)
|
|
|
|
|
|
_lib.llama_kv_cache_seq_cp.argtypes = [
|
|
llama_context_p,
|
|
llama_seq_id,
|
|
llama_seq_id,
|
|
llama_pos,
|
|
llama_pos,
|
|
]
|
|
_lib.llama_kv_cache_seq_cp.restype = None
|
|
|
|
|
|
# // Removes all tokens that do not belong to the specified sequence
|
|
# LLAMA_API void llama_kv_cache_seq_keep(
|
|
# struct llama_context * ctx,
|
|
# llama_seq_id seq_id);
|
|
def llama_kv_cache_seq_keep(
|
|
ctx: llama_context_p,
|
|
seq_id: Union[llama_seq_id, int],
|
|
):
|
|
"""Removes all tokens that do not belong to the specified sequence"""
|
|
return _lib.llama_kv_cache_seq_keep(ctx, seq_id)
|
|
|
|
|
|
_lib.llama_kv_cache_seq_keep.argtypes = [llama_context_p, llama_seq_id]
|
|
_lib.llama_kv_cache_seq_keep.restype = None
|
|
|
|
|
|
# // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
|
|
# // If the KV cache is RoPEd, the KV data is updated accordingly
|
|
# // p0 < 0 : [0, p1]
|
|
# // p1 < 0 : [p0, inf)
|
|
# LLAMA_API void llama_kv_cache_seq_shift(
|
|
# struct llama_context * ctx,
|
|
# llama_seq_id seq_id,
|
|
# llama_pos p0,
|
|
# llama_pos p1,
|
|
# llama_pos delta);
|
|
def llama_kv_cache_seq_shift(
|
|
ctx: llama_context_p,
|
|
seq_id: Union[llama_seq_id, int],
|
|
p0: Union[llama_pos, int],
|
|
p1: Union[llama_pos, int],
|
|
delta: Union[llama_pos, int],
|
|
):
|
|
"""Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
|
|
If the KV cache is RoPEd, the KV data is updated accordingly
|
|
p0 < 0 : [0, p1]
|
|
p1 < 0 : [p0, inf)"""
|
|
return _lib.llama_kv_cache_seq_shift(ctx, seq_id, p0, p1, delta)
|
|
|
|
|
|
_lib.llama_kv_cache_seq_shift.argtypes = [
|
|
llama_context_p,
|
|
llama_seq_id,
|
|
llama_pos,
|
|
llama_pos,
|
|
llama_pos,
|
|
]
|
|
_lib.llama_kv_cache_seq_shift.restype = None
|
|
|
|
# //
|
|
# // State / sessions
|
|
# //
|
|
|
|
|
|
# Returns the maximum size in bytes of the state (rng, logits, embedding
|
|
# and kv_cache) - will often be smaller after compacting tokens
|
|
# LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
|
|
def llama_get_state_size(ctx: llama_context_p) -> int:
|
|
"""Returns the maximum size in bytes of the state (rng, logits, embedding
|
|
and kv_cache) - will often be smaller after compacting tokens"""
|
|
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:
|
|
"""Copies the state to the specified destination address.
|
|
Destination needs to have allocated enough memory.
|
|
Returns the number of bytes copied"""
|
|
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:
|
|
"""Set the state reading from the specified address"""
|
|
return _lib.llama_set_state_data(ctx, src)
|
|
|
|
|
|
_lib.llama_set_state_data.argtypes = [llama_context_p, c_uint8_p]
|
|
_lib.llama_set_state_data.restype = c_size_t
|
|
|
|
|
|
# Save/load session file
|
|
# LLAMA_API bool llama_load_session_file(
|
|
# struct llama_context * ctx,
|
|
# const char * path_session,
|
|
# llama_token * tokens_out,
|
|
# size_t n_token_capacity,
|
|
# size_t * n_token_count_out);
|
|
def llama_load_session_file(
|
|
ctx: llama_context_p,
|
|
path_session: bytes,
|
|
tokens_out, # type: Array[llama_token]
|
|
n_token_capacity: Union[c_size_t, int],
|
|
n_token_count_out, # type: _Pointer[c_size_t]
|
|
) -> int:
|
|
return _lib.llama_load_session_file(
|
|
ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
|
|
)
|
|
|
|
|
|
_lib.llama_load_session_file.argtypes = [
|
|
llama_context_p,
|
|
c_char_p,
|
|
llama_token_p,
|
|
c_size_t,
|
|
c_size_t_p,
|
|
]
|
|
_lib.llama_load_session_file.restype = c_size_t
|
|
|
|
|
|
# LLAMA_API bool llama_save_session_file(
|
|
# struct llama_context * ctx,
|
|
# const char * path_session,
|
|
# const llama_token * tokens,
|
|
# size_t n_token_count);
|
|
def llama_save_session_file(
|
|
ctx: llama_context_p,
|
|
path_session: bytes,
|
|
tokens, # type: Array[llama_token]
|
|
n_token_count: Union[c_size_t, int],
|
|
) -> int:
|
|
return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)
|
|
|
|
|
|
_lib.llama_save_session_file.argtypes = [
|
|
llama_context_p,
|
|
c_char_p,
|
|
llama_token_p,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_save_session_file.restype = c_size_t
|
|
|
|
# //
|
|
# // Decoding
|
|
# //
|
|
|
|
|
|
# // Run the llama inference to obtain the logits and probabilities for the next token(s).
|
|
# // 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
|
|
# // DEPRECATED: use llama_decode() instead
|
|
# LLAMA_API DEPRECATED(int llama_eval(
|
|
# struct llama_context * ctx,
|
|
# llama_token * tokens,
|
|
# int32_t n_tokens,
|
|
# int n_past),
|
|
# "use llama_decode() instead");
|
|
def llama_eval(
|
|
ctx: llama_context_p,
|
|
tokens, # type: Array[llama_token]
|
|
n_tokens: Union[c_int, int],
|
|
n_past: Union[c_int, int],
|
|
) -> int:
|
|
"""Run the llama inference to obtain the logits and probabilities for the next token(s).
|
|
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
|
|
DEPRECATED: use llama_decode() instead"""
|
|
return _lib.llama_eval(ctx, tokens, n_tokens, n_past)
|
|
|
|
|
|
_lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int]
|
|
_lib.llama_eval.restype = c_int
|
|
|
|
|
|
# // Same as llama_eval, but use float matrix input directly.
|
|
# // DEPRECATED: use llama_decode() instead
|
|
# LLAMA_API DEPRECATED(int llama_eval_embd(
|
|
# struct llama_context * ctx,
|
|
# float * embd,
|
|
# int32_t n_tokens,
|
|
# int n_past),
|
|
# "use llama_decode() instead");
|
|
def llama_eval_embd(
|
|
ctx: llama_context_p,
|
|
embd, # type: Array[c_float]
|
|
n_tokens: Union[c_int, int],
|
|
n_past: Union[c_int, int],
|
|
) -> int:
|
|
"""Same as llama_eval, but use float matrix input directly.
|
|
DEPRECATED: use llama_decode() instead"""
|
|
return _lib.llama_eval_embd(ctx, embd, n_tokens, n_past)
|
|
|
|
|
|
_lib.llama_eval_embd.argtypes = [llama_context_p, c_float_p, c_int, c_int]
|
|
_lib.llama_eval_embd.restype = c_int
|
|
|
|
|
|
# // 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);
|
|
def llama_batch_get_one(
|
|
tokens, # type: Array[llama_token]
|
|
n_tokens: Union[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
|
|
"""
|
|
return _lib.llama_batch_get_one(tokens, n_tokens, pos_0, seq_id)
|
|
|
|
|
|
_lib.llama_batch_get_one.argtypes = [
|
|
llama_token_p,
|
|
c_int,
|
|
llama_pos,
|
|
llama_seq_id,
|
|
]
|
|
_lib.llama_batch_get_one.restype = 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
|
|
# LLAMA_API struct llama_batch llama_batch_init(
|
|
# int32_t n_tokens,
|
|
# int32_t embd,
|
|
# int32_t n_seq_max);
|
|
def llama_batch_init(
|
|
n_tokens: Union[c_int32, int],
|
|
embd: Union[c_int32, int],
|
|
n_seq_max: Union[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"""
|
|
return _lib.llama_batch_init(n_tokens, embd, n_seq_max)
|
|
|
|
|
|
_lib.llama_batch_init.argtypes = [c_int32, c_int32, c_int32]
|
|
_lib.llama_batch_init.restype = llama_batch
|
|
|
|
|
|
# // Frees a batch of tokens allocated with llama_batch_init()
|
|
# LLAMA_API void llama_batch_free(struct llama_batch batch);
|
|
def llama_batch_free(batch: llama_batch):
|
|
"""Frees a batch of tokens allocated with llama_batch_init()"""
|
|
return _lib.llama_batch_free(batch)
|
|
|
|
|
|
_lib.llama_batch_free.argtypes = [llama_batch]
|
|
_lib.llama_batch_free.restype = None
|
|
|
|
|
|
# // 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 int llama_decode(
|
|
# struct llama_context * ctx,
|
|
# struct llama_batch batch);
|
|
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"""
|
|
return _lib.llama_decode(ctx, batch)
|
|
|
|
|
|
_lib.llama_decode.argtypes = [llama_context_p, llama_batch]
|
|
_lib.llama_decode.restype = c_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)
|
|
# LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
|
|
def llama_set_n_threads(
|
|
ctx: llama_context_p,
|
|
n_threads: Union[c_uint32, int],
|
|
n_threads_batch: Union[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)
|
|
"""
|
|
return _lib.llama_set_n_threads(ctx, n_threads, n_threads_batch)
|
|
|
|
|
|
_lib.llama_set_n_threads.argtypes = [llama_context_p, c_uint32, c_uint32]
|
|
_lib.llama_set_n_threads.restype = None
|
|
|
|
|
|
# // 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
|
|
# LLAMA_API float * llama_get_logits(struct llama_context * ctx);
|
|
def llama_get_logits(
|
|
ctx: llama_context_p,
|
|
): # type: (...) -> Array[float] # type: ignore
|
|
"""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"""
|
|
return _lib.llama_get_logits(ctx)
|
|
|
|
|
|
_lib.llama_get_logits.argtypes = [llama_context_p]
|
|
_lib.llama_get_logits.restype = c_float_p
|
|
|
|
|
|
# // 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);
|
|
def llama_get_logits_ith(
|
|
ctx: llama_context_p, i: Union[c_int32, int]
|
|
): # type: (...) -> Array[float] # type: ignore
|
|
"""Logits for the ith token. Equivalent to:
|
|
llama_get_logits(ctx) + i*n_vocab"""
|
|
return _lib.llama_get_logits_ith(ctx, i)
|
|
|
|
|
|
_lib.llama_get_logits_ith.argtypes = [llama_context_p, c_int32]
|
|
_lib.llama_get_logits_ith.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
|
|
"""Get the embeddings for the input
|
|
shape: [n_embd] (1-dimensional)"""
|
|
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_model * model, llama_token token);
|
|
def llama_token_get_text(model: llama_model_p, token: Union[llama_token, int]) -> bytes:
|
|
return _lib.llama_token_get_text(model, token)
|
|
|
|
|
|
_lib.llama_token_get_text.argtypes = [llama_model_p, llama_token]
|
|
_lib.llama_token_get_text.restype = c_char_p
|
|
|
|
|
|
# LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
|
|
def llama_token_get_score(
|
|
model: llama_model_p, token: Union[llama_token, int]
|
|
) -> float:
|
|
return _lib.llama_token_get_score(model, token)
|
|
|
|
|
|
_lib.llama_token_get_score.argtypes = [llama_model_p, llama_token]
|
|
_lib.llama_token_get_score.restype = c_float
|
|
|
|
|
|
# LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
|
|
def llama_token_get_type(model: llama_model_p, token: Union[llama_token, int]) -> int:
|
|
return _lib.llama_token_get_type(model, token)
|
|
|
|
|
|
_lib.llama_token_get_type.argtypes = [llama_model_p, llama_token]
|
|
_lib.llama_token_get_type.restype = ctypes.c_int
|
|
|
|
|
|
# // Special tokens
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
|
|
def llama_token_bos(model: llama_model_p) -> int:
|
|
"""beginning-of-sentence"""
|
|
return _lib.llama_token_bos(model)
|
|
|
|
|
|
_lib.llama_token_bos.argtypes = [llama_model_p]
|
|
_lib.llama_token_bos.restype = llama_token
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
|
|
def llama_token_eos(model: llama_model_p) -> int:
|
|
"""end-of-sentence"""
|
|
return _lib.llama_token_eos(model)
|
|
|
|
|
|
_lib.llama_token_eos.argtypes = [llama_model_p]
|
|
_lib.llama_token_eos.restype = llama_token
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
|
|
def llama_token_nl(model: llama_model_p) -> int:
|
|
"""next-line"""
|
|
return _lib.llama_token_nl(model)
|
|
|
|
|
|
_lib.llama_token_nl.argtypes = [llama_model_p]
|
|
_lib.llama_token_nl.restype = llama_token
|
|
|
|
|
|
# // Returns -1 if unknown, 1 for true or 0 for false.
|
|
# LLAMA_API int llama_add_bos_token(const struct llama_model * model);
|
|
def llama_add_bos_token(model: llama_model_p) -> int:
|
|
"""Returns -1 if unknown, 1 for true or 0 for false."""
|
|
return _lib.llama_add_bos_token(model)
|
|
|
|
|
|
_lib.llama_add_bos_token.argtypes = [llama_model_p]
|
|
_lib.llama_add_bos_token.restype = c_int
|
|
|
|
|
|
# // Returns -1 if unknown, 1 for true or 0 for false.
|
|
# LLAMA_API int llama_add_eos_token(const struct llama_model * model);
|
|
def llama_add_eos_token(model: llama_model_p) -> int:
|
|
"""Returns -1 if unknown, 1 for true or 0 for false."""
|
|
return _lib.llama_add_eos_token(model)
|
|
|
|
|
|
_lib.llama_add_eos_token.argtypes = [llama_model_p]
|
|
_lib.llama_add_eos_token.restype = c_int
|
|
|
|
|
|
# // codellama infill tokens
|
|
# LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
|
|
def llama_token_prefix(model: llama_model_p) -> int:
|
|
"""codellama infill tokens"""
|
|
return _lib.llama_token_prefix(model)
|
|
|
|
|
|
_lib.llama_token_prefix.argtypes = [llama_model_p]
|
|
_lib.llama_token_prefix.restype = llama_token
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
|
|
def llama_token_middle(model: llama_model_p) -> int:
|
|
return _lib.llama_token_middle(model)
|
|
|
|
|
|
_lib.llama_token_middle.argtypes = [llama_model_p]
|
|
_lib.llama_token_middle.restype = llama_token
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
|
|
def llama_token_suffix(model: llama_model_p) -> int:
|
|
return _lib.llama_token_suffix(model)
|
|
|
|
|
|
_lib.llama_token_suffix.argtypes = [llama_model_p]
|
|
_lib.llama_token_suffix.restype = llama_token
|
|
|
|
|
|
# LLAMA_API llama_token llama_token_eot (const struct llama_model * model); // End of infill middle
|
|
def llama_token_eot(model: llama_model_p) -> int:
|
|
return _lib.llama_token_eot(model)
|
|
|
|
|
|
_lib.llama_token_eot.argtypes = [llama_model_p]
|
|
_lib.llama_token_eot.restype = llama_token
|
|
|
|
|
|
# //
|
|
# // 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_max_tokens
|
|
# /// @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 int llama_tokenize(
|
|
# const struct llama_model * model,
|
|
# const char * text,
|
|
# int text_len,
|
|
# llama_token * tokens,
|
|
# int n_max_tokens,
|
|
# bool add_bos,
|
|
# bool special);
|
|
def llama_tokenize(
|
|
model: llama_model_p,
|
|
text: bytes,
|
|
text_len: Union[c_int, int],
|
|
tokens, # type: Array[llama_token]
|
|
n_max_tokens: Union[c_int, int],
|
|
add_bos: Union[c_bool, bool],
|
|
special: Union[c_bool, bool],
|
|
) -> int:
|
|
"""Convert the provided text into tokens."""
|
|
return _lib.llama_tokenize(
|
|
model, text, text_len, tokens, n_max_tokens, add_bos, special
|
|
)
|
|
|
|
|
|
_lib.llama_tokenize.argtypes = [
|
|
llama_model_p,
|
|
c_char_p,
|
|
c_int,
|
|
llama_token_p,
|
|
c_int,
|
|
c_bool,
|
|
c_bool,
|
|
]
|
|
_lib.llama_tokenize.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_model * model,
|
|
# llama_token token,
|
|
# char * buf,
|
|
# int length);
|
|
def llama_token_to_piece(
|
|
model: llama_model_p,
|
|
token: Union[llama_token, int],
|
|
buf: Union[c_char_p, bytes],
|
|
length: Union[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.
|
|
"""
|
|
return _lib.llama_token_to_piece(model, token, buf, length)
|
|
|
|
|
|
_lib.llama_token_to_piece.argtypes = [llama_model_p, llama_token, c_char_p, c_int]
|
|
_lib.llama_token_to_piece.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: Union[c_size_t, int],
|
|
start_rule_index: Union[c_size_t, int],
|
|
) -> llama_grammar_p:
|
|
"""Initialize a grammar from a set of rules."""
|
|
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):
|
|
"""Free a grammar."""
|
|
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:
|
|
"""Copy a grammar."""
|
|
return _lib.llama_grammar_copy(grammar)
|
|
|
|
|
|
_lib.llama_grammar_copy.argtypes = [llama_grammar_p]
|
|
_lib.llama_grammar_copy.restype = llama_grammar_p
|
|
|
|
# //
|
|
# // Sampling functions
|
|
# //
|
|
|
|
|
|
# // Sets the current rng seed.
|
|
# LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
|
|
def llama_set_rng_seed(ctx: llama_context_p, seed: Union[c_uint32, int]):
|
|
"""Sets the current rng seed."""
|
|
return _lib.llama_set_rng_seed(ctx, seed)
|
|
|
|
|
|
_lib.llama_set_rng_seed.argtypes = [llama_context_p, c_uint32]
|
|
_lib.llama_set_rng_seed.restype = None
|
|
|
|
|
|
# /// @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);
|
|
def llama_sample_repetition_penalties(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
last_tokens_data, # type: Array[llama_token]
|
|
penalty_last_n: Union[c_size_t, int],
|
|
penalty_repeat: Union[c_float, float],
|
|
penalty_freq: Union[c_float, float],
|
|
penalty_present: Union[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.
|
|
"""
|
|
return _lib.llama_sample_repetition_penalties(
|
|
ctx,
|
|
candidates,
|
|
last_tokens_data,
|
|
penalty_last_n,
|
|
penalty_repeat,
|
|
penalty_freq,
|
|
penalty_present,
|
|
)
|
|
|
|
|
|
_lib.llama_sample_repetition_penalties.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
llama_token_p,
|
|
c_size_t,
|
|
c_float,
|
|
c_float,
|
|
c_float,
|
|
]
|
|
_lib.llama_sample_repetition_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: Union[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"""
|
|
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]
|
|
):
|
|
"""Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits."""
|
|
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: Union[c_int, int],
|
|
min_keep: Union[c_size_t, int],
|
|
):
|
|
"""Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751"""
|
|
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: Union[c_float, float],
|
|
min_keep: Union[c_size_t, int],
|
|
):
|
|
"""Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751"""
|
|
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 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);
|
|
def llama_sample_min_p(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
p: Union[c_float, float],
|
|
min_keep: Union[c_size_t, int],
|
|
):
|
|
"""Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841"""
|
|
return _lib.llama_sample_min_p(ctx, candidates, p, min_keep)
|
|
|
|
|
|
_lib.llama_sample_min_p.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_float,
|
|
c_size_t,
|
|
]
|
|
_lib.llama_sample_min_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: Union[c_float, float],
|
|
min_keep: Union[c_size_t, int],
|
|
):
|
|
"""Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/."""
|
|
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: Union[c_float, float],
|
|
min_keep: Union[c_size_t, int],
|
|
):
|
|
"""Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666."""
|
|
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_temp(
|
|
# struct llama_context * ctx,
|
|
# llama_token_data_array * candidates,
|
|
# float temp);
|
|
def llama_sample_temp(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
temp: Union[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."""
|
|
return _lib.llama_sample_temp(ctx, candidates, temp)
|
|
|
|
|
|
_lib.llama_sample_temp.argtypes = [
|
|
llama_context_p,
|
|
llama_token_data_array_p,
|
|
c_float,
|
|
]
|
|
_lib.llama_sample_temp.restype = None
|
|
|
|
|
|
# LLAMA_API DEPRECATED(void llama_sample_temperature(
|
|
# struct llama_context * ctx,
|
|
# llama_token_data_array * candidates,
|
|
# float temp),
|
|
# "use llama_sample_temp instead");
|
|
def llama_sample_temperature(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
temp: Union[c_float, float],
|
|
):
|
|
"""use llama_sample_temp instead"""
|
|
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
|
|
|
|
|
|
# /// @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);
|
|
def llama_sample_grammar(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[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."""
|
|
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: Union[c_float, float],
|
|
eta: Union[c_float, float],
|
|
m: Union[c_int, int],
|
|
mu, # type: _Pointer[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."""
|
|
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: Union[c_float, float],
|
|
eta: Union[c_float, float],
|
|
mu, # type: _Pointer[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."""
|
|
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.
|
|
# /// 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);
|
|
def llama_sample_token_greedy(
|
|
ctx: llama_context_p,
|
|
candidates, # type: _Pointer[llama_token_data_array]
|
|
) -> int:
|
|
"""Selects the token with the highest probability."""
|
|
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:
|
|
"""Randomly selects a token from the candidates based on their probabilities."""
|
|
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: Union[llama_token, int],
|
|
) -> None:
|
|
"""Accepts the sampled token into the grammar"""
|
|
_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);
|
|
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: Union[c_size_t, int],
|
|
n_past: Union[c_int, int],
|
|
n_predict: Union[c_int, int],
|
|
):
|
|
return _lib.llama_beam_search(
|
|
ctx, callback, callback_data, n_beams, n_past, n_predict
|
|
)
|
|
|
|
|
|
_lib.llama_beam_search.argtypes = [
|
|
llama_context_p,
|
|
llama_beam_search_callback_fn_t,
|
|
c_void_p,
|
|
c_size_t,
|
|
c_int,
|
|
c_int,
|
|
]
|
|
_lib.llama_beam_search.restype = None
|
|
|
|
|
|
# Performance information
|
|
|
|
|
|
# LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
|
|
def llama_get_timings(ctx: llama_context_p) -> llama_timings:
|
|
"""Get performance information"""
|
|
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):
|
|
"""Print performance information"""
|
|
_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):
|
|
"""Reset performance information"""
|
|
_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:
|
|
"""Print system information"""
|
|
return _lib.llama_print_system_info()
|
|
|
|
|
|
_lib.llama_print_system_info.argtypes = []
|
|
_lib.llama_print_system_info.restype = c_char_p
|
|
|
|
|
|
# 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);
|
|
def llama_log_set(
|
|
log_callback: "ctypes._FuncPointer", user_data: c_void_p # type: ignore
|
|
):
|
|
"""Set callback for all future logging events.
|
|
|
|
If this is not called, or NULL is supplied, everything is output on stderr."""
|
|
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
|