656 lines
18 KiB
Python
656 lines
18 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_int,
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c_float,
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c_char_p,
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c_void_p,
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c_bool,
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POINTER,
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Structure,
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Array,
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c_uint8,
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c_size_t,
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)
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import pathlib
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# Load the library
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def _load_shared_library(lib_base_name):
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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_ext = ".so"
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elif sys.platform == "darwin":
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lib_ext = ".so"
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elif sys.platform == "win32":
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lib_ext = ".dll"
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else:
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raise RuntimeError("Unsupported platform")
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# Construct the paths to the possible shared library names
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_base_path = pathlib.Path(__file__).parent.resolve()
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# Searching for the library in the current directory under the name "libllama" (default name
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# for llamacpp) and "llama" (default name for this repo)
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_lib_paths = [
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_base_path / f"lib{lib_base_name}{lib_ext}",
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_base_path / f"{lib_base_name}{lib_ext}",
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]
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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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# 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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# 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))
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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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# C types
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LLAMA_FILE_VERSION = ctypes.c_int(1)
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LLAMA_FILE_MAGIC = b"ggjt"
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LLAMA_FILE_MAGIC_UNVERSIONED = b"ggml"
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LLAMA_SESSION_MAGIC = b"ggsn"
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LLAMA_SESSION_VERSION = ctypes.c_int(0)
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llama_context_p = c_void_p
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llama_token = c_int
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llama_token_p = POINTER(llama_token)
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class llama_token_data(Structure):
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_fields_ = [
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("id", llama_token), # token id
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("logit", c_float), # log-odds of the token
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("p", c_float), # probability of the token
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]
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llama_token_data_p = POINTER(llama_token_data)
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class llama_token_data_array(Structure):
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_fields_ = [
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("data", llama_token_data_p),
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("size", c_size_t),
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("sorted", c_bool),
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]
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llama_token_data_array_p = POINTER(llama_token_data_array)
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llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
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class llama_context_params(Structure):
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_fields_ = [
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("n_ctx", c_int), # text context
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("n_parts", c_int), # -1 for default
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("seed", c_int), # RNG seed, 0 for random
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("f16_kv", c_bool), # use fp16 for KV cache
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(
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"logits_all",
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c_bool,
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), # the llama_eval() call computes all logits, not just the last one
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("vocab_only", c_bool), # only load the vocabulary, no weights
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("use_mmap", c_bool), # use mmap if possible
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("use_mlock", c_bool), # force system to keep model in RAM
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("embedding", c_bool), # embedding mode only
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# called with a progress value between 0 and 1, pass NULL to disable
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("progress_callback", llama_progress_callback),
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# context pointer passed to the progress callback
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("progress_callback_user_data", c_void_p),
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]
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llama_context_params_p = POINTER(llama_context_params)
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LLAMA_FTYPE_ALL_F32 = ctypes.c_int(0)
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LLAMA_FTYPE_MOSTLY_F16 = ctypes.c_int(1) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_0 = ctypes.c_int(2) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1 = ctypes.c_int(3) # except 1d tensors
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LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = ctypes.c_int(
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4
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) # tok_embeddings.weight and output.weight are F16
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LLAMA_FTYPE_MOSTLY_Q4_2 = ctypes.c_int(5) # except 1d tensors
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# LLAMA_FTYPE_MOSTYL_Q4_3 = ctypes.c_int(6) # except 1d tensors
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LLAMA_FTYPE_MOSTYL_Q8_0 = ctypes.c_int(7) # except 1d tensors
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LLAMA_FTYPE_MOSTYL_Q5_0 = ctypes.c_int(8) # except 1d tensors
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LLAMA_FTYPE_MOSTYL_Q5_1 = ctypes.c_int(9) # except 1d tensors
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# Functions
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def llama_context_default_params() -> llama_context_params:
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return _lib.llama_context_default_params()
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_lib.llama_context_default_params.argtypes = []
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_lib.llama_context_default_params.restype = llama_context_params
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def llama_mmap_supported() -> c_bool:
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return _lib.llama_mmap_supported()
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_lib.llama_mmap_supported.argtypes = []
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_lib.llama_mmap_supported.restype = c_bool
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def llama_mlock_supported() -> c_bool:
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return _lib.llama_mlock_supported()
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_lib.llama_mlock_supported.argtypes = []
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_lib.llama_mlock_supported.restype = c_bool
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# Various functions for loading a ggml llama model.
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# Allocate (almost) all memory needed for the model.
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# Return NULL on failure
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def llama_init_from_file(
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path_model: bytes, params: llama_context_params
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) -> llama_context_p:
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return _lib.llama_init_from_file(path_model, params)
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_lib.llama_init_from_file.argtypes = [c_char_p, llama_context_params]
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_lib.llama_init_from_file.restype = llama_context_p
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# Frees all allocated memory
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def llama_free(ctx: llama_context_p):
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_lib.llama_free(ctx)
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_lib.llama_free.argtypes = [llama_context_p]
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_lib.llama_free.restype = None
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# TODO: not great API - very likely to change
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# Returns 0 on success
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# nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
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def llama_model_quantize(
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fname_inp: bytes, fname_out: bytes, ftype: c_int, nthread: c_int
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) -> c_int:
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return _lib.llama_model_quantize(fname_inp, fname_out, ftype, nthread)
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_lib.llama_model_quantize.argtypes = [c_char_p, c_char_p, c_int, c_int]
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_lib.llama_model_quantize.restype = c_int
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# Apply a LoRA adapter to a loaded model
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# path_base_model is the path to a higher quality model to use as a base for
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# the layers modified by the adapter. Can be NULL to use the current loaded model.
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# The model needs to be reloaded before applying a new adapter, otherwise the adapter
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# will be applied on top of the previous one
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# Returns 0 on success
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def llama_apply_lora_from_file(
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ctx: llama_context_p,
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path_lora: ctypes.c_char_p,
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path_base_model: ctypes.c_char_p,
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n_threads: c_int,
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) -> c_int:
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return _lib.llama_apply_lora_from_file(ctx, path_lora, path_base_model, n_threads)
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_lib.llama_apply_lora_from_file.argtypes = [llama_context_p, c_char_p, c_char_p, c_int]
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_lib.llama_apply_lora_from_file.restype = c_int
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# Returns the number of tokens in the KV cache
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def llama_get_kv_cache_token_count(ctx: llama_context_p) -> c_int:
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return _lib.llama_get_kv_cache_token_count(ctx)
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_lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p]
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_lib.llama_get_kv_cache_token_count.restype = c_int
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# Sets the current rng seed.
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def llama_set_rng_seed(ctx: llama_context_p, seed: c_int):
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return _lib.llama_set_rng_seed(ctx, seed)
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_lib.llama_set_rng_seed.argtypes = [llama_context_p, c_int]
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_lib.llama_set_rng_seed.restype = None
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# Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
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def llama_get_state_size(ctx: llama_context_p) -> c_size_t:
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return _lib.llama_get_state_size(ctx)
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_lib.llama_get_state_size.argtypes = [llama_context_p]
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_lib.llama_get_state_size.restype = c_size_t
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# Copies the state to the specified destination address.
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# Destination needs to have allocated enough memory.
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# Returns the number of bytes copied
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def llama_copy_state_data(ctx: llama_context_p, dest) -> c_size_t:
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return _lib.llama_copy_state_data(ctx, dest)
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_lib.llama_copy_state_data.argtypes = [llama_context_p, POINTER(c_uint8)]
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_lib.llama_copy_state_data.restype = c_size_t
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# Set the state reading from the specified address
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# Returns the number of bytes read
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def llama_set_state_data(ctx: llama_context_p, src) -> c_size_t:
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return _lib.llama_set_state_data(ctx, src)
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_lib.llama_set_state_data.argtypes = [llama_context_p, POINTER(c_uint8)]
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_lib.llama_set_state_data.restype = c_size_t
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# Save/load session file
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def llama_load_session_file(
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ctx: llama_context_p,
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path_session: bytes,
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tokens_out,
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n_token_capacity: c_size_t,
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n_token_count_out,
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) -> c_size_t:
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return _lib.llama_load_session_file(
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ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
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)
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_lib.llama_load_session_file.argtypes = [
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llama_context_p,
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c_char_p,
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llama_token_p,
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c_size_t,
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POINTER(c_size_t),
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]
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_lib.llama_load_session_file.restype = c_size_t
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def llama_save_session_file(
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ctx: llama_context_p, path_session: bytes, tokens, n_token_count: c_size_t
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) -> c_size_t:
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return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)
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_lib.llama_save_session_file.argtypes = [
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llama_context_p,
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c_char_p,
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llama_token_p,
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c_size_t,
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]
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_lib.llama_save_session_file.restype = c_size_t
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# Run the llama inference to obtain the logits and probabilities for the next token.
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# tokens + n_tokens is the provided batch of new tokens to process
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# n_past is the number of tokens to use from previous eval calls
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# Returns 0 on success
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def llama_eval(
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ctx: llama_context_p,
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tokens, # type: Array[llama_token]
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n_tokens: c_int,
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n_past: c_int,
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n_threads: c_int,
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) -> c_int:
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return _lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
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_lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_int]
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_lib.llama_eval.restype = c_int
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# Convert the provided text into tokens.
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# The tokens pointer must be large enough to hold the resulting tokens.
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# Returns the number of tokens on success, no more than n_max_tokens
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# Returns a negative number on failure - the number of tokens that would have been returned
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# TODO: not sure if correct
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def llama_tokenize(
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ctx: llama_context_p,
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text: bytes,
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tokens, # type: Array[llama_token]
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n_max_tokens: c_int,
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add_bos: c_bool,
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) -> c_int:
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return _lib.llama_tokenize(ctx, text, tokens, n_max_tokens, add_bos)
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_lib.llama_tokenize.argtypes = [llama_context_p, c_char_p, llama_token_p, c_int, c_bool]
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_lib.llama_tokenize.restype = c_int
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def llama_n_vocab(ctx: llama_context_p) -> c_int:
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return _lib.llama_n_vocab(ctx)
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_lib.llama_n_vocab.argtypes = [llama_context_p]
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_lib.llama_n_vocab.restype = c_int
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def llama_n_ctx(ctx: llama_context_p) -> c_int:
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return _lib.llama_n_ctx(ctx)
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_lib.llama_n_ctx.argtypes = [llama_context_p]
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_lib.llama_n_ctx.restype = c_int
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def llama_n_embd(ctx: llama_context_p) -> c_int:
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return _lib.llama_n_embd(ctx)
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_lib.llama_n_embd.argtypes = [llama_context_p]
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_lib.llama_n_embd.restype = c_int
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# Token logits obtained from the last call to llama_eval()
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# The logits for the last token are stored in the last row
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# Can be mutated in order to change the probabilities of the next token
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# Rows: n_tokens
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# Cols: n_vocab
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def llama_get_logits(ctx: llama_context_p):
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return _lib.llama_get_logits(ctx)
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_lib.llama_get_logits.argtypes = [llama_context_p]
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_lib.llama_get_logits.restype = POINTER(c_float)
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# Get the embeddings for the input
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# shape: [n_embd] (1-dimensional)
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def llama_get_embeddings(ctx: llama_context_p):
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return _lib.llama_get_embeddings(ctx)
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_lib.llama_get_embeddings.argtypes = [llama_context_p]
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_lib.llama_get_embeddings.restype = POINTER(c_float)
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# Token Id -> String. Uses the vocabulary in the provided context
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def llama_token_to_str(ctx: llama_context_p, token: llama_token) -> bytes:
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return _lib.llama_token_to_str(ctx, token)
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_lib.llama_token_to_str.argtypes = [llama_context_p, llama_token]
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_lib.llama_token_to_str.restype = c_char_p
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# Special tokens
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def llama_token_bos() -> llama_token:
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return _lib.llama_token_bos()
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_lib.llama_token_bos.argtypes = []
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_lib.llama_token_bos.restype = llama_token
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def llama_token_eos() -> llama_token:
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return _lib.llama_token_eos()
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_lib.llama_token_eos.argtypes = []
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_lib.llama_token_eos.restype = llama_token
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def llama_token_nl() -> llama_token:
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return _lib.llama_token_nl()
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_lib.llama_token_nl.argtypes = []
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_lib.llama_token_nl.restype = llama_token
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# Sampling functions
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def llama_sample_repetition_penalty(
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ctx: llama_context_p,
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candidates,
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last_tokens_data,
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last_tokens_size: c_int,
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penalty: c_float,
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) -> llama_token:
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return _lib.llama_sample_repetition_penalty(
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ctx, candidates, last_tokens_data, last_tokens_size, penalty
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)
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_lib.llama_sample_repetition_penalty.argtypes = [
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llama_context_p,
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llama_token_data_array_p,
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llama_token_p,
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c_int,
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c_float,
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]
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_lib.llama_sample_repetition_penalty.restype = llama_token
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# LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
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def llama_sample_frequency_and_presence_penalties(
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ctx: llama_context_p,
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candidates,
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last_tokens_data,
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last_tokens_size: c_int,
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alpha_frequency: c_float,
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alpha_presence: c_float,
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) -> llama_token:
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return _lib.llama_sample_frequency_and_presence_penalties(
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ctx,
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candidates,
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last_tokens_data,
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last_tokens_size,
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alpha_frequency,
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alpha_presence,
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)
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_lib.llama_sample_frequency_and_presence_penalties.argtypes = [
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llama_context_p,
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llama_token_data_array_p,
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llama_token_p,
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c_int,
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c_float,
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c_float,
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]
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_lib.llama_sample_frequency_and_presence_penalties.restype = llama_token
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# LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
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def llama_sample_softmax(ctx: llama_context_p, candidates) -> llama_token:
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return _lib.llama_sample_softmax(ctx, candidates)
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|
|
|
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_lib.llama_sample_softmax.argtypes = [
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|
llama_context_p,
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|
llama_token_data_array_p,
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|
]
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_lib.llama_sample_softmax.restype = llama_token
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|
|
|
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# LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep = 1);
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|
def llama_sample_top_k(
|
|
ctx: llama_context_p, candidates, k: c_int, min_keep: c_int
|
|
) -> llama_token:
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|
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_int,
|
|
]
|
|
|
|
|
|
# LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
|
|
def llama_sample_top_p(
|
|
ctx: llama_context_p, candidates, p: c_float, min_keep: c_int
|
|
) -> llama_token:
|
|
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_int,
|
|
]
|
|
_lib.llama_sample_top_p.restype = llama_token
|
|
|
|
|
|
# LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep = 1);
|
|
def llama_sample_tail_free(
|
|
ctx: llama_context_p, candidates, z: c_float, min_keep: c_int
|
|
) -> llama_token:
|
|
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_int,
|
|
]
|
|
_lib.llama_sample_tail_free.restype = llama_token
|
|
|
|
|
|
# LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
|
|
def llama_sample_typical(
|
|
ctx: llama_context_p, candidates, p: c_float, min_keep: c_int
|
|
) -> llama_token:
|
|
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_int,
|
|
]
|
|
_lib.llama_sample_typical.restype = llama_token
|
|
|
|
|
|
# LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
|
|
def llama_sample_temperature(
|
|
ctx: llama_context_p, candidates, temp: c_float
|
|
) -> llama_token:
|
|
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 = llama_token
|
|
|
|
|
|
# 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, tau: c_float, eta: c_float, m: c_int, mu
|
|
) -> llama_token:
|
|
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,
|
|
POINTER(c_float),
|
|
]
|
|
_lib.llama_sample_token_mirostat.restype = llama_token
|
|
|
|
|
|
# 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, tau: c_float, eta: c_float, mu
|
|
) -> llama_token:
|
|
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,
|
|
POINTER(c_float),
|
|
]
|
|
_lib.llama_sample_token_mirostat_v2.restype = llama_token
|
|
|
|
|
|
# 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) -> llama_token:
|
|
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
|
|
|
|
|
|
# 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) -> llama_token:
|
|
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
|
|
|
|
|
|
# Performance information
|
|
|
|
|
|
def llama_print_timings(ctx: llama_context_p):
|
|
_lib.llama_print_timings(ctx)
|
|
|
|
|
|
_lib.llama_print_timings.argtypes = [llama_context_p]
|
|
_lib.llama_print_timings.restype = None
|
|
|
|
|
|
def llama_reset_timings(ctx: llama_context_p):
|
|
_lib.llama_reset_timings(ctx)
|
|
|
|
|
|
_lib.llama_reset_timings.argtypes = [llama_context_p]
|
|
_lib.llama_reset_timings.restype = None
|
|
|
|
|
|
# Print system information
|
|
def llama_print_system_info() -> bytes:
|
|
return _lib.llama_print_system_info()
|
|
|
|
|
|
_lib.llama_print_system_info.argtypes = []
|
|
_lib.llama_print_system_info.restype = c_char_p
|