171 lines
6.5 KiB
Python
171 lines
6.5 KiB
Python
import sys
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import os
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import ctypes
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import functools
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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_uint8,
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c_float,
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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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)
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import pathlib
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from typing import List, Union, NewType, Optional, TypeVar, Callable, Any
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import llama_cpp.llama_cpp as llama_cpp
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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 "LLAVA_CPP_LIB" in os.environ:
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lib_base_name = os.environ["LLAVA_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) # type: ignore
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except Exception as e:
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raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}")
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raise FileNotFoundError(
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f"Shared library with base name '{lib_base_name}' not found"
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)
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# Specify the base name of the shared library to load
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_libllava_base_name = "llava"
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# Load the library
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_libllava = _load_shared_library(_libllava_base_name)
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# ctypes helper
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F = TypeVar("F", bound=Callable[..., Any])
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def ctypes_function_for_shared_library(lib: ctypes.CDLL):
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def ctypes_function(
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name: str, argtypes: List[Any], restype: Any, enabled: bool = True
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):
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def decorator(f: F) -> F:
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if enabled:
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func = getattr(lib, name)
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func.argtypes = argtypes
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func.restype = restype
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functools.wraps(f)(func)
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return func
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else:
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return f
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return decorator
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return ctypes_function
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ctypes_function = ctypes_function_for_shared_library(_libllava)
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################################################
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# llava.h
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################################################
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# struct clip_ctx;
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clip_ctx_p = NewType("clip_ctx_p", int)
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clip_ctx_p_ctypes = c_void_p
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# struct llava_image_embed {
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# float * embed;
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# int n_image_pos;
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# };
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class llava_image_embed(Structure):
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_fields_ = [
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("embed", POINTER(c_float)),
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("n_image_pos", c_int),
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]
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# /** sanity check for clip <-> llava embed size match */
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# LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
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@ctypes_function("llava_validate_embed_size", [llama_cpp.llama_context_p_ctypes, clip_ctx_p_ctypes], c_bool)
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def llava_validate_embed_size(ctx_llama: llama_cpp.llama_context_p, ctx_clip: clip_ctx_p, /) -> bool:
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...
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# /** build an image embed from image file bytes */
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# LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
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@ctypes_function("llava_image_embed_make_with_bytes", [clip_ctx_p_ctypes, c_int, POINTER(c_uint8), c_int], POINTER(llava_image_embed))
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def llava_image_embed_make_with_bytes(ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_bytes: bytes, image_bytes_length: Union[c_int, int], /) -> "_Pointer[llava_image_embed]":
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...
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# /** build an image embed from a path to an image filename */
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# LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
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@ctypes_function("llava_image_embed_make_with_filename", [clip_ctx_p_ctypes, c_int, c_char_p], POINTER(llava_image_embed))
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def llava_image_embed_make_with_filename(ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_path: bytes, /) -> "_Pointer[llava_image_embed]":
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...
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# LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
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# /** free an embedding made with llava_image_embed_make_* */
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@ctypes_function("llava_image_embed_free", [POINTER(llava_image_embed)], None)
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def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /):
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...
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# /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
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# LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
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@ctypes_function("llava_eval_image_embed", [llama_cpp.llama_context_p_ctypes, POINTER(llava_image_embed), c_int, POINTER(c_int)], c_bool)
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def llava_eval_image_embed(ctx_llama: llama_cpp.llama_context_p, embed: "_Pointer[llava_image_embed]", n_batch: Union[c_int, int], n_past: "_Pointer[c_int]", /) -> bool:
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...
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################################################
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# clip.h
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################################################
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# /** load mmproj model */
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# CLIP_API struct clip_ctx * clip_model_load (const char * fname, int verbosity);
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@ctypes_function("clip_model_load", [c_char_p, c_int], clip_ctx_p_ctypes)
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def clip_model_load(fname: bytes, verbosity: Union[c_int, int], /) -> Optional[clip_ctx_p]:
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...
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# /** free mmproj model */
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# CLIP_API void clip_free(struct clip_ctx * ctx);
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@ctypes_function("clip_free", [clip_ctx_p_ctypes], None)
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def clip_free(ctx: clip_ctx_p, /):
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...
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