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171 lines (140 loc) · 6.49 KB
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import sys
import os
import ctypes
import functools
from ctypes import (
c_bool,
c_char_p,
c_int,
c_uint8,
c_float,
c_void_p,
POINTER,
_Pointer, # type: ignore
Structure,
)
import pathlib
from typing import List, Union, NewType, Optional, TypeVar, Callable, Any
import llama_cpp.llama_cpp as llama_cpp
# Load the library
def _load_shared_library(lib_base_name: str):
# Construct the paths to the possible shared library names
_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__)))
# Searching for the library in the current directory under the name "libllama" (default name
# for llamacpp) and "llama" (default name for this repo)
_lib_paths: List[pathlib.Path] = []
# Determine the file extension based on the platform
if sys.platform.startswith("linux"):
_lib_paths += [
_base_path / f"lib{lib_base_name}.so",
]
elif sys.platform == "darwin":
_lib_paths += [
_base_path / f"lib{lib_base_name}.so",
_base_path / f"lib{lib_base_name}.dylib",
]
elif sys.platform == "win32":
_lib_paths += [
_base_path / f"{lib_base_name}.dll",
_base_path / f"lib{lib_base_name}.dll",
]
else:
raise RuntimeError("Unsupported platform")
if "LLAVA_CPP_LIB" in os.environ:
lib_base_name = os.environ["LLAVA_CPP_LIB"]
_lib = pathlib.Path(lib_base_name)
_base_path = _lib.parent.resolve()
_lib_paths = [_lib.resolve()]
cdll_args = dict() # type: ignore
# Add the library directory to the DLL search path on Windows (if needed)
if sys.platform == "win32" and sys.version_info >= (3, 8):
os.add_dll_directory(str(_base_path))
if "CUDA_PATH" in os.environ:
os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "bin"))
os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "lib"))
cdll_args["winmode"] = ctypes.RTLD_GLOBAL
# Try to load the shared library, handling potential errors
for _lib_path in _lib_paths:
if _lib_path.exists():
try:
return ctypes.CDLL(str(_lib_path), **cdll_args) # type: ignore
except Exception as e:
raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}")
raise FileNotFoundError(
f"Shared library with base name '{lib_base_name}' not found"
)
# Specify the base name of the shared library to load
_libllava_base_name = "llava"
# Load the library
_libllava = _load_shared_library(_libllava_base_name)
# ctypes helper
F = TypeVar("F", bound=Callable[..., Any])
def ctypes_function_for_shared_library(lib: ctypes.CDLL):
def ctypes_function(
name: str, argtypes: List[Any], restype: Any, enabled: bool = True
):
def decorator(f: F) -> F:
if enabled:
func = getattr(lib, name)
func.argtypes = argtypes
func.restype = restype
functools.wraps(f)(func)
return func
else:
return f
return decorator
return ctypes_function
ctypes_function = ctypes_function_for_shared_library(_libllava)
################################################
# llava.h
################################################
# struct clip_ctx;
clip_ctx_p = NewType("clip_ctx_p", int)
clip_ctx_p_ctypes = c_void_p
# struct llava_image_embed {
# float * embed;
# int n_image_pos;
# };
class llava_image_embed(Structure):
_fields_ = [
("embed", POINTER(c_float)),
("n_image_pos", c_int),
]
# /** sanity check for clip <-> llava embed size match */
# LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
@ctypes_function("llava_validate_embed_size", [llama_cpp.llama_context_p_ctypes, clip_ctx_p_ctypes], c_bool)
def llava_validate_embed_size(ctx_llama: llama_cpp.llama_context_p, ctx_clip: clip_ctx_p, /) -> bool:
...
# /** build an image embed from image file bytes */
# 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);
@ctypes_function("llava_image_embed_make_with_bytes", [clip_ctx_p_ctypes, c_int, POINTER(c_uint8), c_int], POINTER(llava_image_embed))
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]":
...
# /** build an image embed from a path to an image filename */
# LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
@ctypes_function("llava_image_embed_make_with_filename", [clip_ctx_p_ctypes, c_int, c_char_p], POINTER(llava_image_embed))
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]":
...
# LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
# /** free an embedding made with llava_image_embed_make_* */
@ctypes_function("llava_image_embed_free", [POINTER(llava_image_embed)], None)
def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /):
...
# /** 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. */
# LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
@ctypes_function("llava_eval_image_embed", [llama_cpp.llama_context_p_ctypes, POINTER(llava_image_embed), c_int, POINTER(c_int)], c_bool)
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:
...
################################################
# clip.h
################################################
# /** load mmproj model */
# CLIP_API struct clip_ctx * clip_model_load (const char * fname, int verbosity);
@ctypes_function("clip_model_load", [c_char_p, c_int], clip_ctx_p_ctypes)
def clip_model_load(fname: bytes, verbosity: Union[c_int, int], /) -> Optional[clip_ctx_p]:
...
# /** free mmproj model */
# CLIP_API void clip_free(struct clip_ctx * ctx);
@ctypes_function("clip_free", [clip_ctx_p_ctypes], None)
def clip_free(ctx: clip_ctx_p, /):
...