llama-cpp-python/llama_cpp/llava_cpp.py at main · ExtReMLapin/llama-cpp-python · GitHub

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from __future__ import annotations

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,

TYPE_CHECKING,

Generic,

)

from typing_extensions import TypeAlias

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__))) / "lib"

# 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

if TYPE_CHECKING:

CtypesCData = TypeVar("CtypesCData", bound=ctypes._CData) # type: ignore

CtypesArray: TypeAlias = ctypes.Array[CtypesCData] # type: ignore

CtypesPointer: TypeAlias = ctypes._Pointer[CtypesCData] # type: ignore

CtypesVoidPointer: TypeAlias = ctypes.c_void_p

class CtypesRef(Generic[CtypesCData]):

pass

CtypesPointerOrRef: TypeAlias = Union[

CtypesPointer[CtypesCData], CtypesRef[CtypesCData]

]

CtypesFuncPointer: TypeAlias = ctypes._FuncPointer # type: ignore

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: CtypesArray[c_uint8],

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, /): ...

llama-cpp-python/llama_cpp/llava_cpp.py at main · ExtReMLapin/llama-cpp-python · GitHub

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