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import ctypes
import numpy as np
from enum import IntEnum
from typing import Callable, Any
# Load the shared library
_lib = ctypes.CDLL("./librkllmrt.so") # Adjust the library name if necessary
# Define enums
class LLMCallState(IntEnum):
RKLLM_RUN_NORMAL = 0
RKLLM_RUN_WAITING = 1
RKLLM_RUN_FINISH = 2
RKLLM_RUN_ERROR = 3
RKLLM_RUN_GET_LAST_HIDDEN_LAYER = 4
class RKLLMInputType(IntEnum):
RKLLM_INPUT_PROMPT = 0
RKLLM_INPUT_TOKEN = 1
RKLLM_INPUT_EMBED = 2
RKLLM_INPUT_MULTIMODAL = 3
class RKLLMInferMode(IntEnum):
RKLLM_INFER_GENERATE = 0
RKLLM_INFER_GET_LAST_HIDDEN_LAYER = 1
# Define structures
class RKLLMExtendParam(ctypes.Structure):
_fields_ = [
("base_domain_id", ctypes.c_int32),
("reserved", ctypes.c_uint8 * 112)
]
class RKLLMParam(ctypes.Structure):
_fields_ = [
("model_path", ctypes.c_char_p),
("max_context_len", ctypes.c_int32),
("max_new_tokens", ctypes.c_int32),
("top_k", ctypes.c_int32),
("top_p", ctypes.c_float),
("temperature", ctypes.c_float),
("repeat_penalty", ctypes.c_float),
("frequency_penalty", ctypes.c_float),
("presence_penalty", ctypes.c_float),
("mirostat", ctypes.c_int32),
("mirostat_tau", ctypes.c_float),
("mirostat_eta", ctypes.c_float),
("skip_special_token", ctypes.c_bool),
("is_async", ctypes.c_bool),
("img_start", ctypes.c_char_p),
("img_end", ctypes.c_char_p),
("img_content", ctypes.c_char_p),
("extend_param", RKLLMExtendParam)
]
class RKLLMLoraAdapter(ctypes.Structure):
_fields_ = [
("lora_adapter_path", ctypes.c_char_p),
("lora_adapter_name", ctypes.c_char_p),
("scale", ctypes.c_float)
]
class RKLLMEmbedInput(ctypes.Structure):
_fields_ = [
("embed", ctypes.POINTER(ctypes.c_float)),
("n_tokens", ctypes.c_size_t)
]
class RKLLMTokenInput(ctypes.Structure):
_fields_ = [
("input_ids", ctypes.POINTER(ctypes.c_int32)),
("n_tokens", ctypes.c_size_t)
]
class RKLLMMultiModelInput(ctypes.Structure):
_fields_ = [
("prompt", ctypes.c_char_p),
("image_embed", ctypes.POINTER(ctypes.c_float)),
("n_image_tokens", ctypes.c_size_t)
]
class RKLLMInput(ctypes.Structure):
class _InputUnion(ctypes.Union):
_fields_ = [
("prompt_input", ctypes.c_char_p),
("embed_input", RKLLMEmbedInput),
("token_input", RKLLMTokenInput),
("multimodal_input", RKLLMMultiModelInput)
]
_fields_ = [
("input_type", ctypes.c_int),
("_input", _InputUnion)
]
class RKLLMLoraParam(ctypes.Structure):
_fields_ = [
("lora_adapter_name", ctypes.c_char_p)
]
class RKLLMPromptCacheParam(ctypes.Structure):
_fields_ = [
("save_prompt_cache", ctypes.c_int),
("prompt_cache_path", ctypes.c_char_p)
]
class RKLLMInferParam(ctypes.Structure):
_fields_ = [
("mode", ctypes.c_int),
("lora_params", ctypes.POINTER(RKLLMLoraParam)),
("prompt_cache_params", ctypes.POINTER(RKLLMPromptCacheParam))
]
class RKLLMResultLastHiddenLayer(ctypes.Structure):
_fields_ = [
("hidden_states", ctypes.POINTER(ctypes.c_float)),
("embd_size", ctypes.c_int),
("num_tokens", ctypes.c_int)
]
class RKLLMResult(ctypes.Structure):
_fields_ = [
("text", ctypes.c_char_p),
("token_id", ctypes.c_int32),
("last_hidden_layer", RKLLMResultLastHiddenLayer)
]
# Define callback type
LLMResultCallback = ctypes.CFUNCTYPE(None, ctypes.POINTER(RKLLMResult), ctypes.c_void_p, ctypes.c_int)
# Define function prototypes
_lib.rkllm_createDefaultParam.restype = RKLLMParam
_lib.rkllm_init.argtypes = [ctypes.POINTER(ctypes.c_void_p), ctypes.POINTER(RKLLMParam), LLMResultCallback]
_lib.rkllm_init.restype = ctypes.c_int
_lib.rkllm_load_lora.argtypes = [ctypes.c_void_p, ctypes.POINTER(RKLLMLoraAdapter)]
_lib.rkllm_load_lora.restype = ctypes.c_int
_lib.rkllm_load_prompt_cache.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
_lib.rkllm_load_prompt_cache.restype = ctypes.c_int
_lib.rkllm_release_prompt_cache.argtypes = [ctypes.c_void_p]
_lib.rkllm_release_prompt_cache.restype = ctypes.c_int
_lib.rkllm_destroy.argtypes = [ctypes.c_void_p]
_lib.rkllm_destroy.restype = ctypes.c_int
_lib.rkllm_run.argtypes = [ctypes.c_void_p, ctypes.POINTER(RKLLMInput), ctypes.POINTER(RKLLMInferParam), ctypes.c_void_p]
_lib.rkllm_run.restype = ctypes.c_int
_lib.rkllm_run_async.argtypes = [ctypes.c_void_p, ctypes.POINTER(RKLLMInput), ctypes.POINTER(RKLLMInferParam), ctypes.c_void_p]
_lib.rkllm_run_async.restype = ctypes.c_int
_lib.rkllm_abort.argtypes = [ctypes.c_void_p]
_lib.rkllm_abort.restype = ctypes.c_int
_lib.rkllm_is_running.argtypes = [ctypes.c_void_p]
_lib.rkllm_is_running.restype = ctypes.c_int
# Python wrapper functions
def create_default_param() -> RKLLMParam:
return _lib.rkllm_createDefaultParam()
def init(param: RKLLMParam, callback: Callable[[RKLLMResult, Any, LLMCallState], None]) -> ctypes.c_void_p:
handle = ctypes.c_void_p()
c_callback = LLMResultCallback(callback)
status = _lib.rkllm_init(ctypes.byref(handle), ctypes.byref(param), c_callback)
if status != 0:
raise RuntimeError(f"Failed to initialize RKLLM: {status}")
return handle
def load_lora(handle: ctypes.c_void_p, lora_adapter: RKLLMLoraAdapter) -> None:
status = _lib.rkllm_load_lora(handle, ctypes.byref(lora_adapter))
if status != 0:
raise RuntimeError(f"Failed to load Lora adapter: {status}")
def load_prompt_cache(handle: ctypes.c_void_p, prompt_cache_path: str) -> None:
status = _lib.rkllm_load_prompt_cache(handle, prompt_cache_path.encode())
if status != 0:
raise RuntimeError(f"Failed to load prompt cache: {status}")
def release_prompt_cache(handle: ctypes.c_void_p) -> None:
status = _lib.rkllm_release_prompt_cache(handle)
if status != 0:
raise RuntimeError(f"Failed to release prompt cache: {status}")
def destroy(handle: ctypes.c_void_p) -> None:
status = _lib.rkllm_destroy(handle)
if status != 0:
raise RuntimeError(f"Failed to destroy RKLLM: {status}")
def run(handle: ctypes.c_void_p, rkllm_input: RKLLMInput, rkllm_infer_params: RKLLMInferParam, userdata: Any) -> None:
status = _lib.rkllm_run(handle, ctypes.byref(rkllm_input), ctypes.byref(rkllm_infer_params), ctypes.c_void_p(userdata))
if status != 0:
raise RuntimeError(f"Failed to run RKLLM: {status}")
def run_async(handle: ctypes.c_void_p, rkllm_input: RKLLMInput, rkllm_infer_params: RKLLMInferParam, userdata: Any) -> None:
status = _lib.rkllm_run_async(handle, ctypes.byref(rkllm_input), ctypes.byref(rkllm_infer_params), ctypes.c_void_p(userdata))
if status != 0:
raise RuntimeError(f"Failed to run RKLLM asynchronously: {status}")
def abort(handle: ctypes.c_void_p) -> None:
status = _lib.rkllm_abort(handle)
if status != 0:
raise RuntimeError(f"Failed to abort RKLLM: {status}")
def is_running(handle: ctypes.c_void_p) -> bool:
return _lib.rkllm_is_running(handle) == 0
# Helper function to convert numpy array to C array
def numpy_to_c_array(arr: np.ndarray, c_type):
return arr.ctypes.data_as(ctypes.POINTER(c_type))
# Helper function to create RKLLMInput
def create_rkllm_input(input_type: RKLLMInputType, **kwargs) -> RKLLMInput:
rkllm_input = RKLLMInput()
rkllm_input.input_type = input_type.value
if input_type == RKLLMInputType.RKLLM_INPUT_PROMPT:
rkllm_input._input.prompt_input = kwargs['prompt'].encode()
elif input_type == RKLLMInputType.RKLLM_INPUT_EMBED:
embed = kwargs['embed']
rkllm_input._input.embed_input.embed = numpy_to_c_array(embed, ctypes.c_float)
rkllm_input._input.embed_input.n_tokens = embed.shape[1]
elif input_type == RKLLMInputType.RKLLM_INPUT_TOKEN:
tokens = kwargs['tokens']
rkllm_input._input.token_input.input_ids = numpy_to_c_array(tokens, ctypes.c_int32)
rkllm_input._input.token_input.n_tokens = tokens.shape[1]
elif input_type == RKLLMInputType.RKLLM_INPUT_MULTIMODAL:
rkllm_input._input.multimodal_input.prompt = kwargs['prompt'].encode()
image_embed = kwargs['image_embed']
rkllm_input._input.multimodal_input.image_embed = numpy_to_c_array(image_embed, ctypes.c_float)
rkllm_input._input.multimodal_input.n_image_tokens = image_embed.shape[1]
return rkllm_input |