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llms.py
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llms.py
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import torch
# from queue import Queue
from typing import Union, List, Optional, Callable, Tuple
from peft import PeftModel
from loguru import logger
# from threading import Thread
from typing import Dict
from transformers import GenerationConfig, PreTrainedTokenizer, PreTrainedModel
from transformers_stream_generator.main import StreamGenerationConfig
from transformers.generation.stopping_criteria import (
StoppingCriteriaList,
StoppingCriteria as _StoppingCriteria,
)
class ChatStreamer:
def __init__(self, tokenizer: PreTrainedTokenizer, generation_stream):
self.tokenizer = tokenizer
self.generation_stream = generation_stream
def __iter__(self):
return self
def __next__(self):
return self.tokenizer.decode(self.generation_stream.__next__(), skip_special_tokens=True)
class StoppingCriteria(_StoppingCriteria):
def __init__(self, stop_token_ids: List[int] = None):
self.stop_token_ids = stop_token_ids
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> bool:
return input_ids[0].tolist()[-1] in self.stop_token_ids
class LLM:
def __init__(self,
model_type: str,
model: PreTrainedModel,
tokenizer: Optional[PreTrainedTokenizer] = None,
adapters: Optional[List[str]] = None):
self.__model_type = model_type
self.__model = model
self.__tokenizer = tokenizer
self.__adapters = adapters
'''
# 旧版glm build_input
def _chatglm_build_input(self, tokenizer, query, history):
user_token_id = tokenizer.get_command("<|user|>")
assistant_token_id = tokenizer.get_command("<|assistant|>")
system_token_id = tokenizer.get_command("<|system|>")
inner_tokenizer = tokenizer.tokenizer
line_content = tokenizer.tokenizer.encode("\n")
line_token = [13]
input_ids = []
system = None
if len(history) > 0:
if history[0]["role"] == "system":
system = history.pop(0)
input_ids += [system_token_id] + line_content
input_ids += inner_tokenizer.encode(system["content"])
for his in history:
if his["role"] == "user":
if system is not None:
input_ids += line_token
input_ids += [user_token_id] + line_content
input_ids += inner_tokenizer.encode(his["content"])
elif his["role"] == "assistant":
input_ids += [assistant_token_id] + line_content
input_ids += inner_tokenizer.encode(his["content"])
if system is not None:
input_ids += line_token
input_ids += [user_token_id] + line_content
input_ids += inner_tokenizer.encode(query) + [assistant_token_id] + line_content
return tokenizer.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True)
'''
def _stream_chat(
self,
inputs,
tokenizer: PreTrainedTokenizer,
generation_config: GenerationConfig,
eos_token_id: List[int]
) -> ChatStreamer:
stream_config = StreamGenerationConfig(
**generation_config.to_dict(),
do_stream=True
)
if tokenizer.eos_token_id is not None:
stream_config.eos_token_id = tokenizer.eos_token_id
if tokenizer.pad_token_id is not None:
stream_config.pad_token_id = tokenizer.pad_token_id
if tokenizer.bos_token_id is not None:
stream_config.bos_token_id = tokenizer.bos_token_id
stopping_criteria = StoppingCriteriaList([StoppingCriteria(eos_token_id)])
return ChatStreamer(
tokenizer,
self.__model.generate_stream(
**inputs,
generation_config=stream_config,
stopping_criteria=stopping_criteria,
seed=-1
)
)
def _llama3_chat(self,
tokenizer: PreTrainedTokenizer,
messages: List[Dict],
generation_config: Optional[GenerationConfig] = None,
stream: bool = True) -> Union[ChatStreamer, str]:
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
inputs = tokenizer.batch_encode_plus([text], return_tensors="pt").to(self.__model.device)
if stream:
response = self._stream_chat(inputs, tokenizer, generation_config, eos_token_id)
else:
outputs = self.__model.generate(**inputs, generation_config=generation_config,
eos_token_id=eos_token_id)
response = tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
return response
def _qwen2_chat(self,
tokenizer: PreTrainedTokenizer,
messages: List[Dict],
generation_config: Optional[GenerationConfig] = None,
stream: bool = True) -> Union[ChatStreamer, str]:
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
eos_token_id = self.__model.generation_config.eos_token_id
inputs = tokenizer.batch_encode_plus([text], return_tensors="pt").to(self.__model.device)
if stream:
response = self._stream_chat(inputs, tokenizer, generation_config, eos_token_id)
else:
outputs = self.__model.generate(**inputs, generation_config=generation_config,
eos_token_id=eos_token_id)
response = tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
return response
def _chatglm_chat(self,
tokenizer: PreTrainedTokenizer,
messages: List[Dict],
generation_config: Optional[GenerationConfig] = None,
stream: bool = True) -> Union[ChatStreamer, str]:
query, role = messages[-1]["content"], messages[-1]["role"]
history = messages[:-1]
# 旧版 ---> 已舍弃
# inputs = self._chatglm_build_input(tokenizer, query, history).to(self.__model.device)
inputs = tokenizer.build_chat_input(query, history).to(self.__model.device)
eos_token_id = [
tokenizer.eos_token_id,
tokenizer.get_command("<|user|>"),
tokenizer.get_command("<|observation|>")
]
if stream:
response = self._stream_chat(inputs, tokenizer, generation_config, eos_token_id)
else:
outputs = self.__model.generate(**inputs, generation_config=generation_config,
eos_token_id=eos_token_id)
response = tokenizer.decode(outputs[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
return response
@property
def generation_config(self):
return self.__model.generation_config
@property
def dtype(self):
return self.__model_type
@torch.inference_mode()
def chat(self,
checkpoint_id: str,
messages: List[Dict],
generation_config: Optional[GenerationConfig] = None,
stream: bool = True) -> Union[ChatStreamer, str]:
logger.info(f"=== checkpoint_id ====\n{checkpoint_id}\n")
logger.info(f"==== messages ====\n{messages}\n")
assert checkpoint_id in self.__adapters, f'{checkpoint_id} is not exists'
if isinstance(self.__model, PeftModel):
if checkpoint_id == "baseline":
self.__model.base_model.disable_adapter_layers()
else:
self.__model.base_model.enable_adapter_layers()
self.__model.set_adapter(checkpoint_id)
if generation_config is None:
generation_config = self.__model.generation_config
logger.info(f"==== generation_config ====\n{generation_config}\n")
if self.__model_type == "chatglm":
return self._chatglm_chat(
self.__tokenizer, messages,
stream=stream,
generation_config=generation_config)
elif self.__model_type == "qwen":
return self._qwen2_chat(
self.__tokenizer,
messages,
stream=stream,
generation_config=generation_config)
elif self.__model_type == "llama":
return self._llama3_chat(
self.__tokenizer,
messages,
stream=stream,
generation_config=generation_config)
return self.__model.chat(self.__tokenizer, messages, stream=stream, generation_config=generation_config)
def embedding(self, inputs: List[str], multi_process=False) -> List[List[float]]:
assert self.__model_type == "embedding"
import sentence_transformers
inputs = list(map(lambda x: x.replace("\n", " "), inputs))
if multi_process:
pool = self.__model.start_multi_process_pool()
embeddings = self.__model.encode_multi_process(inputs, pool)
sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
else:
embeddings = self.__model.encode(inputs)
return embeddings.tolist()
def tokenize(self, inputs: List[str]):
return [self.__tokenizer.encode(text) for text in inputs]
def rerank(self, query: str, docs: List[str]) -> Tuple[List[str], List[float]]:
assert self.__model_type == "reranker"
sentence_pairs = [[query, doc] for doc in docs]
results = self.__model.predict(sentences=sentence_pairs, convert_to_tensor=True)
scores, indices = results.topk(len(results))
return [docs[i] for i in indices], scores
class LLMD:
__MODEL_D: Dict[str, LLM] = dict()
def register(self, model_list: List[Tuple[Union[str, LLM, Callable]]]):
for name, model_or_func, size in model_list:
assert isinstance(model_or_func, (LLM, Callable))
if isinstance(model_or_func, LLM):
self(name, model_or_func)
else:
if size is not None:
self(name, LLM(**model_or_func(size=size)))
else:
self(name, LLM(**model_or_func()))
def __call__(self, model_name: str, model: LLM):
if model_name in self.__MODEL_D:
raise ValueError(f"The model name {model_name} already exists, please change one.")
self.__MODEL_D[model_name] = model
def __getattr__(self, model_name: str) -> Union[LLM, None]:
return self.__MODEL_D.get(model_name)
obj_models = LLMD()
def load_custom_models(*model_names):
global obj_models
from utils import (
get_qwen2,
get_llama3,
get_chatglm3,
get_baichuan2,
get_bge_large_zh,
get_bge_reranker_large,
)
model_dict = {
"chatglm": get_chatglm3,
"baichuan": get_baichuan2,
"qwen": get_qwen2,
"llama": get_llama3,
"embedding": get_bge_large_zh,
'reranker': get_bge_reranker_large,
}
model_size_dict = {
'baichuan': '7B',
'qwen': '0.5B',
'llama': '8B',
}
obj_models.register(
[(name, model_dict[name], model_size_dict.get(name)) for name in model_names if name in model_dict]
)
'''
# 旧版 ChatStreamer
# class ChatStreamer:
# def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
# self.tokenizer = tokenizer
# self.skip_prompt = skip_prompt
# self.skip_special_tokens = skip_special_tokens
# self.tokens = []
# self.text_queue = Queue()
# self.next_tokens_are_prompt = True
#
# def put(self, value):
# if self.skip_prompt and self.next_tokens_are_prompt:
# self.next_tokens_are_prompt = False
# else:
# if len(value.shape) > 1:
# value = value[0]
# self.tokens.extend(value.tolist())
# self.text_queue.put(
# self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens))
#
# def end(self):
# self.text_queue.put(None)
#
# def __iter__(self):
# return self
#
# def __next__(self):
# value = self.text_queue.get()
# if value is None:
# raise StopIteration()
# else:
# return value
'''