mirror of
https://github.com/JasonYANG170/CodeGeeX4.git
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82 lines
3.3 KiB
Python
82 lines
3.3 KiB
Python
"""
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coding : utf-8
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@Date : 2024/7/10
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@Author : Shaobo
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@Describe:
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"""
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import torch
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from protocols.openai_api import ChatCompletionRequest, ChatCompletionStreamResponse, ChatCompletionResponse
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from sseclient import Event
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from transformers import AutoTokenizer, AutoModel
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class CodegeexChatModel:
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def __init__(self, args):
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self.tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, trust_remote_code=True)
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if args.bf16:
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self.model = AutoModel.from_pretrained(
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args.model_name_or_path,
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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).to(args.device).eval()
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else:
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self.model = AutoModel.from_pretrained(
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args.model_name_or_path,
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trust_remote_code=True
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).to(args.device).eval()
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print("Model is initialized.")
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def stream_chat(self, request: ChatCompletionRequest):
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try:
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inputs = self.tokenizer.apply_chat_template(
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conversation=[msg.model_dump() for msg in request.messages],
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add_generation_prompt=True,
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return_tensors="pt",
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return_dict=True
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).to(self.model.device)
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gen_configs = {
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"max_new_tokens": request.max_tokens,
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"temperature": request.temperature,
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"top_p": request.top_p,
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"repetition_penalty": request.presence_penalty,
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"do_sample": True if request.temperature else request.temperature,
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}
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length = 0
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for outputs in self.model.stream_generate(**inputs, **gen_configs):
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response = self.tokenizer.decode(outputs.tolist()[0][len(inputs["input_ids"][0]):-1])
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if not response or response[-1] == "<EFBFBD>":
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continue
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resp = ChatCompletionStreamResponse()
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resp.choices[0].delta.content = response[length:]
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event = Event(data=resp.json(), event='message')
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yield event.dump()
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length = len(response)
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resp = ChatCompletionStreamResponse()
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resp.choices[0].finish_reason = 'stop'
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event = Event(data=resp.json(), event='message')
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yield event.dump()
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except Exception as e:
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resp = ChatCompletionStreamResponse()
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resp.choices[0].finish_reason = 'stop'
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event = Event(data=f"请求报错，错误原因：{e}", event='message')
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yield event.dump()
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def chat(self, request: ChatCompletionRequest):
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try:
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response, _ = self.model.chat(
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self.tokenizer,
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query=request.messages[-1].content,
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history=[msg.model_dump() for msg in request.messages[:-1]],
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max_new_tokens=request.max_tokens,
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temperature=request.temperature,
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top_p=request.top_p,
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repetition_penalty=request.presence_penalty
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)
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resp = ChatCompletionResponse()
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resp.choices[0].message.content = response
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resp.choices[0].finish_reason = 'stop'
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return resp.model_dump()
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except Exception as e:
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return f"请求报错，错误原因：{e}"
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