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from fastapi import FastAPI, Request
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from transformers import AutoTokenizer, AutoModel
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import uvicorn, json, datetime
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import torch
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DEVICE = "cuda"
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DEVICE_ID = "0"
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CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
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def torch_gc():
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if torch.cuda.is_available():
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with torch.cuda.device(CUDA_DEVICE):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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app = FastAPI()
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@app.post("/")
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async def create_item(request: Request):
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global model, tokenizer
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json_post_raw = await request.json()
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json_post = json.dumps(json_post_raw)
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json_post_list = json.loads(json_post)
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prompt = json_post_list.get('prompt')
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history = json_post_list.get('history')
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max_length = json_post_list.get('max_length')
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top_p = json_post_list.get('top_p')
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temperature = json_post_list.get('temperature')
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response, history = model.chat(tokenizer,
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prompt,
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history=history,
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max_length=max_length if max_length else 2048,
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top_p=top_p if top_p else 0.7,
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temperature=temperature if temperature else 0.95)
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now = datetime.datetime.now()
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time = now.strftime("%Y-%m-%d %H:%M:%S")
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answer = {
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"response": response,
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"history": history,
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"status": 200,
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"time": time
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}
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log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
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print(log)
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torch_gc()
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return answer
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if __name__ == '__main__':
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
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# 多显卡支持，使用下面三行代替上面两行，将num_gpus改为你实际的显卡数量
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# model_path = "THUDM/chatglm2-6b"
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# tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# model = load_model_on_gpus(model_path, num_gpus=2)
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model.eval()
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uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
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