60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
"""
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Author: LiangSong(sl12160010@gmail.com)
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Date: 2023-03-31 13:26:15
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LastEditors: LiangSong(sl12160010@gmail.com)
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LastEditTime: 2023-03-31 14:05:35
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FilePath: /Open-Llama/server.py
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Description:
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Copyright (c) 2023 by LiangSong(sl12160010@gmail.com), All Rights Reserved.
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"""
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import torch
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import gradio as gr
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import sentencepiece as spm
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from dataset.tokenizer import Tokenizer
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from transformers import LlamaForCausalLM, LlamaConfig
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sp_model = spm.SentencePieceProcessor(
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model_file="configs/10w_vocab_wudao5_pile10.model"
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)
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tokenizer = Tokenizer(sp_model)
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raw_model = LlamaForCausalLM(
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LlamaConfig(
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vocab_size=tokenizer.vocab_size,
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initializer_range=0.01,
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pad_token_id=tokenizer.pad_id,
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rms_norm_eps=1e-5,
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hidden_dropout_prob=0.1,
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attention_dropout_prob=0.1,
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use_stable_embedding=True,
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shared_input_output_embedding=True,
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)
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)
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ckpt = torch.load("data/saved_ckpt/instruction_tuning/12001.pt", map_location="cpu")
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raw_model.load_state_dict(ckpt)
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raw_model.eval()
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model = raw_model.cuda()
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print("ready")
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def question_answer(prompt):
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print(prompt)
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raw_inputs = "user:{}<s>system:".format(prompt)
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inputs_len = len(raw_inputs)
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inputs = tokenizer(raw_inputs, return_tensors=True, add_special_tokens=False)
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for k, v in inputs.items():
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inputs[k] = v.cuda()
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pred = model.generate(**inputs, max_new_tokens=512, do_sample=True)
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pred = tokenizer.decode(pred.cpu())[0]
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pred = pred[inputs_len:]
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print(pred)
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return pred
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demo = gr.Interface(fn=question_answer, inputs="text", outputs="text").queue(
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concurrency_count=1
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)
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demo.launch(share=True)
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