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- import os, sys
-
- import gradio as gr
- import mdtex2html
-
- import torch
- import transformers
- from transformers import (
- AutoConfig,
- AutoModel,
- AutoTokenizer,
- AutoTokenizer,
- DataCollatorForSeq2Seq,
- HfArgumentParser,
- Seq2SeqTrainingArguments,
- set_seed,
- )
-
- from arguments import ModelArguments, DataTrainingArguments
-
-
- model = None
- tokenizer = None
-
- """Override Chatbot.postprocess"""
-
-
- def postprocess(self, y):
- if y is None:
- return []
- for i, (message, response) in enumerate(y):
- y[i] = (
- None if message is None else mdtex2html.convert((message)),
- None if response is None else mdtex2html.convert(response),
- )
- return y
-
-
- gr.Chatbot.postprocess = postprocess
-
-
- def parse_text(text):
- """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
- lines = text.split("\n")
- lines = [line for line in lines if line != ""]
- count = 0
- for i, line in enumerate(lines):
- if "```" in line:
- count += 1
- items = line.split('`')
- if count % 2 == 1:
- lines[i] = f'<pre><code class="language-{items[-1]}">'
- else:
- lines[i] = f'<br></code></pre>'
- else:
- if i > 0:
- if count % 2 == 1:
- line = line.replace("`", "\`")
- line = line.replace("<", "<")
- line = line.replace(">", ">")
- line = line.replace(" ", " ")
- line = line.replace("*", "*")
- line = line.replace("_", "_")
- line = line.replace("-", "-")
- line = line.replace(".", ".")
- line = line.replace("!", "!")
- line = line.replace("(", "(")
- line = line.replace(")", ")")
- line = line.replace("$", "$")
- lines[i] = "<br>"+line
- text = "".join(lines)
- return text
-
-
- def predict(input, chatbot, max_length, top_p, temperature, history):
- chatbot.append((parse_text(input), ""))
- for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
- temperature=temperature):
- chatbot[-1] = (parse_text(input), parse_text(response))
-
- yield chatbot, history
-
-
- def reset_user_input():
- return gr.update(value='')
-
-
- def reset_state():
- return [], []
-
-
- with gr.Blocks() as demo:
- gr.HTML("""<h1 align="center">ChatGLM</h1>""")
-
- chatbot = gr.Chatbot()
- with gr.Row():
- with gr.Column(scale=4):
- with gr.Column(scale=12):
- user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
- container=False)
- with gr.Column(min_width=32, scale=1):
- submitBtn = gr.Button("Submit", variant="primary")
- with gr.Column(scale=1):
- emptyBtn = gr.Button("Clear History")
- max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
- top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
- temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
-
- history = gr.State([])
-
- submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
- show_progress=True)
- submitBtn.click(reset_user_input, [], [user_input])
-
- emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)
-
-
-
- def main():
- global model, tokenizer
-
- parser = HfArgumentParser((
- ModelArguments))
- if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
- # If we pass only one argument to the script and it's the path to a json file,
- # let's parse it to get our arguments.
- model_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0]
- else:
- model_args = parser.parse_args_into_dataclasses()[0]
-
- tokenizer = AutoTokenizer.from_pretrained(
- model_args.model_name_or_path, trust_remote_code=True)
- config = AutoConfig.from_pretrained(
- model_args.model_name_or_path, trust_remote_code=True)
-
- config.pre_seq_len = model_args.pre_seq_len
- config.prefix_projection = model_args.prefix_projection
-
- if model_args.ptuning_checkpoint is not None:
- print(f"Loading prefix_encoder weight from {model_args.ptuning_checkpoint}")
- model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
- prefix_state_dict = torch.load(os.path.join(model_args.ptuning_checkpoint, "pytorch_model.bin"))
- new_prefix_state_dict = {}
- for k, v in prefix_state_dict.items():
- if k.startswith("transformer.prefix_encoder."):
- new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
- model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
- else:
- model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
-
- if model_args.quantization_bit is not None:
- print(f"Quantized to {model_args.quantization_bit} bit")
- model = model.quantize(model_args.quantization_bit)
-
- if model_args.pre_seq_len is not None:
- # P-tuning v2
- model = model.half().cuda()
- model.transformer.prefix_encoder.float().cuda()
-
- model = model.eval()
- demo.queue().launch(share=False, inbrowser=True)
-
-
-
- if __name__ == "__main__":
- main()
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