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import argparse |
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import os |
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import warnings |
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import mdtex2html |
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import gradio as gr |
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import re |
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pattern = re.compile("[\n]+") |
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import torch |
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from accelerate import init_empty_weights, load_checkpoint_and_dispatch |
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from huggingface_hub import snapshot_download |
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from transformers.generation.utils import logger |
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model_name", default=f"DAMO-NLP-MT/polylm-chat-13b", |
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choices=["DAMO-NLP-MT/polylm-chat-13b"], type=str) |
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parser.add_argument("--gpu", default="0", type=str) |
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args = parser.parse_args() |
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu |
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num_gpus = len(args.gpu.split(",")) |
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if ('int8' in args.model_name or 'int4' in args.model_name) and num_gpus > 1: |
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raise ValueError("Quantized models do not support model parallel. Please run on a single GPU (e.g., --gpu 0).") |
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logger.setLevel("ERROR") |
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warnings.filterwarnings("ignore") |
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model_path = args.model_name |
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if not os.path.exists(args.model_name): |
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model_path = snapshot_download(args.model_name) |
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config = AutoConfig.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) |
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if num_gpus > 1: |
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print("Waiting for all devices to be ready, it may take a few minutes...") |
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with init_empty_weights(): |
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raw_model = AutoModelForCausalLM.from_config(config) |
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raw_model.tie_weights() |
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model = load_checkpoint_and_dispatch( |
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raw_model, model_path, device_map="auto", no_split_module_classes=["GPT2Block"] |
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) |
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else: |
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print("Loading model files, it may take a few minutes...") |
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model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).cuda() |
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print(model.dtype) |
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def postprocess(self, y): |
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if y is None: |
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return [] |
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for i, (message, response) in enumerate(y): |
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y[i] = ( |
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None if message is None else mdtex2html.convert((message)), |
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None if response is None else mdtex2html.convert(response), |
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) |
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return y |
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gr.Chatbot.postprocess = postprocess |
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def parse_text(text): |
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"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" |
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lines = text.split("\n") |
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lines = [line for line in lines if line != ""] |
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count = 0 |
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for i, line in enumerate(lines): |
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if "```" in line: |
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count += 1 |
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items = line.split('`') |
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if count % 2 == 1: |
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lines[i] = f'<pre><code class="language-{items[-1]}">' |
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else: |
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lines[i] = f'<br></code></pre>' |
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else: |
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line = line.replace("`", "\`") |
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line = line.replace("<", "<") |
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line = line.replace(">", ">") |
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line = line.replace(" ", " ") |
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line = line.replace("*", "*") |
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line = line.replace("_", "_") |
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line = line.replace("-", "-") |
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line = line.replace(".", ".") |
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line = line.replace("!", "!") |
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line = line.replace("(", "(") |
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line = line.replace(")", ")") |
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line = line.replace("$", "$") |
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if i > 0: |
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lines[i] = "<br>"+line |
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text = "".join(lines) |
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return text |
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def predict(input, chatbot, max_length, top_p, temperature, history): |
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query = input |
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query = query.strip() |
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chatbot.append((query, "")) |
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prompt = "" |
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for i, (old_query, response) in enumerate(history): |
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response = response.strip() |
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prompt += "<|user|>\n" + f"{old_query}\n" + "<|assistant|>\n" + f"{response}\n" |
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prompt += "<|user|>\n" + f"{query}\n" + "<|assistant|>\n" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model.generate( |
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inputs.input_ids.cuda(), |
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attention_mask=inputs.attention_mask.cuda(), |
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max_length=max_length, |
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do_sample=True, |
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top_p=top_p, |
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temperature=temperature, |
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repetition_penalty=1.02, |
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num_return_sequences=1, |
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eos_token_id=2, |
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early_stopping=True) |
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response = tokenizer.decode( |
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outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) |
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chatbot[-1] = (parse_text(query), parse_text(response.replace("\n ", "\n"))) |
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history = history + [(query, response)] |
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print("==========================================================================") |
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print(f"chatbot is {chatbot}") |
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print(f"history is {history}") |
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print("==========================================================================") |
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return chatbot, history |
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def reset_user_input(): |
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return gr.update(value='') |
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def reset_state(): |
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return [], [] |
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with gr.Blocks() as demo: |
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gr.HTML("""<h1 align="center">欢迎使用 PolyLM 多语言人工智能助手！</h1>""") |
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chatbot = gr.Chatbot() |
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with gr.Row(): |
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with gr.Column(scale=4): |
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with gr.Column(scale=12): |
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user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style( |
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container=False) |
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with gr.Column(min_width=32, scale=1): |
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submitBtn = gr.Button("Submit", variant="primary") |
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with gr.Column(scale=1): |
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emptyBtn = gr.Button("Clear History") |
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max_length = gr.Slider( |
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0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True) |
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top_p = gr.Slider(0, 1, value=0.8, step=0.01, |
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label="Top P", interactive=True) |
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temperature = gr.Slider( |
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0, 1, value=0.7, step=0.01, label="Temperature", interactive=True) |
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history = gr.State([]) |
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submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history], |
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show_progress=True) |
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submitBtn.click(reset_user_input, [], [user_input]) |
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emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True) |
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demo.queue().launch(share=False, inbrowser=True) |
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