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web_demo.py

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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("<", "&lt;")
+                    line = line.replace(">", "&gt;")
+                    line = line.replace(" ", "&nbsp;")
+                    line = line.replace("*", "&ast;")
+                    line = line.replace("_", "&lowbar;")
+                    line = line.replace("-", "&#45;")
+                    line = line.replace(".", "&#46;")
+                    line = line.replace("!", "&#33;")
+                    line = line.replace("(", "&#40;")
+                    line = line.replace(")", "&#41;")
+                    line = line.replace("$", "&#36;")
+                lines[i] = "<br>"+line
+    text = "".join(lines)
+    return text
+
+
+def predict(input, chatbot, max_length, top_p, temperature, history, past_key_values):
+    chatbot.append((parse_text(input), ""))
+    for response, history, past_key_values in model.stream_chat(tokenizer, input, history, past_key_values=past_key_values,
+                                                                return_past_key_values=True,
+                                                                max_length=max_length, top_p=top_p,
+                                                                temperature=temperature):
+        chatbot[-1] = (parse_text(input), parse_text(response))
+
+        yield chatbot, history, past_key_values
+
+
+def reset_user_input():
+    return gr.update(value='')
+
+
+def reset_state():
+    return [], [], None
+
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1 align="center">ChatGLM2-6B</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, 32768, value=8192, step=1.0, label="Maximum length", interactive=True)
+            top_p = gr.Slider(0, 1, value=0.8, 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([])
+    past_key_values = gr.State(None)
+
+    submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history, past_key_values],
+                    [chatbot, history, past_key_values], show_progress=True)
+    submitBtn.click(reset_user_input, [], [user_input])
+
+    emptyBtn.click(reset_state, outputs=[chatbot, history, past_key_values], 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)
+    model = model.cuda()
+    if model_args.pre_seq_len is not None:
+        # P-tuning v2
+        model.transformer.prefix_encoder.float()
+    
+    model = model.eval()
+    demo.queue().launch(share=True, inbrowser=True)
+
+
+
+if __name__ == "__main__":
+    main()

web_demo.sh

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+PRE_SEQ_LEN=128
+LR=1e-4
+STEPS=600
+
+CUDA_VISIBLE_DEVICES=0 python3 web_demo.py \
+    --model_name_or_path THUDM/chatglm2-6b \
+    --ptuning_checkpoint ./ \
+    --pre_seq_len $PRE_SEQ_LEN
+