✨ feat: add deploy

README.md

@@ -85,6 +85,12 @@ python finetune.py \
 
 </details>
 
+### 部署
+
+文件夹下`web_demo.py`，`web_demo2.py`与`api.py`的使用方法与官方的一致。
+
+使用前需要修改`peft_path`为你自己训练的模型路径，修改`peft_config`中的`r`与你训练时的`--lora_rank`一致。
+
 
 ## S2. Reward Model
 

api.py

@@ -0,0 +1,76 @@
+from fastapi import FastAPI, Request
+from transformers import AutoTokenizer, AutoModel
+import uvicorn
+import json
+import datetime
+import torch
+from peft import get_peft_model, LoraConfig, TaskType
+
+DEVICE = "cuda"
+DEVICE_ID = "0"
+CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
+
+
+def torch_gc():
+    if torch.cuda.is_available():
+        with torch.cuda.device(CUDA_DEVICE):
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+
+
+app = FastAPI()
+
+
+@app.post("/")
+async def chat(request: Request):
+    global model, tokenizer
+    json_post_raw = await request.json()
+    json_post = json.dumps(json_post_raw)
+    json_post_list = json.loads(json_post)
+    prompt = json_post_list.get('prompt')
+    history = json_post_list.get('history')
+    max_length = json_post_list.get('max_length')
+    top_p = json_post_list.get('top_p')
+    temperature = json_post_list.get('temperature')
+    response, history = model.chat(tokenizer,
+                                   prompt,
+                                   history=history,
+                                   max_length=max_length if max_length else 2048,
+                                   top_p=top_p if top_p else 0.7,
+                                   temperature=temperature if temperature else 0.95)
+    now = datetime.datetime.now()
+    time = now.strftime("%Y-%m-%d %H:%M:%S")
+    answer = {
+        "response": response,
+        "history": history,
+        "status": 200,
+        "time": time
+    }
+    log = "[" + time + "] " + '", prompt:"' + \
+        prompt + '", response:"' + repr(response) + '"'
+    print(log)
+    torch_gc()
+    return answer
+
+
+if __name__ == '__main__':
+    torch.set_default_tensor_type(torch.cuda.HalfTensor)
+    tokenizer = AutoTokenizer.from_pretrained(
+        "THUDM/chatglm-6b", trust_remote_code=True)
+    model = AutoModel.from_pretrained(
+        "THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+
+    peft_path = "output/adapter_model.bin"
+
+    peft_config = LoraConfig(
+        task_type=TaskType.CAUSAL_LM, inference_mode=True,
+        r=8,
+        lora_alpha=32, lora_dropout=0.1
+    )
+
+    model = get_peft_model(model, peft_config)
+    model.load_state_dict(torch.load(peft_path), strict=False)
+
+    model.eval()
+
+    uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)

web_demo.py

@@ -0,0 +1,116 @@
+from transformers import AutoModel, AutoTokenizer
+import gradio as gr
+import mdtex2html
+from peft import get_peft_model, LoraConfig, TaskType
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "THUDM/chatglm-6b", trust_remote_code=True)
+model = AutoModel.from_pretrained(
+    "THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+peft_path = "output/adapter_model.bin"
+peft_config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM, inference_mode=True,
+    r=8,
+    lora_alpha=32, lora_dropout=0.1
+)
+model = get_peft_model(model, peft_config)
+model.load_state_dict(torch.load(peft_path), strict=False)
+model.eval()
+
+"""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):
+    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)
+
+demo.queue().launch(share=False, inbrowser=True)

web_demo2.py

@@ -0,0 +1,82 @@
+from transformers import AutoModel, AutoTokenizer
+import streamlit as st
+from streamlit_chat import message
+from peft import get_peft_model, LoraConfig, TaskType
+import torch
+
+st.set_page_config(
+    page_title="ChatGLM-6b 演示",
+    page_icon=":robot:"
+)
+
+
+@st.cache_resource
+def get_model():
+    tokenizer = AutoTokenizer.from_pretrained(
+        "THUDM/chatglm-6b", trust_remote_code=True)
+    model = AutoModel.from_pretrained(
+        "THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+    peft_path = "output/adapter_model.bin"
+    peft_config = LoraConfig(
+        task_type=TaskType.CAUSAL_LM, inference_mode=True,
+        r=8,
+        lora_alpha=32, lora_dropout=0.1
+    )
+    model = get_peft_model(model, peft_config)
+    model.load_state_dict(torch.load(peft_path), strict=False)
+    model.eval()
+    return tokenizer, model
+
+
+MAX_TURNS = 20
+MAX_BOXES = MAX_TURNS * 2
+
+
+def predict(input, max_length, top_p, temperature, history=None):
+    tokenizer, model = get_model()
+    if history is None:
+        history = []
+
+    with container:
+        if len(history) > 0:
+            for i, (query, response) in enumerate(history):
+                message(query, avatar_style="big-smile", key=str(i) + "_user")
+                message(response, avatar_style="bottts", key=str(i))
+
+        message(input, avatar_style="big-smile",
+                key=str(len(history)) + "_user")
+        st.write("AI正在回复:")
+        with st.empty():
+            for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
+                                                       temperature=temperature):
+                query, response = history[-1]
+                st.write(response)
+
+    return history
+
+
+container = st.container()
+
+# create a prompt text for the text generation
+prompt_text = st.text_area(label="用户命令输入",
+                           height=100,
+                           placeholder="请在这儿输入您的命令")
+
+max_length = st.sidebar.slider(
+    'max_length', 0, 4096, 2048, step=1
+)
+top_p = st.sidebar.slider(
+    'top_p', 0.0, 1.0, 0.6, step=0.01
+)
+temperature = st.sidebar.slider(
+    'temperature', 0.0, 1.0, 0.95, step=0.01
+)
+
+if 'state' not in st.session_state:
+    st.session_state['state'] = []
+
+if st.button("发送", key="predict"):
+    with st.spinner("AI正在思考，请稍等........"):
+        # text generation
+        st.session_state["state"] = predict(
+            prompt_text, max_length, top_p, temperature, st.session_state["state"])