init

MemoAI/qwen2-0.5b/app.py

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+import os
+import copy
+import random
+import threading
+import subprocess
+import gradio as gr
+from typing import List, Optional, Tuple, Dict
+
+
+os.system("pip uninstall -y tensorflow tensorflow-estimator tensorflow-io-gcs-filesystem")
+os.environ["LANG"] = "C"
+os.environ["LC_ALL"] = "C"
+
+default_system = '你是一个微信聊天机器人'
+
+from dashinfer.helper import EngineHelper, ConfigManager
+
+log_lock = threading.Lock()
+
+config_file = "di_config.json"
+config = ConfigManager.get_config_from_json(config_file)
+
+def download_model(model_id, revision, source="modelscope"):
+    print(f"Downloading model {model_id} (revision: {revision}) from {source}")
+    if source == "modelscope":
+        from modelscope import snapshot_download
+        model_dir = snapshot_download(model_id, revision=revision)
+    elif source == "huggingface":
+        from huggingface_hub import snapshot_download
+        model_dir = snapshot_download(repo_id=model_id)
+    else:
+        raise ValueError("Unknown source")
+
+    print(f"Save model to path {model_dir}")
+
+    return model_dir
+
+cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2"
+package_location = subprocess.run(cmd,
+                                  stdout=subprocess.PIPE,
+                                  stderr=subprocess.PIPE,
+                                  shell=True,
+                                  text=True)
+package_location = package_location.stdout.strip()
+os.environ["AS_DAEMON_PATH"] = package_location + "/dashinfer/allspark/bin"
+os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"]))
+os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0])
+
+## download original model
+## download model from modelscope
+original_model = {
+    "source": "modelscope",
+    "model_id": config["model_space"] + config["model_name"],
+    "revision": "master",
+    "model_path": ""
+}
+original_model["model_path"] = download_model(original_model["model_id"],
+                                              original_model["revision"],
+                                              original_model["source"])
+
+engine_helper = EngineHelper(config)
+engine_helper.verbose = True
+engine_helper.init_tokenizer(original_model["model_path"])
+
+## convert huggingface model to dashinfer model
+## only one conversion is required
+engine_helper.convert_model(original_model["model_path"])
+
+engine_helper.init_engine()
+engine_max_batch = engine_helper.engine_config["engine_max_batch"]
+
+###################################################
+
+History = List[Tuple[str, str]]
+Messages = List[Dict[str, str]]
+
+
+class Role:
+    USER = 'user'
+    SYSTEM = 'system'
+    BOT = 'bot'
+    ASSISTANT = 'assistant'
+    ATTACHMENT = 'attachment'
+
+
+def clear_session() -> History:
+    return '', []
+
+
+def modify_system_session(system: str) -> str:
+    if system is None or len(system) == 0:
+        system = default_system
+    return system, system, []
+
+
+def history_to_messages(history: History, system: str) -> Messages:
+    messages = [{'role': Role.SYSTEM, 'content': system}]
+    for h in history:
+        messages.append({'role': Role.USER, 'content': h[0]})
+        messages.append({'role': Role.ASSISTANT, 'content': h[1]})
+    return messages
+
+
+def messages_to_history(messages: Messages) -> Tuple[str, History]:
+    assert messages[0]['role'] == Role.SYSTEM
+    system = messages[0]['content']
+    history = []
+    for q, r in zip(messages[1::2], messages[2::2]):
+        history.append([q['content'], r['content']])
+    return system, history
+
+
+def message_to_prompt(messages: Messages) -> str:
+    prompt = ""
+    for item in messages:
+        im_start, im_end = "<|im_start|>", "<|im_end|>"
+        prompt += f"\n{im_start}{item['role']}\n{item['content']}{im_end}"
+    prompt += f"\n{im_start}assistant\n"
+    return prompt
+
+
+def model_chat(query: Optional[str], history: Optional[History],
+               system: str) -> Tuple[str, str, History]:
+    if query is None:
+        query = ''
+    if history is None:
+        history = []
+
+    messages = history_to_messages(history, system)
+    messages.append({'role': Role.USER, 'content': query})
+    prompt = message_to_prompt(messages)
+
+    gen_cfg = copy.deepcopy(engine_helper.default_gen_cfg)
+    gen_cfg["max_length"] = 1024
+    gen_cfg["seed"] = random.randint(0, 10000)
+
+    request_list = engine_helper.create_request([prompt], [gen_cfg])
+
+    request = request_list[0]
+    gen = engine_helper.process_one_request_stream(request)
+    for response in gen:
+        role = Role.ASSISTANT
+        system, history = messages_to_history(messages + [{'role': role, 'content': response}])
+        yield '', history, system
+
+    json_str = engine_helper.convert_request_to_jsonstr(request)
+    log_lock.acquire()
+    try:
+        print(f"{json_str}\n")
+    finally:
+        log_lock.release()
+
+###################################################
+
+with gr.Blocks() as demo:
+    demo_title = "<center>微信的你</center>"
+    gr.Markdown(demo_title)
+    with gr.Row():
+        with gr.Column(scale=3):
+            system_input = gr.Textbox(value=default_system,
+                                      lines=1,
+                                      label='System')
+        with gr.Column(scale=1):
+            modify_system = gr.Button("🛠️ Set system prompt and clear history.", scale=2)
+        system_state = gr.Textbox(value=default_system, visible=False)
+    chatbot = gr.Chatbot(label=config["model_name"])
+    textbox = gr.Textbox(lines=2, label='Input')
+
+    with gr.Row():
+        clear_history = gr.Button("🧹清除历史记录")
+        sumbit = gr.Button("🚀和我聊天!")
+
+    sumbit.click(model_chat,
+                 inputs=[textbox, chatbot, system_state],
+                 outputs=[textbox, chatbot, system_input],
+                 concurrency_limit=engine_max_batch)
+    clear_history.click(fn=clear_session,
+                        inputs=[],
+                        outputs=[textbox, chatbot],
+                        concurrency_limit=engine_max_batch)
+    modify_system.click(fn=modify_system_session,
+                        inputs=[system_input],
+                        outputs=[system_state, system_input, chatbot],
+                        concurrency_limit=engine_max_batch)
+
+demo.queue(api_open=False).launch(height=800, share=False, server_name="127.0.0.1", server_port=7860)