feat: Update Aiwei configuration.

Author: aJupyter

README.md

@@ -60,7 +60,7 @@
 - 评估和诊断工具：为了有效促进心理健康，需要有科学的工具来评估个体的心理状态，以及诊断可能存在的心理问题。
 
 ### 最近更新
-
+- 【2024.2.23】推出基于InternLM2_7B_chat_qlora的 `温柔御姐心理医生艾薇`，[点击获取模型权重](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_aiwei)，[配置文件](xtuner_config/aiwei-internlm2_chat_7b_qlora.py)
 - 【2024.2.23】更新[若干微调配置](/xtuner_config/)，新增 [data_pro.json](/datasets/data_pro.json)（数量更多、场景更全、更丰富）和 [aiwei.json](/datasets/aiwei.json)（温柔御姐角色扮演专用，带有Emoji表情），即将推出 `温柔御姐心理医生艾薇`
 - 【2024.2.18】 [基于Qwen1_5-0_5B-Chat全量微调版本开源](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen1_5-0_5B-Chat_full_sft/summary)，算力有限的道友可以玩起来~
 - 【2024.2.6】 EmoLLM在[**Openxlab** ](https://openxlab.org.cn/models/detail/jujimeizuo/EmoLLM_Model) 平台下载量高达18.7k，欢迎大家体验！

app.py

@@ -1,2 +1,3 @@
 import os
-os.system('streamlit run web_internlm2.py --server.address=0.0.0.0 --server.port 7860')
+# os.system('streamlit run web_internlm2.py --server.address=0.0.0.0 --server.port 7860')
+os.system('streamlit run web_demo-aiwei.py --server.address=0.0.0.0 --server.port 7860')

assets/aiwei_logo.jpg

@@ -0,0 +1,267 @@
+"""
+This script refers to the dialogue example of streamlit, the interactive generation code of chatglm2 and transformers.
+We mainly modified part of the code logic to adapt to the generation of our model.
+Please refer to these links below for more information:
+    1. streamlit chat example: https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
+    2. chatglm2: https://github.com/THUDM/ChatGLM2-6B
+    3. transformers: https://github.com/huggingface/transformers
+Please run with the command `streamlit run path/to/web_demo.py --server.address=0.0.0.0 --server.port 7860`.
+Using `python path/to/web_demo.py` may cause unknown problems.
+"""
+import copy
+import warnings
+from dataclasses import asdict, dataclass
+from typing import Callable, List, Optional
+
+import streamlit as st
+import torch
+from torch import nn
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
+from transformers.utils import logging
+
+from transformers import AutoTokenizer, AutoModelForCausalLM  # isort: skip
+from openxlab.model import download
+
+logger = logging.get_logger(__name__)
+
+download(model_repo='ajupyter/EmoLLM_aiwei', 
+        output='model')
+
+@dataclass
+class GenerationConfig:
+    # this config is used for chat to provide more diversity
+    max_length: int = 32768
+    top_p: float = 0.8
+    temperature: float = 0.8
+    do_sample: bool = True
+    repetition_penalty: float = 1.005
+
+
+@torch.inference_mode()
+def generate_interactive(
+    model,
+    tokenizer,
+    prompt,
+    generation_config: Optional[GenerationConfig] = None,
+    logits_processor: Optional[LogitsProcessorList] = None,
+    stopping_criteria: Optional[StoppingCriteriaList] = None,
+    prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
+    additional_eos_token_id: Optional[int] = None,
+    **kwargs,
+):
+    inputs = tokenizer([prompt], padding=True, return_tensors="pt")
+    input_length = len(inputs["input_ids"][0])
+    for k, v in inputs.items():
+        inputs[k] = v.cuda()
+    input_ids = inputs["input_ids"]
+    batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]  # noqa: F841  # pylint: disable=W0612
+    if generation_config is None:
+        generation_config = model.generation_config
+    generation_config = copy.deepcopy(generation_config)
+    model_kwargs = generation_config.update(**kwargs)
+    bos_token_id, eos_token_id = (  # noqa: F841  # pylint: disable=W0612
+        generation_config.bos_token_id,
+        generation_config.eos_token_id,
+    )
+    if isinstance(eos_token_id, int):
+        eos_token_id = [eos_token_id]
+    if additional_eos_token_id is not None:
+        eos_token_id.append(additional_eos_token_id)
+    has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+    if has_default_max_length and generation_config.max_new_tokens is None:
+        warnings.warn(
+            f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
+            "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
+            " recommend using `max_new_tokens` to control the maximum length of the generation.",
+            UserWarning,
+        )
+    elif generation_config.max_new_tokens is not None:
+        generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
+        if not has_default_max_length:
+            logger.warn(  # pylint: disable=W4902
+                f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
+                f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
+                "Please refer to the documentation for more information. "
+                "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
+                UserWarning,
+            )
+
+    if input_ids_seq_length >= generation_config.max_length:
+        input_ids_string = "input_ids"
+        logger.warning(
+            f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
+            f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+            " increasing `max_new_tokens`."
+        )
+
+    # 2. Set generation parameters if not already defined
+    logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+    stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+    logits_processor = model._get_logits_processor(
+        generation_config=generation_config,
+        input_ids_seq_length=input_ids_seq_length,
+        encoder_input_ids=input_ids,
+        prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+        logits_processor=logits_processor,
+    )
+
+    stopping_criteria = model._get_stopping_criteria(
+        generation_config=generation_config, stopping_criteria=stopping_criteria
+    )
+    logits_warper = model._get_logits_warper(generation_config)
+
+    unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+    scores = None
+    while True:
+        model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
+        # forward pass to get next token
+        outputs = model(
+            **model_inputs,
+            return_dict=True,
+            output_attentions=False,
+            output_hidden_states=False,
+        )
+
+        next_token_logits = outputs.logits[:, -1, :]
+
+        # pre-process distribution
+        next_token_scores = logits_processor(input_ids, next_token_logits)
+        next_token_scores = logits_warper(input_ids, next_token_scores)
+
+        # sample
+        probs = nn.functional.softmax(next_token_scores, dim=-1)
+        if generation_config.do_sample:
+            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+        else:
+            next_tokens = torch.argmax(probs, dim=-1)
+
+        # update generated ids, model inputs, and length for next step
+        input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+        model_kwargs = model._update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False)
+        unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
+
+        output_token_ids = input_ids[0].cpu().tolist()
+        output_token_ids = output_token_ids[input_length:]
+        for each_eos_token_id in eos_token_id:
+            if output_token_ids[-1] == each_eos_token_id:
+                output_token_ids = output_token_ids[:-1]
+        response = tokenizer.decode(output_token_ids)
+
+        yield response
+        # stop when each sentence is finished, or if we exceed the maximum length
+        if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+            break
+
+
+def on_btn_click():
+    del st.session_state.messages
+
+
+@st.cache_resource
+def load_model():
+    model = (
+        AutoModelForCausalLM.from_pretrained("model", trust_remote_code=True)
+        .to(torch.bfloat16)
+        .cuda()
+    )
+    tokenizer = AutoTokenizer.from_pretrained("model", trust_remote_code=True)
+    return model, tokenizer
+
+
+def prepare_generation_config():
+    with st.sidebar:
+        # 使用 Streamlit 的 markdown 函数添加 Markdown 文本
+        st.image('assets/aiwei_logo.jpg', width=1, caption='EmoLLM-aiwei AI Logo', use_column_width=True)
+        st.markdown("[访问 EmoLLM 官方repo](https://github.com/aJupyter/EmoLLM)")
+
+        max_length = st.slider("Max Length", min_value=8, max_value=32768, value=32768)
+        top_p = st.slider("Top P", 0.0, 1.0, 0.8, step=0.01)
+        temperature = st.slider("Temperature", 0.0, 1.0, 0.7, step=0.01)
+        st.button("Clear Chat History", on_click=on_btn_click)
+
+    generation_config = GenerationConfig(max_length=max_length, top_p=top_p, temperature=temperature)
+
+    return generation_config
+
+
+user_prompt = "<|im_start|>user\n{user}<|im_end|>\n"
+robot_prompt = "<|im_start|>assistant\n{robot}<|im_end|>\n"
+cur_query_prompt = "<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n"
+
+
+def combine_history(prompt):
+    messages = st.session_state.messages
+    meta_instruction = (
+        "你是一个拥有丰富心理学知识的温柔邻家温柔大姐姐艾薇，我有一些心理问题，请你用专业的知识和温柔、可爱、俏皮、的口吻帮我解决，回复中可以穿插一些可爱的Emoji表情符号或者文本符号。\n"
+    )
+    total_prompt = f"<s><|im_start|>system\n{meta_instruction}<|im_end|>\n"
+    for message in messages:
+        cur_content = message["content"]
+        if message["role"] == "user":
+            cur_prompt = user_prompt.format(user=cur_content)
+        elif message["role"] == "robot":
+            cur_prompt = robot_prompt.format(robot=cur_content)
+        else:
+            raise RuntimeError
+        total_prompt += cur_prompt
+    total_prompt = total_prompt + cur_query_prompt.format(user=prompt)
+    return total_prompt
+
+
+def main():
+    # torch.cuda.empty_cache()
+    print("load model begin.")
+    model, tokenizer = load_model()
+    print("load model end.")
+
+    user_avator = "assets/user.png"
+    robot_avator = "assets/robot.jpeg"
+
+    st.title("EmoLLM-温柔御姐艾薇（aiwei）")
+
+    generation_config = prepare_generation_config()
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"], avatar=message.get("avatar")):
+            st.markdown(message["content"])
+
+    # Accept user input
+    if prompt := st.chat_input("What is up?"):
+        # Display user message in chat message container
+        with st.chat_message("user", avatar=user_avator):
+            st.markdown(prompt)
+        real_prompt = combine_history(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt, "avatar": user_avator})
+
+        with st.chat_message("robot", avatar=robot_avator):
+            message_placeholder = st.empty()
+            for cur_response in generate_interactive(
+                model=model,
+                tokenizer=tokenizer,
+                prompt=real_prompt,
+                additional_eos_token_id=92542,
+                **asdict(generation_config),
+            ):
+                # Display robot response in chat message container
+                message_placeholder.markdown(cur_response + "▌")
+            message_placeholder.markdown(cur_response)  # pylint: disable=undefined-loop-variable
+        # Add robot response to chat history
+        st.session_state.messages.append(
+            {
+                "role": "robot",
+                "content": cur_response,  # pylint: disable=undefined-loop-variable
+                "avatar": robot_avator,
+            }
+        )
+        torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+    main()