将微调好的模型部署为 Web Demo

[vansin]

社区的小伙伴们可以使用以下的 python 脚本将 InternLM-20B 或者微调后的模型部署为 Web Demo

web_demo.py

pip install streamlit==1.24.0
pip install transformers==4.30.2
streamlit run web_demo.py

# web_demo.py

# PATH_AB = "work_dirs/internlm_20b_qlora_alpaca_zh_e3_xtuner/hf_merge"
PATH_AB = "Shanghai_AI_Laboratory/internlm-20b"

import copy
import warnings
from dataclasses import dataclass
from typing import Callable, List, Optional

import torch
from torch import nn
# from transformers import AutoTokenizer
from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
from transformers.utils import logging

logger = logging.get_logger(__name__)


@dataclass
class GenerationConfig:
    max_length: Optional[int] = None
    top_p: Optional[float] = None
    temperature: Optional[float] = None
    do_sample: Optional[bool] = True
    repetition_penalty: Optional[float] = 1.0


@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]
    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 = 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(
                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

from dataclasses import asdict

import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.utils import logging

# from tools.transformers.interface import GenerationConfig, generate_interactive

logger = logging.get_logger(__name__)


def on_btn_click():
    del st.session_state.messages




@st.cache_resource
def load_model():
    model = (
        AutoModelForCausalLM.from_pretrained(PATH_AB, trust_remote_code=True)
        .to(torch.bfloat16)
        .cuda()
    )
    tokenizer = AutoTokenizer.from_pretrained(PATH_AB, trust_remote_code=True)
    return model, tokenizer


def prepare_generation_config():
    with st.sidebar:
        max_length = st.slider("Max Length", min_value=32, max_value=2048, value=2048)
        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 = "<|User|>:{user}<eoh>\n"
robot_prompt = "<|Bot|>:{robot}<eoa>\n"
cur_query_prompt = "<|User|>:{user}<eoh>\n<|Bot|>:"


def combine_history(prompt):
    messages = st.session_state.messages
    total_prompt = ""
    for message in messages:
        cur_content = message["content"]
        if message["role"] == "user":
            cur_prompt = user_prompt.replace("{user}", cur_content)
        elif message["role"] == "robot":
            cur_prompt = robot_prompt.replace("{robot}", cur_content)
        else:
            raise RuntimeError
        total_prompt += cur_prompt
    total_prompt = total_prompt + cur_query_prompt.replace("{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 = "docs/imgs/user.png"
    robot_avator = "docs/imgs/robot.png"

    st.title("InternLM-20B")

    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=103028,
                **asdict(generation_config),
            ):
                # Display robot response in chat message container
                message_placeholder.markdown(cur_response + "▌")
            message_placeholder.markdown(cur_response)
        # Add robot response to chat history
        st.session_state.messages.append({"role": "robot", "content": cur_response, "avatar": robot_avator})
        torch.cuda.empty_cache()


if __name__ == "__main__":
    main()