271 lines
11 KiB
Python
271 lines
11 KiB
Python
"""
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This script refers to the dialogue example of streamlit, the interactive generation code of chatglm2 and transformers.
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We mainly modified part of the code logic to adapt to the generation of our model.
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Please refer to these links below for more information:
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1. streamlit chat example: https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
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2. chatglm2: https://github.com/THUDM/ChatGLM2-6B
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3. transformers: https://github.com/huggingface/transformers
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Please run with the command `streamlit run path/to/web_demo.py --server.address=0.0.0.0 --server.port 7860`.
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Using `python path/to/web_demo.py` may cause unknown problems.
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"""
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import copy
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import os
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import warnings
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from dataclasses import asdict, dataclass
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from rag.src.pipeline import EmoLLMRAG
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from typing import Callable, List, Optional
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import streamlit as st
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import torch
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from torch import nn
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from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
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from transformers.utils import logging
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from transformers import AutoTokenizer, AutoModelForCausalLM # isort: skip
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from openxlab.model import download
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logger = logging.get_logger(__name__)
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if not os.path.isdir("model"):
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print("[ERROR] not find model dir")
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exit(0)
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@dataclass
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class GenerationConfig:
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# this config is used for chat to provide more diversity
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max_length: int = 32768
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top_p: float = 0.8
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temperature: float = 0.8
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do_sample: bool = True
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repetition_penalty: float = 1.005
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@torch.inference_mode()
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def generate_interactive(
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model,
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tokenizer,
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prompt,
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generation_config: Optional[GenerationConfig] = None,
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logits_processor: Optional[LogitsProcessorList] = None,
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stopping_criteria: Optional[StoppingCriteriaList] = None,
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prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
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additional_eos_token_id: Optional[int] = None,
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**kwargs,
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):
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inputs = tokenizer([prompt], padding=True, return_tensors="pt")
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input_length = len(inputs["input_ids"][0])
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for k, v in inputs.items():
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inputs[k] = v.cuda()
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input_ids = inputs["input_ids"]
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batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] # noqa: F841 # pylint: disable=W0612
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if generation_config is None:
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generation_config = model.generation_config
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generation_config = copy.deepcopy(generation_config)
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model_kwargs = generation_config.update(**kwargs)
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bos_token_id, eos_token_id = ( # noqa: F841 # pylint: disable=W0612
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generation_config.bos_token_id,
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generation_config.eos_token_id,
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)
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if isinstance(eos_token_id, int):
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eos_token_id = [eos_token_id]
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if additional_eos_token_id is not None:
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eos_token_id.append(additional_eos_token_id)
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has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
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if has_default_max_length and generation_config.max_new_tokens is None:
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warnings.warn(
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f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
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"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
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" recommend using `max_new_tokens` to control the maximum length of the generation.",
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UserWarning,
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)
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elif generation_config.max_new_tokens is not None:
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generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
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if not has_default_max_length:
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logger.warn( # pylint: disable=W4902
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f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
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f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
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"Please refer to the documentation for more information. "
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"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
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UserWarning,
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)
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if input_ids_seq_length >= generation_config.max_length:
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input_ids_string = "input_ids"
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logger.warning(
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f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
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f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
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" increasing `max_new_tokens`."
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)
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# 2. Set generation parameters if not already defined
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logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
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stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
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logits_processor = model._get_logits_processor(
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generation_config=generation_config,
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input_ids_seq_length=input_ids_seq_length,
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encoder_input_ids=input_ids,
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prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
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logits_processor=logits_processor,
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)
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stopping_criteria = model._get_stopping_criteria(
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generation_config=generation_config, stopping_criteria=stopping_criteria
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)
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logits_warper = model._get_logits_warper(generation_config)
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unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
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scores = None
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while True:
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model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
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# forward pass to get next token
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outputs = model(
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**model_inputs,
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return_dict=True,
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output_attentions=False,
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output_hidden_states=False,
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)
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next_token_logits = outputs.logits[:, -1, :]
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# pre-process distribution
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next_token_scores = logits_processor(input_ids, next_token_logits)
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next_token_scores = logits_warper(input_ids, next_token_scores)
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# sample
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probs = nn.functional.softmax(next_token_scores, dim=-1)
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if generation_config.do_sample:
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next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
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else:
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next_tokens = torch.argmax(probs, dim=-1)
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# update generated ids, model inputs, and length for next step
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input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
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model_kwargs = model._update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False)
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unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
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output_token_ids = input_ids[0].cpu().tolist()
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output_token_ids = output_token_ids[input_length:]
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for each_eos_token_id in eos_token_id:
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if output_token_ids[-1] == each_eos_token_id:
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output_token_ids = output_token_ids[:-1]
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response = tokenizer.decode(output_token_ids)
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yield response
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# stop when each sentence is finished, or if we exceed the maximum length
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if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
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break
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def on_btn_click():
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del st.session_state.messages
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@st.cache_resource
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def load_model():
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model = (
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AutoModelForCausalLM.from_pretrained("model", trust_remote_code=True)
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.to(torch.bfloat16)
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.cuda()
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)
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tokenizer = AutoTokenizer.from_pretrained("model", trust_remote_code=True)
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return model, tokenizer
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def prepare_generation_config():
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with st.sidebar:
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max_length = st.slider("Max Length", min_value=8, max_value=32768, value=32768)
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top_p = st.slider("Top P", 0.0, 1.0, 0.8, step=0.01)
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temperature = st.slider("Temperature", 0.0, 1.0, 0.7, step=0.01)
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st.button("Clear Chat History", on_click=on_btn_click)
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generation_config = GenerationConfig(max_length=max_length, top_p=top_p, temperature=temperature)
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return generation_config
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user_prompt = "<|im_start|>user\n{user}<|im_end|>\n"
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robot_prompt = "<|im_start|>assistant\n{robot}<|im_end|>\n"
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cur_query_prompt = "<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n"
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def combine_history(prompt, retrieval_content=''):
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messages = st.session_state.messages
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prompt = f"你需要根据以下从书本中检索到的专业知识:`{retrieval_content}`。从一个心理专家的专业角度来回答后续提问：{prompt}"
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meta_instruction = (
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"你是一个由aJupyter、Farewell、jujimeizuo、Smiling&Weeping研发（排名按字母顺序排序，不分先后）、散步提供技术支持、上海人工智能实验室提供支持开发的心理健康大模型。现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。"
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)
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total_prompt = f"<s><|im_start|>system\n{meta_instruction}<|im_end|>\n"
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for message in messages:
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cur_content = message["content"]
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if message["role"] == "user":
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cur_prompt = user_prompt.format(user=cur_content)
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elif message["role"] == "robot":
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cur_prompt = robot_prompt.format(robot=cur_content)
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else:
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raise RuntimeError
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total_prompt += cur_prompt
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total_prompt = total_prompt + cur_query_prompt.format(user=prompt)
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return total_prompt
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def main():
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# torch.cuda.empty_cache()
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print("load model begin.")
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model, tokenizer = load_model()
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rag_obj = EmoLLMRAG(model)
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print("load model end.")
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user_avator = "assets/user.png"
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robot_avator = "assets/robot.jpeg"
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st.title("EmoLLM")
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generation_config = prepare_generation_config()
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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with st.chat_message(message["role"], avatar=message.get("avatar")):
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st.markdown(message["content"])
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# Accept user input
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if prompt := st.chat_input("What is up?"):
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# Display user message in chat message container
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retrieval_content = rag_obj.get_retrieval_content(prompt)
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with st.chat_message("user", avatar=user_avator):
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st.markdown(prompt)
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#st.markdown(retrieval_content)
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real_prompt = combine_history(prompt, retrieval_content)
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt, "avatar": user_avator})
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with st.chat_message("robot", avatar=robot_avator):
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message_placeholder = st.empty()
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for cur_response in generate_interactive(
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model=model,
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tokenizer=tokenizer,
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prompt=real_prompt,
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additional_eos_token_id=92542,
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**asdict(generation_config),
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):
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# Display robot response in chat message container
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message_placeholder.markdown(cur_response + "▌")
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message_placeholder.markdown(cur_response) # pylint: disable=undefined-loop-variable
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# Add robot response to chat history
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st.session_state.messages.append(
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{
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"role": "robot",
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"content": cur_response, # pylint: disable=undefined-loop-variable
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"avatar": robot_avator,
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}
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)
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torch.cuda.empty_cache()
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if __name__ == "__main__":
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main() |