300 lines
12 KiB
Python
300 lines
12 KiB
Python
"""This script refers to the dialogue example of streamlit, the interactive
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generation code of chatglm2 and transformers.
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We mainly modified part of the code logic to adapt to the
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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:
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https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
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2. chatglm2:
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https://github.com/THUDM/ChatGLM2-6B
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3. transformers:
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https://github.com/huggingface/transformers
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Please run with the command `streamlit run path/to/web_demo.py
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--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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# isort: skip_file
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import copy, os
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import warnings
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from dataclasses import asdict, dataclass
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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,
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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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logger = logging.get_logger(__name__)
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# # local
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# model_path = '/root/EmoLLM/xtuner_config/hf_safe'
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# Online downloading will be added later
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model_path = './EmoLLM_V3.0'
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os.system(f'git clone https://code.openxlab.org.cn/chg0901/EmoLLM_V3.0.git {model_path}')
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os.system(f'cd {model_path} && git lfs pull')
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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],
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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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_, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
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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(
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'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 \
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({repr(generation_config.max_length)}) \
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to control the generation length. "
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'This behaviour is deprecated and will be removed from the \
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config in v5 of Transformers -- we'
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' recommend using `max_new_tokens` to control the maximum \
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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 + \
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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}) "
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f"and 'max_length'(={generation_config.max_length}) seem to "
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"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/'
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'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}, '
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f"but 'max_length' is set to {generation_config.max_length}. "
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'This can lead to unexpected behavior. You should consider'
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" increasing 'max_new_tokens'.")
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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 \
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else LogitsProcessorList()
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stopping_criteria = stopping_criteria if stopping_criteria is not None \
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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,
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stopping_criteria=stopping_criteria)
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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(
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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(
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outputs, model_kwargs, is_encoder_decoder=False)
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unfinished_sequences = unfinished_sequences.mul(
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(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
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# or if we exceed the maximum length
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if unfinished_sequences.max() == 0 or stopping_criteria(
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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 = (AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(torch.bfloat16).cuda())
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tokenizer = AutoTokenizer.from_pretrained(model_path,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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# 使用 Streamlit 的 markdown 函数添加 Markdown 文本
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st.image('assets/EmoLLM_logo_L.png', width=1, caption='EmoLLM Logo', use_column_width=True)
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st.markdown("[访问 EmoLLM 官方repo](https://github.com/SmartFlowAI/EmoLLM)")
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max_length = st.slider('Max Length',
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min_value=8,
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max_value=32768,
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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,
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top_p=top_p,
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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\
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<|im_start|>assistant\n'
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def combine_history(prompt):
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messages = st.session_state.messages
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meta_instruction = ('你是EmoLLM心理咨询师, 由EmoLLM团队打造, 是一个研究过无数具有心理咨询者与顶级专业心理咨询师对话的心理学教授, 在心理方面拥有广博的知识储备和丰富的研究咨询经验。你旨在通过专业心理咨询, 协助来访者完成心理诊断, 利用专业心理学知识与咨询技术一步步帮助来访者解决心理问题。如果有必要，请用“咨询者”称呼对话咨询的用户。')
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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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# st.markdown("我在这里，准备好倾听你的心声了。", unsafe_allow_html=True)
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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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print('load model end.')
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user_avator = 'assets/user.png'
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robot_avator = 'assets/EmoLLM.png'
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st.title('EmoLLM V3.0 心理咨询室')
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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('我在这里准备好倾听你的心声了~'):
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# Display user message in chat message container
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with st.chat_message('user', avatar=user_avator):
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st.markdown(prompt)
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real_prompt = combine_history(prompt)
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# Add user message to chat history
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st.session_state.messages.append({
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'role': 'user',
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'content': prompt,
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'avatar': user_avator
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})
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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)
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# Add robot response to chat history
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st.session_state.messages.append({
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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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torch.cuda.empty_cache()
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if __name__ == '__main__':
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main()
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