web_demo-Llama3_online
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HongCheng
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369
web_demo-Llama3_online.py
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369
web_demo-Llama3_online.py
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# isort: skip_file
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import copy
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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, BitsAndBytesConfig # isort: skip
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from peft import PeftModel
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import warnings
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warnings.filterwarnings("ignore")
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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logger = logging.get_logger(__name__)
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online = True
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if online:
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from openxlab.model import download
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download(model_repo='chg0901/EmoLLM-Llama3-8B-Instruct2.0',
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output='model')
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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 = 500
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top_p: float = 0.9
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temperature: float = 0.6
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do_sample: bool = True
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repetition_penalty: float = 1.1
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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], 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 ({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(arg1):
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# # model = AutoModelForCausalLM.from_pretrained(args.m).cuda()
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# # tokenizer = AutoTokenizer.from_pretrained(args.m, trust_remote_code=True)
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# model = AutoModelForCausalLM.from_pretrained(arg1, torch_dtype=torch.float16).cuda()
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# tokenizer = AutoTokenizer.from_pretrained(arg1, trust_remote_code=True)
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# return model, tokenizer
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@st.cache_resource
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def load_model(model_name_or_path, load_in_4bit=False, adapter_name_or_path=None):
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if load_in_4bit:
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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)
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else:
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quantization_config = None
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# 加载base model
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model = AutoModelForCausalLM.from_pretrained(
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model_name_or_path,
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# load_in_4bit=load_in_4bit,
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# # ValueError: You can't pass `load_in_4bit`or `load_in_8bit` as a kwarg when passing `quantization_config` argument at the same time.
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trust_remote_code=True,
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low_cpu_mem_usage=True,
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torch_dtype=torch.float16,
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device_map='auto',
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quantization_config=quantization_config
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)
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# 加载adapter
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if adapter_name_or_path is not None:
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model = PeftModel.from_pretrained(model, adapter_name_or_path)
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## 加载tokenzier
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tokenizer = AutoTokenizer.from_pretrained(
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model_name_or_path if adapter_name_or_path is None else adapter_name_or_path,
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trust_remote_code=True,
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use_fast=False
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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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: **SmartFlowAI/EmoLLM**](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=8192,
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value=500)
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top_p = st.slider('Top P', 0.0, 1.0, 0.9, step=0.01)
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temperature = st.slider('Temperature', 0.0, 1.0, 0.6, step=0.01)
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repetition_penalty = st.slider('Repetition penalty', 0.0, 1.5, 1.1, 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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repetition_penalty=repetition_penalty,
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do_sample=True)
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return generation_config
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user_prompt = '<|start_header_id|>user<|end_header_id|>\n\n{user}<|eot_id|>'
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robot_prompt = '<|start_header_id|>assistant<|end_header_id|>\n\n{robot}<|eot_id|>'
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cur_query_prompt = '<|start_header_id|>user<|end_header_id|>\n\n{user}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'
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def combine_history(prompt):
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messages = st.session_state.messages
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meta_instruction = (
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"你是心理健康助手EmoLLM, 由EmoLLM团队打造, 是一个研究过无数具有心理健康问题的病人与心理健康医生对话的心理专家, 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。请充分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。。"
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)
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total_prompt = f"<|start_header_id|>system<|end_header_id|>\n{meta_instruction}<|eot_id|>\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(arg1):
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if online:
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model_name_or_path = 'model'
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adapter_name_or_path = None
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else:
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# model_name_or_path = "/root/StableCascade/emollm2/EmoLLM/xtuner_config/merged_Llama3_8b_instruct_e3"
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# adapter_name_or_path = '/root/StableCascade/emollm2/EmoLLM/xtuner_config/hf_llama3_e1_sc2'
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model_name_or_path = "/root/StableCascade/emollm2/EmoLLM/xtuner_config/merged_Llama3_8b_instruct_e1_sc"
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adapter_name_or_path = None
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# 若开启4bit推理能够节省很多显存，但效果可能下降
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load_in_4bit = False # True # 6291MiB
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# torch.cuda.empty_cache()
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print('load model begin.')
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# 加载模型
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print(f'Loading model from: {model_name_or_path}')
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print(f'adapter_name_or_path: {adapter_name_or_path}')
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# model, tokenizer = load_model(arg1)
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model, tokenizer = load_model(
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arg1 if arg1 is not None else model_name_or_path,
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load_in_4bit=load_in_4bit,
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adapter_name_or_path=adapter_name_or_path
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)
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model.eval()
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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 Llama3心理咨询室V2.0')
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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('你好，欢迎来到Llama3 EmoLLM 心理咨询室'):
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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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# stop_token_id = tokenizer.encode('<|eot_id|>', add_special_tokens=True)
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# assert len(stop_token_id) == 1
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# stop_token_id = stop_token_id[0]
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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=128009, # <|eot_id|>
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eos_token_id=128009,
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pad_token_id=128009,
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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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import sys
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arg1 = sys.argv[1]
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main(arg1)
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