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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
logger = logging.get_logger(__name__)
@dataclass
class GenerationConfig:
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']
_, 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 ({repr(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}) "
f"and 'max_length'(={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}, "
f"but 'max_length' is set to {generation_config.max_length}. "
'This can lead to unexpected behavior. You should consider'
" increasing 'max_new_tokens'.")
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)
outputs = model(
**model_inputs,
return_dict=True,
output_attentions=False,
output_hidden_states=False,
)
next_token_logits = outputs.logits[:, -1, :]
next_token_scores = logits_processor(input_ids, next_token_logits)
next_token_scores = logits_warper(input_ids, next_token_scores)
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)
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
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('/root/models/JimmyMa99/BaJie-Chat-mini',
trust_remote_code=True).to(
torch.bfloat16).cuda())
tokenizer = AutoTokenizer.from_pretrained('/root/models/JimmyMa99/BaJie-Chat-mini',
trust_remote_code=True)
return model, tokenizer
def prepare_generation_config():
with st.sidebar:
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 = ('你是猪八戒，猪八戒说话幽默风趣，说话方式通常表现为直率、幽默，有时带有一点自嘲和调侃。'
'你的话语中常常透露出对食物的喜爱和对安逸生活的向往，同时也显示出他机智和有时的懒惰特点。'
'尽量保持回答的自然回答，当然你也可以适当穿插一些文言文，另外，书生·浦语是你的好朋友，是你的AI助手。')
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():
print('load model begin.')
model, tokenizer = load_model()
print('load model end.')
st.title('猪猪Chat-InternLM2')
generation_config = prepare_generation_config()
if 'messages' not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message['role']):
st.markdown(message['content'])
if prompt := st.chat_input('What is up?'):
with st.chat_message('user'):
st.markdown(prompt)
real_prompt = combine_history(prompt)
st.session_state.messages.append({
'role': 'user',
'content': prompt,
})
with st.chat_message('robot'):
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),
):
message_placeholder.markdown(cur_response + '▌')
message_placeholder.markdown(cur_response)
st.session_state.messages.append({
'role': 'robot',
'content': cur_response,
})
torch.cuda.empty_cache()
if __name__ == '__main__':
main()
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