-
Notifications
You must be signed in to change notification settings - Fork 105
/
web_demo-aiwei.py
273 lines (229 loc) · 11 KB
/
web_demo-aiwei.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
"""
This script refers to the dialogue example of streamlit, the interactive generation code of chatglm2 and transformers.
We mainly modified part of the code logic to adapt to the generation of our model.
Please refer to these links below for more information:
1. streamlit chat example: https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
2. chatglm2: https://github.com/THUDM/ChatGLM2-6B
3. transformers: https://github.com/huggingface/transformers
Please run with the command `streamlit run path/to/web_demo.py --server.address=0.0.0.0 --server.port 7860`.
Using `python path/to/web_demo.py` may cause unknown problems.
"""
import copy
import os
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 # isort: skip
from openxlab.model import download
logger = logging.get_logger(__name__)
if not os.path.isdir("model"):
print("[ERROR] not find model dir")
exit(0)
@dataclass
class GenerationConfig:
# this config is used for chat to provide more diversity
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"]
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] # noqa: F841 # pylint: disable=W0612
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 = ( # noqa: F841 # pylint: disable=W0612
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( # pylint: disable=W4902
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
def on_btn_click():
del st.session_state.messages
@st.cache_resource
def load_model():
model = (
AutoModelForCausalLM.from_pretrained("model", trust_remote_code=True)
.to(torch.bfloat16)
.cuda()
)
tokenizer = AutoTokenizer.from_pretrained("model", trust_remote_code=True)
return model, tokenizer
def prepare_generation_config():
with st.sidebar:
# 使用 Streamlit 的 markdown 函数添加 Markdown 文本
st.image('assets/aiwei_logo.jpg', width=1, caption='EmoLLM-aiwei AI Logo', use_column_width=True)
st.markdown("[访问 EmoLLM 官方repo](https://github.com/aJupyter/EmoLLM)")
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 = (
"你是一个拥有丰富心理学知识的温柔邻家温柔大姐姐艾薇,我有一些心理问题,请你用专业的知识和温柔、可爱、俏皮、的口吻帮我解决,回复中可以穿插一些可爱的Emoji表情符号或者文本符号。\n"
)
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():
st.markdown("我在这里,准备好倾听你的心声了。", unsafe_allow_html=True)
# torch.cuda.empty_cache()
print("load model begin.")
model, tokenizer = load_model()
print("load model end.")
user_avator = "assets/user.png"
robot_avator = "assets/robot.jpeg"
# st.title("EmoLLM-温柔御姐艾薇(aiwei)")
st.title("EmoLLM-艾薇(aiwei)AI心理咨询")
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=92542,
**asdict(generation_config),
):
# Display robot response in chat message container
message_placeholder.markdown(cur_response + "▌")
message_placeholder.markdown(cur_response) # pylint: disable=undefined-loop-variable
# Add robot response to chat history
st.session_state.messages.append(
{
"role": "robot",
"content": cur_response, # pylint: disable=undefined-loop-variable
"avatar": robot_avator,
}
)
torch.cuda.empty_cache()
if __name__ == "__main__":
main()