forked from DLYuanGod/TinyGPT-V
-
Notifications
You must be signed in to change notification settings - Fork 0
/
conversation.py
252 lines (213 loc) · 8.66 KB
/
conversation.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
import argparse
import time
from threading import Thread
from PIL import Image
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any
from minigpt4.common.registry import registry
tokenizer = AutoTokenizer.from_pretrained('/root/autodl-tmp/phi-new')
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
@dataclasses.dataclass
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: List[str]
messages: List[List[str]]
offset: int
# system_img: List[Image.Image] = []
sep_style: SeparatorStyle = SeparatorStyle.SINGLE
sep: str = "###"
sep2: str = None
skip_next: bool = False
conv_id: Any = None
def get_prompt(self):
if self.sep_style == SeparatorStyle.SINGLE:
ret = self.system + self.sep
for role, message in self.messages:
if message:
ret += role + message + self.sep
else:
ret += role
return ret
elif self.sep_style == SeparatorStyle.TWO:
seps = [self.sep, self.sep2]
ret = self.system + seps[0]
for i, (role, message) in enumerate(self.messages):
if message:
ret += role + message + seps[i % 2]
else:
ret += role
return ret
else:
raise ValueError(f"Invalid style: {self.sep_style}")
def append_message(self, role, message):
self.messages.append([role, message])
def to_gradio_chatbot(self):
ret = []
for i, (role, msg) in enumerate(self.messages[self.offset:]):
if i % 2 == 0:
ret.append([msg, None])
else:
ret[-1][-1] = msg
return ret
def copy(self):
return Conversation(
system=self.system,
# system_img=self.system_img,
roles=self.roles,
messages=[[x, y] for x, y in self.messages],
offset=self.offset,
sep_style=self.sep_style,
sep=self.sep,
sep2=self.sep2,
conv_id=self.conv_id)
def dict(self):
return {
"system": self.system,
# "system_img": self.system_img,
"roles": self.roles,
"messages": self.messages,
"offset": self.offset,
"sep": self.sep,
"sep2": self.sep2,
"conv_id": self.conv_id,
}
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = stops
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
for stop in self.stops:
if torch.all(input_ids[:, -len(stop):] == stop).item():
return True
return False
CONV_VISION_Vicuna0 = Conversation(
system="Give the following image: <Img>ImageContent</Img>. "
"You will be able to see the image once I provide it to you. Please answer my questions.",
roles=("Human: ", "Assistant: "),
messages=[],
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
CONV_VISION_LLama2 = Conversation(
system="Give the following image: <Img>ImageContent</Img>. "
"You will be able to see the image once I provide it to you. Please answer my questions.",
roles=("Human: ", "Assistant: "),
messages=[],
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
CONV_VISION_minigptv2 = Conversation(
system="",
roles=("Human: ", "Assistant: "),
messages=[],
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
class Chat:
def __init__(self, model, vis_processor, device='cuda:0', stopping_criteria=None):
self.device = device
self.model = model
self.vis_processor = vis_processor
if stopping_criteria is not None:
self.stopping_criteria = stopping_criteria
else:
stop_words_ids = [torch.tensor([2]).to(self.device)]
self.stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
def ask(self, text, conv):
if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[0] \
and conv.messages[-1][1][-6:] == '</Img>': # last message is image.
conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text])
else:
conv.append_message(conv.roles[0], text)
def answer_prepare(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,
repetition_penalty=1.05, length_penalty=1, temperature=1.0, max_length=2000):
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
embs = self.model.get_context_emb(prompt, img_list)
current_max_len = embs.shape[1] + max_new_tokens
if current_max_len - max_length > 0:
print('Warning: The number of tokens in current conversation exceeds the max length. '
'The model will not see the contexts outside the range.')
begin_idx = max(0, current_max_len - max_length)
embs = embs[:, begin_idx:]
generation_kwargs = dict(
inputs_embeds=embs,
max_new_tokens=max_new_tokens,
stopping_criteria=self.stopping_criteria,
num_beams=num_beams,
do_sample=True,
min_length=min_length,
top_p=top_p,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
temperature=float(temperature),
pad_token_id=tokenizer.pad_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
return generation_kwargs
def answer(self, conv, img_list, **kargs):
generation_dict = self.answer_prepare(conv, img_list, **kargs)
output_token = self.model_generate(**generation_dict)[0]
output_text = self.model.llama_tokenizer.decode(output_token, skip_special_tokens=True)
output_text = output_text.split('###')[0] # remove the stop sign '###'
output_text = output_text.split('Assistant:')[-1].strip()
conv.messages[-1][1] = output_text
return output_text, output_token.cpu().numpy()
def stream_answer(self, conv, img_list, **kargs):
generation_kwargs = self.answer_prepare(conv, img_list, **kargs)
streamer = TextIteratorStreamer(self.model.llama_tokenizer, skip_special_tokens=True)
generation_kwargs['streamer'] = streamer
thread = Thread(target=self.model_generate, kwargs=generation_kwargs)
thread.start()
return streamer
generated = input_ids
for _ in range(max_length):
output = self.forward(input_ids=generated).logits
next_word_id = output[:, -1, :].argmax(1)
generated = torch.cat((generated, next_word_id.unsqueeze(-1)), dim=1)
def model_generate(self, *args, **kwargs):
# for 8 bit and 16 bit compatibility
with self.model.maybe_autocast():
output = self.model.llama_model.generate(*args, **kwargs)
return output
# def model_generate(self, *args, **kwargs):
# # for 8 bit and 16 bit compatibility
# with self.model.maybe_autocast():
# max_length=100
# for _ in range(max_length):
# output = self.model(**kwargs).logits
# next_word_id = output[:, -1, :].argmax(1)
# generated = torch.cat((generated, next_word_id.unsqueeze(-1)), dim=1)
# return output
def encode_img(self, img_list):
image = img_list[0]
img_list.pop(0)
if isinstance(image, str): # is a image path
raw_image = Image.open(image).convert('RGB')
image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
elif isinstance(image, Image.Image):
raw_image = image
image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
elif isinstance(image, torch.Tensor):
if len(image.shape) == 3:
image = image.unsqueeze(0)
image = image.to(self.device)
image_emb, _ = self.model.encode_img(image)
img_list.append(image_emb)
def upload_img(self, image, conv, img_list):
conv.append_message(conv.roles[0], "<Img><ImageHere></Img>")
img_list.append(image)
msg = "Received."
return msg