File size: 7,950 Bytes
b656627
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import copy
import os
import sys

dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, dir_path)

import contextlib

import torch.utils.checkpoint
import torch.nn as nn
from torch.nn import LayerNorm
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from PIL import Image

from .modeling_vit import *
from .modeling_InternLM import *
from .modeling_utils import *
from .resampler import create_resampler

from transformers.utils import logging
logger = logging.get_logger(__name__)


class InternLMXComposerForCausalLM(PreTrainedModel):
    config_class = InternLMXComposerConfig
    _auto_class = "AutoModelForCausalLM"

    gen_config = dict(
        num_beams=5,
        do_sample=True,
        min_length=1,
        repetition_penalty=1.5,
        length_penalty=1.0,
        temperature=1.0,
        max_new_tokens=500,
    )

    def __init__(self, config):
        super().__init__(config)

        self.max_length = config.max_length
        print (f'Set max length to {self.max_length}')
        print('Init VIT ... ', end='')
        self.visual_encoder = create_eva_vit_g(img_size=448)
        self.ln_vision = nn.Identity()
        self.supports_gradient_checkpointing = True
        print('Done')
        print('Init Perceive Sampler ... ', end='')
        with all_logging_disabled():
            self.Qformer = create_resampler(num_query_token=256)
        print('Done')

        print('Init InternLM ... ', end='')
        self.flag_image_start = nn.Parameter(torch.zeros([1, 1, 4096]))
        self.flag_image_end = nn.Parameter(torch.zeros([1, 1, 4096]))
        self.flag_image_start.requires_grad = False
        self.flag_image_end.requires_grad = False


        if int(torch.__version__[0]) == 1:
            self.internlm_model = InternLMForCausalLM._from_config(config).to(
                torch.float16)
        else:
            assert int(torch.__version__[0]) == 2
            # speed up init llm
            with torch.device('meta'):
                self.internlm_model = InternLMForCausalLM._from_config(config)
            self.internlm_model.to_empty(device=config.device).to(torch.float16)

        self.internlm_proj = nn.Linear(4096,
                                    self.internlm_model.config.hidden_size)
        print('Done')

        self.vis_processor = transforms.Compose([
            transforms.Resize((448, 448),
                              interpolation=InterpolationMode.BICUBIC),
            transforms.ToTensor(),
            transforms.Normalize((0.48145466, 0.4578275, 0.40821073),
                                 (0.26862954, 0.26130258, 0.27577711)),
        ])

        self.tokenizer = None

    @property
    def eoh(self):
        return '<TOKENS_UNUSED_0>'

    @property
    def eoa(self):
        return '<TOKENS_UNUSED_1>'

    def get_input_embeddings(self):
        return self.internlm_model.get_input_embeddings()
    
    def _set_gradient_checkpointing(self, module, value=False):
        if value:
            self.internlm_model.apply(
                partial(self.internlm_model._set_gradient_checkpointing, value=True)
                )


    def encode_img(self, image):
        if image is None:
            return None
        if isinstance(image, str):
            image = Image.open(image).convert("RGB")
            image = self.vis_processor(image).unsqueeze(0).to(self.device)
        else:
            assert isinstance(image, torch.Tensor)
        device = image.device
        image_embeds = self.ln_vision(
            self.visual_encoder(image)).to(device)
        image_atts = torch.ones(image_embeds.size()[:-1],
                                dtype=torch.long).to(device)
        query_output = self.Qformer(image_embeds)
        inputs_internlm = self.internlm_proj(query_output)

        inputs_internlm = torch.cat([
            self.flag_image_start.expand(inputs_internlm.shape[0], -1, -1),
            inputs_internlm,
            self.flag_image_end.expand(inputs_internlm.shape[0], -1, -1)
        ],
        dim=1)
        return inputs_internlm

    def encode_text(self, text, add_special_tokens=False):
        text_token_ids = self.tokenizer(
            text,
            return_tensors='pt',
            add_special_tokens=add_special_tokens,
        ).input_ids.to(self.device)
        text_embeds = self.internlm_model.model.embed_tokens(text_token_ids)
        return text_embeds

    def decode_text(self, out_embeds):
        out_text = self.tokenizer.batch_decode(out_embeds,
                                               skip_special_tokens=True)[0]
        out_text = out_text.split(self.eoa)[0]
        return out_text

    def wrap_text(self, user_text, bot_text='', add_special=True):
        if add_special:
            eoh = self.eoh
        else:
            eoh = ''
        text = f'<|User|>:{user_text}{eoh}\n<|Bot|>:{bot_text}'
        return text

    def get_gen_args(self, **kwargs):
        new_kargs = copy.deepcopy(self.gen_config)
        new_kargs.update(kwargs)
        return new_kargs
    
    def generate(self, text, image=None, **kwargs):
        text_embeds = self.encode_text(text)
        img_embeds = self.encode_img(image)
        prompt_embeds = self.wrap_prompt(text_embeds, img_embeds)
        out_embeds = self.internlm_model.generate(inputs_embeds=prompt_embeds,
                                                **self.get_gen_args(**kwargs))
        out_text = self.decode_text(out_embeds)
        return out_text

    def chat(self, text, image=None, history=None, **kwargs):
        text_embeds = self.encode_text(text)
        img_embeds = self.encode_img(image)
        prompt_embeds = self.wrap_prompt(text_embeds,
                                         img_embeds,
                                         history=history)
        out_embeds = self.internlm_model.generate(inputs_embeds=prompt_embeds,
                                                **self.get_gen_args(**kwargs))
        out_text = self.decode_text(out_embeds)

        # trunc at eoh and eoa
        clean_out_text_token_ids = self.tokenizer(
            out_text, return_tensors='pt').input_ids.to(self.device)
        clean_out_text_embeds = self.internlm_model.model.embed_tokens(
            clean_out_text_token_ids)
        clean_prompt_embeds = self.wrap_prompt(text_embeds,
                                               img_embeds,
                                               add_special=False)
        cur_history = torch.cat([clean_prompt_embeds, clean_out_text_embeds],
                                dim=1)
        if history is None:
            history = []
        history.append(cur_history)
        return out_text, history

    def wrap_prompt(self,
                    text_embeds,
                    img_embeds=None,
                    history=None,
                    add_special=True):
        if add_special:
            prompt_segs = ['<|User|>:', f'{self.eoh}\n<|Bot|>:']
        else:
            prompt_segs = ['<|User|>:', '<|Bot|>:']  # used in wrap history
        prompt_seg_embeds = []
        for i, seg in enumerate(prompt_segs):
            if history is not None:
                add_special_tokens = False
            else:
                add_special_tokens = i == 0
            seg_embeds = self.encode_text(
                seg, add_special_tokens=add_special_tokens)
            prompt_seg_embeds.append(seg_embeds)
        if img_embeds is None:
            img_embeds = text_embeds.new_empty(text_embeds.size(0), 0,
                                               text_embeds.size(-1))
        prompt_seg_embeds = [
            prompt_seg_embeds[0], img_embeds, text_embeds, prompt_seg_embeds[1]
        ]
        prompt_embeds = torch.cat(prompt_seg_embeds, dim=1)
        if history is not None:
            prompt_embeds = torch.cat([*history, prompt_embeds], dim=1)
        return prompt_embeds