File size: 8,104 Bytes
296c480 cb04891 296c480 |
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 |
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,
)
layers_block_name = "model.layers"
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_model.tie_weights()
# self.internlm_model.to(config.device)
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
|