File size: 7,805 Bytes
a465221 f860a77 a465221 f860a77 a465221 f860a77 a465221 f860a77 a465221 f860a77 a465221 f860a77 a465221 f860a77 a465221 |
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 |
import math
from typing import List, Optional
import json
import torch
import torchvision
from threading import Thread
from copy import deepcopy
from PIL import Image
from transformers import AutoProcessor, Qwen2PreTrainedModel, Qwen2ForCausalLM, TextIteratorStreamer
from .processing_mplugowl3 import mPLUGOwl3Processor
from .image_processing_mplugowl3 import mPLUGOwl3ImageProcessor
from .configuration_mplugowl3 import mPLUGOwl3Config
# from .modeling_navit_siglip import SiglipVisionTransformer
from transformers.models.siglip.modeling_siglip import SiglipVisionTransformer
from .x_sdpa import ScaleDotProductAttention
from .modeling_hyper_qwen2 import HyperQwen2ForCausalLM
from torch import nn
class mPLUGOwl3PreTrainedModel(Qwen2PreTrainedModel):
config_class = mPLUGOwl3Config
class mPLUGOwl3Model(mPLUGOwl3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.language_model = HyperQwen2ForCausalLM(config)
self.vision_model = self.init_vision_module()
self.vision_dim = self.vision_model.embed_dim
self.embed_dim = self.language_model.config.hidden_size
self.vision2text_model = nn.Linear(self.vision_dim, self.embed_dim)
self.processor = None
self.terminators = ['<|im_end|>', '<|endoftext|>']
def init_vision_module(self):
self.config.vision_config._attn_implementation = self.config.vision_config._attn_implementation
model = SiglipVisionTransformer(self.config.vision_config)
setattr(model, 'embed_dim', model.embeddings.embed_dim)
setattr(model, 'patch_size', model.embeddings.patch_size)
return model
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.embed_tokens = value
def get_output_embeddings(self):
return self.language_model.lm_head
def set_output_embeddings(self, new_embeddings):
self.language_model.lm_head = new_embeddings
def set_decoder(self, decoder):
self.language_model = decoder
def get_decoder(self):
return self.language_model
def forward_image(self, pixel_values):
if pixel_values is None:
return None
dtype = self.language_model.model.embed_tokens.weight.dtype
with torch.inference_mode():
image_embeds = self.vision_model(pixel_values.to(dtype), output_hidden_states=True).hidden_states[-2]
if self.vision2text_model is not None:
image_embeds = self.vision2text_model(image_embeds)
else:
pass
return image_embeds
def forward(self, pixel_values=None, **kwargs):
image_embeds = self.forward_image(pixel_values)
return self.language_model(
image_embeds=image_embeds,
**kwargs
)
def _decode(self, input_ids, image_embeds, media_offset, tokenizer, attention_mask, decode_text=False, **kwargs):
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
output = self.language_model.generate(
input_ids=input_ids,
image_embeds=image_embeds,
media_offset=media_offset,
pad_token_id=0,
eos_token_id=terminators,
attention_mask=attention_mask,
**kwargs
)
output = output[:,input_ids.shape[1]:]
if decode_text:
return self._decode_text(output, tokenizer)
return output
def _decode_stream(self, input_ids, image_embeds, media_offset, tokenizer, **kwargs):
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
streamer = TextIteratorStreamer(tokenizer=tokenizer)
generation_kwargs = {
'input_ids': input_ids,
'image_embeds': image_embeds,
'media_offset': media_offset,
'pad_token_id': 0,
'eos_token_id': terminators,
'streamer': streamer
}
generation_kwargs.update(kwargs)
thread = Thread(target=self.language_model.generate, kwargs=generation_kwargs)
thread.start()
return streamer
def _decode_text(self, result_ids, tokenizer):
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
result_text = []
for result in result_ids:
result = result[result != 0]
if result[-1] in terminators:
result = result[:-1]
result_text.append(tokenizer.decode(result).strip())
return result_text
def init_processor(self, tokenizer):
ip = mPLUGOwl3ImageProcessor(image_size=384)
self.processor = mPLUGOwl3Processor(image_processor=ip, tokenizer=tokenizer)
processor = self.processor
return processor
def generate(
self,
input_ids=None,
pixel_values=None,
media_offset=None,
attention_mask=None,
tokenizer=None,
stream=False,
decode_text=False,
**kwargs
):
assert input_ids is not None
with torch.inference_mode():
image_embeds = self.forward_image(pixel_values)
if stream:
result = self._decode_stream(input_ids=input_ids, image_embeds=image_embeds, media_offset=media_offset, tokenizer=tokenizer, **kwargs)
else:
result = self._decode(input_ids=input_ids, image_embeds=image_embeds, media_offset=media_offset, tokenizer=tokenizer, attention_mask=attention_mask, decode_text=decode_text, **kwargs)
return result
def chat(
self,
images,
videos,
messages,
tokenizer,
processor=None,
max_new_tokens=2048,
min_new_tokens=0,
sampling=True,
max_inp_length=8192,
system_prompt='',
stream=False,
max_slice_nums=None,
use_image_id=None,
**kwargs
):
print(messages)
if len(images)>1:
cut_flag=False
else:
cut_flag=True
if processor is None:
if self.processor is None:
processor = self.init_processor(tokenizer)
else:
processor = self.processor
inputs = processor(messages, images=images, videos=videos, cut_enable=cut_flag)
inputs.to('cuda')
inputs.update({
'tokenizer': tokenizer,
'max_new_tokens': max_new_tokens,
# 'stream':True,
})
if sampling:
generation_config = {
"top_p": 0.8,
"top_k": 100,
"temperature": 0.7,
"do_sample": True,
# "repetition_penalty": 1.05
}
else:
generation_config = {
"num_beams": 3,
# "repetition_penalty": 1.2,
}
if min_new_tokens > 0:
generation_config['min_new_tokens'] = min_new_tokens
generation_config.update(
(k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
)
print(inputs)
with torch.inference_mode():
res = self.generate(
**inputs,
stream=stream,
decode_text=True,
**generation_config
)
if stream:
def stream_gen():
for text in res:
for term in self.terminators:
text = text.replace(term, '')
yield text
return stream_gen()
else:
answer = res[0]
return answer
|