File size: 12,888 Bytes
256a159 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 |
import types
from typing import Optional, Tuple
import mmengine
import torch
import torch.nn as nn
from mmengine.device import get_device
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.modeling_outputs import BaseModelOutputWithPast
from opencompass.registry import MM_MODELS
from .generation_utils import decode_tokens, make_context
@MM_MODELS.register_module('qwen-vl-base')
class QwenVLBase(nn.Module):
"""Inference code of Qwen-VL.
We load the Qwen model via Huggingface.
Args:
pretrained_path (str): Path to Qwen checkpoint or repo id.
prompt_constructor (dict): The config of prompt constructor.
post_processor (dict): The config of post processor.
is_caption_task (bool): Whether the task is caption task.
Defaults to False.
commit_id (str): Use given version of Qwen-VL.
Warning: the latest version may have some conflicts.
Recommend to use the given default version.
"""
def __init__(
self,
pretrained_path: str,
prompt_constructor: dict = None,
post_processor: dict = None,
is_caption_task: bool = False,
commit_id: str = '548275c8b99de56dec203c0e793be18e030f2f4c'
) -> None:
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(pretrained_path,
trust_remote_code=True,
revision=commit_id)
self.model = AutoModelForCausalLM.from_pretrained(
pretrained_path,
device_map=get_device(),
trust_remote_code=True,
revision=commit_id)
self.model.generation_config = GenerationConfig.from_pretrained(
pretrained_path, trust_remote_code=True, revision=commit_id)
if prompt_constructor is not None:
self.prompt_constructor = mmengine.registry.build_from_cfg(
prompt_constructor, MM_MODELS)
if post_processor is not None:
self.post_processor = mmengine.registry.build_from_cfg(
post_processor, MM_MODELS)
else:
self.post_processor = None
self.is_caption_task = is_caption_task
self.model.transformer.forward = types.MethodType(
forward_hack, self.model.transformer)
def _build_embeds(self, images, input_ids):
# encode image
images = self.model.transformer.visual(images)
# compute image position
bos_pos = torch.where(input_ids == self.model.transformer.config.
visual['image_start_id'])
eos_pos = torch.where(
input_ids ==
self.model.transformer.config.visual['image_start_id'] + 1)
assert (bos_pos[0] == eos_pos[0]).all()
img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1)
# embed words
inputs_embeds = self.model.transformer.wte(input_ids)
# embed image tokens
for idx, (i, a, b) in enumerate(img_pos):
inputs_embeds[i][a + 1:b] = images[idx]
return inputs_embeds
def generate(self, batch):
images = batch.pop('inputs')
images = torch.stack(images, dim=0)
format_input = self.prompt_constructor(batch)
query = self.tokenizer.from_list_format(format_input)
inputs = self.tokenizer(query, return_tensors='pt')
inputs = inputs.to(get_device())
input_ids, token_type_ids, attention_mask = inputs[
'input_ids'], inputs['token_type_ids'], inputs['attention_mask']
inputs_embeds = self._build_embeds(images, input_ids)
pred = self.model.generate(input_ids=input_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
response = self.post_processor(pred.cpu()[0])
data_sample = batch['data_samples'][0]
if self.is_caption_task:
data_sample.pred_caption = response
else:
data_sample.pred_answer = response
return data_sample
def forward(self, batch):
return self.generate(batch)
@MM_MODELS.register_module('qwen-vl-chat')
class QwenVLChat(QwenVLBase):
"""Inference code of Qwen-VL-Chat.
We load the Qwen model via Huggingface.
Args:
pretrained_path (str): Path to Qwen checkpoint or repo id.
prompt_constructor (dict): The config of prompt constructor.
post_processor (dict): The config of post processor.
is_caption_task (bool): Whether the task is caption task.
Defaults to False.
"""
def __init__(self,
pretrained_path: str,
prompt_constructor: dict = None,
post_processor: dict = None,
is_caption_task: bool = False) -> None:
super().__init__(pretrained_path, prompt_constructor, post_processor,
is_caption_task)
def generate(self, batch):
images = batch.pop('inputs')
images = torch.stack(images, dim=0)
format_input = self.prompt_constructor(batch)
query = self.tokenizer.from_list_format(format_input)
raw_text, context_tokens = make_context(
self.tokenizer,
query,
system='You are a helpful assistant.',
chat_format=self.model.generation_config.chat_format,
)
input_ids = torch.tensor([context_tokens]).to(get_device())
inputs_embeds = self._build_embeds(images, input_ids)
pred = self.model.generate(input_ids=input_ids,
inputs_embeds=inputs_embeds)
response = decode_tokens(
pred[0],
self.tokenizer,
raw_text_len=len(raw_text),
context_length=len(context_tokens),
chat_format=self.model.generation_config.chat_format,
verbose=False,
errors='replace')
if self.post_processor:
response = self.post_processor(response)
data_sample = batch['data_samples'][0]
if self.is_caption_task:
data_sample.pred_caption = response
else:
data_sample.pred_answer = response
return data_sample
def forward_hack(self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None):
if past_key_values is None and input_ids is not None and torch.any(
input_ids == self.config.visual['image_start_id']):
bos_pos = torch.where(
input_ids == self.config.visual['image_start_id'])
eos_pos = torch.where(
input_ids == self.config.visual['image_start_id'] + 1)
assert (bos_pos[0] == eos_pos[0]).all()
img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1)
images = []
for i, a, b in img_pos:
image = input_ids[i][a + 1:b - 1].tolist()
image = image[:image.index(self.config.visual['image_start_id'] +
2)]
images.append(bytes(image).decode('utf-8'))
images = self.visual.encode(images)
assert images.shape[0] == len(images)
else:
images = None
output_attentions = (output_attentions if output_attentions is not None
else self.config.output_attentions)
output_hidden_states = (output_hidden_states if output_hidden_states
is not None else self.config.output_hidden_states)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (return_dict
if return_dict is not None else self.config.use_return_dict)
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
'You cannot specify both input_ids and inputs_embeds at the same time' # noqa
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError(
'You have to specify either input_ids or inputs_embeds')
device = input_ids.device if input_ids is not None else inputs_embeds.device # noqa
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
position_ids = position_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(
past_length,
input_shape[-1] + past_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
encoder_attention_mask = None
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
if batch_size <= 0:
raise ValueError('batch_size has to be defined and > 0')
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, inputs_embeds, past_length)
hidden_states = inputs_embeds
hidden_states = self.drop(hidden_states)
if images is not None:
for idx, (i, a, b) in enumerate(img_pos):
hidden_states[i][a + 1:b] = images[idx]
output_shape = input_shape + (hidden_states.size(-1), )
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states, )
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs, use_cache, output_attentions)
return custom_forward
outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
hidden_states,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[2 if output_attentions else 1], )
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[1], )
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states, )
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states]
if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
|