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from PIL import Image
import torch
from xtuner.model import InternVL_V1_5
from typing import List, Optional, Tuple, Union
from transformers.modeling_outputs import CausalLMOutputWithPast
from torch.nn import CrossEntropyLoss
from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
LlamaTokenizer)
from xtuner.utils import PROMPT_TEMPLATE
from xtuner.tools.utils import get_stop_criteria, is_cn_string
from transformers import GenerationConfig
from projects.llava_sam2.models.preprocess.image_resize import DirectResize
from projects.lisa.datasets.sem_seg_dataset import dynamic_preprocess
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
class InternVL_vlm(InternVL_V1_5):
def forward(self, data, data_samples=None, mode='loss'):
pixel_values = data['pixel_values']
if type(pixel_values) is list or pixel_values.ndim == 5:
if type(pixel_values) is list:
pixel_values = [
x.unsqueeze(0) if x.ndim == 3 else x for x in pixel_values
]
# b*n, c, h, w
concat_images = torch.cat(
[image.to(self.model.vision_model.dtype) for image in pixel_values], dim=0)
else:
raise NotImplementedError()
input_ids = data['input_ids']
position_ids = data['position_ids']
attention_mask = data['attention_mask']
# sum is 0 are text
image_flags = torch.sum(concat_images, dim=(1, 2, 3)) != 0
image_flags = image_flags.long()
labels = data['labels']
use_cache = False
outputs = self._llm_forward(
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
image_flags=image_flags,
pixel_values=concat_images,
labels=labels,
use_cache=use_cache,
output_hidden_states=True)
if mode == 'loss':
return {'llm_loss': outputs.loss,}
else:
return outputs
def _llm_forward(
self,
pixel_values: torch.FloatTensor,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
image_flags: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
return_dict = return_dict if return_dict is not None \
else self.model.config.use_return_dict
image_flags = image_flags.squeeze(-1)
# We only added the clone code here to avoid the error.
input_embeds = self.model.language_model.get_input_embeddings()(
input_ids).clone()
vit_embeds = self.model.extract_feature(pixel_values)
vit_embeds = vit_embeds.to(input_embeds.dtype) # FIXME: why vit_embeds is float16?
vit_embeds = vit_embeds[image_flags == 1]
vit_batch_size = pixel_values.shape[0]
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
self._count += 1
input_ids = input_ids.reshape(B * N)
selected = (input_ids == self.model.img_context_token_id)
try:
input_embeds[selected] = vit_embeds.reshape(-1, C)
except Exception as e:
vit_embeds = vit_embeds.reshape(-1, C)
print(f'warning: {e}, input_embeds[selected].shape='
f'{input_embeds[selected].shape}, '
f'vit_embeds.shape={vit_embeds.shape}')
n_token = selected.sum()
input_embeds[selected] = vit_embeds[:n_token]
input_embeds = input_embeds.reshape(B, N, C)
outputs = self.model.language_model(
inputs_embeds=input_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = outputs.logits
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(
-1, self.model.language_model.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@torch.no_grad()
def generate(
self,
pixel_values: Optional[torch.FloatTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
visual_features: Optional[torch.FloatTensor] = None,
generation_config: Optional[GenerationConfig] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**generate_kwargs,
) -> torch.LongTensor:
device = self.model.device
assert self.model.img_context_token_id is not None
if pixel_values is not None:
if visual_features is not None:
vit_embeds = visual_features
else:
if type(pixel_values) is list or pixel_values.ndim == 5:
if type(pixel_values) is list:
pixel_values = [
x.unsqueeze(0) if x.ndim == 3 else x for x in pixel_values
]
# b*n, c, h, w
pixel_values = torch.cat(
[image.to(self.model.vision_model.dtype) for image in pixel_values], dim=0)
vit_embeds = self.model.extract_feature(pixel_values.to(device))
image_flags = torch.sum(pixel_values, dim=(1, 2, 3)) != 0
image_flags = image_flags.long()
vit_embeds = vit_embeds[image_flags == 1]
input_embeds = self.model.language_model.get_input_embeddings()(input_ids.to(device))
B, N, C = input_embeds.shape
input_embeds = input_embeds.reshape(B * N, C)
input_ids = input_ids.reshape(B * N)
selected = (input_ids == self.model.img_context_token_id)
assert selected.sum() != 0
input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
input_embeds = input_embeds.reshape(B, N, C)
else:
input_embeds = self.model.language_model.get_input_embeddings()(input_ids)
outputs = self.model.language_model.generate(
inputs_embeds=input_embeds,
attention_mask=attention_mask.to(device),
generation_config=generation_config,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=True,
**generate_kwargs,
)
return outputs
def preparing_for_generation(self, metainfo, **kwargs):
# set stop criteria and generation configs for model
self.torch_dtype = torch.bfloat16
assert 'tokenizer' in metainfo
tokenizer = metainfo['tokenizer']
tokenizer_type = tokenizer['type']
del tokenizer['type']
self.tokenizer = tokenizer_type(**tokenizer)
assert hasattr(self, 'tokenizer'), "The Model does not have the tokenizer!!!"
self.bot_name = 'BOT'
if 'template' in metainfo.keys():
template = metainfo['template']
else:
template = PROMPT_TEMPLATE['phi3_chat']
self.template = template
stop_words = []
stop_words += template.get('STOP_WORDS', [])
stop_criteria = get_stop_criteria(
tokenizer=self.tokenizer, stop_words=stop_words)
self.stop_criteria = stop_criteria
default_generation_kwargs = dict(
max_new_tokens=512,
do_sample=False,
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=(
self.tokenizer.pad_token_id
if self.tokenizer.pad_token_id is not None
else self.tokenizer.eos_token_id
),
)
default_generation_kwargs.update(metainfo.get('generation_kwargs', {}))
self.gen_config = GenerationConfig(**default_generation_kwargs)
self.init_prediction_config = True
self.to(self.torch_dtype)
# for multi image process
self.min_dynamic_patch = 1
self.max_dynamic_patch = 12
self.downsample_ratio = 0.5
self.image_size = 448
self.use_thumbnail = True
patch_size = 14
self.patch_token = int((self.image_size // patch_size) ** 2 * (self.downsample_ratio ** 2))
self.IMAGENET_MEAN = (0.485, 0.456, 0.406)
self.IMAGENET_STD = (0.229, 0.224, 0.225)
self.IMG_CONTEXT_TOKEN = '<IMG_CONTEXT>'
self.IMG_START_TOKEN = '<img>'
self.IMG_END_TOKEN = '</img>'
self.transformer = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((self.image_size, self.image_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=self.IMAGENET_MEAN, std=self.IMAGENET_STD)
])
# change phi3 prepare for generation fuction
# self.mllm.model.language_model.prepare_inputs_for_generation = MethodType(prepare_inputs_for_generation, self.mllm.model.language_model)
return
def predict_forward(self, question=None, image_path=None, **kwargs):
assert self.init_prediction_config, "Please set prediction configs using self.preparing_for_generation()"
input_dict = {}
# prepare images
assert image_path is not None, "InternVL2 only support process the image from scratch !!!"
image = Image.open(image_path).convert('RGB')
# for pixel segmentation tasks
images = dynamic_preprocess(image, self.min_dynamic_patch,
self.max_dynamic_patch,
self.image_size, self.use_thumbnail)
pixel_values = [self.transformer(image) for image in images]
pixel_values = torch.stack(pixel_values).to(self.torch_dtype)
input_dict['pixel_values'] = pixel_values
num_image_tokens = pixel_values.shape[0] * self.patch_token
image_token_str = f'{self.IMG_START_TOKEN}' \
f'{self.IMG_CONTEXT_TOKEN * num_image_tokens}' \
f'{self.IMG_END_TOKEN}'
ret_predictions = []
if isinstance(question, str):
text_prompts = [question]
for text_prompt in text_prompts:
# add template for text
text_prompt = text_prompt.replace('<image>', image_token_str)
input_text = ''
input_text += self.template['INSTRUCTION'].format(
input=text_prompt, round=1, bot_name=self.bot_name)
ids = self.tokenizer.encode(input_text)
ids = torch.tensor(ids).cuda().unsqueeze(0)
attention_mask = torch.ones_like(ids, dtype=torch.bool)
mm_inputs = {
'pixel_values': input_dict['pixel_values'],
'input_ids': ids,
'attention_mask': attention_mask,
'position_ids': None,
'past_key_values': None,
'labels': None
}
generate_output = self.generate(
**mm_inputs,
generation_config=self.gen_config,
streamer=None,
bos_token_id=self.tokenizer.bos_token_id,
stopping_criteria=self.stop_criteria,
output_hidden_states=True,
return_dict_in_generate=True
)
predict = self.tokenizer.decode(
generate_output.sequences[0], skip_special_tokens=True).strip()
# print(predict)
ret_predictions.append(predict)
if len(ret_predictions) == 1:
ret_predictions = ret_predictions[0]
print(ret_predictions)
ret_dict = {'prediction': ret_predictions}
ret_dict.update(kwargs)
return ret_dict
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