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import io
import logging
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import MSELoss
from transformers.modeling_outputs import (
CausalLMOutputWithPast,
)
from typing import List, Optional, Tuple, Union
from torch.cuda.amp import autocast as autocast
from .modeling_base import BaseMLLM
logger = logging.getLogger(__name__)
class InternVideo2_VideoChat2(BaseMLLM):
def __init__(
self,
config
):
super().__init__(config=config)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
image: Optional[torch.Tensor] = None,
video: Optional[torch.Tensor] = None,
instruction = None,
video_idx = None,
image_idx = None,
):
# print('Model Forwarding')
if self.use_vision_regression_loss:
text_embeds, visual, visual_idx = self.pad_text_embeds(input_ids=input_ids, image=image,video=video, return_visual=True, video_idx=video_idx, image_idx=image_idx, instruction = instruction)
else:
text_embeds = self.pad_text_embeds(input_ids=input_ids, image=image, video=video, return_visual=False, video_idx=video_idx, image_idx=image_idx, instruction = instruction)
outputs = self.lm(
inputs_embeds=text_embeds,
attention_mask=attention_mask,
labels=labels,
output_hidden_states=True,
return_dict=True,
)
return outputs
def pad_text_embeds(
self,
input_ids: torch.LongTensor = None,
image: Optional[torch.Tensor] = None,
video: Optional[torch.Tensor] = None,
image_idx = None,
video_idx = None,
return_visual: bool = False,
instruction = None,
):
# text_embeds
text_embeds = self.lm.get_input_embeddings()(input_ids.long()).detach()
visual = None
visual_idx = None
if image is not None:
B, T, C, H, W = image.shape
image = image.permute(0, 2, 1, 3, 4)
prompt_image_embeds = self.encode_vision(image, instruction=instruction)
visual = prompt_image_embeds
prompt_image_embeds = self.project_up(prompt_image_embeds)
prompt_image_embeds = prompt_image_embeds.view(-1, prompt_image_embeds.shape[-1])
visual_idx = image_idx
text_embeds[image_idx == 1] = text_embeds[image_idx == 1] * 0 + prompt_image_embeds.to(text_embeds.device)
elif video is not None:
if len(video.shape) == 5:
B, T, C, H, W = video.shape
N = 1
else:
B, N, T, C, H, W = video.shape
video = video.reshape(B*N, T, C, H, W).permute(0, 2, 1, 3, 4)
prompt_video_embeds = self.encode_vision(video, instruction=instruction)
visual = prompt_video_embeds
prompt_video_embeds = self.project_up(prompt_video_embeds)
prompt_video_embeds = prompt_video_embeds.view(-1, prompt_video_embeds.shape[-1])
visual_idx = video_idx
text_embeds[video_idx == 1] = text_embeds[video_idx == 1] * 0 + prompt_video_embeds.to(text_embeds.device).to(text_embeds.dtype)
else:
logger.warn(f"don't get visual input, input_ids: {input_ids}")
if return_visual:
return text_embeds, visual, visual_idx
return text_embeds
def encode_vision(
self,
image,
instruction
):
device = image.device
B = image.shape[0]
T = image.shape[2]
use_image = True if T == 1 else False
image_embeds = self.vision_encoder(image, use_image=use_image)
C = image_embeds.shape[-1]
image_embeds = image_embeds.reshape(B, -1, C)
image_embeds = self.vision_layernorm(image_embeds).to(device) # [B, T*L, C]
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
if self.extra_num_query_token > 0:
query_tokens = torch.cat([self.query_tokens, self.extra_query_tokens], dim=1)
query_tokens = query_tokens.expand(image_embeds.shape[0], -1, -1)
if instruction is not None:
text_Qformer = self.qformer_tokenizer(
instruction,
padding='longest',
truncation=True,
max_length=512,
return_tensors="pt",
).to(image_embeds.device)
query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image_embeds.device)
Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask], dim=1)
query_output = self.qformer.bert(
text_Qformer.input_ids,
attention_mask=Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
else:
query_output = self.qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
return query_output.last_hidden_state[:, :query_tokens.size(1), :]
def generate_caption(
self,
input_ids,
attention_mask,
image_idx = None,
video_idx = None,
image: Optional[torch.Tensor] = None,
video: Optional[torch.Tensor] = None,
num_beams=1,
max_new_tokens=200,
do_sample=True,
top_p=0.9,
top_k=None,
temperature=1.0,
length_penalty=1,
repetition_penalty=1.0,
):
text_embeds = self.pad_text_embeds(input_ids=input_ids, image=image, video=video, image_idx=image_idx, video_idx=video_idx)
outputs = self.lm.generate(
inputs_embeds=text_embeds,
attention_mask=attention_mask,
num_beams=num_beams,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
min_length=1,
top_p=top_p,
top_k=top_k,
temperature=temperature,
length_penalty=length_penalty,
repetition_penalty=repetition_penalty,
)
return outputs |