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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)
class InternVL(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)
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
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