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from typing import ClassVar, Optional
import numpy as np
import torch
from torch import nn
from transformers import LlavaNextPreTrainedModel
from transformers.models.llava_next.modeling_llava_next import LlavaNextForConditionalGeneration
from transformers.models.llava_next.modeling_llava_next import unpad_image, get_anyres_image_grid_shape
from .granite_vision_embedding_config import GraniteVisionEmbConfig
class LlavaNextWithCustomPacking(LlavaNextForConditionalGeneration):
def pack_image_features(
self,
image_features,
image_sizes,
vision_feature_select_strategy,
image_newline=None
):
"""
Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.
Args:
image_features (`List[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`)
List of image feature tensor, each contains all the visual feature of all patches.
image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
Actual image size of each images (H, W).
vision_feature_select_strategy (`str`)
The feature selection strategy used to select the vision feature from the vision backbone.
image_newline (`torch.Tensor` of shape `(embed_dim)`)
New line embedding vector.
Returns:
image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`)
feature_lens (`List[int]`)
token length of each image in image_features
"""
base_image_feature_location = self.config.base_image_feature_location
new_image_features = []
feature_lens = []
for image_idx, image_feature in enumerate(image_features):
if image_feature.shape[0] > 1:
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
image_sizes[image_idx],
self.config.image_grid_pinpoints,
self.config.vision_config.image_size,
)
if (
np.prod(image_feature.shape) % (num_patch_height * num_patch_width * height * width) != 0
and vision_feature_select_strategy == "default"
):
print(
"Image feature shape does not line up with the provided patch size. "
"You may be using the `default` vision_feature_select_strategy with a"
" visual encoder that does not have CLS."
)
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = unpad_image(image_feature, image_sizes[image_idx])
if image_newline is not None:
image_feature = torch.cat(
(
image_feature,
image_newline[:, None, None]
.expand(*image_feature.shape[:-1], 1)
.to(image_feature.device, image_feature.dtype),
),
dim=-1,
)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
if base_image_feature_location == "last":
image_feature = torch.cat((image_feature, base_image_feature), dim=0)
else:
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
else:
image_feature = image_feature[0]
if image_newline is not None:
image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
new_image_features.append(image_feature)
feature_lens.append(image_feature.size(0))
image_features = torch.cat(new_image_features, dim=0)
feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features.device)
return image_features, feature_lens
class GraniteVisionEmb(LlavaNextPreTrainedModel):
"""
GraniteVisionEmb model implementation.
"""
main_input_name: ClassVar[str] = "doc_input_ids" # transformers-related
config_class = GraniteVisionEmbConfig
def __init__(self, config: GraniteVisionEmbConfig):
super().__init__(config=config)
model = LlavaNextWithCustomPacking(config=config)
if model.language_model._tied_weights_keys is not None:
self._tied_weights_keys = [f"model.language_model.{k}" for k in model.language_model._tied_weights_keys]
self.model = model
self.dim = 128
self.custom_text_proj = nn.Linear(self.model.config.text_config.hidden_size, self.dim)
self.post_init()
def forward(self, *args, **kwargs) -> torch.Tensor:
# Delete output_hidden_states from kwargs
kwargs.pop("output_hidden_states", None)
if "pixel_values" in kwargs:
kwargs["pixel_values"] = kwargs["pixel_values"].to(dtype=self.dtype)
outputs = self.model(*args, output_hidden_states=True, **kwargs) # (batch_size, sequence_length, hidden_size)
last_hidden_states = outputs.hidden_states[-1] # (batch_size, sequence_length, hidden_size)
attention_mask = kwargs["attention_mask"]
if "pixel_values" in kwargs:
input_ids = kwargs['input_ids']
image_mask = (input_ids == self.config.image_token_index)
# inputs_embeds = last_hidden_states.masked_scatter(image_mask)
N, M = image_mask.shape
# Create an index matrix: each row is 0, 1, ..., M-1
idx = torch.arange(M, device=image_mask.device).expand(N, M)
# Replace False positions with -1 so they are ignored by topk (since all valid indices are >=0)
masked_idx = torch.where(image_mask, idx, torch.tensor(-1, device=image_mask.device))
topk_values, _ = torch.topk(masked_idx, k=729, dim=1)
last_k_indices, _ = torch.sort(topk_values, dim=1)
last_k_indices_exp = last_k_indices.unsqueeze(-1).expand(-1, -1, last_hidden_states.size(-1))
last_hidden_states = torch.gather(last_hidden_states, 1, last_k_indices_exp)
attention_mask = torch.gather(attention_mask, 1, last_k_indices)
attention_mask = attention_mask.unsqueeze(-1)
proj = self.custom_text_proj(last_hidden_states) # (batch_size, sequence_length, dim)
# L2 normalization
proj = proj / (proj.norm(dim=-1, keepdim=True) + 1e-8)
# proj = proj * kwargs["attention_mask"].unsqueeze(-1) # (batch_size, sequence_length, dim)
proj = proj * attention_mask # (batch_size, sequence_length, dim)
return proj
def get_input_embeddings(self):
return self.model.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.model.language_model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.model.language_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
self.model.language_model.set_output_embeddings(new_embeddings)
def set_decoder(self, decoder):
self.model.language_model.set_decoder(decoder)
def get_decoder(self):
return self.model.language_model.get_decoder()
def tie_weights(self):
return self.model.language_model.tie_weights()
def resize_token_embeddings(
self,
new_num_tokens: Optional[int] = None,
pad_to_multiple_of=None,
) -> nn.Embedding:
model_embeds = self.model.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
# Update vocab size
self.config.text_config.vocab_size = model_embeds.num_embeddings
self.config.vocab_size = model_embeds.num_embeddings
self.model.vocab_size = model_embeds.num_embeddings
return model_embeds
@property
def patch_size(self) -> int:
return self.model.vision_tower.config.patch_size
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