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from abc import ABC, abstractmethod
import torch
from LLaVA.llava.model.multimodal_encoder.builder import build_vision_tower
from LLaVA.llava.model.multimodal_projector.builder import build_vision_projector
from LLaVA.llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, OBJECT_TOKEN_INDEX
class LlavaSearchMetaModel:
def __init__(self, config):
super(LlavaSearchMetaModel, self).__init__(config)
if hasattr(config, "mm_vision_tower"):
self.vision_tower = build_vision_tower(config, delay_load=True)
self.mm_projector = build_vision_projector(config)
self.mm_projector_object = build_vision_projector(config, object_projector=True)
def get_vision_tower(self):
vision_tower = getattr(self, 'vision_tower', None)
if type(vision_tower) is list:
vision_tower = vision_tower[0]
return vision_tower
def initialize_vision_modules(self, model_args, fsdp=None):
vision_tower = model_args.vision_tower
mm_vision_select_layer = model_args.mm_vision_select_layer
mm_vision_select_feature = model_args.mm_vision_select_feature
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
pretrain_mm_perceiver_adapter = model_args.pretrain_mm_perceiver_adapter
self.config.mm_vision_tower = vision_tower
if self.get_vision_tower() is None:
vision_tower = build_vision_tower(model_args)
if fsdp is not None and len(fsdp) > 0:
self.vision_tower = [vision_tower]
else:
self.vision_tower = vision_tower
else:
if fsdp is not None and len(fsdp) > 0:
vision_tower = self.vision_tower[0]
else:
vision_tower = self.vision_tower
vision_tower.load_model()
self.config.use_mm_proj = True
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
self.config.object_mm_projector_type = getattr(model_args, 'object_mm_projector_type', 'perceiver')
self.config.mm_hidden_size = vision_tower.hidden_size
self.config.mm_vision_select_layer = mm_vision_select_layer
self.config.mm_vision_select_feature = mm_vision_select_feature
if getattr(self, 'mm_projector', None) is None:
self.mm_projector = build_vision_projector(self.config)
self.mm_projector_object = build_vision_projector(self.config, object_projector=True)
if pretrain_mm_mlp_adapter is not None:
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
def get_w(weights, keyword):
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
if pretrain_mm_perceiver_adapter is not None:
mm_projector_weights = torch.load(pretrain_mm_perceiver_adapter, map_location='cpu')
def get_w(weights, keyword):
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
self.mm_projector_object.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
class LlavaSearchMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def encode_images(self, images):
image_features = self.get_model().get_vision_tower()(images)
image_features_long = self.get_model().mm_projector(image_features)
image_features_short = self.get_model().mm_projector_object(image_features)
return image_features_long, image_features_short
def project_features(self, object_features):
object_features = self.get_model().get_vision_tower()(object_features)
image_features_long = self.get_model().mm_projector(object_features)
object_features_short = self.get_model().mm_projector_object(object_features)
return image_features_long, object_features_short
def prepare_inputs_labels_for_multimodal(
self, input_ids, attention_mask, past_key_values, labels, images, object_features, images_long=None, objects_long=None
):
vision_tower = self.get_vision_tower()
if vision_tower is None or images is None or input_ids.shape[1] == 1:
if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
return input_ids, attention_mask, past_key_values, None, labels
if type(images) is list or images.ndim == 5:
concat_images = torch.cat([image for image in images], dim=0)
image_features = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
image_features = [x.flatten(0, 1) for x in image_features]
else:
image_features_long, image_features_short = self.encode_images(images)
if object_features is not None and len(object_features) > 0:
projected_object_features_long, projected_object_features_short = self.project_features(object_features)
new_input_embeds = []
new_labels = [] if labels is not None else None
new_attention_mask = [] if attention_mask is not None else None
cur_image_idx = 0
cur_object_idx = 0
for batch_idx, cur_input_ids in enumerate(input_ids):
if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
# multimodal LLM, but the current sample is not multimodal
half_len = cur_input_ids.shape[0] // 2
cur_object_features = projected_object_features_short[cur_object_idx]
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
cat_list = [cur_input_embeds_1, image_features_short[cur_image_idx][0:0], image_features_long[cur_image_idx][0:0]]
for _ in range(3):
cat_list.extend([projected_object_features_short[cur_object_idx][0:0], projected_object_features_long[cur_object_idx][0:0]])
cur_object_idx += 1
cat_list.append(cur_input_embeds_2)
cur_input_embeds = torch.cat(cat_list, dim=0)
new_input_embeds.append(cur_input_embeds)
if labels is not None:
new_labels.append(labels[batch_idx])
cur_image_idx += 1
new_attention_mask.append(attention_mask[batch_idx])
continue
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
cur_new_input_embeds = []
if labels is not None:
cur_labels = labels[batch_idx]
cur_new_labels = []
assert cur_labels.shape == cur_input_ids.shape
if attention_mask is not None:
cur_attention_mask = attention_mask[batch_idx]
cur_new_attention_mask = []
assert cur_attention_mask.shape == cur_input_ids.shape
while image_token_indices.numel() > 0:
if images_long is None or images_long[cur_image_idx]:
cur_image_features = torch.cat([image_features_short[cur_image_idx][0:0], image_features_long[cur_image_idx]])
else:
cur_image_features = torch.cat([image_features_short[cur_image_idx], image_features_long[cur_image_idx][0:0]])
image_token_start = image_token_indices[0]
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start-1]).detach())
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start-1:image_token_start]))
cur_new_input_embeds.append(cur_image_features)
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start+1:image_token_start+2]))
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_new_labels.append(cur_labels[image_token_start:image_token_start+1])
cur_labels = cur_labels[image_token_start+2:]
if attention_mask is not None:
cur_new_attention_mask.append(cur_attention_mask[:image_token_start])
if cur_attention_mask[image_token_start]:
cur_new_attention_mask.append(torch.full((cur_image_features.shape[0],), True, device=attention_mask.device, dtype=attention_mask.dtype))
else:
cur_new_attention_mask.append(torch.full((cur_image_features.shape[0],), False, device=attention_mask.device, dtype=attention_mask.dtype))
cur_new_attention_mask.append(cur_attention_mask[image_token_start:image_token_start+1])
cur_attention_mask = cur_attention_mask[image_token_start+2:]
else:
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start]))
cur_new_input_embeds.append(cur_image_features)
if labels is not None:
cur_new_labels.append(cur_labels[:image_token_start])
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_labels = cur_labels[image_token_start+1:]
if attention_mask is not None:
cur_new_attention_mask.append(cur_attention_mask[:image_token_start])
if cur_attention_mask[image_token_start]:
cur_new_attention_mask.append(torch.full((cur_image_features.shape[0],), True, device=attention_mask.device, dtype=attention_mask.dtype))
else:
cur_new_attention_mask.append(torch.full((cur_image_features.shape[0],), False, device=attention_mask.device, dtype=attention_mask.dtype))
cur_attention_mask = cur_attention_mask[image_token_start+1:]
cur_image_idx += 1
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
cur_input_ids = cur_input_ids[image_token_start+2:]
else:
cur_input_ids = cur_input_ids[image_token_start+1:]
image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
object_token_indices = torch.where(cur_input_ids == OBJECT_TOKEN_INDEX)[0]
cur_object_num = object_token_indices.numel()
while object_token_indices.numel() > 0:
if objects_long is None or not objects_long[cur_object_idx]:
cur_object_features = torch.cat([projected_object_features_short[cur_object_idx], projected_object_features_long[cur_object_idx][0:0]])
else:
cur_object_features = torch.cat([projected_object_features_short[cur_object_idx][0:0],projected_object_features_long[cur_object_idx]])
object_token_start = object_token_indices[0]
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:object_token_start]))
cur_new_input_embeds.append(cur_object_features)
if labels is not None:
cur_new_labels.append(cur_labels[:object_token_start])
cur_new_labels.append(torch.full((cur_object_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
cur_labels = cur_labels[object_token_start+1:]
if attention_mask is not None:
cur_new_attention_mask.append(cur_attention_mask[:object_token_start])
if cur_attention_mask[object_token_start]:
cur_new_attention_mask.append(torch.full((cur_object_features.shape[0],), True, device=attention_mask.device, dtype=attention_mask.dtype))
else:
cur_new_attention_mask.append(torch.full((cur_object_features.shape[0],), False, device=attention_mask.device, dtype=attention_mask.dtype))
cur_attention_mask = cur_attention_mask[object_token_start+1:]
cur_object_idx += 1
cur_input_ids = cur_input_ids[object_token_start+1:]
object_token_indices = torch.where(cur_input_ids == OBJECT_TOKEN_INDEX)[0]
if cur_input_ids.numel() > 0:
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach())
else:
cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
if labels is not None:
cur_new_labels.append(cur_labels)
if attention_mask is not None:
cur_new_attention_mask.append(cur_attention_mask)
cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]
cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
new_input_embeds.append(cur_new_input_embeds)
if labels is not None:
cur_new_labels = torch.cat(cur_new_labels, dim=0)
new_labels.append(cur_new_labels)
if attention_mask is not None:
cur_new_attention_mask = torch.cat(cur_new_attention_mask, dim=0)
new_attention_mask.append(cur_new_attention_mask)
need_padding = False
for i in range(len(new_input_embeds)):
for j in range(i+1, len(new_input_embeds)):
if new_input_embeds[i].shape != new_input_embeds[j].shape:
need_padding = True
break
if need_padding:
break
if need_padding:
max_len = max(x.shape[0] for x in new_input_embeds)
new_input_embeds_align = []
for cur_new_embed in new_input_embeds:
cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0)
new_input_embeds_align.append(cur_new_embed)
new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
if labels is not None:
new_labels_align = []
for cur_new_label in new_labels:
cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0)
new_labels_align.append(cur_new_label)
new_labels = torch.stack(new_labels_align, dim=0)
if attention_mask is not None:
new_attention_mask_align = []
for cur_new_attention_mask in new_attention_mask:
new_attn_mask_pad_right = torch.full((max_len - cur_new_attention_mask.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device)
cur_new_attention_mask = torch.cat((cur_new_attention_mask, new_attn_mask_pad_right), dim=0)
new_attention_mask.append(cur_new_attention_mask)
attention_mask = torch.stack(new_attention_mask, dim=0)
assert attention_mask.shape == new_labels.shape
else:
new_input_embeds = torch.stack(new_input_embeds, dim=0)
if labels is not None:
new_labels = torch.stack(new_labels, dim=0)
if new_attention_mask is not None and len(new_attention_mask):
new_attention_mask = torch.stack(new_attention_mask, dim=0)
attention_mask = new_attention_mask
assert attention_mask.shape == new_input_embeds.shape[:2]
return None, attention_mask, past_key_values, new_input_embeds, new_labels
def initialize_vision_tokenizer(self, model_args, tokenizer):
if model_args.mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if model_args.mm_use_im_start_end:
num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
self.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = self.get_input_embeddings().weight.data
output_embeddings = self.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = True
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
if model_args.pretrain_mm_mlp_adapter:
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
assert num_new_tokens == 2
if input_embeddings.shape == embed_tokens_weight.shape:
input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
elif embed_tokens_weight.shape[0] == num_new_tokens:
input_embeddings[-num_new_tokens:] = embed_tokens_weight
else:
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
elif model_args.mm_use_im_patch_token:
if model_args.tune_mm_mlp_adapter:
for p in self.get_input_embeddings().parameters():
p.requires_grad = False
for p in self.get_output_embeddings().parameters():
p.requires_grad = False
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