import os
import random
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from blip3o.constants import (
DEFAULT_IM_END_TOKEN,
DEFAULT_IM_START_TOKEN,
IGNORE_INDEX,
IMAGE_TOKEN_INDEX,
)
from blip3o.utils import rank0_print
from .multimodal_encoder.builder import build_vision_tower
from .multimodal_decoder.builder import build_sana, build_vae
from diffusers.models.normalization import RMSNorm
from diffusers import AutoencoderDC, FlowMatchEulerDiscreteScheduler, SanaTransformer2DModel
import math
class blip3oMetaModel:
def __init__(self, config):
super(blip3oMetaModel, self).__init__(config)
if hasattr(config, "mm_vision_tower"): # aka TA-Tok: this logic is used during SFT, not used during pretrain
delay_load = getattr(config, "delay_load", False)
self.vision_tower = build_vision_tower(config, delay_load=delay_load)
self.sana = build_sana(config)
self.sana_vae = build_vae(config)
norm = RMSNorm(2304, eps=1e-5, elementwise_affine=True) # 2304 is the hidden size of sana
with torch.no_grad():
norm.weight.fill_(math.sqrt(5.5))
self.diffusion_connector = nn.Sequential( # llm feature to SANA
nn.Linear(config.hidden_size, 2304), # config.hidden_size is the hidden size of the language model (2048)
nn.GELU(approximate="tanh"),
nn.Linear(2304, 2304),
norm,
)
self.vae_connector = nn.Sequential( # vae feature to SANA
nn.Linear(2240, 2304), # inner_dim(2240) = num_attention_heads * attention_head_dim
nn.GELU(approximate="tanh"),
nn.Linear(2304, 2304),
norm,
)
self.noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(config.diffusion_name_or_path, subfolder="scheduler")
self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(config.diffusion_name_or_path, subfolder="scheduler")
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 get_sana(self):
sana = getattr(self, 'sana', None)
if type(sana) is list:
sana = sana[0]
if sana is not None:
sana.to(self.device)
return sana
def get_sana_vae(self):
sana_vae = getattr(self, 'sana_vae', None)
if type(sana_vae) is list:
sana_vae = sana_vae[0]
if sana_vae is not None:
sana_vae.to(self.device)
return sana_vae
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 # sft -2
mm_vision_select_feature = model_args.mm_vision_select_feature # sft "patch"
mm_patch_merge_type = model_args.mm_patch_merge_type # sft 'flat'
self.config.mm_vision_tower = vision_tower
self.config.vision_tower_pretrained = getattr(model_args, "vision_tower_pretrained", "")
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()
if self.get_sana() is None:
sana = build_sana(model_args)
self.noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_args.diffusion_name_or_path, subfolder="scheduler"
)
self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_args.diffusion_name_or_path, subfolder="scheduler")
if fsdp is not None and len(fsdp) > 0:
self.sana = [sana]
else:
self.sana = sana
else:
if fsdp is not None and len(fsdp) > 0:
sana = self.sana[0]
else:
sana = self.sana
if self.get_sana_vae() is None:
sana_vae = build_vae(model_args)
if fsdp is not None and len(fsdp) > 0:
self.sana_vae = [sana_vae]
else:
self.sana_vae = sana_vae
else:
if fsdp is not None and len(fsdp) > 0:
sana_vae = self.sana_vae[0]
else:
sana_vae = self.sana_vae
if getattr(self, 'diffusion_connector', None) is None:
norm = RMSNorm(2304, eps=1e-5, elementwise_affine=True)
with torch.no_grad():
norm.weight.fill_(math.sqrt(5.5))
self.diffusion_connector = nn.Sequential(
nn.Linear(self.config.hidden_size, 2304),
nn.GELU(approximate="tanh"),
nn.Linear(2304, 2304),
norm,
)
else:
for p in self.diffusion_connector.parameters():
p.requires_grad = True
if getattr(self, 'vae_connector', None) is None and hasattr(model_args, "vae_connector") and model_args.vae_connector:
self.vae_connector = nn.Sequential(
nn.Linear(2240, 2304),
nn.GELU(approximate="tanh"),
nn.Linear(2304, 2304),
RMSNorm(2304, eps=1e-5, elementwise_affine=True),
)
self.config.use_mm_proj = True
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
self.config.mm_patch_merge_type = mm_patch_merge_type
class blip3oMetaForCausalLM(ABC):
@abstractmethod
def get_model(self):
pass
def get_vision_tower(self):
return self.get_model().get_vision_tower()
def encode_images(self, images, modalities, pool_scale=None):
image_features = self.get_model().get_vision_tower()(images, pool_scale=pool_scale)
assert 'tokens' in image_features
image_tokens = image_features['tokens']
# discrete features for gen related tasks
image_tokens = image_tokens + self.config.image_start_token_id
image_features = self.get_model().embed_tokens(image_tokens)
return {'image_features': image_features, 'image_tokens': image_tokens}
def prepare_inputs_labels_for_multimodal(self, input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities=None, image_sizes=None):
vision_tower = self.get_vision_tower()
if vision_tower is None or images is None or input_ids.shape[1] == 1:
return input_ids, position_ids, attention_mask, past_key_values, None, labels
if not isinstance(modalities, list):
modalities = [modalities]
# random scale for training, but scale 1 for understanding evaluation
# if self.training:
# pool_scale = random.choice(vision_tower.pool_scales) # vision_tower.pool_scales = [1, 1, 2, 3]
# else:
# pool_scale = 1
pool_scale = 1
if type(images) is list or images.ndim == 5:
if type(images) is list:
images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images] # [(1, 3, 384, 384), (1, 3, 384, 384), (1, 3, 384, 384), ......]
images_list = []
for image in images:
if image.ndim == 4:
images_list.append(image)
else:
images_list.append(image.unsqueeze(0))
concat_images = torch.cat([image for image in images_list], dim=0) # [B, 3, 384, 384]
split_sizes = [image.shape[0] for image in images_list] # [1, 1, 1, 1, ......] length is batch size
encoded_image_features = self.encode_images(concat_images, modalities, pool_scale=pool_scale) # modalities not used # dict(['image_features': torch.Size([B, 729, 2048]), 'image_tokens': torch.Size([B, 729])])
image_tokens = encoded_image_features['image_tokens'] # torch.Size([B, 729])
encoded_image_features = encoded_image_features['image_features'] # torch.Size([B, 729, 2048])
# This is a list, each element is [num_images, patch * patch, dim]
encoded_image_features = torch.split(encoded_image_features, split_sizes) # tuple of length batch size, each element is torch.Size([1, 729, 2048])
if image_tokens is not None:
image_tokens = torch.split(image_tokens, split_sizes) # tuple, each element is torch.Size([1, 729])
image_features = []
for idx, image_feat in enumerate(encoded_image_features):
image_features.append(image_feat)
mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
if mm_patch_merge_type == "flat":
image_features = [x.flatten(0, 1) for x in image_features] # list of length batch size, each element is torch.Size([729, 2048])
if image_tokens is not None:
image_tokens = [x.flatten(0, 1) for x in image_tokens] # list of length batch size, each element is torch.Size([729])
else:
raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
else:
image_features = self.encode_images(images, modalities, pool_scale=pool_scale)
image_tokens = image_features['image_tokens']
image_features = image_features['image_features']
# Let's just add dummy tensors if they do not exist,
# it is a headache to deal with None all the time.
# But it is not ideal, and if you have a better idea,
# please open an issue / submit a PR, thanks.
# import ipdb; ipdb.set_trace()
_labels = labels
_position_ids = position_ids # None
_attention_mask = attention_mask
if attention_mask is None:
attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
else:
attention_mask = attention_mask.bool()
if position_ids is None:
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
if labels is None:
labels = torch.full_like(input_ids, IGNORE_INDEX)
# remove the padding using attention_mask -- FIXME
_input_ids = input_ids
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
new_input_embeds = []
new_labels = []
cur_image_idx = 0
# rank_print("Inserting Images embedding")
for batch_idx, cur_input_ids in enumerate(input_ids):
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
# rank0_print(num_images)
if num_images == 0:
# cur_image_features = image_features[cur_image_idx]
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
# cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_input_embeds_1[0:0]], dim=0) # Unify processing flow: regardless of whether there is an image, follow the same splicing logic to prevent errors during parallel processing.
new_input_embeds.append(cur_input_embeds)
new_labels.append(labels[batch_idx])
cur_image_idx += 1
continue
# [-1, pos1, pos2, ..., posn, cur_input_ids.shape[0]]
image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
cur_input_ids_noim = []
cur_labels = labels[batch_idx]
cur_labels_noim = []
for i in range(len(image_token_indices) - 1): # Use IMAGE_TOKEN_INDEX as the splitting point to split input_ids and labels into num_images+1 parts, and then insert the image embedding
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1 : image_token_indices[i + 1]])
cur_labels_noim.append(cur_labels[image_token_indices[i] + 1 : image_token_indices[i + 1]])
split_sizes = [x.shape[0] for x in cur_labels_noim]
cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim)) # Text embedding after removing images
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0) # Divide again
cur_new_input_embeds = []
cur_new_labels = []
for i in range(num_images + 1): # n images divided into n+1 parts
cur_new_input_embeds.append(cur_input_embeds_no_im[i])
cur_new_labels.append(cur_labels_noim[i]) # Adding text tokens before this line, adding image tokens after this line
if i < num_images: # Image needs to be inserted after the first n parts
try:
cur_image_features = image_features[cur_image_idx]
except IndexError:
rank0_print("Error image_features[cur_image_idx]!")
break
# [Assisant\n<start_image><image><end_image>] Logic for the last image. This if logic is mainly for adding pool tokens before the last image
if self.config.image_start_tag_id == cur_labels_noim[i][-1] and image_tokens is not None: # Current slice's last token is <im_start> tag
cur_image_tokens = image_tokens[cur_image_idx]
if pool_scale is not None:
pool_token = self.config.scale_start_token_id + pool_scale - 1
pool_token = torch.tensor([pool_token], dtype=torch.long, device=cur_image_tokens.device)
cur_image_tokens = torch.cat([pool_token, cur_image_tokens]) # [pool, 0, 1, 2, ..., 728] total 730 tokens
pool_embed = self.get_model().embed_tokens(pool_token)
cur_image_features = torch.cat([pool_embed, cur_image_features])
else: # Previous images go through this logic
cur_image_tokens = torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)
cur_image_idx += 1
cur_new_input_embeds.append(cur_image_features)
cur_new_labels.append(cur_image_tokens)
cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
cur_new_input_embeds = torch.cat(cur_new_input_embeds)
cur_new_labels = torch.cat(cur_new_labels)
new_input_embeds.append(cur_new_input_embeds) # [757, 2048]
new_labels.append(cur_new_labels)
# Truncate sequences to max length as image embeddings can make the sequence longer
tokenizer_model_max_length = getattr(self.config, "tokenizer_model_max_length", None) # 2048: one image is long enough
# import ipdb; ipdb.set_trace()
new_input_embeds = [x[:tokenizer_model_max_length] for x, modality in zip(new_input_embeds, modalities)]
new_labels = [x[:tokenizer_model_max_length] for x, modality in zip(new_labels, modalities)]
# Combine them
max_len = max(x.shape[0] for x in new_input_embeds)
batch_size = len(new_input_embeds)
new_input_embeds_padded = []
new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
cur_len = cur_new_embed.shape[0]
if getattr(self.config, "tokenizer_padding_side", "right") == "left":
new_input_embeds_padded.append(torch.cat((torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device), cur_new_embed), dim=0))
if cur_len > 0:
new_labels_padded[i, -cur_len:] = cur_new_labels
attention_mask[i, -cur_len:] = True
position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
else: # Logic for right padding, pad with 0
new_input_embeds_padded.append(torch.cat((cur_new_embed, torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0))
if cur_len > 0:
new_labels_padded[i, :cur_len] = cur_new_labels
attention_mask[i, :cur_len] = True
position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0) # E.g., torch.Size([8, 901, 2048])
# Up to here we have the new input_embeds, labels, attention_mask, position_ids
if _labels is None:
new_labels = None
else:
new_labels = new_labels_padded
if _attention_mask is None:
attention_mask = None
else:
attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
if _position_ids is None:
position_ids = None
if getattr(self.config, "use_pos_skipping", False) and self.training: # This code implements the **Position Skipping** technique, which is a data augmentation method during training.
position_ids = torch.arange(new_input_embeds.size(1), device=new_input_embeds.device).unsqueeze(0).to(new_input_embeds.device)
split_position = random.randint(0, new_input_embeds.size(1))
left_add = random.randint(0, self.config.pos_skipping_range)
right_add = random.randint(left_add, self.config.pos_skipping_range)
position_ids[:, :split_position] += left_add
position_ids[:, split_position:] += right_add
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
def initialize_vision_tokenizer(self, model_args, tokenizer):
total_num_new_tokens = 0
vocab_size = 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.config.image_start_tag_id = tokenizer.convert_tokens_to_ids(DEFAULT_IM_START_TOKEN)
self.config.image_end_tag_id = tokenizer.convert_tokens_to_ids(DEFAULT_IM_END_TOKEN)
total_num_new_tokens += num_new_tokens
self.resize_token_embeddings(vocab_size + total_num_new_tokens)
if model_args.num_scale_tokens > 0:
scale_tokens = [model_args.scale_token_format.format(str(i)) for i in range(model_args.num_scale_tokens)]
num_new_tokens = tokenizer.add_tokens(scale_tokens, special_tokens=False)
self.config.scale_start_token_id = tokenizer.convert_tokens_to_ids(scale_tokens[0])
self.config.scale_end_token_id = tokenizer.convert_tokens_to_ids(scale_tokens[-1])
self.config.num_scale_tokens = model_args.num_scale_tokens
total_num_new_tokens += num_new_tokens
self.resize_token_embeddings(vocab_size + total_num_new_tokens)
if model_args.num_image_tokens > 0:
image_tokens = [model_args.image_token_format.format(str(i)) for i in range(model_args.num_image_tokens)]
num_new_tokens = tokenizer.add_tokens(image_tokens, special_tokens=False)
self.config.image_start_token_id = tokenizer.convert_tokens_to_ids(image_tokens[0])
self.config.image_end_token_id = tokenizer.convert_tokens_to_ids(image_tokens[-1])
self.config.num_image_tokens = model_args.num_image_tokens
total_num_new_tokens += num_new_tokens
self.resize_token_embeddings(vocab_size + total_num_new_tokens)
if num_new_tokens > 0:
self.config.num_new_tokens = num_new_tokens
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
vision_tower = self.get_vision_tower()
if model_args.load_embeddings_from_vision and vision_tower is not None:
vision_embeddings = vision_tower.get_embedding()
if model_args.num_image_tokens == vision_embeddings.shape[0] and input_embeddings.shape[1] == vision_embeddings.shape[1]:
rank0_print("Load vision embeddings from vision tower.")
input_embeddings[self.config.image_start_token_id:self.config.image_end_token_id+1] = vision_embeddings