# Copyright 2023 Haotian Liu # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod import torch import torch.nn as nn import torch.nn.functional as F from diffusers.models.embeddings import PixArtAlphaTextProjection from .mobile_block import MobileConditioningProjector from .multimodal_llava_encoder.builder import build_vision_tower from .multimodal_llava_projector.builder import build_vision_projector from .multimodal_projector.builder import build_down_projector from .multimodal_decoder.builder import build_vae, build_sana from diffusers import FlowMatchEulerDiscreteScheduler, DPMSolverMultistepScheduler from diffusers.models.normalization import RMSNorm import math from blip3o.constants import DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN, IGNORE_INDEX, IMAGE_TOKEN_INDEX class DiffusionConnector(nn.Module): def __init__(self, input_dim=896, hidden_dim=1024, output_dim=2304, eps=1e-5): super().__init__() self.linear1 = nn.Linear(input_dim, hidden_dim) self.act = nn.GELU(approximate="tanh") self.linear2 = nn.Linear(hidden_dim, output_dim) self.norm = RMSNorm(output_dim, eps=eps, elementwise_affine=True) nn.init.xavier_uniform_(self.linear1.weight) nn.init.zeros_(self.linear1.bias) nn.init.xavier_uniform_(self.linear2.weight) nn.init.zeros_(self.linear2.bias) with torch.no_grad(): self.norm.weight.fill_(math.sqrt(5.5)) def forward(self, x): x = self.linear1(x) x = self.act(x) x = self.linear2(x) x = self.norm(x) return x class LlavaMetaModel: def __init__(self, config): super(LlavaMetaModel, 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) if hasattr(config, "diffusion_name_or_path"): self.dit = build_sana(config) self.vae = build_vae(config) #self.diffusion_connector = DiffusionConnector(input_dim=self.config.hidden_size,hidden_dim=1024,output_dim=2304) self.diffusion_connector = MobileConditioningProjector(input_dim=896, hidden_dim=512, output_dim=2304, num_layers=config.vlm_num_layers) ''' norm = RMSNorm(896, eps=1e-5, elementwise_affine=True) with torch.no_grad(): norm.weight.fill_(math.sqrt(5.5)) self.diffusion_connector = nn.Sequential( nn.Linear(config.hidden_size, 896), nn.GELU(approximate="tanh"), nn.Linear(896, 896), norm, ) ''' if hasattr(config, "is_train"): if config.is_train: print("FLOW MATCHING !!") self.noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(config.diffusion_name_or_path, subfolder="scheduler") else: print("DPM SOLVER !!") self.noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(config.diffusion_name_or_path, subfolder="scheduler") else: print("FLOW MATCHING !!") self.noise_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): dit = getattr(self, 'dit', None) if type(dit) is list: dit = dit[0] if dit is not None: dit.to(self.device) return dit def get_sana_vae(self): vae = getattr(self, 'vae', None) if type(vae) is list: vae = vae[0] if vae is not None: vae.to(self.device) return vae def initialize_vision_modules(self, model_args, fsdp=None): mm_vision_select_layer = model_args.mm_vision_select_layer mm_vision_select_feature = model_args.mm_vision_select_feature mm_patch_merge_type = model_args.mm_patch_merge_type if self.get_sana() is None: dit = build_sana(model_args) if hasattr(model_args, "is_train"): if model_args.is_train: print("FLOW MATCHING !!") self.noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_args.diffusion_name_or_path, subfolder="scheduler") else: print("DPM SOLVER !!") self.noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(model_args.diffusion_name_or_path, subfolder="scheduler") if fsdp is not None and len(fsdp) > 0: self.dit = [dit] else: self.dit = dit else: if fsdp is not None and len(fsdp) > 0: dit = self.dit[0] else: dit = self.dit for p in dit.parameters(): p.requires_grad = False if self.get_sana_vae() is None: vae = build_vae(model_args) if fsdp is not None and len(fsdp) > 0: self.vae = [vae] else: self.vae = vae else: if fsdp is not None and len(fsdp) > 0: vae = self.vae[0] else: vae = self.vae for p in vae.parameters(): p.requires_grad = False if self.get_vision_tower() is None: print("=" * 20, "Building vision tower", "=" * 20) 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 getattr(self, 'diffusion_connector', None) is None: #self.diffusion_connector = DiffusionConnector(input_dim=self.config.hidden_size,hidden_dim=1024,output_dim=2304) self.diffusion_connector = MobileConditioningProjector(input_dim=896, hidden_dim=512, output_dim=2304, num_layers=model_args.vlm_num_layers) ''' 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, 1024), nn.GELU(approximate="tanh"), nn.Linear(1024, 2304), norm, ) ''' else: for p in self.diffusion_connector.parameters(): p.requires_grad = True # freeze all parameters in dit except for caption_projection for name, param in self.dit.named_parameters(): if "caption" in name: param.requires_grad = True else: param.requires_grad = False for p in dit.parameters(): p.requires_grad = True for p in vision_tower.parameters(): p.requires_grad = False # vision_tower().eval() self.config.use_mm_proj = True self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear') 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 self.config.diffusion_name_or_path = model_args.diffusion_name_or_path self.config.is_train = False #model_args.is_train if getattr(self, 'down_projector', None) is None: self.down_projector = build_down_projector(self.config) else: # In case it is frozen by LoRA for p in self.down_projector.parameters(): p.requires_grad = True def unpad_image(tensor, original_size): """ Unpads a PyTorch tensor of a padded and resized image. Args: tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format. original_size (tuple): The original size of PIL image (width, height). Returns: torch.Tensor: The unpadded image tensor. """ original_width, original_height = original_size current_height, current_width = tensor.shape[1:] original_aspect_ratio = original_width / original_height current_aspect_ratio = current_width / current_height if original_aspect_ratio > current_aspect_ratio: scale_factor = current_width / original_width new_height = int(original_height * scale_factor) padding = (current_height - new_height) // 2 unpadded_tensor = tensor[:, padding:current_height - padding, :] else: scale_factor = current_height / original_height new_width = int(original_width * scale_factor) padding = (current_width - new_width) // 2 unpadded_tensor = tensor[:, :, padding:current_width - padding] return unpadded_tensor class LlavaMetaForCausalLM(ABC): @abstractmethod def get_model(self): pass def get_vision_tower(self): return self.get_model().get_vision_tower() def visual(self, pixel_values: torch.Tensor) -> torch.Tensor: image_features = self.get_model().get_vision_tower()(pixel_values) image_features = self.get_model().mm_projector(image_features) return image_features def get_mm_projector(self): return self.get_model().mm_projector def get_sigmas(self, timesteps, device, n_dim=4, dtype=torch.float32): sigmas = self.get_model().noise_scheduler.sigmas.to(device=device, dtype=dtype) schedule_timesteps = self.get_model().noise_scheduler.timesteps.to(device=device) timesteps = timesteps.to(device) step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < n_dim: sigma = sigma.unsqueeze(-1) return sigma def mask_drop(self, latents, drop_prob=0.1): if drop_prob <= 0: return latents mask = torch.bernoulli(torch.zeros(latents.shape[0], device=latents.device, dtype=latents.dtype) + drop_prob) while len(mask.shape) < len(latents.shape): mask = mask.unsqueeze(-1) mask = 1 - mask # need to flip 0 <-> 1 return latents * mask def prepare_inputs_labels_for_multimodal( self, input_ids, position_ids, attention_mask, past_key_values, labels, gen_images=None, und_images=None ): if (gen_images is None and und_images is None) or input_ids.shape[1] == 1 or self.get_vision_tower() is None: return input_ids, position_ids, attention_mask, past_key_values, None, labels, None, None, None if gen_images is not None: vae = self.get_model().get_sana_vae() vae_device = vae.device prompt_image_embeds = vae.encode(gen_images.to(vae_device)).latent if gen_images is not None else None prompt_image_embeds = prompt_image_embeds * vae.config.scaling_factor if prompt_image_embeds is not None else None target_image_embeds = torch.clone(prompt_image_embeds).detach() else: target_image_embeds = None images = und_images 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] concat_images = torch.cat([image for image in images], dim=0) image_features = self.visual(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 = self.visual(images) # [B, image_tokens, hidden_size] # 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. _labels = labels _position_ids = position_ids _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 = [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 = [] new_input_ids = [] cur_image_idx = 0 for batch_idx, cur_input_ids in enumerate(input_ids): num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() 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) new_input_embeds.append(cur_input_embeds) new_labels.append(labels[batch_idx]) cur_image_idx += 1 continue 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): 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)) cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0) cur_new_input_embeds = [] cur_new_labels = [] cur_new_input_ids = [] for i in range(num_images + 1): cur_new_input_embeds.append(cur_input_embeds_no_im[i]) cur_new_labels.append(cur_labels_noim[i]) cur_new_input_ids.append(cur_input_ids_noim[i]) if i < num_images: if cur_image_idx < image_features.shape[0]: cur_image_features = image_features[cur_image_idx] else: cur_image_features = image_features[-1] cur_image_idx += 1 cur_new_input_embeds.append(cur_image_features) cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)) cur_new_input_ids.append(torch.full((cur_image_features.shape[0],), IMAGE_TOKEN_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)) 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, dim=0) cur_new_labels = torch.cat(cur_new_labels, dim=0) cur_new_input_ids = torch.cat(cur_new_input_ids, dim=0) new_input_embeds.append(cur_new_input_embeds) new_labels.append(cur_new_labels) new_input_ids.append(cur_new_input_ids) # 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) new_input_ids_padded = torch.full((batch_size, max_len), -300, dtype=new_input_ids[0].dtype, device=new_input_ids[0].device) if len(new_input_ids) > 0 else None for i, (cur_new_embed, cur_new_labels, cur_new_input_ids) in enumerate(zip(new_input_embeds, new_labels, new_input_ids)): cur_len = cur_new_embed.shape[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_ids_padded[i, :cur_len] = cur_new_input_ids new_input_embeds = torch.stack(new_input_embeds_padded, dim=0) 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 return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels, target_image_embeds 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