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A_MobileO_With_Edit/llava_arch.py

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+#    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 = True
+
+        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