noqueries_code/llava_arch.py

#    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 .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, IMAGE_TOKEN_IDX, UND_IMAGE_TOKEN_IDX, DEFAULT_IMAGE_PATCH_TOKEN


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)
            '''
            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,
            )
            '''
            self.noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(config.diffusion_name_or_path, subfolder="scheduler")
            #self.noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(config.diffusion_name_or_path, subfolder="scheduler")

            #self.latent_queries = nn.Parameter(torch.randn(1, self.config.n_query, self.config.hidden_size))
            
        

    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):
        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
        mm_patch_merge_type = model_args.mm_patch_merge_type
        
        self.config.mm_vision_tower = vision_tower
        self.config.vision_tower_pretrained = getattr(model_args, "vision_tower_pretrained", "")

        if self.get_sana() is None:
            dit = 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")
            
            #self.noise_scheduler = DPMSolverMultistepScheduler.from_pretrained(config.diffusion_name_or_path, subfolder="scheduler")
            #self.scheduler = DPMSolverMultistepScheduler.from_pretrained(config.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)


            '''
            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

        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.gen_pooling = model_args.gen_pooling


        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 encode_image(self, images):
        vision_tower = self.get_vision_tower()
        device = vision_tower.device
        images = images.to(device)
        prompt_image_embeds = vision_tower(images)
        if 'early' in self.get_gen_pooling():
            prompt_image_embeds = self.pool_img(prompt_image_embeds)

        # ------------- compute similarity -------
        all_dist = 0
        count = 0
        for i in range(2, prompt_image_embeds.shape[1]-1):
            diff = (prompt_image_embeds[:,i,:].unsqueeze(1) -  prompt_image_embeds[:,:i,:])
            dist = torch.sqrt(diff.square().sum(-1)).min().item()
            all_dist+=dist
            count+=1
        all_dist /= count
        return prompt_image_embeds
    
    def get_mm_projector(self):
        return self.get_model().mm_projector

    def get_gen_projector(self):
        return None
    

    def get_gen_pooling(self):
        return self.get_model().config.gen_pooling

    def pool_img(self, image_features):
        num_img, n, c = image_features.shape
        gen_pooling = self.get_gen_pooling()
        stride = int(gen_pooling.split('_')[-1])
        sqrt_n = int(n**0.5)
        image_features = image_features.permute(0, 2, 1).view(num_img, c, sqrt_n, sqrt_n)
        image_features = F.avg_pool2d(image_features, kernel_size=(stride, stride), stride=stride)
        return image_features

    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, und_images, grid_thw, i_s_pos, image_sizes=None
    ):
        if (gen_images is None and und_images is None) or input_ids.shape[1] == 1:
            return input_ids, position_ids, attention_mask, past_key_values, None, labels, None, None, 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()
        image_idx = (input_ids == IMAGE_TOKEN_IDX)
        text_embeds = self.get_model().embed_tokens(input_ids)
        labels[image_idx] = -100
        return None, position_ids, attention_mask, past_key_values, text_embeds, 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