blip3o/model/language_model/blip3o_qwen.py

from typing import List, Optional, Tuple, Union, Dict
import torch
import torch.nn as nn
from PIL import Image
import torch.nn.functional as F


import transformers
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput

from blip3o.model.blip3o_arch import blip3oMetaModel, blip3oMetaForCausalLM

from transformers import Qwen2_5_VLConfig, Qwen2_5_VLModel, Qwen2_5_VLForConditionalGeneration

from blip3o.constants import UND_IMAGE_TOKEN_IDX



from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import numpy_to_pil
import numpy as np
from diffusers.models import AutoencoderKL
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler


class blip3oQwenConfig(Qwen2_5_VLConfig):
    model_type = "blip3o_qwen"


class blip3oQwenModel(blip3oMetaModel, Qwen2_5_VLModel):
    config_class = blip3oQwenConfig

    def __init__(self, config: Qwen2_5_VLConfig):
        super(blip3oQwenModel, self).__init__(config)


class blip3oQwenForCausalLM(Qwen2_5_VLForConditionalGeneration, blip3oMetaForCausalLM):
    config_class = blip3oQwenConfig

    def __init__(self, config):
        Qwen2_5_VLForConditionalGeneration.__init__(self, config)
        config.model_type = "blip3o_qwen"

        self.model = blip3oQwenModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        # Initialize weights and apply final processing
        self.post_init()

    def get_model(self):
        return self.model


    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        ids: Optional[list] = None,
        i_s_pos: Optional[list] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        gen_image: Optional[torch.FloatTensor] = None,
        und_image: Optional[torch.FloatTensor] = None,
        grid_thw: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[List[List[int]]] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None
    ) -> Union[Tuple, CausalLMOutputWithPast]:

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        
        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels,
                latents
            ) = self.prepare_inputs_labels_for_multimodal(
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                labels,
                gen_image,
                und_image,
                grid_thw,
                i_s_pos,
                image_sizes
            )

        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        
        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()
        
        total_loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = torch.nn.CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)


            # compute image loss
            # target_img_embeds = torch.clone(inputs_embeds.detach())[:,1:,:] # get target image emb
            img_loss_funct = torch.nn.MSELoss()
            # img_hidden_states = self.get_model().down_projector(hidden_states[:,-self.get_n_query():,:])
            img_hidden_states = []
            
            for b in range(hidden_states.shape[0]):
                img_hidden_states.append(hidden_states[b,i_s_pos[b]:i_s_pos[b]+64,:])
            img_hidden_states = torch.stack(img_hidden_states,dim=0)
            img_hidden_states = self.get_model().down_projector(img_hidden_states)
            # img_loss = 0.0
            if latents is None:
                img_loss = img_loss_funct(img_hidden_states, torch.clone(img_hidden_states.detach()))
            else:
                bsz = latents.shape[0]
                # device = latents.device
                dtype = latents.dtype
                noise = torch.randn_like(latents, device=latents.device)
                u = torch.rand(size=(bsz,), device="cpu")
                indices = (u * self.get_model().noise_scheduler.config.num_train_timesteps).long()
                timesteps = self.get_model().noise_scheduler.timesteps[indices].to(device=latents.device)
                sigmas = self.get_sigmas(timesteps, latents.device, n_dim=latents.ndim, dtype=dtype)
                noisy_latents = (1.0 - sigmas) * latents + sigmas * noise
                noise_pred = self.get_model().dit(
                    x=noisy_latents,
                    timestep=timesteps,
                    z_latents=self.mask_drop(img_hidden_states),
                )
                target = noise - latents
                img_loss = F.mse_loss(noise_pred.float(), target.float(), reduction="mean")
            print(f"img loss {img_loss}")
            total_loss = img_loss

        return CausalLMOutputWithPast(
            loss=total_loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
        

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        images: Optional[torch.Tensor] = None,
        image_sizes: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        position_ids = kwargs.pop("position_ids", None)
        attention_mask = kwargs.pop("attention_mask", None)
        if "inputs_embeds" in kwargs:
            raise NotImplementedError("`inputs_embeds` is not supported")

        if images is not None:
            (
                inputs,
                position_ids,
                attention_mask,
                _,
                inputs_embeds,
                img_indicator,
                _
            ) = self.prepare_inputs_labels_for_understanding(
                inputs,
                position_ids,
                attention_mask,
                None,
                None,
                images,
                image_sizes=image_sizes
            )
        else:
            inputs_embeds = self.get_model().embed_tokens(inputs)

        return super().generate(
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            **kwargs
        )

    @torch.no_grad()
    def generate_image(
        self,
        text: List[str],
        tokenizer: AutoTokenizer,
        pixel_values: Optional[torch.Tensor] = None,
        image_grid_thw: Optional[torch.Tensor] = None,
        max_var: Optional[float] = None,
        # placeholder: str = DEFAULT_IMG_PLACEHOLDER,
    ):  
        scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained("Alpha-VLLM/Lumina-Next-SFT-diffusers", subfolder="scheduler")


        N_QUERY = self.get_n_query()            
        inputs = tokenizer(text, padding="longest", return_tensors="pt")
        device = self.get_model().device
        attention_mask = inputs.attention_mask.to(device)
        input_ids = inputs.input_ids.to(device)  # B x N
        input_ids = torch.cat([input_ids, torch.tensor([[151665]]).to(device)], dim=1)
        # breakpoint()


        text_embeds = self.get_model().embed_tokens(input_ids)
        latent_queries = self.get_model().latent_queries.repeat(text_embeds.shape[0], 1, 1)


        if pixel_values is not None:
            und_image_idx = (input_ids == UND_IMAGE_TOKEN_IDX)
            pixel_values = pixel_values.type(self.visual.dtype)
            und_image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
            text_embeds[und_image_idx] = und_image_embeds.to(text_embeds.device)[:und_image_idx.sum(), :]


        text_embeds = torch.cat([text_embeds, latent_queries], dim=1)
        attention_mask = torch.cat([attention_mask, torch.ones_like(latent_queries[:, :, 0])], dim=1)


        outputs = self.model(
            inputs_embeds=text_embeds,
            attention_mask=attention_mask,
            output_hidden_states=True,
            return_dict=True,
        )
        hidden_states = outputs.hidden_states[-1][:,-N_QUERY:,:]
        img_hidden_states = hidden_states 
        output_img = self.sample_images(img_hidden_states, scheduler)
        output_img = output_img.view(1, 1792, -1).permute(0,2,1).contiguous()

        return output_img

    def sample_images(
        self,
        img_hidden_states,
        scheduler,
        guidance_scale: float = 3.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        num_inference_steps: int = 30,
        num_images_per_prompt: int = 1,
        return_tensor=False,
        **kwargs,
    ):
        
        device = img_hidden_states.device
        dtype = img_hidden_states.dtype


        img_hidden_states_null = torch.zeros_like(img_hidden_states, device=device, dtype=dtype)
        img_hidden_states_input = torch.cat([img_hidden_states_null, img_hidden_states], 0)

        batch_size = img_hidden_states.shape[0]
        latent_size = self.get_model().dit.config.input_size
        latent_channels = self.get_model().dit.config.in_channels

        latents = randn_tensor(
            shape=(batch_size * num_images_per_prompt, latent_channels, latent_size, latent_size),
            generator=generator,
            device=device,
            dtype=dtype,
        )

        # set step values
        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
        scheduler.set_timesteps(num_inference_steps, sigmas=sigmas)

        # Repeat z_latents and conditions for each image per prompt
        img_hidden_states_input = img_hidden_states_input.repeat_interleave(num_images_per_prompt, dim=0)

        for t in scheduler.timesteps:
            latent_model_input = latents.repeat(2, 1, 1, 1)
            if hasattr(scheduler, "scale_model_input"):
                latent_model_input = scheduler.scale_model_input(latent_model_input, t)

            # predict noise model_output
            noise_pred = self.get_model().dit(
                x=latent_model_input,
                timestep=t.unsqueeze(0).expand(latent_model_input.shape[0]).to(latent_model_input.device, torch.long),
                z_latents=img_hidden_states_input,
            )

            # perform guidance
            noise_pred_uncond, noise_pred = noise_pred.chunk(2)
            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

            # compute previous image: x_t -> x_t-1
            latents = scheduler.step(noise_pred, t, latents).prev_sample

        # samples = self.decode_latents(latents, return_tensor=return_tensor)
        # breakpoint()
        return latents

    def decode_latents(self, latents, normalize=True, return_tensor=False):
        if isinstance(self.get_model().vae, AutoencoderKL):
            latents = latents / self.get_model().vae.config.scaling_factor
            if self.get_model().vae.config.shift_factor is not None:
                latents = latents + self.get_model().vae.config.shift_factor
            latents = latents.to(dtype=torch.float32)
            samples = self.get_model().vae.decode(latents).sample
        else:
            samples = self.get_model().vae.decode(latents)
        if normalize:
            samples = (samples / 2 + 0.5).clamp(0, 1)
        else:
            samples = samples.clamp(-1, 1)
        if return_tensor:
            return samples
        samples = samples.cpu().permute(0, 2, 3, 1).float().numpy()
        samples = numpy_to_pil(samples)
        return samples

    def prepare_and_encode_inputs(
        self,
        inputs: List[str | Image.Image],
        tokenizer: AutoTokenizer,
        do_classifier_free_guidance: bool = False,
    ):
        # pdb.set_trace()
        device = self.get_model().device
        dtype = self.get_model().dtype

        has_image, has_text = False, False
        text_prompt, image_prompt = "", []
        img_processor = self.get_vision_tower().image_processor
        negative_prompt = {}

        for x in inputs:
            if isinstance(x, str):
                has_text = True
                text_prompt += x
            else:
                has_image = True
                text_prompt += DEFAULT_IMAGE_TOKEN
                image_prompt.append(img_processor.preprocess(x, return_tensors='pt')['pixel_values'])
        # pdb.set_trace()
        if len(image_prompt) == 0:
            image_prompt = None
        else:
            image_prompt = torch.cat(image_prompt)
            image_prompt = image_prompt.type(dtype).to(device)

        if has_image and not has_text:
            prompt = self.encode_images(image_prompt)
            # pdb.set_trace()
            if do_classifier_free_guidance:
                key = "[NULL_IMAGE]"
                if key not in negative_prompt:
                    negative_image = torch.zeros_like(image_prompt)
                    negative_prompt[key] = self.encode_images(negative_image)
                prompt = torch.cat([prompt, negative_prompt[key]], dim=0)
        else:
            prompt = self.generate_image(text=[text_prompt], image=image_prompt, tokenizer=tokenizer)
            if do_classifier_free_guidance:
                key = ""
                if key not in negative_prompt:
                    negative_prompt[key] = self.generate_image(text=[""], tokenizer=tokenizer)
                prompt = torch.cat([prompt, negative_prompt[key]], dim=0)
        
        gen_pooling = self.get_gen_pooling()
        n_query = self.get_n_query()
        num_img, _, c = prompt.shape
        if 'pool2d' in gen_pooling and has_text and not 'early' in gen_pooling:
            stride = int(gen_pooling.split('_')[1])
            sqrt_n = int(n_query**0.5)
            prompt = prompt.permute(0, 2, 1).reshape(num_img, -1, sqrt_n, sqrt_n)
            prompt = F.avg_pool2d(prompt, kernel_size=(stride, stride), stride=stride)
            prompt = prompt.reshape(num_img, c, -1).permute(0,2,1)
        return prompt


    def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
                                      inputs_embeds=None, **kwargs):
        images = kwargs.pop("images", None)
        image_sizes = kwargs.pop("image_sizes", None)
        inputs = super().prepare_inputs_for_generation(
            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
        )
        if images is not None:
            inputs['images'] = images
        if image_sizes is not None:
            inputs['image_sizes'] = image_sizes
        return inputs

AutoConfig.register("blip3o_qwen", blip3oQwenConfig)
AutoModelForCausalLM.register(blip3oQwenConfig, blip3oQwenForCausalLM)