exp_code/1_benchmark/CausVid/causvid/dmd.py

from causvid.models.model_interface import InferencePipelineInterface
from causvid.models import (
    get_diffusion_wrapper,
    get_text_encoder_wrapper,
    get_vae_wrapper,
    get_inference_pipeline_wrapper
)
from causvid.loss import get_denoising_loss
import torch.nn.functional as F
from typing import Tuple
from torch import nn
import torch


class DMD(nn.Module):
    def __init__(self, args, device):
        """
        Initialize the DMD (Distribution Matching Distillation) module.
        This class is self-contained and compute generator and fake score losses
        in the forward pass.
        """
        super().__init__()

        # Step 1: Initialize all models

        self.generator_model_name = getattr(
            args, "generator_name", args.model_name)
        self.real_model_name = getattr(args, "real_name", args.model_name)
        self.fake_model_name = getattr(args, "fake_name", args.model_name)

        self.generator_task_type = getattr(
            args, "generator_task_type", args.generator_task)
        self.real_task_type = getattr(
            args, "real_task_type", args.generator_task)
        self.fake_task_type = getattr(
            args, "fake_task_type", args.generator_task)

        self.generator = get_diffusion_wrapper(
            model_name=self.generator_model_name)()
        self.generator.set_module_grad(
            module_grad=args.generator_grad
        )

        if getattr(args, "generator_ckpt", False):
            print(f"Loading pretrained generator from {args.generator_ckpt}")
            state_dict = torch.load(args.generator_ckpt, map_location="cpu")[
                'generator']
            self.generator.load_state_dict(
                state_dict, strict=True
            )

        self.num_frame_per_block = getattr(args, "num_frame_per_block", 1)

        if self.num_frame_per_block > 1:
            self.generator.model.num_frame_per_block = self.num_frame_per_block

        self.real_score = get_diffusion_wrapper(
            model_name=self.real_model_name)()
        self.real_score.set_module_grad(
            module_grad=args.real_score_grad
        )

        self.fake_score = get_diffusion_wrapper(
            model_name=self.fake_model_name)()
        self.fake_score.set_module_grad(
            module_grad=args.fake_score_grad
        )

        if args.gradient_checkpointing:
            self.generator.enable_gradient_checkpointing()
            self.fake_score.enable_gradient_checkpointing()

        self.text_encoder = get_text_encoder_wrapper(
            model_name=args.model_name)()
        self.text_encoder.requires_grad_(False)

        self.vae = get_vae_wrapper(model_name=args.model_name)()
        self.vae.requires_grad_(False)

        # this will be init later with fsdp-wrapped modules
        self.inference_pipeline: InferencePipelineInterface = None

        # Step 2: Initialize all dmd hyperparameters

        self.denoising_step_list = torch.tensor(
            args.denoising_step_list, dtype=torch.long, device=device)
        self.num_train_timestep = args.num_train_timestep
        self.min_step = int(0.02 * self.num_train_timestep)
        self.max_step = int(0.98 * self.num_train_timestep)
        self.real_guidance_scale = args.real_guidance_scale
        self.timestep_shift = getattr(args, "timestep_shift", 1.0)

        self.args = args
        self.device = device
        self.dtype = torch.bfloat16 if args.mixed_precision else torch.float32
        self.scheduler = self.generator.get_scheduler()
        self.denoising_loss_func = get_denoising_loss(
            args.denoising_loss_type)()

        if args.warp_denoising_step:  # Warp the denoising step according to the scheduler time
            timesteps = torch.cat((self.scheduler.timesteps.cpu(), torch.tensor([0], dtype=torch.float32))).cuda().cuda()
            self.denoising_step_list = timesteps[1000 - self.denoising_step_list]

        if getattr(self.scheduler, "alphas_cumprod", None) is not None:
            self.scheduler.alphas_cumprod = self.scheduler.alphas_cumprod.to(
                device)
        else:
            self.scheduler.alphas_cumprod = None

    def _process_timestep(self, timestep: torch.Tensor, type: str) -> torch.Tensor:
        """
        Pre-process the randomly generated timestep based on the generator's task type.
        Input:
            - timestep: [batch_size, num_frame] tensor containing the randomly generated timestep.
            - type: a string indicating the type of the current model (image, bidirectional_video, or causal_video).
        Output Behavior:
            - image: check that the second dimension (num_frame) is 1.
            - bidirectional_video: broadcast the timestep to be the same for all frames.
            - causal_video: broadcast the timestep to be the same for all frames **in a block**.
        """
        if type == "image":
            assert timestep.shape[1] == 1
            return timestep
        elif type == "bidirectional_video":
            for index in range(timestep.shape[0]):
                timestep[index] = timestep[index, 0]
            return timestep
        elif type == "causal_video":
            # make the noise level the same within every motion block
            timestep = timestep.reshape(timestep.shape[0], -1, self.num_frame_per_block)
            timestep[:, :, 1:] = timestep[:, :, 0:1]
            timestep = timestep.reshape(timestep.shape[0], -1)
            return timestep
        else:
            raise NotImplementedError("Unsupported model type {}".format(type))

    def _compute_kl_grad(
        self, noisy_image_or_video: torch.Tensor,
        estimated_clean_image_or_video: torch.Tensor,
        timestep: torch.Tensor,
        conditional_dict: dict, unconditional_dict: dict,
        normalization: bool = True
    ) -> Tuple[torch.Tensor, dict]:
        """
        Compute the KL grad (eq 7 in https://arxiv.org/abs/2311.18828).
        Input:
            - noisy_image_or_video: a tensor with shape [B, F, C, H, W] where the number of frame is 1 for images.
            - estimated_clean_image_or_video: a tensor with shape [B, F, C, H, W] representing the estimated clean image or video.
            - timestep: a tensor with shape [B, F] containing the randomly generated timestep.
            - conditional_dict: a dictionary containing the conditional information (e.g. text embeddings, image embeddings).
            - unconditional_dict: a dictionary containing the unconditional information (e.g. null/negative text embeddings, null/negative image embeddings).
            - normalization: a boolean indicating whether to normalize the gradient.
        Output:
            - kl_grad: a tensor representing the KL grad.
            - kl_log_dict: a dictionary containing the intermediate tensors for logging.
        """
        # Step 1: Compute the fake score
        pred_fake_image = self.fake_score(
            noisy_image_or_video=noisy_image_or_video,
            conditional_dict=conditional_dict,
            timestep=timestep
        )

        # Step 2: Compute the real score
        # We compute the conditional and unconditional prediction
        # and add them together to achieve cfg (https://arxiv.org/abs/2207.12598)
        pred_real_image_cond = self.real_score(
            noisy_image_or_video=noisy_image_or_video,
            conditional_dict=conditional_dict,
            timestep=timestep
        )

        pred_real_image_uncond = self.real_score(
            noisy_image_or_video=noisy_image_or_video,
            conditional_dict=unconditional_dict,
            timestep=timestep
        )

        pred_real_image = pred_real_image_cond + (
            pred_real_image_cond - pred_real_image_uncond
        ) * self.real_guidance_scale

        # Step 3: Compute the DMD gradient (DMD paper eq. 7).
        grad = (pred_fake_image - pred_real_image)

        # TODO: Change the normalizer for causal teacher
        if normalization:
            # Step 4: Gradient normalization (DMD paper eq. 8).
            p_real = (estimated_clean_image_or_video - pred_real_image)
            normalizer = torch.abs(p_real).mean(dim=[1, 2, 3, 4], keepdim=True)
            grad = grad / normalizer
        grad = torch.nan_to_num(grad)

        return grad, {
            "dmdtrain_clean_latent": estimated_clean_image_or_video.detach(),
            "dmdtrain_noisy_latent": noisy_image_or_video.detach(),
            "dmdtrain_pred_real_image": pred_real_image.detach(),
            "dmdtrain_pred_fake_image": pred_fake_image.detach(),
            "dmdtrain_gradient_norm": torch.mean(torch.abs(grad)).detach(),
            "timestep": timestep.detach()
        }

    def compute_distribution_matching_loss(
        self, image_or_video: torch.Tensor, conditional_dict: dict,
        unconditional_dict: dict, gradient_mask: torch.Tensor = None
    ) -> Tuple[torch.Tensor, dict]:
        """
        Compute the DMD loss (eq 7 in https://arxiv.org/abs/2311.18828).
        Input:
            - image_or_video: a tensor with shape [B, F, C, H, W] where the number of frame is 1 for images.
            - conditional_dict: a dictionary containing the conditional information (e.g. text embeddings, image embeddings).
            - unconditional_dict: a dictionary containing the unconditional information (e.g. null/negative text embeddings, null/negative image embeddings).
            - gradient_mask: a boolean tensor with the same shape as image_or_video indicating which pixels to compute loss .
        Output:
            - dmd_loss: a scalar tensor representing the DMD loss.
            - dmd_log_dict: a dictionary containing the intermediate tensors for logging.
        """
        original_latent = image_or_video

        batch_size, num_frame = image_or_video.shape[:2]

        with torch.no_grad():
            # Step 1: Randomly sample timestep based on the given schedule and corresponding noise
            timestep = torch.randint(
                0,
                self.num_train_timestep,
                [batch_size, num_frame],
                device=self.device,
                dtype=torch.long
            )

            timestep = self._process_timestep(
                timestep, type=self.real_task_type)

            # TODO: Add timestep warping
            if self.timestep_shift > 1:
                timestep = self.timestep_shift * \
                    (timestep / 1000) / \
                    (1 + (self.timestep_shift - 1) * (timestep / 1000)) * 1000
            timestep = timestep.clamp(self.min_step, self.max_step)

            noise = torch.randn_like(image_or_video)
            noisy_latent = self.scheduler.add_noise(
                image_or_video.flatten(0, 1),
                noise.flatten(0, 1),
                timestep.flatten(0, 1)
            ).detach().unflatten(0, (batch_size, num_frame))

            # Step 2: Compute the KL grad
            grad, dmd_log_dict = self._compute_kl_grad(
                noisy_image_or_video=noisy_latent,
                estimated_clean_image_or_video=original_latent,
                timestep=timestep,
                conditional_dict=conditional_dict,
                unconditional_dict=unconditional_dict
            )

        if gradient_mask is not None:
            dmd_loss = 0.5 * F.mse_loss(original_latent.double()[gradient_mask], (original_latent.double() - grad.double()).detach()[gradient_mask], reduction="mean")
        else:
            dmd_loss = 0.5 * F.mse_loss(original_latent.double(), (original_latent.double() - grad.double()).detach(), reduction="mean")
        return dmd_loss, dmd_log_dict

    def _initialize_inference_pipeline(self):
        """
        Lazy initialize the inference pipeline during the first backward simulation run.
        Here we encapsulate the inference code with a model-dependent outside function.
        We pass our FSDP-wrapped modules into the pipeline to save memory.
        """
        self.inference_pipeline = get_inference_pipeline_wrapper(
            self.generator_model_name,
            denoising_step_list=self.denoising_step_list,
            scheduler=self.scheduler,
            generator=self.generator,
            num_frame_per_block=self.num_frame_per_block
        )

    @torch.no_grad()
    def _consistency_backward_simulation(self, noise: torch.Tensor, conditional_dict: dict) -> torch.Tensor:
        """
        Simulate the generator's input from noise to avoid training/inference mismatch.
        See Sec 4.5 of the DMD2 paper (https://arxiv.org/abs/2405.14867) for details.
        Here we use the consistency sampler (https://arxiv.org/abs/2303.01469)
        Input:
            - noise: a tensor sampled from N(0, 1) with shape [B, F, C, H, W] where the number of frame is 1 for images.
            - conditional_dict: a dictionary containing the conditional information (e.g. text embeddings, image embeddings).
        Output:
            - output: a tensor with shape [B, T, F, C, H, W].
            T is the total number of timesteps. output[0] is a pure noise and output[i] and i>0
            represents the x0 prediction at each timestep.
        """
        if self.inference_pipeline is None:
            self._initialize_inference_pipeline()

        return self.inference_pipeline.inference_with_trajectory(noise=noise, conditional_dict=conditional_dict)

    def _run_generator(self, image_or_video_shape, conditional_dict: dict, unconditional_dict: dict, clean_latent: torch.tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Optionally simulate the generator's input from noise using backward simulation
        and then run the generator for one-step.
        Input:
            - image_or_video_shape: a list containing the shape of the image or video [B, F, C, H, W].
            - conditional_dict: a dictionary containing the conditional information (e.g. text embeddings, image embeddings).
            - unconditional_dict: a dictionary containing the unconditional information (e.g. null/negative text embeddings, null/negative image embeddings).
            - clean_latent: a tensor containing the clean latents [B, F, C, H, W]. Need to be passed when no backward simulation is used.
        Output:
            - pred_image: a tensor with shape [B, F, C, H, W].
        """
        # Step 1: Sample noise and backward simulate the generator's input
        if getattr(self.args, "backward_simulation", True):
            simulated_noisy_input = self._consistency_backward_simulation(
                noise=torch.randn(image_or_video_shape,
                                  device=self.device, dtype=self.dtype),
                conditional_dict=conditional_dict
            )
        else:
            simulated_noisy_input = []
            for timestep in self.denoising_step_list:
                noise = torch.randn(
                    image_or_video_shape, device=self.device, dtype=self.dtype)

                noisy_timestep = timestep * torch.ones(
                    image_or_video_shape[:2], device=self.device, dtype=torch.long)

                if timestep != 0:
                    noisy_image = self.scheduler.add_noise(
                        clean_latent.flatten(0, 1),
                        noise.flatten(0, 1),
                        noisy_timestep.flatten(0, 1)
                    ).unflatten(0, image_or_video_shape[:2])
                else:
                    noisy_image = clean_latent

                simulated_noisy_input.append(noisy_image)

            simulated_noisy_input = torch.stack(simulated_noisy_input, dim=1)

        # Step 2: Randomly sample a timestep and pick the corresponding input
        index = torch.randint(0, len(self.denoising_step_list), [image_or_video_shape[0], image_or_video_shape[1]], device=self.device, dtype=torch.long)
        index = self._process_timestep(index, type=self.generator_task_type)

        # select the corresponding timestep's noisy input from the stacked tensor [B, T, F, C, H, W]
        noisy_input = torch.gather(
            simulated_noisy_input, dim=1,
            index=index.reshape(index.shape[0], 1, index.shape[1], 1, 1, 1).expand(
                -1, -1, -1, *image_or_video_shape[2:])
        ).squeeze(1)

        timestep = self.denoising_step_list[index]

        pred_image_or_video = self.generator(
            noisy_image_or_video=noisy_input,
            conditional_dict=conditional_dict,
            timestep=timestep
        )

        gradient_mask = None  # timestep != 0

        # pred_image_or_video = noisy_input * \
        #     (1-gradient_mask.float()).reshape(*gradient_mask.shape, 1, 1, 1) + \
        #     pred_image_or_video * gradient_mask.float().reshape(*gradient_mask.shape, 1, 1, 1)

        pred_image_or_video = pred_image_or_video.type_as(noisy_input)

        return pred_image_or_video, gradient_mask

    def generator_loss(self, image_or_video_shape, conditional_dict: dict, unconditional_dict: dict, clean_latent: torch.Tensor) -> Tuple[torch.Tensor, dict]:
        """
        Generate image/videos from noise and compute the DMD loss.
        The noisy input to the generator is backward simulated.
        This removes the need of any datasets during distillation.
        See Sec 4.5 of the DMD2 paper (https://arxiv.org/abs/2405.14867) for details.
        Input:
            - image_or_video_shape: a list containing the shape of the image or video [B, F, C, H, W].
            - conditional_dict: a dictionary containing the conditional information (e.g. text embeddings, image embeddings).
            - unconditional_dict: a dictionary containing the unconditional information (e.g. null/negative text embeddings, null/negative image embeddings).
            - clean_latent: a tensor containing the clean latents [B, F, C, H, W]. Need to be passed when no backward simulation is used.
        Output:
            - loss: a scalar tensor representing the generator loss.
            - generator_log_dict: a dictionary containing the intermediate tensors for logging.
        """
        # Step 1: Run generator on backward simulated noisy input
        pred_image, gradient_mask = self._run_generator(
            image_or_video_shape=image_or_video_shape,
            conditional_dict=conditional_dict,
            unconditional_dict=unconditional_dict,
            clean_latent=clean_latent
        )

        # Step 2: Compute the DMD loss
        dmd_loss, dmd_log_dict = self.compute_distribution_matching_loss(
            image_or_video=pred_image,
            conditional_dict=conditional_dict,
            unconditional_dict=unconditional_dict,
            gradient_mask=gradient_mask
        )

        # Step 3: TODO: Implement the GAN loss

        return dmd_loss, dmd_log_dict

    def critic_loss(self, image_or_video_shape, conditional_dict: dict, unconditional_dict: dict, clean_latent: torch.Tensor) -> Tuple[torch.Tensor, dict]:
        """
        Generate image/videos from noise and train the critic with generated samples.
        The noisy input to the generator is backward simulated.
        This removes the need of any datasets during distillation.
        See Sec 4.5 of the DMD2 paper (https://arxiv.org/abs/2405.14867) for details.
        Input:
            - image_or_video_shape: a list containing the shape of the image or video [B, F, C, H, W].
            - conditional_dict: a dictionary containing the conditional information (e.g. text embeddings, image embeddings).
            - unconditional_dict: a dictionary containing the unconditional information (e.g. null/negative text embeddings, null/negative image embeddings).
            - clean_latent: a tensor containing the clean latents [B, F, C, H, W]. Need to be passed when no backward simulation is used.
        Output:
            - loss: a scalar tensor representing the generator loss.
            - critic_log_dict: a dictionary containing the intermediate tensors for logging.
        """

        # Step 1: Run generator on backward simulated noisy input
        with torch.no_grad():
            generated_image, _ = self._run_generator(
                image_or_video_shape=image_or_video_shape,
                conditional_dict=conditional_dict,
                unconditional_dict=unconditional_dict,
                clean_latent=clean_latent
            )

        # Step 2: Compute the fake prediction
        critic_timestep = torch.randint(
            0,
            self.num_train_timestep,
            image_or_video_shape[:2],
            device=self.device,
            dtype=torch.long
        )
        critic_timestep = self._process_timestep(
            critic_timestep, type=self.fake_task_type)

        # TODO: Add timestep warping
        if self.timestep_shift > 1:
            critic_timestep = self.timestep_shift * \
                (critic_timestep / 1000) / (1 + (self.timestep_shift - 1) * (critic_timestep / 1000)) * 1000

        critic_timestep = critic_timestep.clamp(self.min_step, self.max_step)

        critic_noise = torch.randn_like(generated_image)
        noisy_generated_image = self.scheduler.add_noise(
            generated_image.flatten(0, 1),
            critic_noise.flatten(0, 1),
            critic_timestep.flatten(0, 1)
        ).unflatten(0, image_or_video_shape[:2])

        pred_fake_image = self.fake_score(
            noisy_image_or_video=noisy_generated_image,
            conditional_dict=conditional_dict,
            timestep=critic_timestep
        )

        # Step 3: Compute the denoising loss for the fake critic
        if self.args.denoising_loss_type == "flow":
            assert "wan" in self.args.model_name
            from causvid.models.wan.wan_wrapper import WanDiffusionWrapper
            flow_pred = WanDiffusionWrapper._convert_x0_to_flow_pred(
                scheduler=self.scheduler,
                x0_pred=pred_fake_image.flatten(0, 1),
                xt=noisy_generated_image.flatten(0, 1),
                timestep=critic_timestep.flatten(0, 1)
            )
            pred_fake_noise = None
        else:
            flow_pred = None
            pred_fake_noise = self.scheduler.convert_x0_to_noise(
                x0=pred_fake_image.flatten(0, 1),
                xt=noisy_generated_image.flatten(0, 1),
                timestep=critic_timestep.flatten(0, 1)
            ).unflatten(0, image_or_video_shape[:2])

        denoising_loss = self.denoising_loss_func(
            x=generated_image.flatten(0, 1),
            x_pred=pred_fake_image.flatten(0, 1),
            noise=critic_noise.flatten(0, 1),
            noise_pred=pred_fake_noise,
            alphas_cumprod=self.scheduler.alphas_cumprod,
            timestep=critic_timestep.flatten(0, 1),
            flow_pred=flow_pred
        )

        # Step 4: TODO: Compute the GAN loss

        # Step 5: Debugging Log
        critic_log_dict = {
            "critictrain_latent": generated_image.detach(),
            "critictrain_noisy_latent": noisy_generated_image.detach(),
            "critictrain_pred_image": pred_fake_image.detach(),
            "critic_timestep": critic_timestep.detach()
        }

        return denoising_loss, critic_log_dict