sdlm/schedulers/scheduling_simplex_ddpm.py

"""DDPM scheduler for the simplex diffusion model."""

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch
from diffusers import DDPMScheduler
from diffusers.configuration_utils import register_to_config
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
from diffusers.utils import BaseOutput


@dataclass
class SimplexDDPMSchedulerOutput(BaseOutput):
    """
    Output class for the scheduler's step function output.
    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        projected_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, vocab_size)`):
            The projected logits sample (x_{0}) based on the model output from the current timestep.
    """

    prev_sample: torch.FloatTensor
    projected_logits: Optional[torch.FloatTensor] = None


def betas_for_alpha_bar(
    num_diffusion_timesteps, device, max_beta=0.999, improved_ddpm=False
):
    """
    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
    (1-beta) over time from t = [0,1].
    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
    to that part of the diffusion process.
    Args:
        num_diffusion_timesteps (`int`): the number of betas to produce.
        max_beta (`float`): the maximum beta to use; use values lower than 1 to
                     prevent singularities.
    Returns:
        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
    """

    def default_alpha_bar(time_step):
        return math.cos((time_step + 1e-4) / (1 + 1e-4) * math.pi / 2) ** 2

    if improved_ddpm:
        # Implements eqn. 17 in https://arxiv.org/pdf/2102.09672.pdf.
        alpha_bar = lambda x: (  # noqa: E731
            default_alpha_bar(x) / default_alpha_bar(0.0)
        )
        alphas_cumprod = []
    else:
        alpha_bar = default_alpha_bar
    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        alpha_bar_t1 = alpha_bar(t1)
        betas.append(min(1 - alpha_bar(t2) / alpha_bar_t1, max_beta))
        if improved_ddpm:
            alphas_cumprod.append(alpha_bar_t1)
    # TODO(rabeeh): maybe this cause memory issue.
    betas = torch.tensor(betas, dtype=torch.float32, device=device)
    if improved_ddpm:
        return betas, torch.tensor(
            alphas_cumprod, dtype=torch.torch.float32, device=device
        )
    return betas


class SimplexDDPMScheduler(DDPMScheduler):
    @register_to_config
    def __init__(
        self,
        device,
        simplex_value: float,
        num_train_timesteps: int = 1000,
        num_inference_timesteps: int = 1000,
        beta_start: float = 0.0001,
        beta_end: float = 0.02,
        beta_schedule: str = "linear",
        trained_betas: Optional[np.ndarray] = None,
        variance_type: str = "fixed_small",
        clip_sample: bool = False,
    ):
        if trained_betas is not None:
            self.betas = torch.from_numpy(trained_betas)
        elif beta_schedule == "linear":
            self.betas = torch.linspace(
                beta_start,
                beta_end,
                num_train_timesteps,
                dtype=torch.float32,
                device=device,
            )
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            self.betas = (
                torch.linspace(
                    beta_start**0.5,
                    beta_end**0.5,
                    num_train_timesteps,
                    dtype=torch.float32,
                    device=device,
                )
                ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            self.betas = betas_for_alpha_bar(num_train_timesteps, device=device)
        elif beta_schedule == "squaredcos_improved_ddpm":
            self.betas, self.alphas_cumprod = betas_for_alpha_bar(
                num_train_timesteps, device=device, improved_ddpm=True
            )
        elif beta_schedule == "sigmoid":
            # GeoDiff sigmoid schedule
            betas = torch.linspace(-6, 6, num_train_timesteps, device=device)
            self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
        else:
            raise NotImplementedError(
                f"{beta_schedule} does is not implemented for {self.__class__}"
            )

        if beta_schedule == "squaredcos_improved_ddpm":
            self.alphas = None
        else:
            self.alphas = 1.0 - self.betas
            self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)

        self.one = torch.tensor(1.0, device=device)

        # standard deviation of the initial noise distribution
        self.init_noise_sigma = 1.0

        # setable values
        self.num_inference_steps = None
        # TODO(rabeeh): if memory issue, we can not add this to GPU and convert them iteratively.
        self.timesteps = torch.from_numpy(
            np.arange(0, num_train_timesteps)[::-1].copy()
        ).to(device=device)

        self.variance_type = variance_type

    def step(
        self,
        projected_logits: torch.FloatTensor,
        timestep: int,
        t_prev: int,  # previous timestep. recall we are in backward process, so this is the next timestep.
        noise: torch.FloatTensor,
        generator=None,
    ) -> Union[DDPMSchedulerOutput, Tuple]:
        """
        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
        process from the learned model outputs (most often the predicted noise).
        Args:
            projected_logits (`torch.FloatTensor`): projected logits from the diffusion model.
            timestep (`int`): current discrete timestep in the diffusion chain.
            noise (`torch.FloatTensor`): a random noise with simplex_value standard deviation.
            generator: random number generator.
        Returns:
            [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] resulted values.
        """
        t = timestep[0, 0].item()

        # 1. compute alphas, betas
        alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
            projected_logits = torch.clamp(projected_logits, -1, 1)

        # See algorithm 2 in Figure 3 in https://arxiv.org/pdf/2210.17432.pdf.
        predicted_logits_coeff = alpha_prod_t_prev ** (0.5)
        noise_coeff = (1 - alpha_prod_t_prev) ** (0.5)
        pred_prev_sample = (
            predicted_logits_coeff * projected_logits + noise_coeff * noise
        )

        return SimplexDDPMSchedulerOutput(
            prev_sample=pred_prev_sample, projected_logits=projected_logits
        )

    def add_noise(
        self,
        original_samples: torch.FloatTensor,
        noise: torch.FloatTensor,
        timesteps: torch.IntTensor,
    ) -> torch.FloatTensor:
        timesteps = timesteps.long()
        # if same shape, we have per-token timesteps
        if timesteps.shape == noise.shape[:2]:
            alphas_cumprod_timesteps = self.alphas_cumprod[timesteps][:, :, None]
        else:
            alphas_cumprod_timesteps = self.alphas_cumprod[timesteps].view(-1, 1, 1)

        sqrt_alpha_prod = alphas_cumprod_timesteps**0.5
        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod_timesteps) ** 0.5
        noisy_samples = (
            sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        )
        return noisy_samples


class TokenWiseSimplexDDPMScheduler(DDPMScheduler):
    @register_to_config
    def __init__(
        self,
        device,
        simplex_value: float,
        num_train_timesteps: int = 1000,
        num_inference_timesteps: int = 1000,
        beta_start: float = 0.0001,
        beta_end: float = 0.02,
        beta_schedule: str = "linear",
        trained_betas: Optional[np.ndarray] = None,
        variance_type: str = "fixed_small",
        clip_sample: bool = False,
        multiply_factor: float = 1.0,
    ):
        if trained_betas is not None:
            self.betas = torch.from_numpy(trained_betas)
        elif beta_schedule == "linear":
            self.betas = torch.linspace(
                beta_start,
                beta_end,
                num_train_timesteps,
                dtype=torch.float32,
                device=device,
            )
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            self.betas = (
                torch.linspace(
                    beta_start**0.5,
                    beta_end**0.5,
                    num_train_timesteps,
                    dtype=torch.float32,
                    device=device,
                )
                ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            self.betas = betas_for_alpha_bar(num_train_timesteps, device=device)
        elif beta_schedule == "squaredcos_improved_ddpm":
            self.betas, self.alphas_cumprod = betas_for_alpha_bar(
                num_train_timesteps, device=device, improved_ddpm=True
            )
        elif beta_schedule == "sigmoid":
            # GeoDiff sigmoid schedule
            betas = torch.linspace(-6, 6, num_train_timesteps, device=device)
            self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
        else:
            raise NotImplementedError(
                f"{beta_schedule} does is not implemented for {self.__class__}"
            )

        if beta_schedule == "squaredcos_improved_ddpm":
            self.alphas = None
        else:
            self.alphas = 1.0 - self.betas
            self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)

        self.one = torch.tensor(1.0, device=device)
        self.alphas_cumprod = multiply_factor * self.alphas_cumprod

        # standard deviation of the initial noise distribution
        self.init_noise_sigma = 1.0

        # setable values
        self.num_inference_steps = None
        # TODO(rabeeh): if memory issue, we can not add this to GPU and convert them iteratively.
        self.timesteps = torch.from_numpy(
            np.arange(0, num_train_timesteps)[::-1].copy()
        ).to(device=device)

        self.variance_type = variance_type

    def step(
        self,
        projected_logits: torch.FloatTensor,
        timestep: int,
        t_prev: int,  # previous timestep. recall we are in backward process, so this is the next timestep.
        noise: torch.FloatTensor,
        generator=None,
    ) -> Union[DDPMSchedulerOutput, Tuple]:
        """
        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
        process from the learned model outputs (most often the predicted noise).
        Args:
            projected_logits (`torch.FloatTensor`): projected logits from the diffusion model.
            timestep (`int`): current discrete timestep in the diffusion chain.
            noise (`torch.FloatTensor`): a random noise with simplex_value standard deviation.
            generator: random number generator.
        Returns:
            [`~schedulers.scheduling_utils.DDPMSchedulerOutput`] resulted values.
        """
        position_timestep = timestep.long()

        # 1. compute alphas, betas
        # index into alphas cumprod
        alphas_cumprods = []
        for i, pos_timestep in enumerate(position_timestep):
            alphas_cumprods.append(
                torch.where(pos_timestep > 0, self.alphas_cumprod[t_prev[i]], self.one)
            )

        # alphas_cumprods has dim: [batch, positions, timesteps]
        alpha_prod_t_prev = torch.stack(alphas_cumprods, dim=0)[:, :, None]
        # current_timesteps = current_timesteps.unsqueeze(-1)
        # now, we can use gather!
        # alpha_prod_t_prev = torch.where(current_timesteps > 0, alphas_cumprods.gather(dim=-1, index=current_timesteps.long()), self.one)
        # alpha_prod_t_prev = self.alphas_cumprod[position_timestep][t - 1] if t > 0 else self.one
        # alpha_prod_t_prev = alpha_prod_t_prev

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
            projected_logits = torch.clamp(projected_logits, -1, 1)

        # See algorithm 2 in Figure 3 in https://arxiv.org/pdf/2210.17432.pdf.
        predicted_logits_coeff = alpha_prod_t_prev ** (0.5)
        noise_coeff = (1 - alpha_prod_t_prev) ** (0.5)
        pred_prev_sample = (
            predicted_logits_coeff * projected_logits + noise_coeff * noise
        )

        return SimplexDDPMSchedulerOutput(
            prev_sample=pred_prev_sample, projected_logits=projected_logits
        )

    def add_noise(
        self,
        original_samples: torch.FloatTensor,
        noise: torch.FloatTensor,
        timesteps: torch.IntTensor,
    ) -> torch.FloatTensor:
        # if same shape, we have per-token timesteps
        if timesteps.shape == noise.shape[:2]:
            alphas_cumprod_timesteps = self.alphas_cumprod[timesteps.long()][:, :, None]
        else:
            alphas_cumprod_timesteps = self.alphas_cumprod[timesteps.long()].view(
                -1, 1, 1
            )
        sqrt_alpha_prod = alphas_cumprod_timesteps**0.5
        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod_timesteps) ** 0.5
        noisy_samples = (
            sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        )
        return noisy_samples