scheduler/scheduler.py

# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
#
# 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.
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput
from diffusers.schedulers.scheduling_utils import SchedulerMixin
import torch.nn.functional as F

def gumbel_noise(t, generator=None):
    device = generator.device if generator is not None else t.device
    noise = torch.zeros_like(t, device=device).uniform_(0, 1, generator=generator).to(t.device)
    return -torch.log((-torch.log(noise.clamp(1e-20))).clamp(1e-20))


def mask_by_random_topk(mask_len, probs, temperature=1.0, generator=None):
    confidence = torch.log(probs.clamp(1e-20)) + temperature * gumbel_noise(probs, generator=generator)
    sorted_confidence = torch.sort(confidence, dim=-1).values
    cut_off = torch.gather(sorted_confidence, 1, mask_len.long())
    masking = confidence < cut_off
    return masking


@dataclass
class SchedulerOutput(BaseOutput):
    """
    Output class for the scheduler's `step` function output.

    Args:
        prev_sample (`torch.Tensor` 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.
        pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    """

    prev_sample: torch.Tensor
    pred_original_sample: torch.Tensor = None


class Scheduler(SchedulerMixin, ConfigMixin):
    order = 1

    temperatures: torch.Tensor

    @register_to_config
    def __init__(
        self,
        mask_token_id: int,
        masking_schedule: str = "cosine",
    ):
        self.temperatures = None
        self.timesteps = None

    def set_timesteps(
        self,
        num_inference_steps: int,
        temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
        device: Union[str, torch.device] = None,
    ):
        self.timesteps = torch.arange(num_inference_steps, device=device).flip(0)

        if isinstance(temperature, (tuple, list)):
            self.temperatures = torch.linspace(temperature[0], temperature[1], num_inference_steps, device=device)
        else:
            self.temperatures = torch.linspace(temperature, 0.01, num_inference_steps, device=device)


    ### from https://huggingface.co/transformers/v3.2.0/_modules/transformers/generation_utils.html
    def top_k_top_p_filtering(
        self,
        logits,
        top_k: int = 0,
        top_p: float = 1.0,
        filter_value: float = -float("Inf"),
        min_tokens_to_keep: int = 1,
    ):
        """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (batch size, vocabulary size)
            if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
            if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
            Make sure we keep at least min_tokens_to_keep per batch example in the output
        From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
        """
        if top_k > 0:
            top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) 
            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
            logits[indices_to_remove] = filter_value

        if top_p < 1.0:
            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
            cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)

           
            sorted_indices_to_remove = cumulative_probs > top_p
            if min_tokens_to_keep > 1:
                sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
            sorted_indices_to_remove[..., 0] = 0

            indices_to_remove = torch.zeros_like(logits, dtype=torch.bool).scatter_(-1, sorted_indices, sorted_indices_to_remove)
            logits[indices_to_remove] = filter_value

        return logits


    def step(
        self,
        model_output: torch.Tensor,
        timestep: torch.long,
        sample: torch.LongTensor,
        starting_mask_ratio: int = 1,
        generator: Optional[torch.Generator] = None,
        return_dict: bool = True,
        using_topk_topp: Optional[bool] = False,
        sampling_temperature: Optional[float] = 1.0,
    ) -> Union[SchedulerOutput, Tuple]:
        two_dim_input = sample.ndim == 3 and model_output.ndim == 4

        if two_dim_input:
            batch_size, codebook_size, height, width = model_output.shape
            sample = sample.reshape(batch_size, height * width)
            model_output = model_output.reshape(batch_size, codebook_size, height * width).permute(0, 2, 1)

        unknown_map = sample == self.config.mask_token_id

        if using_topk_topp:
            model_output = model_output / max(sampling_temperature, 1e-5)

        if using_topk_topp:
            top_k=8192
            top_p=0.2
            if top_k > 0 or top_p < 1.0:
                model_output = self.top_k_top_p_filtering(model_output, top_k=top_k, top_p=top_p)

        probs = model_output.softmax(dim=-1)

        device = probs.device
        probs_ = probs.to(generator.device) if generator is not None else probs  # handles when generator is on CPU
        if probs_.device.type == "cpu" and probs_.dtype != torch.float32:
            probs_ = probs_.float()  # multinomial is not implemented for cpu half precision
        probs_ = probs_.reshape(-1, probs.size(-1))
        pred_original_sample = torch.multinomial(probs_, 1, generator=generator).to(device=device)
        pred_original_sample = pred_original_sample[:, 0].view(*probs.shape[:-1])
        pred_original_sample = torch.where(unknown_map, pred_original_sample, sample)

        if timestep == 0:
            prev_sample = pred_original_sample
        else:
            seq_len = sample.shape[1]
            step_idx = (self.timesteps == timestep).nonzero()
            ratio = (step_idx + 1) / len(self.timesteps)

            if self.config.masking_schedule == "cosine":
                mask_ratio = torch.cos(ratio * math.pi / 2)
            elif self.config.masking_schedule == "linear":
                mask_ratio = 1 - ratio
            else:
                raise ValueError(f"unknown masking schedule {self.config.masking_schedule}")

            mask_ratio = starting_mask_ratio * mask_ratio

            mask_len = (seq_len * mask_ratio).floor()
            # do not mask more than amount previously masked
            mask_len = torch.min(unknown_map.sum(dim=-1, keepdim=True) - 1, mask_len)
            # mask at least one
            mask_len = torch.max(torch.tensor([1], device=model_output.device), mask_len)

            selected_probs = torch.gather(probs, -1, pred_original_sample[:, :, None])[:, :, 0]
            # Ignores the tokens given in the input by overwriting their confidence.
            selected_probs = torch.where(unknown_map, selected_probs, torch.finfo(selected_probs.dtype).max)

            masking = mask_by_random_topk(mask_len, selected_probs, self.temperatures[step_idx], generator)

            # Masks tokens with lower confidence.
            prev_sample = torch.where(masking, self.config.mask_token_id, pred_original_sample)

        if two_dim_input:
            prev_sample = prev_sample.reshape(batch_size, height, width)
            pred_original_sample = pred_original_sample.reshape(batch_size, height, width)

        if not return_dict:
            return (prev_sample, pred_original_sample)

        return SchedulerOutput(prev_sample, pred_original_sample)

    def add_noise(self, sample, timesteps, generator=None):
        step_idx = (self.timesteps == timesteps).nonzero()
        ratio = (step_idx + 1) / len(self.timesteps)

        if self.config.masking_schedule == "cosine":
            mask_ratio = torch.cos(ratio * math.pi / 2)
        elif self.config.masking_schedule == "linear":
            mask_ratio = 1 - ratio
        else:
            raise ValueError(f"unknown masking schedule {self.config.masking_schedule}")

        mask_indices = (
            torch.rand(
                sample.shape, device=generator.device if generator is not None else sample.device, generator=generator
            ).to(sample.device)
            < mask_ratio
        )

        masked_sample = sample.clone()

        masked_sample[mask_indices] = self.config.mask_token_id

        return masked_sample