src/vqvaes/maskbit/modules/ema_model.py

"""This file contains the definition of the EMA class.

We thank the following public implementations for inspiring this code:
    https://github.com/fadel/pytorch_ema
"""

import copy
from typing import Any, Iterable, Optional, Union

import torch


class EMAModel:
    """
    Exponential Moving Average of models weights
    """

    def __init__(
        self,
        parameters: Iterable[torch.nn.Parameter],
        decay: float = 0.9999,
        min_decay: float = 0.0,
        update_after_step: int = 0,
        update_every: int = 1,
        current_step: int = 0,
        use_ema_warmup: bool = False,
        inv_gamma: Union[float, int] = 1.0,
        power: Union[float, int] = 2 / 3,
        model_cls: Optional[Any] = None,
        **model_config_kwargs
    ):
        """
        Args:
            parameters -> Iterable[torch.nn.Parameter]: The parameters to track.
            decay -> float: The decay factor for the exponential moving average.
            min_decay -> float: The minimum decay factor for the exponential moving average.
            update_after_step -> int: The number of steps to wait before starting to update the EMA weights.
            update_every -> int: The number of steps between each EMA update.
            current_step -> int: The current training step.
            use_ema_warmup -> bool: Whether to use EMA warmup.
            inv_gamma -> float: Inverse multiplicative factor of EMA warmup. Default: 1. Only used if `use_ema_warmup`
                is True.
            power -> float: Exponential factor of EMA warmup. Default: 2/3. Only used if `use_ema_warmup` is True.

        notes on EMA Warmup:
            If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
            to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
            gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
            at 215.4k steps).
        """

        parameters = list(parameters)
        self.shadow_params = [p.clone().detach() for p in parameters]
        self.temp_stored_params = None

        self.decay = decay
        self.min_decay = min_decay
        self.update_after_step = update_after_step
        self.update_every = update_every
        self.use_ema_warmup = use_ema_warmup
        self.inv_gamma = inv_gamma
        self.power = power
        self.optimization_step = current_step
        self.cur_decay_value = None  # set in `step()`

        self.model_cls = model_cls
        self.model_config_kwargs = model_config_kwargs

    @classmethod
    def from_pretrained(
        cls, checkpoint, model_cls, **model_config_kwargs
    ) -> "EMAModel":
        model = model_cls(**model_config_kwargs)
        model.load_pretrained(checkpoint)

        ema_model = cls(model.parameters(), model_cls=model_cls, **model_config_kwargs)
        return ema_model

    def save_pretrained(self, path):
        if self.model_cls is None:
            raise ValueError(
                "`save_pretrained` can only be used if `model_cls` was defined at __init__."
            )

        if self.model_config_kwargs is None:
            raise ValueError(
                "`save_pretrained` can only be used if `model_config_kwargs` was defined at __init__."
            )

        model = self.model_cls(**self.model_config_kwargs)
        self.copy_to(model.parameters())
        model.save_pretrained(path)

    def set_step(self, optimization_step: int):
        """
        Set the current optimization step.

        Args:
            optimization_step -> int: the current optimization step.
        """
        self.optimization_step = optimization_step

    def get_decay(self, optimization_step: int) -> float:
        """
        Compute the decay factor for the exponential moving average.

        Args:
            optimization_step -> int: the current optimization step.
        """
        step = max(0, optimization_step - self.update_after_step - 1)

        if step <= 0:
            return 0.0

        if self.use_ema_warmup:
            cur_decay_value = 1 - (1 + step / self.inv_gamma) ** -self.power
        else:
            cur_decay_value = (1 + step) / (10 + step)

        cur_decay_value = min(cur_decay_value, self.decay)
        # make sure decay is not smaller than min_decay
        cur_decay_value = max(cur_decay_value, self.min_decay)
        return cur_decay_value

    @torch.no_grad()
    def step(self, parameters: Iterable[torch.nn.Parameter]):
        """
        Update the stored parameters with the current parameters.

        Args:
            parameters -> Iterable[torch.nn.Parameter]: the parameters used to update the EMA model.
        """
        parameters = list(parameters)

        self.optimization_step += 1

        if (self.optimization_step - 1) % self.update_every != 0:
            return

        # Compute the decay factor for the exponential moving average.
        decay = self.get_decay(self.optimization_step)
        self.cur_decay_value = decay
        one_minus_decay = 1 - decay

        for s_param, param in zip(self.shadow_params, parameters):
            if param.requires_grad:
                s_param.sub_(one_minus_decay * (s_param - param))
            else:
                s_param.copy_(param)

    def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
        """
        Copy current averaged parameters into given collection of parameters.

        Args:
            parameters -> Iterable[torch.nn.Parameter]: the parameters to be updated with the stored moving averages.
                If `None`, the parameters with which this `ExponentialMovingAverage` was initialized will be used.
        """
        parameters = list(parameters)
        for s_param, param in zip(self.shadow_params, parameters):
            param.data.copy_(s_param.to(param.device).data)

    def to(self, device=None, dtype=None) -> None:
        """Move internal buffers of the ExponentialMovingAverage to `device`.

        Args:
            device: like `device` argument to `torch.Tensor.to`
        """
        # .to() on the tensors handles None correctly
        self.shadow_params = [
            (
                p.to(device=device, dtype=dtype)
                if p.is_floating_point()
                else p.to(device=device)
            )
            for p in self.shadow_params
        ]

    def state_dict(self) -> dict:
        """
        Returns the state of the ExponentialMovingAverage as a dict. This method is used by accelerate during
        checkpointing to save the ema state dict.
        """
        # Following PyTorch conventions, references to tensors are returned:
        # "returns a reference to the state and not its copy!" -
        # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict
        return {
            "decay": self.decay,
            "min_decay": self.min_decay,
            "optimization_step": self.optimization_step,
            "update_after_step": self.update_after_step,
            "use_ema_warmup": self.use_ema_warmup,
            "inv_gamma": self.inv_gamma,
            "power": self.power,
            "shadow_params": self.shadow_params,
        }

    def store(self, parameters: Iterable[torch.nn.Parameter]) -> None:
        """
        Save the current parameters for restoring later.

        Args:
            parameters -> Iterable[torch.nn.Parameter]: the parameters to be temporarily stored.
        """
        self.temp_stored_params = [param.detach().cpu().clone() for param in parameters]

    def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None:
        """
        Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without:
        affecting the original optimization process. Store the parameters before the `copy_to()` method. After
        validation (or model saving), use this to restore the former parameters.

        Args:
            parameters -> Iterable[torch.nn.Parameter]: the parameters to be updated with the stored parameters.
                If `None`, the parameters with which this `ExponentialMovingAverage` was initialized will be used.
        """
        if self.temp_stored_params is None:
            raise RuntimeError(
                "This ExponentialMovingAverage has no `store()`ed weights to `restore()`"
            )
        for c_param, param in zip(self.temp_stored_params, parameters):
            param.data.copy_(c_param.data)

        # Better memory-wise.
        self.temp_stored_params = None

    def load_state_dict(self, state_dict: dict) -> None:
        """
        Loads the ExponentialMovingAverage state. This method is used by accelerate during checkpointing to save the
        ema state dict.

        Args:
            state_dict -> dict: EMA state. Should be an object returned from a call to `state_dict`.
        """
        # deepcopy, to be consistent with module API
        state_dict = copy.deepcopy(state_dict)

        self.decay = state_dict.get("decay", self.decay)
        if self.decay < 0.0 or self.decay > 1.0:
            raise ValueError("Decay must be between 0 and 1")

        self.min_decay = state_dict.get("min_decay", self.min_decay)
        if not isinstance(self.min_decay, float):
            raise ValueError("Invalid min_decay")

        self.optimization_step = state_dict.get(
            "optimization_step", self.optimization_step
        )
        if not isinstance(self.optimization_step, int):
            raise ValueError("Invalid optimization_step")

        self.update_after_step = state_dict.get(
            "update_after_step", self.update_after_step
        )
        if not isinstance(self.update_after_step, int):
            raise ValueError("Invalid update_after_step")

        self.use_ema_warmup = state_dict.get("use_ema_warmup", self.use_ema_warmup)
        if not isinstance(self.use_ema_warmup, bool):
            raise ValueError("Invalid use_ema_warmup")

        self.inv_gamma = state_dict.get("inv_gamma", self.inv_gamma)
        if not isinstance(self.inv_gamma, (float, int)):
            raise ValueError("Invalid inv_gamma")

        self.power = state_dict.get("power", self.power)
        if not isinstance(self.power, (float, int)):
            raise ValueError("Invalid power")

        shadow_params = state_dict.get("shadow_params", None)
        if shadow_params is not None:
            self.shadow_params = shadow_params
            if not isinstance(self.shadow_params, list):
                raise ValueError("shadow_params must be a list")
            if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):
                raise ValueError("shadow_params must all be Tensors")