models/ema_model.py

# Reference: DiffusionPolicy [https://github.com/real-stanford/diffusion_policy]

import torch
from torch.nn.modules.batchnorm import _BatchNorm


class EMAModel:
    """
    Exponential Moving Average of models weights
    """
    def __init__(
        self,
        model,
        update_after_step=0,
        inv_gamma=1.0,
        power=2 / 3,
        min_value=0.0,
        max_value=0.9999
    ):
        """
        @crowsonkb's 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).
        Args:
            inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
            power (float): Exponential factor of EMA warmup. Default: 2/3.
            min_value (float): The minimum EMA decay rate. Default: 0.
        """

        self.averaged_model = model
        self.averaged_model.eval()
        self.averaged_model.requires_grad_(False)

        self.update_after_step = update_after_step
        self.inv_gamma = inv_gamma
        self.power = power
        self.min_value = min_value
        self.max_value = max_value

        self.decay = 0.0
        self.optimization_step = 0

    def get_decay(self, optimization_step):
        """
        Compute the decay factor for the exponential moving average.
        """
        step = max(0, optimization_step - self.update_after_step - 1)
        value = 1 - (1 + step / self.inv_gamma) ** -self.power

        if step <= 0:
            return 0.0

        return max(self.min_value, min(value, self.max_value))

    @torch.no_grad()
    def step(self, new_model):
        self.decay = self.get_decay(self.optimization_step)

        # old_all_dataptrs = set()
        # for param in new_model.parameters():
        #     data_ptr = param.data_ptr()
        #     if data_ptr != 0:
        #         old_all_dataptrs.add(data_ptr)

        all_dataptrs = set()
        for module, ema_module in zip(new_model.modules(), self.averaged_model.modules()):            
            for param, ema_param in zip(module.parameters(recurse=False), ema_module.parameters(recurse=False)):
                # iterative over immediate parameters only.
                if isinstance(param, dict):
                    raise RuntimeError('Dict parameter not supported')
                
                # data_ptr = param.data_ptr()
                # if data_ptr != 0:
                #     all_dataptrs.add(data_ptr)

                if isinstance(module, _BatchNorm):
                    # skip batchnorms
                    ema_param.copy_(param.to(dtype=ema_param.dtype).data)
                elif not param.requires_grad:
                    ema_param.copy_(param.to(dtype=ema_param.dtype).data)
                else:
                    ema_param.mul_(self.decay)
                    ema_param.add_(param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay)

        # verify that iterating over module and then parameters is identical to parameters recursively.
        # assert old_all_dataptrs == all_dataptrs
        self.optimization_step += 1