from __future__ import annotations from copy import deepcopy from functools import partial import torch from torch import Tensor from torch.nn import Module from typing import Set def exists(val): return val is not None def inplace_copy(tgt: Tensor, src: Tensor, *, auto_move_device=False): if auto_move_device: src = src.to(tgt.device) tgt.copy_(src) def inplace_lerp(tgt: Tensor, src: Tensor, weight, *, auto_move_device=False): if auto_move_device: src = src.to(tgt.device) tgt.lerp_(src, weight) class EMA(Module): """ Implements exponential moving average shadowing for your model. Utilizes an inverse decay schedule to manage longer term training runs. By adjusting the power, you can control how fast EMA will ramp up to your specified beta. @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. """ def __init__( self, model: Module, ema_model: Module | None = None, # if your model has lazylinears or other types of non-deepcopyable modules, you can pass in your own ema model beta=0.9999, update_after_step=100, update_every=10, inv_gamma=1.0, power=2 / 3, min_value=0.0, param_or_buffer_names_no_ema: Set[str] = set(), ignore_names: Set[str] = set(), ignore_startswith_names: Set[str] = set(), include_online_model=True, # set this to False if you do not wish for the online model to be saved along with the ema model (managed externally) allow_different_devices=False, # if the EMA model is on a different device (say CPU), automatically move the tensor use_foreach=False ): super().__init__() self.beta = beta self.is_frozen = beta == 1. # whether to include the online model within the module tree, so that state_dict also saves it self.include_online_model = include_online_model if include_online_model: self.online_model = model else: self.online_model = [model] # hack # ema model self.ema_model = ema_model if not exists(self.ema_model): try: self.ema_model = deepcopy(model) except Exception as e: print(f'Error: While trying to deepcopy model: {e}') print('Your model was not copyable. Please make sure you are not using any LazyLinear') exit() for p in self.ema_model.parameters(): p.detach_() # parameter and buffer names self.parameter_names = {name for name, param in self.ema_model.named_parameters() if torch.is_floating_point(param) or torch.is_complex(param)} self.buffer_names = {name for name, buffer in self.ema_model.named_buffers() if torch.is_floating_point(buffer) or torch.is_complex(buffer)} # tensor update functions self.inplace_copy = partial(inplace_copy, auto_move_device=allow_different_devices) self.inplace_lerp = partial(inplace_lerp, auto_move_device=allow_different_devices) # updating hyperparameters self.update_every = update_every self.update_after_step = update_after_step self.inv_gamma = inv_gamma self.power = power self.min_value = min_value assert isinstance(param_or_buffer_names_no_ema, (set, list)) self.param_or_buffer_names_no_ema = param_or_buffer_names_no_ema # parameter or buffer self.ignore_names = ignore_names self.ignore_startswith_names = ignore_startswith_names # whether to manage if EMA model is kept on a different device self.allow_different_devices = allow_different_devices # whether to use foreach if use_foreach: assert hasattr(torch, '_foreach_lerp_') and hasattr(torch, '_foreach_copy_'), 'your version of torch does not have the prerequisite foreach functions' self.use_foreach = use_foreach # init and step states self.register_buffer('initted', torch.tensor(False)) self.register_buffer('step', torch.tensor(0)) @property def model(self): return self.online_model if self.include_online_model else self.online_model[0] def eval(self): return self.ema_model.eval() def restore_ema_model_device(self): device = self.initted.device self.ema_model.to(device) def get_params_iter(self, model): for name, param in model.named_parameters(): if name not in self.parameter_names: continue yield name, param def get_buffers_iter(self, model): for name, buffer in model.named_buffers(): if name not in self.buffer_names: continue yield name, buffer def copy_params_from_model_to_ema(self): copy = self.inplace_copy for (_, ma_params), (_, current_params) in zip(self.get_params_iter(self.ema_model), self.get_params_iter(self.model)): copy(ma_params.data, current_params.data) for (_, ma_buffers), (_, current_buffers) in zip(self.get_buffers_iter(self.ema_model), self.get_buffers_iter(self.model)): copy(ma_buffers.data, current_buffers.data) def copy_params_from_ema_to_model(self): copy = self.inplace_copy for (_, ma_params), (_, current_params) in zip(self.get_params_iter(self.ema_model), self.get_params_iter(self.model)): copy(current_params.data, ma_params.data) for (_, ma_buffers), (_, current_buffers) in zip(self.get_buffers_iter(self.ema_model), self.get_buffers_iter(self.model)): copy(current_buffers.data, ma_buffers.data) def get_current_decay(self): epoch = (self.step - self.update_after_step - 1).clamp(min=0.) value = 1 - (1 + epoch / self.inv_gamma) ** - self.power if epoch.item() <= 0: return 0. return value.clamp(min=self.min_value, max=self.beta).item() def update(self): step = self.step.item() self.step += 1 if (step % self.update_every) != 0: return if step <= self.update_after_step: self.copy_params_from_model_to_ema() return if not self.initted.item(): self.copy_params_from_model_to_ema() self.initted.data.copy_(torch.tensor(True)) self.update_moving_average(self.ema_model, self.model) @torch.no_grad() def update_moving_average(self, ma_model, current_model): if self.is_frozen: return current_decay = self.get_current_decay() # store all source and target tensors to copy or lerp tensors_to_copy = [] tensors_to_lerp = [] # loop through parameters for (name, current_params), (_, ma_params) in zip(self.get_params_iter(current_model), self.get_params_iter(ma_model)): if name in self.ignore_names: continue if any([name.startswith(prefix) for prefix in self.ignore_startswith_names]): continue if name in self.param_or_buffer_names_no_ema: tensors_to_copy.append((ma_params.data, current_params.data)) continue tensors_to_lerp.append((ma_params.data, current_params.data)) # loop through buffers for (name, current_buffer), (_, ma_buffer) in zip(self.get_buffers_iter(current_model), self.get_buffers_iter(ma_model)): if name in self.ignore_names: continue if any([name.startswith(prefix) for prefix in self.ignore_startswith_names]): continue if name in self.param_or_buffer_names_no_ema: tensors_to_copy.append((ma_buffer.data, current_buffer.data)) continue tensors_to_lerp.append((ma_buffer.data, current_buffer.data)) # execute inplace copy or lerp if not self.use_foreach: for tgt, src in tensors_to_copy: self.inplace_copy(tgt, src) for tgt, src in tensors_to_lerp: self.inplace_lerp(tgt, src, 1. - current_decay) else: # use foreach if available and specified if self.allow_different_devices: tensors_to_copy = [(tgt, src.to(tgt.device)) for tgt, src in tensors_to_copy] tensors_to_lerp = [(tgt, src.to(tgt.device)) for tgt, src in tensors_to_lerp] if len(tensors_to_copy) > 0: tgt_copy, src_copy = zip(*tensors_to_copy) torch._foreach_copy_(tgt_copy, src_copy) if len(tensors_to_lerp) > 0: tgt_lerp, src_lerp = zip(*tensors_to_lerp) torch._foreach_lerp_(tgt_lerp, src_lerp, 1. - current_decay) def __call__(self, *args, **kwargs): return self.ema_model(*args, **kwargs)