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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| """ | |
| Used for EMA tracking a given pytorch module. The user is responsible for calling step() | |
| and setting the appropriate decay | |
| """ | |
| import copy | |
| import logging | |
| import torch | |
| class EMAModule: | |
| """Exponential Moving Average of Fairseq Models""" | |
| def __init__( | |
| self, model, ema_decay=0.9999, ema_fp32=False, device=None, skip_keys=None | |
| ): | |
| """ | |
| @param model model to initialize the EMA with | |
| @param config EMAConfig object with configuration like | |
| ema_decay, ema_update_freq, ema_fp32 | |
| @param device If provided, copy EMA to this device (e.g. gpu). | |
| Otherwise EMA is in the same device as the model. | |
| """ | |
| self.decay = ema_decay | |
| self.ema_fp32 = ema_fp32 | |
| self.model = copy.deepcopy(model) | |
| self.model.requires_grad_(False) | |
| self.skip_keys = skip_keys or set() | |
| self.fp32_params = {} | |
| if device is not None: | |
| logging.info(f"Copying EMA model to device {device}") | |
| self.model = self.model.to(device=device) | |
| if self.ema_fp32: | |
| self.build_fp32_params() | |
| self.update_freq_counter = 0 | |
| def build_fp32_params(self, state_dict=None): | |
| """ | |
| Store a copy of the EMA params in fp32. | |
| If state dict is passed, the EMA params is copied from | |
| the provided state dict. Otherwise, it is copied from the | |
| current EMA model parameters. | |
| """ | |
| if not self.ema_fp32: | |
| raise RuntimeError( | |
| "build_fp32_params should not be called if ema_fp32=False. " | |
| "Use ema_fp32=True if this is really intended." | |
| ) | |
| if state_dict is None: | |
| state_dict = self.model.state_dict() | |
| def _to_float(t): | |
| return t.float() if torch.is_floating_point(t) else t | |
| for param_key in state_dict: | |
| if param_key in self.fp32_params: | |
| self.fp32_params[param_key].copy_(state_dict[param_key]) | |
| else: | |
| self.fp32_params[param_key] = _to_float(state_dict[param_key]) | |
| def restore(self, state_dict, build_fp32_params=False): | |
| """Load data from a model spec into EMA model""" | |
| self.model.load_state_dict(state_dict, strict=False) | |
| if build_fp32_params: | |
| self.build_fp32_params(state_dict) | |
| def set_decay(self, decay): | |
| self.decay = decay | |
| def get_decay(self): | |
| return self.decay | |
| def _step_internal(self, new_model): | |
| """One update of the EMA model based on new model weights""" | |
| decay = self.decay | |
| ema_state_dict = {} | |
| ema_params = self.fp32_params if self.ema_fp32 else self.model.state_dict() | |
| for key, param in new_model.state_dict().items(): | |
| if isinstance(param, dict): | |
| continue | |
| try: | |
| ema_param = ema_params[key] | |
| except KeyError: | |
| ema_param = ( | |
| param.float().clone() if param.ndim == 1 else copy.deepcopy(param) | |
| ) | |
| if param.shape != ema_param.shape: | |
| raise ValueError( | |
| "incompatible tensor shapes between model param and ema param" | |
| + "{} vs. {}".format(param.shape, ema_param.shape) | |
| ) | |
| if "version" in key: | |
| # Do not decay a model.version pytorch param | |
| continue | |
| if key in self.skip_keys or ( | |
| "num_batches_tracked" in key and ema_param.dtype == torch.int64 | |
| ): | |
| ema_param = param.to(dtype=ema_param.dtype).clone() | |
| ema_params[key].copy_(ema_param) | |
| else: | |
| ema_param.mul_(decay) | |
| ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1 - decay) | |
| ema_state_dict[key] = ema_param | |
| self.restore(ema_state_dict, build_fp32_params=False) | |
| def step(self, new_model): | |
| self._step_internal(new_model) | |
| def reverse(self, model): | |
| """ | |
| Load the model parameters from EMA model. | |
| Useful for inference or fine-tuning from the EMA model. | |
| """ | |
| d = self.model.state_dict() | |
| if "_ema" in d: | |
| del d["_ema"] | |
| model.load_state_dict(d, strict=False) | |
| return model | |