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""" |
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This module has the EMA class used to store a copy of the exponentially decayed |
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model params. |
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|
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Typical usage of EMA class involves initializing an object using an existing |
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model (random or from a seed model) and setting the config like ema_decay, |
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ema_start_update which determine how the EMA model is updated. After every |
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update of the model i.e. at the end of the train_step, the EMA should be updated |
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by passing the new model to the EMA.step function. The EMA model state dict |
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can be stored in the extra state under the key of "ema" and dumped |
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into a checkpoint and loaded. The EMA object can be passed to tasks |
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by setting task.uses_ema property. |
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EMA is a smoothed/ensemble model which might have better performance |
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when used for inference or further fine-tuning. EMA class has a |
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reverse function to load the EMA params into a model and use it |
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like a regular model. |
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|
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This implementation is used for trainer-level ema tracking. For EMA tracking |
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inside the model, please use fairseq/modules/ema_module.py instead. |
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""" |
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|
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import copy |
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import logging |
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import torch |
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|
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from fairseq import checkpoint_utils |
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class EMA(object): |
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"""Exponential Moving Average of Fairseq Models |
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EMA keeps a copy of the exponentially decayed model params. |
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The set of params should include both gradient-descent and |
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non-gradient descent params, such as batch mean/var and buffers. |
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This is a modified implementation of |
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the open source code in https://github.com/zhawe01/fairseq-gec.git, |
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and internal source code in |
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fbcode/mobile-vision/projects/classification_pytorch/lib/utils/model_ema.py. |
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Similar to TF EMA. |
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https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage. |
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EMA provides a averaged and smoothed set of model weights, and has been shown to |
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improve vision models. EMA class does all necessary functions to update, reload, |
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or init EMA methods. |
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|
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EMA object is initialized from an arbitrary model. By default, it is stored in |
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the same device (unless device specified at initialization) and with the |
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same precision as the model (unless ema_fp32 is True). ema_fp32 is recommended. |
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This stores the EMA parameters in fp32 only for the EMA update step, and |
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is used at the default precision otherwise. |
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EMA is usually enabled using EMAConfig with store_ema=True. Some important |
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parameters to configure EMA are |
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1) ema_decay - The decay of EMA |
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2) ema_update_freq - EMA is updated every this many model updates. |
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3) ema_start_update - Start EMA update after this many model updates [default 0] |
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Key methods: |
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1) step - One update of EMA using new model |
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2) restore - Update EMA from a state dict |
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3) reverse - Load EMA into a model |
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4) get_decay, _set_decay - Used to get or set the decay. Note _set_decay is |
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called from step. |
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5) build_fp32_params - Used to initialize or update the fp32 copy of EMA params. |
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Note this is enabled only when ema_fp32=True |
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""" |
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def __init__(self, model, config, device=None, skip_keys=None): |
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""" |
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@param model model to initialize the EMA with |
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@param config EMAConfig object with configuration like |
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ema_decay, ema_update_freq, ema_fp32 |
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@param device If provided, copy EMA to this device (e.g. gpu). |
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Otherwise EMA is in the same device as the model. |
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""" |
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self.decay = config.ema_decay |
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self.model = copy.deepcopy(model) |
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self.model.requires_grad_(False) |
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self.config = config |
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self.skip_keys = skip_keys or set() |
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self.fp32_params = {} |
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if self.config.ema_seed_model is not None: |
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state = checkpoint_utils.load_ema_from_checkpoint( |
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self.config.ema_seed_model |
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) |
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self.model.load_state_dict(state["model"], strict=True) |
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if device is not None: |
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logging.info(f"Copying EMA model to device {device}") |
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self.model = self.model.to(device=device) |
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if self.config.ema_fp32: |
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self.build_fp32_params() |
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self.update_freq_counter = 0 |
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|
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def get_model(self): |
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return self.model |
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def build_fp32_params(self, state_dict=None): |
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""" |
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Store a copy of the EMA params in fp32. |
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If state dict is passed, the EMA params is copied from |
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the provided state dict. Otherwise, it is copied from the |
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current EMA model parameters. |
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""" |
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if not self.config.ema_fp32: |
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raise RuntimeError( |
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"build_fp32_params should not be called if ema_fp32=False. " |
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"Use ema_fp32=True if this is really intended." |
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) |
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if state_dict is None: |
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state_dict = self.model.state_dict() |
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|
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def _to_float(t): |
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return t.float() if torch.is_floating_point(t) else t |
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for param_key in state_dict: |
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if param_key in self.fp32_params: |
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self.fp32_params[param_key].copy_(state_dict[param_key]) |
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else: |
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self.fp32_params[param_key] = _to_float(state_dict[param_key]) |
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def restore(self, state_dict, build_fp32_params=False): |
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"""Load data from a model spec into EMA model""" |
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self.model.load_state_dict(state_dict, strict=False) |
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if build_fp32_params: |
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self.build_fp32_params(state_dict) |
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def _set_decay(self, decay): |
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self.decay = decay |
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def get_decay(self): |
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return self.decay |
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def _step_internal(self, new_model, updates=None): |
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"""One update of the EMA model based on new model weights""" |
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decay = self.decay |
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ema_state_dict = {} |
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ema_params = ( |
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self.fp32_params if self.config.ema_fp32 else self.model.state_dict() |
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) |
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for key, param in new_model.state_dict().items(): |
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if isinstance(param, dict): |
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continue |
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try: |
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ema_param = ema_params[key] |
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except KeyError: |
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ema_param = ( |
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param.float().clone() if param.ndim == 1 else copy.deepcopy(param) |
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) |
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|
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if param.shape != ema_param.shape: |
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raise ValueError( |
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"incompatible tensor shapes between model param and ema param" |
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+ "{} vs. {}".format(param.shape, ema_param.shape) |
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) |
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if "version" in key: |
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continue |
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if key in self.skip_keys: |
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ema_param = param.to(dtype=ema_param.dtype).clone() |
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else: |
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ema_param.mul_(decay) |
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ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1 - decay) |
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ema_state_dict[key] = ema_param |
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self.restore(ema_state_dict, build_fp32_params=False) |
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def step(self, new_model, updates=None): |
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""" |
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One update of EMA which is done every self.config.ema_update_freq |
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updates of the model. |
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|
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@param updates The current number of model updates done. |
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Decay is set of 0 if model updates < ema_start_update, which means |
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the model will be simply copied over to the EMA. |
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When model updates >= ema_start_updates, then EMA is updated with |
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a decay of self.config.ema_decay. |
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""" |
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if updates is not None: |
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self._set_decay( |
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0 if updates < self.config.ema_start_update else self.config.ema_decay |
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) |
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if self.config.ema_update_freq > 1: |
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self.update_freq_counter += 1 |
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if self.update_freq_counter >= self.config.ema_update_freq: |
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self._step_internal(new_model, updates) |
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self.update_freq_counter = 0 |
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else: |
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self._step_internal(new_model, updates) |
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|
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def reverse(self, model): |
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""" |
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Load the model parameters from EMA model. |
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Useful for inference or fine-tuning from the EMA model. |
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""" |
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d = self.model.state_dict() |
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if "_ema" in d: |
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del d["_ema"] |
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|
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model.load_state_dict(d, strict=False) |
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return model |
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|
