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import copy
import torch
import torch.nn as nn
__all__ = ['EMA']
class EMA(nn.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: 1.
min_value (float): The minimum EMA decay rate. Default: 0.
"""
def __init__(
self,
model,
# if your model has lazylinears or other types of non-deepcopyable modules,
# you can pass in your own ema model
ema_model=None,
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(),
ignore_names=set(),
ignore_startswith_names=set(),
# set this to False if you do not wish for the online model to be
# saved along with the ema model (managed externally)
include_online_model=True
):
super().__init__()
self.beta = beta
# 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 = copy.deepcopy(model)
except:
print('Your model was not copyable. Please make sure you are not using any LazyLinear')
exit()
self.ema_model.requires_grad_(False)
self.parameter_names = {name for name, param in self.ema_model.named_parameters() if param.dtype == torch.float}
self.buffer_names = {name for name, buffer in self.ema_model.named_buffers() if buffer.dtype == torch.float}
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
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 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):
for (_, ma_params), (_, current_params) in zip(self.get_params_iter(self.ema_model),
self.get_params_iter(self.model)):
ma_params.data.copy_(current_params.data)
for (_, ma_buffers), (_, current_buffers) in zip(self.get_buffers_iter(self.ema_model),
self.get_buffers_iter(self.model)):
ma_buffers.data.copy_(current_buffers.data)
def get_current_decay(self):
epoch = clamp(self.step.item() - self.update_after_step - 1, min_value=0.)
value = 1 - (1 + epoch / self.inv_gamma) ** - self.power
if epoch <= 0:
return 0.
return clamp(value, min_value=self.min_value, max_value=self.beta)
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):
current_decay = self.get_current_decay()
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:
ma_params.data.copy_(current_params.data)
continue
ma_params.data.lerp_(current_params.data, 1. - current_decay)
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:
ma_buffer.data.copy_(current_buffer.data)
continue
ma_buffer.data.lerp_(current_buffer.data, 1. - current_decay)
def __call__(self, *args, **kwargs):
return self.ema_model(*args, **kwargs)
def exists(val):
return val is not None
def is_float_dtype(dtype):
return any([dtype == float_dtype for float_dtype in (torch.float64, torch.float32, torch.float16, torch.bfloat16)])
def clamp(value, min_value=None, max_value=None):
assert exists(min_value) or exists(max_value)
if exists(min_value):
value = max(value, min_value)
if exists(max_value):
value = min(value, max_value)
return value
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