LossScaleOptimizer
classkeras.optimizers.LossScaleOptimizer(
inner_optimizer, initial_scale=32768.0, dynamic_growth_steps=2000, **kwargs
)
An optimizer that dynamically scales the loss to prevent underflow.
Loss scaling is a technique to prevent numeric underflow in intermediate gradients when float16 is used. To prevent underflow, the loss is multiplied (or "scaled") by a certain factor called the "loss scale", which causes intermediate gradients to be scaled by the loss scale as well. The final gradients are divided (or "unscaled") by the loss scale to bring them back to their original value.
LossScaleOptimizer
wraps another optimizer and applies dynamic loss
scaling to it. This loss scale is dynamically updated over time as follows:
- On any train step, if a nonfinite gradient is encountered, the loss scale
is halved, and the train step is skipped.
- If dynamic_growth_steps
have occurred since the last time the loss scale
was updated, and no nonfinite gradients have occurred, the loss scale
is doubled.
Arguments
keras.optimizers.Optimizer
instance to wrap.dynamic_growth_steps
steps with finite gradients, the
loss scale is doubled.False
.
If True
, exponential moving average
(EMA) is applied. EMA consists of computing an exponential moving
average of the weights of the model (as the weight values change
after each training batch), and periodically overwriting the
weights with their moving average.use_ema=True
.
This is the momentum to use when computing
the EMA of the model's weights:
new_average = ema_momentum * old_average + (1 - ema_momentum) *
current_variable_value
.use_ema=True
. Every ema_overwrite_frequency
steps of iterations,
we overwrite the model variable by its moving average.
If None, the optimizer
does not overwrite model variables in the middle of training,
and you need to explicitly overwrite the variables
at the end of training by calling
optimizer.finalize_variable_values()
(which updates the model
variables in-place). When using the built-in fit()
training loop,
this happens automatically after the last epoch,
and you don't need to do anything.None
. If a float, the scale factor will
be multiplied the loss before computing gradients, and the inverse
of the scale factor will be multiplied by the gradients before
updating variables. Useful for preventing underflow during
mixed precision training. Alternately,
keras.optimizers.LossScaleOptimizer
will
automatically set a loss scale factor.None
. If an int, model & optimizer
variables will not be updated at every step; instead they will be
updated every gradient_accumulation_steps
steps, using the average
value of the gradients since the last update. This is known as
"gradient accumulation". This can be useful
when your batch size is very small, in order to reduce gradient
noise at each update step. EMA frequency will look at "accumulated"
iterations value (optimizer steps // gradient_accumulation_steps).
Learning rate schedules will look at "real" iterations value
(optimizer steps).