# Copyright 2022 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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from typing import Any, Callable
from flax import core
from flax import struct
import optax
[docs]class TrainState(struct.PyTreeNode):
"""Simple train state for the common case with a single Optax optimizer.
Synopsis::
state = TrainState.create(
apply_fn=model.apply,
params=variables['params'],
tx=tx)
grad_fn = jax.grad(make_loss_fn(state.apply_fn))
for batch in data:
grads = grad_fn(state.params, batch)
state = state.apply_gradients(grads=grads)
Note that you can easily extend this dataclass by subclassing it for storing
additional data (e.g. additional variable collections).
For more exotic usecases (e.g. multiple optimizers) it's probably best to
fork the class and modify it.
Args:
step: Counter starts at 0 and is incremented by every call to
`.apply_gradients()`.
apply_fn: Usually set to `model.apply()`. Kept in this dataclass for
convenience to have a shorter params list for the `train_step()` function
in your training loop.
params: The parameters to be updated by `tx` and used by `apply_fn`.
tx: An Optax gradient transformation.
opt_state: The state for `tx`.
"""
step: int
apply_fn: Callable = struct.field(pytree_node=False)
params: core.FrozenDict[str, Any]
tx: optax.GradientTransformation = struct.field(pytree_node=False)
opt_state: optax.OptState
[docs] def apply_gradients(self, *, grads, **kwargs):
"""Updates `step`, `params`, `opt_state` and `**kwargs` in return value.
Note that internally this function calls `.tx.update()` followed by a call
to `optax.apply_updates()` to update `params` and `opt_state`.
Args:
grads: Gradients that have the same pytree structure as `.params`.
**kwargs: Additional dataclass attributes that should be `.replace()`-ed.
Returns:
An updated instance of `self` with `step` incremented by one, `params`
and `opt_state` updated by applying `grads`, and additional attributes
replaced as specified by `kwargs`.
"""
updates, new_opt_state = self.tx.update(
grads, self.opt_state, self.params)
new_params = optax.apply_updates(self.params, updates)
return self.replace(
step=self.step + 1,
params=new_params,
opt_state=new_opt_state,
**kwargs,
)
[docs] @classmethod
def create(cls, *, apply_fn, params, tx, **kwargs):
"""Creates a new instance with `step=0` and initialized `opt_state`."""
opt_state = tx.init(params)
return cls(
step=0,
apply_fn=apply_fn,
params=params,
tx=tx,
opt_state=opt_state,
**kwargs,
)
