Source code for flax.experimental.nnx.nnx.nn.stochastic

# Copyright 2024 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Copyright 2024 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations

import dataclasses
from typing import Sequence

import jax
import jax.numpy as jnp
from jax import lax, random

from flax.experimental.nnx.nnx import rnglib
from flax.experimental.nnx.nnx.module import Module, first_from


@dataclasses.dataclass
class Dropout(Module):
  """Create a dropout layer.

  Attributes:
    rate: the dropout probability.  (_not_ the keep rate!)
    broadcast_dims: dimensions that will share the same dropout mask
    deterministic: if false the inputs are scaled by `1 / (1 - rate)` and
      masked, whereas if true, no mask is applied and the inputs are returned
      as is.
    rng_collection: the rng collection name to use when requesting an rng key.
  """

  rate: float
  broadcast_dims: Sequence[int] = ()
  deterministic: bool = False
  rng_collection: str = 'dropout'
  rngs: rnglib.Rngs | None = None

  def __call__(
    self,
    inputs,
    *,
    deterministic: bool | None = None,
    rngs: rnglib.Rngs | None = None,
  ) -> jax.Array:
    """Applies a random dropout mask to the input.

    Args:
      inputs: the inputs that should be randomly masked.
      deterministic: if false the inputs are scaled by `1 / (1 - rate)` and
        masked, whereas if true, no mask is applied and the inputs are returned
        as is.

    Returns:
      The masked inputs reweighted to preserve mean.
    """
    deterministic = first_from(
      deterministic,
      self.deterministic,
      error_msg="""No `deterministic` argument was provided to Dropout
          as either a __call__ argument or class attribute""",
    )

    if (self.rate == 0.0) or deterministic:
      return inputs

    # Prevent gradient NaNs in 1.0 edge-case.
    if self.rate == 1.0:
      return jnp.zeros_like(inputs)

    rngs = first_from(
      rngs,
      self.rngs,
      error_msg="""`deterministic` is False, but no `rngs` argument was provided to Dropout
          as either a __call__ argument or class attribute.""",
    )

    keep_prob = 1.0 - self.rate
    rng = rngs[self.rng_collection]()
    broadcast_shape = list(inputs.shape)
    for dim in self.broadcast_dims:
      broadcast_shape[dim] = 1
    mask = random.bernoulli(rng, p=keep_prob, shape=broadcast_shape)
    mask = jnp.broadcast_to(mask, inputs.shape)
    return lax.select(mask, inputs / keep_prob, jnp.zeros_like(inputs))