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# Copyright 2021 DeepMind Technologies Limited
#
# 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.
"""A collection of utilities surrounding PRNG usage in protein folding."""
import haiku as hk
import jax
def safe_dropout(*, tensor, safe_key, rate, is_deterministic, is_training):
"""Applies dropout to a tensor."""
if is_training and not is_deterministic:
keep_rate = 1.0 - rate
keep = jax.random.bernoulli(safe_key.get(), keep_rate, shape=tensor.shape)
return keep * tensor / keep_rate
else:
return tensor
class SafeKey:
"""Safety wrapper for PRNG keys."""
def __init__(self, key):
self._key = key
self._used = False
def _assert_not_used(self):
if self._used:
raise RuntimeError('Random key has been used previously.')
def get(self):
self._assert_not_used()
self._used = True
return self._key
def split(self, num_keys=2):
self._assert_not_used()
self._used = True
new_keys = jax.random.split(self._key, num_keys)
return jax.tree_map(SafeKey, tuple(new_keys))
def duplicate(self, num_keys=2):
self._assert_not_used()
self._used = True
return tuple(SafeKey(self._key) for _ in range(num_keys))
def _safe_key_flatten(safe_key):
# Flatten transfers "ownership" to the tree
return (safe_key._key,), safe_key._used # pylint: disable=protected-access
def _safe_key_unflatten(aux_data, children):
ret = SafeKey(children[0])
ret._used = aux_data # pylint: disable=protected-access
return ret
jax.tree_util.register_pytree_node(
SafeKey, _safe_key_flatten, _safe_key_unflatten)
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