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#
# 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.
# ==============================================================================
"""Tests for optax.transforms._conditionality."""
from absl.testing import absltest
from absl.testing import parameterized
import chex
import jax
import jax.numpy as jnp
from optax._src import alias
from optax._src import base
from optax._src import combine
from optax._src import transform
from optax._src import update
from optax.transforms import _conditionality
from optax.transforms import _constraining
def _build_sgd():
return alias.sgd(1.)
def _build_stateful_sgd():
# This SGD behaves like _build_sgd but also tests the optimizer state. The
# momentum is set to zero rather than None so that the momentum terms are
# calculated, but do not change the results.
return alias.sgd(1., momentum=0.)
def _build_sgd_extra_args():
def init_fn(params):
del params
return {'foo': 1}
def update_fn(grads, state, params=None, *, foo=None, **extra_args):
del extra_args, foo, params
return grads, state
t = base.GradientTransformationExtraArgs(init_fn, update_fn)
return combine.chain(_build_sgd(), t)
class ConditionalityTest(parameterized.TestCase):
@chex.variants(with_jit=True, without_jit=True, with_pmap=True)
@parameterized.named_parameters(
('sgd', _build_sgd),
('stateful_sgd', _build_stateful_sgd),
('sgd_extra_args', _build_sgd_extra_args),
)
def test_apply_if_finite(self, opt_builder):
one = jnp.array(1.)
nan = jnp.array(jnp.nan)
def fn(p, x):
return p * x
params = jnp.array(0.)
opt = _conditionality.apply_if_finite(opt_builder(), 2)
state = opt.init(params)
grads_fn = jax.grad(self.variant(fn))
# Do one successful param update
grads = grads_fn(params, one)
updates, state = opt.update(grads, state, params)
params = update.apply_updates(params, updates)
# We know exactly what should be the value of params since we are
# effectively using sgd in all cases.
self.assertEqual(-1., float(jax.tree_util.tree_flatten(params)[0][0]))
self.assertTrue(bool(getattr(state, 'last_finite')))
# Check 2 rejected param updates
for step in range(2):
grads = grads_fn(params, nan)
updates, state = opt.update(grads, state, params)
params = update.apply_updates(params, updates)
self.assertEqual(-1., float(jax.tree_util.tree_flatten(params)[0][0]))
self.assertFalse(bool(getattr(state, 'last_finite')))
self.assertEqual(step + 1, int(getattr(state, 'notfinite_count')))
# Next successful param update
grads = grads_fn(params, one)
updates, state = opt.update(grads, state, params)
params = update.apply_updates(params, updates)
self.assertEqual(-2., float(jax.tree_util.tree_flatten(params)[0][0]))
self.assertTrue(bool(getattr(state, 'last_finite')))
# Again 2 rejected param updates
for step in range(2):
grads = grads_fn(params, nan)
updates, state = opt.update(grads, state, params)
params = update.apply_updates(params, updates)
self.assertEqual(-2., float(jax.tree_util.tree_flatten(params)[0][0]))
self.assertFalse(bool(getattr(state, 'last_finite')))
self.assertEqual(step + 1, int(getattr(state, 'notfinite_count')))
# Next param update with NaN is accepted since we reached maximum
grads = grads_fn(params, nan)
updates, state = opt.update(grads, state, params)
params = update.apply_updates(params, updates)
self.assertTrue(bool(jnp.isnan(jax.tree_util.tree_flatten(params)[0][0])))
self.assertEqual(5, int(getattr(state, 'total_notfinite')))
def test_apply_if_finite_pmap(self):
# Unlike in `test_apply_if_finite`:
# * pmap is applied to the gradient computation and the optimisation;
# * the NaNs are caused inside the function and do not come from the inputs.
half = jnp.ones([1]) / 2.
two = jnp.ones([1]) * 2. # Causes a NaN in arctanh
def fn(p, x):
return jnp.arctanh(x) * p
opt = _conditionality.apply_if_finite(alias.sgd(1.), 2)
def fn_update(params, opt_state, x):
grads = jax.grad(fn)(params, x)
grads = jax.lax.psum(grads, axis_name='i')
updates, new_opt_state = opt.update(grads, opt_state, params)
new_params = update.apply_updates(params, updates)
return new_params, new_opt_state
fn_update = jax.pmap(fn_update, axis_name='i')
params = jnp.array(0.)
opt_state = opt.init(params)
params = jax.tree_util.tree_map(lambda x: x[None], params)
opt_state = jax.tree_util.tree_map(lambda x: x[None], opt_state)
# Do one successful param update
params, opt_state = fn_update(params, opt_state, half)
self.assertTrue(bool(opt_state.last_finite))
# Check 2 rejected param updates
for step in range(2):
params, opt_state = fn_update(params, opt_state, two)
self.assertFalse(bool(opt_state.last_finite))
self.assertEqual(step + 1, opt_state.notfinite_count.item())
# Next successful param update
params, opt_state = fn_update(params, opt_state, half)
self.assertTrue(bool(opt_state.last_finite))
# Again 2 rejected param updates
for step in range(2):
params, opt_state = fn_update(params, opt_state, two)
self.assertFalse(bool(opt_state.last_finite))
self.assertEqual(step + 1, opt_state.notfinite_count.item())
# Next param update with NaN is accepted since we reached maximum
_, opt_state = fn_update(params, opt_state, two)
self.assertEqual(5, opt_state.total_notfinite.item())
class ConditionallyTransformTest(chex.TestCase):
"""Tests for the conditionally_transform wrapper."""
NUM_STEPS = 3
@chex.all_variants
def test_stateless_inner(self):
params = jnp.zeros([])
grads = jnp.ones([])
def should_update(step):
return step < ConditionallyTransformTest.NUM_STEPS
opt = _conditionality.conditionally_transform(
transform.scale(2.), should_update)
state = opt.init(params)
update_fn = self.variant(opt.update)
for _ in range(ConditionallyTransformTest.NUM_STEPS):
updates, state = update_fn(grads, state)
self.assertEqual(updates, 2.)
# Further updates stop calling the inner optimiser.
for _ in range(5):
updates, state = update_fn(grads, state)
self.assertEqual(updates, 1.)
@chex.all_variants
def test_statefull_inner(self):
params = jnp.zeros([])
grads_with_nan = jnp.array(float('nan'))
grads = jnp.ones([])
def should_update(step):
return step < ConditionallyTransformTest.NUM_STEPS
opt = _conditionality.conditionally_transform(
_constraining.zero_nans(), should_update)
state = opt.init(params)
update_fn = self.variant(opt.update)
for _ in range(ConditionallyTransformTest.NUM_STEPS - 1):
updates, state = update_fn(grads_with_nan, state)
self.assertEqual(updates, 0.)
self.assertEqual(state.inner_state.found_nan, True)
updates, state = update_fn(grads, state)
self.assertEqual(updates, 1.)
self.assertEqual(state.inner_state.found_nan, False)
# Further updates stop calling the inner optimiser.
for _ in range(5):
updates, state = update_fn(grads_with_nan, state)
# Warning: do not use assertEqual with a NaN as NaN == NaN returns False.
self.assertTrue(jnp.isnan(updates))
# Inner state is not be updated.
self.assertEqual(state.inner_state.found_nan, False)
class ConditionallyMaskTest(chex.TestCase):
"""Tests for the conditionally_mask wrapper."""
NUM_STEPS = 3
MIN_LOSS = 0.1
@chex.all_variants
def test_stateless_inner(self):
params = jnp.zeros([])
grads = jnp.ones([])
def should_update(step):
return step < ConditionallyMaskTest.NUM_STEPS
opt = _conditionality.conditionally_mask(transform.scale(2.), should_update)
state = opt.init(params)
update_fn = self.variant(opt.update)
for _ in range(ConditionallyMaskTest.NUM_STEPS):
updates, state = update_fn(grads, state)
self.assertEqual(updates, 2.)
# Further updates stop calling the inner optimiser.
for _ in range(5):
updates, state = update_fn(grads, state)
self.assertEqual(updates, 0.)
@chex.all_variants
def test_statefull_inner(self):
params = jnp.zeros([])
grads_with_nan = jnp.array(float('nan'))
grads = jnp.ones([])
def should_update(step):
return step < ConditionallyMaskTest.NUM_STEPS
opt = _conditionality.conditionally_mask(
_constraining.zero_nans(), should_update)
state = opt.init(params)
update_fn = self.variant(opt.update)
for _ in range(ConditionallyMaskTest.NUM_STEPS - 1):
updates, state = update_fn(grads_with_nan, state)
self.assertEqual(updates, 0.)
self.assertEqual(state.inner_state.found_nan, True)
updates, state = update_fn(grads, state)
self.assertEqual(updates, 1.)
self.assertEqual(state.inner_state.found_nan, False)
# Further updates stop calling the inner optimiser.
for _ in range(5):
updates, state = update_fn(grads_with_nan, state)
self.assertEqual(updates, 0.)
# Inner state is not be updated.
self.assertEqual(state.inner_state.found_nan, False)
@chex.all_variants
def test_stateless_inner_with_extra_args(self):
params = jnp.zeros([])
grads = jnp.ones([])
def should_update(step, loss, **extra_args):
del step, extra_args
return loss > ConditionallyMaskTest.MIN_LOSS
opt = _conditionality.conditionally_mask(
transform.scale(2.), should_update, forward_extra_args=True)
state = opt.init(params)
update_fn = self.variant(opt.update)
for _ in range(ConditionallyMaskTest.NUM_STEPS):
updates, state = update_fn(grads, state, loss=0.2)
self.assertEqual(updates, 2.)
# Further updates stop calling the inner optimiser.
for _ in range(5):
updates, state = update_fn(grads, state, loss=0.)
self.assertEqual(updates, 0.)
if __name__ == '__main__':
absltest.main()
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