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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 `inject.py`."""
import functools
from typing import NamedTuple
from absl.testing import absltest
from absl.testing import parameterized
import chex
import jax
import jax.numpy as jnp
import numpy as np
from optax._src import base
from optax._src import clipping
from optax._src import transform
from optax._src import wrappers
from optax.schedules import _inject
from optax.schedules import _schedule
from optax.tree_utils import _state_utils
class ExampleState(NamedTuple):
total: chex.Numeric
class ExampleStatefulSchedule(base.StatefulSchedule):
def init(self) -> ExampleState:
return ExampleState(total=jnp.zeros([], dtype=jnp.int32))
def update(self, state: ExampleState, **extra_args) -> ExampleState:
total = state.total + extra_args['addendum']
return ExampleState(total=total)
def __call__(self, state: ExampleState, **extra_args) -> chex.Numeric:
return state.total
class InjectHyperparamsTest(chex.TestCase):
"""Tests for the inject_hyperparams wrapper."""
@chex.all_variants
def test_updates(self):
optim = _inject.inject_hyperparams(transform.scale)( # stateless
step_size=_schedule.piecewise_constant_schedule(
3.0, {1: 5, 7: 2, 12: 1.5}))
params = [jnp.zeros([], dtype=jnp.float32)]
state = self.variant(optim.init)(params)
# A no-op change, to verify that tree map works.
state = _state_utils.tree_map_params(optim, lambda v: v, state)
update_fn = self.variant(optim.update)
expected_step_size = [3.0]*2 + [15.0]*6 + [30.0]*5 + [45.0]*3
grads = [jnp.ones([], dtype=jnp.float32)]
for i in range(15):
updates, state = update_fn(grads, state, params=params)
np.testing.assert_almost_equal(updates[0], expected_step_size[i+1])
@chex.all_variants
def test_hyperparams_state(self):
optim = _inject.inject_hyperparams(transform.trace)( # stateful
decay=_schedule.piecewise_constant_schedule(
0.8, {3: 0.5, 9: 1.25}),
nesterov=True)
params = [jnp.zeros([2, 3]) for _ in range(3)]
state = self.variant(optim.init)(params)
update_fn = self.variant(optim.update)
expected_mom = [0.8]*4 + [0.4]*6 + [0.5]*2
grads = jax.tree_util.tree_map(jnp.ones_like, params)
for i in range(12):
np.testing.assert_almost_equal(state.hyperparams['decay'],
expected_mom[i])
_, state = update_fn(grads, state)
np.testing.assert_almost_equal(state.hyperparams['decay'],
expected_mom[-1])
@chex.all_variants
def test_constant_hyperparams(self):
optim = _inject.inject_hyperparams(transform.scale_by_adam)(b1=0., b2=0.)
params = [jnp.zeros([2, 3]) for _ in range(3)]
state = self.variant(optim.init)(params)
update_fn = self.variant(optim.update)
grads = jax.tree_util.tree_map(jnp.ones_like, params)
for _ in range(5):
updates, state = update_fn(grads, state, params)
np.testing.assert_almost_equal(state.hyperparams['b1'], 0.0)
np.testing.assert_almost_equal(state.hyperparams['b2'], 0.0)
np.testing.assert_almost_equal(state.hyperparams['eps'], 1e-8)
np.testing.assert_almost_equal(state.hyperparams['eps_root'], 0.0)
assert 'eps' in state.hyperparams
chex.assert_trees_all_close(updates, grads)
@chex.all_variants
def test_overriding_hyperparam(self):
optim = _inject.inject_hyperparams(clipping.clip_by_global_norm)(0.1)
params = jnp.zeros((3, 5, 7))
state = self.variant(optim.init)(params)
update_fn = self.variant(optim.update)
grads = jnp.ones_like(params)
for i in range(5):
state.hyperparams['max_norm'] = i
updates, state = update_fn(grads, state)
assert np.isclose(jnp.linalg.norm(updates.ravel()), i)
@chex.all_variants
@parameterized.named_parameters(('string', 'mask'), ('list', ['mask']))
def test_static_args(self, static_args):
@functools.partial(_inject.inject_hyperparams, static_args=static_args)
def custom_optim(learning_rate, mask):
return wrappers.masked(transform.scale(-learning_rate), mask)
optim = custom_optim(
0.1, functools.partial(jax.tree_util.tree_map, lambda x: x.ndim > 1))
params = [jnp.ones((1, 2)), jnp.ones(2), jnp.ones((1, 1, 1))]
grads = params
state = self.variant(optim.init)(params)
updates, state = self.variant(optim.update)(grads, state)
expected_updates = jax.tree_util.tree_map(
lambda x: -0.1 * x if x.ndim > 1 else x, grads)
assert set(state.hyperparams.keys()) == {'learning_rate'}, state.hyperparams
chex.assert_trees_all_close(updates, expected_updates)
@chex.all_variants
@parameterized.named_parameters(('one_arg', 'b1'), ('two_arg', ['b1', 'b2']))
def test_numeric_static_args(self, static_args):
optim = _inject.inject_hyperparams(
transform.scale_by_adam, static_args=static_args)(b1=0.9, b2=0.95)
params = [jnp.ones((1, 2)), jnp.ones(2), jnp.ones((1, 1, 1))]
grads = params
state = self.variant(optim.init)(params)
_, state = self.variant(optim.update)(grads, state)
assert not set(state.hyperparams.keys()).intersection(set(static_args))
@chex.all_variants
@parameterized.named_parameters(
('bf16hyp f32param bf16grad', jnp.bfloat16, jnp.float32, jnp.bfloat16),
('bf16hyp f32param f32_grads', jnp.bfloat16, jnp.float32, jnp.float32),
('f32hyp bf16param bf16grad', jnp.float32, jnp.bfloat16, jnp.bfloat16),
('f32hyp f32param bf16grad', jnp.float32, jnp.float32, jnp.bfloat16),
('f32hyp bf16param f32grad', jnp.float32, jnp.bfloat16, jnp.float32),
)
def test_hyperparam_dtypes(self,
hyperparam_dtype,
param_dtype,
grad_dtype):
"""Tests that hyperparam dtype override works as desired."""
optim = _inject.inject_hyperparams(
transform.scale_by_adam,
hyperparam_dtype=hyperparam_dtype)(b1=0.9, b2=0.95)
params = [jnp.ones((1, 2), dtype=param_dtype),
jnp.ones(2, dtype=param_dtype),
jnp.ones((1, 1, 1), dtype=param_dtype)]
grads = jax.tree_util.tree_map(lambda x: x.astype(grad_dtype), params)
state = self.variant(optim.init)(params)
# Check that the hyperparams are overridden
self.assertEqual(state.hyperparams['b1'].dtype, hyperparam_dtype)
self.assertEqual(state.hyperparams['b2'].dtype, hyperparam_dtype)
_, state = self.variant(optim.update)(grads, state)
self.assertEqual(state.hyperparams['b1'].dtype, hyperparam_dtype)
self.assertEqual(state.hyperparams['b2'].dtype, hyperparam_dtype)
@parameterized.named_parameters(('string', 'lr'), ('list', ['lr']))
def test_static_args_error(self, static_args):
with self.assertRaises(ValueError):
_inject.inject_hyperparams(transform.scale, static_args=static_args)
@chex.all_variants
def test_inject_hyperparams_starts_with_step_count_zero(self):
"""Checks that inject_hyperparams uses step count 0 in the first update."""
# See also: https://github.com/deepmind/optax/issues/415.
opt = _inject.inject_hyperparams(transform.scale)(lambda count: count)
params = jnp.zeros(3)
grads = jnp.array([-1, 0, 1])
updates, _ = self.variant(opt.update)(grads, opt.init(params))
np.testing.assert_array_equal(updates, np.zeros(3))
class StatefulTest(chex.TestCase):
def test_wrap_stateless_schedule(self):
my_schedule = _schedule.linear_schedule(1., 1., 10)
my_wrapped_schedule = _inject.WrappedSchedule(my_schedule)
count = jnp.zeros([], dtype=jnp.int32)
state = my_wrapped_schedule.init()
np.testing.assert_allclose(count, state, atol=0.0)
for _ in range(8):
np.testing.assert_allclose(
my_schedule(count), my_wrapped_schedule(state), atol=0.0)
count = count + 1
extra_args = dict(loss=jnp.ones([], dtype=jnp.float32))
state = my_wrapped_schedule.update(state, **extra_args)
np.testing.assert_allclose(count, state, atol=0.0)
@chex.all_variants
def test_inject_stateful_hyperparams(self):
grads = (
jnp.ones((3,), dtype=jnp.float32),
jnp.ones((2,), dtype=jnp.float32),)
params = grads
my_stateful_schedule = ExampleStatefulSchedule()
tx = _inject.inject_hyperparams(
transform.scale)(step_size=my_stateful_schedule)
state = self.variant(tx.init)(params)
extra_args = dict(addendum=0.3 * jnp.ones((), dtype=jnp.float32))
_, state = self.variant(tx.update)(
grads, state, params=params, **extra_args)
_, state = self.variant(tx.update)(
grads, state, params=params, **extra_args)
lr = state.hyperparams['step_size']
total = state.hyperparams_states['step_size']
np.testing.assert_allclose(lr, extra_args['addendum'], atol=0.0)
np.testing.assert_allclose(total, 2 * extra_args['addendum'], atol=0.0)
if __name__ == '__main__':
absltest.main()
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