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fc0f7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | # Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
#
# 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 `transform.py`."""
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 combine
from optax._src import transform
from optax._src import update
STEPS = 50
LR = 1e-2
class TransformTest(parameterized.TestCase):
def setUp(self):
super().setUp()
self.init_params = (jnp.array([1., 2.]), jnp.array([3., 4.]))
self.per_step_updates = (jnp.array([500., 5.]), jnp.array([300., 3.]))
@chex.all_variants
@parameterized.named_parameters([
('adadelta', transform.scale_by_adadelta),
('adam', transform.scale_by_adam),
('adamax', transform.scale_by_adamax),
('lion', transform.scale_by_lion),
('polyak', transform.scale_by_polyak),
('rmsprop', transform.scale_by_rms),
('stddev', transform.scale_by_stddev),
('trust_ratio', transform.scale_by_trust_ratio),
('param_block_norm', transform.scale_by_param_block_norm),
('param_block_rms', transform.scale_by_param_block_rms),
('distance_over_gradients', transform.scale_by_distance_over_gradients),
])
def test_scalers(self, scaler_constr):
params = self.init_params
scaler = scaler_constr()
init_fn = self.variant(scaler.init)
transform_fn = self.variant(scaler.update)
state = init_fn(params)
chex.assert_tree_all_finite(state)
if scaler_constr.__name__ == 'scale_by_polyak':
extra_args = {'value': jnp.array(0.0)}
else:
extra_args = {}
updates, state = transform_fn(
self.per_step_updates, state, params, **extra_args
)
chex.assert_tree_all_finite((params, updates, state))
jax.tree_util.tree_map(
lambda *args: chex.assert_equal_shape(args), params, updates)
@chex.all_variants
def test_apply_every(self):
# The frequency of the application of sgd
k = 4
zero_update = (jnp.array([0., 0.]), jnp.array([0., 0.]))
# optax sgd
optax_sgd_params = self.init_params
sgd = alias.sgd(LR, 0.0)
state_sgd = sgd.init(optax_sgd_params)
# optax sgd plus apply every
optax_sgd_apply_every_params = self.init_params
sgd_apply_every = combine.chain(
transform.apply_every(k=k),
transform.trace(decay=0, nesterov=False),
transform.scale(-LR))
state_sgd_apply_every = sgd_apply_every.init(optax_sgd_apply_every_params)
transform_fn = self.variant(sgd_apply_every.update)
for i in range(STEPS):
# Apply a step of sgd
updates_sgd, state_sgd = sgd.update(self.per_step_updates, state_sgd)
optax_sgd_params = update.apply_updates(optax_sgd_params, updates_sgd)
# Apply a step of sgd_apply_every
updates_sgd_apply_every, state_sgd_apply_every = transform_fn(
self.per_step_updates, state_sgd_apply_every)
optax_sgd_apply_every_params = update.apply_updates(
optax_sgd_apply_every_params, updates_sgd_apply_every)
# Every k steps, check equivalence.
if i % k == k-1:
chex.assert_trees_all_close(
optax_sgd_apply_every_params, optax_sgd_params,
atol=1e-6, rtol=1e-5)
# Otherwise, check update is zero.
else:
chex.assert_trees_all_close(
updates_sgd_apply_every, zero_update, atol=0.0, rtol=0.0)
def test_scale(self):
updates = self.per_step_updates
for i in range(1, STEPS + 1):
factor = 0.1 ** i
rescaler = transform.scale(factor)
# Apply rescaling.
scaled_updates, _ = rescaler.update(updates, {})
# Manually scale updates.
def rescale(t):
return t * factor # pylint:disable=cell-var-from-loop
manual_updates = jax.tree_util.tree_map(rescale, updates)
# Check the rescaled updates match.
chex.assert_trees_all_close(scaled_updates, manual_updates)
@parameterized.named_parameters([
('1d', [1.0, 2.0], [1.0, 2.0]),
('2d', [[1.0, 2.0], [3.0, 4.0]], [[-0.5, 0.5], [-0.5, 0.5]]),
('3d', [[[1., 2.], [3., 4.]],
[[5., 6.], [7., 8.]]], [[[-1.5, -0.5], [0.5, 1.5]],
[[-1.5, -0.5], [0.5, 1.5]]]),
])
def test_centralize(self, inputs, outputs):
inputs = jnp.asarray(inputs)
outputs = jnp.asarray(outputs)
centralizer = transform.centralize()
centralized_inputs, _ = centralizer.update(inputs, {})
chex.assert_trees_all_close(centralized_inputs, outputs)
def test_scale_by_optimistic_gradient(self):
def f(params: jnp.ndarray) -> jnp.ndarray:
return params['x'] ** 2
initial_params = {
'x': jnp.array(2.0)
}
og = transform.scale_by_optimistic_gradient()
og_state = og.init(initial_params)
# Provide some arbitrary previous gradient.
getattr(og_state, 'trace')['x'] = 1.5
g = jax.grad(f)(initial_params)
og_true = 2 * g['x'] - getattr(og_state, 'trace')['x']
og, _ = og.update(g, og_state)
# Compare transformation output with manually computed optimistic gradient.
chex.assert_trees_all_close(og_true, og['x'])
def test_scale_by_polyak_l1_norm(self, tol=1e-10):
"""Polyak step-size on L1 norm."""
# for this objective, the Polyak step-size has an exact model and should
# converge to the minimizer in one step
objective = lambda x: jnp.abs(x).sum()
init_params = jnp.array([1.0, -1.0])
polyak = transform.scale_by_polyak()
polyak_state = polyak.init(init_params)
# check that polyak state raises an error if it called without a value
with self.assertRaises(TypeError):
polyak.update(self.per_step_updates, polyak_state, init_params)
value, grad = jax.value_and_grad(objective)(init_params)
updates, _ = polyak.update(
grad, polyak_state, init_params, value=value
)
# check that objective at (init_params - updates) is smaller than tol
print(grad, value, updates)
self.assertLess(objective(init_params - updates), tol)
if __name__ == '__main__':
absltest.main()
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