def test_mime(self):
base_optimizer = optimizers.sgd(learning_rate=1.0)
train_batch_hparams = client_datasets.ShuffleRepeatBatchHParams(
batch_size=2, num_epochs=1, seed=0)
grad_batch_hparams = client_datasets.PaddedBatchHParams(batch_size=2)
server_learning_rate = 0.2
algorithm = mime.mime(per_example_loss, base_optimizer, train_batch_hparams,
grad_batch_hparams, server_learning_rate)
with self.subTest('init'):
state = algorithm.init({'w': jnp.array(4.)})
npt.assert_equal(state.params, {'w': jnp.array(4.)})
self.assertLen(state.opt_state, 2)
with self.subTest('apply'):
clients = [
(b'cid0',
client_datasets.ClientDataset({'x': jnp.array([2., 4., 6.])}),
jax.random.PRNGKey(0)),
(b'cid1', client_datasets.ClientDataset({'x': jnp.array([8., 10.])}),
jax.random.PRNGKey(1)),
]
state, client_diagnostics = algorithm.apply(state, clients)
npt.assert_allclose(state.params['w'], 2.08)
npt.assert_allclose(client_diagnostics[b'cid0']['delta_l2_norm'], 12.)
npt.assert_allclose(client_diagnostics[b'cid1']['delta_l2_norm'], 6.)
def test_federated_averaging(self):
client_optimizer = optimizers.sgd(learning_rate=1.0)
server_optimizer = optimizers.sgd(learning_rate=1.0)
client_batch_hparams = client_datasets.ShuffleRepeatBatchHParams(
batch_size=2, num_epochs=1, seed=0)
algorithm = fed_avg.federated_averaging(grad_fn, client_optimizer,
server_optimizer,
client_batch_hparams)
with self.subTest('init'):
state = algorithm.init({'w': jnp.array([0., 2., 4.])})
npt.assert_array_equal(state.params['w'], [0., 2., 4.])
self.assertLen(state.opt_state, 2)
with self.subTest('apply'):
clients = [
(b'cid0',
client_datasets.ClientDataset({'x': jnp.array([2., 4., 6.])}),
jax.random.PRNGKey(0)),
(b'cid1',
client_datasets.ClientDataset({'x': jnp.array([8., 10.])}),
jax.random.PRNGKey(1)),
]
state, client_diagnostics = algorithm.apply(state, clients)
npt.assert_allclose(state.params['w'], [0., 1.5655555, 3.131111])
npt.assert_allclose(client_diagnostics[b'cid0']['delta_l2_norm'],
1.4534444262)
npt.assert_allclose(client_diagnostics[b'cid1']['delta_l2_norm'],
0.2484521282)
def test_client_delta_clip_norm(self):
base_optimizer = optimizers.sgd(learning_rate=1.0)
train_batch_hparams = client_datasets.ShuffleRepeatBatchHParams(
batch_size=2, num_epochs=1, seed=0)
grad_batch_hparams = client_datasets.PaddedBatchHParams(batch_size=2)
server_learning_rate = 0.2
algorithm = mime_lite.mime_lite(per_example_loss,
base_optimizer,
train_batch_hparams,
grad_batch_hparams,
server_learning_rate,
client_delta_clip_norm=0.5)
clients = [
(b'cid0',
client_datasets.ClientDataset({'x': jnp.array([0.2, 0.4, 0.6])}),
jax.random.PRNGKey(0)),
(b'cid1',
client_datasets.ClientDataset({'x': jnp.array([0.8, 0.1])}),
jax.random.PRNGKey(1)),
]
state = algorithm.init({'w': jnp.array(4.)})
state, client_diagnostics = algorithm.apply(state, clients)
npt.assert_allclose(state.params['w'], 3.904)
npt.assert_allclose(
client_diagnostics[b'cid0']['clipped_delta_l2_norm'], 0.5)
npt.assert_allclose(
client_diagnostics[b'cid1']['clipped_delta_l2_norm'], 0.45000005)
def test_fed_prox(self):
client_optimizer = optimizers.sgd(learning_rate=1.0)
server_optimizer = optimizers.sgd(learning_rate=1.0)
client_batch_hparams = client_datasets.ShuffleRepeatBatchHParams(
batch_size=2, num_epochs=1, seed=0)
algorithm = fed_prox.fed_prox(per_example_loss,
client_optimizer,
server_optimizer,
client_batch_hparams,
proximal_weight=0.01)
with self.subTest('init'):
state = algorithm.init({'w': jnp.array(4.)})
npt.assert_array_equal(state.params['w'], 4.)
self.assertLen(state.opt_state, 2)
with self.subTest('apply'):
clients = [
(b'cid0',
client_datasets.ClientDataset({'x': jnp.array([2., 4., 6.])}),
jax.random.PRNGKey(0)),
(b'cid1',
client_datasets.ClientDataset({'x': jnp.array([8., 10.])}),
jax.random.PRNGKey(1)),
]
state, client_diagnostics = algorithm.apply(state, clients)
npt.assert_allclose(state.params['w'], -3.77)
npt.assert_allclose(client_diagnostics[b'cid0']['delta_l2_norm'],
6.95)
npt.assert_allclose(client_diagnostics[b'cid1']['delta_l2_norm'],
9.)
def __init__(self,
name: Optional[str] = None,
default_batch_size: int = 128):
super().__init__(name)
defaults = client_datasets.ShuffleRepeatBatchHParams(batch_size=-1)
# TODO(wuke): Support other fields.
self._integer('batch_size', default_batch_size, 'Batch size')
self._integer('num_epochs', defaults.num_epochs, 'Number of epochs')
self._integer('num_steps', defaults.num_steps, 'Number of steps')
def test_get(self):
with self.subTest('default'):
self.assertEqual(
self.SHUFFLE_BATCH_REPEAT.get(),
client_datasets.ShuffleRepeatBatchHParams(batch_size=128))
self.assertEqual(
self.PADDED_BATCH.get(),
client_datasets.PaddedBatchHParams(batch_size=128))
self.assertEqual(self.BATCH.get(),
client_datasets.BatchHParams(batch_size=128))
with self.subTest('custom'):
with flagsaver.flagsaver(shuffle_batch_repeat_batch_size=12,
shuffle_batch_repeat_num_epochs=21,
padded_batch_size=34,
batch_size=56):
self.assertEqual(
self.SHUFFLE_BATCH_REPEAT.get(),
client_datasets.ShuffleRepeatBatchHParams(batch_size=12,
num_epochs=21))
self.assertEqual(
self.PADDED_BATCH.get(),
client_datasets.PaddedBatchHParams(batch_size=34))
self.assertEqual(self.BATCH.get(),
client_datasets.BatchHParams(batch_size=56))
def test_expectation_step(self):
def per_example_loss(params, batch, rng):
self.assertIsNotNone(rng)
return jnp.square(params - batch['x'])
trainer = hyp_cluster.ClientDeltaTrainer(models.grad(per_example_loss),
optimizers.sgd(0.5))
batch_hparams = client_datasets.ShuffleRepeatBatchHParams(batch_size=1,
num_epochs=5)
cluster_params = [jnp.array(1.), jnp.array(-1.), jnp.array(3.14)]
client_cluster_ids = {b'0': 0, b'1': 0, b'2': 1, b'3': 1, b'4': 0}
# RNGs are not actually used.
clients = [
(b'0', client_datasets.ClientDataset({'x': np.array([1.1])}),
jax.random.PRNGKey(0)),
(b'1', client_datasets.ClientDataset({'x': np.array([0.9, 0.9])}),
jax.random.PRNGKey(1)),
(b'2', client_datasets.ClientDataset({'x': np.array([-1.1])}),
jax.random.PRNGKey(2)),
(b'3',
client_datasets.ClientDataset({'x': np.array([-0.9, -0.9,
-0.9])}),
jax.random.PRNGKey(3)),
(b'4', client_datasets.ClientDataset({'x': np.array([-0.1])}),
jax.random.PRNGKey(4)),
]
cluster_delta_params = hyp_cluster.expectation_step(
trainer=trainer,
cluster_params=cluster_params,
client_cluster_ids=client_cluster_ids,
clients=clients,
batch_hparams=batch_hparams)
self.assertIsInstance(cluster_delta_params, list)
self.assertLen(cluster_delta_params, 3)
npt.assert_allclose(cluster_delta_params[0],
(-0.1 + 0.1 * 2 + 1.1) / 4)
npt.assert_allclose(cluster_delta_params[1], (0.1 - 0.1 * 3) / 4,
rtol=1e-6)
self.assertIsNone(cluster_delta_params[2])
def test_agnostic_federated_averaging(self):
algorithm = agnostic_fed_avg.agnostic_federated_averaging(
per_example_loss=per_example_loss,
client_optimizer=optimizers.sgd(learning_rate=1.0),
server_optimizer=optimizers.sgd(learning_rate=0.1),
client_batch_hparams=client_datasets.ShuffleRepeatBatchHParams(
batch_size=3, num_epochs=1, seed=0),
domain_batch_hparams=client_datasets.PaddedBatchHParams(
batch_size=3),
init_domain_weights=[0.1, 0.2, 0.3, 0.4],
domain_learning_rate=0.01,
domain_algorithm='eg',
domain_window_size=2,
init_domain_window=[1., 2., 3., 4.])
with self.subTest('init'):
state = algorithm.init({'w': jnp.array(4.)})
npt.assert_equal(state.params, {'w': jnp.array(4.)})
self.assertLen(state.opt_state, 2)
npt.assert_allclose(state.domain_weights, [0.1, 0.2, 0.3, 0.4])
npt.assert_allclose(state.domain_window,
[[1., 2., 3., 4.], [1., 2., 3., 4.]])
with self.subTest('apply'):
clients = [
(b'cid0',
client_datasets.ClientDataset({
'x':
jnp.array([1., 2., 4., 3., 6., 1.]),
'domain_id':
jnp.array([1, 0, 0, 0, 2, 2])
}), jax.random.PRNGKey(0)),
(b'cid1',
client_datasets.ClientDataset({
'x':
jnp.array([8., 10., 5.]),
'domain_id':
jnp.array([1, 3, 1])
}), jax.random.PRNGKey(1)),
]
next_state, client_diagnostics = algorithm.apply(state, clients)
npt.assert_allclose(next_state.params['w'], 3.5555556)
npt.assert_allclose(
next_state.domain_weights,
[0.08702461, 0.18604803, 0.2663479, 0.46057943])
npt.assert_allclose(next_state.domain_window,
[[1., 2., 3., 4.], [3., 3., 2., 1.]])
npt.assert_allclose(client_diagnostics[b'cid0']['delta_l2_norm'],
2.8333335)
npt.assert_allclose(client_diagnostics[b'cid1']['delta_l2_norm'],
7.666667)
with self.subTest('invalid init_domain_weights'):
with self.assertRaisesRegex(
ValueError,
'init_domain_weights must sum to approximately 1.'):
agnostic_fed_avg.agnostic_federated_averaging(
per_example_loss=per_example_loss,
client_optimizer=optimizers.sgd(learning_rate=1.0),
server_optimizer=optimizers.sgd(learning_rate=1.0),
client_batch_hparams=client_datasets.
ShuffleRepeatBatchHParams(batch_size=3),
domain_batch_hparams=client_datasets.PaddedBatchHParams(
batch_size=3),
init_domain_weights=[50., 0., 0., 0.],
domain_learning_rate=0.5)
with self.subTest('unequal lengths'):
with self.assertRaisesRegex(
ValueError,
'init_domain_weights and init_domain_window must be equal lengths.'
):
agnostic_fed_avg.agnostic_federated_averaging(
per_example_loss=per_example_loss,
client_optimizer=optimizers.sgd(learning_rate=1.0),
server_optimizer=optimizers.sgd(learning_rate=1.0),
client_batch_hparams=client_datasets.
ShuffleRepeatBatchHParams(batch_size=3),
domain_batch_hparams=client_datasets.PaddedBatchHParams(
batch_size=3),
init_domain_weights=[0.1, 0.2, 0.3, 0.4],
domain_learning_rate=0.5,
init_domain_window=[1, 2])
def test_hyp_cluster(self):
functions_called = set()
def per_example_loss(params, batch, rng):
self.assertIsNotNone(rng)
functions_called.add('per_example_loss')
return jnp.square(params - batch['x'])
def regularizer(params):
del params
functions_called.add('regularizer')
return 0
client_optimizer = optimizers.sgd(0.5)
server_optimizer = optimizers.sgd(0.25)
maximization_batch_hparams = client_datasets.PaddedBatchHParams(
batch_size=2)
expectation_batch_hparams = client_datasets.ShuffleRepeatBatchHParams(
batch_size=1, num_epochs=5)
algorithm = hyp_cluster.hyp_cluster(
per_example_loss=per_example_loss,
client_optimizer=client_optimizer,
server_optimizer=server_optimizer,
maximization_batch_hparams=maximization_batch_hparams,
expectation_batch_hparams=expectation_batch_hparams,
regularizer=regularizer)
init_state = algorithm.init([jnp.array(1.), jnp.array(-1.)])
# Nothing happens with empty data.
no_op_state, diagnostics = algorithm.apply(init_state, clients=[])
npt.assert_array_equal(init_state.cluster_params,
no_op_state.cluster_params)
self.assertEmpty(diagnostics)
# Some actual training. PRNGKeys are not actually used.
clients = [
(b'0', client_datasets.ClientDataset({'x': np.array([1.1])}),
jax.random.PRNGKey(0)),
(b'1', client_datasets.ClientDataset({'x': np.array([0.9, 0.9])}),
jax.random.PRNGKey(1)),
(b'2', client_datasets.ClientDataset({'x': np.array([-1.1])}),
jax.random.PRNGKey(2)),
(b'3',
client_datasets.ClientDataset({'x': np.array([-0.9, -0.9,
-0.9])}),
jax.random.PRNGKey(3)),
]
next_state, diagnostics = algorithm.apply(init_state, clients)
npt.assert_equal(
diagnostics, {
b'0': {
'cluster_id': 0
},
b'1': {
'cluster_id': 0
},
b'2': {
'cluster_id': 1
},
b'3': {
'cluster_id': 1
},
})
cluster_params = next_state.cluster_params
self.assertIsInstance(cluster_params, list)
self.assertLen(cluster_params, 2)
npt.assert_allclose(cluster_params[0], [1. - 0.25 * 0.1 / 3])
npt.assert_allclose(cluster_params[1], [-1. + 0.25 * 0.2 / 4])
self.assertCountEqual(functions_called,
['per_example_loss', 'regularizer'])
def test_kmeans_init(self):
functions_called = set()
def init(rng):
functions_called.add('init')
return jax.random.uniform(rng)
def apply_for_train(params, batch, rng):
functions_called.add('apply_for_train')
self.assertIsNotNone(rng)
return params - batch['x']
def train_loss(batch, out):
functions_called.add('train_loss')
return jnp.square(out) + batch['bias']
def regularizer(params):
del params
functions_called.add('regularizer')
return 0
initializer = hyp_cluster.ModelKMeansInitializer(
models.Model(init=init,
apply_for_train=apply_for_train,
apply_for_eval=None,
train_loss=train_loss,
eval_metrics={}), optimizers.sgd(0.5), regularizer)
# Each client has 1 example, so it's very easy to reach minimal loss, at
# which point the loss entirely depends on bias.
clients = [
(b'0',
client_datasets.ClientDataset({
'x': np.array([1.01]),
'bias': np.array([-2.])
}), jax.random.PRNGKey(1)),
(b'1',
client_datasets.ClientDataset({
'x': np.array([3.02]),
'bias': np.array([-1.])
}), jax.random.PRNGKey(2)),
(b'2',
client_datasets.ClientDataset({
'x': np.array([3.03]),
'bias': np.array([1.])
}), jax.random.PRNGKey(3)),
(b'3',
client_datasets.ClientDataset({
'x': np.array([1.04]),
'bias': np.array([2.])
}), jax.random.PRNGKey(3)),
]
train_batch_hparams = client_datasets.ShuffleRepeatBatchHParams(
batch_size=1, num_epochs=5)
eval_batch_hparams = client_datasets.PaddedBatchHParams(batch_size=2)
# Using a rng that leads to b'0' being the initial center.
cluster_params = initializer.cluster_params(
num_clusters=3,
rng=jax.random.PRNGKey(0),
clients=clients,
train_batch_hparams=train_batch_hparams,
eval_batch_hparams=eval_batch_hparams)
self.assertIsInstance(cluster_params, list)
self.assertLen(cluster_params, 3)
npt.assert_allclose(cluster_params, [1.01, 3.03, 1.04])
self.assertCountEqual(
functions_called,
['init', 'apply_for_train', 'train_loss', 'regularizer'])
# Using a rng that leads to b'2' being the initial center.
cluster_params = initializer.cluster_params(
num_clusters=3,
rng=jax.random.PRNGKey(1),
clients=clients,
train_batch_hparams=train_batch_hparams,
eval_batch_hparams=eval_batch_hparams)
self.assertIsInstance(cluster_params, list)
self.assertLen(cluster_params, 3)
npt.assert_allclose(cluster_params, [3.03, 1.04, 1.04])
def get(self):
return client_datasets.ShuffleRepeatBatchHParams(
batch_size=self._get_flag('batch_size'),
num_epochs=self._get_flag('num_epochs'),
num_steps=self._get_flag('num_steps'))
