def make_jd_sequential(axis_name):
return jd.JointDistributionSequential([
tfd.Normal(0., 1.),
lambda w: sharded.ShardedSample( # pylint: disable=g-long-lambda
tfd.Normal(w, 1.), test_lib.NUM_DEVICES, shard_axis_name=axis_name),
lambda x: sharded.ShardedIndependent( # pylint: disable=g-long-lambda
tfd.Normal(x, 1.), 1, shard_axis_name=axis_name),
], shard_axis_name=axis_name)
def make_jd_sequential(axis_name):
return jd.JointDistributionSequential([
tfd.Normal(0., 1.),
lambda w: sharded.Sharded( # pylint: disable=g-long-lambda
tfd.Sample(tfd.Normal(w, 1.), 1),
shard_axis_name=axis_name),
lambda x: sharded.Sharded( # pylint: disable=g-long-lambda
tfd.Independent(tfd.Normal(x, 1.), 1),
shard_axis_name=axis_name),
])
lambda per_replica: tf.stack(per_replica.values, axis=0), value)
def model_coroutine():
w = yield tfd.JointDistributionCoroutine.Root(tfd.Normal(0., 1.))
x = yield sharded.ShardedSample(tfd.Normal(w, 1.), NUM_DEVICES)
yield sharded.ShardedIndependent(tfd.Normal(x, 1.), 1)
distributions = (
('coroutine', lambda: jd.JointDistributionCoroutine(model_coroutine)),
(
'sequential',
lambda: jd.JointDistributionSequential([ # pylint: disable=g-long-lambda
tfd.Normal(0., 1.),
lambda w: sharded.ShardedSample(tfd.Normal(w, 1.), NUM_DEVICES),
lambda x: sharded.ShardedIndependent(tfd.Normal(x, 1.), 1),
])),
(
'named',
lambda: jd.JointDistributionNamed( # pylint: disable=g-long-lambda
dict(
w=tfd.Normal(0., 1.),
x=lambda w: sharded.ShardedSample(tfd.Normal(w, 1.),
NUM_DEVICES),
data=lambda x: sharded.ShardedIndependent(
tfd.Normal(x, 1.), 1),
))),
)
