def test_build_encoded_broadcast_process_raises_incompatible_encoder(self):
value_type = computation_types.TensorType(tf.float32, shape=[2])
incompatible_encoder = te.encoders.as_simple_encoder(
te.encoders.identity(), tf.TensorSpec((3, )))
with self.assertRaises(TypeError):
encoding_utils.build_encoded_broadcast_process(
value_type, incompatible_encoder)
def test_build_encoded_broadcast_process_raises_bad_structure(self):
value_type = computation_types.StructType([
computation_types.TensorType(tf.float32, shape=[2]),
computation_types.TensorType(tf.float32, shape=[2])
])
encoder = te.encoders.as_simple_encoder(te.encoders.identity(),
tf.TensorSpec((2, )))
with self.assertRaises(ValueError):
encoding_utils.build_encoded_broadcast_process(value_type, encoder)
def build_encoded_broadcast_process_from_model(
model_fn: _ModelConstructor,
encoder_fn: _EncoderConstructor) -> measured_process.MeasuredProcess:
"""Builds `MeasuredProcess` for weights of model returned by `model_fn`.
This method creates a `SimpleEncoder` for every weight of model created by
`model_fn`, as returned by `encoder_fn`.
Args:
model_fn: A Python callable with no arguments function that returns a
`tff.learning.Model`.
encoder_fn: A Python callable with a single argument, which is expected to
be a `tf.Tensor` of shape and dtype to be encoded. The function must
return a `tensor_encoding.core.SimpleEncoder`, which expects a `tf.Tensor`
with compatible type as the input to its `encode` method.
Returns:
A `MeasuredProcess` for encoding and broadcasting the weights of model
created by `model_fn`.
Raises:
TypeError: If `model_fn` or `encoder_fn` are not callable objects.
"""
py_typecheck.check_callable(model_fn)
py_typecheck.check_callable(encoder_fn)
weights = _weights_from_model_fn(model_fn)
encoders = tf.nest.map_structure(encoder_fn, weights)
weight_type = type_conversions.type_from_tensors(weights)
return encoding_utils.build_encoded_broadcast_process(
weight_type, encoders)
def test_build_encoded_broadcast_process(self, value_constructor,
encoder_constructor):
value = value_constructor(np.random.rand(20))
value_spec = tf.TensorSpec(value.shape, tf.dtypes.as_dtype(value.dtype))
value_type = computation_types.to_type(value_spec)
encoder = te.encoders.as_simple_encoder(encoder_constructor(), value_spec)
broadcast_process = encoding_utils.build_encoded_broadcast_process(
value_type, encoder)
state_type = broadcast_process._initialize_fn.type_signature.result
broadcast_signature = broadcast_process._next_fn.type_signature
self.assertIsInstance(broadcast_process, MeasuredProcess)
self.assertEqual(state_type, broadcast_signature.result[0])
self.assertEqual(placements.SERVER, broadcast_signature.result[0].placement)
self.assertEqual(value_type, broadcast_signature.result[1].member)
self.assertEqual(placements.CLIENTS,
broadcast_signature.result[1].placement)
def test_build_encoded_broadcast_process_raises_bad_encoder(
self, bad_encoder):
value_type = computation_types.TensorType(tf.float32, shape=[2])
with self.assertRaises(TypeError):
encoding_utils.build_encoded_broadcast_process(
value_type, bad_encoder)
