def test_validating_dataset_input_tensors_with_dtype_mismatch(
self, distribution):
with self.cached_session():
@tf.function
def run():
ctx = tf.distribute.get_replica_context()
if ctx.replica_id_in_sync_group.device.endswith("GPU:0"):
return tf.constant([[1, 2]], dtype=tf.int32)
else:
return tf.constant([[1, 2]], dtype=tf.float64)
x = distribution.run(run)
# Removed device and input tensor dtype details from the error message
# since the order of the device and the corresponding input tensor dtype
# is not deterministic over different runs.
with self.assertRaisesRegex(
ValueError,
"Input tensor dtypes do not match for "
"distributed tensor inputs "
"PerReplica:.+",
):
with distribution.scope():
distributed_training_utils_v1.validate_distributed_dataset_inputs(
distribution, x, None)
def test_validating_dataset_input_tensors_with_dtype_mismatch(
self, distribution):
with self.cached_session():
a = tf.constant([1, 2], shape=(1, 2), dtype=tf.int32)
b = tf.constant([1, 2], shape=(1, 2), dtype=tf.float64)
x = tf.distribute.DistributedValues((a, b))
y = tf.distribute.DistributedValues((a, a))
# Removed device and input tensor dtype details from the error message
# since the order of the device and the corresponding input tensor dtype
# is not deterministic over different runs.
with self.assertRaisesRegex(
ValueError, 'Input tensor dtypes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
with distribution.scope():
distributed_training_utils_v1.validate_distributed_dataset_inputs(
distribution, x, y)
