def test_validating_dataset_input_tensors_with_shape_mismatch(
self, distribution):
with self.cached_session():
a = constant_op.constant([1, 2], shape=(1, 2))
b = constant_op.constant([[1, 2], [1, 2]], shape=(2, 2))
x = values.DistributedValues((a, b))
y = values.DistributedValues((a, a))
# Removed device and input tensor shape details from the error message
# since the order of the device and the corresponding input tensor shape
# is not deterministic over different runs.
with self.assertRaisesRegex(
ValueError, 'Input tensor shapes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
with distribution.scope():
distributed_training_utils.validate_distributed_dataset_inputs(
distribution, x, y)
def test_validating_dataset_input_tensors_with_dtype_mismatch(
self, distribution):
with self.cached_session():
a = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.int32)
b = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.float64)
device_map = values.ReplicaDeviceMap(('/device:CPU:0', '/device:GPU:0'))
x = values.DistributedValues(device_map, (a, b))
y = values.DistributedValues(device_map, (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.assertRaisesRegexp(
ValueError, 'Input tensor dtypes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
with distribution.scope():
distributed_training_utils.validate_distributed_dataset_inputs(
distribution, x, y)
def testGetEager(self):
with ops.device("/device:CPU:0"):
one = constant_op.constant(1)
two = constant_op.constant(2)
v = values.DistributedValues({"/device:CPU:0": one, "/device:GPU:0": two})
self.assertEqual(two, v.get("/device:GPU:0"))
self.assertEqual(one, v.get())
with self.assertRaises(ValueError):
self.assertIsNone(v.get("/device:GPU:2"))
def testGetEager(self):
with ops.device("/device:CPU:0"):
one = constant_op.constant(1)
two = constant_op.constant(2)
device_map = values.ReplicaDeviceMap(("/device:CPU:0", "/device:GPU:0"))
v = values.DistributedValues(device_map, (one, two))
self.assertEqual(two, v.get("/device:GPU:0"))
self.assertEqual(one, v.get())
with self.assertRaises(ValueError):
self.assertIsNone(v.get("/device:GPU:2"))
def testIsTensorLikeWithAConstant(self):
with context.graph_mode(), \
ops.Graph().as_default(), \
ops.device("/device:CPU:0"):
one = constant_op.constant(1)
two = 2.0
v = values.DistributedValues({"/device:CPU:0": one, "/device:GPU:0": two})
self.assertEqual(two, v.get("/device:GPU:0"))
self.assertEqual(one, v.get())
self.assertFalse(v.is_tensor_like)
self.assertFalse(tensor_util.is_tensor(v))
def testGetGraph(self):
with context.graph_mode(), \
ops.Graph().as_default(), \
ops.device("/device:CPU:0"):
one = constant_op.constant(1)
two = constant_op.constant(2)
v = values.DistributedValues({"/device:CPU:0": one, "/device:GPU:0": two})
self.assertEqual(two, v.get("/device:GPU:0"))
self.assertEqual(one, v.get())
with self.assertRaises(ValueError):
self.assertIsNone(v.get("/device:GPU:2"))
def testIsTensorLike(self):
with context.graph_mode(), \
ops.Graph().as_default(), \
ops.device("/device:CPU:0"):
one = constant_op.constant(1)
two = constant_op.constant(2)
device_map = values.ReplicaDeviceMap(("/device:CPU:0", "/device:GPU:0"))
v = values.DistributedValues(device_map, (one, two))
self.assertEqual(two, v.get("/device:GPU:0"))
self.assertEqual(one, v.get())
self.assertTrue(v.is_tensor_like)
self.assertTrue(tensor_util.is_tensor(v))
def testNonMatchingVariableCreation(self, distribution):
self.skipTest("b/123075960")
def model_fn(name):
v = variable_scope.variable(1.0, name=name)
ds_context.get_replica_context().merge_call(lambda _: _)
return v
with distribution.scope():
names = values.DistributedValues(("foo", "bar"))
with self.assertRaises(RuntimeError):
_ = distribution.extended.call_for_each_replica(model_fn, args=(names,))