def test_explicit_input_signature(self, cycles):
@def_function.function(
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
def func(x):
return 2 * x
root = tracking.AutoCheckpointable()
root.f = func
imported = self.cycle(root, cycles)
self.assertEqual(4., imported.f(constant_op.constant(2.0)).numpy())
def test_unused_asset(self):
root = tracking.AutoCheckpointable()
root.f = def_function.function(
lambda x: 2. * x,
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
root.asset = tracking.TrackableAsset(self._vocab_path)
export_dir = os.path.join(self.get_temp_dir(), "save_dir")
save.save(root, export_dir)
self.assertAllClose({"output_0": [0.2]},
_import_and_infer(export_dir, {"x": [0.1]}))
def test_capture_assets(self, cycles):
root = tracking.AutoCheckpointable()
root.vocab = tracking.TrackableAsset(self._make_asset("contents"))
root.f = def_function.function(lambda: root.vocab.asset_path,
input_signature=[])
imported = self.cycle(root, cycles)
original_output = root.f().numpy()
imported_output = imported.f().numpy()
self.assertNotEqual(original_output, imported_output)
with open(imported_output, "r") as f:
self.assertEqual("contents", f.read())
def test_method_save_signature(self):
root = tracking.AutoCheckpointable()
root.f = def_function.function(
lambda x: 2. * x,
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
root.f(constant_op.constant(1.))
save_dir = os.path.join(self.get_temp_dir(), "saved_model")
save.save(root, save_dir, root.f)
self.assertEqual(
{"output_0": 2.},
_import_and_infer(save_dir, {"x": 1.}))
def test_overwritten_signatures_error(self, cycles):
exported = tracking.AutoCheckpointable()
exported.f = def_function.function(lambda: constant_op.constant(1.))
imported = self.cycle(
exported,
cycles,
signatures={"key": exported.f.get_concrete_function()})
self.assertEqual(1., imported.signatures["key"]()["output_0"].numpy())
imported.signatures = {"key1": imported.signatures["key"]}
with self.assertRaisesRegexp(ValueError, "signatures"):
save.save(imported, tempfile.mkdtemp(prefix=self.get_temp_dir()))
def test_variable(self):
root = tracking.AutoCheckpointable()
root.v1 = variables.Variable(3.)
root.v2 = variables.Variable(2.)
root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
root.f(constant_op.constant(1.))
to_save = root.f.get_concrete_function(constant_op.constant(1.))
save_dir = os.path.join(self.get_temp_dir(), "saved_model")
save.save(root, save_dir, to_save)
self.assertAllEqual({"output_0": 12.},
_import_and_infer(save_dir, {"x": 2.}))
def test_concrete_function_no_signature(self):
@def_function.function
def func(x):
return 2 * x
root = tracking.AutoCheckpointable()
root.f = func.get_concrete_function(constant_op.constant([1]))
self.assertAllEqual([4], root.f(constant_op.constant([2])).numpy())
imported = self.cycle(root)
self.assertAllEqual([6],
imported.f(constant_op.constant([3])).numpy())
def test_concrete_function_no_signature(self, cycles):
@def_function.function
def func(x):
return 2 * x
root = tracking.AutoCheckpointable()
root.f = func.get_concrete_function(constant_op.constant([1]))
self.assertAllEqual([4], root.f(constant_op.constant([2])).numpy())
# TODO(andresp): Fix exporting of loaded concrete functions as signatures.
imported = self.cycle(root, cycles, signatures={})
self.assertAllEqual([6], imported.f(constant_op.constant([3])).numpy())
def test_perserve_argspec(self, cycles):
def f(a, b, c): # pylint: disable=unused-argument
return None
original_fullargspec = tf_inspect.getfullargspec(f)
root = tracking.AutoCheckpointable()
root.f = def_function.function(f)
imported = self.cycle(root, cycles)
restored_fullargspec = tf_inspect.getfullargspec(imported.f)
self.assertEqual(original_fullargspec, restored_fullargspec)
def testAssertions(self):
a = tracking.AutoCheckpointable()
a.l = {"k": [numpy.zeros([2, 2])]}
self.assertAllEqual(nest.flatten({"k": [numpy.zeros([2, 2])]}),
nest.flatten(a.l))
self.assertAllClose({"k": [numpy.zeros([2, 2])]}, a.l)
nest.map_structure(self.assertAllClose, a.l,
{"k": [numpy.zeros([2, 2])]})
a.tensors = {"k": [array_ops.ones([2, 2]), array_ops.zeros([3, 3])]}
self.assertAllClose(
{"k": [numpy.ones([2, 2]), numpy.zeros([3, 3])]},
self.evaluate(a.tensors))
def _get_checkpoint_name(self, name):
root = tracking.AutoCheckpointable()
checkpointable_utils.add_variable(root,
name=name,
shape=[1, 2],
dtype=dtypes.float64)
(named_variable,
), _, _ = checkpointable_utils._serialize_object_graph(
root, saveables_cache=None)
with ops.name_scope("root/" + named_variable.name):
pass # Make sure we can use this as an op name if we prefix it.
return named_variable.name
def test_explicit_save_signature(self):
@def_function.function
def func(x):
return 2 * x
root = tracking.AutoCheckpointable()
root.f = func
imported = self.cycle(
root, {"f": root.f.get_concrete_function(
tensor_spec.TensorSpec(None, dtypes.float32))})
self.assertEqual(4., imported.f(constant_op.constant(2.0)).numpy())
def test_nested_functions(self):
f = def_function.function(
lambda x: x * 2.0,
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
g = def_function.function(
lambda x: f(x) + 1.0,
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
root = tracking.AutoCheckpointable()
root.g = g
imported = self.cycle(root)
imported.g(constant_op.constant([1.0]))
def test_nested_inputs(self):
root = tracking.AutoCheckpointable()
root.f = def_function.function(
lambda x: 2. * x[0],
input_signature=([tensor_spec.TensorSpec(None, dtypes.float32),
tensor_spec.TensorSpec(None, dtypes.float32)],))
root.f([constant_op.constant(1.), constant_op.constant(1.)])
# Concrete functions must always have uniquely named Tensor inputs. Save
# relies on this.
with self.assertRaisesRegexp(
ValueError, "two arguments named 'x'"):
root.f.get_concrete_function()
def testNestedLists(self):
a = tracking.AutoCheckpointable()
a.l = []
b = tracking.AutoCheckpointable()
a.l.append([b])
c = tracking.AutoCheckpointable()
a.l[0].append(c)
a_deps = util.list_objects(a)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
a.l[0].append(1)
d = tracking.AutoCheckpointable()
a.l[0].append(d)
a_deps = util.list_objects(a)
self.assertIn(d, a_deps)
self.assertIn(b, a_deps)
self.assertIn(c, a_deps)
self.assertNotIn(1, a_deps)
e = tracking.AutoCheckpointable()
f = tracking.AutoCheckpointable()
a.l1 = [[], [e]]
a.l1[0].append(f)
a_deps = util.list_objects(a)
self.assertIn(e, a_deps)
self.assertIn(f, a_deps)
checkpoint = util.Checkpoint(a=a)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
a.l[0].append(data_structures.NoDependency([]))
a.l[0][-1].append(5)
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
# Dirtying the inner list means the root object is unsaveable.
a.l[0][1] = 2
with self.assertRaisesRegexp(ValueError,
"A list element was replaced"):
checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt"))
def testHashing(self):
has_mappings = set([data_structures.Mapping(),
data_structures.Mapping()])
self.assertEqual(2, len(has_mappings))
self.assertNotIn(data_structures.Mapping(), has_mappings)
# In contrast to Mapping, dict wrappers are not hashable
a = tracking.AutoCheckpointable()
a.d = {}
self.assertEqual({}, a.d)
self.assertFalse({} != a.d) # pylint: disable=g-explicit-bool-comparison
self.assertNotEqual({1: 2}, a.d)
with self.assertRaisesRegexp(TypeError, "unhashable"):
set([a.d])
def testLayerCollectionWithExternalMutation(self):
d = {}
root = tracking.AutoCheckpointable()
root.wrapper = d
self.assertEqual([], root.wrapper.layers)
self.assertEqual([], root.wrapper.trainable_weights)
layer1 = core.Dense(1)
layer2 = core.Dense(1)
d["a"] = layer1
d["b"] = layer2
self.assertEqual([layer1, layer2], root.wrapper.layers)
# The layers have still not created variables
self.assertEqual([], root.wrapper.trainable_weights)
def test_asset_path_returned(self):
root = tracking.AutoCheckpointable()
root.path = tracking.TrackableAsset(self._vocab_path)
save_dir = os.path.join(self.get_temp_dir(), "saved_model")
root.get_asset = def_function.function(lambda: root.path.asset_path)
save.save(root,
save_dir,
signatures=root.get_asset.get_concrete_function())
second_dir = os.path.join(self.get_temp_dir(), "second_dir")
file_io.rename(save_dir, second_dir)
imported_path = _import_and_infer(second_dir, {})["output_0"]
self.assertIn(compat.as_str_any(second_dir),
compat.as_str_any(imported_path))
def test_load_in_func_graph(self, cycles):
root = tracking.AutoCheckpointable()
root.v1 = variables.Variable(1.)
root.v2 = variables.Variable(2.)
root.f = def_function.function(
lambda x: root.v2 * x,
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
if cycles > 1:
root = self.cycle(root, cycles - 1)
path = tempfile.mkdtemp(prefix=self.get_temp_dir())
save.save(root, path)
closure = tracking.AutoCheckpointable()
@def_function.function
def func(x):
if not hasattr(closure, "model"):
closure.model = load.load(path)
return closure.model.f(x)
inputs = constant_op.constant(2.)
self.assertEqual(4.0, func(inputs).numpy())
def test_method_save_concrete(self):
root = tracking.AutoCheckpointable()
root.f = def_function.function(lambda z: {"out": 2. * z})
root.f(constant_op.constant(1.))
save_dir = os.path.join(self.get_temp_dir(), "saved_model")
save.save(
root, save_dir, {
"non_default_key":
root.f.get_concrete_function(
tensor_spec.TensorSpec(None, dtypes.float32))
})
self.assertEqual({"out": 2.},
_import_and_infer(save_dir, {"z": 1.},
signature_key="non_default_key"))
def test_dict(self, cycles):
root = tracking.AutoCheckpointable()
root.variables = dict(a=variables.Variable(1.))
root.variables["b"] = variables.Variable(2.)
root.variables["c"] = 1
root.funcs = dict(
a=def_function.function(lambda: constant_op.constant(100.)))
root.funcs["conc"] = root.funcs["a"].get_concrete_function()
imported = self.cycle(root, cycles)
self.assertEqual(1., imported.variables["a"].numpy())
self.assertEqual(2., imported.variables["b"].numpy())
self.assertEqual(set(["a", "b"]), set(imported.variables.keys()))
self.assertEqual(100., imported.funcs["a"]().numpy())
self.assertEqual(100., imported.funcs["conc"]().numpy())
def test_nested_functions(self, cycles):
f = def_function.function(
lambda x: x * 2.0,
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
g = def_function.function(
lambda x: f(x) + 1.0,
input_signature=[tensor_spec.TensorSpec(None, dtypes.float32)])
root = tracking.AutoCheckpointable()
root.g = g
# TODO(vbardiovsky): Enable this test. For this to work, we must ensure that
# concrete_function._inference_function._graph._functions contains all
# functions that were on the graph before saving.
imported = self.cycle(root, 1)
imported.g(constant_op.constant([1.0]))
def test_revived_concrete_function_tensorspec_kwargs(self, cycles):
@def_function.function
def func(*args):
x, y = args
return x * (y + 1.)
root = tracking.AutoCheckpointable()
root.f = func.get_concrete_function(
tensor_spec.TensorSpec([], dtypes.float32, name="x"),
tensor_spec.TensorSpec([], dtypes.float32, name="y"))
self.assertEqual(8., root.f(y=constant_op.constant(3.),
x=constant_op.constant(2.)).numpy())
imported = self.cycle(root, cycles, signatures={})
self.assertEqual(8., imported.f(y=constant_op.constant(3.),
x=constant_op.constant(2.)).numpy())
def test_revived_concrete_function_kwargs(self, cycles):
@def_function.function
def func(x, y):
return x * (y + 1.)
root = tracking.AutoCheckpointable()
root.f = func.get_concrete_function(
tensor_spec.TensorSpec([], dtypes.float32),
tensor_spec.TensorSpec([], dtypes.float32))
self.assertEqual(8., root.f(y=constant_op.constant(3.),
x=constant_op.constant(2.)).numpy())
# TODO(andresp): Fix exporting of loaded concrete functions as signatures.
imported = self.cycle(root, cycles, signatures={})
self.assertEqual(8., imported.f(y=constant_op.constant(3.),
x=constant_op.constant(2.)).numpy())
def test_function_and_component(self, cycles):
@def_function.function
def func(v):
return v + 1
root = tracking.AutoCheckpointable()
root.func = func
root.concrete_func = func.get_concrete_function(
tensor_spec.TensorSpec(None, dtypes.int32))
one = constant_op.constant(1)
self.assertEqual(2, root.func(one).numpy())
self.assertEqual(2, root.concrete_func(one).numpy())
imported = self.cycle(root, cycles)
self.assertEqual(2, imported.func(one).numpy())
self.assertEqual(2, imported.concrete_func(one).numpy())
def testListWrapperBasic(self):
# _ListWrapper, unlike List, compares like the built-in list type (since it
# is used to automatically replace lists).
a = tracking.AutoCheckpointable()
b = tracking.AutoCheckpointable()
self.assertEqual([a, a],
[a, a])
self.assertEqual(data_structures._ListWrapper([a, a]),
data_structures._ListWrapper([a, a]))
self.assertEqual([a, a],
data_structures._ListWrapper([a, a]))
self.assertEqual(data_structures._ListWrapper([a, a]),
[a, a])
self.assertNotEqual([a, a],
[b, a])
self.assertNotEqual(data_structures._ListWrapper([a, a]),
data_structures._ListWrapper([b, a]))
self.assertNotEqual([a, a],
data_structures._ListWrapper([b, a]))
self.assertLess([a], [a, b])
self.assertLess(data_structures._ListWrapper([a]),
data_structures._ListWrapper([a, b]))
self.assertLessEqual([a], [a, b])
self.assertLessEqual(data_structures._ListWrapper([a]),
data_structures._ListWrapper([a, b]))
self.assertGreater([a, b], [a])
self.assertGreater(data_structures._ListWrapper([a, b]),
data_structures._ListWrapper([a]))
self.assertGreaterEqual([a, b], [a])
self.assertGreaterEqual(data_structures._ListWrapper([a, b]),
data_structures._ListWrapper([a]))
self.assertEqual([a], data_structures._ListWrapper([a]))
self.assertEqual([a], list(data_structures.List([a])))
self.assertEqual([a, a], data_structures._ListWrapper([a]) + [a])
self.assertEqual([a, a], [a] + data_structures._ListWrapper([a]))
self.assertIsInstance(data_structures._ListWrapper([a]), list)
def test_implicit_input_signature(self):
@def_function.function
def func(x):
return 2 * x
root = tracking.AutoCheckpointable()
root.f = func
# Add two traces.
root.f(constant_op.constant(1.))
root.f(constant_op.constant(1))
imported = self.cycle(root)
self.assertEqual(4., imported.f(constant_op.constant(2.)).numpy())
self.assertEqual(14, imported.f(constant_op.constant(7)).numpy())
def testDictDeepCopy(self):
root = tracking.AutoCheckpointable()
orig_dict = {"a": [1.]}
root.a = orig_dict
copied = copy.deepcopy(root.a)
self.assertAllEqual([1.], copied["a"])
self.assertIsNot(root.a, copied)
self.assertIsNot(root.a["a"], copied["a"])
# Dirtiness should be inherited
util.list_objects(root.a)
orig_dict["b"] = []
with self.assertRaises(ValueError):
util.list_objects(root.a)
with self.assertRaises(ValueError):
util.list_objects(copy.deepcopy(root.a))
def testListDeepCopy(self):
root = tracking.AutoCheckpointable()
orig_list = [[1.]]
root.a = orig_list
copied = copy.deepcopy(root.a)
self.assertAllEqual([[1.]], copied)
self.assertIsNot(root.a, copied)
self.assertIsNot(root.a[0], copied[0])
# Dirtiness should be inherited
util.list_objects(root.a)
orig_list.append(1.)
with self.assertRaises(ValueError):
util.list_objects(root.a)
with self.assertRaises(ValueError):
util.list_objects(copy.deepcopy(root.a))
def test_concrete_function_arg_names(self):
@def_function.function(
input_signature=[tensor_spec.TensorSpec([None], dtypes.int32)])
def func(x):
return 2 * x
root = tracking.AutoCheckpointable()
root.f = func.get_concrete_function()
self.assertAllEqual([2], root.f(constant_op.constant([1])).numpy())
imported = self.cycle(root)
self.assertAllEqual([2, 4, 6],
imported.f(x=constant_op.constant([1, 2, 3])).numpy())
