def testSaveLoadCheckpoint(self):
init_var = m["custom_model"]().trainable_variables
with tempfile.TemporaryDirectory() as tempdir:
ckpt_dir = os.path.join(tempdir, "testSaveLoadCheckpoint")
os.makedirs(ckpt_dir)
checkpoint_saver = CheckpointSaver(ckpt_dir, 3, 5, False)
self.assertTrue(checkpoint_saver.is_enabled())
params = Parameters()
for var in init_var:
params.non_embedding_params[var.name] = var
model_pb = params.to_model_pb()
checkpoint_saver.save(0, model_pb, False)
ckpt_version_dir = os.path.join(ckpt_dir, "version-0")
restore_params = CheckpointSaver.restore_params_from_checkpoint(
ckpt_version_dir, 0, 1)
self.assertEqual(restore_params.version, params.version)
for var_name in params.non_embedding_params:
self.assertTrue(
np.array_equal(
params.non_embedding_params[var_name].numpy(),
restore_params.non_embedding_params[var_name].numpy(),
))
def save_checkpoint_without_embedding(model, checkpoint_dir, version=100):
checkpoint_saver = CheckpointSaver(checkpoint_dir, 0, 0, False)
params = Parameters()
for var in model.trainable_variables:
params.non_embedding_params[var.name] = var
params.version = version
model_pb = params.to_model_pb()
checkpoint_saver.save(version, model_pb, False)
def test_restore_parameters_from_checkpoint(self):
checkpoint_dir = "elasticdl/python/tests/testdata/ps_ckpt"
checkpoint_saver = CheckpointSaver(checkpoint_dir, 0, 0, False)
params = Parameters()
table = EmbeddingTable("embedding", 2, "random_uniform")
table.set([0, 1, 2, 3], np.ones((4, 2), dtype=np.float32))
params.embedding_params["embedding"] = table
params.non_embedding_params["dense/kernel:0"] = tf.Variable(
[[1.0], [1.0]]
)
params.non_embedding_params["dense/bias:0"] = tf.Variable([1.0])
params.version = 100
model_pb = params.to_model_pb()
checkpoint_saver.save(100, model_pb, False)
checkpoint_dir_for_init = checkpoint_dir + "/version-100"
args = PserverArgs(
ps_id=0,
num_ps_pods=2,
model_zoo=_test_model_zoo_path,
model_def="test_module.custom_model",
checkpoint_dir_for_init=checkpoint_dir_for_init,
)
pserver_0 = ParameterServer(args)
embedding_table = pserver_0.parameters.embedding_params["embedding"]
self.assertEqual(
list(embedding_table.embedding_vectors.keys()), [0, 2]
)
self.assertEqual(
list(pserver_0.parameters.non_embedding_params.keys()),
["dense/kernel:0"],
)
self.assertTrue(
np.array_equal(
pserver_0.parameters.non_embedding_params[
"dense/kernel:0"
].numpy(),
np.array([[1], [1]], dtype=int),
)
)
self.assertEqual(pserver_0.parameters.version, 100)
args = PserverArgs(
ps_id=1,
num_ps_pods=2,
model_zoo=_test_model_zoo_path,
model_def="test_module.custom_model",
checkpoint_dir_for_init=checkpoint_dir_for_init,
)
pserver_1 = ParameterServer(args)
embedding_table = pserver_1.parameters.embedding_params["embedding"]
self.assertEqual(
list(embedding_table.embedding_vectors.keys()), [1, 3]
)
self.assertEqual(
list(pserver_1.parameters.non_embedding_params.keys()),
["dense/bias:0"],
)
self.assertTrue(
np.array_equal(
pserver_1.parameters.non_embedding_params[
"dense/bias:0"
].numpy(),
np.array([1], dtype=int),
)
)
self.assertEqual(pserver_1.parameters.version, 100)
class ParametersTest(unittest.TestCase):
def setUp(self):
self.params = Parameters()
self.model_pb = Model()
self.infos_pb = self.model_pb.embedding_table_infos
self.tensors_pb = self.model_pb.dense_parameters
self.embedding_tables_pb = self.model_pb.embedding_tables
self.embedding_table_name = "embedding_1"
self.embedding_dim = 10
embedding_pb = self.infos_pb.add()
embedding_pb.name = self.embedding_table_name
embedding_pb.dim = self.embedding_dim
embedding_pb.initializer = "uniform"
arr1 = np.random.uniform(size=(3, 4))
serialize_ndarray(arr1, self.tensors_pb["x"])
arr2 = np.random.uniform(size=(4, 5))
serialize_ndarray(arr2, self.tensors_pb["y"])
embedding_vectors = np.random.uniform(size=(2, 10))
embedding_indices = np.array([0, 8])
serialize_indexed_slices(
Tensor(None, embedding_vectors, embedding_indices),
self.embedding_tables_pb[self.embedding_table_name],
)
def _test_get_embedding_param(self, slot_names=[], slot_init_value={}):
indices = [0, 3, 7]
res = self.params.get_embedding_param(
self.embedding_table_name, indices
)
self.assertTupleEqual(res.shape, (3, 10))
for slot in slot_names:
res = self.params.get_embedding_param(
get_slot_table_name(self.embedding_table_name, slot), indices
)
self.assertTrue(((res - slot_init_value[slot]) < 0.0001).all())
res = self.params.get_embedding_param(self.embedding_table_name, [])
self.assertIsNone(res)
with self.assertRaises(ValueError):
self.params.get_embedding_param("tom", indices)
def test_init_from_model_pb(self):
self.params.reset()
self.params.init_from_model_pb(self.model_pb)
res = self.params.non_embedding_params
self.assertTrue("x" in res)
self.assertTrue("y" in res)
self.assertTrue(res["x"].trainable)
self.assertTupleEqual(tuple(res["y"].shape.as_list()), (4, 5))
self._test_get_embedding_param()
def test_non_embedding_params(self):
self.params.reset()
res = self.params.non_embedding_params
self.assertFalse(any(res))
variables = {
"x": tf.Variable(1, name="x"),
"y": tf.Variable(2, name="y"),
}
self.params.non_embedding_params = variables
self.assertTrue("x" in self.params.non_embedding_params)
self.assertTrue("y" in self.params.non_embedding_params)
def test_get_embedding_param(self):
self.params.reset()
self.params.init_embedding_params(self.infos_pb)
self._test_get_embedding_param()
def test_set_embedding_param(self):
self.params.reset()
self.params.init_embedding_params(self.infos_pb)
indices = [100, 34, 8]
x = len(indices)
values = np.random.uniform(size=x * self.embedding_dim).reshape(
(x, self.embedding_dim)
)
self.params.set_embedding_param(
self.embedding_table_name, indices, values
)
row0 = self.params.get_embedding_param(
self.embedding_table_name, [100]
)
row1 = self.params.get_embedding_param(self.embedding_table_name, [34])
row2 = self.params.get_embedding_param(self.embedding_table_name, [8])
rows = [row0, row1, row2]
rows = np.concatenate(rows)
np.testing.assert_array_equal(rows, values)
with self.assertRaises(ValueError):
self.params.set_embedding_param("tom", [0, 1, 2], values)
def test_check_grad(self):
self.params.reset()
self.params.init_from_model_pb(self.model_pb)
grad0 = Tensor("z", None, None)
with self.assertRaisesRegex(ValueError, "Name error"):
self.params.check_grad(grad0)
grad1 = Tensor("x", np.random.uniform(size=(3, 5)), None)
with self.assertRaisesRegex(ValueError, "Non embedding param error"):
self.params.check_grad(grad1)
grad2 = Tensor(
name="embedding_1",
values=np.random.uniform(size=(3, 11)),
indices=np.array([1, 2, 3]),
)
with self.assertRaisesRegex(
ValueError, "ElasticDL embedding param error"
):
self.params.check_grad(grad2)
grad3 = Tensor(
name="x",
values=np.random.uniform(size=(4, 4)),
indices=np.array([1, 2, 3, 4]),
)
with self.assertRaisesRegex(ValueError, "Keras embedding param error"):
self.params.check_grad(grad3)
def test_create_slot_params(self):
# At first, no embedding table are in the parameters
self.assertFalse(self.params.has_embedding_params())
# create embedding tables in the parameters
self.params.init_embedding_params(self.infos_pb)
self.assertTrue(self.params.has_embedding_params())
slot_names = ["accumulator", "linear"]
slot_init_value = {slot_names[0]: 3.5, slot_names[1]: 0.0}
self.params.create_slot_params(slot_names, slot_init_value)
self._test_get_embedding_param(slot_names, slot_init_value)
def test_export_to_model_pb(self):
self.params.init_from_model_pb(self.model_pb)
self.params.version = 15
model_pb = self.params.to_model_pb()
params = Parameters()
params.init_from_model_pb(model_pb)
self.assertEqual(params.version, self.params.version)
self.assertEqual(
params.non_embedding_params.keys(),
self.params.non_embedding_params.keys(),
)
self.assertEqual(
params.embedding_params["embedding_1"].get([0]).tolist(),
self.params.embedding_params["embedding_1"].get([0]).tolist(),
)