def test_cpu_sequence_lookup_ragged(self):
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_user, name='friends', max_sequence_length=2), )
optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_config, optimizer=optimizer)
features = self._get_ragged_tensors()[2:3]
result = tpu_embedding_v2.cpu_embedding_lookup(
features,
weights=None,
tables=mid_level.embedding_tables,
feature_config=feature_config)
sparse_ver = features[0].to_sparse()
golden = self._numpy_sequence_lookup(
mid_level.embedding_tables[self.table_user].numpy(),
sparse_ver.indices.numpy(), sparse_ver.values.numpy(),
self.data_batch_size, feature_config[0].max_sequence_length,
self.table_user.dim)
self.assertAllClose(result[0], golden)
def test_missing_feature(self, is_sparse):
strategy = self._get_strategy()
with strategy.scope():
optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
mid_level_api = tpu_embedding_v2.TPUEmbedding(
feature_config=tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='watched'),
optimizer=optimizer)
# Create sparse or ragged feature with last sample missing.
if is_sparse:
features = sparse_tensor.SparseTensor(
indices=self.feature_watched_indices[:-1],
values=self.feature_watched_values[:-1],
dense_shape=[self.data_batch_size, 2])
else:
features = ragged_tensor.RaggedTensor.from_row_lengths(
row_lengths=[1, 2, 2, 0],
values=self.feature_watched_values[:-1])
dataset = dataset_ops.DatasetV2.from_tensors(features)
dataset = dataset.unbatch().repeat().batch(
self.batch_size * strategy.num_replicas_in_sync,
drop_remainder=True)
dataset_iter = iter(
strategy.experimental_distribute_dataset(
dataset,
options=distribute_lib.InputOptions(
experimental_fetch_to_device=False)))
@def_function.function
def test_fn():
def get_activations():
return mid_level_api.dequeue()
mid_level_api.enqueue(next(dataset_iter), training=False)
return strategy.run(get_activations)
test_fn()
def setUp(self):
super(CPUEmbeddingTest, self).setUp()
self.embedding_values = np.array(list(range(32)), dtype=np.float64)
self.initializer = init_ops_v2.Constant(self.embedding_values)
# Embedding for video initialized to
# 0 1 2 3
# 4 5 6 7
# ...
self.table_video = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=8,
dim=4,
initializer=self.initializer,
combiner='sum',
name='video')
# Embedding for user initialized to
# 0 1
# 2 3
# 4 5
# 6 7
# ...
self.table_user = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=16,
dim=2,
initializer=self.initializer,
combiner='mean',
name='user')
self.feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='watched'),
tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='favorited'),
tpu_embedding_v2_utils.FeatureConfig(
table=self.table_user, name='friends'))
self.batch_size = 2
self.data_batch_size = 4
# One (global) batch of inputs
# sparse tensor for watched:
# row 0: 0
# row 1: 0, 1
# row 2: 0, 1
# row 3: 1
self.feature_watched_indices = [[0, 0], [1, 0], [1, 1], [2, 0], [2, 1],
[3, 0]]
self.feature_watched_values = [0, 0, 1, 0, 1, 1]
self.feature_watched_row_lengths = [1, 2, 2, 1]
# sparse tensor for favorited:
# row 0: 0, 1
# row 1: 1
# row 2: 0
# row 3: 0, 1
self.feature_favorited_indices = [[0, 0], [0, 1], [1, 0], [2, 0],
[3, 0], [3, 1]]
self.feature_favorited_values = [0, 1, 1, 0, 0, 1]
self.feature_favorited_row_lengths = [2, 1, 1, 2]
# sparse tensor for friends:
# row 0: 3
# row 1: 0, 1, 2
# row 2: 3
# row 3: 0, 1, 2
self.feature_friends_indices = [[0, 0], [1, 0], [1, 1], [1, 2], [2, 0],
[3, 0], [3, 1], [3, 2]]
self.feature_friends_values = [3, 0, 1, 2, 3, 0, 1, 2]
self.feature_friends_row_lengths = [1, 3, 1, 3]
def test_model_export_cpu(self):
strategy = self._get_strategy()
with strategy.scope():
first_mid_level_contents = np.ones((4, 4))
first_mid_level_optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
initializer = init_ops_v2.Constant(first_mid_level_contents)
table = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=4,
dim=4,
initializer=initializer,
combiner='sum',
name='table')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=table, name='feature'),)
first_mid_level = tpu_embedding_v1.TPUEmbeddingV0(
feature_config, first_mid_level_optimizer)
first_mid_level.build()
cpu_mid_level_optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
cpu_mid_level = tpu_embedding_for_serving.TPUEmbeddingForServing(
feature_config, cpu_mid_level_optimizer)
cpu_mid_level.build()
tpu_checkpoint = util.Checkpoint(model=first_mid_level)
tpu_checkpoint.save(self._get_tmpdir('export_cpu', 'save'))
# We restore the checkpoint of our tpu mid level onto our cpu mid level.
cpu_checkpoint = util.Checkpoint(model=cpu_mid_level)
cpu_checkpoint.restore(self._get_tmpdir('export_cpu', 'save-1'))
@def_function.function
def serve_tensors(features):
features = tpu_embedding_for_serving.cpu_embedding_lookup(
features, None, cpu_mid_level.embedding_tables,
cpu_mid_level._feature_config)
return features[0]
signatures = {
'serving_default':
serve_tensors.get_concrete_function((tensor_spec.TensorSpec(
shape=(2,), dtype=dtypes.int32, name='feature'),))
}
save.save(
cpu_mid_level,
export_dir=self._get_tmpdir('export_cpu', 'exported_model'),
signatures=signatures)
imported = load.load(self._get_tmpdir('export_cpu', 'exported_model'))
predict_fn = imported.signatures['serving_default']
input_feature_value = np.array([1, 0])
input_batch = (constant_op.constant(
input_feature_value, dtype=dtypes.int32),)
prediction = predict_fn(*input_batch)['output_0']
self.assertAllClose(prediction.numpy(),
first_mid_level_contents[input_feature_value])
parser.add_argument("--warmups", type=int, default=0)
parser.add_argument("--randomseed", type=int, default=0)
parser.add_argument("--testtpu", type=int, default=0)
parser.add_argument("--verify", type=int, default=0)
args = parser.parse_args()
# embedding_values = np.array(list(range(32)), dtype=np.float64)
# initializer = init_ops_v2.Constant(embedding_values)
table_test = tpu_embedding_v2_utils.TableConfig(vocabulary_size=args.features,
dim=args.em,
initializer=None,
combiner='sum',
name='test')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(table=table_test,
name='watched'))
batch = args.batch
nnz = args.nnz
features = args.features
feature_watched_values = np.random.randint(0, features, (batch * nnz * 2, ))
# print("Feature: ", feature_watched_values)
batch_size = batch * nnz
# feature_watched_values = [0, 0, 1, 0, 1, 1]
# feature_watched_row_lengths = [1, 2, 2, 1]
resolver = None
# 126-129
def test_sequence_embeddings(self, sparse):
feature_config = (
tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='watched',
max_sequence_length=2),
tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='favorited',
max_sequence_length=2),
tpu_embedding_v2_utils.FeatureConfig(
table=self.table_user, name='friends',
max_sequence_length=3))
optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
strategy = self._get_strategy()
num_replicas = strategy.num_replicas_in_sync
with strategy.scope():
mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_config,
optimizer=optimizer)
# Call build here. We call 'next' outside of the tf.function and this
# results in data where the shape of the sparse tensor is a tensor which we
# can't tell the shape of at tracing time.
mid_level.build(self.batch_size)
if sparse:
dataset = self._create_sparse_dataset(strategy)
else:
dataset = self._create_ragged_dataset(strategy)
data = next(
iter(
strategy.experimental_distribute_dataset(
dataset,
options=distribute_lib.InputOptions(
experimental_fetch_to_device=False))))
@def_function.function
def embedding_and_set_gradients(data):
def tpu_fn():
activations = mid_level.dequeue()
mid_level.apply_gradients(nest.map_structure(array_ops.ones_like,
activations))
return activations
mid_level.enqueue(data)
return strategy.run(tpu_fn)
@def_function.function
def embedding_only(data):
def tpu_fn():
return mid_level.dequeue()
mid_level.enqueue(data)
return strategy.run(tpu_fn)
# Only check core 0.
before_update = self._get_replica_numpy(
embedding_and_set_gradients(data), strategy, 0)
after_update = self._get_replica_numpy(embedding_only(data), strategy, 0)
# For videos table, row 0 and row 1 are looked up 3*num_replicas times as
# they occur 3 times per replica (considering the features 0 and 1 which are
# both looked up in the videos table).
# Feature 0 has ids [0, 0, 1], [0, 1, 1], ... repeated over num_replicas
# Feature 1 has ids [0, 1, 1], [0, 0, 1], ... repeated over num_replicas
# This means that both rows 0 and 1 get a -0.1*3*num_replicas update
# For users table, each row is looked up twice:
# Feature 2 has ids [3, 0, 1, 2], .. repeated over num_replicas
# This means that we get a -0.1*num_replicas update to the third feature.
# In general this means that after the update, if we lookup feature 0 and 1
# the values will be 0.3*num_replicas lower per entry and for feature 2 they
# will be 0.1*num_replicas lower.
# The one issue is that these lookups contain padding values.
# For core 0, we get the first 2 elements of the 4 element batch.
# For feature 0, the indices are [[0, 0], [1, 0], [1, 1]] with max sequence
# length of 2, which means that [0, 1] will be 0s.
# For feature 1, the indices are [[0, 0], [0, 1], [1, 0]] with max sequence
# length of 2, which means that [1, 1] will be 0s.
# For feature 2, the indices are [[0, 0], [1, 0], [1, 1], [1, 2]] with max
# sequence length of 3, which means that [0, 1], [0, 2] will be 0s.
# The following masks represent that so that we only apply the above updates
# to the non-padding rows:
masks = (
np.array([[[1], [0]], [[1], [1]]]),
np.array([[[1], [1]], [[1], [0]]]),
np.array([[[1], [0], [0]], [[1], [1], [1]]]))
per_row_update = (0.3 * num_replicas,
0.3 * num_replicas,
0.1 * num_replicas)
golden = tuple([before - update * mask for before, update, mask in
zip(before_update, per_row_update, masks)])
self.assertAllClose(golden, after_update)
def setUp(self):
super(TPUEmbeddingBaseTest, self).setUp()
self.embedding_values = np.array(list(range(32)), dtype=np.float64)
self.initializer = init_ops_v2.Constant(self.embedding_values)
# Embedding for video initialized to
# 0 1 2 3
# 4 5 6 7
# ...
self.table_video = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=8,
dim=4,
initializer=self.initializer,
combiner='sum',
name='video')
# Embedding for user initialized to
# 0 1
# 2 3
# 4 5
# 6 7
# ...
self.table_user = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=16,
dim=2,
initializer=self.initializer,
combiner='mean',
name='user')
self.feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='watched'),
tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='favorited'),
tpu_embedding_v2_utils.FeatureConfig(
table=self.table_user, name='friends'))
self.batch_size = 2
self.data_batch_size = 4
# One (global) batch of inputs
# sparse tensor for watched:
# row 0: 0
# row 1: 0, 1
# row 2: 0, 1
# row 3: 1
self.feature_watched_indices = [[0, 0], [1, 0], [1, 1], [2, 0], [2, 1],
[3, 0]]
self.feature_watched_values = [0, 0, 1, 0, 1, 1]
self.feature_watched_row_lengths = [1, 2, 2, 1]
# sparse tensor for favorited:
# row 0: 0, 1
# row 1: 1
# row 2: 0
# row 3: 0, 1
self.feature_favorited_indices = [[0, 0], [0, 1], [1, 0], [2, 0],
[3, 0], [3, 1]]
self.feature_favorited_values = [0, 1, 1, 0, 0, 1]
self.feature_favorited_row_lengths = [2, 1, 1, 2]
# sparse tensor for friends:
# row 0: 3
# row 1: 0, 1, 2
# row 2: 3
# row 3: 0, 1, 2
self.feature_friends_indices = [[0, 0], [1, 0], [1, 1], [1, 2], [2, 0],
[3, 0], [3, 1], [3, 2]]
self.feature_friends_values = [3, 0, 1, 2, 3, 0, 1, 2]
self.feature_friends_row_lengths = [1, 3, 1, 3]
self.resolver = None
# Basically we are expand the dims of the old feature by 1 and repeat
# batch size times for the first dimension.
def create_hight_dimensional_indices(indices):
indices = np.array(indices, dtype=np.int32)
batch_size_index = np.repeat(np.arange(self.data_batch_size),
len(indices)).reshape(-1, 1)
repeated_indices = np.tile(indices, (self.data_batch_size, 1))
return np.concatenate([batch_size_index, repeated_indices], axis=1)
# Create high dimensional features with shape(4, 4, 2)
self.feature_watched_indices_high_dimensional = create_hight_dimensional_indices(
self.feature_watched_indices)
self.feature_watched_values_high_dimensional = self.feature_watched_values * self.data_batch_size
self.feature_watched_row_lengths_high_dimensional = self.feature_watched_row_lengths * self.data_batch_size
# Create high dimensional features with shape(4, 4, 2)
self.feature_favorited_indices_high_dimensional = create_hight_dimensional_indices(
self.feature_favorited_indices)
self.feature_favorited_values_high_dimensional = self.feature_favorited_values * self.data_batch_size
self.feature_favorited_row_lengths_high_dimensional = self.feature_favorited_row_lengths * self.data_batch_size
# Create high dimensional features with shape(4, 4, 3)
self.feature_friends_indices_high_dimensional = create_hight_dimensional_indices(
self.feature_friends_indices)
self.feature_friends_values_high_dimensional = self.feature_friends_values * self.data_batch_size
self.feature_friends_row_lengths_high_dimensional = self.feature_friends_row_lengths * self.data_batch_size
def test_variable_learning_rate(self):
num_steps = 10
num_steps_float = float(num_steps)
starting_lr = 1.0
ending_lr = 0.5
strategy = self._get_strategy()
num_replicas = strategy.num_replicas_in_sync
# Create model with Keras.
with strategy.scope():
step_counter = tf_variables.Variable(0.0, dtypes.float32)
def lr_function():
return gen_math_ops.maximum(
ending_lr,
starting_lr + ((ending_lr - starting_lr) * step_counter) /
num_steps_float)
optimizer = tpu_embedding_v2_utils.SGD(learning_rate=lr_function)
table_config = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=num_replicas,
dim=4,
initializer=init_ops_v2.Constant(np.zeros((num_replicas, 4))),
combiner='sum', name='table')
mid_level_api = tpu_embedding_v2.TPUEmbedding(
feature_config={
'feature': tpu_embedding_v2_utils.FeatureConfig(
table=table_config, name='feature')},
optimizer=optimizer)
feature = {
'feature': constant_op.constant([0], shape=(1, 1), dtype=dtypes.int32)
}
def input_fn(ctx):
del ctx
return dataset_ops.DatasetV2.from_tensors(feature).repeat()
dist = strategy.distribute_datasets_from_function(
input_fn,
options=distribute_lib.InputOptions(experimental_fetch_to_device=False))
dist_iter = iter(dist)
@def_function.function
def test_fn():
def step():
with backprop.GradientTape() as tape:
activations = mid_level_api.dequeue()
tape.watch(activations)
result = math_ops.reduce_sum(activations['feature'])
loss = result / num_replicas
grads = tape.gradient(loss, activations)
mid_level_api.apply_gradients(grads)
return activations['feature']
mid_level_api.enqueue(next(dist_iter), training=True)
return strategy.run(step)
# Run model.
results = []
for _ in range(num_steps):
result = test_fn()
results.append(self._unpack(strategy, result))
step_counter.assign_add(1.0)
# Table is 2 elements wide, per-replica batch size of 1, with id 0.
# Loss for the gradient is the sum of the entries divided by the number of
# replicas. Thus the per replica gradient is 1/#of replicas for row 0 and no
# other updates. The reduced gradient is therefore 1.
# Learning rate schedule over num_steps steps:
# 1.0 0.95 0.9 0.85 0.8 ...
# Since use SGD and the gradient is one, the first row of the table is
# [0, 0] [-1.0, -1.0] [-1.95, -1.95] [-2.85, -2.85] ... (the negative
# partial sums of the above).
learning_rates = [starting_lr - (starting_lr - ending_lr) / num_steps * j
for j in range(num_steps)]
cumsum = [sum(learning_rates[0:j]) for j in range(num_steps)]
goldens = [[[-cumsum[i]] * table_config.dim] * num_replicas
for i in range(10)]
self.assertAllClose(results, goldens)
def test_checkpoint_save_retrieves(self):
strategy = self._get_strategy()
num_rows = strategy.num_replicas_in_sync
with strategy.scope():
first_mid_level_contents = np.ones((num_rows, 4))
first_mid_level_optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
initializer = init_ops_v2.Constant(first_mid_level_contents)
table = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=num_rows,
dim=4,
initializer=initializer,
combiner='sum',
name='table')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=table, name='feature'),)
first_mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config, first_mid_level_optimizer)
first_mid_level.build(64)
# Ensure that the variables from the first model are loaded.
first_mid_level._load_variables()
self.assertAllClose(
first_mid_level_contents,
self.make_checkpoint_and_get_embedding('before_load', first_mid_level,
num_rows),
msg='Checkpoint should contain values from the first api object.')
# Reinitialize the tpu.
tpu_strategy_util.initialize_tpu_system(self.resolver)
with strategy.scope():
second_mid_level_contents = np.ones((num_rows, 4)) * 2
second_mid_level_optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
initializer = init_ops_v2.Constant(second_mid_level_contents)
table = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=num_rows,
dim=4,
initializer=initializer,
combiner='sum',
name='table')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=table, name='feature'),)
second_mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config, second_mid_level_optimizer)
second_mid_level.build(64)
second_mid_level._load_variables()
# When we load the variables from the second mid level API object to the TPU
# we expect that checkpointing the first mid level API object will now
# retrieve the values from the TPU which are now different from the current
# variables in the first mid level.
self.assertAllClose(
second_mid_level_contents,
self.make_checkpoint_and_get_embedding('after_load', first_mid_level,
num_rows),
msg='Checkpoint should contain values from the second api object.')
def test_checkpoint_restore_loads(self):
strategy = self._get_strategy()
num_rows = strategy.num_replicas_in_sync
def get_values(mid):
return ops.convert_to_tensor(
mid._variables['table']['parameters'].variables[0])
with strategy.scope():
first_mid_level_contents = np.ones((num_rows, 4))
first_mid_level_optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
initializer = init_ops_v2.Constant(first_mid_level_contents)
table = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=num_rows,
dim=4,
initializer=initializer,
combiner='sum',
name='table')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=table, name='feature'),)
first_mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config, first_mid_level_optimizer)
first_mid_level.build(64)
first_mid_level._load_variables()
first_checkpoint = util.Checkpoint(model=first_mid_level)
first_checkpoint.save(self._get_tmpdir('restore', 'save'))
tpu_strategy_util.initialize_tpu_system(self.resolver)
with strategy.scope():
second_mid_level_contents = np.ones((num_rows, 4)) * 2
second_mid_level_optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
initializer = init_ops_v2.Constant(second_mid_level_contents)
table = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=num_rows,
dim=4,
initializer=initializer,
combiner='sum',
name='table')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=table, name='feature'),)
second_mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config, second_mid_level_optimizer)
second_mid_level.build(64)
second_mid_level._load_variables()
self.assertAllClose(
second_mid_level_contents,
get_values(second_mid_level),
msg='Second mid level api should contain its initial values.',
)
# We restore the checkpoint of our first model into our second model.
# This should load the first mid level API object onto the TPU.
second_checkpoint = util.Checkpoint(model=second_mid_level)
second_checkpoint.restore(self._get_tmpdir('restore', 'save-1'))
# Call retrieve here as a way to check what the TPU contains.
# Calling the retrieve ops directly might make for a cleaner separation of
# test and module, though.
second_mid_level._retrieve_variables()
self.assertAllClose(
first_mid_level_contents,
get_values(second_mid_level),
msg='Second mid level api should have retrieved the first model values.'
)
