def test_cpu_multiple_creation(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)
embedding_one = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_config, optimizer=optimizer)
embedding_two = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_config, optimizer=optimizer)
# Both of the tpu embedding tables should be able to build on cpu.
embedding_one.build()
embedding_two.build()
def test_optimizer_with_slot_creation_fn_non_partial(self):
def slot_creation_fn(table, slot_names, _):
slots = {}
for slot in slot_names:
# Note that we don't pass functools.partial here, so on TPU we can't
# extract the shape. We expect the error below.
slots[slot] = tf_variables.Variable(
name='{}_{}'.format(table.name, slot),
initial_value=init_ops_v2.Zeros()(shape=table.shape,
dtype=dtypes.float32),
trainable=False)
return slots
optimizer = tpu_embedding_v2_utils.Adagrad(
learning_rate=0.1,
slot_variable_creation_fn=slot_creation_fn)
strategy = self._get_strategy()
with strategy.scope():
mid_level_api = tpu_embedding_v2.TPUEmbedding(
feature_config=self.feature_config,
optimizer=optimizer)
with self.assertRaisesRegex(ValueError,
'Unable to extract initializer function'):
# We aren't going to actually run anything, so the batch_size here does
# not matter.
mid_level_api.build(self.batch_size)
def test_unsupported_optimizer(self):
with self.assertRaisesRegex(
ValueError, 'is an unsupported optimizer class.'):
with self._get_strategy().scope():
tpu_embedding_v2.TPUEmbedding(
self.feature_config,
tpu_embedding.AdagradParameters(learning_rate=0.1))
def _create_mid_level(self, optimizer=None):
# Create `TPUEmbedding` object.
if optimizer is None:
optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
return tpu_embedding_v2.TPUEmbedding(
feature_config=self.feature_config, optimizer=optimizer)
def create_mid_level(optimizer=None):
# Create `TPUEmbedding` object.
if optimizer is None:
optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
return tpu_embedding_v2.TPUEmbedding(feature_config=feature_config,
batch_size=batch_size,
optimizer=optimizer)
def build_mid_level(self, embedding_values, optimizer,
initialize_tpu_embedding=True):
"""Creates an embedding api object initialized to embedding_values."""
initializer = init_ops_v2.Constant(embedding_values)
table = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=self.num_rows, dim=4, initializer=initializer,
combiner='sum', name='table')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=table, name='feature'),)
mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config, optimizer)
# We want to create a second object (with its own variables) but not
# initialize the TPU.
if not initialize_tpu_embedding:
saved_fn = tpu.initialize_system_for_tpu_embedding
tpu.initialize_system_for_tpu_embedding = lambda x: None
# batch_size here does not matter as we aren't training in any of these
# tests.
mid_level.build(64)
if not initialize_tpu_embedding:
tpu.initialize_system_for_tpu_embedding = saved_fn
return mid_level
def test_cpu_sequence_lookup_ragged(self):
feature_config = (
tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='watched', 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()[:1]
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_video].numpy(),
sparse_ver.indices.numpy(),
sparse_ver.values.numpy(),
self.data_batch_size,
feature_config[0].max_sequence_length,
self.table_video.dim)
self.assertAllClose(result[0], golden)
def test_multiple_creation(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)
strategy = self._get_strategy()
with strategy.scope():
embedding_one = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_config, optimizer=optimizer)
embedding_two = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_config, optimizer=optimizer)
# The first TPU embedding should be able to be built.
# The second one should fail with a runtime error indicating another TPU
# embedding has already been initialized on TPU.
embedding_one.build(64)
with self.assertRaisesRegex(
RuntimeError, 'TPU is already initialized for embeddings.'):
embedding_two.build(64)
def test_check_checkpoint_variable_names_are_same_on_cpu_and_tpu(
self, optimizer):
# Reinitialize the TPU so that we can re-initialize the embeddings with the
# given optimizer.
if optimizer != tpu_embedding_v2_utils.SGD:
self.skip_if_oss()
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 = optimizer(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)
cpu_mid_level_optimizer = optimizer(learning_rate=0.1)
cpu_mid_level = tpu_embedding_v2.TPUEmbedding(feature_config,
cpu_mid_level_optimizer)
cpu_mid_level.build(64)
tpu_checkpoint = util.Checkpoint(model=first_mid_level)
tpu_checkpoint.save(self._get_tmpdir('save-tpu', 'save'))
tpu_variables = checkpoint_utils.list_variables(
self._get_tmpdir('save-tpu'))
cpu_checkpoint = util.Checkpoint(model=cpu_mid_level)
cpu_checkpoint.save(self._get_tmpdir('save-cpu', 'save'))
cpu_variables = checkpoint_utils.list_variables(
self._get_tmpdir('save-cpu'))
self.assertAllEqual(tpu_variables, cpu_variables)
def test_cpu_no_optimizer(self):
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='watched', max_sequence_length=2), )
mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_config,
batch_size=self.batch_size,
optimizer=None)
self.assertEqual(
list(mid_level._variables[self.table_video.name].keys()),
['parameters'])
def create_embedding(self):
table = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=self._rows, dim=4, initializer=self._initializer,
combiner='sum', name='table')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=table, name='feature'),)
optimizer = tpu_embedding_v2_utils.SGD()
self.tpu_embedding = tpu_embedding_v2.TPUEmbedding(
feature_config, self._rows, optimizer)
def _create_mid_level(self, optimizer=None):
# Create `TPUEmbedding` object.
if optimizer is None:
optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
num_replicas = (
distribution_strategy_context.get_strategy().num_replicas_in_sync)
return tpu_embedding_v2.TPUEmbedding(
feature_config=self.feature_config,
batch_size=self.batch_size * num_replicas,
optimizer=optimizer)
def test_cpu_no_optimizer(self):
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='watched', max_sequence_length=2), )
mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_config, optimizer=None)
# Build the layer manually to create the variables. Normally calling enqueue
# would do this.
mid_level.build()
self.assertEqual(
list(mid_level._variables[self.table_video.name].keys()),
['parameters'])
def test_cpu_high_dimensional_lookup_ragged(self):
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_user, name='friends', output_shape=[2, 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)
self.assertAllClose(result[0].shape, (2, 2, 2))
def test_cpu_sequence_lookup(self):
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_video, name='watched', 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 = tuple(self._get_sparse_tensors()[:1])
with self.assertRaisesRegex(
ValueError, 'Sequence features unsupported at this time.'):
tpu_embedding_v2.cpu_embedding_lookup(
features,
weights=None,
tables=mid_level.embedding_tables,
feature_config=feature_config)
def test_cpu_high_dimensional_sequence_lookup_ragged(self):
# Prod of output shape is a factor of the data batch size.
# The divide result will be the sequence length.
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_user, name='friends', output_shape=[2, 4]), )
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)
self.assertAllClose(result[0].shape, (2, 4, 2))
def tpu_embedding_config():
feature_configs = []
for dim, vocab, name in table_data:
feature_configs.append(tpu_embedding_v2_utils.FeatureConfig(
table=tpu_embedding_v2_utils.TableConfig(
vocabulary_size=int(vocab), dim=int(dim),
initializer=init_ops_v2.Zeros(), name=name)))
optimizer = tpu_embedding_v2_utils.Adagrad(
learning_rate=0.1)
with strategy.scope():
mid_level_api = tpu_embedding_v2.TPUEmbedding(
feature_config=feature_configs,
optimizer=optimizer)
mid_level_api._output_shapes = [TensorShape(128)] * len(feature_configs)
return mid_level_api._create_config_proto()
def build_mid_level(self, embedding_values, optimizer,
initialize_tpu_embedding=True):
"""Creates an embedding api object initialized to embedding_values."""
initializer = init_ops_v2.Constant(embedding_values)
table = tpu_embedding_v2_utils.TableConfig(
vocabulary_size=self.num_rows, dim=4, initializer=initializer,
combiner='sum', name='table')
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=table, name='feature'),)
# batch_size here does not matter as we aren't training in any of these
# tests.
return tpu_embedding_v2.TPUEmbedding(
feature_config, 64, optimizer,
initialize_tpu_embedding=initialize_tpu_embedding)
def test_cpu_high_dimensional_invalid_lookup_ragged(self):
# Prod of output shape is not a factor of the data batch size.
# An error will be raised in this case.
feature_config = (tpu_embedding_v2_utils.FeatureConfig(
table=self.table_user, name='friends', output_shape=[3]), )
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]
with self.assertRaisesRegex(
ValueError,
'Output shape set in the FeatureConfig should be the factor'):
tpu_embedding_v2.cpu_embedding_lookup(
features,
weights=None,
tables=mid_level.embedding_tables,
feature_config=feature_config)
def test_tables_with_same_name(self):
with self.assertRaisesRegex(
ValueError, 'Multiple tables with name table found.'):
with self._get_strategy().scope():
tpu_embedding_v2.TPUEmbedding(
(tpu_embedding_v2_utils.FeatureConfig(
table=tpu_embedding_v2_utils.TableConfig(
name='table',
vocabulary_size=4,
dim=2,
initializer=self.initializer,),
name='watched'),
tpu_embedding_v2_utils.FeatureConfig(
table=tpu_embedding_v2_utils.TableConfig(
name='table',
vocabulary_size=4,
dim=2,
initializer=self.initializer),
name='favorited')),
tpu_embedding_v2_utils.SGD(learning_rate=0.1))
def test_optimizer_with_slot_creation_fn(self, use_tpu):
def slot_creation_fn(table, slot_names, _):
slots = {}
for slot in slot_names:
slots[slot] = tf_variables.Variable(
name='{}_{}'.format(table.name, slot),
initial_value=functools.partial(
init_ops_v2.Zeros(), shape=table.shape, dtype=dtypes.float32),
trainable=False)
return slots
optimizer = tpu_embedding_v2_utils.Adagrad(
learning_rate=0.1,
slot_variable_creation_fn=slot_creation_fn)
if use_tpu:
strategy = self._get_strategy()
else:
strategy = distribution_strategy_context.get_strategy()
with strategy.scope():
mid_level = tpu_embedding_v2.TPUEmbedding(
feature_config=self.feature_config,
optimizer=optimizer)
# We aren't going to actually run anything, so the batch_size here does
# not matter.
mid_level.build(self.batch_size)
video_accumulator = mid_level._variables['video']['accumulators']
user_accumulator = mid_level._variables['user']['accumulators']
if use_tpu:
# To check the table contents (ensure that it is zero rather than the
# normal initial accumulator value specified to in the optimizer config),
# we need to select the underlying table variable on TPU.
# We only have one shard on Forge.
video_accumulator = video_accumulator.variables[0]
user_accumulator = user_accumulator.variables[0]
self.assertAllClose(video_accumulator.numpy(),
np.zeros((self.table_video.vocabulary_size,
self.table_video.dim)))
self.assertAllClose(user_accumulator.numpy(),
np.zeros((self.table_user.vocabulary_size,
self.table_user.dim)))
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 test_optimizer_with_slot_creation_fn_non_partial(self):
def slot_creation_fn(table, slot_names):
slots = {}
for slot in slot_names:
# Note that we don't pass functools.partial here, so on TPU we can't
# extract the shape. We expect the error below.
slots[slot] = tf_variables.Variable(
name='{}_{}'.format(table.name, slot),
initial_value=init_ops_v2.Zeros()(shape=table.shape,
dtype=dtypes.float32),
trainable=False)
return slots
optimizer = tpu_embedding_v2_utils.Adagrad(
learning_rate=0.1,
slot_variable_creation_fn=slot_creation_fn)
strategy = self._get_strategy()
num_replicas = strategy.num_replicas_in_sync
with strategy.scope():
with self.assertRaisesRegex(ValueError,
'Unable to extract initializer function'):
tpu_embedding_v2.TPUEmbedding(
feature_config=self.feature_config,
batch_size=self.batch_size*num_replicas,
optimizer=optimizer)
def _create_mid_level(self):
optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
return tpu_embedding_v2.TPUEmbedding(
feature_config=self.feature_config, optimizer=optimizer)
def test_model_export_cpu(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)
cpu_mid_level_optimizer = tpu_embedding_v2_utils.SGD(learning_rate=0.1)
cpu_mid_level = tpu_embedding_v2.TPUEmbedding(feature_config,
cpu_mid_level_optimizer)
cpu_mid_level.build(64)
first_mid_level._load_variables()
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_v2.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])
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 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.'
)
