class EnablingTF2Behavior(test.TestCase, parameterized.TestCase):
def __init__(self, methodName):
super().__init__(methodName)
self._set_default_seed = False
@combinations.generate(test_base.v1_only_combinations())
def test_tf1_enable_tf2_behaviour(self):
self.assertFalse(tf2.enabled())
self.assertFalse(_pywrap_tf2.is_enabled())
v2_compat.enable_v2_behavior()
self.assertTrue(tf2.enabled())
self.assertTrue(_pywrap_tf2.is_enabled())
v2_compat.disable_v2_behavior()
self.assertFalse(tf2.enabled())
self.assertFalse(_pywrap_tf2.is_enabled())
@combinations.generate(test_base.v1_only_combinations())
def test_tf1_disable_tf2_behaviour(self):
self.assertFalse(tf2.enabled())
self.assertFalse(_pywrap_tf2.is_enabled())
v2_compat.disable_v2_behavior()
self.assertFalse(tf2.enabled())
self.assertFalse(_pywrap_tf2.is_enabled())
v2_compat.enable_v2_behavior()
self.assertTrue(tf2.enabled())
self.assertTrue(_pywrap_tf2.is_enabled())
@combinations.generate(test_base.v2_only_combinations())
def test_tf2_enable_tf2_behaviour(self):
self.assertTrue(tf2.enabled())
self.assertTrue(_pywrap_tf2.is_enabled())
v2_compat.enable_v2_behavior()
self.assertTrue(tf2.enabled())
self.assertTrue(_pywrap_tf2.is_enabled())
v2_compat.disable_v2_behavior()
self.assertFalse(tf2.enabled())
self.assertFalse(_pywrap_tf2.is_enabled())
@combinations.generate(test_base.v2_only_combinations())
def test_tf2_disable_tf2_behaviour(self):
self.assertTrue(tf2.enabled())
self.assertTrue(_pywrap_tf2.is_enabled())
v2_compat.disable_v2_behavior()
self.assertFalse(tf2.enabled())
self.assertFalse(_pywrap_tf2.is_enabled())
v2_compat.enable_v2_behavior()
self.assertTrue(tf2.enabled())
self.assertTrue(_pywrap_tf2.is_enabled())
class FromSparseTensorSlicesCheckpointTest(
checkpoint_test_base.CheckpointTestBase, parameterized.TestCase):
def _build_sparse_tensor_slice_dataset(self, slices):
# pylint: disable=g-complex-comprehension
indices = np.array([[i, j] for i in range(len(slices))
for j in range(len(slices[i]))],
dtype=np.int64)
values = np.array([val for s in slices for val in s], dtype=np.float64)
# pylint: enable=g-complex-comprehension
dense_shape = np.array(
[len(slices), max(len(s) for s in slices) + 1], dtype=np.int64)
sparse_components = sparse_tensor.SparseTensor(indices, values,
dense_shape)
return dataset_ops.Dataset.from_sparse_tensor_slices(sparse_components)
@combinations.generate(
combinations.times(test_base.v1_only_combinations(),
checkpoint_test_base.default_test_combinations()))
def test(self, verify_fn):
slices = [[1., 2., 3.], [1.], [1.], [1., 2.], [], [1., 2.], [], [], []]
verify_fn(self,
lambda: self._build_sparse_tensor_slice_dataset(slices),
num_outputs=9,
sparse_tensors=True)
class DataServiceOpsTest(data_service_test_base.TestBase,
parameterized.TestCase):
@combinations.generate(
combinations.times(test_base.eager_only_combinations(),
data_service_test_base.all_cluster_configurations())
)
def testDistributeBasic(self, work_dir, fault_tolerant_mode):
cluster = self.create_cluster(num_workers=1,
work_dir=work_dir,
fault_tolerant_mode=fault_tolerant_mode)
num_elements = 10
ds = self.make_distributed_range_dataset(10, cluster)
results = [elem.numpy() for elem in ds]
self.assertEqual(list(range(num_elements)), results)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeSparse(self):
cluster = self.create_cluster(num_workers=1)
element = sparse_tensor.SparseTensor(indices=[[0]],
values=constant_op.constant(
[0], dtype=dtypes.int32),
dense_shape=[1])
ds = dataset_ops.Dataset.from_tensors(element)
ds = self.make_distributed_dataset(ds, cluster)
results = [sparse_ops.sparse_tensor_to_dense(elem) for elem in ds]
self.assertAllEqual(results, [[0]])
@combinations.generate(test_base.eager_only_combinations())
def testDistributeRagged(self):
cluster = self.create_cluster(num_workers=1)
ds = dataset_ops.Dataset.from_tensor_slices([1, 5, 3, 2, 8])
ds = ds.map(math_ops.range)
ds = ds.apply(batching.dense_to_ragged_batch(2))
ds = self.make_distributed_dataset(ds, cluster)
results = [elem.to_tensor() for elem in ds]
self.assertAllEqual(results[0], [[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]])
self.assertAllEqual(results[1], [[0, 1, 2], [0, 1, 0]])
self.assertAllEqual(results[2], [[0, 1, 2, 3, 4, 5, 6, 7]])
@combinations.generate(test_base.eager_only_combinations())
def testDifferentShuffleOrders(self):
random_seed.set_random_seed(None)
num_elements = 100
cluster = self.create_cluster(num_workers=2)
ds = dataset_ops.Dataset.range(num_elements)
ds = ds.shuffle(num_elements)
ds = self.make_distributed_dataset(ds, cluster)
output = [elem.numpy() for elem in ds]
# The output will be two sequences of range(num_elements)
# non-deterministically interleaved together. If the orders of the elements
# were the same, first_order and second_order computed below will be equal.
first_order = {}
second_order = {}
for element in output:
if element in first_order:
second_order[element] = len(second_order)
else:
first_order[element] = len(first_order)
self.assertNotEqual(first_order, second_order)
@combinations.generate(test_base.eager_only_combinations())
def testMultipleEpochs(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 3
ds = self.make_distributed_range_dataset(num_elements, cluster)
for _ in range(10):
self.assertEqual(list(range(num_elements)),
[elem.numpy() for elem in ds])
@combinations.generate(test_base.eager_only_combinations())
def testRepeatedDataset(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 10
num_repetitions = 5
ds = self.make_distributed_range_dataset(num_elements, cluster)
ds = ds.repeat(num_repetitions)
self.assertDatasetProduces(ds,
expected_output=num_repetitions *
list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testConcurrentEpoch(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 10
num_datasets = 3
iterators = []
results = []
for _ in range(num_datasets):
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterators.append(iter(ds))
results.append([])
for _ in range(num_elements):
for dataset_ind in range(num_datasets):
result = next(iterators[dataset_ind]).numpy()
results[dataset_ind].append(result)
for result in results:
self.assertEqual(list(range(num_elements)), result)
@combinations.generate(test_base.eager_only_combinations())
def testSharedEpoch(self):
self.skipTest("Not yet implemented")
cluster = self.create_cluster(num_workers=1)
num_elements = 10
num_iterators = 3
ds = self.make_distributed_range_dataset(num_elements, cluster)
result = []
iterators = []
for _ in range(num_iterators):
iterators.append(iter(ds))
# Alternate reading between the iterators.
for _ in range(2):
for it in iterators:
result.append(next(it).numpy())
# Drain the rest of the elements.
for it in iterators:
for elem in it:
result.append(elem.numpy())
self.assertCountEqual(list(range(num_elements)), result)
@combinations.generate(test_base.eager_only_combinations())
def testMultiWorker(self):
num_workers = 3
cluster = self.create_cluster(num_workers=num_workers)
num_elements = 10
ds = self.make_distributed_range_dataset(num_elements, cluster)
results = [elem.numpy() for elem in ds]
self.assertCountEqual(num_workers * list(range(num_elements)), results)
@combinations.generate(test_base.eager_only_combinations())
def testMaxOutstandingRequests(self):
num_workers = 3
cluster = self.create_cluster(num_workers=num_workers)
num_elements = 10
ds = self.make_distributed_range_dataset(num_elements,
cluster,
max_outstanding_requests=1)
self.assertCountEqual(num_workers * list(range(num_elements)),
self.getDatasetOutput(ds))
@combinations.generate(test_base.eager_only_combinations())
def testInsideFunction(self):
num_workers = 3
cluster = self.create_cluster(num_workers=num_workers)
num_elements = 10
@def_function.function
def f():
ds = self.make_distributed_range_dataset(num_elements, cluster)
result = tensor_array_ops.TensorArray(dtypes.int64,
size=num_workers *
num_elements,
dynamic_size=True)
i = 0
for elem in ds:
result = result.write(i, elem)
i += 1
return result.stack()
result = list(f().numpy())
self.assertCountEqual(num_workers * list(range(num_elements)), result)
@combinations.generate(test_base.eager_only_combinations())
def testSharedJobName(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 1000
def make_ds():
return dataset_ops.Dataset.range(num_elements).shuffle(
num_elements)
ds1 = self.make_distributed_dataset(make_ds(),
cluster,
job_name="job_name")
ds2 = self.make_distributed_dataset(make_ds(),
cluster,
job_name="job_name")
iter1 = iter(ds1)
iter2 = iter(ds2)
results = []
for _ in range(num_elements // 5):
results.append(next(iter1).numpy())
results.append(next(iter2).numpy())
for elem in iter1:
results.append(elem.numpy())
for elem in iter2:
results.append(elem.numpy())
self.assertCountEqual(list(range(num_elements)), results)
@combinations.generate(test_base.eager_only_combinations())
def testDifferentJobNames(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 10
ds1 = self.make_distributed_range_dataset(num_elements,
cluster,
job_name="job_name1")
ds2 = self.make_distributed_range_dataset(num_elements,
cluster,
job_name="job_name2")
self.assertDatasetProduces(ds1, list(range(num_elements)))
self.assertDatasetProduces(ds2, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testSharedJobNameMultiIteration(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 10
ds1 = self.make_distributed_range_dataset(num_elements,
cluster,
job_name="job_name")
ds2 = self.make_distributed_range_dataset(num_elements,
cluster,
job_name="job_name")
# iteration 1
self.assertDatasetProduces(ds1, list(range(num_elements)))
self.assertDatasetProduces(ds2, [])
# iteration 2
self.assertDatasetProduces(ds2, list(range(num_elements)))
self.assertDatasetProduces(ds1, [])
@combinations.generate(test_base.eager_only_combinations())
def testSharedJobNameRepeat(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
num_repetitions = 3
ds1 = self.make_distributed_range_dataset(num_elements,
cluster,
job_name="job_name")
ds1 = ds1.repeat(num_repetitions)
ds2 = self.make_distributed_range_dataset(num_elements,
cluster,
job_name="job_name")
ds2 = ds2.repeat(num_repetitions)
results = []
iter1 = iter(ds1)
iter2 = iter(ds2)
for _ in range((num_elements * num_repetitions) // 5):
results.append(next(iter1).numpy())
for _ in range((num_elements * num_repetitions) // 5):
results.append(next(iter2).numpy())
for elem in iter1:
results.append(elem.numpy())
for elem in iter2:
results.append(elem.numpy())
self.assertCountEqual(num_repetitions * list(range(num_elements)),
results)
@combinations.generate(
combinations.times(
test_base.default_test_combinations(),
combinations.combine(num_workers=[1, 3], num_consumers=[1, 2, 5])))
def testRoundRobin(self, num_workers, num_consumers):
cluster = self.create_cluster(num_workers=num_workers)
# Round robin reads can cause slow cluster shutdown.
data_service_test_base.GLOBAL_CLUSTERS.add(cluster)
ds = dataset_ops.Dataset.range(10000000)
ds = ds.repeat()
consumers = []
for consumer_index in range(num_consumers):
consumers.append(
self.make_distributed_dataset(ds,
cluster,
job_name="test",
consumer_index=consumer_index,
num_consumers=num_consumers))
# Use parallel interleave to read from consumers in parallel.
ds = dataset_ops.Dataset.from_tensor_slices(consumers)
ds = ds.interleave(lambda x: x,
cycle_length=num_consumers,
num_parallel_calls=num_consumers)
ds = ds.take(1000)
results = self.getDatasetOutput(ds, requires_initialization=True)
for i in range(0, len(results), num_consumers):
self.assertEqual(0, results[i] % num_consumers)
# Check that each group of `num_consumers` results are consecutive.
for offset in range(1, num_consumers):
if i + offset < len(results):
self.assertEqual(results[i] + offset, results[i + offset])
@combinations.generate(test_base.default_test_combinations())
def testRoundRobinBucketizing(self):
# Tests a common use case for round robin reads. At each step, all
# consumers should get batches with the same bucket size.
cluster = self.create_cluster(num_workers=4)
# Round robin reads can cause slow cluster shutdown.
data_service_test_base.GLOBAL_CLUSTERS.add(cluster)
num_elements = 100
low_bucket_max = 30
mid_bucket_max = 60
bucket_boundaries = [low_bucket_max, mid_bucket_max]
batch_size = 10
num_consumer_hosts = 3
replicas_per_consumer_host = 5
num_consumers = num_consumer_hosts * replicas_per_consumer_host
bucket_batch_sizes = [batch_size] * (len(bucket_boundaries) + 1)
# Set up the dataset that will run on the tf.data workers.
ds = dataset_ops.Dataset.range(num_elements, output_type=dtypes.int32)
ds = ds.shuffle(num_elements)
ds = ds.repeat()
ds = ds.apply(
grouping.bucket_by_sequence_length(lambda x: x,
bucket_boundaries,
bucket_batch_sizes,
drop_remainder=True))
ds = ds.apply(
grouping.group_by_window(
lambda x: math_ops.cast(x[1], dtypes.int64),
lambda _, x: dataset_ops.Dataset.from_tensors(x),
window_size=num_consumers))
ds = ds.flat_map(lambda x: x)
# Set up the per-consumer-host datasets. During each global step, we pull
# `replicas_per_consumer_host` batches from each of these datasets.
host_datasets = []
for host_index in range(num_consumer_hosts):
per_replica_datasets = []
for i in range(replicas_per_consumer_host):
consumer_index = host_index * replicas_per_consumer_host + i
per_replica_datasets.append(
self.make_distributed_dataset(
ds,
cluster,
job_name="test",
consumer_index=consumer_index,
num_consumers=num_consumers))
host_dataset = dataset_ops.Dataset.from_tensor_slices(
per_replica_datasets)
host_dataset = host_dataset.interleave(
lambda x: x,
cycle_length=len(per_replica_datasets),
num_parallel_calls=len(per_replica_datasets),
deterministic=True)
host_datasets.append(host_dataset)
# Use parallel interleave to read from host datasets in parallel.
ds = dataset_ops.Dataset.from_tensor_slices(host_datasets)
ds = ds.interleave(lambda x: x,
block_length=replicas_per_consumer_host,
cycle_length=len(host_datasets),
num_parallel_calls=len(host_datasets),
deterministic=True)
num_rounds = 10
get_next = self.getNext(ds, requires_initialization=True)
results = []
for _ in range(num_rounds * num_consumers):
results.append(self.evaluate(get_next()))
def get_bucket(elem):
bucket_ind = 0
while bucket_ind < len(bucket_boundaries
) and elem >= bucket_boundaries[bucket_ind]:
bucket_ind += 1
return bucket_ind
# Check that the batches for each step contain elements from the same
# bucket.
for i in range(0, len(results), num_consumers):
batches = results[num_consumers * i:num_consumers * (i + 1)]
bucket_inds = [get_bucket(batch[0]) for batch in batches]
for bucket_ind in bucket_inds[1:]:
self.assertEqual(bucket_inds[0], bucket_ind)
@combinations.generate(test_base.v1_only_combinations())
def testRoundRobinFiniteV1(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = dataset_ops.Dataset.range(num_elements)
ds = self.make_distributed_dataset(ds,
cluster,
job_name="test",
consumer_index=0,
num_consumers=1)
with self.assertRaisesRegex(
errors.FailedPreconditionError,
"Encountered end of sequence on a "
"round-robin read iterator"):
self.getDatasetOutput(ds, requires_initialization=True)
@combinations.generate(test_base.v2_only_combinations())
def testRoundRobinFiniteV2(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = dataset_ops.Dataset.range(num_elements)
ds = self.make_distributed_dataset(ds,
cluster,
job_name="test",
consumer_index=0,
num_consumers=1)
with self.assertRaisesRegex(
errors.FailedPreconditionError, "Round robin reads "
"require that the input dataset has infinite "
"cardinality, but the dataset has cardinality " +
str(num_elements)):
self.getDatasetOutput(ds, requires_initialization=True)
@combinations.generate(
combinations.times(test_base.eager_only_combinations(),
combinations.combine(job_name=[None, "test"])))
def testGcUnusedJob(self, job_name):
cluster = self.create_cluster(num_workers=1,
job_gc_check_interval_ms=50,
job_gc_timeout_ms=20)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements,
cluster,
job_name=job_name)
it = iter(ds)
self.assertEqual(next(it).numpy(), 0)
self.assertEqual(cluster.num_tasks_on_worker(), 1)
del it
while cluster.num_tasks_on_worker() > 0:
time.sleep(0.1)
@combinations.generate(test_base.eager_only_combinations())
def testDontGcUsedJob(self):
cluster = self.create_cluster(num_workers=1,
job_gc_check_interval_ms=50,
job_gc_timeout_ms=20)
num_elements = 10
it1 = iter(
self.make_distributed_range_dataset(num_elements,
cluster,
job_name="test1"))
it2 = iter(
self.make_distributed_range_dataset(num_elements,
cluster,
job_name="test2"))
it3 = iter( # this iterator keeps the task alive. pylint: disable=unused-variable
self.make_distributed_range_dataset(num_elements,
cluster,
job_name="test2"))
self.assertEqual(2, cluster.num_tasks_on_worker())
del it1
del it2
# Check that only the first job is gced. The second job will not be gced
# because there is still an outstanding iterator for it.
while cluster.num_tasks_on_worker() > 1:
time.sleep(0.1)
self.assertEqual(1, cluster.num_tasks_on_worker())
@combinations.generate(test_base.eager_only_combinations())
def testApplyDeterminismOption(self):
elements = list(range(10))
cluster = self.create_cluster(num_workers=1)
def dataset_fn(delay_ms):
def interleave_fn(x):
ds = dataset_ops.Dataset.from_tensors(x)
if math_ops.equal(x, 0):
ds = ds.apply(testing.sleep(delay_ms * 1000))
else:
ds = ds.apply(testing.sleep(0))
return ds
ds = dataset_ops.Dataset.from_tensor_slices(elements)
ds = ds.interleave(interleave_fn,
cycle_length=10,
num_parallel_calls=10)
opts = dataset_ops.Options()
opts.experimental_deterministic = False
ds = ds.with_options(opts)
ds = self.make_distributed_dataset(ds, cluster)
return ds
self.checkDeterminism(dataset_fn=dataset_fn,
expect_determinism=False,
expected_elements=elements)
def run_stateful(self, external_state_policy):
num_elements = 10
ds = dataset_ops.Dataset.range(num_elements).map(
lambda _: random_ops.random_uniform(()))
options = dataset_ops.Options()
options.experimental_external_state_policy = external_state_policy
ds = ds.with_options(options)
cluster = self.create_cluster(num_workers=3)
ds = self.make_distributed_dataset(ds, cluster)
next(iter(ds))
@combinations.generate(
combinations.times(
test_base.eager_only_combinations(),
combinations.combine(external_state_policy=[
distribute_options.ExternalStatePolicy.IGNORE,
distribute_options.ExternalStatePolicy.WARN
])))
def testStatefulNoError(self, external_state_policy):
self.run_stateful(external_state_policy)
@combinations.generate(test_base.eager_only_combinations())
def testStatefulError(self):
with self.assertRaises(errors.FailedPreconditionError):
self.run_stateful(distribute_options.ExternalStatePolicy.FAIL)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpochTensorSlices(self):
cluster = self.create_cluster(num_workers=2)
vals = [5, 1, 2, 4]
ds = dataset_ops.Dataset.from_tensor_slices(vals)
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
self.assertDatasetProduces(ds, vals, assert_items_equal=True)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpochInterleave(self):
cluster = self.create_cluster(num_workers=2)
elements = [1, 5, 0]
ds = dataset_ops.Dataset.from_tensor_slices(elements)
ds = ds.interleave(
lambda x: dataset_ops.Dataset.from_tensor_slices([x]))
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
self.assertDatasetProduces(ds, elements, assert_items_equal=True)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpochParallelInterleave(self):
cluster = self.create_cluster(num_workers=2)
elements = [1, 5, 0]
ds = dataset_ops.Dataset.from_tensor_slices(elements)
ds = ds.interleave(
lambda x: dataset_ops.Dataset.from_tensor_slices([x]),
num_parallel_calls=dataset_ops.AUTOTUNE)
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
self.assertDatasetProduces(ds, elements, assert_items_equal=True)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpochFlatMap(self):
cluster = self.create_cluster(num_workers=2)
elements = [1, 5, 0]
ds = dataset_ops.Dataset.from_tensor_slices(elements)
ds = ds.flat_map(lambda x: dataset_ops.Dataset.from_tensor_slices([x]))
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
self.assertDatasetProduces(ds, elements, assert_items_equal=True)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpochRepeat(self):
cluster = self.create_cluster(num_workers=2)
num_repeats = 5
num_elements = 20
ds = dataset_ops.Dataset.range(num_elements).repeat(num_repeats)
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
self.assertDatasetProduces(ds,
num_repeats * list(range(num_elements)),
assert_items_equal=True)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpochForeverRepeat(self):
cluster = self.create_cluster(num_workers=2)
num_elements = 20
elements_to_read = 1000
ds = dataset_ops.Dataset.range(num_elements).repeat()
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
it = iter(ds)
results = {}
for _ in range(elements_to_read):
val = next(it).numpy()
if val not in results:
results[val] = 0
results[val] += 1
for i in range(num_elements):
self.assertGreater(results[i], elements_to_read / num_elements / 2)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpochForeverRepeatFewElements(self):
num_workers = 5
cluster = self.create_cluster(num_workers=num_workers)
# Less than the number of workers, so that some workers get zero elements on
# the first repetition.
num_elements = 1
ds = dataset_ops.Dataset.range(num_elements).repeat()
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
it = iter(ds)
for _ in range(100):
self.assertEqual(next(it).numpy(), 0)
# Stop all but one worker and check that we can still read.
for i in range(num_workers - 1):
cluster.workers[i]._stop()
for _ in range(100):
self.assertEqual(next(it).numpy(), 0)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpochShuffleAndRepeat(self):
cluster = self.create_cluster(num_workers=2)
num_repeats = 5
num_elements = 20
ds = dataset_ops.Dataset.range(num_elements).shuffle(
num_elements).repeat(num_repeats)
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
self.assertDatasetProduces(ds,
num_repeats * list(range(num_elements)),
assert_items_equal=True)
def testDistributeFromInterleave(self):
cluster = self.create_cluster(num_workers=1)
ds = dataset_ops.Dataset.range(2)
def interleave_fn(_):
dataset = dataset_ops.Dataset.range(2)
self.make_distributed_dataset(dataset, cluster)
return dataset
ds = ds.interleave(interleave_fn, cycle_length=2)
self.assertDatasetProduces(ds, [0, 0, 1, 1])
@combinations.generate(test_base.eager_only_combinations())
def testDistributeDistributedEpoch(self):
cluster = self.create_cluster(num_workers=2)
num_elements = 100
ds = dataset_ops.Dataset.range(num_elements)
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
self.assertDatasetProduces(ds,
list(range(num_elements)),
assert_items_equal=True)
@combinations.generate(test_base.eager_only_combinations())
def testDistributeNonStringAddresses(self):
ds = dataset_ops.Dataset.range(10)
with self.assertRaisesRegex(ValueError, "service must be a string"):
ds = ds.apply(
data_service_ops.distribute(processing_mode="parallel_epochs",
service=1))
@combinations.generate(test_base.eager_only_combinations())
def testDistributeEmptyAddress(self):
ds = dataset_ops.Dataset.range(10)
with self.assertRaisesWithLiteralMatch(ValueError,
"service must not be empty"):
ds = ds.apply(
data_service_ops.distribute(processing_mode="parallel_epochs",
service=""))
@combinations.generate(test_base.eager_only_combinations())
def testDistributeInvalidProcessingMode(self):
ds = dataset_ops.Dataset.range(10)
with self.assertRaisesRegex(ValueError,
"invalid is not a valid processing mode"):
ds = ds.apply(
data_service_ops.distribute(processing_mode="invalid",
service="grpc://localhost:5000"))
@combinations.generate(test_base.eager_only_combinations())
def testZipDifferentProcessingModesDatasets(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds1 = dataset_ops.Dataset.range(num_elements)
ds1 = self.make_distributed_dataset(
ds1, cluster, processing_mode="distributed_epoch")
ds2 = dataset_ops.Dataset.range(num_elements)
ds2 = self.make_distributed_dataset(ds2,
cluster,
processing_mode="parallel_epochs")
ds = dataset_ops.Dataset.zip((ds1, ds2))
self.assertDatasetProduces(ds,
list(
zip(range(num_elements),
range(num_elements))),
assert_items_equal=True)
@combinations.generate(test_base.eager_only_combinations())
def testZipDifferentProcessingModesDatasetsSharedJobName(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds1 = dataset_ops.Dataset.range(num_elements)
ds1 = self.make_distributed_dataset(
ds1,
cluster,
processing_mode="distributed_epoch",
job_name="job_name")
ds2 = dataset_ops.Dataset.range(num_elements)
ds2 = self.make_distributed_dataset(ds2,
cluster,
processing_mode="parallel_epochs",
job_name="job_name")
ds = dataset_ops.Dataset.zip((ds1, ds2))
with self.assertRaisesRegex(errors.FailedPreconditionError,
"but there is already an existing job"):
self.getDatasetOutput(ds)
@combinations.generate(test_base.eager_only_combinations())
def testFromDatasetId(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 10
ds = dataset_ops.Dataset.range(num_elements)
dataset_id = data_service_ops.register_dataset(cluster.target, ds)
from_dataset_id_ds = data_service_ops.from_dataset_id(
"parallel_epochs", cluster.target, dataset_id, ds.element_spec)
self.assertDatasetProduces(from_dataset_id_ds,
list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testFromDatasetIdMultipleComponents(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 10
ds = dataset_ops.Dataset.range(num_elements)
ds = dataset_ops.Dataset.zip({"a": (ds, ds), "b": ds})
dataset_id = data_service_ops.register_dataset(cluster.target, ds)
from_dataset_id_ds = data_service_ops.from_dataset_id(
"parallel_epochs", cluster.target, dataset_id, ds.element_spec)
output = self.getDatasetOutput(from_dataset_id_ds)
for i in range(num_elements):
self.assertEqual(i, output[i]["a"][0])
self.assertEqual(i, output[i]["a"][1])
self.assertEqual(i, output[i]["b"])
@combinations.generate(test_base.eager_only_combinations())
def testFromDatasetIdWrongElementSpec(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 10
ds = dataset_ops.Dataset.range(num_elements)
dataset_id = data_service_ops.register_dataset(cluster.target, ds)
wrong_spec = tensor_spec.TensorSpec(shape=(), dtype=dtypes.variant)
from_dataset_id_ds = data_service_ops.from_dataset_id(
"parallel_epochs", cluster.target, dataset_id, wrong_spec)
with self.assertRaisesRegex(errors.FailedPreconditionError,
"Expected a tensor of type variant"):
self.evaluate(self.getNext(from_dataset_id_ds)())
@combinations.generate(test_base.eager_only_combinations())
def testFromDatasetIdNotRegistered(self):
cluster = self.create_cluster(num_workers=1)
dataset_id = 0
element_spec = tensor_spec.TensorSpec(shape=(), dtype=dtypes.variant)
from_dataset_id_ds = data_service_ops.from_dataset_id(
"parallel_epochs", cluster.target, dataset_id, element_spec)
with self.assertRaisesRegex(errors.NotFoundError, "Dataset id"):
self.evaluate(self.getNext(from_dataset_id_ds)())
@combinations.generate(test_base.default_test_combinations())
def testCancellation(self):
self.skipTest("b/162521601")
sleep_microseconds = int(1e6) * 1000
cluster = self.create_cluster(num_workers=1)
# Create a dataset which produces the first element quickly, and the second
# element slowly. Fetching the first element triggers prefetching of the
# second element, which we should be able to cancel.
slow = dataset_ops.Dataset.range(1)
slow = slow.apply(testing.sleep(sleep_microseconds))
ds = dataset_ops.Dataset.range(1).concatenate(slow)
ds = self.make_distributed_dataset(ds, cluster)
ds = ds.prefetch(1)
get_next = self.getNext(ds, requires_initialization=True)
self.assertEqual(0, self.evaluate(get_next()))
# Without properly implemented cancellation, we will hang here while trying
# to garbage collect the dataset iterator.
@combinations.generate(test_base.eager_only_combinations())
def testRegisterEquivalentDatasets(self):
ds_1 = dataset_ops.Dataset.range(10)
ds_2 = dataset_ops.Dataset.range(10)
cluster = self.create_cluster(num_workers=1)
id_1 = data_service_ops.register_dataset(cluster.target, ds_1)
id_2 = data_service_ops.register_dataset(cluster.target, ds_2)
self.assertEqual(id_1.numpy(), id_2.numpy())
@combinations.generate(test_base.eager_only_combinations())
def testRegisterDifferentDatasets(self):
ds_1 = dataset_ops.Dataset.range(10)
ds_2 = dataset_ops.Dataset.range(20)
cluster = self.create_cluster(num_workers=1)
id_1 = data_service_ops.register_dataset(cluster.target, ds_1)
id_2 = data_service_ops.register_dataset(cluster.target, ds_2)
self.assertNotEqual(id_1.numpy(), id_2.numpy())
@combinations.generate(test_base.default_test_combinations())
def testDistributedEpochOnZippedDataset(self):
ds_1 = dataset_ops.Dataset.range(10)
ds_2 = dataset_ops.Dataset.range(10)
cluster = self.create_cluster(num_workers=1)
ds_3 = dataset_ops.Dataset.zip((ds_1, ds_2))
ds_3 = self.make_distributed_dataset(
ds_3, cluster, processing_mode="distributed_epoch")
error_regex = "Cannot create a split provider for dataset " + \
"of type ZipDataset"
with self.assertRaisesRegex(errors.UnimplementedError, error_regex):
self.getDatasetOutput(ds_3, requires_initialization=True)
@combinations.generate(test_base.default_test_combinations())
def testDistributedEpochOnDistributedDataset(self):
cluster_1 = self.create_cluster(num_workers=1)
cluster_2 = self.create_cluster(num_workers=1)
num_sizes = 10
size_repeats = 5
numbers = [1 * i for i in range(num_sizes)] * size_repeats
ds = dataset_ops.Dataset.from_tensor_slices(numbers)
ds = self.make_distributed_dataset(ds,
cluster_1,
processing_mode="parallel_epochs")
ds = ds.map(lambda x: x + 1)
ds = self.make_distributed_dataset(ds,
cluster_2,
processing_mode="distributed_epoch")
error_regex = "Cannot create a split provider for dataset " + \
"of type DataServiceDataset"
with self.assertRaisesRegex(errors.UnimplementedError, error_regex):
self.getDatasetOutput(ds, requires_initialization=True)
@combinations.generate(test_base.eager_only_combinations())
def testTwoLevelDistribute(self):
cluster_1_size = 3
cluster_1 = self.create_cluster(num_workers=cluster_1_size)
cluster_2 = self.create_cluster(num_workers=1)
num_sizes = 10
size_repeats = 5
strings = ["a" * i for i in range(num_sizes)] * size_repeats
ds = dataset_ops.Dataset.from_tensor_slices(strings)
ds = ds.shuffle(len(strings))
ds = self.make_distributed_dataset(ds, cluster_1)
# Large enough so that all strings of the same size are windowed together.
window_size = cluster_1_size * size_repeats
batch_size = size_repeats
def key_func(x):
return math_ops.cast(string_ops.string_length_v2(x), dtypes.int64)
ds = ds.apply(
grouping.group_by_window(
key_func=key_func,
reduce_func=lambda _, x: x.batch(batch_size),
window_size=window_size))
ds = self.make_distributed_dataset(ds, cluster_2)
it = iter(ds)
for _ in range(num_sizes):
element = next(it).numpy()
for _ in range(1, cluster_1_size):
self.assertAllEqual(next(it).numpy(), element)
self.assertEmpty(list(it))
@combinations.generate(
combinations.times(test_base.eager_only_combinations()))
def testDistributeLargeGraph(self):
cluster = self.create_cluster(num_workers=1,
work_dir=NO_WORK_DIR,
fault_tolerant_mode=False)
# Larger than default OSS grpc message size limit of 4MB.
tensor = array_ops.ones((2, 1000, 1000), dtype=dtypes.float32)
ds = dataset_ops.Dataset.from_tensors(tensor)
ds = self.make_distributed_dataset(ds, cluster)
self.assertDatasetProduces(ds, [tensor])
class CoordinatedReadTest(data_service_test_base.TestBase,
parameterized.TestCase):
@combinations.generate(
combinations.times(
test_base.default_test_combinations(),
combinations.combine(num_workers=[1, 3], num_consumers=[1, 2, 5])))
def testBasic(self, num_workers, num_consumers):
cluster = data_service_test_base.TestCluster(num_workers=num_workers)
# Round robin reads can cause slow cluster shutdown.
data_service_test_base.GLOBAL_CLUSTERS.add(cluster)
ds = self.make_coordinated_read_dataset(cluster, num_consumers)
ds = ds.take(100)
results = self.getDatasetOutput(ds)
self.checkCoordinatedReadGroups(results, num_consumers)
@combinations.generate(
combinations.times(test_base.default_test_combinations()))
def testConsumerRestart(self):
cluster = data_service_test_base.TestCluster(num_workers=1)
# Round robin reads can cause slow cluster shutdown.
data_service_test_base.GLOBAL_CLUSTERS.add(cluster)
num_consumers = 3
ds = self.make_coordinated_read_dataset(cluster, num_consumers)
ds = ds.take(20)
self.getDatasetOutput(ds)
ds2 = self.make_coordinated_read_dataset(cluster, num_consumers)
ds2 = ds2.take(20)
with self.assertRaisesRegex(errors.FailedPreconditionError,
"current round has already reached"):
self.getDatasetOutput(ds2)
@combinations.generate(test_base.default_test_combinations())
def testBucketizing(self):
# Tests a common use case for round robin reads. At each step, all
# consumers should get batches with the same bucket size.
cluster = data_service_test_base.TestCluster(num_workers=4)
# Round robin reads can cause slow cluster shutdown.
data_service_test_base.GLOBAL_CLUSTERS.add(cluster)
num_elements = 100
low_bucket_max = 30
mid_bucket_max = 60
bucket_boundaries = [low_bucket_max, mid_bucket_max]
batch_size = 10
num_consumer_hosts = 3
replicas_per_consumer_host = 5
num_consumers = num_consumer_hosts * replicas_per_consumer_host
bucket_batch_sizes = [batch_size] * (len(bucket_boundaries) + 1)
# Set up the dataset that will run on the tf.data workers.
ds = dataset_ops.Dataset.range(num_elements, output_type=dtypes.int32)
ds = ds.shuffle(num_elements)
ds = ds.repeat()
ds = ds.apply(
grouping.bucket_by_sequence_length(
lambda x: x,
bucket_boundaries,
bucket_batch_sizes,
drop_remainder=True))
ds = ds.apply(
grouping.group_by_window(
lambda x: math_ops.cast(x[1], dtypes.int64),
lambda _, x: dataset_ops.Dataset.from_tensors(x),
window_size=num_consumers))
ds = ds.flat_map(lambda x: x)
# Set up the per-consumer-host datasets. During each global step, we pull
# `replicas_per_consumer_host` batches from each of these datasets.
host_datasets = []
for host_index in range(num_consumer_hosts):
per_replica_datasets = []
for i in range(replicas_per_consumer_host):
consumer_index = host_index * replicas_per_consumer_host + i
per_replica_datasets.append(
self.make_distributed_dataset(
ds,
cluster,
job_name="test",
consumer_index=consumer_index,
num_consumers=num_consumers))
host_dataset = dataset_ops.Dataset.from_tensor_slices(
per_replica_datasets)
host_dataset = host_dataset.interleave(
lambda x: x,
cycle_length=len(per_replica_datasets),
num_parallel_calls=len(per_replica_datasets),
deterministic=True)
host_datasets.append(host_dataset)
# Use parallel interleave to read from host datasets in parallel.
ds = dataset_ops.Dataset.from_tensor_slices(host_datasets)
ds = ds.interleave(
lambda x: x,
block_length=replicas_per_consumer_host,
cycle_length=len(host_datasets),
num_parallel_calls=len(host_datasets),
deterministic=True)
num_rounds = 4
get_next = self.getNext(ds)
results = []
for i in range(num_rounds * num_consumers):
results.append(self.evaluate(get_next()))
def get_bucket(elem):
bucket_ind = 0
while bucket_ind < len(
bucket_boundaries) and elem >= bucket_boundaries[bucket_ind]:
bucket_ind += 1
return bucket_ind
# Check that the batches for each step contain elements from the same
# bucket.
for i in range(0, len(results), num_consumers):
batches = results[num_consumers * i:num_consumers * (i + 1)]
bucket_inds = [get_bucket(batch[0]) for batch in batches]
for bucket_ind in bucket_inds[1:]:
self.assertEqual(
bucket_inds[0], bucket_ind,
"Batches: {}, Buckets: {}".format(batches, bucket_inds))
@combinations.generate(test_base.v1_only_combinations())
def testFiniteV1(self):
cluster = data_service_test_base.TestCluster(num_workers=1)
num_elements = 100
ds = dataset_ops.Dataset.range(num_elements)
ds = self.make_distributed_dataset(
ds, cluster, job_name="test", consumer_index=0, num_consumers=1)
with self.assertRaisesRegex(
errors.FailedPreconditionError, "Encountered end of sequence on a "
"round-robin read iterator"):
self.getDatasetOutput(ds)
@combinations.generate(test_base.v2_only_combinations())
def testFiniteV2(self):
cluster = data_service_test_base.TestCluster(num_workers=1)
num_elements = 100
ds = dataset_ops.Dataset.range(num_elements)
ds = self.make_distributed_dataset(
ds, cluster, job_name="test", consumer_index=0, num_consumers=1)
with self.assertRaisesRegex(
errors.FailedPreconditionError, "Round robin reads "
"require that the input dataset has infinite "
"cardinality, but the dataset has cardinality " + str(num_elements)):
self.getDatasetOutput(ds)
class MultiDeviceIteratorTest(test_base.DatasetTestBase,
parameterized.TestCase):
@combinations.generate(
combinations.times(test_base.v1_only_combinations(),
combinations.combine(num_inits=[0, 1, 42])))
def testInitOnly(self, num_inits):
dataset = dataset_ops.Dataset.range(10)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2"])
config = config_pb2.ConfigProto(device_count={"CPU": 3})
with self.test_session(config=config):
for _ in range(num_inits):
self.evaluate(multi_device_iterator.initializer)
@combinations.generate(test_base.v1_only_combinations())
def testBasic(self):
dataset = dataset_ops.Dataset.range(10)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2"])
config = config_pb2.ConfigProto(device_count={"CPU": 3})
with self.test_session(config=config):
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 10, 2):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.assertEqual(i, self.evaluate(elem_on_1))
self.assertEqual(i + 1, self.evaluate(elem_on_2))
with self.assertRaises(errors.OutOfRangeError):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.evaluate(elem_on_1)
self.evaluate(elem_on_2)
@combinations.generate(test_base.v1_only_combinations())
def testOneOnSameDevice(self):
with ops.device("/cpu:0"):
dataset = dataset_ops.Dataset.range(10)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:0", "/cpu:1"])
config = config_pb2.ConfigProto(device_count={"CPU": 2})
with self.test_session(config=config):
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 10, 2):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.assertEqual(i, self.evaluate(elem_on_1))
self.assertEqual(i + 1, self.evaluate(elem_on_2))
with self.assertRaises(errors.OutOfRangeError):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.evaluate(elem_on_1)
self.evaluate(elem_on_2)
@combinations.generate(test_base.v1_only_combinations())
def testRepeatDevices(self):
with ops.device("/cpu:0"):
dataset = dataset_ops.Dataset.range(20)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2", "/cpu:1", "/cpu:2"])
config = config_pb2.ConfigProto(device_count={"CPU": 3})
with self.test_session(config=config):
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 20, 4):
elements = multi_device_iterator.get_next()
elem_on_1, elem_on_2, elem_on_3, elem_on_4 = elements
self.assertEqual(i, self.evaluate(elem_on_1))
self.assertEqual(i + 1, self.evaluate(elem_on_2))
self.assertEqual(i + 2, self.evaluate(elem_on_3))
self.assertEqual(i + 3, self.evaluate(elem_on_4))
with self.assertRaises(errors.OutOfRangeError):
elements = multi_device_iterator.get_next()
elem_on_1, elem_on_2, elem_on_3, elem_on_4 = elements
self.evaluate(elem_on_1)
self.evaluate(elem_on_2)
self.evaluate(elem_on_3)
self.evaluate(elem_on_4)
@combinations.generate(test_base.v1_only_combinations())
def testNotFullyDivisible(self):
dataset = dataset_ops.Dataset.range(9)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2"])
config = config_pb2.ConfigProto(device_count={"CPU": 3})
with self.test_session(config=config):
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 8, 2):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.assertEqual(i, self.evaluate(elem_on_1))
self.assertEqual(i + 1, self.evaluate(elem_on_2))
elem_on_1 = multi_device_iterator.get_next("/cpu:1")
self.assertEqual(8, self.evaluate(elem_on_1))
with self.assertRaises(errors.OutOfRangeError):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.evaluate(elem_on_1)
self.evaluate(elem_on_2)
@combinations.generate(test_base.v1_only_combinations())
def testGetNextAsOptional(self):
if context.executing_eagerly():
return
dataset = dataset_ops.Dataset.range(9)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2"])
elem_on_1, elem_on_2 = multi_device_iterator.get_next_as_optional()
elem_on_1_has_value_t = elem_on_1.has_value()
elem_on_1_t = elem_on_1.get_value()
elem_on_2_has_value_t = elem_on_2.has_value()
elem_on_2_t = elem_on_2.get_value()
config = config_pb2.ConfigProto(device_count={"CPU": 3})
with self.test_session(config=config) as sess:
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 8, 2):
elem_on_1_has_value, elem_on_1_value = sess.run(
[elem_on_1_has_value_t, elem_on_1_t])
self.assertTrue(elem_on_1_has_value)
self.assertEqual(i, elem_on_1_value)
elem_on_2_has_value, elem_on_2_value = sess.run(
[elem_on_2_has_value_t, elem_on_2_t])
self.assertTrue(elem_on_2_has_value)
self.assertEqual(i + 1, elem_on_2_value)
elem_on_1_has_value, elem_on_1_value = sess.run(
[elem_on_1_has_value_t, elem_on_1_t])
self.assertTrue(elem_on_1_has_value)
self.assertEqual(8, elem_on_1_value)
self.assertFalse(self.evaluate(elem_on_1_has_value_t))
self.assertFalse(self.evaluate(elem_on_2_has_value_t))
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(elem_on_1_t)
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(elem_on_2_t)
@combinations.generate(test_base.v1_only_combinations())
def testUneven(self):
dataset = dataset_ops.Dataset.range(10)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2"], max_buffer_size=4)
config = config_pb2.ConfigProto(device_count={"CPU": 3})
with self.test_session(config=config):
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 10, 2):
elem_on_1 = multi_device_iterator.get_next("/cpu:1")
self.assertEqual(i, self.evaluate(elem_on_1))
for i in range(0, 10, 2):
elem_on_2 = multi_device_iterator.get_next("/cpu:2")
self.assertEqual(i + 1, self.evaluate(elem_on_2))
with self.assertRaises(errors.OutOfRangeError):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.evaluate(elem_on_1)
self.evaluate(elem_on_2)
@combinations.generate(test_base.v1_only_combinations())
def testMultipleInitializationsGraph(self):
if context.executing_eagerly():
return
with ops.device("/cpu:0"):
epoch = array_ops.placeholder(dtypes.int64, shape=[])
dataset1 = dataset_ops.Dataset.from_tensors(epoch).repeat(1000)
dataset2 = dataset_ops.Dataset.range(1000)
dataset = dataset_ops.Dataset.zip((dataset1, dataset2))
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2"], prefetch_buffer_size=4)
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
init_op = multi_device_iterator.initializer
config = config_pb2.ConfigProto(device_count={"CPU": 3})
pool = config.session_inter_op_thread_pool.add()
pool.num_threads = 2
with session.Session(config=config) as sess:
for i in range(1000):
sess.run(init_op, feed_dict={epoch: i})
self.assertEqual([(i, 0), (i, 1)],
self.evaluate([elem_on_1, elem_on_2]))
@combinations.generate(test_base.v1_only_combinations())
def testMultipleInitializationsEager(self):
if not context.executing_eagerly():
return
with ops.device("/cpu:0"):
dataset1 = dataset_ops.Dataset.range(1000)
dataset2 = dataset_ops.Dataset.range(1000)
dataset = dataset_ops.Dataset.zip((dataset1, dataset2))
for _ in range(5):
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2"], prefetch_buffer_size=4)
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.assertEqual([(0, 0), (1, 1)],
self.evaluate([elem_on_1, elem_on_2]))
@combinations.generate(test_base.v1_only_combinations())
def testBasicGpu(self):
if not test_util.is_gpu_available():
self.skipTest("No GPU available")
dataset = dataset_ops.Dataset.range(10)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/gpu:0"])
config = config_pb2.ConfigProto(device_count={"CPU": 2, "GPU": 1})
with self.test_session(config=config):
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 10, 2):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.assertEqual(i, self.evaluate(elem_on_1))
self.assertEqual(i + 1, self.evaluate(elem_on_2))
with self.assertRaises(errors.OutOfRangeError):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.evaluate(elem_on_1)
self.evaluate(elem_on_2)
@combinations.generate(test_base.v1_only_combinations())
def testUnevenGpu(self):
if not test_util.is_gpu_available():
self.skipTest("No GPU available")
dataset = dataset_ops.Dataset.range(10)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/gpu:0"], max_buffer_size=4)
config = config_pb2.ConfigProto(device_count={"CPU": 2, "GPU": 1})
with self.test_session(config=config):
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 10, 2):
elem_on_1 = multi_device_iterator.get_next("/cpu:1")
self.assertEqual(i, self.evaluate(elem_on_1))
for i in range(0, 10, 2):
elem_on_2 = multi_device_iterator.get_next("/gpu:0")
self.assertEqual(i + 1, self.evaluate(elem_on_2))
with self.assertRaises(errors.OutOfRangeError):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.evaluate(elem_on_1)
self.evaluate(elem_on_2)
@combinations.generate(test_base.v1_only_combinations())
def testGetNextAsOptionalGpu(self):
if not test_util.is_gpu_available() or context.executing_eagerly():
self.skipTest("No GPU available")
dataset = dataset_ops.Dataset.range(9)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/gpu:0"])
elem_on_1, elem_on_2 = multi_device_iterator.get_next_as_optional()
elem_on_1_has_value_t = elem_on_1.has_value()
elem_on_1_t = elem_on_1.get_value()
elem_on_2_has_value_t = elem_on_2.has_value()
elem_on_2_t = elem_on_2.get_value()
config = config_pb2.ConfigProto(device_count={"CPU": 2, "GPU": 1})
with self.test_session(config=config) as sess:
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 8, 2):
elem_on_1_has_value, elem_on_1_value = sess.run(
[elem_on_1_has_value_t, elem_on_1_t])
self.assertTrue(elem_on_1_has_value)
self.assertEqual(i, elem_on_1_value)
elem_on_2_has_value, elem_on_2_value = sess.run(
[elem_on_2_has_value_t, elem_on_2_t])
self.assertTrue(elem_on_2_has_value)
self.assertEqual(i + 1, elem_on_2_value)
elem_on_1_has_value, elem_on_1_value = sess.run(
[elem_on_1_has_value_t, elem_on_1_t])
self.assertTrue(elem_on_1_has_value)
self.assertEqual(8, elem_on_1_value)
self.assertFalse(self.evaluate(elem_on_1_has_value_t))
self.assertFalse(self.evaluate(elem_on_2_has_value_t))
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(elem_on_1_t)
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(elem_on_2_t)
@combinations.generate(test_base.v1_only_combinations())
def testOptimization(self):
dataset = dataset_ops.Dataset.range(10)
dataset = dataset.apply(testing.assert_next(["MemoryCacheImpl"]))
dataset = dataset.skip(0) # this should be optimized away
dataset = dataset.cache()
options = dataset_ops.Options()
options.experimental_optimization.noop_elimination = True
dataset = dataset.with_options(options)
multi_device_iterator = multi_device_iterator_ops.MultiDeviceIterator(
dataset, ["/cpu:1", "/cpu:2"])
config = config_pb2.ConfigProto(device_count={"CPU": 3})
with self.test_session(config=config):
self.evaluate(multi_device_iterator.initializer)
for i in range(0, 10, 2):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.assertEqual(i, self.evaluate(elem_on_1))
self.assertEqual(i + 1, self.evaluate(elem_on_2))
with self.assertRaises(errors.OutOfRangeError):
elem_on_1, elem_on_2 = multi_device_iterator.get_next()
self.evaluate(elem_on_1)
self.evaluate(elem_on_2)
class CheckpointInputPipelineHookTest(test.TestCase, parameterized.TestCase):
@staticmethod
def _model_fn(features, labels, mode, config):
del labels
del mode
del config
global_step = training_util.get_or_create_global_step()
update_global_step_op = global_step.assign_add(1)
latest_feature = variables.VariableV1(0,
name='latest_feature',
dtype=dtypes.int64)
store_latest_feature_op = latest_feature.assign(features)
ops.add_to_collection('my_vars', global_step)
ops.add_to_collection('my_vars', latest_feature)
return model_fn.EstimatorSpec(mode='train',
train_op=control_flow_ops.group([
update_global_step_op,
store_latest_feature_op
]),
loss=constant_op.constant(2.0))
def _read_vars(self, model_dir):
"""Returns (global_step, latest_feature)."""
with ops.Graph().as_default() as g:
ckpt_path = checkpoint_management.latest_checkpoint(model_dir)
meta_filename = ckpt_path + '.meta'
saver_lib.import_meta_graph(meta_filename)
saver = saver_lib.Saver()
with self.session(graph=g) as sess:
saver.restore(sess, ckpt_path)
return sess.run(ops.get_collection('my_vars'))
def _build_iterator_saver_hook(self, est):
return iterator_ops.CheckpointInputPipelineHook(est)
@combinations.generate(test_base.v1_only_combinations())
def testReturnDatasetFromInputFn(self):
def _input_fn():
return dataset_ops.Dataset.range(10)
est = estimator.Estimator(model_fn=self._model_fn)
est.train(_input_fn,
steps=2,
hooks=[self._build_iterator_saver_hook(est)])
self.assertSequenceEqual(self._read_vars(est.model_dir), (2, 1))
est.train(_input_fn,
steps=2,
hooks=[self._build_iterator_saver_hook(est)])
self.assertSequenceEqual(self._read_vars(est.model_dir), (4, 3))
@combinations.generate(test_base.v1_only_combinations())
def testBuildIteratorInInputFn(self):
def _input_fn():
ds = dataset_ops.Dataset.range(10)
iterator = ds.make_one_shot_iterator()
return iterator.get_next()
est = estimator.Estimator(model_fn=self._model_fn)
est.train(_input_fn,
steps=2,
hooks=[self._build_iterator_saver_hook(est)])
self.assertSequenceEqual(self._read_vars(est.model_dir), (2, 1))
est.train(_input_fn,
steps=2,
hooks=[self._build_iterator_saver_hook(est)])
self.assertSequenceEqual(self._read_vars(est.model_dir), (4, 3))
@combinations.generate(test_base.v1_only_combinations())
def testDoNotRestore(self):
def _input_fn():
return dataset_ops.Dataset.range(10)
est = estimator.Estimator(model_fn=self._model_fn)
est.train(_input_fn,
steps=2,
hooks=[self._build_iterator_saver_hook(est)])
self.assertSequenceEqual(self._read_vars(est.model_dir), (2, 1))
est.train(_input_fn,
steps=2,
hooks=[self._build_iterator_saver_hook(est)])
self.assertSequenceEqual(self._read_vars(est.model_dir), (4, 3))
# Hook not provided, input pipeline was not restored.
est.train(_input_fn, steps=2)
self.assertSequenceEqual(self._read_vars(est.model_dir), (6, 1))
@combinations.generate(test_base.v1_only_combinations())
def testRaiseErrorIfNoIterator(self):
def _input_fn():
return constant_op.constant(1, dtype=dtypes.int64)
est = estimator.Estimator(model_fn=self._model_fn)
with self.assertRaises(ValueError):
est.train(_input_fn,
steps=2,
hooks=[self._build_iterator_saver_hook(est)])
