class DataServiceOpsTest(data_service_test_base.TestBase,
parameterized.TestCase):
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherStop(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
results = []
results.append(next(iterator).numpy())
cluster.stop_dispatcher()
# After the dispatcher dies, the worker should continue providing the rest
# of the dataset's elements.
for _ in range(num_elements - 1):
results.append(next(iterator).numpy())
self.assertEqual(results, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherRestartBeforeReading(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
cluster.restart_dispatcher()
self.assertDatasetProduces(ds, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherRestartDuringReading(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
results = []
for _ in range(num_elements // 2):
results.append(next(iterator).numpy())
cluster.restart_dispatcher()
for elem in iterator:
results.append(elem.numpy())
self.assertEqual(list(range(num_elements)), results)
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherRestartDuringDistributedEpoch(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(
num_elements, cluster, processing_mode="distributed_epoch")
iterator = iter(ds)
results = []
for _ in range(num_elements // 2):
results.append(next(iterator).numpy())
cluster.restart_dispatcher()
for elem in iterator:
results.append(elem.numpy())
self.assertEqual(list(range(num_elements)), results)
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherRestartDuringDistributedEpochRepeat(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
repetitions = 5
breakpoints = [50, 250, 450, 500]
ds = dataset_ops.Dataset.range(num_elements)
ds = ds.repeat(repetitions)
ds = self.make_distributed_dataset(ds,
cluster,
processing_mode="distributed_epoch")
iterator = iter(ds)
results = []
for breakpoint in breakpoints:
for _ in range(len(results), breakpoint):
results.append(next(iterator).numpy())
cluster.restart_dispatcher()
self.assertCountEqual(repetitions * list(range(num_elements)), results)
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherRestartBetweenIterations(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(100, cluster)
self.assertDatasetProduces(ds, list(range(num_elements)))
cluster.restart_dispatcher()
self.assertDatasetProduces(ds, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherManyRestarts(self):
cluster = self.create_cluster(num_workers=1)
num_elements_start = 10
num_elements_end = 15
datasets = []
for num_elements in range(num_elements_start, num_elements_end):
datasets.append(
self.make_distributed_range_dataset(num_elements, cluster))
cluster.restart_dispatcher()
for ds, num_elements in zip(
datasets, range(num_elements_start, num_elements_end)):
self.assertDatasetProduces(ds, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherAndWorkerRestart(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
cluster.restart_dispatcher()
cluster.restart_worker()
self.assertDatasetProduces(ds, list(range(num_elements)))
cluster.restart_dispatcher()
cluster.restart_worker()
self.assertDatasetProduces(ds, list(range(num_elements)))
@combinations.generate(
combinations.times(test_base.default_test_combinations(),
combinations.combine(workers_to_add=[1, 3])))
def testRoundRobinAddWorkers(self, workers_to_add):
starting_workers = 3
cluster = self.create_cluster(num_workers=starting_workers)
# Round robin reads can cause slow cluster shutdown.
data_service_test_base.GLOBAL_CLUSTERS.add(cluster)
num_consumers = 7
ds = dataset_ops.Dataset.range(100000000)
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)
get_next = self.getNext(ds, requires_initialization=True)
results = []
zeros_seen = 0
for _ in range(50):
results.append(self.evaluate(get_next()))
if results[-1] == 0:
zeros_seen += 1
for _ in range(workers_to_add):
cluster.add_worker()
# Read until all new workers have joined.
while zeros_seen < starting_workers + workers_to_add:
results.append(self.evaluate(get_next()))
if results[-1] == 0:
zeros_seen += 1
# Read some more.
for _ in range(100):
results.append(self.evaluate(get_next()))
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.eager_only_combinations())
def testDispatcherAndMultiWorkerRestart(self):
num_workers = 2
cluster = self.create_cluster(num_workers=num_workers)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
results = []
cluster.restart_dispatcher()
for worker_index in range(num_workers):
cluster.restart_worker(worker_index=worker_index)
for elem in iterator:
results.append(elem.numpy())
self.assertCountEqual(num_workers * list(range(num_elements)), results)
cluster.restart_dispatcher()
for worker_index in range(num_workers):
cluster.restart_worker(worker_index=worker_index)
for elem in iterator:
results.append(elem.numpy())
self.assertCountEqual(num_workers * list(range(num_elements)), results)
@combinations.generate(test_base.eager_only_combinations())
def testStartServersLate(self):
# Test that the data service client performs retries instead of failing when
# the dataset is created before the master and worker are started.
try:
import portpicker # pylint: disable=g-import-not-at-top
dispatcher_port = portpicker.pick_unused_port()
except:
raise self.skipTest(
"Flakes in portpicker library do not represent "
"TensorFlow errors.")
cluster = self.create_cluster(num_workers=1,
dispatcher_port=dispatcher_port,
start=False)
def start_servers():
time.sleep(0.5)
cluster.start_dispatcher()
cluster.start_workers()
start_servers_thread = threading.Thread(target=start_servers,
daemon=True)
start_servers_thread.start()
num_elements = 10
ds = self.make_distributed_range_dataset(num_elements, cluster)
results = [elem.numpy() for elem in ds]
self.assertEqual(list(range(num_elements)), results)
start_servers_thread.join()
@combinations.generate(test_base.eager_only_combinations())
def testAddWorkerMidJob(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
results = []
# Read halfway through the dataset.
for _ in range(num_elements // 2):
results.append(next(iterator).numpy())
cluster.add_worker()
# Wait for the new worker to register with the dispatcher.
while cluster.num_registered_workers() < 2:
time.sleep(10 / 1000) # 10ms
for elem in iterator:
results.append(elem.numpy())
self.assertCountEqual(2 * list(range(num_elements)), results)
@combinations.generate(
combinations.times(test_base.eager_only_combinations(),
combinations.combine(use_same_port=[True, False]),
data_service_test_base.all_cluster_configurations())
)
def testRestartWorker(self, use_same_port, work_dir, fault_tolerant_mode):
cluster = self.create_cluster(num_workers=1,
work_dir=work_dir,
fault_tolerant_mode=fault_tolerant_mode)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
# Read halfway through the dataset.
midpoint = num_elements // 2
for i in range(midpoint):
self.assertEqual(i, next(iterator).numpy())
# Stop the original worker and start a new one.
cluster.restart_worker(use_same_port=use_same_port)
# There may have been some elements prefetched from the first worker
# before it was stopped.
while True:
val = next(iterator).numpy()
if val == 0:
break
# The dataset starts over now that we read from the new worker.
# TODO(b/157086991): Iterate until end of sequence when we support
# detecting lost workers.
for i in range(1, num_elements // 2):
val = next(iterator).numpy()
self.assertEqual(i, val)
@combinations.generate(test_base.eager_only_combinations())
def testChangeProcessingModeAfterRestart(self):
self.skipTest("b/170910141")
cluster = self.create_cluster(num_workers=1)
num_elements = 100
range_dataset = dataset_ops.Dataset.range(num_elements)
ds = range_dataset.apply(
data_service_ops.distribute(processing_mode="parallel_epochs",
service=cluster.target,
job_name="test"))
iterator = iter(ds)
for i in range(num_elements // 2):
self.assertEqual(i, next(iterator).numpy())
cluster.restart_dispatcher()
ds = range_dataset.apply(
data_service_ops.distribute(processing_mode="distributed_epoch",
service=cluster.target,
job_name="test"))
with self.assertRaisesOpError(
"already an existing job with that name "
"using processing mode <parallel_epochs>"):
next(iter(ds)).numpy()
@combinations.generate(
combinations.times(
test_base.eager_only_combinations(),
combinations.combine(work_dir=[TMP_WORK_DIR, NO_WORK_DIR])))
def testDistributeLargeGraphThenRegisterWorker(self, work_dir):
cluster = self.create_cluster(num_workers=0,
work_dir=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)
it = iter(ds)
cluster.add_worker()
self.assertAllEqual(next(it), tensor)
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 = 100
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.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 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 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.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 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 DataServiceOpsTest(data_service_test_base.TestBase,
parameterized.TestCase):
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherStop(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
results = []
results.append(next(iterator).numpy())
cluster.stop_dispatcher()
# After the dispatcher dies, the worker should continue providing the rest
# of the dataset's elements.
for _ in range(num_elements - 1):
results.append(next(iterator).numpy())
self.assertEqual(results, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherRestartBeforeReading(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
cluster.restart_dispatcher()
self.assertDatasetProduces(ds, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherRestartDuringReading(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
results = []
for _ in range(num_elements // 2):
results.append(next(iterator).numpy())
cluster.restart_dispatcher()
for elem in iterator:
results.append(elem.numpy())
self.assertEqual(list(range(num_elements)), results)
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherRestartBetweenIterations(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(100, cluster)
self.assertDatasetProduces(ds, list(range(num_elements)))
cluster.restart_dispatcher()
self.assertDatasetProduces(ds, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherManyRestarts(self):
cluster = self.create_cluster(num_workers=1)
num_elements_start = 10
num_elements_end = 15
datasets = []
for num_elements in range(num_elements_start, num_elements_end):
datasets.append(
self.make_distributed_range_dataset(num_elements, cluster))
cluster.restart_dispatcher()
for ds, num_elements in zip(
datasets, range(num_elements_start, num_elements_end)):
self.assertDatasetProduces(ds, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testDispatcherAndWorkerRestart(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)
cluster.restart_dispatcher()
cluster.restart_worker()
self.assertDatasetProduces(ds, list(range(num_elements)))
cluster.restart_dispatcher()
cluster.restart_worker()
self.assertDatasetProduces(ds, list(range(num_elements)))
@combinations.generate(test_base.eager_only_combinations())
def testStartServersLate(self):
# Test that the data service client performs retries instead of failing when
# the dataset is created before the master and worker are started.
try:
import portpicker # pylint: disable=g-import-not-at-top
dispatcher_port = portpicker.pick_unused_port()
except:
raise self.skipTest(
"Flakes in portpicker library do not represent "
"TensorFlow errors.")
cluster = self.create_cluster(num_workers=1,
dispatcher_port=dispatcher_port,
start=False)
def start_servers():
time.sleep(0.5)
cluster.start_dispatcher()
cluster.start_workers()
start_servers_thread = threading.Thread(target=start_servers,
daemon=True)
start_servers_thread.start()
num_elements = 10
ds = self.make_distributed_range_dataset(num_elements, cluster)
results = [elem.numpy() for elem in ds]
self.assertEqual(list(range(num_elements)), results)
start_servers_thread.join()
@combinations.generate(test_base.eager_only_combinations())
def testAddWorkerMidJob(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
results = []
# Read halfway through the dataset.
for _ in range(num_elements // 2):
results.append(next(iterator).numpy())
cluster.add_worker()
# Wait for the new worker to register with the dispatcher.
while cluster.num_registered_workers() < 2:
time.sleep(10 / 1000) # 10ms
for elem in iterator:
results.append(elem.numpy())
self.assertCountEqual(2 * list(range(num_elements)), results)
@combinations.generate(
combinations.times(test_base.eager_only_combinations(),
combinations.combine(use_same_port=[True, False]),
data_service_test_base.all_cluster_configurations())
)
def testRestartWorker(self, use_same_port, work_dir, fault_tolerant_mode):
cluster = self.create_cluster(num_workers=1,
work_dir=work_dir,
fault_tolerant_mode=fault_tolerant_mode)
num_elements = 100
ds = self.make_distributed_range_dataset(num_elements, cluster)
iterator = iter(ds)
# Read halfway through the dataset.
midpoint = num_elements // 2
for i in range(midpoint):
self.assertEqual(i, next(iterator).numpy())
# Stop the original worker and start a new one.
cluster.restart_worker(use_same_port=use_same_port)
# There may have been some elements prefetched from the first worker
# before it was stopped.
while True:
val = next(iterator).numpy()
if val == 0:
break
# The dataset starts over now that we read from the new worker.
# TODO(b/157086991): Iterate until end of sequence when we support
# detecting lost workers.
for i in range(1, num_elements // 2):
val = next(iterator).numpy()
self.assertEqual(i, val)
@combinations.generate(test_base.eager_only_combinations())
def testChangeProcessingModeAfterRestart(self):
cluster = self.create_cluster(num_workers=1)
num_elements = 100
range_dataset = dataset_ops.Dataset.range(num_elements)
ds = range_dataset.apply(
data_service_ops.distribute(processing_mode="parallel_epochs",
service=cluster.target,
job_name="test"))
iterator = iter(ds)
for i in range(num_elements // 2):
self.assertEqual(i, next(iterator).numpy())
cluster.restart_dispatcher()
ds = range_dataset.apply(
data_service_ops.distribute(processing_mode="distributed_epoch",
service=cluster.target,
job_name="test"))
with self.assertRaisesOpError(
"already an existing job with that name "
"using processing mode <parallel_epochs>"):
next(iter(ds)).numpy()
@combinations.generate(
combinations.times(
test_base.eager_only_combinations(),
combinations.combine(work_dir=[TMP_WORK_DIR, NO_WORK_DIR])))
def testDistributeLargeGraphThenRegisterWorker(self, work_dir):
cluster = self.create_cluster(num_workers=0,
work_dir=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)
it = iter(ds)
cluster.add_worker()
self.assertAllEqual(next(it), tensor)
