[tf.data service] Increase the number of client-side uncompress threads.

This is necessary to prevent uncompression from becoming the bottleneck.

The change required updating the unit tests because now the `distribute` transformation may prefetch up to 16 elements.

PiperOrigin-RevId: 316919714
Change-Id: I4e0c0b2985792a2a2a0f216de2143a645076b1c8

tensorflow/python/data/experimental/ops/data_service_ops.py

@@ -240,8 +240,11 @@ def _distribute(processing_mode,
         task_refresh_interval_hint_ms=task_refresh_interval_hint_ms)
     # TODO(b/157105111): Make this an autotuned parallel map when we have a way
     # to limit memory usage.
+    # The value 16 is chosen based on experience with pipelines that require
+    # more than 8 parallel calls to prevent this stage from being a bottleneck.
     dataset = dataset.map(
-        lambda x: compression_ops.uncompress(x, output_spec=uncompressed_spec))
+        lambda x: compression_ops.uncompress(x, output_spec=uncompressed_spec),
+        num_parallel_calls=16)
 
     # Disable autosharding for shared jobs.
     if job_name:

tensorflow/python/data/kernel_tests/data_service_ops_test.py

@@ -201,12 +201,17 @@ class DataServiceOpsTest(test_base.DatasetTestBase, parameterized.TestCase):
     self._new_worker = server_lib.WorkerServer(
         port=port, master_address=self._master._address, protocol=PROTOCOL)
 
-    # The dataset starts over now that we read from the new worker.
-    for i in range(num_elements):
-      val = next(iterator).numpy()
-      if val == midpoint and i != midpoint:
-        # There may have been one last element prefetched from the first worker
+    # 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)
 
@@ -248,7 +253,7 @@ class DataServiceOpsTest(test_base.DatasetTestBase, parameterized.TestCase):
 
   @combinations.generate(test_base.eager_only_combinations())
   def testSharedJobName(self):
-    num_elements = 10
+    num_elements = 100
     master_address = self.create_cluster(1)
     ds = dataset_ops.Dataset.range(num_elements)
     ds1 = _make_distributed_dataset(ds, master_address, job_name="job_name")
@@ -256,7 +261,7 @@ class DataServiceOpsTest(test_base.DatasetTestBase, parameterized.TestCase):
     iter1 = iter(ds1)
     iter2 = iter(ds2)
     results = []
-    for _ in range(3):
+    for _ in range(num_elements // 5):
       results.append(next(iter1).numpy())
       results.append(next(iter2).numpy())
     for elem in iter1:
@@ -291,7 +296,7 @@ class DataServiceOpsTest(test_base.DatasetTestBase, parameterized.TestCase):
 
   @combinations.generate(test_base.eager_only_combinations())
   def testSharedJobNameRepeat(self):
-    num_elements = 10
+    num_elements = 100
     num_repetitions = 3
     master_address = self.create_cluster(1)
     ds = dataset_ops.Dataset.range(num_elements)
@@ -302,9 +307,9 @@ class DataServiceOpsTest(test_base.DatasetTestBase, parameterized.TestCase):
     results = []
     iter1 = iter(ds1)
     iter2 = iter(ds2)
-    for _ in range(((num_elements * num_repetitions) // 2) - 1):
+    for _ in range((num_elements * num_repetitions) // 5):
       results.append(next(iter1).numpy())
-    for _ in range(((num_elements * num_repetitions) // 2) - 1):
+    for _ in range((num_elements * num_repetitions) // 5):
       results.append(next(iter2).numpy())
     for elem in iter1:
       results.append(elem.numpy())