Enable last partial batch for MWMS in TF2.x

PiperOrigin-RevId: 317760674
Change-Id: Ib7e0adbf4f8f013f21faef07ed4961c078806093

tensorflow/python/distribute/collective_all_reduce_strategy.py

@@ -178,6 +178,7 @@ class CollectiveAllReduceExtended(mirrored_strategy.MirroredExtended):
     self._communication = communication
     self._initialize_strategy(self._cluster_resolver)
     self._cfer_fn_cache = weakref.WeakKeyDictionary()
+    self.experimental_enable_get_next_as_optional = True
     assert isinstance(self._cross_device_ops,
                       cross_device_ops_lib.CollectiveAllReduce)
 

tensorflow/python/distribute/collective_all_reduce_strategy_test.py

@@ -370,7 +370,8 @@ class CollectiveAllReduceStrategyTestBase(
         else:
           self.assertEqual(list(expected_value), list(computed_value))
 
-      with self.assertRaises(errors.OutOfRangeError):
+      # error raised by calling optional_get_value on an Optional of None
+      with self.assertRaises(errors.InvalidArgumentError):
         next_element = iterator.get_next()
         sess.run([distribute_utils.select_replica(r, next_element)
                   for r in range(len(devices))])
@@ -449,31 +450,35 @@ class DistributedCollectiveAllReduceStrategyTest(
       combinations.combine(
           mode=['graph'], required_gpus=[0, 1, 2], use_dataset=[True, False]))
   def testMakeInputFnIterator(self, required_gpus, use_dataset):
-    if use_dataset:
-      fn = lambda: dataset_ops.Dataset.range(100)
-    else:
-      def fn():
-        dataset = dataset_ops.Dataset.range(100)
-        it = dataset_ops.make_one_shot_iterator(dataset)
-        return it.get_next
-    # We use CPU as the device when required_gpus = 0
-    devices_per_worker = max(1, required_gpus)
-    expected_values = [[i+j for j in range(devices_per_worker)]
-                       for i in range(0, 100, devices_per_worker)]
+    def _worker_fn(task_type, task_id, required_gpus):
+      if use_dataset:
+        fn = lambda: dataset_ops.Dataset.range(100)
+      else:
+        def fn():
+          dataset = dataset_ops.Dataset.range(100)
+          it = dataset_ops.make_one_shot_iterator(dataset)
+          return it.get_next
+      # We use CPU as the device when required_gpus = 0
+      devices_per_worker = max(1, required_gpus)
+      expected_values = [[i+j for j in range(devices_per_worker)]
+                         for i in range(0, 100, devices_per_worker)]
 
-    input_fn = self._input_fn_to_test_input_context(
-        fn,
-        expected_num_replicas_in_sync=3*devices_per_worker,
-        expected_num_input_pipelines=3,
-        expected_input_pipeline_id=1)  # because task_id = 1
-    self._test_input_fn_iterator(
-        'worker',
-        1,
-        required_gpus,
-        input_fn,
-        expected_values,
-        test_reinitialize=use_dataset,
-        ignore_order=not use_dataset)
+      input_fn = self._input_fn_to_test_input_context(
+          fn,
+          expected_num_replicas_in_sync=3*devices_per_worker,
+          expected_num_input_pipelines=3,
+          expected_input_pipeline_id=task_id)
+      self._test_input_fn_iterator(
+          task_type,
+          task_id,
+          required_gpus,
+          input_fn,
+          expected_values,
+          test_reinitialize=use_dataset,
+          ignore_order=not use_dataset)
+
+    self._run_between_graph_clients(_worker_fn, self._cluster_spec,
+                                    required_gpus)
 
   @combinations.generate(combinations.combine(mode=['graph']))
   def testUpdateConfigProto(self):

tensorflow/python/distribute/input_lib.py

@@ -549,7 +549,7 @@ def _get_next_as_optional(iterator, strategy, name=None):
       # Collective all-reduce requires explicit devices for inputs.
       with ops.device("/cpu:0"):
         # Converting to integers for all-reduce.
-        worker_has_value = math_ops.cast(worker_has_value, dtypes.int32)
+        worker_has_value = math_ops.cast(worker_has_value, dtypes.int64)
         worker_devices.append(worker_has_value.device)
         worker_has_values.append(worker_has_value)
       # Make `replicas` a flat list of values across all replicas.
@@ -624,16 +624,12 @@ class DistributedIteratorBase(DistributedIteratorInterface):
     # get_next_as_optional(). And we only enable get_next_as_optional when the
     # output shapes are not static.
     #
-    # TODO(yuefengz): Currently `experimental_enable_get_next_as_optional` is
-    # always set to False in CollectiveAllReduceStrategy. We want to have a way
-    # to distinguish multi workers/single worker between graph, so we can enable
-    # the behavior in single worker case.
-    #
     # TODO(rxsang): We want to always enable the get_next_as_optional behavior
     # when user passed input_fn instead of dataset.
     if getattr(
         strategy.extended, "experimental_enable_get_next_as_optional", False):
-      self._enable_get_next_as_optional = not static_shape
+      self._enable_get_next_as_optional = (
+          not static_shape) or strategy.extended._in_multi_worker_mode()
     else:
       self._enable_get_next_as_optional = False
 
@@ -906,7 +902,8 @@ class DistributedIterator(DistributedIteratorBase,
       self._strategy = strategy
       if getattr(
           strategy.extended, "experimental_enable_get_next_as_optional", False):
-        self._enable_get_next_as_optional = not static_shape
+        self._enable_get_next_as_optional = (
+            not static_shape) or strategy.extended._in_multi_worker_mode()
       else:
         self._enable_get_next_as_optional = False
     else:

tensorflow/python/distribute/input_lib_test.py

@@ -1144,7 +1144,6 @@ class DistributedIteratorMultiWorkerTest(
           expected_values = [[[0, 1]], [[2, 3]], [[4]]]
           input_context = None
 
-        strategy.extended.experimental_enable_get_next_as_optional = True
         self._test_input_iteration(
             input_type,
             api_type,

tensorflow/python/distribute/strategy_common_test.py

@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 from absl.testing import parameterized
+import numpy as np
 
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.distribute import combinations
@@ -29,7 +30,6 @@ from tensorflow.python.distribute import strategy_test_lib
 from tensorflow.python.eager import def_function
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 
 
@@ -81,7 +81,7 @@ class DistributedCollectiveAllReduceStrategyTest(
       return d.shard(input_context.num_input_pipelines,
                      input_context.input_pipeline_id)
 
-    expected_sum_on_workers = [10, 35]
+    expected_data_on_worker = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
     input_iterator = iter(
         strategy.experimental_distribute_datasets_from_function(dataset_fn))
 
@@ -90,10 +90,59 @@ class DistributedCollectiveAllReduceStrategyTest(
       return strategy.experimental_local_results(iterator.get_next())
 
     result = run(input_iterator)
-    sum_value = math_ops.reduce_sum(result)
-    self.assertEqual(
-        sum_value.numpy(),
-        expected_sum_on_workers[multi_worker_test_base.get_task_index()])
+    self.assertTrue(
+        np.array_equal(
+            result[0].numpy(),
+            expected_data_on_worker[multi_worker_test_base.get_task_index()]))
+
+  def testSimpleInputFromDatasetLastPartialBatch(self, strategy):
+    global_batch_size = 8
+    dataset = dataset_ops.DatasetV2.range(14).batch(
+        global_batch_size, drop_remainder=False)
+    input_iterator = iter(strategy.experimental_distribute_dataset(dataset))
+
+    @def_function.function
+    def run(input_iterator):
+      return strategy.run(lambda x: x, args=(next(input_iterator),))
+
+    # Let the complete batch go.
+    run(input_iterator)
+
+    # `result` is an incomplete batch
+    result = run(input_iterator)
+    expected_data_on_worker = [[8, 9, 10], [11, 12, 13]]
+    self.assertTrue(
+        np.array_equal(
+            result.numpy(),
+            expected_data_on_worker[multi_worker_test_base.get_task_index()]))
+
+  def testSimpleInputFromFnLastPartialBatch(self, strategy):
+
+    def dataset_fn(input_context):
+      global_batch_size = 8
+      batch_size = input_context.get_per_replica_batch_size(global_batch_size)
+      dataset = dataset_ops.DatasetV2.range(14).batch(
+          batch_size, drop_remainder=False)
+      return dataset.shard(input_context.num_input_pipelines,
+                           input_context.input_pipeline_id)
+
+    input_iterator = iter(
+        strategy.experimental_distribute_datasets_from_function(dataset_fn))
+
+    @def_function.function
+    def run(input_iterator):
+      return strategy.run(lambda x: x, args=(next(input_iterator),))
+
+    # Let the complete batch go.
+    run(input_iterator)
+    # `result` is an incomplete batch
+    result = run(input_iterator)
+
+    expected_data_on_worker = [[8, 9, 10, 11], [12, 13]]
+    self.assertTrue(
+        np.array_equal(
+            result.numpy(), expected_data_on_worker[
+                multi_worker_test_base.get_task_index()]))
 
   def testReduceHostTensor(self, strategy):
     reduced = strategy.reduce(