Replace usage of math_ops.maximum with math_ops.reduce_max when getting max length from SparseTensors.

PiperOrigin-RevId: 170748309

tensorflow/contrib/training/python/training/batch_sequences_with_states_test.py

@@ -30,6 +30,7 @@ from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -527,6 +528,50 @@ class PaddingTest(test.TestCase):
         self.assertTrue(
             math_ops.reduce_all(math_ops.equal(val, padded_seq[key])).eval())
 
+  def testPaddingOnlySparse(self):
+    ind1 = np.array([[0], [2]])
+    val1 = np.array([3, 4])
+    shape1 = np.array([4])
+
+    ind2 = np.array([[1], [2]])
+    val2 = np.array([9, 12])
+    shape2 = np.array([5])
+
+    with ops.Graph().as_default() as g, self.test_session(graph=g):
+      sp_tensor1 = sparse_tensor.SparseTensor(
+          indices=array_ops.constant(ind1, dtypes.int64),
+          values=array_ops.constant(val1, dtypes.int64),
+          dense_shape=array_ops.constant(shape1, dtypes.int64))
+      sp_tensor2 = sparse_tensor.SparseTensor(
+          indices=array_ops.constant(ind2, dtypes.int64),
+          values=array_ops.constant(val2, dtypes.int64),
+          dense_shape=array_ops.constant(shape2, dtypes.int64))
+
+      sp_tensor1_expected = sparse_tensor.SparseTensor(
+          indices=sp_tensor1.indices,
+          values=sp_tensor1.values,
+          dense_shape=[8])
+      sp_tensor2_expected = sparse_tensor.SparseTensor(
+          indices=sp_tensor2.indices,
+          values=sp_tensor2.values,
+          dense_shape=[8])
+
+      sequences = {
+          "key_1": sp_tensor1,
+          "key_2": sp_tensor2,
+      }
+      _, padded_seq = sqss._padding(sequences, 4)
+
+      expected_padded_seq = {
+          "key_1": sp_tensor1_expected,
+          "key_2": sp_tensor2_expected,
+      }
+
+      for key, val in expected_padded_seq.items():
+        self.assertAllEqual(
+            sparse_ops.sparse_tensor_to_dense(val).eval(),
+            sparse_ops.sparse_tensor_to_dense(padded_seq[key]).eval())
+
 
 class SparseTensorReConstructionTest(test.TestCase):
 

tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py

@@ -1596,7 +1596,7 @@ def _padding(sequences, num_unroll):
   else:  # Only have SparseTensors
     sparse_lengths = [value.dense_shape[0] for value in sequences_dict.values()
                       if isinstance(value, sparse_tensor.SparseTensor)]
-    length = math_ops.maximum(sparse_lengths)
+    length = math_ops.reduce_max(math_ops.to_int32(sparse_lengths))
 
   unroll = array_ops.constant(num_unroll)
   padded_length = length + ((unroll - (length % unroll)) % unroll)