Fix repeat, and add tests

PiperOrigin-RevId: 302538059
Change-Id: Ie8a17b4fe5818d04260cc5a3f8868178b92e0014

tensorflow/python/kernel_tests/array_ops_test.py

@@ -21,6 +21,7 @@ import re
 import time
 import unittest
 
+from absl.testing import parameterized
 import numpy as np
 
 from tensorflow.core.protobuf import config_pb2
@@ -1890,48 +1891,33 @@ class BatchGatherNdTest(test_util.TensorFlowTestCase):
     self.assertEqual(None, tensor_shape.dimension_value(shape[0]))
 
 
-class RepeatTest(test_util.TensorFlowTestCase):
+@test_util.run_all_in_graph_and_eager_modes
+class RepeatTest(test_util.TensorFlowTestCase, parameterized.TestCase):
 
-  @test_util.run_deprecated_v1
+  @parameterized.parameters(
-  def testRepeatScalar(self):
+      (3, 4, None),
-    with self.test_session():
+      ([[1, 2], [3, 4]], 2, None),
-      v_tf = array_ops.repeat(constant_op.constant(3), 4)
+      ([[1, 2], [3, 4]], [1, 2], 0),
-      v_np = np.repeat(3, 4)
+      ([[1, 2], [3, 4]], [1, 2], 1),
-      self.assertAllEqual(v_tf.eval(), v_np)
+      ([[1, 2], [3, 4]], 3, 1),
+      ([[1, 2], [3, 4]], [1, 2, 3, 4], None),
+      (np.ones([0, 4]), 0, 1),
+      (np.ones([1, 2]), [2], None),
+  )
+  def testRepeat(self, array, repeats, axis):
+    array = np.array(array)
 
-  @test_util.run_deprecated_v1
+    @def_function.function(
-  def testRepeatMatrix(self):
+        input_signature=[tensor_spec.TensorSpec(None, dtypes.int32)] * 2)
-    with self.test_session():
+    def repeat_fn(array, repeats):
-      x = np.array([[1, 2], [3, 4]], dtype=np.int32)
+      return array_ops.repeat(array, repeats, axis)
-      v_tf = array_ops.repeat(constant_op.constant(x), 2)
-      v_np = np.repeat(x, 2)
-      self.assertAllEqual(v_tf.eval(), v_np)
 
-  @test_util.run_deprecated_v1
+    v_tf = array_ops.repeat(constant_op.constant(array), repeats, axis)
-  def testRepeatMatrixAxis0(self):
+    v_tf_fn = repeat_fn(
-    with self.test_session():
+        constant_op.constant(array, dtype=dtypes.int32), repeats)
-      x = np.array([[1, 2], [3, 4]], dtype=np.int32)
+    v_np = np.repeat(array, repeats, axis)
-      v_tf = array_ops.repeat(
+    self.assertAllEqual(v_tf, v_np)
-          constant_op.constant(x), constant_op.constant([1, 2]), axis=0)
+    self.assertAllEqual(v_tf_fn, v_np)
-      v_np = np.repeat(x, [1, 2], axis=0)
-      self.assertAllEqual(v_tf.eval(), v_np)
-
-  @test_util.run_deprecated_v1
-  def testRepeatMatrixAxis1(self):
-    with self.test_session():
-      x = np.array([[1, 2], [3, 4]], dtype=np.int32)
-      v_tf = array_ops.repeat(
-          constant_op.constant(x), constant_op.constant(3), axis=1)
-      v_np = np.repeat(x, 3, axis=1)
-      self.assertAllEqual(v_tf.eval(), v_np)
-
-  @test_util.run_deprecated_v1
-  def testRepeatMatrixRepeatArray(self):
-    with self.test_session():
-      x = np.array([[1, 2], [3, 4]], dtype=np.int32)
-      v_tf = array_ops.repeat(constant_op.constant(x), [1, 2, 3, 4])
-      v_np = np.repeat(x, [1, 2, 3, 4])
-      self.assertAllEqual(v_tf.eval(), v_np)
 
 
 if __name__ == "__main__":

tensorflow/python/kernel_tests/parsing_ops_test.py

@@ -2278,13 +2278,13 @@ class ParseSequenceExampleTest(test.TestCase):
             serialized=ops.convert_to_tensor(original.SerializeToString()),
             sequence_features=sequence_features),
         expected_err=(
-            (errors_impl.OpError, ValueError),
+            (errors_impl.InvalidArgumentError, ValueError),
             # Message for batch=true:
             "Feature b: values and partitions are not aligned"
             # Message for batch=false in graph mode:
             "|.* do not form a valid RaggedTensor"
             # Message for batch=false in eager mode:
-            "|Dimensions 2 and 1 are not compatible"))
+            "|Incompatible shapes"))
 
 
 @test_util.run_all_in_graph_and_eager_modes

tensorflow/python/ops/array_ops.py

@@ -5511,17 +5511,24 @@ def repeat_with_axis(data, repeats, axis, name=None):
     # If `axis` is negative, then convert it to a positive value.
     axis = get_positive_axis(axis, data.shape.rank, ndims_name="rank(data)")
 
+    # If we know that `repeats` is a scalar, then we can just tile & reshape.
+    if repeats.shape.num_elements() == 1:
+      repeats = reshape(repeats, [])
+      expanded = expand_dims(data, axis + 1)
+      tiled = tile_one_dimension(expanded, axis + 1, repeats)
+      result_shape = concat([
+          data_shape[:axis], [repeats * data_shape[axis]], data_shape[axis + 1:]
+      ],
+                            axis=0)
+      return reshape(tiled, result_shape)
+
+
     # Check data Tensor shapes.
     if repeats.shape.ndims == 1:
       data.shape.dims[axis].assert_is_compatible_with(repeats.shape[0])
 
-    # If we know that `repeats` is a scalar, then we can just tile & reshape.
+    repeats = broadcast_to(repeats, [data_shape[axis]])
-    if repeats.shape.ndims == 0:
+    repeats_original = repeats
-      expanded = expand_dims(data, axis + 1)
-      tiled = tile_one_dimension(expanded, axis + 1, repeats)
-      result_shape = concat([data_shape[:axis], [-1], data_shape[axis + 1:]],
-                            axis=0)
-      return reshape(tiled, result_shape)
 
     # Broadcast the `repeats` tensor so rank(repeats) == axis + 1.
     if repeats.shape.ndims != axis + 1:
@@ -5552,7 +5559,11 @@ def repeat_with_axis(data, repeats, axis, name=None):
     if axis == 0:
       result = masked
     else:
-      result_shape = concat([data_shape[:axis], [-1], data_shape[axis + 1:]],
+      repeated_dim_size = gen_math_ops._sum(
+          repeats_original,
+          axis=gen_math_ops._range(0, rank(repeats_original), 1))
+      result_shape = concat(
+          [data_shape[:axis], [repeated_dim_size], data_shape[axis + 1:]],
           axis=0)
       result = reshape(masked, result_shape)