Merge pull request #39241 from yongtang:39229-map_fn-empty-list

PiperOrigin-RevId: 318950663
Change-Id: Ia75f5bf028a94abbb7ebf2c7d3193309bdef9029

tensorflow/python/kernel_tests/map_fn_test.py

@@ -237,6 +237,12 @@ class MapFnTest(test.TestCase):
       self.assertAllEqual([0, 3, 2], map_return.get_shape().dims)
       self.assertAllEqual([0, 3, 2], self.evaluate(map_return).shape)
 
+  @test_util.run_in_graph_and_eager_modes
+  def testMapEmptyList(self):
+    x = []
+    with self.assertRaisesRegexp(ValueError, r"elems must be a Tensor or"):
+      _ = map_fn.map_fn(lambda e: e, x)
+
 
 if __name__ == "__main__":
   test.main()

tensorflow/python/ops/map_fn.py

@@ -267,7 +267,7 @@ def map_fn(fn,
     elems: A tensor or (possibly nested) sequence of tensors, each of which will
       be unstacked along their first dimension.  `fn` will be applied to the
       nested sequence of the resulting slices.  `elems` may include ragged and
-      sparse tensors.
+      sparse tensors. `elems` must consist of at least one tensor.
     dtype: Deprecated: Equivalent to `fn_output_signature`.
     parallel_iterations: (optional) The number of iterations allowed to run in
       parallel. When graph building, the default value is 10. While executing
@@ -296,7 +296,7 @@ def map_fn(fn,
     TypeError: if `fn` is not callable or the structure of the output of
       `fn` and `fn_output_signature` do not match.
     ValueError: if the lengths of the output of `fn` and `fn_output_signature`
-      do not match.
+      do not match, or if the `elems` does not contain any tensor.
 
   Examples:
 
@@ -375,6 +375,13 @@ def map_fn(fn,
 
   # Flatten the input tensors, and get the TypeSpec for each one.
   elems_flat = nest.flatten(elems)
+
+  # Check in case this is an empty list
+  if len(elems_flat) == 0:
+    raise ValueError(
+        "elems must be a Tensor or (possibly nested) sequence of Tensors. "
+        "Got {}, which does not contain any Tensors.".format(elems))
+
   elems_flat_signature = [type_spec.type_spec_from_value(e) for e in elems_flat]
   elems_unflatten = lambda x: nest.pack_sequence_as(elems, x)