diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py
index 653ca525dce..758cba74877 100644
--- a/tensorflow/python/util/nest.py
+++ b/tensorflow/python/util/nest.py
@@ -19,6 +19,9 @@ This module can perform operations on nested structures. A nested structure is a
 Python sequence, tuple (including `namedtuple`), or dict that can contain
 further sequences, tuples, and dicts.
 
+attr.s decorated classes (http://www.attrs.org) are also supported, in the
+same way as `namedtuple`.
+
 The utilities here assume (and do not check) that the nested structures form a
 'tree', i.e., no references in the structure of the input of these functions
 should be recursive.
@@ -38,6 +41,12 @@ import six as _six
 from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
 
 
+def _get_attrs_values(obj):
+  """Returns the list of values from an attrs instance."""
+  attrs = getattr(obj.__class__, "__attrs_attrs__")
+  return [getattr(obj, a.name) for a in attrs]
+
+
 def _sorted(dict_):
   """Returns a sorted list of the dict keys, with error if keys not sortable."""
   try:
@@ -64,6 +73,7 @@ def _is_namedtuple(instance, strict=False):
 
 # See the swig file (util.i) for documentation.
 _is_mapping = _pywrap_tensorflow.IsMapping
+_is_attrs = _pywrap_tensorflow.IsAttrs
 
 
 def _sequence_like(instance, args):
@@ -85,7 +95,7 @@ def _sequence_like(instance, args):
     # corresponding `OrderedDict` to pack it back).
     result = dict(zip(_sorted(instance), args))
     return type(instance)((key, result[key]) for key in _six.iterkeys(instance))
-  elif _is_namedtuple(instance):
+  elif _is_namedtuple(instance) or _is_attrs(instance):
     return type(instance)(*args)
   else:
     # Not a namedtuple
@@ -93,6 +103,7 @@ def _sequence_like(instance, args):
 
 
 def _yield_value(iterable):
+  """Yields the next value from the given iterable."""
   if _is_mapping(iterable):
     # Iterate through dictionaries in a deterministic order by sorting the
     # keys. Notice this means that we ignore the original order of `OrderedDict`
@@ -101,6 +112,9 @@ def _yield_value(iterable):
     # corresponding `OrderedDict` to pack it back).
     for key in _sorted(iterable):
       yield iterable[key]
+  elif _is_attrs(iterable):
+    for value in _get_attrs_values(iterable):
+      yield value
   else:
     for value in iterable:
       yield value
diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py
index bfb4c6f910f..e03a8daaa19 100644
--- a/tensorflow/python/util/nest_test.py
+++ b/tensorflow/python/util/nest_test.py
@@ -33,6 +33,11 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test
 from tensorflow.python.util import nest
 
+try:
+  import attr  # pylint:disable=g-import-not-at-top
+except ImportError:
+  attr = None
+
 
 class _CustomMapping(collections.Mapping):
 
@@ -53,6 +58,35 @@ class NestTest(parameterized.TestCase, test.TestCase):
 
   PointXY = collections.namedtuple("Point", ["x", "y"])  # pylint: disable=invalid-name
 
+  if attr:
+    class BadAttr(object):
+      """Class that has a non-iterable __attrs_attrs__."""
+      __attrs_attrs__ = None
+
+    @attr.s
+    class SampleAttr(object):
+      field1 = attr.ib()
+      field2 = attr.ib()
+
+  @test_util.assert_no_new_pyobjects_executing_eagerly
+  def testAttrsFlattenAndPack(self):
+    if attr is None:
+      self.skipTest("attr module is unavailable.")
+
+    field_values = [1, 2]
+    sample_attr = NestTest.SampleAttr(*field_values)
+    self.assertFalse(nest._is_attrs(field_values))
+    self.assertTrue(nest._is_attrs(sample_attr))
+    flat = nest.flatten(sample_attr)
+    self.assertEqual(field_values, flat)
+    restructured_from_flat = nest.pack_sequence_as(sample_attr, flat)
+    self.assertIsInstance(restructured_from_flat, NestTest.SampleAttr)
+    self.assertEqual(restructured_from_flat, sample_attr)
+
+    # Check that flatten fails if attributes are not iterable
+    with self.assertRaisesRegexp(TypeError, "object is not iterable"):
+      flat = nest.flatten(NestTest.BadAttr())
+
   @test_util.assert_no_new_pyobjects_executing_eagerly
   def testFlattenAndPack(self):
     structure = ((3, 4), 5, (6, 7, (9, 10), 8))
diff --git a/tensorflow/python/util/util.cc b/tensorflow/python/util/util.cc
index 2087957b311..38b8491c664 100644
--- a/tensorflow/python/util/util.cc
+++ b/tensorflow/python/util/util.cc
@@ -192,6 +192,19 @@ int IsMappingHelper(PyObject* o) {
   return check_cache->CachedLookup(o);
 }
 
+// Returns 1 if `o` is an instance of attrs-decorated class.
+// Returns 0 otherwise.
+int IsAttrsHelper(PyObject* o) {
+  Safe_PyObjectPtr cls(PyObject_GetAttrString(o, "__class__"));
+  if (cls) {
+    return PyObject_HasAttrString(cls.get(), "__attrs_attrs__");
+  } else {
+    // PyObject_GetAttrString returns null on error
+    PyErr_Clear();
+    return 0;
+  }
+}
+
 // Returns 1 if `o` is considered a sequence for the purposes of Flatten().
 // Returns 0 otherwise.
 // Returns -1 if an error occurred.
@@ -206,6 +219,7 @@ int IsSequenceHelper(PyObject* o) {
   });
   // We treat dicts and other mappings as special cases of sequences.
   if (IsMappingHelper(o)) return true;
+  if (IsAttrsHelper(o)) return true;
   if (PySet_Check(o) && !WarnedThatSetIsNotSequence) {
     LOG(WARNING) << "Sets are not currently considered sequences, "
                     "but this may change in the future, "
@@ -354,6 +368,38 @@ class SparseTensorValueIterator : public ValueIterator {
   Safe_PyObjectPtr tensor_;
 };
 
+class AttrsValueIterator : public ValueIterator {
+ public:
+  explicit AttrsValueIterator(PyObject* nested) : nested_(nested) {
+    Py_INCREF(nested);
+    cls_.reset(PyObject_GetAttrString(nested_.get(), "__class__"));
+    if (cls_) {
+      attrs_.reset(PyObject_GetAttrString(cls_.get(), "__attrs_attrs__"));
+      if (attrs_) {
+        iter_.reset(PyObject_GetIter(attrs_.get()));
+      }
+    }
+    if (!iter_ || PyErr_Occurred()) invalidate();
+  }
+
+  Safe_PyObjectPtr next() override {
+    Safe_PyObjectPtr result;
+    Safe_PyObjectPtr item(PyIter_Next(iter_.get()));
+    if (item) {
+      Safe_PyObjectPtr name(PyObject_GetAttrString(item.get(), "name"));
+      result.reset(PyObject_GetAttr(nested_.get(), name.get()));
+    }
+
+    return result;
+  }
+
+ private:
+  Safe_PyObjectPtr nested_;
+  Safe_PyObjectPtr cls_;
+  Safe_PyObjectPtr attrs_;
+  Safe_PyObjectPtr iter_;
+};
+
 bool IsSparseTensorValueType(PyObject* o) {
   if (TF_PREDICT_FALSE(SparseTensorValueType == nullptr)) {
     return false;
@@ -372,6 +418,8 @@ ValueIteratorPtr GetValueIterator(PyObject* nested) {
     return absl::make_unique<DictValueIterator>(nested);
   } else if (IsMappingHelper(nested)) {
     return absl::make_unique<MappingValueIterator>(nested);
+  } else if (IsAttrsHelper(nested)) {
+    return absl::make_unique<AttrsValueIterator>(nested);
   } else {
     return absl::make_unique<SequenceValueIterator>(nested);
   }
@@ -383,6 +431,8 @@ ValueIteratorPtr GetValueIteratorForData(PyObject* nested) {
     return absl::make_unique<DictValueIterator>(nested);
   } else if (IsMappingHelper(nested)) {
     return absl::make_unique<MappingValueIterator>(nested);
+  } else if (IsAttrsHelper(nested)) {
+    return absl::make_unique<AttrsValueIterator>(nested);
   } else if (IsSparseTensorValueType(nested)) {
     return absl::make_unique<SparseTensorValueIterator>(nested);
   } else {
@@ -639,6 +689,7 @@ void RegisterSparseTensorValueClass(PyObject* sparse_tensor_value_class) {
 
 bool IsSequence(PyObject* o) { return IsSequenceHelper(o) == 1; }
 bool IsMapping(PyObject* o) { return IsMappingHelper(o) == 1; }
+bool IsAttrs(PyObject* o) { return IsAttrsHelper(o) == 1; }
 
 PyObject* Flatten(PyObject* nested) {
   PyObject* list = PyList_New(0);
diff --git a/tensorflow/python/util/util.h b/tensorflow/python/util/util.h
index 343605285ea..01f85ea1dc9 100644
--- a/tensorflow/python/util/util.h
+++ b/tensorflow/python/util/util.h
@@ -56,6 +56,15 @@ PyObject* IsNamedtuple(PyObject* o, bool strict);
 //   True if the sequence subclasses mapping.
 bool IsMapping(PyObject* o);
 
+// Returns a true if its input is an instance of an attr.s decorated class.
+//
+// Args:
+//   o: the input to be checked.
+//
+// Returns:
+//   True if the object is an instance of an attr.s decorated class.
+bool IsAttrs(PyObject* o);
+
 // Implements the same interface as tensorflow.util.nest._same_namedtuples
 // Returns Py_True iff the two namedtuples have the same name and fields.
 // Raises RuntimeError if `o1` or `o2` don't look like namedtuples (don't have
diff --git a/tensorflow/python/util/util.i b/tensorflow/python/util/util.i
index 104a6156367..32a6e684fa9 100644
--- a/tensorflow/python/util/util.i
+++ b/tensorflow/python/util/util.i
@@ -65,6 +65,18 @@ Returns:
 %unignore tensorflow::swig::IsMapping;
 %noexception tensorflow::swig::IsMapping;
 
+%feature("docstring") tensorflow::swig::IsAttrs
+"""Returns True iff `instance` is an instance of an `attr.s` decorated class.
+
+Args:
+  instance: An instance of a Python object.
+
+Returns:
+  True if `instance` is an instance of an `attr.s` decorated class.
+"""
+%unignore tensorflow::swig::IsAttrs;
+%noexception tensorflow::swig::IsAttrs;
+
 %feature("docstring") tensorflow::swig::SameNamedtuples
 "Returns True if the two namedtuples have the same name and fields."
 %unignore tensorflow::swig::SameNamedtuples;