diff --git a/tensorflow/python/distribute/BUILD b/tensorflow/python/distribute/BUILD
index acbffb84089..01ae1b61f6a 100644
--- a/tensorflow/python/distribute/BUILD
+++ b/tensorflow/python/distribute/BUILD
@@ -1181,6 +1181,23 @@ distribute_py_test(
     ],
 )
 
+distribute_py_test(
+    name = "strategy_reduce_test",
+    srcs = ["strategy_reduce_test.py"],
+    main = "strategy_reduce_test.py",
+    tags = [
+        "multi_and_single_gpu",
+    ],
+    deps = [
+        ":combinations",
+        ":strategy_combinations",
+        "//tensorflow/python:errors",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/eager:test",
+        "@absl_py//absl/testing:parameterized",
+    ],
+)
+
 distribute_py_test(
     name = "minimize_loss_test",
     srcs = ["minimize_loss_test.py"],
diff --git a/tensorflow/python/distribute/custom_training_loop_models_test.py b/tensorflow/python/distribute/custom_training_loop_models_test.py
index 48f2af0349a..5a9384bb7e0 100644
--- a/tensorflow/python/distribute/custom_training_loop_models_test.py
+++ b/tensorflow/python/distribute/custom_training_loop_models_test.py
@@ -26,6 +26,7 @@ import numpy as np
 from tensorflow.python import keras
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.distribute import combinations
+from tensorflow.python.distribute import reduce_util
 from tensorflow.python.distribute import strategy_combinations
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import def_function
@@ -448,6 +449,35 @@ class KerasModelsTest(test.TestCase, parameterized.TestCase):
 
     train_step(input_iterator)
 
+  @combinations.generate(
+      combinations.combine(
+          distribution=strategy_combinations.all_strategies, mode=["eager"]))
+  def test_reduce_loss(self, distribution):
+    inputs = np.zeros((10, 4), dtype=np.float32)
+    targets = np.zeros((10, 1), dtype=np.float32)
+    dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
+    dataset = dataset.batch(10, drop_remainder=False)
+    input_iterator = iter(distribution.experimental_distribute_dataset(dataset))
+
+    with distribution.scope():
+      x = keras.layers.Input(shape=(4), name="input")
+      y = keras.layers.Dense(3, name="dense")(x)
+      model = keras.Model(x, y)
+
+    @def_function.function
+    def train_step(iterator):
+
+      def step_fn(inputs):
+        images, targets = inputs
+        outputs = model(images)
+        loss = keras.losses.sparse_categorical_crossentropy(targets, outputs)
+        return loss
+
+      return distribution.run(step_fn, args=(next(iterator),))
+
+    loss = train_step(input_iterator)
+    loss = distribution.reduce(reduce_util.ReduceOp.MEAN, loss, axis=0)
+
   @combinations.generate(
       combinations.combine(
           distribution=strategy_combinations.tpu_strategies, mode=["eager"]))
diff --git a/tensorflow/python/distribute/distribute_lib.py b/tensorflow/python/distribute/distribute_lib.py
index 4531e922840..ecdc4fad159 100644
--- a/tensorflow/python/distribute/distribute_lib.py
+++ b/tensorflow/python/distribute/distribute_lib.py
@@ -114,6 +114,7 @@ from tensorflow.python.distribute import distribution_strategy_context
 from tensorflow.python.distribute import numpy_dataset
 from tensorflow.python.distribute import reduce_util
 from tensorflow.python.eager import context as eager_context
+from tensorflow.python.eager import def_function
 from tensorflow.python.eager import monitoring
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -628,6 +629,10 @@ class StrategyBase(object):
         # a sensible value.
         extended._retrace_functions_for_each_device = True
 
+    # Below are the dicts of axis(int) -> `tf.function`.
+    self._mean_reduce_helper_fns = {}
+    self._reduce_sum_fns = {}
+
   @property
   def extended(self):
     """`tf.distribute.StrategyExtended` with additional methods."""
@@ -1014,8 +1019,25 @@ class StrategyBase(object):
     if axis is None:
       return self._extended._reduce(reduce_op, value)  # pylint: disable=protected-access
     if reduce_op == reduce_util.ReduceOp.SUM:
-      value = self.run(
-          lambda v: math_ops.reduce_sum(v, axis=axis), args=(value,))
+
+      def reduce_sum(v):
+        return math_ops.reduce_sum(v, axis=axis)
+
+      if eager_context.executing_eagerly():
+        # As some strategies (e.g. TPUStrategy) doesn't support pure eager
+        # execution, wrap the `reduce_sum_fn` with a `tf.function` so it can be
+        # run from eager mode. Cache the tf.function by `axis` to avoid the
+        # same function to be traced again.
+        if axis not in self._reduce_sum_fns:
+
+          def reduce_sum_fn(v):
+            return self.run(reduce_sum, args=(v,))
+
+          self._reduce_sum_fns[axis] = def_function.function(reduce_sum_fn)
+        value = self._reduce_sum_fns[axis](value)
+      else:
+        value = self.run(reduce_sum, args=(value,))
+
       return self._extended._reduce(reduce_op, value)  # pylint: disable=protected-access
     if reduce_op != reduce_util.ReduceOp.MEAN:
       raise TypeError("Expected `reduce_op` to be a `tf.distribute.ReduceOp`, "
@@ -1062,7 +1084,22 @@ class StrategyBase(object):
       # reduce is complete?
       return numer, denom
 
-    numer, denom = self.run(mean_reduce_helper, args=(value,))
+    if eager_context.executing_eagerly():
+      # As some strategies (e.g. TPUStrategy) doesn't support pure eager
+      # execution, wrap the `mean_reduce_helper` with a `tf.function` so it can
+      # be run from eager mode. Cache the tf.function by `axis` to avoid the
+      # same function to be traced again.
+      if axis not in self._mean_reduce_helper_fns:
+
+        def mean_reduce_fn(v):
+          return self.run(mean_reduce_helper, args=(v,))
+
+        self._mean_reduce_helper_fns[axis] = def_function.function(
+            mean_reduce_fn)
+      numer, denom = self._mean_reduce_helper_fns[axis](value)
+    else:
+      numer, denom = self.run(mean_reduce_helper, args=(value,))
+
     # TODO(josh11b): Should batch reduce here instead of doing two.
     numer = self._extended._reduce(reduce_util.ReduceOp.SUM, numer)  # pylint: disable=protected-access
     denom = self._extended._reduce(reduce_util.ReduceOp.SUM, denom)  # pylint: disable=protected-access
diff --git a/tensorflow/python/distribute/strategy_reduce_test.py b/tensorflow/python/distribute/strategy_reduce_test.py
new file mode 100644
index 00000000000..a87cce2f0b8
--- /dev/null
+++ b/tensorflow/python/distribute/strategy_reduce_test.py
@@ -0,0 +1,52 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for `strategy.reduce`."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl.testing import parameterized
+
+from tensorflow.python.distribute import combinations
+from tensorflow.python.distribute import reduce_util
+from tensorflow.python.distribute import strategy_combinations
+from tensorflow.python.eager import def_function
+from tensorflow.python.eager import test
+from tensorflow.python.framework import constant_op
+
+
+class StrategyReduceTest(test.TestCase, parameterized.TestCase):
+
+  @combinations.generate(
+      combinations.combine(
+          distribution=strategy_combinations.all_strategies,
+          mode=["eager"]
+      ))
+  def test_reduce_with_axis(self, distribution):
+
+    @def_function.function
+    def fn():
+      return constant_op.constant([1., 2.])
+    x = distribution.run(fn)
+
+    x_m = distribution.reduce(reduce_util.ReduceOp.MEAN, x, axis=0)
+    self.assertEqual(1.5, self.evaluate(x_m))
+    x_s = distribution.reduce(reduce_util.ReduceOp.SUM, x, axis=0)
+    self.assertEqual(3 * distribution.num_replicas_in_sync, self.evaluate(x_s))
+
+
+if __name__ == "__main__":
+  test.main()