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diff --git a/tensorflow/python/data/experimental/kernel_tests/filter_dataset_op_test.py b/tensorflow/python/data/experimental/kernel_tests/filter_dataset_op_test.py
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+++ b/tensorflow/python/data/experimental/kernel_tests/filter_dataset_op_test.py
@@ -0,0 +1,76 @@
+# Copyright 2018 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.
+# ==============================================================================
+"""Benchmarks FilterDataset input pipeline op."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+
+import numpy as np
+
+from tensorflow.python.client import session
+from tensorflow.python.data.experimental.ops import optimization
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+
+
+class FilterBenchmark(test.Benchmark):
+
+  # This benchmark compares the performance of pipeline with multiple chained
+  # filter with and without filter fusion.
+  def benchmarkFilters(self):
+    chain_lengths = [0, 1, 2, 5, 10, 20, 50]
+    for chain_length in chain_lengths:
+      self._benchmarkFilters(chain_length, False)
+      self._benchmarkFilters(chain_length, True)
+
+  def _benchmarkFilters(self, chain_length, optimize_dataset):
+    with ops.Graph().as_default():
+      dataset = dataset_ops.Dataset.from_tensors(5).repeat(None)
+      for _ in range(chain_length):
+        dataset = dataset.filter(lambda x: math_ops.greater_equal(x - 5, 0))
+      if optimize_dataset:
+        dataset = dataset.apply(optimization.optimize(["filter_fusion"]))
+
+      iterator = dataset.make_one_shot_iterator()
+      next_element = iterator.get_next()
+
+      with session.Session() as sess:
+        for _ in range(10):
+          sess.run(next_element.op)
+        deltas = []
+        for _ in range(100):
+          start = time.time()
+          for _ in range(100):
+            sess.run(next_element.op)
+          end = time.time()
+          deltas.append(end - start)
+
+        median_wall_time = np.median(deltas) / 100
+        opt_mark = "opt" if optimize_dataset else "no-opt"
+        print("Filter dataset {} chain length: {} Median wall time: {}".format(
+            opt_mark, chain_length, median_wall_time))
+        self.report_benchmark(
+            iters=1000,
+            wall_time=median_wall_time,
+            name="benchmark_filter_dataset_chain_latency_{}_{}".format(
+                opt_mark, chain_length))
+
+
+if __name__ == "__main__":
+  test.main()