diff --git a/tensorflow/python/keras/layers/preprocessing/benchmarks/BUILD b/tensorflow/python/keras/layers/preprocessing/benchmarks/BUILD
index f488e1da34f..276fb4767af 100644
--- a/tensorflow/python/keras/layers/preprocessing/benchmarks/BUILD
+++ b/tensorflow/python/keras/layers/preprocessing/benchmarks/BUILD
@@ -7,6 +7,16 @@ package(
 
 exports_files(["LICENSE"])
 
+tf_py_test(
+    name = "categorical_encoding_benchmark",
+    srcs = ["categorical_encoding_benchmark.py"],
+    python_version = "PY3",
+    deps = [
+        "//tensorflow:tensorflow_py",
+        "//tensorflow/python/keras/layers/preprocessing:categorical_encoding",
+    ],
+)
+
 tf_py_test(
     name = "index_lookup_adapt_benchmark",
     srcs = ["index_lookup_adapt_benchmark.py"],
diff --git a/tensorflow/python/keras/layers/preprocessing/benchmarks/categorical_encoding_benchmark.py b/tensorflow/python/keras/layers/preprocessing/benchmarks/categorical_encoding_benchmark.py
new file mode 100644
index 00000000000..e68b77ebef9
--- /dev/null
+++ b/tensorflow/python/keras/layers/preprocessing/benchmarks/categorical_encoding_benchmark.py
@@ -0,0 +1,87 @@
+# 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.
+# ==============================================================================
+"""Benchmark for Keras categorical_encoding preprocessing layer."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import time
+
+from absl import flags
+import numpy as np
+
+from tensorflow.python import keras
+from tensorflow.python.compat import v2_compat
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.keras.layers.preprocessing import categorical_encoding
+from tensorflow.python.ops import random_ops
+from tensorflow.python.platform import benchmark
+from tensorflow.python.platform import test
+
+FLAGS = flags.FLAGS
+
+v2_compat.enable_v2_behavior()
+
+
+class BenchmarkLayer(benchmark.Benchmark):
+  """Benchmark the layer forward pass."""
+
+  def run_dataset_implementation(self, output_mode, batch_size, sequence_length,
+                                 max_tokens):
+    input_t = keras.Input(shape=(sequence_length,), dtype=dtypes.int32)
+    layer = categorical_encoding.CategoricalEncoding(
+        max_tokens=max_tokens, output_mode=output_mode)
+    _ = layer(input_t)
+
+    num_repeats = 5
+    starts = []
+    ends = []
+    for _ in range(num_repeats):
+      ds = dataset_ops.Dataset.from_tensor_slices(
+          random_ops.random_uniform([batch_size * 10, sequence_length],
+                                    minval=0,
+                                    maxval=max_tokens - 1,
+                                    dtype=dtypes.int32))
+      ds = ds.shuffle(batch_size * 100)
+      ds = ds.batch(batch_size)
+      num_batches = 5
+      ds = ds.take(num_batches)
+      ds = ds.prefetch(num_batches)
+      starts.append(time.time())
+      # Benchmarked code begins here.
+      for i in ds:
+        _ = layer(i)
+      # Benchmarked code ends here.
+      ends.append(time.time())
+
+    avg_time = np.mean(np.array(ends) - np.array(starts)) / num_batches
+    name = "categorical_encoding|batch_%s|seq_length_%s|%s_max_tokens" % (
+        batch_size, sequence_length, max_tokens)
+    self.report_benchmark(iters=num_repeats, wall_time=avg_time, name=name)
+
+  def benchmark_vocab_size_by_batch(self):
+    for batch in [32, 256, 2048]:
+      for sequence_length in [10, 1000]:
+        for num_tokens in [100, 1000, 20000]:
+          self.run_dataset_implementation(
+              output_mode="count",
+              batch_size=batch,
+              sequence_length=sequence_length,
+              max_tokens=num_tokens)
+
+
+if __name__ == "__main__":
+  test.main()