diff --git a/tensorflow/python/keras/benchmarks/layer_benchmarks/layer_benchmarks_test.py b/tensorflow/python/keras/benchmarks/layer_benchmarks/layer_benchmarks_test.py
index 850e7efdf45..2a7bede8fcb 100644
--- a/tensorflow/python/keras/benchmarks/layer_benchmarks/layer_benchmarks_test.py
+++ b/tensorflow/python/keras/benchmarks/layer_benchmarks/layer_benchmarks_test.py
@@ -68,38 +68,38 @@ class KerasLayerBenchmarks(six.with_metaclass(
           "activation": "relu"
       }, {
           "input_shape": (1, 1, 1, 1)
-      }, 10),
+      }, 100),
       ("Conv2D_normal_shape", tf.keras.layers.Conv2D, {
           "filters": 1,
           "kernel_size": 1,
           "activation": "relu"
       }, {
           "input_shape": (64, 28, 28, 3)
-      }, 10),
+      }, 100),
       ("LSTM_small_shape", tf.keras.layers.LSTM, {
           "units": 1
       }, {
           "input_shape": (1, 1, 1)
-      }, 10),
+      }, 100),
       ("LSTM_normal_shape", tf.keras.layers.LSTM, {
           "units": 4
       }, {
           "input_shape": (32, 10, 8)
-      }, 10),
+      }, 100),
       ("Embedding_small_shape", tf.keras.layers.Embedding, {
           "input_dim": 1,
           "output_dim": 1,
           "input_length": 1
       }, {
           "input": np.random.randint(1, size=(1, 1))
-      }, 10),
+      }, 100),
       ("Embedding_normal_shape", tf.keras.layers.Embedding, {
           "input_dim": 1000,
           "output_dim": 64,
           "input_length": 10
       }, {
           "input": np.random.randint(1000, size=(32, 10))
-      }, 10),
+      }, 100),
   ])
 
   def benchmark_layer_call(self, layer_cls, layer_args, inputs, num_iters):
@@ -163,50 +163,12 @@ class KerasLayerBenchmarks(six.with_metaclass(
     metadata.update(_get_metadata(name))
     self.run_report(fn, num_iters, metadata)
 
-
-class KerasLayerBenchmarksBackwardXLA(six.with_metaclass(
-    benchmark.ParameterizedBenchmark,
-    layer_benchmarks_test_base.LayerBenchmarksBase)):
-
-  _benchmark_parameters = benchmark_util.generate_benchmark_params_cpu_gpu([
-      ("Conv2D_small_shape", tf.keras.layers.Conv2D, {
-          "filters": 1,
-          "kernel_size": 1,
-          "activation": "relu"
-      }, {
-          "input_shape": (1, 1, 1, 1)
-      }, 10000),
-      ("Conv2D_normal_shape", tf.keras.layers.Conv2D, {
-          "filters": 1,
-          "kernel_size": 1,
-          "activation": "relu"
-      }, {
-          "input_shape": (64, 28, 28, 3)
-      }, 10000),
-      # TODO(b/153480400)
-      # ("LSTM_small_shape", tf.keras.layers.LSTM,
-      #  {"units": 1}, {"input_shape": (1, 1, 1)}, 10000),
-      # ("LSTM_normal_shape", tf.keras.layers.LSTM,
-      #  {"units": 4}, {"input_shape": (32, 10, 8)}, 10000),
-      ("Embedding_small_shape", tf.keras.layers.Embedding, {
-          "input_dim": 1,
-          "output_dim": 1,
-          "input_length": 1
-      }, {
-          "input": np.random.randint(1, size=(1, 1))
-      }, 10),
-      ("Embedding_normal_shape", tf.keras.layers.Embedding, {
-          "input_dim": 1000,
-          "output_dim": 64,
-          "input_length": 10
-      }, {
-          "input": np.random.randint(1000, size=(32, 10))
-      }, 10),
-  ])
-
   def benchmark_layer_call_backward_with_xla(
       self, layer_cls, layer_args, inputs, num_iters):
     name = benchmark_util.get_benchmark_name(self._get_name())
+    # TODO(b/153480400)
+    if layer_cls is tf.keras.layers.LSTM:
+      return
     # TODO(b/173461426)
     if layer_cls is tf.keras.layers.Embedding and name[-1] == "GPU":
       return