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diff --git a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc
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+++ b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc
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+/* Copyright 2017 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.
+==============================================================================*/
+
+#include <algorithm>
+#include <array>
+#include <memory>
+
+#include "tensorflow/compiler/xla/array4d.h"
+#include "tensorflow/compiler/xla/client/computation_builder.h"
+#include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/client/padding.h"
+#include "tensorflow/compiler/xla/legacy_flags/cpu_compiler_flags.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/reference_util.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/test_helpers.h"
+#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
+#include "tensorflow/compiler/xla/tests/literal_test_util.h"
+#include "tensorflow/compiler/xla/tests/test_macros.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace xla {
+namespace {
+
+class ConvolutionDimensionNumbersTest : public ClientLibraryTestBase {};
+
+// Tests the convolution operation with invalid input dimension numbers.
+TEST_F(ConvolutionDimensionNumbersTest, InvalidInputDimensionNumbers) {
+  auto dimension_numbers_status =
+      ComputationBuilder::CreateConvDimensionNumbers(0, 2, 2, 3, 0, 1, 2, 3);
+  ASSERT_FALSE(dimension_numbers_status.ok());
+  ASSERT_MATCH(dimension_numbers_status.status().error_message(),
+               testing::ContainsRegex("input are not unique"));
+}
+
+// Tests the convolution operation with invalid weight dimension numbers.
+TEST_F(ConvolutionDimensionNumbersTest, InvalidWeightDimensionNumbers) {
+  auto dimension_numbers_status =
+      ComputationBuilder::CreateConvDimensionNumbers(0, 1, 2, 3, 2, 3, 2, 3);
+  ASSERT_FALSE(dimension_numbers_status.ok());
+  ASSERT_MATCH(dimension_numbers_status.status().error_message(),
+               testing::ContainsRegex("weight are not unique"));
+}
+
+XLA_TEST_F(ConvolutionDimensionNumbersTest,
+           TwoConvsWithDifferentDimensionNumbers) {
+  auto input_array = MakeUnique<Array4D<float>>(2, 3, 5, 5);
+  input_array->FillWithMultiples(0.1);
+  auto weight_array = MakeUnique<Array4D<float>>(4, 3, 1, 1);
+  weight_array->FillWithMultiples(0.2);
+  auto weight_data =
+      client_
+          ->TransferToServer(*LiteralUtil::CreateR4FromArray4D(*weight_array))
+          .ConsumeValueOrDie();
+
+  ComputationBuilder builder(client_, TestName());
+  auto input = builder.ConstantR4FromArray4D<float>(*input_array);
+  auto weight =
+      builder.Parameter(0, ShapeUtil::MakeShape(F32, {4, 3, 1, 1}), "weight");
+  auto conv1 = builder.Conv(input, weight, {1, 1}, Padding::kValid);
+
+  ConvolutionDimensionNumbers dim_nums =
+      ComputationBuilder::CreateDefaultConvDimensionNumbers();
+  // Swap batch_dimension and feature_dimension.
+  int64 tmp = dim_nums.batch_dimension();
+  dim_nums.set_batch_dimension(dim_nums.feature_dimension());
+  dim_nums.set_feature_dimension(tmp);
+  // Swap kernel_input_feature_dimension and kernel_output_feature_dimension.
+  tmp = dim_nums.kernel_input_feature_dimension();
+  dim_nums.set_kernel_input_feature_dimension(
+      dim_nums.kernel_output_feature_dimension());
+  dim_nums.set_kernel_output_feature_dimension(tmp);
+  builder.ConvWithGeneralDimensions(input, conv1, {1, 1}, Padding::kValid,
+                                    dim_nums);
+
+  auto expected_conv1 = ReferenceUtil::ConvArray4D(*input_array, *weight_array,
+                                                   {1, 1}, Padding::kValid);
+  auto expected_conv2 = ReferenceUtil::ConvArray4DGeneralDimensions(
+      *input_array, *expected_conv1, {1, 1}, Padding::kValid, dim_nums);
+
+  ComputeAndCompareR4<float>(&builder, *expected_conv2, {weight_data.get()},
+                             ErrorSpec(0.001, 0.01));
+}
+
+}  // namespace
+}  // namespace xla
+
+int main(int argc, char** argv) {
+  std::vector<tensorflow::Flag> flag_list;
+  xla::legacy_flags::AppendCpuCompilerFlags(&flag_list);
+  xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
+  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
+  if (!parse_result) {
+    LOG(ERROR) << "\n" << usage;
+    return 2;
+  }
+  testing::InitGoogleTest(&argc, argv);
+  if (argc > 1) {
+    LOG(ERROR) << "Unknown argument " << argv[1] << "\n" << usage;
+    return 2;
+  }
+  return RUN_ALL_TESTS();
+}