disabling subtests that test features not yet supported on the ROCm platform

tensorflow/c/eager/c_api_experimental_test.cc

@@ -83,7 +83,10 @@ void ExecuteWithProfiling(bool async) {
   if (!gpu_device_name.empty()) {
     EXPECT_TRUE(HasSubstr(profile_proto_str, "/device:GPU:0"));
     // device name with "stream:all" is collected by Device Tracer.
+#ifndef TENSORFLOW_USE_ROCM
+    // ROCm platform does not yet support stream level tracing
     EXPECT_TRUE(HasSubstr(profile_proto_str, "stream:all"));
+#endif
   }
   // "/host:CPU" is collected by TraceMe
   EXPECT_TRUE(HasSubstr(profile_proto_str, "/host:CPU"));

tensorflow/core/kernels/conv_ops_test.cc

@@ -1001,6 +1001,10 @@ class FusedConv2DWithBatchNormOpTest : public FusedConv2DOpTest<T> {};
 TYPED_TEST_SUITE_P(FusedConv2DWithBiasOpTest);
 TYPED_TEST_SUITE_P(FusedConv2DWithBatchNormOpTest);
 
+// ROCm does not yet support the _FusedConv2D op,
+// Therefore disable tests that check _FusedConv2D, when building with ROCm
+
+#ifndef TENSORFLOW_USE_ROCM
 // -------------------------------------------------------------------------- //
 // Conv2D + BiasAdd + {Activation}                                            //
 // -------------------------------------------------------------------------- //
@@ -1165,4 +1169,5 @@ using FusedBatchNormDataTypes = ::testing::Types<float>;
 INSTANTIATE_TYPED_TEST_SUITE_P(Test, FusedConv2DWithBatchNormOpTest,
                                FusedBatchNormDataTypes);
 
+#endif // TENSORFLOW_USE_ROCM
 }  // namespace tensorflow

tensorflow/python/kernel_tests/init_ops_test.py

@@ -753,6 +753,13 @@ class ConvolutionDeltaOrthogonalInitializerTest(test.TestCase):
         else:
           shape = [4, 16, 16, 16, 64]
           convolution = convolutional.conv3d
+
+          if test.is_built_with_rocm():
+            # This subtest triggers a known bug in ROCm runtime code
+            # The bug has been fixed and will be available in ROCm 2.7
+            # Re-enable this test once ROCm 2.7 is released
+            continue
+
         inputs = random_ops.random_normal(shape, dtype=dtype)
         inputs_2norm = linalg_ops.norm(inputs)
         outputs = convolution(