[ROCm] Re-enabling unit-tests that are now passing on ROCm platform

tensorflow/core/common_runtime/constant_folding_test.cc

@@ -631,13 +631,6 @@ TEST_F(ConstantFoldingTest, ConstShapeKnown) {
   }
 }
 
-// Disabling the following test on the ROCm platform because it relies on the
-// "topK" operator being supported on the ROCm platform (which is currently not
-// the case)
-// TODO(rocm) :
-// re-enable this test once support for "topK" operator is available on ROCm
-
-#ifndef TENSORFLOW_USE_ROCM
 TEST_F(ConstantFoldingTest, NoReplacePartialOutput) {
   Graph g(OpRegistry::Global());
   {
@@ -662,7 +655,6 @@ TEST_F(ConstantFoldingTest, NoReplacePartialOutput) {
       &g, &was_mutated));
   EXPECT_FALSE(was_mutated);
 }
-#endif  // TENSORFLOW_USE_ROCM
 
 namespace {
 

tensorflow/core/kernels/depthwise_conv_ops_test.cc

@@ -102,7 +102,7 @@ TEST_F(DepthwiseConvOpTest, DepthwiseConvHalfCpu) {
   Run<Eigen::half>(Device::CPU);
 }
 
-#ifdef GOOGLE_CUDA
+#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
 TEST_F(DepthwiseConvOpTest, DepthwiseConvFloatGpu) { Run<float>(Device::GPU); }
 TEST_F(DepthwiseConvOpTest, DepthwiseConvDoubleGpu) {
   Run<double>(Device::GPU);

tensorflow/core/kernels/image/resize_bilinear_op_test.cc

@@ -533,7 +533,7 @@ INSTANTIATE_TEST_SUITE_P(ResizeBilinearHalfPixelCentersOpTestCpu,
 INSTANTIATE_TEST_SUITE_P(ResizeBilinearOpAlignCornersTestCpu,
                          ResizeBilinearOpAlignCornersTest,
                          ::testing::Values(TestDevice::CPU));
-#if GOOGLE_CUDA
+#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
 // Instantiate tests for GPU.
 INSTANTIATE_TEST_SUITE_P(ResizeBilinearOpTestGpu, ResizeBilinearOpTest,
                          ::testing::Values(TestDevice::GPU));
@@ -543,7 +543,7 @@ INSTANTIATE_TEST_SUITE_P(ResizeBilinearHalfPixelCentersOpTestGpu,
 INSTANTIATE_TEST_SUITE_P(ResizeBilinearOpAlignCornersTestGpu,
                          ResizeBilinearOpAlignCornersTest,
                          ::testing::Values(TestDevice::GPU));
-#endif  // GOOGLE_CUDA
+#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
 
 class ResizeBM : public ResizeBilinearOpTest {
  public:

tensorflow/python/compiler/xla/jit_compile_test.py

@@ -37,20 +37,14 @@ class JitCompileTest(test.TestCase):
 
       xla_func = def_function.function(fn, jit_compile=True)
       inputs = array_ops.placeholder(dtypes.float32, [5])
-      # XLA support is not yet enabled for TF ROCm
+      x = xla_func(inputs, 1)
-      if not test.is_built_with_rocm():
+      with session.Session(graph=g) as sess:
-        x = xla_func(inputs, 1)
+        y = sess.run(x, feed_dict={inputs: [1, 2, 2, 3, 3]})
-        with session.Session(graph=g) as sess:
+        self.assertTrue(x.graph.as_graph_def().library.function[0]
-          y = sess.run(x, feed_dict={inputs: [1, 2, 2, 3, 3]})
+                        .attr["_XlaMustCompile"].b)
-          self.assertTrue(x.graph.as_graph_def().library.function[0]
+        self.assertAllClose([2, 3, 3, 4, 4], y)
-                          .attr["_XlaMustCompile"].b)
-          self.assertAllClose([2, 3, 3, 4, 4], y)
 
   def testDerivative(self):
-    # XLA support is not yet enabled for TF ROCm
-    if test.is_built_with_rocm():
-      return
-
     def fn(x, a):
       return 2 * x + a
 
@@ -81,14 +75,12 @@ class JitCompileTest(test.TestCase):
 
       xla_func = def_function.function(fn, jit_compile=True)
       inputs = array_ops.placeholder(dtypes.int32, [5])
-      # XLA support is not yet enabled for TF ROCm
+      x = xla_func(inputs, 1)
-      if not test.is_built_with_rocm():
+      with session.Session(graph=g) as sess:
-        x = xla_func(inputs, 1)
+        y = sess.run(x, feed_dict={inputs: [1, 2, 2, 3, 3]})
-        with session.Session(graph=g) as sess:
+        self.assertTrue(x.graph.as_graph_def().library.function[0]
-          y = sess.run(x, feed_dict={inputs: [1, 2, 2, 3, 3]})
+                        .attr["_XlaMustCompile"].b)
-          self.assertTrue(x.graph.as_graph_def().library.function[0]
+        self.assertAllClose([2, 3, 3, 4, 4], y)
-                          .attr["_XlaMustCompile"].b)
-          self.assertAllClose([2, 3, 3, 4, 4], y)
 
   # Checking that we crash on an unsupported operation lets us test that the XLA
   # compiler was actually invoked.
@@ -101,12 +93,10 @@ class JitCompileTest(test.TestCase):
       xla_func = def_function.function(fn, jit_compile=True)
       inputs = array_ops.placeholder(dtypes.float32, [5])
       x = xla_func(inputs)
-      # XLA support is not yet enabled for TF ROCm
+      with self.assertRaisesRegex(errors.InvalidArgumentError,
-      if not test.is_built_with_rocm():
+                                  "not compilable"):
-        with self.assertRaisesRegex(errors.InvalidArgumentError,
+        with session.Session(graph=g) as sess:
-                                    "not compilable"):
+          sess.run(x, feed_dict={inputs: [1, 2, 2, 3, 3]})
-          with session.Session(graph=g) as sess:
-            sess.run(x, feed_dict={inputs: [1, 2, 2, 3, 3]})
 
 
 if __name__ == "__main__":

tensorflow/python/eager/forwardprop_test.py

@@ -472,12 +472,6 @@ class ForwardpropTest(test.TestCase, parameterized.TestCase):
 
   def testFusedBatchNormGradsInference(self):
 
-    if test.is_built_with_rocm():
-      # This test was added recently and has been failing on the ROCm
-      # platform, since it was added.
-      # TODO(rocm): do root cause analysis of test failure and fix it.
-      self.skipTest("Test fails on ROCm platform, needs further analysis")
-
     x_shape = [4, 10, 10, 2]
     increment = 3. / math_ops.reduce_prod(
         constant_op.constant(x_shape, dtype=dtypes.float32))

tensorflow/python/keras/backend_test.py

@@ -847,8 +847,6 @@ class BackendNNOpsTest(test.TestCase, parameterized.TestCase):
       y = backend.pool2d(x, (2, 2), strides=(2, 2), pool_mode='other')
 
   def test_pool3d(self):
-    if test.is_built_with_rocm():
-      self.skipTest('Pooling with 3D tensors is not supported in ROCm')
     val = np.random.random((10, 3, 10, 10, 10))
     x = backend.variable(val)
     y = backend.pool3d(

tensorflow/python/keras/integration_test/forwardprop_test.py

@@ -218,12 +218,6 @@ class ForwardpropTest(tf.test.TestCase, parameterized.TestCase):
                                    ("NoFunction", lambda f: f)])
   def testVariablesHVP(self, decorator):
 
-    if tf.test.is_built_with_rocm():
-      # TODO(rocm)
-      # This test was recently added and has never passed on the
-      # ROCm platform. Remove this skip once the test is passing again
-      self.skipTest("NoFunction decorator test fails on the ROCm platform")
-
     class _Model(tf.Module):
 
       def __init__(self):

tensorflow/python/keras/layers/pooling_test.py

@@ -194,8 +194,6 @@ class Pooling2DTest(test.TestCase, parameterized.TestCase):
 class Pooling3DTest(test.TestCase, parameterized.TestCase):
 
   def test_maxpooling_3d(self):
-    if test.is_built_with_rocm():
-      self.skipTest('Pooling with 3D tensors is not supported in ROCm')
     pool_size = (3, 3, 3)
     testing_utils.layer_test(
         keras.layers.MaxPooling3D,
@@ -214,8 +212,6 @@ class Pooling3DTest(test.TestCase, parameterized.TestCase):
         input_shape=(3, 4, 11, 12, 10))
 
   def test_averagepooling_3d(self):
-    if test.is_built_with_rocm():
-      self.skipTest('Pooling with 3D tensors is not supported in ROCm')
     pool_size = (3, 3, 3)
     testing_utils.layer_test(
         keras.layers.AveragePooling3D,

tensorflow/python/keras/layers/preprocessing/image_preprocessing_test.py

@@ -228,11 +228,6 @@ class RandomCropTest(keras_parameterized.TestCase):
         self._run_test(expected_height, expected_width)
 
   def test_training_with_mock(self):
-    if test.is_built_with_rocm():
-      # TODO(rocm):
-      # re-enable this test once ROCm adds support for
-      # the StatefulUniformFullInt Op (on the GPU)
-      self.skipTest('Feature not supported on ROCm')
     np.random.seed(1337)
     height, width = 3, 4
     height_offset = np.random.randint(low=0, high=3)
@@ -253,11 +248,6 @@ class RandomCropTest(keras_parameterized.TestCase):
       ('random_crop_4_by_6', 4, 6),
       ('random_crop_3_by_2', 3, 2))
   def test_random_crop_output_shape(self, expected_height, expected_width):
-    if test.is_built_with_rocm():
-      # TODO(rocm):
-      # re-enable this test once ROCm adds support for
-      # the StatefulUniformFullInt Op (on the GPU)
-      self.skipTest('Feature not supported on ROCm')
     with CustomObjectScope({'RandomCrop': image_preprocessing.RandomCrop}):
       self._run_test(expected_height, expected_width)
 

tensorflow/python/kernel_tests/linalg_grad_test.py

@@ -200,13 +200,6 @@ if __name__ == '__main__':
 
           for lower in True, False:
             name = '%s_low_%s' % (name, lower)
-            if (name == 'float32_10_10_adj_False_low_True') and \
-               test_lib.is_built_with_rocm():
-              # Skip this one particular subtest on the ROCm platform
-              # It will fail because of 1 element in 10,000 mismatch,
-              # and the mismatch is minor (tolerance is 0.20, mismatch is 0,22)
-              # TODO(rocm) : investigate cause of mismatch and fix
-              continue
             _AddTest(MatrixBinaryFunctorGradientTest,
                      'MatrixTriangularSolveGradient', name,
                      _GetMatrixBinaryFunctorGradientTest(

tensorflow/python/kernel_tests/pool_test.py

@@ -274,9 +274,6 @@ class PoolingTest(test.TestCase):
               strides=[1, 2],
               dilation_rate=[1, 1],
               data_format="NCHW")
-          if test.is_built_with_rocm():
-            # Pooling with 3D tensors is not supported in ROCm
-            continue
           self._test(
               input_shape=[2, 2, 7, 5, 3],
               window_shape=[2, 2, 2],

tensorflow/python/ops/control_flow_ops_test.py

@@ -57,7 +57,6 @@ from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 import tensorflow.python.ops.tensor_array_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import googletest
-from tensorflow.python.platform import test
 from tensorflow.python.training import momentum
 from tensorflow.python.util import nest
 
@@ -1113,9 +1112,6 @@ class IndexedCaseTest(test_util.TensorFlowTestCase, parameterized.TestCase):
     """Verify disjoint branches across while iterations are run in parallel."""
     if control_flow_v2_toggles.control_flow_v2_enabled():
       self.skipTest("b/138870290")
-    if test.is_built_with_rocm():
-      self.skipTest(
-          "Disable subtest on ROCm due to missing Cholesky op support")
 
     with ops.Graph().as_default() as g:
       nbranches = 7

tensorflow/python/ops/init_ops_v2_test.py

@@ -443,10 +443,6 @@ class OrthogonalInitializerTest(InitializersTest):
 
   @test_util.run_in_graph_and_eager_modes
   def testShapesValues(self):
-
-    if test.is_built_with_rocm():
-      self.skipTest("Disable subtest on ROCm due to missing QR op support")
-
     for shape in [(10, 10), (10, 9, 8), (100, 5, 5), (50, 40), (40, 50)]:
       init = init_ops_v2.Orthogonal()
       tol = 1e-5