diff --git a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_dense_mat_mul_grad_test.py b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_dense_mat_mul_grad_test.py
index c56ac88249f..5cd206ccbc1 100644
--- a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_dense_mat_mul_grad_test.py
+++ b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_dense_mat_mul_grad_test.py
@@ -106,7 +106,11 @@ class CSRSparseMatrixDenseMatMulGradTest(test.TestCase):
 
 # These tests are refactored from sparse_csr_matrix_grad_test to keep its size
 # "medium".
-for dtype in (np.float32, np.complex64):
+dtypes_to_test = [np.float32]
+if not test.is_built_with_rocm:
+  # complex type is not supported on the ROCm platform
+  dtypes_to_test += [np.complex64]
+for dtype in dtypes_to_test:
   for (t_a, t_b, adj_a, adj_b, t_out,
        conj_out) in itertools.product(*(([False, True],) * 6)):
 
diff --git a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_grad_test.py b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_grad_test.py
index e6425fcdc94..a8da71be4d9 100644
--- a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_grad_test.py
+++ b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_grad_test.py
@@ -84,6 +84,10 @@ class CSRSparseMatrixGradTest(test.TestCase):
     if not self._gpu_available:
       return
 
+    if test.is_built_with_rocm():
+      # sparse-matrix-add op is not yet supported on the ROCm platform
+      self.skipTest("sparse-matrix-add op not supported on ROCm")
+
     sparsify = lambda m: m * (m > 0)
     for dense_shape in ([53, 65, 127], [127, 65]):
       a_mats_val = sparsify(np.random.randn(*dense_shape))
diff --git a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py
index c05e50664b2..958e413940d 100644
--- a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py
+++ b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py
@@ -432,6 +432,10 @@ class CSRSparseMatrixOpsTest(test.TestCase):
     if not self._gpu_available:
       return
 
+    if test.is_built_with_rocm():
+      # sparse-matrix-add op is not yet supported on the ROCm platform
+      self.skipTest("sparse-matrix-add op not supported on ROCm")
+
     a_indices = np.array([[0, 0], [2, 3]])
     a_values = np.array([1.0, 5.0]).astype(np.float32)
     a_dense_shape = [5, 6]
@@ -469,6 +473,10 @@ class CSRSparseMatrixOpsTest(test.TestCase):
     if not self._gpu_available:
       return
 
+    if test.is_built_with_rocm():
+      # sparse-matrix-add op is not yet supported on the ROCm platform
+      self.skipTest("sparse-matrix-add op not supported on ROCm")
+
     sparsify = lambda m: m * (m > 0)
     dense_shape = [53, 65, 127]
     a_mats = sparsify(np.random.randn(*dense_shape)).astype(np.float32)
@@ -511,6 +519,10 @@ class CSRSparseMatrixOpsTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes
   def testSparseMatrixMatMulConjugateOutput(self):
+    if test.is_built_with_rocm():
+      # complex types are not yet supported on the ROCm platform
+      self.skipTest("complex type not supported on ROCm")
+
     for shapes in [[(5, 6), (6, 1)], [(5, 6), (6, 2)]]:
       a_indices = np.array([[0, 0], [2, 3]])
       a_values = np.array([1.0 + 1.j, 5.0 - 2.j]).astype(np.complex64)
@@ -533,8 +545,17 @@ class CSRSparseMatrixOpsTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes
   def testLargeBatchSparseMatrixMatMul(self):
+    dtypes_to_test = [np.float32]
+    if not test.is_built_with_rocm():
+      # complex types is not supported on the ROCm platform
+      dtypes_to_test += [np.complex64]
+
+    if test.is_built_with_rocm():
+      # TODO(rocm): fix this
+      self.skipTest("hipSPARSE all failure on the ROCm platform")
+
     sparsify = lambda m: m * (m > 0)
-    for dtype in np.float32, np.complex64:
+    for dtype in dtypes_to_test:
       for (transpose_a, transpose_b) in ((False, False), (False, True),
                                          (True, False), (True, True)):
         for (adjoint_a, adjoint_b) in ((False, False), (False, True),
@@ -584,8 +605,17 @@ class CSRSparseMatrixOpsTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes
   def testLargeBatchSparseMatrixMatMulTransposed(self):
+    dtypes_to_test = [np.float32]
+    if not test.is_built_with_rocm():
+      # complex types is not supported on the ROCm platform
+      dtypes_to_test += [np.complex64]
+
+    if test.is_built_with_rocm():
+      # TODO(rocm): fix this
+      self.skipTest("hipSPARSE all failure on the ROCm platform")
+
     sparsify = lambda m: m * (m > 0)
-    for dtype in np.float32, np.complex64:
+    for dtype in dtypes_to_test:
       for (transpose_a, transpose_b) in ((False, False), (False, True),
                                          (True, False), (True, True)):
         for (adjoint_a, adjoint_b) in ((False, False), (False, True),
@@ -636,6 +666,10 @@ class CSRSparseMatrixOpsTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes
   def testLargeBatchSparseMatrixMatMulConjugate(self):
+    if test.is_built_with_rocm():
+      # complex types are not yet supported on the ROCm platform
+      self.skipTest("complex type not supported on ROCm")
+
     sparsify = lambda m: m * (m > 0)
     a_dense_shape = [53, 65, 127]
     b_dense_shape = [53, 127, 67]
@@ -767,6 +801,10 @@ class CSRSparseMatrixOpsTest(test.TestCase):
     if not self._gpu_available:
       return
 
+    if test.is_built_with_rocm():
+      # sparse-matrix-add op is not yet supported on the ROCm platform
+      self.skipTest("sparse-matrix-add op not supported on ROCm")
+
     sparsify = lambda m: m * (m > 0)
     dense_shape = [53, 65, 127]
     matrices = [
@@ -1154,9 +1192,10 @@ class CSRSparseMatrixOpsTest(test.TestCase):
         ]  #
     ]).astype(np.complex128)
 
-    data_types = [
-        dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128
-    ]
+    data_types = [dtypes.float32, dtypes.float64]
+    if not test.is_built_with_rocm():
+      # complex type is not supported on the ROCm platform
+      data_types += [dtypes.complex64, dtypes.complex128]
     for dtype in data_types:
       sparse_matrix = dense_to_csr_sparse_matrix(
           math_ops.cast(dense_mat, dtype))
diff --git a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_test.py b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_test.py
index 74456229b49..66077f5b2d2 100644
--- a/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_test.py
+++ b/tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_test.py
@@ -154,7 +154,11 @@ class SparseMatrixMatmulTest(test.TestCase):
     sparsify = lambda m: m * (m > 0)
     dense_shape_a = [5, 13, 7] if transpose_a or adjoint_a else [5, 7, 13]
     dense_shape_b = [5, 15, 13] if transpose_b or adjoint_b else [5, 13, 15]
-    for dtype in np.float32, np.complex64:
+    dtypes_to_test = [np.float32]
+    if not test.is_built_with_rocm():
+      # complex type is not supported on the ROCm platform
+      dtypes_to_test += [np.complex64]
+    for dtype in dtypes_to_test:
       a_mats = sparsify((np.random.randn(*dense_shape_a) +
                          1.j * np.random.randn(*dense_shape_a))).astype(dtype)
       b_mats = sparsify((np.random.randn(*dense_shape_b) +
@@ -194,7 +198,11 @@ class SparseMatrixMatmulTest(test.TestCase):
     sparsify = lambda m: m * (m > 0)
     dense_shape_a = [5, 13, 7] if transpose_a or adjoint_a else [5, 7, 13]
     dense_shape_b = [5, 15, 13] if transpose_b or adjoint_b else [5, 13, 15]
-    for dtype in np.float32, np.complex64:
+    dtypes_to_test = [np.float32]
+    if not test.is_built_with_rocm():
+      # complex type is not supported on the ROCm platform
+      dtypes_to_test += [np.complex64]
+    for dtype in dtypes_to_test:
       a_mats = sparsify((np.random.randn(*dense_shape_a) +
                          1.j * np.random.randn(*dense_shape_a))).astype(dtype)
       b_mats = (np.random.randn(*dense_shape_b) +
@@ -231,7 +239,11 @@ class SparseMatrixMatmulTest(test.TestCase):
     sparsify = lambda m: m * (m > 0)
     dense_shape_a = [5, 13, 7] if transpose_a or adjoint_a else [5, 7, 13]
     dense_shape_b = [5, 15, 13] if transpose_b or adjoint_b else [5, 13, 15]
-    for dtype in np.float32, np.complex64:
+    dtypes_to_test = [np.float32]
+    if not test.is_built_with_rocm():
+      # complex type is not supported on the ROCm platform
+      dtypes_to_test += [np.complex64]
+    for dtype in dtypes_to_test:
       a_mats = (np.random.randn(*dense_shape_a) +
                 1.j * np.random.randn(*dense_shape_a)).astype(dtype)
       b_mats = sparsify((np.random.randn(*dense_shape_b) +