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Skippping failing subtests within the CSR Sparse Matrix unit-tests.

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The failures are because either
* the subtests require support for complex type (which is not yet supported by ROCm)
* or they require a GPU kernel implementation for the SparseMatrixAdd op
  (which is also not supported by ROCm, because the underlying hipSPARSE API routine - csrgeam - does not exist).

There are also a couple of subtests commented out because hipSPARSE API errors out with an unknown error for them. Those will be looked into and fixed soon
This commit is contained in:
Deven Desai 2019-11-19 22:55:48 +00:00
parent 7e8ccbd22b
commit 5ad7620d6f
4 changed files with 68 additions and 9 deletions
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6
tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_dense_mat_mul_grad_test.py
View File

@ -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)):

4
tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_grad_test.py
View File

@ -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))

49
tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_ops_test.py
View File

@ -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))

18
tensorflow/python/kernel_tests/linalg/sparse/csr_sparse_matrix_test.py
View File

@ -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) +