experiments/STT-tensorflow
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Decorates a few more tests to pass in tf2 nightlies

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PiperOrigin-RevId: 236012401
This commit is contained in:
A. Unique TensorFlower 2019-02-27 16:08:21 -08:00 committed by TensorFlower Gardener
parent 08e48b6138
commit 4951bd6b6e
3 changed files with 10 additions and 5 deletions

2
tensorflow/python/kernel_tests/basic_gpu_test.py
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@ -257,7 +257,7 @@ class GpuMultiSessionMemoryTest(test_util.TensorFlowTestCase):
if len(results) != 1: if len(results) != 1:
break break
@test_util.run_deprecated_v1 @test_util.run_v1_only('b/126596827 needs graph mode in multiple threads')
def testConcurrentSessions(self): def testConcurrentSessions(self):
n_threads = 4 n_threads = 4
threads = [] threads = []

1
tensorflow/python/kernel_tests/matrix_triangular_solve_op_test.py
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@ -167,6 +167,7 @@ class MatrixTriangularSolveOpTest(test.TestCase):
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
self._verifySolve(matrix, rhs, batch_dims=[2, 3]) self._verifySolve(matrix, rhs, batch_dims=[2, 3])
@test_util.run_deprecated_v1
def testNotInvertible(self): def testNotInvertible(self):
# The input should be invertible. # The input should be invertible.
# The matrix is singular because it has a zero on the diagonal. # The matrix is singular because it has a zero on the diagonal.

12
tensorflow/python/ops/matmul_benchmark.py
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@ -49,13 +49,17 @@ def build_graph(device, n, m, k, transpose_a, transpose_b, dtype):
""" """
with ops.device('%s' % device): with ops.device('%s' % device):
if not transpose_a: if not transpose_a:
x = variables.VariableV1(random_ops.random_uniform([n, m], dtype=dtype)) x = variables.VariableV1(random_ops.random_uniform([n, m], dtype=dtype),
use_resource=False)
else: else:
x = variables.VariableV1(random_ops.random_uniform([m, n], dtype=dtype)) x = variables.VariableV1(random_ops.random_uniform([m, n], dtype=dtype),
use_resource=False)
if not transpose_b: if not transpose_b:
y = variables.VariableV1(random_ops.random_uniform([m, k], dtype=dtype)) y = variables.VariableV1(random_ops.random_uniform([m, k], dtype=dtype),
use_resource=False)
else: else:
y = variables.VariableV1(random_ops.random_uniform([k, m], dtype=dtype)) y = variables.VariableV1(random_ops.random_uniform([k, m], dtype=dtype),
use_resource=False)
z = math_ops.matmul(x, y, transpose_a=transpose_a, transpose_b=transpose_b) z = math_ops.matmul(x, y, transpose_a=transpose_a, transpose_b=transpose_b)
return control_flow_ops.group(z) return control_flow_ops.group(z)