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authorGravatar A. Unique TensorFlower <gardener@tensorflow.org>2018-09-12 01:49:11 -0700
committerGravatar TensorFlower Gardener <gardener@tensorflow.org>2018-09-12 01:53:01 -0700
commit6bb429b7772bead4e386cb22b6ab2aefa520442e (patch)
tree3e8850d0ee4db999a281d15418431efdc6aba942 /tensorflow/compiler/tests
parent4c936f1b220676d0d427f5f38b4111cfb9011b5a (diff)
Automated rollback of commit 4c936f1b220676d0d427f5f38b4111cfb9011b5a
PiperOrigin-RevId: 212600364
Diffstat (limited to 'tensorflow/compiler/tests')
-rw-r--r--tensorflow/compiler/tests/concat_ops_test.py35
1 files changed, 0 insertions, 35 deletions
diff --git a/tensorflow/compiler/tests/concat_ops_test.py b/tensorflow/compiler/tests/concat_ops_test.py
index 2d225ad226..37e5318bb5 100644
--- a/tensorflow/compiler/tests/concat_ops_test.py
+++ b/tensorflow/compiler/tests/concat_ops_test.py
@@ -291,41 +291,6 @@ class ConcatTest(xla_test.XLATestCase):
ValueError, r"Can't concatenate scalars \(use tf\.stack instead\)"):
array_ops.concat([scalar, scalar, scalar], dim)
- # The purpose of this is to ensure that XLA on GPU will not run out of memory
- # with too many arguments.
- def testConcatLargeNumberOfTensors(self):
- with self.cached_session():
- with self.test_scope():
- for concat_dim in range(2):
- params = {}
- p = []
- shape = np.array([7, 13])
- num_tensors = 1001
- for i in np.arange(num_tensors):
- input_shape = shape
- placeholder = array_ops.placeholder(
- dtypes.float32, shape=input_shape)
- p.append(placeholder)
- params[placeholder] = np.random.rand(*input_shape).astype(
- np.float32)
-
- concat_inputs = p
- c = array_ops.concat(concat_inputs, concat_dim)
- result = c.eval(feed_dict=params)
-
- self.assertEqual(result.shape, c.get_shape())
- cur_offset = 0
-
- for i in np.arange(num_tensors):
- # The index into the result is the ':' along all dimensions
- # except the concat_dim. slice(0, size) is used for ':', and
- # a list of slices is used to index into result.
- index = [slice(0, params[p[i]].shape[j]) for j in np.arange(2)]
- index[concat_dim] = slice(
- cur_offset, cur_offset + params[p[i]].shape[concat_dim])
- cur_offset += params[p[i]].shape[concat_dim]
- self.assertAllEqual(result[index], params[p[i]])
-
class ConcatOffsetTest(xla_test.XLATestCase):