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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functional tests for out-of-memory conditions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.platform import googletest
class OutOfMemoryTest(xla_test.XLATestCase):
def testOutputOutOfMemory(self):
"""Allocates tensors until out of memory.
Generates a large rank-1 tensor. The tensor is an output of an XLA
computation, not constant.
Check that a ResourceExhaustedError is raised and can be caught.
We spin in a loop generating larger and larger tensors until an OOM event
happens. We may be running sandboxed, so have a small host memory limit, so
any hardcoded value is unlikely to land in the sweet spot between device
memory size and host memory size with stability.
"""
def test_loop():
size = int(2e8)
while True:
with self.cached_session():
# Force the compiled code to not be constant by feeding in a
# parameter.
p = array_ops.placeholder(dtypes.float32, shape=[2, 1, 1])
with self.test_scope():
# Create a computation that produces a large R1 tensor as an
# intermediate result. Reduce it down so that if this file was
# compiled without --config=cuda, we don't force a D2H copy of a
# large tensor and potentially OOM the host.
#
# This is a bit tricky because XLA:GPU doesn't currently support RNG
# ops. Here we rely on the fact that XLA doesn't do algebraic
# simplifications on conv(<ones>, <filter>).
c = math_ops.reduce_sum(
nn_ops.convolution(
array_ops.ones([1, size, 1]),
p,
padding='SAME',
data_format='NWC'))
c.eval(feed_dict={p: [[[1.0]], [[2.0]]]})
size *= 2
self.assertRaises(errors.ResourceExhaustedError, test_loop)
if __name__ == '__main__':
googletest.main()
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