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# Copyright 2017 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.
# ==============================================================================
"""Tests for memory statistics ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.memory_stats.python.ops import memory_stats_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_util
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test
class MemoryStatsOpsTest(test_util.TensorFlowTestCase):
def testBytesLimit(self):
# AllocatorStats.bytes_limit is set to zero for CPU allocators, so we skip
# the check.
if not test.is_gpu_available():
return
with self.test_session(use_gpu=True) as sess:
bytes_limit = sess.run(memory_stats_ops.BytesLimit())
self.assertLess(0, bytes_limit)
# Tests the peak memory usage of the following computation.
# a b
# | / |
# c |
# \ |
# \ |
# d
# The memory for matrix "a" can be reused for matrix "d". Therefore, this
# computation needs space for only three matrix plus some small overhead.
def testChainOfMatmul(self):
# MaxBytesInUse is registerd on GPU only. See kernels/memory_stats_ops.cc.
if not test.is_gpu_available():
return
with self.test_session(use_gpu=True) as sess:
matrix_size = 64
matrix_shape = tensor_shape.TensorShape([matrix_size, matrix_size])
dtype = dtypes.float32
matrix_size_in_bytes = matrix_shape.num_elements() * dtype.size
a = random_ops.random_uniform(matrix_shape, dtype=dtype)
b = random_ops.random_uniform(matrix_shape, dtype=dtype)
c = math_ops.matmul(a, b)
d = math_ops.matmul(c, b)
sess.run(d)
max_bytes_in_use = sess.run(memory_stats_ops.MaxBytesInUse())
self.assertGreaterEqual(max_bytes_in_use, matrix_size_in_bytes * 3)
self.assertLess(max_bytes_in_use, matrix_size_in_bytes * 4)
if __name__ == '__main__':
test.main()
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