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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 the behavior of the auto-compilation pass."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0"
class ClusteringTest(xla_test.XLATestCase):
def testAdd(self):
val1 = np.array([4, 3, 2, 1], dtype=np.float32)
val2 = np.array([5, 6, 7, 8], dtype=np.float32)
expected = val1 + val2
with self.test_session():
with self.test_scope():
input1 = constant_op.constant(val1, name="const1")
input2 = constant_op.constant(val2, name="const2")
output = math_ops.add(input1, input2)
result = output.eval()
self.assertAllClose(result, expected, rtol=1e-3)
def testAddFromCpuMultiple(self):
val1 = np.array([4, 3, 2, 1]).astype(np.float32)
val2 = np.array([5, 6, 7, 8]).astype(np.float32)
expected = val1 + val2
with self.test_session():
with ops.device(CPU_DEVICE):
input1 = constant_op.constant(val1, name="const1")
input2 = constant_op.constant(val2, name="const2")
with self.test_scope():
output = math_ops.add(input1, input2)
for _ in xrange(10):
result = output.eval()
self.assertAllClose(result, expected, rtol=1e-3)
def testDeadlock(self):
# Builds a graph of the form:
# x -> y
# | \
# z -> w
# where x and z are placed on the CPU and y and w are placed on the XLA
# device. If y and w are clustered for compilation, then the graph will
# deadlock since the clustered graph will contain a self-loop.
with self.test_session() as sess:
with ops.device(CPU_DEVICE):
x = array_ops.placeholder(dtypes.float32, [2])
with self.test_scope():
y = x * 2
with ops.device(CPU_DEVICE):
z = y * y
with self.test_scope():
w = y + z
result = sess.run(w, {x: [1.5, 0.5]})
self.assertAllClose(result, [12., 2.], rtol=1e-3)
def testHostMemory(self):
with self.test_session() as sess:
x = array_ops.placeholder(dtypes.int32)
with self.test_scope():
y = x + 1
with ops.device(CPU_DEVICE):
# Place a computation on the CPU, so y and w cannot be merged into the
# same JIT compilation.
z = y * 2
with self.test_scope():
# Argument 'y' is a non-constant output of a previous cluster. Make sure
# it is properly copied to host memory so it can be used as a
# compile-time constant input for this cluster.
w = array_ops.reshape(z, y)
result = sess.run(w, {x: [1, 0]})
expected = np.array([[4], [2]], dtype=np.int32)
self.assertAllClose(expected, result, rtol=1e-3)
if __name__ == "__main__":
googletest.main()
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