1e67c90e2c
XLA is a compiler-based linear algebra execution engine that targets CPUs, GPUs and custom accelerators. XLA is still experimental; we are releasing it early to get the community involved. Change: 143990941
103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for the behavior of the auto-compilation pass."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.compiler.tests.xla_test import XLATestCase
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import googletest
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CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0"
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class ClusteringTest(XLATestCase):
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def testAdd(self):
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val1 = np.array([4, 3, 2, 1], dtype=np.float32)
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val2 = np.array([5, 6, 7, 8], dtype=np.float32)
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expected = val1 + val2
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with self.test_session():
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with self.test_scope():
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input1 = constant_op.constant(val1, name="const1")
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input2 = constant_op.constant(val2, name="const2")
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output = math_ops.add(input1, input2)
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result = output.eval()
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self.assertAllClose(result, expected, rtol=1e-3)
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def testAddFromCpuMultiple(self):
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val1 = np.array([4, 3, 2, 1]).astype(np.float32)
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val2 = np.array([5, 6, 7, 8]).astype(np.float32)
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expected = val1 + val2
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with self.test_session():
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with ops.device(CPU_DEVICE):
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input1 = constant_op.constant(val1, name="const1")
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input2 = constant_op.constant(val2, name="const2")
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with self.test_scope():
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output = math_ops.add(input1, input2)
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for _ in xrange(10):
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result = output.eval()
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self.assertAllClose(result, expected, rtol=1e-3)
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def testDeadlock(self):
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# Builds a graph of the form:
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# x -> y
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# | \
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# z -> w
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# where x and z are placed on the CPU and y and w are placed on the XLA
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# device. If y and w are clustered for compilation, then the graph will
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# deadlock since the clustered graph will contain a self-loop.
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with self.test_session() as sess:
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with ops.device(CPU_DEVICE):
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x = array_ops.placeholder(dtypes.float32, [2])
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with self.test_scope():
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y = x * 2
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with ops.device(CPU_DEVICE):
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z = y * y
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with self.test_scope():
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w = y + z
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result = sess.run(w, {x: [1.5, 0.5]})
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self.assertAllClose(result, [12., 2.], rtol=1e-3)
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def testHostMemory(self):
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with self.test_session() as sess:
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x = array_ops.placeholder(dtypes.int32)
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with self.test_scope():
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y = x + 1
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with ops.device(CPU_DEVICE):
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# Place a computation on the CPU, so y and w cannot be merged into the
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# same JIT compilation.
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z = y * 2
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with self.test_scope():
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# Argument 'y' is a non-constant output of a previous cluster. Make sure
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# it is properly copied to host memory so it can be used as a
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# compile-time constant input for this cluster.
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w = array_ops.reshape(z, y)
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result = sess.run(w, {x: [1, 0]})
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expected = np.array([[4], [2]], dtype=np.int32)
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self.assertAllClose(expected, result, rtol=1e-3)
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if __name__ == "__main__":
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googletest.main()
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