6762ca15c4
The session returned by cached_session uses soft placement, something we don't want for XLA_* devices. With soft placement ops lacking XLA kernels silently fall back and run on the CPU, misleading us into thinking we have more test coverage than we actually do. With this test some tests (rightly) start failing because they were testing ops with dtypes the XLA kernels do not support. I've removed these dtypes from the tests. This CL partially addresses b/132430685. It stubs out "cached_session" and "test_session" to raise errors, so we have more confidence that the compiler is being exercised. However, we still use XLA_* devices to exercise XLA, which has a different code path than xla.compile and tpu.rewrite. This needs to be incrementally fixed. PiperOrigin-RevId: 248437673
202 lines
6.9 KiB
Python
202 lines
6.9 KiB
Python
# Copyright 2015 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 tensorflow.ops.data_flow_ops.FIFOQueue."""
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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 time
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from six.moves import xrange # pylint: disable=redefined-builtin
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes as dtypes_lib
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from tensorflow.python.ops import data_flow_ops
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from tensorflow.python.platform import test
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class FIFOQueueTest(xla_test.XLATestCase):
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def testEnqueue(self):
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with self.session(), self.test_scope():
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q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32)
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enqueue_op = q.enqueue((10.0,))
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enqueue_op.run()
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def testEnqueueWithShape(self):
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with self.session(), self.test_scope():
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q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32, shapes=(3, 2))
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enqueue_correct_op = q.enqueue(([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],))
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enqueue_correct_op.run()
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with self.assertRaises(ValueError):
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q.enqueue(([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],))
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self.assertEqual(1, q.size().eval())
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def testMultipleDequeues(self):
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with self.session(), self.test_scope():
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q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()])
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self.evaluate(q.enqueue([1]))
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self.evaluate(q.enqueue([2]))
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self.evaluate(q.enqueue([3]))
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a, b, c = self.evaluate([q.dequeue(), q.dequeue(), q.dequeue()])
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self.assertAllEqual(set([1, 2, 3]), set([a, b, c]))
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def testQueuesDontShare(self):
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with self.session(), self.test_scope():
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q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()])
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self.evaluate(q.enqueue(1))
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q2 = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()])
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self.evaluate(q2.enqueue(2))
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self.assertAllEqual(self.evaluate(q2.dequeue()), 2)
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self.assertAllEqual(self.evaluate(q.dequeue()), 1)
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def testEnqueueDictWithoutNames(self):
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with self.session(), self.test_scope():
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q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32)
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with self.assertRaisesRegexp(ValueError, "must have names"):
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q.enqueue({"a": 12.0})
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def testParallelEnqueue(self):
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with self.session() as sess, self.test_scope():
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q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32)
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elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]
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enqueue_ops = [q.enqueue((x,)) for x in elems]
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dequeued_t = q.dequeue()
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# Run one producer thread for each element in elems.
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def enqueue(enqueue_op):
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sess.run(enqueue_op)
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threads = [
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self.checkedThread(target=enqueue, args=(e,)) for e in enqueue_ops
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]
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for thread in threads:
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thread.start()
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for thread in threads:
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thread.join()
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# Dequeue every element using a single thread.
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results = []
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for _ in xrange(len(elems)):
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results.append(dequeued_t.eval())
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self.assertItemsEqual(elems, results)
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def testParallelDequeue(self):
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with self.session() as sess, self.test_scope():
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q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32)
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elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]
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enqueue_ops = [q.enqueue((x,)) for x in elems]
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dequeued_t = q.dequeue()
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# Enqueue every element using a single thread.
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for enqueue_op in enqueue_ops:
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enqueue_op.run()
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# Run one consumer thread for each element in elems.
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results = []
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def dequeue():
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results.append(sess.run(dequeued_t))
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threads = [self.checkedThread(target=dequeue) for _ in enqueue_ops]
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for thread in threads:
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thread.start()
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for thread in threads:
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thread.join()
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self.assertItemsEqual(elems, results)
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def testDequeue(self):
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with self.session(), self.test_scope():
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q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32)
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elems = [10.0, 20.0, 30.0]
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enqueue_ops = [q.enqueue((x,)) for x in elems]
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dequeued_t = q.dequeue()
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for enqueue_op in enqueue_ops:
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enqueue_op.run()
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for i in xrange(len(elems)):
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vals = self.evaluate(dequeued_t)
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self.assertEqual([elems[i]], vals)
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def testEnqueueAndBlockingDequeue(self):
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with self.session() as sess, self.test_scope():
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q = data_flow_ops.FIFOQueue(3, dtypes_lib.float32)
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elems = [10.0, 20.0, 30.0]
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enqueue_ops = [q.enqueue((x,)) for x in elems]
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dequeued_t = q.dequeue()
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def enqueue():
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# The enqueue_ops should run after the dequeue op has blocked.
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# TODO(mrry): Figure out how to do this without sleeping.
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time.sleep(0.1)
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for enqueue_op in enqueue_ops:
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sess.run(enqueue_op)
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results = []
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def dequeue():
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for _ in xrange(len(elems)):
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results.append(sess.run(dequeued_t))
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enqueue_thread = self.checkedThread(target=enqueue)
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dequeue_thread = self.checkedThread(target=dequeue)
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enqueue_thread.start()
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dequeue_thread.start()
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enqueue_thread.join()
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dequeue_thread.join()
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for elem, result in zip(elems, results):
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self.assertEqual([elem], result)
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def testMultiEnqueueAndDequeue(self):
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with self.session() as sess, self.test_scope():
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q = data_flow_ops.FIFOQueue(10, (dtypes_lib.int32, dtypes_lib.float32))
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elems = [(5, 10.0), (10, 20.0), (15, 30.0)]
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enqueue_ops = [q.enqueue((x, y)) for x, y in elems]
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dequeued_t = q.dequeue()
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for enqueue_op in enqueue_ops:
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enqueue_op.run()
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for i in xrange(len(elems)):
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x_val, y_val = sess.run(dequeued_t)
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x, y = elems[i]
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self.assertEqual([x], x_val)
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self.assertEqual([y], y_val)
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def testQueueSizeEmpty(self):
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with self.session(), self.test_scope():
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q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32)
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self.assertEqual([0], q.size().eval())
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def testQueueSizeAfterEnqueueAndDequeue(self):
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with self.session(), self.test_scope():
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q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32)
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enqueue_op = q.enqueue((10.0,))
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dequeued_t = q.dequeue()
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size = q.size()
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self.assertEqual([], size.get_shape())
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enqueue_op.run()
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self.assertEqual(1, self.evaluate(size))
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dequeued_t.op.run()
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self.assertEqual(0, self.evaluate(size))
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if __name__ == "__main__":
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test.main()
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