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[TF:XLA] Add partial implementation of tf.FIFOQueue for XLA devices (…
…e.g., TPU). The idea is to have a host-side queue of device tensors. Operators dequeue_many, enqueue_many, and dequeue_up_to are not yet implemented because they require splitting/concatenating tensors, which will require calling into a compiled XLA compilation. Refactor queue operator implementations into libraries separate from the kernel registrations. Add support for ResourceOpKernels that are placed on non-CPU devices. Add support for allocating host-memory tensors during OpKernel construction. PiperOrigin-RevId: 202590292
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| # Copyright 2015 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 tensorflow.ops.data_flow_ops.FIFOQueue.""" | ||
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| from __future__ import absolute_import | ||
| from __future__ import division | ||
| 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 | ||
| from tensorflow.python.framework import dtypes as dtypes_lib | ||
| from tensorflow.python.ops import data_flow_ops | ||
| from tensorflow.python.platform import test | ||
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| class FIFOQueueTest(xla_test.XLATestCase): | ||
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| def testEnqueue(self): | ||
| with self.test_session(), self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) | ||
| enqueue_op = q.enqueue((10.0,)) | ||
| enqueue_op.run() | ||
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| def testEnqueueWithShape(self): | ||
| with self.test_session(), self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32, shapes=(3, 2)) | ||
| enqueue_correct_op = q.enqueue(([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],)) | ||
| enqueue_correct_op.run() | ||
| with self.assertRaises(ValueError): | ||
| q.enqueue(([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],)) | ||
| self.assertEqual(1, q.size().eval()) | ||
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| def testMultipleDequeues(self): | ||
| with self.test_session(), self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) | ||
| self.evaluate(q.enqueue([1])) | ||
| self.evaluate(q.enqueue([2])) | ||
| self.evaluate(q.enqueue([3])) | ||
| a, b, c = self.evaluate([q.dequeue(), q.dequeue(), q.dequeue()]) | ||
| self.assertAllEqual(set([1, 2, 3]), set([a, b, c])) | ||
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| def testQueuesDontShare(self): | ||
| with self.test_session(), self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) | ||
| self.evaluate(q.enqueue(1)) | ||
| q2 = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) | ||
| self.evaluate(q2.enqueue(2)) | ||
| self.assertAllEqual(self.evaluate(q2.dequeue()), 2) | ||
| self.assertAllEqual(self.evaluate(q.dequeue()), 1) | ||
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| def testEnqueueDictWithoutNames(self): | ||
| with self.test_session(), self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) | ||
| with self.assertRaisesRegexp(ValueError, "must have names"): | ||
| q.enqueue({"a": 12.0}) | ||
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| def testParallelEnqueue(self): | ||
| with self.test_session() as sess, self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) | ||
| elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] | ||
| enqueue_ops = [q.enqueue((x,)) for x in elems] | ||
| dequeued_t = q.dequeue() | ||
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| # Run one producer thread for each element in elems. | ||
| def enqueue(enqueue_op): | ||
| sess.run(enqueue_op) | ||
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| threads = [ | ||
| self.checkedThread(target=enqueue, args=(e,)) for e in enqueue_ops | ||
| ] | ||
| for thread in threads: | ||
| thread.start() | ||
| for thread in threads: | ||
| thread.join() | ||
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| # Dequeue every element using a single thread. | ||
| results = [] | ||
| for _ in xrange(len(elems)): | ||
| results.append(dequeued_t.eval()) | ||
| self.assertItemsEqual(elems, results) | ||
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| def testParallelDequeue(self): | ||
| with self.test_session() as sess, self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) | ||
| elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] | ||
| enqueue_ops = [q.enqueue((x,)) for x in elems] | ||
| dequeued_t = q.dequeue() | ||
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| # Enqueue every element using a single thread. | ||
| for enqueue_op in enqueue_ops: | ||
| enqueue_op.run() | ||
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| # Run one consumer thread for each element in elems. | ||
| results = [] | ||
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| def dequeue(): | ||
| results.append(sess.run(dequeued_t)) | ||
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| threads = [self.checkedThread(target=dequeue) for _ in enqueue_ops] | ||
| for thread in threads: | ||
| thread.start() | ||
| for thread in threads: | ||
| thread.join() | ||
| self.assertItemsEqual(elems, results) | ||
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| def testDequeue(self): | ||
| with self.test_session(), self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) | ||
| elems = [10.0, 20.0, 30.0] | ||
| enqueue_ops = [q.enqueue((x,)) for x in elems] | ||
| dequeued_t = q.dequeue() | ||
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| for enqueue_op in enqueue_ops: | ||
| enqueue_op.run() | ||
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| for i in xrange(len(elems)): | ||
| vals = dequeued_t.eval() | ||
| self.assertEqual([elems[i]], vals) | ||
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| def testEnqueueAndBlockingDequeue(self): | ||
| with self.test_session() as sess, self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(3, dtypes_lib.float32) | ||
| elems = [10.0, 20.0, 30.0] | ||
| enqueue_ops = [q.enqueue((x,)) for x in elems] | ||
| dequeued_t = q.dequeue() | ||
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| def enqueue(): | ||
| # The enqueue_ops should run after the dequeue op has blocked. | ||
| # TODO(mrry): Figure out how to do this without sleeping. | ||
| time.sleep(0.1) | ||
| for enqueue_op in enqueue_ops: | ||
| sess.run(enqueue_op) | ||
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| results = [] | ||
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| def dequeue(): | ||
| for _ in xrange(len(elems)): | ||
| results.append(sess.run(dequeued_t)) | ||
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| enqueue_thread = self.checkedThread(target=enqueue) | ||
| dequeue_thread = self.checkedThread(target=dequeue) | ||
| enqueue_thread.start() | ||
| dequeue_thread.start() | ||
| enqueue_thread.join() | ||
| dequeue_thread.join() | ||
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| for elem, result in zip(elems, results): | ||
| self.assertEqual([elem], result) | ||
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| def testMultiEnqueueAndDequeue(self): | ||
| with self.test_session() as sess, self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, (dtypes_lib.int32, dtypes_lib.float32)) | ||
| elems = [(5, 10.0), (10, 20.0), (15, 30.0)] | ||
| enqueue_ops = [q.enqueue((x, y)) for x, y in elems] | ||
| dequeued_t = q.dequeue() | ||
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| for enqueue_op in enqueue_ops: | ||
| enqueue_op.run() | ||
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| for i in xrange(len(elems)): | ||
| x_val, y_val = sess.run(dequeued_t) | ||
| x, y = elems[i] | ||
| self.assertEqual([x], x_val) | ||
| self.assertEqual([y], y_val) | ||
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| def testQueueSizeEmpty(self): | ||
| with self.test_session(), self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) | ||
| self.assertEqual([0], q.size().eval()) | ||
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| def testQueueSizeAfterEnqueueAndDequeue(self): | ||
| with self.test_session(), self.test_scope(): | ||
| q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) | ||
| enqueue_op = q.enqueue((10.0,)) | ||
| dequeued_t = q.dequeue() | ||
| size = q.size() | ||
| self.assertEqual([], size.get_shape()) | ||
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| enqueue_op.run() | ||
| self.assertEqual(1, size.eval()) | ||
| dequeued_t.op.run() | ||
| self.assertEqual(0, size.eval()) | ||
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| if __name__ == "__main__": | ||
| test.main() |
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