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path: root/tensorflow/compiler/tests/tensor_array_ops_test.py
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* Emit xla::Or in TensorArrayScatterV3 for PRED types instead of xla::AddGravatar A. Unique TensorFlower2018-10-10
| | | | | | | Previosuly we emitted xla::Add what isn't supported by some XLA backend on PRED types. PiperOrigin-RevId: 216497939
* Move from deprecated self.test_session() to self.cached_session().Gravatar A. Unique TensorFlower2018-08-22
| | | | | | | | self.test_session() has been deprecated in 9962eb5e84b15e309410071b06c2ed2d6148ed44 as its name confuses readers of the test. Moving to cached_session() instead which is more explicit about: * the fact that the session may be reused. * the session is not closed even when doing a "with self.test_session()" statement. PiperOrigin-RevId: 209837298
* For tf.gradients(), do not backpropagate through integer tensors.Gravatar A. Unique TensorFlower2018-04-26
| | | | | | | All integer tensors are now considered constant with respect to all `xs`. This fixes a bug in gradients through tf.while_loop. PiperOrigin-RevId: 194438529
* Removing underscore prefixes from hidden generated Python functions.Gravatar Anna R2018-02-28
| | | | PiperOrigin-RevId: 187386941
* Enable bfloat16 tests and add a filter for currentlyGravatar A. Unique TensorFlower2017-12-14
| | | | | | failed tests. PiperOrigin-RevId: 179069257
* [TF:XLA] Make the shape of a TensorArray flow value a scalar.Gravatar Peter Hawkins2017-07-25
| | | | | | Previously we used an f32[0] value, since the exact flow value does not matter, however this causes problems when a TensorArray computation is placed in a loop since the shape of the flow value is no longer loop invariant. PiperOrigin-RevId: 163082452
* Merge changes from github.Gravatar Frank Chen2017-07-13
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | END_PUBLIC --- Commit fe5338177 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 161727345 --- Commit c65f69119 authored by Eugene Brevdo<ebrevdo@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Factor out DenseUpdate ops into dense_update_functor build dep. Also add support for complex types. PiperOrigin-RevId: 161726749 --- Commit 9a172989e authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Update ops-related pbtxt files. PiperOrigin-RevId: 161726324 --- Commit fd5530d6e authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: adding bazel-toolchains repo to workspace. This repo will be necessary for remote execution (specifically for cross OS compilation) PiperOrigin-RevId: 161719899 --- Commit 71c4ec8ed authored by Derek Murray<mrry@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add a mechanism for switching between multiple iterators by feeding a handle. With this change, you can do the following: 1. Fetch a string handle for any iterator, by evaluating the result of `Iterator.string_handle()`. 2. Define an `Iterator` object based on a `tf.string` placeholder handle. 3. Feed the placeholder using an evaluated string handle to use a particular iterator in a particular step. Concretely, this allows you to define two iterators for a training dataset and a test dataset, and choose which one to use on a per-run basis: ```python train_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator() train_iterator_handle = sess.run(train_iterator.string_handle()) test_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator() test_iterator_handle = sess.run(test_iterator.string_handle()) handle = tf.placeholder(tf.string, shape=[]) iterator = tf.contrib.data.Iterator.from_string_handle( handle, train_iterator.output_types) next_element = iterator.get_next() loss = f(next_element) train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle}) test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle}) ``` PiperOrigin-RevId: 161719836 --- Commit 6d6dda807 authored by Kay Zhu<kayzhu@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: [TF:XLA] Fix an issue where plugin/Executor backend is used by default when TF is built from source with XLA support. See Github issue #11122. The priority of the executor backend is set to be higher than the default (50) and CPUs (<100), and is therefore selected as the default when tf.device is not explicitly specified. PiperOrigin-RevId: 161717173 --- Commit 6b28eb084 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Rename HloLocation to HloPosition, to avoid ambiguity with MemoryLocation. PiperOrigin-RevId: 161716528 --- Commit 8e7f57371 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Expose tf.contrib.nn.rank_sampled_softmax_loss. PiperOrigin-RevId: 161716450 --- Commit e424d209a authored by Peter Hawkins<phawkins@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: [TF:XLA] Use a more numerically accurate formulation of ResourceApplyRMSProp. PiperOrigin-RevId: 161706120 --- Commit 45a58d378 authored by Skye Wanderman-Milne<skyewm@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Introduce Python-only extensions to the C API Implements an incomplete version of Operation._add_control_input() using a new extension to make sure the plumbing works. This also adds header guards to c_api_internal.h, which were missing. For some reason the missing guards caused problems in the cmake build even though there doesn't appear to be any #include cycles. PiperOrigin-RevId: 161705859 --- Commit 4f5433634 authored by Jonathan Hseu<jhseu@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Rename TpuEstimator to TPUEstimator and TpuConfig to TPUConfig to follow PEP8 naming conventions. PiperOrigin-RevId: 161704561 --- Commit 38180d7bb authored by Yun Peng<pcloudy@google.com> Committed by gunan<gunan@google.com>: Disable nn_test on Windows (#11445) --- Commit e1de7a1b0 authored by Yun Peng<pcloudy@google.com> Committed by gunan<gunan@google.com>: Windows Bazel Build: Build TensorFlow with wrapper-less CROSSTOOL (#11454) --- Commit c9d03a568 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add tf.contrib.nn.rank_sampled_softmax_loss, a variant of tf.nn.sampled_softmax_loss that has been shown to improve rank loss. Paper: https://arxiv.org/abs/1707.03073 PiperOrigin-RevId: 161702455 --- Commit 9aa0dcbf2 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add shape check for MakeQuantileSummariesOp. PiperOrigin-RevId: 161698801 --- Commit 9c4da4a24 authored by vhasanov<KyotoSunshine@users.noreply.github.com> Committed by Frank Chen<frankchn@gmail.com>: Deleted unnecessary repetition of the same text. (#11459) The same text was repeated two times. I deleted the repetition. --- Commit d1e3cadda authored by DimanNe<dimanne@gmail.com> Committed by drpngx<drpngx@users.noreply.github.com>: Fix linking options issued by bazel in oorder to make gradients register (#11449) --- Commit 8605f7ab8 authored by Taehoon Lee<me@taehoonlee.com> Committed by Frank Chen<frankchn@gmail.com>: Fix typos (#11444) --- Commit 7c1fe9068 authored by Karl Lessard<karllessard@users.noreply.github.com> Committed by Frank Chen<frankchn@gmail.com>: [Java] Add base classes and utilities for operation wrappers. (#11188) * Add base classes and utilities for operation wrappers. * Rename Input interface to Operand * Introduce changes after code review --- Commit 2195db6d8 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Remove unused flag: xla_hlo_graph_for_compute_constant PiperOrigin-RevId: 161686867 --- Commit a72fc31bc authored by Martin Wicke<martin.wicke@gmail.com> Committed by Martin Wicke<martin.wicke@gmail.com>: Remove tabs. Unassign contrib/framework. --- Commit 6e74bd65a authored by Martin Wicke<martin.wicke@gmail.com> Committed by Martin Wicke<martin.wicke@gmail.com>: Add CODEOWNERS Added what we know about contrib mainly, and some well-separated components. --- Commit de546d066 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: BUILD cleanup in tensorflow/compiler/... PiperOrigin-RevId: 161679855 --- Commit 576c7b1ec authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: BEGIN_PUBLIC Automated g4 rollback of changelist 161218103 PiperOrigin-RevId: 161868747
* [TF:XLA] Refactor handling of Resources (Variables and TensorArrays) in the ↵Gravatar Peter Hawkins2017-06-16
| | | | | | | | | | | XLA bridge. * Rename "Variable" to "Resource" in many places where non-Variable resources might be used. * Add kTensorArray to the XlaCompiler::Argument enum. Remove kUninitializedVariable and make "initialized" a separate boolean field. * Add a kind field to XlaResource. Add checks that Variables are not used where TensorArrays are expected, and vice-versa. * Clean ups to the TensorArray operators. PiperOrigin-RevId: 159244478
* [TF:XLA] Add no-op implementation of TensorArrayCloseV3 to the XLA bridge.Gravatar Peter Hawkins2017-06-15
| | | | PiperOrigin-RevId: 159185414
* [TF:XLA] Initial implementation of TensorArray ops.Gravatar Peter Hawkins2017-06-07
The XLA implementation of TensorArrays is more restrictive than regular TensorArrays: * XLA TensorArrays must have dynamic_size=False. * all elements in an XLA TensorArray must have the same shape. * writes always add their values to any existing values; neither reads nor writes ever issue errors. Out-of-bounds writes currently wrap. Refactor Variable handling in the TF/XLA bridge. Use a XlaVariable* to refer to variables inside compilation rather than a numerical ID. Allow for variables that don't correspond to variables known to the user. Also use XlaVariable to handle TensorArrays. PiperOrigin-RevId: 158322041
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