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* cond_v2: raise an error if pred is a Python bool.Gravatar Skye Wanderman-Milne2018-10-10
| | | | | | This is to match the existing behavior of tf.cond. PiperOrigin-RevId: 216534084
* Use lambdas when converting ifexps, since they are now supported.Gravatar Dan Moldovan2018-10-10
| | | | PiperOrigin-RevId: 216533613
* [tf.data] `Dataset.make_one_shot_iterator()` inherits the random seed from ↵Gravatar Derek Murray2018-10-10
| | | | | | | | | | | | | | | the calling graph. This change makes a subtle difference to the behavior of existing programs that create multiple iterators. Previously, one-shot iterators would not inherit the graph seed, and so their values would be non-deterministic (unless explicit seeds were set). After this change, an iterator will inherit its seed from the outer graph. Multiple one-shot iterators created from the same dataset will inherit different seeds, matching the semantics of creating multiple ops with the same graph seed. PiperOrigin-RevId: 216532256
* Use overloaded operators for the assert statement. This should remove the ↵Gravatar Dan Moldovan2018-10-10
| | | | | | reliance on importing tensorflow in the generated code. PiperOrigin-RevId: 216528047
* Automated rollback of commit 950cf87104bfee28e2165fe368f66337b8a1336dGravatar A. Unique TensorFlower2018-10-10
| | | | PiperOrigin-RevId: 216500702
* compat: Update forward compatibility horizon to 2018-10-10Gravatar A. Unique TensorFlower2018-10-10
| | | | PiperOrigin-RevId: 216495091
* Delete dead code in batch_scatter_ops_test.Gravatar A. Unique TensorFlower2018-10-10
| | | | PiperOrigin-RevId: 216483746
* Run while loop test that was not being run before.Gravatar A. Unique TensorFlower2018-10-10
| | | | PiperOrigin-RevId: 216483744
* Enable support for lambda functions in static analyses.Gravatar Dan Moldovan2018-10-09
| | | | | | | The CFG treats lambdas as ordinary expressions. The activity analysis ensures that variables masked by the lambda's arguments are not being tracked. Note: lambdas do not allow direct modification (we exclude indirect mutation via function or methods). PiperOrigin-RevId: 216456682
* Remove the deprecated created and IS_LOCAL abstractions from activity analysis.Gravatar Dan Moldovan2018-10-09
| | | | PiperOrigin-RevId: 216446750
* Part 2/3 of the update of tf.keras to the Keras 2.2.4 API.Gravatar Francois Chollet2018-10-09
| | | | PiperOrigin-RevId: 216442569
* Raises an appropriate error if `add_weight` is called on a Keras network.Gravatar A. Unique TensorFlower2018-10-09
| | | | PiperOrigin-RevId: 216432358
* Make defun work under distributed strategies.Gravatar Igor Ganichev2018-10-09
| | | | | | | | | | | | | The core of the change is have the gradient tape capture distributed variables instead of plain ResourceVariables. In other words, we move the distribution awareness from defun down to tape and rely on distributed variable magic to provide us with the right variable at runtime. In tower context, we always watch the container (e.g. MirroredVariable). In cross tower context, we always watch all the components. PiperOrigin-RevId: 216430530
* [tf.data vectorization] Add vectorizer for `Add` opGravatar Rachel Lim2018-10-09
| | | | PiperOrigin-RevId: 216424512
* [tf.data] Lift parameterized test parameters into lambdas if they create TF ops.Gravatar Derek Murray2018-10-09
| | | | | | | | The existing code triggers parts of the TensorFlow runtime that may not have been fully initialized at the time the parameters are evaluated. Lifting into a lambda and invoking the lambda inside the test method will achieve the proper order. PiperOrigin-RevId: 216419757
* Improve the control flow conversion for loops by using dataflow analysis to ↵Gravatar Dan Moldovan2018-10-09
| | | | | | construct the state. This is part of a larger refactoring which removes the reliance on the deprecated Scope.created field. PiperOrigin-RevId: 216418556
* Small cleanup in function_test.Gravatar Gunhan Gulsoy2018-10-09
| | | | PiperOrigin-RevId: 216412380
* Silence tf.distributions deprecation messages caused by internal global ↵Gravatar Pavel Sountsov2018-10-09
| | | | | | | | | | | | function calls. E.g. register_kl calls would trigger such warnings. This spam was exacerbated by the fact that it happens before logging is initialized, so it is dumped prominently to STDERR. Worse yet it also happened no matter whether the user imported any symbols from tf.distributions or not as the relevant code is executed when you import TensorFlow. PiperOrigin-RevId: 216396036
* [tf.data] NUMA-aware MapAndBatch dataset.Gravatar Brennan Saeta2018-10-09
| | | | PiperOrigin-RevId: 216395709
* Improves tf.function prototype.Gravatar Alexandre Passos2018-10-09
| | | | | | | | | | Specifically: - renames from def_function - returns an object with well-defined methods - doesn't force-retrace twice - uses the python descriptor API ( https://docs.python.org/3/howto/descriptor.html ) to remove the need for a tf.method PiperOrigin-RevId: 216388957
* Removing the _SHOULD_RECORD_SUMMARIES_NAME andGravatar Rohan Jain2018-10-09
| | | | | | | _SUMMARY_WRITER_INIT_COLLECTION_NAME collections from the summaryV2 implementation. Replacing them with global variables. PiperOrigin-RevId: 216383152
* [tf.data vectorization] Handle captured inputs in MapVectorization optimizationGravatar Rachel Lim2018-10-09
| | | | PiperOrigin-RevId: 216381943
* Include live-in symbols in liveness analysis. These are required for control ↵Gravatar Dan Moldovan2018-10-09
| | | | | | flow conversion. PiperOrigin-RevId: 216370439
* Improve docstring for tf.data.Dataset.shuffle()Gravatar A. Unique TensorFlower2018-10-09
| | | | PiperOrigin-RevId: 216370329
* Create SDCAOptimizerV2 op to fix the "adaptative" typo.Gravatar Yuefeng Zhou2018-10-09
| | | | PiperOrigin-RevId: 216370193
* Throw error when evaluating have variable target in GradientTape.Gravatar Tamara Norman2018-10-09
| | | | PiperOrigin-RevId: 216368178
* Avoid extra calls to set_random_seed, as it is already called inGravatar Gunhan Gulsoy2018-10-09
| | | | | | tensorflowtestcase. PiperOrigin-RevId: 216363450
* Allowing for mixture of V1 and V2 feature columns usage in canned ↵Gravatar Rohan Jain2018-10-09
| | | | | | | | | | estimators. This is required for TF hub use cases where users might send in new feature columns to old model code. Implemented this support by making V2 feature columns support the V1 API. This is needed temporarily and would definitely be removed by TF 2.0, possibly earlier depending on what guarantees are provided by TF hub. The only case we don't allow here is mixing in V2 shared embedding columns with V1 Feature columns. V2 Shared FC's depend on a SharedEmbeddingState manager that would have to be passed in to the various API's and there wasn't really a very clean way to make that work. Mixing V2 feature columns with V1 shared embedding columns is fine though and along with all other combinations PiperOrigin-RevId: 216359041
* compat: Update forward compatibility horizon to 2018-10-09Gravatar A. Unique TensorFlower2018-10-09
| | | | PiperOrigin-RevId: 216323343
* Fix the seeding for `Dataset.shuffle(..., reshuffle_each_iteration=False)`.Gravatar Derek Murray2018-10-08
| | | | | | | | | Previously, we were passing the first (graph-level) seed for both the graph-level and op-level seeds when creating a C++ dataset. This change passes the op-level seed to the appropriate point, and adds a test for the behavior with graph-but-not-op-level seeds. PiperOrigin-RevId: 216280641
* Consolidate device parameter arguments into a shared DeviceInfo structGravatar A. Unique TensorFlower2018-10-08
| | | | PiperOrigin-RevId: 216280197
* Avoid calling get_default_graph() during tf.enable_eager_execution()Gravatar Gunhan Gulsoy2018-10-08
| | | | PiperOrigin-RevId: 216270497
* Internal Change.Gravatar Michael Case2018-10-08
| | | | PiperOrigin-RevId: 216260437
* Simple comment fix in CheckpointInputPipelineHook.Gravatar Ruoxin Sang2018-10-08
| | | | PiperOrigin-RevId: 216260216
* Automated rollback of commit 09b0fc199129e0f487a39741bdf674cf09035cbcGravatar Derek Murray2018-10-08
| | | | PiperOrigin-RevId: 216256115
* Ignore args and kwargs for defun's get_concrete_fn if `PolymorphicFunction` ↵Gravatar Shivani Agrawal2018-10-08
| | | | | | | | was created with an input_signature. PiperOrigin-RevId: 216253122
* [tf.data] Choose non-deterministic seed once per Python-level `Dataset` object.Gravatar Derek Murray2018-10-08
| | | | | | | | | | This changes the behavior of randomness-introducing datasets (`tf.data.Dataset.shuffle()`, `tf.data.experimental.shuffle_and_repeat()`, and `tf.data.experimental.RandomDataset`). Previously, when you used the same `tf.data.Dataset` object multiple times in a pipeline (e.g. by zipping two datasets derived from the same randomness-introducing dataset) *and* you did not specify an explicit `seed`, the implementation would choose different non-deterministic seeds for each use of the `Dataset` object. With this change, the seed will be chosen once per `Dataset` (technically, once per `Dataset`-`Graph` combination, due to the vagaries of capturing state in `Dataset.make_one_shot_iterator()`), which means that all uses of the same dataset object will observe the same sequence of values. This change also revealed a small bug in how `Dataset.shuffle(..., reshuffle_each_iteration=False)` is serialized when an explicit seed is specified. The op-level seed was dropped, which could lead to non-deterministic behavior. This change fixes that issue by forwarding the op-level seed to the appropriate place. PiperOrigin-RevId: 216248013
* Partial support tfe.defun in tf.gradients.Gravatar Alexandre Passos2018-10-08
| | | | | | | | Doesn't attempt to deal with cases where we might have already generated the functiondef for the parent function as in that case we cannot easily modify the forward pass. PiperOrigin-RevId: 216243224
* Allow using more than one converter in the testing harness.Gravatar Dan Moldovan2018-10-08
| | | | PiperOrigin-RevId: 216242862
* Add tf.BenchmarkConfig that returns a session config appropriate for ↵Gravatar A. Unique TensorFlower2018-10-08
| | | | | | benchmarking. At the moment, it returns a default config with only Grappler dependency optimizer disabled. Many benchmarks wrap the subgraph they want to time in control_flow_ops.group() to avoid including the overhead of copying the output back to the Python client in the measurement. In the graph, this only adds a control dependency between the subgraph output and the fetch node, which in turn (often) causes the dependency optimizer to turn all nodes in the graph into no-ops. PiperOrigin-RevId: 216242463
* Fix a couple of reference leaksGravatar A. Unique TensorFlower2018-10-08
| | | | PiperOrigin-RevId: 216230391
* Merge pull request #21658 from lowintelligence:masterGravatar TensorFlower Gardener2018-10-08
|\ | | | | | | PiperOrigin-RevId: 216217509
* | Fix support for a single tensor to be passed to target_tensorsGravatar Sourabh Bajaj2018-10-08
| | | | | | | | PiperOrigin-RevId: 216212953
* | Add a utility that allows finding a name for an entity, relative to an ↵Gravatar Dan Moldovan2018-10-08
| | | | | | | | | | | | existing namespace. PiperOrigin-RevId: 216211286
* | Part 1/3 of the feature sync to the Keras 2.2.4 API.Gravatar Francois Chollet2018-10-08
| | | | | | | | PiperOrigin-RevId: 216211279
* | Add support for SequenceExamples to sequence_feature_columnsGravatar Karmel Allison2018-10-08
| | | | | | | | PiperOrigin-RevId: 216210141
* | Wait for shared resources to initialize before initializing local resources.Gravatar A. Unique TensorFlower2018-10-08
| | | | | | | | | | | | shared resources are very similar to global variables functionally and they are initialized at the same time but since workers are only waiting for global variables being initialized, there is a race condition that sometimes the shared resource is not ready. PiperOrigin-RevId: 216208679
* | Allow TensorSpec objects as arguments to defun's get_concrete_functionGravatar Allen Lavoie2018-10-08
| | | | | | | | | | | | Will be helpful for specifying serving signatures when exporting SavedModels PiperOrigin-RevId: 216207284
* | [tf.data] Adding specialization for `MapDataset`, `ParallelMapDataset`, and ↵Gravatar Jiri Simsa2018-10-08
| | | | | | | | | | | | `MapAndBatchDataset` whose user-provided functions have the property that each output argument take its value directly from an input argument (e.g. `lambda x, y: y, x`). This specialization can produce the result without having to schedule the function using the executor. PiperOrigin-RevId: 216206232
* | Fix typoGravatar Makoto Uchida2018-10-08
| | | | | | | | PiperOrigin-RevId: 216203408
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