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* [StreamExecutor] When a kernel launch fails, print the kernel's name.Gravatar Justin Lebar2017-12-05
| | | | | | | Previously, we printed everything about the kernel *except* its name. :) PiperOrigin-RevId: 178037469
* Fix some build incompatibilities with new versions of BazelGravatar Austin Anderson2017-12-05
| | | | | | See #15137. PiperOrigin-RevId: 178037461
* Adds an optional dict to hold tensors that are concatenated into the returnedGravatar A. Unique TensorFlower2017-12-05
| | | | | | | | | | tensor Notes: for learning tasks built on sparse signals, most of the tensors that go into the returned tensors are embedding, which are potentially useful for applications that consume embeddings from other models. This makes it easy for the caller to retrieve these tensors and make their customized signatures. PiperOrigin-RevId: 178033410
* Always include the function library when exporting a MetaGraphDef.Gravatar Derek Murray2017-12-05
| | | | | | | | | | | | Previously, some code paths through `tf.train.export_meta_graph()` did not ensure that the function library was persisted in the resulting `MetaGraphDef`. This would break serialization for meta-graphs that included `tf.data` pipelines that used functions. This fix ensures that the library is copied to all such meta-graphs. Fixes #15019. Fixes #14143. PiperOrigin-RevId: 178033103
* Support a vector and a 4D tensor as inputs to a binary op.Gravatar Yao Zhang2017-12-05
| | | | PiperOrigin-RevId: 178033021
* Improve module docstrings, which show up in Google search.Gravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 178032838
* Change InputArray.shape from being a repeated int field to beingGravatar A. Unique TensorFlower2017-12-05
| | | | | | | | | | | | an optional embedded message itself containing a repeated int field (now called 'dims'). This matches existing shape structurs (both in Toco internally, and in TensorFlow) and is necessary in order to disambiguate between a 0-dimensional shape and an undefined/unknown shape. This is a necessary prerequisite, in particular, for allowing toco to operate without given fixed input shapes, as so far these were impossible to disambiguate from fixed 0-dimensional shapes. PiperOrigin-RevId: 178027064
* Implement faster and less memory hungry version of topological sort that is ↵Gravatar A. Unique TensorFlower2017-12-05
| | | | | | idempotent. PiperOrigin-RevId: 178026253
* Add SaveRestoreMeasuringCostEstimator to measure the memory and runtime cost ↵Gravatar Yuefeng Zhou2017-12-05
| | | | | | of a grappler item's save/restore subgraph. PiperOrigin-RevId: 178025696
* Sets the master to '' for single node cluster.Gravatar Jianwei Xie2017-12-05
| | | | PiperOrigin-RevId: 178021454
* Replace `FunctionCallFrame` with a pure-virtual `CallFrameInterface`.Gravatar Derek Murray2017-12-05
| | | | | | | | Current users are unaffected. Running `//tensorflow/core/common_runtime_direct_session_test --benchmarks=all`, which stresses the Arg and Retval ops, reveals no performance change. PiperOrigin-RevId: 178015803
* Adds shards and increases size to dnn_linear_combined_test to prevent timeouts.Gravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 178013302
* Use a macro to determine whether BF16 is supported.Gravatar Yuanzhong Xu2017-12-05
| | | | PiperOrigin-RevId: 178010405
* [TF:XLA] Add test with while loop and many parameters.Gravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 178009859
* Rather than make potentially complex modifications to the Hlo graph, simply ↵Gravatar Nick Desaulniers2017-12-05
| | | | | | | | | generate input data that is constrained for certain entry computation parameters. Generate fake literals that are within bounds for DynamicSlice and other operations that accept dynamically computed indices. PiperOrigin-RevId: 178006866
* New document for Getting Started section about saving models.Gravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 177999275
* Internal change.Gravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 177994155
* Add android rule helpers and cleanup input loopsGravatar Austin Anderson2017-12-05
| | | | | | | | | | | | | | | | | | | | This change teaches the configure script how to search for Android NDK and SDK installations and create new WORKSPACE rules pointing to them. It also refactors many similar loop-over-user-input functions into using a reusable method (not the more complex ones). Specifying an SDK directory will further query for the available SDK API levels and build tools versions, but it won't perform any compatibility checks. Like other settings, every android-related setting can be set beforehand via an env param. The script will not ask for any Android settings if there are already any android repository rules in the WORKSPACE. The script will emit a warning if using an NDK version newer than 14 due to https://github.com/bazelbuild/bazel/issues/4068. PiperOrigin-RevId: 177989785
* Automated g4 rollback of changelist 177799252Gravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 177989542
* [TF:XLA] Add support for FusedBatchNormGrad where is_training=False.Gravatar Peter Hawkins2017-12-05
| | | | | | Also add support for rank != 4 tensors to the TF/XLA fused batchnorm operators, although the TF core ops don't actually support other ranks yet so this is not tested. PiperOrigin-RevId: 177987592
* Only parse known flags in tf.app.run().Gravatar Yilei Yang2017-12-05
| | | | | | | | This requires absl-py 0.1.6. Also remove the manual tag on //tensorflow/python:app_test. PiperOrigin-RevId: 177986813
* Add the tf2xla_supported_ops tool, which dumps ops supported by tf2xla.Gravatar A. Unique TensorFlower2017-12-05
| | | | | | | | Also fix a TODO in XlaOpRegistry to filter by the types allowed by the OpDef. Also see #14798 PiperOrigin-RevId: 177986664
* Add ImportGraphDefOptions::uniquify_prefix.Gravatar Skye Wanderman-Milne2017-12-05
| | | | | | | This option is necessary to mimic the Python import_graph_def method's behavior. PiperOrigin-RevId: 177986165
* nn_impl.py cleanup: used keepdims instead of deprecated keep_dims.Gravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 177972555
* Adding variant-based serialization and deserialization for sparse tensors.Gravatar Jiri Simsa2017-12-05
| | | | PiperOrigin-RevId: 177971801
* Simplify code in dependency optimizer.Gravatar A. Unique TensorFlower2017-12-05
| | | | | | | Change dependency optimizer to remove isolated NoOps when it is safe. Fix bug in arithmetic optimizer: Only remove deduped nodes if we know the fetches. PiperOrigin-RevId: 177970063
* Improve handling of operations that are known to TOCO but not to TF Lite.Gravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 177966156
* Make RevBlock a subclass of LayerGravatar A. Unique TensorFlower2017-12-05
| | | | PiperOrigin-RevId: 177964932
* Add a helper to HloSharding to easily create trivial flat tuples without ↵Gravatar A. Unique TensorFlower2017-12-05
| | | | | | | | requiring a ShapeTree. PiperOrigin-RevId: 177956572
* Estimate Placeholder as a no-op.Gravatar Max Galkin2017-12-05
| | | | PiperOrigin-RevId: 177956552
* [TF:XLA] Add support for NCHW format to SpaceToDepth and DepthToSpace.Gravatar Peter Hawkins2017-12-05
| | | | PiperOrigin-RevId: 177953076
* [XLA] Mark Rng as side-effecting and add a rematerialization test to ensure ↵Gravatar Blake Hechtman2017-12-05
| | | | | | that rng instructions are not rematerialized. This also lists Rng as non-rematerializable. PiperOrigin-RevId: 177932160
* Fix bugs in neutral element code and add more unit tests to cover matmul ↵Gravatar A. Unique TensorFlower2017-12-05
| | | | | | with input shape != output shape. PiperOrigin-RevId: 177920882
* Generates a warning if the global step is not increased.Gravatar Jianwei Xie2017-12-04
| | | | PiperOrigin-RevId: 177908680
* Enable transferring a tuple literal to a replicated device.Gravatar Mark Heffernan2017-12-04
| | | | | | | | Use ShapedBuffer to allocate required memory for the shape, then transfer the literal to the allocated addresses on each replica. Also, add Allocate() method to ShapedBuffer. PiperOrigin-RevId: 177900588
* [TF2XLA] Change the implementation of Diag and MatrixDiag to use arithmetic ↵Gravatar A. Unique TensorFlower2017-12-04
| | | | | | rather than Pad. PiperOrigin-RevId: 177896187
* Reproduce an issue with MonitoredSession when saving a variable on a GPU.Gravatar Igor Saprykin2017-12-04
| | | | | | Also arrange for continuous testing with GPUs. PiperOrigin-RevId: 177895214
* Modifying _get_examples in graph_io.py to utilize tf.cond.Gravatar A. Unique TensorFlower2017-12-04
| | | | PiperOrigin-RevId: 177892591
* Treat integer default initializers like floating point ones.Gravatar Alexandre Passos2017-12-04
| | | | | | This fixes subtle problems with partitioned variables. PiperOrigin-RevId: 177892499
* Fix tf.identity(resource variable) with eager execution and a deviceGravatar A. Unique TensorFlower2017-12-04
| | | | | | copy. PiperOrigin-RevId: 177891209
* Add BF16 tests for reduce-window.Gravatar Yuanzhong Xu2017-12-04
| | | | PiperOrigin-RevId: 177890892
* Fix bug with uniquified colocation attrs in ImportGraphDef.Gravatar Skye Wanderman-Milne2017-12-04
| | | | | | | | | | | | | | | | The colocation attrs must be updated after all NodeDefs have been processed. The nodes are processed and uniquified in topological order, which allows us to update the inputs simultaneously due to the topological ordering, but this doesn't work for the colocation groups. I also considered updating all the NodeDefs with prefixes or unique names at the very beginning, before starting conversion. This would make the logic simpler, but require us to potentially keep a full copy of all the NodeDefs in memory (so we could edit them), so I decided to edit in-place after construction. We might want to consider this alternate in future though. PiperOrigin-RevId: 177890362
* [XLA] Add --print_result flag to replay_computation tool.Gravatar Justin Lebar2017-12-04
| | | | | | | | | Before, we assumed that if you passed --use_fake_data, you didn't care about the output of the computation. With this patch, we decouple the decision of using fake data from the decision of whether or not to print the results. PiperOrigin-RevId: 177889877
* Correct trivial spelling error in internal_convert_to_tensorGravatar A. Unique TensorFlower2017-12-04
| | | | PiperOrigin-RevId: 177886163
* Fix minor typos in the doc of SpaceToDepth and DepthToSpace.Gravatar Jingyue Wu2017-12-04
| | | | PiperOrigin-RevId: 177884096
* [XLA:GPU] Use more threads per thread block.Gravatar Justin Lebar2017-12-04
| | | | | | | | | | | | | | | | | | | | | | | | | | | | Before this change, we supported two algorithms for choosing the number of threads per block: * "optimize-for-latency" algorithm assumed that each thread would want the maximum number of registers it could have, and chose a block size small enough to accommodate this. * "optimize-for-throughput" algorithm packed as many threads into a block as possible. In practice we always chose the optimize-for-latency algorithm. This change removes the choice of algorithm and changes us to unconditionally use a new one. In our new algorithm, we choose the smallest block size that still has the potential to allow the GPU to reach maximum occupancy. When each thread's register usage is small, we can pack many of these blocks into one SM and hit maximum occupancy. When the threads' register usage is larger, we degrade gracefully (unlike with larger block sizes, where the occupancy degredation is more quantized). On our benchmarks, this is a moderate (0-10%) speedup on K40, and a large (10-25%) speedup on P100. PiperOrigin-RevId: 177879741
* [XLA] Add a default implementation of Literal::ToString for rank >= 6 tensors.Gravatar Peter Hawkins2017-12-04
| | | | PiperOrigin-RevId: 177878887
* Add a single capacity prefetch to `tf.contrib.data.read_batch_features`.Gravatar A. Unique TensorFlower2017-12-04
| | | | PiperOrigin-RevId: 177877751
* hsv_in_yiq gpu implementation.Gravatar A. Unique TensorFlower2017-12-04
| | | | PiperOrigin-RevId: 177876455
* Enable bfloat16 use from Python:Gravatar Peter Hawkins2017-12-04
| | | | | | | * add a bfloat16 Python type and NumPy extension. * allow the bfloat16 type in a number places in the Python libraries. PiperOrigin-RevId: 177875784
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