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* Change HandleFromInput() to return a `const ResourceHandle&` and avoid ↵Gravatar Derek Murray2018-09-12
| | | | | | | | copying that type. This avoids unnecessary string copies and deallocations in the ReadVariableOp, and similar ops. PiperOrigin-RevId: 212652588
* [TF:XLA] Add partial implementation of tf.FIFOQueue for XLA devices (e.g., TPU).Gravatar Peter Hawkins2018-06-28
| | | | | | | | | | | | 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
* Cleanup: Ran clang-format on all *.{cc,h} files in tensorflow/core/kernels.Gravatar A. Unique TensorFlower2018-01-26
| | | | PiperOrigin-RevId: 183423961
* Delete trailing whitespaceGravatar A. Unique TensorFlower2017-11-27
| | | | PiperOrigin-RevId: 177008504
* Merge changes from github.Gravatar Shanqing Cai2017-07-10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | END_PUBLIC --- Commit d0f53f77f authored by Penghao Cen<scorpiocph@gmail.com> Committed by Shanqing Cai<cais@google.com>: Minor fix typo (#11323) --- Commit 02fcf564e authored by Chris Song<sjhshy@gmail.com> Committed by Chris Song<sjhshy@gmail.com>: Fix misspells. --- Commit 764c9b6b4 authored by Louis Tiao<ltiao@users.noreply.github.com> Committed by GitHub<noreply@github.com>: Fixed typo in docstring --- Commit f8cd1283e authored by Shanqing Cai<cais@google.com> Committed by Shanqing Cai<cais@google.com>: Chaser --- Commit 01383b946 authored by Shanqing Cai<cais@google.com> Committed by Shanqing Cai<cais@google.com>: Adapt TensorFlowTestCase.setUp() to new reset_default_graph() semantics Avoid calling reset_default_graph() directly to prevent exceptions in cases where test methods error out from within nested graph contexts, which can leave _default_graph_stack non-empty in certain Python versions. --- Commit 0ffc37890 authored by Amit Patankar<amitpatankar@google.com> Committed by Amit Patankar<amitpatankar@google.com>: Removing second declaration of functions. --- Commit f9c9cacb0 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Refactor ElementalIrEmitter's slice index finding code into IrArray::Index::SourceIndexOfSlice(). PiperOrigin-RevId: 161140653 --- Commit ba297aec9 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Update ops-related pbtxt files. PiperOrigin-RevId: 161138258 --- Commit 68d666737 authored by Alexandre Passos<apassos@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Fixes a reentrant lock issue with tensors using ndarray memory which uses tensor memory. PiperOrigin-RevId: 161137788 --- Commit a2ee8bca3 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add support for int8 x int8 -> int32 matrix multiplication via cublasGemmEx to stream_executor. PiperOrigin-RevId: 161137741 --- Commit 755fa7b50 authored by Mark Daoust<markdaoust@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Block generate_test, and docs generating from running in python3. - Doc generation is currently unsupported in python3 - These both end in errors in python 3.5.1+ PiperOrigin-RevId: 161137467 --- Commit 97cbcac45 authored by Peter Hawkins<phawkins@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: [TF:XLA] Fix failure in functionalize_control_flow rewrite for Enter nodes that are unused. Make sure we ignore such nodes without producing an error. PiperOrigin-RevId: 161136545 --- Commit dabcb60bc authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: [XLA] Add reasonable error messages to Builder::Build for bad parameter numbers. PiperOrigin-RevId: 161136262 --- Commit 0cbd249e8 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add complex tensors support to `matrix_determinant`. PiperOrigin-RevId: 161132422 --- Commit 335f1f14d authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Extend static shape inference for SparseTensors with dense_shapes constructed using slicing. PiperOrigin-RevId: 161132391 --- Commit 53604916e authored by Jianwei Xie<xiejw@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Fixed the missing labels test in TPUEstimator. PiperOrigin-RevId: 161131282 --- Commit 9f57dc8dd authored by Bruno Rosa<bruno.rosa@eldorado.org.br> Committed by Bruno Rosa<bruno.rosa@eldorado.org.br>: Use mcpu instead of march for ppc64le march is not support by gcc on ppc64le --- Commit 7d5c74a9c authored by Skye Wanderman-Milne<skyewm@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Move duplicate detection logic from Graph to FunctionLibraryDefinition Turns out this is more useful, since there are many function libraries that don't belong to a graph. This will be used in a future change. Note that this maintains the current behavior of Graph. In addition, updates FunctionDefsEqual() to handle unset attr entries (I ran into this when using this in said future change). PiperOrigin-RevId: 161126628 --- Commit 2caec3af1 authored by Shanqing Cai<cais@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Disable more timeseries py tests failing in OSS PIP GPU builds PiperOrigin-RevId: 161124799 --- Commit 0b5cce367 authored by Eugene Brevdo<ebrevdo@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Get TopK op working on GPU again. Extend using cub's radix sort. 1. Undo rollback of Andreas Kirsch's initial implementation. 2. Use cub segmented radix sort if Andreas' heap-based impl for large k and small num_cols (thresholds of k=100, n=1000 determined empirically). 3. Use cub segmented radix sort if k == num_cols (this case is always faster). 4. Added benchmarks. Benchmarks show that the GPU implementation is up to 3x slower for small k but can be 10x faster for large num_cols and k. Benchmarks: Benchmark: m_128_n_10_k_5_use_gpu_False wall_time: 0.000166 s Throughput: 0.0077 GB/s Benchmark: m_128_n_10_k_5_use_gpu_True wall_time: 0.000796 s Throughput: 0.00161 GB/s Benchmark: m_128_n_10_k_9_use_gpu_False wall_time: 0.00017 s Throughput: 0.00751 GB/s Benchmark: m_128_n_10_k_9_use_gpu_True wall_time: 0.000796 s Throughput: 0.00161 GB/s Benchmark: m_128_n_10_k_10_use_gpu_False wall_time: 0.00017 s Throughput: 0.00753 GB/s Benchmark: m_128_n_10_k_10_use_gpu_True wall_time: 0.000775 s Throughput: 0.00165 GB/s Benchmark: m_128_n_100_k_1_use_gpu_False wall_time: 0.000155 s Throughput: 0.0826 GB/s Benchmark: m_128_n_100_k_1_use_gpu_True wall_time: 0.000796 s Throughput: 0.0161 GB/s Benchmark: m_128_n_100_k_50_use_gpu_False wall_time: 0.000247 s Throughput: 0.0519 GB/s Benchmark: m_128_n_100_k_50_use_gpu_True wall_time: 0.0008 s Throughput: 0.016 GB/s Benchmark: m_128_n_100_k_99_use_gpu_False wall_time: 0.000261 s Throughput: 0.049 GB/s Benchmark: m_128_n_100_k_99_use_gpu_True wall_time: 0.000794 s Throughput: 0.0161 GB/s Benchmark: m_128_n_100_k_100_use_gpu_False wall_time: 0.000239 s Throughput: 0.0536 GB/s Benchmark: m_128_n_100_k_100_use_gpu_True wall_time: 0.000777 s Throughput: 0.0165 GB/s Benchmark: m_128_n_1000_k_1_use_gpu_False wall_time: 0.000324 s Throughput: 0.395 GB/s Benchmark: m_128_n_1000_k_1_use_gpu_True wall_time: 0.000916 s Throughput: 0.14 GB/s Benchmark: m_128_n_1000_k_10_use_gpu_False wall_time: 0.00042 s Throughput: 0.305 GB/s Benchmark: m_128_n_1000_k_10_use_gpu_True wall_time: 0.000902 s Throughput: 0.142 GB/s Benchmark: m_128_n_1000_k_500_use_gpu_False wall_time: 0.0011 s Throughput: 0.116 GB/s Benchmark: m_128_n_1000_k_500_use_gpu_True wall_time: 0.00097 s Throughput: 0.132 GB/s Benchmark: m_128_n_1000_k_990_use_gpu_False wall_time: 0.00133 s Throughput: 0.0962 GB/s Benchmark: m_128_n_1000_k_990_use_gpu_True wall_time: 0.000993 s Throughput: 0.129 GB/s Benchmark: m_128_n_1000_k_1000_use_gpu_False wall_time: 0.00102 s Throughput: 0.126 GB/s Benchmark: m_128_n_1000_k_1000_use_gpu_True wall_time: 0.000964 s Throughput: 0.133 GB/s Benchmark: m_128_n_10000_k_10_use_gpu_False wall_time: 0.002 s Throughput: 0.64 GB/s Benchmark: m_128_n_10000_k_10_use_gpu_True wall_time: 0.00288 s Throughput: 0.445 GB/s Benchmark: m_128_n_10000_k_100_use_gpu_False wall_time: 0.00233 s Throughput: 0.549 GB/s Benchmark: m_128_n_10000_k_100_use_gpu_True wall_time: 0.00325 s Throughput: 0.394 GB/s Benchmark: m_128_n_10000_k_5000_use_gpu_False wall_time: 0.0127 s Throughput: 0.101 GB/s Benchmark: m_128_n_10000_k_5000_use_gpu_True wall_time: 0.00381 s Throughput: 0.336 GB/s Benchmark: m_128_n_10000_k_9900_use_gpu_False wall_time: 0.015 s Throughput: 0.0853 GB/s Benchmark: m_128_n_10000_k_9900_use_gpu_True wall_time: 0.00438 s Throughput: 0.292 GB/s Benchmark: m_128_n_10000_k_10000_use_gpu_False wall_time: 0.0104 s Throughput: 0.123 GB/s Benchmark: m_128_n_10000_k_10000_use_gpu_True wall_time: 0.00427 s Throughput: 0.3 GB/s Benchmark: m_128_n_100000_k_100_use_gpu_False wall_time: 0.0148 s Throughput: 0.865 GB/s Benchmark: m_128_n_100000_k_100_use_gpu_True wall_time: 0.0262 s Throughput: 0.488 GB/s Benchmark: m_128_n_100000_k_1000_use_gpu_False wall_time: 0.0201 s Throughput: 0.636 GB/s Benchmark: m_128_n_100000_k_1000_use_gpu_True wall_time: 0.0263 s Throughput: 0.486 GB/s Benchmark: m_128_n_100000_k_50000_use_gpu_False wall_time: 0.214 s Throughput: 0.0599 GB/s Benchmark: m_128_n_100000_k_50000_use_gpu_True wall_time: 0.0322 s Throughput: 0.398 GB/s Benchmark: m_128_n_100000_k_99000_use_gpu_False wall_time: 0.262 s Throughput: 0.0489 GB/s Benchmark: m_128_n_100000_k_99000_use_gpu_True wall_time: 0.0377 s Throughput: 0.34 GB/s Benchmark: m_128_n_100000_k_100000_use_gpu_False wall_time: 0.118 s Throughput: 0.108 GB/s Benchmark: m_128_n_100000_k_100000_use_gpu_True wall_time: 0.0365 s Throughput: 0.351 GB/s END_PUBLIC BEGIN_PUBLIC BEGIN_PUBLIC Automated g4 rollback of changelist 157169178 PiperOrigin-RevId: 161476569
* Annotate overriding functions with "override" or "final" (and not with ↵Gravatar A. Unique TensorFlower2017-05-26
| | | | | | "virtual") PiperOrigin-RevId: 157284709
* Fix persistent memory tracking for queue ops.Gravatar Yuefeng Zhou2017-04-24
| | | | Change: 154081725
* Tracking memory usage of queues.Gravatar Yuefeng Zhou2017-03-02
| | | | Change: 149048750
* Adds V2 versions of Queue and Reader ops using ResourceHandles.Gravatar A. Unique TensorFlower2017-01-04
| | | | Change: 143570831
* Fix dequeue error messagesGravatar Geoffrey Irving2016-07-22
| | | | Change: 128191932
* Update copyright for 3p/tf/core.Gravatar A. Unique TensorFlower2016-06-02
| | | | Change: 123900938
* Add allow_small_batch attribute to QueueInterface, and a new opGravatar Eugene Brevdo2016-04-20
| | | | | | | | called DequeueUpToOp for Queues. In python land, there is a new Queue.dequeue_up_to method. No queues support this dequeue option for now. If a user calls dequeue_up_to, an error is currently returned at runtime. Change: 120341224
* Global search & replace to move to the new location forGravatar Josh Levenberg2016-01-26
| | | | | tensorflow/core/ files and build targets. Change: 113075177
* #include third_party/tensorflow/core/platform/macros.hGravatar Josh Levenberg2016-01-07
| | | | | directly so we can drop it from port.h. Change: 111612474
* TensorFlow: Improve performance of AlexnetGravatar Manjunath Kudlur2015-11-20
| | | | | | | | | | | | | | | | | | | | | | Changes: * error message that refers to removed `DefaultSession` method. * -Wnull-conversion warnings * the "_start_time" attr for recvs when the flag "--brain_enable_scheduling_for_recvs" is set. * typo in tutorial data download progress message. * a typo ("however their installing"=>"however installing"). * typo, rename "TensorFlow Mechanics" to "How To" to be consistent with the website. * a typo ("subtact"=>"subtract"). * protobuf examples in comments in tensorflow::Example.proto. * formula formatting in MNIST beginner tutorial * negative fraction-of-queue-full stats * protobuf inclusion path so that Android demo will build under Blaze. * small typo (moderatly > moderately) * Session.run() to check that tensor arguments come from the session's graph. * another six import * seq2seq typo in bazel command Base CL: 108349164
* TensorFlow: more features, performance improvements, and doc fixes.Gravatar Vijay Vasudevan2015-11-18
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Changes: - Add Split/Concat() methods to TensorUtil (meant for convenience, not speed) by Chris. - Changes to linear algebra ops interface by Rasmus - Tests for tensorboard by Daniel - Fix bug in histogram calculation by Cassandra - Added tool for backwards compatibility of OpDefs. Tool Checks in history of opdefs and their changes, checks for backwards-incompatible changes. All done by @josh11b - Fix some protobuf example proto docs by Oliver - Add derivative of MatrixDeterminant by @yaroslavvb - Add a priority queue queue by @ebrevdo - Doc and typo fixes by Aurelien and @dave-andersen - Speed improvements to ConvBackwardFilter by @andydavis - Improve speed of Alexnet on TitanX by @zheng-xq - Add some host memory annotations to some GPU kernels by Yuan. - Add support for doubles in histogram summary by @jmchen-g Base CL: 108158338
* TensorFlow: Initial commit of TensorFlow library.Gravatar Manjunath Kudlur2015-11-06
TensorFlow is an open source software library for numerical computation using data flow graphs. Base CL: 107276108
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