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* Destroy the task before unblocking its waiters.Gravatar Tim Shen2018-08-13
| | | | PiperOrigin-RevId: 208508212
* Automated rollback of commit 56e4ea405d13125a3dcb6459019a83d12330bf84Gravatar Peter Hawkins2018-08-13
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* Automated rollback of commit b306f5f9458feddbdb89b7db557cb74dc9408d07Gravatar Peter Hawkins2018-08-10
| | | | PiperOrigin-RevId: 208200028
* [TF:XLA] Add a real implementation of XlaDevice::Sync() so Session::Run() ↵Gravatar Peter Hawkins2018-08-09
| | | | | | | | will correctly wait for all computations to complete on an XLA device before termination. [TF:XLA] Change the XlaTensor definition event to be a shared pointer to an stream_executor::Event. This allows many tensors to share the same definition event. PiperOrigin-RevId: 208128264
* Automated rollback of commit 0ea6847c892497afdd20c1150fee1e532612ca17Gravatar A. Unique TensorFlower2018-07-24
| | | | PiperOrigin-RevId: 205885304
* [ROCm] Interface changes for StreamExecutor to support both CUDA and ROCmGravatar Wen-Heng (Jack) Chung2018-07-12
| | | | | | | | | | | | | | | | | | | 1) StreamInterface::CudaStreamMemberHack() Despite the fact that StreamExecutor and GPU common runtime are largely orthogonal, this particular routine in StreamExecutor is used in GPU common runtime and a couple of other operators. In this commit it's renamed as StreamInterface::GpuStreamMemberHack() and their call sites are also changed. 2) StreamExecutorInterface::CudaContextHack() This member is renamed to StramExecutorInterface::GpuContextHack(). Changes introduced in this commit includes: - some StreamExecutor interfaces and CUDA implementation - GPU common runtime related to interface changes in StreamExecutor - operators affected by interface changes in StreamExecutor
* [SE] Re-enable acquiring real cpu frequencyGravatar A. Unique TensorFlower2018-07-10
| | | | PiperOrigin-RevId: 203959955
* Allow is_initialized and initializer to be called on MirroredVariables and ↵Gravatar Anjali Sridhar2018-07-06
| | | | | | TowerLocalVariables. PiperOrigin-RevId: 203520287
* Automated g4 rollback of changelist 202347723Gravatar A. Unique TensorFlower2018-06-28
| | | | PiperOrigin-RevId: 202392792
* [SE] Re-enable acquiring real cpu frequencyGravatar A. Unique TensorFlower2018-06-28
| | | | PiperOrigin-RevId: 202347723
* [XLA] Support asynchronous execution through XLAGravatar A. Unique TensorFlower2018-06-28
| | | | PiperOrigin-RevId: 202292422
* [TF:STREAM_EXECUTOR] move declaration of multi_platform_manager to headerGravatar Nick Desaulniers2018-05-17
| | | | PiperOrigin-RevId: 197051272
* Use parenthesis based construction instead of brace initializationGravatar Smit Hinsu2018-05-09
| | | | | | | | Updates all the construction calls for Status, ScopedActivateContext and mutexes withing stream_executor to follow the recommendation in https://abseil.io/tips/88 PiperOrigin-RevId: 196007931
* [StreamExecutor] [XLA] Delete copy/pasted implementations of MakeUnique.Gravatar Justin Lebar2018-04-22
| | | | | | | | | | | | | | | | | StreamExecutor and XLA had a copy/pasted implementation of MakeUnique, in namespaces stream_executor::port and xla. This change removes those implementations and instead pulls tensorflow::MakeUnique into namespace stream_executor and namespace xla. We pull it into stream_executor rather than stream_executor::port for consistency with TF and XLA, which both pull MakeUnique into their own namespace. This change also moves MakeUnique and WrapUnique out of namespace tensorflow::scam_ops::internal -- scam can simply use tensorflow::{Make,Wrap}Unique. I suspect the reason it was this way originally was that TF didn't have Make/WrapUnique. PiperOrigin-RevId: 193849330
* [StreamExecutor] Rename ::perftools::gputools -> ::stream_executor, part 1.Gravatar Justin Lebar2018-04-17
| | | | | | | | | | | | | | | | | | | | | | | | | | Step 1 of re-namespace'ing StreamExecutor into ::stream_executor. This moves everything inside of stream_executor/..., and leaves a namespace alias into ::perftools::gputools. The next steps will clean up users to use the new namespace. This is mostly a mechanical change, but it also includes a bunch of non-mechanical changes that ideally would be split out into separate patches. Unfortunately they all sort of need to be shoved in here for various reasons: - forward declarations need to be in the same namespace as the actual types, so we need to change all forward declarations of StreamExecutor types in this one patch. - Uses of these forward declarations need to be changed to the new namespace (or otherwise we need to add a namespace alias to the relevant header, but this is pretty ugly). - Various initialization code needs to live in StreamExecutor's "real" namespace, so all this needs to be changed. PiperOrigin-RevId: 193256128
* Rename StreamExecutorInterface::BlockHostUntilDoneWithStatus to ↵Gravatar A. Unique TensorFlower2017-12-15
| | | | | | BlockHostUntilDone. PiperOrigin-RevId: 179198370
* Use BlockHostUntilDoneWithStatus in various places.Gravatar A. Unique TensorFlower2017-12-11
| | | | PiperOrigin-RevId: 178723711
* Replace StreamExecutorInterface::BlockHostUntilDone with ↵Gravatar A. Unique TensorFlower2017-12-09
| | | | | | | | | BlockHostUntilDoneWithStatus All known overrides of StreamExecutorInterface::BlockHostUntilDone are changed by this CL. PiperOrigin-RevId: 178492517
* Make tensorflow::mutex implement a shared (reader/writer) lock, usingGravatar A. Unique TensorFlower2017-08-17
| | | | | | open source nsync library. PiperOrigin-RevId: 165633487
* Merge changes from github.Gravatar A. Unique TensorFlower2017-08-15
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | END_PUBLIC --- Commit 9f81374c3 authored by raymondxyang<zihao.yang@microsoft.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: Add option for build more python tests in Cmake (#11853) * Ignore Windows built project * Fix deprecated methods in tf.contrib.python * Fix regex match for Windows build in contrib.keras * Fix Regex match for Windows build in session_bundle * * Fix deprecated methods * Fix regex match for Windows * Fix compatibility issue with Python 3.x * Add missing ops into Windows build for test * Enabled more testcases for Windows build * Clean code and fix typo * Add conditional cmake mode for enabling more unit testcase * Add Cmake mode for major Contrib packages * Add supplementary info in RAEDME for new cmake option * * Update tf_tests after testing with TF 1.3 * Clean code and resolve conflicts * Fix unsafe regex matches and format code * Update exclude list after testing with latest master branch * Fix missing module --- Commit 98f0e1efe authored by Yong Tang<yong.tang.github@outlook.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: Dynamic ksize and strides with MaxPool (#11875) * Dynamic ksize with max_pool This fix tries to fix the issue raised in 4746 where ksize is static (attr) with max_pool. This fix changes ksize to input tensor so that it is dynamic now. This fix fixes 4746. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add dynamic ksize to MaxPoolGrad and MaxPoolGradGrad Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add test cases for max_pool_v2 Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Fix GPU Jenkins issue. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Enable MaxPoolV2 in GPU Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Hide MaxPoolV2 and other fixes. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit 02d6bc185 authored by Bairen Yi<byronyi@users.noreply.github.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: remove useless variable (#12212) --- Commit ed6b0d905 authored by namrata-ibm<bhavenamrata@gmail.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: Adding support for s390x in calculation of cpu_frequency (#12201) --- Commit 627dfc9dd authored by Taehoon Lee<taehoonlee@snu.ac.kr> Committed by Taehoon Lee<taehoonlee@snu.ac.kr>: Fix typos --- Commit c0f9b0a91 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: In fast-math mode emit a tanh that has a faster min/max. PiperOrigin-RevId: 164943597 --- Commit 87605f3d6 authored by Kay Zhu<kayzhu@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: [TF:XLA] Use HloEvaluator for ComputeConstant, remove the need of a dedicated compute constant backend. PiperOrigin-RevId: 164940970 --- Commit 881de45c2 authored by Taehoon Lee<me@taehoonlee.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: Add bool type supports for GPU kernels (#11927) * Add bool type supports for GPU kernels * Add bool type test codes for GPU kernels --- Commit eeacdcdb1 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add missing "CPU" suffix in registrations. PiperOrigin-RevId: 164939527 --- Commit de01be952 authored by namrata-ibm<bhavenamrata@gmail.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: Adding support for Big Endian in graph_constructor_test and wav_io (#12179) --- Commit 26719d29f authored by QingYing Chen<pkudysj@126.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: Implement CRF decode (Viterbi decode) for tensor (#12056) * Implement CRF decoding for tensors * add test code for tensor version's CRF decoding * made modifications according to pylint * add some comments for crf decode * remove useless code * add comments at the top comment of crf module and add more comments in crf_test * capitalize first char of first word in comments * replace crf_decode test code with a deterministic example --- Commit f9a81ca2f authored by Pete Warden<pete@petewarden.com> Committed by gunan<gunan@google.com>: Create CI build script for Raspberry Pi (#12190) * Create CI build script for Raspberry Pi * Moved location of Pi build script --- Commit e2a163a90 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Merge code from PR #11940 with internal changes from cl/164796436, and update Python tests to also run on GPU. PiperOrigin-RevId: 164929133 --- Commit 08bbfa187 authored by Taehoon Lee<me@taehoonlee.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: Fix typos (#12195) --- Commit ab96f41fb authored by Luke Iwanski<luke@codeplay.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: [OpenCL] Extends matmul_benchmark.py to cover SYCL (#11697) * [OpenCL] Extends matmul_benchmark.py to cover SYCL * Fixed typo * /gpu:0 -> /device:GPU:0 * Fixes control_flow_ops_py_test * /gpu: -> /device:GPU: * Fixes //tensorflow/python/profiler/internal:run_metadata_test * gpu: -> GPU: * Fixes tfprof_node * [OpenCL] Fixes device path to name with many colons (#123) The device path is constructed from a device name by replacing all colons with underscores. Some device names contain more than one colon, for example 'device:SYCL:0' which gives a path 'device_SYCL_0'. The previous code would not convert this back to the original device name, but rather to 'device:SYCL_0'. An alternative fix would be to convert all underscores to colons in the device name (i.e. remove the restriction inside `replace("_", ":", 1)`), however I'm not sure if there are any device names which contain underscores. * If no gpu device aviable fake one * gpu: -> device:GPU * Fixes profiler test * /gpu:x -> /device:GPU:x * Fixes debug_io_utils_test.cc test * Fixes device_name_utils_test.cc --- Commit 35e7a3665 authored by Yong Tang<yong.tang.github@outlook.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: Remove unneeded casting of int64 for reverse_sequence (#12192) This fix remove unneeded cast of int64 for reverse_sequence: ``` lengths = math_ops.to_int64(lengths) ``` as int32 has already been enabled for reverse_sequence. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit 9fba8c185 authored by Anna R<annarev@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add benchmark dashboard link to benchmarks doc. Also, I added a link and description for Benchmarks page to Community index page. PiperOrigin-RevId: 164924906 --- Commit bb6f32fa7 authored by Mark Heffernan<meheff@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Make HloAliasAnalysis updatable after changes to the HLO graph. As part of this change make HloAliasAnalysis a thinner layer which basically only holds a map from HloValue to HloBuffer and vice versa. PiperOrigin-RevId: 164923041 --- Commit 9103096c1 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by Thomas K?ppe<tkoeppe@google.com>: Merged commit includes the following changes: 164923041 by meheff: Make HloAliasAnalysis updatable after changes to the HLO graph. As part of this change make HloAliasAnalysis a thinner layer which basically only holds a map from HloValue to HloBuffer and vice versa. -- PiperOrigin-RevId: 164923041 --- Commit 822603aed authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Merging sibling fusion instruction using multi_output_fusion PiperOrigin-RevId: 164920220 --- Commit c035aa2a8 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: 164917891 --- Commit e1e81d9ba authored by Luke Iwanski<luke@codeplay.com> Committed by Rasmus Munk Larsen<rmlarsen@google.com>: [OpenCL] Fixes double memcpy bug (#151) (#12173) * [OpenCL] Fixes double memcpy bug (#151) As the debg CopyOp is called on a Tensor without type, we need to use the DataType enum to get type information, and use this to pass the type on to Eigen. This is a workaround Eigen's need to have a type when calling memcpy. If the Eigen memcpy can be provided without a type requirement, then the memcpy in sycl_util is unnecessary. * Acts on feedback from: #12173/files/32cb12a9001b672425867b5a3110fd98e737a20b#r132496277 --- Commit d9ca2d86d authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Internal change PiperOrigin-RevId: 164916465 --- Commit b8d13d218 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Remove more parts of DCASGD missed in the first pass. (47949b) PiperOrigin-RevId: 164914552 --- Commit 73b3d52c7 authored by Alexandre Passos<apassos@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: cmake fix PiperOrigin-RevId: 164911656 --- Commit 2173b5b0a authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Allow TFE_TensorHandleCopyToDevice to have the same device as src and destination. It will reuse the same underlying buffer in those cases. PiperOrigin-RevId: 164909906 --- Commit 13eb3b90e authored by Alexandre Passos<apassos@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Experimental C and Python APIs to invoke TensorFlow kernels on concrete values. PiperOrigin-RevId: 164902588 --- Commit 7dfabcc01 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Initialize ExecutionOptions in ComputeConstant to default values. PiperOrigin-RevId: 164894867 --- Commit c8897e9bc authored by Benoit Steiner<bsteiner@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Static required time computation PiperOrigin-RevId: 164894645 --- Commit 076158f9b authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Enable implicit->explicit conversion by default. PiperOrigin-RevId: 164890915 --- Commit 58c4a4cb1 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Bugfix: number of input channels is not necessarily in the last dimension, after introduction of data_format param. PiperOrigin-RevId: 164889729 --- Commit 8f9b1af8a authored by Igor Saprykin<isaprykin@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Recover MonitoredSession when the Coordinator is requested to stop with one of the _PREEMPTION_ERRORS. When SyncReplicasOptimizer is used, a preemption in the Coordinator may result in two cases: Case 1) the session gets silently marked as complete Case 2) the session gets stuck This CL aims to solve and verify solutions for both of these problems. Fix 1 changes the should_stop logic. Fix 2 changes the CoordinatedSession.run() logic. SyncReplicasOptimizer runs a separate set of threads using a Coordinator instance. Those threads do FIFOQueue.enqueue; the main thread does a blocking FIFOQueue.dequeue. `sync_token_q` FIFOQueue is on parameter-servers. When one of the PS instances gets preempted, an AbortedError causes the Coordinator to stop via request_stop(ex). That by itself changes the state of MonitoredSession.should_stop() to True (Fix 1). Results of the blocking Dequeue operation are sent to the chief worker via Recv. What happens next depends on the amount of tokens in `sync_token_q`. If there are enough for the next call to Dequeue to return, then the low-level "tf session run() call" returns. The next iteration of the `while not MonitoredSession.should_stop()` loop decides that the training is complete (Case 1). If there are not enough tokens in `sync_token_q`, then the blocking Dequeue is going to keep waiting for them. This results in the graph execution getting stuck and the whole session getting garbage collected after 10 minutes (Case 2). We decided to fix that by re-creating a session after it gets garbage collected (Fix 2). An alternative was to try to cancel the pending Dequeue operation, but it's not clear that it is the right thing to do and it is also not easy. PiperOrigin-RevId: 164888390 --- Commit 46e4de6e5 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Undo loop fusion changes for now as they seem to be altering a few results. END_PUBLIC RELNOTES: n/a BEGIN_PUBLIC BEGIN_PUBLIC Automated g4 rollback of changelist 164825735 PiperOrigin-RevId: 165340331
* [SE] Make ExecutorCache thread-safe, change ExecutorCache::Insert to ↵Gravatar Peter Hawkins2017-07-31
| | | | | | | | ExecutorCache::GetOrCreate. Add support for creating Executors for different device ordinals in parallel. [XLA] Create Executors in parallel. PiperOrigin-RevId: 163734988
* Remove unused namespace aliasesGravatar A. Unique TensorFlower2017-05-30
| | | | PiperOrigin-RevId: 157468609
* [SE:Host] Temporarily revert the determination of host frequency.Gravatar A. Unique TensorFlower2017-03-14
| | | | | | Need to investigate some fork/exec related bugs and this is not critical functionality. Change: 150099703
* Set the clock rate for the host accuratelyGravatar David Majnemer2017-02-27
| | | | | | We used CLOCKS_PER_SEC which is defined only in terms of the clock system call. What we needed to use was the hardware clock rate. Change: 148655561
* Change the way the host platform stream blocks so that it is usable with a ↵Gravatar A. Unique TensorFlower2017-01-26
| | | | | | | | | condition variable implementation that doesn't support waiting with a predicate. Remove unnecessary ToString calls in cuda_gpu_executor.cc. Minor tweaks to header inclusions which should have no effect on code behavior. Change: 145732882
* Better StreamExecutor constructorGravatar A. Unique TensorFlower2017-01-20
| | | | | | Using std::unique for the implementation argument makes it clear that StreamExecutor is taking ownership of that argument. Change: 145107302
* Add hooks for StreamExecutor module initialization orderingGravatar A. Unique TensorFlower2017-01-19
| | | | Change: 145010633
* Plumb port::Status through the internal synchronous memcopy routines.Gravatar A. Unique TensorFlower2017-01-17
| | | | | | | Now, at least for the public APIs that return port::Status, they can grab the port::Status that the implementation would like to return and use its additional information in reporting to the user. Change: 144741667
* Add a Host platform to the stream executor library that runs code on the CPU.Gravatar Peter Hawkins2016-12-05
Avoid using a linker-initialized mutex in multi_platform_manager to work around a initialization/destruction ordering problem on Mac. Include the CUDA platform ID module even in non-CUDA builds. Change: 141045549