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* Merge pull request #20609 from karllessard:java-add-gradients-with-nameGravatar TensorFlower Gardener2018-07-31
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* | Added a C API to get a TF function name.Gravatar Mingsheng Hong2018-07-30
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| * Enforce uniqueness of custom prefixes for gradientsGravatar karl@kubx.ca2018-07-25
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| * 1st code review: rename 'scope_name' to 'prefix', etc.Gravatar karl@kubx.ca2018-07-25
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| * Add scope name to TF_AddGradientsGravatar karl@kubx.ca2018-07-25
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* Allow TF_Delete* functions to accept nullptrGravatar James Keeling2018-07-24
| | | | | | The TF_Delete* functions in TensorFlow's C API now safely do nothing when asked to delete a null pointer. This mirrors the behaviour of free in C and delete in C++. PiperOrigin-RevId: 205844191
* Add C API for kernel infoGravatar James Keeling2018-07-23
| | | | | | This is part of the work to make available kernels possible to query, to support Swift For TensorFlow. There are also a number of github issues asking for the functionality. PiperOrigin-RevId: 205660862
* Expose each device's incarnation via `Session.list_devices()`.Gravatar Derek Murray2018-07-19
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* Make ImportGraphDefOptions and ImportGraphDefResults own all strings.Gravatar Skye Wanderman-Milne2018-06-21
| | | | | | | | | | | This change introduces SafeTensorId, an alternative to TensorId that uses strings instead of StringPieces. It makes ImportGraphDefOptions/Results use SafeTensorIds instead of TensorIds so the caller isn't responsible for managing the string lifetime. import_graph_def in Python was broken before this change, although it wasn't caught in tests. PiperOrigin-RevId: 201622030
* Merge changes from github.Gravatar Yifei Feng2018-04-23
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* Raise exception in SWIG on bad TF_Status from C API.Gravatar Skye Wanderman-Milne2018-03-30
| | | | | | | | | | | | | | | This change provides an alternative mechanism to tf.raise_exception_on_not_ok_status(), which is inefficient and error-prone (people often use the status multiple times in the with block, but it's only checked when the context manager exits). Instead, it uses SWIG to automatically raise an exception when a C API method fails. Note that this removes the status argument from affected methods. For now, I've only applied this typemap to C API methods. It would be good to expand this to all uses of raise_exception_on_not_ok_status. PiperOrigin-RevId: 191121016
* Add TF_TryEvaluateConstant to the C API and have smart_cond call it.Gravatar Skye Wanderman-Milne2018-03-06
| | | | | | | This effectively plumbs EvaluateConstantTensor to smart_cond. This makes smart_cond even smarter by trying to evaluate the predicate if it can't statically infer it. PiperOrigin-RevId: 188073244
* Added C-API based unit tests for GPU and XLA GPU testing.Gravatar Mingsheng Hong2018-02-14
| | | | | | Also refined the API comment for TF_NewSession(). PiperOrigin-RevId: 185739196
* C API: Fixes #7394Gravatar Asim Shankar2018-02-08
| | | | | | | | Ideally, when TF_NewTensor is provided with invalid arguments it would provide a detailed error message. However, for now, to keep the existing API, signal failure by returning nullptr. PiperOrigin-RevId: 185040858
* Add TF_GraphNumFunctions and TF_GraphGetFunctionsGravatar Igor Ganichev2018-01-17
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* Rename the newly introduced TF_SetAttrFunc function to make it clear that it isGravatar A. Unique TensorFlower2018-01-17
| | | | | | | setting the attribute to a function name. This permits future patches to add things like TF_SetAttrFuncAttrList if necessary later. PiperOrigin-RevId: 182216098
* Introduce a new C API entrypoint to set a 'func' attribute on an opGravatar A. Unique TensorFlower2018-01-16
| | | | | | description. PiperOrigin-RevId: 182146876
* Read ApiDef from TensorFlow Go API.Gravatar Anna R2017-12-20
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* Raise exception on missing unused input_map keys with C API enabled.Gravatar Skye Wanderman-Milne2017-12-12
| | | | | | | | | | | | | | | | | Without this change, the C++ ImportGraphDef API returns unused input_map keys (which are plumbed through to the C API as well). However, the Python import_graph_def API requires slightly different semantics: it throws an error for unused input_map keys that are missing from the GraphDef. This change modifies the C and C++ APIs to limit the returned keys to those missing from the GraphDef, and plumbs this through to the C API-enabled import_graph_def implementation. Note that this is a change to the existing C API. Luckily the modified method hasn't been released yet, so it's ok to change it. PiperOrigin-RevId: 178783957
* Uniquify names and prefixes in import_graph_def with C API enabled.Gravatar Skye Wanderman-Milne2017-12-06
| | | | | | This makes the C API-enabled behavior the same as the current behavior. PiperOrigin-RevId: 178133381
* Add TF_GraphVersions() to C API and use in Graph.graph_def_versions()Gravatar Skye Wanderman-Milne2017-10-30
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* Add ability to fetch return nodes and unused input mappings from C API ↵Gravatar Skye Wanderman-Milne2017-10-30
| | | | | | | | | | | | | GraphDef import This change introduces yet another ImportGraphDef function to the C API (TF_GraphImportGraphDefWithResults), but this one has extensible return values so we shouldn't have to add more in the future. This change also modifies the ImportGraphDef C interface to manage all string data for the user. PiperOrigin-RevId: 173894710
* Merge changes from github.Gravatar Benoit Steiner2017-10-24
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | END_PUBLIC --- Commit 9f8523640 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Update ops-related pbtxt files. PiperOrigin-RevId: 173145770 --- Commit 01b6b0638 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Cut tracing memory cost PiperOrigin-RevId: 173144626 --- Commit 5e23e0e67 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: [XLA] Erase cloned instructions on the fly when merging fusion nodes. This avoids the awkward situation where an RNG which is clearly eligible for fusion becomes ineligible mid-fusion because it suddenly has an extra (dead) user. PiperOrigin-RevId: 173141716 --- Commit 1038927c0 authored by Saurabh Saxena<srbs@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add SerializeIterator op that serializes an IteratorResource into a variant tensor. Add DeserializeIterator op that builds IteratorResource from a variant tensor. Move BundleReaderWrapper and BundleWriterWrapper from dataset.h to iterator_ops.cc. Add generic key-value store interfaces IteratorStateReader and IteratorStateWriter for reading/writing state of iterators. Get rid of IteratorBundleReader and IteratorBundleWriter. PiperOrigin-RevId: 173140858 --- Commit 57f3e529d authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Internal change PiperOrigin-RevId: 173136642 --- Commit 0e56ffb7b authored by Shanqing Cai<cais@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Fix breakages in OSS builds See example breakages logs at: http://ci.tensorflow.org/job/tensorflow-cl-cpu-python3-pip/10847/console http://ci.tensorflow.org/job/tensorflow-cl-gpu/11008/console 1. CL/172477381 added the no_oss tag to tests with oss_serial tags, which broke the logic of OSS_SERIAL tests in pip.sh and run_pip_test.sh. This CL fixes that. 2. The nccl_kernels BUILD target in contrib/nccl/BUILD was missing some dependencies. This CL adds the missing ones. Fixes: #13918 PiperOrigin-RevId: 173133914 --- Commit 3ed049b67 authored by Alexandre Passos<apassos@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Allows calling keras layers in eager mode. PiperOrigin-RevId: 173129805 --- Commit 4ec6f2b07 authored by Alexandre Passos<apassos@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Switching contrib.summaries API to be context-manager-centric PiperOrigin-RevId: 173129793 --- Commit 03b02ffc9 authored by Justine Tunney<jart@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Put Bazel mirror URLs first PiperOrigin-RevId: 173127955 --- Commit 46ab25e4d authored by David Majnemer<majnemer@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: [XLA] Add support for convolutions with no spatial dimensions PiperOrigin-RevId: 173126950 --- Commit fc56349b7 authored by Derek Murray<mrry@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: [tf.data] Convert dataset arguments to tensors as early as possible. This change raises a `TypeError` earlier if (for example) the `batch_size` argument to `Dataset.batch()` has the incorrect type. PiperOrigin-RevId: 173126678 --- Commit 4f7503a87 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: K-FAC: Support for registering multiple minibatches with register_fully_connected() PiperOrigin-RevId: 173121735 --- Commit 2845bfcd6 authored by Tim Harley<tharley@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Avoid listing all modified Enter/RefEnter nodes on INFO, use VLOG(1) instead. Leave a single, simple, message on INFO. PiperOrigin-RevId: 173121726 --- Commit 434695921 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: K-FAC: _check_registration() supports multiple towers. PiperOrigin-RevId: 173115870 --- Commit 670dddf4a authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Multi-minibatch support for tf.contrib.kfac.fisher_blocks.FullyConnectedKFACBasicFB. PiperOrigin-RevId: 173109677 --- Commit dc13a8e2f authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Fix import of meta graphs with partitioned variables into a scope. Saver inspects SliceInfo to decide the variable name when creating a checkpoint. Before this fix even if a partitioned variable ("weights") was imported into a scope "a" it would still be checkpointed as ("weights") instead of ("a/weights") since import_scoped_meta_graph was not adjusting the SliceInfo. WARNING: if you use import_meta_graph on graphs with partitioned_variables WITH an import_scope argument AND then create a Saver to write/read checkpoints this change may break your checkpoint loading. PiperOrigin-RevId: 173105796 --- Commit eea089bdb authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: K-FAC: Multi-tower support for ConvDiagonalFB. PiperOrigin-RevId: 173105412 --- Commit 9b9cbbe2a authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Add int64 Tperm type support for `Transpose` (#13909) * Add int64 Tperm type support for `Transpose` This fix adds int64 Tperm support for `Transpose`. In `array_ops.cc`, `Transpose` and `ConjugateTranspose` have been specified as accepting int32 and int64 perm types. However, only int32 kernels has been registered. This fix adds the int64 perm support by removing the constraint on Tperm, resolve the type at runtime, and copying the data type accordingly to correctly handle the int64/int32 types. Additional tests have been added as well. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add test cases for int64 of perm in Transpose. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add namespace to hide PermutationHelper Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Enable use_gpu=True for perm type test. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * extra // namespace annotation * Adding a comment about int32 casting that should be safe. Permutations only contain values that refer to dimensions, and the maximum number of dimensions we have is 254, so an int32 is always safe here. --- Commit ac0004e71 authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Add int64 shape support on GPU for stateless random ops. (#13908) * Add int64 shape support on GPU for stateless random ops. This fix adds int64 shape support on GPU for stateless random ops `StatelessRandomUniform`, `StatelessRandomNormal`, `StatelessTruncatedNormal`. The int64 shape for stateless random ops is already supported on CPU with int32/int64 processed properly through `MakeShape`. However, on GPU a type constraint `.TypeConstraint<int32>("T")` has been improperly added. Such a type constraint actually prevents an int64 shape type to run on GPU. (As a comparision, no type constraint on CPU). This fix removes the type constraint and allows int64 shape to be run on GPU. This fix also adds test cases for int64 shape support on stateless random ops. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add test cases for int64 shape support for stateless random ops. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add int32 to shape types tested. --- Commit 0d437c3be authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Add int64 padding support for MirrorPad (#13907) * Add int64 padding support for MirrorPad This fix adds int64 padding support for `MirrorPad`. In the `array_ops.cc` the `MirrorPad`/`MirrorPadGrad` has been specified as supporting int64 padding. The related kernels does not have the int64 padding registered though. This fix adds the int64 padding support. This fix also adds additional test cases for coverage. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Update template for CPU and GPU support of int64 paddings. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add int64 padding support for MirrorPad Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Put eigen header first like before, just in case. --- Commit 690003cc0 authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Add `int64` type `multiples` support for `tf.tile` (#13884) * Add `int64` type `multiples` support for `tf.tile` In the doc of `tf.tile` (tf.tile.__doc__) both `int32` and `int64` are supported for `multiples`. However, the kernel for `int64` is not registered yet. This fix adds the support of `int64` `multiples` so that the behavior matches the description of the docs. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Update functors for int64 multiples support in `tf.tile` Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Update test cases for int64 of multiples in `tf.tile` Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add GPU and non GPU tests Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * format with clang-format -i Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Move Tmultiples after T (as it is auxilliary) And use `use_gpu=True` Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit fd8d517b9 authored by Yunxing Dai<yunxing@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add tests for convolution 1D RELNOTES: n/a PiperOrigin-RevId: 173060283 --- Commit 40c475b48 authored by formath<jinpengliu@163.com> Committed by Vijay Vasudevan<vrv@google.com>: add segment_reduction_ops to tf_op_files (#13901) --- Commit bfa4ec194 authored by Tayo Oguntebi<10927929+tayo@users.noreply.github.com> Committed by Vijay Vasudevan<vrv@google.com>: Update node_def.proto comments (#13874) The device field had outdated comments. Note: We could consider adding tpu as an example here, e.g. "gpu" | "cpu" | "tpu". Thoughts? --- Commit c9cb5a58d authored by formath<jinpengliu@163.com> Committed by Vijay Vasudevan<vrv@google.com>: protobuf lib path bug fix for benckmark on osx (#13878) --- Commit 1c1dad105 authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Add int64 axis support for reduction ops. (#13891) * Add int64 axis support for reduction ops. This fix is a follow up to PR 13863. In PR 13863 the program crash is fixed if int64 axis is passed to reduction ops, e.g. reduce_sum, reduce_max, etc. However, 13863 does not process the case of int64 support, it merely fixes the crash. This fix adds the support for int64 axis of reduction ops. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add int64 axis support for mean, prod, sum Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add int64 axis support for min and max. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add int64 axis support for reduce_all and reduce_any Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add test cases for int64 axis support of reduce_any and reduce_all Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit 17096081e authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Improve resize_bicubic performance by reorganizing loops (#13840) * Improve resize_bicubic performance by reorganizing loops This fix tries to address the issue raised in 13693 where performance of `resize_bicubic` is not on par with opencv. This fix rearranges the loops so that it is the same for num_channel=40 and num_channel=3: Pre-fix: ``` CHANNEL=40 opencv: 145.08ms tf: 314.26ms CHANNEL=3 opencv: 11.95ms tf: 8.95ms ``` Post-fix: ``` CHANNEL=40 opencv: 144.25ms tf: 214.55ms CHANNEL=3 opencv: 11.78ms tf: 14.07ms ``` This fix fixes 13693. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Keep special handling of `num_channels=3` for `resize_bicubic` This commit keeps special handling of `num_channels=3` for `resize_bicubic`: Without special handling: ``` opencv: 11.78ms tf: 14.07ms ``` With special handling: ``` opencv: 11.74ms tf: 9.46ms ``` Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Expand Benchmark test for resize_bicubic Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Update from review feedback. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit b927df57f authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Update protobuf.cmake to b04e5cba356212e4e8c66c61bbe0c3a20537c5b9 (#13893) This fix tries to address the issue raised in 8187 where protobuf.cmake used different version as bazel. The reason for discrepancy was due to the fact that a customerized protobuf was needed with Windows patch. Since the patch has been merged in (https://github.com/google/protobuf/pull/2203), it makes sense to update protobuf.cmake so that the same version of cmake is used. This fix fixes 8187. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit d1183ca6a authored by Vijay Vasudevan<vrv@google.com> Committed by GitHub<noreply@github.com>: Give each variable a unique name in accumulate_n_v2_eager_test. (#13886) --- Commit a69945810 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Update pin for bazel-toolchains to latest version PiperOrigin-RevId: 173002530 --- Commit 9d55c249c authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Fix doc in TF_CALL_ when invoked in mobile platform (#13881) * Fix doc in TF_CALL_ when defined(IS_MOBILE_PLATFORM) && !defined(__ANDROID_TYPES_FULL__) This is a small doc fix that includes bool as part of the types that is supported in mobile (IS_MOBILE_PLATFORM && !__ANDROID_TYPES_FULL__), as bool is clearly invoked in the following define. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Also add bool to android full version. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit ba49d8583 authored by Bjarke Hammersholt Roune<broune@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Slight change to reduce_test to avoid generating inf, which was triggering an inf detector unnecessarily. PiperOrigin-RevId: 172965466 --- Commit 93e8f3c67 authored by Anna R<annarev@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Adding Python ApiDef overrides. PiperOrigin-RevId: 172960496 --- Commit 0d6a2e353 authored by Anna R<annarev@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Internal change. PiperOrigin-RevId: 172960439 --- Commit 62df65c72 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add dtype argument to Mean and Accuracy object-oriented metrics. PiperOrigin-RevId: 172957714 --- Commit d7409d32b authored by Simone Cirillo<my.accounts@gmx.se> Committed by Vijay Vasudevan<vrv@google.com>: Fix import of spatial_softmax from tensorflow.contrib.layers (#13833) --- Commit df8bce63d authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Fix crash when `int64` axis is passed to `tf.reduce_sum` (#13863) * Fix crash when `int64` axis is passed to `tf.reduce_sum` This fix tries to fix the crash triggered by `int64` axis passed to `tf.reduce_sum`: ``` ubuntu@ubuntu:~/tensorflow2$ (cd && python) Python 2.7.12 (default, Nov 19 2016, 06:48:10) [GCC 5.4.0 20160609] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf >>> v = tf.reduce_sum([1,2,3], tf.constant(0, tf.int64)) 2017-10-20 15:55:06.993430: F tensorflow/core/framework/tensor.cc:601] Check failed: dtype() == expected_dtype (9 vs. 3) ubuntu@ubuntu:~/tensorflow2$ ``` The issue is caused by the fact that shape inference in `common_shape_fns.cc` only assumes int32 without proper handling of diffent types. In `math_ops.cc` both int32 and int64 are mentioned. NOTE that this fix does not address the issue that int64 is not supported. To allow int64 axis it is more than adding a template in `ReductionOp` as the type of the axis seems to be decided by some other ways in Eigen. This fix merely fixed the crash so that an error message will return without exit from the python program "No OpKernel was registered to support Op 'Sum' with these attrs". Still, I think its worth to at least allow the program to continue in case of unsupported kernel. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Update implementation with a template helper function. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit 29c7b4658 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Adding the Stanford Tensorflow class to community resources. PiperOrigin-RevId: 172956049 --- Commit f758b24a8 authored by Alexandre Passos<apassos@google.com> Committed by Vijay Vasudevan<vrv@google.com>: Variable name for the eager test (#13873) --- Commit a5fe66b15 authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Removed some unnecessary broadcasts in binary ops where only one input needs broadcasting (which is a fairly common case, even in the fallback path). PiperOrigin-RevId: 172950493 --- Commit c77090a0a authored by Yong Tang<yong.tang.github@outlook.com> Committed by Vijay Vasudevan<vrv@google.com>: Fix issues where int64 crops could not be passed to batch_to_space. (#13862) * Fix issues where int64 crops could not be passed to batch_to_space. This fix tries to address the issue where int64 `crops` could not be passed to `batch_to_space` even though both int32 and int64 are specified as supported in the docs (tf.batch_to_space.__doc__) The reason is that BatchToSpace kernel puts a constraint of int32 to crops data types. This fix removed the constraint so that int64 `crops` could be supported. NOTE: Just removing the constraint should work and it is not necessary to add specification to the kernel class template, as `SubtleMustCopyFlat` called in the class already correctly handled both int32 and int64 cases. Besides, other data types (e.g., float or double) will not be passed to the kernel as they are guarded by the specification in `array_ops.cc`. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Also remove int64/int32 type constraints for SpaceToBatch kernels Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Add test cases for int64 crops of batch_to_space and space_to_batch Signed-off-by: Yong Tang <yong.tang.github@outlook.com> * Fix test failures. Signed-off-by: Yong Tang <yong.tang.github@outlook.com> --- Commit 494837936 authored by Joshua V. Dillon<jvdillon@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Make `tf.contrib.distributions` quadrature family accept a `Tensor` for `quadrature_grid_and_probs` argument. PiperOrigin-RevId: 172950094 --- Commit 9c825d32c authored by Jinze Bai<baijinze1994@163.com> Committed by Vijay Vasudevan<vrv@google.com>: Merge two GPU kernel launching to one in DiagOp. (#13859) --- Commit c0ca50a47 authored by Yan Facai (???)<facai.yan@gmail.com> Committed by Vijay Vasudevan<vrv@google.com>: ENH: add Relu6GradGrad (#13268) * ENH: add Relu6GradGrad * TST: add test case * CLN: import nn_grad * TST: add init value --- Commit 8ff33271e authored by Justin Lebar<jlebar@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Dump the computation's SessionModule as part of the tf_compile rule. PiperOrigin-RevId: 172946149 --- Commit ebcae4a5e authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add streaming_precision_recall_at_equal_thresholds This helper method computes streaming tp, fp, tn, fp, precision, and recall for the user in a way that exhibits O(T + N) time and space complexity (instead of O(T * N)), where T is the number of thresholds and N is the size of the predictions tensor. Thanks to Frank Chu for the efficient algorithm! PiperOrigin-RevId: 172946073 --- Commit ccfd9c1e5 authored by Sanjoy Das<sanjoy@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Log Hlo IR during AOT compilation PiperOrigin-RevId: 172944165 --- Commit 985031a10 authored by Alexandre Passos<apassos@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Allows tfe.enable_eager_execution(device_policy=tfe.DEVICE_POLICY_WARN). PiperOrigin-RevId: 172943398 --- Commit 703182d85 authored by Mingxing Tan<tanmingxing@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Add performance guide for fused decode_and_crop_jpeg optimization. PiperOrigin-RevId: 172943116 --- Commit 66b1f4383 authored by Francois Chollet<fchollet@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Make Network compatible with eager mode. Currently it only allows to instantiate a Network in eager mode using the regular Keras API, and call it on eager tensors. PiperOrigin-RevId: 172942569 --- Commit 41df2cec2 authored by ashankar<ashankar@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Testing pending CL: 172939383 --- Commit 37fd95179 authored by Alexandre Passos<apassos@google.com> Committed by TensorFlower Gardener<gardener@tensorflow.org>: Simplifies capturing code in graph_callable to use recent function improvements. PiperOrigin-RevId: 172937003 --- Commit d1e7382af authored by A. Unique TensorFlower<gardener@tensorflow.org> Committed by TensorFlower Gardener<gardener@tensorflow.org>: BEGIN_PUBLIC Automated g4 rollback of changelist 172924803 PiperOrigin-RevId: 173347587
* Add TF_GraphGetOpDef() to C API and use in Operation.op_def()Gravatar Skye Wanderman-Milne2017-10-18
| | | | | | | | | Note that this creates a small change in behavior with the C API enabled, since previously not all Python Operations had an OpDef (op_def() returns None). With the C API enabled, op_def() always returns an OpDef. PiperOrigin-RevId: 172634411
* Add uint32 and uint64 types to TensorFlow.Gravatar Peter Hawkins2017-10-10
| | | | | | | | This change merely creates the types, but does not register kernels that act on uint32/uint64 values. It also does not alter most op registration lists to include uint32/uint64 values. If desirable, that can be done in a subsequent change, although binary size will likely prove problematic if adding more kernels. The intent of the change is so XLA-compiled code can make use uint32/uint64 types. Since XLA does not use traditional TensorFlow kernels, using uint32/uint64 operators from XLA will require only uint32/uint64 op registrations, but will require few new kernel registrations. PiperOrigin-RevId: 171681867
* Use void* intead of TF_Buffer in TF_FunctionImportFunctionDefGravatar Igor Ganichev2017-09-28
| | | | | | | void* is more common (and more convenient) for passing in serialized protobufs in c_api.h. PiperOrigin-RevId: 170386128
* Add append_hash_to_fn_name arg to TF_GraphToFunctionGravatar Igor Ganichev2017-09-28
| | | | PiperOrigin-RevId: 170379490
* Add function description parameter to TF_GraphToFunctionGravatar Igor Ganichev2017-09-20
| | | | PiperOrigin-RevId: 169421145
* Relax restriction on ref types in bodyGravatar Igor Ganichev2017-09-19
| | | | PiperOrigin-RevId: 169356209
* Add attribute setting and getting support to TF_FunctionGravatar Igor Ganichev2017-09-19
| | | | PiperOrigin-RevId: 169337159
* Implement TF_FunctionImportFunctionDefGravatar Igor Ganichev2017-09-19
| | | | PiperOrigin-RevId: 169304057
* Support specifying output names during TF_Function creationGravatar Igor Ganichev2017-09-19
| | | | PiperOrigin-RevId: 169245946
* Add function gradient support to C APIGravatar Igor Ganichev2017-09-18
| | | | | | | | | | Also, change the internal representation of TF_Function and rename TF_GraphAddFunction to TF_GraphAddFunctionCopy to make it clear that a copy of the function is added to the graph. Any subsequent modifications to the function will not be reflected in the copy added to the graph. PiperOrigin-RevId: 169187793
* Add function support to Tensorflow C APIGravatar Igor Ganichev2017-08-30
| | | | | | | | This change adds minimal functionality. Support for FunctionOptions, attributes, output name rewriting, function name generation, etc is comming next. PiperOrigin-RevId: 167091238
* C API: TF_Tensors will always be in host memory.Gravatar Asim Shankar2017-08-03
| | | | | | | | This change undoes some experimentation in commit 22651083406ca01ac9d481e3367a3510d25f88cd and restores TF_Tensor behavior to what is was prior to that change. PiperOrigin-RevId: 164146670
* Minor comment improvement.Gravatar A. Unique TensorFlower2017-07-28
| | | | PiperOrigin-RevId: 163514331
* C API: Groundwork for experimenting with TF_Tensor in device memory.Gravatar Asim Shankar2017-07-27
| | | | | | | | TF_Tensor objects are always backed by host memory. This commit lays the groundwork for allowing TF_Tensor objects to refer to tensor data on device (e.g., GPU) memory. PiperOrigin-RevId: 163388079
* Added DT_VARIANT type.Gravatar Manjunath Kudlur2017-07-21
| | | | | | | | | A tensor with DT_VARIANT type can store arbitrary C++ data structures. DT_VARIANT is implemented using a type-erased data structure similar to std::any, but with extensions to make it compatible with tensorflow::Tensor. In particular, Encode and Decode methods need to be provided by C++ classes whose objects are stored in Variant. PiperOrigin-RevId: 162754827
* Automated g4 rollback of changelist 162668355Gravatar Manjunath Kudlur2017-07-21
| | | | PiperOrigin-RevId: 162733043
* Added DT_VARIANT type.Gravatar Manjunath Kudlur2017-07-20
| | | | | | | | | A tensor with DT_VARIANT type can store arbitrary C++ data structures. DT_VARIANT is implemented using a type-erased data structure similar to std::any, but with extensions to make it compatible with tensorflow::Tensor. In particular, Encode and Decode methods need to be provided by C++ classes whose objects are stored in Variant. PiperOrigin-RevId: 162668355
* Use new C API for Session.partial_run and its setupGravatar A. Unique TensorFlower2017-06-16
| | | | PiperOrigin-RevId: 159298135
* Add session.list_devices() APIGravatar Brennan Saeta2017-05-26
| | | | | | | | | | | | | | | | | | | | | | | | | | | In order to debug a TensorFlow cluster or check whether devices are available in a local session (e.g. GPU drivers are loaded), this change adds a `sess.list_devices` API to list all devices within the cluster. This CL implements the list_devices() feature via extensions to the TensorFlow C API, and the corresponding additions to the session.h session class and corresponding subclasses for both direct sessions, grpc_sessions, tensorflow_serving, and others. Additionally, in order to accomidate ClusterSpec propagation clusters, Master::ListDevices now also includes a session_handle in order to identify the appropriate master_session on which it should list the available devices. (With ClusterSpec propagation, different sessions can have different servers with different device capabilities.) This CL adds a ListDevices() API to MasterSession. It is most efficient to implement this API call there, because the MasterSession already has a list of devices. Additionally, this change upgrades the implementation of Master::ListDevices() to delegate to the MasterSession if a session handle is specified, and to return an error if no corresponding session is found. PiperOrigin-RevId: 157239656
* C API/Windows: Export API functions that were accidentally left out.Gravatar Asim Shankar2017-05-24
| | | | PiperOrigin-RevId: 157037270
* Name enums in C API (in addition to typedefs).Gravatar Skye Wanderman-Milne2017-04-26
| | | | | | SWIG will sometimes generate type references like "enum TF_DataType", which doesn't compile if only the typedef is named. Change: 154369467
* C API: Export functions in WindowsGravatar Asim Shankar2017-04-19
| | | | | | Adds visibility attributes to function declarations so that the symbols are visible in the shared library (tensorflow.dll) built on Windows. Change: 153622880
* Add C++ gradients to c_api.Gravatar Suharsh Sivakumar2017-04-13
| | | | | | | | | | | #6268 This CL does the following: (1) Adds TF_AddGradients function to C_API which adds gradient nodes for the specified inputs. (2) Adds internal constructor for Scope, need to create a scope from an existing graph in the c_api. (3) Adds constructor for AddSymbolicGradients that assumes OnesLike when grad_inputs aren't provided. (4) Improves error message when gradients aren't provided. Change: 153092774
* C API: fix misnamed function in c_api.hGravatar Skye Wanderman-Milne2017-04-13
| | | | | | | | | | | TF_ImportGraphDefOptionsRemapControlDependency() was accidentally called TF_GraphImportGraphDefOptionsRemapControlDependency() in the header file. This change is technically a breaking change to the public API, but given that the public function was never defined it seems ok to change it to the intended name. This change also adds a test for TF_ImportGraphDefOptionsRemapControlDependency(), which was missing before, allowing this to happen. Change: 153075645
* Fetch / py_func arguments no longer copy.Gravatar Alexandre Passos2017-04-12
| | | | Change: 153003331
* Merge changes from github.Gravatar Dandelion Mané2017-03-10
| | | | Change: 149800363
* C API: Keep the TF_LoadSessionFromSavedModel symbol in Android builds.Gravatar Asim Shankar2017-02-27
| | | | | | | Though, the function will return an error. Required for #7688 Change: 148664204
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