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TF_AddGradients could create nodes in the graph with names that conflicted with
other nodes in the graph. This would most clearly happen if TF_AddGradients()
was called twice on the same graph, and could also happen if there were other
nodes in the graph that happened to have "gradients" as a prefix of their name.
Fix that.
The added test in c_api_test.cc would fail in the call to TF_SessionRun() with
Node 'gradients/OnesLike' is not unique
without the changes to c_api.cc and c_api_internal.h
While at it, also fixed a possible name collision bug when using the C++ API
to constructor graphs (using Scope).
Thanks @karllessard for pointing this out.
PiperOrigin-RevId: 202087996
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PiperOrigin-RevId: 201701040
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PiperOrigin-RevId: 201110240
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PiperOrigin-RevId: 201033171
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PiperOrigin-RevId: 201011811
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Previously, the generate would emit code like this:
struct Attrs {
ArraySlice<int> dilations_ = {1, 1, 1, 1};
};
This code is incorrect, since the array slice references a temporary object
that dies after initialization finishes.
After this change change the generator will produce static functions to
initialize the values:
struct Attrs {
ArraySlice<int> dilations_ = Default_dilations();
private:
ArraySlice<int> Default_dilations() {
static int kStorage[] = {1, 1, 1, 1};
return ArraySlice<int>(kStorage);
}
};
Presumably, it used to work because all compilers chose to use static storage
in those cases anyway. However, new versions of clang tend to miscompile this
code, causing test failures. (This error was found when trying to upgrade our
clang revision from r328903 to r331746).
PiperOrigin-RevId: 200110952
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C++ gradients: Fractional*Pool, Soft{Plus,Sign}
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* unused using-declarations
* redundant string conversions
* C-style casts
* redundant get() call on smart pointer
* the 'empty' method should be used to check for emptiness instead of 'size'
PiperOrigin-RevId: 196585984
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PiperOrigin-RevId: 196139886
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PiperOrigin-RevId: 196055377
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conversions.
That is, instances of sp.ToString() are replaced with std::string(sp).
This will allow tensorflow::StringPiece::ToString to be removed, which is necessary before it can be replaced with absl::string_view.
PiperOrigin-RevId: 195188185
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PiperOrigin-RevId: 194997009
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C++ gradient for StridedSlice
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* Fix docstring.
PiperOrigin-RevId: 191747417
* Use constants in tf.zeros if the constant won't be too big.
Using fill saves on GraphDef size, but can slow down models since the
total number of ops is greater (fill + shape + constant op). This
change makes us only use fill for large shapes.
PiperOrigin-RevId: 191747456
* Fix typos in "Profile Model Float Operations" documentation.
PiperOrigin-RevId: 191751175
* Added a call in CheckpointSaverHook.after_create_session to always save
checkpoint before the first training step.
PiperOrigin-RevId: 191753026
* Document expected regular structure of the statistical testing library.
PiperOrigin-RevId: 191753693
* Refine BatchReshape error messages.
PiperOrigin-RevId: 191754120
* Include the operators module in the test framework as well.
PiperOrigin-RevId: 191756100
* Expand activity analysis to the test nodes of if and while statements.
PiperOrigin-RevId: 191756234
* Inline more functions
PiperOrigin-RevId: 191761109
* Sync only the convolutional_recurrent file to Keras 2.1.5.
PiperOrigin-RevId: 191763101
* Internal change
PiperOrigin-RevId: 191769724
* Expose odeint_fixed in tf.contrib.integrate
PiperOrigin-RevId: 191769890
* Automated g4 rollback of changelist 191761109
PiperOrigin-RevId: 191771969
* Fix final eval bottleneck creation to work in cases where it isn't cached already.
Fixes #17423
PiperOrigin-RevId: 191773001
* Fix regression caused by cl/191020868: Re-use materialized shapes for other broadcast gradient shape nodes.
PiperOrigin-RevId: 191779263
* Save the original from_proto method before calling it to avoid infinite loop.
PiperOrigin-RevId: 191784430
* Automated g4 rollback of changelist 191753026
PiperOrigin-RevId: 191784709
* [XLA] Remove a dead function and a stale todo.
PiperOrigin-RevId: 191786563
* Enable branch prediction in TensorFlow
PiperOrigin-RevId: 191788253
* Changes loss_reduction default to SUM_OVER_BATCH_SIZE for multi_class_head and binary_classification_head.
PiperOrigin-RevId: 191793392
* quantized LSTM support improvements
PiperOrigin-RevId: 191794956
* Fix TF_ImportGraphDefResults and TF_Function leaks in Python API.
PiperOrigin-RevId: 191797853
* [XLA] Better support for mul reductions in MakeFakeArguments()
Mul reductions want a 1 as their init value, not a 0 or a random value.
PiperOrigin-RevId: 191802819
* Disable tests that are currently failing with cuda 9
PiperOrigin-RevId: 191805453
* Make tf.contrib.estimator.add_metrics work with warm-starting.
PiperOrigin-RevId: 191805682
* Add Raspberry Pi section and link to github build instructions.
PiperOrigin-RevId: 191807862
* Add for and while loops to the list of operators. Do not use them yet.
PiperOrigin-RevId: 191807973
* [TF:XLA] No need to set return value in the while loop's condition.
PiperOrigin-RevId: 191809110
* Add functions to extract the basic symbols on which a composite name relies. This in turn allows to statically obtain a block's syntactic closure.
PiperOrigin-RevId: 191809965
* Add link for index file in performance tab.
PiperOrigin-RevId: 191811610
* Added an option to inline all functions in aggressive mode.
PiperOrigin-RevId: 191819577
* Make concat handler support mixed range input
PiperOrigin-RevId: 191822664
* Automated g4 rollback of changelist 191605505
PiperOrigin-RevId: 191824447
* Add a command line parameter to toco to change the way toco rescales input and output tensors.
PiperOrigin-RevId: 191825756
* refactor and add proto field required by POD support.
PiperOrigin-RevId: 191826636
* Lazily evaluate shapes with the C API enabled.
This change makes it so shapes are computed only when requested with
_USE_C_API = True. Note that the C API will still raise a shape error
if necessary when the op is created.
In addition, it cleans up the logic for _USE_C_SHAPES = True. In this
case, we lazily fetch and cache shapes directly from the C API. We no
longer need set_shapes_for_outputs at all in this case.
PiperOrigin-RevId: 191830565
* [XLA] Don't call Literal::Get in HloEvaluator's convolution loop.
This speeds up the implementation of conv because Literal::Get calls
Literal::Piece::data, which is relatively slow.
Instead, we call Literal::Data() once and cache the result.
Before: ConvolutionTest/0.StridedFilter (59094 ms)
After: ConvolutionTest/0.StridedFilter (41812 ms)
Speedup: 59/42 = 1.4x
PiperOrigin-RevId: 191830741
* Added `drop_final_batch` argument to make_batched_features_dataset. This allows the batch_and_drop_remainder function to be used instead of the default batch function.
PiperOrigin-RevId: 191831842
* Add RunMetadata logging to tf.train.ProfilerHook for Tensorboard Memeory/CPU usage visualization
PiperOrigin-RevId: 191832832
* [XLA] Don't call MultidimensionalIndexToLinearIndex in HloEvaluator's convolution routine.
Before: ConvolutionTest/0.StridedFilter (41812 ms)
After: ConvolutionTest/0.StridedFilter (28054 ms)
Speedup: 42 / 28 = 1.5x
PiperOrigin-RevId: 191835735
* Expose the adaptive sampling option for SDCA and shuffle the data when adaptive sampling is off.
PiperOrigin-RevId: 191836004
* Swap in the new implementation of while and for loops.
PiperOrigin-RevId: 191838806
* Upgrade libpng
PiperOrigin-RevId: 191840652
* Fix StringPiece use-after-free in MasterSession::ReffedClientGraph.
Use the owned ClientGraph as the source for the node_to_name_ map, rather than the borrowed GraphExecutionState (which can be deleted while the ReffedClientGraph is in use).
PiperOrigin-RevId: 191847023
* Add a test to check graceful handling of out-of-memory conditions.
PiperOrigin-RevId: 191860462
* internal change
PiperOrigin-RevId: 191869400
* Fix typos in XlaCompilationCache
PiperOrigin-RevId: 191881135
* Define PRNG seeding style for new code in Distributions and TF Probability, with rationales.
Implement lightweight PRNG for seed generation in that style.
Enables incremental refactoring of existing code into this style.
PiperOrigin-RevId: 191884573
* Avoid marking clusters containing only Identity ops for compilation.
This would produce clusters where XLA cannot optimize anything.
PiperOrigin-RevId: 191887414
* Add description to the LPIRC 2018 competition benchmarker.
PiperOrigin-RevId: 191889484
* The training model need not be built when the kfac optimizer is initialized so the
self._variables will be empty list. So pass a function which returns list of trainable variables to estimator.
PiperOrigin-RevId: 191893084
* Fix up the support for the case where a given array name occurs multiple
times in the inputs/outputs list of an op. The (non-essential) computation
of the optimal workspace size had not been updated for that case, causing it
to fail on a simple test case. Moreover, the initial implementation had some
redundant usage of std::find that this CL moves to a shared helper function.
PiperOrigin-RevId: 191894081
* Support override of device filters for gRPC, by overriding the requests with default session config.
PiperOrigin-RevId: 191895856
* Tweaked docstrings in LayerCollection.
PiperOrigin-RevId: 191897098
* [TPUClusterResolver] Start a TFServer when running in GKE
This change allows advanced input pipelines (e.g. StreamingFilesDataset, or split-pipelines that use py_func's) to run in GKE- and GKE-like enviornments.
PiperOrigin-RevId: 191897639
* [tf.data] Enable using `tf.contrib.data.prefetch_to_device()` in eager mode.
The added functionality is a substitute for the implicit prefetching in
`tfe.Iterator`, and the two paths will converge in a future change.
Fixes #18260.
PiperOrigin-RevId: 191897666
* Materialize tensor array sizes whenever possible
PiperOrigin-RevId: 191900015
* Object-based checkpointing support for unidirectional cuDNN LSTM cells
Once checked in, this will be the only way I know of to save canonical weights
when executing eagerly. Eager's name-based saving support will only do the
opaque parameter buffer.
I'm not going to try converting everything in one go, but it's a start at
least. And everything else should raise a NotImplementedError rather than
silently not saving correctly.
Single-layer cuDNN cells can be swapped for un-wrapped cuDNN compatible cells or
single cells wrapped in MultiRNNCells. Multi-layer cells need MultiRNNCell
wrapping.
PiperOrigin-RevId: 191905703
* Allow TFE_NewContext to fail more reasonably when SWIG is checking status.
Before:
TFE_Context would check nullptr, and the function would fail straight away.
Now:
TFE_Context is nullptr, so it skips down to checking the status, and an error
is raised.
I'm not able to find in SWIG documentation how to order typemaps in the
generated code - ideally, I'd order it to check the status typemap first. This
code makes it not dependent on this ordering either way.
PiperOrigin-RevId: 191905893
* Change GetInstructionCallContext to take an opcode instead of an
HloInstruction.
This enables use of the function without an actual instruction (eg, if you just
have an HloProto).
PiperOrigin-RevId: 191905914
* TPU Cost Estimator has been modified to also account for the memory cost in the execution time. Until more sophisticated methods are added, we resort to the roofline model to calculate such cost.
PiperOrigin-RevId: 191913626
* Properly handle callable objects.
PiperOrigin-RevId: 191913834
* Minor doc clarification for reduce_sum return type
PiperOrigin-RevId: 191914398
* Added headers only version of tensorflow/core/kernels:cwise_lib, cwise_lib_hdrs. This is for clients that want to use the cwise_ops machinery when making their own custom ops, including cwise_lib directly causes multiple definition linker errors.
PiperOrigin-RevId: 191914445
* [TF:XLA] Create Despecializing Pass Pipeline
When comparing backends, it is useful to take an HLO optimized for one backend and perform transformations in order to match numerics. This can be thought of as finding a lowest common denominator.
Move this grouping of passes into its own HloPassPipeline that can be reused in a few different places.
PiperOrigin-RevId: 191914799
* Update tf.keras to keras 2.1.5 version.
PiperOrigin-RevId: 191914904
* Remove `TF_InitializeTPU` and `TF_ShutdownTPU` from experimental C API as they are no longer needed. Also remove a duplicate function declaration.
PiperOrigin-RevId: 191918408
* Fix small performance regression in microbenchmarks.
PiperOrigin-RevId: 191919464
* Support RNN profiling in StreamExecutor for CUDA GPUs.
This change hasn't applied autotune on TF Cudnn kernels, only provides lower level support.
PiperOrigin-RevId: 191919566
* Validate errorReporter and improve the documentation on it.
PiperOrigin-RevId: 191920009
* Fix a few bugs in ArithmeticOptimizer and make it robust to failures of shape inference.
PiperOrigin-RevId: 191922788
* Update the rewriter options with the optimizer options
PiperOrigin-RevId: 191923287
* Pull changes from prefetching_ops to support dicts in prefetching_ops_v2 in distribute, and update estimator test to use prefetching.
Also update readme to reflect the support of dictionaries.
PiperOrigin-RevId: 191924990
* Replaced calls to deprecated tensorflow::StringPiece methods with their
tensorflow::str_util equivalents.
This will allow the deprecated methods to be removed.
PiperOrigin-RevId: 191925087
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PiperOrigin-RevId: 191805453
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tensorflow::str_util equivalents.
This will allow the deprecated methods to be removed.
PiperOrigin-RevId: 191350894
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PiperOrigin-RevId: 190878279
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tensorflow::str_util equivalents.
This will allow the deprecated methods to be removed.
PiperOrigin-RevId: 190650553
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PiperOrigin-RevId: 189224522
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PiperOrigin-RevId: 189110935
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a hash<StringPiece>).
PiperOrigin-RevId: 189060958
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PiperOrigin-RevId: 188817194
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PiperOrigin-RevId: 188747641
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See https://github.com/tensorflow/tensorflow/issues/9645
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o Fixed simple merge issue in tf/contrib/timeseries/python/timeseries/BUILD
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Also enabled TF_MUST_USE_RESULT for the generated Attr API, so we
can catch any new errors early.
Fixes #17360
PiperOrigin-RevId: 187925761
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PiperOrigin-RevId: 187653676
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1. Adds gradients for four nn ops:
FractionalAvgPool
FractionalMaxPool
SoftPlus
SoftSign
2. Update randomization to allow numeric gradient
checks on max pooling algorithms with more than
one pool.
Resolves https://github.com/tensorflow/tensorflow/issues/17330
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Resolves tensorflow/tensorflow#17195
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PiperOrigin-RevId: 184897758
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PiperOrigin-RevId: 184236409
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PiperOrigin-RevId: 183874527
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returns null
PiperOrigin-RevId: 183846994
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* Half migrated to manip
Half migrated to tf.manip
* Roll op: polymorphism & GPU attempt
* Roll op: Added support for gradients
Added compile script
* Rebase for roll op code
* Roll op: Migrated to manip namespace
* Roll op: Supports CPU thread pooling
fix namespace error
* Remove roll from user_ops
* Roll op: Optimization
* Roll op: Pylint fix
* Roll op: Updated documentation
* Roll op: Two versions for CPU thread pooling
* Roll op: Huge CPU speed up
Fixed thread pooling issue that was due to a bad cost_per_unit parameter
Also improved readability
* Roll op: Rough draft of DoRollV2
DoRollV2 copies memory in groups instead of element by element.
Not thoroughly tested yet.
Polished DoRollV2 algorithm
* Roll op: Restrict tensor size for GPU implementation
* Roll op: Fixed clang-format and missing include
* Roll op: Minor change
* Roll op GPU bug fix
Roll op GPU bug fix
GPU bug fix
Roll op GPU bug fix
Roll op GPU fix
Roll GPU test BUILD update
* Roll op: Remove GPU code
Fully remove roll op GPU code
Remove compile_cpu.sh
* Roll op: Fixes problems with array_ops_test.py and a size 1 dimension bug
* Roll op: Migrated to manip
Migrated to tf.manip
Roll op registered
Roll op uses InlinedVector
Small improvements
* Roll op: Revert array op changes
* Roll op: Api def fix
* Roll op: review changes
* Roll op: API review changes
Roll op: Docstring fix
* Roll op: Review changes round 1
* Roll op: resolve conflicts
* Roll op: Resolve conflicts
* Roll op: clang-tidy
* Roll op: Review round 2 changes
Roll op: fixed BUILD file
Roll op: api docs update
* Roll op: failure fixes 1
- updates goldens and fixes api compatibility issue
- fixes python op test issue for windows
- fixes makefile issues
* Roll op: Windows CMake failure fix
Windows CMake checks were failing because numpy was on an older version that did not support np.roll on multiple shifts that was use to check the correctness of tf.manip.roll. manip_ops_test.py now checks for numpy version 1.12.0 before testing multiple shifts, otherwise it'll just test single shift roll.
* Roll op: pylint changes
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They don't make sense in the open source repository.
PiperOrigin-RevId: 183140889
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PiperOrigin-RevId: 182959711
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PiperOrigin-RevId: 182463327
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Asim Shankar
Gunhan Gulsoy
Akshay Modi
Ilya Biryukov
Florian Courtial
Yan Facai (颜发才)
Asim Shankar
A. Unique TensorFlower
Suharsh Sivakumar
Patrick Nguyen
Martin Wicke
Rasmus Munk Larsen
Martin Wicke
Shanqing Cai
Benoit Steiner
KB Sriram
Jonathan Hseu
MandarJKulkarni
Michael Case
Jean Flaherty
Andrew Harp
Amit Patankar
Sanjoy Das
Jianwei Xie
Taehoon Lee
Anna R