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* Add RaggedTensors to tf.core. Moving the RaggedGather op kernel.Gravatar A. Unique TensorFlower2018-10-09
| | | | PiperOrigin-RevId: 216400726
* Removed unused load statements from the core BUILD.Gravatar A. Unique TensorFlower2018-10-09
| | | | PiperOrigin-RevId: 216354906
* Declare that stateless random ops are not differentiable in C++ code.Gravatar Tong Shen2018-10-05
| | | | PiperOrigin-RevId: 215935319
* Change hierarchical_tree_broadcaster_test from small to medium.Gravatar Todd Wang2018-10-03
| | | | PiperOrigin-RevId: 215607769
* Internal build specification changeGravatar A. Unique TensorFlower2018-10-01
| | | | PiperOrigin-RevId: 215263951
* CleanupGravatar A. Unique TensorFlower2018-09-29
| | | | PiperOrigin-RevId: 215073641
* Introduce the abstraction of RunHandler which each DirectSession can use forGravatar A. Unique TensorFlower2018-09-28
| | | | | | | | | the duration of a single RunInternal() call from RunHandlerPool. It is used for running inter-op closures with a global scheduler (which in the future) to improve both median and tail latency (for use-cases like CPU inference). In the case that global pools aren't used, this change should be a no-op. PiperOrigin-RevId: 214992852
* [tf.data] Move `tf.contrib.data` C++ code to a core "experimental" directory.Gravatar Derek Murray2018-09-28
| | | | | | | | NOTE: All ops and kernels previously previously defined in tensorflow/contrib/data have had their name prefixed with "Experimental" to indicate that they are not (yet) stable, and thus not subject to backwards or forwards compatibility guarantees. PiperOrigin-RevId: 214940819
* Automated rollback of commit 750466c6e6624d279de7f9a43accd682d487509cGravatar Revan Sopher2018-09-27
| | | | PiperOrigin-RevId: 214853846
* Introduce the abstraction of RunHandler which each DirectSession can use forGravatar A. Unique TensorFlower2018-09-27
| | | | | | | | | the duration of a single RunInternal() call from RunHandlerPool. We want to leverage this abstraction for improving the cross-session inter-op parallelism for lower latency inference in the future. In the case that global pools aren't used, this change should be a no-op. PiperOrigin-RevId: 214818187
* Merge pull request #20277 from ROCmSoftwarePlatform:upstream-stagingGravatar TensorFlower Gardener2018-09-27
|\ | | | | | | PiperOrigin-RevId: 214793113
* \ Merge pull request #22076 from Intel-tensorflow:feature/daoxin/sliceGravatar TensorFlower Gardener2018-09-26
|\ \ | | | | | | | | | PiperOrigin-RevId: 214726180
* | | Refactor build deps by making :framework depend on :feature_util to not use ↵Gravatar A. Unique TensorFlower2018-09-26
| | | | | | | | | | | | | | | | | | the same source dependency twice. PiperOrigin-RevId: 214704620
* | | [TF] Add new internal ops _VarHandlesOp and _ReadVariablesOp.Gravatar Peter Hawkins2018-09-26
| | | | | | | | | | | | | | | | | | | | | | | | The purpose of these ops is to fix a latency problem observed for an inference benchmark. Often a inference step starts by reading the value of many (hundreds) of weights. For a resource variable, this requires a VarHandleOp and a ReadVariableOp per variable. Running hundreds of trivial ops can add hundreds of microseconds of latency to the critical path of an inference step. The inter-op latency of the executor can be hundreds of nanoseconds, which rapidly adds up. This change introduces two fused ops _VarHandlesOp and _ReadVariablesOp that allow us to read many variables in a pair of larger ops, rather than many tiny ops. PiperOrigin-RevId: 214662338
* | | Fix dynamic kernels dependencies for python build and test rules.Gravatar Gunhan Gulsoy2018-09-25
| | | | | | | | | | | | | | | | | | | | | | | | Bazel does not allow python rules to directly depend on c++ rules. So I have to separately manage static dependencies, unfortunately avoiding "kernels" option for now. PiperOrigin-RevId: 214532631
* | | Automated rollback of commit 7a1096f424b1adcb4152db80a01a163ddb1a0173Gravatar Anna R2018-09-24
| | | | | | | | | | | | PiperOrigin-RevId: 214366272
* | | Replace usage of base::GetFlag with Abseil Flags public API absl::GetFlag.Gravatar A. Unique TensorFlower2018-09-24
| | | | | | | | | | | | PiperOrigin-RevId: 214354104
* | | Inline kernel tracing logic into `ExecutorState::Process()`.Gravatar Derek Murray2018-09-24
| | | | | | | | | | | | | | | | | | All devices implement the same tracing logic in an override of `Device::Compute()`. However, that logic does not have access to the cached `NodeItem::kernel_is_expensive` bit for the kernel, so it must make a virtual call to `OpKernel::IsExpensive()`. By inlining the logic into `ExecutorState::Process()`, we avoid making an unnecessary virtual call on each kernel invocation (when a trace controller is attached). PiperOrigin-RevId: 214332492
* | | Fully depend on external repositories (instead of just headers) in ↵Gravatar A. Unique TensorFlower2018-09-21
| | | | | | | | | | | | | | | | | | | | | | | | cc_header_only_library. This allows TensorFlow to be build from another bazel repo. PiperOrigin-RevId: 214091199
* | | Implement TF graph capture.Gravatar Russell Power2018-09-20
| | | | | | | | | | | | PiperOrigin-RevId: 213875284
* | | Add searchsorted (ie lower/upper bound) op.Gravatar A. Unique TensorFlower2018-09-20
| | | | | | | | | | | | PiperOrigin-RevId: 213863392
* | | This CL adds a new `tf.print` operator that more closely aligns with the ↵Gravatar A. Unique TensorFlower2018-09-19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | standard python `print` method, and deprecates the old `tf.Print` operator (to be removed in in v2.0). It follows the design doc specified in https://github.com/tensorflow/community/pull/14 and additionally incorporates the community feedback and design review decisions. This CL adds two new internal graph operators: a StringFormat operator that formats a template string with a list of input tensors to insert into the string and outputs a string scalar containing the result, and a PrintV2 operator that prints a string scalar to a specified output stream or logging level. The formatting op is exposed at `tf.strings.Format`. A new python method is exposed at `tf.print` that takes a list of inputs that may be nested structures and may contain tensors, formats them nicely using the formatting op, and returns a PrintV2 operator that prints them. In Eager mode and inside defuns this PrintV2 operator will automatically be executed, but in graph mode it will need to be either added to `sess.run`, or used as a control dependency for other operators being executed. As compared to the previous print function, the new print function: - Has an API that more closely aligns with the standard python3 print - Supports changing the print logging level/output stream - allows printing arbitrary (optionally nested) data structures as opposed to just flat lists of tensors - support printing sparse tensors - changes printed tensor format to show more meaningful summary (recursively print the first and last elements of each tensor dimension, instead of just the first few elements of the tensor irregardless of dimension). PiperOrigin-RevId: 213709924
* | | Added ABSL_DEPRECATED annotations to various deprecated TensorFlow functions.Gravatar A. Unique TensorFlower2018-09-19
| | | | | | | | | | | | PiperOrigin-RevId: 213693027
* | | Eliminate VisitableAllocator.Gravatar A. Unique TensorFlower2018-09-18
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The visitor pattern is used to allow pre-registration of memory for DMA access, e.g. for fast GPU/CPU i/o and for RDMA networking. The VisitableAllocator interface was introduced to support this use some time ago, prior to SubAllocators. Memory registration works best if it's done infrequently, on large pieces of memory, rather than on every piece that's dynamically allocated/freed. This usage pattern fits the SubAllocator better than a general Allocator. This change moves memory allocation visitor access to SubAllocator and eliminates the VisitableAllocator subclass of Allocator. This change also more rigorously enforces the requirement that all Visitors be declared prior to memory allocation begining. This is accomplished by requiring that Visitors be provided to the SubAllocator constructor. This refactoring will ease an upcoming CL introducing NUMA specific CPU devices. It also should fix some performance pitfalls (e.g. accidental use of PoolAllocator) introduced by an earlier refactoring of ProcessState that was also in preparation for NUMA. It restores the default use of the cpu_allocator() value (i.e. no SubAllocator) by model executions that don't use allocation visitors (since visitor registration must precede the first allocation, hence can be detected at that time). PiperOrigin-RevId: 213505655
* | | Automated rollback of commit 185aa89912376d4088c22615908696cd30f9951bGravatar A. Unique TensorFlower2018-09-17
| | | | | | | | | | | | PiperOrigin-RevId: 213394522
* | | Eliminate VisitableAllocator.Gravatar A. Unique TensorFlower2018-09-17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The visitor pattern is used to allow pre-registration of memory for DMA access, e.g. for fast GPU/CPU i/o and for RDMA networking. The VisitableAllocator interface was introduced to support this use some time ago, prior to SubAllocators. Memory registration works best if it's done infrequently, on large pieces of memory, rather than on every piece that's dynamically allocated/freed. This usage pattern fits the SubAllocator better than a general Allocator. This change moves memory allocation visitor access to SubAllocator and eliminates the VisitableAllocator subclass of Allocator. This change also more rigorously enforces the requirement that all Visitors be declared prior to memory allocation begining. This is accomplished by requiring that Visitors be provided to the SubAllocator constructor. This refactoring will ease an upcoming CL introducing NUMA specific CPU devices. It also should fix some performance pitfalls (e.g. accidental use of PoolAllocator) introduced by an earlier refactoring of ProcessState that was also in preparation for NUMA. It restores the default use of the cpu_allocator() value (i.e. no SubAllocator) by model executions that don't use allocation visitors (since visitor registration must precede the first allocation, hence can be detected at that time). PiperOrigin-RevId: 213371553
* | | Merge pull request #22061 from perfinion:install_headersGravatar TensorFlower Gardener2018-09-17
|\ \ \ | | | | | | | | | | | | PiperOrigin-RevId: 213343364
* | | | [tf.data] Adding support for `tf.data.AUTOTUNE` as a special value for the ↵Gravatar Jiri Simsa2018-09-17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | `num_parallel_calls` argument of `tf.data.Dataset.map()`, `tf.data.Dataset.interleave()`, and `tf.contrib.data.map_and_batch()`. When `tf.data.AUTOTUNE` is specified, the level of parallelism is determined at runtime. The underlying mechanism instruments the input pipeline to build a performance model and then uses the model to find the optimal values for the parallelism knobs. PiperOrigin-RevId: 213283297
* | | | [tf.data] Introducing an optimization that parallelizes map transformations.Gravatar Piotr Padlewski2018-09-14
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Stateless MapDatasets can be paralellized by switching to ParallelMapDataset. We set `num_parallel_calls` to 2 for now, but in the future a special value will be used that result in the optimal value to be selected dynamically at runtime. This patch also exposed a memory leak which was fixed. PiperOrigin-RevId: 213015223
* | | | Added Pyclif binding rule for config.proto.Gravatar Sung Jin Hwang2018-09-13
| | | | | | | | | | | | | | | | PiperOrigin-RevId: 212920113
* | | | Modify signature of WritableFile::Append to accept StringPiece data by value.Gravatar Eugene Brevdo2018-09-12
| | | | | | | | | | | | | | | | PiperOrigin-RevId: 212736286
* | | | Internal change.Gravatar Anna R2018-09-12
| | | | | | | | | | | | | | | | PiperOrigin-RevId: 212684548
* | | | [tf.data] Mechanism for collecting processing time information and modeling ↵Gravatar Jiri Simsa2018-09-11
| | | | | | | | | | | | | | | | | | | | | | | | performance. PiperOrigin-RevId: 212557406
* | | | [TF] Variant improvements.Gravatar Eugene Brevdo2018-09-11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1. Change Variant Decode to accept VariantTensorData (non-ref). This should allow some optimization in the future. In the meantime it means removing the variant.h include from tensor.h, since variant_encode_decode.h now relies on tensor.h and variant.h now relies on that. It also means we found a bunch of places where tensor.proto.h, variant.h, and mutex.h were being imported through tensor.h (along with a bunch of other crap); so now we directly import them in order to compile. 2. Move Variant registry to use TypeIndex instead of a TypeName string; this should speed up registry lookups. PiperOrigin-RevId: 212478896
* | | | Add missing dependency to ↵Gravatar A. Unique TensorFlower2018-09-07
| | | | | | | | | | | | | | | | | | | | | | | | tensorflow/core:common_runtime/mkl_threadpool_device_test. PiperOrigin-RevId: 212060726
* | | | Simplify BUILD rule for MKL transpose op.Gravatar A. Unique TensorFlower2018-09-06
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | There is no reason for outside dependents to make a distinction between the Eigen or MKL transpose operation, as the substitution is transparent. There is also no need for transpose_op.cc itself to be compiled differently based on whether MKL is in use or not. Therefore we remove external dependencies on :mkl_transpose_op and make :transpose_op depend on it if needed (i.e., if using MKL). This is consistent with how other transparent MKL operations (e.g. matmul) are built. PiperOrigin-RevId: 211874336
* | | | Add feature_util build target so the library can be included in a ↵Gravatar A. Unique TensorFlower2018-09-06
| | | | | | | | | | | | | | | | | | | | | | | | lightweight way PiperOrigin-RevId: 211833556
| | | * [ROCm] bazel build system and continuous integration logicGravatar Wen-Heng (Jack) Chung2018-09-05
| |_|/ |/| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The commit contains following components to support TensorFlow on ROCm platform - bazel build system - continuous integration logic Authors: - Jack Chung: jack.chung@amd.com - Jeffrey Poznanovic: Jeffrey.Poznanovic@amd.com - Peng Sun: Peng.Sun@amd.com
* | | Alias tensorflow::gtl::InlinedVector to absl::InlinedVectorGravatar Benjamin Kramer2018-09-05
| | | | | | | | | | | | PiperOrigin-RevId: 211639440
| * | Add //tensorflow:install_headers targetGravatar Jason Zaman2018-09-05
|/ / | | | | | | | | | | | | Used to prepare all the header files so they can easily be installed into /usr/include when packaging TF. Signed-off-by: Jason Zaman <jason@perfinion.com>
| * Add MklSlice op.Gravatar Pan Daoxin2018-09-04
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* BEGIN_PUBLICGravatar Ayush Dubey2018-08-31
| | | | | | | | | | | Rollback of rollback. Fix: make access to collective_graph_key thread-safe. The original change introduced a collective_graph_key_ integer to DirectSession, but it did not protect accesses to this integer. This change protects access with a mutex. END_PUBLIC Automated rollback of commit cb9443831283c2366e3dd91001db6362d6594f66 PiperOrigin-RevId: 211161961
* Currently there are two formats for error reporting inside TensorFlow. This ↵Gravatar A. Unique TensorFlower2018-08-31
| | | | | | is first of a series of CL to merge these into one. In this change, we remove the format tag from the errors. PiperOrigin-RevId: 211146036
* Alias gtl::optional to absl::optional and remove the old implementation.Gravatar Benjamin Kramer2018-08-31
| | | | PiperOrigin-RevId: 211110958
* Automated rollback of commit 73a3477356990f2451e220f553c9d7782df836acGravatar Ayush Dubey2018-08-30
| | | | PiperOrigin-RevId: 211037202
* Initialize collective_graph_key based on the graph if unspecified in RunOptions.Gravatar Ayush Dubey2018-08-30
| | | | | | | | | | | Before this CL, for collective_ops to work, the client had to specify a collective_graph_key in the RunOptions of a session.Run call. After this change, if a client does not specify a collective_graph_key for a graph that contains collective ops, a graph key is generated automatically as a hash of the set of keys of collective instances in the placed graph. PiperOrigin-RevId: 211024617
* Call new tflite buffer quantize weights transformation tool from TOCO.Gravatar Suharsh Sivakumar2018-08-30
| | | | PiperOrigin-RevId: 211020126
* Add lowering pass for functional While op.Gravatar Saurabh Saxena2018-08-30
| | | | | | | This will allow the functional tf.while_loop proposed in https://github.com/tensorflow/community/pull/13 to achieve feature parity with the current implementation. Lowering is performed only when the "_lower_using_switch_merge" attr is set to True. PiperOrigin-RevId: 210956432
* Remove (Mutable)ArraySlice implementation and alias them to absl::Span.Gravatar Tim Shen2018-08-30
| | | | | | | | There are several API migrations happening: * ArraySlice's sub-slice constructor => .subspan * MutableArraySlice's container pointer constructor => absl::MakeSpan PiperOrigin-RevId: 210946124
* Replaced tensorflow::StringPiece with an alias of absl::string_view.Gravatar A. Unique TensorFlower2018-08-30
| | | | PiperOrigin-RevId: 210929192
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