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* Benchmark for comparing original cond and cond_v2 performance.Gravatar Skye Wanderman-Milne2018-10-08
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This benchmark creates many intermediates values, so we can make sure there's no performance overhead (it looks like there might be currently, or it might be from some other difference). It also runs in a defun and in legacy graph mode. Results from my machine: entry { name: "CondWithManyIntermediatesBenchmark.benchmark_cond_v1_defun" iters: 500 wall_time: 1.25822591782 } entry { name: "CondWithManyIntermediatesBenchmark.benchmark_cond_v2_defun" iters: 500 wall_time: 5.99376106262 } entry { name: "CondWithManyIntermediatesBenchmark.benchmark_cond_v1_graph" iters: 500 wall_time: 2.05277585983 } entry { name: "CondWithManyIntermediatesBenchmark.benchmark_cond_v2_graph" iters: 500 wall_time: 2.84808516502 } Clearly we have some work to do! I haven't looked into the time differences at all yet. PiperOrigin-RevId: 216202325
* Add option in tf.gradients() to return zero tensors for unconnected gradients.Gravatar A. Unique TensorFlower2018-10-04
| | | | | | | | tf.gradients currently returns [NONE] when the gradient of unconnected variables is required. This backwards compatable change adds in the option to have zero tensors returned that match the dimensions of the input tensor. PiperOrigin-RevId: 215725488
* [tf.data] Deprecate `tf.contrib.data` and introduce `tf.data.experimental` ↵Gravatar Derek Murray2018-10-01
| | | | | | | | | | | | | | | | | | | | to replace it. This change prepares `tf.data` for TensorFlow 2.0, where `tf.contrib` will no longer exist. It retains the pre-existing endpoints in `tf.contrib.data` with deprecation warnings. Note there are some exceptions to the move: * Deprecated symbols in `tf.contrib.data` have not been moved to `tf.data.experimental`, because replacements already exist. * `tf.contrib.data.LMDBDataset` has not been moved, because we plan to move it to a SIG-maintained repository. * `tf.contrib.data.assert_element_shape()` has not yet been moved, because it depends on functionality in `tf.contrib`, and it will move in a later change. * `tf.contrib.data.AUTOTUNE` has not yet been moved, because we have not yet determined how to `tf_export()` a Python integer. * The stats-related API endpoints have not yet appeared in a released version of TensorFlow, so these are moved to `tf.data.experimental` without retaining an endpoint in `tf.contrib.data`. In addition, this change includes some build rule and ApiDef refactoring: * Some of the "//third_party/tensorflow/python:training" dependencies had to be split in order to avoid a circular dependency. * The `tf.contrib.stateless` ops now have a private core library for the generated wrappers (and accordingly are hidden in their ApiDef) so that `tf.data.experimental.sample_from_datasets()` can depend on them. PiperOrigin-RevId: 215304249
* Add flag for enabling while_v2.Gravatar Saurabh Saxena2018-09-28
| | | | | | | | | | Add a single test flag for enabling v2 control flow in tests since we do not plan to support v2 ops with legacy control flow. We have 2 test decorators now: @with_control_flow_v2: Enables all tests in a class to run with v2 control flow. @disable_control_flow_v2: Disables a test function from running in v2. I have removed the skiptests to avoid setup/teardown overheads. Enable tests in control_flow_ops_py_test that run with control_flow_v2. PiperOrigin-RevId: 214980108
* [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
* Refactoring of nest value getters.Gravatar A. Unique TensorFlower2018-09-26
| | | | PiperOrigin-RevId: 214587760
* Implement TF graph capture.Gravatar Russell Power2018-09-20
| | | | PiperOrigin-RevId: 213875284
* 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
* First commit for functional while loop.Gravatar Saurabh Saxena2018-09-18
| | | | | | | | Supports single and double derivatives but does not supporting nesting yet. https://github.com/tensorflow/community/pull/13 PiperOrigin-RevId: 213565971
* Increase test timeout for image_grad_test to de-flake.Gravatar A. Unique TensorFlower2018-09-17
| | | | PiperOrigin-RevId: 213372241
* Support providing default gradient for variant tensors in tf.gradients call.Gravatar Saurabh Saxena2018-09-12
| | | | PiperOrigin-RevId: 212645190
* Add tf_api_version flag. If --define=tf_api_version=2 flag is passed in, ↵Gravatar Anna R2018-09-07
| | | | | | then bazel will build TensorFlow API version 2.0. In all other cases, it would build API version 1.*. PiperOrigin-RevId: 212016666
* This CL changes the graph-mode API of the learning_rate_decay functions in ↵Gravatar A. Unique TensorFlower2018-09-05
| | | | | | | | TF 2.0 to return a no-arg callable to output a learning rate, instead of directly outputting a learning rate tensor. This brings the graph mode API in line with the eager execution API, where this change was made to allow changing the learning rate value across different invocations of optimizer functions. PiperOrigin-RevId: 211726295
* Allow gradients() calls from inside a tfe.defun wrt captured tensors.Gravatar Skye Wanderman-Milne2018-09-05
| | | | | | | | This modifies https://github.com/tensorflow/tensorflow/commit/834da2c3fddab1bbbce742db572cfe65dd320fcd to work with tfe.defun in addition to the legacy Defun implementation. PiperOrigin-RevId: 211663702
* Open source graph analyzer.Gravatar Yao Zhang2018-08-27
| | | | PiperOrigin-RevId: 210439649
* Allow `tf.enable_eager_execution()` to be run multiple times.Gravatar Tom Hennigan2018-08-27
| | | | | | | | Subsequent calls after a successful call to enable eager execution are a no op. This is mostly to support colab where I might accidentally re-execute this and have to clear a large stack trace / split my import cell. PiperOrigin-RevId: 210344960
* Add an option to RunConfig and train_and_evaluate to run distribute coordinator.Gravatar Yuefeng Zhou2018-08-24
| | | | | | This is necessary to run multi-worker MirroredStrategy and CollectiveAllReduceStrategy with estimator. PiperOrigin-RevId: 210192378
* Fix BUILD typo.Gravatar A. Unique TensorFlower2018-08-24
| | | | PiperOrigin-RevId: 210107257
* Switch cond_v2 to using tfe.defun instead of function.Defun.Gravatar Skye Wanderman-Milne2018-08-23
| | | | | | | | | | | | | This requires a few changes: - Make func_graph_from_py_func public (not part of the official public API though) - Make function_def_to_graph return the new FuncGraph implementation. - Disables some cond_v2 tests until we get them working with the new FuncGraph implementation. - Add outer_graph field to FuncGraph. - Add external_captures and internal_captures properties to FuncGraph for readability. - Remove extra_inputs/extra_args terminology from cond_v2_impl for readability. - Use compat.as_str() around Graph._functions keys. In Python 3, we were somehow getting a mix of str and bytes objects. PiperOrigin-RevId: 210015940
* Adds a sparse.expand_dims operation to TensorFlow.Gravatar A. Unique TensorFlower2018-08-23
| | | | PiperOrigin-RevId: 209986849
* Adds a sparse_eye operation to tensorflow.Gravatar A. Unique TensorFlower2018-08-21
| | | | PiperOrigin-RevId: 209683367
* Internal Change.Gravatar Michael Case2018-08-21
| | | | PiperOrigin-RevId: 209637025
* Merge pull request #21122 from NervanaSystems:masterGravatar TensorFlower Gardener2018-08-21
|\ | | | | | | PiperOrigin-RevId: 209623532
* | Add `batch_scatter_update`, analogous to `batch_gather`.Gravatar Adria Puigdomenech2018-08-21
| | | | | | | | | | | | | | | | | | | | This operation computes: ref[i_1, ..., i_n, indices[i_1, ..., i_n, j]] = updates[i_1, ..., i_n, j] That is, it assumes that `ref`, `indices` and `updates` have a series of leading dimensions that are the same for all of them, and the updates are performed on the last dimension of indices. PiperOrigin-RevId: 209566652
* | Internal Change.Gravatar Michael Case2018-08-18
| | | | | | | | PiperOrigin-RevId: 209299599
* | Use distribution strategy to configure distribute coordinator.Gravatar Yuefeng Zhou2018-08-16
| | | | | | | | | | | | Add session_creator and a couple properties to worker context which then are used to configure monitored sessions. PiperOrigin-RevId: 209026599
| * Merge remote-tracking branch 'upstream/master'Gravatar Avijit2018-08-15
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* | Re-enable some tests on WindowsGravatar A. Unique TensorFlower2018-08-14
| | | | | | | | PiperOrigin-RevId: 208695032
* | 1. Move distribution strategy context utility methods to a separate file ↵Gravatar Priya Gupta2018-08-14
| | | | | | | | | | | | | | | | | | with few dependencies. This allows us to import this in some places without creating circular dependencies as the original file imported many things. 2. Move the stack used in distribution strategy context to the graph. This allows us to use different strategies in different graphs (for e.g. in train and eval). This fixes #21412 and #21180. PiperOrigin-RevId: 208680454
* | Add missing numerics dependency in clip_opsGravatar Yanan Cao2018-08-13
| | | | | | | | PiperOrigin-RevId: 208503071
| * Merge remote-tracking branch 'upstream/master'Gravatar Avijit2018-08-12
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| * Updated based on PR feedback.Gravatar Avijit2018-08-12
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* | [TF] Extend the Softmax kernels so they accept shapes with rank >= 1, rather ↵Gravatar Peter Hawkins2018-08-06
| | | | | | | | | | | | | | | | than relying on the caller to reshape to rank 2. Guard the Python library code that reshapes softmax inputs to rank 1 with a forward compatibility check; after the forward compatibility window expires, the Python code will no longer reshape to rank 1. PiperOrigin-RevId: 207606326
| * Merge remote-tracking branch 'upstream/master'Gravatar avijit-nervana2018-08-06
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* | Add missing dependency of ops.pyGravatar Igor Ganichev2018-08-02
| | | | | | | | PiperOrigin-RevId: 207215039
* | Split checkpoint management utility functions out of saver.pyGravatar Allen Lavoie2018-08-02
| | | | | | | | | | | | Pure refactor, in preparation for adding a higher level checkpoint management utility. This utility will also need to work with the Checkpoint proto, and globbing it on to saver.py seems dirty. PiperOrigin-RevId: 207179646
* | Use device and colocation stack metadata to improve an error message in ↵Gravatar A. Unique TensorFlower2018-08-02
| | | | | | | | | | | | ops.py. This change does not depend on the new config.experimental.client_handles_error_formatting flag. I also attempted to modify relevant interpolated error strings so an uninterpolated error message still read correclty if you removed the interpolation tokens. PiperOrigin-RevId: 207075862
* | [Checkpointable] Make Iterator checkpointable.Gravatar Shivani Agrawal2018-08-01
| | | | | | | | | | | | Use object-based save/restore to make dataset/iterator checkpointable in both graph as well as eager mode. PiperOrigin-RevId: 206998349
| * Merge remote-tracking branch 'upstream/master' and changesGravatar avijit-nervana2018-07-31
| |\ | |/ |/| | | based on PR review comments.
* | Automated rollback of commit b8a9d163d9cbb4b581c044d9c4b1b256c801a9c4Gravatar Russell Power2018-07-26
| | | | | | | | PiperOrigin-RevId: 206166233
| * Merge remote-tracking branch 'upstream/master'Gravatar Avijit2018-07-25
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| * nGraph integration with TensorFlowGravatar Avijit2018-07-24
| | | | | | | | * Added nGraph bridge as a third_party to be built with TensorFlow based on user selection. * Added a limited set of C++ unit tests to verify the correctness of the computation
* | Automated rollback of commit 568727eed199dba04e37f500265b50f96fed455eGravatar Nick Felt2018-07-24
| | | | | | | | PiperOrigin-RevId: 205875586
* | Add support for custom mapping types to util.nestGravatar Allen Lavoie2018-07-24
| | | | | | | | | | | | | | | | | | | | Analagous to the existing support for custom collections.Sequence types. They need to be constructable with the same arguments as the base type for pack_sequence_as to work. Leaves PyDict_* calls for dict subclasses, but adds more general (and likely much slower) fallbacks for instances of collections.Mapping which are not dict subclasses. My hope is that this support will be enough so I can use a wrapper around dicts which does not inherit from dict in __setattr__ tracking (some tests failed without it). Inheriting from dict and properly shadowing a real dict seems impossible with CPython (since to shadow without synchronization issues, the wrapper needs to respond to updates to the original dict, but to work with e.g. {}.update(dict_subclass) the wrapper's C storage needs to also be updated). PiperOrigin-RevId: 205858082
* | Add v2 summary support to Estimator.train() and MonitoredSession hooksGravatar Nick Felt2018-07-24
| | | | | | | | | | | | | | | | | | | | | | | | | | This change makes Estimator.train() support v2 summaries (tf.contrib.summary.*) out-of-the-box, to match the support for v1 summaries. Estimator.train() will now handle the boilerplate necessary to initialize a file writer and enable summary writing every N steps, and will ensure that its own automatically exported summaries (for loss and global_step/sec) get written to the same underlying events file. As part of this change, tf.train.SummarySaverHook, tf.train.CheckpointSaverHook, tf.train.StepCounterHook, and tf.train.ProfilerHook have also been adapted to write summaries using the v2 summary system (via a compatibility layer), instead of using FileWriterCache. A couple additional smaller changes are: - the 'session' parameter to FileWriter() can now be a callable returning a tf.Session instance. - the introduction of tf.contrib.summary.record_summaries_if() which takes a boolean tensor for direct control of tf.contrib.summary.should_record_summaries(). - EstimatorSpec.train_op, besides a tf.Operation, is now allowed to be any Tensor-equivalent object rather than just a tf.Tensor. PiperOrigin-RevId: 205843986
* | Add Python API functions to query kernelsGravatar James Keeling2018-07-24
| | | | | | | | | | | | This is part of the work to make available kernels easier to query at runtime. PiperOrigin-RevId: 205802663
* | Automated rollback of commit 8048b3a53d5a919fef74874fcffbec9039e6acd1Gravatar Russell Power2018-07-23
| | | | | | | | PiperOrigin-RevId: 205679162
* | Destroy tf.Session when the session is closed.Gravatar Russell Power2018-07-23
| | | | | | | | | | | | | | | | Previously session destruction was delayed until the destructor for the Python session object. If the session ends up requiring the Python cycle collector for deallocation, it could end up persisting for a long, non-deterministic period. This can tie up resources and lead to out of memory issues. This change introduces a SessionRef which causes session.close() to block until all outstanding run operations are finished and tears down the underlying session. PiperOrigin-RevId: 205670577
* | Support Defuns and nested Defuns inside cond_v2 branches.Gravatar Saurabh Saxena2018-07-19
| | | | | | | | | | | | Support nested cond_v2s. PiperOrigin-RevId: 205356562
* | Track colocation context manager locations (file:line) and add colocation ↵Gravatar A. Unique TensorFlower2018-07-18
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | information support to error interpolation. This CL add a new private property on ops: Operation._colocation_dict. This property will return a dictionary for which the keys are nodes with which this Operation is colocated, and for which the values are traceable_stack.TraceableObject instances. The TraceableObject instances record the location of the relevant colocation context manager but have the "obj" field set to None to prevent leaking private data. For example, suppose file_a contained these lines: file_a.py: 14: node_a = tf.constant(3, name='NODE_A') 15: with tf.colocate_with(node_a): 16: node_b = tf.constant(4, name='NODE_B') Then a TraceableObject t_obj representing the colocation context manager would have these member values: t_obj.obj -> None t_obj.name = 'NODE_A' t_obj.filename = 'file_a.py' t_obj.lineno = 15 and node_b.op._colocation_dict would return the dictionary { 'NODE_A': t_obj } PiperOrigin-RevId: 205035378
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