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* Fix a couple of reference leaksGravatar A. Unique TensorFlower2018-10-08
| | | | PiperOrigin-RevId: 216230391
* Minor speed improvements to defun.Gravatar Akshay Modi2018-10-01
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - EncodeArg in C instead of python. - Also caches parsed device specs, and device spec hashes - Adds a common way to register python types in C. - Fastpath canonicalize function inputs when no kwargs are passed - Set the func name attr directly instead of creating an op to wrap it. - Rewrite IsAttrsHelper without caching Before: entry { name: "MicroBenchmarks.benchmark_defun_matmul_2_by_2_CPU" iters: 30000 wall_time: 101.803263028 extras { key: "examples_per_sec" value { double_value: 9822.86785562 } } } After: entry { name: "MicroBenchmarks.benchmark_defun_matmul_2_by_2_CPU" iters: 30000 wall_time: 47.2899993261 extras { key: "examples_per_sec" value { double_value: 21146.1199884 } } } PiperOrigin-RevId: 215272962
* eager: Graceful failure on invalid inputs.Gravatar Asim Shankar2018-09-12
| | | | | | | | | | | | | | | Tests added to pywrap_tfe_test.py would fail (segmentation fault / infinite loop) without corresponding fixes to pywrap_tfe.i and pywrap_tfe_src.cc Other statements that would fail ungracefully without this fix (and with eager execution enabled) include: tf.split(value=0, num_or_size_splits=-1) tf.dynamic_partition(data=0, partitions=0, num_partitions=-1) tf.split(value=0, num_or_size_splits=1.23, num=-1) tf.unstack(value=0, num=-1) PiperOrigin-RevId: 212731927
* Support not automatically watching (trainable) accessed variables in ↵Gravatar Tom Hennigan2018-09-07
| | | | | | | | | GradientTape. For more complex use cases this allows fine grained control over what is tracked by the tape. PiperOrigin-RevId: 211948236
* Set Vspace only one timeGravatar Akshay Modi2018-09-06
| | | | | | | | I don't believe there is currently a use-case for a different VSpace (and it doesn't seem to be controllable through any public method). If it is a usecase we want to support, it should be simple enough to add an overload of TFE_Py_TapeGradient. PiperOrigin-RevId: 211917235
* Only watch tensors on the current tape rather than all of them.Gravatar Tom Hennigan2018-09-01
| | | | | | | | | | | | | | | | | | | | | This allows fine grained control over recording in some cases, for example the following where we want d2y but not d2z: x1 = tf.Variable(2.0, trainable=False) x2 = tf.Variable(2.0, trainable=False) with tf.GradientTape() as tape1: with tf.GradientTape() as tape2: tape1.watch(x1) tape2.watch([x1, x2]) y = x1 ** 3 z = x2 ** 2 dy, dz = tape2.gradient([y, z], [x1, x2]) d2y, d2z = tape1.gradient([dy, dz], [x1, x2]) assert d2z is None PiperOrigin-RevId: 211206506
* commentsGravatar Ben2018-08-26
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* py37Gravatar bstriner2018-08-14
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* Allows differentiating tfe.defun functions with loops in eager mode.Gravatar Alexandre Passos2018-08-08
| | | | | | | | | | | Adopts a minimal sensible policy for step containers: starting a graident tape creates a step container; inner tapes do nothing; popping out of the outermost tape will reset that step container. This should allow us to have reasonable behavior in the presence of step-container-scoped things for a while. Ideally we'll move away from them in favor of lists but the infrastructure isn't ready yet. PiperOrigin-RevId: 207911091
* Allow setting server_def directly on TFE_Context.Gravatar Akshay Modi2018-08-03
| | | | | | | | | Any time that the server def is updated, the context is effectively "reset" by clearing all the caches. - Check that the FLR returned is not a nullptr instead of seg faulting. - Consolidate caches within the context object. PiperOrigin-RevId: 207308086
* Allow setting server def on the eager context, and add the eager service to ↵Gravatar Akshay Modi2018-06-19
| | | | | | the grpc_tensorflow_server. PiperOrigin-RevId: 201198350
* Add EagerTensor profiler and device shape utilitiesGravatar Igor Ganichev2018-05-25
| | | | | | | | | | | | | | | | | | | | | | | | | | This change includes the following steps to make EagerTensor profiler work: - Add a PaddedShapeFn to XlaDevice::Metadata. We need a backend-independent way to get a fully-padded shape and its layout on the device. This function is set during device construction. CPU and GPU devices effectively get an identity function since they neither change the layout nor pad. TPU gets the appropriate function. - Add TFE_TensorDebugInfo struct and C API methods for it. These methods are necessary to fetch the shape and layout from under the C API to the Python level. This can be a home for more debug information later. - Make EagerTensor weak referencable. This involves adding a pointer to the list of current weak references. This addition should have negligible overhead when profiler is not used. The only operations on this field are setting it to null on construction and checking if it is null on destruction. - Adding C++ functions callable from Python to register an instance of EagerTensorProfiler and retrieve debug information for a given EagerTensor. These functions are used in the new "inspect" module. - Finally, writing the actual profiler. PiperOrigin-RevId: 198098380
* Remove _get_backward_fn and depend on _gradient_function directly.Gravatar Akshay Modi2018-05-24
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | (_magic_gradient_function was renamed to _gradient_function) Before: entry { name: "MicroBenchmarks.benchmark_tf_gradient_forward_identity" iters: 30000 wall_time: 5.88456789653 extras { key: "examples_per_sec" value { double_value: 169936.011885 } } } After: entry { name: "MicroBenchmarks.benchmark_tf_gradient_forward_identity" iters: 30000 wall_time: 5.04853725433 extras { key: "examples_per_sec" value { double_value: 198077.175551 } } } PiperOrigin-RevId: 197972668
* Misc changes found when debugging an unrelated problem.Gravatar Akshay Modi2018-05-17
| | | | | | | | - (pywrap_tfe.i) Improve error message for better debugging TFE_Py_Execute failures. - (pywrap_tfe_src.cc) Accept _value of None - (base.i) Remove unnecessary temporary PiperOrigin-RevId: 197073571
* Methods to stop and reset tf.GradientTape()Gravatar Alexandre Passos2018-05-17
| | | | PiperOrigin-RevId: 196995160
* Make TF functions work with _USE_C_SHAPES=True.Gravatar Skye Wanderman-Milne2018-04-24
| | | | | | | | | | It turns out regular functions need to manually copy handle data in addition to eager GraphModeFunctions, so I moved the C extensions to python_api.h from eager/c_api.h. This also cleans up function_test.py to assume the C API is enabled. PiperOrigin-RevId: 194158700
* Merged commit includes the following changes:Gravatar A. Unique TensorFlower2018-04-18
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 193422827 by yifeif: Fix buildifier error. -- 193421691 by skyewm: Make GraphModeFunctions work with _USE_C_SHAPES=True. Tensor._handle_data is going away. This change adds special hooks for propagating the resource handle shape information through EagerTensors. -- 193421473 by A. Unique TensorFlower: Register dynamic_stitch for DT_VARIANT type. -- 193421175 by nolivia: disabling flaky tsan test -- 193420117 by nolivia: disabling flaky test in tensorflow that has no apparent culprit -- PiperOrigin-RevId: 193422827
* Allow TFE_NewContext to fail more reasonably when SWIG is checking status.Gravatar Akshay Modi2018-04-06
| | | | | | | | | | | | | | | 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
* Apply "Raise exception in SWIG on bad TF_Status" to base.iGravatar Akshay Modi2018-04-03
| | | | | | Minor fixes to make this work. PiperOrigin-RevId: 191457070
* Enable async eager mode in python. Add some benchmarks.Gravatar A. Unique TensorFlower2018-03-14
| | | | PiperOrigin-RevId: 189101670
* ReadVariableOp in C for eager (only for the fastpath)Gravatar Akshay Modi2018-03-02
| | | | PiperOrigin-RevId: 187676012
* Record gradient in CGravatar Akshay Modi2018-02-21
| | | | PiperOrigin-RevId: 186522240
* Make TFE_Py_FastpathExecute work for all types of opsGravatar Akshay Modi2018-02-06
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | MatMul benchmarks: entry { name: "MicroBenchmarks.benchmark_gen_math_ops_matmul_2_by_2_CPU" iters: 30000 wall_time: 11.580435435 extras { key: "examples_per_sec" value { double_value: 86352.538781 } } } entry { name: "MicroBenchmarks.benchmark_tfe_py_fastpath_execute_matmul_2_by_2_CPU" iters: 30000 wall_time: 7.02576637268 extras { key: "examples_per_sec" value { double_value: 142333.227004 } } } PiperOrigin-RevId: 184734289
* Silent copies for int32 tensors in TFE.Gravatar Alexandre Passos2018-01-24
| | | | | | Fixes #16106 PiperOrigin-RevId: 183137298
* Allow setting per-thread device copying policies in TFE.Gravatar Alexandre Passos2018-01-24
| | | | PiperOrigin-RevId: 183093407
* Reducing op overhead for eager.Gravatar Akshay Modi2018-01-22
| | | | | | | | | | | | | | | | | | | | | Benchmarks: (new) tfe_py_fastpath_execute_matmul: 7.86213080088 walltime (old) gen_math_ops_matmul: 11.2566947937 walltime (The slowdown is due to adding the record_gradient callback) This will be a 3-step process: 1. (This CL) Add a function that allows execution of an op on the fastpath. 2. Update python generation code to create 2 new python functions (_op/_op_gradient_callback and _op_slowpath_fallback): -- first function (_op) checks if it is in graph mode, if so, do the normal thing, else call out to the function added in step 1. -- second function does the else part similar to today (calling out to args_to_matching_eager etc.) 3. Rename the first function generated above to be the canonical _op function. PiperOrigin-RevId: 182791741
* Exposes runmetadata from tfe in python.Gravatar Alexandre Passos2018-01-09
| | | | PiperOrigin-RevId: 181317960
* Fix the threading model of gradient tapes.Gravatar Alexandre Passos2018-01-08
| | | | | | | | | | | | | The set of tapes needs to be global to enable multithreaded programming (when it's natural for tensors to cross threads during reduction operations) but each thread still needs to be able to locally pause recording while it does gradient-related bookkeeping (like custom gradients or initialization). Also removes a mutex from the thread-local structure since it's unnecessary as we're always holding the GIL while calling across the python-c boundary unless we explicitly release it. PiperOrigin-RevId: 181246570
* Clear eager kernel cache when resetting random seed.Gravatar Alexandre Passos2018-01-05
| | | | | | "big hammer" required for reproducibility. PiperOrigin-RevId: 180961787
* Uses C API for eager functions.Gravatar Alexandre Passos2017-11-30
| | | | | | Rolls back the rollback with some swiggery to get python3 to work. PiperOrigin-RevId: 177470328
* Automated g4 rollback of changelist 177375237Gravatar Gunhan Gulsoy2017-11-30
| | | | PiperOrigin-RevId: 177418947
* Using the C API in eager mode for graph functions.Gravatar Alexandre Passos2017-11-29
| | | | PiperOrigin-RevId: 177375237
* Tape stack in C++ instead of python.Gravatar Alexandre Passos2017-11-14
| | | | PiperOrigin-RevId: 175704617
* Moves tape.watch_variable to C. Prequel to moving the tape stack to C.Gravatar Alexandre Passos2017-11-13
| | | | PiperOrigin-RevId: 175531148
* Moves imperative_grad to CGravatar Alexandre Passos2017-11-10
| | | | | | Neutral-to-positive on all benchmarks. Also reduces overhead of should_record. PiperOrigin-RevId: 175057104
* Add TFE_Py_TensorShapeSlice functionGravatar Igor Ganichev2017-10-31
| | | | | | | | TFE_Py_TensorShapeSlice takes a list of EagerTensors and returns a list of their i'th dimensions. This utility is fairly niche but it is simple and reduces SPINN training time by over 12%. PiperOrigin-RevId: 174065044
* Allows tfe.enable_eager_execution(device_policy=tfe.DEVICE_POLICY_WARN).Gravatar Alexandre Passos2017-10-20
| | | | PiperOrigin-RevId: 172943398
* Context-specific C API to set options other than configproto (still unused)Gravatar Alexandre Passos2017-10-19
| | | | PiperOrigin-RevId: 172818175
* Ports the eager gradient tape to C.Gravatar Alexandre Passos2017-10-13
| | | | | | The tape stack is still in python as is the backprop code. PiperOrigin-RevId: 172151189
* Fuse TFE_NewOp and TFE_OpGetAttrType to avoid leaking memory.Gravatar Allen Lavoie2017-10-06
| | | | | | Removes TFE_NewOp and TFE_OpGetAttrType from pywrap_tensorflow, adds TFE_OpNameGetAttrType. PiperOrigin-RevId: 171302338
* Move EagerTensor from python to C.Gravatar A. Unique TensorFlower2017-09-30
| | | | PiperOrigin-RevId: 170617321
* Fix capitalization of some TFE/Python C APIs.Gravatar A. Unique TensorFlower2017-09-17
| | | | PiperOrigin-RevId: 169043830
* Move Python sequence -> TensorFlow Tensor conversion fromGravatar A. Unique TensorFlower2017-09-16
| | | | | | Python/NumPy to C/C++. PiperOrigin-RevId: 168983402
* Initial tests for gradient code.Gravatar Alexandre Passos2017-08-16
| | | | PiperOrigin-RevId: 165467540
* Expose TFE_DeleteContext in pywrap_tensorflow.Gravatar A. Unique TensorFlower2017-08-15
| | | | PiperOrigin-RevId: 165379414
* Make tf.ResourceVariable, tf.constant_op, tf.identity, tf.colocate_with,Gravatar A. Unique TensorFlower2017-08-14
| | | | | | tf.name_scope, tf.device, tf.control_dependencies EAGER friendly. PiperOrigin-RevId: 165269572
* Experimental C and Python APIs to invoke TensorFlow kernels on concrete values.Gravatar Alexandre Passos2017-08-10
PiperOrigin-RevId: 164902588
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