diff options
| author |  Andrew Harp <andrewharp@google.com> | 2017-08-21 12:10:44 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2017-08-21 12:14:49 -0700 |
| commit | 6e3e7d18f42cb4237ce6dbe2ffd0f9f158c36daf (patch) | |
| tree | be8357ec40fb227cdbb1e329bdfda0bd9e6f46a2 | |
| parent | 2ba7ec1680f6462122a11f1748f04e1747d8d2e0 (diff) | |
Merge changes from github.
END_PUBLIC
---
Commit 575bd01d4 authored by Vijay Vasudevan<vrv@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Remove /replica:0 declaration in device functions and allow them
to be freely bound based on cluster names present.
When more than one value matches, it will choose the first
lexicographically available device that matches the specification,
which in practice will do pretty much the same thing as hardcoding
/replica:0.
PiperOrigin-RevId: 165766815
---
Commit d685bbc54 authored by Alexandre Passos<apassos@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Benchmarks with backprop enabled (and removes overhead).
Before:
np.array([[3]]) took 1.50us (30000 iterations)
Tensor([[3]]) took 16.30us (30000 iterations)
MatMul [2, 2]: np.dot took 0.61us (30000 iterations)
MatMul [2, 2]: tf.matmul took 60.53us (30000 iterations)
MatMul [2, 2]: gen_math_ops.mat_mul took 25.72us (30000 iterations)
MatMul [2, 2]: TFE_Py_Execute took 2.82us (30000 iterations)
MatMul [2, 2]: defun(tf.matmul) took 45.70us (30000 iterations)
MatMul [100, 784]: np.dot took 383.32us (1000 iterations)
MatMul [100, 784]: tf.matmul took 350.35us (1000 iterations)
MatMul [100, 784]: gen_math_ops.mat_mul took 315.97us (1000 iterations)
MatMul [100, 784]: TFE_Py_Execute took 249.42us (1000 iterations)
MatMul [100, 784]: defun(tf.matmul) took 280.95us (1000 iterations)
If backprop is enabled:
np.array([[3]]) took 0.83us (30000 iterations)
Tensor([[3]]) took 15.21us (30000 iterations)
MatMul [2, 2]: np.dot took 0.63us (30000 iterations)
MatMul [2, 2]: tf.matmul took 76.31us (30000 iterations)
MatMul [2, 2]: gen_math_ops.mat_mul took 38.66us (30000 iterations)
MatMul [2, 2]: TFE_Py_Execute took 2.31us (30000 iterations)
MatMul [2, 2]: defun(tf.matmul) took 51.96us (30000 iterations)
MatMul [100, 784]: np.dot took 378.34us (1000 iterations)
MatMul [100, 784]: tf.matmul took 352.09us (1000 iterations)
MatMul [100, 784]: gen_math_ops.mat_mul took 364.28us (1000 iterations)
MatMul [100, 784]: TFE_Py_Execute took 350.68us (1000 iterations)
MatMul [100, 784]: defun(tf.matmul) took 377.19us (1000 iterations)
After:
np.array([[3]]) took 0.86us (30000 iterations)
Tensor([[3]]) took 15.19us (30000 iterations)
MatMul [2, 2]: np.dot took 0.60us (30000 iterations)
MatMul [2, 2]: tf.matmul took 64.51us (30000 iterations)
MatMul [2, 2]: gen_math_ops.mat_mul took 28.34us (30000 iterations)
MatMul [2, 2]: TFE_Py_Execute took 2.38us (30000 iterations)
MatMul [2, 2]: defun(tf.matmul) took 48.50us (30000 iterations)
MatMul [100, 784]: np.dot took 475.27us (1000 iterations)
MatMul [100, 784]: tf.matmul took 399.50us (1000 iterations)
MatMul [100, 784]: gen_math_ops.mat_mul took 307.80us (1000 iterations)
MatMul [100, 784]: TFE_Py_Execute took 272.83us (1000 iterations)
MatMul [100, 784]: defun(tf.matmul) took 350.06us (1000 iterations)
PiperOrigin-RevId: 165765641
---
Commit d902babbd authored by David Majnemer<majnemer@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Algebraic simplifier incorrectly transformed convolutions into bitcasts
PiperOrigin-RevId: 165765575
---
Commit 8e78e10ef authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
disable test temporarily
PiperOrigin-RevId: 165763204
---
Commit a271c37db authored by Benoit Steiner<bsteiner@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Small improvements to the arithmetic optimizer
PiperOrigin-RevId: 165760972
---
Commit b6409594d authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Convert some tests to cover both eager and graph.
PiperOrigin-RevId: 165760364
---
Commit 5ead76420 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Reduce XLA compile time by ~7% for a convolutional image model:
* Added CompactPointerSet<T>, which is optimized for set size <= 1.
* Changed expensive CHECKs to DCHECKS in buffer_assignment.cc
* Reserve space in DFS state array before starting DFS.
* Use unsigned arithmetic in DFS state maintenance.
* HloInstruction:
- Moved frequently used fields to start for better cache locality.
- Use InlinedVector instead of vector for operand array.
- Use InlinedVector instead of vector for DFS stack.
* Pre-compute "is array" and "is tuple" for LogicalBuffer.
* PointsToSet:
- Combine two ShapeTrees into one.
- Use CompactPointerSet instead of std::set to hold sources.
- Use CompactPointerSet instead of std::set to hold flattened buffers.
* ShapeTree: use unique_ptr instead of optional for shape storage
(reduces size and destruction overhead).
* Add proper const qualifiers to some FlatSet iterator methods.
Co-author=jeff
PiperOrigin-RevId: 165759117
---
Commit a0544b0b8 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make TPU symbols more easily accessible from contrib.
PiperOrigin-RevId: 165753322
---
Commit cdc08afbb authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Slightly relax numeric tolerance for sinlge precision tests of matrix_solve_ls (and tighten it for double precision).
PiperOrigin-RevId: 165750936
---
Commit eebcc861a authored by Jianwei Xie<xiejw@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fixed the race condition between multi eval step increments.
PiperOrigin-RevId: 165750595
---
Commit bbc0b8471 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 165748384
---
Commit 65f87c967 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Change device string in RecvNodeDescriptor in VirtualScheduler from const
reference to const as the RecvNodeDescriptor (and cached_recv_nodes map)
outlives device string from the NodeDef.
PiperOrigin-RevId: 165748244
---
Commit 57b0276cf authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Update ops-related pbtxt files.
PiperOrigin-RevId: 165747467
---
Commit 64e54423b authored by Derek Murray<mrry@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[tf.contrib.data] Fix nested dictionary handling in dataset elements.
Backports recent changes to the core version of the nest.py library.
Fixes #12372.
PiperOrigin-RevId: 165746517
---
Commit 378463ae8 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make tf.eye accept Python integer shapes and avoid generating unnecessary shape handling ops.
Clean up test and add tests with placeholders.
PiperOrigin-RevId: 165746090
---
Commit 109ecf823 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add support for complex in matrix_solve_ls_op.
Split into separate files for each data type to speed up build.
PiperOrigin-RevId: 165744539
---
Commit 51441302d authored by Alexandre Passos<apassos@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Internal change.
PiperOrigin-RevId: 165737455
---
Commit d0cb32c2a authored by Alexandre Passos<apassos@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Docstring for ResourceVariable.
PiperOrigin-RevId: 165735441
---
Commit 32f4c5b6e authored by Chris Leary<leary@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Add IsFinite op in tf2xla.
PiperOrigin-RevId: 165734702
---
Commit 5f5c3eb0a authored by Mark Daoust<markdaoust@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Move "supervisor.md" from programmer's guide to api_guides.
PiperOrigin-RevId: 165732026
---
Commit d001b58de authored by Derek Murray<mrry@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[tf.contrib.data] Fix handling of multi-output tf.py_func() in Dataset.map().
If the `map_func` returns a list of tensors, the current code will
attempt to stack it into a single tensor and raise an unintuitive
error. Some multi-output ops (such as `tf.py_func()`) return lists of
typically-not-stackable tensors. This change treats lists returned
from `map_func` as tuples; users who were relying on this
auto-stacking behavior should manually call `tf.stack()` (or
`tf.convert_to_tensor()`) on the list being returned.
Fixes #12396.
PiperOrigin-RevId: 165731970
---
Commit e6c60fb36 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fix flakyness, sometimes the op takes ms to run.
PiperOrigin-RevId: 165728705
---
Commit 360bff8ae authored by Ali Yahya<alive@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Makes tape.watch() work with ResourceVariables.
To this end, also adds a property, `device`, to TensorNode.
PiperOrigin-RevId: 165726368
---
Commit 80bd004cd authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Implements SVDF model for keyword spotting tutorial.
PiperOrigin-RevId: 165725938
---
Commit aaabf6b90 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fix bug: Using a ComputationDataHandle from the wrong ComputationBuilder.
PiperOrigin-RevId: 165724017
---
Commit 107d165d9 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Use 2-arg TraceMe constructor to prevent unnecessary StrCat computation when
tracing is disabled.
PiperOrigin-RevId: 165722280
---
Commit 7d01f89cc authored by Pete Warden<petewarden@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Android demo app for speech recognition
PiperOrigin-RevId: 165714459
---
Commit a6729325a authored by Alexandre Passos<apassos@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Deletes convert_n_to_eager_tensor. Moves convert_to_eager_tensor to constant_op.
PiperOrigin-RevId: 165704074
---
Commit 573b303ac authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BUILD cleanup in tensorflow/core/kernels
PiperOrigin-RevId: 165688864
---
Commit 711be6adc authored by Derek Murray<mrry@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
`Dataset.from_generator()` constructs a dataset from a Python generator.
With this change, it becomes possible to use a Python generator as the source
dataset for a `tf.contrib.data` input pipeline. This enables easier integration
with non-TensorFlow data sources. The generator can yield a nested structure of
NumPy arrays, or values convertible to NumPy arrays.
This addresses a concern raised in issue #7951.
PiperOrigin-RevId: 165663857
---
Commit 00594ecdd authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
New landing page and leftnav for Programmer's Guide.
PiperOrigin-RevId: 165660897
---
Commit 7359fec79 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Implement Batchnorm Inference by expanding them into smaller ops.
1. Add batch norm inference support in batchnorm_rewriter
2. Connect xla's batchnorm inference to tf's FusedBatchNorm
RELNOTES: n/a
PiperOrigin-RevId: 165655351
---
Commit f0da8bf56 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[Rematerialization] Reconsider to remat operations with control dependencies
We added a conservartive logic to not rematerialize operations with control dependencies since the rematerialized operations could result in undesired ordering. However, we now realize that when we remat an operation, we also copy the dependencies of them, which guarantees the rematerialized operation has the same constraint as the original operation.
PiperOrigin-RevId: 165654629
---
Commit a1225879c authored by Chris Leary<leary@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Propagate error code in computation replay tool.
PiperOrigin-RevId: 165654497
---
Commit 513def0bb authored by Benoit Steiner<bsteiner@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fixed BuildOpInfoWithoutDevice
PiperOrigin-RevId: 165653933
---
Commit d7e425f0b authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fix linear algebra benchmarks.
PiperOrigin-RevId: 165653891
---
Commit 465c40819 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fix the shape information propagation for Enter op.
PiperOrigin-RevId: 165653579
---
Commit c0198fd8d authored by Derek Murray<derek.murray@gmail.com>
Committed by gunan<gunan@google.com>:
[CMake] Add missing dependencies on boosted_trees protos and other fixes (#12315)
* [CMake] Add missing dependencies
* Avoid rebuilding boosted_trees protos for Python.
* Add GPU implementation ZeroInitializerOp to the CMake build.
---
Commit 641943fd7 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Update ops-related pbtxt files.
PiperOrigin-RevId: 165652758
---
Commit e31346452 authored by Jonathan Hseu<jhseu@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
TPUEstimator: Fix the outfeed thread join.
PiperOrigin-RevId: 165651781
---
Commit 565a9d350 authored by Vijay Vasudevan<vrv@google.com>
Committed by Andrew Harp<andrewharp@users.noreply.github.com>:
Add missing 'type' keyword to ArgumentParser add_argument (#12275)
Fixes #12210
---
Commit 19a55725a authored by Rohan Jain<rohanj@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Allowing functions to run across devices. This change expands the ProcessFunctionLibraryRuntime library to Instantiate and Run functions on different devices. When a FunctionLibraryRuntime encounters a function with a target that is another device, it delegates Instantiate() and Run() calls to the ProcessFunctionLibraryRuntime.
This change also moves the table_ containing all function instantiations to the PFLR instead of the FunctionLibraryRuntime.
PiperOrigin-RevId: 165651194
---
Commit 8c0853db7 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add a test for negative and zero pow() input.
PiperOrigin-RevId: 165650096
---
Commit a3c4e980e authored by Pete Warden<petewarden@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fixed input shape for freezing audio graphs
PiperOrigin-RevId: 165649546
---
Commit 9b9e5989d authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add a call_logit_fn utility for logit_fn's, similar to Estimator's _call_model_fn.
PiperOrigin-RevId: 165649388
---
Commit 4ff1f4442 authored by Amit Patankar<amitpatankar@google.com>
Committed by Amit Patankar<amitpatankar@google.com>:
Remove the script as well if building tf_nightly.
---
Commit 373d78987 authored by Amit Patankar<amitpatankar@google.com>
Committed by Amit Patankar<amitpatankar@google.com>:
Adding the break.
---
Commit 0139ac983 authored by Amit Patankar<amitpatankar@google.com>
Committed by Amit Patankar<amitpatankar@google.com>:
Remove tensorboard as a required package if we are building tf_nightly.
---
Commit a92bd5d5c authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 165630063
PiperOrigin-RevId: 165957821
68 files changed, 1969 insertions, 140 deletions
@@ -35,11 +35,11 @@ and discussion, and please direct specific questions to [Stack Overflow](https:/ People who are a little more adventurous can also try our nightly binaries: -* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/)) -* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0rc2-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0rc2-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0rc2-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/)) -* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc2-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0rc2-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/)) -* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.3.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.3.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/)) -* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.3.0rc2-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.3.0rc2-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/)) +* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/)) +* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/)) +* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/)) +* Windows CPU-only: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.3.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow-1.3.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows,PY=36/)) +* Windows GPU: [Python 3.5 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.3.0-cp35-cp35m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=35/)) / [Python 3.6 64-bit](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/lastSuccessfulBuild/artifact/cmake_build/tf_python/dist/tensorflow_gpu-1.3.0-cp36-cp36m-win_amd64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-win/M=windows-gpu,PY=36/)) * Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/)) diff --git a/RELEASE.md b/RELEASE.md index ffe38004a2..3203f0aec1 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,5 +1,7 @@ # Release 1.3.0 +See also [TensorBoard 0.1.4](https://github.com/tensorflow/tensorboard/releases/tag/0.1.4) release notes. + ## Major Features and Improvements * Added canned estimators to Tensorflow library. List of added estimators: * `DNNClassifier` @@ -8,7 +10,7 @@ * `LinearRegressor` * `DNNLinearCombinedClassifier` * `DNNLinearCombinedRegressor`. -* All our prebuilt binaries have been built with cuDNN 6. +* All our prebuilt binaries have been built with cuDNN 6. We anticipate releasing TensorFlow 1.4 with cuDNN 7. * `import tensorflow` now goes much faster. * Adds a file cache to the GCS filesystem with configurable max staleness for file contents. This permits caching of file contents across close/open boundaries. * Added an axis parameter to `tf.gather`. @@ -44,6 +46,9 @@ * Adds time series models to contrib. See contrib/timeseries/README.md for details. * Adds FULLY_CONNECTED Op to tensorflow/contrib/lite/schema.fbs +## Known Issues +* Tensorflow_gpu compilation fails with Bazel 0.5.3. + ## Bug Fixes and Other Changes * Fixes `strides` and `begin` dtype mismatch when slicing using int64 Tensor index in python. * Improved convolution padding documentation. diff --git a/configure.py b/configure.py index 3646670263..5a024fb0e4 100644 --- a/configure.py +++ b/configure.py @@ -22,7 +22,6 @@ import errno import os import platform import re -import site import subprocess import sys @@ -131,16 +130,27 @@ def cygpath(path): return run_shell('cygpath -m "%s"' % path) -def get_python_path(environ_cp): +def get_python_path(environ_cp, python_bin_path): """Get the python site package paths.""" python_paths = [] if environ_cp.get('PYTHONPATH'): python_paths = environ_cp.get('PYTHONPATH').split(':') try: - library_paths = site.getsitepackages() - except AttributeError: - from distutils.sysconfig import get_python_lib # pylint: disable=g-import-not-at-top - library_paths = [get_python_lib()] + check_input = [ + python_bin_path, '-c', + 'import site; print("\\n".join(site.getsitepackages()))' + ] + library_paths = subprocess.check_output(check_input).decode( + 'UTF-8').strip().split('\n') + except subprocess.CalledProcessError: + check_input = [ + python_bin_path, '-c', 'from distutils.sysconfig import get_python_lib;' + + 'print(get_python_lib())' + ] + library_paths = [ + subprocess.check_output(check_input).decode('UTF-8').strip() + ] + all_paths = set(python_paths + library_paths) paths = [] @@ -150,6 +160,12 @@ def get_python_path(environ_cp): return paths +def get_python_major_version(python_bin_path): + """Get the python major version.""" + check_input = [python_bin_path, '-c', 'import sys; print(sys.version[0])'] + return subprocess.check_output(check_input).decode('UTF-8').strip() + + def setup_python(environ_cp, bazel_version): """Setup python related env variables.""" # Get PYTHON_BIN_PATH, default is the current running python. @@ -170,10 +186,14 @@ def setup_python(environ_cp, bazel_version): print('%s is not executable. Is it the python binary?' % python_bin_path) environ_cp['PYTHON_BIN_PATH'] = '' + # Convert python path to Windows style before checking lib and version + if is_windows(): + python_bin_path = cygpath(python_bin_path) + # Get PYTHON_LIB_PATH python_lib_path = environ_cp.get('PYTHON_LIB_PATH') if not python_lib_path: - python_lib_paths = get_python_path(environ_cp) + python_lib_paths = get_python_path(environ_cp, python_bin_path) if environ_cp.get('USE_DEFAULT_PYTHON_LIB_PATH') == '1': python_lib_path = python_lib_paths[0] else: @@ -187,10 +207,10 @@ def setup_python(environ_cp, bazel_version): python_lib_path = default_python_lib_path environ_cp['PYTHON_LIB_PATH'] = python_lib_path - python_major_version = sys.version_info[0] + python_major_version = get_python_major_version(python_bin_path) + # Convert python path to Windows style before writing into bazel.rc if is_windows(): - python_bin_path = cygpath(python_bin_path) python_lib_path = cygpath(python_lib_path) # Set-up env variables used by python_configure.bzl @@ -726,9 +746,15 @@ def set_tf_cuda_compute_capabilities(environ_cp): # Check whether all capabilities from the input is valid all_valid = True for compute_capability in tf_cuda_compute_capabilities.split(','): - if not re.match('[0-9]+.[0-9]+', compute_capability): + m = re.match('[0-9]+.[0-9]+', compute_capability) + if not m: print('Invalid compute capability: ' % compute_capability) all_valid = False + else: + ver = int(m.group(0).split('.')[0]) + if ver < 3: + print('Only compute capabilities 3.0 or higher are supported.') + all_valid = False if all_valid: break diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 585240ec89..80646fb602 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -312,6 +312,7 @@ filegroup( "//tensorflow/contrib/nn:all_files", "//tensorflow/contrib/opt:all_files", "//tensorflow/contrib/predictor:all_files", + "//tensorflow/contrib/reduce_slice_ops:all_files", "//tensorflow/contrib/remote_fused_graph/pylib:all_files", "//tensorflow/contrib/resampler:all_files", "//tensorflow/contrib/rnn:all_files", diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc index 4ef2dc5bfa..f0426c998b 100644 --- a/tensorflow/cc/gradients/math_grad.cc +++ b/tensorflow/cc/gradients/math_grad.cc @@ -537,6 +537,22 @@ Status ImagGrad(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("Imag", ImagGrad); +Status AngleGrad(const Scope& scope, const Operation& op, + const std::vector<Output>& grad_inputs, + std::vector<Output>* grad_outputs) { + // y = Angle(x) + // dx = -dy / (Im(x) + iRe(x)) = -dy * z + auto re = Real(scope, op.input(0)); + auto im = Imag(scope, op.input(0)); + auto z_inv = Reciprocal(scope, Complex(scope, im, re)); + auto zero = Cast(scope, Const(scope, 0), grad_inputs[0].type()); + auto grad = Complex(scope, grad_inputs[0], zero); + auto dx = Neg(scope, Mul(scope, grad, z_inv)); + grad_outputs->push_back(dx); + return scope.status(); +} +REGISTER_GRADIENT_OP("Angle", AngleGrad); + Status ConjGrad(const Scope& scope, const Operation& op, const std::vector<Output>& grad_inputs, std::vector<Output>* grad_outputs) { diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc index 011e119353..bd78331309 100644 --- a/tensorflow/cc/gradients/math_grad_test.cc +++ b/tensorflow/cc/gradients/math_grad_test.cc @@ -684,7 +684,7 @@ class CWiseUnaryComplexGradTest : public ::testing::Test { CWiseUnaryComplexGradTest() : scope_(Scope::NewRootScope().WithDevice("/cpu:0")) {} - enum UnaryOpType { REAL, IMAG, CONJ }; + enum UnaryOpType { REAL, IMAG, ANGLE, CONJ }; void TestCWiseGradComplex(UnaryOpType op_type, const Tensor& x, const Tensor& dy, const Tensor& dx_expected) { @@ -696,6 +696,9 @@ class CWiseUnaryComplexGradTest : public ::testing::Test { case IMAG: y = Imag(scope_, x); break; + case ANGLE: + y = Angle(scope_, x); + break; case CONJ: y = Conj(scope_, x); break; @@ -730,6 +733,21 @@ TEST_F(CWiseUnaryComplexGradTest, Imag) { TestCWiseGradComplex(IMAG, x, dy, dx_expected); } +TEST_F(CWiseUnaryComplexGradTest, Angle) { + Tensor x = test::AsTensor<complex64>( + {{1, -1}, {-2, 2}, {3, -3}, {-4, 4}, {8, -8}, {-9, 9}}, {2, 3}); + Tensor dy = test::AsTensor<float>({11, -12, 13, -14, 15, -16}, {2, 3}); + Tensor dx_expected = + test::AsTensor<complex64>({{5.5, 5.5}, + {3, 3}, + {2.1666666666666665, 2.1666666666666665}, + {1.75, 1.75}, + {0.9375, 0.9375}, + {0.8888888888888888, 0.8888888888888888}}, + {2, 3}); + TestCWiseGradComplex(ANGLE, x, dy, dx_expected); +} + TEST_F(CWiseUnaryComplexGradTest, Conj) { Tensor x = test::AsTensor<complex64>( {{1, -1}, {-2, 2}, {3, -3}, {-4, 4}, {8, -8}, {-9, 9}}, {2, 3}); diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index 167821a51a..ee703e040e 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -57,6 +57,7 @@ py_library( "//tensorflow/contrib/opt:opt_py", "//tensorflow/contrib/predictor", "//tensorflow/contrib/quantization:quantization_py", + "//tensorflow/contrib/reduce_slice_ops:reduce_slice_ops_py", "//tensorflow/contrib/remote_fused_graph/pylib:remote_fused_graph_ops_py", "//tensorflow/contrib/resampler:resampler_py", "//tensorflow/contrib/rnn:rnn_py", diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index a0f8896f98..315ea943cf 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -53,6 +53,7 @@ from tensorflow.contrib import nn from tensorflow.contrib import opt from tensorflow.contrib import predictor from tensorflow.contrib import quantization +from tensorflow.contrib import reduce_slice_ops from tensorflow.contrib import resampler from tensorflow.contrib import rnn from tensorflow.contrib import saved_model diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake index d2a817390a..f7470d3bce 100644 --- a/tensorflow/contrib/cmake/tf_core_framework.cmake +++ b/tensorflow/contrib/cmake/tf_core_framework.cmake @@ -87,6 +87,7 @@ endfunction() file(GLOB_RECURSE tf_protos_cc_srcs RELATIVE ${tensorflow_source_dir} "${tensorflow_source_dir}/tensorflow/core/*.proto" + "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/proto/*.proto" ) RELATIVE_PROTOBUF_GENERATE_CPP(PROTO_SRCS PROTO_HDRS ${tensorflow_source_dir} ${tf_protos_cc_srcs} diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake index a86eb44d3f..1c7cb9476d 100644 --- a/tensorflow/contrib/cmake/tf_core_kernels.cmake +++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake @@ -137,6 +137,7 @@ file(GLOB_RECURSE tf_core_kernels_exclude_srcs "${tensorflow_source_dir}/tensorflow/core/kernels/*test_utils.cc" "${tensorflow_source_dir}/tensorflow/core/kernels/*main.cc" "${tensorflow_source_dir}/tensorflow/core/kernels/*.cu.cc" + "${tensorflow_source_dir}/tensorflow/core/kernels/fuzzing/*" "${tensorflow_source_dir}/tensorflow/core/kernels/hexagon/*" "${tensorflow_source_dir}/tensorflow/core/kernels/remote_fused_graph_rewriter_transform*.cc" ) @@ -169,9 +170,10 @@ if(WIN32) endif(WIN32) file(GLOB_RECURSE tf_core_gpu_kernels_srcs - "${tensorflow_source_dir}/tensorflow/core/kernels/*.cu.cc" - "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/*.cu.cc" - "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/kernels/*.cu.cc" + "${tensorflow_source_dir}/tensorflow/core/kernels/*.cu.cc" + "${tensorflow_source_dir}/tensorflow/contrib/framework/kernels/zero_initializer_op_gpu.cu.cc" + "${tensorflow_source_dir}/tensorflow/contrib/rnn/kernels/*.cu.cc" + "${tensorflow_source_dir}/tensorflow/contrib/seq2seq/kernels/*.cu.cc" ) if(WIN32 AND tensorflow_ENABLE_GPU) diff --git a/tensorflow/contrib/cmake/tf_core_ops.cmake b/tensorflow/contrib/cmake/tf_core_ops.cmake index a9770bc731..f9b5d31088 100644 --- a/tensorflow/contrib/cmake/tf_core_ops.cmake +++ b/tensorflow/contrib/cmake/tf_core_ops.cmake @@ -100,6 +100,7 @@ GENERATE_CONTRIB_OP_LIBRARY(tensor_forest_stats "${tensorflow_source_dir}/tensor GENERATE_CONTRIB_OP_LIBRARY(text_skip_gram "${tensorflow_source_dir}/tensorflow/contrib/text/ops/skip_gram_ops.cc") GENERATE_CONTRIB_OP_LIBRARY(tpu "${tpu_ops_srcs}") GENERATE_CONTRIB_OP_LIBRARY(bigquery_reader "${tensorflow_source_dir}/tensorflow/contrib/cloud/ops/bigquery_reader_ops.cc") +GENERATE_CONTRIB_OP_LIBRARY(reduce_slice_ops "${tensorflow_source_dir}/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc") ######################################################## # tf_user_ops library diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 170feb582b..704f0d09f3 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -142,7 +142,6 @@ RELATIVE_PROTOBUF_GENERATE_PYTHON( file(GLOB_RECURSE tf_python_protos_cc_srcs RELATIVE ${tensorflow_source_dir} "${tensorflow_source_dir}/tensorflow/core/profiler/*.proto" "${tensorflow_source_dir}/tensorflow/python/*.proto" - "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/proto/*.proto" "${tensorflow_source_dir}/tensorflow/contrib/session_bundle/*.proto" "${tensorflow_source_dir}/tensorflow/contrib/tensorboard/*.proto" "${tensorflow_source_dir}/tensorflow/contrib/training/*.proto" @@ -567,6 +566,12 @@ add_python_module("tensorflow/contrib/training") add_python_module("tensorflow/contrib/training/python") add_python_module("tensorflow/contrib/training/python/training") add_python_module("tensorflow/contrib/util") +add_python_module("tensorflow/contrib/reduce_slice_ops") +add_python_module("tensorflow/contrib/reduce_slice_ops/kernels") +add_python_module("tensorflow/contrib/reduce_slice_ops/ops") +add_python_module("tensorflow/contrib/reduce_slice_ops/python") +add_python_module("tensorflow/contrib/reduce_slice_ops/python/kernel_tests") +add_python_module("tensorflow/contrib/reduce_slice_ops/python/ops") ######################################################## diff --git a/tensorflow/contrib/framework/python/framework/tensor_util.py b/tensorflow/contrib/framework/python/framework/tensor_util.py index 6852dee979..470fb15fd6 100644 --- a/tensorflow/contrib/framework/python/framework/tensor_util.py +++ b/tensorflow/contrib/framework/python/framework/tensor_util.py @@ -28,6 +28,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops +from tensorflow.python.util.deprecation import deprecated __all__ = [ @@ -77,6 +78,11 @@ def reduce_sum_n(tensors, name=None): return math_ops.add_n(tensors, name=name_scope) +@deprecated( + None, + 'Please switch to tf.confusion_matrix.remove_squeezable_dimensions. Note ' + 'that order of the inputs and ouputs of labels and predictions have also ' + 'been switched.') def remove_squeezable_dimensions(predictions, labels, name=None): """Squeeze last dim if ranks of `predictions` and `labels` differ by 1. diff --git a/tensorflow/contrib/gdr/gdr_server_lib.cc b/tensorflow/contrib/gdr/gdr_server_lib.cc index ea4d3a10ee..1f9dd0decb 100644 --- a/tensorflow/contrib/gdr/gdr_server_lib.cc +++ b/tensorflow/contrib/gdr/gdr_server_lib.cc @@ -14,6 +14,8 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/gdr/gdr_server_lib.h" + +#include "grpc/support/alloc.h" #include "tensorflow/contrib/gdr/gdr_memory_manager.h" #include "tensorflow/contrib/gdr/gdr_rendezvous_mgr.h" #include "tensorflow/contrib/gdr/gdr_worker.h" diff --git a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py index 8623768d33..884faf8335 100644 --- a/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py +++ b/tensorflow/contrib/learn/python/learn/learn_io/generator_io.py @@ -31,8 +31,10 @@ def generator_input_fn(x, num_epochs=1, shuffle=True, queue_capacity=1000, - num_threads=1): - """Returns input function that returns dicts yielded by a generator. + num_threads=1, + pad_value=None): + """Returns input function that returns dicts of numpy arrays + yielded from a generator. It is assumed that every dict of numpy arrays yielded from the dictionary represents a single sample. The generator should consume a single epoch of the @@ -68,6 +70,7 @@ def generator_input_fn(x, time. queue_capacity: Integer, size of queue to accumulate. num_threads: Integer, number of threads used for reading and enqueueing. + pad_value: default value for dynamic padding of data samples, if provided. Returns: Function, that returns a feature `dict` with `Tensors` and an optional @@ -117,7 +120,8 @@ def generator_input_fn(x, shuffle=shuffle, num_threads=num_threads, enqueue_size=batch_size, - num_epochs=num_epochs) + num_epochs=num_epochs, + pad_value=pad_value) features = (queue.dequeue_many(batch_size) if num_epochs is None else queue.dequeue_up_to(batch_size)) diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index b5d8c95678..463bd60300 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -22,12 +22,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.framework import deprecated -from tensorflow.contrib.framework.python.framework import tensor_util from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops +from tensorflow.python.ops import confusion_matrix from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics @@ -35,6 +34,7 @@ from tensorflow.python.ops import metrics_impl from tensorflow.python.ops import nn from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.util.deprecation import deprecated def _safe_div(numerator, denominator, name): @@ -2445,8 +2445,8 @@ def _remove_squeezable_dimensions(predictions, labels, weights): Tuple of `predictions`, `labels` and `weights`, possibly with the last dimension squeezed. """ - predictions, labels = tensor_util.remove_squeezable_dimensions( - predictions, labels) + labels, predictions = confusion_matrix.remove_squeezable_dimensions( + labels, predictions) predictions.get_shape().assert_is_compatible_with(labels.get_shape()) if weights is not None: diff --git a/tensorflow/contrib/reduce_slice_ops/BUILD b/tensorflow/contrib/reduce_slice_ops/BUILD new file mode 100644 index 0000000000..5340a51e00 --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/BUILD @@ -0,0 +1,110 @@ +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "tf_custom_op_library") +load("//tensorflow:tensorflow.bzl", "tf_gen_op_libs") +load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py") +load("//tensorflow:tensorflow.bzl", "tf_custom_op_py_library") +load("//tensorflow:tensorflow.bzl", "tf_cc_test") +load("//tensorflow:tensorflow.bzl", "cuda_py_test") +load("//tensorflow:tensorflow.bzl", "tf_kernel_library") +load("//tensorflow/core:platform/default/build_config.bzl", "tf_kernel_tests_linkstatic") + +tf_custom_op_library( + name = "python/ops/_reduce_slice_ops.so", + srcs = [ + "kernels/reduce_slice_ops.cc", + "kernels/reduce_slice_ops.h", + "ops/reduce_slice_ops.cc", + ], + gpu_srcs = [ + "kernels/reduce_slice_ops.h", + "kernels/reduce_slice_ops_gpu.cu.cc", + ], +) + +tf_kernel_library( + name = "reduce_slice_ops_kernels", + srcs = [ + "kernels/reduce_slice_ops.cc", + ], + hdrs = [ + "kernels/reduce_slice_ops.h", + ], + gpu_srcs = [ + "kernels/reduce_slice_ops.h", + "kernels/reduce_slice_ops_gpu.cu.cc", + ], + deps = [ + "//tensorflow/core:framework", + "//tensorflow/core:lib", + "//third_party/eigen3", + ], +) + +tf_gen_op_libs( + op_lib_names = ["reduce_slice_ops"], +) + +tf_gen_op_wrapper_py( + name = "reduce_slice_ops", + deps = [":reduce_slice_ops_op_lib"], +) + +tf_custom_op_py_library( + name = "reduce_slice_ops_py", + srcs = [ + "__init__.py", + "python/ops/reduce_slice_ops.py", + ], + dso = [ + ":python/ops/_reduce_slice_ops.so", + ], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":reduce_slice_ops", + "//tensorflow/contrib/util:util_py", + "//tensorflow/python:framework", + ], +) + +cuda_py_test( + name = "reduce_slice_ops_test", + size = "small", + srcs = ["python/kernel_tests/reduce_slice_ops_test.py"], + additional_deps = [ + ":reduce_slice_ops_py", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + "//third_party/py/numpy", + ], +) + +tf_cc_test( + name = "reduce_slice_ops_test_cc", + size = "small", + srcs = [ + "ops/reduce_slice_ops_test.cc", + ], + linkstatic = tf_kernel_tests_linkstatic(), + deps = [ + ":reduce_slice_ops_op_lib", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + ], +) + +filegroup( + name = "all_files", + srcs = glob( + ["**/*"], + exclude = [ + "**/METADATA", + "**/OWNERS", + ], + ), + visibility = ["//tensorflow:__subpackages__"], +) diff --git a/tensorflow/contrib/reduce_slice_ops/__init__.py b/tensorflow/contrib/reduce_slice_ops/__init__.py new file mode 100644 index 0000000000..d0364587b5 --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/__init__.py @@ -0,0 +1,26 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""reduce by slice + +@@reduce_slice_sum +@@reduce_slice_prod +@@reduce_slice_min +@@reduce_slice_max +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.reduce_slice_ops.python.ops import * diff --git a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.cc b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.cc new file mode 100644 index 0000000000..2def4f3f17 --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.cc @@ -0,0 +1,239 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#define EIGEN_USE_THREADS + +#include "tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h" +#include <algorithm> +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/lib/core/threadpool.h" + +namespace tensorflow { + +using GPUDevice = Eigen::GpuDevice; +using CPUDevice = Eigen::ThreadPoolDevice; +using thread::ThreadPool; + +namespace functor { + +#define CPUReduceSliceFunctorReduceop(reduceop, beginning) \ + template <typename T, typename Index> \ + struct ReduceSliceFunctor##reduceop<CPUDevice, T, Index> { \ + private: \ + struct XYZ { \ + Index x, y, z; \ + XYZ() = default; \ + XYZ(Index x, Index y, Index z) : x(x), y(y), z(z) {} \ + }; \ + inline static XYZ global_index_to_xyz(Index global, XYZ size) { \ + XYZ ret; \ + ret.x = global / (size.y * size.z); \ + ret.y = global % (size.y * size.z) / size.z; \ + ret.z = global % size.z; \ + return ret; \ + } \ + \ + public: \ + virtual ~ReduceSliceFunctor##reduceop() {} \ + virtual void operator()(OpKernelContext* ctx, const CPUDevice& d, \ + Index indices_width, \ + typename TTypes<Index, 1>::ConstTensor indices, \ + typename TTypes<T, 3>::ConstTensor data, \ + typename TTypes<T, 3>::Tensor output) { \ + Index bound = data.dimension(1); \ + Index dim1 = output.dimension(0); \ + Index dim2 = output.dimension(1); \ + Index dim3 = output.dimension(2); \ + Index size = dim1 * dim2 * dim3; \ + if (size == 0) { \ + return; \ + } \ + T zero = beginning<T>(); \ + ThreadPool* thread_pool = \ + ctx->device()->tensorflow_cpu_worker_threads()->workers; \ + /* shard the work */ \ + auto work = [&](Index start, Index end) { \ + for (Index global = start; global < end; ++global) { \ + XYZ xyz = global_index_to_xyz(global, XYZ(dim1, dim2, dim3)); \ + Index x = xyz.x; \ + Index y = xyz.y; \ + Index z = xyz.z; \ + output(x, y, z) = zero; \ + Index slice_head = indices(y * indices_width); \ + Index slice_end = std::min(indices(y * indices_width + 1), bound); \ + for (Index i = slice_head; i < slice_end; ++i) { \ + output(x, y, z) = reduceop(output(x, y, z), data(x, i, z)); \ + } \ + } \ + }; \ + /* Here assumes the number of average CPU cycles for each slice equals \ + * the average length of each slice */ \ + thread_pool->ParallelFor(size, std::max(bound / dim2, (Index)1), work); \ + } \ + }; + +CALL_ALL_REDUCEOPS(CPUReduceSliceFunctorReduceop) +#undef CPUReduceSliceFunctorReduceop + +#define DEFINE_CPU_SUMPROD_SPECS_INDEX(T, Index) \ + template struct ReduceSliceFunctorSum<CPUDevice, T, Index>; \ + template struct ReduceSliceFunctorProd<CPUDevice, T, Index>; + +#define DEFINE_CPU_MINMAX_SPECS_INDEX(T, Index) \ + template struct ReduceSliceFunctorMax<CPUDevice, T, Index>; \ + template struct ReduceSliceFunctorMin<CPUDevice, T, Index>; + +#define DEFINE_CPU_SUMPROD_SPECS(T) \ + DEFINE_CPU_SUMPROD_SPECS_INDEX(T, int32); \ + DEFINE_CPU_SUMPROD_SPECS_INDEX(T, int64); + +#define DEFINE_CPU_MINMAX_SPECS(T) \ + DEFINE_CPU_MINMAX_SPECS_INDEX(T, int32); \ + DEFINE_CPU_MINMAX_SPECS_INDEX(T, int64); + +TF_CALL_NUMBER_TYPES(DEFINE_CPU_SUMPROD_SPECS) +TF_CALL_REAL_NUMBER_TYPES(DEFINE_CPU_MINMAX_SPECS) + +#undef DEFINE_CPU_SUMPROD_SPECS_INDEX +#undef DEFINE_CPU_MINMAX_SPECS_INDEX +#undef DEFINE_CPU_SUMPROD_SPECS +#undef DEFINE_CPU_MINMAX_SPECS + +} // namespace functor + +template <typename Device, typename T, typename Index, + template <typename Device2, typename T2, typename Index2> + class Functor> +class ReduceSliceKernel : public OpKernel { + public: + explicit ReduceSliceKernel(OpKernelConstruction* context) + : OpKernel(context) {} + + void Compute(OpKernelContext* context) override { + const Tensor& data = context->input(0); + const Tensor& indices = context->input(1); + const Tensor& _axis = context->input(2); + int64 axis = _axis.scalar<int64>()(); + + int indices_width = 2; + int out_axis_dim_size = indices.shape().dim_size(0); + if (indices.dims() == 1 || indices.shape().dim_size(1) == 1) { + indices_width = 1; + if (out_axis_dim_size > 0) { + out_axis_dim_size--; + } + } + + TensorShape output_shape = data.shape(); + output_shape.set_dim(axis, out_axis_dim_size); + Tensor* output = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output)); + auto functor = Functor<Device, T, Index>(); + functor(context, context->eigen_device<Device>(), indices_width, + indices.flat<Index>(), data.flat_inner_outer_dims<T, 3>(axis - 1), + output->flat_inner_outer_dims<T, 3>(axis - 1)); + } +}; + +#define REGISTER_CPU_SUMPROD_REDUCE_SLICE_KERNELS(type, index_type) \ + REGISTER_KERNEL_BUILDER(Name("ReduceSliceSum") \ + .Device(DEVICE_CPU) \ + .TypeConstraint<type>("T") \ + .TypeConstraint<index_type>("Tindices"), \ + ReduceSliceKernel<CPUDevice, type, index_type, \ + functor::ReduceSliceFunctorSum>); \ + REGISTER_KERNEL_BUILDER(Name("ReduceSliceProd") \ + .Device(DEVICE_CPU) \ + .TypeConstraint<type>("T") \ + .TypeConstraint<index_type>("Tindices"), \ + ReduceSliceKernel<CPUDevice, type, index_type, \ + functor::ReduceSliceFunctorProd>); + +#define REGISTER_CPU_MINMAX_REDUCE_SLICE_KERNELS(type, index_type) \ + REGISTER_KERNEL_BUILDER(Name("ReduceSliceMax") \ + .Device(DEVICE_CPU) \ + .TypeConstraint<type>("T") \ + .TypeConstraint<index_type>("Tindices"), \ + ReduceSliceKernel<CPUDevice, type, index_type, \ + functor::ReduceSliceFunctorMax>); \ + REGISTER_KERNEL_BUILDER(Name("ReduceSliceMin") \ + .Device(DEVICE_CPU) \ + .TypeConstraint<type>("T") \ + .TypeConstraint<index_type>("Tindices"), \ + ReduceSliceKernel<CPUDevice, type, index_type, \ + functor::ReduceSliceFunctorMin>); + +#define REGISTER_CPU_SUMPROD_REDUCE_SLICE_KERNELS_ALL(type) \ + REGISTER_CPU_SUMPROD_REDUCE_SLICE_KERNELS(type, int32); \ + REGISTER_CPU_SUMPROD_REDUCE_SLICE_KERNELS(type, int64); + +#define REGISTER_CPU_MINMAX_REDUCE_SLICE_KERNELS_ALL(type) \ + REGISTER_CPU_MINMAX_REDUCE_SLICE_KERNELS(type, int32); \ + REGISTER_CPU_MINMAX_REDUCE_SLICE_KERNELS(type, int64); + +TF_CALL_REAL_NUMBER_TYPES(REGISTER_CPU_MINMAX_REDUCE_SLICE_KERNELS_ALL) +TF_CALL_NUMBER_TYPES(REGISTER_CPU_SUMPROD_REDUCE_SLICE_KERNELS_ALL) + +#undef REGISTER_CPU_SUMPROD_REDUCE_SLICE_KERNELS +#undef REGISTER_CPU_MINMAX_REDUCE_SLICE_KERNELS +#undef REGISTER_CPU_SUMPROD_REDUCE_SLICE_KERNELS_ALL +#undef REGISTER_CPU_MINMAX_REDUCE_SLICE_KERNELS_ALL + +#if GOOGLE_CUDA + +#define REGISTER_GPU_REDUCE_SLICE_KERNELS(type, index_type) \ + REGISTER_KERNEL_BUILDER(Name("ReduceSliceSum") \ + .Device(DEVICE_GPU) \ + .HostMemory("axis") \ + .TypeConstraint<type>("T") \ + .TypeConstraint<index_type>("Tindices"), \ + ReduceSliceKernel<GPUDevice, type, index_type, \ + functor::ReduceSliceFunctorSum>); \ + REGISTER_KERNEL_BUILDER(Name("ReduceSliceProd") \ + .Device(DEVICE_GPU) \ + .HostMemory("axis") \ + .TypeConstraint<type>("T") \ + .TypeConstraint<index_type>("Tindices"), \ + ReduceSliceKernel<GPUDevice, type, index_type, \ + functor::ReduceSliceFunctorProd>); \ + REGISTER_KERNEL_BUILDER(Name("ReduceSliceMax") \ + .Device(DEVICE_GPU) \ + .HostMemory("axis") \ + .TypeConstraint<type>("T") \ + .TypeConstraint<index_type>("Tindices"), \ + ReduceSliceKernel<GPUDevice, type, index_type, \ + functor::ReduceSliceFunctorMax>); \ + REGISTER_KERNEL_BUILDER(Name("ReduceSliceMin") \ + .Device(DEVICE_GPU) \ + .HostMemory("axis") \ + .TypeConstraint<type>("T") \ + .TypeConstraint<index_type>("Tindices"), \ + ReduceSliceKernel<GPUDevice, type, index_type, \ + functor::ReduceSliceFunctorMin>); + +#define REGISTER_GPU_REDUCE_SLICE_KERNELS_ALL(type) \ + REGISTER_GPU_REDUCE_SLICE_KERNELS(type, int32); \ + REGISTER_GPU_REDUCE_SLICE_KERNELS(type, int64); + +TF_CALL_REAL_NUMBER_TYPES(REGISTER_GPU_REDUCE_SLICE_KERNELS_ALL); + +#undef REGISTER_GPU_REDUCE_SLICE_KERNELS +#undef REGISTER_GPU_REDUCE_SLICE_KERNELS_ALL + +#endif // GOOGLE_CUDA + +} // namespace tensorflow diff --git a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h new file mode 100644 index 0000000000..c62a7b20d6 --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h @@ -0,0 +1,84 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_PARTIAL_REDUCTION_OPS_H_ +#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_PARTIAL_REDUCTION_OPS_H_ + +#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/tensor_shape.h" +#include "tensorflow/core/framework/tensor_types.h" + +#define Sum(a, b) ((a) + (b)) +#define Prod(a, b) ((a) * (b)) +#define Max(a, b) ((a) > (b) ? (a) : (b)) +#define Min(a, b) ((a) < (b) ? (a) : (b)) + +namespace tensorflow { + +class OpKernelContext; + +namespace functor { + +namespace reduce_functions { + +template <typename T> +inline T zero() { + return T(0); +} + +template <typename T> +inline T one() { + return T(1); +} + +template <typename T> +inline T infinity() { + return std::max<T>(std::numeric_limits<T>::max(), + std::numeric_limits<T>::infinity()); +} + +template <typename T> +inline T negative_infinity() { + return std::min<T>(-std::numeric_limits<T>::infinity(), + std::numeric_limits<T>::min()); +} + +} // namespace reduce_functions + +#define CALL_ALL_REDUCEOPS(func, ...) \ + func(Sum, functor::reduce_functions::zero, ##__VA_ARGS__) \ + func(Prod, functor::reduce_functions::one, ##__VA_ARGS__) func( \ + Max, functor::reduce_functions::negative_infinity, ##__VA_ARGS__) \ + func(Min, functor::reduce_functions::infinity, ##__VA_ARGS__) + +#define ReduceSliceFunctorReduceop(reduceop, dummy) \ + template <typename Device, typename T, typename Index> \ + struct ReduceSliceFunctor##reduceop { \ + virtual ~ReduceSliceFunctor##reduceop() {} \ + virtual void operator()(OpKernelContext* ctx, const Device& d, \ + Index indices_width, \ + typename TTypes<Index, 1>::ConstTensor indices, \ + typename TTypes<T, 3>::ConstTensor data, \ + typename TTypes<T, 3>::Tensor output); \ + }; + +CALL_ALL_REDUCEOPS(ReduceSliceFunctorReduceop) +#undef ReduceSliceFunctorReduceop + +} // namespace functor +} // namespace tensorflow + +#endif // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_PARTIAL_REDUCTION_OPS_H_ diff --git a/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc new file mode 100644 index 0000000000..8b205f7dd5 --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops_gpu.cu.cc @@ -0,0 +1,100 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/contrib/reduce_slice_ops/kernels/reduce_slice_ops.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/register_types.h" +#include "tensorflow/core/util/cuda_kernel_helper.h" + +namespace tensorflow { + +using GPUDevice = Eigen::GpuDevice; + +namespace functor { + +#define GPUReduceSliceFunctorReduceop(reduceop, beginning) \ + template <typename T, typename Index> \ + __global__ void ReduceSliceDeviceKernel##reduceop( \ + Cuda3DLaunchConfig config, Index indices_width, Index bound, \ + const T begin, const Index *indices, const T *input, T *out) { \ + CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count, x) { \ + CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count, y) { \ + CUDA_AXIS_KERNEL_LOOP(z, config.virtual_thread_count, z) { \ + Index outidx = x * config.virtual_thread_count.y * \ + config.virtual_thread_count.z + \ + y * config.virtual_thread_count.z + z; \ + out[outidx] = begin; \ + Index start = indices[y * indices_width]; \ + Index end = Min(bound, indices[y * indices_width + 1]); \ + for (Index yin = start; yin < end; yin++) { \ + Index inidx = x * bound * config.virtual_thread_count.z + \ + yin * config.virtual_thread_count.z + z; \ + out[outidx] = reduceop(out[outidx], input[inidx]); \ + } \ + } \ + } \ + } \ + } \ + \ + template <typename T, typename Index> \ + struct ReduceSliceFunctor##reduceop<GPUDevice, T, Index> { \ + virtual ~ReduceSliceFunctor##reduceop() {} \ + virtual void operator()(OpKernelContext *ctx, const GPUDevice &d, \ + Index indices_width, \ + typename TTypes<Index, 1>::ConstTensor indices, \ + typename TTypes<T, 3>::ConstTensor data, \ + typename TTypes<T, 3>::Tensor output) { \ + Index bound = data.dimension(1); \ + int sizex = output.dimension(0); \ + int sizey = output.dimension(1); \ + int sizez = output.dimension(2); \ + if (sizex * sizey * sizez == 0) { \ + return; \ + } \ + Cuda3DLaunchConfig config = GetCuda3DLaunchConfig( \ + sizex, sizey, sizez, d, ReduceSliceDeviceKernel##reduceop<T, Index>, \ + 0, 0); \ + \ + ReduceSliceDeviceKernel##reduceop<T, Index> \ + <<<config.block_count, config.thread_per_block, 0, d.stream()>>>( \ + config, indices_width, bound, beginning<T>(), indices.data(), \ + data.data(), output.data()); \ + } \ + }; + +CALL_ALL_REDUCEOPS(GPUReduceSliceFunctorReduceop) +#undef GPUReduceSliceFunctorReduceop + +#define DEFINE_GPU_REDUCEOP_SPECS_INDEX(reduceop, dummy, T) \ + template struct ReduceSliceFunctor##reduceop<GPUDevice, T, int32>; \ + template struct ReduceSliceFunctor##reduceop<GPUDevice, T, int64>; + +#define DEFINE_GPU_SPECS(T) \ + CALL_ALL_REDUCEOPS(DEFINE_GPU_REDUCEOP_SPECS_INDEX, T) + +TF_CALL_REAL_NUMBER_TYPES(DEFINE_GPU_SPECS) + +#undef DEFINE_GPU_REDUCEOP_SPECS_INDEX +#undef DEFINE_GPU_SPECS + +} // namespace functor +} // namespace tensorflow + +#endif diff --git a/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc new file mode 100644 index 0000000000..b8b56c0e22 --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops.cc @@ -0,0 +1,282 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +using shape_inference::DimensionHandle; +using shape_inference::InferenceContext; +using shape_inference::ShapeHandle; + +namespace { + +Status ReduceSliceShapeFn(InferenceContext* c) { + ShapeHandle handle; + DimensionHandle dimhandle; + DimensionHandle dim_axis = c->UnknownDim(); + // "axis" must be a scala + TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &handle)); + // "data" must have rank at least 1 + TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &handle)); + // "indices" must have have rank 1 or rank 2 with the number of columns must + // be 2 + if (c->RankKnown(c->input(1))) { + TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &handle)); + TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(1), 2, &handle)); + if (c->Rank(c->input(1)) == 1) { + // if "indices" is a vector of 0 elements, then the axis dimension of + // output tensor should be of dimension 0. + DimensionHandle raw_dim_axis; + TF_RETURN_IF_ERROR(c->Max(c->Dim(c->input(1), 0), 1, &raw_dim_axis)); + TF_RETURN_IF_ERROR(c->Subtract(raw_dim_axis, 1, &dim_axis)); + } else { // c->Rank(c->input(1)) == 2 + TF_RETURN_IF_ERROR( + c->Merge(c->Dim(c->input(1), 1), c->MakeDim(2), &dimhandle)); + dim_axis = c->Dim(c->input(1), 0); + } + } + // shape of output tensor + const Tensor* _axis = c->input_tensor(2); + if (nullptr == _axis) { + c->set_output(0, c->UnknownShapeOfRank(c->Rank(c->input(0)))); + } else { + int64 axis = _axis->scalar<int64>()(); + TF_RETURN_IF_ERROR(c->ReplaceDim(handle, axis, dim_axis, &handle)); + c->set_output(0, handle); + } + return Status::OK(); +} + +} // namespace + +REGISTER_OP("ReduceSliceSum") + .Input("data: T") + .Input("indices: Tindices") + .Input("axis: int64") + .Output("output: T") + .Attr("T: numbertype") + .Attr("Tindices: {int32,int64}") + .SetShapeFn(ReduceSliceShapeFn) + .Doc(R"doc( +Dynamically sum over the first dimension of a tensor according to start and end +indices specified at 'index'. + +For example: + +```prettyprint +# if 'data' is [[ 1, 2, 3] + [ 40, 50, 60] + [ 700, 800, 900] + [1000,2000,3000]], + +and 'indices' is [[0,1] + [1,1] + [0,2]], + +the the output will be [[ 1, 2, 3] + [ 0, 0, 0] + [41,52,63]]. +``` + +The data must be at least rank 1. The indices must be of shape (?,2) where the +first column is start indices and the second column is end indices. The end indices +are not included in the reduce operation, which means, if you want to do a reduce +over indices 0,1,2, then you should have start index 0 and end index 3. If end +index is smaller than or equal to start, the result will be zero. If end index is +out of bounds, then the reduce operation will automatically stop at the bound, so +feel free to put a large number as your end of your index if you want to do the +reduction until the bound. + +data: The source of data where the computation will be taken from. +indices: start, end indices that controls which part to be included. +T: the type of data. +Tindices: the type of indices, must be int32 or int64. +output: the computed sum values. +)doc"); + +REGISTER_OP("ReduceSliceProd") + .Input("data: T") + .Input("indices: Tindices") + .Input("axis: int64") + .Output("output: T") + .Attr("T: numbertype") + .Attr("Tindices: {int32,int64}") + .SetShapeFn(ReduceSliceShapeFn) + .Doc(R"doc( +Dynamically compute the product over the first dimension of a tensor according +to start and end indices specified at 'indices'. + +For example: + +```prettyprint +# if 'data' is [[ 1, 2, 3] + [ 40, 50, 60] + [ 700, 800, 900] + [1000,2000,3000]], + +and 'indices' is [[0,1] + [1,1] + [0,2]], + +the the output will be [[ 1, 2, 3] + [ 1, 1, 1] + [40,100,180]]. +``` + +The data must be at least rank 1. The indices can be of shape (?,2) where the +first column is start indices and the second column is end indices. The end indices +are not included in the reduce operation, which means, if you want to do a reduce +over indices 0,1,2, then you should have start index 0 and end index 3. If end +index is smaller than or equal to start, the result will be 1. If end index is +out of bounds, then the reduce operation will automatically stop at the bound, so +feel free to put a large number as your end of your index if you want to do the +reduction until the bound. The indices can also be of shape (?), in this case, the +start index of i will be the element at i, then end index of i will be the element +at i+1. That is: + +```prettyprint +indices = [0,5,11,115] + +is equivalent to + +indices = [ [0,5], + [5,11], + [11,115]] +``` + +data: The source of data where the computation will be taken from. +indices: start, end indices that controls which part to be included. +T: the type of data. +Tindices: the type of indices, must be int32 or int64. +output: the computed product values. +)doc"); + +REGISTER_OP("ReduceSliceMax") + .Input("data: T") + .Input("indices: Tindices") + .Input("axis: int64") + .Output("output: T") + .Attr("T: numbertype") + .Attr("Tindices: {int32,int64}") + .SetShapeFn(ReduceSliceShapeFn) + .Doc(R"doc( +Dynamically compute the maximum over the first dimension of a tensor according +to start and end indices specified at "indices". + +For example: + +```prettyprint +# if 'data' is [[ 1, 20, 3] + [ 400, 5, 60] + [ 70, 8, 900] + [1000,2000,3000]], + +and 'indices' is [[0,1] + [1,1] + [0,2]], + +the the output will be [[ 1, 20, 3] + [ -BIG_VALUE, -BIG_VALUE, -BIG_VALUE] + [ 400, 20, 60]]. +``` + +The data must be at least rank 1. The indices can be of shape (?,2) where the +first column is start indices and the second column is end indices. The end indices +are not included in the reduce operation, which means, if you want to do a reduce +over indices 0,1,2, then you should have start index 0 and end index 3. If end +index is smaller than or equal to start, the result will be 1. If end index is +out of bounds, then the reduce operation will automatically stop at the bound, so +feel free to put a large number as your end of your index if you want to do the +reduction until the bound. The indices can also be of shape (?), in this case, the +start index of i will be the element at i, then end index of i will be the element +at i+1. That is: + +```prettyprint +indices = [0,5,11,115] + +is equivalent to + +indices = [ [0,5], + [5,11], + [11,115]] +``` + +data: The source of data where the computation will be taken from. +indices: start, end indices that controls which part to be included. +T: the type of data. +Tindices: the type of indices, must be int32 or int64. +output: the computed product values. +)doc"); + +REGISTER_OP("ReduceSliceMin") + .Input("data: T") + .Input("indices: Tindices") + .Input("axis: int64") + .Output("output: T") + .Attr("T: numbertype") + .Attr("Tindices: {int32,int64}") + .SetShapeFn(ReduceSliceShapeFn) + .Doc(R"doc( +Dynamically compute the minimum over the first dimension of a tensor according +to start and end indices specified at 'indices'. + +For example: + +```prettyprint +# if 'data' is [[ 1, 20, 3] + [ 400, 5, 60] + [ 70, 8, 900] + [1000,2000,3000]], + +and 'indices' is [[0,1] + [1,1] + [0,2]], + +the the output will be [[ 1, 20, 3] + [ +BIG_VALUE, +BIG_VALUE, +BIG_VALUE] + [ 1, 5, 3]]. +``` + +The data must be at least rank 1. The indices can be of shape (?,2) where the +first column is start indices and the second column is end indices. The end indices +are not included in the reduce operation, which means, if you want to do a reduce +over indices 0,1,2, then you should have start index 0 and end index 3. If end +index is smaller than or equal to start, the result will be 1. If end index is +out of bounds, then the reduce operation will automatically stop at the bound, so +feel free to put a large number as your end of your index if you want to do the +reduction until the bound. The indices can also be of shape (?), in this case, the +start index of i will be the element at i, then end index of i will be the element +at i+1. That is: + +```prettyprint +indices = [0,5,11,115] + +is equivalent to + +indices = [ [0,5], + [5,11], + [11,115]] +``` + +data: The source of data where the computation will be taken from. +indices: start, end indices that controls which part to be included. +T: the type of data. +Tindices: the type of indices, must be int32 or int64. +output: the computed product values. +)doc"); + +} // namespace tensorflow diff --git a/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops_test.cc b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops_test.cc new file mode 100644 index 0000000000..777ad9bf15 --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/ops/reduce_slice_ops_test.cc @@ -0,0 +1,41 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); + +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/shape_inference_testutil.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { + +TEST(ReduceSliceOpsTest, ReduceSliceSum_ShapeFn) { + ShapeInferenceTestOp op("ReduceSliceSum"); + INFER_OK(op, "?;?;?", "?"); + INFER_OK(op, "[10,20];[100,2];[]", "[?,?]"); + INFER_OK(op, "[10,20];[?,2];[]", "[?,?]"); + INFER_OK(op, "[10,20];[0];[]", "[?,?]"); + INFER_OK(op, "[10,20];[1];[]", "[?,?]"); + INFER_OK(op, "[10,20];[?];[]", "[?,?]"); + INFER_OK(op, "[?,?];[?,2];[]", "[?,?]"); + INFER_OK(op, "[?,?];[25,2];[]", "[?,?]"); + INFER_OK(op, "[?];[123,2];[]", "[?]"); + INFER_OK(op, "[1,2,3,4];[100,2];[]", "[?,?,?,?]"); + + INFER_ERROR("must be rank 0", op, "?;[?,2];[?]"); + INFER_ERROR("must be at least rank 1", op, "?;[];[]"); + INFER_ERROR("must be at most rank 2", op, "?;[1,2,3];[]"); + INFER_ERROR("must be equal, but are 1 and 2", op, "?;[?,1];[]"); + INFER_ERROR("must be at least rank 1", op, "[];?;[]"); +} + +} // end namespace tensorflow diff --git a/tensorflow/contrib/reduce_slice_ops/python/kernel_tests/reduce_slice_ops_test.py b/tensorflow/contrib/reduce_slice_ops/python/kernel_tests/reduce_slice_ops_test.py new file mode 100644 index 0000000000..8c8db295ff --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/python/kernel_tests/reduce_slice_ops_test.py @@ -0,0 +1,158 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for tensorflow.contrib.reduce_slice_ops.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import unittest + +from tensorflow.contrib.reduce_slice_ops.python.ops import reduce_slice_ops +from tensorflow.python.framework.test_util import TensorFlowTestCase +from tensorflow.python.platform import googletest + + +class ReduceSliceTest(TensorFlowTestCase): + + def testReduceSliceSum1D(self): + x = np.array([1, 40, 700], dtype=np.int32) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.array([1, 741, 40, 740, 41], dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceSum2D(self): + x = np.array([[1, 2, 3], [40, 50, 60], [700, 800, 900]], dtype=np.int32) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.array( + [[1, 2, 3], [741, 852, 963], [40, 50, 60], [740, 850, 960], + [41, 52, 63]], + dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceSum3D(self): + x = np.array( + [[[1, 2], [3, 4]], [[50, 60], [70, 80]], [[600, 700], [800, 900]]], + dtype=np.int32) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.array( + [[[1, 2], [3, 4]], [[651, 762], [873, 984]], [[50, 60], [70, 80]], + [[650, 760], [870, 980]], [[51, 62], [73, 84]]], + dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceSumAxis1(self): + x = np.transpose( + np.array([[1, 2, 3], [40, 50, 60], [700, 800, 900]], dtype=np.int32)) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.transpose( + np.array( + [[1, 2, 3], [741, 852, 963], [40, 50, 60], [740, 850, 960], + [41, 52, 63]], + dtype=np.int32)) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 1).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceSum1DIndices(self): + x = np.array( + [[1, 2, 3], [40, 50, 60], [700, 800, 900], [1000, 2000, 3000], + [40000, 50000, 60000]], + dtype=np.int32) + indices = np.array([0, 0, 2, 5], dtype=np.int32) + result = np.array( + [[0, 0, 0], [41, 52, 63], [41700, 52800, 63900]], dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceProd(self): + x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.array( + [[1, 2, 3], [28, 80, 162], [4, 5, 6], [28, 40, 54], [4, 10, 18]], + dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_prod(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceMax(self): + x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.array( + [[1, 2, 3], [7, 8, 9], [4, 5, 6], [7, 8, 9], [4, 5, 6]], dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_max(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceMin(self): + x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.array( + [[1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6], [1, 2, 3]], dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_min(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceEmptyDataRows(self): + x = np.empty((0, 1, 2, 3, 4, 5, 6), dtype=np.int32) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.zeros((5, 1, 2, 3, 4, 5, 6), dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceEmptyDataCols(self): + x = np.empty((100, 0, 2, 3, 4, 5, 6), dtype=np.int32) + indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32) + result = np.empty((5, 0, 2, 3, 4, 5, 6), dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceEmptyIndicesRows(self): + x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) + indices = np.empty((0, 2), dtype=np.int32) + result = np.empty((0, 3), dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceEmpty0Indices1D(self): + x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) + indices = np.empty((0,), dtype=np.int32) + result = np.empty((0, 3), dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + def testReduceSliceEmpty1Indices1D(self): + x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32) + indices = np.array([0], dtype=np.int32) + result = np.empty((0, 3), dtype=np.int32) + with self.test_session(use_gpu=True): + y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval() + self.assertAllEqual(y_tf, result) + + +if __name__ == "__main__": + googletest.main() diff --git a/tensorflow/contrib/reduce_slice_ops/python/ops/reduce_slice_ops.py b/tensorflow/contrib/reduce_slice_ops/python/ops/reduce_slice_ops.py new file mode 100644 index 0000000000..d0f02489bd --- /dev/null +++ b/tensorflow/contrib/reduce_slice_ops/python/ops/reduce_slice_ops.py @@ -0,0 +1,30 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Python wrapper for the reduce slice operators.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.util import loader +from tensorflow.python.platform import resource_loader + +_reduce_slice_ops = loader.load_op_library( + resource_loader.get_path_to_datafile("_reduce_slice_ops.so")) + +reduce_slice_sum = _reduce_slice_ops.reduce_slice_sum +reduce_slice_prod = _reduce_slice_ops.reduce_slice_prod +reduce_slice_max = _reduce_slice_ops.reduce_slice_max +reduce_slice_min = _reduce_slice_ops.reduce_slice_min diff --git a/tensorflow/contrib/session_bundle/example/export_half_plus_two.py b/tensorflow/contrib/session_bundle/example/export_half_plus_two.py index 4a56509e59..83e91d390f 100644 --- a/tensorflow/contrib/session_bundle/example/export_half_plus_two.py +++ b/tensorflow/contrib/session_bundle/example/export_half_plus_two.py @@ -152,11 +152,10 @@ if __name__ == "__main__": ) parser.add_argument( "--use_checkpoint_v2", - "bool", + type="bool", nargs="?", const=True, default=False, - help="If true, write v2 checkpoint files." - ) + help="If true, write v2 checkpoint files.") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/contrib/slim/python/slim/data/parallel_reader.py b/tensorflow/contrib/slim/python/slim/data/parallel_reader.py index e97f500572..ad5e985487 100644 --- a/tensorflow/contrib/slim/python/slim/data/parallel_reader.py +++ b/tensorflow/contrib/slim/python/slim/data/parallel_reader.py @@ -127,6 +127,36 @@ class ParallelReader(io_ops.ReaderBase): The next record (i.e. (key, value pair)) from the common_queue. """ + self._configure_readers_by(queue) + return self._common_queue.dequeue(name=name) + + def read_up_to(self, queue, num_records, name=None): + """Returns up to num_records (key, value pairs) produced by a reader. + + Will dequeue a work unit from queue if necessary (e.g., when the + Reader needs to start reading from a new file since it has + finished with the previous file). + It may return less than num_records even before the last batch. + + **Note** This operation is not supported by all types of `common_queue`s. + If a `common_queue` does not support `dequeue_up_to()`, then a + `tf.errors.UnimplementedError` is raised. + + Args: + queue: A Queue or a mutable string Tensor representing a handle + to a Queue, with string work items. + num_records: Number of records to read. + name: A name for the operation (optional). + + Returns: + A tuple of Tensors (keys, values) from common_queue. + keys: A 1-D string Tensor. + values: A 1-D string Tensor. + """ + self._configure_readers_by(queue) + return self._common_queue.dequeue_up_to(num_records, name) + + def _configure_readers_by(self, queue): enqueue_ops = [] for reader in self._readers: enqueue_ops.append(self._common_queue.enqueue(reader.read(queue))) @@ -134,8 +164,6 @@ class ParallelReader(io_ops.ReaderBase): queue_runner.add_queue_runner( queue_runner.QueueRunner(self._common_queue, enqueue_ops)) - return self._common_queue.dequeue(name=name) - def num_records_produced(self, name=None): """Returns the number of records this reader has produced. diff --git a/tensorflow/contrib/slim/python/slim/data/parallel_reader_test.py b/tensorflow/contrib/slim/python/slim/data/parallel_reader_test.py index a46e4b00f9..10ea883e1f 100644 --- a/tensorflow/contrib/slim/python/slim/data/parallel_reader_test.py +++ b/tensorflow/contrib/slim/python/slim/data/parallel_reader_test.py @@ -24,6 +24,7 @@ from tensorflow.contrib.slim.python.slim.data import test_utils from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import io_ops from tensorflow.python.ops import variables @@ -74,6 +75,54 @@ class ParallelReaderTest(test.TestCase): self.assertGreater(count2, 0) self.assertEquals(count0 + count1 + count2, num_reads) + def _verify_read_up_to_out(self, shared_queue): + with self.test_session(): + num_files = 3 + num_records_per_file = 7 + tfrecord_paths = test_utils.create_tfrecord_files( + self.get_temp_dir(), + num_files=num_files, + num_records_per_file=num_records_per_file) + + p_reader = parallel_reader.ParallelReader( + io_ops.TFRecordReader, shared_queue, num_readers=5) + + data_files = parallel_reader.get_data_files(tfrecord_paths) + filename_queue = input_lib.string_input_producer(data_files, num_epochs=1) + key, value = p_reader.read_up_to(filename_queue, 4) + + count0 = 0 + count1 = 0 + count2 = 0 + all_keys_count = 0 + all_values_count = 0 + + sv = supervisor.Supervisor(logdir=self.get_temp_dir()) + with sv.prepare_or_wait_for_session() as sess: + sv.start_queue_runners(sess) + while True: + try: + current_keys, current_values = sess.run([key, value]) + self.assertEquals(len(current_keys), len(current_values)) + all_keys_count += len(current_keys) + all_values_count += len(current_values) + for current_key in current_keys: + if '0-of-3' in str(current_key): + count0 += 1 + if '1-of-3' in str(current_key): + count1 += 1 + if '2-of-3' in str(current_key): + count2 += 1 + except errors_impl.OutOfRangeError: + break + + self.assertEquals(count0, num_records_per_file) + self.assertEquals(count1, num_records_per_file) + self.assertEquals(count2, num_records_per_file) + self.assertEquals(all_keys_count, num_files * num_records_per_file) + self.assertEquals(all_values_count, all_keys_count) + self.assertEquals(count0 + count1 + count2, all_keys_count) + def testRandomShuffleQueue(self): shared_queue = data_flow_ops.RandomShuffleQueue( capacity=256, @@ -86,6 +135,23 @@ class ParallelReaderTest(test.TestCase): capacity=256, dtypes=[dtypes_lib.string, dtypes_lib.string]) self._verify_all_data_sources_read(shared_queue) + def testReadUpToFromRandomShuffleQueue(self): + shared_queue = data_flow_ops.RandomShuffleQueue( + capacity=55, + min_after_dequeue=28, + dtypes=[dtypes_lib.string, dtypes_lib.string], + shapes=[tensor_shape.scalar(), + tensor_shape.scalar()]) + self._verify_read_up_to_out(shared_queue) + + def testReadUpToFromFIFOQueue(self): + shared_queue = data_flow_ops.FIFOQueue( + capacity=99, + dtypes=[dtypes_lib.string, dtypes_lib.string], + shapes=[tensor_shape.scalar(), + tensor_shape.scalar()]) + self._verify_read_up_to_out(shared_queue) + class ParallelReadTest(test.TestCase): diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 1f7eb87f18..6b51d3bc80 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2192,6 +2192,7 @@ tf_cc_test( srcs = ["platform/setround_test.cc"], tags = [ "noasan", + "noclang", "nomsan", "notsan", ], diff --git a/tensorflow/core/framework/types.cc b/tensorflow/core/framework/types.cc index 39dd5b435e..1a5fd10f52 100644 --- a/tensorflow/core/framework/types.cc +++ b/tensorflow/core/framework/types.cc @@ -39,6 +39,14 @@ const char* const DEVICE_CPU = "CPU"; const char* const DEVICE_GPU = "GPU"; const char* const DEVICE_SYCL = "SYCL"; +const std::string DeviceName<Eigen::ThreadPoolDevice>::value = DEVICE_CPU; +#if GOOGLE_CUDA +const std::string DeviceName<Eigen::GpuDevice>::value = DEVICE_GPU; +#endif // GOOGLE_CUDA +#ifdef TENSORFLOW_USE_SYCL +const std::string DeviceName<Eigen::SyclDevice>::value = DEVICE_SYCL; +#endif // TENSORFLOW_USE_SYCL + string DataTypeString(DataType dtype) { if (IsRefType(dtype)) { DataType non_ref = static_cast<DataType>(dtype - kDataTypeRefOffset); diff --git a/tensorflow/core/framework/types.h b/tensorflow/core/framework/types.h index 9127750d68..3b4362bcc9 100644 --- a/tensorflow/core/framework/types.h +++ b/tensorflow/core/framework/types.h @@ -74,6 +74,28 @@ TF_EXPORT extern const char* const DEVICE_CPU; // "CPU" TF_EXPORT extern const char* const DEVICE_GPU; // "GPU" TF_EXPORT extern const char* const DEVICE_SYCL; // "SYCL" +template <typename Device> +struct DeviceName {}; + +template <> +struct DeviceName<Eigen::ThreadPoolDevice> { + static const std::string value; +}; + +#if GOOGLE_CUDA +template <> +struct DeviceName<Eigen::GpuDevice> { + static const std::string value; +}; +#endif // GOOGLE_CUDA + +#ifdef TENSORFLOW_USE_SYCL +template <> +struct DeviceName<Eigen::SyclDevice> { + static const std::string value; +}; +#endif // TENSORFLOW_USE_SYCL + typedef gtl::InlinedVector<MemoryType, 4> MemoryTypeVector; typedef gtl::ArraySlice<MemoryType> MemoryTypeSlice; diff --git a/tensorflow/core/kernels/cwise_op_arg.cc b/tensorflow/core/kernels/cwise_op_arg.cc new file mode 100644 index 0000000000..62ffa0718f --- /dev/null +++ b/tensorflow/core/kernels/cwise_op_arg.cc @@ -0,0 +1,37 @@ +/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/kernels/cwise_ops_common.h" + +namespace tensorflow { +#define REGISTER_COMPLEX(D, R, C) \ + REGISTER_KERNEL_BUILDER(Name("Angle") \ + .Device(DEVICE_##D) \ + .TypeConstraint<C>("T") \ + .TypeConstraint<R>("Tout"), \ + UnaryOp<D##Device, functor::get_angle<C>>); + +REGISTER_COMPLEX(CPU, float, complex64); +REGISTER_COMPLEX(CPU, double, complex128); + +// TODO: Enable GPU support for angle op after resolving +// build failures on GPU (See #10643 for context). +#if 0 && GOOGLE_CUDA +REGISTER_COMPLEX(GPU, float, complex64); +REGISTER_COMPLEX(GPU, double, complex128); +#endif + +#undef REGISTER_COMPLEX +} // namespace tensorflow diff --git a/tensorflow/core/kernels/cwise_op_gpu_arg.cu.cc b/tensorflow/core/kernels/cwise_op_gpu_arg.cu.cc new file mode 100644 index 0000000000..9b3f8200bd --- /dev/null +++ b/tensorflow/core/kernels/cwise_op_gpu_arg.cu.cc @@ -0,0 +1,28 @@ +/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// TODO: Enable GPU support for angle op after resolving +// build failures on GPU (See #10643 for context). +#if 0 && GOOGLE_CUDA + +#include "tensorflow/core/kernels/cwise_ops_gpu_common.cu.h" + +namespace tensorflow { +namespace functor { +DEFINE_UNARY2(get_angle, complex64, complex128); +} // namespace functor +} // namespace tensorflow + +#endif // GOOGLE_CUDA diff --git a/tensorflow/core/kernels/cwise_ops.h b/tensorflow/core/kernels/cwise_ops.h index 65a60720dd..d935331904 100644 --- a/tensorflow/core/kernels/cwise_ops.h +++ b/tensorflow/core/kernels/cwise_ops.h @@ -831,6 +831,10 @@ struct get_imag : base<T, Eigen::internal::scalar_imag_op<T>, typename T::value_type> {}; template <typename T> +struct get_angle + : base<T, Eigen::internal::scalar_arg_op<T>, typename T::value_type> {}; + +template <typename T> struct conj : base<T, Eigen::internal::scalar_conjugate_op<T>> {}; //////////////////////////////////////////////////////////////////////////////// diff --git a/tensorflow/core/kernels/string_split_op.cc b/tensorflow/core/kernels/string_split_op.cc index d7b804daeb..9efbd66ef7 100644 --- a/tensorflow/core/kernels/string_split_op.cc +++ b/tensorflow/core/kernels/string_split_op.cc @@ -28,9 +28,13 @@ namespace tensorflow { namespace { -std::vector<string> Split(const string& str, const string& delimiter) { +std::vector<string> Split(const string& str, const string& delimiter, + const bool skipEmpty) { if (!delimiter.empty()) { - return str_util::Split(str, delimiter, str_util::SkipEmpty()); + if (skipEmpty) { + return str_util::Split(str, delimiter, str_util::SkipEmpty()); + } + return str_util::Split(str, delimiter); } std::vector<string> char_vector(str.size()); for (size_t i = 0; i < str.size(); ++i) { @@ -43,7 +47,15 @@ std::vector<string> Split(const string& str, const string& delimiter) { class StringSplitOp : public OpKernel { public: - using OpKernel::OpKernel; + explicit StringSplitOp(OpKernelConstruction* context) + : OpKernel(context), skip_empty_(true) { + bool skip_empty; + // By default skip_empty_ is true. We only get the value from attr if it is + // available, so that it is backward compatible. + if (context->GetAttr("skip_empty", &skip_empty).ok()) { + skip_empty_ = skip_empty; + } + } void Compute(OpKernelContext* ctx) override { const Tensor* input_tensor; @@ -73,7 +85,7 @@ class StringSplitOp : public OpKernel { int64 max_num_entries = 0; std::vector<int64> num_indices(batch_size); for (int64 i = 0; i < batch_size; ++i) { - std::vector<string> parts = Split(input_vec(i), delimiter); + std::vector<string> parts = Split(input_vec(i), delimiter, skip_empty_); int64 n_entries = parts.size(); num_indices[i] = n_entries; output_size += n_entries; @@ -105,6 +117,9 @@ class StringSplitOp : public OpKernel { } } } + + private: + bool skip_empty_; }; REGISTER_KERNEL_BUILDER(Name("StringSplit").Device(DEVICE_CPU), StringSplitOp); diff --git a/tensorflow/core/ops/math_grad.cc b/tensorflow/core/ops/math_grad.cc index 5e082ce8f5..1290d3103e 100644 --- a/tensorflow/core/ops/math_grad.cc +++ b/tensorflow/core/ops/math_grad.cc @@ -349,6 +349,20 @@ Status ImagGrad(const AttrSlice& attrs, FunctionDef* g) { } REGISTER_OP_GRADIENT("Imag", ImagGrad); +Status AngleGrad(const AttrSlice& attrs, FunctionDef* g) { + // clang-format off + return GradForUnaryCwise(g, { + {{"re"}, "Real", {"x"}}, + {{"im"}, "Imag", {"x"}}, + {{"z"}, "Complex", {"im", "re"}}, + {{"z_inv"}, "Reciprocal", {"z"}}, + {{"neg"}, "Neg", {"z_inv"}}, + {{"dx"}, "Mul", {"neg", "dy"}}, + }); + // clang-format on +} +REGISTER_OP_GRADIENT("Angle", AngleGrad); + Status ConjGrad(const AttrSlice& attrs, FunctionDef* g) { // clang-format off return GradForUnaryCwise(g, { diff --git a/tensorflow/core/ops/math_grad_test.cc b/tensorflow/core/ops/math_grad_test.cc index 1393bffb91..2b4b35547b 100644 --- a/tensorflow/core/ops/math_grad_test.cc +++ b/tensorflow/core/ops/math_grad_test.cc @@ -612,6 +612,7 @@ TEST_F(MathGradTest, Cos) { // TODO(zhifengc) // TEST_F(MathGradSComplexTest, Real) {} // TEST_F(MathGradSComplexTest, Imag) {} +// TEST_F(MathGradSComplexTest, Angle) {} // TEST_F(MathGradSComplexTest, Conj) {} // TEST_F(MathGradTernary, Select) {} diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index 36f999ff60..6ff05bd2a6 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -2065,6 +2065,34 @@ tf.imag(input) ==> [4.75, 5.75] ``` )doc"); +REGISTER_OP("Angle") + .Input("input: T") + .Output("output: Tout") + .Attr("T: {complex64, complex128} = DT_COMPLEX64") + .Attr("Tout: {float, double} = DT_FLOAT") + .SetShapeFn(shape_inference::UnchangedShape) + .Doc(R"doc( +Returns the argument of a complex number. + +Given a tensor `input` of complex numbers, this operation returns a tensor of +type `float` that is the argument of each element in `input`. All elements in +`input` must be complex numbers of the form \\(a + bj\\), where *a* +is the real part and *b* is the imaginary part. + +The argument returned by this operation is of the form \\(atan2(b, a)\\). + +For example: + +``` +# tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +tf.angle(input) ==> [2.0132, 1.056] +``` + +@compatibility(numpy) +Equivalent to np.angle. +@end_compatibility +)doc"); + REGISTER_OP("Conj") .Input("input: T") .Output("output: T") diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index b68d05a8e8..5419301992 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -593,6 +593,45 @@ op { is_stateful: true } op { + name: "Angle" + input_arg { + name: "input" + type_attr: "T" + } + output_arg { + name: "output" + type_attr: "Tout" + } + attr { + name: "T" + type: "type" + default_value { + type: DT_COMPLEX64 + } + allowed_values { + list { + type: DT_COMPLEX64 + type: DT_COMPLEX128 + } + } + } + attr { + name: "Tout" + type: "type" + default_value { + type: DT_FLOAT + } + allowed_values { + list { + type: DT_FLOAT + type: DT_DOUBLE + } + } + } + summary: "Returns the argument of a complex number." + description: "Given a tensor `input` of complex numbers, this operation returns a tensor of\ntype `float` that is the argument of each element in `input`. All elements in\n`input` must be complex numbers of the form \\(a + bj\\), where *a*\nis the real part and *b* is the imaginary part.\n\nThe argument returned by this operation is of the form \\(atan2(b, a)\\).\n\nFor example:\n```\n # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]\ntf.angle(input) ==> [2.0132, 1.056]\n```" +} +op { name: "Any" input_arg { name: "input" diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc index d6fe8ec342..5e99187d50 100644 --- a/tensorflow/core/ops/string_ops.cc +++ b/tensorflow/core/ops/string_ops.cc @@ -202,6 +202,7 @@ REGISTER_OP("StringSplit") .Output("indices: int64") .Output("values: string") .Output("shape: int64") + .Attr("skip_empty: bool = true") .SetShapeFn([](InferenceContext* c) { ShapeHandle unused; TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused)); @@ -238,6 +239,7 @@ For example: input: 1-D. Strings to split. delimiter: 0-D. Delimiter characters (bytes), or empty string. +skip_empty: A `bool`. If `True`, skip the empty strings from the result. indices: A dense matrix of int64 representing the indices of the sparse tensor. values: A vector of strings corresponding to the splited values. shape: a length-2 vector of int64 representing the shape of the sparse diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 2fefa67d7d..ccb861c93a 100644 --- a/tensorflow/core/public/version.h +++ b/tensorflow/core/public/version.h @@ -24,7 +24,7 @@ limitations under the License. // TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1", // "-beta", "-rc", "-rc.1") -#define TF_VERSION_SUFFIX "-rc2" +#define TF_VERSION_SUFFIX "" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/docs_src/api_guides/python/math_ops.md b/tensorflow/docs_src/api_guides/python/math_ops.md index b3c7a0c010..dee7f1618a 100644 --- a/tensorflow/docs_src/api_guides/python/math_ops.md +++ b/tensorflow/docs_src/api_guides/python/math_ops.md @@ -122,6 +122,7 @@ functions to your graph. * @{tf.complex} * @{tf.conj} * @{tf.imag} +* @{tf.angle} * @{tf.real} diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index ec37311624..7ebf5c4a2c 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -35,7 +35,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for Mac OS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.3.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.3.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index b7dc033efc..b991fd0f93 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.3.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.3.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index f9b7b322ca..2adcd4da73 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: <dependency> <groupId>org.tensorflow</groupId> <artifactId>tensorflow</artifactId> - <version>1.3.0-rc2</version> + <version>1.3.0</version> </dependency> ``` @@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: <dependency> <groupId>org.tensorflow</groupId> <artifactId>tensorflow</artifactId> - <version>1.3.0-rc2</version> + <version>1.3.0</version> </dependency> </dependencies> </project> @@ -122,7 +122,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or Mac OS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.3.0-rc2.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.3.0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.3.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.3.0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.3.0-rc2.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.3.0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.3.0-rc2.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.3.0.zip). 3. Extract this .zip file. @@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -<pre><b>javac -cp libtensorflow-1.3.0-rc2.jar HelloTF.java</b></pre> +<pre><b>javac -cp libtensorflow-1.3.0.jar HelloTF.java</b></pre> ### Running @@ -214,11 +214,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and Mac OS X: -<pre><b>java -cp libtensorflow-1.3.0-rc2.jar:. -Djava.library.path=./jni HelloTF</b></pre> +<pre><b>java -cp libtensorflow-1.3.0.jar:. -Djava.library.path=./jni HelloTF</b></pre> And the following command line executes the `HelloTF` program on Windows: -<pre><b>java -cp libtensorflow-1.3.0-rc2.jar;. -Djava.library.path=jni HelloTF</b></pre> +<pre><b>java -cp libtensorflow-1.3.0.jar;. -Djava.library.path=jni HelloTF</b></pre> If the program prints <tt>Hello from <i>version</i></tt>, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 85182cc74f..a961c6b1ce 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -172,7 +172,7 @@ Take the following steps to install TensorFlow with Virtualenv: virtualenv environment: <pre>(tensorflow)$ <b>pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre> + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl</b></pre> If you encounter installation problems, see [Common Installation Problems](#common_installation_problems). @@ -277,7 +277,7 @@ take the following steps: <pre> $ <b>sudo pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc2-cp34-cp34m-linux_x86_64.whl</b> + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl</b> </pre> If this step fails, see @@ -464,7 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \ - https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre> + https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl</b></pre> <a name="ValidateYourInstallation"></a> @@ -632,14 +632,14 @@ This section documents the relevant values for Linux installations. CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc2-cp27-none-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp27-none-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0rc2-cp27-none-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp27-none-linux_x86_64.whl </pre> Note that GPU support requires the NVIDIA hardware and software described in @@ -651,14 +651,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc2-cp34-cp34m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0rc2-cp34-cp34m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp34-cp34m-linux_x86_64.whl </pre> Note that GPU support requires the NVIDIA hardware and software described in @@ -670,14 +670,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc2-cp35-cp35m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0rc2-cp35-cp35m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp35-cp35m-linux_x86_64.whl </pre> @@ -689,14 +689,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: <pre> -https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0rc2-cp36-cp36m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp36-cp36m-linux_x86_64.whl </pre> GPU support: <pre> -https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0rc2-cp36-cp36m-linux_x86_64.whl +https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.3.0-cp36-cp36m-linux_x86_64.whl </pre> diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 733ecc37fb..6bae3c03d1 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -109,7 +109,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: <pre> $ <b>pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc2-py2-none-any.whl</b></pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0-py2-none-any.whl</b></pre> If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -230,7 +230,7 @@ take the following steps: issue the following command: <pre> $ <b>sudo pip3 install --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc2-py2-none-any.whl</b> </pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0-py2-none-any.whl</b> </pre> If the preceding command fails, see [installation problems](#common-installation-problems). @@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \ - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc2-py2-none-any.whl</b></pre> + https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0-py2-none-any.whl</b></pre> <a name="ValidateYourInstallation"></a> @@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations. <pre> -https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc2-py2-none-any.whl +https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0-py2-none-any.whl </pre> @@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc2-py2-none-a <pre> -https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0rc2-py3-none-any.whl +https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0-py3-none-any.whl </pre> diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index a69f982d76..810a27d1ab 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -343,10 +343,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.3.0rc2 on Linux: +for TensorFlow 1.3.0 on Linux: <pre> -$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.3.0rc2-py2-none-any.whl</b> +$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.3.0-py2-none-any.whl</b> </pre> ## Validate your installation diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index a9d7dd955a..a06ab88046 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -115,12 +115,12 @@ Take the following steps to install TensorFlow in an Anaconda environment: environment. To install the CPU-only version of TensorFlow, enter the following command: - <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.3.0rc2-cp35-cp35m-win_amd64.whl</b> </pre> + <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.3.0-cp35-cp35m-win_amd64.whl</b> </pre> To install the GPU version of TensorFlow, enter the following command (on a single line): - <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.3.0rc2-cp35-cp35m-win_amd64.whl</b> </pre> + <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.3.0-cp35-cp35m-win_amd64.whl</b> </pre> ## Validate your installation diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index e464d98a15..4dcd88befa 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -9981,6 +9981,52 @@ func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf return op.Output(0) } +// ArgAttr is an optional argument to Arg. +type ArgAttr func(optionalAttr) + +// ArgTout sets the optional Tout attribute to value. +// If not specified, defaults to DT_FLOAT +func ArgTout(value tf.DataType) ArgAttr { + return func(m optionalAttr) { + m["Tout"] = value + } +} + +// Returns the argument of a complex number. +// +// Given a tensor `input` of complex numbers, this operation returns a tensor of +// type `float` that is the argument of each element in `input`. All elements in +// `input` must be complex numbers of the form \\(a + bj\\), where *a* +// is the real part and *b* is the imaginary part. +// +// +// The argument returned by this operation is of the form \\(atan2(b, a)\\). +// +// For example: +// +// ``` +// # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] +// tf.angle(input) ==> [2.0132, 1.056] +// ``` +func Angle(scope *Scope, input tf.Output, optional ...ArgAttr) (output tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "Angle", + Input: []tf.Input{ + input, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes fingerprints of the input strings. // // Arguments: diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 42fdd72053..40589528f2 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -4038,6 +4038,7 @@ cuda_py_test( "//third_party/py/numpy", "//tensorflow/core:protos_all_py", ], + tags = ["manual"], ) py_library( diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py index b4f0fd6f40..32c738f0f1 100644 --- a/tensorflow/python/client/session_test.py +++ b/tensorflow/python/client/session_test.py @@ -1701,43 +1701,6 @@ class SessionTest(test_util.TensorFlowTestCase): server = server_lib.Server.create_local_server() self.runTestBuildGraphError(session.Session(server.target)) - def testGraphOptimizer(self): - rewrite_options = rewriter_config_pb2.RewriterConfig( - disable_model_pruning=False, constant_folding=True) - graph_options = config_pb2.GraphOptions( - rewrite_options=rewrite_options, build_cost_model=1) - config = config_pb2.ConfigProto(graph_options=graph_options) - - with ops.Graph().as_default() as g: - r1 = random_ops.random_normal(shape=[2, 3], name='R1') - r2 = random_ops.random_normal(shape=[2, 3], name='R2') - copy1 = array_ops.stop_gradient(r1) - copy2 = array_ops.identity(r2) - result = copy1 + copy2 - - with session.Session(graph=g, config=config) as sess: - metadata = config_pb2.RunMetadata() - sess.run(result, run_metadata=metadata) - - # Check that we optimized the graph by looking at the cost model: the add - # node should have been reconnected directly to the R1 and R2 nodes. - found_valid_nodes = 0 - for node in metadata.cost_graph.node: - if node.name == 'R1': - r1_cost_id = node.id - found_valid_nodes += 1 - if node.name == 'R2': - r2_cost_id = node.id - found_valid_nodes += 1 - if node.name == 'add': - if node.input_info[0].preceding_node == r1_cost_id: - self.assertEqual(node.input_info[1].preceding_node, r2_cost_id) - found_valid_nodes += 1 - elif node.input_info[0].preceding_node == r2_cost_id: - self.assertEqual(node.input_info[1].preceding_node, r1_cost_id) - found_valid_nodes += 1 - self.assertEqual(3, found_valid_nodes) - def testDeviceAttributes(self): attrs = session._DeviceAttributes( '/job:worker/replica:0/task:3/device:CPU:2', 'TYPE', 1337) diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions.py b/tensorflow/python/estimator/inputs/queues/feeding_functions.py index 847b27b904..149425436a 100644 --- a/tensorflow/python/estimator/inputs/queues/feeding_functions.py +++ b/tensorflow/python/estimator/inputs/queues/feeding_functions.py @@ -46,6 +46,64 @@ except ImportError: HAS_PANDAS = False +def _fill_array(arr, seq, fillvalue=0): + """Recursively fills padded arr with elements from seq. + + If lenght of seq is less then arr padded length, fillvalue used. + Args: + arr: Padded tensor of shape [batch_size, ..., max_padded_dim_len]. + seq: Non-padded list of data sampels of shape + [batch_size, ..., padded_dim(None)] + fillvalue: Default fillvalue to use. + """ + if arr.ndim == 1: + try: + len_ = len(seq) + except TypeError: + len_ = 0 + arr[:len_] = seq + arr[len_:] = fillvalue + else: + for subarr, subseq in six.moves.zip_longest(arr, seq, fillvalue=()): + _fill_array(subarr, subseq, fillvalue) + + +def _pad_if_needed(batch_key_item, fillvalue=0): + """ Returns padded batch. + + Args: + batch_key_item: List of data samples of any type with shape + [batch_size, ..., padded_dim(None)]. + fillvalue: Default fillvalue to use. + + Returns: + Padded with zeros tensor of same type and shape + [batch_size, ..., max_padded_dim_len]. + + Raises: + ValueError if data samples have different shapes (except last padded dim). + """ + shapes = [ + seq.shape[:-1] if len(seq.shape) > 0 else -1 for seq in batch_key_item + ] + if not all(shapes[0] == x for x in shapes): + raise ValueError("Array shapes must match.") + + last_length = [ + seq.shape[-1] if len(seq.shape) > 0 else 0 for seq in batch_key_item + ] + if all([x == last_length[0] for x in last_length]): + return batch_key_item + + batch_size = len(batch_key_item) + max_sequence_length = max(last_length) + result_batch = np.zeros( + shape=[batch_size] + list(shapes[0]) + [max_sequence_length], + dtype=batch_key_item[0].dtype) + _fill_array(result_batch, batch_key_item, fillvalue) + return result_batch + + def _get_integer_indices_for_next_batch( batch_indices_start, batch_size, epoch_end, array_length, current_epoch, total_epochs): @@ -229,7 +287,8 @@ class _GeneratorFeedFn(object): batch_size, random_start=False, seed=None, - num_epochs=None): + num_epochs=None, + pad_value=None): first_sample = next(generator()) if len(placeholders) != len(first_sample): raise ValueError("Expected {} placeholders; got {}.".format( @@ -241,6 +300,7 @@ class _GeneratorFeedFn(object): self._batch_size = batch_size self._num_epochs = num_epochs self._epoch = 0 + self._pad_value = pad_value random.seed(seed) def __call__(self): @@ -264,7 +324,17 @@ class _GeneratorFeedFn(object): list_dict.setdefault(self._col_placeholders[index], list()).append(data_row[key]) list_dict_size += 1 - feed_dict = {key: np.asarray(item) for key, item in list(list_dict.items())} + + if self._pad_value is not None: + feed_dict = { + key: np.asarray(_pad_if_needed(item, self._pad_value)) + for key, item in list(list_dict.items()) + } + else: + feed_dict = { + key: np.asarray(item) + for key, item in list(list_dict.items()) + } return feed_dict @@ -276,7 +346,8 @@ def _enqueue_data(data, seed=None, name="enqueue_input", enqueue_size=1, - num_epochs=None): + num_epochs=None, + pad_value=None): """Creates a queue filled from a numpy array or pandas `DataFrame`. Returns a queue filled with the rows of the given (`OrderedDict` of) array @@ -298,6 +369,7 @@ def _enqueue_data(data, name: a scope name identifying the data. enqueue_size: the number of rows to enqueue per step. num_epochs: limit enqueuing to a specified number of epochs, if provided. + pad_value: default value for dynamic padding of data samples, if provided. Returns: A queue filled with the rows of the given (`OrderedDict` of) array or @@ -306,6 +378,8 @@ def _enqueue_data(data, Raises: TypeError: `data` is not a Pandas `DataFrame`, an `OrderedDict` of numpy arrays, a numpy `ndarray`, or a generator producing these. + NotImplementedError: padding and shuffling data at the same time. + NotImplementedError: padding usage with non generator data type. """ with ops.name_scope(name): if isinstance(data, np.ndarray): @@ -336,6 +410,14 @@ def _enqueue_data(data, "data must be either a numpy array or pandas DataFrame if pandas is " "installed; got {}".format(type(data).__name__)) + pad_data = pad_value is not None + if pad_data and get_feed_fn is not _GeneratorFeedFn: + raise NotImplementedError( + "padding is only available with generator usage") + if shuffle and pad_data: + raise NotImplementedError( + "padding and shuffling data at the same time is not implemented") + # TODO(jamieas): TensorBoard warnings for all warnings below once available. if num_threads > 1 and num_epochs is not None: @@ -368,6 +450,13 @@ def _enqueue_data(data, dtypes=types, shapes=queue_shapes, seed=seed) + elif pad_data: + min_after_dequeue = 0 # just for the summary text + queue_shapes = list( + map(lambda x: tuple(list(x[:-1]) + [None]) if len(x) > 0 else x, + queue_shapes)) + queue = data_flow_ops.PaddingFIFOQueue( + capacity, dtypes=types, shapes=queue_shapes) else: min_after_dequeue = 0 # just for the summary text queue = data_flow_ops.FIFOQueue( @@ -383,14 +472,26 @@ def _enqueue_data(data, enqueue_ops.append(queue.enqueue_many(placeholders)) seed_i = None if seed is None else (i + 1) * seed - feed_fns.append( - get_feed_fn( - placeholders, - data, - enqueue_size, - random_start=shuffle, - seed=seed_i, - num_epochs=num_epochs)) + + if not pad_data: + feed_fns.append( + get_feed_fn( + placeholders, + data, + enqueue_size, + random_start=shuffle, + seed=seed_i, + num_epochs=num_epochs)) + else: + feed_fns.append( + get_feed_fn( + placeholders, + data, + enqueue_size, + random_start=shuffle, + seed=seed_i, + num_epochs=num_epochs, + pad_value=pad_value)) runner = fqr._FeedingQueueRunner( # pylint: disable=protected-access queue=queue, enqueue_ops=enqueue_ops, feed_fns=feed_fns) diff --git a/tensorflow/python/estimator/inputs/queues/feeding_functions_test.py b/tensorflow/python/estimator/inputs/queues/feeding_functions_test.py index 0e602d3f33..3508f5aa3c 100644 --- a/tensorflow/python/estimator/inputs/queues/feeding_functions_test.py +++ b/tensorflow/python/estimator/inputs/queues/feeding_functions_test.py @@ -290,6 +290,102 @@ class _FeedingFunctionsTestCase(test.TestCase): actual = aff() self.assertEqual(expected, vals_to_list(actual)) + def testFillArraySmall(self): + a = (np.ones(shape=[32, 32], dtype=np.int32).tolist() + np.ones( + shape=[32, 36], dtype=np.int32).tolist()) + actual = np.ones(shape=[64, 36], dtype=np.int32) + ff._fill_array(actual, a) + expected = np.ones(shape=[64, 36], dtype=np.int32) + expected[:32, 32:] = 0 + self.assertEqual(expected.tolist(), actual.tolist()) + + def testFillArrayLarge(self): + a = (np.ones(shape=[8, 8, 8, 8, 32], dtype=np.int32).tolist() + np.ones( + shape=[8, 8, 8, 8, 36], dtype=np.int32).tolist()) + actual = np.ones(shape=[16, 8, 8, 8, 36], dtype=np.int32) + ff._fill_array(actual, a) + expected = np.ones(shape=[16, 8, 8, 8, 36], dtype=np.int32) + expected[:8, ..., 32:] = 0 + self.assertEqual(expected.tolist(), actual.tolist()) + + def testFillArraySmallWithSpecifiedValue(self): + fill_value = 8 + a = (np.ones(shape=[32, 32], dtype=np.int32).tolist() + np.ones( + shape=[32, 36], dtype=np.int32).tolist()) + actual = np.ones(shape=[64, 36], dtype=np.int32) + ff._fill_array(actual, a, fill_value) + expected = np.ones(shape=[64, 36], dtype=np.int32) + expected[:32, 32:] = fill_value + self.assertEqual(expected.tolist(), actual.tolist()) + + def testFillArrayLargeWithSpecifiedValue(self): + fill_value = 8 + a = (np.ones(shape=[8, 8, 8, 8, 32], dtype=np.int32).tolist() + np.ones( + shape=[8, 8, 8, 8, 36], dtype=np.int32).tolist()) + actual = np.ones(shape=[16, 8, 8, 8, 36], dtype=np.int32) + ff._fill_array(actual, a, fill_value) + expected = np.ones(shape=[16, 8, 8, 8, 36], dtype=np.int32) + expected[:8, ..., 32:] = fill_value + self.assertEqual(expected.tolist(), actual.tolist()) + + def testPadIfNeededSmall(self): + a = (np.ones(shape=[32, 32], dtype=np.int32).tolist() + np.ones( + shape=[32, 36], dtype=np.int32).tolist()) + a = list(map(np.array, a)) + actual = ff._pad_if_needed(a) + expected = np.ones(shape=[64, 36], dtype=np.int32) + expected[:32, 32:] = 0 + self.assertEqual(expected.tolist(), actual.tolist()) + + def testPadIfNeededLarge(self): + a = (np.ones(shape=[8, 8, 8, 8, 32], dtype=np.int32).tolist() + np.ones( + shape=[8, 8, 8, 8, 36], dtype=np.int32).tolist()) + a = list(map(np.array, a)) + actual = ff._pad_if_needed(a) + expected = np.ones(shape=[16, 8, 8, 8, 36], dtype=np.int32) + expected[:8, ..., 32:] = 0 + self.assertEqual(expected.tolist(), actual.tolist()) + + def testPadIfNeededSmallWithSpecifiedValue(self): + fill_value = 8 + a = (np.ones(shape=[32, 32], dtype=np.int32).tolist() + np.ones( + shape=[32, 36], dtype=np.int32).tolist()) + a = list(map(np.array, a)) + actual = ff._pad_if_needed(a, fill_value) + expected = np.ones(shape=[64, 36], dtype=np.int32) + expected[:32, 32:] = fill_value + self.assertEqual(expected.tolist(), actual.tolist()) + + def testPadIfNeededLargeWithSpecifiedValue(self): + fill_value = 8 + a = (np.ones(shape=[8, 8, 8, 8, 32], dtype=np.int32).tolist() + np.ones( + shape=[8, 8, 8, 8, 36], dtype=np.int32).tolist()) + a = list(map(np.array, a)) + actual = ff._pad_if_needed(a, fill_value) + expected = np.ones(shape=[16, 8, 8, 8, 36], dtype=np.int32) + expected[:8, ..., 32:] = fill_value + self.assertEqual(expected.tolist(), actual.tolist()) + + def testPadIfNeededSmallWithSpecifiedNonNumericValue(self): + fill_value = False + a = (np.ones(shape=[32, 32], dtype=np.bool).tolist() + np.ones( + shape=[32, 36], dtype=np.bool).tolist()) + a = list(map(np.array, a)) + actual = ff._pad_if_needed(a, fill_value) + expected = np.ones(shape=[64, 36], dtype=np.bool) + expected[:32, 32:] = fill_value + self.assertEqual(expected.tolist(), actual.tolist()) + + def testPadIfNeededLargeWithSpecifiedNonNumericValue(self): + fill_value = False + a = (np.ones(shape=[8, 8, 8, 8, 32], dtype=np.bool).tolist() + np.ones( + shape=[8, 8, 8, 8, 36], dtype=np.bool).tolist()) + a = list(map(np.array, a)) + actual = ff._pad_if_needed(a, fill_value) + expected = np.ones(shape=[16, 8, 8, 8, 36], dtype=np.bool) + expected[:8, ..., 32:] = fill_value + self.assertEqual(expected.tolist(), actual.tolist()) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py index d03a04b24e..95cd27ebad 100644 --- a/tensorflow/python/kernel_tests/cwise_ops_test.py +++ b/tensorflow/python/kernel_tests/cwise_ops_test.py @@ -71,9 +71,26 @@ def _sparsify(x, thresh=0.5, index_dtype=np.int64): indices=x_indices, values=x_values, dense_shape=x_shape), x_values +def _default_tolerance(dtype): + """Returns a sensible default tolerance for comparing results of a given + type""" + if dtype == np.float16: + return 5e-3 + elif dtype in (np.float32, np.complex64): + return 1e-3 + elif dtype in (np.float64, np.complex128): + return 1e-5 + else: + return None # Fail fast for unexpected types + + class UnaryOpTest(test.TestCase): - def _compareCpu(self, x, np_func, tf_func): + def _compareCpu(self, x, np_func, tf_func, grad_rtol=None, grad_atol=None): + if grad_rtol is None: + grad_rtol = _default_tolerance(x.dtype) + if grad_atol is None: + grad_atol = _default_tolerance(x.dtype) np_ans = np_func(x) with self.test_session(use_gpu=False): inx = ops.convert_to_tensor(x) @@ -102,17 +119,17 @@ class UnaryOpTest(test.TestCase): _, jacob_n = gradient_checker.compute_gradient( inxf, s, yf, s, x_init_value=xf, delta=1e-2) jacob_n = jacob_n.astype(np.float16) - self.assertAllClose(jacob_t, jacob_n, rtol=5e-3, atol=5e-3) + self.assertAllClose(jacob_t, jacob_n, rtol=grad_rtol, atol=grad_atol) elif x.dtype in (np.float32, np.complex64): s = list(np.shape(x)) jacob_t, jacob_n = gradient_checker.compute_gradient( inx, s, y, s, x_init_value=x, delta=1e-3) - self.assertAllClose(jacob_t, jacob_n, rtol=1e-3, atol=1e-3) + self.assertAllClose(jacob_t, jacob_n, rtol=grad_rtol, atol=grad_atol) elif x.dtype in (np.float64, np.complex128): s = list(np.shape(x)) jacob_t, jacob_n = gradient_checker.compute_gradient( inx, s, y, s, x_init_value=x, delta=1e-5) - self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5) + self.assertAllClose(jacob_t, jacob_n, rtol=grad_rtol, atol=grad_atol) def _check(self, result_tensor, result_np, input_sp_t, tol): self.assertTrue(isinstance(result_tensor, sparse_tensor.SparseTensor)) @@ -407,8 +424,13 @@ class UnaryOpTest(test.TestCase): self._compareCpu(x, np.sinh, math_ops.sinh) self._compareCpu(x, np.cosh, math_ops.cosh) self._compareCpu(x, np.tanh, math_ops.tanh) - self._compareCpu(y, np.arcsinh, math_ops.asinh) - self._compareCpu(y, np.arccosh, math_ops.acosh) + + # Complex64 versions of asinh() and acosh() in libstdc++ only have 6 digits + # of precision. + # Small gradient values + low precision --> High relative error + self._compareCpu(y, np.arcsinh, math_ops.asinh, grad_rtol=1e-2) + self._compareCpu(y, np.arccosh, math_ops.acosh, grad_rtol=1e-2) + self._compareCpu(y, np.arctanh, math_ops.atanh) self._compareCpu(x, self._sigmoid, math_ops.sigmoid) self._compareCpu(x, np.sin, math_ops.sin) @@ -1930,6 +1952,33 @@ class ComplexMakeRealImagTest(test.TestCase): self._compareRealImag(cplx, use_gpu=False) self._compareRealImag(cplx, use_gpu=True) + def _compareAngle(self, cplx, use_gpu): + np_angle = np.angle(cplx) + with self.test_session(use_gpu=use_gpu) as sess: + inx = ops.convert_to_tensor(cplx) + tf_angle = math_ops.angle(inx) + tf_angle_val = tf_angle.eval() + self.assertAllEqual(np_angle, tf_angle_val) + self.assertShapeEqual(np_angle, tf_angle) + + def testAngle64(self): + real = (np.arange(-3, 3) / 4.).reshape([1, 3, 2]).astype(np.float32) + imag = (np.arange(-3, 3) / 5.).reshape([1, 3, 2]).astype(np.float32) + cplx = real + 1j * imag + self._compareAngle(cplx, use_gpu=False) + # TODO: Enable GPU tests for angle op after resolving + # build failures on GPU (See #10643 for context). + # self._compareAngle(cplx, use_gpu=True) + + def testAngle(self): + real = (np.arange(-3, 3) / 4.).reshape([1, 3, 2]).astype(np.float64) + imag = (np.arange(-3, 3) / 5.).reshape([1, 3, 2]).astype(np.float64) + cplx = real + 1j * imag + self._compareAngle(cplx, use_gpu=False) + # TODO: Enable GPU tests for angle op after resolving + # build failures on GPU (See #10643 for context). + # self._compareAngle(cplx, use_gpu=True) + def testRealReal(self): for dtype in dtypes_lib.int32, dtypes_lib.int64, dtypes_lib.float32, dtypes_lib.float64: x = array_ops.placeholder(dtype) @@ -2062,6 +2111,28 @@ class AccumulateTest(test.TestCase): tf_val = math_ops.accumulate_n([]) tf_val.eval() + def testWrongShape(self): + with self.test_session(): + with self.assertRaises(ValueError): + a = variables.Variable(0.2) + b = variables.Variable(0.1) + tf_val = math_ops.accumulate_n( + [a, b], shape=[2, 2]) # Should be shape=[] + + def testWrongType(self): + with self.test_session(): + with self.assertRaises(TypeError): + a = variables.Variable(0.2, dtype=np.float32) + b = variables.Variable(0.1, dtype=np.float32) + tf_val = math_ops.accumulate_n([a, b], tensor_dtype=np.int32) + + def testWrongTypeOneInput(self): + # Scenario that used to trigger a bug, even when testWrongType() worked + with self.test_session(): + with self.assertRaises(TypeError): + a = variables.Variable(0.2, dtype=np.float32) + tf_val = math_ops.accumulate_n([a], tensor_dtype=np.int32) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/string_split_op_test.py b/tensorflow/python/kernel_tests/string_split_op_test.py index 60ba16c1ac..f3731bad38 100644 --- a/tensorflow/python/kernel_tests/string_split_op_test.py +++ b/tensorflow/python/kernel_tests/string_split_op_test.py @@ -126,6 +126,24 @@ class StringSplitOpTest(test.TestCase): [b"hello", b"cruel", b"world", b"hello cruel world"]) self.assertAllEqual(shape, [2, 3]) + def testStringSplitWithNoSkipEmpty(self): + strings = ["#a", "b#", "#c#"] + + with self.test_session() as sess: + tokens = string_ops.string_split(strings, "#", skip_empty=False) + indices, values, shape = sess.run(tokens) + self.assertAllEqual(indices, [[0, 0], [0, 1], [1, 0], [1, 1], [2, 0], + [2, 1], [2, 2]]) + self.assertAllEqual(values, [b"", b"a", b"b", b"", b"", b"c", b""]) + self.assertAllEqual(shape, [3, 3]) + + with self.test_session() as sess: + tokens = string_ops.string_split(strings, "#") + indices, values, shape = sess.run(tokens) + self.assertAllEqual(values, [b"a", b"b", b"c"]) + self.assertAllEqual(indices, [[0, 0], [1, 0], [2, 0]]) + self.assertAllEqual(shape, [3, 1]) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py index 8f88a9b30d..3d70465e68 100644 --- a/tensorflow/python/ops/math_grad.py +++ b/tensorflow/python/ops/math_grad.py @@ -1023,6 +1023,19 @@ def _ImagGrad(_, grad): return math_ops.complex(zero, grad) +@ops.RegisterGradient("Angle") +def _AngleGrad(op, grad): + """Returns -grad / (Im(x) + iRe(x))""" + x = op.inputs[0] + with ops.control_dependencies([grad.op]): + re = math_ops.real(x) + im = math_ops.imag(x) + z = math_ops.reciprocal(math_ops.complex(im, re)) + zero = constant_op.constant(0, dtype=grad.dtype) + complex_grad = math_ops.complex(grad, zero) + return -complex_grad * z + + @ops.RegisterGradient("Conj") def _ConjGrad(_, grad): """Returns the complex conjugate of grad.""" diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 7ee095745a..dc1a17829e 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -100,6 +100,7 @@ See the @{$python/math_ops} guide. @@complex @@conj @@imag +@@angle @@real @@fft @@ifft @@ -622,10 +623,9 @@ def imag(input, name=None): r"""Returns the imaginary part of a complex number. Given a tensor `input` of complex numbers, this operation returns a tensor of - type `float32` or `float64` that is the imaginary part of each element in - `input`. All elements in `input` must be complex numbers of the form \\(a + - bj\\), where *a* is the real part and *b* is the imaginary part returned by - this operation. + type `float` that is the argument of each element in `input`. All elements in + `input` must be complex numbers of the form \\(a + bj\\), where *a* + is the real part and *b* is the imaginary part returned by the operation. For example: @@ -646,6 +646,35 @@ def imag(input, name=None): return gen_math_ops.imag(input, Tout=input.dtype.real_dtype, name=name) +def angle(input, name=None): + r"""Returns the argument of a complex number. + + Given a tensor `input` of complex numbers, this operation returns a tensor of + type `float32` or `float64` that is the argument of each element in `input`. + All elements in `input` must be complex numbers of the form \\(a + bj\\), + where *a* is the real part and *b* is the imaginary part. + + The argument returned by this function is of the form \\(atan2(b, a)\\). + + For example: + + ``` + # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] + tf.angle(input) ==> [2.0132, 1.056] + ``` + + Args: + input: A `Tensor`. Must be one of the following types: `complex64`, + `complex128`. + name: A name for the operation (optional). + + Returns: + A `Tensor` of type `float32` or `float64`. + """ + with ops.name_scope(name, "Angle", [input]) as name: + return gen_math_ops.angle(input, Tout=input.dtype.real_dtype, name=name) + + # pylint: enable=redefined-outer-name,redefined-builtin @@ -2027,10 +2056,13 @@ def accumulate_n(inputs, shape=None, tensor_dtype=None, name=None): for input_tensor in inputs: if isinstance(input_tensor, ops.Tensor): shape = shape.merge_with(input_tensor.get_shape()) - if len(inputs) == 1: - return inputs[0] if tensor_dtype is None: tensor_dtype = inputs[0].dtype + if tensor_dtype != inputs[0].dtype: + raise TypeError("tensor_dtype is {}, but input is of type {}" + .format(tensor_dtype, inputs[0].dtype)) + if len(inputs) == 1: + return inputs[0] with ops.name_scope(name, "AccumulateN", inputs) as name: var = gen_state_ops._temporary_variable( shape=tensor_shape.vector(0), dtype=tensor_dtype) diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index 46a5792474..b36cfeb2eb 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -317,6 +317,21 @@ class AddNTest(test_util.TensorFlowTestCase): self.assertAllEqual(x[0] * num_inputs, math_ops.add_n([tf_x[0]] * num_inputs).eval()) + def testGrad(self): + np.random.seed(42) + for num_inputs in range(1, 10): + with self.test_session(use_gpu=True) as sess: + input_vars = [ + variables.Variable(10.0 * np.random.random()) + for i in range(0, num_inputs) + ] + addn = math_ops.add_n(input_vars) + sess.run(variables.global_variables_initializer()) + add_n_grad = gradients.gradients(addn, input_vars) + self.assertAllEqual( + np.repeat(1.0, num_inputs), # d/dx (x + y + ...) = 1 + [g.eval() for g in add_n_grad]) + class DivAndModTest(test_util.TensorFlowTestCase): # TODO(aselle): Test more types before exposing new division operators. diff --git a/tensorflow/python/ops/string_ops.py b/tensorflow/python/ops/string_ops.py index 97f2f761a6..f30e79a108 100644 --- a/tensorflow/python/ops/string_ops.py +++ b/tensorflow/python/ops/string_ops.py @@ -50,7 +50,7 @@ from tensorflow.python.util import deprecation # pylint: enable=wildcard-import -def string_split(source, delimiter=" "): # pylint: disable=invalid-name +def string_split(source, delimiter=" ", skip_empty=True): # pylint: disable=invalid-name """Split elements of `source` based on `delimiter` into a `SparseTensor`. Let N be the size of source (typically N will be the batch size). Split each @@ -78,6 +78,7 @@ def string_split(source, delimiter=" "): # pylint: disable=invalid-name source: `1-D` string `Tensor`, the strings to split. delimiter: `0-D` string `Tensor`, the delimiter character, the string should be length 0 or 1. + skip_empty: A `bool`. If `True`, skip the empty strings from the result. Raises: ValueError: If delimiter is not a string. @@ -92,7 +93,7 @@ def string_split(source, delimiter=" "): # pylint: disable=invalid-name # pylint: disable=protected-access indices, values, shape = gen_string_ops._string_split( - source, delimiter=delimiter) + source, delimiter=delimiter, skip_empty=skip_empty) # pylint: enable=protected-access indices.set_shape([None, 2]) values.set_shape([None]) diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py index e5484e02b5..1562f65675 100644 --- a/tensorflow/python/training/monitored_session.py +++ b/tensorflow/python/training/monitored_session.py @@ -566,6 +566,8 @@ class _MonitoredSession(object): h.end(self._coordinated_creator.tf_sess) finally: try: + if self._sess is None: + raise RuntimeError('Session is already closed.') self._sess.close() finally: self._sess = None diff --git a/tensorflow/python/training/monitored_session_test.py b/tensorflow/python/training/monitored_session_test.py index ffd9ed311f..a7c34cdd1b 100644 --- a/tensorflow/python/training/monitored_session_test.py +++ b/tensorflow/python/training/monitored_session_test.py @@ -1414,6 +1414,13 @@ class MonitoredSessionTest(test.TestCase): isinstance(hook.run_metadata_list[0], config_pb2.RunMetadata)) self.assertGreater(len(hook.run_metadata_list[0].partition_graphs), 0) + def test_with_statement_and_close(self): + # Test case for https://github.com/tensorflow/tensorflow/issues/12224 + # where close() inside the with should have a better error message. + with self.assertRaisesRegexp(RuntimeError, 'Session is already closed'): + with monitored_session.MonitoredSession() as session: + session.close() + class SingularMonitoredSessionTest(test.TestCase): """Tests SingularMonitoredSession.""" diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/tensorflow.pbtxt index f9733b4963..a7a044f64e 100644 --- a/tensorflow/tools/api/golden/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.pbtxt @@ -561,6 +561,10 @@ tf_module { argspec: "args=[], varargs=None, keywords=None, defaults=None" } member_method { + name: "angle" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { name: "arg_max" argspec: "args=[\'input\', \'dimension\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\"<dtype: \'int64\'>\", \'None\'], " } @@ -1878,7 +1882,7 @@ tf_module { } member_method { name: "string_split" - argspec: "args=[\'source\', \'delimiter\'], varargs=None, keywords=None, defaults=[\' \'], " + argspec: "args=[\'source\', \'delimiter\', \'skip_empty\'], varargs=None, keywords=None, defaults=[\' \', \'True\'], " } member_method { name: "string_to_hash_bucket" diff --git a/tensorflow/tools/dist_test/Dockerfile b/tensorflow/tools/dist_test/Dockerfile index 83bbeeca8a..cd64e2c518 100644 --- a/tensorflow/tools/dist_test/Dockerfile +++ b/tensorflow/tools/dist_test/Dockerfile @@ -26,7 +26,6 @@ RUN apt-get update RUN apt-get install -y \ curl \ python \ - python-numpy \ python-pip \ && \ apt-get clean && \ diff --git a/tensorflow/tools/dist_test/Dockerfile.local b/tensorflow/tools/dist_test/Dockerfile.local index 0cfb8d529e..7a896ab611 100644 --- a/tensorflow/tools/dist_test/Dockerfile.local +++ b/tensorflow/tools/dist_test/Dockerfile.local @@ -23,7 +23,6 @@ MAINTAINER Shanqing Cai <cais@google.com> # Pick up some TF dependencies. RUN apt-get update && apt-get install -y \ - python-numpy \ python-pip \ && \ apt-get clean && \ diff --git a/tensorflow/tools/gcs_test/Dockerfile b/tensorflow/tools/gcs_test/Dockerfile index 581bded65d..5af753226f 100644 --- a/tensorflow/tools/gcs_test/Dockerfile +++ b/tensorflow/tools/gcs_test/Dockerfile @@ -7,7 +7,6 @@ RUN apt-get install -y \ curl \ libcurl4-openssl-dev \ python \ - python-numpy \ python-pip # Install Google Cloud SDK diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index cdce308a50..f48fdcc9ec 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -54,11 +54,10 @@ function main() { while true; do if [[ "$1" == "--nightly_flag" ]]; then NIGHTLY_BUILD=1 - fi - if [[ "$1" == "--gpu" ]]; then + elif [[ "$1" == "--gpu" ]]; then GPU_BUILD=1 elif [[ "$1" == "--gpudirect" ]]; then - GPU_FLAG="--project_name tensorflow_gpudirect" + PKG_NAME_FLAG="--project_name tensorflow_gpudirect" fi shift diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 2b840c1a3a..1c45e9e5f6 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -29,11 +29,11 @@ from setuptools.dist import Distribution # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.3.0-rc2' +_VERSION = '1.3.0' REQUIRED_PACKAGES = [ 'enum34 >= 1.1.6', - 'numpy >= 1.11.0', + 'numpy >= 1.12.1', 'six >= 1.10.0', 'protobuf >= 3.3.0', 'tensorflow-tensorboard >= 0.1.0, < 0.2.0', @@ -55,6 +55,13 @@ else: # mock comes with unittest.mock for python3, need to install for python2 REQUIRED_PACKAGES.append('mock >= 2.0.0') +# remove tensorboard from tf-nightly packages +if 'tf_nightly' in project_name: + for package in REQUIRED_PACKAGES: + if 'tensorflow-tensorboard' in package: + REQUIRED_PACKAGES.remove(package) + break + # weakref.finalize was introduced in Python 3.4 if sys.version_info < (3, 4): REQUIRED_PACKAGES.append('backports.weakref >= 1.0rc1') @@ -70,6 +77,10 @@ CONSOLE_SCRIPTS = [ ] # pylint: enable=line-too-long +# remove the tensorboard console script if building tf_nightly +if 'tf_nightly' in project_name: + CONSOLE_SCRIPTS.remove('tensorboard = tensorboard.main:main') + TEST_PACKAGES = [ 'scipy >= 0.15.1', ] |