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END_PUBLIC
---
Commit 9f81374c3 authored by raymondxyang<zihao.yang@microsoft.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
Add option for build more python tests in Cmake (#11853)
* Ignore Windows built project
* Fix deprecated methods in tf.contrib.python
* Fix regex match for Windows build in contrib.keras
* Fix Regex match for Windows build in session_bundle
* * Fix deprecated methods
* Fix regex match for Windows
* Fix compatibility issue with Python 3.x
* Add missing ops into Windows build for test
* Enabled more testcases for Windows build
* Clean code and fix typo
* Add conditional cmake mode for enabling more unit testcase
* Add Cmake mode for major Contrib packages
* Add supplementary info in RAEDME for new cmake option
* * Update tf_tests after testing with TF 1.3
* Clean code and resolve conflicts
* Fix unsafe regex matches and format code
* Update exclude list after testing with latest master branch
* Fix missing module
---
Commit 98f0e1efe authored by Yong Tang<yong.tang.github@outlook.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
Dynamic ksize and strides with MaxPool (#11875)
* Dynamic ksize with max_pool
This fix tries to fix the issue raised in 4746 where ksize
is static (attr) with max_pool.
This fix changes ksize to input tensor so that it is dynamic now.
This fix fixes 4746.
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
* Add dynamic ksize to MaxPoolGrad and MaxPoolGradGrad
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
* Add test cases for max_pool_v2
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
* Fix GPU Jenkins issue.
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
* Enable MaxPoolV2 in GPU
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
* Hide MaxPoolV2 and other fixes.
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
Commit 02d6bc185 authored by Bairen Yi<byronyi@users.noreply.github.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
remove useless variable (#12212)
---
Commit ed6b0d905 authored by namrata-ibm<bhavenamrata@gmail.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
Adding support for s390x in calculation of cpu_frequency (#12201)
---
Commit 627dfc9dd authored by Taehoon Lee<taehoonlee@snu.ac.kr>
Committed by Taehoon Lee<taehoonlee@snu.ac.kr>:
Fix typos
---
Commit c0f9b0a91 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
In fast-math mode emit a tanh that has a faster min/max.
PiperOrigin-RevId: 164943597
---
Commit 87605f3d6 authored by Kay Zhu<kayzhu@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Use HloEvaluator for ComputeConstant, remove the need of a dedicated
compute constant backend.
PiperOrigin-RevId: 164940970
---
Commit 881de45c2 authored by Taehoon Lee<me@taehoonlee.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
Add bool type supports for GPU kernels (#11927)
* Add bool type supports for GPU kernels
* Add bool type test codes for GPU kernels
---
Commit eeacdcdb1 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add missing "CPU" suffix in registrations.
PiperOrigin-RevId: 164939527
---
Commit de01be952 authored by namrata-ibm<bhavenamrata@gmail.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
Adding support for Big Endian in graph_constructor_test and wav_io (#12179)
---
Commit 26719d29f authored by QingYing Chen<pkudysj@126.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
Implement CRF decode (Viterbi decode) for tensor (#12056)
* Implement CRF decoding for tensors
* add test code for tensor version's CRF decoding
* made modifications according to pylint
* add some comments for crf decode
* remove useless code
* add comments at the top comment of crf module and add more comments in crf_test
* capitalize first char of first word in comments
* replace crf_decode test code with a deterministic example
---
Commit f9a81ca2f authored by Pete Warden<pete@petewarden.com>
Committed by gunan<gunan@google.com>:
Create CI build script for Raspberry Pi (#12190)
* Create CI build script for Raspberry Pi
* Moved location of Pi build script
---
Commit e2a163a90 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Merge code from PR #11940 with internal changes from cl/164796436, and update Python tests to also run on GPU.
PiperOrigin-RevId: 164929133
---
Commit 08bbfa187 authored by Taehoon Lee<me@taehoonlee.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
Fix typos (#12195)
---
Commit ab96f41fb authored by Luke Iwanski<luke@codeplay.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
[OpenCL] Extends matmul_benchmark.py to cover SYCL (#11697)
* [OpenCL] Extends matmul_benchmark.py to cover SYCL
* Fixed typo
* /gpu:0 -> /device:GPU:0
* Fixes control_flow_ops_py_test
* /gpu: -> /device:GPU:
* Fixes //tensorflow/python/profiler/internal:run_metadata_test
* gpu: -> GPU:
* Fixes tfprof_node
* [OpenCL] Fixes device path to name with many colons (#123)
The device path is constructed from a device name by replacing all
colons with underscores. Some device names contain more than one colon,
for example 'device:SYCL:0' which gives a path 'device_SYCL_0'. The
previous code would not convert this back to the original device name,
but rather to 'device:SYCL_0'.
An alternative fix would be to convert all underscores to colons in the
device name (i.e. remove the restriction inside `replace("_", ":", 1)`),
however I'm not sure if there are any device names which contain
underscores.
* If no gpu device aviable fake one
* gpu: -> device:GPU
* Fixes profiler test
* /gpu:x -> /device:GPU:x
* Fixes debug_io_utils_test.cc test
* Fixes device_name_utils_test.cc
---
Commit 35e7a3665 authored by Yong Tang<yong.tang.github@outlook.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
Remove unneeded casting of int64 for reverse_sequence (#12192)
This fix remove unneeded cast of int64 for reverse_sequence:
```
lengths = math_ops.to_int64(lengths)
```
as int32 has already been enabled for reverse_sequence.
Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
---
Commit 9fba8c185 authored by Anna R<annarev@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add benchmark dashboard link to benchmarks doc. Also, I added a link and
description for Benchmarks page to Community index page.
PiperOrigin-RevId: 164924906
---
Commit bb6f32fa7 authored by Mark Heffernan<meheff@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make HloAliasAnalysis updatable after changes to the HLO graph.
As part of this change make HloAliasAnalysis a thinner layer which
basically only holds a map from HloValue to HloBuffer and vice versa.
PiperOrigin-RevId: 164923041
---
Commit 9103096c1 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by Thomas K?ppe<tkoeppe@google.com>:
Merged commit includes the following changes:
164923041 by meheff:
Make HloAliasAnalysis updatable after changes to the HLO graph.
As part of this change make HloAliasAnalysis a thinner layer which
basically only holds a map from HloValue to HloBuffer and vice versa.
--
PiperOrigin-RevId: 164923041
---
Commit 822603aed authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Merging sibling fusion instruction using multi_output_fusion
PiperOrigin-RevId: 164920220
---
Commit c035aa2a8 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: 164917891
---
Commit e1e81d9ba authored by Luke Iwanski<luke@codeplay.com>
Committed by Rasmus Munk Larsen<rmlarsen@google.com>:
[OpenCL] Fixes double memcpy bug (#151) (#12173)
* [OpenCL] Fixes double memcpy bug (#151)
As the debg CopyOp is called on a Tensor without type, we need to use
the DataType enum to get type information, and use this to pass the type
on to Eigen. This is a workaround Eigen's need to have a type when
calling memcpy. If the Eigen memcpy can be provided without a type
requirement, then the memcpy in sycl_util is unnecessary.
* Acts on feedback from: #12173/files/32cb12a9001b672425867b5a3110fd98e737a20b#r132496277
---
Commit d9ca2d86d authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Internal change
PiperOrigin-RevId: 164916465
---
Commit b8d13d218 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Remove more parts of DCASGD missed in the first pass. (47949b)
PiperOrigin-RevId: 164914552
---
Commit 73b3d52c7 authored by Alexandre Passos<apassos@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
cmake fix
PiperOrigin-RevId: 164911656
---
Commit 2173b5b0a authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Allow TFE_TensorHandleCopyToDevice to have the same device as src and
destination. It will reuse the same underlying buffer in those cases.
PiperOrigin-RevId: 164909906
---
Commit 13eb3b90e authored by Alexandre Passos<apassos@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Experimental C and Python APIs to invoke TensorFlow kernels on concrete values.
PiperOrigin-RevId: 164902588
---
Commit 7dfabcc01 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Initialize ExecutionOptions in ComputeConstant to default values.
PiperOrigin-RevId: 164894867
---
Commit c8897e9bc authored by Benoit Steiner<bsteiner@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Static required time computation
PiperOrigin-RevId: 164894645
---
Commit 076158f9b authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Enable implicit->explicit conversion by default.
PiperOrigin-RevId: 164890915
---
Commit 58c4a4cb1 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Bugfix: number of input channels is not necessarily in the last dimension, after introduction of data_format param.
PiperOrigin-RevId: 164889729
---
Commit 8f9b1af8a authored by Igor Saprykin<isaprykin@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Recover MonitoredSession when the Coordinator is requested to stop with one of the _PREEMPTION_ERRORS.
When SyncReplicasOptimizer is used, a preemption in the Coordinator may result in two cases:
Case 1) the session gets silently marked as complete
Case 2) the session gets stuck
This CL aims to solve and verify solutions for both of these problems. Fix 1 changes the should_stop logic. Fix 2 changes the CoordinatedSession.run() logic.
SyncReplicasOptimizer runs a separate set of threads using a Coordinator instance. Those threads do FIFOQueue.enqueue; the main thread does a blocking FIFOQueue.dequeue.
`sync_token_q` FIFOQueue is on parameter-servers. When one of the PS instances gets preempted, an AbortedError causes the Coordinator to stop via request_stop(ex). That by itself changes the state of MonitoredSession.should_stop() to True (Fix 1).
Results of the blocking Dequeue operation are sent to the chief worker via Recv. What happens next depends on the amount of tokens in `sync_token_q`. If there are enough for the next call to Dequeue to return, then the low-level "tf session run() call" returns. The next iteration of the `while not MonitoredSession.should_stop()` loop decides that the training is complete (Case 1).
If there are not enough tokens in `sync_token_q`, then the blocking Dequeue is going to keep waiting for them. This results in the graph execution getting stuck and the whole session getting garbage collected after 10 minutes (Case 2).
We decided to fix that by re-creating a session after it gets garbage collected (Fix 2). An alternative was to try to cancel the pending Dequeue operation, but it's not clear that it is the right thing to do and it is also not easy.
PiperOrigin-RevId: 164888390
---
Commit 46e4de6e5 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Undo loop fusion changes for now as they seem to be altering a few results.
END_PUBLIC
RELNOTES: n/a
BEGIN_PUBLIC
BEGIN_PUBLIC
Automated g4 rollback of changelist 164825735
PiperOrigin-RevId: 165340331
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END_PUBLIC
---
Commit d0f53f77f authored by Penghao Cen<scorpiocph@gmail.com>
Committed by Shanqing Cai<cais@google.com>:
Minor fix typo (#11323)
---
Commit 02fcf564e authored by Chris Song<sjhshy@gmail.com>
Committed by Chris Song<sjhshy@gmail.com>:
Fix misspells.
---
Commit 764c9b6b4 authored by Louis Tiao<ltiao@users.noreply.github.com>
Committed by GitHub<noreply@github.com>:
Fixed typo in docstring
---
Commit f8cd1283e authored by Shanqing Cai<cais@google.com>
Committed by Shanqing Cai<cais@google.com>:
Chaser
---
Commit 01383b946 authored by Shanqing Cai<cais@google.com>
Committed by Shanqing Cai<cais@google.com>:
Adapt TensorFlowTestCase.setUp() to new reset_default_graph() semantics
Avoid calling reset_default_graph() directly to prevent exceptions in
cases where test methods error out from within nested graph contexts,
which can leave _default_graph_stack non-empty in certain Python
versions.
---
Commit 0ffc37890 authored by Amit Patankar<amitpatankar@google.com>
Committed by Amit Patankar<amitpatankar@google.com>:
Removing second declaration of functions.
---
Commit f9c9cacb0 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Refactor ElementalIrEmitter's slice index finding code into
IrArray::Index::SourceIndexOfSlice().
PiperOrigin-RevId: 161140653
---
Commit ba297aec9 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Update ops-related pbtxt files.
PiperOrigin-RevId: 161138258
---
Commit 68d666737 authored by Alexandre Passos<apassos@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fixes a reentrant lock issue with tensors using ndarray memory which uses tensor memory.
PiperOrigin-RevId: 161137788
---
Commit a2ee8bca3 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add support for int8 x int8 -> int32 matrix multiplication via cublasGemmEx to stream_executor.
PiperOrigin-RevId: 161137741
---
Commit 755fa7b50 authored by Mark Daoust<markdaoust@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Block generate_test, and docs generating from running in python3.
- Doc generation is currently unsupported in python3
- These both end in errors in python 3.5.1+
PiperOrigin-RevId: 161137467
---
Commit 97cbcac45 authored by Peter Hawkins<phawkins@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Fix failure in functionalize_control_flow rewrite for Enter nodes that are unused. Make sure we ignore such nodes without producing an error.
PiperOrigin-RevId: 161136545
---
Commit dabcb60bc authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Add reasonable error messages to Builder::Build for bad parameter numbers.
PiperOrigin-RevId: 161136262
---
Commit 0cbd249e8 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add complex tensors support to `matrix_determinant`.
PiperOrigin-RevId: 161132422
---
Commit 335f1f14d authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Extend static shape inference for SparseTensors with dense_shapes constructed using slicing.
PiperOrigin-RevId: 161132391
---
Commit 53604916e authored by Jianwei Xie<xiejw@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Fixed the missing labels test in TPUEstimator.
PiperOrigin-RevId: 161131282
---
Commit 9f57dc8dd authored by Bruno Rosa<bruno.rosa@eldorado.org.br>
Committed by Bruno Rosa<bruno.rosa@eldorado.org.br>:
Use mcpu instead of march for ppc64le
march is not support by gcc on ppc64le
---
Commit 7d5c74a9c authored by Skye Wanderman-Milne<skyewm@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Move duplicate detection logic from Graph to FunctionLibraryDefinition
Turns out this is more useful, since there are many function libraries
that don't belong to a graph. This will be used in a future
change. Note that this maintains the current behavior of Graph.
In addition, updates FunctionDefsEqual() to handle unset attr entries
(I ran into this when using this in said future change).
PiperOrigin-RevId: 161126628
---
Commit 2caec3af1 authored by Shanqing Cai<cais@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Disable more timeseries py tests failing in OSS PIP GPU builds
PiperOrigin-RevId: 161124799
---
Commit 0b5cce367 authored by Eugene Brevdo<ebrevdo@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Get TopK op working on GPU again. Extend using cub's radix sort.
1. Undo rollback of Andreas Kirsch's initial implementation.
2. Use cub segmented radix sort if Andreas' heap-based impl
for large k and small num_cols (thresholds of k=100, n=1000
determined empirically).
3. Use cub segmented radix sort if k == num_cols (this case is always faster).
4. Added benchmarks.
Benchmarks show that the GPU implementation is up to 3x slower for small k but
can be 10x faster for large num_cols and k.
Benchmarks:
Benchmark: m_128_n_10_k_5_use_gpu_False wall_time: 0.000166 s Throughput: 0.0077 GB/s
Benchmark: m_128_n_10_k_5_use_gpu_True wall_time: 0.000796 s Throughput: 0.00161 GB/s
Benchmark: m_128_n_10_k_9_use_gpu_False wall_time: 0.00017 s Throughput: 0.00751 GB/s
Benchmark: m_128_n_10_k_9_use_gpu_True wall_time: 0.000796 s Throughput: 0.00161 GB/s
Benchmark: m_128_n_10_k_10_use_gpu_False wall_time: 0.00017 s Throughput: 0.00753 GB/s
Benchmark: m_128_n_10_k_10_use_gpu_True wall_time: 0.000775 s Throughput: 0.00165 GB/s
Benchmark: m_128_n_100_k_1_use_gpu_False wall_time: 0.000155 s Throughput: 0.0826 GB/s
Benchmark: m_128_n_100_k_1_use_gpu_True wall_time: 0.000796 s Throughput: 0.0161 GB/s
Benchmark: m_128_n_100_k_50_use_gpu_False wall_time: 0.000247 s Throughput: 0.0519 GB/s
Benchmark: m_128_n_100_k_50_use_gpu_True wall_time: 0.0008 s Throughput: 0.016 GB/s
Benchmark: m_128_n_100_k_99_use_gpu_False wall_time: 0.000261 s Throughput: 0.049 GB/s
Benchmark: m_128_n_100_k_99_use_gpu_True wall_time: 0.000794 s Throughput: 0.0161 GB/s
Benchmark: m_128_n_100_k_100_use_gpu_False wall_time: 0.000239 s Throughput: 0.0536 GB/s
Benchmark: m_128_n_100_k_100_use_gpu_True wall_time: 0.000777 s Throughput: 0.0165 GB/s
Benchmark: m_128_n_1000_k_1_use_gpu_False wall_time: 0.000324 s Throughput: 0.395 GB/s
Benchmark: m_128_n_1000_k_1_use_gpu_True wall_time: 0.000916 s Throughput: 0.14 GB/s
Benchmark: m_128_n_1000_k_10_use_gpu_False wall_time: 0.00042 s Throughput: 0.305 GB/s
Benchmark: m_128_n_1000_k_10_use_gpu_True wall_time: 0.000902 s Throughput: 0.142 GB/s
Benchmark: m_128_n_1000_k_500_use_gpu_False wall_time: 0.0011 s Throughput: 0.116 GB/s
Benchmark: m_128_n_1000_k_500_use_gpu_True wall_time: 0.00097 s Throughput: 0.132 GB/s
Benchmark: m_128_n_1000_k_990_use_gpu_False wall_time: 0.00133 s Throughput: 0.0962 GB/s
Benchmark: m_128_n_1000_k_990_use_gpu_True wall_time: 0.000993 s Throughput: 0.129 GB/s
Benchmark: m_128_n_1000_k_1000_use_gpu_False wall_time: 0.00102 s Throughput: 0.126 GB/s
Benchmark: m_128_n_1000_k_1000_use_gpu_True wall_time: 0.000964 s Throughput: 0.133 GB/s
Benchmark: m_128_n_10000_k_10_use_gpu_False wall_time: 0.002 s Throughput: 0.64 GB/s
Benchmark: m_128_n_10000_k_10_use_gpu_True wall_time: 0.00288 s Throughput: 0.445 GB/s
Benchmark: m_128_n_10000_k_100_use_gpu_False wall_time: 0.00233 s Throughput: 0.549 GB/s
Benchmark: m_128_n_10000_k_100_use_gpu_True wall_time: 0.00325 s Throughput: 0.394 GB/s
Benchmark: m_128_n_10000_k_5000_use_gpu_False wall_time: 0.0127 s Throughput: 0.101 GB/s
Benchmark: m_128_n_10000_k_5000_use_gpu_True wall_time: 0.00381 s Throughput: 0.336 GB/s
Benchmark: m_128_n_10000_k_9900_use_gpu_False wall_time: 0.015 s Throughput: 0.0853 GB/s
Benchmark: m_128_n_10000_k_9900_use_gpu_True wall_time: 0.00438 s Throughput: 0.292 GB/s
Benchmark: m_128_n_10000_k_10000_use_gpu_False wall_time: 0.0104 s Throughput: 0.123 GB/s
Benchmark: m_128_n_10000_k_10000_use_gpu_True wall_time: 0.00427 s Throughput: 0.3 GB/s
Benchmark: m_128_n_100000_k_100_use_gpu_False wall_time: 0.0148 s Throughput: 0.865 GB/s
Benchmark: m_128_n_100000_k_100_use_gpu_True wall_time: 0.0262 s Throughput: 0.488 GB/s
Benchmark: m_128_n_100000_k_1000_use_gpu_False wall_time: 0.0201 s Throughput: 0.636 GB/s
Benchmark: m_128_n_100000_k_1000_use_gpu_True wall_time: 0.0263 s Throughput: 0.486 GB/s
Benchmark: m_128_n_100000_k_50000_use_gpu_False wall_time: 0.214 s Throughput: 0.0599 GB/s
Benchmark: m_128_n_100000_k_50000_use_gpu_True wall_time: 0.0322 s Throughput: 0.398 GB/s
Benchmark: m_128_n_100000_k_99000_use_gpu_False wall_time: 0.262 s Throughput: 0.0489 GB/s
Benchmark: m_128_n_100000_k_99000_use_gpu_True wall_time: 0.0377 s Throughput: 0.34 GB/s
Benchmark: m_128_n_100000_k_100000_use_gpu_False wall_time: 0.118 s Throughput: 0.108 GB/s
Benchmark: m_128_n_100000_k_100000_use_gpu_True wall_time: 0.0365 s Throughput: 0.351 GB/s
END_PUBLIC
BEGIN_PUBLIC
BEGIN_PUBLIC
Automated g4 rollback of changelist 157169178
PiperOrigin-RevId: 161476569
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A. Unique TensorFlower
Shanqing Cai