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android / platform / external / pytorch / 080fab8f6c
commit080fab8f6c82daa93b478267f3a604e1bdb8083f[log] [tgz]
authorMisha Smelyanskiy <msmelyan@fb.com>Wed Aug 30 16:11:39 2017 -0700
committerFacebook Github Bot <facebook-github-bot@users.noreply.github.com>Wed Aug 30 16:22:11 2017 -0700
treedf9e4922f288955b27aa4fe4609d678729f52ca2
parent45e6e71198ab027fca61b6de6596479374a9b76c [diff]
Code generator for and high-performance emebding look-up kernels, supporting

Summary:
Code generator for and high-performance emebding look-up kernels, supporting
Sum, WeightedSum, and Mean reducers.
Achieve at least 1.5x speedup on float and over 2x speedup for float16, compared to existing code
These are results on Broadwell, using sparse_lengths_sum_benchmar.par benchmark

Old
==============
[root@fblearner001.01.ftw1 /home/msmelyan]# numactl -m 0 -C 0 ./sparse_lengths_sum_benchmark.par  --iteration 10000
Preparing lookup table. 2017-08-08 00:10:23.101848
Preparation finished. 2017-08-08 00:10:27.955680
I0808 00:10:27.955732 30700 net.cc:177] Starting benchmark.
I0808 00:10:27.955759 30700 net.cc:178] Running warmup runs.
I0808 00:10:27.956367 30700 net.cc:188] Main runs.
I0808 00:10:31.839035 30700 net.cc:199] Main run finished. Milliseconds per iter: 0.388264. Iters per second: 2575.56
I0808 00:10:35.704169 30700 net.cc:233] Operator #0 (indices, Python) 0.0583264 ms/iter
I0808 00:10:35.704210 30700 net.cc:233] Operator #1 (Y, SparseLengthsSum) 0.327694 ms/iter
I0808 00:10:35.704213 30700 net.cc:237] Time per operator type:
I0808 00:10:35.704217 30700 net.cc:246]        0.327694 SparseLengthsSum
I0808 00:10:35.704221 30700 net.cc:246]       0.0583264 Python
[root@fblearner001.01.ftw1 /home/msmelyan]# numactl -m 0 -C 0 ./sparse_lengths_sum_benchmark.par  --iteration 10000 --dtype float16
Preparing lookup table. 2017-08-08 00:10:59.047159
Preparation finished. 2017-08-08 00:11:05.140565
I0808 00:11:05.140612 31725 net.cc:177] Starting benchmark.
I0808 00:11:05.140635 31725 net.cc:178] Running warmup runs.
I0808 00:11:05.141104 31725 net.cc:188] Main runs.
I0808 00:11:08.371510 31725 net.cc:199] Main run finished. Milliseconds per iter: 0.323039. Iters per second: 3095.6
I0808 00:11:11.671450 31725 net.cc:233] Operator #0 (indices, Python) 0.0609876 ms/iter
I0808 00:11:11.671489 31725 net.cc:233] Operator #1 (Y, SparseLengthsSum) 0.26856 ms/iter
I0808 00:11:11.671494 31725 net.cc:237] Time per operator type:
I0808 00:11:11.671497 31725 net.cc:246]         0.26856 SparseLengthsSum
I0808 00:11:11.671500 31725 net.cc:246]       0.0609876 Python

New (Misha's)
==============
[root@fblearner001.01.ftw1 /home/msmelyan]# numactl -m 0 -C 0 ./sparse_lengths_sum_benchmark.par  --iteration 10000
Preparing lookup table. 2017-08-07 23:44:55.897748
Preparation finished. 2017-08-07 23:45:00.708896
I0807 23:45:00.708945 4178361 net.cc:177] Starting benchmark.
I0807 23:45:00.708971 4178361 net.cc:178] Running warmup runs.
I0807 23:45:00.709444 4178361 net.cc:188] Main runs.
I0807 23:45:03.608551 4178361 net.cc:199] Main run finished. Milliseconds per iter: 0.289909. Iters per second: 3449.36
I0807 23:45:06.536182 4178361 net.cc:233] Operator #0 (indices, Python) 0.0572399 ms/iter
I0807 23:45:06.536224 4178361 net.cc:233] Operator #1 (Y, SparseLengthsSum) 0.23512 ms/iter
I0807 23:45:06.536228 4178361 net.cc:237] Time per operator type:
I0807 23:45:06.536232 4178361 net.cc:246]         0.23512 SparseLengthsSum
I0807 23:45:06.536236 4178361 net.cc:246]       0.0572399 Python
[root@fblearner001.01.ftw1 /home/msmelyan]# numactl -m 0 -C 0 ./sparse_lengths_sum_benchmark.par  --iteration 10000 --dtype float16
Preparing lookup table. 2017-08-07 23:45:17.191579
Preparation finished. 2017-08-07 23:45:23.173668
I0807 23:45:23.173715 4179316 net.cc:177] Starting benchmark.
I0807 23:45:23.173743 4179316 net.cc:178] Running warmup runs.
I0807 23:45:23.174090 4179316 net.cc:188] Main runs.
I0807 23:45:24.939749 4179316 net.cc:199] Main run finished. Milliseconds per iter: 0.176564. Iters per second: 5663.67
I0807 23:45:26.698885 4179316 net.cc:233] Operator #0 (indices, Python) 0.0557303 ms/iter
I0807 23:45:26.698923 4179316 net.cc:233] Operator #1 (Y, SparseLengthsSum) 0.119794 ms/iter
I0807 23:45:26.698927 4179316 net.cc:237] Time per operator type:
I0807 23:45:26.698931 4179316 net.cc:246]        0.119794 SparseLengthsSum
I0807 23:45:26.698935 4179316 net.cc:246]       0.0557303 Python

Reviewed By: salexspb

Differential Revision: D5582172

fbshipit-source-id: d71f5a55580b734a51b8f30852b75f379acfdaf2
3 files changed
tree: df9e4922f288955b27aa4fe4609d678729f52ca2
  1. .travis/
  2. caffe/
  3. caffe2/
  4. cmake/
  5. conda/
  6. docker/
  7. docs/
  8. scripts/
  9. third_party/
  10. .Doxyfile
  11. .Doxyfile-c
  12. .Doxyfile-python
  13. .gitignore
  14. .gitmodules
  15. .travis.yml
  16. appveyor.yml
  17. cmake_uninstall.cmake.in
  18. CMakeLists.txt
  19. LICENSE
  20. Makefile
  21. PATENTS
  22. README.md
  23. release-notes.md
README.md

Caffe2

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Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.

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