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jjsjann123 8d753c8062 [WIP] Upstream push 0627 (#80355) 
Syncing nvfuser devel branch to upstream master. https://github.com/csarofeen/pytorch/

Code changes includes:

- TransformPropagator refactor: switched to Dijkstra instead of exhaustive enumeration on all possible paths to reduce compilation time on transform propagation;
- Indexing refactor: remove reference tensor creation in all tensor indexing logic (#1690)
- (more) generic grouped grid reduction kernel;
- Minor parser/fuser patches:
  1. zero-dim tensor reduction support
  3. no-op binary removal within fused graph
  4. expand supported in fusion

Squashed commits to WAR github API
Commits that's actually in this PR from the devel branch:

```
a054b3efcf5af58ea518de283f55aaf9fe06ff5f Refactor TransormPropagator to allow specifying a position and propagating to part of the DAG (#1775)
d67e1cda9b802036841a371318014a818a849b0a Indexing refactor stage 1: remove reference tensor creation in all tensor indexing logic (#1690)
1b6529956a1ace220898ad09dde0bf85e49827f7 Issue 1770 (#1774)
35b04276b648c9b55cdb6a67f3889f54e745c3d2 Avoid compilation errors like below: (#1773)
452c77326a340d2a4130b7802f4f319aec60e72a Ignore reductions of zero-dim tensors per PyTorch conventions (#1771)
31d6c56d88afba09ac53b2d5dd3493d625f8cd57 TransformPropagator refactor (#1769)
570c5a84b91a3cf67207331be9650d26a2d37e3d Merge pull request #1767 from csarofeen/upstream_merge_0621
9d6c3d84be86da643df6fd51695543938111f20d merging upstream 61305cd638
0ed815f76b08f285bda855dd500692ff10a8abce New TransformPropagator algorithm (#1763)
6c195200c0a92fb0f38c833431a8940ed07569b9 no-op binary removal (#1764)
ec7fa4187c177186527409dfc5c7b1754d30bc92 Proper propagation of IterType (#1762)
b263562dbc3c865007ad7d7d42a58a20be8d7922 Fix dimensionality check (#1759)
2d6343f6cc1e47b63ef20a50d1446f6480736478 More generic grouped grid reduction kernel (#1740)
64e2b56df2c8b9fd22a362d9cc05974a8607ef3d [nvfuser] prevent spamming warning message (#77777) (#1758)
0c431624ff15b6458b9f9b674a3852373fc426b1 [nvFuser] Improving bitwise ops support (#77158) (#1757)
b93a14777fde3b9b39684b9cf1715651a806b281 Parser expand (#1754)
```

RUN_TORCHBENCH: nvfuser
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80355
Approved by: https://github.com/davidberard98
2022-07-13 19:34:31 +00:00
..
cpp [WIP] Upstream push 0627 (#80355) 2022-07-13 19:34:31 +00:00
distributed Fix some typos. 2022-04-11 21:55:59 +00:00
fastrnns [libkineto] Re-enable user-annotations in PyTorch (#75601) 2022-04-26 23:54:22 +00:00
framework_overhead_benchmark Remove py2 compatible future imports (#44735) 2020-09-16 12:55:57 -07:00
functional_autograd_benchmark Added functorch to functional_autograd_benchmark 2022-04-22 14:04:26 +00:00
fuser Benchmarks for various fusers (#67622) 2021-11-04 18:57:17 -07:00
instruction_counts [lint] upgrade mypy to latest version 2022-05-03 20:51:34 +00:00
operator_benchmark [ao][sparsity] Vectorized WeightNormSparsifier (#80059) 2022-07-12 19:16:44 +00:00
overrides_benchmark Use classmethods for overrides (#64841) 2021-09-17 08:32:49 -07:00
profiler_benchmark Use libkineto in profiler (#46470) 2020-11-25 04:32:16 -08:00
record_function_benchmark Fix D23995953 import. 2020-09-29 19:30:23 -07:00
serialization [JIT] Make new zip serialization for torch save/load significantly (~70%) faster (#38379) 2020-05-29 01:56:18 -07:00
sparse Add CSR (compressed sparse row) layout for sparse tensors (#50937) 2021-04-12 10:09:12 -07:00
static_runtime [Static Runtime] Fix precision error in test cases (#80935) 2022-07-06 16:31:18 +00:00
tensorexpr Fix some typos. 2022-04-11 21:55:59 +00:00
compare-fastrnn-results.py Benchmarks: add scripts for FastRNNs results comparison. (#44134) 2020-09-03 13:44:42 -07:00
compare.sh Benchmarks: add scripts for FastRNNs results comparison. (#44134) 2020-09-03 13:44:42 -07:00
README.md Add CSR (compressed sparse row) layout for sparse tensors (#50937) 2021-04-12 10:09:12 -07:00
upload_scribe.py Fix benchmark's import module and remove its usage of tools.stats.scribe (#61808) 2021-07-19 09:45:05 -07:00

README.md

PyTorch Benchmarks

This folder contains scripts that produce reproducible timings of various PyTorch features.

It also provides mechanisms to compare PyTorch with other frameworks.

Setup environment

Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:

# Install torchvision. It comes with the pytorch stable release binary
conda install pytorch torchvision -c pytorch

# Install the latest pytorch master from source.
# It should supersede the installation from the release binary.
cd $PYTORCH_HOME
python setup.py build develop

# Check the pytorch installation version
python -c "import torch; print(torch.__version__)"

Benchmark List

Please refer to each subfolder to discover each benchmark suite