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Mike Iovine 3dcd785cac [Static Runtime] Add tests for all aten ops (#62347) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62347

This diff includes tests for all `aten` ops that did not already have test coverage.

Test Plan: `buck test //caffe2/benchmarks/static_runtime/static_runtime:static_runtime_cpptest`

Reviewed By: hlu1

Differential Revision: D29968280

fbshipit-source-id: 768655ca535f9e37422711673168dce193de45d2
2021-08-09 12:09:59 -07:00
..
cpp Disable avoid-non-const-global-variables lint check (#62008) 2021-07-22 18:04:40 -07:00
distributed [DDP Communication Hook] Update get_tensor and set_tensor to be cleaner naming conventions (buffer() and set_buffer()) (#62662) 2021-08-04 09:27:31 -07:00
fastrnns Add lint for unqualified noqa (#56272) 2021-04-19 13:16:18 -07:00
framework_overhead_benchmark
functional_autograd_benchmark faster generate_square_subsequent_mask in nn.Transformer (#60631) 2021-06-25 16:07:01 -07:00
instruction_counts Allow instruction counting to use shared memory as a staging ground. (And a couple other tweaks.) (#56711) 2021-05-12 20:37:41 -07:00
operator_benchmark [quant] update FakeQuant modules to use tensor qparams (#61318) 2021-07-10 19:43:02 -07:00
overrides_benchmark Remove legacy constructor calls from pytorch codebase. (#54142) 2021-04-11 15:45:17 -07:00
profiler_benchmark
record_function_benchmark
serialization
sparse Add CSR (compressed sparse row) layout for sparse tensors (#50937) 2021-04-12 10:09:12 -07:00
static_runtime [Static Runtime] Add tests for all aten ops (#62347) 2021-08-09 12:09:59 -07:00
tensorexpr [nnc] Added micro-benchmark to show perf improvement with cat subgraph optimization (#59581) 2021-06-18 14:32:09 -07:00
compare-fastrnn-results.py
compare.sh
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