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zaf 78c8a0d752 [quant][ao_migration] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional` (#78712 ) Context: In order to avoid the cluttering of the `torch.nn` namespace the quantized modules namespace is moved to `torch.ao.nn`. The list of the `nn.quantized` files that are being migrated: - [ ] `torch.nn.quantized` → `torch.ao.nn.quantized` - [X] [Current PR] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional` - [ ] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules` - [ ] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic` - [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference` - [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable` - [ ] `torch.nn.qat` → `torch.ao.nn.qat` - [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules` - [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic` - [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic` - [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules` - [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat` - [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized` - [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules` - [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic` Majority of the files are just moved to the new location. However, specific files need to be double checked: - [Documentation](docs/source/quantization-support.rst) @vkuzo - [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10 Differential Revision: [D36792967](https://our.internmc.facebook.com/intern/diff/D36792967/) NOTE FOR REVIEWERS: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36792967/)! Pull Request resolved: https://github.com/pytorch/pytorch/pull/78712 Approved by: https://github.com/jerryzh168		2022-08-18 17:51:54 +00:00
..
cpp	[NVFuser] Upstream push 0809 (#83067 )	2022-08-10 21:02:56 +00:00
distributed
fastrnns
framework_overhead_benchmark
functional_autograd_benchmark
fuser
instruction_counts
operator_benchmark	[quant][ao_migration] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional` (#78712 )	2022-08-18 17:51:54 +00:00
overrides_benchmark
profiler_benchmark
record_function_benchmark
serialization
sparse
static_runtime	[static-runtime] change the backend for permute_copy (#83532 )	2022-08-17 18:10:36 +00:00
tensorexpr
compare-fastrnn-results.py
compare.sh
README.md
upload_scribe.py

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

Fast RNNs benchmarks