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angelayi b126adcdee [aotinductor] Pass TorchIR to AOTInductor (#110020) 
Updates `_export.aot_compile` to pass a torch IR graph to inductor, allowing inductor to now run the pre_grad_passes, and reuse more of inductor's code.
Also updates the API to only return the `so_path`, and not returning the exported program. The pytree call spec is now serialized and placed inside of the generated model code. When calling the model, because there is no c++ pytree implementation linked yet, we can access the call specs through `get_call_spec()`, and call pytree flatten/unflattenin python.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/110020
Approved by: https://github.com/desertfire
2023-10-26 15:54:31 +00:00
..
cpp Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
distributed Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
dynamo [aotinductor] Pass TorchIR to AOTInductor (#110020) 2023-10-26 15:54:31 +00:00
fastrnns Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
framework_overhead_benchmark Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
functional_autograd_benchmark [BE]: Update ruff to 0.285 (#107519) 2023-08-22 23:16:38 +00:00
fuser Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
instruction_counts Fix some typos, mostly "that that" (#106901) 2023-08-10 19:46:53 +00:00
nested Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
operator_benchmark [BE]: Update ruff to 0.285 (#107519) 2023-08-22 23:16:38 +00:00
overrides_benchmark [BE]: Update ruff to 0.285 (#107519) 2023-08-22 23:16:38 +00:00
profiler_benchmark Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
record_function_benchmark Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
serialization Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
sparse Add NVIDIA A100 optimized meta parameters to bsr_dense_mm (#111760) 2023-10-23 23:52:49 +00:00
static_runtime fix some typos (#106018) 2023-07-26 18:14:44 +00:00
tensorexpr Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
transformer Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
compare-fastrnn-results.py Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
compare.sh Benchmarks: add scripts for FastRNNs results comparison. (#44134) 2020-09-03 13:44:42 -07:00
README.md Add more child links to benchmark readme (#104627) 2023-07-06 12:11:00 +00:00
upload_scribe.py Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00

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. Links are provided where descriptions exist: