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Files

Evgeni Burovski 48989bc820 trace frames with np.ndarray (#110512 )

Fixes #109604

Resubmit gh-109715 + several skips and small fixes to make tests pass.

The main fix here is by @ysiraichi : previously, dynamo did not resume tracing numpy ndarrays after a graph break.
While at it, fix several small issues Yukio's fix uncovers:

- graph break gracefully on numpy dtypes which do not map to torch.dtypes (uint16 etc)
- recognize array scalars in dynamo, treat them as 0D ndarrays
- make sure that iterating over torch.ndarray generates arrays not bare tensors

Pull Request resolved: https://github.com/pytorch/pytorch/pull/110512
Approved by: https://github.com/lezcano

2023-10-15 00:56:10 +00:00

cpp

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

distributed

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2023-07-26 01:18:48 +00:00

dynamo

trace frames with np.ndarray (#110512 )

2023-10-15 00:56:10 +00:00

fastrnns

Apply UFMT to all files in benchmarks/ (#105928 )

2023-07-26 01:18:48 +00:00

framework_overhead_benchmark

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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

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2023-07-26 01:18:48 +00:00

record_function_benchmark

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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

[sparse] Add padding for dense matrices in semi-structured sparse (#110583 )

2023-10-13 20:04:23 +00:00

static_runtime

fix some typos (#106018 )

2023-07-26 18:14:44 +00:00

tensorexpr

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2023-07-26 01:18:48 +00:00

transformer

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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

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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: