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Fixes https://github.com/pytorch/pytorch/issues/123503. https://github.com/pytorch/pytorch/pull/121866 makes GPT2ForSequenceClassification hit the SDPA pattern 18 and then encounter the accuracy issue. The issue only happens with BF16 inference single thread. This PR tends to increase the model tolerance from 4e-3 to 5e-3 and make the check pass. Note that the issue is due to some small implementation diff. For example, the sdpa math backend scales q, k before matmul for stability; the flash attention backend has more diffs as a new algorithm. Pull Request resolved: https://github.com/pytorch/pytorch/pull/136749 Approved by: https://github.com/jgong5, https://github.com/jansel |
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|---|---|---|
| .. | ||
| distributed | ||
| dynamo | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| gpt_fast | ||
| inference | ||
| instruction_counts | ||
| nested | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| transformer | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
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: