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725bbb6b5f
Fixes #151930 This PR updates the `assert_size_stride` and `assert_alignment` functions in [guards.cpp](https://github.com/pytorch/pytorch/blob/main/torch/csrc/dynamo/guards.cpp) to accept an optional `op_name` argument and includes it in the error messages. The corresponding type stubs in [guards.pyi](https://github.com/pytorch/pytorch/blob/main/torch/_C/_dynamo/guards.pyi) are updated to match the new function arg. In [inductor/ir.py](https://github.com/pytorch/pytorch/blob/main/torch/_inductor/ir.py) extracts the operator name from the FX graph and passes it into the `codegen_size_asserts` and `codegen_alignment_asserts` functions, so that generated assertions in Triton code include the op name for better debugging. Added unit tests inside [test_torchinductor.py](https://github.com/pytorch/pytorch/blob/main/test/inductor/test_torchinductor.py). - Verified both successful and failing assertion cases include the operator name. - Verified that generated Triton code contains the op name inside the asserts. Pull Request resolved: https://github.com/pytorch/pytorch/pull/152353 Approved by: https://github.com/jansel |
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| .. | ||
| distributed/ddp | ||
| dynamo | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| gpt_fast | ||
| inductor_backends | ||
| 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: