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e02c038a23
FIXES https://github.com/pytorch/pytorch/issues/144775 frfr See details on the problem: https://github.com/pytorch/pytorch/issues/144775#issuecomment-2611699385 We fixed some silent incorrectness, but it results in less nodes DCE'd. The benchmark iteration loop had some dead code which could contain side effect ops that aren't safe to DCE. The regression is expected. This PR removes the compile time benchmarking of the dead code, which should reduce the noise of the benchmark and aligns with the benchmarking used by performance tests New benchmark results: ```python dev,name,batch_size,accuracy,calls_captured,unique_graphs,graph_breaks,unique_graph_breaks,autograd_captures,autograd_compiles,cudagraph_skips,compilation_latency cuda,BartForConditionalGeneration,1,pass,897,1,0,0,0,0,0,39.322364 # after https://github.com/pytorch/pytorch/pull/144319 cuda,BartForConditionalGeneration,1,pass,897,1,0,0,0,0,0,38.972257 # before https://github.com/pytorch/pytorch/pull/144319 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/145590 Approved by: https://github.com/jansel ghstack dependencies: #145447 |
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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: