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pytorch/benchmarks
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leslie-fang-intel 8072ebc36c SKIP llama for dynamic size testing (#135960) 
Running Torchbench llama with dynamic size failed with
```
  File "/localdisk/leslie/torch_inductor_community/pytorch/torch/fx/experimental/symbolic_shapes.py", line 4182, in produce_guards
    raise ConstraintViolationError(
torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (L['inputs'][0].size()[0])! For more information, run with TORCH_LOGS="+dynamic".
  - Not all values of RelaxedUnspecConstraint(L['inputs'][0].size()[0]) are valid because L['inputs'][0].size()[0] was inferred to be a constant (32).
```
Skip this model for marking dynamic dim.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/135960
Approved by: https://github.com/ezyang
2024-09-15 00:06:49 +00:00
..
distributed [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
dynamo SKIP llama for dynamic size testing (#135960) 2024-09-15 00:06:49 +00:00
fastrnns [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
framework_overhead_benchmark [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
functional_autograd_benchmark [BE][Easy] enable ruff rule PIE790: unnecessary pass statement (#133200) 2024-08-15 15:50:19 +00:00
fuser [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
gpt_fast [GPT-fast] Update compilation time target for Llama & Mixtral (#135817) 2024-09-12 07:13:44 +00:00
inference [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
instruction_counts Add instruction count benchmark to run on pull requests (#131475) 2024-08-12 05:20:26 +00:00
nested Apply UFMT to all files in benchmarks/ (#105928) 2023-07-26 01:18:48 +00:00
operator_benchmark Fix out_tensor device in diag_test.py (#134020) 2024-08-21 20:43:39 +00:00
overrides_benchmark [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
profiler_benchmark [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
record_function_benchmark [Caffe2]Remove Caffe2 scripts and benchmarks (#126747) 2024-06-05 23:46:31 +00:00
serialization [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
sparse remove fast_flush arguments (#135387) 2024-09-13 08:13:46 +00:00
static_runtime [9/N] Replace c10::optional with std::optional (#130674) 2024-07-15 00:48:43 +00:00
tensorexpr [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
transformer Add explicit GQA support. (#131559) 2024-08-09 21:25:35 +00:00
compare-fastrnn-results.py [BE][Easy][3/19] enforce style for empty lines in import segments in benchmarks/ (#129754) 2024-07-17 14:34:42 +00:00
compare.sh
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: