TritonBench

TritonBench features two distinct channels: TritonBench-G and TritonBench-T, each with its own evaluation framework. For detailed information, refer to the paper TRITONBENCH: Benchmarking Large Language Model Capabilities for Generating Triton Operators.

Data

• TritonBench-G offers two versions of Alpaca-format instructions:
  • Simple instruction: TritonBench_G_simp_alpac_v1.json
  • Complex instruction: TritonBench_G_comp_alpac_v1.json
• It also includes executable folders (TritonBench_G_v1) and associated statistics (TritonBench_G_v1.json).
• TritonBench-T offers two versions of Alpaca-format instructions:
  • Simple instruction: TritonBench_T_simp_alpac_v1.json
  • Complex instruction: TritonBench_T_comp_alpac_v1.json
• It also includes executable folders (TritonBench_T_v1) and associated statistics (TritonBench_T_v1.json).
• Additionally, there are two sets of filtered GitHub data:
  • train_crawl.json (4024 entries) – de-duplicated using BERT score similarity.
  • train_synth.json (4133 entries) – data synthesized using Jiuci.
• The combined 8k dataset can be used for RAG (Retrieval-Augmented Generation).

LLM Generated

We also provide the output results from all major models used in the paper.

Python Environment

• triton = 3.1.0
• torch >= 2.5.1
• After installation, update the py_interpreter paths in eval_G and eval_T.

Evaluation Process

TritonBench-G

1. Code Similarity Evaluation: First, use CodeBLEU to evaluate code similarity. For detailed instructions, refer to ../readme_4similarity.md.
2. Execution Accuracy:
   • Run 0_call_acc.py with the following command:

   0_call_acc.py --source source/path/or/folder --target target/path/or/folder --GPUs [0,1,2,3]

   • Multiple GPUs can accelerate the execution.
3. Execution Performance:
   • Run 1_exe_acc.py with:

   1_exe_acc.py --folder root/of/multiple/folders/or/folder --GPUs [0,1,2,3]

4. Efficiency:
   • First run the correctly executable operators and get the performance:

   cd performance_metrics/perf_G
   python run_bench/write_file.py --input_folder_path /folder/of/pyfiles --results_path /folder/of/output/results
   python run_bench/multiprocess_gpu_run.py

   • Finally, run 2_efficiency.py to evaluate the performance:

   cd EVAL/eval_G
   python 2_efficiency.py --gen_folder /folder/of/output/results

TritonBench-T

For TritonBench-T, there is no code similarity evaluation. Only call accuracy, execution accuracy, and speedup are assessed. The process is similar:

1. Run 0_call_acc.py as above:

   0_call_acc.py --source source/path/or/folder --target target/path/or/folder --GPUs [0,1,2,3]

2. Run 1_exe_acc.py with the appropriate folders and GPUs:

   1_exe_acc.py --folder root/of/multiple/folders/or/folder --GPUs [0,1,2,3]

3. Get the performance and evaluate
   • First run the correctly executable operators and get the performance:

   cd performance_metrics/perf_T
   python run_bench/write_file.py --input_folder_path /folder/of/pyfiles --results_path /folder/of/output/results
   python run_bench/multiprocess_gpu_run.py

   • Finally, run 2_efficiency.py to evaluate the performance:

   cd EVAL/eval_T
   python 2_efficiency.py --gen_folder /folder/of/output/results

Note: Ensure that accuracy and efficiency evaluations are performed sequentially.

Hugging face

We have published our dataset on Hugging Face.

📩 Contact Us

If you have any questions, feel free to reach out to us at:
✉️ Email: [[email protected]]