🤗 Transformers Benchmarking
Benchmark the performance of 🤗 Transformers on PyTorch. Quick project done out of curiosity.
Live link: https://share.streamlit.io/tlkh/transformers-benchmarking/main/app.py
docker pull nvcr.io/nvidia/pytorch:20.10-py3
# inside container
pip install -r requirements.txt -U
# edit EXP_PREFIX to change directory to record results
nano run_benchmarks_1gpu.sh
# run benchmarking script
run_benchmarks_1gpu.shMore Information
Important disclaimer
Benchmarks are done by myself independently on with NVIDIA NGC PyTorch 20.10 container. The information is provided on a best-effort basis and I make no guarantees of the accuracy of the information. Feel free to open a GitHub issue if you have any questions or suggestions.
This dashboard shows the measured performance of GPUs when training various configurations of Transformer networks, showing throughput (seq/s) and GPU memory (VRAM) usage. The idea is to allow users have an easy reference for choosing model configuration (model size/batch size/sequence length) and GPU model.
Each HuggingFace Transformer model is loaded in PyTorch as a AutoModelForSequenceClassification and used in a PyTorch Lightning LightningModule for training.
Forward and backward pass computation timings and provided by the PyTorch Lightning built-in SimpleProfiler.
GPU utilization shown is likely lower than actual since measuring GPU utilization (especially using DLProf to measure Tensor Core utilization) likely reduces GPU utilization due to overheads. This is not present in the throughput measurements.
All current results are using PyTorch native automatic mixed precision and Apex fused AdamW optimizer.
Systems used:
- V100 - local OEM system
- A100 - AWS p4d.24xlarge instance
Only single GPU configurations have been tested.