This showcases the autotuning feature in DeepSpeed (DS) with Hugging Face (HF) models.

• DistilBERT
• BERT-base
• BERT-large
• GPT2
• GPT2-medium
• GPT2-large
• GPT2-xl
• DeBERTa

Each model folder has a test_tune.sh script:

• ./test_tune.sh tune tunes the model training and then runs it using the selected tuned DeepSpeed configuration.
• ./test_tune.sh 0 runs the model using HF without DeepSpeed.
• ./test_tune.sh z0 runs the model using HF + DS with ZeRO optimization disabled.
• ./test_tune.sh z1 runs the model using HF + DS with ZeRO optimization stage 1.
• ./test_tune.sh z2 runs the model using HF + DS with ZeRO optimization stage 2.
• ./test_tune.sh z3 runs the model using HF + DS with ZeRO optimization stage 3.

The training runs on 1 node with 16 Nvidia V100 GPUs. The autotuning uses the same hardware resource as the training. The HF packages below are used.

HF examples require installing the transformers package from source:

    git clone https://github.com/huggingface/transformers.git
    cd transformers
    pip install .

The datasets package can be installed by pip install datasets

Below are the versions used in this test.

• transformers (4.12.0)
• datasets (1.11.0)

The table below shows the throughput (samples per second) comparison. The corresponding train micro-batch size per GPU (mbs or tmbspg) and ZeRO stage used to achieve the throughput value is also shown in the parentheses. Assume the strategy users would use in the handtuning process is to start from mbs = 1 and increase mbs by 2 each time until running out of GPU memory.

• baseline is the vanila HF without DeepSpeed (DS) and mbs is hand-tuned.
• HF + DS hand-tuned is HF with DS, and mbs is hand-tuned while other DS configuration uses default values.
• HF + DS autotuning is HF with DS, and the DS configuration is selected from autotuning.

Notation: Hugging Face (HF), DeepSpeed (DS), ZeRO stage (z), gradient accumulation steps (gas), train micro-batch size per GPU (mbs or tmbspg).