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esm2_accelerate_te

README.md

TransformerEngine-accelerated ESM-2 training with Hugging Face Trainer

This folder demonstrates how to train TE-accelerated ESM-2 using the Hugging Face Transformers Trainer class and Hugging Face Accelerate, including sequence packing and FP8 precision, using distributed training frameworks like FSDP and DeepSpeed.

How to use this recipe

This folder contains an independent, minimal training example. It does not depend on any other code in the top-level bionemo-framework repository. You can download a zipped directory of this folder alone by clicking here.

How to deploy this recipe on cloud providers

🚧 Under development

Supported Models and Training Features

Model BF16 FP8[1] THD Input Format FP8 with THD Input Format MXFP8[2] Context Parallelism
ESM-2 ✅ ✅ 🚧 🚧 🚧 ❌
AMPLIFY ✅ ❌ 🚧 ❌ ❌ ❌

✅: Supported
🚧: Under development
❌: Not supported

[1]: Requires compute capacity 9.0 and above (Hopper+)
[2]: Requires compute capacity 10.0 and 10.3 (Blackwell), 12.0 support pending

Distributed Training

This recipe leverages Hugging Face Accelerate for distributed training, which supports multiple distributed training frameworks through configuration files:

The training strategy is configured through Accelerate's configuration system rather than separate training scripts.

Commands to Launch Training

Single Process Training

To run single-process training on one GPU:

python train.py --config-name=L0_sanity

To train the AMPLIFY model instead of ESM-2, use the L0_sanity_amplify config:

python train.py --config-name=L0_sanity_amplify

Multi-Process Training

To run distributed training using Accelerate's launch command:

accelerate launch --config_file accelerate_config/fsdp2_te.yaml \
    --num_processes 2 train.py \
    --config-name=L0_sanity

Multi-Node Training

For multi-node training, configure your accelerate setup with the appropriate machine rank, main process IP, and port:

accelerate launch --config_file accelerate_config/fsdp2_te.yaml \
    --main_process_ip 192.168.20.1 \
    --main_process_port 9898 \
    --num_machines 2 \
    --machine_rank 0 \
    train.py --config-name=L0_sanity

Refer to slurm.sh for an example SLURM script.

FP8 Training

FP8 precision is enabled with an accelerate config file, shown in accelerate_config/fp8.yaml.

accelerate launch --config_file accelerate_config/fp8.yaml \
    train.py --config-name L0_sanity

Torch Dynamo (torch.compile) Support

An example accelerate config file, shown in accelerate_config/dynamo.yaml, is provided for torch.compile support.

accelerate launch --config_file accelerate_config/dynamo.yaml \
    train.py --config-name L0_sanity

Known Limitations

  • Combining FP8 and FSDP1 or FSDP2 does not seem to be supported currently.

Saving and Loading Checkpoints

The Hugging Face Trainer automatically handles checkpointing based on the TrainingArguments configuration. Checkpointing behavior is controlled through the trainer configuration in your hydra config.

Enabling Checkpoints

To enable checkpoint saving, ensure that trainer.output_dir is set to a writable directory. Checkpointing frequency is controlled by the trainer.save_steps configuration parameter.

accelerate launch train.py --config-name L0_sanity \
  trainer.output_dir=/path/to/ckpt_dir \
  trainer.save_steps=100

Resuming from Checkpoints

The Trainer automatically detects and resumes from the latest checkpoint in the output directory. You can also specify a specific checkpoint:

accelerate launch train.py --config-name L0_sanity \
  trainer.output_dir=/path/to/ckpt_dir \
  trainer.resume_from_checkpoint=/path/to/specific/checkpoint

Evaluation

Configure evaluation strategy and frequency through TrainingArguments:

accelerate launch train.py --config-name L0_sanity \
  trainer.eval_strategy=steps \
  trainer.eval_steps=500 \

Running Inference with the Trained Model

Models trained with this recipe can be loaded using standard Hugging Face methods. The final model is saved in a format compatible with the AutoModel.from_pretrained method:

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("path/to/checkpoint")
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")

gfp_P42212 = (
    "MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTL"
    "VTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLV"
    "NRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLAD"
    "HYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK"
)

inputs = tokenizer(gfp_P42212, return_tensors="pt")
model.eval()
output = model(**inputs)

Performance

🚧 Under development

References

Developer Guide

Running Tests

To run tests locally, run recipes_local_test.py from the repository root with the recipe directory as an argument.

./ci/scripts/recipes_local_test.py bionemo-recipes/recipes/esm2_accelerate_te/

Tests should be kept relatively fast, using the smallest model and number of training steps required to validate the feature. Hardware requirements beyond those used in CI, like a single L4, should be annotated with pytest.mark.requires, such as requires_fp8 and requires_multi_gpu.

Development Container

To use the provided devcontainer, use "Dev Containers: Reopen in Container" from the VSCode menu, and choose the "BioNeMo Recipes Dev Container" option. To run the tests inside the container, run pytest -v . in the recipe directory.

Hydra Tips

Hydra is a powerful configuration management library for Python. This recipe uses Hydra to manage training configurations, allowing for easy modification of training hyper-parameters and model settings.

Configuration parameters can be overridden from the command line, for example:

accelerate launch train.py --config-name L0_sanity fp8_config.enabled=true trainer.learning_rate=2e-5