This project aims to fine-tune various large language model (LLM) architectures on a multi-task dataset for Accelerated Discovery (AD). It provides scripts for:

• PEFT-based LaMDA fine-tuning of Seq2Seq and CLM models using Hugging Face (HF) transformers Trainer and DeepSpeed
• Inference on the fine-tuned models
• Model evaluation

Standard Installation

git clone [email protected]:GT4SD/mtl4ad.git
cd mtl4ad
poetry install

CUDA setup helper

In the scripts directory, there is a script called torch_setup.sh that ensures that your PyTorch installation is configured to use the correct CUDA version.

The multi-task dataset used in this project was described in the paper by Christiofidelis et al. "Unifying Molecular and Textual Representations via Multi-task Language Modelling". The goal is to eventually incorporate additional modalities beyond text and chemistry.

The training dataset should be placed in src/mtl4ad/resources/train

Format and Structure

The data resides in PARQUET format and is automatically loaded using Hugging Face Datasets. Here's an example of a data point:

{"source": "Caption the following smile: CC(=O)NC(CC1=CC=C(C=C1)O)C(=O)O", 
"target": "The molecule is an N-acetyl-amino acid that is tyrosine with an amine hydrogen substituted by an acetyl group. It has a role as a human urinary metabolite. It is a tyrosine derivative, a N-acetyl-amino acid and a member of phenols. It derives from a tyrosine."}

The data is organized into configurations and splits defined using YAML formatting, detailed in a separate README.md file within dataset location. Here's a summary of the configurations:

• main_data: Training and validation sets as described in Christiofidelis et al. (see above link).
• spectra_data: Training and validation sets with an additional spectral modality task (1H-NMR spectra to SMILES).
• backward_test, forward_test, d2s_test, s2d_test, and p2a_test: Test sets for specific tasks (retrosynthesis, forward synthesis, description to SMILES, SMILES to description, paragraph to actions) described in Christiofidelis et al.
• spectra2smi_test: Test set for the 1H-NMR spectra to SMILES task (data curator: Marvin Alberts).

Sequence Length Considerations

Based on sequence length analysis, source and target prompts are potentially truncated to a maximum length of 512 (max_length = 512) for Seq2Seq modeling. For CLM modeling, both prompts are merged and might be truncated to a maximum length of 1024 (max_length = 1024). Remember to implement the correct prompt format (currently supporting mistral-instruct) when using instruction-based models.

This repository is organized as follows:

• scripts: This directory contains scripts for launching training and inference tasks.
• src/mtl4ad: This directory holds the core source code for the projectand a directory called resources which contains a sample test dataset and configuration file

This project leverages DeepSpeed through Hugging Face's accelerate library for multi-GPU training. DeepSpeed Config File: An example is located at /configs/deepspeed_config.json. This file contains DeepSpeed-specific training parameters.

For more details check out this page.

The training with torchrun command requires specific parameters to configure the training environment. While these can be set directly within the command, it's recommended to use a separate configuration file for improved readability and maintainability.

An example file is provided below:

export CONFIG_FILE="/configs/config_opt_125m.yaml"

./scripts/train.sh ${CONFIG_FILE}  # To initiate training

Explanation:

• CONFIG_FILE: Configuration file for training optimization.

For a list of the arguments please refer to HF TrainingArguments class, as well as custom classes under src/mtl4ad/train.py. Further examples of training scripts and launching scripts can be found in this repository under scripts.

Known issues:

• Use of Seq2Seq Collator in CLM Modules: To force the learning of eos token addition for CLM models, the Seq2Seq collator is used (cf. this issue)
• Model specific prompt formatting in CLM Modules for instruction-based models may need to be implemented.
• For tracking, we employed mlflow throughout the project. However, it seems to only track HF Training Arguments.

Inference can be launched using the following the same indications as for training but using the inference.sh file instead of the train.sh. Here is an example:

export CONFIG_FILE="/configs/config_opt_125m.yaml"

./scripts/inference.sh ${CONFIG_FILE}  # To initiate evaluation

Use ruff to check the styling and format of your script.

poetry run ruff check .          # Lint all files in the current directory.
poetry run ruff check . --fix    # Lint all files in the current directory, and fix any fixable errors.
poetry run ruff check . --watch  # Lint all files in the current directory, and re-lint on change.

Known issues:

• Model specific post-processing may need to be implemented if specific prompt formatting is employed.

Use the following command to run the testing

poetry run pytest -sv --cov=mtl4ad tests