A training script for LWM model designed for use with the JAX library using a LLaMA (Large Language Model) and its variations, including ones for video and text processing. The script is structured to support distributed training across multiple devices or nodes, using functionalities from JAX for parallel execution and the flax library for model state management. Here's a high-level overview of how it works:
Default configurations and flags, include data modality (text or vision+text), dataset loading, model configuration, optimizer setup, logging, and checkpointing.
The main function initializes distributed training using JaxDistributedConfig and sets up logging with tux.WandBLogger using the integration with the Weights & Biases platform for experiment tracking.
Depending on the specified modality (text or vision+text), you can select appropriate model configuration and class (LLaMAConfig and FlaxLLaMAForCausalLMModule for text, VideoLLaMAConfig and FlaxVideoLLaMAForCausalLMModule for vision+text).
The dataset is loaded using a DatasetFactory, which provides a way to load and preprocess data suitable for training the model. There's support for resuming training from a checkpoint or loading a specific dataset state.
The model is initialized with the specified configuration, and the script prepares for distributed training by setting up a computational mesh using pjit (parallel JIT compilation in JAX). This involves defining how the model's parameters and operations should be partitioned across the available hardware.
Training Loop:
The main training loop iterates over the total number of training steps. For each step, it processes a batch of data, performs a forward pass and backward pass (computing gradients), and updates the model parameters using the defined optimizer. The script supports different data modalities by branching the logic within the training step function (train_step), handling text and vision+text differently in terms of how the model is applied and how losses are computed.
Optionally, the script can perform evaluation steps at a specified frequency, computing metrics on a separate evaluation dataset. Metrics from both training and evaluation are logged using the configured logger, allowing for monitoring of the training process through the Weights & Biases platform.
The script includes functionality for saving model checkpoints at specified intervals, supporting both regular checkpoints and milestone checkpoints. This allows for resuming training from a specific point and provides a way to save model states for later use or analysis.
After completing the training loop, a final checkpoint may be saved, capturing the final state of the model. The script is designed with modularity and flexibility in mind, allowing for various configurations and supporting complex distributed training setups. It leverages advanced features of JAX and Flax for efficient, scalable training of potentially large models on specialized hardware.