Resuming Training From Hugging Face Checkpoints A Comprehensive Guide

Jul 26, 2025 by ADMIN 70 views

How to Resume Training from Checkpoint in Hugging Face Format

Hey guys! So, you're diving into the world of training models with Hugging Face and running into the checkpoint conundrum, huh? No worries, it’s a super common question, especially when you're just starting out. Let's break down how to resume training from a checkpoint in Hugging Face format, and clear up those confusing bits about file paths and different ways to load your model.

Understanding Checkpoints in Hugging Face

When you're training a model, especially a big one, you don't want to lose all your progress if something goes sideways. That's where checkpoints come in. Checkpoints are like save points in a video game – they capture the state of your model and training at a specific moment. This includes the model's weights, the optimizer's state, and other training-related information. Using these saved checkpoints, you can continue training from any given point, saving you a ton of time and computational resources.

Hugging Face's Trainer class makes saving and resuming from checkpoints pretty straightforward, but there are a few key things to keep in mind. When you set trainer.save_freq=50, you're telling the Trainer to save a checkpoint every 50 steps (or epochs, depending on your setup). As you've noticed, this can result in checkpoints being saved in both Fully Sharded Data Parallel (FSDP) format and Hugging Face format. The FSDP format is optimized for distributed training, where the model is split across multiple devices. However, for resuming training, especially if you've converted your checkpoints to the standard Hugging Face format, you need to know which path to use and why.

FSDP vs. Hugging Face Format

Before we dive into the specifics of resume_from_path, let's quickly touch on the difference between FSDP and Hugging Face formats. FSDP is a distributed training technique that shards the model across multiple GPUs, making it possible to train very large models. When using FSDP, the checkpoints are often saved in a way that reflects this sharding. Hugging Face format, on the other hand, typically refers to the standard way models are saved using transformers library, which includes a pytorch_model.bin file (or similar) and a config.json file. These files contain the model's weights and configuration, respectively. You mentioned converting your FSDP checkpoints to HF model files, which is a common practice for easier loading and sharing.

Choosing the Correct Path for `resume_from_path`

Okay, let's get to your first question: Which directory path should you use for resume_from_path?

This is a crucial point. When you're using trainer.resume_from_path=xxx, you should point it to the directory containing the Hugging Face model files, not the FSDP model file path. Think of it like this: resume_from_path is designed to load the entire training state, including the model, optimizer, and scheduler states, from a single checkpoint directory. This directory should contain the standard Hugging Face model files like pytorch_model.bin, config.json, training_args.bin, and potentially other files related to the training state.

So, if you've converted your FSDP checkpoints to Hugging Face format, you'll have a directory (or multiple directories, depending on how frequently you saved checkpoints) containing these files. The xxx in trainer.resume_from_path=xxx should be the path to one of these directories.

Here’s a breakdown of why this matters: The Trainer expects a specific directory structure when resuming from a checkpoint. If you point it to the FSDP model file path, it won't find the necessary files to properly restore the training state, and you'll likely run into errors. By using the Hugging Face model path, you ensure that the Trainer can load all the components it needs to resume training seamlessly. Remember, the checkpoint directory saved by the Trainer includes not just the model weights but also the optimizer state, learning rate scheduler state, and other crucial training metadata. Loading from the correct path ensures that your training picks up exactly where it left off, avoiding any unexpected behavior or loss of progress.

When setting trainer.resume_from_path, it is vital to ensure the directory pointed to contains all the necessary files for the Trainer to properly restore the training state. This includes, but is not limited to, the model's weights (pytorch_model.bin), the configuration file (config.json), and the training arguments (training_args.bin). Additionally, the directory may contain files related to the optimizer state (optimizer.pt) and the learning rate scheduler state (scheduler.pt), which are crucial for continuing the training process without losing momentum. By including these components in the checkpoint, the Trainer can accurately resume training from the exact point it was paused, maintaining the integrity of the learning process. Omitting any of these files can lead to errors or unexpected behavior, potentially undoing previous training progress. Thus, carefully selecting the correct checkpoint directory is a critical step in resuming training successfully and efficiently.

`trainer.resume_from_path` vs. `actor_rollout_ref.model.path`: What's the Difference?

Now, let's tackle your second question: What's the difference between using the checkpoint model file in trainer.resume_from_path versus actor_rollout_ref.model.path?

This is where things get a bit more nuanced. Both trainer.resume_from_path and actor_rollout_ref.model.path can be used to load a model, but they serve different purposes and operate at different levels of the training process.

`trainer.resume_from_path`

As we've discussed, trainer.resume_from_path is used to resume an entire training run from a checkpoint. It's a high-level function that loads not just the model weights but also the optimizer state, learning rate scheduler, and other training-related information. When you use trainer.resume_from_path, you're essentially telling the Trainer to pick up exactly where it left off, as if the training run was never interrupted. This is particularly useful when you've trained for a while and want to continue training from a specific point, preserving the training momentum and settings.

In essence, trainer.resume_from_path is your go-to method when you need to restart a training session and maintain the continuity of your learning process. It ensures that all the critical components of your training setup are restored, allowing you to seamlessly continue improving your model without starting from scratch. This holistic approach to resuming training is what sets it apart from simply loading model weights, as it takes into account the entire context of the training run.

`actor_rollout_ref.model.path`

On the other hand, actor_rollout_ref.model.path (or a similar attribute) typically refers to loading the model weights specifically for inference or evaluation purposes, or for use within a specific part of your training pipeline, such as an actor in a reinforcement learning setup. When you load a model using this method, you're generally only loading the model's architecture and weights, not the optimizer state, learning rate scheduler, or other training-specific components. This is more of a surgical operation – you're just swapping out the model weights, not resuming the entire training process.

Think of actor_rollout_ref.model.path as a way to inject a specific model state into a particular part of your system. For example, in a reinforcement learning context, you might use this to update the actor network with the latest weights from a checkpoint, while the training process continues independently. This allows you to evaluate or deploy the model at different stages of training without disrupting the overall learning process.

Here's an analogy to help clarify: Imagine trainer.resume_from_path as restoring an entire virtual machine to a previous state – everything, including the operating system, applications, and data, is rolled back to the checkpoint. In contrast, actor_rollout_ref.model.path is like copying a single file (the model weights) from a backup to a specific location – only that file is restored, and the rest of the system remains unchanged.

To summarize the key differences:

trainer.resume_from_path: Resumes the entire training process, including model weights, optimizer state, learning rate scheduler, etc.
actor_rollout_ref.model.path: Loads only the model weights, typically for inference, evaluation, or specific parts of the training pipeline.

Choosing between these two methods depends entirely on your goal. If you want to continue training from a checkpoint, use trainer.resume_from_path. If you just want to load the model weights for a specific purpose, use actor_rollout_ref.model.path (or a similar mechanism). Understanding this distinction is crucial for managing your training runs effectively and leveraging checkpoints to their full potential.

Practical Example

Let’s make this super practical with a quick example. Suppose you have a checkpoint saved at path/to/my/checkpoint. This directory contains the usual suspects: pytorch_model.bin, config.json, training_args.bin, and potentially optimizer.pt and scheduler.pt. To resume training, you would set:

trainer.resume_from_path = "path/to/my/checkpoint"
trainer.train()

The Trainer will then load the model, optimizer, scheduler, and other training-related information from this directory and continue training from where it left off. No sweat!

On the other hand, if you just wanted to load the model weights into an actor network, you might do something like this:

actor_rollout_ref.model = AutoModelForCausalLM.from_pretrained("path/to/my/checkpoint")

In this case, you're only loading the model weights, not the entire training state. This is perfect for scenarios where you want to use the model for inference or evaluation without affecting the ongoing training process.

Conclusion

So, there you have it! Resuming training from a checkpoint in Hugging Face format is all about understanding the purpose of trainer.resume_from_path and distinguishing it from other ways of loading model weights. Remember to always point trainer.resume_from_path to the directory containing the Hugging Face model files, and be clear about whether you want to resume the entire training process or just load the model weights for a specific task. By keeping these distinctions in mind, you'll be well-equipped to manage your training runs effectively and make the most of your checkpoints. Happy training, guys!