Pytorch torchtune

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torchtune is a PyTorch-based library designed to streamline the authoring, fine-tuning, and experimentation processes for large language models (LLMs). Additionally, torchtune has built-in support for logging with W&B, enhancing tracking and visualization of training processes.

Check the W&B blog post on Fine-tuning Mistral 7B using torchtune.

W&B logging at your fingertips

Recipe's config

Overriding command line arguments at launch:

tune run lora_finetune_single_device --config llama3/8B_lora_single_device \
  metric_logger._component_=torchtune.utils.metric_logging.WandBLogger \
  metric_logger.project="llama3_lora" \
  log_every_n_steps=5

Command line

Enable W&B logging on the recipe's config

# inside llama3/8B_lora_single_device.yaml
metric_logger:
  _component_: torchtune.utils.metric_logging.WandBLogger
  project: llama3_lora
log_every_n_steps: 5

Using the W&B metric logger

Enable W&B logging on the recipe's config file by modifying the metric_logger section. Change the _component_ to torchtune.utils.metric_logging.WandBLogger class. You can also pass a project name and log_every_n_steps to customize the logging behavior.

You can also pass any other kwargs as you would to the wandb.init method. For example, if you are working on a team, you can pass the entity argument to the WandBLogger class to specify the team name.

Recipe's Config

tune run lora_finetune_single_device --config llama3/8B_lora_single_device \
  metric_logger._component_=torchtune.utils.metric_logging.WandBLogger \
  metric_logger.project="llama3_lora" \
  metric_logger.entity="my_project" \
  metric_logger.job_type="lora_finetune_single_device" \
  metric_logger.group="my_awesome_experiments" \
  log_every_n_steps=5

Command Line

# inside llama3/8B_lora_single_device.yaml
metric_logger:
  _component_: torchtune.utils.metric_logging.WandBLogger
  project: llama3_lora
  entity: my_project
  job_type: lora_finetune_single_device
  group: my_awesome_experiments
log_every_n_steps: 5

What is logged?

After running the above command, you can explore the W&B dashboard to see the logged metrics. By default W&B grabs all of the hyperparameters from the config file and the launch override ones.

W&B captures the resolved config for you on the Overview tab. W&B also stores the config as a YAML on the Files tab.

Logged Metrics

Each recipe has their own training loop, so check each individual recipe to see what metrics are logged. The default metrics logged are:

Metric	Description
loss	The loss of the model
lr	The learning rate
tokens_per_second	The tokens per second of the model
grad_norm	The gradient norm of the model
global_step	Corresponds to the current step in the training loop. Takes into account gradient accumulation, basically every time an optimizer step is taken, the model is updated, the gradients are accumulated and the model is updated once every gradient_accumulation_steps

info

global_step is not the same as the number of training steps. It corresponds to the current step in the training loop. Takes into account gradient accumulation, basically every time an optimizer step is taken the global_step is incremented by 1. For example, if the dataloader has 10 batches, gradient accumulation steps is 2 and run for 3 epochs, the optimizer will step 15 times, in this case global_step will range from 1 to 15.

The streamlined design of torchtune allows to easily add custom metrics or modify the existing ones. It suffices to modify the corresponding recipe file, for example, computing one could log current_epoch as a percentage of the total number of epochs as following:

# inside `train.py` function in the recipe file
self._metric_logger.log_dict(
    {"current_epoch": self.epochs * self.global_step / self._steps_per_epoch},
    step=self.global_step,
)

info

This is a fast evolving library, the current metrics are subject to change. If you want to add a custom metric, you should modify the recipe and call the corresponding self._metric_logger.* function.

Saving and loading checkpoints

The torchtune library supports various checkpoint formats. Depending on the origin of the model you are using, you should switch to the appropriate checkpointer class.

If you want to save the model checkpoints to W&B Artifacts, the simplest solution is to override the save_checkpoint functions inside the corresponding recipe.

Here is an example of how you can override the save_checkpoint function to save the model checkpoints to W&B Artifacts.

def save_checkpoint(self, epoch: int) -> None:
    ...
    ## Let's save the checkpoint to W&B
    ## depending on the Checkpointer Class the file will be named differently
    ## Here is an example for the full_finetune case
    checkpoint_file = Path.joinpath(
        self._checkpointer._output_dir, f"torchtune_model_{epoch}"
    ).with_suffix(".pt")
    wandb_artifact = wandb.Artifact(
        name=f"torchtune_model_{epoch}",
        type="model",
        # description of the model checkpoint
        description="Model checkpoint",
        # you can add whatever metadata you want as a dict
        metadata={
            utils.SEED_KEY: self.seed,
            utils.EPOCHS_KEY: self.epochs_run,
            utils.TOTAL_EPOCHS_KEY: self.total_epochs,
            utils.MAX_STEPS_KEY: self.max_steps_per_epoch,
        }
    )
    wandb_artifact.add_file(checkpoint_file)
    wandb.log_artifact(wandb_artifact)