Resolving PyTorch Lightning Rich Progress Bar AssertionError

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Introduction

Hey guys! Today, we're diving deep into a tricky issue some of you might have encountered while using PyTorch Lightning: an AssertionError related to the Rich Progress Bar when restoring training state from a checkpoint. Specifically, the error pops up as AssertionError in .../python3.10/site-packages/pytorch_lightning/callbacks/progress/rich_progress.py, line 452, which states assert progress_bar_id is not None. This can be a real headache, especially when you're trying to resume training from a specific point. We will explore this issue, understand why it happens, and provide a comprehensive guide on how to resolve it. We'll break down the problem, look at the configurations that might be causing it, and offer solutions to get your training back on track. So, let's get started and make sure your PyTorch Lightning journey is smooth sailing! This error typically occurs when the progress_bar_id is unexpectedly None during an update of the Rich Progress Bar. This can happen in scenarios where the internal state of the progress bar isn't correctly restored when resuming from a checkpoint. The goal of this article is to provide a detailed explanation of the issue and step-by-step instructions on how to resolve it, ensuring a seamless training experience with PyTorch Lightning.

Understanding the Bug

To really grasp this issue, let's break it down a bit. The Rich Progress Bar in PyTorch Lightning is a fantastic tool that gives you a visually appealing and informative way to monitor your training progress. It uses the rich library to display live updates in your console. However, like any complex system, it has its quirks. The error message AssertionError: assert progress_bar_id is not None tells us that somewhere in the code, the progress_bar_id is expected to have a value, but it's showing up as None. This ID is crucial for the progress bar to keep track of different tasks and update them correctly. When you're restoring training from a checkpoint, PyTorch Lightning needs to reload the state of the progress bar along with everything else. If something goes wrong during this restoration process, the progress_bar_id might not get set properly, leading to our dreaded error. The assertion failure occurs within the _update method of the RichProgressBar class, indicating that the progress bar update is being attempted without a valid ID. This suggests that the progress bar's internal state is not being correctly managed during the restoration process. The error typically arises when the training state is restored from a checkpoint saved during a training step. This is because the progress bar's internal state might not be fully synchronized with the training loop when checkpoints are saved mid-step. Switching to the default tqdm progress bar bypasses the issue because it uses a different mechanism for tracking progress, which doesn't rely on the same internal state management as the Rich Progress Bar. Understanding this distinction is key to diagnosing and resolving the problem.

Key Components Involved

  1. RichProgressBar: This is the class responsible for displaying the progress bar using the rich library. It handles the visual representation of training progress, including metrics and time elapsed.
  2. ModelCheckpoint: This callback saves the state of your training, including the model's weights, optimizer state, and other relevant information. It's crucial for resuming training from a specific point.
  3. Training State: This includes all the information needed to resume training seamlessly, such as the current epoch, step, model weights, optimizer state, and the state of callbacks like the progress bar.

Configuration Details

Let's take a closer look at the configurations that might be contributing to this issue. We'll examine the provided code snippets for the TrainingCheckpoint and CustomRichProgressBar to identify any potential areas of concern. Properly configuring these components is essential for ensuring smooth training and restoration processes. Let's dive into the specifics and see how we can fine-tune these configurations for optimal performance.

TrainingCheckpoint Configuration

First up, we have the TrainingCheckpoint callback. This is a custom implementation that extends PyTorch Lightning's ModelCheckpoint to save training states at specific intervals. Here’s the breakdown:

from torch import Tensor
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from datetime import timedelta
import torch
from copy import deepcopy
from typing_extensions import override

class TrainingCheckpoint(ModelCheckpoint):
    def __init__(self, last_n=1, every_n_minites: int = None, every_n_iterations: int =None, last_name: str = 'training-last'):
        monitor = "totalsteps"
        super().__init__(
            filename='training-{epoch}-{step}-{monitor}'.replace("monitor", monitor),
            monitor=monitor,
            mode="max",
            save_last=True,
            save_top_k=last_n,
            every_n_train_steps = every_n_iterations,
            save_weights_only=False,
            train_time_interval=timedelta(minutes=every_n_minites),
            save_on_train_epoch_end = False,
        )
        self.monitor = None
        self.CHECKPOINT_NAME_LAST = last_name

    @override
    def _monitor_candidates(self, trainer: "pl.Trainer") -> dict[str, Tensor]:
        monitor_candidates = deepcopy(trainer.callback_metrics)
        # cast to int if necessary because `self.log("epoch", 123)` will convert it to float. if it's not a tensor
        # or does not exist we overwrite it as it's likely an error
        epoch = monitor_candidates.get("epoch")
        monitor_candidates["epoch"] = epoch.int() if isinstance(epoch, Tensor) else torch.tensor(trainer.current_epoch)
        step = monitor_candidates.get("step")
        monitor_candidates["step"] = step.int() if isinstance(step, Tensor) else torch.tensor(trainer.global_step)
        monitor_candidates[self.monitor] = torch.tensor(trainer.global_step)
        return monitor_candidates
    
    @override
    def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        pass
  • Key Parameters: The checkpoint saves the training state based on totalsteps, keeps the last n checkpoints, and can save every n minutes or iterations. Importantly, save_on_train_epoch_end is set to False, meaning checkpoints are saved during training steps, which could be a factor in the progress bar issue.
  • _monitor_candidates Override: This method ensures that the monitored metrics (epoch, step, totalsteps) are correctly captured and cast to integers if necessary. This is vital for proper checkpoint naming and tracking.
  • on_validation_end Override: This method is intentionally left empty, preventing checkpoint saving at the end of validation, which aligns with the goal of saving checkpoints during training steps.

CustomRichProgressBar Configuration

Next, we have the CustomRichProgressBar, which customizes the appearance of the progress bar. Let's break it down:

from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBar, RichProgressBarTheme
from typing import List, Optional, Any

class CustomRichProgressBar(RichProgressBar):
    def __init__(self):
        refresh_rate: int = 1
        leave: bool = False
        theme: RichProgressBarTheme = RichProgressBarTheme(
            description="green_yellow",
            progress_bar="green1",
            progress_bar_finished="green1",
            progress_bar_pulse="#6206E0",
            batch_progress="green_yellow",
            time="grey82",
            processing_speed="grey82",
            metrics="grey82",
            metrics_text_delimiter="\n",
            metrics_format=".4f",
        )
        console_kwargs: Optional[dict[str, Any]] = None

        super().__init__(refresh_rate, leave, theme, console_kwargs)
  • Customization: This class extends RichProgressBar to provide a custom theme, setting colors and formatting options for different progress bar elements.
  • Theme: The RichProgressBarTheme defines the visual style of the progress bar, including colors for the description, progress bar, batch progress, and metrics. This allows for a more visually appealing and informative training experience.
  • Refresh Rate: The progress bar updates every step (refresh_rate = 1), providing real-time feedback during training.

Potential Issue

The core issue likely stems from saving checkpoints during training steps (save_on_train_epoch_end = False) in combination with the Rich Progress Bar. When a checkpoint is saved mid-step, the internal state of the progress bar might not be fully consistent, leading to the progress_bar_id being None when restored. This inconsistency is a critical factor in the AssertionError.

Reproducing the Bug

To reproduce the bug, you'd need to set up a PyTorch Lightning training script that uses the TrainingCheckpoint and CustomRichProgressBar configurations. The key is to save checkpoints during training steps and then attempt to resume training from one of these checkpoints. Here’s a general outline of how to reproduce the issue:

  1. Define a PyTorch Lightning Module: Create a simple LightningModule for training.
  2. Configure Callbacks: Instantiate TrainingCheckpoint and CustomRichProgressBar and pass them to the Trainer.
  3. Train the Model: Run the training loop, ensuring checkpoints are saved during training steps.
  4. Resume Training: Attempt to resume training from a saved checkpoint.

If the bug is present, the AssertionError will occur when the Rich Progress Bar attempts to update its state after the training is resumed.

Steps to Reproduce

While the exact code to reproduce the bug wasn't provided, the following steps can help you create a scenario where the issue is likely to occur:

  1. Set up a PyTorch Lightning project: Start with a basic PyTorch Lightning project structure.
  2. Define a LightningModule: Create a simple LightningModule with a training step, validation step, and optimizer configuration.
  3. Implement the Custom Callbacks: Use the TrainingCheckpoint and CustomRichProgressBar classes provided in the bug report.
  4. Configure the Trainer: Instantiate the PyTorch Lightning Trainer, passing in the custom callbacks. Make sure to set save_on_train_epoch_end=False in the TrainingCheckpoint configuration.
  5. Run Training: Start the training process and let it run for a few steps, ensuring that checkpoints are saved during training steps.
  6. Resume from Checkpoint: Stop the training and attempt to resume it from the latest saved checkpoint.
  7. Observe the Error: If the bug is present, the AssertionError: assert progress_bar_id is not None will occur when training resumes and the Rich Progress Bar attempts to update.

Example Code Snippet (Illustrative)

import pytorch_lightning as pl
import torch
from torch.nn import Linear, functional as F
from torch.optim import Adam
from torch.utils.data import DataLoader, TensorDataset

# Custom Callbacks (as provided in the bug report)
from pytorch_lightning.callbacks import ModelCheckpoint
from datetime import timedelta
from copy import deepcopy
from typing_extensions import override
from torch import Tensor
from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBar, RichProgressBarTheme
from typing import List, Optional, Any

class TrainingCheckpoint(ModelCheckpoint):
    def __init__(self, last_n=1, every_n_minites: int = None, every_n_iterations: int =None, last_name: str = 'training-last'):
        monitor = "totalsteps"
        super().__init__(
            filename='training-{epoch}-{step}-{monitor}'.replace("monitor", monitor),
            monitor=monitor,
            mode="max",
            save_last=True,
            save_top_k=last_n,
            every_n_train_steps = every_n_iterations,
            save_weights_only=False,
            train_time_interval=timedelta(minutes=every_n_minites),
            save_on_train_epoch_end = False,
        )
        self.monitor = None
        self.CHECKPOINT_NAME_LAST = last_name

    @override
    def _monitor_candidates(self, trainer: "pl.Trainer") -> dict[str, Tensor]:
        monitor_candidates = deepcopy(trainer.callback_metrics)
        # cast to int if necessary because `self.log("epoch", 123)` will convert it to float. if it's not a tensor
        # or does not exist we overwrite it as it's likely an error
        epoch = monitor_candidates.get("epoch")
        monitor_candidates["epoch"] = epoch.int() if isinstance(epoch, Tensor) else torch.tensor(trainer.current_epoch)
        step = monitor_candidates.get("step")
        monitor_candidates["step"] = step.int() if isinstance(step, Tensor) else torch.tensor(trainer.global_step)
        monitor_candidates[self.monitor] = torch.tensor(trainer.global_step)
        return monitor_candidates
    
    @override
    def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        pass

class CustomRichProgressBar(RichProgressBar):
    def __init__(self):
        refresh_rate: int = 1
        leave: bool = False
        theme: RichProgressBarTheme = RichProgressBarTheme(
            description="green_yellow",
            progress_bar="green1",
            progress_bar_finished="green1",
            progress_bar_pulse="#6206E0",
            batch_progress="green_yellow",
            time="grey82",
            processing_speed="grey82",
            metrics="grey82",
            metrics_text_delimiter="\n",
            metrics_format=".4f",
        )
        console_kwargs: Optional[dict[str, Any]] = None

        super().__init__(refresh_rate, leave, theme, console_kwargs)


# Dummy LightningModule
class SimpleModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.linear = Linear(32, 2)

    def forward(self, x):
        return self.linear(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = F.cross_entropy(logits, y)
        self.log('train_loss', loss)
        return loss

    def configure_optimizers(self):
        return Adam(self.parameters(), lr=1e-3)


# Dummy Data
train_data = TensorDataset(torch.randn(100, 32), torch.randint(0, 2, (100,)))
train_loader = DataLoader(train_data, batch_size=32)

# Initialize Model, Callbacks, and Trainer
model = SimpleModel()
checkpoint_callback = TrainingCheckpoint(every_n_iterations=5, save_on_train_epoch_end=False)
progress_bar = CustomRichProgressBar()
trainer = pl.Trainer(
    max_epochs=2,
    callbacks=[checkpoint_callback, progress_bar],
)

# Train
trainer.fit(model, train_loader)

# Attempt to resume from checkpoint
# trainer = pl.Trainer(
#     resume_from_checkpoint='path/to/checkpoint.ckpt',
#     max_epochs=2,
#     callbacks=[checkpoint_callback, progress_bar],
# )
# trainer.fit(model, train_loader)

This code snippet provides a basic structure. To fully reproduce the bug, you would need to:

  • Uncomment the section to resume from a checkpoint.
  • Replace 'path/to/checkpoint.ckpt' with the actual path to a saved checkpoint.

Solutions and Workarounds

Alright, let's get down to brass tacks and talk about how to fix this pesky issue. We've identified that the problem likely arises from inconsistencies in the Rich Progress Bar's state when saving checkpoints during training steps. So, what can we do about it? Here are a few solutions and workarounds you can try to get your training back on track.

1. Save Checkpoints at the End of Epoch

The most straightforward solution is to ensure that checkpoints are saved at the end of each epoch rather than during training steps. This allows the progress bar to complete its update cycle and ensures a consistent state when the checkpoint is saved. To implement this, modify your TrainingCheckpoint configuration:

class TrainingCheckpoint(ModelCheckpoint):
    def __init__(self, last_n=1, every_n_minites: int = None, every_n_iterations: int =None, last_name: str = 'training-last'):
        monitor = "totalsteps"
        super().__init__(
            filename='training-{epoch}-{step}-{monitor}'.replace("monitor", monitor),
            monitor=monitor,
            mode="max",
            save_last=True,
            save_top_k=last_n,
            every_n_train_steps = every_n_iterations,
            save_weights_only=False,
            train_time_interval=timedelta(minutes=every_n_minites),
            save_on_train_epoch_end = True,  # Set this to True
        )
        self.monitor = None
        self.CHECKPOINT_NAME_LAST = last_name

By setting save_on_train_epoch_end = True, you ensure that checkpoints are saved only after an epoch is completed, which provides a more stable state for the progress bar to be restored from.

2. Use the Default tqdm Progress Bar

If you're not heavily reliant on the Rich Progress Bar's features, switching back to the default tqdm progress bar can be a quick workaround. The tqdm progress bar uses a different mechanism for tracking progress and doesn't suffer from the same state inconsistency issues. To switch back, simply remove the CustomRichProgressBar callback from your Trainer configuration:

trainer = pl.Trainer(
    max_epochs=2,
    callbacks=[checkpoint_callback],  # Removed progress_bar
)

This will use the default tqdm progress bar, which should allow you to resume training without the AssertionError.

3. Implement a Custom State Management for the Progress Bar

For a more robust solution, you can implement a custom state management mechanism for the Rich Progress Bar. This involves saving and restoring the progress bar's internal state manually during the checkpointing process. Here’s a general approach:

  1. Save the Progress Bar State: Override the on_save_checkpoint method in your LightningModule or a custom callback to save the relevant state of the Rich Progress Bar.
  2. Restore the Progress Bar State: Override the on_load_checkpoint method to restore the saved state when resuming training.

Here’s an example of how you might implement this:

import pytorch_lightning as pl
import torch
from torch.nn import Linear, functional as F
from torch.optim import Adam
from torch.utils.data import DataLoader, TensorDataset
from pytorch_lightning.callbacks import ModelCheckpoint
from datetime import timedelta
from copy import deepcopy
from typing_extensions import override
from torch import Tensor
from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBar, RichProgressBarTheme
from typing import List, Optional, Any

class TrainingCheckpoint(ModelCheckpoint):
    def __init__(self, last_n=1, every_n_minites: int = None, every_n_iterations: int =None, last_name: str = 'training-last'):
        monitor = "totalsteps"
        super().__init__(
            filename='training-{epoch}-{step}-{monitor}'.replace("monitor", monitor),
            monitor=monitor,
            mode="max",
            save_last=True,
            save_top_k=last_n,
            every_n_train_steps = every_n_iterations,
            save_weights_only=False,
            train_time_interval=timedelta(minutes=every_n_minites),
            save_on_train_epoch_end = False,
        )
        self.monitor = None
        self.CHECKPOINT_NAME_LAST = last_name

    @override
    def _monitor_candidates(self, trainer: "pl.Trainer") -> dict[str, Tensor]:
        monitor_candidates = deepcopy(trainer.callback_metrics)
        # cast to int if necessary because `self.log("epoch", 123)` will convert it to float. if it's not a tensor
        # or does not exist we overwrite it as it's likely an error
        epoch = monitor_candidates.get("epoch")
        monitor_candidates["epoch"] = epoch.int() if isinstance(epoch, Tensor) else torch.tensor(trainer.current_epoch)
        step = monitor_candidates.get("step")
        monitor_candidates["step"] = step.int() if isinstance(step, Tensor) else torch.tensor(trainer.global_step)
        monitor_candidates[self.monitor] = torch.tensor(trainer.global_step)
        return monitor_candidates
    
    @override
    def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        pass

class CustomRichProgressBar(RichProgressBar):
    def __init__(self):
        refresh_rate: int = 1
        leave: bool = False
        theme: RichProgressBarTheme = RichProgressBarTheme(
            description="green_yellow",
            progress_bar="green1",
            progress_bar_finished="green1",
            progress_bar_pulse="#6206E0",
            batch_progress="green_yellow",
            time="grey82",
            processing_speed="grey82",
            metrics="grey82",
            metrics_text_delimiter="\n",
            metrics_format=".4f",
        )
        console_kwargs: Optional[dict[str, Any]] = None
        self._progress_bar_id = None

        super().__init__(refresh_rate, leave, theme, console_kwargs)

    def on_save_checkpoint(self, trainer, pl_module, checkpoint):
        # Save the progress bar state
        checkpoint['progress_bar_id'] = self._progress_bar_id

    def on_load_checkpoint(self, trainer, pl_module, checkpoint):
        # Restore the progress bar state
        self._progress_bar_id = checkpoint.get('progress_bar_id', None)


class SimpleModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.linear = Linear(32, 2)

    def forward(self, x):
        return self.linear(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = F.cross_entropy(logits, y)
        self.log('train_loss', loss)
        return loss

    def configure_optimizers(self):
        return Adam(self.parameters(), lr=1e-3)

    def on_save_checkpoint(self, trainer, checkpoint):
        # Call the progress bar's on_save_checkpoint
        for callback in trainer.callbacks:
            if isinstance(callback, CustomRichProgressBar):
                callback.on_save_checkpoint(trainer, self, checkpoint)

    def on_load_checkpoint(self, checkpoint):
        # Call the progress bar's on_load_checkpoint
        for callback in self.trainer.callbacks:
            if isinstance(callback, CustomRichProgressBar):
                callback.on_load_checkpoint(self.trainer, self, checkpoint)


# Dummy Data
train_data = TensorDataset(torch.randn(100, 32), torch.randint(0, 2, (100,)))
train_loader = DataLoader(train_data, batch_size=32)

# Initialize Model, Callbacks, and Trainer
model = SimpleModel()
checkpoint_callback = TrainingCheckpoint(every_n_iterations=5, save_on_train_epoch_end=False)
progress_bar = CustomRichProgressBar()
trainer = pl.Trainer(
    max_epochs=2,
    callbacks=[checkpoint_callback, progress_bar],
)

# Train
trainer.fit(model, train_loader)

# Attempt to resume from checkpoint
trainer = pl.Trainer(
    resume_from_checkpoint='path/to/checkpoint.ckpt',
    max_epochs=2,
    callbacks=[checkpoint_callback, progress_bar],
)
trainer.fit(model, train_loader)

Explanation:

  • on_save_checkpoint: This method is called before saving the checkpoint. We save the _progress_bar_id to the checkpoint dictionary.
  • on_load_checkpoint: This method is called when loading the checkpoint. We restore the _progress_bar_id from the checkpoint dictionary.

By implementing this custom state management, you can ensure that the Rich Progress Bar's state is correctly saved and restored, even when saving checkpoints during training steps.

4. Update PyTorch Lightning

Always a good practice, updating to the latest version of PyTorch Lightning can resolve issues that have been addressed in newer releases. The bug you're encountering might have been fixed in a more recent version. To update, use pip:

pip install pytorch-lightning --upgrade

Or, if you're using conda:

conda update pytorch-lightning

Summary of Solutions

  • Save Checkpoints at the End of Epoch: Ensure save_on_train_epoch_end = True in your TrainingCheckpoint configuration.
  • Use the Default tqdm Progress Bar: Remove the CustomRichProgressBar callback from your Trainer.
  • Implement Custom State Management: Save and restore the progress bar's internal state manually using on_save_checkpoint and on_load_checkpoint.
  • Update PyTorch Lightning: Keep your PyTorch Lightning version up to date.

By applying one or more of these solutions, you should be able to resolve the AssertionError and resume your training smoothly. Each solution offers a different trade-off between convenience and robustness, so choose the one that best fits your needs.

Additional Information

In addition to the solutions, let's consider some extra details provided in the bug report. The environment information can be crucial for pinpointing the root cause, and understanding the context in which the error occurs can guide us toward more effective solutions.

Environment Details

The bug report includes a section for environment details, which can be helpful in diagnosing the issue. Here's what the environment information typically includes:

  • PyTorch Lightning Version: The version of PyTorch Lightning being used (e.g., 2.5.0).
  • PyTorch Version: The version of PyTorch (e.g., 2.5).
  • Python Version: The Python version (e.g., 3.12).
  • OS: The operating system (e.g., Linux).
  • CUDA/cuDNN Version: The versions of CUDA and cuDNN, if applicable.
  • GPU Models and Configuration: Information about the GPUs being used.
  • Installation Method: How PyTorch Lightning was installed (conda, pip, source).

This information can help identify compatibility issues or specific environment configurations that might be contributing to the bug. For instance, certain versions of PyTorch Lightning might have known issues with specific Python versions or CUDA configurations. If you encounter this issue, providing these details can help the community or the PyTorch Lightning team to assist you more effectively.

Error Messages and Logs

The bug report also mentions the importance of including error messages and logs. The complete traceback and any relevant logs can provide valuable clues about the source of the error. In this case, the key error message is:

AssertionError: assert progress_bar_id is not None

This error message, along with the traceback, points directly to the RichProgressBar class and the assertion failure related to progress_bar_id. Analyzing the traceback can help you understand the sequence of function calls that led to the error and identify the exact location where the issue occurs. Always include these details when reporting bugs or seeking help, as they can significantly speed up the diagnosis process.

Conclusion

Okay, guys, we've covered a lot of ground in this article! We started by diving into the specifics of the AssertionError related to the Rich Progress Bar in PyTorch Lightning. We explored why this error occurs when restoring training state from checkpoints saved during training steps. We dissected the configurations of the TrainingCheckpoint and CustomRichProgressBar, identifying potential areas of concern. Then, we walked through several solutions and workarounds, from saving checkpoints at the end of each epoch to implementing custom state management for the progress bar. By now, you should have a solid understanding of the issue and the tools to tackle it head-on.

Remember, the key takeaway is that the AssertionError arises from inconsistencies in the Rich Progress Bar's state when checkpoints are saved mid-step. By ensuring checkpoints are saved at the end of an epoch or implementing custom state management, you can mitigate this issue. And if all else fails, switching to the default tqdm progress bar is a reliable workaround.

We also highlighted the importance of providing detailed environment information and error messages when reporting bugs or seeking help. This context is crucial for diagnosing issues and finding effective solutions.

So, next time you encounter this error, don't fret! You now have a comprehensive guide to help you resolve it and get back to training your models smoothly. Happy training, and may your progress bars always be rich and informative!