FSDP With AveragedModel

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Introduction

FSDP (Fully Sharded Data Parallel) is a data parallelism technique developed by Meta AI that allows for efficient training of large models on multiple GPUs. However, when using FSDP with torch.optim.swa_utils.AveragedModel, you may encounter a TypeError due to the inability to pickle the torch.cuda module. In this article, we will explore whether FSDP is supposed to work with torch.optim.swa_utils.AveragedModel, and if not, how to implement it.

FSDP and AveragedModel Compatibility

FSDP is designed to work with PyTorch's built-in data parallelism modules, including torch.nn.DataParallel and torch.nn.parallel.DistributedDataParallel. However, torch.optim.swa_utils.AveragedModel is not explicitly designed to work with FSDP.

The TypeError you are encountering is due to the fact that torch.optim.swa_utils.AveragedModel uses the deepcopy function to create a copy of the model, which fails when trying to pickle the torch.cuda module. This is because the torch.cuda module is not pickleable, as it contains CUDA-specific code that cannot be serialized.

Implementing FSDP with AveragedModel

To implement FSDP with torch.optim.swa_utils.AveragedModel, you can try the following approaches:

1. Use a Sharded State Dict

As you mentioned, one way to avoid the deepcopy issue is to use a sharded state dict. This involves splitting the model's state dict into smaller chunks, each of which is stored on a separate GPU. You can then compute the average of the state dict on each GPU separately, without having to create a full copy of the state dict.

However, as you noted, this approach requires a way to convert the sharded state dict back to a full state dict when you need to save the state dict. Unfortunately, PyTorch does not provide a built-in way to do this.

2. Use a Custom State Dict Class

Another approach is to create a custom state dict class that can handle sharded state dicts. This would involve creating a class that inherits from PyTorch's OrderedDict class, and overrides the __getstate__ and __setstate__ methods to handle the sharding of the state dict.

Here is an example of what such a class might look like:

import torch
import torch.nn as nn

class ShardedStateDict(OrderedDict):
    def __getstate__(self):
        # Get the state dict as a regular OrderedDict
        state_dict = super().__getstate__()
        
        # Shard the state dict into smaller chunks
        sharded_state_dict = {}
        for key, value in state_dict.items():
            sharded_state_dict[key] = value.split(1)
        
        return sharded_state_dict
    
    def __setstate__(self, state_dict):
        # Unshard the state dict
        unsharded_state_dict = {}
        for key, values in state_dict.items():
            unsharded_state_dict[key] = torch.cat(values)
        
        # Set the state dict as a regular OrderedDict
        super().__setstate__(unsharded_state_dict)

You can then use this custom state dict class with FSDP and torch.optim.swa_utils.AveragedModel as follows:

model = MyModel()
state_dict = ShardedStateDict(model.state_dict())
averaged_model = torch.optim.swa_utils.AveragedModel(model, state_dict)

Note that this is just one possible implementation, and you may need to modify it to suit your specific use case.

3. Use a Different Averaging Strategy

Another approach is to use a different averaging strategy that does not rely on torch.optim.swa_utils.AveragedModel. For example, you could use a simple moving average of the model's parameters, or a more sophisticated averaging strategy such as exponential moving average.

Here is an example of how you might implement a simple moving average:

class MovingAverageModel(nn.Module):
    def __init__(self, model, alpha):
        super().__init__()
        self.model = model
        self.alpha = alpha
        self.moving_average = {}
    
    def forward(self, x):
        return self.model(x)
    
    def update_moving_average(self):
        for key, value in self.model.state_dict().items():
            if key in self.moving_average:
                self.moving_average[key] = self.alpha * self.moving_average[key] + (1 - self.alpha) * value
            else:
                self.moving_average[key] = value

You can then use this custom model class with FSDP as follows:

model = MyModel()
moving_average_model = MovingAverageModel(model, alpha=0.9)
fsdp_model = FSDP(moving_average_model)

Note that this is just one possible implementation, and you may need to modify it to suit your specific use case.

Conclusion

In conclusion, while FSDP is not explicitly designed to work with torch.optim.swa_utils.AveragedModel, it is possible to implement it using a custom state dict class or a different averaging strategy. However, these approaches may require significant modifications to your code, and may not be suitable for all use cases.

We hope this article has provided a helpful overview of the issues and potential solutions for using FSDP with torch.optim.swa_utils.AveragedModel. If you have any further questions or would like to discuss this topic further, please don't hesitate to contact us.

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Q: What is the main issue with using FSDP with torch.optim.swa_utils.AveragedModel?

A: The main issue is that torch.optim.swa_utils.AveragedModel uses the deepcopy function to create a copy of the model, which fails when trying to pickle the torch.cuda module.

Q: Why is the torch.cuda module not pickleable?

A: The torch.cuda module is not pickleable because it contains CUDA-specific code that cannot be serialized. This is a limitation of the PyTorch library.

Q: What are some potential solutions to this issue?

A: Some potential solutions include:

  • Using a sharded state dict, which involves splitting the model's state dict into smaller chunks, each of which is stored on a separate GPU.
  • Creating a custom state dict class that can handle sharded state dicts.
  • Using a different averaging strategy that does not rely on torch.optim.swa_utils.AveragedModel.

Q: How can I implement a sharded state dict?

A: To implement a sharded state dict, you can create a custom state dict class that inherits from PyTorch's OrderedDict class. You can then override the __getstate__ and __setstate__ methods to handle the sharding of the state dict.

Q: What is an example of a custom state dict class?

A: Here is an example of a custom state dict class that handles sharded state dicts:

import torch
import torch.nn as nn

class ShardedStateDict(OrderedDict):
    def __getstate__(self):
        # Get the state dict as a regular OrderedDict
        state_dict = super().__getstate__()
        
        # Shard the state dict into smaller chunks
        sharded_state_dict = {}
        for key, value in state_dict.items():
            sharded_state_dict[key] = value.split(1)
        
        return sharded_state_dict
    
    def __setstate__(self, state_dict):
        # Unshard the state dict
        unsharded_state_dict = {}
        for key, values in state_dict.items():
            unsharded_state_dict[key] = torch.cat(values)
        
        # Set the state dict as a regular OrderedDict
        super().__setstate__(unsharded_state_dict)

Q: How can I use a custom state dict class with FSDP and torch.optim.swa_utils.AveragedModel?

A: To use a custom state dict class with FSDP and torch.optim.swa_utils.AveragedModel, you can create an instance of the custom state dict class and pass it to the torch.optim.swa_utils.AveragedModel constructor.

Q: What are some other potential solutions to this issue?

A: Some other potential solutions include:

  • Using a different averaging strategy that does not rely on torch.optim.swa_utils.AveragedModel.
  • Creating a custom model class that handles the averaging of the model's parameters.
  • Using a library or framework that provides a built-in solution to this issue.

Q: How can I implement a different averaging strategy?

A: To implement a different averaging strategy, you can create a custom model class that handles the averaging of the model's parameters. You can then use this custom model class with FSDP.

Q: What is an example of a custom model class that handles the averaging of the model's parameters?

A: Here is an example of a custom model class that handles the averaging of the model's parameters:

class MovingAverageModel(nn.Module):
    def __init__(self, model, alpha):
        super().__init__()
        self.model = model
        self.alpha = alpha
        self.moving_average = {}
    
    def forward(self, x):
        return self.model(x)
    
    def update_moving_average(self):
        for key, value in self.model.state_dict().items():
            if key in self.moving_average:
                self.moving_average[key] = self.alpha * self.moving_average[key] + (1 - self.alpha) * value
            else:
                self.moving_average[key] = value

Q: How can I use a custom model class with FSDP?

A: To use a custom model class with FSDP, you can create an instance of the custom model class and pass it to the FSDP constructor.

Q: What are some other potential solutions to this issue?

A: Some other potential solutions include:

  • Using a library or framework that provides a built-in solution to this issue.
  • Creating a custom solution that handles the averaging of the model's parameters.
  • Using a different data parallelism technique that does not rely on FSDP.

Q: How can I determine the best solution for my use case?

A: To determine the best solution for your use case, you can consider the following factors:

  • The size and complexity of your model.
  • The number of GPUs available for training.
  • The desired level of accuracy and precision.
  • The computational resources available for training.

By considering these factors, you can determine the best solution for your use case and implement it using the techniques and strategies outlined in this article.