Understanding FSDP Parameter Access: Alternatives to named_parameters()

• FSDP Doesn't Support named_parameters()
  Due to this sharding, FSDP wrapped models don't inherit the nn.Module class and consequently lack the named_parameters() method.

• FSDP Shards Parameters
  FSDP is a technique for distributing model parameters across multiple devices during training. This sharding process alters the internal structure of the model, making it difficult to directly access named parameters using the standard method.

1. Access Parameters Directly
   Iterate through the FSDP module's children and access their parameters directly. This approach requires traversing the module hierarchy.

2. Use Unwrapped Model
   If you need named parameters for tasks like weight decay configuration, temporarily unwrap the FSDP module using fsdp.unwrap(model). This unwrapped model will have the standard named_parameters() method. However, be cautious as this might not be suitable for all use cases within the FSDP training loop.

Understanding FSDP internals for context

While directly using named_parameters() isn't recommended, it's helpful to understand how FSDP works under the hood. Here are some resources:

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Accessing Parameters Directly

import torch
from torch.distributed import fsdp

# Wrap a model with FSDP
model = fsdp.FullyShardedDataParallel(your_model)

# Iterate through children and access parameters
for name, child in model.named_children():
  for param_name, param in child.named_parameters():
    # Access individual parameter
    print(f"Param: {name}.{param_name}, Value: {param.data}")

This code loops through the children of the FSDP module (model.named_children()) and then iterates through the parameters of each child (child.named_parameters()) allowing access to individual parameter values using param.data.

Using Unwrapped Model (for specific tasks)

import torch
from torch.distributed import fsdp

# Wrap a model with FSDP
model = fsdp.FullyShardedDataParallel(your_model)

# Temporarily unwrap the model for tasks like weight decay
unwrapped_model = fsdp.unwrap(model)

# Use unwrapped model's named_parameters()
for name, param in unwrapped_model.named_parameters():
  # Configure weight decay for specific parameters
  if name in ['fc.weight', 'fc.bias']:
    param.requires_grad = True
    param.register_hook(lambda grad: grad * 0.01)  # Example weight decay

# Wrap the model back with FSDP for training
model = fsdp.shard(unwrapped_model)

This example unwraps the FSDP model using fsdp.unwrap(model) to gain access to the named_parameters() method. It then demonstrates configuring weight decay for specific parameters within the unwrapped model. Finally, the model is wrapped back with FSDP using fsdp.shard(unwrapped_model) before resuming training.

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1. Custom Named Parameters Class
   

   For more control, you can create a custom class that mimics the behavior of named_parameters() for FSDP models. This class would need to handle the specific structure of FSDP modules and their parameters.

   Note
   Implementing a custom class requires a deeper understanding of FSDP internals and might be more complex.

2. Leveraging FSDP Hooks
   

   FSDP offers hooks that allow you to intercept operations during the training process. You could potentially utilize a hook to capture information about the parameters being used at specific points in the training loop. However, this approach might be less intuitive for simply accessing parameter names and values.

Choosing the Right Approach

• FSDP hooks are less common for this specific task and might be more appropriate for advanced use cases.
• A custom named parameters class is suitable if you need more control and flexibility, but requires more development effort.
• Iterating through children is a straightforward solution for most use cases.