`ParallelWrapper`¶

===============

The ParallelWrapper class is a simple wrapper designed to facilitate the use of data parallelism in PyTorch. It is particularly suited for transformer architectures. The class wraps a given neural network model and allows it to be moved to a specified device. If data parallelism is enabled, the model is wrapped in PyTorch's nn.DataParallel class, which splits the input across the specified device's GPUs and parallelizes the forward pass.

Class Definition¶

class ParallelWrapper:
    def __init__(self, model: nn.Module, device: str = "cuda", use_data_parallel: bool = True):
        pass

    def forward(self, *args, **kwargs):
        pass

    def to(self, device: str):
        pass

    def __getattr__(self, name: str):
        pass

Parameters¶

Parameter	Type	Default	Description
`model`	`nn.Module`	-	The neural network model to be parallelized.
`device`	`str`	`"cuda"`	The device to which the model should be moved.
`use_data_parallel`	`bool`	`True`	A flag to indicate whether to use data parallelism or not.

Methods¶

`init(self, model, device="cuda", use_data_parallel=True)`¶

The constructor for the ParallelWrapper class. It initializes the instance and moves the model to the specified device. If data parallelism is enabled and more than one GPU is available, it wraps the model in nn.DataParallel.

`forward(self, *args, **kwargs)`¶

The forward method for the ParallelWrapper class. It simply calls the forward method of the wrapped model with the provided arguments and keyword arguments.

`to(self, device)`¶

This method moves the model to the specified device and updates the device attribute of the instance.

`getattr(self, name)`¶

This method redirects attribute access to the internal model to allow direct access to its methods and properties.

Usage Examples¶

Example 1: Basic Usage¶

import torch.nn as nn

from zeta.training import ParallelWrapper

# Define a model
model = nn.Linear(512, 512)

# Wrap the model
model = ParallelWrapper(model)

# Now you can use the model as usual
input = torch.randn(128, 512)
output = model(input)

Example 2: Using a Different Device¶

import torch.nn as nn

from zeta.training import ParallelWrapper

# Define a model
model = nn.Linear(512, 512)

# Wrap the model and move it to CPU
model = ParallelWrapper(model, device="cpu")

# Now you can use the model as usual
input = torch.randn(128, 512)
output = model(input)

Example 3: Disabling Data Parallelism¶

import torch.nn as nn

from zeta.training import ParallelWrapper

# Define a model
model = nn.Linear(512, 512)

# Wrap the model and disable data parallelism
model = ParallelWrapper(model, use_data_parallel=False)

# Now you can use the model as usual
input = torch.randn(128, 512)
output = model(input)

Note¶

The ParallelWrapper class is a utility that simplifies the use of data parallelism in PyTorch. It does not provide any additional functionality beyond what is already provided by PyTorch's nn.DataParallel class. It is intended to be used as a convenience wrapper to reduce boilerplate code.

ParallelWrapper¶