`StableAdamWUnfused` Documentation¶

Table of Contents¶

Introduction
Class: StableAdamWUnfused
Initialization
Key Functions
Usage Examples
Training a Deep Learning Model
Using Custom Floating Point Precision
Hyperparameter Tuning
Additional Information
StableAdamW Algorithm
Setting Precision to "custom_fp16"
References

1. Introduction ¶

Welcome to the documentation for the StableAdamWUnfused optimizer in the Shapeless library! StableAdamWUnfused is designed to provide a stable and efficient implementation of the AdamW optimizer with optional features like custom floating point precision and update clipping.

Key Features¶

StableAdamW: Stable implementation of the AdamW optimizer.
Custom Floating Point Precision: Choose between standard precision and custom precision for gradients.
Update Clipping: Apply update clipping to prevent excessively large updates.

In this documentation, you will learn how to use the StableAdamWUnfused optimizer effectively, understand its architecture, and explore examples of its applications.

2. Class: `StableAdamWUnfused` ¶

The StableAdamWUnfused class is the core component of the Shapeless library, providing advanced optimization techniques for deep learning models. Below, we'll delve into its initialization and key functions.

Initialization ¶

optimizer = StableAdamWUnfused(
    params,
    lr=0.002,
    weight_decay=0.2,
    betas=(0.9, 0.99),
    eps=1e-8,
    clip_thresh=1.0,
    precision="amp_bfloat16",
    custom_scalar=65536,
)

Parameters:¶

params (iterable): Model parameters for optimization.
lr (float): Learning rate (default: 0.002).
weight_decay (float): Weight decay (L2 penalty) (default: 0.2).
betas (Tuple[float, float]): Coefficients for computing running averages of gradient and its square (default: (0.9, 0.99)).
eps (float): Small constant to prevent division by zero (default: 1e-8).
clip_thresh (float): Threshold for update clipping (default: 1.0).
precision (str): Precision mode ("amp_bfloat16" or "custom_fp16") (default: "amp_bfloat16").
custom_scalar (int): Custom scalar for gradients (default: 65536).

Key Functions ¶

`step(closure=None)`¶

Performs a single optimization step. Computes gradients and updates model parameters.

closure (Optional[Callable]): A closure that computes the loss (default: None).

3. Usage Examples ¶

Now, let's explore practical examples of using the StableAdamWUnfused optimizer in various scenarios.

Training a Deep Learning Model ¶

# Initialize the optimizer
optimizer = StableAdamWUnfused(model.parameters(), lr=0.001, weight_decay=0.0001)

# Inside the training loop
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()

Using Custom Floating Point Precision ¶

# Initialize the optimizer with custom_fp16 precision
optimizer = StableAdamWUnfused(model.parameters(), lr=0.001, precision="custom_fp16")

# Inside the training loop, use custom scalar
custom_scalar = 65536  # Custom scalar value
(loss * custom_scalar).backward()  # Backward pass with custom scalar
optimizer.step()

Hyperparameter Tuning ¶

# Define a grid of hyperparameters
learning_rates = [0.001, 0.01, 0.1]
weight_decays = [0.0001, 0.001, 0.01]

# Loop through hyperparameters
for lr in learning_rates:
    for wd in weight_decays:
        optimizer = StableAdamWUnfused(model.parameters(), lr=lr, weight_decay=wd)

        # Training and evaluation code here

These examples showcase how the StableAdamWUnfused optimizer can be used in training deep learning models with various configurations.

4. Additional Information ¶

StableAdamW Algorithm ¶

The StableAdamWUnfused optimizer implements the StableAdamW algorithm, which is a stable version of the AdamW optimizer. It provides stability and efficiency in deep learning optimization.

Setting Precision to "custom_fp16" ¶

You can set the precision mode to "custom_fp16" to use a custom scalar value for gradients. This mode allows fine-grained control over the precision of gradient calculations.

5. References ¶

For further information and research papers related to the StableAdamWUnfused optimizer and its stability improvements, please refer to the following resources:

Explore these references to gain a deeper understanding of the optimization techniques implemented in StableAdamWUnfused.

Feel free to reach out to the Shapeless community for any questions or discussions regarding this optimizer. Happy optimizing!

StableAdamWUnfused Documentation¶