cosine_beta_schedule¶

Module Function Name: cosine_beta_schedule¶

The cosine_beta_schedule function is a utility used to generate a schedule based on the cosine beta function. This schedule can be useful in numerous areas including machine learning and deep learning applications, particularly in regularization and training.

Here, we provide a comprehensive, step-by-step explanation of the cosine_beta_schedule function, from its argument, types, and method to usage examples.

Function Definition¶

def cosine_beta_schedule(timesteps, s=0.008):
    """
    Generates a cosine beta schedule for the given number of timesteps.

    Parameters:
    - timesteps (int): The number of timesteps for the schedule.
    - s (float): A small constant used in the calculation. Default: 0.008.

    Returns:
    - betas (torch.Tensor): The computed beta values for each timestep.
    """
    steps = timesteps + 1
    x = torch.linspace(0, timesteps, steps, dtype=torch.float64)
    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2
    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
    return torch.clip(betas, 0, 0.9999)

Parameters & Return¶

Parameters	Type	Description	Default
timesteps	int	The number of timesteps for the schedule	None
s	float	A small constant used in the calculation	0.008

Return	Type	Description
betas	torch.Tensor	The computed beta values for each timestep

Example¶

Import necessary library:

import torch

from zeta.utils import cosine_beta_schedule

Create an instance and use the function:

beta_values = cosine_beta_schedule(1000)

# To access the beta value at timestep t=500
print(beta_values[500])

In the above code, cosine_beta_schedule function generates beta_values for the given number of timesteps (1000). The beta value at a particular timestep can be assessed by index.

Description¶

Essentially, this function generates a schedule based on the cosine beta function. This can be used to control the learning process in training algorithms. The function uses two parameters: timesteps and s.

The timesteps parameter is an integer representing the number of time intervals. The s parameter is a small constant used in the calculation to ensure numerical stability and it helps to control the shape of the beta schedule. In the function, s defaults to 0.008 if not provided.

The function first creates a 1D tensor x with elements from 0 to timesteps and then calculates cumulative product of alphas using cosine function on x. The calculated values form a sequence which is then normalized by the first element. Finally, the function computes the beta_values which are differences between subsequent alphas and clips the values between 0 and 0.9999. These beta_values are returned as a tensor.

This function assures that the return beta_values gradually decrease from 1 towards 0 as the timesteps progress, thus controlling the scheduling process in the learning algorithms. The rate of the decrease in the beta_values is influenced by the s parameter and can be adjusted by the user.

Note¶

Be careful when selecting the number of timesteps. Higher timesteps might lead to a more finely tuned beta schedule, but it would also require more computational resources.
The s parameter affects the shape of the beta schedule. Adjust it according to your need.

For further understanding and usage of this function, refer to the PyTorch documentation and communities.