While PyTorch offers a wide array of established optimization algorithms in torch.optim, from SGD to Adam, research and practical applications often benefit from custom optimization strategies. You might need to implement a novel algorithm from a recent paper, adapt an existing optimizer for specific constraints (like layer-wise adaptive learning rates not covered by parameter groups alone), or combine steps from different optimizers. This section details how to create your own optimizers by subclassing torch.optim.Optimizer, giving you full control over the parameter update process.

The torch.optim.Optimizer Base Class

At its core, any PyTorch optimizer inherits from the torch.optim.Optimizer base class. Understanding its structure is essential for building your own. Main components include:

1. __init__(self, params, defaults): The constructor.

   • params: An iterable of parameters (tensors) to optimize or an iterable of dictionaries defining parameter groups. Parameter groups allow applying different hyperparameters (like learning rates) to different parts of your model.
   • defaults: A dictionary containing the default hyperparameters for the optimizer (e.g., {'lr': 0.01, 'momentum': 0.9}). These defaults are used for parameter groups that don't explicitly override them.
   • The first step in your custom optimizer's __init__ should always be super().__init__(params, defaults). This call handles the setup of self.param_groups, which stores the parameters and their associated hyperparameters.

2. step(self, closure=None): This method performs a single optimization step (parameter update).

   • It's called typically once per training iteration after loss.backward().
   • It uses the gradients stored in the .grad attribute of each parameter.
   • The optional closure argument is a callable function that re-evaluates the model and returns the loss. Some optimization algorithms, like L-BFGS, require re-evaluating the loss multiple times per step, making the closure necessary. For most common optimizers (SGD, Adam, etc.), the closure is not needed.
   • This is the primary method you need to implement.

3. zero_grad(self, set_to_none=False): Clears the gradients of all optimized parameters.

   • You typically don't need to override this method.
   • Setting set_to_none=True assigns param.grad = None instead of filling it with zeros. This can sometimes offer a minor performance benefit by freeing memory sooner and avoiding a memory write operation, but requires careful handling downstream if code expects .grad to always be a Tensor.

4. state: A dictionary (usually collections.defaultdict(dict)) that holds the optimizer's state for each parameter. For example, momentum optimizers store momentum buffers here, and Adam stores moving averages of gradients and squared gradients. The state is typically keyed by the parameter object itself (self.state[param]).

5. param_groups: A list of dictionaries. Each dictionary represents a group of parameters and contains the keys:

   • 'params': A list of parameter tensors belonging to this group.
   • Other keys corresponding to the hyperparameters for this group (e.g., 'lr', 'momentum', 'weight_decay').

Implementing a Custom Optimizer: SGD with Momentum Example

Let's implement Stochastic Gradient Descent (SGD) with momentum from scratch to illustrate the process. The update rule for momentum SGD is:

$$v_{t+1} = \mu v_t + g_{t+1}$$ $$p_{t+1} = p_t - \alpha v_{t+1}$$

Where:

• $p_t$ is the parameter at timestep $t$.
• $g_{t+1}$ is the gradient of the loss with respect to $p$ at timestep $t+1$.
• $v_t$ is the momentum buffer (velocity) at timestep $t$.
• $\mu$ is the momentum coefficient.
• $\alpha$ is the learning rate.

Here's how you can implement this:

import torch
from torch.optim import Optimizer
from collections import defaultdict

class CustomSGD(Optimizer):
    """Implements Stochastic Gradient Descent with Momentum."""

    def __init__(self, params, lr=0.01, momentum=0.0, weight_decay=0.0):
        if lr < 0.0:
            raise ValueError(f"Invalid learning rate: {lr}")
        if momentum < 0.0:
            raise ValueError(f"Invalid momentum value: {momentum}")
        if weight_decay < 0.0:
            raise ValueError(f"Invalid weight_decay value: {weight_decay}")

        defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay)
        super().__init__(params, defaults)

        # Initialize state (although often done lazily in step)
        # We don't strictly need this here if we initialize in step
        # for group in self.param_groups:
        #     for p in group['params']:
        #         self.state[p] = dict(momentum_buffer=None)

    @torch.no_grad() # Important: disable gradient tracking within the optimizer step
    def step(self, closure=None):
        """Performs a single optimization step.

        Args:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            with torch.enable_grad(): # Ensure gradients are enabled for closure
                loss = closure()

        for group in self.param_groups:
            lr = group['lr']
            momentum = group['momentum']
            weight_decay = group['weight_decay']

            for p in group['params']:
                if p.grad is None:
                    continue # Skip parameters without gradients

                grad = p.grad # Get the gradient tensor

                # Apply weight decay (L2 penalty) if specified
                # Note: This is the standard way, modifying the gradient
                if weight_decay != 0:
                    grad = grad.add(p, alpha=weight_decay)

                # Access and update parameter state (momentum buffer)
                param_state = self.state[p]

                if 'momentum_buffer' not in param_state:
                    # Initialize momentum buffer lazily on first step
                    param_state['momentum_buffer'] = torch.clone(grad).detach()
                else:
                    param_state['momentum_buffer'].mul_(momentum).add_(grad) # v = mu*v + grad

                # Get the momentum buffer after update
                momentum_buffer = param_state['momentum_buffer']

                # Perform the parameter update step
                # p = p - lr * momentum_buffer
                p.add_(momentum_buffer, alpha=-lr)

        return loss

Implementation Points:

• @torch.no_grad(): Decorating the step method with @torch.no_grad() is important. Optimization steps should not be part of the computational graph tracked by autograd.
• Iteration: The code iterates through param_groups and then through the params within each group.
• Gradient Check: It checks if p.grad is None: because some parameters in a model might not receive gradients (e.g., if they are not used in the forward pass or detached from the graph).
• State Management: The momentum_buffer is stored in self.state[p]. It's initialized lazily (the first time step is called for a parameter) to avoid allocating memory upfront if the parameter never gets a gradient.
• In-place Updates: Parameter updates use in-place operations like add_ and mul_. This modifies the parameter tensor directly without creating new tensors, which is essential for the optimizer to actually update the model weights.
• Hyperparameters: Hyperparameters (lr, momentum, weight_decay) are accessed from the group dictionary, allowing different groups to have different settings.
• Weight Decay: Weight decay (L2 regularization) is often implemented by adding the scaled parameter value to the gradient before the main update step.

Using the Custom Optimizer

Using your custom optimizer is just like using a built-in one:

# Assuming 'model' is your torch.nn.Module
# Instantiate the custom optimizer
optimizer = CustomSGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-4)

# In your training loop:
for inputs, targets in dataloader:
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step() # Performs the update using CustomSGD logic

Advanced Considerations

• Parameter Groups: You can define parameter groups when initializing the optimizer to apply different settings:

  optimizer = CustomSGD([
      {'params': model.base.parameters()},
      {'params': model.classifier.parameters(), 'lr': 1e-3} # Different LR for classifier
  ], lr=1e-4, momentum=0.9) # Default LR for other params (e.g., base)
  

• Closures: If your algorithm needs multiple loss evaluations (like L-BFGS), you'll define a closure function and pass it to optimizer.step(closure). Your step implementation must then call closure() appropriately, potentially multiple times, usually within a with torch.enable_grad(): block.

Example closure usage (specific optimizer logic varies)

def closure():
    optimizer.zero_grad()
    output = model(input)
    loss = loss_fn(output, target)
    loss.backward()
    return loss

In step method (for L-BFGS like optimizers)

# loss = closure() # May be called multiple times internally
# Use loss and gradients to update parameters...
```

• Interaction with LR Schedulers: Custom optimizers that correctly manage self.param_groups work with PyTorch's learning rate schedulers (torch.optim.lr_scheduler). The schedulers modify the 'lr' value within each param_group, which your custom step function then reads.
• Complex State: Optimizers like Adam or AdamW require more state per parameter (e.g., first and second moment estimates, potentially a step count). You manage this by adding more entries to the self.state[p] dictionary.
• Performance: While Python allows for rapid prototyping of new optimizer ideas, computationally intensive update rules might become bottlenecks. If profiling reveals the optimizer step is slow, you might consider implementing the core logic as a custom C++ or CUDA extension for maximum performance, as discussed in other sections of this chapter. However, for most algorithms, the overhead of the Python iteration is negligible compared to the gradient computation, and a pure Python implementation is perfectly adequate and much easier to maintain.

By subclassing torch.optim.Optimizer, you gain the power to implement virtually any parameter update rule, integrating novel optimization research directly into your PyTorch training workflows and fine-tuning the learning process for your specific needs.