PyTorch 101: Understanding Hooks

In this article, we will explore PyTorch Hooks — a powerful feature that allows you to visualize your model during the forward and backward passes. Hooks give you the ability to debug your training process, visualize activations, and even modify gradients without altering your model’s architecture. We are covering hooks because they are essential tools for diagnosing issues like vanishing gradients, understanding intermediate layer behaviors, and gaining fine-grained control over training dynamics. This tutorial is designed for beginner to intermediate PyTorch users who have experience building and training models and now want deeper insights into model behavior, as well as for researchers and developers looking to optimize and customize their networks. By the end of this tutorial, you’ll have the tools to open the “black box” of deep learning and better understand what happens inside your models during training.

Introduction to PyTorch Hooks

Hooks in PyTorch are severely under-documented for the functionality they bring to the table.

One of the reasons hooks have a major role in PyTorch is that they allow you to interact with your model during backpropagation. Think of a hook as a clever device — like the kind heroes plant inside a villain’s base to secretly gather information. In PyTorch, a hook is simply a function that you attach to either a Tensor or a nn.Module, and it gets executed automatically when the forward or backward pass happens.

Now, when I say “forward,” I don’t just mean the forward() method you define inside a nn.Module class. Instead, I’m referring to the internal forward operation of PyTorch’s torch.autograd.Function — which is the underlying mechanism that handles computation graphs and automatic differentiation. Every tensor that results from an operation (like addition or multiplication) has a grad_fn attached to it. This grad_fn is actually an instance of a torch.autograd.Function, responsible for creating that tensor.
For example, if you compute tens = tens1 + tens2, the resulting tensor tens will have a grad_fn of type AddBackward. This means the system internally knows how to compute the gradients when backpropagating through this addition operation.

If this explanation feels a bit confusing, I highly recommend reviewing our earlier article on computation graphs in PyTorch. However, if you just want the quick version, remember that every tensor created through operations (not manually) in PyTorch tracks how it was created through its grad_fn.

Now, here’s something important: nn.Module objects, like nn.Linear, are composed of multiple operations internally. For instance, a Linear layer computes its output using two operations: matrix multiplication followed by addition (Y = W * X + B). That means, at the autograd level, there will be multiple forward operations happening — one for multiplication and one for addition — not just a single forward() function call.
If you’re using hooks without keeping this in mind, you might accidentally hook onto each individual operation rather than the full layer, leading to multiple outputs or unexpected behavior. We’ll dive deeper into how to handle this properly later in the tutorial.

PyTorch provides two types of hooks.

1. The Forward Hook
2. The Backward Hook

A forward hook is executed during the forward pass, while the backward hook is, well, you guessed it, executed when the backward function is called. Time to remind you again: these are the forward and backward functions of an Autograd.Function object.