Autograd: Building A TODO.md For Missing Features

Hey guys! Let's talk about something super important for our Autograd project: creating a TODO.md file. This file will be our go-to spot for documenting all the things we still need to do in Autograd, especially the features that are missing or incomplete. Think of it as our roadmap to make Autograd even better, more robust, and way more powerful. We're going to dive into what needs to be in this TODO.md and why it's so crucial for the project's success. This is a collaborative effort, so buckle up, and let's get started!

Why a TODO.md is Essential for Autograd

Alright, so why are we even bothering with a TODO.md file, you might ask? Well, it's pretty simple, actually. A TODO.md file serves as a central hub for all the tasks that need to be tackled, acting as a living document that evolves as the project grows. It's like having a project manager right inside our code repository! This is especially critical for a project like Autograd, which is all about automating the process of calculating gradients, a fundamental concept in machine learning.

First off, it keeps us organized. Imagine trying to keep track of all the missing pieces in your head. Nightmare, right? The TODO.md gives us a clear overview of what's missing, what needs work, and what we’re aiming for in the future. It’s a single source of truth. Secondly, it helps with collaboration. When multiple people are working on a project, it's easy for things to get missed or for different people to work on the same thing. The TODO.md ensures everyone is on the same page. Thirdly, it aids in prioritization. We can use the TODO.md to prioritize tasks based on their importance and the overall project goals. This helps us focus our efforts on what matters most. Finally, it provides transparency. Anyone who's interested in contributing or just curious about the project can quickly see what's going on and where they can jump in to help. It keeps the development process open and accessible.

By documenting everything, we avoid the pitfalls of forgetting important features or losing track of the design decisions. As the project evolves, this document will be our guiding light. It will keep us focused, efficient, and prevent duplicated efforts, which is a common problem in any software project.

Diving into the Missing Backward Functions

Now, let's get into the nitty-gritty of what's going into our TODO.md. The most important section right now will be about missing backward functions. Backward functions, or the 'backprop' functions, are the heart of automatic differentiation. They calculate the gradients and are essential for training models. Without them, Autograd is, well, pretty useless! So, we need to list out all the backward functions that are missing. This isn’t a list of everything, but rather a focused compilation of the critical functions we still need to build to get the project working.

For example, we'll need to document dropout_backward. Dropout is a regularization technique that randomly sets some activations to zero during training, which needs a corresponding backward function to work correctly. We'll also mention that we need mask caching for this, linking it back to the specific issue (#10) in our issue tracker. This provides context, allowing anyone to easily find out more about the problem and how it's being addressed. Then, there's global_avgpool2d_backward. Global average pooling is a common operation in convolutional neural networks, and without its backward function, you can't properly backpropagate through your model. Again, we'll include a link to the relevant issue (#11). Then, we have other essential functions, such as concat_backward and stack_backward, which are important for handling how we join tensors together. Think of it like a chain: if one link is missing, the whole thing breaks. That’s why we need each and every backward function to be implemented and working flawlessly. So, in our TODO.md, we're going to make sure to clearly state what's missing, why it's missing, and, if possible, where to find more information, such as linking back to any related issues. This transparency is going to be incredibly useful.

Peeking into the Future: Autograd's Upcoming Features

Beyond just documenting what's missing, our TODO.md will also look ahead. We'll outline future features that we want to implement. This isn't just about what's missing right now but also about where we want to take Autograd in the future. This is where we get to dream big and discuss the new things that could make Autograd really shine.

We need to list out the features we are planning to implement. Top of the list should be full tape-based automatic differentiation. Think of this as the ultimate form of Autograd. This would allow us to record all operations during the forward pass and then replay them in reverse to compute gradients. This is a very powerful technique, and it would give us much more flexibility and control. Next, we will be working on higher-order gradients (Hessian computation). This is something that gets pretty complex, as it involves calculating the gradients of gradients, which can be useful in tasks like optimization and model diagnostics. Implementing Hessian computation would add a significant layer of sophistication to Autograd. This makes it a tool that's useful not just for the basics but also for more advanced research.

We should also think about JIT compilation support. Just-In-Time (JIT) compilation can drastically improve the speed of our code by compiling it on the fly. This will dramatically improve performance, especially for larger models, and it will also make Autograd more attractive to users. Finally, we must mention custom gradient registration. This would give users the ability to define their own gradients for custom operations, allowing for maximum flexibility and control. Adding this capability will make Autograd really user-friendly, allowing it to easily integrate with new custom layers. By including these future features in our TODO.md, we ensure we stay focused on our long-term goals. We also create a vision of what Autograd could become, attracting contributors and keeping us motivated.

The Current Approach: Keeping It Simple and Flexible

In our TODO.md, we also need to clearly explain the current approach of Autograd, so that everyone understands how the system is built. This is particularly important because it gives people the context they need to contribute effectively and also informs our future decisions. This section should cover the design principles we're using, so people can quickly get up to speed on the project.

The main thing to remember is that we are using a functional approach with explicit backward functions. This means that our operations and gradients are defined as separate functions. This gives us a lot of flexibility and control over how gradients are computed. It also makes the code easier to understand and maintain. And that is a win-win. We should also touch upon the YAGNI/KISS principles. YAGNI, which stands for “You Ain’t Gonna Need It”, tells us to avoid over-engineering. We implement only what we need right now. KISS stands for