TinyTorch: Build Your Own PyTorch from Scratch — Tensors to Transformers

From "Using Libraries" to "Building Them"

For most machine learning learners, the journey starts with import torch or import tensorflow. Frameworks hide the complexity of gradient computation, tensor operations, and memory management, making it easy to get started. But this convenience comes with a hidden cost: when a model behaves unexpectedly, training fails to converge, or deep performance optimization is needed, many practitioners have no idea what's actually happening inside the framework.

TinyTorch takes the opposite approach — instead of importing PyTorch, you build it yourself. It's a free, open-source educational curriculum that guides learners through implementing a fully functional machine learning framework from scratch, covering the complete stack from tensors to Transformers.

The logic behind the project is straightforward: every mature systems-level project eventually needs a "teaching version." Just as understanding operating systems means reading xv6, and understanding compilers means writing a toy one, the best way to truly understand a deep learning framework is to implement its core mechanisms yourself.

What TinyTorch Actually Teaches

TinyTorch is positioned as a curriculum, not a production-ready library. Learners build a functional mini machine learning framework, with the goal of connecting the dots from low-level data structures all the way up to high-level model architectures.

The knowledge path is clear from its structure:

Starting with Tensors

Tensors are the foundation of every deep learning framework. Understanding tensor storage layout, broadcasting mechanics, and basic operations is a prerequisite for everything that follows. Implementing a tensor class yourself means confronting the memory organization and computation rules of multi-dimensional arrays directly — no black boxes allowed.

At the implementation level, a tensor is a contiguous block of one-dimensional memory, described by two pieces of metadata: shape and stride. Strides record how many memory units must be skipped to move one step along each dimension, which allows operations like transpose and slicing to be performed by simply modifying strides — without copying data (i.e., "view" operations). Broadcasting defines how tensors with different shapes can participate in the same operation: dimensions are aligned from the right, and dimensions of size 1 are implicitly expanded to match the other operand, again without actually copying memory. When implementing a tensor class yourself, correctly handling strides and broadcasting is where most mistakes happen — and also where the most intuition is built. Many seemingly mysterious shape errors become immediately explainable once you understand stride mechanics.

Autograd Is the Critical Bridge

Moving from tensor operations to trainable models requires crossing one essential hurdle: automatic differentiation (autograd). Backpropagation is, at its core, automatic differentiation over a computation graph. Implementing a mechanism that records operations, builds a computation graph, and propagates gradients backward is often the "aha moment" where learners finally understand why frameworks can compute gradients automatically.

Automatic Differentiation (autograd) is fundamentally different from manually deriving formulas or using numerical finite differences. During each forward operation, it simultaneously records the operation and its inputs, dynamically constructing a directed acyclic graph (DAG) — the computation graph. During backpropagation, the framework starts from the loss node and traverses the graph in reverse, calling each operation's local gradient function and accumulating gradients back to the input tensors via the chain rule. PyTorch uses a "define-by-run" (dynamic graph) approach, building the computation graph in real time during each forward pass. This makes debugging and dynamic control flow feel natural, but it also means graph construction overhead occurs at every training step. Once you've implemented this mechanism yourself, every step behind loss.backward() — operation push, gradient function registration, topological sort and reverse traversal — becomes clearly visible. It's no longer magic.

All the Way Up to Transformer

The curriculum culminates in the Transformer — the core architecture powering today's large models. When learners use their own tensor and autograd engine to stack up attention mechanisms and feed-forward networks layer by layer, and finally get a Transformer running, their understanding of modern deep learning shifts from "knowing how to use it" to "understanding why it works."

The Transformer was introduced by Vaswani et al. in the 2017 paper Attention Is All You Need. Its core innovation was replacing the recurrent structure of RNNs and LSTMs entirely with Self-Attention. Self-attention computes the representation of each position in a sequence as a weighted sum of all other positions, where weights are derived from the dot product of Query and Key vectors, normalized via softmax. This allows the model to directly capture dependencies at any distance while being highly parallelizable. The full architecture also includes Multi-Head Attention, Positional Encoding, feed-forward sublayers, and residual connections. Building these components with your own tensor and autograd engine means every gradient flow path through every matrix multiplication and softmax must be designed by you — the most direct way to understand how large language models work under the hood.

Why "Building It Yourself" Beats "Just Using It"

Using a mature framework is certainly efficient, but during the learning phase, efficiency doesn't equal depth of understanding. The value of projects like TinyTorch lies precisely in forcing you to expose and fill your knowledge gaps.

When you must implement backpropagation yourself, you can't avoid confronting how the chain rule is applied node by node across a computation graph. When you manage tensor operations yourself, you must understand the rules behind shape alignment and broadcasting. These details — automatically handled when calling high-level APIs — are exactly what separates "getting a demo to run" from "being able to diagnose problems."

This "educational reimplementation" learning paradigm has a strong track record in systems education: MIT's xv6 has helped countless people truly understand operating systems; MinGPT and nanoGPT have made the skeleton of GPT visible and legible. TinyTorch applies this same methodology to the entire deep learning framework layer.

Who It's For, and What It Isn't

TinyTorch is free and open-source, with clear value for learners who want to go deep, CS students, and engineers looking to build a solid foundational understanding of deep learning. It's especially suited for people who already know how to use PyTorch but always feel like there's "a layer between them and the real thing."

Its scope matters: TinyTorch is a teaching tool, not a production framework meant to replace PyTorch. Its purpose is to help you understand PyTorch, not replace it. After working through it, when you return to PyTorch, you'll bring a clear understanding of the mechanisms behind every API call — and that's its real payoff.

For those willing to invest the time, the process of building a framework from scratch is itself the best teacher. No longer just import-ing a black box, but understanding exactly how every gear inside it meshes and turns.