Building makemore Part 5: Building a WaveNet

Andrej Karpathy

20 Nov 202256:21

Summary

TLDRIn this detailed tutorial, the speaker embarks on a journey to enhance a character-level language model named 'Make More'. Broadcasting from Kyoto, they explore the transition from a simple multi-layer perceptron to a more complex architecture inspired by WaveNet, a model originally designed for audio sequences. The tutorial covers increasing input character sequences, introducing hierarchical processing, and meticulously constructing a deeper model for better prediction accuracy. The process involves critical modifications, including custom layer implementation and debugging, aiming to optimize the model's performance from a validation loss of 2.10 to an improved metric, all while navigating through the intricacies of neural network development and pytorch utilization.

Takeaways

😊 Increased context length from 3 to 8 characters improves model performance
👍 PyTorch matrix multiplication works on last tensor dimensions, allowing higher-D inputs
😮 Replacing layers list with PyTorch-style Sequential container simplifies code
🤔 BatchNorm1D assumes channels in middle dimension, unlike our assumption
📈 Hierarchical model architecture lifts validation loss slightly
💡 Residual connections reuse intermediate outputs, improving efficiency
🔍 Inspection critical for debugging complex neural network architectures
📚 PyTorch documentation incomplete - use notebooks to prototype layers
⏳ Lack of experimental harness limits hyperparameter search
🌟 Hierarchical model architecture enables future RNN and transformer networks

Q & A

What is the goal of implementing the make more architecture?
-The goal is to build a character-level language model that can predict the next character in a sequence. The make more architecture is a multi-layer perceptron that takes in previous characters as input and tries to predict the next one.
How does the wave net architecture work for predicting the next character?
-The wave net architecture fuses information from previous characters progressively, instead of crushing them all into a single layer. It takes in characters two at a time, fuses them into bigram representations, then fuses bigrams into 4-grams, and so on in a hierarchical tree-like structure.
Why is the bachelor layer tricky to work with?
-The bachelor layer has different behavior during training and evaluation due to the training parameter. It also couples computation across batch elements to estimate statistics. This statefulness and changed behavior can introduce bugs if not properly handled.
What is the benefit of using convolutional layers?
-Convolutional layers provide efficiency gains in the wave net architecture. They allow sliding a computational graph over the input sequence, calculating many outputs in parallel while reusing intermediate activations. This avoids redundant computation.
How can the model performance be further improved?
-There are many ways the 1.993 validation loss could be improved - tuning hyperparameters, trying different layer sizes, implementing more wave net components like gated linear units, adding residual/skip connections, improving the optimization and initialization strategies, etc.
What was the initial performance before modifications?
-The initial model had a validation loss of 2.1. Simply increasing the context length brought this down to 2.02, giving a good baseline.
How did the flattened consecutive layer work?
-The flattened consecutive layer concatenates n consecutive embedding vectors in the last dimension. This allowed grouping embeddings into bigrams and 4-grams to be processed hierarchically instead of fully flattening everything.
What was the issue with the initial Bachelor 1D layer?
-The initial Bachelor 1D implementation only computed mean and variance over the first input dimension. For 3D inputs, it should have computed over the first two dimensions to properly pool over multiple batch dimensions.
How can the code be pytorchified further?
-The custom layers can be replaced with torch.nn to leverage its optimized and well-tested module implementations. Containers like Sequential can be used to organize layers instead of manual lists.
What is the typical deep learning development process like?
-There is a lot of trial-and-error, shape debugging, documentation checks, and prototyping in Jupyter notebooks. Experiments are managed systematically once basic functionality is proven to tune hyperparameters and performance.