ML Debugging (Transformer)

Question

# Question

## Transformer Debugging + Follow-up Modification Question

You are given a small GPT-style transformer implemented in PyTorch. It is intended to train an autoregressive character-level model with causal self-attention and KV-cache style decoding.

This code contains **4 bugs**:
- **Two bugs are in the self-attention mechanism**
- **One bug is in the initialization / handling of positional embeddings**
- **One bug is in the loss function / training path**

The bug locations are already labeled for you as:
- `BUG 1`
- `BUG 2`
- `BUG 3`
- `BUG 4`

Your tasks:

1. Identify and explain each of the 4 labeled bugs.
2. Describe the failure mode caused by each bug.
3. Provide a correct fix for each bug.
4. Point out one suspicious-looking line that is actually correct in this implementation.
5. **Follow-up:** modify the model so that instead of doing next-token prediction, it becomes a **classifier** that predicts whether a number is **odd or even**.

For the follow-up, assume the input is still a token sequence representing a number, and the final output should be a binary class:
- `0` for even
- `1` for odd

You should describe:
- what parts of the architecture need to change

Accepted Answer

A detailed reference solution is available to Crack ML Interview members.