Start by understanding the expected behavior of a Transformer encoder and the toy task, then systematically inspect the code for common bugs in attention, normalization, and training setup. Prioritize bugs that prevent training (e.g., shape mismatches, incorrect masking) and validate fixes with a minimal test case.
Pro tip: Demonstrate a debugging mindset by first running the code to observe errors, then using unit tests for individual components (e.g., attention scores) to isolate issues. Mention that you'd check gradients for vanishing/exploding values to catch subtle bugs.
Review the standard Transformer encoder components: multi-head attention, feed-forward network, residual connections, layer normalization, and positional encoding. Ensure you know the correct shapes and operations for each.
Execute the code on the toy task to see immediate errors (e.g., runtime exceptions, NaN losses). Use print statements or a debugger to trace tensor shapes and values through the forward pass.
Check attention computation (scaling, masking, softmax dimension), layer norm placement (pre/post), residual connections, and positional encoding implementation. Verify that the output shape matches the target.
Fix one bug at a time and re-run the code to confirm the fix. Write small unit tests for each component (e.g., attention output shape, masking effect) to ensure correctness.
After all fixes, train on the toy task and monitor loss. Ensure the model can overfit a small dataset, indicating that gradients flow and the architecture is correct.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The pooling choice was the first decision point and I went with mean pooling over the last hidden states instead of a CLS token approach.
Start by clarifying the scope: the fixed Transformer encoder is already implemented, so focus on adding a classification head (e.g., linear layer on the [CLS] token or mean-pooled output), switching to cross-entropy loss, and writing a minimal training loop. Then demonstrate convergence on a toy dataset (e.g., synthetic sequences with binary labels) by tracking loss and accuracy over epochs. Emphasize modularity, reproducibility, and clear separation of model, loss, and training logic.
Pro tip: Mention that you would first verify the fixed Transformer's output shape and ensure the classification head is properly initialized (e.g., Xavier) to avoid vanishing gradients. Also, use a small learning rate with Adam and monitor both training and validation loss to confirm convergence, not just accuracy.
Confirm the fixed Transformer's output format (e.g., sequence of hidden states) and decide on the pooling strategy (CLS token or mean pooling). Define a toy dataset with clear patterns (e.g., sequences of 0s and 1s where label is parity) to ensure learnability.
Attach a linear layer (with dropout optionally) mapping the pooled representation to the number of classes. Replace any existing loss with cross-entropy loss (using logits, not softmax).
Implement a standard loop: forward pass, compute loss, backpropagate, and update weights. Include optimizer (e.g., Adam), learning rate scheduling if needed, and track metrics (loss, accuracy) per epoch.
Run the loop on the toy dataset, plot or print loss/accuracy over epochs, and show that the model reaches high accuracy (e.g., >95%) within a reasonable number of steps. Discuss any hyperparameter tuning or debugging if convergence is slow.
Highlight design choices: why cross-entropy over MSE, why a particular pooling method, and how this setup scales to real data. Mention potential pitfalls like overfitting on small toy data and how to address them (e.g., regularization).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.