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The architecture itself wasn't the hard part.
Start by clarifying the task: next-token prediction with a simple Embedding -> Linear -> ReLU -> Linear architecture. Then walk through the implementation step-by-step: dataset and DataLoader setup, model definition with proper dimensions, and the forward pass. Emphasize shape transformations and the importance of matching output dimensions to vocabulary size.
Pro tip: Mention that the model outputs logits, so you should use CrossEntropyLoss which combines LogSoftmax and NLLLoss, and ensure the target is a 1D tensor of token indices. Also, note that the embedding dimension and hidden dimension are hyperparameters, but the output dimension must equal the vocabulary size.
Confirm that the input is a sequence of token indices and the target is the next token index. Define the vocabulary size and sequence length.
Create a Dataset that returns (input_sequence, target_token) pairs, and wrap it in a DataLoader with appropriate batch size and shuffling.
In __init__, define nn.Embedding(vocab_size, embed_dim), nn.Linear(embed_dim, hidden_dim), nn.ReLU(), and nn.Linear(hidden_dim, vocab_size).
In forward, pass input through embedding, then linear, ReLU, and final linear. Ensure the output shape is (batch_size, vocab_size) by taking the last time step if needed.
Mention using CrossEntropyLoss and an optimizer like Adam. Highlight that the model outputs logits and no softmax is needed before the loss.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the kind of thing that seems obvious in retrospect.
Start by framing the problem as a shift-by-one alignment task: inputs are tokens 0..n-1 and targets are tokens 1..n. Then walk through the concrete pipeline: tokenization, padding/truncation, creating shifted sequences, and masking padding labels so they don't contribute to the loss.
Pro tip: Mention that you typically set padding token labels to -100 (or ignore_index) so the loss ignores them, and that for causal models you also need a causal attention mask to prevent attending to future tokens.
Convert raw text into token IDs using the model's tokenizer, then group sequences into a batch. Handle variable lengths via padding or truncation to a fixed max length.
For each sequence of tokens [t0, t1, ..., tn], set input = [t0, t1, ..., t_{n-1}] and target = [t1, t2, ..., tn]. This ensures each position predicts the next token.
Pad inputs to the same length within the batch, and create an attention mask (1 for real tokens, 0 for padding). For causal models, also apply a causal mask to prevent attending to future tokens.
Set target labels for padding positions to -100 (or the framework's ignore index) so they are excluded from the cross-entropy loss. This prevents the model from learning to predict padding.
Double-check that input and target sequences are correctly shifted and have the same shape. Ensure masks are correctly aligned with the sequences before feeding into the model.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Flatten the token positions into one batch dimension before CrossEntropyLoss.
Explain that after slicing inputs and targets from the sequence batch, you flatten the batch and sequence dimensions using a view or reshape operation, such as inputs.view(-1, vocab_size) and targets.view(-1). This treats every token position as an independent example, allowing the loss function to compute a single scalar loss over all tokens.
Pro tip: Mention that using view(-1) is efficient because it avoids copying data when the tensor is contiguous, and note that ignoring padding tokens via ignore_index is crucial to avoid skewing the loss.
Extract the input and target tensors from the sequence batch, ensuring they have shape (batch_size, seq_len) or (batch_size, seq_len, features).
Use .view(-1, ...) or .reshape(-1, ...) to collapse the batch and sequence dimensions into a single dimension, resulting in shape (batch_size * seq_len, ...).
Pass the flattened inputs and targets to the loss function (e.g., CrossEntropyLoss) to compute a single loss value over all token positions.
If the sequence contains padding tokens, use ignore_index in the loss function to exclude them from the loss calculation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the standard PyTorch training loop structure, emphasizing the correct order of operations: zero gradients, forward pass, loss computation, backward pass, and optimizer step. Then, walk through each step with a concrete code example, explaining the purpose and common pitfalls. Finally, mention best practices like using model.train(), moving data to device, and handling gradients.
Pro tip: Always call optimizer.zero_grad() before the backward pass to prevent gradient accumulation, and consider using torch.nn.utils.clip_grad_norm_ if you encounter exploding gradients. Also, ensure the model is in training mode with model.train() to enable dropout and batch normalization updates.
Instantiate the model, define the loss function (CrossEntropyLoss), and create the Adam optimizer with model parameters and learning rate. Ensure the model is in training mode.
Call optimizer.zero_grad() to clear any previously accumulated gradients. This is crucial because PyTorch accumulates gradients by default.
Pass the input batch through the model to get predictions, then compute the loss using CrossEntropyLoss between predictions and target labels.
Call loss.backward() to compute gradients of the loss with respect to all model parameters via backpropagation.
Call optimizer.step() to update the model parameters using the computed gradients and the Adam update rule.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.