Batch Size and Training Data Iteration | Generated by AI
That’s a very good breakdown of the context length (block size) and target definition in training an autoregressive language model like nanoGPT (which is based on GPT-2 architecture).
Here’s an assessment and clarification of the points, especially focusing on the batch size and the iteration/data loading process.
🧠 Understanding Batch Size and Training Data
Your understanding of the sequence, target, and tokenization is generally correct, but the relationship between the tokens in the training set and how they are used across iterations needs clarification, especially when considering the batch size.
1. Sequence, Target, and Block Size (Context Length)
- Tokenization: Correct. The GPT-2 encoder changes text into a sequence of numerical tokens.
- Sequence/Context (\(X\)): This is the input block size (e.g., 1024 tokens). It represents the history the model uses to make a prediction.
- Target (\(Y\)): The target is the input sequence shifted one token to the left. This is because an autoregressive model predicts the next token given the current sequence.
- If your input sequence (\(X\)) is \([t_1, t_2, \dots, t_N]\),
- The corresponding target sequence (\(Y\)) is \([t_2, t_3, \dots, t_{N+1}]\).
- The model learns to predict \(t_{i+1}\) given the context \([t_1, \dots, t_i]\).
2. What Batch Size Means
- Batch Size (\(B\)): The batch size is the number of independent sequences (context blocks) that are processed simultaneously in one forward and backward pass during training.
- If \(B=4\), the model loads 4 different, non-overlapping sequences (each of size \(N\)) from the dataset at the same time.
- Tensor Shape: The input tensor to the model in one training step has the shape (Batch Size, Block Size), e.g., \((B, N)\).
\(\)\text{Input Tensor Shape} = (B, N)\(\)
- Loss Calculation: The Cross-Entropy Loss is calculated for all \(B \times N\) predictions in that single step. The final loss is usually the average of the loss calculated over all these tokens.
3. The Training Set Iteration
The tokens you mentioned refer to the sliding window technique used to extract the \(X\) and \(Y\) pairs from the large training text, but they do not typically represent successive training iterations when batch size is involved.
What typically happens in the dataset loading:
- First Example: If your block size is \(N=1024\).
- Input Sequence 1 (\(X_1\)): \([t_1, t_2, \dots, t_{1024}]\)
- Target Sequence 1 (\(Y_1\)): \([t_2, t_3, \dots, t_{1025}]\)
- Second Example (Next Block): To maximize data usage, the next block often starts immediately after the first one, meaning the first token of the next block is \(t_{1025}\).
- Input Sequence 2 (\(X_2\)): \([t_{1025}, t_{1026}, \dots, t_{2048}]\)
- Target Sequence 2 (\(Y_2\)): \([t_{1026}, t_{1027}, \dots, t_{2049}]\)
What happens in one training step (iteration):
If your Batch Size (\(B\)) is 4, then one training step involves simultaneously using the first \(B=4\) non-overlapping blocks from the dataset.
- Step 1: Calculate loss/gradients for:
- \(X_1 \to Y_1\) (Tokens \(t_1\) to \(t_{1025}\))
- \(X_2 \to Y_2\) (Tokens \(t_{1025}\) to \(t_{2049}\))
- \(X_3 \to Y_3\) (Tokens \(t_{2049}\) to \(t_{3073}\))
- \(X_4 \to Y_4\) (Tokens \(t_{3073}\) to \(t_{4097}\))
- Then, update the model parameters.
- Step 2 (The second iteration): The next batch will pick up from the tokens immediately following the last token of the previous batch’s last sequence.
- Next Batch: Start at \(t_{4097}\) for \(X_5\), and continue to \(X_8\).
Your point about \(t_{1025}\) being the target for \(t_{1024}\) is correct. However, your statement that “token 2 to token 1025 , token 1026 , is the second iteration” is only accurate if your batch size is 1. With a standard batch size greater than 1, the model is processing multiple independent sequences at once.
Would you like to explore how gradient accumulation is sometimes used to effectively use a larger batch size than fits in memory?