Calculate the padding length needed to align sequences to a fixed model input length.
Padding brings variable-length sequences up to a fixed maximum length so they can be batched into a single tensor, but every padding token still consumes compute during training (unless masked efficiently), which is pure overhead. This calculator computes the number of padding tokens added for a given sequence and max length, the resulting padding ratio, and the total wasted tokens across a full batch — high ratios signal that bucketing or dynamic padding could meaningfully speed up training.
padding_tokens = max_seq_len - actual_length
Properly masked padding tokens shouldn't affect accuracy directly since attention masks exclude them from loss and attention computation, but they still consume memory and compute.
Use dynamic padding (pad only to the longest sequence in each batch) or length-based bucketing (group similar-length sequences together) instead of a single global max length.
In that case the sequence must be truncated rather than padded — see the Sequence Truncation Calculator for that scenario.