Sequence Padding Calculator
Calculate how many padding tokens are added when sequences are padded to a fixed maximum length.
Inputs
Padding Tokens Added
384tokens
Padded Length
512tokens
Padding Ratio
75.0%
Total Padding in Batch
12,288tokens
Step by step
Padded length
max_seq_len
= 512 tokens
Padding tokens added
512 − 128
= 384 tokens
Padding ratio
384 ÷ 512
= 75.0%
How it works
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.
Formula
padding_tokens = max_seq_len - actual_length
- max_seq_len
- Fixed maximum sequence length
- actual_length
- Actual sequence length in tokens
Frequently Asked Questions
Does padding affect model accuracy?
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.
How can I reduce wasted compute from padding?
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.
What if actual length exceeds max length?
In that case the sequence must be truncated rather than padded — see the Sequence Truncation Calculator for that scenario.