Reducing Memory Usage

Truncation

Sequence lengths in the dataset can vary widely, and by default, TRL does not modify the data. When data is batched, sequences are padded to match the longest one in the batch, which can cause high memory usage, even if most sequences are relatively short.

To reduce memory usage, it’s important to truncate sequences to a reasonable length. Even discarding just a few tokens from the dataset can result in significant memory savings by minimizing unnecessary padding. Truncation is a good practice and should always be applied to ensure efficient use of resources. While the truncation limit doesn’t need to be overly restrictive, setting a sensible value is essential for optimal performance.

DPO truncation is applied first to the prompt and to the completion via the max_prompt_length and max_completion_length parameters. The max_length parameter is then used to truncate the resulting sequence.

To set the truncation parameters, use the following code snippet:

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from trl import DPOConfig

training_args = DPOConfig(..., max_prompt_length=..., max_completion_length=..., max_length=...)

Packing

Truncation has several drawbacks:

1. Loss of information: Key data at the end of a sequence may be discarded.
2. Choosing truncation length: Too short loses data; too long undermines efficiency.

Packing, introduced in Raffel et al., 2020, addresses these issues by grouping sequences instead of truncating. It concatenates and splits dataset sequences into the desired lengths.

Packing eliminates padding, preserves all sequence information, and allows for flexible sequence lengths, making it a more efficient alternative to truncation. To enable packing, use packing=True in the SFTConfig:

from trl import SFTConfig

training_args = SFTConfig(..., packing=True, max_seq_length=512)