Imagine you're studying for a test where you are given an image and need to answer the correct class. To prepare, you're given a deck of flashcards with an image on the front and the class on the back.
(Random) You shuffle the deck every time you go through it. You're forced to learn the images and their classifications without relying on any specific sequence, as the data has no signal from sequence order.
(Fixed order) Every time you go through the deck, the images appear in the exact same order. Over time you may start to unconsciously memorize the sequence of flashcards, rather than the actual classification of each image.
When it comes to actually training a model, if the batches are sampled sequentially from a dataset, it risks learning from correlations caused by the sequencing of the data, resulting in poor generalization. In contrast, when you sample the batches randomly, the model is biased and encouraged to learn features from the data itself rather than from any signals that arise from artifacts of the ordering.
Why, then are so many successful models trained on multiple passes through sequential data? Note, I'm not naive in this field, but as an infra person, random reads make my life very difficult, and if they're not necessary, I'd rather not deal with them by having to switch to an approach that can't use readahead and other strategies for high throughput io.
The engineers at DeepSeek seem extremely smart and well-funded, so my guess is they looked at the alternatives and concluded none of them would allow them to make their models work well enough.
They did this back in their trading firm days, and...
Imagine that you have a sequence of numbers. You want to randomly select a window of, say, 1024 consecutive numbers, a sequence, as input to your model. Now, say, you have n items in this sequence, you want to sample n/c (c is a constant and << 1024) sequences in total. How to do fixed shuffle?
The key is, we have overlap in data we want to read. If we brute force fixed shuffle and expand, we need to save 1024/c times more than original data.
This isn't useful for LLMs, but hey, wonder how it started?
I guess I'm much more of the "materialize the shuffle asychronously from the training loop" kind of person. I agree, the materialization storage cost is very high, but that's normally been a cost I've been willing to accept.
As an ML infra guy I have had to debug a lot of failing jobs over the years, and randomizing datapipes are one of the hardest to debug. Sometimes there will be a "record-of-death" that randomly gets shuffled into a batch, but only causes problems when it is (extremely rarely) coupled with a few other records.
I guess I'll just have to update my priors and accept that inline synchronous randomization with random reads is a useful-enough access pattern in HPC that it should be optimized for. Certainly a lot more work and complexity, hence my question of just how necessary it is.
Yeah, I don't want to do this either. This is a super special case, after exploring alternatives with our researchers it's unfortunately needed. As for record-of-death, we made sure that we do serialize all rng state and have our data pipeline perfectly reproducible even when starting from checkpoint.
Building a system for serving read-only data at NVMe SSD speed (as in IOPS) took surprisingly few effort, and is mostly enough for training data. Kudos to DeepSeek who decided to spend extra effort to build a full PFS and share it.
(Random) You shuffle the deck every time you go through it. You're forced to learn the images and their classifications without relying on any specific sequence, as the data has no signal from sequence order.
(Fixed order) Every time you go through the deck, the images appear in the exact same order. Over time you may start to unconsciously memorize the sequence of flashcards, rather than the actual classification of each image.
When it comes to actually training a model, if the batches are sampled sequentially from a dataset, it risks learning from correlations caused by the sequencing of the data, resulting in poor generalization. In contrast, when you sample the batches randomly, the model is biased and encouraged to learn features from the data itself rather than from any signals that arise from artifacts of the ordering.