I managed to use SwAV on 1 GPU (8GB), batch size 240, 224x224 images, FP16, ResNet18.

Of course it works, the problem isn't just the batch size but the accuracy - batchsize trade-off, and the accuracy was quite bad (still usable for my task though). If 50% top5 on imagenet is ok for you, you can do it. But I'm not sure there are many tasks where it makes sense.

Perhaps contrastive learning isn't the best for single GPU. I'm not sure about the current SOTA on this task.

For 224x224 images, sure. But for images with large sizes, for example satellite images, it is hard to get 200+ batch size for a single gpu.

I think single GPU for SSL contrastive learning is a research direction to pursue, I'm not sure if anyone published papers on it, but if there's none, I'm surprised.

How about mocov2? That should work on a single gpu

If you are willing to trade time for batch size you can try with gradient accumulation

Gradient accumulation is tricky for contrastive methods that rely on having lots of negatives in a batch.

Curious about this, I have not read any paper related. What is its effect on the performance (accuracy, etc) ?

It depends on implementation. Naive gradient accumulation will probably give better results than small batches, but as u/RaptorDotCpp mentioned, if you relay on many negative samples inside one batch, it will still be worse than a large batch training.

There is also a cool paper about gradient caching, which somehow solves this issue, but again with an additional penalty on training speed. https://arxiv.org/pdf/2101.06983v2.pdf

exactly the paper I need to read! Thanks!

As said in the topic, gradient accumulation is not a solution. However, gradient checkpointing could be. https://paperswithcode.com/method/gradient-checkpointing It recompute some of the features map during backwards pass so that they are not stored in memory. So you can fit bigger batch size

There is a great paper about analyzing batch size vs accuracy correlation. They propose loss function, which is able to learn SimClr on bs=256 instead of 4k. So, there is some research in this domain. https://arxiv.org/abs/2110.06848

Barlow twins, maybe? Easy to implement and batch size effective.

FastSiam: SimSiam that fit on one GPU small batch size (down to 32 smth) https://dl.acm.org/doi/abs/10.1007/978-3-031-16788-1_4

I'm confused. Gradient accumulation is exactly equivalent to batching as long as the data is the same, unless you use things like batch norm (you shouldn't).

contrastive methods require in-batch negatives, you can't replicate that with grad accumulation

cache your predictions on each smaller batch w/ labels until you get a similar batch size, then run your loss function

so instead of calculating loss and accumulating like gradient accumulation, you only calculate loss once you reach the target batch size

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The trouble is that contrastive methods often compare elements from the same batch, instead of treating elements as independent like pretty much all other ML (except batchnorm).

As a simple example with a really weird version of contrastive learning: with a batch of 2N, contrastive learning might use the 4N^2 distances between batch elements to calculate a loss, while with two accumulated batches of N, contrastive learning could only use 2N^2 pairs for loss.

If you don't have 8 GPUs you can always run the same computation 8x in series on one GPU. Then you merge the results the same way the parallel implementation would do it. In most cases that's probably gonna end up being a form of gradient accumulation. Think of it this way: you basically compute your distances on a subset of n, but since there are much fewer pairs of distances, the gradient would be noisy. So you just run it a couple of times and average the result to get an approximation of the real thing. Very likely that this is what the parallel implementation does too.

Non-contrastive approaches e.g. SWaV can handle lower batch sizes https://arxiv.org/abs/2006.09882

(https://arxiv.org/pdf/2106.04156.pdf ) This was a cool paper from NeurIPS 2020 which aimed to theoretically explain the success of CL by relating it spectral clustering. They present a loss with a very similar form to InfoNCE, which they use for their theory. One of the plus sides found was it worked well with small batch sizes.

(https://arxiv.org/abs/2110.06848) I skimmed this work a while back, one of their main claims is that this approach works with small batch sizes.

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Thats not the same thing...

Gradient accumulation calcs the loss on each batch, it doesnt work with in batch negatives because you need compare input from batch 1 to inputs of batch 2, hence offloading and caching predictions, then calculating the loss with 1 batch

Thats why gradient accumulation doesnt work to simulate large batch sizes for contrastive learning, if youre familiar with it

Momentum encoder based approaches don’t need large batch size because they use a queue for storing negatives rather than taking negatives from the mini-batch

https://github.com/facebookresearch/moco

I saw an implementation of that paper here: https://github.com/raminnakhli/Decoupled-Contrastive-Learning

And I saw also that the same paper is rejected at NeurIPS'21 becuase of its similar impact on other methods like Barlow Twins, SimSiam, BYOL, etc.

However, at first glance at the re-implemented results, it works great on small batch-size indeed.

How do you implement the cacheing? You have to cache all the activations to do the bawards pass

VICReg

Is there any reason you'd like a contrastive algorithm? (intra-class discrimination?)

Barlow twins showed to work quite well with lower batches (32) and HSIC-SSL is a nice variant on this style of learning if you only care about clusters. Im sure simsiam is fine too (avoid BYOL for small batches).

In terms of contrastive approaches, methods that avoid any "coupling" mentioned in DCL for the negative terms will work with smaller batch sizes (contrastive estimates converge to mle assuming large noise samples). This is seen in the spectral algorithm or in align-uniform. These work because they ignore the comparing the representations from the same augmented samples. SWAV also does this by contrastive prototypes which are basically free variables which don't have gradients that conflict with any alignment goal. I think it's fair to say that algorithms with LSE transforms are less stable for small batch sizes since the gradients will be biases to randomly coupled terms. With sufficiently many terms this coupling matters less.

From what i've noticed, methods that avoid comparing the augmented views of the same base sample will require slightly more tuning to get things just right. (align + weight * diversity)

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Notes: NNCLR is nicer than moco imo. VicReg is good but is a mess to finetune. I am assuming youre using a CNN and have omitted transformer and masked based algorithms.