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altmly t1_j5uglpx wrote

Reply to comment by RaptorDotCpp in [D] Self-Supervised Contrastive Approaches that don’t use large batch size. by shingekichan1996

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).

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Paedor t1_j5ur6tx wrote

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.

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satireplusplus t1_j5v24u2 wrote

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.

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koolaidman123 t1_j5ujfpv wrote

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

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