Claude Sonnet-3.5 New, commenting on the limited scalability of RNNs, when prompted with ‘comment on what this would imply for the scalability of RNNs, refering (parts of) the post’ and fed https://epoch.ai/blog/data-movement-bottlenecks-scaling-past-1e28-flop (relevant to opaque reasoning, out-of-context reasoning, scheming):
’Based on the article’s discussion of data movement bottlenecks, RNNs (Recurrent Neural Networks) would likely face even more severe scaling challenges than Transformers for several reasons:
Sequential Nature: The article mentions pipeline parallelism and discusses how sequential dependencies affect scaling. From the text:
Pipeline parallelism: Different GPUs handle different sequential blocks, or layers, of a Transformer. The output of one GPU is fed to the next as an input sequentially until an input makes it through all of the blocks of the model.
RNNs have even stronger sequential dependencies than Transformers, as each token’s processing depends directly on the previous token’s hidden state. This would make them much harder to parallelize effectively.
Latency Wall Impact: The article notes that:
The basic reason behind the latency wall is that a bigger model typically requires more gradient steps during training, and if these have to be done in a fixed time window, this means each gradient step must take a shorter time as we scale the models we’re training.
RNNs would hit this latency wall even harder because their sequential processing means they can’t take advantage of some of the parallelization strategies discussed in the article, particularly tensor parallelism which requires:
after every one or two matrix multiplications (depending on the exact implementation) the GPUs need to synchronize results with each other.
Batch Size Scaling: The article suggests that one potential solution to scaling limits is:
If we can find a way to aggressively scale batch sizes together with model sizes, we can push out the latency wall.
However, RNNs are typically trained with truncated backpropagation through time, which limits how much you can effectively increase the batch size without losing long-range dependencies. This would make it harder to apply this particular solution to RNNs.
This analysis suggests that while Transformers are approaching scaling limits around 2e28 FLOP, RNNs would likely hit prohibitive scaling bottlenecks at significantly lower compute levels due to their inherently sequential nature and limited parallelization options.′
Claude Sonnet-3.5 New, commenting on the limited scalability of RNNs, when prompted with ‘comment on what this would imply for the scalability of RNNs, refering (parts of) the post’ and fed https://epoch.ai/blog/data-movement-bottlenecks-scaling-past-1e28-flop (relevant to opaque reasoning, out-of-context reasoning, scheming):
’Based on the article’s discussion of data movement bottlenecks, RNNs (Recurrent Neural Networks) would likely face even more severe scaling challenges than Transformers for several reasons:
Sequential Nature: The article mentions pipeline parallelism and discusses how sequential dependencies affect scaling. From the text:
RNNs have even stronger sequential dependencies than Transformers, as each token’s processing depends directly on the previous token’s hidden state. This would make them much harder to parallelize effectively.
Latency Wall Impact: The article notes that:
RNNs would hit this latency wall even harder because their sequential processing means they can’t take advantage of some of the parallelization strategies discussed in the article, particularly tensor parallelism which requires:
Batch Size Scaling: The article suggests that one potential solution to scaling limits is:
However, RNNs are typically trained with truncated backpropagation through time, which limits how much you can effectively increase the batch size without losing long-range dependencies. This would make it harder to apply this particular solution to RNNs.
This analysis suggests that while Transformers are approaching scaling limits around 2e28 FLOP, RNNs would likely hit prohibitive scaling bottlenecks at significantly lower compute levels due to their inherently sequential nature and limited parallelization options.′
I’m curious how these claims relate to what’s proposed by this paper. (note, I haven’t read either in depth)