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maizeq t1_jd6u4kb wrote

Reply to comment by artsybashev in [R] SPDF - Sparse Pre-training and Dense Fine-tuning for Large Language Models by CS-fan-101

The sparsity they describe in this link entails randomly pruning weights (i.e. not specific channels like depthwise convolutions), which is what Graphcore is calling "unstructured".

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osdd_alt_123 t1_jd6ufjz wrote

Nvidia has 2:4 structured sparsity in the Ampere architecture and one or two below as well, if memory serves.

So in a block of 4, you have to have 2 dropped and 2 retained. It's how they claim their 2x throughput at the hardware level.

You can, however, emulate sparsity in a variety of other ways that are higher than the hardware level. Hope this helps.

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maizeq t1_jd76a7x wrote

Ah I see, thank you for the clarification.

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brownmamba94 t1_jd8lqry wrote

Also, the N:M sparsity structure is much more constrained in terms of mask diversity compared to unstructured sparsity. In Table 1 in the N:M Transposable sparsity paper, they present the mask diversity constraint between different sparsity techniques (both unstructured and structured), and as expected unstructured sparsity achieves the best. I think this is important especially for dynamic sparse training because now the algorithm has a much larger search space to explore sparse subnetworks. Also, imposing structured sparsity like N:M sparsity tends to reduce the expressivity of a weight matrix at higher sparsity levels, which can be a constraint if you want to get high compression ratios.

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