13 ms·

Attention calculations aren't shared across more than one vector during next token prediction (thinking and writing) which this sounds almost perfect for. Per a

Attention calculations aren't shared across more than one vector during next token prediction (thinking and writing) which this sounds almost perfect for. Per attention layer, for deepseek at 1M context, you want to broadcast a single 1KB vector to 4GB of dot products, and map reduce a 1KB vector back.

reliabilityguy 18d ago
How exactly the map-reduce will happen though? Won’t you need to do it host-side, or make a lot of reads and writes? Also, doesn’t it mean that you forgo batching?
- deleted 18d ago
  [deleted]
- ACCount37 18d ago
  Map-reduce is implemented as a rolling calc, see: online softmax in FlashAttention kernels.
  - reliabilityguy 18d ago
    Rollie calculation, like the online softmax in FA, implies a centralized computing unit that does the compute and stores the intermediate results in its registers. With PIM you have no centralized compute unit, you have a bunch of memory, and a bunch of MACs all over the place. How would you do map-reduce across multiple DIMMs w/o extra reads/writes? PIM implies some sort of distributed compute, which can work for some cases, but I am not sure LLMs are one of them.
    - whatshisface 18d ago
      Re-arrange x+(y+(z+w)) into (x+y)+(z+w) to accomplish a distributed sum. Shared bus, interconnect fabric. This is a supercomputer on a chip.
    - ACCount37 17d ago
      Is there a single reason why we can't just "distribute" the online softmax? Each die-attached PIM accelerator computes online softmax for its own KVs. Then the central unit gathers the softmax intermediates, one intermediate per die, and uses those to compute the final softmax. The PIM win is that we crater the memory traffic between the central accelerator and the memory dies for attention ops. Most of the attention bandwidth never leaves the memory. This isn't "run the entire LLM in PIM", no - this is "offload the parts of LLM that benefit from PIM the most to PIM".

reliabilityguy 18d ago

How exactly the map-reduce will happen though? Won’t you need to do it host-side, or make a lot of reads and writes? Also, doesn’t it mean that you forgo batching?

deleted 18d ago
[deleted]
ACCount37 18d ago
Map-reduce is implemented as a rolling calc, see: online softmax in FlashAttention kernels.
- reliabilityguy 18d ago
  Rollie calculation, like the online softmax in FA, implies a centralized computing unit that does the compute and stores the intermediate results in its registers. With PIM you have no centralized compute unit, you have a bunch of memory, and a bunch of MACs all over the place. How would you do map-reduce across multiple DIMMs w/o extra reads/writes? PIM implies some sort of distributed compute, which can work for some cases, but I am not sure LLMs are one of them.
  - whatshisface 18d ago
    Re-arrange x+(y+(z+w)) into (x+y)+(z+w) to accomplish a distributed sum. Shared bus, interconnect fabric. This is a supercomputer on a chip.
  - ACCount37 17d ago
    Is there a single reason why we can't just "distribute" the online softmax? Each die-attached PIM accelerator computes online softmax for its own KVs. Then the central unit gathers the softmax intermediates, one intermediate per die, and uses those to compute the final softmax. The PIM win is that we crater the memory traffic between the central accelerator and the memory dies for attention ops. Most of the attention bandwidth never leaves the memory. This isn't "run the entire LLM in PIM", no - this is "offload the parts of LLM that benefit from PIM the most to PIM".