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What are the potential consequences? Does this open doors to faster edge inference or improved capabilities?
Cool. Important note: """ One may ask whether the conditionality introduced by the use of CMM does not make FFFs incompatible with the processes and hardware already in place for dense matrix multiplication and deep learning more broadly. In short, the answer is “No, it does not, save for some increased caching complexity." """ It's hard to beat the hardware lottery!
Infact, as stated in the paper, this is bad news > We therefore leave the attention layers untouched Meaning, presumably, that the GPU memory remains the bottleneck Flops really are quite cheap by now, e.g. vision inference chip ~$2/teraflop/s !!
What are the potential consequences? Does this open doors to faster edge inference or improved capabilities?
Both. Cheaper CPU-based inference, GPUs are not as competitive for sparse linear algebra. This could lead to much larger models, as you only touch a small portion of the matrix during inference. However, the training here is still dense-LA on a GPU, so you still blow up the compute cost when increasing model size.
https://arxiv.org/abs/2308.14711
An attempt at a summary: They use a sigmoid function to make differentiable "soft" branches, and stack them to construct a binary tree, with the goal of only taking one branch at inference time (but training the whole tree) leading to log(W) instead of W inference cost. They gradually harden the branches so they become hard branches at the end of training.
A branch is computed as branch(input, N), with a neural network N computing a scalar c=N(input), then using a sigmoid to do a soft branch by returning the weighted sum of the recursive call s(c)*branch(input, N_left) + (1-s(c)) * branch(input, N_right) (the two weights s(c) and 1-s(c) sum to 1). They only do "proper processing" using the leaf nodes.
Then they add a new loss term that encourages hard decisions by minimising the entropy of the Bernoulli distribution, making the 2 weights converge to 0 and 1, at which point only one branch needs to be taken at inference. They also state that this hardening often happens automatically though.
It's a simple idea but the loss formulation is nice, you usually want your loss terms to be a measure of information.
Could this be applied to other models like Llama2 or Mistral?
What are the potential consequences? Does this open doors to faster edge inference or improved capabilities?
Cool. Important note: """ One may ask whether the conditionality introduced by the use of CMM does not make FFFs incompatible with the processes and hardware already in place for dense matrix multiplication and deep learning more broadly. In short, the answer is “No, it does not, save for some increased caching complexity." """ It's hard to beat the hardware lottery!
Infact, as stated in the paper, this is bad news > We therefore leave the attention layers untouched Meaning, presumably, that the GPU memory remains the bottleneck Flops really are quite cheap by now, e.g. vision inference chip ~$2/teraflop/s !!
We are creating a monster.