This is quite useful, as this should make training this type of LLMs much more efficient.
So this is a ternary weight LLM using quantization aware training (QAT). The activations are quantized to 8 bits. The matmal is still there, but it is multiplying the 8 bit activations by one bit values.
Quantization aware training with low bit weights seems to lead to reduced overfitting by an intrensic tendency to regularize. However, also the model capacity should be reduced compared to a model with the same number of weights and a higher number of bits per weights. It's quite possible that this only becomes apparent after the models have been trained with a significant number of tokens, as LLMs seem to be quite sparse.
Edit: In addition to the QAT they also changed the model architecture to use a linear transformer to reduce reliance on multiplications in the attention mechanism. Thanks to logicchains for pointing this out.
So a mishmash of ideas from other papers(Not to downplay the results). This is exciting times of hackery and basically using puzzle pieces and piecing together stuff.
This is the kind of stuff that can only be done so quickly by having more and more people brought into the field to try these ideas out. The more people the more permutations of ideas.
Am I reading the paper right, or is there any reason not to be cynical about these points?
1) It’s weird to choose linear attention for their implementation because that’s not what their paper is about and they claim no insights relevant to attention mechanisms.
2) By benchmarking all models this way (linear vs linear) it likely inflated their numbers over comparing their removal of matmul in a quadratic vs quadratic scenario.
3) This claim implies a comparison to the state of the art in language models where the standard is quadratic attention, and is therefore a flawed comparison:
“We processed billion-parameter scale models at 13W beyond human readable throughput, moving LLMs closer to brain-like efficiency.”
4) Those type of brain comparisons fall apart under scrutiny, are not standard in ML research and don’t mean much anyway.
5) Right up front in the abstract they make specific performance claims and imply they come from removing matmul, but don’t mention linear attention until section 4 on experiments.
Comments
The quantization approach is basically identical to the 1.58bit LLM paper:
https://arxiv.org/abs/2402.17764
The main addition of the new paper seems to be the implementation of optimized and fused kernels using triton, as seen here:
https://github.com/ridgerchu/matmulfreellm/blob/master/mmfre...
This is quite useful, as this should make training this type of LLMs much more efficient.
So this is a ternary weight LLM using quantization aware training (QAT). The activations are quantized to 8 bits. The matmal is still there, but it is multiplying the 8 bit activations by one bit values.
Quantization aware training with low bit weights seems to lead to reduced overfitting by an intrensic tendency to regularize. However, also the model capacity should be reduced compared to a model with the same number of weights and a higher number of bits per weights. It's quite possible that this only becomes apparent after the models have been trained with a significant number of tokens, as LLMs seem to be quite sparse.
Edit: In addition to the QAT they also changed the model architecture to use a linear transformer to reduce reliance on multiplications in the attention mechanism. Thanks to logicchains for pointing this out.
It's also a fundamentally different class of model than the original BitNet; it's a linear transformer, without quadratic attention.
So a mishmash of ideas from other papers(Not to downplay the results). This is exciting times of hackery and basically using puzzle pieces and piecing together stuff.
This is the kind of stuff that can only be done so quickly by having more and more people brought into the field to try these ideas out. The more people the more permutations of ideas.
Am I reading the paper right, or is there any reason not to be cynical about these points?
1) It’s weird to choose linear attention for their implementation because that’s not what their paper is about and they claim no insights relevant to attention mechanisms.
2) By benchmarking all models this way (linear vs linear) it likely inflated their numbers over comparing their removal of matmul in a quadratic vs quadratic scenario.
3) This claim implies a comparison to the state of the art in language models where the standard is quadratic attention, and is therefore a flawed comparison:
“We processed billion-parameter scale models at 13W beyond human readable throughput, moving LLMs closer to brain-like efficiency.”
4) Those type of brain comparisons fall apart under scrutiny, are not standard in ML research and don’t mean much anyway.
5) Right up front in the abstract they make specific performance claims and imply they come from removing matmul, but don’t mention linear attention until section 4 on experiments.
Good point, I went right to the bitnet code. I will correct my original post.