Mixture-of-Depths: Dynamically allocating compute in transformer-based
  language models
Transformer-based language models spread FLOPs uniformly across input
sequences. In this work we demonstrate that transformers can instead learn to
dynamically allocate FLOPs (or compute) to specific positions in a sequence,
optimising the allocation along the sequence for different layers across the
model depth. Our method enforces a total compute budget by capping the number
of tokens (k) that can participate in the self-attention and MLP computations
at a given layer. The tokens to be processed are determined by the network
using a top-k routing mechanism. Since k is defined a priori, this simple
procedure uses a static computation graph with known tensor sizes, unlike other
conditional computation techniques. Nevertheless, since the identities of the
k tokens are fluid, this method can expend FLOPs non-uniformly across the
time and model depth dimensions. Thus, compute expenditure is entirely
predictable in sum total, but dynamic and context-sensitive at the token-level.
Not only do models trained in this way learn to dynamically allocate compute,
they do so efficiently. These models match baseline performance for equivalent
FLOPS and wall-clock times to train, but require a fraction of the FLOPs per
forward pass, and can be upwards of 50\% faster to step during post-training
sampling.
660e2a251714de5d5bd49825
David Raposo
660e2a251714de5d5bd49826
Sam Ritter
660e2a251714de5d5bd49827
Blake Richards
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Timothy Lillicrap
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Peter Conway Humphreys
admin_assigned
2024-04-25T07:34:08.695Z
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/avatars/b2167b6777364b24ea3ad433655fa66d.svg
Peter Humphreys
user
peterhumphreys
660e2a251714de5d5bd4982a
Adam Santoro
Mixture-of-Depths: Dynamically allocating compute in transformer-based
  language models
Transformer-based language models spread FLOPs uniformly across input
sequences. In this work we demonstrate that transformers can instead learn to
dynamically allocate FLOPs (or compute) to specific positions in a sequence,
optimising the allocation along the sequence for different layers across the
model depth. Our method enforces a total compute budget by capping the number
of tokens (k) that can participate in the self-attention and MLP computations
at a given layer. The tokens to be processed are determined by the network
using a top-k routing mechanism. Since k is defined a priori, this simple
procedure uses a static computation graph with known tensor sizes, unlike other
conditional computation techniques. Nevertheless, since the identities of the
k tokens are fluid, this method can expend FLOPs non-uniformly across the
time and model depth dimensions. Thus, compute expenditure is entirely
predictable in sum total, but dynamic and context-sensitive at the token-level.
Not only do models trained in this way learn to dynamically allocate compute,
they do so efficiently. These models match baseline performance for equivalent
FLOPS and wall-clock times to train, but require a fraction of the FLOPs per
forward pass, and can be upwards of 50\% faster to step during post-training
sampling.
Transformer-based models can dynamically allocate computational resources to specific positions in input sequences, leading to efficient performance with reduced FLOPs.
Transformer-based language models FLOPs self-attention MLP top-k routing mechanism model depth tensor sizes compute expenditure wall-clock times forward pass post-training sampling