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What a Transformer reuses—and what it doesn’t
A Transformer reuses a layer’s computation rules and learned parameters; it does not necessarily reuse the same state by state dynamics. Attention patterns and changes to internal representations depend on the current activations.[13] Under specific pre LayerNorm assumptions, a uniform shift to a token’s features ca...
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Key takeaways
- A Transformer reuses a layer’s computation rules and learned parameters; it does not necessarily reuse the same state by state dynamics.
- Attention patterns and changes to internal representations depend on the current activations.[13]
- Under specific pre LayerNorm assumptions, a uniform shift to a token’s features can produce an identical perturbation response along that direction.[12]
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The key distinction is between reusing a computation rule and reusing the same response. In a Transformer, a given layer applies the same learned parameters and computational structure to different activation states. But the results of that computation can vary with the input state.13
A layer’s structure includes its attention and feed-forward sublayers, along with residual connections. In self-attention, the current activations determine the queries, keys and values, so the attention pattern—and the effect of a particular perturbation—can change from one state to another.13
In other words:
- Reused: The wiring and learned parameters of a particular layer. This does not mean different layers necessarily share weights.
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- Not guaranteed to be reused: The layer’s specific local response to a disturbance. That response can depend on the current activations, including how queries and keys interact.
- A special case: In the stipulated pre-LayerNorm setup, a uniform shift across a token’s features can be removed by normalization and then carried through the residual path. If the stated conditions hold for every branch along the way, the internal response to that shift is identical along that direction.
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So the precise answer is: Transformers reuse fixed operators across activation states; that alone does not establish identical dynamics across those states. The pre-LayerNorm shift is one specific case in which identical responses can arise. Other directions require their own justification.
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What is the short answer to "What a Transformer reuses—and what it doesn’t"?
A Transformer reuses a layer’s computation rules and learned parameters; it does not necessarily reuse the same state by state dynamics.
What are the key points to validate first?
A Transformer reuses a layer’s computation rules and learned parameters; it does not necessarily reuse the same state by state dynamics. Attention patterns and changes to internal representations depend on the current activations.[13]
What should I do next in practice?
Under specific pre LayerNorm assumptions, a uniform shift to a token’s features can produce an identical perturbation response along that direction.[12]
Sources
doi.orgCan Graph Learning Learn Circuits?