Add selectable masking strategies for attention#204
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LoserCheems merged 1 commit intomainfrom Nov 6, 2025
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Introduces selectable masking strategies to support both top-k and ReLU flows, enabling experimentation with bias-driven sparsity. Normalizes the top-k path to use detached bias casting and rejects unsupported mask types to avoid silent misuse.
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ftgreat,
juliohsu,
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zacliu2023
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ftgreat,
juliohsu,
SNHuan,
Thanksyy,
wubingheng111 and
zacliu2023
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Pull Request Overview
This PR refactors the mask generation functionality to support multiple mask types. It renames dynamic_mask to topk_mask, introduces a new relu_mask function, and adds a type parameter to create_mask to select between different masking strategies.
- Renamed
dynamic_maskfunction totopk_maskfor clarity - Added new
relu_maskfunction implementing ReLU-based masking - Updated
create_maskto support both "topk" and "relu" mask types via a newtypeparameter
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| attention_mask = relu_mask( | ||
| attention_bias=attention_bias, | ||
| attention_mask=attention_mask, | ||
| window_size=window_size, |
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The relu_mask function does not accept a window_size parameter, but it's being passed here. This will cause an error when type == 'relu' is used. The window_size parameter should be removed from this function call, or the relu_mask function signature should be updated to accept and use it.
Suggested change
| window_size=window_size, |
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Introduce selectable masking strategies for attention mechanisms, enabling both top-k and ReLU flows. This allows for experimentation with bias-driven sparsity and normalizes the top-k path to prevent misuse of unsupported mask types.