fix: optimize attention decomposition - #4448
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| assert attn_mask is None, "attn_mask must be None when is_causal=True" | ||
| attn_bias = torch.zeros((L, S), dtype=query.dtype, device=device) | ||
| temp_mask = torch.ones((L, S), dtype=torch.bool, device=device).tril(diagonal=0) | ||
| attn_bias = attn_bias.masked_fill(temp_mask.logical_not(), float("-inf")) |
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For the bool attn_mask path we switched to scalar where(0 / -inf), but is_causal still uses masked_fill. Should the causal path use the same scalar where pattern?
zewenli98
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Aug 4, 2026
Restore the original -inf value and invalid-mask polarity while retaining scalar Select inputs required for compact TensorRT MHA masks.
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Description
Align attention decomposition behavior with the ONNX graph.
This PR does the following:
With this change, Torch-TensorRT is able to generate optimized engine with a pattern that an attention using a causal mask AND a padding mask, when
decompose_attention=True.Fixes # (issue)
Type of change
Checklist: