feat: add attention dimension dispatch
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@@ -244,3 +244,55 @@ def attn_paged_decode(
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return _torch_fallback(
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q, k, v, mask, causal_offset, scale, q_layout=li, kv_layout=1
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)
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def attention(
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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mask: torch.Tensor | None = None,
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causal_offset: int = -1,
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scale: float = 0.0,
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layout: str = "bhld",
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) -> torch.Tensor:
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"""Dispatch to decode or prefill attention based on the query length.
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A query length of one is the decode case; longer queries use prefill.
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The paged-cache decode path cannot be selected here because its page-table
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arguments are not part of this interface.
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"""
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li = _parse_layout(layout)
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if q.ndim not in (2, 3, 4) or k.ndim != q.ndim or v.ndim != q.ndim:
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raise ValueError(
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"q, k, and v must all have the same rank in {2, 3, 4}, "
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f"got {q.ndim}D, {k.ndim}D, {v.ndim}D"
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)
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if k.shape != v.shape:
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raise ValueError(
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f"k and v must have the same shape, got {k.shape} and {v.shape}"
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)
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original_ndim = q.ndim
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if original_ndim == 2:
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# [L, D] -> [1, 1, L, D] or [1, L, 1, D]
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q = q.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
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k = k.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
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v = v.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
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elif original_ndim == 3:
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# [B, L, D] -> single-head 4D input.
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q = q.unsqueeze(1 if li == 0 else 2)
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k = k.unsqueeze(1 if li == 0 else 2)
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v = v.unsqueeze(1 if li == 0 else 2)
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q_len = q.size(2 if li == 0 else 1)
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if q_len == 1:
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out = attn_decode(q, k, v, mask, causal_offset, scale, layout)
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else:
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out = attn_prefill(q, k, v, mask, causal_offset, scale, layout)
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if original_ndim == 2:
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return out.squeeze(0).squeeze(0 if li == 0 else 1)
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if original_ndim == 3:
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return out.squeeze(1 if li == 0 else 2)
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return out
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