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[perf] Use direct copy (broadcast) instead of cat for k_nope/k_pe in MLA prefill #29710

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[perf] Use direct copy (broadcast) instead of cat for k_nope/k_pe in MLA prefill #29710

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[perf] Use direct copy (broadcast) instead of cat for k_nope/k_pe in …
minosfuture Nov 29, 2025
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Merge branch 'main' into optimize_cat
minosfuture Nov 29, 2025
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33 changes: 30 additions & 3 deletions vllm/v1/attention/backends/mla/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1645,6 +1645,33 @@ def get_and_maybe_dequant_weights(layer: LinearBase):
# Convert from (L, N, P) to (N, P, L)
self.W_UK_T = W_UK.permute(1, 2, 0)

def _concat_k_nope_k_pe(
self, k_nope: torch.Tensor, k_pe: torch.Tensor
) -> torch.Tensor:
"""
Efficiently concatenate k_nope and k_pe tensors along the last dimension.

This function avoids the performance penalty of torch.cat with expanded
non-contiguous tensors by pre-allocating the output and using direct copies.

Args:
k_nope: Tensor of shape [..., nope_dim]
k_pe: Tensor to broadcast and concatenate, typically shape [..., 1, pe_dim]
or [..., pe_dim]

Returns:
Tensor of shape [..., nope_dim + pe_dim]
"""
k = torch.empty(
(*k_nope.shape[:-1], k_nope.shape[-1] + k_pe.shape[-1]),
dtype=k_nope.dtype,
device=k_nope.device,
)
# Direct copies with efficient broadcasting
k[..., : k_nope.shape[-1]] = k_nope
k[..., k_nope.shape[-1] :] = k_pe
return k

def _compute_prefill_context(
self,
q: torch.Tensor,
Expand Down Expand Up @@ -1681,7 +1708,7 @@ def _compute_prefill_context(
)
k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)

k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
k = self._concat_k_nope_k_pe(k_nope, k_pe)

attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
prefill=prefill_metadata,
Expand Down Expand Up @@ -1785,7 +1812,7 @@ def _context_parallel_compute_prefill_context(
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
)
k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
k = self._concat_k_nope_k_pe(k_nope, k_pe)

attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
prefill=prefill_metadata,
Expand Down Expand Up @@ -1834,7 +1861,7 @@ def _forward_prefill(
)
k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)

k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
k = self._concat_k_nope_k_pe(k_nope, k_pe)

output_prefill = self._run_prefill_new_tokens(
prefill=attn_metadata.prefill,
Expand Down