[ROCm][Performance] Eliminate per-decode allocations and output copy … - #50566
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…in sparse MLA decode Reduce decode latency for DeepSeek V4 sparse MLA attention on ROCm by removing redundant memory allocations and a device-to-device copy that occurred on every decode forward pass. Changes: 1. Thread the caller's output buffer through the decode call chain (rocm_sparse_attn_decode → _rocm_sparse_attn_decode_triton → _rocm_sparse_attn_decode_ragged_triton) so the Triton kernel writes directly into the final destination, eliminating one torch.empty_like allocation and one .copy_() per decode step. 2. Pre-allocate split-K partition buffers (part_m, part_l, part_acc) at maximum size on first use and slice into them per decode call, avoiding three torch.empty allocations per decode step on gfx942/gfx950. Both optimizations are backward-compatible: all new parameters are optional with None defaults, so existing callers and tests continue to work unchanged. Signed-off-by: wangchuanbo <wangchuanbo@xcoresigma.com>
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Coordination note: I opened draft #52212 for the default Triton DSV4 sparse-decode long-context work (split ceiling 16→32, constexpr cache geometry, UE8M0 exponent-bit decoding, and an 8,192-row correctness case). It includes direct-output reuse as part of the integrated tested kernel and explicitly credits this PR for the overlapping idea. If #50566 lands first, I will reconcile that hunk before marking #52212 ready. One integration detail: #50566 currently sizes reusable split-K buffers with |
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Summary
Reduce decode latency for DeepSeek V4 sparse MLA attention on ROCm by removing redundant memory allocations and a device-to-device copy on every decode forward pass.
Changes
1. Eliminate output tensor allocation + copy (
vllm/v1/attention/ops/rocm_aiter_mla_sparse.py)Previously,
rocm_sparse_attn_decode()allocated a fresh bf16 output tensor inside_rocm_sparse_attn_decode_ragged_triton, then copied it back to the caller'soutputbuffer via
output.copy_(attn_out.to(output.dtype)). Since both tensors are bf16 with identical shape and the Triton kernel uses explicit stride parameters, the caller'soutput buffer is now passed directly to the kernel, removing one
torch.empty_like+ one.copy_()per decode step.2. Pre-allocate split-K partition buffers (
vllm/models/deepseek_v4/amd/rocm.py+vllm/v1/attention/ops/rocm_aiter_mla_sparse.py)On gfx942/gfx950, the split-K decode path allocates three scratch tensors (
part_m,part_l,part_acc) on every forward call. These are now lazily allocated once atmaximum size (
[max_num_batched_tokens, 16, n_local_heads, ...]) and sliced per-call, eliminating 3×torch.emptyper decode step.Backward Compatibility
All new parameters (
out,split_k_buffers) areOptionalwithNonedefaults. WhenNone, the functions allocate internally as before. Existing tests and callers areunaffected.
Testing
test_sparse_attn_decode_ragged_kernel,test_sparse_attn_decode_split_k_kernel) pass unchanged (new parameters are optional)Expected Impact
torch.emptyallocations per decode forward (1 output + 3 split-K).copy_()per decode forward