[XPU] Fix sparse-MLA metadata sync - #52066
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xinyu-intel
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Aug 13, 2026
majian4work
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Aug 13, 2026
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land https://github.com/vllm-project/vllm/pull/52118/changes to fix this issue. thanks. |
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Since issue 1 was resolved by another pr， could you split issue 2 into a standalone fix？ |
The shared MLA layer (mla_attention.py::forward_impl) reads
num_decodes/num_prefills/num_decode_tokens on every MLA metadata (it asserts
they are not None and uses num_decode_tokens to split MQA vs dense-MHA
tokens). The CUDA sparse backends supply these via
SparseMLACommonMetadataBuilder, but the XPU sparse backend builds its own
metadata and omitted them, so a sparse-MLA (DeepSeek DSA / GLM) run on XPU
crashed with:
'XPUMLASparseMetadata' object has no attribute 'num_decode_tokens'
Add the fields and populate them so all tokens route through the top-k sparse
MQA path (num_decode_tokens == num_actual_tokens, num_prefills == 0), which
this backend already implements for both prefill and decode. This keeps the
shared layer's num_mha_tokens at 0 and never enters the dense-MHA prefill
branch (whose prefill-only fields this backend does not provide).
XPU-only; does not touch CUDA/ROCm/CPU backends. The ragged-N FP8 block-scale
GEMM fix that previously accompanied this change is now covered upstream by
vllm-project#52118, so it is dropped here.
Signed-off-by: Libin Tang <libin.tang@intel.com>
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✅ Triggered Buildkite CI #83859 for commit |
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Fix issue below for sparse-MLA FP8 MoE models (e.g. GLM-5.2, DeepSeek DSA) on Intel GPUs.
Sparse-MLA metadata sync (xpu_mla_sparse.py): The shared MLA layer (mla_attention.py::forward_impl) unconditionally reads num_decodes/num_prefills/num_decode_tokens on every MLA metadata; the CUDA sparse backends carry them via SparseMLACommonMetadataBuilder, but the XPU sparse backend built its own metadata without them, so a sparse-MLA run on XPU crashed with "'XPUMLASparseMetadata' object has no attribute 'num_decode_tokens'". Add the fields and populate them so all tokens route through the top-k sparse MQA path (num_decode_tokens == num_actual_tokens, num_prefills == 0), which this backend already implements for both prefill and decode; this keeps num_mha_tokens at 0 and avoids the dense-MHA prefill branch (whose prefill-only fields this backend does not provide).
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supported_models.mdandexamplesfor a new model.