[XPU] Add sparse-MLA decode/prefill metadata for the shared MLA layer

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
#52118, so it is dropped here.

Signed-off-by: Libin Tang <libin.tang@intel.com>

Author: libinta

vllm/v1/attention/backends/mla/xpu_mla_sparse.py

@@ -97,6 +97,23 @@ class XPUMLASparseMetadata(AttentionMetadata):
     block_size: int = 1
     topk_tokens: int = 2048
 
+    # The shared MLA layer (`mla_attention.py::forward_impl`) reads these
+    # decode/prefill counts unconditionally for every MLA metadata (it asserts
+    # `num_decodes`/`num_prefills`/`num_decode_tokens is not None` and uses
+    # `num_decode_tokens` to split MQA vs dense-MHA tokens). The CUDA sparse
+    # backends carry them via `SparseMLACommonMetadataBuilder`; this XPU backend
+    # builds its own metadata and previously omitted them, so a sparse-MLA
+    # (DeepSeek / GLM DSA) run on XPU crashed with
+    # `'XPUMLASparseMetadata' object has no attribute 'num_decode_tokens'`.
+    # This backend serves both prefill and decode through the top-k sparse MQA
+    # path (see `forward_mqa`), so all tokens are routed as "decode"
+    # (`num_decode_tokens == num_actual_tokens`, `num_prefills == 0`); that keeps
+    # the shared layer's `num_mha_tokens` at 0 and never enters the dense-MHA
+    # prefill branch (which needs prefill-only fields this backend lacks).
+    num_decodes: int = 0
+    num_prefills: int = 0
+    num_decode_tokens: int = 0
+
 
 @dataclass
 class XPUMLASparseMetadataBuilder(AttentionMetadataBuilder[XPUMLASparseMetadata]):
@@ -166,6 +183,11 @@ def build(
             req_id_per_token=req_id_per_token,
             block_size=self.kv_cache_spec.block_size,
             topk_tokens=self.topk_tokens,
+            # Route every token through the sparse MQA path (see the field
+            # definitions above); this backend has no dense-MHA prefill.
+            num_decodes=common_attn_metadata.num_reqs,
+            num_prefills=0,
+            num_decode_tokens=common_attn_metadata.num_actual_tokens,
         )
         return metadata