feat: SGLang-style paged attention kernels replace page-table path

- PagedAttentionParams uses flat KV pool + req_to_token + kv_indptr/qo_indptr instead of page_table
- MMA split-KV decode and split-Q prefill kernels with indirect ragged-batch addressing
- Prefill kernel accepts 4D mask (causal-aware); decode kernel supports 2D mask
- CudaBackend is inference-only: kv_cache=None raises, no torch fallback
- benchmark.py: required --ckpt, --backend/--compare options
- Parallel build isolates build-temp/build-lib per subprocess
- Standalone test covers decode/prefill with mask, 27 cases pass

csrc/build.py

@@ -72,4 +72,5 @@ def register(name: str, sources: list[str] | None = None, **kwargs):
 register("attn_decode")
 register("attn_prefill")
 register("attn_paged_decode")
+register("attn_paged_prefill")
 register("rotary_emb")