Shared CP KV materialization remained on the critical path for cached
NSA/MLA prefill batches. This change introduces a one-layer-ahead
prefetcher that materializes the cached prefix for the next layer on a
separate CUDA stream and consumes it when that layer reaches attention.
The prefetch path keeps the existing dense page-table semantics, defers
waiting until the prefetched buffer is actually consumed, and uses the
TAI optimized materialize/remap helpers when enabled before falling back
to the torch implementation.
The implementation is intentionally gated by environment variables and
keeps layer-2-only probe logging for functional confirmation without
making normal profiling noisy.
Constraint: Prefill CP shared KV must preserve existing page-table and dense KV semantics for NSA paged topk attention
Constraint: The production performance path requires SGLANG_CP_SHARED_KV_USE_TAI_MATERIALIZE=1 and logging disabled
Rejected: Wait immediately after the producer layer attention | this truncated the overlap window and hid less work
Rejected: Torch-only prefetch materialize | it bypassed the optimized TAI materialize/remap path and could erase the expected win
Confidence: medium
Scope-risk: moderate
Directive: Do not evaluate Phase8 throughput with SGLANG_CP_SHARED_KV_LOG_MLA_PREFETCH=1; use it only to confirm create/start/consume_hit behavior
Tested: Local AST parse for modified Python files
Tested: Local git diff --check
Tested: Remote g0034 container AST parse for modified files under /sgl-workspace/sglang-tai
Tested: Remote g0034 container pytest target covering Phase8 log env, TAI range materialize, optimized slot inverse/remap, and existing token TAI path
Not-tested: Full prefill/decode/router throughput after the TAI prefetch-path fix
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