feat: support Qwen3.8 Flash Next with ReplaySSM

docs/qwen38_adaptation.rst

@@ -0,0 +1,48 @@
+Qwen3.8-Flash-Next
+==================
+
+Qwen3.8-Flash-Next checkpoints use the ``qwen4_exp`` model type. The model
+implementation combines gated residuals, n-gram embeddings, QSA and GDN
+attention, with dedicated multimodal, MTP and cache-state integration.
+Reusable operations live in ``lightllm/common/gated_residual``,
+``lightllm/common/ngram_embedding`` and ``lightllm/common/qsa``.
+
+CPU embedding offload is disabled by default. The optional QSA TopK selector
+is enabled by the model only for its qualified H100/Torch/Triton geometry;
+other configurations use Torch. Shared-prefix admission is limited to the
+model's single-node, DP1 normal serving mode with one-page state blocks.
+
+Validation
+----------
+
+Historical H100 measurements and their configuration are recorded in
+``qwen38_h100_performance.rst``. They cover TP8 BF16, FP32 SSM states,
+fixed MTP2 and one-page linear-attention state blocks. These results do not
+qualify PD/cross-DP serving or other cache block configurations.
+
+The four-page-block pressure-recovery configuration has unresolved text
+differences. Low-load latency still trails SGLang in some metrics. Archived
+HC and sampling experiments are not part of the runtime implementation.
+
+Rebase to upstream, 2026-11-03
+------------------------------
+
+The implementation now uses AttentionStateManager. QSA keys and positions are
+owned by the model's GQA State and participate in token copies and CPU/PD/DP
+transfer layouts. Lexical histories use a separate bounded State for reset,
+prefix snapshots, CPU/PD/DP transfers and accepted MTP candidate commits.
+The removed request/cache manager classes are not restored.
+
+Model and draft registration use the upstream lazy registry. HTTP slot waiting
+preserves atomic group allocation and current PD timeouts. The GDN MTP path uses
+the upstream recurrent kernel and its explicit run configuration.
+
+The historical H100 results above predate this interface migration. They do not
+validate the rebased runtime. GPU generation, complete PD transfer and performance
+must be rechecked before deploying a new image of this revision.
+
+CPU validation after migration: 1022 checks passed and 930 GPU cases were
+skipped. The DeepSeek V4 integration module could not be collected because
+FlashMLA is absent. Two upstream State identity cases require CUDA-backed
+pinned memory; both failures reproduce on unmodified upstream/main with CUDA
+hidden. No GPU serving, PD end-to-end or performance result is claimed here.

docs/qwen38_h100_performance.rst

@@ -0,0 +1,76 @@
+Qwen3.8-Flash-Next H100 性能与精度
+=================================
+
+当前结论：4× 的两轮吞吐、TTFT P95、完整 HTTP P95 和末次可见文本 P95 均优于冻结基线。
+低负载下仍有延迟差距，尚未完成“超过 vLLM 和 SGLang”的整体目标。
+
+测试口径
+--------
+
+* 8 × NVIDIA H100 80GB HBM3，TP8，BF16，SSM 状态 FP32；MTP 固定 2。
+* 模型版本 de4b8e4d43b917e7706784d8bb445c9af86a3540；4096 上下文、最多 8 个并发请求、512-token prefill chunk、16384 KV 容量。
+* 当前已验证的线性注意力状态页为 512 tokens，每块一页。
+* AIPerf 0.9 统一回放公开 Mooncake 轨迹中的 256 个请求，倍率为 1×、2×、4×。
+* 公开轨迹提供匿名长度、时间戳和 hash；请求正文由 AIPerf 合成，并非原始私有正文。
+* 每个 LightLLM 样本使用全新服务；vLLM、SGLang 只使用此前冻结的原始结果，未重新推理。
+* 原生 Torch 2.9.1+cu128 / Triton 3.5.1；vLLM 0.31；SGLang 0.5.22。
+* 1×/2× 吞吐受请求到达速率限制，不能当作最大处理容量；保留所有重复样本。
+
+已完成的性能样本
+----------------
+
+.. csv-table:: 延迟单位 ms；吞吐单位成功输出 tok/s
+   :header: "倍率", "框架", "样本", "吞吐 ↑", "TTFT P95 ↓", "完整 HTTP P95 ↓", "末次文本 P95 ↓", "空文本错误/256"
+
+   "1×", "LightLLM", "1", "276.14", "516.09", "3832.07", "3615.36", "2"
+   "1×", "LightLLM", "2", "276.16", "515.16", "3818.23", "3622.51", "2"
+   "1×", "SGLang", "冻结", "276.99", "610.40", "3864.63", "3329.87", "2"
+   "1×", "vLLM", "冻结", "277.72", "804.93", "4255.93", "3548.29", "1"
+   "2×", "LightLLM", "1", "546.33", "475.34", "4466.54", "4187.74", "2"
+   "2×", "SGLang", "冻结", "545.80", "600.51", "5302.52", "4178.47", "2"
+   "2×", "vLLM", "冻结", "513.20", "2933.67", "8695.77", "7227.73", "2"
+   "4×", "LightLLM", "1", "887.26", "376.17", "5794.25", "5188.47", "2"
+   "4×", "LightLLM", "2", "886.14", "390.13", "5770.75", "5151.58", "2"
+   "4×", "SGLang", "冻结", "835.28", "864.93", "5924.62", "5204.95", "2"
+   "4×", "vLLM", "冻结", "523.95", "3367.76", "9085.75", "7930.38", "1"
+
+完整 HTTP 统计包含全部 256 个请求；TTFT 和末次文本仅包含有可见文本的请求。
+空文本仍计为错误，其输出 token 不计入成功吞吐。AIPerf 导出的 request_latency
+在这些记录中对应末次可见文本，不能替代完整 HTTP/DONE 边界。
+
+精度与兼容性
+------------
+
+* 数学题正确 1272/1319，准确率 96.44%。
+* 全部 1319 个数学样例的生成 token 序列与原实现一致；分别保留 19 例截断、19 例无有效最终答案。
+* 480 个冷缓存请求和 48 个调度、缓存、EOS 请求核对通过；2788 个训练模型中间张量完全一致。
+* H100 共享组件/默认行为回归 134 项通过；本地 CPU 兼容性检查 124 项通过，零失败、错误、跳过。
+* 已完成的 5 轮回放共 1280 个输出文本与原实现一致，空文本错误单独保留。
+* TopK 为独立组件，共享选项默认关闭，Qwen4 在工厂启用，select 接口保持兼容。
+
+剩余工作与限制
+--------------
+
+* 1× 首轮末次文本 P95 为 3615.36 ms，高于 SGLang 3329.87 ms；中位请求延迟也仍落后。
+* 2× 末次文本 P95 为 4187.74 ms，比 SGLang 4178.47 ms 高约 9.28 ms。
+* 历史四页块压力恢复的文本差异仍未解决，不能将当前一页块精度结论推广到该配置。
+* 五轮回放及低负载剖析已完成；按用户要求收尾，优化目标已暂停，未标记为全面达成。
+
+原始结果与证据
+--------------
+
+* 五轮独立汇总：``artifacts/h100/261019/audits/topk-short-five-sample-summary.json``。
+* 性能 CSV：``artifacts/h100/261019/audits/topk-short-performance-current.csv``。
+* 完整精度：``artifacts/h100/261019/audits/topk-short-full-math-token-parity.json``。
+* 源码与本地验证：``artifacts/h100/261019/audits/topk-short-local-adoption.json``。
+* 重复回放状态：``artifacts/h100/261019/tuning/topk-short-canonical-candidate/progress.json``。
+
+收尾状态
+--------
+
+* 最终保留完整精度和五轮回放验证通过的 TopK 版本；H100 隔离源码已恢复到同一版本。
+* HC 小批量上投影候选已归档：30 项 CPU、203 项 GPU 检查通过，2788 个模型中间张量一致。
+  完整模型轨迹实际覆盖 8 行 BD8；其余选择形状只有组件检查。HC 尚未完成服务精度与公开回放验证，未纳入最终实现或上述性能结果。
+* 首次 HC 模型轨迹因测试插桩作用域断言失败而回滚；修复插桩并验证后重跑通过，失败记录一并保留。
+* H100 恢复后核对 949 项源码和 42 项测试/基准文件哈希；GPU 无遗留计算进程。未启动新的推理测试。
+* 收尾记录：``artifacts/h100/261019/audits/user-wrapup-qualified-topk-restored.json``。

lightllm/common/attention_state/bounded_att/gdn/create_gdn_state.py

@@ -13,8 +13,24 @@ def create_gdn_state(
     request_capacity: int,
     mtp_step: int = 0,
     device: str | torch.device = "cuda",
+    ssm_state_mode: str = "native",
+    ssm_replay_config: dict | None = None,
+    gate_lower_bound: float = -5.0,
 ) -> GDNState:
-    """创建 GDN 请求运行态，大小页池由后续容量分配流程初始化。"""
+    """创建 GDN 请求运行态。replay 模式使用独立的 ReplayGDNState。"""
+    if ssm_state_mode == "replay":
+        from .replay_gdn_state import ReplayGDNState
+
+        return ReplayGDNState(
+            config=config,
+            request_capacity=request_capacity,
+            mtp_step=mtp_step,
+            device=device,
+            ssm_replay_config=ssm_replay_config,
+            gate_lower_bound=gate_lower_bound,
+        )
+    if ssm_state_mode != "native":
+        raise ValueError(f"unknown GDN state mode: {ssm_state_mode}")
     return GDNState(
         config=config,
         request_capacity=request_capacity,