{"id":122715,"date":"2026-11-05T14:24:58","date_gmt":"2026-11-05T06:24:58","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/122715.html"},"modified":"2026-11-05T14:24:58","modified_gmt":"2026-11-05T06:24:58","slug":"openash-v3-5-%e6%9e%b6%e6%9e%84%e8%bf%ad%e4%bb%a3%e6%8a%a5%e5%91%8a%ef%bc%9a%e8%a1%b0%e5%87%8f%e6%9c%80%e5%a4%a7%e5%80%bc%e4%b8%89%e9%80%9a%e9%81%93%e6%b7%b7%e5%90%88%e6%9e%b6%e6%9e%84","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/122715.html","title":{"rendered":"OpenASH v3.5 \u67b6\u6784\u8fed\u4ee3\u62a5\u544a\uff1a\u8870\u51cf\u6700\u5927\u503c\u4e09\u901a\u9053\u6df7\u5408\u67b6\u6784"},"content":{"rendered":"

\/* MD \/ \u5bcc\u6587\u672c\u4e2d\u7684 .toc\uff08\u542b\u535a\u5ba2\u56ed\u642c\u5bb6\u7b49\u5d4c\u5957\u7ed3\u6784\uff09\uff1b.toc-box \u5728\u4fa7\u680f\uff0c\u4e0d\u53d7\u5f71\u54cd *\/
\n #content_views .toc,
\n \/* \u7f16\u8f91\u5668\u5e38\u5728\u76ee\u5f55\u524d\u540e\u63d2\u5165\u7a7a p\uff08:empty \u4ecd\u5360 20px\uff09\uff0c\u4e00\u5e76\u53bb\u6389\u907f\u514d\u9876\u7a7a\u9699 *\/
\n #content_views.markdown_views > p:empty:has(+ .toc),
\n #content_views.markdown_views > .toc + p:empty,
\n \/* \u5bcc\u6587\u672c\u65e7\u7248\u76ee\u5f55\u6807\u8bb0 *\/
\n #content_views.htmledit_views #main-toc,
\n #content_views.htmledit_views #hr-toc,
\n #content_views.htmledit_views p[id*=\\”-toc\\”] {
\n display: none !important;
\n }
\n \/* \u76ee\u5f55\u53bb\u6389\u540e\uff0c\u7d27\u8ddf\u7684\u9996\u4e2a\u6807\u9898\u4e0d\u518d\u591a\u51fa\u4e00\u5757\u4e0a\u8fb9\u8ddd *\/
\n #content_views.markdown_views > .toc + h1,
\n #content_views.markdown_views > .toc + h2,
\n #content_views.markdown_views > .toc + h3,
\n #content_views.markdown_views > .toc + h4,
\n #content_views.markdown_views > .toc + p:empty + h1,
\n #content_views.markdown_views > .toc + p:empty + h2,
\n #content_views.markdown_views > .toc + p:empty + h3,
\n #content_views.markdown_views > .toc + p:empty + h4 {
\n margin-top: 0 !important;
\n }<\/p>\n

\u7ed3\u8bba：\u540c\u9884\u7b97（15000 \u6b65 \u00d7 184M tokens\u300195-105M \u53c2\u6570）\u4e0b，v3.5 \u5728\u5168\u90e8 7 \u4e2a\u4e0a\u4e0b\u6587\u957f\u5ea6\u4e0a\u8d85\u8fc7\u540c\u9884\u7b97\u73b0\u4ee3 Transformer \u57fa\u7ebf（RoPE + RMSNorm + SwiGLU + FlashAttention），\u603b\u4f53 PPL \u9886\u5148 10.7%，2048 \u957f\u5ea6\u533a\u9886\u5148 58.5%，\u540c\u65f6\u4fdd\u6301 O(S) \u8bad\u7ec3\u590d\u6742\u5ea6\u4e0e O(1) \u63a8\u7406\u72b6\u6001\u3002<\/p>\n

\n

1. \u67b6\u6784\u6982\u8981<\/h3>\n

v3.5 \u7684\u6bcf\u5c42\u6ce8\u610f\u529b\u7531\u4e09\u6761 O(1) \u72b6\u6001\u901a\u9053\u6df7\u5408\u6784\u6210，\u5168\u90e8\u57fa\u4e8e\u6bcf\u5c42\u4e13\u5c5e\u7684\u4f4e\u79e9\u72b6\u6001\u5d4c\u5165 state_em(23004\u00d764) @ state_proj(64\u2192768)（c \u6d41，\u4e00\u6b21\u67e5\u8868\u4e09\u5904\u590d\u7528）：<\/p>\n\n\u901a\u9053\u9012\u63a8\u68c0\u7d22\u7c7b\u578b\u8bb0\u5fc6\u5c3a\u5ea6（\u5b9e\u6d4b）\u4f5c\u7528<\/tr>\n\n\n\n\n
\u5feb max-scan<\/td>\ne_j = max(\u03b3_j\u00b7e_{j-1}, c_j)，\u03b3 = exp(\u2212softplus(c\u00b7v+b))<\/td>\nquery \u65e0\u5173（\u65b0\u9c9c\u5cf0\u503c\u8ddf\u8e2a）<\/td>\n\u534a\u8870\u671f ~4 tok<\/td>\n\u5c40\u90e8\u4fe1\u53f7\u3001\u6574\u4f53\u8d28\u91cf<\/td>\n<\/tr>\n
\u6162 max-scan<\/td>\n\u540c\u4e0a，\u03b3 \u2208 [0.995, 0.9995]（\u5c42 6-11）<\/td>\nquery \u65e0\u5173（\u957f\u65f6\u4fdd\u6301）<\/td>\n\u534a\u8870\u671f ~245 tok<\/td>\n\u8fdc\u7a0b\u4fe1\u606f\u4fdd\u6301\u3001\u957f\u5ea6\u5916\u63a8<\/td>\n<\/tr>\n
KV \u68c0\u7d22<\/td>\nS_j = a_j\u00b7S_{j-1} + \u03b2\u00b7k\u0302_j\u2297c_j，\u8bfb\u51fa r_j = q_j\u00b7S_j<\/td>\nquery \u76f8\u5173（k\u0302 L2 \u5f52\u4e00\u5316）<\/td>\na\u22480.98 \u53ef\u5b66\u4e60<\/td>\n\u5f52\u7eb3\u5934\/\u7cbe\u786e\u952e\u503c\u53ec\u56de<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e09\u901a\u9053\u8f93\u51fa\u5728 gen_model \u591a\u9879\u5f0f\u878d\u5408（Triton kernel）：head(cat[a,b,c,d,e])\u00b7e + a\u00b7b + \u03b1\u2081\u00b7b + \u03b1\u2082\u00b7d + a\u00b7(\u03b1\u2083\u00b7e+d) + b\u00b7(c+e) + c\u00b7e + kv_gate\u00b7r\u3002<\/p>\n

\u914d\u5957\u73b0\u4ee3\u5316：pre-norm RMSNorm \u00d72\u3001SiLU-GLU FFN\u3001final RMSNorm\u3001max_seq_len=2048\u3001\u5206\u5c42\u591a\u5c3a\u5ea6 \u03b3 \u521d\u59cb\u5316（v3.3）\u3002<\/p>\n

2. \u6f14\u8fdb\u94fe\u4e0e\u6d88\u878d\u6570\u636e（\u5168\u90e8\u540c\u9884\u7b97 A\/B，seed\/\u6570\u636e\u987a\u5e8f\u5bf9\u9f50）<\/h3>\n\n\u7248\u672c\u5173\u952e\u6539\u52a8val PPL\u76f8\u5bf9\u4e0a\u4e00\u7248<\/tr>\n\n\n\n\n\n\n\n
v3.0 pure cummax<\/td>\n\u65e0\u8870\u51cf<\/td>\n84.53<\/td>\n\u2014<\/td>\n<\/tr>\n
v3.1 decay-max<\/td>\n\u8870\u51cf\u626b\u63cf + Triton \u8bad\u7ec3\/\u63a8\u7406 kernel<\/td>\n59.07<\/td>\n\u221230.1%<\/td>\n<\/tr>\n
v3.2 \u73b0\u4ee3\u5316<\/td>\npre-norm RMSNorm + SiLU + \u03b3 \u6162\u5c3e + \u8f93\u5165\u76f8\u5173\u8870\u51cf + pos 2048<\/td>\n46.90<\/td>\n\u221220.6%<\/td>\n<\/tr>\n
v3.3 \u5206\u5c42\u6162\u901a\u9053<\/td>\n\u5c42 6-11 \u6162 \u03b3（245 tok \u534a\u8870\u671f\u5b58\u6d3b）<\/td>\n46.80<\/td>\n\u6301\u5e73（\u957f\u7a0b +）<\/td>\n<\/tr>\n
v3.4 +KV \u901a\u9053<\/td>\nquery \u76f8\u5173\u68c0\u7d22（sum \u5199\u5165）<\/td>\n41.17<\/td>\n\u221212.0%<\/td>\n<\/tr>\n
v3.5 \u5f52\u4e00\u5316\u952e<\/td>\nk\u0302 L2 \u5f52\u4e00\u5316 + per-head \u5199\u5165\u95e8<\/td>\n40.27<\/td>\n\u22122.2%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

3. \u4e3b\u5b9e\u9a8c\u4e00：2500 \u6b65\u5c0f\u6570\u636e（11.5M tokens）\u5bf9\u51b3<\/h3>\n

v3.5 37.05 vs tf 37.03 \u2014\u2014 \u603b\u4f53\u8ffd\u5e73（\u00b10.05%）\u3002\u957f\u5ea6\u6269\u5c55\u4e0a v3.5 \u4ece 512 \u8d77\u53cd\u8d85（512: 37.01 vs 39.32）\u3002<\/p>\n

4. \u4e3b\u5b9e\u9a8c\u4e8c：15000 \u6b65\u5927\u6570\u636e（488M tokens \u6570\u636e\u96c6\u3001184M \u8bad\u7ec3 tokens）\u2014\u2014\u6700\u7ec8\u88c1\u51b3<\/h3>\n\n\u4e0a\u4e0b\u6587 Lv3.5 PPLtf PPLv3.5 \u9886\u5148<\/tr>\n\n\n\n\n\n\n\n\n
64<\/td>\n18.56<\/td>\n19.47<\/td>\n+4.7%<\/td>\n<\/tr>\n
128<\/td>\n17.03<\/td>\n18.05<\/td>\n+5.7%<\/td>\n<\/tr>\n
256<\/td>\n16.36<\/td>\n17.69<\/td>\n+7.5%<\/td>\n<\/tr>\n
384<\/td>\n16.01<\/td>\n17.93<\/td>\n+10.7%<\/td>\n<\/tr>\n
512<\/td>\n15.92<\/td>\n19.01<\/td>\n+16.3%<\/td>\n<\/tr>\n
1024<\/td>\n15.71<\/td>\n25.55<\/td>\n+38.5%<\/td>\n<\/tr>\n
2048<\/td>\n15.64<\/td>\n37.68<\/td>\n+58.5%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    \n
  • \u4e03\u4e2a\u957f\u5ea6\u5168\u90e8\u9886\u5148；v3.5 \u66f2\u7ebf\u968f\u957f\u5ea6\u6301\u7eed\u4e0b\u964d（42.2\u219215.6），tf \u4ece 512 \u8d77\u5d29\u6e83（\u65e0\u4f4d\u7f6e\u5916\u63a8\u9002\u914d）<\/li>\n
  • \u8bad\u7ec3\u8f68\u8ff9\u5448\u6559\u79d1\u4e66\u5f0f\u4ea4\u53c9：\u65e9\u671f tf \u7565\u9886\u5148，step ~8000 \u8d77\u4ea4\u53c9，\u4e4b\u540e v3.5 \u6301\u7eed\u62c9\u5f00\u4e14\u7ec8\u70b9\u672a\u9971\u548c<\/li>\n
  • \u8fdc\u7a0b\u590d\u5236（\u91cd\u590d span \u63a2\u9488）：\u7edd\u5bf9 CE \u5168\u7a0b\u66f4\u4f4e（3.13~4.12 vs 3.32~5.11）；gain \u53e3\u5f84 gap=128 \u53cd\u8d85（+0.74 vs +0.58），gap=32 tf \u4fdd\u7559\u4f18\u52bf（+1.28 vs +0.89）<\/li>\n
  • \u4e0a\u4e0b\u6587\u5229\u7528\u66f2\u7ebf：\u5168\u6876\u9886\u5148，tf \u5c3e\u6876\u6076\u5316（3.01）\u800c v3.5 \u4fdd\u6301（2.79）<\/li>\n<\/ul>\n

    5. \u957f\u7a0b\u80fd\u529b\u63a2\u9488\u5957\u4ef6（bench_longrange.py）<\/h3>\n
  • \u957f\u5ea6\u6269\u5c55 PPL：64\u21922048 \u4e03\u6863，>512 \u4e3a\u4f4d\u7f6e\u5916\u63a8\u533a（\u53ef\u5b66\u4e60\u4f4d\u7f6e\u7f16\u7801 clamp \u9971\u548c，\u4e13\u6d4b scan \u72b6\u6001\u4fdd\u6301）<\/li>\n
  • \u8fdc\u7a0b\u590d\u5236\u53ec\u56de：[prefix][span A][gap G][span A] \u91cd\u590d\u6bb5 CE \u4e0b\u964d\u91cf，gap \u5373\u53d7\u63a7\u8bb0\u5fc6\u8ddd\u79bb<\/li>\n
  • \u5206\u4f4d\u7f6e loss \u66f2\u7ebf：8 \u6876\u4e0a\u4e0b\u6587\u5229\u7528\u66f2\u7ebf<\/li>\n
  • \u03b3 \u6709\u6548\u8bb0\u5fc6\u957f\u5ea6：\u771f\u5b9e\u8bed\u6599\u4e0a \u03b3 \u5206\u5e03\u7edf\u8ba1（v3.5 \u5b9e\u6d4b：\u5feb\u5c42 4.1 tok \/ \u6162\u5c42 245 tok，\u5feb\u6162\u5206\u5de5\u88ab\u8bad\u7ec3\u4fdd\u7559）<\/li>\n

    6. \u5de5\u7a0b\u5b9e\u73b0\u8981\u70b9<\/h3>\n
      \n
    • \u8bad\u7ec3 297 ms\/step（bf16 autocast，bs=32\u00d7S=384；KV Triton \u524d\u5411 0.42 ms\/\u5c42 + torch \u91cd\u7b97\u53cd\u5411 4.3 ms\/\u5c42）<\/li>\n
    • O(1) \u53cc\u72b6\u6001\u589e\u91cf\u63a8\u7406：state \u5143\u7ec4\u643a\u5e26 (max-scan e \u5411\u91cf, KV \u77e9\u9635 S)；fp32 \u4e0b\u9010 token \u89e3\u7801 vs \u5168\u5e8f\u5217\u524d\u5411 diff = 3.7e-06<\/li>\n
    • \u663e\u5b58：\u8bad\u7ec3\u5cf0\u503c 12.4 GiB（bs=32），DecayScanFn \u7cfb\u6570\u53cd\u5411\u91cd\u7b97\u7701 1.8 GB<\/li>\n
    • \u7a33\u5b9a\u6027\u53d1\u73b0（\u91cd\u8981）：\u672c\u73af\u5883 Triton \u5bf9"\u5faa\u73af\u643a\u5e26\u7d2f\u52a0\u5bc4\u5b58\u5668 + \u5faa\u73af\u51fa\u53e3 store"\u5b58\u5728 codegen \u975e\u786e\u5b9a\u6027（miscompilation \u5acc\u7591）；\u5faa\u73af\u5185\u9010\u6b65 store \u6a21\u5f0f\u5b8c\u5168\u7a33\u5b9a\u2014\u2014\u6240\u6709 kernel \u9075\u5faa\u8be5\u6a21\u5f0f\u8bbe\u8ba1，\u591a\u5f62\u72b6 \u00d7 \u591a\u6b21\u53d1\u5c04\u96f6\u6f02\u79fb\u9a8c\u8bc1<\/li>\n
    • \u6570\u503c\u5b89\u5168：max-scan \u65e0\u9664\u6cd5；KV \u5206\u5757\u6743\u91cd C[j,i] = exp(l_j \u2212 l_i) \u56e0\u679c\u63a9\u7801\u540e \u2264 1，\u65e0\u9664\u6cd5\u65e0\u4e0b\u6ea2<\/li>\n<\/ul>\n

      7. \u590d\u73b0\u6307\u5357<\/h3>\n

      # \u540c\u9884\u7b97 A\/B（\u67b6\u6784\u53ef\u9009 v30\/v31\/v32\/v33\/v34\/v35\/v35d\/tf）<\/span>
      \npython train_ab.py –arch<\/span> v35 –steps<\/span> 3000<\/span> –out<\/span> ab_v35_3k
      \n# \u5927\u6570\u636e\u7f29\u653e<\/span>
      \npython train_ab.py –arch<\/span> v35 –steps<\/span> 15000<\/span> –warmup<\/span> 500<\/span> \\\\<\/span>
      \n –data<\/span> minimind_data\/pretrain_cached_1270238_384.pt –out<\/span> big_v35
      \n# \u957f\u7a0b\u56db\u63a2\u9488（\u7f16\u8f91 MODEL_KEYS \u9009\u62e9\u5bf9\u6bd4\u6a21\u578b）<\/span>
      \npython bench_longrange.py
      \n# \u6b63\u786e\u6027\u56de\u5f52（58 \u9879：max-scan \u524d\u5411\/\u53cd\u5411\u3001gen_model\u3001fusedCE\u3001KV scan）<\/span>
      \npython bench_v3_triton.py
      \n# \u589e\u91cf\u63a8\u7406\u7b49\u4ef7\u6027<\/span>
      \npython <<\/span>\u89c1 test_incremental.py \u6a21\u5f0f><\/span> # fp32 \u5168\u5e8f\u5217 vs \u9010 token：3.7e-06<\/span><\/p>\n

      \u4ea7\u7269：longrange_results.json（\u56db\u63a2\u9488\u539f\u59cb\u6570\u636e）\u3001\u5404 ab_*\/big_*\/train_log.json（\u8bad\u7ec3\u66f2\u7ebf）\u3002<\/p>\n

      8. \u8fb9\u754c\u4e0e\u540e\u7eed\u65b9\u5411<\/h3>\n
    • \u9a8c\u8bc1\u5c3a\u5ea6\u4e3a 488M tokens \/ 96-105M \u53c2\u6570\u2014\u2014\u76f8\u5bf9\u771f\u5b9e LLM \u9884\u8bad\u7ec3\u4ecd\u5c0f；\u66f4\u5927\u89c4\u6a21\u9700\u6269\u5c55\u7b97\u529b<\/li>\n
    • delta-rule \u5206\u5757（UT\/WY \u53d8\u6362）\u4e0e KV scan \u624b\u5de5 Triton \u53cd\u5411\u662f\u660e\u786e\u7684\u4e0b\u4e00\u7ea7\u4f18\u5316（\u5f53\u524d delta \u9010\u6b65\u7248\u4e0d\u53ef\u8bad\u7ec3\u3001\u91cd\u7b97\u53cd\u5411 ~4.3 ms\/\u5c42）<\/li>\n
    • gap=128 \u4e2d\u8ddd\u68c0\u7d22\u4ecd\u843d\u540e tf（+0.25 vs +0.62 @3000 \u6b65\u7248；\u5927\u6570\u636e\u7248 gap=128 \u5df2\u53cd\u8d85）\u2014\u2014delta \u5199\u5165\u9884\u671f\u8fdb\u4e00\u6b65\u6269\u5927\u4f18\u52bf<\/li>\n
    • tf \u57fa\u7ebf\u672a\u505a\u4f4d\u7f6e\u5916\u63a8\u9002\u914d（YaRN\/\u63d2\u503c）；v3.5 \u7684\u957f\u5ea6\u65e0\u5173\u6027\u4e3a\u67b6\u6784\u81ea\u5e26<\/li>\n
    • \u901f\u5ea6\u4e0a KV \u901a\u9053\u4e3a max-scan \u7684 ~15\u00d7 \u5355 token \u6210\u672c，Triton \u524d\u5411\u5df2\u56de\u6536\u5927\u5934；\u8fdb\u4e00\u6b65\u53ef\u505a KV \u7626\u8eab（dh \u51cf\u534a）\u6d88\u878d<\/li>\n

      9. \u540e\u7eed\u4efb\u52a1\u6267\u884c\u7ed3\u679c（2026-11-05 \u66f4\u65b0）<\/h3>\n\n\u4efb\u52a1\u7ed3\u679c<\/tr>\n\n\n\n\n\n\n
      tf \u4f4d\u7f6e\u63d2\u503c\u516c\u5e73\u6027（PI \u00d74）<\/td>\ntf@2048 \u6062\u590d 37.68\u219235.20，v3.5 \u4ecd\u9886\u5148 22.8%（15.64）；\u4f18\u52bf\u5bf9\u516c\u5e73\u6027\u8865\u5f3a\u7a33\u5065<\/td>\n<\/tr>\n
      \u589e\u91cf O(1) \u63a8\u7406\u5151\u73b0<\/td>\nstate \u5143\u7ec4\u643a\u5e26 (e, S)；fp32 \u9010 token vs \u5168\u5e8f\u5217 diff 3.7e-06；demo_o1_decode.py \u7aef\u5230\u7aef\u6f14\u793a（\u5e38\u6570\u72b6\u6001 894k \u6570\u503c\/12 \u5c42，\u751f\u6210\u8d28\u91cf\u8fde\u8d2f）<\/td>\n<\/tr>\n
      KV \u7626\u8eab\u6d88\u878d（kv_dh 96\u219248）<\/td>\nv35s PPL 40.87 vs \u5b8c\u6574\u7248 41.34（3000 \u6b65）\u2014\u2014\u53c2\u6570 -3.5M\u3001KV \u72b6\u6001\/\u8ba1\u7b97\u51cf\u534a\u4e14\u8d28\u91cf\u7565\u4f18；48 \u7ef4\u952e\u7a7a\u95f4\u68c0\u7d22\u66f4\u9510\u5229\u3002v35s \u6210\u4e3a\u65b0\u63a8\u8350\u914d\u7f6e<\/td>\n<\/tr>\n
      delta-rule \u5199\u5165<\/td>\nnaive \u7248\u6027\u8d28\u9a8c\u8bc1 PASS（\u91cd\u590d\u952e\u7cbe\u786e\u8986\u76d6）；\u5206\u5757 GPU \u7248\u5b58\u5728\u53cc\u5411\u72b6\u6001\u4f9d\u8d56（\u53cd\u5411 gq_j = \u03a3_v \u03bb_j\u00b7S_j \u9700 \u03bb \u53cd\u5411\u9012\u63a8\u4e0e S \u6b63\u5411\u91cd\u7b97\u540c\u573a）\u2014\u2014\u7cbe\u786e\u5f62\u5f0f\u9700 Gated DeltaNet（arXiv 2412.06464）\u8bba\u6587\u7ea7 UT\/WY \u5b9e\u73b0；\u5df2\u4ea4\u4ed8 naive \u53c2\u8003\u5b9e\u73b0（\u6027\u8d28\u9a8c\u8bc1 PASS）\u4e0e TBPTT \u8bda\u5b9e\u8bc4\u4f30（384 \u6b65 Python \u5faa\u73af 18s\/step \u4e0d\u53ef\u8bad\u7ec3）\u3002\u5927\u6570\u636e\u7248 gap=128 \u68c0\u7d22\u5df2\u53cd\u8d85 tf，\u7d27\u8feb\u6027\u4e0b\u964d<\/td>\n<\/tr>\n
      KV scan \u624b\u5de5 Triton \u53cd\u5411<\/td>\n\u964d\u7ea7：\u91cd\u7b97\u53cd\u5411（4.3 ms\/\u5c42）\u5df2\u591f\u5feb，\u624b\u5de5\u63a8\u5bfc\u6536\u76ca ~2 ms\/\u5c42\u3001\u98ce\u9669\u9ad8<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      v35s \u589e\u91cf\u53d1\u73b0<\/h4>\n

      KV \u901a\u9053 48 \u7ef4\u952e\u7a7a\u95f4\u76f8\u5bf9 96 \u7ef4\u662f\u8d28\u91cf-\u6548\u7387\u53cc\u4f18（\u66f4\u5c11\u53c2\u6570\u3001\u66f4\u9510\u68c0\u7d22\u3001\u66f4\u5c0f\u72b6\u6001 36,864\u219218,432 \u6570\u503c\/\u5c42）\u3002\u67b6\u6784\u53d9\u8ff0\u66f4\u65b0\u4e3a：\u5feb\/\u6162 max-scan + 48 \u7ef4\u5f52\u4e00\u5316 KV \u68c0\u7d22\u3002<\/p>\n

      https:\/\/github.com\/dfytensor\/OpenAshAnyWhere\/tree\/master<\/p>\n","protected":false},"excerpt":{"rendered":"

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