{"id":98817,"date":"2026-09-01T13:16:57","date_gmt":"2026-09-01T05:16:57","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/98817.html"},"modified":"2026-09-01T13:16:57","modified_gmt":"2026-09-01T05:16:57","slug":"glm-5-3-nvfp4-%e9%83%a8%e7%bd%b2%e5%ae%9e%e6%88%98%e7%b3%bb%e5%88%97%e5%8d%81%e7%95%aa%e5%a4%961%ef%bc%9aflexattention-%e6%98%af%e4%bb%80%e4%b9%88%ef%bc%8c%e4%bb%a5%e5%8f%8a-dflash2-%e4%b9%8b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/98817.html","title":{"rendered":"GLM-5.3-NVFP4 \u90e8\u7f72\u5b9e\u6218\u7cfb\u5217[\u5341]\u756a\u59161\uff1aFlexAttention \u662f\u4ec0\u4e48\uff0c\u4ee5\u53ca DFlash2 \u4e4b\u8c1c\u7684\u5b8c\u6574\u4fa6\u7834"},"content":{"rendered":"<h2>10 \u756a\u5916&#xff1a;FlexAttention \u662f\u4ec0\u4e48&#xff0c;\u4ee5\u53ca DFlash2 \u4e4b\u8c1c\u7684\u5b8c\u6574\u4fa6\u7834<\/h2>\n<ul>\n<li>\u7cfb\u5217&#xff1a;GLM-5.3-NVFP4 \u90e8\u7f72\u5b9e\u5f55&#xff08;8\u00d7A800 \/ sm80&#xff09;\u3002<\/li>\n<li>\u4e0a\u4e00\u7bc7&#xff1a;09 \u6027\u80fd\u5b9e\u6d4b<\/li>\n<li>\u672c\u6587\u6027\u8d28&#xff1a;\u7814\u7a76\u7eaa\u5b9e&#xff0c;\u4ece\u4e00\u7bc7&#034;\u7814\u7a76\u89c4\u5212&#034;&#xff08;\u521d\u7248&#xff09;\u6f14\u5316\u4e3a\u672c\u7cfb\u5217\u6700\u540e\u4e00\u4e2a\u7ed3\u6848\u7684\u756a\u5916&#xff1a;FlexAttention \u662f\u4ec0\u4e48\u3001DFlash2 \u5728 A800 \u4e0a\u63a5\u53d7\u7387\u5d29\u584c\u4e4b\u8c1c\u5982\u4f55\u88ab\u9759\u6001\u6e90\u7801\u9a8c\u8bc1 &#043; GPU \u5224\u51b3\u5b9e\u9a8c\u9010\u5c42\u4fa6\u7834\u3001\u4ee5\u53ca\u5b98\u65b9&#034;DFlash2 \u4f18\u4e8e MTP&#034;\u4e3a\u4f55\u5728\u6211\u4eec\u7684\u73af\u5883\u91cc\u590d\u73b0\u4e0d\u51fa\u6765\u3002 GLM5.3 nvfp4 VLLM\u90e8\u7f72\u65b9\u6848\u6e90\u7801<\/li>\n<\/ul>\n<p>\u6458\u8981&#xff1a;\u672c\u6587\u5b8c\u6574\u590d\u76d8 DFlash2 \u5728 A800 \u4e0a\u63a5\u53d7\u7387\u5d29\u584c\u4e4b\u8c1c\u7684\u4fa6\u7834\u8fc7\u7a0b\u3002\u6838\u5fc3\u7ed3\u8bba&#xff1a;0.75 token\/\u6b65\u7684&#034;\u786c\u4ef6\u5bbf\u547d&#034;\u5b9e\u4e3a\u5171\u4eab .compile_cache \u7f13\u5b58\u6c61\u67d3\u6240\u81f4&#xff0c;\u6362\u5e72\u51c0\u7f13\u5b58\u540e\u63a5\u53d7\u7387\u6062\u590d\u81f3 2.26-2.47\/\u6b65&#xff1b;FA2 \u5185\u6838\u3001mask \u8bed\u4e49\u3001rope \u540c\u6b65\u3001aux \u63a5\u7ebf\u7ecf\u9759\u6001\u6e90\u7801\u9a8c\u8bc1\u4e0e GPU \u5224\u51b3\u5b9e\u9a8c\u5168\u90e8\u65e0\u7f6a\u3002\u4f46&#034;\u4fee\u597d&#034;\u4e0d\u7b49\u4e8e&#034;\u7ffb\u76d8&#034;\u2014\u2014A800&#043;vLLM \u4e0a DFlash2 \u4ecd\u5168\u9762\u843d\u540e MTP&#xff08;~2.0-2.5 vs ~3.0-3.3\/\u6b65&#xff09;&#xff0c;\u5b98\u65b9&#xff08;GB300&#043;SGLang&#043;FA4&#xff09;\u7684\u4f18\u52bf\u672a\u80fd\u590d\u73b0&#xff0c;\u90e8\u7f72\u9ed8\u8ba4\u7ef4\u6301 MTP\u3002\u6587\u7ae0\u987a\u5e26\u8bb2\u6e05 FlexAttention \u8fd9\u4e00&#034;\u7528 Python \u51fd\u6570\u63cf\u8ff0\u6ce8\u610f\u529b&#034;\u7684\u901a\u7528\u5de5\u5177&#xff0c;\u5e76\u6c89\u6dc0\u4e09\u6761\u53ef\u590d\u7528\u7684\u6392\u969c\u6559\u8bad\u3002<\/p>\n<ul>\n<li>GLM-5.3-NVFP4 \u90e8\u7f72\u5b9e\u6218\u7cfb\u5217[\u516d]\u95ee\u9898\u6df1\u62c6\u2462&#xff1a;DFlash2 \u63a5\u53d7\u7387\u5d29\u584c\u2014\u2014\u4e3a\u4ec0\u4e48\u5728 A800 \u4e0a\u9ed8\u8ba4MTP\u7559\u4e0b\u4e86\u672c\u9879\u76ee\u6700\u5927\u7684\u4e00\u4e2a\u672a\u7ed3\u6848\u95ee\u9898&#xff1a;DFlash2 \u5728 A800 \u4e0a\u63a5\u53d7\u7387\u5d29\u584c&#xff08;0.75 token\/\u6b65 vs \u5b98\u65b9 5.9&#xff09;&#xff0c;\u6211\u4eec\u9ed8\u8ba4\u4e86 MTP\u3002\u5f53\u65f6\u7ed9\u51fa\u7684\u5f52\u56e0\u662f&#034;draft \u7684\u975e\u56e0\u679c\u5757\u6269\u6563\u6ce8\u610f\u529b\u5728 A800 \u53ea\u6709 FA2 \u53ef\u9009&#xff08;\u5b98\u65b9\u4e3a GB300 \u4e0a\u7684 FA4&#xff09;&#xff0c;\u5e76\u884c\u8349\u7a3f\u8d28\u91cf\u9010\u4f4d\u8870\u51cf&#034;&#xff0c;\u8fd9\u4e2a\u5f52\u56e0\u6700\u7ec8\u88ab\u8bc1\u660e\u662f\u9519\u7684\u3002<\/li>\n<li>\u771f\u5b9e\u6839\u56e0\u5e73\u6de1\u5f97\u8ba9\u4eba\u5c34\u5c2c&#xff1a;\u5171\u4eab torch.compile \u7f13\u5b58\u88ab\u6c61\u67d3\u3002\u672c\u6587\u5b8c\u6574\u590d\u76d8\u8fd9\u573a&#034;\u5dee\u70b9\u628a\u7f13\u5b58\u95ee\u9898\u5f53\u786c\u4ef6\u5bbf\u547d&#034;\u7684\u4fa6\u7834&#xff0c;\u987a\u5e26\u628a FlexAttention \u8fd9\u4e2a\u6709\u4ef7\u503c\u7684\u5de5\u5177\u8bb2\u6e05\u695a\u3002<\/li>\n<\/ul>\n<h3>\u6700\u7ec8\u7ed3\u8bba&#xff1a;<\/h3>\n<table>\n<tr>\u95ee\u9898\u7b54\u6848<\/tr>\n<tbody>\n<tr>\n<td>0.75 \u5931\u8d25\u7684\u6839\u56e0&#xff1f;<\/td>\n<td>\u5171\u4eab .compile_cache \u88ab\u5931\u8d25\u7684 DFlash2 \u8fd0\u884c\u6c61\u67d3&#xff1b;\u5e72\u51c0\u7f13\u5b58\u4e0b\u63a5\u53d7\u7387\u6062\u590d 3 \u500d&#xff08;0.75\u21922.26-2.47\/\u6b65&#xff09;&#xff0c;\u9010\u4f4d\u8870\u51cf\u5f62\u6001\u6b63\u5e38<\/td>\n<\/tr>\n<tr>\n<td>FA2 \u80fd\u4e0d\u80fd\u505a\u5757\u6269\u6563&#xff1f;<\/td>\n<td>\u80fd&#xff0c;\u800c\u4e14\u662f vLLM 0.28.0 \u7684\u4e00\u7b49\u516c\u6c11&#xff08;\u6e90\u7801\u91cc\u751a\u81f3\u6709\u4e13\u4e3a DFlash \u8bbe\u8ba1\u7684\u7a97\u53e3\u5bf9\u79f0\u5316\u903b\u8f91&#xff09;&#xff1b;\u5185\u6838\u7b49\u4ef7\u6027\u5b9e\u6d4b\u6700\u5927\u504f\u5dee 0.0071<\/td>\n<\/tr>\n<tr>\n<td>DFlash2 \u73b0\u5728\u80fd\u7528\u5417&#xff1f;<\/td>\n<td>\u80fd\u8dd1\u901a&#xff0c;\u4f46\u5728 A800&#043;vLLM \u4e0a\u63a5\u53d7\u7387 ~2.0-2.5\/\u6b65&#xff0c;\u4ecd\u5168\u9762\u843d\u540e MTP&#xff08;3.0-3.3\/\u6b65&#xff0c;\u541e\u5410 52-63 vs 70-91 tok\/s&#xff09;&#xff0c;\u90e8\u7f72\u9ed8\u8ba4\u7ef4\u6301 MTP<\/td>\n<\/tr>\n<tr>\n<td>\u5b98\u65b9&#034;DFlash2 \u4f18\u4e8e MTP&#034;\u662f\u771f\u7684\u5417&#xff1f;<\/td>\n<td>\u662f&#xff08;GB300&#043;SGLang&#043;FA4 \u5b98\u65b9\u5168\u8868\u80dc\u51fa&#xff09;&#xff0c;\u4f46\u5728\u6211\u4eec\u7684\u73af\u5883\u590d\u73b0\u4e0d\u51fa\u2014\u2014\u5dee\u8ddd\u6307\u5411\u8fd0\u884c\u65f6\u5b9e\u73b0&#xff08;SGLang vs vLLM&#xff09;\u6216\u786c\u4ef6\u4ee3\u9645<\/td>\n<\/tr>\n<tr>\n<td>FlexAttention \u7684\u89d2\u8272&#xff1f;<\/td>\n<td>\u4ece&#034;\u4e3b\u653b\u65b9\u5411&#034;\u964d\u7ea7\u4e3a&#034;\u540e\u5907&#034;\u2014\u2014\u56db\u4e2a\u5019\u9009\u540e\u7aef\u90fd\u539f\u751f\u652f\u6301\u975e\u56e0\u679c&#043;\u6ed1\u7a97&#xff0c;\u6700\u7ec8\u8c01\u90fd\u6ca1\u7528\u4e0a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>1. FlexAttention&#xff1a;\u4e3a&#034;\u975e\u6807\u51c6\u6ce8\u610f\u529b&#034;\u800c\u751f\u7684\u7f16\u7a0b\u6a21\u578b<\/h3>\n<ul>\n<li>\u521d\u7248\u672c\u6587\u7684\u51fa\u53d1\u70b9\u662f\u4e00\u4e2a\u5047\u8bbe&#xff1a;\u201csm80 \u7f3a\u652f\u6301\u975e\u56e0\u679c&#043;\u6ed1\u7a97\u7684\u540e\u7aef&#xff0c;\u9700\u8981 FlexAttention \u8865\u4f4d\u201d\u3002\u8fd9\u4e2a\u5047\u8bbe\u540e\u6765\u88ab\u63a8\u7ffb\u4e86&#xff0c;\u4f46 FlexAttention \u672c\u8eab\u503c\u5f97\u8ba4\u8bc6\u2014\u2014\u5b83\u662f\u8fd9\u7c7b\u95ee\u9898\u771f\u6b63\u7684\u901a\u7528\u89e3\u6cd5\u3002<\/li>\n<\/ul>\n<p>PyTorch 2.5 \u5f15\u5165\u7684 torch.nn.attention.flex_attention \u7528\u4e24\u4e2a\u7eaf Python \u51fd\u6570\u5b9a\u4e49\u4efb\u610f\u6ce8\u610f\u529b\u53d8\u4f53&#xff1a;<\/p>\n<ul>\n<li>mask_mod(b, h, q_idx, kv_idx) -&gt; bool&#xff1a;\u5b9a\u4e49&#034;query \u80fd\u770b\u89c1\u54ea\u4e9b key&#034;\u2014\u2014\u56e0\u679c\u3001\u6ed1\u7a97\u3001\u975e\u56e0\u679c\u3001\u6587\u6863\u63a9\u7801\u3001\u5757\u7a00\u758f&#xff0c;\u4efb\u610f\u6a21\u5f0f\u90fd\u80fd\u7cbe\u786e\u8868\u8fbe&#xff1b;<\/li>\n<li>score_mod(score, b, h, q_idx, kv_idx) -&gt; score&#xff1a;\u5b9a\u4e49\u6253\u5206\u4fee\u6539&#xff08;ALiBi\u3001\u4f4d\u7f6e\u504f\u7f6e\u3001\u53ef\u5b66\u4e60 bias \u7b49&#xff09;\u3002<\/li>\n<\/ul>\n<p>torch.compile\/inductor \u628a\u8fd9\u4e24\u4e2a\u51fd\u6570\u7f16\u8bd1\u6210\u878d\u5408\u7684 Triton \u5185\u6838\u3002\u63a8\u7406\u4fa7\u914d\u5957 FlexDecoding \u540e\u7aef&#xff1a;\u68c0\u6d4b\u5230&#034;\u77ed query &#043; \u957f KV&#034;\u7684 decode \u5f62\u6001\u65f6\u81ea\u52a8\u5207\u6362\u4e3a FlashDecoding \u98ce\u683c\u7684 split-KV \u5185\u6838&#xff0c;\u5b98\u65b9\u57fa\u51c6\u4e0e FlashDecoding \u6301\u5e73\u3001\u663e\u8457\u4f18\u4e8e SDPA&#xff1b;\u53e6\u652f\u6301 GQA \u548c PagedAttention&#xff08;\u7528 BlockMask \u505a\u903b\u8f91\/\u7269\u7406\u9875\u8868\u8f6c\u6362&#xff0c;\u5f00\u9500 &lt;5%&#xff0c;\u4e0e FA2 \u76f8\u5f53&#xff09;\u3002vLLM \u91cc\u6709\u5bf9\u5e94\u5b9e\u73b0&#xff1a;FLEX_ATTENTION \u540e\u7aef&#xff08;v1\/attention\/backends\/flex_attention.py&#xff09;&#xff0c;\u5bf9 paged \u975e\u56e0\u679c\u573a\u666f\u8fd8\u6709\u5185\u7f6e\u7684 bidirectional_mask_mod\u3002<\/p>\n<p>\u4e3a\u4ec0\u4e48\u5b83\u66fe\u88ab\u8ba4\u4e3a\u662f sm80 \u7684\u5e0c\u671b\u2014\u2014\u5185\u6838\u662f Triton \u751f\u6210\u7684 bf16 \u8def\u5f84&#xff0c;\u4e0d\u4f9d\u8d56 sm89&#043; \u7684 fp8e4nv\u3001\u4e0d\u4f9d\u8d56 SM90&#043; \u7684 deep_gemm&#xff08;\u5751 11 \u7684\u95e8\u69db&#xff09;&#xff1b;\u4e14&#034;\u975e\u56e0\u679c&#043;\u6ed1\u7a97&#034;\u6070\u662f mask_mod \u7684\u8868\u8fbe\u751c\u533a\u3002\u8fd9\u4e2a\u5224\u65ad\u5bf9 FlexAttention \u4f9d\u7136\u6210\u7acb&#xff0c;\u53ea\u662f DFlash2 \u6700\u7ec8\u4e0d\u9700\u8981\u5b83\u2014\u2014\u89c1\u4e0b\u6587\u4fa6\u7834\u3002<\/p>\n<h3>2. \u5916\u90e8\u5b9e\u8bc1&#xff1a;DFlash \u7ea2\u5229\u5728 Ampere \u4e0a\u662f\u771f\u5b9e\u5b58\u5728\u7684<\/h3>\n<ul>\n<li>&#xff08;Qwen3.5 9B \u672c\u5730 DFlash \u63a8\u7406\u52a0\u901f\u5b9e\u8df5&#xff09;\u7ed9\u4e86\u5173\u952e\u53c2\u7167&#xff1a;2\u00d7RTX 3090&#xff08;Ampere sm86&#xff0c;24GB&#xff0c;PCIe \u65e0 NVLink&#xff09;\u8dd1 Qwen3.5-9B &#043; DFlash \u8349\u7a3f&#xff08;\u6bcf\u6b65 15 \u8349\u7a3f token&#xff09;&#043; flash_attn \u540e\u7aef&#xff0c;\u7efc\u5408\u52a0\u901f 2.67\u00d7,\u4ee3\u7801\u751f\u6210 4.52\u00d7\u3001\u6570\u5b66\u63a8\u7406 4.31\u00d7\u3001\u53d1\u6563\u6027\u5185\u5bb9\u7ea6 2\u00d7&#xff0c;\u7aef\u5230\u7aef\u5ef6\u8fdf\u964d 63%\u3002\u4e8b\u540e\u770b&#xff0c;\u8fd9\u6761\u5916\u90e8\u8bc1\u636e\u7684\u63a8\u8bba\u5168\u90e8\u88ab GPU-day \u5b9e\u6d4b\u8bc1\u5b9e&#xff1a;<\/li>\n<\/ul>\n<li>Ampere \u786c\u4ef6\u4e0d\u662f DFlash \u7684\u969c\u788d\u2014\u2014\u6211\u4eec\u7684\u5d29\u584c\u540e\u6765\u786e\u8ba4\u4e0e\u73af\u5883\u72b6\u6001\u6709\u5173&#xff0c;\u4e0e\u786c\u4ef6\u65e0\u5173&#xff1b;<\/li>\n<li>PCIe \u673a\u5668\u662f DFlash \u7684\u989d\u5916\u53d7\u76ca\u8005\u2014\u2014\u4e00\u6b21\u9a8c\u8bc1 N \u4e2a\u8349\u7a3f token&#xff0c;\u628a\u8de8\u5361 All-Reduce \u9891\u7387\u964d\u4f4e N \u500d\u3002\u6211\u4eec\u662f 8\u00d7A800 PCIe \u578b\u53f7&#xff0c;\u8fd9\u9879\u7ea2\u5229\u771f\u5b9e\u5b58\u5728&#xff1b;<\/li>\n<li>\u8fb9\u754c\u63d0\u9192&#xff1a;3090 \u5b9e\u9a8c\u662f 9B dense \u53cc\u5361&#xff0c;GLM-5.3-NVFP4 \u662f 433G MoE \u9700\u8981 TP8\u2014\u2014\u5b83\u8bc1\u660e\u7684\u662f&#034;Ampere &#043; DFlash \u9ad8\u63a5\u53d7\u7387&#034;\u7ec4\u5408\u6210\u7acb&#xff0c;\u4e0d\u80fd\u76f4\u63a5\u5916\u63a8\u5177\u4f53\u6570\u5b57\u3002<\/li>\n<h3>3. \u7b2c\u4e00\u6b65&#xff1a;\u4e0d\u9700\u8981 GPU \u7684\u9759\u6001\u6e90\u7801\u9a8c\u8bc1<\/h3>\n<ul>\n<li>\n<p>GPU \u88ab\u8bad\u7ec3\u4efb\u52a1\u5360\u6ee1\u7684\u90a3\u6bb5\u65f6\u95f4&#xff0c;\u6211\u4eec\u628a vllm\/vllm-openai:v0.28.0-glm53-sm80 \u955c\u50cf\u5185\u7684 vLLM \u6e90\u7801\u6574\u5305\u63d0\u53d6&#xff08;docker create &#043; docker cp&#xff0c;\u4e0d\u5360 GPU&#xff09;&#xff0c;\u5bf9 DFlash2 \u5b9e\u9645\u8fd0\u884c\u94fe\u8def\u505a\u4e86\u5b8c\u6574\u7684\u9759\u6001\u9a8c\u8bc1\u3002\u65b9\u6cd5\u503c\u5f97\u4e00\u63d0&#xff0c;\u56e0\u4e3a\u5b83\u4fbf\u5b9c\u5f97\u60ca\u4eba&#xff1a;<\/p>\n<\/li>\n<li>\n<p>ast \u63d0\u53d6\u771f\u5b9e\u51fd\u6570\u8dd1\u771f\u5b9e\u914d\u7f6e&#xff1a;\u628a dflash_has_any_non_causal\u3001_resolve_layer_attention\u3001get_eagle3_aux_layers_from_config \u7b49\u51fd\u6570\u4ece\u6e90\u7801\u4e2d\u6309 AST \u539f\u6837\u53d6\u51fa&#xff0c;\u914d\u6700\u5c0f stub \u540e\u76f4\u63a5\u6267\u884c&#xff0c;\u5582\u7ed9\u5b83\u4eec\u7684\u662f\u771f\u5b9e\u7684 GLM-5.3-DFlash2\/config.json\u2014\u2014\u6d4b\u7684\u662f\u771f\u5b9e\u4ee3\u7801&#xff0c;\u4e0d\u662f\u590d\u8ff0&#xff1b;<\/p>\n<\/li>\n<li>\n<p>mask \u8bed\u4e49\u9010\u4f4d\u5bf9\u6bd4&#xff1a;\u7eaf Python \u590d\u523b\u4e09\u79cd\u5b9e\u73b0\u4f1a\u4e0b\u53d1\u7684\u6ce8\u610f\u529b\u63a9\u7801&#xff08;FA2 \u5b9e\u9645\u8c03\u7528\u5f62\u6001 \/ FlexAttention mask_mod \/ DFlash \u5757\u6269\u6563\u5b9a\u4e49&#xff09;&#xff0c;\u9010\u4f4d diff\u3002<\/p>\n<\/li>\n<\/ul>\n<p>14\/14 \u9879\u68c0\u67e5\u901a\u8fc7&#xff0c;\u4e09\u4e2a\u5173\u952e\u53d1\u73b0&#xff1a;<\/p>\n<li>&#034;FA2 \u5e72\u4e0d\u4e86\u5757\u6269\u6563&#034;\u88ab\u63a8\u7ffb\u3002v0.28.0 \u7684 FA2 \u540e\u7aef supports_non_causal&#061;True&#xff0c;\u4e14 _maybe_symmetrize_window \u4f1a\u628a\u56e0\u679c\u6ed1\u7a97 (w,0) \u5bf9\u79f0\u5316\u6210 (w,w)&#xff0c;\u6e90\u7801\u6ce8\u91ca\u539f\u6587 \u201cso bidirectional queries attend in both directions\u201d\u2014\u2014\u8fd9\u662f\u4e13\u95e8\u4e3a DFlash \u8bbe\u8ba1\u7684\u3002mask \u9010\u4f4d\u5bf9\u6bd4\u8bc1\u660e&#xff1a;\u5728\u6211\u4eec\u6240\u6709\u538b\u6d4b\u7684\u77ed\u4e0a\u4e0b\u6587\u533a\u95f4&#xff0c;FA2 \u5b9e\u9645\u4e0b\u53d1\u7684 mask \u4e0e\u5757\u6269\u6563\u5b9a\u4e49\u5b8c\u5168\u76f8\u540c\u3002TRITON_ATTN \/ FLASHINFER \/ FLEX_ATTENTION \u4e5f\u5168\u90e8\u58f0\u660e\u652f\u6301\u975e\u56e0\u679c&#043;\u6ed1\u7a97\u3002<\/li>\n<li>\u6574\u6761\u94fe\u8def\u63a5\u7ebf\u81ea\u6d3d&#xff1a;DFlash2DraftModel \u5f3a\u5236\u8d70 V2 runner&#xff08;\u6e90\u7801\u6ce8\u91ca&#xff1a;V1 \u4f1a&#034;silently degrade DFlash2 to DFlash1&#034;&#xff09;\u3001aux \u5c42\u89e3\u6790 (6,20,34,48,62,76)\u3001rope \u98ce\u683c\u540c\u6b65&#xff08;GLM rope_interleave&#061;True \u2194 draft \u62ff\u5230 is_neox_style&#061;False&#xff09;\u3001query\u2194query \u7ecf\u8349\u7a3f\u81ea\u8eab SWA cache \u4e92\u89c1\u3001\u91c7\u6837\u9ed8\u8ba4\u786e\u5b9a\u6027\u3002<\/li>\n<li>\u6839\u56e0\u6536\u7a84\u4e3a 4 \u4e2a\u5047\u8bbe&#xff1a;H1 FA2 \u5185\u6838\u6570\u503c\u3001H2 compile \u7f13\u5b58\u6c61\u67d3\u3001H3 rope \u540c\u6b65\u9759\u9ed8\u5931\u8d25\u3001H5 NVFP4 \u76ee\u6807 hidden states \u566a\u58f0\u2014\u2014\u6bcf\u4e2a\u90fd\u914d\u4e86 \u226430 \u5206\u949f\u7684\u5224\u51b3\u5b9e\u9a8c\u811a\u672c\u3002<\/li>\n<p>\u6e90\u7801\u91cc\u8fd8\u6709\u4e00\u6761\u4f0f\u7b14\u503c\u5f97\u4e00\u63d0&#xff1a;dflash_target_rope_is_neox_style \u7684 docstring \u5199\u7740 \u201ca mismatch is silent \u2014 acceptance collapses but nothing errors and the output stays correct\u201d&#xff08;rope \u98ce\u683c\u4e0d\u5339\u914d\u4f1a\u9759\u9ed8\u5d29\u63a5\u53d7\u7387\u3001\u4e0d\u62a5\u9519\u3001\u8f93\u51fa\u4f9d\u7136\u6b63\u786e&#xff09;\u3002\u8fd9\u4e2a&#034;\u9759\u9ed8\u5d29\u584c&#034;\u7684\u63cf\u8ff0\u4e0e\u5751 13 \u75c7\u72b6\u5b8c\u7f8e\u543b\u5408&#xff0c;\u4e00\u5ea6\u662f\u5934\u53f7\u5acc\u7591\u2014\u2014\u8fd9\u6b63\u662f\u5fc5\u987b\u505a\u5b9e\u6d4b\u5224\u51b3\u7684\u539f\u56e0&#xff1a;\u9759\u6001\u9a8c\u8bc1\u53ea\u80fd\u6392\u9664&#034;\u63a5\u7ebf\u9519\u8bef&#034;&#xff0c;\u6392\u9664\u4e0d\u4e86&#034;\u72b6\u6001\u6c61\u67d3&#034;\u3002<\/p>\n<h3>4. \u7b2c\u4e8c\u6b65&#xff1a;GPU \u5224\u51b3&#xff08;\u4e09\u4e2a\u5b9e\u9a8c\u9501\u6b7b\u6839\u56e0&#xff09;<\/h3>\n<p>GPU \u91ca\u653e\u540e\u6309 runbook \u6267\u884c\u4e86\u4e09\u4e2a\u5224\u51b3\u5b9e\u9a8c&#xff1a;<\/p>\n<p>\u5b9e\u9a8c 1&#xff1a;FA2 \u5185\u6838\u7b49\u4ef7\u6027&#xff08;\u5224\u51b3 H1&#xff0c;5 \u5206\u949f&#xff09;\u3002\u5728\u955c\u50cf\u5185\u6784\u9020 DFlash \u771f\u5b9e\u8c03\u7528\u5f62\u6001&#xff08;causal&#061;False &#043; \u5bf9\u79f0\u6ed1\u7a97 (2048,2048) &#043; \u5206\u9875 block_table &#043; 8 \u4e2a query&#xff09;&#xff0c;\u4e0e fp32 SDPA \u53c2\u8003\u9010\u5143\u7d20\u5bf9\u6bd4\u3002\u4e94\u7ec4\u5f62\u72b6&#xff08;\u77ed\u4e0a\u4e0b\u6587\/\u5355\u6d41\/\u7a97\u53e3\u8fb9\u7f18\/\u8d85\u7a97&#xff09;\u6700\u5927\u504f\u5dee 0.0071\u2014\u2014bf16 \u91cf\u7ea7&#xff0c;FA2 \u5185\u6838\u65e0\u7f6a\u3002<\/p>\n<p>\u5b9e\u9a8c 2&#xff1a;\u63a2\u9488\u542f\u52a8&#xff08;\u5224\u51b3 H2\/H3&#xff09;\u3002\u5168\u65b0 compile \u7f13\u5b58\u76ee\u5f55 &#043; sitecustomize.py \u63a2\u9488&#xff08;\u81ea\u52a8\u6302 hook \u6253\u5370\u8fd0\u884c\u65f6\u771f\u503c&#xff09;\u30028 \u4e2a worker \u5168\u90e8\u4e00\u81f4&#xff1a;rope sync \u2192 False \u2713\u3001aux layers \u2192 (6,20,34,48,62,76) \u2713\u3001DFlash2 speculator FULL CUDA \u56fe\u6355\u83b7\u6210\u529f\u3002\u63a5\u7ebf\u5c42\u5b9e\u6d4b\u4e0e\u9759\u6001\u9a8c\u8bc1\u5b8c\u5168\u543b\u5408\u3002<\/p>\n<p>\u5b9e\u9a8c 3&#xff1a;\u63a5\u53d7\u7387\u590d\u6d4b&#xff08;\u5224\u51b3 H2&#xff09;\u3002\u540c\u6837\u7684\u955c\u50cf\u3001\u540c\u6837\u7684\u914d\u7f6e\u3001\u540c\u6837\u7684 FA2 \u540e\u7aef&#xff0c;\u552f\u4e00\u7684\u533a\u522b\u662f\u6362\u4e86\u5e72\u51c0\u7f13\u5b58\u2014\u2014<\/p>\n<table>\n<tr>\u89c2\u6d4b\u9879\u810f\u7f13\u5b58&#xff08;2026-08 \u5751 13&#xff09;\u5e72\u51c0\u7f13\u5b58&#xff08;2026-08-30&#xff09;MTP&#xff08;\u53c2\u7167&#xff09;<\/tr>\n<tbody>\n<tr>\n<td>\u63a5\u53d7\u957f\u5ea6<\/td>\n<td>0.75\/\u6b65&#xff08;pos2&#043; \u5f52\u96f6\u7684\u60ac\u5d16&#xff09;<\/td>\n<td>2.26-2.47\/\u6b65&#xff08;0.75\/0.36\/0.11\/0.04\/0.02\/0\/0&#xff0c;\u5065\u5eb7\u5355\u8c03\u8870\u51cf&#xff09;<\/td>\n<td>3.06\/\u6b65<\/td>\n<\/tr>\n<tr>\n<td>\u5355\u6d41\u541e\u5410<\/td>\n<td>33 tok\/s<\/td>\n<td>~50 tok\/s<\/td>\n<td>75.6 tok\/s<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>H2 \u5b9e\u9524&#xff1a;0.75 \u60ac\u5d16 &#061; \u5171\u4eab .compile_cache \u6c61\u67d3\u3002\u5f53\u5e74\u5931\u8d25\u7684 DFlash2 \u8fd0\u884c\u628a\u8349\u7a3f\u7f16\u8bd1\u4ea7\u7269\u5199\u8fdb\u4e86\u7f13\u5b58&#xff0c;\u540e\u7eed\u91cd\u8dd1\u590d\u7528\u4e86\u810f\u4ea7\u7269\u3002\u6574\u4e2a&#034;\u786c\u4ef6\u5bbf\u547d&#034;\u53d9\u4e8b\u5d29\u584c\u2014\u2014\u4fee\u590d\u65b9\u5f0f\u662f\u6362\u4e00\u4e2a\u7f13\u5b58\u76ee\u5f55\u3002<\/p>\n<p>&#xff08;\u5b9e\u64cd\u5907\u5fd8&#xff1a;\u4ee5\u540e\u542f\u7528 DFlash2 \u524d\u5fc5\u987b\u6362\u5168\u65b0 compile \u7f13\u5b58\u76ee\u5f55&#xff1b;\u672c\u6b21\u5b9e\u9a8c\u4ea7\u51fa\u7684 .compile_cache_probe\/ \u662f\u5408\u6cd5\u7f13\u5b58&#xff0c;\u5df2\u52a0\u5165 .gitignore\u3002&#xff09;<\/p>\n<h3>5. \u7b2c\u4e09\u6b65&#xff1a;\u53c2\u6570\u626b\u63cf\u4e0e\u5b98\u65b9\u5bf9\u7167\u2014\u2014&#034;\u5b98\u65b9\u4f18\u4e8e MTP&#034;\u4e3a\u4f55\u590d\u73b0\u4e0d\u51fa<\/h3>\n<ul>\n<li>\u7ed3\u6848\u4e4b\u540e\u95ee\u9898\u5347\u7ea7\u4e3a&#xff1a;\u4fee\u590d\u540e\u7684 DFlash2 \u80fd\u5426\u50cf\u5b98\u65b9\u8bf4\u7684\u90a3\u6837\u8d85\u8fc7 MTP&#xff1f;\u6211\u4eec\u7528\u540c\u4e00\u4f1a\u8bdd\u3001\u540c\u4e00\u6d4b\u91cf\u7535\u6c60&#xff08;essay\/code \u53cc prompt \u00d7 greedy\/\u5b98\u65b9\u91c7\u6837\u53cc\u53e3\u5f84 \u00d7 256 token&#xff09;\u505a\u4e86\u626b\u63cf&#xff1a;<\/li>\n<\/ul>\n<table>\n<tr>\u914d\u7f6eessay greedycode greedy\u5b98\u65b9\u91c7\u6837 essay\/codeconc&#061;8 \u805a\u5408\u63a5\u53d7\u957f\u5ea6<\/tr>\n<tbody>\n<tr>\n<td>DFlash2 &#064;7<\/td>\n<td>~50-52<\/td>\n<td>\u2014<\/td>\n<td>\u2014<\/td>\n<td>171.6<\/td>\n<td>1.99-2.47<\/td>\n<\/tr>\n<tr>\n<td>DFlash2 &#064;5<\/td>\n<td>51.9-54.4<\/td>\n<td>54.5-63.4<\/td>\n<td>53-56 \/ 55-57<\/td>\n<td>155.1<\/td>\n<td>1.62-2.29<\/td>\n<\/tr>\n<tr>\n<td>MTP &#064;5<\/td>\n<td>70.5-74.4<\/td>\n<td>88.2-90.9<\/td>\n<td>66-74 \/ 78-80<\/td>\n<td>168.3<\/td>\n<td>2.92-3.34&#xff08;0.84\/0.61\/0.43\/0.27\/0.17&#xff09;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5b98\u65b9 README \u5168\u8868 DFlash2 \u80dc\u51fa&#xff08;\u63a5\u53d7\u957f\u5ea6 4.19-6.02 vs 3.81-5.12&#xff0c;\u4e94\u4e2a\u4efb\u52a1\u5168\u80dc&#xff09;&#xff0c;\u4f46\u6211\u4eec\u7684\u56db\u79cd\u7ec4\u5408\u5168\u90e8\u662f MTP \u9886\u5148\u3002\u4e09\u4e2a\u5019\u9009\u89e3\u91ca&#xff0c;\u6309\u53ef\u80fd\u6027\u6392\u5e8f&#xff1a;<\/p>\n<li>\u8fd0\u884c\u65f6\u5b9e\u73b0\u5dee\u5f02&#xff1a;\u5b98\u65b9\u8dd1\u7684\u662f SGLang \u7684 DFLASH \u96c6\u6210&#xff0c;\u6211\u4eec\u662f vLLM \u7684 V2 speculator\u2014\u2014\u8349\u7a3f\u8fd0\u884c\u65f6&#xff08;\u5757\u5377\u79ef\u5e94\u7528\u65b9\u5f0f\u3001selector walk\u3001\u9a8c\u8bc1\u8def\u5f84&#xff09;\u4e0d\u662f\u540c\u4e00\u4efd\u4ee3\u7801&#xff1b;<\/li>\n<li>\u786c\u4ef6\/\u5185\u6838\u4ee3\u9645&#xff1a;GB300 TP4 &#043; FA4 vs A800 TP8 &#043; FA2&#xff08;\u5185\u6838&#034;\u6b63\u786e\u6027&#034;\u5df2\u6392\u9664&#xff0c;\u4f46 FA4 \u7684\u6570\u503c\u8def\u5f84\u4e0e sm80 \u7684 bf16 \u8def\u5f84\u4e0d\u5b8c\u5168\u540c\u6784&#xff0c;TP8 \u7684\u9a8c\u8bc1\u540c\u6b65\u5f00\u9500\u5bf9\u4e0d\u540c\u63a5\u53d7\u957f\u5ea6\u5206\u5e03\u7684\u654f\u611f\u5ea6\u4e5f\u4e0d\u540c&#xff09;&#xff1b;<\/li>\n<li>\u8349\u7a3f\u5bf9\u975e\u76ee\u6807\u4ee3\u9645\u7684\u6cdb\u5316\u2014\u2014\u8bc1\u636e\u6700\u5f31\u3002<\/li>\n<p>\u4e24\u4e2a\u9644\u52a0\u53d1\u73b0&#xff1a;num_spec&#061;5 \u2248 7&#xff08;\u74f6\u9888\u5728 pos2&#043; \u63a5\u53d7\u7387\u8d8b\u96f6&#xff0c;\u4e0d\u5728\u8349\u7a3f\u957f\u5ea6&#xff1b;\u4e14 checkpoint \u7684\u5757\u5377\u79ef\u6309 block_size&#061;8 \u8bad\u7ec3&#xff0c;\u6539 num_spec \u504f\u79bb\u8bad\u7ec3\u5f62\u6001&#xff0c;\u8c03\u53c2\u7a7a\u95f4\u6709\u9650&#xff09;&#xff1b;\u5b98\u65b9\u91c7\u6837\u53e3\u5f84&#xff08;temp&#061;1.0\/top-p 0.95&#xff09;\u4e0e code \u4efb\u52a1\u90fd\u4e0d\u6539\u53d8\u6392\u5e8f\u3002<\/p>\n<h3>6. \u6559\u8bad&#xff1a;\u6211\u4eec\u5dee\u70b9\u628a\u7f13\u5b58\u95ee\u9898\u5199\u6210\u786c\u4ef6\u5bbf\u547d<\/h3>\n<p>\u590d\u76d8\u6574\u6761\u5f2f\u8def&#xff1a;\u5751 13 \u53d1\u751f\u65f6&#xff0c;\u89c2\u5bdf\u5230\u7684&#034;pos0 ~50% \u2192 pos2&#043; \u5f52\u96f6&#034;\u88ab\u89e3\u91ca\u4e3a&#034;FA2 \u8bed\u4e49\u9000\u5316&#034;&#xff0c;\u4e0e\u5b98\u65b9 FA4 \u73af\u5883\u7684\u5dee\u5f02\u88ab\u5f53\u4f5c\u4f50\u8bc1&#xff0c;\u751a\u81f3\u5199\u8fdb\u4e86\u535a\u5ba2\u548c\u90e8\u7f72\u6587\u6863\u3002\u800c\u771f\u76f8\u662f\u8fd0\u884c\u73af\u5883\u7684\u53ef\u53d8\u72b6\u6001&#xff08;compile 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