{"id":38319,"date":"2025-02-14T04:00:59","date_gmt":"2025-02-13T20:00:59","guid":{"rendered":"https:\/\/17aitech.com\/?p=38319"},"modified":"2025-02-14T04:00:59","modified_gmt":"2025-02-13T20:00:59","slug":"deepseek-r1%e4%b8%8d%e7%bc%96%e7%a8%8b%e5%b0%b1%e8%83%bd%e7%94%9f%e6%88%90gpu%e5%86%85%e6%a0%b8%ef%bc%8c%e6%af%94%e7%86%9f%e7%bb%83%e5%b7%a5%e7%a8%8b%e5%b8%88%e5%a5%bd%ef%bc%8c%e6%83%8a%e5%88%b0","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=38319","title":{"rendered":"DeepSeek R1\u4e0d\u7f16\u7a0b\u5c31\u80fd\u751f\u6210GPU\u5185\u6838\uff0c\u6bd4\u719f\u7ec3\u5de5\u7a0b\u5e08\u597d\uff0c\u60ca\u5230\u4e86\u82f1\u4f1f\u8fbe"},"content":{"rendered":"

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:DeepSeek R1\u4e0d\u7f16\u7a0b\u5c31\u80fd\u751f\u6210GPU\u5185\u6838\uff0c\u6bd4\u719f\u7ec3\u5de5\u7a0b\u5e08\u597d\uff0c\u60ca\u5230\u4e86\u82f1\u4f1f\u8fbe<\/a><\/p>\n

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DeepSeek \u5f15\u7206 AI \u793e\u533a\u540e\uff0c\u4eba\u4eec\u90fd\u5728\u5c1d\u8bd5\u672c\u5730\u90e8\u7f72\u548c\u5404\u9886\u57df\u5e94\u7528\uff0c\u5728\u65b0\u6a21\u578b\u57fa\u7840\u4e0a\u6301\u7eed\u6539\u8fdb\u7684\u65b9\u5411\u4e5f\u88ab\u4e0d\u65ad\u63d0\u51fa\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u82f1\u4f1f\u8fbe\u5374\u5728\u5c1d\u8bd5\u7528 DeepSeek \u7ed9\u5927\u6a21\u578b pipeline \u672c\u8eab\u641e\u81ea\u52a8\u5316\u3002<\/section>\n
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\u672c\u5468\u4e09\uff0c\u82f1\u4f1f\u8fbe\u5728\u535a\u5ba2\u4e2d\u4ecb\u7ecd\u4e86\u5229\u7528 DeepSeek-R1 \u548c\u63a8\u7406\u65f6\u6269\u5c55\u6280\u672f\u6765\u81ea\u52a8\u751f\u6210\u4f18\u5316 GPU \u5185\u6838\u7684\u6700\u65b0\u7814\u7a76\u6210\u679c\uff0c\u6548\u679c\u5f02\u5e38\u7684\u597d\u3002<\/a><\/section>\n
\u5bf9\u6b64\u6709\u4eba\u8bc4\u4ef7\u9053\uff1a\u96be\u4e0d\u6210\u82f1\u4f1f\u8fbe\u5728\u81ea\u62c6\u62a4\u57ce\u6cb3\uff1f<\/section>\n
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\u968f\u7740 AI \u5927\u6a21\u578b\u89c4\u6a21\u4e0d\u65ad\u6269\u5c55\uff0c\u80fd\u529b\u6301\u7eed\u8fdb\u6b65\uff0c\u6d4b\u8bd5\u65f6\u6269\u5c55\uff08TTS\uff1aTest-Time Scaling\uff09\u6216\u63a8\u7406\u65f6\u6269\u5c55\uff08Inference-Time Scaling\uff09\u6cd5\u5219\u6b63\u5728\u5174\u8d77\u3002\u8fd9\u9879\u6280\u672f\u4e5f\u88ab\u79f0\u4e3a AI \u63a8\u7406\u6216\u957f\u601d\u8003\uff0c\u5b83\u901a\u8fc7\u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\u5206\u914d\u989d\u5916\u7684\u8ba1\u7b97\u8d44\u6e90\u6765\u8bc4\u4f30\u591a\u79cd\u53ef\u80fd\u7684\u7ed3\u679c\uff0c\u7136\u540e\u9009\u62e9\u6700\u4f73\u7684\u4e00\u4e2a\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u6574\u4f53\u6027\u80fd\u3002<\/section>\n
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\u63a8\u7406\u80fd\u529b\u7684\u52a0\u5f3a\u4f7f\u5f97 AI \u521d\u6b65\u638c\u63e1\u4e86\u7c7b\u4f3c\u4e8e\u4eba\u7c7b\u5256\u6790\u590d\u6742\u95ee\u9898\u7684\u80fd\u529b\uff0c\u80fd\u9010\u4e2a\u89e3\u51b3\u4ee5\u5f97\u51fa\u6700\u7ec8\u89e3\u51b3\u65b9\u6848\u7684\u65b9\u5f0f\uff0c\u8fdb\u884c\u7b56\u7565\u6027\u601d\u8003\u548c\u7cfb\u7edf\u6027\u5730\u89e3\u51b3\u590d\u6742\u95ee\u9898\u3002<\/section>\n
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\u5728\u82f1\u4f1f\u8fbe\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u5de5\u7a0b\u5e08\u4eec\u8fdb\u884c\u4e86\u4e00\u9879\u5b9e\u9a8c\uff0c\u4ed6\u4eec\u4f7f\u7528\u6700\u65b0\u3001\u6700\u70ed\u95e8\u7684\u5f00\u6e90\u5927\u6a21\u578b DeepSeek-R1 \u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\u5229\u7528\u989d\u5916\u7684\u8ba1\u7b97\u80fd\u529b\u6765\u89e3\u51b3\u4e00\u4e2a\u590d\u6742\u95ee\u9898 \u2014\u2014 \u81ea\u52a8\u751f\u6210\u6570\u503c\u6b63\u786e\uff0c\u4e14\u9488\u5bf9\u4e0d\u540c\u6ce8\u610f\u529b\u53d8\u4f53\u4f18\u5316\u7684 GPU \u6ce8\u610f\u529b\u5185\u6838\uff0c\u800c\u65e0\u9700\u4efb\u4f55\u663e\u5f0f\u7f16\u7a0b\u3002<\/section>\n
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\u4eba\u4eec\u53d1\u73b0\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0cR1 \u8f93\u51fa\u7684\u7ed3\u679c\u751a\u81f3\u4f18\u4e8e\u7531\u719f\u7ec3\u5de5\u7a0b\u5e08\u5f00\u53d1\u51fa\u6765\u7684\u4f18\u5316\u5185\u6838\u3002<\/section>\n
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\u6ce8\u610f\u529b\u673a\u5236\u662f\u5f7b\u5e95\u6539\u53d8\u5927\u578b\u8bed\u8a00\u6a21\u578b\uff08LLM\uff09\u53d1\u5c55\u7684\u4e00\u4e2a\u5173\u952e\u6982\u5ff5\u3002\u5b83\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u673a\u5236\uff0c\u4f7f AI \u6a21\u578b\u5728\u6267\u884c\u4efb\u52a1\u65f6\u80fd\u591f\u9009\u62e9\u6027\u5730\u5173\u6ce8\u8f93\u5165\u4e2d\u6700\u76f8\u5173\u7684\u90e8\u5206\u3002\u901a\u8fc7\u4e13\u6ce8\u4e8e\u91cd\u8981\u4fe1\u606f\uff0c\u6ce8\u610f\u529b\u64cd\u4f5c\u5e2e\u52a9\u6a21\u578b\u505a\u51fa\u66f4\u597d\u7684\u9884\u6d4b\u5e76\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u9690\u85cf\u6a21\u5f0f\u3002<\/section>\n
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\u6ce8\u610f\u529b\u64cd\u4f5c\u7684\u8ba1\u7b97\u590d\u6742\u5ea6\u4e0e\u8f93\u5165\u5e8f\u5217\u957f\u5ea6\u7684\u5e73\u65b9\u6210\u6b63\u6bd4\u589e\u957f\u3002\u8fd9\u4fc3\u4f7f\u6211\u4eec\u9700\u8981\u5f00\u53d1\u4f18\u5316\u7684\u5e95\u5c42\u5b9e\u73b0\uff08\u5373 GPU \u5185\u6838\uff09\uff0c\u4ee5\u9632\u6b62\u7b80\u5355\u5b9e\u73b0\u5bfc\u81f4\u7684\u8fd0\u884c\u65f6\u9519\u8bef\uff08\u5982\u5185\u5b58\u4e0d\u8db3\u9519\u8bef\uff09\uff0c\u5e76\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002<\/section>\n
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\u53e6\u5916\uff0c\u6ce8\u610f\u529b\u6709\u591a\u79cd\u53d8\u4f53\uff08\u56e0\u679c\u6ce8\u610f\u529b\u3001\u76f8\u5bf9\u4f4d\u7f6e\u5d4c\u5165\u3001ALiBi \u7b49\uff09\uff0c\u5de5\u7a0b\u5e08\u901a\u5e38\u9700\u8981\u4e3a\u7279\u5b9a\u4efb\u52a1\u7ec4\u5408\u4f7f\u7528\u8fd9\u4e9b\u53d8\u4f53\u3002<\/section>\n
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\u591a\u6a21\u6001\u6a21\u578b\uff08\u4f8b\u5982\u89c6\u89c9 Transformer\uff09\u5f15\u5165\u4e86\u989d\u5916\u7684\u6311\u6218\uff0c\u56e0\u4e3a\u5b83\u4eec\u9700\u8981\u4e13\u95e8\u7684\u6ce8\u610f\u529b\u673a\u5236\uff08\u5982\u7a7a\u95f4\u90bb\u57df\u6ce8\u610f\u529b\uff09\u6765\u7ef4\u62a4\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u89c6\u9891\u751f\u6210\u6a21\u578b\u7b49\u4e2d\u5e38\u89c1\u7684\u65f6\u7a7a\u4fe1\u606f\u3002<\/section>\n
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\u6700\u8fd1\u7684\u5927\u6a21\u578b\uff08\u5982 DeepSeek-R1\uff09\u5728\u4ee3\u7801\u751f\u6210\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u4e86\u5f88\u5927\u7684\u6f5c\u529b\uff0c\u4f46\u5b83\u4eec\u5728\u7b2c\u4e00\u6b21\u5c1d\u8bd5\u521b\u5efa\u4f18\u5316\u4ee3\u7801\u65f6\u4ecd\u7136\u6548\u679c\u4e0d\u597d\u3002\u8fd9\u4f7f\u5f97\u5728\u63a8\u7406\u65f6\u4f7f\u7528\u5176\u4ed6\u7b56\u7565\u6765\u751f\u6210\u4f18\u5316\u4ee3\u7801\u6210\u4e3a\u4e86\u5fc5\u8981\u3002<\/section>\n
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\u4ee5\u4e0b Prompt \u662f\u76f8\u5bf9\u4f4d\u7f6e\u5d4c\u5165\u6ce8\u610f\u529b\u5185\u6838\u7684\u793a\u4f8b\u7528\u6237\u8f93\u5165\u3002<\/section>\n
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Please write a GPU attention kernel to support relative position encodings. Implement the relative positional encoding on the fly within the kernel. The complete code should be returned, including the necessary modifications.<\/code>\r\n<\/code><\/section>
\r\n<\/section>
Use the following function to compute the relative positional encoding:<\/code>\r\n<\/code><\/section>
\r\n<\/section>
def relative_positional(score, b, h, q_idx, kv_idx):<\/code>\r\n<\/code>   <\/code><\/section>
\u00a0 \u00a0 \u00a0return score + (q_idx - kv_idx)<\/code>\r\n<\/code><\/section>
\r\n<\/section>
When implementing the kernel, keep in mind that a constant scaling factor 1.44269504 should be applied to the relative positional encoding due to qk_scale = sm_scale * 1.44269504. The PyTorch reference does not need to scale the relative positional encoding, but in the GPU kernel, use:<\/code>\r\n<\/code><\/section>
\r\n<\/section>
qk = qk * qk_scale + rel_pos * 1.44269504<\/code>\r\n<\/code><\/section>
\r\n<\/section>
Please provide the complete updated kernel code that incorporates these changes, ensuring that the relative positional encoding is applied efficiently within the kernel operations.<\/code><\/section><\/pre>\n<\/section>\n
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\u5927\u6a21\u578b\u6709\u65f6\u4f1a\u4ea7\u751f\u5e7b\u89c9\uff0c\u6216\u8f93\u51fa\u6df7\u5408\u4e0d\u540c\u8bed\u8a00\u6216\u6846\u67b6\u7684\u8bed\u6cd5\uff0c\u5bfc\u81f4\u751f\u6210\u7684\u4ee3\u7801\u9519\u8bef\u6216\u6548\u7387\u4f4e\u4e0b\u3002\u8ba1\u7b97\u6700\u4f73 GPU \u7ebf\u7a0b\u6620\u5c04\u4e5f\u662f\u4e00\u9879\u8270\u5de8\u800c\u5177\u6709\u6311\u6218\u6027\u7684\u4efb\u52a1\uff0c\u901a\u5e38\u9700\u8981\u8fed\u4ee3\u7ec6\u5316\u624d\u80fd\u83b7\u5f97\u6b63\u786e\u4e14\u9ad8\u6548\u7684\u5185\u6838\u3002<\/section>\n
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\u7528\u4e8e\u751f\u6210\u4f18\u5316 GPU \u5185\u6838\u7684\u63a8\u7406\u65f6\u6269\u5c55<\/strong><\/section>\n
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\u4e3a\u4e86\u5229\u7528\u4f18\u5316\u7684\u6ce8\u610f\u529b\u5185\u6838\u83b7\u5f97\u6700\u4f73\u7ed3\u679c\uff0c\u82f1\u4f1f\u8fbe\u5de5\u7a0b\u5e08\u521b\u5efa\u4e86\u4e00\u4e2a\u65b0\u7684\u5de5\u4f5c\u6d41\u7a0b\uff0c\u5305\u62ec\u4e86\u4e00\u4e2a\u7279\u6b8a\u7684\u9a8c\u8bc1\u5668\u4ee5\u53ca\u4e00\u4e2a\u5728\u9884\u5b9a\u65f6\u95f4\u5185\u4ee5\u95ed\u73af\u65b9\u5f0f\u8fdb\u884c\u63a8\u7406\u7684 DeepSeek-R1 \u6a21\u578b\u3002<\/section>\n
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\u5177\u4f53\u5730\u8bb2\uff0c\u8be5\u5de5\u4f5c\u6d41\u7a0b\u9996\u5148\u7531\u624b\u52a8\u63d0\u793a\u8fdb\u884c\u521d\u59cb\u5316\uff0c\u7136\u540e DeepSeek-R1 \u5728\u7b2c\u4e00\u6b21\u904d\u5386\u4e2d\u751f\u6210 GPU \u4ee3\u7801\uff08\u5373\u5185\u6838\uff09\u3002\u9a8c\u8bc1\u5668\u5728\u4e00\u5757\u82f1\u4f1f\u8fbe H100 GPU \u4e0a\u8fd0\u884c\uff0c\u5b83\u5bf9\u751f\u6210\u7684\u5185\u6838\u8fdb\u884c\u5206\u6790\uff0c\u5e76\u521b\u9020\u65b0\u7684\u63d0\u793a\u4ee5\u4f5c\u4e3a\u8f93\u5165\u63d0\u4f9b\u7ed9 DeepSeek-R1\u3002<\/section>\n
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\u8fd9\u79cd\u95ed\u73af\u65b9\u6cd5\u6bcf\u6b21\u90fd\u4ee5\u4e0d\u540c\u7684\u65b9\u5f0f\u5f15\u5bfc\u4ee3\u7801\u751f\u6210\u8fc7\u7a0b\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u597d\u7684\u6548\u679c\u3002\u82f1\u4f1f\u8fbe\u53d1\u73b0\uff0c\u8fd9\u4e2a\u8fc7\u7a0b\u6301\u7eed 15 \u5206\u949f\u5c31\u53ef\u4ee5\u5f97\u5230\u4e00\u4e2a\u6539\u8fdb\u7684\u6ce8\u610f\u529b\u5185\u6838\u3002<\/section>\n
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\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u56fe 3\uff1a\u4f7f\u7528 flex attention \u81ea\u52a8\u751f\u6210\u7684\u4f18\u5316\u6ce8\u610f\u529b\u5185\u6838\u7684\u6027\u80fd\u3002<\/sup><\/em><\/section>\n
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\u6839\u636e\u65af\u5766\u798f\u7684 KernelBench \u57fa\u51c6\u6d4b\u8bd5\uff0c\u8be5\u5de5\u4f5c\u6d41\u7a0b\u53ef\u4ee5\u4e3a 100% \u7684 Level-1 \u95ee\u9898\u548c 96% \u7684 Level-2 \u95ee\u9898\u751f\u6210\u6570\u503c\u6b63\u786e\u7684\u5185\u6838\u3002\u200c<\/section>\n
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\u5176\u4e2d\uff0cKernelBench \u4e2d\u7684 Level-1 \u89e3\u51b3\u7387\u662f\u6307\uff1a\u8bc4\u4f30 LLM \u4e3a\u7279\u5b9a\u8ba1\u7b97\u4efb\u52a1\u751f\u6210\u9ad8\u6548 GPU \u5185\u6838\u7684\u80fd\u529b\u7684\u6570\u503c\u6b63\u786e\u6307\u6807\u3002\u8be5\u6d4b\u8bd5\u662f\u4e00\u7cfb\u5217\u300c\u6d4b\u8bd5\u6700\u65b0 LLM GPU \u7f16\u7a0b\u80fd\u529b\u300d\u6311\u6218\u7684\u4e00\u90e8\u5206\u3002<\/section>\n
<\/section>\n
\u56fe 4 \u663e\u793a\u4e86\u63a8\u7406\u65f6\u9884\u7b97\u5bf9 agent \u89e3\u51b3\u7387\u7684\u5f71\u54cd\uff0c\u7ed3\u679c\u663e\u793a\uff0c\u5728 Level-1 \u7c7b\u522b\u4e2d\u4e3a\u6bcf\u4e2a\u95ee\u9898\u5206\u914d\u8d85\u8fc7 10 \u5206\u949f\u7684\u65f6\u95f4\uff0c\u53ef\u4ee5\u4f7f\u5de5\u4f5c\u6d41\u7a0b\u4e3a 100 \u4e2a\u95ee\u9898\u4e2d\u7684\u5927\u591a\u6570\u751f\u6210\u6570\u503c\u6b63\u786e\u7684\u4ee3\u7801\u3002<\/a><\/section>\n
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<\/section>\n
\u53c2\u8003\u5185\u5bb9\uff1ahttps:\/\/developer.nvidia.com\/blog\/automating-gpu-kernel-generation-with-deepseek-r1-and-inference-time-scaling\/<\/sup><\/em><\/section>\n
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