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105ms&#xff0c;\u8fd9\u987f\u996d\u503c\u4e0d\u503c<\/h3>\n<p>\u6709\u3002\u800c\u4e14\u5f88\u76f4\u767d&#xff1a;\u901f\u5ea6\u3002<\/p>\n<p>\u539f\u59cb bf16 \u63a8\u7406&#xff0c;\u4e00\u6b21\u524d\u5411 36 \u6beb\u79d2\u3002BF16X \u8981\u5b9e\u65f6\u89e3\u7801&#xff0c;105 \u6beb\u79d2\u3002\u6162\u4e86\u5dee\u4e0d\u591a 3 \u500d\u3002<\/p>\n<p>\u542c\u8d77\u6765\u5f88\u4e8f\u5bf9\u5427&#xff1f;\u4f46\u4f60\u5f97\u770b\u6362\u6765\u4ec0\u4e48&#xff1a;GPU \u663e\u5b58\u4ece 2.2GB \u964d\u5230 2.0GB&#xff0c;\u78c1\u76d8\u7a7a\u95f4\u7701\u4e00\u534a\u591a\u3002\u800c\u5982\u679c\u8d70 CPU \u6d41\u5f0f\u65b9\u6848&#xff0c;\u663e\u5b58\u80fd\u538b\u5230 0.9GB\u2014\u2014\u4e0d\u5230\u539f\u59cb\u7684\u4e00\u534a\u3002<\/p>\n<p>\u800c\u4e14\u522b\u5fd8\u4e86\u90a3\u4e2a\u6700\u8981\u547d\u7684\u6570\u5b57&#xff1a;ppl\u3002\u539f\u59cb 56.02&#xff0c;BF16X \u4e4b\u540e&#xff0c;\u8fd8\u662f 56.02\u3002<\/p>\n<p>\u5bf9\u6bd4\u4e00\u4e0b 4-bit \u91cf\u5316&#xff1a;ppl \u76f4\u63a5\u6da8\u5230 61.5&#xff0c;\u8d28\u91cf\u635f\u5931\u8d85\u8fc7 10%&#xff0c;\u8fd8\u5f97\u9760\u5fae\u8c03\u8865\u6551\u3002BF16X \u4e00\u5206\u94b1\u7cbe\u5ea6\u6ca1\u6389&#xff0c;\u53ea\u662f\u591a\u82b1\u70b9\u65f6\u95f4\u3002<\/p>\n<p>\u8fd9\u5c31\u50cf\u2026\u2026\u4e00\u4e2a\u65e0\u635f\u7684 ZIP&#xff0c;\u6bd4\u6709\u635f\u7684 JPEG \u6162&#xff0c;\u4f46\u653e\u5927\u770b&#xff0c;\u6bcf\u4e00\u9897\u50cf\u7d20\u90fd\u4e00\u6a21\u4e00\u6837\u3002\u4f60\u8981\u7ed9\u7532\u65b9\u4ea4\u4ed8\u3001\u8981\u8dd1\u7ebf\u4e0a\u670d\u52a1&#xff0c;\u4f60\u6562\u62ff JPEG \u7cca\u5f04\u5417&#xff1f;<\/p>\n<p>\u901f\u5ea6\u7684\u95ee\u9898&#xff0c;\u65b9\u5411\u4e5f\u5df2\u7ecf\u6e05\u695a\u4e86&#xff1a;CUDA graph \u4e00\u6b21\u5c31\u80fd\u6355\u83b7\u5168\u90e8 168 \u5c42&#xff0c;\u7406\u8bba\u4e0a\u80fd\u628a\u89e3\u7801\u5f00\u9500\u57fa\u672c\u62b9\u5e73\u3002\u73b0\u5728\u662f\u7f13\u51b2\u65f6\u5e8f\u7684 bug \u8fd8\u6ca1\u4fee\u5b8c\u2014\u2014\u4f46\u8fd9\u5df2\u7ecf\u662f\u9ece\u660e\u524d\u4e86\u3002<\/p>\n<p>\u8bf4\u5230 bug&#xff0c;\u6211\u5f97\u8ddf\u4f60\u804a\u804a&#xff0c;\u90a3\u4e09\u4e2a\u5dee\u70b9\u8ba9\u4eba\u5d29\u6e83\u7684\u6df1\u591c\u3002<\/p>\n<h3>\u5355\u51435 | \u4e09\u4e2a bug \u4e4b\u591c&#xff1a;Triton \u5185\u6838\u8c03\u8bd5\u5b9e\u5f55<\/h3>\n<p>\u8fd9\u4e2a\u9879\u76ee\u6700\u6709\u610f\u601d\u7684&#xff0c;\u4e0d\u662f\u539f\u7406&#xff0c;\u662f\u5de5\u7a0b\u3002<\/p>\n<p>Triton \u5185\u6838&#xff0c;\u5f88\u591a\u4eba\u542c\u8bf4\u8fc7&#xff0c;\u5199\u8d77\u6765\u50cf Python&#xff0c;\u8dd1\u8d77\u6765\u50cf CUDA\u3002\u4f46&#034;\u50cf&#034;\u662f\u539f\u7f6a\u3002\u6211\u8e29\u4e86\u4e09\u4e2a\u5751&#xff0c;\u6bcf\u4e00\u4e2a\u90fd\u8db3\u591f\u8ba9\u4eba\u6000\u7591\u4eba\u751f\u3002<\/p>\n<p>\u7b2c\u4e00\u4e2a\u5751&#xff0c;\u53eb&#034;\u7b26\u53f7\u6269\u5c55&#034;\u3002<\/p>\n<p>Triton \u91cc\u7684\u53f3\u79fb&#xff0c;\u9ed8\u8ba4\u662f\u7b97\u672f\u53f3\u79fb\u3002\u4ec0\u4e48\u610f\u601d&#xff1f;\u4e00\u4e2a int32 \u7684\u8d1f\u6570&#xff0c;\u6700\u9ad8\u4f4d\u662f 1&#xff0c;\u53f3\u79fb\u7684\u65f6\u5019&#xff0c;\u9ad8\u4f4d\u8865\u7684\u5168\u662f 1\u3002\u6211\u538b\u7f29\u597d\u7684\u4f4d\u6d41&#xff0c;\u53ea\u8981\u67d0\u4e2a\u5b57\u6700\u9ad8\u4f4d\u662f 1&#xff0c;\u89e3\u538b\u51fa\u6765\u5c31\u6709 10% \u7684\u6743\u91cd\u662f\u9519\u7684\u3002<\/p>\n<p>10% \u7684\u6743\u91cd\u9519\u662f\u4ec0\u4e48\u6982\u5ff5&#xff1f;\u6a21\u578b\u76f4\u63a5\u53d8\u6210\u667a\u969c\u3002\u800c\u4e14\u5b83\u9519\u5f97\u5f88\u6709\u89c4\u5f8b\u2014\u2014\u89c4\u5f8b\u5230\u4f60\u4f1a\u6000\u7591&#xff0c;\u662f\u4e0d\u662f\u81ea\u5df1\u7684\u6570\u5b66\u51fa\u4e86\u95ee\u9898&#xff0c;\u800c\u4e0d\u662f\u4ee3\u7801\u3002<\/p>\n<p>\u7b2c\u4e8c\u4e2a\u5751&#xff0c;\u66f4\u9634\u9669\u3002\u5c3e\u6570\u8de8\u5b57\u8fb9\u754c\u7684\u65f6\u5019&#xff0c;\u6211\u7b97\u4e86\u4e00\u4e2a\u6307\u9488&#xff0c;\u6307\u5411&#034;\u4e0b\u4e00\u4e2a\u5b57&#034;&#xff0c;\u4f46\u5b83\u5076\u5c14\u8ddf\u5f53\u524d\u5b57\u662f\u540c\u4e00\u4e2a\u3002\u540c\u4e00\u4e2a word \u548c\u81ea\u5df1 OR \u4e24\u6b21&#xff0c;\u7ed3\u679c\u4e0d\u53d8&#xff0c;\u4ee3\u7801\u4e0d\u62a5\u9519&#xff0c;\u6570\u636e\u770b\u7740\u4e5f\u5bf9\u2014\u2014\u53ea\u6709\u8de8\u5b57\u8fb9\u754c\u7684\u90a3\u4e00\u6279\u5143\u7d20&#xff0c;\u6084\u6084\u9519\u4e86\u3002<\/p>\n<p>\u8fd9\u79cd bug \u6700\u6298\u78e8\u4eba&#xff1a;\u5b83\u4e0d\u5d29\u6e83\u3001\u4e0d\u62a5\u9519\u3001\u5927\u90e8\u5206\u65f6\u5019\u7ed3\u679c\u6b63\u786e&#xff0c;\u53ea\u662f\u5076\u5c14\u9519\u51e0\u4e2a\u6570\u5b57\u3002\u4f60\u5bf9\u7740\u4e00\u4e2a 99% \u6b63\u786e\u7684\u6a21\u578b&#xff0c;\u53bb\u627e\u90a3 1% \u7684\u6bdb\u75c5&#xff0c;\u4e00\u627e\u5c31\u662f\u4e09\u5929\u4e09\u591c\u3002<\/p>\n<p>\u7b2c\u4e09\u4e2a\u5751&#xff0c;\u662f\u6700\u8352\u8bde\u7684&#xff1a;.to(tl.int16)&#xff0c;Triton \u91cc\u7684\u7c7b\u578b\u8f6c\u6362&#xff0c;\u5c45\u7136\u4e0d\u662f\u4f4d\u91cd\u89e3\u91ca&#xff0c;\u800c\u662f\u6570\u503c\u8f6c\u6362\u3002\u6ea2\u51fa\u4fee\u590d\u5199\u5b8c&#xff0c;0x0000 \u88ab\u76f4\u63a5\u5199\u8fdb\u6743\u91cd\u2014\u2014\u90a3\u662f\u771f\u6b63\u7684&#034;\u4e00\u7b14\u52fe\u9500&#034;&#xff0c;\u628a\u6574\u4e2a\u6743\u91cd\u6e05\u96f6\u3002<\/p>\n<p>\u770b\u5230\u7ed3\u679c\u7684\u90a3\u4e00\u523b&#xff0c;\u6211\u53cd\u800c\u7b11\u4e86\u3002\u56e0\u4e3a\u8fd9\u79cd bug \u7684\u4ef7\u503c\u5728\u4e8e&#xff1a;\u4fee\u5b8c\u4e4b\u540e&#xff0c;\u4f60\u624b\u91cc\u7684\u4ee3\u7801\u662f\u771f\u7684\u61c2\u4e86&#xff0c;\u4e0d\u662f\u78b0\u5de7\u80fd\u8dd1\u3002<\/p>\n<p>\u8fd9\u4e09\u4e2a bug&#xff0c;\u6bcf\u4e00\u4e2a\u90fd\u5728\u6df1\u591c\u4e24\u70b9\u534a\u5de6\u53f3\u88ab\u6211\u627e\u5230\u3002\u4fee\u5b8c\u7684\u90a3\u4e00\u523b&#xff0c;\u6211\u8dd1\u4e86\u4e00\u904d\u5168\u6a21\u578b\u9010\u6bd4\u7279\u5bf9\u6bd4&#xff0c;\u770b\u7740 assert \u901a\u8fc7\u7684\u90a3\u4e00\u884c\u7eff\u5b57\u2014\u2014\u8bf4\u53e5\u5b9e\u8bdd&#xff0c;\u6bd4\u4e2d\u5f69\u7968\u8fd8\u723d\u3002<\/p>\n<p>\u56e0\u4e3a\u90a3\u610f\u5473\u7740&#xff1a;\u8fd9\u4e2a&#034;\u4e0d\u53ef\u80fd&#034;&#xff0c;\u771f\u7684\u6210\u4e86\u3002<\/p>\n<h3>\u5355\u51436 | \u5b9e\u6d4b&#xff1a;RTX 4090 \u4e0a\u7684\u771f\u6570\u636e<\/h3>\n<p>\u7a7a\u53e3\u65e0\u51ed&#xff0c;\u4e0a\u6570\u636e\u3002RTX 4090&#xff0c;24GB \u663e\u5b58&#xff0c;MiniCPM5-1B&#xff0c;168 \u5c42 Linear&#xff0c;\u5168\u90e8 Triton \u89e3\u7801&#xff0c;\u6ca1\u6709\u4efb\u4f55 PyTorch \u540e\u5904\u7406\u3002<\/p>\n<table>\n<tr>\u6a21\u5f0f\u63a8\u7406\u5ef6\u8fdf\u663e\u5b58ppl\u8d28\u91cf<\/tr>\n<tbody>\n<tr>\n<td>bf16 \u539f\u59cb<\/td>\n<td>36ms<\/td>\n<td>2.2GB<\/td>\n<td>56.02<\/td>\n<td>100%<\/td>\n<\/tr>\n<tr>\n<td>BF16X GPU\u5b9e\u65f6<\/td>\n<td>105ms<\/td>\n<td>2.0GB<\/td>\n<td>56.02<\/td>\n<td>100%\u65e0\u635f<\/td>\n<\/tr>\n<tr>\n<td>BF16X CPU\u6d41\u5f0f<\/td>\n<td>128ms<\/td>\n<td>0.9GB<\/td>\n<td>56.02<\/td>\n<td>100%\u65e0\u635f<\/td>\n<\/tr>\n<tr>\n<td>4-bit \u6253\u5305<\/td>\n<td>112ms<\/td>\n<td>1.4GB<\/td>\n<td>61.50<\/td>\n<td>\u6709\u635f&#043;10%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u6ce8\u610f\u770b ppl \u90a3\u4e00\u5217&#xff1a;\u4e0d\u7ba1\u600e\u4e48\u538b\u7f29&#xff0c;56.02&#xff0c;\u7eb9\u4e1d\u4e0d\u52a8\u3002\u800c 4-bit \u65b9\u6848&#xff0c;61.5&#xff0c;\u6da8\u4e86 10%\u3002<\/p>\n<p>\u89e3\u7801\u5185\u6838\u5feb\u6210\u4ec0\u4e48\u6837\u5462&#xff1f;\u5355\u5c42 3.15M \u6743\u91cd&#xff0c;20 \u5fae\u79d2\u3002\u4ec0\u4e48\u6982\u5ff5&#xff1f;\u4e00\u7728\u773c\u662f 30 \u4e07\u5fae\u79d2&#xff0c;\u4f60\u7728\u4e00\u4e0b\u773c\u775b&#xff0c;\u8fd9\u4e2a\u5185\u6838\u5df2\u7ecf\u8dd1\u5b8c\u4e86 15 \u4e07\u6b21\u3002<\/p>\n<p>\u4f46\u6211\u4e0d\u6253\u7b97\u628a\u5b83\u5439\u6210\u4e07\u80fd\u836f\u3002\u6070\u6070\u76f8\u53cd&#xff0c;\u63a5\u4e0b\u6765\u8fd9\u6bb5&#xff0c;\u662f\u7ed9\u4f60\u6cfc\u51b7\u6c34\u7684\u3002<\/p>\n<h3>\u5355\u51437 | \u6cfc\u51b7\u6c34&#xff1a;BF16X \u4e0d\u662f\u94f6\u5f39<\/h3>\n<p>\u4e09\u4e2a\u4f60\u5fc5\u987b\u77e5\u9053\u7684\u4e8b\u5b9e\u3002<\/p>\n<p>\u7b2c\u4e00&#xff0c;\u89e3\u7801\u5b8c\u8fd8\u662f bf16\u3002\u4e5f\u5c31\u662f\u8bf4&#xff0c;\u8ba1\u7b97\u7684\u65f6\u5019\u663e\u5b58\u5360\u7528\u548c\u539f\u59cb\u4e00\u6837&#xff0c;\u7701\u4e0b\u7684\u7a7a\u95f4\u4e3b\u8981\u9760 CPU \u6d41\u5f0f\u65b9\u6848\u2014\u2014\u628a\u6253\u5305\u6570\u636e\u653e\u5728\u5185\u5b58&#xff0c;\u7528\u7684\u65f6\u5019 DMA \u8fdb\u6765\u3002\u8fd9\u4e5f\u662f\u4e3a\u4ec0\u4e48\u6781\u9650\u80fd\u5230 0.9GB\u3002<\/p>\n<p>\u7b2c\u4e8c&#xff0c;\u5b83\u53ea\u538b Linear \u6743\u91cd\u3002\u5f52\u4e00\u5316\u5c42\u3001\u504f\u7f6e\u3001Embedding&#xff0c;\u4e00\u6982\u4e0d\u78b0\u3002<\/p>\n<p>\u7b2c\u4e09&#xff0c;\u4e5f\u662f\u6700\u5927\u7684\u4e00\u5200&#xff1a;Embedding \u52a8\u8f84\u51e0\u767e MB&#xff0c;MiniCPM \u7684 embedding \u5c31 400MB&#xff0c;\u4e0d\u538b\u7f29\u3002\u7b97\u4e0b\u6765 GPU \u603b\u7701 10%\u2014\u20142.0 \u5bf9 2.2GB\u3002<\/p>\n<p>\u542c\u8d77\u6765\u4e0d\u591a\u662f\u5427&#xff1f;\u4f46\u6362\u4e2a\u89c6\u89d2&#xff1a;\u78c1\u76d8\u4ece 2.1GB \u538b\u5230 1GB&#xff0c;\u7701\u4e0b\u7684\u662f\u5b9e\u6253\u5b9e\u7684\u90e8\u7f72\u6210\u672c\u3001\u4e0b\u8f7d\u6d41\u91cf\u3001\u52a0\u8f7d\u65f6\u95f4\u3002\u800c\u4e14\u65b9\u5411\u5df2\u7ecf\u5b8c\u5168\u8dd1\u901a&#xff0c;\u5269\u4e0b\u7684\u662f\u628a 10% \u53d8\u6210 30% \u7684\u5de5\u7a0b\u95ee\u9898&#xff0c;\u4e0d\u662f\u539f\u7406\u95ee\u9898\u3002<\/p>\n<p>\u5927\u6a21\u578b\u538b\u7f29\u8fd9\u6761\u8def&#xff0c;\u771f\u6b63\u7684\u96be\u70b9\u4ece\u6765\u4e0d\u662f\u7b97\u6cd5&#xff0c;\u800c\u662f\u4f60\u6562\u4e0d\u6562\u5148\u8981\u4e00\u4e2a&#034;\u4e00\u4e2a\u6bd4\u7279\u90fd\u4e0d\u80fd\u9519&#034;\u7684\u7b54\u6848\u3002<\/p>\n<h3>\u7ed3\u5c3e<\/h3>\n<p>\u6211\u89c1\u8fc7\u592a\u591a\u4eba\u505a\u91cf\u5316&#xff0c;\u7b2c\u4e00\u6b65\u5c31\u662f\u63a5\u53d7\u635f\u5931&#xff0c;\u7136\u540e\u5b89\u6170\u81ea\u5df1&#034;\u635f\u5931\u4e00\u70b9\u70b9\u6ca1\u5173\u7cfb&#034;\u3002<\/p>\n<p>\u4f46 BF16X \u8fd9\u4e2a\u6545\u4e8b\u60f3\u8bf4\u7684\u662f&#xff1a;\u6709\u4e9b\u6807\u51c6&#xff0c;\u4e0d\u80fd\u56e0\u4e3a\u96be&#xff0c;\u5c31\u5148\u81ea\u5df1\u964d\u4e0b\u6765\u3002<\/p>\n<p>100% \u65e0\u635f&#xff0c;\u542c\u7740\u50cf\u5f3a\u8feb\u75c7&#xff0c;\u50cf\u6d01\u7656&#xff0c;\u50cf\u6280\u672f\u4eba\u7684\u6267\u5ff5\u3002\u4f46\u6070\u6070\u662f\u8fd9\u79cd\u6267\u5ff5&#xff0c;\u5728\u903c\u7740\u6574\u4e2a\u884c\u4e1a\u5f80\u524d\u8d70\u2014\u2014\u56e0\u4e3a\u5f53\u6240\u6709\u4eba\u90fd\u9ed8\u8ba4&#034;\u538b\u7f29\u5fc5\u987b\u8981\u6709\u635f&#034;\u7684\u65f6\u5019&#xff0c;\u4f60\u80fd\u505a\u5230\u7684\u6781\u9650&#xff0c;\u4e5f\u53ea\u662f\u522b\u4eba\u7684\u9ed8\u8ba4\u503c\u3002<\/p>\n<p>\u65e0\u635f\u538b\u7f29&#xff0c;2.08 \u500d&#xff0c;56.02 \u7eb9\u4e1d\u4e0d\u52a8\u3002<\/p>\n<p>\u8fd9\u4e09\u4ef6\u4e8b&#xff0c;\u4efb\u4f55\u4e00\u4ef6\u5355\u62ce\u51fa\u6765\u90fd\u4e0d\u7a00\u5947\u3002\u4f46\u5b83\u4eec\u540c\u65f6\u6210\u7acb\u7684\u65f6\u5019&#xff0c;\u5c31\u662f\u4e00\u4ef6\u503c\u5f97\u559d\u4e00\u676f\u7684\u4e8b\u60c5\u3002<\/p>\n<p>\u5982\u679c\u4f60\u4e5f\u5728\u505a\u6a21\u578b\u90e8\u7f72&#xff0c;\u6216\u8005\u6b63\u5728\u4e3a\u663e\u5b58\u4e0d\u591f\u800c\u5931\u7720\u2014\u2014\u628a\u8fd9\u6761\u8f6c\u53d1\u7ed9\u4f60\u7684\u540c\u4e8b&#xff0c;\u7136\u540e\u544a\u8bc9\u4ed6&#xff1a;\u522b\u6025\u7740\u59a5\u534f&#xff0c;\u5148\u95ee\u95ee\u81ea\u5df1&#xff0c;\u80fd\u4e0d\u80fd\u505a\u5230\u4e00\u4e2a\u6bd4\u7279\u90fd\u4e0d\u5dee\u3002<\/p>\n<p>\u8bc4\u8bba\u533a\u804a\u804a&#xff1a;\u4e3a\u4e86\u65e0\u635f&#xff0c;\u4f60\u613f\u610f\u591a\u7b49\u591a\u4e45&#xff1f;<\/p>\n<p>https:\/\/github.com\/dfytensor\/bfloat16x<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u90fd\u8bf4\u6709\u5f97\u5fc5\u6709\u5931&#xff0c;\u4f46\u4eca\u5929\u8fd9\u4e2a\u6545\u4e8b&#xff0c;\u8bb2\u7684\u662f\u600e\u4e48\\&#8221;\u767d\u62ff\\&#8221;\u300290%\u7684\u4eba\u538b\u7f29\u5927\u6a21\u578b&#xff0c;\u90fd\u9ed8\u8ba4\u5fc5\u987b\u727a\u7272\u7cbe\u5ea6\u3002\u4eca\u5929\u6211\u8981\u544a\u8bc9\u4f60&#xff1a;\u4e0d\u7528\u3002<br 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