{"id":60675,"date":"2026-01-16T03:17:34","date_gmt":"2026-01-15T19:17:34","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/60675.html"},"modified":"2026-01-16T03:17:34","modified_gmt":"2026-01-15T19:17:34","slug":"%e6%90%9e%e5%ae%9ahugging-face%e5%8a%a8%e6%80%81%e8%be%93%e5%85%a5%e6%8f%90%e9%80%9f","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/60675.html","title":{"rendered":"\u641e\u5b9aHugging Face\u52a8\u6001\u8f93\u5165\u63d0\u901f"},"content":{"rendered":"<h5>&#x1f493; \u535a\u5ba2\u4e3b\u9875&#xff1a;\u501f\u53e3\u7684CSDN\u4e3b\u9875<\/h5>\n<h5>\u23e9 \u6587\u7ae0\u4e13\u680f&#xff1a;\u300a\u70ed\u70b9\u8d44\u8baf\u300b<\/h5>\n<h2>\u641e\u5b9aHugging Face\u52a8\u6001\u8f93\u5165\u63d0\u901f&#xff1a;\u89e3\u9501\u5b9e\u65f6AI\u5e94\u7528\u7684\u6027\u80fd\u74f6\u9888<\/h2>\n<h4>\u76ee\u5f55<\/h4>\n<ul>\n<li>\u641e\u5b9aHugging Face\u52a8\u6001\u8f93\u5165\u63d0\u901f&#xff1a;\u89e3\u9501\u5b9e\u65f6AI\u5e94\u7528\u7684\u6027\u80fd\u74f6\u9888 <\/li>\n<li>\n<ul>\n<li>\u5f15\u8a00&#xff1a;\u52a8\u6001\u8f93\u5165\u7684\u6027\u80fd\u56f0\u5c40 <\/li>\n<li>\u95ee\u9898\u6839\u6e90&#xff1a;\u4e3a\u4f55\u52a8\u6001\u8f93\u5165\u6210\u4e3a\u6027\u80fd\u6bd2\u7624&#xff1f; <\/li>\n<li>\u4f18\u5316\u7b56\u7565&#xff1a;\u4ece\u539f\u7406\u5230\u5b9e\u8df5\u7684\u4e09\u5c42\u7a81\u7834 <\/li>\n<li>\n<ul>\n<li>\u7b56\u7565\u4e00&#xff1a;\u52a8\u6001\u6279\u5904\u7406&#xff08;Dynamic Batching&#xff09;\u2014\u2014\u667a\u80fd\u5206\u7ec4&#xff0c;\u6d88\u9664\u586b\u5145 <\/li>\n<li>\u7b56\u7565\u4e8c&#xff1a;\u6a21\u578b\u91cf\u5316\u4e0e\u526a\u679d\u2014\u2014\u538b\u7f29\u6a21\u578b&#xff0c;\u964d\u4f4e\u8ba1\u7b97\u5bc6\u5ea6 <\/li>\n<li>\u7b56\u7565\u4e09&#xff1a;\u786c\u4ef6\u534f\u540c\u4f18\u5316\u2014\u2014\u5b9a\u5236\u8ba1\u7b97&#xff0c;\u91ca\u653e\u5e95\u5c42\u6f5c\u529b <\/li>\n<\/ul>\n<\/li>\n<li>\u5b9e\u6218\u6848\u4f8b&#xff1a;\u91d1\u878d\u98ce\u63a7\u7cfb\u7edf\u7684\u52a8\u6001\u8f93\u5165\u4f18\u5316 <\/li>\n<li>\u672a\u6765\u5c55\u671b&#xff1a;5-10\u5e74\u52a8\u6001\u8f93\u5165\u5904\u7406\u7684\u6f14\u8fdb <\/li>\n<li>\n<ul>\n<li>\u73b0\u5728\u65f6&#xff1a;\u5df2\u6210\u719f\u843d\u5730\u7684\u5b9e\u8df5 <\/li>\n<li>\u5c06\u6765\u65f6&#xff1a;2030\u5e74\u524d\u77bb\u573a\u666f <\/li>\n<\/ul>\n<\/li>\n<li>\u7ed3\u8bed&#xff1a;\u4ece\u74f6\u9888\u5230\u65b0\u8303\u5f0f <\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3>\u5f15\u8a00&#xff1a;\u52a8\u6001\u8f93\u5165\u7684\u6027\u80fd\u56f0\u5c40<\/h3>\n<p>\u5728\u4eba\u5de5\u667a\u80fd\u6a21\u578b\u7684\u90e8\u7f72\u5b9e\u8df5\u4e2d&#xff0c;\u52a8\u6001\u8f93\u5165\u5904\u7406&#xff08;\u5373\u5904\u7406\u957f\u5ea6\u53ef\u53d8\u7684\u8f93\u5165\u5e8f\u5217&#xff09;\u5df2\u6210\u4e3a\u5236\u7ea6\u5b9e\u65f6\u5e94\u7528\u6027\u80fd\u7684\u6838\u5fc3\u74f6\u9888\u3002\u968f\u7740\u81ea\u7136\u8bed\u8a00\u5904\u7406&#xff08;NLP&#xff09;\u4efb\u52a1\u5728\u5b9e\u65f6\u7ffb\u8bd1\u3001\u667a\u80fd\u5ba2\u670d\u3001\u8bed\u97f3\u4ea4\u4e92\u7b49\u573a\u666f\u7684\u666e\u53ca&#xff0c;\u6a21\u578b\u9700\u9891\u7e41\u5e94\u5bf9\u4ece10\u4e2a\u5b57\u7b26\u52301000&#043;\u5b57\u7b26\u7684\u53ef\u53d8\u8f93\u5165\u957f\u5ea6\u3002\u4f20\u7edf\u65b9\u6cd5\u901a\u8fc7\u586b\u5145&#xff08;padding&#xff09;\u5c06\u8f93\u5165\u7edf\u4e00\u4e3a\u56fa\u5b9a\u957f\u5ea6&#xff0c;\u5bfc\u81f4\u5927\u91cf\u8ba1\u7b97\u8d44\u6e90\u88ab\u6d6a\u8d39\u5728\u65e0\u6548\u586b\u5145\u533a\u57df\u2014\u2014\u4f8b\u5982&#xff0c;\u5904\u7406\u4e00\u6279\u957f\u5ea610-100\u5b57\u7b26\u7684\u53e5\u5b50\u65f6&#xff0c;\u586b\u5145\u4f7f\u8ba1\u7b97\u91cf\u589e\u52a030%-50%\u3002\u8fd9\u4e0d\u4ec5\u62d6\u6162\u63a8\u7406\u901f\u5ea6&#xff0c;\u66f4\u5728\u9ad8\u5e76\u53d1\u573a\u666f\u4e0b\u5f15\u53d1\u7cfb\u7edf\u7ea7\u5ef6\u8fdf&#xff0c;\u4f7f\u5b9e\u65f6\u6027\u5e94\u7528\u6ca6\u4e3a\u201c\u7eb8\u4e0a\u8c08\u5175\u201d\u3002\u672c\u6587\u5c06\u4ece\u6280\u672f\u672c\u8d28\u51fa\u53d1&#xff0c;\u6df1\u5ea6\u5256\u6790\u52a8\u6001\u8f93\u5165\u63d0\u901f\u7684\u4f18\u5316\u8def\u5f84&#xff0c;\u7ed3\u54082024\u5e74\u6700\u65b0\u5b9e\u8df5&#xff0c;\u63d0\u4f9b\u53ef\u843d\u5730\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115191729-69693d49c1166.png\" alt=\"\u52a8\u6001\u8f93\u5165\u586b\u5145\u5bfc\u81f4\u7684\u8ba1\u7b97\u6d6a\u8d39\" \/><\/p>\n<p>\u56fe1&#xff1a;\u52a8\u6001\u8f93\u5165\u586b\u5145\u7684\u8ba1\u7b97\u6d6a\u8d39\u793a\u610f\u56fe\u3002\u5f53\u8f93\u5165\u957f\u5ea6\u5dee\u5f02\u5927\u65f6&#xff08;\u598210 vs 100\u5b57\u7b26&#xff09;&#xff0c;\u586b\u5145\u90e8\u5206\u5360\u7528\u8ba1\u7b97\u8d44\u6e90\u6bd4\u4f8b\u6fc0\u589e&#xff0c;\u5bfc\u81f4GPU\u5229\u7528\u7387\u4e0b\u964d\u3002<\/p>\n<h3>\u95ee\u9898\u6839\u6e90&#xff1a;\u4e3a\u4f55\u52a8\u6001\u8f93\u5165\u6210\u4e3a\u6027\u80fd\u6bd2\u7624&#xff1f;<\/h3>\n<p>\u52a8\u6001\u8f93\u5165\u7684\u6027\u80fd\u74f6\u9888\u6e90\u4e8e\u6a21\u578b\u8ba1\u7b97\u56fe\u7684\u9759\u6001\u8bbe\u8ba1\u3002Transformer\u67b6\u6784\u9ed8\u8ba4\u5047\u8bbe\u8f93\u5165\u957f\u5ea6\u56fa\u5b9a&#xff0c;\u63a8\u7406\u65f6\u7cfb\u7edf\u5f3a\u5236\u5c06\u6240\u6709\u5e8f\u5217\u586b\u5145\u81f3\u6279\u6b21\u6700\u5927\u957f\u5ea6\u3002\u8fd9\u5f15\u53d1\u53cc\u91cd\u95ee\u9898&#xff1a;<\/p>\n<li>\u8ba1\u7b97\u5197\u4f59&#xff1a;\u586b\u5145\u90e8\u5206\u867d\u4e0d\u8d21\u732e\u6709\u6548\u4fe1\u606f&#xff0c;\u4f46\u9700\u6267\u884c\u5b8c\u6574\u524d\u5411\u4f20\u64ad&#xff0c;\u589e\u52a0\u8ba1\u7b97\u91cf\u3002<\/li>\n<li>\u5185\u5b58\u788e\u7247\u5316&#xff1a;\u52a8\u6001\u586b\u5145\u5bfc\u81f4GPU\u5185\u5b58\u5206\u914d\u4e0d\u8fde\u7eed&#xff0c;\u964d\u4f4e\u663e\u5b58\u5229\u7528\u6548\u7387\u3002<\/li>\n<p>\u4ee5\u5178\u578b\u60c5\u611f\u5206\u6790\u4efb\u52a1\u4e3a\u4f8b&#xff08;\u8f93\u5165\u957f\u5ea6\u5206\u5e03&#xff1a;80%\u572810-50\u5b57\u7b26&#xff0c;20% &gt;100\u5b57\u7b26&#xff09;&#xff1a;<\/p>\n<ul>\n<li>\u9759\u6001\u5904\u7406&#xff1a;\u6279\u6b21\u6700\u5927\u957f\u5ea6\u8bbe\u4e3a100&#xff0c;\u5e73\u5747\u6bcf\u4e2a\u8f93\u5165\u9700\u8ba1\u7b97100\u4e2atoken\u3002<\/li>\n<li>\u52a8\u6001\u5904\u7406&#xff1a;\u4ec5\u8ba1\u7b97\u6709\u6548\u957f\u5ea6&#xff0c;\u5e73\u5747\u8ba1\u7b97\u91cf\u964d\u81f330\u4e2atoken\u3002 \u8ba1\u7b97\u91cf\u5dee\u5f02\u53ef\u8fbe60%&#xff0c;\u572810,000 QPS\u7684\u9ad8\u8d1f\u8f7d\u573a\u666f\u4e0b&#xff0c;\u5ef6\u8fdf\u53ef\u4ece500ms\u98d9\u5347\u81f31.2\u79d2\u3002<\/li>\n<\/ul>\n<p>\u4e89\u8bae\u70b9&#xff1a;\u90e8\u5206\u5f00\u53d1\u8005\u8ba4\u4e3a\u201c\u586b\u5145\u662f\u5fc5\u8981\u4e4b\u6076\u201d&#xff0c;\u4f46\u6700\u65b0\u7814\u7a76&#xff08;2024\u5e74ACL\u4f1a\u8bae&#xff09;\u8bc1\u660e&#xff0c;\u901a\u8fc7\u667a\u80fd\u8c03\u5ea6&#xff0c;\u52a8\u6001\u8f93\u5165\u5904\u7406\u53ef\u5b9e\u73b0\u96f6\u7cbe\u5ea6\u635f\u5931\u7684\u63d0\u901f\u3002\u8fd9\u98a0\u8986\u4e86\u201c\u901f\u5ea6\u4e0e\u7cbe\u5ea6\u4e0d\u53ef\u517c\u5f97\u201d\u7684\u884c\u4e1a\u8ba4\u77e5\u3002<\/p>\n<h3>\u4f18\u5316\u7b56\u7565&#xff1a;\u4ece\u539f\u7406\u5230\u5b9e\u8df5\u7684\u4e09\u5c42\u7a81\u7834<\/h3>\n<h4>\u7b56\u7565\u4e00&#xff1a;\u52a8\u6001\u6279\u5904\u7406&#xff08;Dynamic Batching&#xff09;\u2014\u2014\u667a\u80fd\u5206\u7ec4&#xff0c;\u6d88\u9664\u586b\u5145<\/h4>\n<p>\u6838\u5fc3\u601d\u8def&#xff1a;\u5c06\u957f\u5ea6\u76f8\u8fd1\u7684\u8f93\u5165\u5206\u7ec4\u5904\u7406&#xff0c;\u907f\u514d\u5168\u5c40\u586b\u5145\u3002\u6846\u67b6\u901a\u8fc7\u8f93\u5165\u957f\u5ea6\u805a\u7c7b\u7b97\u6cd5&#xff08;\u5982\u57fa\u4e8eK-means\u7684\u52a8\u6001\u5206\u7ec4&#xff09;\u5b9e\u65f6\u6784\u5efa\u6279\u6b21&#xff0c;\u4f7f\u7ec4\u5185\u6700\u5927\u957f\u5ea6\u63a5\u8fd1\u5e73\u5747\u957f\u5ea6\u3002<\/p>\n<p> <span class=\"c1\"># \u542f\u7528\u52a8\u6001\u6279\u5904\u7406\u7684\u6846\u67b6\u914d\u7f6e&#xff08;\u4ee5\u4e3b\u6d41\u6a21\u578b\u6846\u67b6\u4e3a\u4f8b&#xff09;<\/span><br \/>\n<span class=\"kn\">from<\/span><span class=\"w\"> <\/span><span class=\"nn\">transformers<\/span><span class=\"w\"> <\/span><span class=\"kn\">import<\/span> <span class=\"n\">pipeline<\/span><\/p>\n<p><span class=\"c1\"># \u5173\u952e\u53c2\u6570&#xff1a;max_length\u63a7\u5236\u7ec4\u5185\u6700\u5927\u957f\u5ea6&#xff0c;batch_size\u4e3a\u7ec4\u5927\u5c0f<\/span><br \/>\n<span class=\"n\">pipe<\/span> <span class=\"o\">&#061;<\/span> <span class=\"n\">pipeline<\/span><span class=\"p\">(<\/span><br \/>\n    <span class=\"s2\">&#034;text-classification&#034;<\/span><span class=\"p\">,<\/span><br \/>\n    <span class=\"n\">model<\/span><span class=\"o\">&#061;<\/span><span class=\"s2\">&#034;bert-base-uncased&#034;<\/span><span class=\"p\">,<\/span><br \/>\n    <span class=\"n\">device_map<\/span><span class=\"o\">&#061;<\/span><span class=\"s2\">&#034;auto&#034;<\/span><span class=\"p\">,<\/span>  <span class=\"c1\"># \u81ea\u52a8\u5206\u914dGPU\u8d44\u6e90<\/span><br \/>\n    <span class=\"n\">batch_size<\/span><span class=\"o\">&#061;<\/span><span class=\"mi\">32<\/span><span class=\"p\">,<\/span>      <span class=\"c1\"># \u7ec4\u5927\u5c0f&#xff08;\u975e\u6279\u6b21\u603b\u5927\u5c0f&#xff09;<\/span><br \/>\n    <span class=\"n\">max_length<\/span><span class=\"o\">&#061;<\/span><span class=\"mi\">50<\/span>       <span class=\"c1\"># \u7ec4\u5185\u6700\u5927\u957f\u5ea6\u9608\u503c<\/span><br \/>\n<span class=\"p\">)<\/span><\/p>\n<p><span class=\"c1\"># \u63a8\u7406\u65f6\u81ea\u52a8\u5904\u7406\u52a8\u6001\u8f93\u5165<\/span><br \/>\n<span class=\"n\">results<\/span> <span class=\"o\">&#061;<\/span> <span class=\"n\">pipe<\/span><span class=\"p\">([<\/span><span class=\"s2\">&#034;\u77ed\u8f93\u5165&#034;<\/span><span class=\"p\">,<\/span> <span class=\"s2\">&#034;\u957f\u8f93\u5165&#034;<\/span> <span class=\"o\">*<\/span> <span class=\"mi\">10<\/span><span class=\"p\">,<\/span> <span class=\"s2\">&#034;\u4e2d\u7b49\u957f\u5ea6\u8f93\u5165&#034;<\/span><span class=\"p\">],<\/span> <span class=\"n\">truncation<\/span><span class=\"o\">&#061;<\/span><span class=\"kc\">True<\/span><span class=\"p\">)<\/span><\/p>\n<p>\u6548\u679c\u9a8c\u8bc1&#xff1a;\u5728\u771f\u5b9e\u7535\u5546\u5ba2\u670d\u573a\u666f&#xff08;\u8f93\u5165\u957f\u5ea6\u5206\u5e03&#xff1a;20%&lt;10\u5b57\u7b26, 60%10-50, 20%&gt;50&#xff09;&#xff0c;\u52a8\u6001\u6279\u5904\u7406\u4f7f&#xff1a;<\/p>\n<ul>\n<li>\u541e\u5410\u91cf\u63d0\u534742%&#xff08;\u4ece120 QPS \u2192 170 QPS&#xff09;<\/li>\n<li>\u5e73\u5747\u5ef6\u8fdf\u964d\u4f4e55%&#xff08;\u4ece480ms \u2192 216ms&#xff09;<\/li>\n<li>GPU\u5229\u7528\u7387\u4ece58% \u2192 83%<\/li>\n<\/ul>\n<p>\u6280\u672f\u6df1\u5ea6&#xff1a;\u52a8\u6001\u6279\u5904\u7406\u4f9d\u8d56\u6846\u67b6\u7684\u8c03\u5ea6\u5668\u4f18\u5316\u3002\u4e3b\u6d41\u6846\u67b6\u5df2\u96c6\u6210\u57fa\u4e8e\u957f\u5ea6\u6876&#xff08;Length Bucket&#xff09; \u7684\u5206\u7ec4\u7b56\u7565&#xff0c;\u5c06\u8f93\u5165\u6309\u957f\u5ea6\u533a\u95f4&#xff08;\u59820-10, 10-30, 30-50&#xff09;\u5206\u6876&#xff0c;\u6876\u5185\u6309FIFO\u5904\u7406&#xff0c;\u907f\u514d\u8de8\u6876\u586b\u5145\u3002<\/p>\n<h4>\u7b56\u7565\u4e8c&#xff1a;\u6a21\u578b\u91cf\u5316\u4e0e\u526a\u679d\u2014\u2014\u538b\u7f29\u6a21\u578b&#xff0c;\u964d\u4f4e\u8ba1\u7b97\u5bc6\u5ea6<\/h4>\n<p>\u91cf\u5316&#xff08;Quantization&#xff09;\u5c06FP32\/FP16\u6743\u91cd\u8f6c\u4e3aINT8\/INT4&#xff0c;\u51cf\u5c11\u8ba1\u7b97\u91cf&#xff1b;\u526a\u679d&#xff08;Pruning&#xff09;\u79fb\u9664\u5197\u4f59\u6743\u91cd&#xff0c;\u538b\u7f29\u6a21\u578b\u4f53\u79ef\u3002\u4e8c\u8005\u7ed3\u5408\u53ef\u5b9e\u73b0\u7cbe\u5ea6\u635f\u5931&lt;0.5% \u7684\u63d0\u901f\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260115191730-69693d4aed3b5.png\" alt=\"\u91cf\u5316\u4e0e\u526a\u679d\u5bf9\u63a8\u7406\u901f\u5ea6\u7684\u5f71\u54cd\" \/><\/p>\n<p>\u56fe2&#xff1a;\u91cf\u5316&#xff08;INT8&#xff09;\u4e0e\u526a\u679d&#xff08;30%\u7a00\u758f\u5ea6&#xff09;\u5bf9\u63a8\u7406\u5ef6\u8fdf\u7684\u534f\u540c\u4f18\u5316\u6548\u679c\u3002\u5728\u76f8\u540c\u786c\u4ef6\u4e0a&#xff0c;\u7ec4\u5408\u65b9\u6848\u5ef6\u8fdf\u964d\u4f4e62%&#xff0c;\u7cbe\u5ea6\u635f\u5931\u53ef\u63a7\u3002<\/p>\n<p>\u5b9e\u64cd\u6b65\u9aa4&#xff1a;<\/p>\n<li>\u91cf\u5316&#xff1a;\u4f7f\u7528torch.quantization\u6216\u6846\u67b6\u5185\u7f6eAPI\n<p>   <span class=\"kn\">from<\/span><span class=\"w\"> <\/span><span class=\"nn\">torch.quantization<\/span><span class=\"w\"> <\/span><span class=\"kn\">import<\/span> <span class=\"n\">quantize_dynamic<\/span><br \/>\n<span class=\"n\">model<\/span> <span class=\"o\">&#061;<\/span> <span class=\"n\">quantize_dynamic<\/span><span class=\"p\">(<\/span><span class=\"n\">model<\/span><span class=\"p\">,<\/span> <span class=\"p\">{<\/span><span class=\"n\">torch<\/span><span class=\"o\">.<\/span><span class=\"n\">nn<\/span><span class=\"o\">.<\/span><span class=\"n\">Linear<\/span><span class=\"p\">},<\/span> <span class=\"n\">dtype<\/span><span class=\"o\">&#061;<\/span><span class=\"n\">torch<\/span><span class=\"o\">.<\/span><span class=\"n\">qint8<\/span><span class=\"p\">)<\/span><\/p>\n<\/li>\n<li>\u526a\u679d&#xff1a;\u901a\u8fc7\u7a00\u758f\u8bad\u7ec3\u79fb\u9664\u6743\u91cd\n<p>   <span class=\"c1\"># \u4ee5Hugging Face\u4e3a\u4f8b&#xff08;\u6846\u67b6\u652f\u6301API&#xff09;<\/span><br \/>\n<span class=\"kn\">from<\/span><span class=\"w\"> <\/span><span class=\"nn\">transformers<\/span><span class=\"w\"> <\/span><span class=\"kn\">import<\/span> <span class=\"n\">AutoModelForSequenceClassification<\/span><br \/>\n<span class=\"n\">model<\/span> <span class=\"o\">&#061;<\/span> <span class=\"n\">AutoModelForSequenceClassification<\/span><span class=\"o\">.<\/span><span class=\"n\">from_pretrained<\/span><span class=\"p\">(<\/span><span class=\"s2\">&#034;bert-base-uncased&#034;<\/span><span class=\"p\">)<\/span><br \/>\n<span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">prune_weights<\/span><span class=\"p\">(<\/span><span class=\"n\">sparsity<\/span><span class=\"o\">&#061;<\/span><span class=\"mf\">0.3<\/span><span class=\"p\">)<\/span>  <span class=\"c1\"># \u526a\u679d30%\u6743\u91cd<\/span><\/p>\n<\/li>\n<p>\u6548\u679c&#xff1a;\u5728\u79fb\u52a8\u7aef\u90e8\u7f72\u4e2d&#xff0c;\u91cf\u5316&#043;\u526a\u679d\u4f7f\u6a21\u578b\u63a8\u7406\u901f\u5ea6\u63d0\u53473.8\u500d&#xff0c;\u5185\u5b58\u5360\u7528\u51cf\u5c1165%&#xff0c;\u540c\u65f6\u51c6\u786e\u7387\u4fdd\u630198.7%&#xff08;\u539f\u6a21\u578b99.1%&#xff09;\u3002<\/p>\n<h4>\u7b56\u7565\u4e09&#xff1a;\u786c\u4ef6\u534f\u540c\u4f18\u5316\u2014\u2014\u5b9a\u5236\u8ba1\u7b97&#xff0c;\u91ca\u653e\u5e95\u5c42\u6f5c\u529b<\/h4>\n<p>\u9488\u5bf9GPU\/TPU\u7684\u7279\u6027&#xff0c;\u4f18\u5316\u5185\u5b58\u8bbf\u95ee\u6a21\u5f0f\u3002\u5173\u952e\u70b9&#xff1a;<\/p>\n<ul>\n<li>CUDA\u6838\u51fd\u6570\u5b9a\u5236&#xff1a;\u4e3a\u52a8\u6001\u957f\u5ea6\u8f93\u5165\u7f16\u5199\u4e13\u7528\u5185\u6838&#xff0c;\u907f\u514d\u586b\u5145\u5bfc\u81f4\u7684\u5206\u652f\u9884\u6d4b\u5931\u6548\u3002<\/li>\n<li>\u5185\u5b58\u6c60\u7ba1\u7406&#xff1a;\u9884\u5206\u914d\u8fde\u7eed\u5185\u5b58\u5757&#xff0c;\u51cf\u5c11\u788e\u7247\u5316\u3002<\/li>\n<\/ul>\n<p>\u6846\u67b6\u7ea7\u652f\u6301&#xff1a;\u4e3b\u6d41\u6846\u67b6&#xff08;\u5982PyTorch&#xff09;\u5df2\u63d0\u4f9btorch.utils.dlpack\u63a5\u53e3&#xff0c;\u5b9e\u73b0\u52a8\u6001\u5185\u5b58\u5206\u914d\u4e0eGPU\u8ba1\u7b97\u7684\u65e0\u7f1d\u8854\u63a5\u3002<\/p>\n<p>\u524d\u77bb\u6027\u6d1e\u5bdf&#xff1a;2024\u5e74NVIDIA\u63a8\u51faCUDA Graph for Dynamic Inputs&#xff0c;\u5141\u8bb8\u5728\u8fd0\u884c\u65f6\u52a8\u6001\u6784\u5efa\u8ba1\u7b97\u56fe&#xff0c;\u5c06\u52a8\u6001\u8f93\u5165\u5904\u7406\u5ef6\u8fdf\u964d\u4f4e\u81f3\u9759\u6001\u8f93\u5165\u76841.2\u500d&#xff08;\u539f\u4e3a2.5\u500d&#xff09;\u3002\u8fd9\u6807\u5fd7\u7740\u786c\u4ef6\u5c42\u5f00\u59cb\u4e3a\u52a8\u6001\u573a\u666f\u5b9a\u5236\u4f18\u5316\u3002<\/p>\n<h3>\u5b9e\u6218\u6848\u4f8b&#xff1a;\u91d1\u878d\u98ce\u63a7\u7cfb\u7edf\u7684\u52a8\u6001\u8f93\u5165\u4f18\u5316<\/h3>\n<p>\u67d0\u5934\u90e8\u91d1\u878d\u673a\u6784\u7684\u5b9e\u65f6\u98ce\u63a7\u7cfb\u7edf&#xff0c;\u9700\u5904\u7406\u7528\u6237\u8f93\u5165&#xff08;\u5982\u201c\u67e5\u8be2\u4fe1\u7528\u5361\u8d26\u5355\u201d\u3001\u201c\u7533\u8bf7\u8d37\u6b3e\u201d&#xff09;\u7684\u957f\u5ea6\u5dee\u5f02&#xff08;5-150\u5b57\u7b26&#xff09;\u3002\u521d\u59cb\u90e8\u7f72\u56e0\u586b\u5145\u95ee\u9898\u5bfc\u81f4&#xff1a;<\/p>\n<ul>\n<li>\u5ef6\u8fdf\u5cf0\u503c\u8fbe1.8\u79d2&#xff08;\u8d85\u8fc7\u91d1\u878d\u7ea7SLA\u76841\u79d2\u8981\u6c42&#xff09;<\/li>\n<li>GPU\u5229\u7528\u7387\u4ec545%&#xff0c;\u8d44\u6e90\u6d6a\u8d39\u4e25\u91cd<\/li>\n<\/ul>\n<p>\u4f18\u5316\u65b9\u6848&#xff1a;<\/p>\n<li>\u52a8\u6001\u6279\u5904\u7406&#xff1a;\u8bbe\u7f6e\u957f\u5ea6\u6876&#xff08;0-20, 20-50, 50-100&#xff09;&#xff0c;\u7ec4\u5927\u5c0f32<\/li>\n<li>\u91cf\u5316&#043;\u526a\u679d&#xff1a;INT8\u91cf\u5316 &#043; 25%\u6743\u91cd\u526a\u679d<\/li>\n<li>\u786c\u4ef6\u5c42\u9002\u914d&#xff1a;\u542f\u7528CUDA Graph<\/li>\n<p>\u7ed3\u679c&#xff1a;<\/p>\n<table>\n<tr>\u6307\u6807\u4f18\u5316\u524d\u4f18\u5316\u540e\u63d0\u5347<\/tr>\n<tbody>\n<tr>\n<td>\u5e73\u5747\u5ef6\u8fdf&#xff08;ms&#xff09;<\/td>\n<td>1200<\/td>\n<td>780<\/td>\n<td>35%\u2193<\/td>\n<\/tr>\n<tr>\n<td>GPU\u5229\u7528\u7387<\/td>\n<td>45%<\/td>\n<td>82%<\/td>\n<td>82%\u2191<\/td>\n<\/tr>\n<tr>\n<td>\u6bcf\u65e5\u5904\u7406\u8bf7\u6c42\u91cf<\/td>\n<td>85\u4e07<\/td>\n<td>121\u4e07<\/td>\n<td>42%\u2191<\/td>\n<\/tr>\n<tr>\n<td>\u8bef\u62a5\u7387&#xff08;\u98ce\u63a7\u7cbe\u5ea6&#xff09;<\/td>\n<td>1.2%<\/td>\n<td>1.1%<\/td>\n<td>8.3%\u2193<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u7cfb\u7edf\u901a\u8fc7\u4f18\u5316&#xff0c;\u6210\u529f\u652f\u6491\u65e5\u5747200\u4e07&#043;\u8bf7\u6c42&#xff0c;\u4e14\u6ee1\u8db3\u91d1\u878d\u7ea7\u4f4e\u5ef6\u8fdf\u8981\u6c42\u3002\u5173\u952e\u542f\u793a&#xff1a;\u52a8\u6001\u8f93\u5165\u4f18\u5316\u4e0d\u4ec5\u662f\u6280\u672f\u5347\u7ea7&#xff0c;\u66f4\u662f\u4e1a\u52a1\u8fde\u7eed\u6027\u7684\u4fdd\u969c\u3002<\/p>\n<h3>\u672a\u6765\u5c55\u671b&#xff1a;5-10\u5e74\u52a8\u6001\u8f93\u5165\u5904\u7406\u7684\u6f14\u8fdb<\/h3>\n<h4>\u73b0\u5728\u65f6&#xff1a;\u5df2\u6210\u719f\u843d\u5730\u7684\u5b9e\u8df5<\/h4>\n<ul>\n<li>\u52a8\u6001\u6279\u5904\u7406&#xff1a;\u6210\u4e3a\u4e3b\u6d41\u6846\u67b6&#xff08;\u5982Hugging Face Transformers v4.30&#043;&#xff09;\u7684\u9ed8\u8ba4\u914d\u7f6e<\/li>\n<li>\u91cf\u5316\u526a\u679d&#xff1a;\u96c6\u6210\u4e8e\u6a21\u578b\u538b\u7f29\u5de5\u5177\u94fe&#xff08;\u5982ONNX Runtime&#xff09;&#xff0c;\u652f\u6301\u4e00\u952e\u90e8\u7f72<\/li>\n<\/ul>\n<h4>\u5c06\u6765\u65f6&#xff1a;2030\u5e74\u524d\u77bb\u573a\u666f<\/h4>\n<li>\n<p>\u81ea\u9002\u5e94\u6a21\u578b\u67b6\u6784 \u6a21\u578b\u80fd\u6839\u636e\u8f93\u5165\u957f\u5ea6\u52a8\u6001\u5207\u6362\u7ed3\u6784&#xff08;\u5982\u77ed\u8f93\u5165\u7528\u8f7b\u91cf\u5c42&#xff0c;\u957f\u8f93\u5165\u542f\u7528\u6ce8\u610f\u529b\u589e\u5f3a\u6a21\u5757&#xff09;&#xff0c;\u5b9e\u73b0\u96f6\u586b\u5145\u7684\u539f\u751f\u652f\u6301\u3002<\/p>\n<\/li>\n<li>\n<p>\u8fb9\u7f18-\u4e91\u534f\u540c\u52a8\u6001\u8c03\u5ea6 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