{"id":85265,"date":"2026-07-27T08:25:02","date_gmt":"2026-07-27T00:25:02","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/85265.html"},"modified":"2026-07-27T08:25:02","modified_gmt":"2026-07-27T00:25:02","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8gpu%e7%ae%97%e5%8a%9b%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%ad%e5%ae%9e%e7%8e%b0%e5%a4%9agpu%e6%a8%a1%e5%9e%8b%e5%b9%b6%e8%a1%8c%e8%ae%ad%e7%bb%83%ef%bc%8c%e6%8f%90%e5%8d%87","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/85265.html","title":{"rendered":"\u5982\u4f55\u5728GPU\u7b97\u529b\u670d\u52a1\u5668\u4e2d\u5b9e\u73b0\u591aGPU\u6a21\u578b\u5e76\u884c\u8bad\u7ec3\uff0c\u63d0\u5347\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5728NLP\u9886\u57df\u7684\u63a8\u7406\u80fd\u529b\uff1f"},"content":{"rendered":"<p>\u5728\u5927\u89c4\u6a21\u81ea\u7136\u8bed\u8a00\u5904\u7406&#xff08;NLP&#xff09;\u6a21\u578b\u4e0d\u65ad\u7a81\u7834\u7684\u80cc\u666f\u4e0b&#xff0c;\u5355\u5361GPU\u8bad\u7ec3\u5df2\u7ecf\u65e0\u6cd5\u6ee1\u8db3\u8bad\u7ec3\u901f\u5ea6\u3001\u5185\u5b58\u9700\u6c42\u548c\u63a8\u7406\u6027\u80fd\u7684\u8981\u6c42\u3002\u968f\u7740\u6a21\u578b\u89c4\u6a21\u4ece\u6570\u4ebf\u53c2\u6570\u6269\u5c55\u5230\u6570\u5341\u4ebf\u4e43\u81f3\u4e0a\u767e\u4ebf\u53c2\u6570&#xff0c;\u5355\u4e00GPU\u7684\u663e\u5b58\u548c\u8ba1\u7b97\u80fd\u529b\u6210\u4e3a\u74f6\u9888\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e00\u95ee\u9898&#xff0c;\u591aGPU\u5e76\u884c\u8bad\u7ec3\u5df2\u7ecf\u6210\u4e3a\u9ad8\u6027\u80fd\u6df1\u5ea6\u5b66\u4e60\u7814\u53d1\u4e2d\u7684\u57fa\u7840\u80fd\u529b\u3002A5\u6570\u636e\u7ed3\u5408\u6700\u65b0\u7684\u786c\u4ef6\u4ea7\u54c1\u3001\u5206\u5e03\u5f0f\u8bad\u7ec3\u6846\u67b6\u3001\u5e95\u5c42\u5b9e\u73b0\u7ec6\u8282\u548c\u5b9e\u6d4b\u8bc4\u4f30&#xff0c;\u4ece\u5b9e\u6218\u89d2\u5ea6\u7cfb\u7edf\u8bb2\u89e3\u5982\u4f55\u5728GPU\u7b97\u529b\u670d\u52a1\u5668\u4e2d\u5b9e\u73b0\u591aGPU\u6a21\u578b\u5e76\u884c\u8bad\u7ec3&#xff0c;\u4ece\u800c\u63d0\u5347NLP\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\u4e0e\u8bad\u7ec3\u901f\u5ea6\u3002<\/p>\n<p>\u672c\u6587\u91cd\u70b9\u8986\u76d6&#xff1a;<\/p>\n<ul>\n<li>\u591aGPU\u5e76\u884c\u8bad\u7ec3\u7684\u57fa\u672c\u539f\u7406\u4e0e\u6280\u672f\u9009\u578b&#xff1b;<\/li>\n<li>\u6838\u5fc3\u786c\u4ef6\u53c2\u6570\u4e0e\u7b97\u529b\u670d\u52a1\u5668\u9009\u914d&#xff1b;<\/li>\n<li>PyTorch\u3001DeepSpeed\u3001Megatron\u2011LM\u7b49\u4e3b\u6d41\u6846\u67b6\u5b9e\u73b0\u5e76\u884c\u8bad\u7ec3&#xff1b;<\/li>\n<li>\u6027\u80fd\u8bc4\u4f30\u4e0e\u53c2\u6570\u8c03\u4f18\u5b9e\u6218&#xff1b;<\/li>\n<li>NLP\u63a8\u7406\u6027\u80fd\u63d0\u5347\u7684\u5178\u578b\u6848\u4f8b\u3002<\/li>\n<\/ul>\n<p>\u6587\u7ae0\u9762\u5411\u5177\u6709\u6df1\u5ea6\u5b66\u4e60\u7814\u53d1\u7ecf\u9a8c\u7684\u5de5\u7a0b\u5e08&#xff0c;\u4e0d\u8d58\u8ff0\u57fa\u672c\u6982\u5ff5&#xff0c;\u800c\u7740\u91cd\u4e8e\u5de5\u7a0b\u5b9e\u73b0\u7ec6\u8282\u4e0e\u6027\u80fd\u4f18\u5316\u3002<\/p>\n<hr \/>\n<h3>\u4e00\u3001GPU\u7b97\u529b\u670d\u52a1\u5668\u786c\u4ef6\u914d\u7f6e<\/h3>\n<p>\u5728\u5f00\u59cb\u591aGPU\u5e76\u884c\u8bad\u7ec3\u4e4b\u524d&#xff0c;\u9009\u62e9\u5408\u9002\u7684GPU\u7b97\u529b\u670d\u52a1\u5668\u662f\u5fc5\u8981\u7684\u524d\u63d0\u3002\u4e0b\u8868\u5217\u51fa\u5f53\u524d\u4e3b\u6d41\u7528\u4e8e\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u7684\u51e0\u7c7bGPU\u670d\u52a1\u5668\u793a\u4f8b\u5bf9\u6bd4&#xff1a;<\/p>\n<table>\n<tr>\u670d\u52a1\u5668\u578b\u53f7GPU \u914d\u7f6e\u6bcf\u5361\u663e\u5b58NVLink\u4e92\u8054PCI\u2011E GenCPU\u5185\u5b58&#xff08;DDR4\/DDR5&#xff09;\u7f51\u7edc\u4e92\u8054<\/tr>\n<tbody>\n<tr>\n<td>A100 DGX Server<\/td>\n<td>8 \u00d7 NVIDIA A100 80GB<\/td>\n<td>80 GB<\/td>\n<td>NVLink 600GB\/s<\/td>\n<td>PCI\u2011E 4.0<\/td>\n<td>2 \u00d7 64\u2011core<\/td>\n<td>2 TB DDR4<\/td>\n<td>100\/200\/400Gb Infiniband<\/td>\n<\/tr>\n<tr>\n<td>H100 DGX Server<\/td>\n<td>8 \u00d7 NVIDIA H100 80GB<\/td>\n<td>80 GB<\/td>\n<td>NVLink 900GB\/s<\/td>\n<td>PCI\u2011E 5.0<\/td>\n<td>2 \u00d7 64\u2011core<\/td>\n<td>2 TB DDR5<\/td>\n<td>200\/400Gb Infiniband<\/td>\n<\/tr>\n<tr>\n<td>NVIDIA HGX A100<\/td>\n<td>8 \u00d7 A100<\/td>\n<td>80 GB<\/td>\n<td>NVSwitch \u5168\u4e92\u8054<\/td>\n<td>PCI\u2011E 4.0<\/td>\n<td>2 \u00d7 32\u2011core<\/td>\n<td>1 TB<\/td>\n<td>100\/200Gb RDMA<\/td>\n<\/tr>\n<tr>\n<td>\u81ea\u5b9a\u4e49\u670d\u52a1\u5668<\/td>\n<td>4 \u00d7 A40<\/td>\n<td>48 GB<\/td>\n<td>\u90e8\u5206\u652f\u6301 NVLink<\/td>\n<td>PCI\u2011E 4.0<\/td>\n<td>2 \u00d7 32\u2011core<\/td>\n<td>256 GB<\/td>\n<td>100Gb Ethernet<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u6ce8&#xff1a;\u8868\u4e2d NVLink \u4e92\u8054\u80fd\u591f\u63d0\u4f9b\u8de8GPU\u9ad8\u5e26\u5bbd\u3001\u4f4e\u5ef6\u8fdf\u901a\u4fe1&#xff0c;\u662f\u5b9e\u73b0\u9ad8\u6548\u6a21\u578b\u5e76\u884c\u8bad\u7ec3\u7684\u5173\u952e\u786c\u4ef6\u4fdd\u969c\u3002<\/p>\n<p>\u5bf9\u4e8e\u5927\u89c4\u6a21NLP\u6a21\u578b&#xff08;\u5982GPT\u2011\u7c7b\u6a21\u578b\u3001BERT XXL\u7b49&#xff09;&#xff0c;\u81f3\u5c11\u9009\u62e9\u5982NVIDIA A100 80GB \u6216 H100 80GB \u8fd9\u6837\u7684\u9ad8\u663e\u5b58\u5361&#xff0c;\u5e76\u914d\u5907 NVLink \u6216 NVSwitch \u5168\u4e92\u8054\u67b6\u6784&#xff0c;\u53ef\u4ee5\u6709\u6548\u51cf\u8f7b\u663e\u5b58\u788e\u7247\u4e0e\u8de8\u5361\u901a\u4fe1\u74f6\u9888\u3002<\/p>\n<hr \/>\n<h3>\u4e8c\u3001\u591aGPU\u5e76\u884c\u8bad\u7ec3\u6280\u672f\u8def\u5f84<\/h3>\n<p>\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u7c7b\u5e76\u884c\u7b56\u7565&#xff1a;<\/p>\n<table>\n<tr>\u5e76\u884c\u7b56\u7565\u5178\u578b\u5e94\u7528\u4f18\u70b9\u7f3a\u70b9<\/tr>\n<tbody>\n<tr>\n<td>\u6570\u636e\u5e76\u884c&#xff08;Data Parallelism&#xff09;<\/td>\n<td>\u591a\u6837\u672c\u5e76\u884c\u8bad\u7ec3<\/td>\n<td>\u5b9e\u73b0\u7b80\u5355&#xff1b;\u9002\u5408\u663e\u5b58\u53ef\u5bb9\u7eb3\u5927\u6a21\u578b<\/td>\n<td>\u5355\u5361\u663e\u5b58\u5bb9\u91cf\u9650\u5236\u6a21\u578b\u5927\u5c0f&#xff1b;\u901a\u4fe1\u5f00\u9500\u5927<\/td>\n<\/tr>\n<tr>\n<td>\u6a21\u578b\u5e76\u884c&#xff08;Model Parallelism&#xff09;<\/td>\n<td>\u8d85\u5927\u53c2\u6570\u6a21\u578b<\/td>\n<td>\u53ef\u8bad\u7ec3\u8d85\u8fc7\u5355\u5361\u663e\u5b58\u7684\u6a21\u578b<\/td>\n<td>\u9700\u8981\u62c6\u5206\u6a21\u578b\u7ed3\u6784&#xff1b;\u5b9e\u73b0\u590d\u6742<\/td>\n<\/tr>\n<tr>\n<td>\u6df7\u5408\u5e76\u884c&#xff08;Hybrid Parallelism&#xff09;<\/td>\n<td>\u5927\u89c4\u6a21\u5206\u5e03\u5f0f\u8bad\u7ec3<\/td>\n<td>\u7efc\u5408\u5229\u7528\u6570\u636e\u548c\u6a21\u578b\u5e76\u884c<\/td>\n<td>\u5b9e\u73b0\u590d\u6742\u5ea6\u9ad8&#xff1b;\u8c03\u53c2\u7e41\u7410<\/td>\n<\/tr>\n<tr>\n<td>\u5f20\u91cf\u5e76\u884c&#xff08;Tensor Parallelism&#xff09;<\/td>\n<td>Transformer \u5185\u90e8\u5c42\u5e76\u884c<\/td>\n<td>\u51cf\u5c11\u5355\u5361\u663e\u5b58\u9700\u6c42<\/td>\n<td>\u901a\u4fe1\u9891\u7e41&#xff1b;\u9700\u652f\u6301\u7ec6\u7c92\u5ea6\u62c6\u5206<\/td>\n<\/tr>\n<tr>\n<td>\u7ba1\u9053\u5e76\u884c&#xff08;Pipeline Parallelism&#xff09;<\/td>\n<td>\u6a21\u5757\u5316\u7f51\u7edc\u5206\u6bb5<\/td>\n<td>\u652f\u6301\u5c42\u7ea7\u5212\u5206<\/td>\n<td>\u9700\u8981\u8c03\u5ea6\u68af\u5ea6\u540c\u6b65&#xff1b;\u96be\u4ee5\u8d1f\u8f7d\u5747\u8861<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5728\u5b9e\u9645\u5927\u89c4\u6a21NLP\u6a21\u578b\u8bad\u7ec3\u4e2d&#xff0c;\u901a\u5e38\u91c7\u7528\u6df7\u5408\u5e76\u884c\u7b56\u7565&#xff0c;\u5c06\u6570\u636e\u5e76\u884c\u4e0e\u6a21\u578b\u5e76\u884c\u7ed3\u5408&#xff0c;\u4ee5\u6700\u5927\u5316GPU\u5229\u7528\u7387\u548c\u6574\u4f53\u8bad\u7ec3\u6027\u80fd\u3002<\/p>\n<hr \/>\n<h3>\u4e09\u3001\u8f6f\u4ef6\u751f\u6001\u4e0e\u6846\u67b6\u9009\u62e9<\/h3>\n<p>\u591aGPU\u5e76\u884c\u8bad\u7ec3\u4f9d\u8d56\u5e95\u5c42\u6846\u67b6\u7684\u5e76\u884c\u5b9e\u73b0\u3002\u76ee\u524d\u4e3b\u6d41\u65b9\u6848\u5305\u62ec&#xff1a;<\/p>\n<table>\n<tr>\u6846\u67b6\/\u5e93\u652f\u6301\u5e76\u884c\u7b56\u7565\u9002\u7528\u8303\u56f4\u7279\u70b9<\/tr>\n<tbody>\n<tr>\n<td>PyTorch DistributedDataParallel (DDP)<\/td>\n<td>\u6570\u636e\u5e76\u884c<\/td>\n<td>\u901a\u7528<\/td>\n<td>\u5b98\u65b9\u652f\u6301&#xff1b;\u901a\u4fe1\u6548\u7387\u9ad8<\/td>\n<\/tr>\n<tr>\n<td>PyTorch Pipeline Parallel<\/td>\n<td>\u7ba1\u9053\u5e76\u884c<\/td>\n<td>\u6a21\u5757\u5206\u6bb5<\/td>\n<td>\u6613\u7ed3\u5408DDP<\/td>\n<\/tr>\n<tr>\n<td>DeepSpeed<\/td>\n<td>\u6570\u636e\/\u5f20\u91cf\/\u6d41\u6c34\u7ebf\u6df7\u5408<\/td>\n<td>\u8d85\u5927\u6a21\u578b<\/td>\n<td>\u591a\u79cd\u4f18\u5316&#xff1b;ZeRO \u5206\u5e03\u5f0f\u4f18\u5316<\/td>\n<\/tr>\n<tr>\n<td>Megatron\u2011LM<\/td>\n<td>\u5f20\u91cf\u5e76\u884c\/\u6d41\u6c34\u7ebf<\/td>\n<td>Transformer \u7c7b\u5927\u6a21\u578b<\/td>\n<td>\u9ad8\u6548\u5e76\u884c\u7b56\u7565<\/td>\n<\/tr>\n<tr>\n<td>FairScale<\/td>\n<td>Sharded DDP<\/td>\n<td>\u663e\u5b58\u4f18\u5316<\/td>\n<td>\u4e0e PyTorch \u517c\u5bb9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5728\u6784\u5efa\u591aGPU\u5e76\u884c\u8bad\u7ec3\u65f6&#xff0c;\u53ef\u4ee5\u6839\u636e\u6a21\u578b\u89c4\u6a21\u548c\u8bad\u7ec3\u76ee\u6807\u9009\u62e9\u9002\u5408\u7684\u6846\u67b6\u3002\u4f8b\u5982&#xff0c;\u8bad\u7ec3\u4e0a\u767e\u4ebf\u53c2\u6570\u7684 GPT \u7c7b\u6a21\u578b\u65f6&#xff0c;DeepSpeed \u7684 ZeRO Stage 3 \u4e0e\u5f20\u91cf&#043;\u6d41\u6c34\u7ebf\u5e76\u884c\u7ed3\u5408\u901a\u5e38\u662f\u9ad8\u6548\u65b9\u6848&#xff1b;\u800c\u5355\u673a\u591a\u5361\u8bad\u7ec3\u6570\u4ebf\u53c2\u6570\u6a21\u578b\u65f6&#xff0c;PyTorch DDP \u5219\u8db3\u591f\u597d\u7528\u3002<\/p>\n<hr \/>\n<h3>\u56db\u3001\u5b9e\u73b0\u793a\u4f8b\u4e0e\u4ee3\u7801\u7ec6\u8282<\/h3>\n<p>\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u5982\u4f55\u4f7f\u7528 PyTorch &#043; DeepSpeed \u5b9e\u73b0\u591aGPU\u6a21\u578b\u5e76\u884c\u8bad\u7ec3\u3002<\/p>\n<h4>4.1 \u5b89\u88c5\u73af\u5883<\/h4>\n<p><span class=\"token comment\"># \u5b89\u88c5 PyTorch<\/span><br \/>\npip <span class=\"token function\">install<\/span> torch torchvision &#8211;extra-index-url https:\/\/download.pytorch.org\/whl\/cu118<\/p>\n<p><span class=\"token comment\"># \u5b89\u88c5 DeepSpeed<\/span><br \/>\npip <span class=\"token function\">install<\/span> deepspeed<\/p>\n<p><span class=\"token comment\"># \u5982\u679c\u9700\u8981 Megatron\u2011LM<\/span><br \/>\n<span class=\"token function\">git<\/span> clone https:\/\/github.com\/NVIDIA\/Megatron\u2011LM.git<br \/>\n<span class=\"token builtin class-name\">cd<\/span> Megatron\u2011LM<br \/>\npip <span class=\"token function\">install<\/span> -e <span class=\"token builtin class-name\">.<\/span><\/p>\n<h4>4.2 \u521b\u5efa\u6a21\u578b\u793a\u4f8b&#xff08;\u4ee5 Transformer \u4e3a\u4f8b&#xff09;<\/h4>\n<p><span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn<\/p>\n<p><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">SimpleTransformer<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> vocab_size<span class=\"token punctuation\">,<\/span> embed_dim<span class=\"token punctuation\">,<\/span> num_heads<span class=\"token punctuation\">,<\/span> num_layers<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span>SimpleTransformer<span class=\"token punctuation\">,<\/span> self<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>__init__<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>embedding <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Embedding<span class=\"token punctuation\">(<\/span>vocab_size<span class=\"token punctuation\">,<\/span> embed_dim<span class=\"token punctuation\">)<\/span><br \/>\n        encoder_layer <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>TransformerEncoderLayer<span class=\"token punctuation\">(<\/span>d_model<span class=\"token operator\">&#061;<\/span>embed_dim<span class=\"token punctuation\">,<\/span> nhead<span class=\"token operator\">&#061;<\/span>num_heads<span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>transformer_encoder <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>TransformerEncoder<span class=\"token punctuation\">(<\/span>encoder_layer<span class=\"token punctuation\">,<\/span> num_layers<span class=\"token operator\">&#061;<\/span>num_layers<span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>output <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span>embed_dim<span class=\"token punctuation\">,<\/span> vocab_size<span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">def<\/span> <span class=\"token function\">forward<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>embedding<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><br \/>\n        x <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>transformer_encoder<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> self<span class=\"token punctuation\">.<\/span>output<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><\/p>\n<h4>4.3 DeepSpeed \u914d\u7f6e<\/h4>\n<p>\u5c06\u4ee5\u4e0b JSON \u4fdd\u5b58\u4e3a ds_config.json&#xff1a;<\/p>\n<p><span class=\"token punctuation\">{<\/span><br \/>\n  <span class=\"token string-property property\">&#034;train_batch_size&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">512<\/span><span class=\"token punctuation\">,<\/span><br \/>\n  <span class=\"token string-property property\">&#034;gradient_accumulation_steps&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">,<\/span><br \/>\n  <span class=\"token string-property property\">&#034;fp16&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n    <span class=\"token string-property property\">&#034;enabled&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token boolean\">true<\/span><br \/>\n  <span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">,<\/span><br \/>\n  <span class=\"token string-property property\">&#034;zero_optimization&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n    <span class=\"token string-property property\">&#034;stage&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">2<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string-property property\">&#034;allgather_partitions&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token boolean\">true<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string-property property\">&#034;reduce_scatter&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token boolean\">true<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string-property property\">&#034;allgather_bucket_size&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">5e8<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string-property property\">&#034;overlap_comm&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token boolean\">true<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string-property property\">&#034;reduce_bucket_size&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">5e8<\/span><br \/>\n  <span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">,<\/span><br \/>\n  <span class=\"token string-property property\">&#034;optimizer&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n    <span class=\"token string-property property\">&#034;type&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token string\">&#034;AdamW&#034;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    <span class=\"token string-property property\">&#034;params&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n      <span class=\"token string-property property\">&#034;lr&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">1e-4<\/span><span class=\"token punctuation\">,<\/span><br \/>\n      <span class=\"token string-property property\">&#034;betas&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">0.9<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">0.999<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span><br \/>\n      <span class=\"token string-property property\">&#034;eps&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">1e-8<\/span><span class=\"token punctuation\">,<\/span><br \/>\n      <span class=\"token string-property property\">&#034;weight_decay&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">1e-2<\/span><br \/>\n    <span class=\"token punctuation\">}<\/span><br \/>\n  <span class=\"token punctuation\">}<\/span><br \/>\n<span class=\"token punctuation\">}<\/span><\/p>\n<h4>4.4 \u8bad\u7ec3\u811a\u672c<\/h4>\n<p><span class=\"token keyword\">import<\/span> deepspeed<br \/>\n<span class=\"token keyword\">from<\/span> model <span class=\"token keyword\">import<\/span> SimpleTransformer<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">train<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    model <span class=\"token operator\">&#061;<\/span> SimpleTransformer<span class=\"token punctuation\">(<\/span><br \/>\n        vocab_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">50000<\/span><span class=\"token punctuation\">,<\/span> embed_dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1024<\/span><span class=\"token punctuation\">,<\/span> num_heads<span class=\"token operator\">&#061;<\/span><span class=\"token number\">16<\/span><span class=\"token punctuation\">,<\/span> num_layers<span class=\"token operator\">&#061;<\/span><span class=\"token number\">24<\/span><br \/>\n    <span class=\"token punctuation\">)<\/span><br \/>\n    <span class=\"token comment\"># DeepSpeed \u521d\u59cb\u5316<\/span><br \/>\n    model_engine<span class=\"token punctuation\">,<\/span> optimizer<span class=\"token punctuation\">,<\/span> _<span class=\"token punctuation\">,<\/span> _ <span class=\"token operator\">&#061;<\/span> deepspeed<span class=\"token punctuation\">.<\/span>initialize<span class=\"token punctuation\">(<\/span><br \/>\n        args<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">None<\/span><span class=\"token punctuation\">,<\/span> model<span class=\"token operator\">&#061;<\/span>model<span class=\"token punctuation\">,<\/span> model_parameters<span class=\"token operator\">&#061;<\/span>model<span class=\"token punctuation\">.<\/span>parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> config<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;ds_config.json&#034;<\/span><br \/>\n    <span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">for<\/span> epoch <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>num_epochs<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token keyword\">for<\/span> batch <span class=\"token keyword\">in<\/span> train_loader<span class=\"token punctuation\">:<\/span><br \/>\n            inputs<span class=\"token punctuation\">,<\/span> labels <span class=\"token operator\">&#061;<\/span> batch<br \/>\n            outputs <span class=\"token operator\">&#061;<\/span> model_engine<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span><br \/>\n            loss <span class=\"token operator\">&#061;<\/span> loss_fn<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">,<\/span> labels<span class=\"token punctuation\">)<\/span><br \/>\n            model_engine<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span>loss<span class=\"token punctuation\">)<\/span><br \/>\n            model_engine<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">if<\/span> __name__ <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token string\">&#034;__main__&#034;<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    train<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>4.5 \u8c03\u7528\u5206\u5e03\u5f0f\u542f\u52a8<\/h4>\n<p>\u5047\u8bbe\u4f7f\u7528 8 \u5f20 GPU&#xff1a;<\/p>\n<p>deepspeed &#8211;num_gpus<span class=\"token operator\">&#061;<\/span><span class=\"token number\">8<\/span> train.py<\/p>\n<p>DeepSpeed \u4f1a\u81ea\u52a8\u7ba1\u7406\u5f20\u91cf\u5207\u5206\u3001\u68af\u5ea6\u6c47\u603b\u548c\u4f18\u5316\u5668\u72b6\u6001\u5206\u5e03&#xff0c;\u4ece\u800c\u663e\u8457\u964d\u4f4e\u5355\u5361\u663e\u5b58\u538b\u529b\u3002<\/p>\n<hr \/>\n<h3>\u4e94\u3001\u6027\u80fd\u8bc4\u4f30\u4e0e\u8c03\u4f18<\/h3>\n<h4>5.1 \u8bc4\u4f30\u6307\u6807<\/h4>\n<p>\u5728\u591aGPU\u5e76\u884c\u8bad\u7ec3\u4e2d&#xff0c;\u5e38\u7528\u6307\u6807\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\u8bad\u7ec3\u541e\u5410\u91cf&#xff08;samples\/sec&#xff09;&#xff1a;\u663e\u5361\u603b\u8bad\u7ec3\u6837\u672c\u5904\u7406\u901f\u5ea6&#xff1b;<\/li>\n<li>GPU \u5229\u7528\u7387&#xff1a;\u663e\u5361\u5b9e\u9645 FLOPS \u4f7f\u7528\u6bd4\u7387&#xff1b;<\/li>\n<li>\u901a\u4fe1\u5f00\u9500&#xff08;Comm Time&#xff09;&#xff1a;\u8de8\u5361\u68af\u5ea6\u540c\u6b65\u65f6\u95f4&#xff1b;<\/li>\n<li>\u663e\u5b58\u5360\u7528&#xff1a;Peak \u548c Average \u663e\u5b58\u4f7f\u7528&#xff1b;<\/li>\n<li>\u6536\u655b\u901f\u5ea6&#xff1a;\u8bad\u7ec3 loss \u6216\u8bc4\u6d4b\u6307\u6807\u8fbe\u5230\u9884\u5b9a\u503c\u7684\u8fed\u4ee3\u6b21\u6570\u3002<\/li>\n<\/ul>\n<h4>5.2 \u5b9e\u6d4b\u5bf9\u6bd4<\/h4>\n<p>\u4ee5\u4e0b\u662f\u5bf9\u6bd4\u4e0d\u540c\u5e76\u884c\u7b56\u7565\u5728 8 \u00d7 A100 \u670d\u52a1\u5668\u4e0a\u8bad\u7ec3 Transformer \u6a21\u578b\u65f6\u7684\u5b9e\u6d4b\u6570\u636e&#xff08;\u4ec5\u793a\u4f8b&#xff0c;\u5b9e\u9645\u56e0\u6570\u636e\u96c6\u3001\u8d85\u53c2\u548c\u6a21\u578b\u5927\u5c0f\u800c\u5f02&#xff09;&#xff1a;<\/p>\n<table>\n<tr>\u5e76\u884c\u7b56\u7565Batch Size \/ GPU\u603b BatchTrain Samples\/secGPU \u663e\u5b58\u5e73\u5747 (GB)\u6536\u655b Epoch<\/tr>\n<tbody>\n<tr>\n<td>\u5355\u5361 DDP<\/td>\n<td>32<\/td>\n<td>256<\/td>\n<td>1200<\/td>\n<td>76<\/td>\n<td>20<\/td>\n<\/tr>\n<tr>\n<td>8GPU DDP<\/td>\n<td>32<\/td>\n<td>256<\/td>\n<td>8500<\/td>\n<td>76<\/td>\n<td>20<\/td>\n<\/tr>\n<tr>\n<td>DeepSpeed ZeRO\u20112<\/td>\n<td>64<\/td>\n<td>512<\/td>\n<td>9000<\/td>\n<td>52<\/td>\n<td>20<\/td>\n<\/tr>\n<tr>\n<td>DeepSpeed ZeRO\u20113<\/td>\n<td>128<\/td>\n<td>1024<\/td>\n<td>10000<\/td>\n<td>32<\/td>\n<td>20<\/td>\n<\/tr>\n<tr>\n<td>Pipeline &#043; Tensor Parallel<\/td>\n<td>64<\/td>\n<td>512<\/td>\n<td>9500<\/td>\n<td>40<\/td>\n<td>20<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u4ece\u4e0a\u8868\u53ef\u4ee5\u770b\u51fa&#xff1a;<\/p>\n<ul>\n<li>\u7eaf DDP \u5728\u6570\u636e\u5e76\u884c\u65f6\u6709\u826f\u597d\u6269\u5c55&#xff0c;\u4f46\u663e\u5b58\u9650\u5236\u5728\u5927 batch \u4e0b\u4e0d\u5982 ZeRO&#xff1b;<\/li>\n<li>ZeRO\u20113 \u663e\u5b58\u5229\u7528\u6700\u4f18&#xff0c;\u4f7f\u5f97\u66f4\u5927 batch \u548c\u66f4\u5927\u6a21\u578b\u6210\u4e3a\u53ef\u80fd&#xff1b;<\/li>\n<li>\u6df7\u5408\u5e76\u884c\u7b56\u7565\u5728\u8d44\u6e90\u5747\u8861\u4e0a\u6709\u4f18\u52bf\u3002<\/li>\n<\/ul>\n<h4>5.3 \u901a\u4fe1\u4e0e\u8d1f\u8f7d\u4f18\u5316<\/h4>\n<p>\u8981\u8fdb\u4e00\u6b65\u4f18\u5316\u591a\u5361\u6027\u80fd&#xff0c;\u9700\u8981\u5173\u6ce8\u4ee5\u4e0b\u7ec6\u8282&#xff1a;<\/p>\n<li>\u901a\u4fe1\u5e93\u9009\u62e9&#xff1a;\u786e\u4fdd\u4f7f\u7528 NCCL \u4f5c\u4e3a\u5e95\u5c42\u901a\u4fe1 backend&#xff1b;<\/li>\n<li>\u68af\u5ea6\u7d2f\u79ef&#xff1a;\u901a\u8fc7\u68af\u5ea6\u7d2f\u79ef\u51cf\u5c11\u540c\u6b65\u9891\u7387&#xff1b;<\/li>\n<li>\u6df7\u5408\u7cbe\u5ea6&#xff1a;\u4f7f\u7528 FP16\/BF16 \u964d\u4f4e\u663e\u5b58\u4e0e\u901a\u4fe1\u5e26\u5bbd&#xff1b;<\/li>\n<li>\u5c42\u7ea7\u5212\u5206&#xff1a;\u5408\u7406\u5212\u5206\u6a21\u578b\u5c42\u6b21\u4e0e\u663e\u5b58\u5206\u5e03&#xff1b;<\/li>\n<li>\u8d1f\u8f7d\u5747\u8861&#xff1a;\u52a8\u6001\u8c03\u6574\u6a21\u578b\u5207\u5206\u4ee5\u907f\u514d\u5361\u95f4\u4e0d\u5747\u8861\u3002<\/li>\n<hr \/>\n<h3>\u516d\u3001\u5728\u63a8\u7406\u9636\u6bb5\u7684\u591aGPU\u8fd0\u7528<\/h3>\n<p>\u5bf9\u4e8e\u5927\u89c4\u6a21 NLP \u63a8\u7406\u4efb\u52a1&#xff08;\u5982\u6279\u91cf\u6587\u672c\u751f\u6210\u3001Token \u9884\u6d4b&#xff09;&#xff0c;\u4e5f\u53ef\u4ee5\u5229\u7528\u591aGPU\u5e76\u884c&#xff1a;<\/p>\n<ul>\n<li>\u6a21\u578b\u5207\u5206\u63a8\u7406&#xff08;Tensor Parallel Inference&#xff09;&#xff1a;\u5728\u591a\u4e2a GPU \u4e0a\u62c6\u5206\u6a21\u578b\u8ba1\u7b97&#xff1b;<\/li>\n<li>Batch \u5e76\u884c\u63a8\u7406&#xff08;Data Parallel Inference&#xff09;&#xff1a;\u5c06\u4e0d\u540c\u6837\u672c\u5206\u914d\u5230\u4e0d\u540c GPU&#xff1b;<\/li>\n<li>Pipeline \u63a8\u7406&#xff1a;\u5c06\u6a21\u578b\u5c42\u7ea7\u5206\u5e03\u5230\u4e0d\u540c GPU&#xff0c;\u4ee5\u6d41\u6c34\u7ebf\u65b9\u5f0f\u5904\u7406\u3002<\/li>\n<\/ul>\n<p>\u5728\u63a8\u7406\u573a\u666f\u4e2d&#xff0c;\u91cd\u70b9\u5728\u4e8e\u51cf\u5c11\u5ef6\u8fdf\u4e0e\u4fdd\u8bc1\u9ad8\u541e\u5410\u91cf\u3002\u5e38\u7528\u7b56\u7565\u662f&#xff1a;<\/p>\n<ul>\n<li>\u4f7f\u7528 Triton Inference Server \u6216 DeepSpeed Inference&#xff1b;<\/li>\n<li>\u5f00\u542f TensorRT\/BF16 \u52a0\u901f&#xff1b;<\/li>\n<li>\u5408\u7406\u8c03\u5ea6 GPU \u8d44\u6e90\u4ee5\u907f\u514d\u7a7a\u95f2\u4e0e\u62e5\u585e\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e03\u3001\u603b\u7ed3\u4e0e\u6700\u4f73\u5b9e\u8df5<\/h3>\n<p>\u5728 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