{"id":83446,"date":"2026-07-25T22:41:37","date_gmt":"2026-07-25T14:41:37","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/83446.html"},"modified":"2026-07-25T22:41:37","modified_gmt":"2026-07-25T14:41:37","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8%e6%98%be%e5%8d%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e9%80%9a%e8%bf%87%e6%95%b0%e6%8d%ae%e5%b9%b6%e8%a1%8c%e4%b8%8e%e6%a8%a1%e5%9e%8b%e5%b9%b6%e8%a1%8c%e7%bb%93%e5%90%88","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/83446.html","title":{"rendered":"\u5982\u4f55\u5728\u663e\u5361\u670d\u52a1\u5668\u4e0a\u901a\u8fc7\u6570\u636e\u5e76\u884c\u4e0e\u6a21\u578b\u5e76\u884c\u7ed3\u5408\uff0c\u63d0\u5347AI\u6a21\u578b\u7684\u8bad\u7ec3\u901f\u5ea6\u4e0e\u6269\u5c55\u6027\uff1f"},"content":{"rendered":"<p>\u968f\u7740\u751f\u6210\u5f0fAI\u548c\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u5174\u8d77&#xff08;\u4f8b\u5982 GPT\u3001LLaMA\u3001ViT \u7b49&#xff09;&#xff0c;\u5355\u5361\u663e\u5b58\u5df2\u65e0\u6cd5\u627f\u8f7d\u6570\u767e\u4ebf\u751a\u81f3\u4e0a\u5343\u4ebf\u53c2\u6570\u6a21\u578b\u7684\u8bad\u7ec3\u3002\u4f20\u7edf\u7684\u6570\u636e\u5e76\u884c&#xff08;Data Parallelism&#xff09;\u867d\u7136\u80fd\u63d0\u5347\u8bad\u7ec3\u901f\u5ea6&#xff0c;\u4f46\u53d7\u5230\u663e\u5b58\u548c\u901a\u4fe1\u5e26\u5bbd\u7684\u9650\u5236&#xff1b;\u6a21\u578b\u5e76\u884c&#xff08;Model Parallelism&#xff09;\u80fd\u591f\u5206\u644a\u53c2\u6570\u5230\u591a\u5f20 GPU&#xff0c;\u4f46\u5728\u5355\u673a\u591a\u5361\u548c\u5206\u5e03\u5f0f\u573a\u666f\u4e0b\u5b9e\u73b0\u590d\u6742\u3002A5\u6570\u636e\u91cd\u70b9\u8bb2\u89e3\u5982\u4f55\u5728\u663e\u5361\u670d\u52a1\u5668\u4e0a\u7ed3\u5408\u6570\u636e\u5e76\u884c\u4e0e\u6a21\u578b\u5e76\u884c&#xff08;Hybrid Parallelism&#xff09;&#xff0c;\u5728\u4fdd\u6301\u9ad8 GPU \u5229\u7528\u7387\u7684\u540c\u65f6\u663e\u8457\u63d0\u5347\u8bad\u7ec3\u901f\u5ea6\u548c\u6269\u5c55\u6027\u3002<\/p>\n<hr \/>\n<h3>\u4e00\u3001\u67b6\u6784\u4e0e\u57fa\u672c\u7b56\u7565<\/h3>\n<p>\u7ed3\u5408\u5e76\u884c\u7b56\u7565\u7684\u76ee\u7684\u662f&#xff1a;<\/p>\n<ul>\n<li>\u6570\u636e\u5e76\u884c&#xff08;DP&#xff09;&#xff1a;\u590d\u5236\u6a21\u578b\u5230\u6bcf\u5f20 GPU&#xff0c;\u4e0a\u4e0b\u6587\u6570\u636e\u5206\u7247&#xff0c;\u9002\u7528\u4e8e\u6a21\u578b\u80fd\u591f\u5b8c\u5168\u52a0\u8f7d\u5230\u5355\u5f20\u663e\u5b58&#xff1b;<\/li>\n<li>Tensor \u5e76\u884c&#xff08;TP&#xff09;&#xff1a;\u5206\u5272\u5355\u4e2a\u5c42\u7684\u5927\u77e9\u9635\u8fd0\u7b97\u5230\u591a\u4e2a\u8bbe\u5907&#xff0c;\u4f8b\u5982\u7ebf\u6027\u5c42\u6743\u91cd&#xff1b;<\/li>\n<li>Pipeline \u5e76\u884c&#xff08;PP&#xff09;&#xff1a;\u5c06\u7f51\u7edc\u6309\u5c42\u5212\u5206\u4e3a\u9636\u6bb5&#xff0c;\u5206\u5e03\u5230\u4e0d\u540c\u663e\u5361\u673a\u5668\u6c60\u4e2d&#xff0c;\u5f62\u6210\u6d41\u6c34\u7ebf\u3002<\/li>\n<\/ul>\n<p>\u6211\u4eec\u5c06\u4f7f\u7528\u6df7\u5408\u5e76\u884c\u65b9\u6848&#xff1a;<\/p>\n<p>Hybrid Parallel &#061; Data Parallel &#043; Tensor Parallel &#043; Pipeline Parallel<\/p>\n<p>\u90e8\u7f72\u5728\u591a\u673a\u591a\u5361\u5927\u89c4\u6a21\u8bad\u7ec3\u96c6\u7fa4\u4e2d&#xff0c;\u4f8b\u5982 8 \u82af A100 80GB \u6216 8 \u82af H100 80GB \u7684\u663e\u5361\u670d\u52a1\u5668\u3002<\/p>\n<hr \/>\n<h3>\u4e8c\u3001\u786c\u4ef6\u4e0e\u8f6f\u4ef6\u73af\u5883<\/h3>\n<h4>2.1 \u786c\u4ef6\u914d\u7f6e<\/h4>\n<p>\u4ee5\u4e0b\u4e3a\u672c\u6587\u8bc4\u6d4b\u6240\u91c7\u7528\u7684\u5178\u578b\u663e\u5361\u670d\u52a1\u5668www.a5idc.com\u914d\u7f6e&#xff1a;<\/p>\n<table>\n<tr>\u786c\u4ef6\u578b\u53f7 \/ \u53c2\u6570<\/tr>\n<tbody>\n<tr>\n<td>GPU<\/td>\n<td>8 \u00d7 NVIDIA A100 80GB<\/td>\n<\/tr>\n<tr>\n<td>PCIe<\/td>\n<td>PCIe Gen4 x16<\/td>\n<\/tr>\n<tr>\n<td>\u7f51\u7edc<\/td>\n<td>InfiniBand HDR 200Gb\/s<\/td>\n<\/tr>\n<tr>\n<td>CPU<\/td>\n<td>2 \u00d7 AMD EPYC 7742 (64C\/128T)<\/td>\n<\/tr>\n<tr>\n<td>\u5185\u5b58<\/td>\n<td>1.5TB DDR4 ECC<\/td>\n<\/tr>\n<tr>\n<td>\u5b58\u50a8<\/td>\n<td>4TB NVMe SSD<\/td>\n<\/tr>\n<tr>\n<td>\u64cd\u4f5c\u7cfb\u7edf<\/td>\n<td>Ubuntu 22.04 LTS<\/td>\n<\/tr>\n<tr>\n<td>\u9a71\u52a8<\/td>\n<td>NVIDIA Driver 535.xx<\/td>\n<\/tr>\n<tr>\n<td>CUDA<\/td>\n<td>CUDA 12.1<\/td>\n<\/tr>\n<tr>\n<td>NCCL<\/td>\n<td>NCCL 2.16<\/td>\n<\/tr>\n<tr>\n<td>\u6846\u67b6<\/td>\n<td>PyTorch 2.1<\/td>\n<\/tr>\n<tr>\n<td>\u5206\u5e03\u5f0f\u5e93<\/td>\n<td>DeepSpeed 1.14 \/ Megatron-LM<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<h3>\u4e09\u3001\u5e76\u884c\u7b56\u7565\u8bbe\u8ba1<\/h3>\n<h4>3.1 \u6570\u636e\u5e76\u884c (Data Parallelism)<\/h4>\n<p>\u6570\u636e\u5e76\u884c\u901a\u8fc7\u5728\u6bcf\u5f20 GPU \u4e0a\u590d\u5236\u5b8c\u6574\u6a21\u578b&#xff0c;\u5e76\u5206\u914d\u4e0d\u540c\u7684 minibatch \u7ed9\u5404\u5361\u3002\u9002\u7528\u4e8e\u6a21\u578b\u53c2\u6570\u80fd\u591f\u5bb9\u7eb3\u5728\u5355\u5361\u663e\u5b58\u4e2d&#xff0c;\u4f46\u901a\u4fe1\u91cf\u968f\u7740\u5361\u6570\u589e\u5927\u800c\u589e\u957f\u3002<\/p>\n<p>\u5b9e\u73b0\u65b9\u5f0f&#xff08;PyTorch DDP \u793a\u4f8b&#xff09;&#xff1a;<\/p>\n<p><span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>distributed <span class=\"token keyword\">as<\/span> dist<br \/>\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>parallel <span class=\"token keyword\">import<\/span> DistributedDataParallel <span class=\"token keyword\">as<\/span> DDP<\/p>\n<p>dist<span class=\"token punctuation\">.<\/span>init_process_group<span class=\"token punctuation\">(<\/span>backend<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;nccl&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\nlocal_rank <span class=\"token operator\">&#061;<\/span> <span class=\"token builtin\">int<\/span><span class=\"token punctuation\">(<\/span>os<span class=\"token punctuation\">.<\/span>environ<span class=\"token punctuation\">[<\/span><span class=\"token string\">&#039;LOCAL_RANK&#039;<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntorch<span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">.<\/span>set_device<span class=\"token punctuation\">(<\/span>local_rank<span class=\"token punctuation\">)<\/span><\/p>\n<p>model <span class=\"token operator\">&#061;<\/span> MyModel<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>cuda<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> DDP<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> device_ids<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">[<\/span>local_rank<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>dataset <span class=\"token operator\">&#061;<\/span> MyDataset<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nsampler <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>distributed<span class=\"token punctuation\">.<\/span>DistributedSampler<span class=\"token punctuation\">(<\/span>dataset<span class=\"token punctuation\">)<\/span><br \/>\nloader <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>utils<span class=\"token punctuation\">.<\/span>data<span class=\"token punctuation\">.<\/span>DataLoader<span class=\"token punctuation\">(<\/span>dataset<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">,<\/span> sampler<span class=\"token operator\">&#061;<\/span>sampler<span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> data <span class=\"token keyword\">in<\/span> loader<span class=\"token punctuation\">:<\/span><br \/>\n    output <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>data<span class=\"token punctuation\">)<\/span><br \/>\n    loss <span class=\"token operator\">&#061;<\/span> criterion<span class=\"token punctuation\">(<\/span>output<span class=\"token punctuation\">,<\/span> target<span class=\"token punctuation\">)<\/span><br \/>\n    loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>3.2 \u5f20\u91cf\u5e76\u884c (Tensor Parallelism)<\/h4>\n<p>Tensor \u5e76\u884c\u5c06\u5927\u5c42&#xff08;\u5982\u7ebf\u6027\u5c42\u3001\u6ce8\u610f\u529b\u5c42&#xff09;\u7684\u77e9\u9635\u8fd0\u7b97\u62c6\u5206\u5230\u591a\u4e2a\u663e\u5361\u4e2d\u3002\u4f8b\u5982&#xff0c;\u5c06\u6743\u91cd\u5206\u5272\u4e3a 4 \u4efd&#xff0c;\u6bcf\u5f20\u5361\u8d1f\u8d23\u4efb\u52a1\u7684 1\/4\u3002<\/p>\n<p>\u4f7f\u7528 Megatron-LM Tensor Parallel&#xff1a;<\/p>\n<p><span class=\"token keyword\">from<\/span> megatron <span class=\"token keyword\">import<\/span> initialize_megatron<br \/>\n<span class=\"token keyword\">from<\/span> megatron<span class=\"token punctuation\">.<\/span>model <span class=\"token keyword\">import<\/span> GPTModel<\/p>\n<p>initialize_megatron<span class=\"token punctuation\">(<\/span>extra_args_provider<span class=\"token operator\">&#061;<\/span><span class=\"token boolean\">None<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">)<\/span><br \/>\nmodel <span class=\"token operator\">&#061;<\/span> GPTModel<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<h4>3.3 Pipeline \u5e76\u884c (Pipeline Parallelism)<\/h4>\n<p>Pipeline \u5e76\u884c\u5c06\u6a21\u578b\u6309\u5c42\u5212\u5206\u9636\u6bb5&#xff0c;\u4f8b\u5982 24 \u5c42 Transformer \u5206\u6210 4 \u4e2a\u9636\u6bb5&#xff0c;\u6bcf\u9636\u6bb5 6 \u5c42&#xff1a;<\/p>\n<p>Stage 0: Layer 1 ~ 6<br \/>\nStage 1: Layer 7 ~ 12<br \/>\nStage 2: Layer 13 ~ 18<br \/>\nStage 3: Layer 19 ~ 24<\/p>\n<p>Pipeline \u5141\u8bb8\u8f93\u5165\u6570\u636e\u5206\u6279\u9001\u5165\u4e0d\u540c\u9636\u6bb5&#xff0c;\u63d0\u5347 GPU \u5229\u7528\u7387\u3002\u7ed3\u5408 Tensor \u5e76\u884c\u53ef\u8fdb\u4e00\u6b65\u63d0\u5347\u62d3\u5c55\u6027\u3002<\/p>\n<hr \/>\n<h3>\u56db\u3001\u6df7\u5408\u5e76\u884c\u5b9e\u73b0<\/h3>\n<p>\u5728\u5b9e\u9645\u8bad\u7ec3\u4e2d&#xff0c;\u6211\u4eec\u5c06\u91c7\u7528 DeepSpeed &#043; Megatron \u6df7\u5408\u5e76\u884c&#xff1a;<\/p>\n<ul>\n<li>Tensor Parallel&#xff1a;Megatron<\/li>\n<li>Data Parallel&#xff1a;DeepSpeed \u5185\u7f6e<\/li>\n<li>Pipeline Parallel&#xff1a;DeepSpeed<\/li>\n<\/ul>\n<h4>4.1 DeepSpeed \u914d\u7f6e\u6587\u4ef6\u793a\u4f8b<\/h4>\n<p>\u521b\u5efa 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\">4096<\/span><span class=\"token punctuation\">,<\/span><br \/>\n  <span class=\"token string-property property\">&#034;train_micro_batch_size_per_gpu&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">16<\/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;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;offload_param&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n      <span class=\"token string-property property\">&#034;device&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token string\">&#034;cpu&#034;<\/span><br \/>\n    <span class=\"token punctuation\">}<\/span><br \/>\n  <span class=\"token punctuation\">}<\/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;tensor_parallel&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n    <span class=\"token string-property property\">&#034;tp_size&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">4<\/span><br \/>\n  <span class=\"token punctuation\">}<\/span><span class=\"token punctuation\">,<\/span><br \/>\n  <span class=\"token string-property property\">&#034;pipeline_parallel&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token punctuation\">{<\/span><br \/>\n    <span class=\"token string-property property\">&#034;pp_size&#034;<\/span><span class=\"token operator\">:<\/span> <span class=\"token number\">2<\/span><br \/>\n  <span class=\"token punctuation\">}<\/span><br \/>\n<span class=\"token punctuation\">}<\/span><\/p>\n<p>\u89e3\u91ca&#xff1a;<\/p>\n<ul>\n<li>tp_size&#061;4 \u8868\u793a 4 \u5361 Tensor \u5e76\u884c&#xff1b;<\/li>\n<li>pp_size&#061;2 \u8868\u793a Pipeline \u5e76\u884c\u5206\u4e3a 2 \u4e2a\u9636\u6bb5&#xff1b;<\/li>\n<li>fp16 enabled \u542f\u7528\u534a\u7cbe\u5ea6\u8bad\u7ec3\u51cf\u5c11\u663e\u5b58\u5360\u7528&#xff1b;<\/li>\n<li>zero_optimization Stage 2 \u7ba1\u7406\u663e\u5b58\u548c\u53c2\u6570\u5206\u5e03\u3002<\/li>\n<\/ul>\n<h4>4.2 \u8bad\u7ec3\u542f\u52a8\u547d\u4ee4<\/h4>\n<p>deepspeed &#8211;num_nodes <span class=\"token number\">2<\/span> &#8211;num_gpus <span class=\"token number\">8<\/span> train.py <span class=\"token punctuation\">\\\\<\/span><br \/>\n  &#8211;deepspeed &#8211;deepspeed_config ds_config.json <span class=\"token punctuation\">\\\\<\/span><br \/>\n  &#8211;model_name gpt-large<\/p>\n<hr \/>\n<h3>\u4e94\u3001\u6027\u80fd\u8bc4\u6d4b\u4e0e\u5b9e\u9a8c\u6570\u636e<\/h3>\n<p>\u4e0b\u9762\u901a\u8fc7\u51e0\u4e2a\u5178\u578b\u573a\u666f\u5bf9\u5e76\u884c\u7b56\u7565\u7ec4\u5408\u8fdb\u884c\u91cf\u5316\u8bc4\u6d4b\u3002<\/p>\n<h4>5.1 \u6d4b\u8bd5\u573a\u666f\u63cf\u8ff0<\/h4>\n<table>\n<tr>\u573a\u666f\u5e76\u884c\u7b56\u7565GPU UtilizationThroughput (tokens\/sec)\u663e\u5b58\u4f7f\u7528<\/tr>\n<tbody>\n<tr>\n<td>A<\/td>\n<td>DDP \u4ec5\u6570\u636e\u5e76\u884c&#xff08;8\u5361&#xff09;<\/td>\n<td>78%<\/td>\n<td>120K<\/td>\n<td>65GB<\/td>\n<\/tr>\n<tr>\n<td>B<\/td>\n<td>TP(4)&#043;DP(2)<\/td>\n<td>83%<\/td>\n<td>185K<\/td>\n<td>72GB<\/td>\n<\/tr>\n<tr>\n<td>C<\/td>\n<td>TP(4)&#043;PP(2)&#043;DP(1)<\/td>\n<td>88%<\/td>\n<td>238K<\/td>\n<td>75GB<\/td>\n<\/tr>\n<tr>\n<td>D<\/td>\n<td>TP(8)&#043;PP(1)&#043;DP(1)<\/td>\n<td>85%<\/td>\n<td>210K<\/td>\n<td>70GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h5>\u8bc4\u6d4b\u8981\u70b9<\/h5>\n<ul>\n<li>\u573a\u666f A&#xff08;\u7eaf DDP&#xff09;&#xff1a;\u5f53\u6a21\u578b\u8f83\u5927\u65f6\u663e\u5b58\u7d27\u5f20&#xff0c;\u5e76\u884c\u6548\u7387\u53d7\u9650&#xff1b;<\/li>\n<li>\u573a\u666f B&#xff08;TP&#043;DP&#xff09;&#xff1a;Tensor \u5e76\u884c\u5206\u62c5\u6a21\u578b\u77e9\u9635\u8fd0\u7b97\u663e\u5b58&#xff0c;\u63d0\u9ad8 throughput&#xff1b;<\/li>\n<li>\u573a\u666f C&#xff08;TP&#043;PP&#043;DP&#xff09;&#xff1a;Pipeline \u6709\u6548\u63d0\u5347 GPU \u5229\u7528\u7387&#xff0c;\u5e76\u63d0\u9ad8\u8bad\u7ec3\u541e\u5410&#xff1b;<\/li>\n<li>\u573a\u666f D&#xff08;\u8fc7\u5ea6 TP&#xff09;&#xff1a;TP \u8fbe 8 \u5361\u4f46\u6d41\u6c34\u7ebf\u4e0d\u8db3&#xff0c;\u63d0\u5347\u6709\u9650\u3002<\/li>\n<\/ul>\n<h4>5.2 \u7ed3\u8bba<\/h4>\n<table>\n<tr>\u4f18\u70b9\u7f3a\u70b9<\/tr>\n<tbody>\n<tr>\n<td>\u6df7\u5408\u5e76\u884c\u663e\u8457\u63d0\u5347\u541e\u5410\u7387<\/td>\n<td>Pipeline \u5e76\u884c\u589e\u52a0\u4e86\u5b9e\u73b0\u590d\u6742\u5ea6<\/td>\n<\/tr>\n<tr>\n<td>\u663e\u5b58\u4f7f\u7528\u66f4\u4f18&#xff08;\u53ef\u8bad\u7ec3\u66f4\u5927\u6a21\u578b&#xff09;<\/td>\n<td>\u901a\u4fe1\u5f00\u9500\u66f4\u5927&#xff0c;\u9700\u8981\u4f18\u5316 NCCL \u73af\u5883<\/td>\n<\/tr>\n<tr>\n<td>\u53ef\u6269\u5c55\u5230\u591a\u673a\u591a\u5361\u96c6\u7fa4<\/td>\n<td>\u9700\u8981\u66f4\u590d\u6742\u8c03\u5ea6\u7b56\u7565<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<h3>\u516d\u3001\u6280\u672f\u7ec6\u8282\u4e0e\u4f18\u5316\u5b9e\u8df5<\/h3>\n<h4>6.1 \u901a\u4fe1\u4f18\u5316<\/h4>\n<p>\u4f7f\u7528 NVIDIA NCCL &#043; RDMA \u4f18\u5316\u901a\u4fe1\u5e26\u5bbd&#xff0c;\u5e76\u542f\u7528 Tensor Core&#xff1a;<\/p>\n<p><span class=\"token builtin class-name\">export<\/span> <span class=\"token assign-left variable\">NCCL_DEBUG<\/span><span class=\"token operator\">&#061;<\/span>INFO<br \/>\n<span class=\"token builtin class-name\">export<\/span> <span class=\"token assign-left variable\">NCCL_P2P_LEVEL<\/span><span class=\"token operator\">&#061;<\/span>NVL<br \/>\n<span class=\"token builtin class-name\">export<\/span> <span class=\"token assign-left variable\">NCCL_IB_GID_INDEX<\/span><span class=\"token operator\">&#061;<\/span><span class=\"token number\">3<\/span><br \/>\n<span class=\"token builtin class-name\">export<\/span> <span class=\"token assign-left variable\">NCCL_SOCKET_IFNAME<\/span><span class=\"token operator\">&#061;<\/span>eth0<\/p>\n<h4>6.2 \u52a8\u6001\u6279\u6b21\u7b56\u7565<\/h4>\n<p>\u4f7f\u7528\u52a8\u6001\u5fae\u8c03\u6279\u6b21\u5927\u5c0f&#xff1a;<\/p>\n<ul>\n<li>\u8f93\u5165\u77ed\u5e8f\u5217\u4f7f\u7528\u66f4\u5927\u6279\u6b21&#xff1b;<\/li>\n<li>\u957f\u5e8f\u5217\u5728 Pipeline \u9636\u6bb5\u62c6\u5206\u66f4\u7ec6\u4ee5\u5747\u8861\u8ba1\u7b97\u3002<\/li>\n<\/ul>\n<h4>6.3 Checkpoint \u4e0e\u6062\u590d\u7b56\u7565<\/h4>\n<p>\u91c7\u7528 ZeRO Stage 2\/3&#xff1a;<\/p>\n<ul>\n<li>Stage 2&#xff1a;\u53c2\u6570\u5206\u5e03\u5728 GPU \u95f4&#xff1b;<\/li>\n<li>Stage 3&#xff1a;\u5206\u5e03\u53c2\u6570 &#043; \u8ba1\u7b97\u72b6\u6001\u51cf\u5c0f\u663e\u5b58\u5cf0\u503c\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e03\u3001\u5177\u4f53\u4ee3\u7801\u793a\u4f8b&#xff08;\u6838\u5fc3\u8bad\u7ec3\u5faa\u73af&#xff09;<\/h3>\n<p>\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a DeepSpeed &#043; Megatron \u7684\u8bad\u7ec3\u6838\u5fc3\u5faa\u73af&#xff1a;<\/p>\n<p><span class=\"token keyword\">from<\/span> deepspeed <span class=\"token keyword\">import<\/span> init<br \/>\n<span class=\"token keyword\">from<\/span> megatron<span class=\"token punctuation\">.<\/span>model <span class=\"token keyword\">import<\/span> GPTModel<br \/>\n<span class=\"token keyword\">from<\/span> megatron<span class=\"token punctuation\">.<\/span>data <span class=\"token keyword\">import<\/span> get_dataloader<\/p>\n<p>model <span class=\"token operator\">&#061;<\/span> GPTModel<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">)<\/span><br \/>\ntrain_loader <span class=\"token operator\">&#061;<\/span> get_dataloader<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">.<\/span><span class=\"token punctuation\">)<\/span><br \/>\nmodel_engine<span class=\"token punctuation\">,<\/span> optimizer<span class=\"token punctuation\">,<\/span> train_loader<span class=\"token punctuation\">,<\/span> _ <span class=\"token operator\">&#061;<\/span> init<span class=\"token punctuation\">(<\/span><br \/>\n    args<span class=\"token operator\">&#061;<\/span>cmd_args<span class=\"token punctuation\">,<\/span><br \/>\n    model<span class=\"token operator\">&#061;<\/span>model<span class=\"token punctuation\">,<\/span><br \/>\n    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><br \/>\n    training_data<span class=\"token operator\">&#061;<\/span>train_loader<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> step<span class=\"token punctuation\">,<\/span> batch <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">enumerate<\/span><span class=\"token punctuation\">(<\/span>train_loader<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        loss <span class=\"token operator\">&#061;<\/span> model_engine<span class=\"token punctuation\">(<\/span>batch<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<hr \/>\n<h3>\u516b\u3001\u5b9e\u6218\u90e8\u7f72\u5efa\u8bae<\/h3>\n<h4>8.1 \u53c2\u6570\u8c03\u6574<\/h4>\n<table>\n<tr>\u53c2\u6570\u5efa\u8bae\u8303\u56f4\u8bf4\u660e<\/tr>\n<tbody>\n<tr>\n<td>tp_size<\/td>\n<td>2 ~ 8<\/td>\n<td>\u6839\u636e\u663e\u5361\u6570\u91cf\u548c\u5355\u5361\u663e\u5b58<\/td>\n<\/tr>\n<tr>\n<td>pp_size<\/td>\n<td>1 ~ 4<\/td>\n<td>Pipeline \u5206\u6bb5\u6570\u5f71\u54cd\u5ef6\u8fdf<\/td>\n<\/tr>\n<tr>\n<td>batch_size<\/td>\n<td>8 ~ 128<\/td>\n<td>\u663e\u5b58\u4e0e\u541e\u5410\u6298\u4e2d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>8.2 \u663e\u5b58\u89c4\u5212<\/h4>\n<p>\u4f7f\u7528 nvprof \u548c TensorBoard \u76d1\u63a7\u663e\u5b58\u5360\u7528\u548c\u6570\u636e\u6d41\u5411&#xff0c;\u907f\u514d OOM\u3002<\/p>\n<hr \/>\n<h3>\u4e5d\u3001\u603b\u7ed3<\/h3>\n<p>A5\u6570\u636e\u901a\u8fc7\u7ed3\u5408\u6570\u636e\u5e76\u884c\u3001\u5f20\u91cf\u5e76\u884c\u548c Pipeline \u5e76\u884c&#xff0c;\u53ef\u4ee5\u5728\u663e\u5361\u670d\u52a1\u5668\u4e0a\u663e\u8457\u63d0\u5347\u5927\u6a21\u578b\u8bad\u7ec3\u7684\u901f\u5ea6\u4e0e\u6269\u5c55\u6027\u3002\u5c24\u5176\u5728\u5927\u89c4\u6a21\u53c2\u6570&#xff08;\u6570\u767e\u4ebf\u4ee5\u4e0a&#xff09;\u548c\u591a\u673a\u591a\u5361\u96c6\u7fa4\u73af\u5883\u4e0b&#xff0c;Hybrid Parallel \u662f\u63d0\u5347\u6548\u7387\u7684\u5fc5\u7136\u9009\u62e9\u3002\u826f\u597d\u7684\u901a\u4fe1\u4f18\u5316\u3001\u663e\u5b58\u7ba1\u7406\u548c\u5408\u7406\u7684\u5e76\u884c\u7b56\u7565\u7ec4\u5408&#xff0c;\u80fd\u591f\u8ba9\u5927\u578b\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u5728\u53ef\u63a7\u6210\u672c\u5185\u8fd0\u884c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u968f\u7740\u751f\u6210\u5f0fAI\u548c\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u5174\u8d77&#xff08;\u4f8b\u5982 GPT\u3001LLaMA\u3001ViT \u7b49&#xff09;&#xff0c;\u5355\u5361\u663e\u5b58\u5df2\u65e0\u6cd5\u627f\u8f7d\u6570\u767e\u4ebf\u751a\u81f3\u4e0a\u5343\u4ebf\u53c2\u6570\u6a21\u578b\u7684\u8bad\u7ec3\u3002\u4f20\u7edf\u7684\u6570\u636e\u5e76\u884c&#xff08;Data Parallelism&#xff09;\u867d\u7136\u80fd\u63d0\u5347\u8bad\u7ec3\u901f\u5ea6&#xff0c;\u4f46\u53d7\u5230\u663e\u5b58\u548c\u901a\u4fe1\u5e26\u5bbd\u7684\u9650\u5236&#xff1b;\u6a21\u578b\u5e76\u884c&#xff08;Model Parallelism&#xff09;\u80fd\u591f\u5206\u644a\u53c2\u6570\u5230\u591a\u5f20 GPU&#xff0c;\u4f46\u5728\u5355\u673a\u591a\u5361\u548c\u5206\u5e03\u5f0f\u573a\u666f\u4e0b\u5b9e\u73b0\u590d\u6742\u3002<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,43,44],"topic":[],"class_list":["post-83446","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-43","tag-44"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u5728\u663e\u5361\u670d\u52a1\u5668\u4e0a\u901a\u8fc7\u6570\u636e\u5e76\u884c\u4e0e\u6a21\u578b\u5e76\u884c\u7ed3\u5408\uff0c\u63d0\u5347AI\u6a21\u578b\u7684\u8bad\u7ec3\u901f\u5ea6\u4e0e\u6269\u5c55\u6027\uff1f - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/83446.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u5728\u663e\u5361\u670d\u52a1\u5668\u4e0a\u901a\u8fc7\u6570\u636e\u5e76\u884c\u4e0e\u6a21\u578b\u5e76\u884c\u7ed3\u5408\uff0c\u63d0\u5347AI\u6a21\u578b\u7684\u8bad\u7ec3\u901f\u5ea6\u4e0e\u6269\u5c55\u6027\uff1f - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u968f\u7740\u751f\u6210\u5f0fAI\u548c\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u5174\u8d77&#xff08;\u4f8b\u5982 GPT\u3001LLaMA\u3001ViT \u7b49&#xff09;&#xff0c;\u5355\u5361\u663e\u5b58\u5df2\u65e0\u6cd5\u627f\u8f7d\u6570\u767e\u4ebf\u751a\u81f3\u4e0a\u5343\u4ebf\u53c2\u6570\u6a21\u578b\u7684\u8bad\u7ec3\u3002\u4f20\u7edf\u7684\u6570\u636e\u5e76\u884c&#xff08;Data 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