{"id":66748,"date":"2026-01-27T14:09:06","date_gmt":"2026-01-27T06:09:06","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/66748.html"},"modified":"2026-01-27T14:09:06","modified_gmt":"2026-01-27T06:09:06","slug":"%e5%a4%a7%e6%a8%a1%e5%9e%8bmcp%e5%bc%80%e5%8f%91%e5%ae%9e%e6%88%98%e8%af%a6%e8%a7%a3","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/66748.html","title":{"rendered":"\u5927\u6a21\u578bMCP\u5f00\u53d1\u5b9e\u6218\u8be6\u89e3"},"content":{"rendered":"<h3>\u4e00\u3001\u4ec0\u4e48\u662fMCP&#xff08;Model-Compute Parallelism&#xff09;<\/h3>\n<p>MCP&#xff08;\u6a21\u578b-\u8ba1\u7b97\u5e76\u884c&#xff09;\u662f\u6307\u5c06\u5927\u578b\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u8ba1\u7b97\u4efb\u52a1\u5728\u591a\u53f0\u8bbe\u5907\u3001\u591a\u79cd\u8d44\u6e90\u4e0a\u8fdb\u884c\u5e76\u884c\u6267\u884c&#xff0c;\u4ee5\u63d0\u5347\u8bad\u7ec3\u4e0e\u63a8\u7406\u6548\u7387\u3002\u5b83\u901a\u5e38\u5305\u62ec\u6570\u636e\u5e76\u884c&#xff08;Data Parallelism&#xff09;\u3001\u6a21\u578b\u5e76\u884c&#xff08;Model Parallelism&#xff09;\u3001\u4ee5\u53ca\u4e00\u4e9b\u65b0\u7684\u6df7\u5408\u5e76\u884c\u7b56\u7565&#xff08;\u5982\u6d41\u6c34\u5e76\u884cPipeline Parallelism\u3001\u5f20\u91cf\u5e76\u884cTensor Parallelism\u7b49&#xff09;\u3002<\/p>\n<hr \/>\n<h3>\u4e8c\u3001MCP\u7684\u5e38\u89c1\u573a\u666f<\/h3>\n<ul>\n<li>\u5927\u578b\u8bed\u8a00\u6a21\u578b&#xff08;\u5982GPT\u3001BERT\u3001LLAMA\u7b49&#xff09;\u7684\u8bad\u7ec3\u4e0e\u63a8\u7406<\/li>\n<li>\u8d85\u5927\u89c4\u6a21\u89c6\u89c9\u6a21\u578b&#xff08;ViT\u3001SAM\u7b49&#xff09;\u90e8\u7f72<\/li>\n<li>\u591a\u673a\u591a\u5361\u52a0\u901f\u5206\u5e03\u5f0f\u63a8\u7406\u3001\u5fae\u8c03<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e09\u3001\u6838\u5fc3\u6280\u672f\u65b9\u6848<\/h3>\n<h5>1. \u6570\u636e\u5e76\u884c&#xff08;Data Parallelism&#xff09;<\/h5>\n<p>\u4e0d\u540c\u8bbe\u5907\u5904\u7406\u4e0d\u540c\u6279\u6b21\u7684\u6570\u636e&#xff0c;\u6a21\u578b\u53c2\u6570\u5b9a\u671f\u540c\u6b65\u3002<\/p>\n<h5>2. \u6a21\u578b\u5e76\u884c&#xff08;Model Parallelism&#xff09;<\/h5>\n<p>\u5c06\u6a21\u578b\u7684\u4e0d\u540c\u5c42\u6216\u90e8\u5206\u5206\u5e03\u5230\u4e0d\u540c\u7684\u8bbe\u5907\u4e0a&#xff0c;\u6bcf\u4e2a\u8bbe\u5907\u8d1f\u8d23\u4e00\u90e8\u5206\u8ba1\u7b97\u3002<\/p>\n<h5>3. \u6df7\u5408\u5e76\u884c&#xff08;Hybrid Parallelism&#xff09;<\/h5>\n<p>\u7ed3\u5408\u4e0a\u4e24\u79cd\u65b9\u5f0f&#xff0c;\u53ef\u4ee5\u91c7\u7528\u5982 Tensor Parallel&#xff08;\u5207\u5206\u6743\u91cd\u77e9\u9635&#xff09;\u3001Pipeline Parallel&#xff08;\u5207\u5206\u6a21\u578b\u5c42\u7ea7&#xff09;\u7684\u624b\u6bb5\u3002\u4f8b\u5982 Megatron-LM\u3001DeepSpeed-Chat \u5b9e\u73b0\u4e86\u9ad8\u6548\u7684\u6df7\u5408\u5e76\u884c\u3002<\/p>\n<h5>4. \u901a\u4fe1\u4f18\u5316<\/h5>\n<p>\u4f7f\u7528\u9ad8\u6548\u901a\u4fe1\u5e93&#xff08;NCCL\u3001MPI\u3001Gloo&#xff09;\u6765\u51cf\u5c11\u6a21\u578b\u548c\u68af\u5ea6\u540c\u6b65\u7684\u5f00\u9500\u3002<\/p>\n<hr \/>\n<h3>\u56db\u3001\u4e3b\u6d41\u6846\u67b6\u9009\u578b<\/h3>\n<li>PyTorch Distributed&#xff1a;\u5e7f\u6cdb&#xff0c;\u7528\u4e8e\u5206\u5e03\u5f0f\u8bad\u7ec3\u3001\u6570\u636e\u5e76\u884c\u3001\u6a21\u578b\u5e76\u884c<\/li>\n<li>DeepSpeed&#xff1a;\u5fae\u8f6f\u5f00\u6e90&#xff0c;\u652f\u6301ZeRO&#xff08;\u788e\u7247\u4f18\u5316&#xff09;\u3001\u6df7\u5408\u5e76\u884c\u3001\u5185\u5b58\u4f18\u5316<\/li>\n<li>Megatron-LM&#xff1a;\u4e13\u6ce8\u56fe\u4f18\u5316\u4e0e\u6d41\u6c34\u7ebf\u5e76\u884c<\/li>\n<li>ColossalAI\u3001OneFlow\u7b49\u56fd\u5185\u5916\u65b0\u6846\u67b6&#xff0c;\u4e5f\u5728\u63a2\u7d22MCP\u6781\u81f4\u6027\u80fd\u3002<\/li>\n<hr \/>\n<h3>\u4e94\u3001\u5f00\u53d1\u5b9e\u6218\u6d41\u7a0b\u8be6\u89e3<\/h3>\n<h4>Step 1&#xff1a;\u73af\u5883\u51c6\u5907<\/h4>\n<ul>\n<li>\u591aGPU\u670d\u52a1\u5668\u6216\u4e91\u5e73\u53f0&#xff08;\u5982AWS\u3001\u817e\u8baf\u4e91GPU\u5b9e\u4f8b&#xff09;<\/li>\n<li>\u5b89\u88c5\u5bf9\u5e94\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u53ca\u901a\u4fe1\u5e93 pip install torch torchvision torchaudio<br \/>\npip install deepspeed\n  <\/li>\n<\/ul>\n<h4>Step 2&#xff1a;\u6a21\u578b\u5207\u5206<\/h4>\n<p>\u4ee5PyTorch\u4e3a\u4f8b&#xff0c;\u90e8\u5206\u6a21\u578b\u5c42\u5207\u5230\u4e0d\u540cGPU\u4e0a&#xff1a;<\/p>\n<p>import torch<br \/>\nfrom torch import nn<\/p>\n<p>class ParallelModel(nn.Module):<br \/>\n    def __init__(self):<br \/>\n        super().__init__()<br \/>\n        self.layer1 &#061; nn.Linear(1024, 2048).to(&#039;cuda:0&#039;)<br \/>\n        self.layer2 &#061; nn.Linear(2048, 4096).to(&#039;cuda:1&#039;)<\/p>\n<p>    def forward(self, x):<br \/>\n        x &#061; self.layer1(x)<br \/>\n        x &#061; x.to(&#039;cuda:1&#039;)<br \/>\n        x &#061; self.layer2(x)<br \/>\n        return x<\/p>\n<p>\u5b9e\u9645\u5927\u6a21\u578b\u5efa\u8bae\u7528\u6846\u67b6\u7684\u81ea\u52a8\u6a21\u578b\u5e76\u884c\u652f\u6301&#xff0c;\u5982 Megatron-LM \/ DeepSpeed<\/p>\n<h4>Step 3&#xff1a;\u8bbe\u7f6e\u6570\u636e\u5e76\u884c<\/h4>\n<p># torch.distributed.launch \u542f\u52a8\u811a\u672c<br \/>\nimport torch.distributed as dist<br \/>\ndist.init_process_group(backend&#061;&#039;nccl&#039;)<\/p>\n<p>\u901a\u8fc7 DDP (torch.nn.parallel.DistributedDataParallel) \u5305\u88c5\u6a21\u578b&#xff1a;<\/p>\n<p>model &#061; torch.nn.parallel.DistributedDataParallel(model)<\/p>\n<h4>Step 4&#xff1a;\u8bad\u7ec3\u4e0e\u8fed\u4ee3<\/h4>\n<ul>\n<li>\u914d\u7f6e\u5206\u5e03\u5f0f\u4f18\u5316\u5668\u3001\u68af\u5ea6\u540c\u6b65<\/li>\n<li>\u6ce8\u610f\u5185\u5b58\u8c03\u4f18\u3001\u663e\u5b58\u5206\u914d<\/li>\n<li>\u76d1\u63a7&#xff08;NVIDIA Nsight\u3001TensorBoard&#xff09;<\/li>\n<\/ul>\n<h4>Step 5&#xff1a;\u6027\u80fd\u4f18\u5316<\/h4>\n<ul>\n<li>\u901a\u4fe1\u91cf\u51cf\u5c11&#xff08;\u68af\u5ea6\u88c1\u526a\u3001\u5206\u7ec4\u540c\u6b65\u3001FP16\u6df7\u5408\u7cbe\u5ea6\u7b49&#xff09;<\/li>\n<li>\u52a8\u6001\u8d1f\u8f7d\u5747\u8861<\/li>\n<li>I\/O\u6d41\u6c34\u7ebf\u9884\u53d6<\/li>\n<\/ul>\n<hr \/>\n<h3>\u516d\u3001\u5178\u578b\u5b9e\u6218\u6848\u4f8b<\/h3>\n<h4>1. \u4f7f\u7528DeepSpeed\u8bad\u7ec3GPT<\/h4>\n<p>\u914d\u7f6e\u00a0deepspeed_config.json\u00a0\u63d0\u53d6\u6d41\u6c34\u7ebf\u5e76\u884c\u548cZeRO\u4f18\u5316&#xff0c;\u8fd0\u884c\u5982\u4e0b&#xff1a;<\/p>\n<p>deepspeed &#8211;num_gpus&#061;4 train.py &#8211;deepspeed &#8211;deepspeed_config deepspeed_config.json<\/p>\n<h4>2. Megatron-LM \u7684\u5f20\u91cf\u5e76\u884c\u8bad\u7ec3<\/h4>\n<p>\u914d\u7f6e\u597d\u00a0model_parallel_size&#xff0c;\u5373\u53ef\u8fdb\u884c\u53c2\u6570\u5207\u5206\u4e0e\u5927\u89c4\u6a21\u5e76\u884c\u5316\u3002<\/p>\n<hr \/>\n<h3>\u4e03\u3001\u5e38\u89c1\u96be\u70b9&amp;\u89e3\u51b3\u601d\u8def<\/h3>\n<ul>\n<li>\u5185\u5b58\u7206\u70b8\u00a0\u2192 ZeRO\u4f18\u5316\u3001FP16<\/li>\n<li>\u5e26\u5bbd\u74f6\u9888\u00a0\u2192 \u9ad8\u901f\u7f51\u7edc\u3001Infiniband\u3001\u901a\u4fe1\u878d\u5408<\/li>\n<li>\u8d1f\u8f7d\u4e0d\u5747\u00a0\u2192 \u7cbe\u7ec6\u6a21\u578b\u5207\u5206\u4e0e\u6620\u5c04<\/li>\n<\/ul>\n<h3>\u516b\u3001\u53c2\u8003\u8d44\u6e90<\/h3>\n<ul>\n<li>PyTorch\u5206\u5e03\u5f0f\u5b98\u65b9\u6587\u6863<\/li>\n<li>DeepSpeed\u5b98\u65b9\u6559\u7a0b<\/li>\n<li>Megatron-LM GitHub<\/li>\n<li>ColossalAI\u5206\u5e03\u5f0f\u6559\u7a0b<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e5d\u3001\u8fdb\u9636\u5185\u5bb9\u8bb2\u89e3<\/h3>\n<h4>1. \u6df7\u5408\u5e76\u884c\u7684\u6df1\u5ea6\u5b9e\u73b0<\/h4>\n<h5>&#xff08;1&#xff09;Pipeline Parallel &#043; Tensor Parallel<\/h5>\n<p>\u5e38\u89c1\u4e8eGPT\u3001LLAMA\u7b49\u8d85\u5927\u89c4\u6a21\u6a21\u578b&#xff0c;\u6a21\u578b\u4e00\u90e8\u5206\u6a2a\u5411\u62c6\u5206&#xff08;\u5982\u5f20\u91cf\u5e76\u884c\u5206\u5230\u4e0d\u540c\u5361&#xff09;&#xff0c;\u4e00\u90e8\u5206\u7eb5\u5411\u6d41\u6c34&#xff08;\u5982\u7f16\u7801\u5c42\u4e00\u7ec4\u3001\u89e3\u7801\u5c42\u4e00\u7ec4&#xff09;\u3002<\/p>\n<p>\u76f8\u5173\u4ee3\u7801\u6846\u67b6&#xff1a;Megatron-LM \u7684 hybrid parallel<\/p>\n<p># Megatron-LM\u90e8\u5206\u521d\u59cb\u5316\u4ee3\u7801\u793a\u4f8b<br \/>\nfrom megatron import get_args, initialize_model_parallel<\/p>\n<p>args &#061; get_args()<br \/>\ninitialize_model_parallel(args.tensor_model_parallel_size, args.pipeline_model_parallel_size)<\/p>\n<p># \u5728\u5b9e\u9645\u6a21\u578b\u4e2d\u53ea\u9700\u6307\u5b9a\u5e76\u884csize&#xff0c;\u6a21\u578b\u81ea\u52a8\u5206\u914d<\/p>\n<h5>&#xff08;2&#xff09;DeepSpeed ZeRO-3\u8d85\u5927\u5206\u5e03\u5f0f\u4f18\u5316<\/h5>\n<p>ZeRO Stage 3 \u53ef\u5c06\u53c2\u6570\u3001\u4f18\u5316\u5668\u72b6\u6001\u3001\u68af\u5ea6\u5168\u90e8\u5206\u7247\u5230\u5404\u4e2aGPU\u4e0a&#xff0c;\u89e3\u51b3\u5355\u5361\u663e\u5b58\u74f6\u9888\u3002<\/p>\n<p>\u6838\u5fc3\u914d\u7f6e\u7247\u6bb5&#xff1a;<\/p>\n<p>{<br \/>\n  &#034;zero_optimization&#034;: {<br \/>\n    &#034;stage&#034;: 3,<br \/>\n    &#034;offload_param&#034;: {<br \/>\n      &#034;device&#034;: &#034;cpu&#034;<br \/>\n    },<br \/>\n    &#034;offload_optimizer&#034;: {<br \/>\n      &#034;device&#034;: &#034;cpu&#034;<br \/>\n    }<br \/>\n  }<br \/>\n}<\/p>\n<hr \/>\n<h4>2. \u8bad\u7ec3\u4e0e\u63a8\u7406\u5b9e\u6218\u6848\u4f8b\u6269\u5c55<\/h4>\n<h5>&#xff08;1&#xff09;4\u8282\u70b932GPU\u5927\u6a21\u578b\u5206\u5e03\u5f0f\u8bad\u7ec3&#xff08;DeepSpeed&#xff09;<\/h5>\n<p># \u5404\u8282\u70b9\u542f\u52a8\u793a\u4f8b<br \/>\ndeepspeed &#8211;hostfile hostfile train.py &#8211;deepspeed &#8211;deepspeed_config ds_config.json<\/p>\n<p>hostfile\u793a\u4f8b&#xff1a;<\/p>\n<p>192.168.1.1 slots&#061;8<br \/>\n192.168.1.2 slots&#061;8<br \/>\n192.168.1.3 slots&#061;8<br \/>\n192.168.1.4 slots&#061;8<\/p>\n<h5>&#xff08;2&#xff09;ColossalAI\u7684\u6d41\u6c34\u7ebf\u5e76\u884c\u4e0e\u5fae\u8c03<\/h5>\n<p>ColossalAI\u652f\u6301\u8f7b\u91cf\u914d\u7f6e\u5373\u53ef\u5b9e\u73b0\u9ad8\u6548\u6d41\u6c34\u7ebf\u5e76\u884c\u4e0e\u5206\u5e03\u5f0f\u5fae\u8c03\u3002<\/p>\n<p>from colossalai.launch import launch<br \/>\nlaunch(config&#061;&#039;.\/config.py&#039;, rank&#061;0, world_size&#061;4, host&#061;&#039;localhost&#039;, port&#061;29500)<\/p>\n<hr \/>\n<h4>3. \u65e5\u5fd7\u76d1\u63a7\u4e0e\u6392\u9519\u6280\u5de7<\/h4>\n<ul>\n<li>GPU\u5206\u5e03\u3001\u901a\u4fe1\u95ee\u9898&#xff1a;\u00a0\u7528\u00a0nvidia-smi\u3001nvtop\u00a0\u5b9e\u65f6\u67e5\u770b\u6bcf\u5361\u5229\u7528\u7387&#xff0c;\u5224\u65ad\u662f\u5426\u6a21\u578b\u5207\u5206\u5747\u8861\u3002<\/li>\n<li>\u901a\u4fe1\u5e93\u7248\u672c\u517c\u5bb9&#xff1a;\u00a0NCCL\/torch\/cuda\u9700\u5bf9\u5e94&#xff0c;\u51fa\u9519\u591a\u4e3a\u7248\u672c\u4e0d\u5339\u914d\u6216\u7aef\u53e3\u8bbe\u7f6e\u95ee\u9898\u3002<\/li>\n<li>OOM\u663e\u5b58\u6ea2\u51fa&#xff1a;\u00a0\u9010\u6b65\u51cf\u5c0fbatch&#xff0c;\u6216\u7528\u00a0deepspeed.Zero.offload_param\u3002<\/li>\n<li>\u68af\u5ea6\u540c\u6b65\u6162\/\u963b\u585e&#xff1a;\u00a0\u4f18\u5316\u7f51\u7edc\u5e26\u5bbd\u6216\u8005\u7528\u68af\u5ea6\u7d2f\u8ba1&#xff08;Gradient Accumulation Steps&#xff09;\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3>\u5341\u3001\u6a21\u578b\u90e8\u7f72\u5b9e\u6218Tips<\/h3>\n<h4>1. \u63a8\u7406\u5e76\u884c&#xff08;\u591a\u670d\u52a1\u5668\u63a8\u7406&#xff09;<\/h4>\n<ul>\n<li>\u4f7f\u7528 TensorRT \/ FasterTransformer \u8fdb\u884c\u63a8\u7406\u6a21\u578b\u5207\u5206\u5e76\u90e8\u7f72&#xff0c;API\u5982&#xff1a;<\/li>\n<\/ul>\n<p>from tensorrt import InferModel  # \u5047\u8bbeAPI<br \/>\ninfer_model &#061; InferModel(config, model_path)<br \/>\ninfer_model.deploy(cluster&#061;[&#039;server1&#039;, &#039;server2&#039;, &#8230;])<\/p>\n<ul>\n<li>\u524d\u7aef\u7edf\u4e00\u6d88\u606f\u5165\u53e3&#xff0c;\u540e\u7aef\u591a\u8282\u70b9\u534f\u540c\u5e76\u884c\u3002<\/li>\n<\/ul>\n<h4>2. \u81ea\u52a8\u6269\u5c55\u548c\u5f39\u6027\u4f38\u7f29<\/h4>\n<ul>\n<li>\u4f7f\u7528 Kubernetes \u90e8\u7f72\u5bb9\u5668\u5316\u63a8\u7406\u670d\u52a1&#xff0c;\u7ed3\u5408\u5206\u5e03\u5f0f\u7b56\u7565\u81ea\u52a8\u6269\u5bb9\u3002<\/li>\n<li>PyTorch Elastic\/Elastic Training \u53ef\u5e94\u5bf9\u5c11\u91cf\u8282\u70b9\u6545\u969c&#xff0c;\u5b9e\u73b0\u9ad8\u53ef\u7528\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3>\u5341\u4e00\u3001\u771f\u5b9e\u9879\u76ee\u5b9e\u6218\u6d41\u7a0b\u603b\u7ed3<\/h3>\n<h4>1. \u9700\u6c42\u5206\u6790<\/h4>\n<ul>\n<li>\u660e\u786e\u6a21\u578b\u53c2\u6570\u91cf\u3001\u4e1a\u52a1\u573a\u666f\u3001\u6240\u9700\u541e\u5410\u91cf<\/li>\n<\/ul>\n<h4>2. \u67b6\u6784\u8bbe\u8ba1<\/h4>\n<ul>\n<li>\u9009\u62e9\u9002\u5408\u7684\u5e76\u884c\u6846\u67b6\u548c\u6df7\u5408\u5e76\u884c\u6a21\u5f0f&#xff0c;\u5408\u7406\u5207\u5206\u6a21\u578b\u548c\u6570\u636e<\/li>\n<\/ul>\n<h4>3. \u4ee3\u7801\u5b9e\u73b0<\/h4>\n<ul>\n<li>\u6839\u636e\u5e76\u884c\u7b56\u7565\u914d\u7f6e\u8bad\u7ec3\u3001\u63a8\u7406\u811a\u672c&#xff0c;\u96c6\u6210\u76d1\u63a7\u548c\u81ea\u52a8\u6062\u590d\u673a\u5236<\/li>\n<\/ul>\n<h4>4. \u6027\u80fd\u6d4b\u8bd5\u4e0e\u4f18\u5316<\/h4>\n<ul>\n<li>\u6301\u7eed\u8c03\u4f18 batch size\u3001\u540c\u6b65\u7b56\u7565\u3001\u663e\u5b58\u5206\u914d\u3001\u7f51\u7edc\u5e26\u5bbd<\/li>\n<\/ul>\n<h4>5. \u751f\u4ea7\u90e8\u7f72\u4e0e\u8fd0\u7ef4<\/h4>\n<ul>\n<li>\u7070\u5ea6\u53d1\u5e03&#xff0c;\u81ea\u52a8\u6269\u5bb9&#xff0c;\u9ad8\u53ef\u7528\u5bb9\u707e<\/li>\n<\/ul>\n<hr \/>\n<h3>\u5341\u4e8c\u3001\u5e38\u89c1\u95ee\u9898\u5408\u96c6<\/h3>\n<p>Q1:\u00a0\u5927\u6a21\u578b\u663e\u5b58\u7206\u70b8&#xff0c;\u5982\u4f55\u89e3\u51b3&#xff1f; 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