{"id":66385,"date":"2026-11-07T09:07:06","date_gmt":"2026-11-07T01:07:06","guid":{"rendered":"https:\/\/www.biocloudservice.com\/wordpress\/?p=66385"},"modified":"2026-11-07T09:07:06","modified_gmt":"2026-11-07T01:07:06","slug":"turbopairformer-openfold3-h100-training","status":"publish","type":"post","link":"https:\/\/www.biocloudservice.com\/wordpress\/turbopairformer-openfold3-h100-training\/","title":{"rendered":"16\u5f20H100\uff0c\u8bad\u7ec3\u6b65\u63d0\u901f1.73\u500d\uff1aTurboPairFormer\u4e3a\u4ec0\u4e48\u4e0d\u662f\u201c\u88c5\u4e2a\u5305\u5c31\u80fd\u52a0\u901fAlphaFold3\u201d"},"content":{"rendered":"
\n

AlphaFold3\u7c7b\u6a21\u578b\u4e0d\u4ec5\u201c\u6a21\u578b\u5927\u201d\uff0c\u5176Pairformer\u4e2d\u7684\u4e09\u89d2\u6ce8\u610f\u529b\u8fd8\u4f1a\u968f\u7740token\u957f\u5ea6\u8fc5\u901f\u653e\u5927\u8ba1\u7b97\u548c\u663e\u5b58\u538b\u529b\u3002\u8bad\u7ec3\u77ed\u5e8f\u5217\u65f6\u770b\u4f3c\u6b63\u5e38\uff0c\u4e00\u65e6\u628acrop size\u4ece384\u63d0\u9ad8\u5230640\u6216768\uff0c\u5355\u6b65\u65f6\u95f4\u3001\u68af\u5ea6\u5b58\u50a8\u548c\u591a\u5361\u540c\u6b65\u5c31\u4f1a\u660e\u663e\u589e\u52a0\u3002\u5f88\u591a\u56e2\u961f\u56e0\u6b64\u628a\u95ee\u9898\u7b80\u5355\u5f52\u7ed3\u4e3a\u201c\u663e\u5361\u4e0d\u591f\u591a\u201d\uff0c\u4f46\u771f\u6b63\u5361\u4f4f\u8bad\u7ec3\u7684\uff0c\u5f80\u5f80\u662f\u5c11\u6570\u9ad8\u590d\u6742\u5ea6\u7b97\u5b50\u3001\u7cbe\u5ea6\u7a33\u5b9a\u6027\uff0c\u4ee5\u53ca\u8f6f\u4ef6\u6808\u80fd\u5426\u5339\u914dGPU\u67b6\u6784\u3002<\/p>\n

2026\u5e7410\u6708\u53d1\u5e03\u7684TurboPairFormer\uff0c\u4e13\u95e8\u4f18\u5316AlphaFold3\u98ce\u683c\u7684Triangle Attention\u548cTriangle Multiplicative Update\u3002\u8bba\u6587\u628a\u5b83\u96c6\u6210\u5230OpenFold3\uff0c\u572816\u5f20NVIDIA H100\u4e0a\u3001crop size 768\u6761\u4ef6\u4e0b\uff0c\u5355\u4e2a\u4f18\u5316\u5668\u8bad\u7ec3\u6b65\u7684GPU\u8ba1\u7b97\u65f6\u95f4\u76f8\u5bf9OpenFold3 Triton\u540e\u7aef\u63d0\u901f1.73\u500d\uff0c\u76f8\u5bf9cuEquivariance\u63d0\u901f1.13\u500d\u3002\u8fd9\u4e2a\u7ed3\u679c\u5f88\u6709\u4ef7\u503c\uff0c\u4f46\u5b83\u662f\u7279\u5b9a\u8bad\u7ec3\u5185\u6838\u548c\u73af\u5883\u4e2d\u7684\u52a0\u901f\uff0c\u4e0d\u4ee3\u8868\u6240\u6709\u7ed3\u6784\u9884\u6d4b\u63a8\u7406\u90fd\u80fd\u76f4\u63a5\u5feb1.73\u500d\u3002<\/p>\n

\"TurboPairFormer\u4e0eOpenFold3\u8bad\u7ec3\u7b97\u529b\u89c4\u5212\u6d77\u62a5\"
TurboPairFormer\u9002\u7528\u5224\u65ad\uff1a\u5185\u6838\u901f\u5ea6\u3001\u6570\u503c\u7a33\u5b9a\u6027\u548cHopper\u73af\u5883\u5fc5\u987b\u4e00\u8d77\u9a8c\u6536\u3002<\/figcaption><\/figure>\n

\u5148\u770b\u6e05\u201c1.73\u500d\u201d\u5bf9\u5e94\u4ec0\u4e48<\/h2>\n
\n\n\n\n\n\n\n\n\n\n
\u6838\u5bf9\u9879<\/th>\n\u8bba\u6587\u6216\u5b98\u65b9\u5305\u6761\u4ef6<\/th>\n\u5b9e\u9645\u542b\u4e49<\/th>\n<\/tr>\n<\/thead>\n
\u4efb\u52a1<\/td>\nOpenFold3\u8bad\u7ec3\u3001optimizer step GPU\u8ba1\u7b97<\/td>\n\u4e0d\u662fMSA\u641c\u7d22\uff0c\u4e5f\u4e0d\u662f\u5b8c\u6574\u7aef\u5230\u7aef\u63a8\u7406<\/td>\n<\/tr>\n
\u786c\u4ef6<\/td>\n16\u5f20H100\uff0cHopper\u67b6\u6784<\/td>\nA100\u30014090\u7b49\u4e0d\u5728\u5f53\u524d\u9884\u7f16\u8bd1wheel\u652f\u6301\u8303\u56f4<\/td>\n<\/tr>\n
\u89c4\u6a21<\/td>\ncrop size 768\uff0cBF16\u8f93\u5165<\/td>\n\u66f4\u77ed\u5e8f\u5217\u6216\u4e0d\u540chead\u7ef4\u5ea6\u7684\u6536\u76ca\u53ef\u80fd\u4e0d\u540c<\/td>\n<\/tr>\n
\u5bf9\u6bd4<\/td>\n1.73\u00d7\u5bf9Triton\uff1b1.13\u00d7\u5bf9cuEquivariance<\/td>\n\u4e0d\u80fd\u5199\u6210\u201c\u6bd4\u6240\u6709\u540e\u7aef\u5feb1.73\u500d\u201d<\/td>\n<\/tr>\n
\u7a33\u5b9a\u6027<\/td>\n600\u7ec4\u56fa\u5b9a\u8f93\u5165\u4e2d\u56db\u7c7b\u68af\u5ea6\u91cd\u590d\u8c03\u7528\u9010\u4f4d\u4e00\u81f4<\/td>\n\u4ec5\u5728\u56fa\u5b9a\u6267\u884c\u6761\u4ef6\u548c\u6d4b\u8bd5\u8303\u56f4\u5185\u6210\u7acb<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

\u4e09\u89d2\u6ce8\u610f\u529b\u4ee5\u6210\u5bf9\u8868\u793a\u4e3a\u6838\u5fc3\uff0c\u8ba1\u7b97\u6210\u672c\u968f\u5e8f\u5217\u957f\u5ea6\u8fd1\u4f3c\u7acb\u65b9\u589e\u957f\u3002crop size\u7ffb\u500d\uff0c\u4e0d\u53ea\u662f\u8f93\u5165\u53d8\u6210\u4e24\u500d\uff1b\u8ba1\u7b97\u91cf\u7406\u8bba\u4e0a\u53ef\u63a5\u8fd1\u516b\u500d\u3002\u56e0\u6b64\uff0c\u7b97\u5b50\u7ea7\u4f18\u5316\u80fd\u663e\u8457\u5f71\u54cd\u80fd\u5426\u628a\u66f4\u957fcrop\u653e\u8fdb\u8bad\u7ec3\u3002\u4f46\u9879\u76ee\u603b\u65f6\u95f4\u8fd8\u5305\u542b\u6570\u636e\u52a0\u8f7d\u3001\u7279\u5f81\u6784\u5efa\u3001\u5176\u4ed6\u7f51\u7edc\u6a21\u5757\u3001\u68af\u5ea6\u540c\u6b65\u3001\u68c0\u67e5\u70b9\u5199\u5165\u548c\u9a8c\u8bc1\uff0c\u5185\u6838\u63d0\u901f\u4e0d\u4f1a\u7b49\u6bd4\u4f8b\u8f6c\u5316\u4e3a\u6574\u4e2a\u8bad\u7ec3\u4efb\u52a1\u7684\u5899\u949f\u65f6\u95f4\u3002<\/p>\n

\u5b83\u4f18\u5316\u4e86\u4ec0\u4e48\uff0c\u800c\u4e0d\u662f\u53ea\u201c\u5c11\u5b58\u4e00\u70b9\u5f20\u91cf\u201d<\/h2>\n

\u666e\u901a\u5b9e\u73b0\u9700\u8981\u5728\u53cd\u5411\u4f20\u64ad\u4e2d\u5904\u7406query\u3001key\u3001value\u548c\u5171\u4eabpair bias\u7684\u591a\u91cd\u5f52\u7ea6\u3002\u6709\u7684\u540e\u7aef\u53cd\u590d\u91cd\u7b97\u6982\u7387\uff0c\u6709\u7684\u4f7f\u7528\u6d6e\u70b9\u539f\u5b50\u64cd\u4f5c\uff0c\u8fd8\u6709\u7684\u4fdd\u5b58\u5b8c\u6574score\u68af\u5ea6\uff0c\u5206\u522b\u5e26\u6765\u65f6\u95f4\u3001\u786e\u5b9a\u6027\u6216\u663e\u5b58\u4ee3\u4ef7\u3002TurboPairFormer\u91c7\u7528key-tile\u5e76\u884c\u53cd\u5411\u7b97\u6cd5\uff0c\u6bcf\u4e2a\u6982\u7387tile\u53ea\u91cd\u7b97\u4e00\u6b21\uff0c\u518d\u901a\u8fc7\u6709\u5e8f\u5c40\u90e8\u5f52\u7ea6\u751f\u6210\u56db\u7c7b\u68af\u5ea6\uff0c\u907f\u514d\u6d6e\u70b9\u539f\u5b50\u548c\u5b8c\u6574score\u68af\u5ea6\u843d\u76d8\u3002<\/p>\n

\u53e6\u4e00\u4e2a\u95ee\u9898\u6765\u81eaBF16\u820d\u5165\uff1a\u5982\u679csoftmax\u53cd\u5411\u6821\u6b63\u53ea\u4f7f\u7528\u5df2\u7ecf\u820d\u5165\u7684\u524d\u5411\u8f93\u51fa\uff0c\u68af\u5ea6\u8bef\u5dee\u4f1a\u88ab\u653e\u5927\u3002\u8be5\u65b9\u6cd5\u989d\u5916\u4fdd\u5b58\u524d\u5411\u820d\u5165\u6b8b\u5dee\uff0c\u5e76\u7528FP32\u8ba1\u7b97\u6821\u6b63\u9879\u3002\u8bba\u6587\u7684\u53d7\u63a7\u6d88\u878d\u4e2d\uff0c\u8fd9\u9879\u8865\u507f\u4f7fquery\u3001key\u548cpair-bias\u68af\u5ea6\u76f8\u5bf9FP64\u53c2\u8003\u7684RMSE\u4e0b\u964d28%\u201447%\u3002\u8fd9\u8bf4\u660e\u8bad\u7ec3\u52a0\u901f\u4e0d\u80fd\u53ea\u770b\u541e\u5410\uff0c\u8fd8\u8981\u540c\u65f6\u68c0\u67e5\u6570\u503c\u8bef\u5dee\u4e0e\u91cd\u590d\u6027\u3002<\/p>\n

\u5f53\u524d\u53ef\u7528\u73af\u5883\u975e\u5e38\u7a84\uff0c\u5148\u505a\u517c\u5bb9\u6027\u9884\u68c0<\/h2>\n

\u5b98\u65b90.1.0 wheel\u9762\u5411Linux x86-64\u3001CPython 3.14\u3001PyTorch 2.10.0\u53ca\u5176CUDA 12.8 runtime\uff0c\u5e76\u4e3aHopper sm_90a<\/code>\u9884\u7f16\u8bd1\u3002\u6784\u5efawheel\u4f7f\u7528CUDA Toolkit 12.9\uff0c\u4f46\u5b89\u88c5\u73b0\u6210wheel\u8fd0\u884c\u65f6\u4e0d\u9700\u8981\u672c\u5730\u7f16\u8bd1\u5de5\u5177\u94fe\u3002H100\u5df2\u7ecf\u5b8c\u6210\u5b89\u88c5\u4e0e\u6570\u503c\u6d4b\u8bd5\uff1bH200\u540c\u5c5e\u8ba1\u7b97\u80fd\u529b9.0\uff0c\u5305\u53ef\u8bc6\u522b\uff0c\u4f46\u5b98\u65b9\u9875\u9762\u660e\u786e\u8bf4H200\u6267\u884c\u5c1a\u672a\u9a8c\u8bc1\u3002<\/p>\n

python --version\r\npython -c "import torch; print(torch.__version__, torch.version.cuda)"\r\nnvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv\r\n\r\n# \u53ea\u5b89\u88c5\u4e0e\u5f53\u524dABI\u5b8c\u5168\u5339\u914d\u7684\u5b98\u65b9wheel\r\npython -m pip install \/path\/to\/turbopairformer-0.1.0-cp314-cp314-manylinux_2_28_x86_64.whl\r\npython -c "import turbopairformer; print(turbopairformer.__file__)"\r\n<\/code><\/pre>\n
\u5305\u52a0\u8f7d\u5668\u4f1a\u6838\u5bf9ABI\u548c\u4e8c\u8fdb\u5236\u54c8\u5e0c\uff0c\u53d1\u73b0\u4e0d\u5339\u914d\u65f6\u76f4\u63a5\u62a5\u9519\uff0c\u800c\u4e0d\u662f\u9759\u9ed8JIT\u7f16\u8bd1\u3002\u8fd9\u4e00\u70b9\u5bf9\u96c6\u7fa4\u5f88\u91cd\u8981\uff1a\u767b\u5f55\u8282\u70b9\u88c5\u5f97\u4e0a\uff0c\u4e0d\u7b49\u4e8e\u8ba1\u7b97\u8282\u70b9\u80fd\u52a0\u8f7d\uff1b\u5bb9\u5668\u91cc\u7684Python\u3001Torch\u3001CUDA runtime\u548cGPU\u8ba1\u7b97\u80fd\u529b\u5fc5\u987b\u4e00\u8d77\u6838\u5bf9\u3002A100\u3001L40S\u3001\u6d88\u8d39\u7ea7RTX\u6216Windows\u73af\u5883\u4e0d\u5e94\u5f3a\u884c\u5957\u7528\u5f53\u524dwheel\u3002<\/p>\n
\u670d\u52a1\u5668\u8bc4\u4f30\u4e0d\u80fd\u4ece16\u5f20H100\u76f4\u63a5\u8d77\u6b65<\/h2>\n
\u8bba\u6587\u752816\u5361\u9a8c\u8bc1OpenFold3\u8bad\u7ec3\u6b65\uff0c\u5e76\u4e0d\u610f\u5473\u7740\u6bcf\u4e2a\u56e2\u961f\u90fd\u9700\u8981\u4e00\u6b21\u7533\u8bf716\u5f20H100\u3002\u66f4\u7a33\u59a5\u7684\u8def\u5f84\u662f\u5148\u5728\u5355\u5f20H100\u4e0a\u8dd1standalone smoke test\uff0c\u8986\u76d6\u524d\u5411\u3001\u53cd\u5411\u548c\u663e\u5b58\u5cf0\u503c\uff1b\u518d\u7528\u4e0e\u9879\u76ee\u4e00\u81f4\u7684crop size\u3001batch size\u3001head dimension\u4e0epair channel\u505a\u5355\u5361\u5bf9\u7167\uff1b\u53ea\u6709\u5355\u5361\u6570\u503c\u548c\u541e\u5410\u901a\u8fc7\uff0c\u624d\u6269\u5c55\u52302\u5361\u30014\u5361\u548c\u591a\u8282\u70b9\u3002<\/p>\n
\u6bcf\u4e00\u7ea7\u90fd\u8981\u8bb0\u5f55\u6bcf\u6b65\u65f6\u95f4\u3001\u5cf0\u503c\u663e\u5b58\u3001GPU\u5229\u7528\u7387\u3001\u901a\u4fe1\u7b49\u5f85\u3001\u68af\u5ea6\u662f\u5426\u6709\u9650\u503c\u3001\u56fa\u5b9aseed\u91cd\u590d\u7ed3\u679c\uff0c\u4ee5\u53ca\u68c0\u67e5\u70b9\u80fd\u5426\u6062\u590d\u3002\u591a\u5361\u63d0\u901f\u53d8\u5dee\u65f6\uff0c\u95ee\u9898\u53ef\u80fd\u5728\u901a\u4fe1\u3001\u6570\u636e\u4f9b\u7ed9\u6216\u8d1f\u8f7d\u4e0d\u5747\uff0c\u800c\u4e0d\u5728\u5185\u6838\u3002\u670d\u52a1\u5668\u9700\u8981\u9ad8\u901fGPU\u4e92\u8fde\u3001\u8db3\u591fCPU\u4e0e\u672c\u5730NVMe\uff0c\u4e5f\u9700\u8981\u5bb9\u5668\u3001\u7f16\u8bd1\u4ea7\u7269\u3001wheel\u6458\u8981\u548c\u6d4b\u8bd5\u56de\u6267\u7684\u7248\u672c\u7ba1\u7406\u3002<\/p>\n
crop size\u4e0d\u662f\u4e00\u4e2a\u666e\u901a\u53c2\u6570<\/h2>\n
\u7ed3\u6784\u6a21\u578b\u8bad\u7ec3\u4e2d\u7684crop\u51b3\u5b9a\u6bcf\u4e2a\u6837\u672c\u53c2\u4e0e\u6210\u5bf9\u8868\u793a\u8ba1\u7b97\u7684token\u6570\u3002\u4e09\u89d2\u6ce8\u610f\u529b\u8fd1\u4f3c\u7acb\u65b9\u590d\u6742\u5ea6\uff0ccrop\u4ece384\u589e\u52a0\u5230768\u65f6\uff0c\u7406\u8bba\u8ba1\u7b97\u91cf\u53ef\u63a5\u8fd1\u516b\u500d\uff1b\u4e0e\u6b64\u540c\u65f6\uff0cpair tensor\u7684\u5b58\u50a8\u89c4\u6a21\u4e5f\u4f1a\u5feb\u901f\u589e\u957f\u3002\u56e0\u6b64\u4e0d\u80fd\u53ea\u62ff\u77edcrop\u6d4b\u51fa\u7684\u663e\u5b58\u4f59\u91cf\u5916\u63a8\u957fcrop\uff0c\u66f4\u4e0d\u80fd\u4e3a\u4e86\u585e\u8fdb\u663e\u5361\u5c31\u968f\u610f\u7f29\u77edcrop\u800c\u4e0d\u8bc4\u4f30\u5bf9\u8bad\u7ec3\u76ee\u6807\u7684\u5f71\u54cd\u3002<\/p>\n
\u5efa\u8bae\u6309384\u3001640\u3001768\u4e09\u4e2a\u6863\u4f4d\u5206\u522b\u505a\u81f3\u5c1150\u4e2a\u7a33\u5b9a\u8bad\u7ec3\u6b65\uff0c\u5254\u9664\u9996\u6b21\u7f16\u8bd1\u548c\u7f13\u5b58\u9884\u70ed\uff0c\u518d\u7edf\u8ba1\u4e2d\u4f4d\u6570\u53ca\u9ad8\u5206\u4f4d\u5355\u6b65\u65f6\u95f4\u3002\u6bcf\u6863\u540c\u65f6\u8bb0\u5f55\u6700\u5927\u663e\u5b58\u3001\u662f\u5426\u89e6\u53d1\u56de\u9000\u3001\u6570\u636e\u7b49\u5f85\u548c\u901a\u4fe1\u5360\u6bd4\u3002\u82e5\u9879\u76ee\u771f\u5b9e\u6837\u672c\u957f\u5ea6\u5206\u5e03\u504f\u77ed\uff0c768\u4e0a\u7684\u6700\u597d\u52a0\u901f\u53ef\u80fd\u5e76\u4e0d\u4ee3\u8868\u65e5\u5e38\u541e\u5410\uff1b\u53cd\u4e4b\uff0c\u957f\u590d\u5408\u7269\u8bad\u7ec3\u82e5\u9891\u7e41\u5230\u8fbe\u4e0a\u9650\uff0c\u5219\u5e94\u4f18\u5148\u4fdd\u8bc1\u957fcrop\u4e0b\u7684\u7a33\u5b9a\u6027\u548c\u65ad\u70b9\u6062\u590d\u3002<\/p>\n
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