第14回 配信講義　計算科学技術特論A （2023）

計算科学技術特論A 第１４回: 深層学習フレームワークの基礎と実践１ 東京工業大学 学術国際情報センター 横田理央 rioyokota@gsic.titech.ac.jp

主要な深層ニューラルネットモデルの変遷 MobileNet: Squeeze and excite AlexNet: ReLU, Dropout, GPU LeNet-5:畳み込み ResNet: Skip connection, batch norm Ef cientNet: Neural architecture search Transformer: 注意機構 Vision Transformer: 画像パッチ LSTM 1995 2012 2015 2017 2019 2021 fi fi https://towardsdatascience.com/from-lenet-to-ef cientnet-the-evolution-of-cnns-3a57eb34672f

• https://towardsdatascience.com/from-lenet-to-efficientnet-the-evolution-of-cnns-3a57eb34672f

深層ニューラルネットの学習 y：ラベル Cross entropy loss <latexit sha1_base64="eGta8ATILI7rVM5edzp7/uMnFog=">AAAB+3icbVDLSsNAFJ3UV62vqks3g0VwVRIVdVl047IF+4A2lMnkph06mYSZiRBCvsCtfoE7cevH+AH+h5M2C1s9MHA4517umePFnClt219WZW19Y3Orul3b2d3bP6gfHvVUlEgKXRrxSA48ooAzAV3NNIdBLIGEHoe+N7sv/P4TSMUi8ajTGNyQTAQLGCXaSJ10XG/YTXsO/Jc4JWmgEu1x/XvkRzQJQWjKiVJDx461mxGpGeWQ10aJgpjQGZnA0FBBQlBuNg+a4zOj+DiIpHlC47n6eyMjoVJp6JnJkOipWvUK8T9vmOjg1s2YiBMNgi4OBQnHOsLFr7HPJFDNU0MIlcxkxXRKJKHadLN0xVdFtNz04qy28Jf0LprOdfOyc9Vo3ZUNVdEJOkXnyEE3qIUeUBt1EUWAntELerVy6816tz4WoxWr3DlGS7A+fwCA+ZVy</latexit> Labradoodle 1 0.9 0 <latexit 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sha1_base64="rQepUHmc6aWrxxB1wV5j6ZVGC6k=">AAACHXicdVDLSsNAFJ3UV62vqEs3o0VwVRKt2mXRjcsK9gFNCJPJpB06mYSZiVBC136GX+BWv8CduBU/wP9w0ka0ohcGzj3nvub4CaNSWda7UVpYXFpeKa9W1tY3NrfM7Z2OjFOBSRvHLBY9H0nCKCdtRRUjvUQQFPmMdP3RZa53b4mQNOY3apwQN0IDTkOKkdKUZ+47oUA4cxIkFEUMpp49+c66OvPMql2zpgGt2lm9bp00NCiYL6kKimh55ocTxDiNCFeYISn7tpUoN8tHYkYmFSeVJEF4hAakryFHEZFuNv3KBB5qJoBhLPTjCk7Znx0ZiqQcR76ujJAayt9aTv6l9VMVNtyM8iRVhOPZojBlUMUw9wUGVBCs2FgDhAXVt0I8RNobpd2b2xLI/LQ5X/4HneOafVqzruvV5kXhUBnsgQNwBGxwDprgCrRAG2BwBx7AI3gy7o1n48V4nZWWjKJnF8yF8fYJ8zGjJg==</latexit> <latexit sha1_base64="2g++4FK2qtbTNVizFSiWmGPzmRk=">AAACHXicdVDLSsNAFJ34rPUVdelmtAiuQlJabXdFNy4r2Ac0IUwmk3bo5MHMRCihaz/DL3CrX+BO3Iof4H84aSNa0QMD555779x7j5cwKqRpvmtLyyura+uljfLm1vbOrr633xVxyjHp4JjFvO8hQRiNSEdSyUg/4QSFHiM9b3yZ53u3hAsaRzdykhAnRMOIBhQjqSRXP7IDjnBmJ4hLihhMXXP6HfVU5OoV02g26s1aA5qGOUNOqmfNugWtQqmAAm1X/7D9GKchiSRmSIiBZSbSyfIvMSPTsp0KkiA8RkMyUDRCIRFONjtlCk+U4sMg5upFEs7Unx0ZCoWYhJ6qDJEcid+5XPwrN0hl0HAyGiWpJBGeDwpSBmUMc1+gTznBkk0UQZhTtSvEI6S8kcq9hSm+yFfLffk6Hv5PulXDqhvmda3SuigcKoFDcAxOgQXOQQtcgTboAAzuwAN4BE/avfasvWiv89Ilreg5AAvQ3j4BLYSjTA==</latexit> data X <latexit sha1_base64="/J5Xk+dXiOlf6omGGiJXLYgOMI8=">AAACBXicdVDLSsNAFJ34rPVVdelmsAiuQlLb2u6KblxWsA9IY5lMJu3QmUmYmQgldO0XuNUvcCdu/Q4/wP8waSNY0QMXDufcy733eBGjSlvWh7Gyura+sVnYKm7v7O7tlw4OuyqMJSYdHLJQ9j2kCKOCdDTVjPQjSRD3GOl5k6vM790TqWgobvU0Ii5HI0EDipFOJWegYn6X+Eij2bBUtsxmo9asNqBlWnNkpFJv1mxo50oZ5GgPS58DP8QxJ0JjhpRybCvSboKkppiRWXEQKxIhPEEj4qRUIE6Um8xPnsHTVPFhEMq0hIZz9edEgrhSU+6lnRzpsfrtZeJfnhProOEmVESxJgIvFgUxgzqE2f/Qp5JgzaYpQVjS9FaIx0girNOUlrb4Kjsty+X7efg/6VZMu26e31TLrcs8oQI4BifgDNjgArTANWiDDsAgBI/gCTwbD8aL8Wq8LVpXjHzmCCzBeP8CCxKaQA==</latexit> 四則演算や初等関数の微分は内部で定義されている それらを連鎖させれば行列積で勾配が計算できる 後ろからかければ全て行列ベクトル積になる 画像ごとにこれが行われ最後に和をとる @u0 @W0 15 @W1 10 <latexit sha1_base64="RlrtYxiGwNDm/OSOojM6YjHJMWs=">AAACI3icbVC7TsMwFHV4lvIKMLJYVAimKgEEjBUsDAxFog+piSrHdVqrjmPZDlIV5RP4DL6AFb6ADbEwMPIfOG0kaMuRLB2d+zo+gWBUacf5tBYWl5ZXVktr5fWNza1te2e3qeJEYtLAMYtlO0CKMMpJQ1PNSFtIgqKAkVYwvM7rrQciFY35vR4J4keoz2lIMdJG6tpHXigRTj2BpKaIQS9CeoARS2+z7FcVWdeuOFVnDDhP3IJUQIF61/72ejFOIsI1ZkipjusI7af5QsxIVvYSRQTCQ9QnHUM5iojy0/GHMnholB4MY2ke13Cs/p1IUaTUKApMZ+5XzdZy8b9aJ9HhpZ9SLhJNOJ4cChMGdQzzdGCPSoI1GxmCsKTGK8QDZBLSJsOpKz2VW8tzcWdTmCfNk6p7Xj29O6vUroqESmAfHIBj4IILUAM3oA4aAINH8AxewKv1ZL1Z79bHpHXBKmb2wBSsrx/HuKZK</latexit> @L @p 2

最適化手法 重みWとバイアスbを合わせてθとする https://losslandscape.com SGD <latexit sha1_base64="IOyR436oWLZbrKn8O0Lz+NybarM=">AAACMXicbVDLSsNAFJ34rPVVdekmWARFLInvjSC6ceGigrVCU8rNdGqHTiZh5kYoIV/iZ/gFbvUL3Ingyp9w0kaw1QvDnDnnXu6Z40eCa3ScN2ticmp6ZrYwV5xfWFxaLq2s3uowVpTVaChCdeeDZoJLVkOOgt1FikHgC1b3exeZXn9gSvNQ3mA/Ys0A7iXvcApoqFbp0MMuQ2gluOOmp/kDdz1zeRJ8AV4A2KUgkqt060febpXKTsUZlP0XuDkok7yqrdKn1w5pHDCJVIDWDdeJsJmAQk4FS4terFkEtAf3rGGghIDpZjL4XmpvGqZtd0JljkR7wP6eSCDQuh/4pjMzq8e1jPxPa8TYOWkmXEYxMkmHizqxsDG0s6zsNleMougbAFRx49WmXVBA0SQ6sqWtM2upycUdT+EvuN2ruEeV/euD8tl5nlCBrJMNskVcckzOyCWpkhqh5JE8kxfyaj1Zb9a79TFsnbDymTUyUtbXNx2yq3o=</latexit> ✓t+1 = ✓t ⌘rL(✓t ) momentum SGD <latexit sha1_base64="EMAKBAJcozE6InSyo+X7qysKpy0=">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</latexit> vt+1 = vt + ⌘rL(✓t ) ✓t+1 = ✓t vt+1 <latexit sha1_base64="so4WvEfNBhzQ2+k0DIcQ37xIf6o=">AAACGXicbVDLSsNAFJ3UV62vqEsRgkUQxJKoqBuh6MZlBfuANoTJZNoOnUzCzE2hhK78DL/ArX6BO3Hryg/wP5y0WdjqgYFzz7mXe+f4MWcKbPvLKCwsLi2vFFdLa+sbm1vm9k5DRYkktE4iHsmWjxXlTNA6MOC0FUuKQ5/Tpj+4zfzmkErFIvEAo5i6Ie4J1mUEg5Y8c78DfQrYS+HYGV/nBZwMp4Jnlu2KPYH1lzg5KaMcNc/87gQRSUIqgHCsVNuxY3BTLIERTselTqJojMkA92hbU4FDqtx08o2xdaiVwOpGUj8B1kT9PZHiUKlR6OvOEENfzXuZ+J/XTqB75aZMxAlQQaaLugm3ILKyTKyASUqAjzTBRDJ9q0X6WGICOrmZLYHKTstyceZT+EsapxXnonJ2f16u3uQJFdEeOkBHyEGXqIruUA3VEUGP6Bm9oFfjyXgz3o2PaWvByGd20QyMzx85KqEn</latexit> semi-implicit Euler風に書くと 慣性項 at = rL(✓t ) <latexit sha1_base64="MjuWH5G898k351kRZEFDIU570Os=">AAACHHicbVDLSsNAFJ34rPVVdelmsAi6sCQq6kYQ3bhwoWC10JRyM5naoZNJmLkRSujWz/AL3OoXuBO3gh/gfzhps9DqgYHDOfdyz5wgkcKg6346E5NT0zOzpbny/MLi0nJlZfXGxKlmvM5iGetGAIZLoXgdBUreSDSHKJD8Nuid5f7tPddGxOoa+wlvRXCnREcwQCu1KxTaeLzjKwgk+BFgl4HMLgZbPnY5Wm+7Xam6NXcI+pd4BamSApftypcfxiyNuEImwZim5ybYykCjYJIPyn5qeAKsB3e8aamCiJtWNvzJgG5aJaSdWNunkA7VnxsZRMb0o8BO5mHNuJeL/3nNFDtHrUyoJEWu2OhQJ5UUY5rXQkOhOUPZtwSYFjYrZV3QwNCW9+tKaPJoA9uLN97CX3KzW/MOantX+9WT06KhElknG2SLeOSQnJBzcknqhJEH8kSeyYvz6Lw6b877aHTCKXbWyC84H9+Vz6Jo</latexit> <latexit sha1_base64="6uRgnqbwem00uROeNjK2GJZ+8vY=">AAACHnicbZDPSsNAEMY39f//qkcvwSIIhZKoqBeh6MWjglWhKWGy3bRLd5OwOymUkLuP4RN41SfwJl71AXwPNzUHa/1g4eObGWb2FySCa3ScT6syMzs3v7C4tLyyura+Ud3cutVxqihr0VjE6j4AzQSPWAs5CnafKAYyEOwuGFwU9bshU5rH0Q2OEtaR0It4yCmgifzq7tDPsO7mZ0Mf6+CjFyqgmccQ8szrgZSQ+9Wa03DGsqeNW5oaKXXlV7+8bkxTySKkArRuu06CnQwUcipYvuylmiVAB9BjbWMjkEx3svFfcnvPJF07jJV5Edrj9PdEBlLrkQxMpwTs67+1Ivyv1k4xPO1kPEpSZBH9WRSmwsbYLsDYXa4YRTEyBqji5lab9sHQQINvYktXF6cVXNy/FKbN7UHDPW4cXh/VmucloUWyQ3bJPnHJCWmSS3JFWoSSB/JEnsmL9Wi9Wm/W+09rxSpntsmErI9vHfqj0w==</latexit> vt+1 = vt + at Nesterov momentum vt+1 = vt + ⌘rL(✓t ✓t+1 = ✓t vt+1 RMSProp ✓t+1 = ✓t + vt+1 vt+1 = ⇢vt + (1 mt+1 ✓t+1 <latexit sha1_base64="4YEOH6DoswNYNGJQU7KvSQoMDRc=">AAACGXicbVDLSsNAFJ3UV62vqEsRBosgiCVRUTdC0Y3LCvYBbQiTybQdOpOEmRuhhK78DL/ArX6BO3Hryg/wP0zaLGzrgYFzz7mXe+d4keAaLOvbKCwsLi2vFFdLa+sbm1vm9k5Dh7GirE5DEaqWRzQTPGB14CBYK1KMSE+wpje4zfzmI1Oah8EDDCPmSNILeJdTAqnkmvsd6DMgbgLH9ug6L+BETgTXLFsVaww8T+yclFGOmmv+dPyQxpIFQAXRum1bETgJUcCpYKNSJ9YsInRAeqyd0oBIpp1k/I0RPkwVH3dDlb4A8Fj9O5EQqfVQemmnJNDXs14m/ue1Y+heOQkPohhYQCeLurHAEOIsE+xzxSiIYUoIVTy9FdM+UYRCmtzUFl9np2W52LMpzJPGacW+qJzdn5erN3lCRbSHDtARstElqqI7VEN1RNETekGv6M14Nt6ND+Nz0low8pldNAXj6xcqpaEe</latexit> vt ) <latexit sha1_base64="ir9w3iQmgCVKvRKp5bEPzuOE5aE=">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</latexit> 物理的整合性を 持たせるためには t= = momentum ⌘ vt <latexit sha1_base64="2qXPSiX6g4ESw8yJGT+gqcvZYr8=">AAACB3icbVDLTgIxFO3gC/GFunTTSEzcOJkBFd0R3bjERMAEJqTTKdDQdsa2Q0ImfIBf4Fa/wJ1x62f4Af6HHZgYQU9yk5Nz7s09OX7EqNKO82nllpZXVtfy64WNza3tneLuXlOFscSkgUMWynsfKcKoIA1NNSP3kSSI+4y0/OF16rdGRCoaijs9jojHUV/QHsVIG8k76fQR5wiOuomedIslx3amgI59Vi07lxX4o7gZKYEM9W7xqxOEOOZEaMyQUm3XibSXIKkpZmRS6MSKRAgPUZ+0DRWIE+Ul09ATeGSUAPZCaUZoOFV/XySIKzXmvtnkSA/UopeK/3ntWPcuvISKKNZE4NmjXsygDmHaAAyoJFizsSEIS2qyQjxAEmFtepr7Eqg0WtqLu9jCX9Is2+65Xbk9LdWusoby4AAcgmPggiqogRtQBw2AwQN4As/gxXq0Xq036322mrOym30wB+vjG5S9mng=</latexit> ✓t ✓t+1 <latexit sha1_base64="dYecKxTxjwKqcAllQPGL/cN5Q/M=">AAACBnicdVDLSsNAFJ3UV62vqks3g0UQhJKoaLsrunFZwdpCE8pkOmmHTiZh5kYooXu/wK1+gTtx62/4Af6HkzZCK3pg4HDOvdwzx48F12Dbn1ZhaXllda24XtrY3NreKe/u3esoUZS1aCQi1fGJZoJL1gIOgnVixUjoC9b2R9eZ335gSvNI3sE4Zl5IBpIHnBIwkuvCkAHppXDiTHrlil21p8BzpF6vObU6dnKlgnI0e+Uvtx/RJGQSqCBadx07Bi8lCjgVbFJyE81iQkdkwLqGShIy7aXTzBN8ZJQ+DiJlngQ8Vec3UhJqPQ59MxkSGOrfXib+5XUTCGpeymWcAJN0dihIBIYIZwXgPleMghgbQqjiJiumQ6IIBVPTwpW+zqJlvfx8Hv9P7k+rzkX17Pa80rjKGyqiA3SIjpGDLlED3aAmaiGKYvSEntGL9Wi9Wm/W+2y0YOU7+2gB1sc3Az6aLA==</latexit> Nesterov momentum <latexit sha1_base64="pYEI99nU86rqt2I07iUb+/8icC4=">AAACUHicdVBNbxMxEJ0NHy3hKy1HLhYRUpFotFv6kdwquHDgUKSmrZRdrWYdJ7Fqe1f2bKVotX+Mn8GNG5ceyi/gBt40K1EEI1l+ejN+8/yyQklHYfgt6Ny7/+Dhxuaj7uMnT589721tn7m8tFyMea5ye5GhE0oaMSZJSlwUVqDOlDjPLj80/fMrYZ3MzSktC5FonBs5kxzJU2nvtIpXIhM7z5IqHIyGB6P94dtwEK6qAXuHo4Oo3o0FYWwwUxhrpAVHVX2qd2JaeD6l3XiOWiO7SulNnfb6rRJrlVirxKI104d1naS963ia81ILQ1yhc5MoLCip0JLkStTduHSiQH6JczHx0KAWLqlWxmv22jNTNsutP4bYiv3zRYXauaXO/GRj3P3da8h/9SYlzYZJJU1RkjD8dtGsVIxy1kTJptIKTmrpAXIrvVfGF2iRkw/8zpapa6w1ubSfZ/8HZ3uD6HDw7vN+//j9OqFNeAmvYAciOIJj+AgnMAYOX+A73MCP4GvwM/jVCW5H2xtewJ3qdH8DuV+yQA==</latexit> ⌘rL(✓t vt <latexit sha1_base64="2qXPSiX6g4ESw8yJGT+gqcvZYr8=">AAACB3icbVDLTgIxFO3gC/GFunTTSEzcOJkBFd0R3bjERMAEJqTTKdDQdsa2Q0ImfIBf4Fa/wJ1x62f4Af6HHZgYQU9yk5Nz7s09OX7EqNKO82nllpZXVtfy64WNza3tneLuXlOFscSkgUMWynsfKcKoIA1NNSP3kSSI+4y0/OF16rdGRCoaijs9jojHUV/QHsVIG8k76fQR5wiOuomedIslx3amgI59Vi07lxX4o7gZKYEM9W7xqxOEOOZEaMyQUm3XibSXIKkpZmRS6MSKRAgPUZ+0DRWIE+Ul09ATeGSUAPZCaUZoOFV/XySIKzXmvtnkSA/UopeK/3ntWPcuvISKKNZE4NmjXsygDmHaAAyoJFizsSEIS2qyQjxAEmFtepr7Eqg0WtqLu9jCX9Is2+65Xbk9LdWusoby4AAcgmPggiqogRtQBw2AwQN4As/gxXq0Xq036322mrOym30wB+vjG5S9mng=</latexit> vt ) ✓t+1 <latexit sha1_base64="dYecKxTxjwKqcAllQPGL/cN5Q/M=">AAACBnicdVDLSsNAFJ3UV62vqks3g0UQhJKoaLsrunFZwdpCE8pkOmmHTiZh5kYooXu/wK1+gTtx62/4Af6HkzZCK3pg4HDOvdwzx48F12Dbn1ZhaXllda24XtrY3NreKe/u3esoUZS1aCQi1fGJZoJL1gIOgnVixUjoC9b2R9eZ335gSvNI3sE4Zl5IBpIHnBIwkuvCkAHppXDiTHrlil21p8BzpF6vObU6dnKlgnI0e+Uvtx/RJGQSqCBadx07Bi8lCjgVbFJyE81iQkdkwLqGShIy7aXTzBN8ZJQ+DiJlngQ8Vec3UhJqPQ59MxkSGOrfXib+5XUTCGpeymWcAJN0dihIBIYIZwXgPleMghgbQqjiJiumQ6IIBVPTwpW+zqJlvfx8Hv9P7k+rzkX17Pa80rjKGyqiA3SIjpGDLlED3aAmaiGKYvSEntGL9Wi9Wm/W+2y0YOU7+2gB1sc3Az6aLA==</latexit> <latexit sha1_base64="jikt4Srxl5+zqapEUBoYmyFrypA=">AAACAnicdVDLSsNAFJ3UV62vqks3g0VwVRIVbXdFNy4r2Ae0oUwmk3boZBJmboQSuvML3OoXuBO3/ogf4H84aSO0ogcuHM65l3vv8WLBNdj2p1VYWV1b3yhulra2d3b3yvsHbR0lirIWjUSkuh7RTHDJWsBBsG6sGAk9wTre+CbzOw9MaR7Je5jEzA3JUPKAUwJG6vZhxIAMYFCu2FV7BrxA6vWaU6tjJ1cqKEdzUP7q+xFNQiaBCqJ1z7FjcFOigFPBpqV+ollM6JgMWc9QSUKm3XR27xSfGMXHQaRMScAzdXEiJaHWk9AznSGBkf7tZeJfXi+BoOamXMYJMEnni4JEYIhw9jz2uWIUxMQQQhU3t2I6IopQMBEtbfF1dtrU5PLzPP6ftM+qzmX1/O6i0rjOEyqiI3SMTpGDrlAD3aImaiGKBHpCz+jFerRerTfrfd5asPKZQ7QE6+MbQ1KYsA==</latexit> <latexit sha1_base64="4YkWyJvS7AvVbbmFVeff+OrbRbQ=">AAACMnicbVDLSsNAFJ34rO+qSzeDRaiIJVGpbgTRjQsXClaFpoab6dQOTiZh5qZQQv7Ez/AL3OoP6E7EnR/hpHbh68DA4Zx7uWdOmEhh0HWfnZHRsfGJydLU9Mzs3PxCeXHpwsSpZrzBYhnrqxAMl0LxBgqU/CrRHKJQ8svw9qjwL3tcGxGrc+wnvBXBjRIdwQCtFJTrvSDDDS/f93U3pr0AN6reZsHXfQWhBD8C7DKQ2Ule9bHLEQJcv94KyhW35g5A/xJvSCpkiNOg/O63Y5ZGXCGTYEzTcxNsZaBRMMnzaT81PAF2Cze8aamCiJtWNvhfTtes0qadWNunkA7U7xsZRMb0o9BOFnHNb68Q//OaKXb2WplQSYpcsa9DnVRSjGlRFm0LzRnKviXAtLBZKeuCBoa20h9X2qaIlttevN8t/CUXWzWvXts+26kcHA4bKpEVskqqxCO75IAck1PSIIzckQfySJ6ce+fFeXXevkZHnOHOMvkB5+MTv8+qnQ==</latexit> <latexit sha1_base64="jPT370zvmdQBBRtoAnxlbXWW5BM=">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</latexit> ミニバッチごとに損失関数の形状は変化する <latexit sha1_base64="+wvl9bAzEQ9hRPZ+KyDyIC4ih2s=">AAACH3icbVBLSgNBEO2Jvxh/UZduBoMgBMKMiroRgm5cRjAfSEKo6XSSJt0zQ3dNIAw5gMfwBG71BO7EbQ7gPexJZmESHzS8eq+Kqn5eKLhGx5lambX1jc2t7HZuZ3dv/yB/eFTTQaQoq9JABKrhgWaC+6yKHAVrhIqB9ASre8OHxK+PmNI88J9xHLK2hL7Pe5wCGqmTL7RwwBA6MRbdyV1aYHE0F1p9kBJMl1NyZrBXiZuSAklR6eR/Wt2ARpL5SAVo3XSdENsxKORUsEmuFWkWAh1CnzUN9UEy3Y5nn5nYZ0bp2r1AmeejPVP/TsQgtR5Lz3RKwIFe9hLxP68ZYe+2HXM/jJD5dL6oFwkbAztJxu5yxSiKsSFAFTe32nQACiia/Ba2dHVy2sTk4i6nsEpqFyX3unT5dFUo36cJZckJOSXnxCU3pEweSYVUCSUv5I28kw/r1fq0vqzveWvGSmeOyQKs6S8iWKPA</latexit> <latexit sha1_base64="PPIS633nLP5qR61KoXjzVGg7aSY=">AAACOXicbVBNa9tAFFylH3HdplXbYy9LTcDFxEhtSXsxmPaSQw8u1InBMuJpvbYX767E7pPBCP2a/oz8glyTU489FEKu+QNd2YbWcQYWhpn3eLOTZFJYDIJf3t6Dh48e79ee1J8+O3j+wn/56tSmuWG8z1KZmkEClkuheR8FSj7IDAeVSH6WzL9W/tmCGytS/QOXGR8pmGoxEQzQSbHfWcQFtsKyE01BKaCLGFsRR4g0JBIiBThjIItvZTPCmdNjPPo3+S72G0E7WIHuknBDGmSDXuz/icYpyxXXyCRYOwyDDEcFGBRM8rIe5ZZnwOYw5UNHNShuR8XqmyU9dMqYTlLjnka6Uv/fKEBZu1SJm6xy27teJd7nDXOcfB4VQmc5cs3Whya5pJjSqjM6FoYzlEtHgBnhslI2AwMMXbNbV8a2ila6XsK7LeyS0/ft8Lj94fvHRvfLpqEaeUPekiYJySfSJSekR/qEkZ/kglySK+/c++1dezfr0T1vs/OabMG7/QvV+65E</latexit> <latexit sha1_base64="so4WvEfNBhzQ2+k0DIcQ37xIf6o=">AAACGXicbVDLSsNAFJ3UV62vqEsRgkUQxJKoqBuh6MZlBfuANoTJZNoOnUzCzE2hhK78DL/ArX6BO3Hryg/wP5y0WdjqgYFzz7mXe+f4MWcKbPvLKCwsLi2vFFdLa+sbm1vm9k5DRYkktE4iHsmWjxXlTNA6MOC0FUuKQ5/Tpj+4zfzmkErFIvEAo5i6Ie4J1mUEg5Y8c78DfQrYS+HYGV/nBZwMp4Jnlu2KPYH1lzg5KaMcNc/87gQRSUIqgHCsVNuxY3BTLIERTselTqJojMkA92hbU4FDqtx08o2xdaiVwOpGUj8B1kT9PZHiUKlR6OvOEENfzXuZ+J/XTqB75aZMxAlQQaaLugm3ILKyTKyASUqAjzTBRDJ9q0X6WGICOrmZLYHKTstyceZT+EsapxXnonJ2f16u3uQJFdEeOkBHyEGXqIruUA3VEUGP6Bm9oFfjyXgz3o2PaWvByGd20QyMzx85KqEn</latexit> ⌘ 2 ⇢)rL(✓t ) 勾配分散項 ⌘ = mt + p rL(✓t ) 慣性項+正規化 vt+1 + ✏ = ✓t mt+1 ✓t <latexit sha1_base64="jikt4Srxl5+zqapEUBoYmyFrypA=">AAACAnicdVDLSsNAFJ3UV62vqks3g0VwVRIVbXdFNy4r2Ae0oUwmk3boZBJmboQSuvML3OoXuBO3/ogf4H84aSO0ogcuHM65l3vv8WLBNdj2p1VYWV1b3yhulra2d3b3yvsHbR0lirIWjUSkuh7RTHDJWsBBsG6sGAk9wTre+CbzOw9MaR7Je5jEzA3JUPKAUwJG6vZhxIAMYFCu2FV7BrxA6vWaU6tjJ1cqKEdzUP7q+xFNQiaBCqJ1z7FjcFOigFPBpqV+ollM6JgMWc9QSUKm3XR27xSfGMXHQaRMScAzdXEiJaHWk9AznSGBkf7tZeJfXi+BoOamXMYJMEnni4JEYIhw9jz2uWIUxMQQQhU3t2I6IopQMBEtbfF1dtrU5PLzPP6ftM+qzmX1/O6i0rjOEyqiI3SMTpGDrlAD3aImaiGKBHpCz+jFerRerTfrfd5asPKZQ7QE6+MbQ1KYsA==</latexit> <latexit sha1_base64="cMJEeTuJyWkIIo1/oFATGDoX7ho=">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</latexit> ⌘rL(✓t ) Adam <latexit sha1_base64="hcu7JIK5zJuWJREk9Bj+qzd8oyg=">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</latexit> mt+1 = vt+1 = <latexit sha1_base64="b0A57HXrWLeK1gdAKZRl7DCDL2w=">AAACN3icbVDLSsNAFJ34rO+qSzfBIlTEklRRQYSiGxcuFKwKTQ0306kdnEzCzE2hhHyMn+EXuNWlK1eKW//ASc3C14WBc8+5l3vmBLHgGh3n2RoZHRufmCxNTc/Mzs0vlBeXLnSUKMqaNBKRugpAM8ElayJHwa5ixSAMBLsMbo9y/bLPlOaRPMdBzNoh3Eje5RTQUH55v++nuOFmB17AEPx638eNqrtZdOuehECAFwL2KIj0JKt62MslXL+u++WKU3OGZf8FbgEqpKhTv/zqdSKahEwiFaB1y3VibKegkFPBsmkv0SwGegs3rGWghJDpdjr8ZGavGaZjdyNlnkR7yH7fSCHUehAGZjK3q39rOfmf1kqwu9dOuYwTZJJ+HeomwsbIzhOzO1wximJgAFDFjVeb9kABRZPrjysdnVvLTC7u7xT+got6zd2pbZ1tVxqHRUIlskJWSZW4ZJc0yDE5JU1CyR15II/kybq3Xqw36/1rdMQqdpbJj7I+PgF9f6x3</latexit> <latexit sha1_base64="xhnZcIhN39z2hCSemrfnC5bRMYE=">AAACMnicbVDLSsNAFJ34rPVVdekmWARBLEmV6kYounFZwT6gqWEynbRDJw9nboQS8id+hl/gVn9AdyLu/AgnaRa29TADh3Pu5d57nJAzCYbxri0sLi2vrBbWiusbm1vbpZ3dlgwiQWiTBDwQHQdLyplPm8CA004oKPYcTtvO6Dr1249USBb4dzAOac/DA5+5jGBQkl2qOXYMx2ZyabkCk9iSDwJi88RyKOD7zLGrSTKjqFcqGxUjgz5PzJyUUY6GXfq2+gGJPOoD4VjKrmmE0IuxAEY4TYpWJGmIyQgPaFdRH3tU9uLsvkQ/VEpfdwOhvg96pv7tiLEn5dhzVKWHYShnvVT8z+tG4F70YuaHEVCfTAa5Edch0NOw9D4TlAAfK4KJYGpXnQyxCgpUpFNT+jJdLc3FnE1hnrSqFbNWOb09K9ev8oQKaB8doCNkonNURzeogZqIoCf0gl7Rm/asfWif2tekdEHLe/bQFLSfX6nFqyE=</latexit> bt+1 = 1 mt + (1 2 vt + (1 q t+1 1 2 1 <latexit sha1_base64="9xePHLSefWqCHhmPtXC5CMrtgpA=">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</latexit> ✓t+1 = ✓t t+1 1 1 )rL(✓t ) 慣性項 2 )rL(✓ ) 2 t 勾配分散項 初期バイアス補正項 mt+1 ↵p bt+1 vt+1 + ✏ 正規化 https://arxiv.org/pdf/2007.01547.pdf

• https://losslandscape.com
• https://arxiv.org/pdf/2007.01547.pdf

https://cs231n.github.io/convolutional-networks/ 畳み込みニューラルネット 入力チャネル３，出力チャネル２の例 入力画像 GEMM フィルタ https://arxiv.org/abs/1410.0759 Winograd 入力画像 フィルタ [入出力テンソルの次元] N: バッチサイズ C: チャネル数 H: 画像の高さ W: 画像の幅 [畳み込みのパラメータ] F: フィルタの大きさ P: パディングの幅 S: ストライド batched GEMM [入力] [出力] N: 1 Cin: 3 Hin: 5 Win: 5 N: 1 Cout: 2 Hout: 3 Wout: 3 F: 3 P: 1 S: 2 出力画像 https://www.slideshare.net/nervanasys/an-analysis-of-convolution-for-inference FFT http://cs231n.stanford.edu/reports/2016/pdfs/117_Report.pdf

• https://cs231n.github.io/convolutional-networks/
• http://cs231n.stanford.edu/reports/2016/pdfs/117_Report.pdf
• https://arxiv.org/abs/1410.0759
• https://www.slideshare.net/nervanasys/an-analysis-of-convolution-for-inference

正規化 (normalization) Batch normalization (BN) <latexit sha1_base64="RqN4YpnAkqmVd2gk3UwpB5hXPg8=">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</latexit> x̂ = <latexit sha1_base64="dpuT1CUY56ArR9bRnSs4Ys0fxb4=">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</latexit> <latexit sha1_base64="mMlCkP7IjuvtvnbWTjoEnGSHv/w=">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</latexit> ✓ x µ(x) (x) N ◆ H Group normalization (GN) <latexit sha1_base64="RqN4YpnAkqmVd2gk3UwpB5hXPg8=">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</latexit> + x̂ = W XXX 1 µ(x) = xnchw N HW n=1 h=1 w=1 v u N X H X W u 1 X t (x) = (xnchw N HW n=1 w=1 <latexit sha1_base64="+mSuVgDmFbFdQz9MORmrfpjKhzI=">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</latexit> <latexit sha1_base64="VgwH0utETMAfcEv0IoDUtgzOTWk=">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</latexit> µ(x))2 h=1 x̂ = <latexit sha1_base64="jWwokzwi9lm2DSevW/HMsFrzSug=">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</latexit> <latexit sha1_base64="ih89Pzc2EBidrxV+yRGOgCPpnQE=">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</latexit> ✓ x µ(x) (x) ◆ ✓ x µ(x) (x) ◆ + C/2 H W 2 XX X µ(x) = xnchw CHW c=1 h=1 w=1 v u C/2 H W u 2 X XX (x) = t (xnchw CHW c=1 w=1 µ(x))2 h=1 Layer normalization (LN) <latexit sha1_base64="RqN4YpnAkqmVd2gk3UwpB5hXPg8=">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</latexit> https://theaisummer.com/normalization/ Weight standardization (WS) ✓ ◆ <latexit sha1_base64="Pt2kdQmIa3vxI/2P4C+R6402oY0=">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</latexit> Ŵ = + h=1 µ(W ) (W ) <latexit sha1_base64="kB8kd70X3SXw/F5W+lkWy3s6d8o=">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</latexit> C X H X W X 1 µ(x) = xnchw CHW c=1 h=1 w=1 v u C X H X W u 1 X (x) = t (xnchw CHW c=1 w=1 W µ(W ) = <latexit sha1_base64="PSqEPc30bQy6FHyPWYe6IWsLh00=">AAACZnicbVFNS8MwGM7q9/dUxIOX4BDmwdFOUS/CcDB2VHBWWGdJs3SLS9qapMII/Y9e/QGCv8CrprMH3Xwh8LzP837xJEgYlcq230rW3PzC4tLyyura+sbmVnl7517GqcCkg2MWi4cAScJoRDqKKkYeEkEQDxhxg1Ez190XIiSNozs1TkiPo0FEQ4qRMpRffvIkHXBUdY+vPPkslPZCgbB2Mt1s+e2W72aeTPmjbma+xldOkRnJ5KHf/sW4E8Y1TNU1tUY0WXbi8dQMP36sZ365YtfsScBZ4BSgAoq48cvvXj/GKSeRwgxJ2XXsRPU0EopiRrJVL5UkQXiEBqRrYIQ4kT098SSDR4bpwzAW5kUKTtjfHRpxKcc8MJUcqaGc1nLyP62bqvCyp2mUpIpE+GdRmDKoYpgbDPtUEKzY2ACEBTW3QjxExlRlvuHPlr7MT8t9caZdmAX39ZpzXju9Pas0rguHlsEBOARV4IAL0ABtcAM6AINX8Am+SqD0YW1ae9b+T6lVKnp2wZ+w4Dc1/7tI</latexit> µ(x))2 2 CFH FW v u u (W ) = t FH FW C X X X WcfH fW FH FW C X X X (WcfH fW c=1 fH =1 fW =1 1 CFH FW c=1 fH =1 fW =1 (higher is better) BN + LN µ(W ))2 BN + LN BN + LN BN + LN

• https://theaisummer.com/normalization/

データ拡張 (augmentation) Flipping Mixup AutoAugment 強化学習を使って最適なデータ拡張を探索 Fast AutoAugment 強化学習＋ベイズ最適化により探索時間短縮 Random crop CutMix Scale Faster AutoAugment AugMix 勾配ベースの探索によりさらに時間短縮 Rotation Cutout Random Erasing https://github.com/xkumiyu/numpy-data-augmentation https://openreview.net/pdf?id=S1gmrxHFvB (lower is better)

• https://openreview.net/pdf?id=S1gmrxHFvB
• https://github.com/xkumiyu/numpy-data-augmentation

正則化 (regularization) 損失関数 <latexit sha1_base64="PTOETKQJ9sDV108G8utVEdMYksY=">AAACGnicbVDLSsNAFJ34rPUVdSnIYBHcWJIi6kYounHhooJ9QBPLzWTaDp08mJkIJWTnZ/gFbvUL3IlbN36A/+Gk7cK2Hhg4nHMv98zxYs6ksqxvY2FxaXlltbBWXN/Y3No2d3YbMkoEoXUS8Ui0PJCUs5DWFVOctmJBIfA4bXqD69xvPlIhWRTeq2FM3QB6IesyAkpLHfPACUD1CfD0Nrt0ZBI8pD4oyE4cHvVw3DFLVtkaAc8Te0JKaIJax/xx/IgkAQ0V4SBl27Zi5aYgFCOcZkUnkTQGMoAebWsaQkClm47+keEjrfi4Gwn9QoVH6t+NFAIph4GnJ/PUctbLxf+8dqK6F27KwjhRNCTjQ92EYxXhvBTsM0GJ4kNNgAims2LSBwFE6eqmrvgyj5bpXuzZFuZJo1K2z8qVu9NS9WrSUAHto0N0jGx0jqroBtVQHRH0hF7QK3ozno1348P4HI8uGJOdPTQF4+sXt5uh/g==</latexit> L= Sharpness Aware Minimization (SAM) data X log p <latexit sha1_base64="9vmQ2Pyai29yDGuUrPziv2Rg5HE=">AAACPnicbVBNSxxBEO0xfn+uydFL4yIo4jKjol6EZb3kkIOBrCvsjEtNT+1uY0/30N0jLsP8n/yM/IJcE39AvEmuOaZnXcGvBwWP96qoqhdnghvr+3fe1Ifpmdm5+YXFpeWV1bXa+scLo3LNsM2UUPoyBoOCS2xbbgVeZhohjQV24uuzyu/coDZcyW92lGGUwkDyPmdgndSrtcIU7JCBKL6Up6HJ06siAQulk297RYiZ4ULJUGCoh6rs7oVCDWi23dl9snaiXq3uN/wx6FsSTEidTHDeq/0JE8XyFKVlAozpBn5mowK05UxguRjmBjNg1zDArqMSUjRRMf61pFtOSWhfaVfS0rH6fKKA1JhRGrvO6jPz2qvE97xubvsnUcFllluU7HFRPxfUKloFRxOukVkxcgSY5u5WyoaggVkX74stialOK10uwesU3pKL/UZw1Dj4elhvtiYJzZMNskm2SUCOSZN8JuekTRj5Tn6SX+S398O79x68v4+tU95k5hN5Ae/ff/ElsWk=</latexit> L= L2正則化 <latexit sha1_base64="Yy8xPfnTLiqjWpL4JDjdct97CAw=">AAACJ3icbVDLSgMxFM34rPU16tJNsAhCscxUUTdC0Y0LFxXsAzq13MmkNTTzIMkIZTof4Wf4BW71C9yJLl34H2baLmz1QOBwzr259x434kwqy/o05uYXFpeWcyv51bX1jU1za7suw1gQWiMhD0XTBUk5C2hNMcVpMxIUfJfThtu/zPzGAxWShcGtGkS07UMvYF1GQGmpYxYdH9Q9AZ5cp+eOjP27xAMF6aHDwx6Oig7Xf3kwbAzvyh2zYJWsEfBfYk9IAU1Q7ZjfjheS2KeBIhykbNlWpNoJCMUIp2neiSWNgPShR1uaBuBT2U5GR6V4Xyse7oZCv0Dhkfq7IwFfyoHv6srsBDnrZeJ/XitW3bN2woIoVjQg40HdmGMV4iwh7DFBieIDTYAIpnfF5B4EEKVznJriyWy1VOdiz6bwl9TLJfukdHRzXKhcTBLKoV20hw6QjU5RBV2hKqohgh7RM3pBr8aT8Wa8Gx/j0jlj0rODpmB8/QDq3qdI</latexit> L= data X data X max[ log p(W + ✏)] ✏⇢ https://arxiv.org/abs/2010.01412 log p + |W |2 L1正則化 <latexit sha1_base64="qqFUGj5bRbXJTuGZgDLorSbuXL0=">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</latexit> L= data X log p + |W | Dropout Flooding <latexit sha1_base64="+29JRp4dO+SSQAn2+lrjhc+5WsE=">AAACIHicbVDLSgMxFM34rPVVdekmWAVBWmZU1I1QdOPCRQX7gE4tdzJpG5p5kGSEMp0f8DP8Arf6Be7Epe79DzNtF7b1QOBwzr3ck+OEnEllml/G3PzC4tJyZiW7ura+sZnb2q7KIBKEVkjAA1F3QFLOfFpRTHFaDwUFz+G05vSuU7/2SIVkgX+v+iFtetDxWZsRUFpq5fZtD1SXAI9vk0tbRt5D7IKCZFCwedDBYcEZHDmtXN4smkPgWWKNSR6NUW7lfmw3IJFHfUU4SNmwzFA1YxCKEU6TrB1JGgLpQYc2NPXBo7IZD3+T4AOtuLgdCP18hYfq340YPCn7nqMn0+xy2kvF/7xGpNoXzZj5YaSoT0aH2hHHKsBpNdhlghLF+5oAEUxnxaQLAojSBU5ccWUaLdG9WNMtzJLqcdE6K57cneZLV+OGMmgX7aFDZKFzVEI3qIwqiKAn9IJe0ZvxbLwbH8bnaHTOGO/soAkY378ypqRP</latexit> L= data X | log p b| + b https://arxiv.org/abs/2002.08709 Stochastic depth https://arxiv.org/abs/1603.09382

• https://arxiv.org/abs/2002.08709
• https://arxiv.org/abs/2010.01412
• https://arxiv.org/abs/1603.09382

分散並列化 データ並列 テンソル並列 パイプライン並列 or ZeRO/FSDP データは分散 データは冗長 データは冗長 モデルは冗長 モデルは分散 モデルは分散 勾配のAllReduce 活性のAllReduce or パラメータのAllGather 活性のSendRecv 課題：バッチサイズの 増大に伴う汎化性能の低下 解決策：正則化・最適化 手法の工夫 課題：パイプラインバブル 課題：通信頻度の増加 解決策：通信のオーバーラップ 解決策：パイプライン の工夫

深層学習における強スケーリングとは？ 科学技術計算 格子を分散処理 深層学習 Given a certain model and data size モデルを分散処理 データを分散処理

パイプライン並列（層間並列） fi Chimera: Ef ciently Training Large-Scale Neural Networks with Bidirectional Pipelines, https://arxiv.org/abs/2107.06925

• https://arxiv.org/abs/2107.06925

テンソル並列（層内並列） Apply SUMMA to Attention Layer Ef cient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM https://arxiv.org/abs/2104.04473 fi fi Tesseract: Parallelize the Tensor Parallelism Ef ciently https://arxiv.org/abs/2105.14500

• https://arxiv.org/abs/2104.04473
• https://arxiv.org/abs/2105.14500

Zero Redundancy Optimizer (ZeRO) fi AllGather Parameters and Optimizer States ReduceScatter Gradients ZeRO-In nity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning, https://arxiv.org/abs/2104.07857

• https://arxiv.org/abs/2104.07857

大規模データ並列分散学習の問題 バッチサイズ ＝ GPUあたりのバッチサイズ × GPU数

LARS ラージバッチ用の最適化手法？ <latexit sha1_base64="o+M96FcBaWGVEj1D/kpYk4bZkPU=">AAACPXicbVA9TyMxEPXycQccHDkoaSyik2iIdhESlBE0FCmCRAhSNopmnVmw8HpX9uxF0SZ/534Gv4AWJHroEC0t3iQFX0+y/PxmRvP8okxJS77/4M3NLyz++Lm0vPJrde33euXPxrlNcyOwJVKVmosILCqpsUWSFF5kBiGJFLaj6+Oy3v6HxspUn9Eww24Cl1rGUgA5qVepD3ioMCYwJh3wwW6IBJyHsQFRjEaD0WjsrlBDpIA33GtKwwToSoAqGuNeperX/An4VxLMSJXN0OxVnsJ+KvIENQkF1nYCP6NuAYakUDheCXOLGYhruMSOoxoStN1i8tMx/+uUPo9T444mPlHfTxSQWDtMItdZWrSfa6X4Xa2TU3zYLaTOckItpoviXHFKeRkb70uDgtTQERBGOq9cXIFLiVy4H7b0bWmtzCX4nMJX0t6rBfu1IDjdr9aPZhEtsS22zXZYwA5YnZ2wJmsxwf6zW3bH7r0b79F79l6mrXPebGaTfYD3+gZIWrEN</latexit> w w ||w|| ⌘ rL ||rL|| LAMB (ADAM+LARS) 32kのハッチサイズにおいても： NesterovでLARSと同じ性能を達成 AdamでLAMBと同じ性能を達成 結局ハイパラチューニング次第

東工大のHPCの講義 https://github.com/rioyokotalab/hpsc-2023

• https://github.com/rioyokotalab/hpsc-2023

batch_size(BS)=2 H=5 D_in=3 Data 2層の全結合NN D_out=2 1 X L= (yp NO y)2 <latexit sha1_base64="HuDWCNBZ3fiwLb58W34hfFqbE4Y=">AAACGHicbZDLSsNAFIYn9VbrLepSF8Ei1IUlKYJuhKIbF6IV7AWaGCaTSTt0cmFmIoSQjY/hE7jVJ3Anbt35AL6HkzYL2/rDwM9/zuGc+ZyIEi50/VspLSwuLa+UVytr6xubW+r2ToeHMUO4jUIasp4DOaYkwG1BBMW9iGHoOxR3ndFlXu8+YsZJGNyLJMKWDwcB8QiCQka2un99bnoMotTI0hv7NjN57NcSOzpOjh4atlrV6/pY2rwxClMFhVq2+mO6IYp9HAhEIed9Q4+ElUImCKI4q5gxxxFEIzjAfWkD6GNupeNfZNqhTFzNC5l8gdDG6d+JFPqcJ74jO30ohny2lof/1fqx8M6slARRLHCAJou8mGoi1HIkmksYRoIm0kDEiLxVQ0MooQgJbmqLy/PTMsnFmKUwbzqNuqHXjbuTavOiIFQGe+AA1IABTkETXIEWaAMEnsALeAVvyrPyrnwon5PWklLM7IIpKV+/xXGgTg==</latexit> y_p(BS,D_out) x(BS,D_in) h_r=f(x*w1) y_p=h_r*w2 w1(D_in,H) w2(H,D_out) w2 w2 <latexit sha1_base64="XDVYNpwB7UZogd7iSX6oklwpHpw=">AAACMXicbVDLSgNBEJz1bXxFPXoZDIIXw64oehS9ePAQwZhANoTeSa8Ozj6Y6TWEJV/iZ/gFXvULchPBkz/hbAz4iA0DNVXV0z0VpEoact2hMzU9Mzs3v7BYWlpeWV0rr29cmyTTAusiUYluBmBQyRjrJElhM9UIUaCwEdydFXrjHrWRSXxF/RTbEdzEMpQCyFKd8mGvs+8rDAm0Tnrc3vZ8JPBDDSL3U9AkQfGLwTe2lkGnXHGr7qj4JPDGoMLGVeuU3/1uIrIIYxIKjGl5bkrtvHhSKByU/MxgCuIObrBlYQwRmnY++t6A71imy8NE2xMTH7E/O3KIjOlHgXVGQLfmr1aQ/2mtjMLjdi7jNCOMxdegMFOcEl5kxbtSoyDVtwCElnZXLm7BJkM20V9TuqZYrcjF+5vCJLjer3pu1bs8qJycjhNaYFtsm+0yjx2xE3bOaqzOBHtgT+yZvTiPztB5dd6+rFPOuGeT/Srn4xP07atd</latexit> w1 w1 <latexit sha1_base64="kN3sQo8OP8glKG68w4PsUtC/f3k=">AAACMXicbVDLSgNBEJyN73fUo5fBIHgx7IqiR9GLBw8RjArZEHonvcmQ2QczvYaw5Ev8DL/Aq35BbiJ48iecjQGfDQM1VdXTPRWkShpy3ZFTmpqemZ2bX1hcWl5ZXSuvb1ybJNMC6yJRib4NwKCSMdZJksLbVCNEgcKboHdW6Dd3qI1M4isapNiMoBPLUAogS7XKh/2W5ysMCbRO+tze9nwk8EMNIvdT0CRB8YvhF7aWYatccavuuPhf4E1AhU2q1iq/+e1EZBHGJBQY0/DclJp58aRQOFz0M4MpiB50sGFhDBGaZj7+3pDvWKbNw0TbExMfs987coiMGUSBdUZAXfNbK8j/tEZG4XEzl3GaEcbic1CYKU4JL7LibalRkBpYAEJLuysXXbDJkE30x5S2KVYrcvF+p/AXXO9XPbfqXR5UTk4nCc2zLbbNdpnHjtgJO2c1VmeC3bNH9sSenQdn5Lw4r5/WkjPp2WQ/ynn/AO/Rq1o=</latexit> @L ⌘ @w2 @L ⌘ @w1 Back propagation ReLU (Recti ed Linear Unit) y=f(x) <latexit sha1_base64="0rAPpB7aTBShnIAwO9pRmo0aRkQ=">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</latexit> sha1_base64="KD+WkL8b4iPtxogxdCifBK+sduE=">AAAB7HicbVDLSgNBEOyNrxijxrOXwSB4Crte9Ch48RjBPCBZwuxsJxkyO7vM9AphyQ949Qu8iX/kB/gfziY5mMSCgaKqm66pKFPSku9/e5W9/YPDo+px7aReOz07b9S7Ns2NwI5IVWr6EbeopMYOSVLYzwzyJFLYi2aPpd97RWNlql9onmGY8ImWYyk4Oak9ajT9lr8E2yXBmjRhjVHjZxinIk9Qk1Dc2kHgZxQW3JAUChe1YW4x42LGJzhwVPMEbVgsYy7YtVNiNk6Ne5rYUv27UfDE2nkSucmE09Rue6X4nzfIaXwfFlJnOaEWq0PjXDFKWflnFkuDgtTcES6MdFmZmHLDBblmNq7Etoy2cLUE2yXsku5tK/BbwbMPVbiEK7iBAO7gAZ6gDR0QEMMbvHuF9+F9ruqreOseL2AD3tcvU8WSkg==</latexit> sha1_base64="BPl6LZUWEc7LnKT4OpXuCsHjQG0=">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</latexit> sha1_base64="9s6RO95OuhDQX1hzGQv1yWyzpFM=">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</latexit> @L @L @yp 1 = = 2(yp @w2 @yp @w2 NO <latexit sha1_base64="V1OkoDW7pmfxcKKULrjvVELuV8s=">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</latexit> y)hr @L @L @yp @hr 1 = = 2(yp @w1 @yp @hr @w1 NO y)w2 x hr > 0 fi <latexit 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00_numpy.py

NumPyだけによる実装

import numpy as np
epochs = 300
batch_size = 32
D_in = 784
H = 100
D_out = 10
learning_rate = 1.0e-06
# create random input and output data
x = np.random.randn(batch_size, D_in)
y = np.random.randn(batch_size, D_out)
# randomly initialize weights
w1 = np.random.randn(D_in, H)
w2 = np.random.randn(H, D_out)
for epoch in range(epochs):
# forward pass
h = x.dot(w1) # h = x * w1
h_r = np.maximum(h, 0) # h_r = ReLU(h)
y_p = h_r.dot(w2) # y_p = h_r * w2

1 X
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# compute mean squared error and print loss
loss = np.square(y_p - y).sum()
print(epoch, loss)
# backward pass: compute gradients of loss with respect to w2
grad_y_p = 2.0 * (y_p - y)
grad_w2 = h_r.T.dot(grad_y_p)
# backward pass: compute gradients of loss with respect to w1
grad_h_r = grad_y_p.dot(w2.T)
grad_h = grad_h_r.copy()
grad_h[h < 0] = 0
grad_w1 = x.T.dot(grad_h)
# update weights
w1 -= learning_rate * grad_w1
w2 -= learning_rate * grad_w2

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w2
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01_tensors.py PyTorch の導入 import torch epochs = 300 batch_size = 32 D_in = 784 H = 100 D_out = 10 learning_rate = 1.0e-06 # create random input and output data x = torch.randn(batch_size, D_in) y = torch.randn(batch_size, D_out) # randomly initialize weights w1 = torch.randn(D_in, H) w2 = torch.randn(H, D_out) for epoch in range(epochs): # forward pass: compute predicted y h = x.mm(w1) h_r = h.clamp(min=0) y_p = h_r.mm(w2) # compute and print loss loss = (y_p - y).pow(2).sum().item() print(t, loss) # backward pass: compute gradients of loss with respect to w2 grad_y_p = 2.0 * (y_p - y) grad_w2 = h_r.t().mm(grad_y_p) # backward pass: compute gradients of loss with respect to w1 grad_h_r = grad_y_p.mm(w2.t()) grad_h = grad_h_r.clone() grad_h[h < 0] = 0 grad_w1 = x.t().mm(grad_h) # update weights w1 -= learning_rate * grad_w1 w2 -= learning_rate * grad_w2 np torch np.random torch x.dot(w1) x.mm(w1) np.maximum(h, 0) h.clamp(min=0) np.square(y_p-y) (y_p-y).pow(2) copy() clone()

01_tensor.py

自動微分の導入

02_autograd.py

# randomly initialize weights
w1 = torch.randn(D_in, H)
w2 = torch.randn(H, D_out)

# randomly initialize weights
w1 = torch.randn(D_in, H, requires_grad=True)
w2 = torch.randn(H, D_out, requires_grad=True)

for epoch in range(epochs):
# forward pass: compute predicted y
h = x.mm(w1)
h_r = h.clamp(min=0)
y_p = h_r.mm(w2)

for epoch in range(epochs):
# forward pass: compute predicted y
h = x.mm(w1)
h_r = h.clamp(min=0)
y_p = h_r.mm(w2)

# compute and print loss
loss = (y_p - y).pow(2).sum().item()
print(t, loss)

# compute and print loss
loss = (y_p - y).pow(2).sum()
print(t, loss.item())

# backward pass: compute gradients of loss
with respect to w2
grad_y_p = 2.0 * (y_p - y)
grad_w2 = h_r.t().mm(grad_y_p)

# backward pass
loss.backward()
with torch.no_grad():
# update weights
w1 -= learning_rate * w1.grad
w2 -= learning_rate * w2.grad

# backward pass: compute gradients of loss
with respect to w1
grad_h_r = grad_y_p.mm(w2.t())
grad_h = grad_h_r.clone()
grad_h[h < 0] = 0
grad_w1 = x.t().mm(grad_h)
# update weights
w1 -= learning_rate * grad_w1
w2 -= learning_rate * grad_w2

# initialize weights
w1.grad.zero_()
w2.grad.zero_()

<latexit sha1_base64="V1OkoDW7pmfxcKKULrjvVELuV8s=">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</latexit>

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02_autograd.py torch.nnの利用 03_model.py # create random input and output data x = torch.randn(batch_size, D_in) y = torch.randn(batch_size, D_out) # create random input and output data x = torch.randn(batch_size, D_in) y = torch.randn(batch_size, D_out) # randomly initialize weights w1 = torch.randn(D_in, H, requires_grad=True) w2 = torch.randn(H, D_out, requires_grad=True) # define model model = torch.nn.Sequential( torch.nn.Linear(D_in, H), torch.nn.ReLU(), torch.nn.Linear(H, D_out), ) for epoch in range(epochs): # forward pass: compute predicted y h = x.mm(w1) h_r = h.clamp(min=0) y_p = h_r.mm(w2) # compute and print loss loss = (y_p - y).pow(2).sum() print(t, loss.item()) # backward pass loss.backward() with torch.no_grad(): # update weights w1 -= learning_rate * w1.grad w2 -= learning_rate * w2.grad # initialize weights w1.grad.zero_() w2.grad.zero_() # define loss function criterion = torch.nn.MSELoss(reduction='sum') for epoch in range(epochs): # forward pass: compute predicted y y_p = model(x) # compute and print loss loss = criterion(y_p, y) print(t, loss.item()) # backward pass model.zero_grad() loss.backward() with torch.no_grad(): # update weights for param in model.parameters(): param -= learning_rate * param.grad

03_model.py 最適化関数の呼び出し 04_optimizer.py # define loss function criterion = torch.nn.MSELoss(reduction='sum') # define loss function criterion = torch.nn.MSELoss(reduction='sum') for t in range(epochs): # forward pass: compute predicted y y_p = model(x) # define optimizer optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) # compute and print loss loss = criterion(y_p, y) print(t, loss.item()) for epoch in range(epochs): # forward pass: compute predicted y y_p = model(x) # backward pass model.zero_grad() loss.backward() # compute and print loss loss = criterion(y_p, y) print(t, loss.item()) with torch.no_grad(): # update weights for param in model.parameters(): param -= learning_rate * param.grad # backward pass optimizer.zero_grad() loss.backward() # update weights optimizer.step()

04_optimizer.py MNIST Datasetのロード import torch.nn as nn import torch.nn.functional as F .. . # create random input and output data x = torch.randn(batch_size, D_in) y = torch.randn(batch_size, D_out) .. . for t in range(epochs): # forward pass: compute predicted y y_p = model(x) 05_mnist.py import torch.nn as nn import torch.nn.functional as F from torchvision import datasets, transforms # read input data and labels train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transforms.ToTensor()) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) .. . for epoch in range(epochs): # Set model to training mode model.train() # Loop over each batch from the training set for batch_idx, (x, y) in enumerate(train_loader): # forward pass: compute predicted y y_p = model(x)

06_validate.py Validationデータによる検証 def validate(): model.eval() val_loss, val_acc = 0, 0 for data, target in val_loader: output = model(data) loss = criterion(output, target) Validation dataのloss val_loss += loss.item() sum()はGPUでやると遅いのでCPUで pred = output.data.max(1)[1] val_acc += 100. * pred.eq(target.data).cpu().sum() / target.size(0) パーセンテージに変換 val_loss /= len(val_loader) 予測クラスがラベルと一致しているか？ val_acc /= len(val_loader) print('\nValidation set: Average loss: {:.4f}, Accuracy: {:.1f}%\n'.format( val_loss, val_acc)) 最終的な精度の評価 に使うデータ 学習時に使うデータ ハイパラやモデル を変えて試すとき に使うデータ

06_validate.py 畳み込みNNモデル class TwoLayerNet(nn.Module): def __init__(self, D_in, H, D_out): super(TwoLayerNet, self).__init__() self.fc1 = nn.Linear(D_in, H) self.fc2 = nn.Linear(H, D_out) def forward(self, x): x = x.view(-1, D_in) h = self.fc1(x) h_r = F.relu(h) y_p = self.fc2(h_r) return F.log_softmax(y_p, dim=1) 07_cnn.py class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output

08_cifar10.py CIFAR10 train_dataset = datasets.CIFAR10('./data', データセットの変更 train=True, download=True, transform=transforms.ToTensor()) val_dataset = datasets.CIFAR10('./data', train=False, download=True, transform=transforms.ToTensor()) model = VGG('VGG19').to(device) モデルの変更 model = DDP(model, device_ids=[rank % 4]) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

09_gpu.py GPUを利用 device = torch.device('cuda') model = .. . CNN().to(device) .. . def train(train_loader,model,criterion,optimizer,epoch): model.train() t = time.perf_counter() for batch_idx, (data, target) in enumerate(train_loader): data = data.to(device) target = target.to(device) .. . def validate(loss_vector, accuracy_vector): model.eval() val_loss, correct = 0, 0 for data, target in validation_loader: data = data.to(device) target = target.to(device) PyTorchは裏でcuDNNを呼んでいる 1. torch.device(‘cuda’)でデバイスを指定 2. data, targetをデバイスに送る 3. 計算は全て自動的にGPUを用いて行われる

10_distributed.py

分散並列

import os
import torch
import torch.distributed as dist
master_addr = os.getenv("MASTER_ADDR", default="localhost")
通信に用いるホストアドレスとポート番号を指定
master_port = os.getenv('MASTER_PORT', default='8888')
method = "tcp://{}:{}".format(master_addr, master_port)
rank = int(os.getenv('OMPI_COMM_WORLD_RANK', '0'))
OpenMPI環境変数からrankとsizeを取得
world_size = int(os.getenv('OMPI_COMM_WORLD_SIZE', '1'))
dist.init_process_group("nccl", init_method=method, rank=rank, world_size=world_size)
PyTorchにこれらを設定
print('Rank: {}, Size: {}'.format(dist.get_rank(),dist.get_world_size()))
ngpus = 4
device = rank % ngpus
x = torch.randn(1).to(device)
print('rank {}: {}'.format(rank, x))
dist.broadcast(x, src=0)
print('rank {}: {}'.format(rank, x))

PyTorchによる集団通信

.bashrcに以下を記入
if [ -f "$SGE_JOB_SPOOL_DIR/pe_hostfile" ]; then
export MASTER_ADDR=`head -n 1 $SGE_JOB_SPOOL_DIR/pe_hostfile | cut -d " " -f 1`
fi
mpirun -np 4 python 10_distributed.py

分散並列学習 11_ddp.py def print0(message): if torch.distributed.is_initialized(): if torch.distributed.get_rank() == 0: print(message, flush=True) else: print(message, flush=True) .. . train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset, num_replicas=torch.distributed.get_world_size(), rank=torch.distributed.get_rank()) .. . model = DDP(model, device_ids=[rank % 4]) 全プロセスがprintすると見づらいので１プロセスだけprintするようなprint関数を定義 train dataの読み込みで異なるプロセスが異なるデータを読むようにする モデルをDDP()に通すことで分散並列計算を行う

ImageNet download Select this Download these mkdir train && cd train wget --no-check-certificate https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar ~/hpsc-2023/15_pytorch/untarimage.sh cd .. mkdir val && cd val wget --no-check-certificate https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar tar -xvf ILSVRC2012_img_val.tar && rm -f ILSVRC2012_img_val.tar ~/hpsc-2023/15_pytorch/valprep.sh

11_ddp.py ImageNet train_dataset = datasets.CIFAR10('./data', train=True, download=True, transform=transforms.ToTensor()) val_dataset = datasets.CIFAR10('./data', train=False, transform=transforms.ToTensor()) 12_imagenet.py normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_dataset = datasets.ImageFolder('/gs/hs1/tga-hpc-lecture/ILSVRC2012/train', transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize ])) val_dataset = datasets.ImageFolder('/gs/hs1/tga-hpc-lecture/ILSVRC2012/val', transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize ]))

参考文献 Learning PyTorch with Examples https://pytorch.org/tutorials/beginner/pytorch_with_examples.html PyTorch Examples github https://github.com/pytorch/examples PyTorch Tutorial github https://github.com/yunjey/pytorch-tutorial Understanding PyTorch with an example: a step-by-step tutorial by Daniel Godoy https://towardsdatascience.com/understanding-pytorch-with-an-example-a-step-by-step-tutorial-81fc5f8c4e8e Practical Deep Learning for Coders, v3 by fast.ai https://course.fast.ai PyTorch by Beeren Sahu https://beerensahu.wordpress.com/2018/03/21/pytorch-tutorial-lesson-1-tensor/ Writing Distributed Applications with PyTorch by Séb Arnold https://pytorch.org/tutorials/intermediate/dist_tuto.html

• https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
• https://github.com/pytorch/examples
• https://github.com/yunjey/pytorch-tutorial
• https://towardsdatascience.com/understanding-pytorch-with-an-example-a-step-by-step-tutorial-81fc5f8c4e8e
• https://course.fast.ai
• https://beerensahu.wordpress.com/2018/03/21/pytorch-tutorial-lesson-1-tensor/
• https://pytorch.org/tutorials/intermediate/dist_tuto.html