diff options
| author | SIPB | 2024-11-17 20:17:29 +0000 |
|---|---|---|
| committer | SIPB | 2024-11-17 20:17:29 +0000 |
| commit | e953b3752e2ce588395de7ec48e32f3276c0c435 (patch) | |
| tree | 82f3776c4ee1335abf4b14757ea051c5052ea995 | |
| parent | e460b0ae66be6c8c897d5880c54e4c2bc1b38aad (diff) | |
Use bfloat16
| -rw-r--r-- | transformer_shortest_paths.ipynb | 410 |
1 files changed, 313 insertions, 97 deletions
diff --git a/transformer_shortest_paths.ipynb b/transformer_shortest_paths.ipynb index fcb24c7..fe0223c 100644 --- a/transformer_shortest_paths.ipynb +++ b/transformer_shortest_paths.ipynb @@ -11,8 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, - "execution_state": "idle", + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -23,13 +22,11 @@ "outputs": [], "source": [ "from collections import deque\n", - "import pickle\n", "# using tqdm.auto glitches out collaborative editing\n", "from tqdm import tqdm\n", "\n", "import torch\n", "import torch.nn as nn\n", - "import pickle\n", "from math import sqrt\n", "from torch.utils.data import DataLoader, TensorDataset\n", "import matplotlib.pyplot as plt\n", @@ -37,9 +34,6 @@ "torch.manual_seed(42)\n", "\n", "import os\n", - "from IPython.display import clear_output\n", - "import ipdb\n", - "\n", "import random\n", "random.seed(42)\n", "\n", @@ -49,8 +43,7 @@ }, { "cell_type": "code", - "execution_count": 3, - "execution_state": "idle", + "execution_count": 2, "metadata": { "id": "lylOX2POPwFL" }, @@ -78,8 +71,7 @@ }, { "cell_type": "code", - "execution_count": 18, - "execution_state": "idle", + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -150,7 +142,7 @@ " distance1.append(dist)\n", " \n", " data = torch.tensor(graphs1, device=device)\n", - " labels = torch.tensor(distance1, dtype=torch.float32, device=device)\n", + " labels = torch.tensor(distance1, dtype=torch.bfloat16, device=device)\n", " padding = data == PAD_TOKEN\n", " return data, labels, padding\n", "\n", @@ -193,26 +185,25 @@ " break\n", " \n", " data = torch.tensor(graphs2, device=device)\n", - " labels = torch.tensor(distance2, dtype=torch.float32, device=device)\n", + " labels = torch.tensor(distance2, dtype=torch.bfloat16, device=device)\n", " padding = data == PAD_TOKEN\n", " return data, labels, padding" ] }, { "cell_type": "code", - "execution_count": 41, - "execution_state": "idle", + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([255., 0., 298., 0., 231., 0., 210., 0., 123., 0., 63.,\n", - " 0., 31., 0., 46., 0., 39., 0., 35., 0., 40., 0.,\n", - " 35., 0., 44., 0., 24., 0., 37., 0., 26., 0., 0.,\n", - " 39., 0., 31., 0., 31., 0., 38., 0., 34., 0., 36.,\n", - " 0., 33., 0., 33., 0., 22., 0., 38., 0., 28., 0.,\n", - " 34., 0., 26., 0., 30., 0., 26., 0., 32.]),\n", + "(array([252., 0., 309., 0., 278., 0., 199., 0., 119., 0., 63.,\n", + " 0., 23., 0., 45., 0., 36., 0., 36., 0., 28., 0.,\n", + " 37., 0., 23., 0., 33., 0., 24., 0., 34., 0., 0.,\n", + " 30., 0., 33., 0., 34., 0., 34., 0., 25., 0., 33.,\n", + " 0., 39., 0., 33., 0., 20., 0., 29., 0., 27., 0.,\n", + " 35., 0., 37., 0., 30., 0., 33., 0., 37.]),\n", " array([ 1. , 1.484375, 1.96875 , 2.453125, 2.9375 , 3.421875,\n", " 3.90625 , 4.390625, 4.875 , 5.359375, 5.84375 , 6.328125,\n", " 6.8125 , 7.296875, 7.78125 , 8.265625, 8.75 , 9.234375,\n", @@ -227,13 +218,13 @@ " <BarContainer object of 64 artists>)" ] }, - "execution_count": 41, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -243,46 +234,46 @@ } ], "source": [ - "plt.hist(mkbatch(2048)[1].cpu(), bins=64)" + "plt.hist(mkbatch(2048)[1].to(torch.float32).cpu(), bins=64)" ] }, { "cell_type": "code", - "execution_count": 42, - "execution_state": "idle", + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([1162., 0., 0., 0., 0., 0., 0., 0., 0.,\n", - " 0., 525., 0., 0., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 242., 0., 0., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 87., 0., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0., 22., 0., 0.,\n", - " 0., 0., 0., 0., 0., 0., 0., 0., 7.,\n", + "(array([1157., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 570., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 210., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", - " 3.]),\n", - " array([1. , 1.09375, 1.1875 , 1.28125, 1.375 , 1.46875, 1.5625 ,\n", - " 1.65625, 1.75 , 1.84375, 1.9375 , 2.03125, 2.125 , 2.21875,\n", - " 2.3125 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3.578125, 3.65625 , 3.734375,\n", + " 3.8125 , 3.890625, 3.96875 , 4.046875, 4.125 , 4.203125,\n", + " 4.28125 , 4.359375, 4.4375 , 4.515625, 4.59375 , 4.671875,\n", + " 4.75 , 4.828125, 4.90625 , 4.984375, 5.0625 , 5.140625,\n", + " 5.21875 , 5.296875, 5.375 , 5.453125, 5.53125 , 5.609375,\n", + " 5.6875 , 5.765625, 5.84375 , 5.921875, 6. ]),\n", " <BarContainer object of 64 artists>)" ] }, - "execution_count": 42, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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AADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOB0SMF988YVuvvlm9enTR3FxcRoyZIh27dplH7csS/Pnz1dKSori4uKUlZWlQ4cOBTxGY2Oj8vLylJCQoMTERE2fPl3Nzc0dMS4AADBM0APm6NGjGjVqlLp3767XX39d+/fv16OPPqrevXvba5YuXarS0lItX75cVVVV6tmzp7Kzs3Xs2DF7TV5envbt26eKigpt2rRJ27ZtU0FBQbDHBQAABoqwLMsK5gPOnTtX27dv1z/+8Y/THrcsS263W3feeafuuusuSZLP55PT6dTq1as1efJkHThwQOnp6aqurlZGRoYkqby8XBMmTNDnn38ut9v9g3P4/X45HA75fD4lJCQE7wlKOn/uq2c89uninKCeCwCAn5Kz/fkd9CswL7/8sjIyMnTdddcpOTlZw4cP18qVK+3jhw8fltfrVVZWlr3P4XAoMzNTlZWVkqTKykolJiba8SJJWVlZioyMVFVV1WnP29raKr/fH7ABAIDwFPSA+eSTT1RWVqYLLrhAb7zxhm699VbddtttWrNmjSTJ6/VKkpxOZ8D9nE6nfczr9So5OTngeFRUlJKSkuw131VSUiKHw2FvqampwX5qAACgiwh6wLS3t+viiy/WokWLNHz4cBUUFGjGjBlavnx5sE8VoLi4WD6fz97q6uo69HwAACB0gh4wKSkpSk9PD9g3aNAgHTlyRJLkcrkkSfX19QFr6uvr7WMul0sNDQ0Bx0+cOKHGxkZ7zXfFxMQoISEhYAMAAOEp6AEzatQoHTx4MGDfRx99pP79+0uS0tLS5HK5tHnzZvu43+9XVVWVPB6PJMnj8aipqUk1NTX2mi1btqi9vV2ZmZnBHhkAABgmKtgPeMcdd+iyyy7TokWLdP3112vnzp1asWKFVqxYIUmKiIjQrFmztHDhQl1wwQVKS0vTvHnz5Ha7NWnSJEn/f8Vm3Lhx9q+e2traVFRUpMmTJ5/VJ5AAAEB4C3rAjBw5Uhs2bFBxcbEeeughpaWl6YknnlBeXp695p577lFLS4sKCgrU1NSk0aNHq7y8XLGxsfaatWvXqqioSGPGjFFkZKRyc3NVWloa7HEBAICBgv49MF0F3wMDAIB5QvY9MAAAAB2NgAEAAMYJ+ntggB+DX8sBAM4FV2AAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgnA4PmMWLFysiIkKzZs2y9x07dkyFhYXq06ePevXqpdzcXNXX1wfc78iRI8rJyVGPHj2UnJysu+++WydOnOjocQEAgAE6NGCqq6v1zDPP6KKLLgrYf8cdd+iVV17Riy++qK1bt+rLL7/Utddeax8/efKkcnJydPz4ce3YsUNr1qzR6tWrNX/+/I4cFwAAGKLDAqa5uVl5eXlauXKlevfube/3+Xx69tln9dhjj+nqq6/WiBEjtGrVKu3YsUPvvfeeJOnNN9/U/v379dxzz2nYsGEaP368Hn74YS1btkzHjx/vqJEBAIAhOixgCgsLlZOTo6ysrID9NTU1amtrC9g/cOBA9evXT5WVlZKkyspKDRkyRE6n016TnZ0tv9+vffv2ddTIAADAEFEd8aDr16/X7t27VV1d/b1jXq9X0dHRSkxMDNjvdDrl9XrtNd+Ol1PHTx07ndbWVrW2ttq3/X7///IUAABAFxb0KzB1dXW6/fbbtXbtWsXGxgb74c+opKREDofD3lJTUzvt3AAAoHMFPWBqamrU0NCgiy++WFFRUYqKitLWrVtVWlqqqKgoOZ1OHT9+XE1NTQH3q6+vl8vlkiS5XK7vfSrp1O1Ta76ruLhYPp/P3urq6oL91AAAQBcR9IAZM2aM9uzZo9raWnvLyMhQXl6e/c/du3fX5s2b7fscPHhQR44ckcfjkSR5PB7t2bNHDQ0N9pqKigolJCQoPT39tOeNiYlRQkJCwAYAAMJT0N8DEx8fr8GDBwfs69mzp/r06WPvnz59umbPnq2kpCQlJCRo5syZ8ng8uvTSSyVJY8eOVXp6uqZMmaKlS5fK6/Xq/vvvV2FhoWJiYoI9MgAAMEyHvIn3hzz++OOKjIxUbm6uWltblZ2draeffto+3q1bN23atEm33nqrPB6Pevbsqfz8fD300EOhGBcAAHQxnRIw77zzTsDt2NhYLVu2TMuWLTvjffr376/XXnutgycDAAAm4m8hAQAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOAQMAAAwDgEDAACMQ8AAAADjEDAAAMA4BAwAADAOAQMAAIxDwAAAAOMQMAAAwDgEDAAAMA4BAwAAjEPAAAAA4xAwAADAOEEPmJKSEo0cOVLx8fFKTk7WpEmTdPDgwYA1x44dU2Fhofr06aNevXopNzdX9fX1AWuOHDminJwc9ejRQ8nJybr77rt14sSJYI8LAAAMFBXsB9y6dasKCws1cuRInThxQvfee6/Gjh2r/fv3q2fPnpKkO+64Q6+++qpefPFFORwOFRUV6dprr9X27dslSSdPnlROTo5cLpd27Nihr776Srfccou6d++uRYsWBXtk4Cfn/LmvnvHYp4tzOnESAPhxgh4w5eXlAbdXr16t5ORk1dTU6IorrpDP59Ozzz6rdevW6eqrr5YkrVq1SoMGDdJ7772nSy+9VG+++ab279+vt956S06nU8OGDdPDDz+sOXPm6IEHHlB0dHSwxwYAAAbp8PfA+Hw+SVJSUpIkqaamRm1tbcrKyrLXDBw4UP369VNlZaUkqbKyUkOGDJHT6bTXZGdny+/3a9++fac9T2trq/x+f8AGAADCU4cGTHt7u2bNmqVRo0Zp8ODBkiSv16vo6GglJiYGrHU6nfJ6vfaab8fLqeOnjp1OSUmJHA6HvaWmpgb52QAAgK6iQwOmsLBQe/fu1fr16zvyNJKk4uJi+Xw+e6urq+vwcwIAgNAI+ntgTikqKtKmTZu0bds29e3b197vcrl0/PhxNTU1BVyFqa+vl8vlstfs3Lkz4PFOfUrp1JrviomJUUxMTJCfBQAA6IqCfgXGsiwVFRVpw4YN2rJli9LS0gKOjxgxQt27d9fmzZvtfQcPHtSRI0fk8XgkSR6PR3v27FFDQ4O9pqKiQgkJCUpPTw/2yAAAwDBBvwJTWFiodevW6aWXXlJ8fLz9nhWHw6G4uDg5HA5Nnz5ds2fPVlJSkhISEjRz5kx5PB5deumlkqSxY8cqPT1dU6ZM0dKlS+X1enX//fersLCQqywAACD4AVNWViZJuvLKKwP2r1q1Sn/4wx8kSY8//rgiIyOVm5ur1tZWZWdn6+mnn7bXduvWTZs2bdKtt94qj8ejnj17Kj8/Xw899FCwxwUAAAYKesBYlvWDa2JjY7Vs2TItW7bsjGv69++v1157LZijAQCAMMHfQgIAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGCcq1AMAQDg7f+6rZzz26eKcTpwECC9cgQEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADGIWAAAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgnKhQDwAAQLCcP/fVMx77dHFOJ06CjsYVGAAAYJwuHTDLli3T+eefr9jYWGVmZmrnzp2hHgkAAHQBXTZgnn/+ec2ePVsLFizQ7t27NXToUGVnZ6uhoSHUowEAgBDrsgHz2GOPacaMGZo6darS09O1fPly9ejRQ3/7299CPRoAAAixLvkm3uPHj6umpkbFxcX2vsjISGVlZamysvK092ltbVVra6t92+fzSZL8fn/Q52tv/eaMxzrifD8FvKadi9e78/Bady5e7+AavOCNMx7b+2B2h5zz1L8ny7L++0KrC/riiy8sSdaOHTsC9t99993WJZdcctr7LFiwwJLExsbGxsbGFgZbXV3df22FLnkF5scoLi7W7Nmz7dvt7e1qbGxUnz59FBEREbTz+P1+paamqq6uTgkJCUF7XHwfr3Xn4HXuHLzOnYPXuXN05OtsWZa+/vprud3u/7quSwbMeeedp27duqm+vj5gf319vVwu12nvExMTo5iYmIB9iYmJHTWiEhIS+I+jk/Badw5e587B69w5eJ07R0e9zg6H4wfXdMk38UZHR2vEiBHavHmzva+9vV2bN2+Wx+MJ4WQAAKAr6JJXYCRp9uzZys/PV0ZGhi655BI98cQTamlp0dSpU0M9GgAACLEuGzA33HCD/v3vf2v+/Pnyer0aNmyYysvL5XQ6QzpXTEyMFixY8L1fVyH4eK07B69z5+B17hy8zp2jK7zOEZb1Q59TAgAA6Fq65HtgAAAA/hsCBgAAGIeAAQAAxiFgAACAcQiYc7Bt2zZNnDhRbrdbERER2rhxY6hHCjslJSUaOXKk4uPjlZycrEmTJungwYOhHivslJWV6aKLLrK/hMrj8ej1118P9Vhhb/HixYqIiNCsWbNCPUrYeeCBBxQRERGwDRw4MNRjhaUvvvhCN998s/r06aO4uDgNGTJEu3bt6vQ5CJhz0NLSoqFDh2rZsmWhHiVsbd26VYWFhXrvvfdUUVGhtrY2jR07Vi0tLaEeLaz07dtXixcvVk1NjXbt2qWrr75av/vd77Rv375Qjxa2qqur9cwzz+iiiy4K9Shh68ILL9RXX31lb++++26oRwo7R48e1ahRo9S9e3e9/vrr2r9/vx599FH17t2702fpst8D0xWNHz9e48ePD/UYYa28vDzg9urVq5WcnKyamhpdccUVIZoq/EycODHg9iOPPKKysjK99957uvDCC0M0Vfhqbm5WXl6eVq5cqYULF4Z6nLAVFRV1xj83g+BYsmSJUlNTtWrVKntfWlpaSGbhCgy6NJ/PJ0lKSkoK8STh6+TJk1q/fr1aWlr4Ux0dpLCwUDk5OcrKygr1KGHt0KFDcrvd+sUvfqG8vDwdOXIk1COFnZdfflkZGRm67rrrlJycrOHDh2vlypUhmYUrMOiy2tvbNWvWLI0aNUqDBw8O9ThhZ8+ePfJ4PDp27Jh69eqlDRs2KD09PdRjhZ3169dr9+7dqq6uDvUoYS0zM1OrV6/WgAED9NVXX+nBBx/U5Zdfrr179yo+Pj7U44WNTz75RGVlZZo9e7buvfdeVVdX67bbblN0dLTy8/M7dRYCBl1WYWGh9u7dy++xO8iAAQNUW1srn8+nv//978rPz9fWrVuJmCCqq6vT7bffroqKCsXGxoZ6nLD27V/vX3TRRcrMzFT//v31wgsvaPr06SGcLLy0t7crIyNDixYtkiQNHz5ce/fu1fLlyzs9YPgVErqkoqIibdq0SW+//bb69u0b6nHCUnR0tH71q19pxIgRKikp0dChQ/Xkk0+GeqywUlNTo4aGBl188cWKiopSVFSUtm7dqtLSUkVFRenkyZOhHjFsJSYm6te//rU+/vjjUI8SVlJSUr73PzmDBg0Kya/ruAKDLsWyLM2cOVMbNmzQO++8E7I3h/0Utbe3q7W1NdRjhJUxY8Zoz549AfumTp2qgQMHas6cOerWrVuIJgt/zc3N+te//qUpU6aEepSwMmrUqO99tcVHH32k/v37d/osBMw5aG5uDqj5w4cPq7a2VklJSerXr18IJwsfhYWFWrdunV566SXFx8fL6/VKkhwOh+Li4kI8XfgoLi7W+PHj1a9fP3399ddat26d3nnnHb3xxhuhHi2sxMfHf+/9Wz179lSfPn14X1eQ3XXXXZo4caL69++vL7/8UgsWLFC3bt104403hnq0sHLHHXfosssu06JFi3T99ddr586dWrFihVasWNH5w1g4a2+//bYl6Xtbfn5+qEcLG6d7fSVZq1atCvVoYWXatGlW//79rejoaOtnP/uZNWbMGOvNN98M9Vg/Cb/5zW+s22+/PdRjhJ0bbrjBSklJsaKjo62f//zn1g033GB9/PHHoR4rLL3yyivW4MGDrZiYGGvgwIHWihUrQjJHhGVZVudnEwAAwI/Hm3gBAIBxCBgAAGAcAgYAABiHgAEAAMYhYAAAgHEIGAAAYBwCBgAAGIeAAQAAxiFgAACAcQgYAABgHAIGAAAYh4ABAADG+T+/FGJF6kiv7QAAAABJRU5ErkJggg==", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -292,7 +283,7 @@ } ], "source": [ - "plt.hist(mktunebatch(2048)[1].cpu(), bins=64)" + "plt.hist(mktunebatch(2048)[1].to(torch.float32).cpu(), bins=64)" ] }, { @@ -306,8 +297,7 @@ }, { "cell_type": "code", - "execution_count": 5, - "execution_state": "idle", + "execution_count": 6, "metadata": { "id": "tLOWhg_CeWzH" }, @@ -316,9 +306,9 @@ "class TransformerModel(nn.Module):\n", " def __init__(self, input_dim, model_dim, output_dim, num_heads, num_layers, seq_len, device, dropout):\n", " super().__init__()\n", - " self.embedding = nn.Embedding(input_dim, model_dim//2)\n", + " self.embedding = nn.Embedding(input_dim, model_dim//2, dtype=torch.bfloat16)\n", " # seq_len is odd\n", - " self.fancy_encoding = torch.repeat_interleave(torch.rand((1, seq_len // 2 + 1, model_dim // 2), device=device), 2, dim=1)\n", + " self.fancy_encoding = torch.repeat_interleave(torch.rand((1, seq_len // 2 + 1, model_dim // 2), device=device, dtype=torch.bfloat16), 2, dim=1)\n", " # cut off last element since the target vertex is not repeated\n", " self.fancy_encoding = self.fancy_encoding[:, :seq_len, :]\n", " \n", @@ -328,10 +318,10 @@ "\n", " encoder_layer = nn.TransformerEncoderLayer(d_model=model_dim, nhead=num_heads,\n", " dim_feedforward=model_dim*4,\n", - " dropout=dropout, batch_first=True)\n", + " dropout=dropout, batch_first=True, dtype=torch.bfloat16)\n", " self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers)\n", "\n", - " self.fc_out = nn.Linear(model_dim*seq_len, output_dim)\n", + " self.fc_out = nn.Linear(model_dim*seq_len, output_dim, dtype=torch.bfloat16)\n", "\n", " def forward(self, src, key_padding_mask):\n", " batch_size, src_len = src.size(0), src.size(1)\n", @@ -356,8 +346,7 @@ }, { "cell_type": "code", - "execution_count": 11, - "execution_state": "idle", + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -370,8 +359,66 @@ "name": "stdout", "output_type": "stream", "text": [ - "Training data: 131M\n", - "Trainable parameters in the model: 605K\n" + "T", + "r", + "a", + "i", + "n", + "i", + "n", + "g", + " ", + "d", + "a", + "t", + "a", + ":", + " ", + "2", + "6", + "2", + "M", + "\n", + "T", + "r", + "a", + "i", + "n", + "a", + "b", + "l", + "e", + " ", + "p", + "a", + "r", + "a", + "m", + "e", + "t", + "e", + "r", + "s", + " ", + "i", + "n", + " ", + "t", + "h", + "e", + " ", + "m", + "o", + "d", + "e", + "l", + ":", + " ", + "6", + "0", + "5", + "K", + "\n" ] } ], @@ -379,9 +426,9 @@ "# PARAMS\n", "VOCAB_SIZE = 1 + MAX_VTXS # one more than the max number of vertices\n", "MODEL_DIM = 64 # Dimension of model (embedding and transformer)\n", - "NEPOCHS = 1000\n", - "BSZ = 2048 # Batch size\n", - "BPE = 64 # Batches per epoch\n", + "NEPOCHS = 100\n", + "BSZ = 10244 # Batch size\n", + "BPE = 256 # Batches per epoch\n", "LR = 0.003\n", "WD = 0.002\n", "NHEADS = 4\n", @@ -391,6 +438,7 @@ " output_dim=1, num_heads=NHEADS,\n", " num_layers=NLAYERS, seq_len=SEQ_LEN,\n", " dropout=DROPOUT, device=device).to(device)\n", + "model = torch.compile(model)\n", "\n", "criterion = nn.MSELoss()\n", "optimizer = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=WD)\n", @@ -411,8 +459,7 @@ }, { "cell_type": "code", - "execution_count": 46, - "execution_state": "idle", + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -428,25 +475,24 @@ }, { "cell_type": "code", - "execution_count": 51, - "execution_state": "idle", + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ba829714ada43c184a04b0a0b4d06f2", + "model_id": "4eb2d57cb7c948da8e9bd201f70d8d19", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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", 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' 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' 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\u001b[38;5;241m=\u001b[39m \u001b[43mmkbatch\u001b[49m(BSZ)\n\u001b[1;32m 9\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 10\u001b[0m output \u001b[38;5;241m=\u001b[39m model(batch_src, batch_padding_mask)\n", - "\u001b[0;31mNameError\u001b[0m: name 'mkbatch' is not defined" + " ", + "9", + "3", + "%", + "|", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "█", + "▌", + " ", + " ", + " ", + " ", + " ", + " ", + " ", + " ", + " ", + " ", + "|", + " ", + "2", + "3", + "8", + "/", + "2", + "5", + "6", + " ", + "[", + "0", + "1", + ":", + "1", + "3", + "<", + "0", + "0", + ":", + "0", + "5", + ",", + " ", + " ", + "3", + ".", + "5", + "3", + "i", + "t", + "/", + "s", + "]" ] } ], @@ -503,6 +724,9 @@ "train_err = []\n", "test_err = []\n", "\n", + "# clear loss file\n", + "open('loss', 'w').close()\n", + "\n", "for epoch in range(NEPOCHS):\n", " model.train()\n", " train_loss = 0\n", @@ -511,15 +735,15 @@ " optimizer.zero_grad()\n", " output = model(batch_src, batch_padding_mask)\n", " loss = criterion(output.squeeze(1), batch_labels)\n", - " train_loss += loss.item() / BPEREPOCH\n", + " train_loss += loss.item() / BPE\n", " loss.backward()\n", " optimizer.step()\n", "\n", " test_loss = evaluate()\n", - " \n", + " \n", " test_err.append(test_loss)\n", " train_err.append(train_loss)\n", - " with open(\"loss\", \"a\") as f:\n", + " with open('loss', 'a') as f:\n", " f.write(f\"{train_loss} {test_loss}\\n\")\n", " print(f\"Epoch {epoch + 1}/{NEPOCHS} \\t Train Err: {train_loss:.4f} \\t Test Err: {test_loss:.4f}\")\n", " ax.plot(train_err, label='Train', color='blue')\n", @@ -528,14 +752,13 @@ " ax.set_ylabel('MSE')\n", " fig.canvas.draw()\n", "\n", - " if epoch % 100 == 99:\n", + " if epoch % 10 == 9:\n", " torch.save(model.state_dict(), f\"model_weights_{epoch}.pth\")" ] }, { "cell_type": "code", "execution_count": 57, - "execution_state": "idle", "metadata": {}, "outputs": [ { @@ -556,7 +779,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": { "id": "LoGEmM5lH7_A" }, @@ -570,7 +792,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": {}, "outputs": [], "source": [ @@ -580,7 +801,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": {}, "outputs": [], "source": [ @@ -590,7 +810,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": {}, "outputs": [], "source": [ @@ -600,7 +819,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": {}, "outputs": [], "source": [ @@ -613,7 +831,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": {}, "outputs": [], "source": [ @@ -632,7 +849,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": {}, "outputs": [], "source": [ @@ -647,7 +863,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": {}, "outputs": [], "source": [ @@ -664,7 +879,6 @@ { "cell_type": "code", "execution_count": null, - "execution_state": "running", "metadata": {}, "outputs": [], "source": [ @@ -679,7 +893,6 @@ { "cell_type": "code", "execution_count": 13, - "execution_state": "idle", "metadata": {}, "outputs": [ { @@ -698,6 +911,9 @@ "tune_train_err = []\n", "tune_test_err = []\n", "\n", + "# clear loss file\n", + "open('tune_loss', 'w').close()\n", + "\n", "for epoch in range(N_TUNE_EPOCHS):\n", " model.train()\n", " train_loss = 0\n", @@ -714,7 +930,7 @@ " \n", " tune_test_err.append(test_loss)\n", " tune_train_err.append(train_loss)\n", - " with open(\"tune_loss\", \"a\") as f:\n", + " with open('tune_loss', 'a') as f:\n", " f.write(f\"{train_loss} {test_loss}\\n\")\n", " ax.plot(tune_train_err, label='Train', color='blue')\n", " ax.plot(tune_test_err, label='Test', color='red')\n", @@ -757,7 +973,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.3" } }, "nbformat": 4, |