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doctor and model test
2025-10-08 22:51:36 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ddfb4457",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EPOCH 1\n",
"\tLoss: 7.424792\n",
"[0] \n",
"[1] \n",
"[2] \n",
"[3] \n",
"[4] \n",
"[5] \n",
"[6] \n",
"[7] \n",
"[8] \n",
"[9] \n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 114\u001b[39m\n\u001b[32m 112\u001b[39m loss_t = cross_entropy(logits_t, tgt[:, t]) \u001b[38;5;66;03m# CE expects raw logits; PAD ignored\u001b[39;00m\n\u001b[32m 113\u001b[39m loss_t.backward() \u001b[38;5;66;03m# backprop for this step\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m114\u001b[39m \u001b[43moptimizer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# update params\u001b[39;00m\n\u001b[32m 115\u001b[39m scheduler.step() \u001b[38;5;66;03m# Noam/warmup: step per optimizer step\u001b[39;00m\n\u001b[32m 117\u001b[39m total_loss = \u001b[38;5;28mfloat\u001b[39m(loss_t.detach()) \u001b[38;5;66;03m# keep last step loss for logging\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/optim/optimizer.py:516\u001b[39m, in \u001b[36mOptimizer.profile_hook_step.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 511\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 512\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 513\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 514\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m516\u001b[39m out = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 517\u001b[39m \u001b[38;5;28mself\u001b[39m._optimizer_step_code()\n\u001b[32m 519\u001b[39m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/optim/optimizer.py:81\u001b[39m, in \u001b[36m_use_grad_for_differentiable.<locals>._use_grad\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 79\u001b[39m torch.set_grad_enabled(\u001b[38;5;28mself\u001b[39m.defaults[\u001b[33m\"\u001b[39m\u001b[33mdifferentiable\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 80\u001b[39m torch._dynamo.graph_break()\n\u001b[32m---> \u001b[39m\u001b[32m81\u001b[39m ret = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 82\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 83\u001b[39m torch._dynamo.graph_break()\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/optim/adam.py:247\u001b[39m, in \u001b[36mAdam.step\u001b[39m\u001b[34m(self, closure)\u001b[39m\n\u001b[32m 235\u001b[39m beta1, beta2 = group[\u001b[33m\"\u001b[39m\u001b[33mbetas\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 237\u001b[39m has_complex = \u001b[38;5;28mself\u001b[39m._init_group(\n\u001b[32m 238\u001b[39m group,\n\u001b[32m 239\u001b[39m params_with_grad,\n\u001b[32m (...)\u001b[39m\u001b[32m 244\u001b[39m state_steps,\n\u001b[32m 245\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m247\u001b[39m \u001b[43madam\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 248\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams_with_grad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 249\u001b[39m \u001b[43m \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 250\u001b[39m \u001b[43m \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 251\u001b[39m \u001b[43m \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 253\u001b[39m \u001b[43m \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 254\u001b[39m \u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mamsgrad\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 255\u001b[39m \u001b[43m \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 256\u001b[39m \u001b[43m \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 257\u001b[39m \u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 258\u001b[39m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mlr\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 259\u001b[39m \u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mweight_decay\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 260\u001b[39m \u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43meps\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 261\u001b[39m \u001b[43m \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmaximize\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 262\u001b[39m \u001b[43m \u001b[49m\u001b[43mforeach\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mforeach\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 263\u001b[39m \u001b[43m \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcapturable\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 264\u001b[39m \u001b[43m \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdifferentiable\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/optim/optimizer.py:149\u001b[39m, in \u001b[36m_disable_dynamo_if_unsupported.<locals>.wrapper.<locals>.maybe_fallback\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 147\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m disabled_func(*args, **kwargs)\n\u001b[32m 148\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m149\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/optim/adam.py:949\u001b[39m, in \u001b[36madam\u001b[39m\u001b[34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, has_complex, decoupled_weight_decay, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[39m\n\u001b[32m 946\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 947\u001b[39m func = _single_tensor_adam\n\u001b[32m--> \u001b[39m\u001b[32m949\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 950\u001b[39m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 951\u001b[39m \u001b[43m \u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 952\u001b[39m \u001b[43m \u001b[49m\u001b[43mexp_avgs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 953\u001b[39m \u001b[43m \u001b[49m\u001b[43mexp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 954\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_exp_avg_sqs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 955\u001b[39m \u001b[43m \u001b[49m\u001b[43mstate_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 956\u001b[39m \u001b[43m \u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[43m=\u001b[49m\u001b[43mamsgrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 957\u001b[39m \u001b[43m \u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhas_complex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 958\u001b[39m \u001b[43m \u001b[49m\u001b[43mbeta1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbeta1\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 959\u001b[39m \u001b[43m \u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbeta2\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 960\u001b[39m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 961\u001b[39m \u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[43m=\u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 962\u001b[39m \u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m=\u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 963\u001b[39m \u001b[43m \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 964\u001b[39m \u001b[43m \u001b[49m\u001b[43mcapturable\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcapturable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 965\u001b[39m \u001b[43m \u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdifferentiable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 966\u001b[39m \u001b[43m \u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mgrad_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 967\u001b[39m \u001b[43m \u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfound_inf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 968\u001b[39m \u001b[43m \u001b[49m\u001b[43mdecoupled_weight_decay\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdecoupled_weight_decay\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 969\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/optim/adam.py:411\u001b[39m, in \u001b[36m_single_tensor_adam\u001b[39m\u001b[34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, has_complex, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable, decoupled_weight_decay)\u001b[39m\n\u001b[32m 408\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m weight_decay != \u001b[32m0\u001b[39m:\n\u001b[32m 409\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m decoupled_weight_decay:\n\u001b[32m 410\u001b[39m \u001b[38;5;66;03m# Perform stepweight decay\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m411\u001b[39m \u001b[43mparam\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmul_\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m-\u001b[49m\u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 412\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 413\u001b[39m \u001b[38;5;66;03m# Nested if is necessary to bypass jitscript rules\u001b[39;00m\n\u001b[32m 414\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m differentiable \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(weight_decay, Tensor):\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/deep_learning/lib/python3.13/site-packages/torch/utils/_device.py:103\u001b[39m, in \u001b[36mDeviceContext.__torch_function__\u001b[39m\u001b[34m(self, func, types, args, kwargs)\u001b[39m\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m _device_constructors() \u001b[38;5;129;01mand\u001b[39;00m kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mdevice\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 102\u001b[39m kwargs[\u001b[33m\"\u001b[39m\u001b[33mdevice\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28mself\u001b[39m.device\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[31mKeyboardInterrupt\u001b[39m: "
]
}
],
"source": [
"import random\n",
"import torch\n",
"import pandas as pd\n",
"from pathlib import Path\n",
"import Project_Model.Libs.Embedder as Embedder\n",
"import Project_Model.Libs.BPE as BPE\n",
"import Project_Model.Libs.Transformer as Transformer\n",
"import Project_Model.Libs.TorchShims as torch_shims\n",
"from Project_Model.Libs.Training.learning_rade_shedulers import Custom_lr\n",
"from Project_Model.Libs.Training.logistic_collector import LogitsCollector # import the external collector\n",
"\n",
"# set a fixed seed\n",
"torch.manual_seed(0)\n",
"random.seed(0)\n",
"DEVICE = torch_shims.get_default_device()\n",
"torch.set_default_device(DEVICE)\n",
"\n",
"# BPE Init\n",
"VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
"SPECIAL_VOC = BPE.default_special_tokens()\n",
"\n",
"VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
"TOKENANO = BPE.TokeNanoCore(VOCABULARY, SPECIAL_VOC)\n",
"\n",
"# Constants\n",
"TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size + 1\n",
"EMBEDDED_SIZE = 256\n",
"FEED_FORWARD_MULTIPLIER = 4\n",
"ATTENTION_HEADS = 4\n",
"SENTENCE_LENGTH = 256\n",
"NUMBER_OF_BLOCKS = 2\n",
"MAX_EPOCHS = int(1e3)\n",
"\n",
"PAD_TOKEN = TOKENANO.encode(\"<PAD>\")[0]\n",
"END_TOKEN = TOKENANO.encode(\"<END>\")[0]\n",
"\n",
"# Load CSV\n",
"TOY_DATASET_PATH = Path(\"Assets/Dataset/1-hop/toy/rdf_text.csv\")\n",
"TOY_DATASET = pd.read_csv(TOY_DATASET_PATH)\n",
"\n",
"TOY_BATCH_INPUT_LIST: list[list[int]] = []\n",
"TOY_BATCH_PADDING_LIST: list[list[bool]] = []\n",
"TOY_BATCH_TARGET_LIST: list[list[int]] = []\n",
"TOY_BATCH_DECODER_DEFAULT: list[list[int]] = []\n",
"\n",
"for index, row in TOY_DATASET.iterrows():\n",
" RDFs: str = row[\"RDFs\"]\n",
" Abstract: str = row[\"Abstract\"]\n",
"\n",
" input_tokens = TOKENANO.encode(RDFs) # encoder input ids\n",
" output_tokens = TOKENANO.encode(Abstract)[1:] # decoder target ids (shifted left)\n",
" decoder_default_tokens = TOKENANO.encode(\"<SOS>\") # decoder input starts with <SOS>\n",
"\n",
" input_tokens, padding = Transformer.normalize_sequence(\n",
" input_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
" ) # pad/trim + end token\n",
" output_tokens, _ = Transformer.normalize_sequence(\n",
" output_tokens, SENTENCE_LENGTH, PAD_TOKEN, END_TOKEN\n",
" ) # pad/trim + end token\n",
" decoder_default_tokens = Transformer.pad_sequence(\n",
" decoder_default_tokens, SENTENCE_LENGTH, PAD_TOKEN\n",
" ) # pad with PAD up to SENTENCE_LENGTH\n",
"\n",
" TOY_BATCH_INPUT_LIST.append(input_tokens)\n",
" TOY_BATCH_PADDING_LIST.append(padding)\n",
" TOY_BATCH_TARGET_LIST.append(output_tokens)\n",
" TOY_BATCH_DECODER_DEFAULT.append(decoder_default_tokens)\n",
"\n",
"# Training loop\n",
"LOSS_HISTORY = []\n",
"NANOSOCRATES = Transformer.TrainingModel(\n",
" TOKEN_SPACE_SIZE,\n",
" EMBEDDED_SIZE,\n",
" FEED_FORWARD_MULTIPLIER,\n",
" ATTENTION_HEADS,\n",
" NUMBER_OF_BLOCKS,\n",
")\n",
"\n",
"collector = LogitsCollector(PAD_TOKEN, END_TOKEN, TOKENANO) # collects logits and decodes\n",
"\n",
"NANOSOCRATES.train()\n",
"cross_entropy = torch.nn.CrossEntropyLoss(ignore_index=PAD_TOKEN)\n",
"optimizer = torch.optim.AdamW(NANOSOCRATES.parameters())\n",
"scheduler = Custom_lr(EMBEDDED_SIZE, 4000) # step each optimizer step\n",
"\n",
"current_epoch = 0\n",
"BATCH_SIZE = min(32, len(TOY_BATCH_INPUT_LIST)) # small batch to stabilize\n",
"\n",
"while current_epoch < MAX_EPOCHS:\n",
" # simple fixed mini-batch from the top; later you can shuffle/slice\n",
" enc = torch.tensor(TOY_BATCH_INPUT_LIST[:BATCH_SIZE], dtype=torch.long) # [B,T] encoder token ids\n",
" pad = torch.tensor(TOY_BATCH_PADDING_LIST[:BATCH_SIZE], dtype=torch.bool) # [B,T] True where encoder PAD is present\n",
" tgt = torch.tensor(TOY_BATCH_TARGET_LIST[:BATCH_SIZE], dtype=torch.long) # [B,T] decoder targets (ground-truth)\n",
"\n",
" # decoder prefix buffer: <SOS> at pos 0, PAD elsewhere (no shift here) # we will fill it step by step\n",
" dec = torch.tensor(TOY_BATCH_DECODER_DEFAULT[:BATCH_SIZE], dtype=torch.long) # [B,T]\n",
"\n",
" total_loss = 0.0\n",
" collector.reset() # start fresh for this epoch\n",
"\n",
" T = tgt.size(1) # sequence length\n",
" for t in range(T):\n",
" optimizer.zero_grad(set_to_none=True) # clear grads for this token step\n",
"\n",
" prefix = dec[:, : t + 1] # [B, t+1] current decoder prefix\n",
" dec_pad_mask = prefix.eq(PAD_TOKEN) # [B, t+1] True where PAD inside prefix\n",
"\n",
" # one-step logits given prefix (trainer model expects 4 args now)\n",
" logits_t: torch.Tensor = NANOSOCRATES((enc, pad, prefix, dec_pad_mask)) # [B,V] logits for step t\n",
" collector.add(logits_t) # store logits for decoding later\n",
"\n",
" loss_t = cross_entropy(logits_t, tgt[:, t]) # CE expects raw logits; PAD ignored\n",
" loss_t.backward() # backprop for this step\n",
" optimizer.step() # update params\n",
" scheduler.step() # Noam/warmup: step per optimizer step\n",
"\n",
" total_loss = float(loss_t.detach()) # keep last step loss for logging\n",
"\n",
" # teacher forcing: reveal the correct token for next position\n",
" if t < T - 1:\n",
" dec[:, t + 1] = tgt[:, t] # write ground-truth into next slot\n",
"\n",
" current_epoch += 1\n",
" print(f\"EPOCH {current_epoch}\\n\\tLoss: {total_loss:.6f}\") # simple log\n",
" collector.print_decoded() # print decoded predictions for the batch\n"
]
}
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