SuperposPrompt_Thesis/01_ResidualPrompt/04_T5_custom.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "896de91a-4ab9-40f5-a3c1-914535b6e0a7",
   "metadata": {},
   "source": [
    "# intro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5f17aae6-73f5-4793-95a3-09147ea89e04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version is: 3.10.11\n",
      "Scikit-learn version is: 1.2.2\n",
      "Torch version is: 1.13.1+cu117\n",
      "Nvidia device is: NVIDIA GeForce RTX 4090\n",
      "Transformers version is: 4.32.1\n",
      "Adapterhub not found!!!\n"
     ]
    }
   ],
   "source": [
    "from typing import Optional\n",
    "\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from transformers import T5TokenizerFast, T5ForConditionalGeneration\n",
    "\n",
    "from _utils import print_system_info, generate_dataloader\n",
    "from _datasets import AutoLoad\n",
    "from _mydelta import T5Wrapper, auto_freeze\n",
    "from _trainer import train_loop, valid_loop\n",
    "\n",
    "print_system_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb5ef784-fef0-4b7b-98e7-ec5d3575a9a8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from types import SimpleNamespace\n",
    "config = SimpleNamespace(\n",
    "    model_name='google/t5-base-lm-adapt',\n",
    "    n_tokens=30,\n",
    "    n_layers=6,\n",
    "    random_seed=42,\n",
    "    task=['glue:cola'],\n",
    "    hot_modules=['sadcl'],\n",
    "    train_batch_size=32,\n",
    "    valid_batch_size=32,\n",
    "    balancify_sample=False,\n",
    "    learning_rate=0.01,\n",
    "    num_epochs=200\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3802d01-7c5a-4c11-beaf-f683a2fb9d80",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "np.random.seed(config.random_seed)\n",
    "slected_tokens = torch.from_numpy(np.random.randint(0, 32128, size=(config.n_tokens,)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e785d49-beca-4333-986e-b198bbaadf7d",
   "metadata": {},
   "source": [
    "# load model and date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afcc6244-978a-425a-9fa9-8b11dd0df8ba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = T5ForConditionalGeneration.from_pretrained(config.model_name)\n",
    "tokenizer = T5TokenizerFast.from_pretrained(config.model_name, model_max_length=2048)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "894a8474-e2e1-4f9d-b9ab-58d911808ec0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoder.block.6.soft_prompt.sadcl_learned_embedding\n",
      "encoder.block.7.soft_prompt.sadcl_learned_embedding\n",
      "encoder.block.8.soft_prompt.sadcl_learned_embedding\n",
      "encoder.block.9.soft_prompt.sadcl_learned_embedding\n",
      "encoder.block.10.soft_prompt.sadcl_learned_embedding\n",
      "encoder.block.11.soft_prompt.sadcl_learned_embedding\n"
     ]
    }
   ],
   "source": [
    "delta_module = T5Wrapper.mutate(\n",
    "    model=model,\n",
    "    config=config,\n",
    "    slected_tokens=slected_tokens\n",
    ")\n",
    "auto_freeze(model, config.hot_modules, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9453d3cc-c04c-4a27-83aa-eaac3e49c14e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "decoder.block.9.layer.1.EncDecAttention.q.weight\n",
      "decoder.block.9.layer.1.EncDecAttention.k.weight\n",
      "decoder.block.9.layer.1.EncDecAttention.v.weight\n",
      "decoder.block.9.layer.1.EncDecAttention.o.weight\n",
      "decoder.block.9.layer.1.layer_norm.weight\n",
      "decoder.block.9.layer.2.DenseReluDense.wi_0.weight\n",
      "decoder.block.9.layer.2.DenseReluDense.wi_1.weight\n",
      "decoder.block.9.layer.2.DenseReluDense.wo.weight\n",
      "decoder.block.9.layer.2.layer_norm.weight\n",
      "decoder.block.10.layer.0.SelfAttention.q.weight\n",
      "decoder.block.10.layer.0.SelfAttention.k.weight\n",
      "decoder.block.10.layer.0.SelfAttention.v.weight\n",
      "decoder.block.10.layer.0.SelfAttention.o.weight\n",
      "decoder.block.10.layer.0.layer_norm.weight\n",
      "decoder.block.10.layer.1.EncDecAttention.q.weight\n",
      "decoder.block.10.layer.1.EncDecAttention.k.weight\n",
      "decoder.block.10.layer.1.EncDecAttention.v.weight\n",
      "decoder.block.10.layer.1.EncDecAttention.o.weight\n",
      "decoder.block.10.layer.1.layer_norm.weight\n",
      "decoder.block.10.layer.2.DenseReluDense.wi_0.weight\n",
      "decoder.block.10.layer.2.DenseReluDense.wi_1.weight\n",
      "decoder.block.10.layer.2.DenseReluDense.wo.weight\n",
      "decoder.block.10.layer.2.layer_norm.weight\n",
      "decoder.block.11.layer.0.SelfAttention.q.weight\n",
      "decoder.block.11.layer.0.SelfAttention.k.weight\n",
      "decoder.block.11.layer.0.SelfAttention.v.weight\n",
      "decoder.block.11.layer.0.SelfAttention.o.weight\n",
      "decoder.block.11.layer.0.layer_norm.weight\n",
      "decoder.block.11.layer.1.EncDecAttention.q.weight\n",
      "decoder.block.11.layer.1.EncDecAttention.k.weight\n",
      "decoder.block.11.layer.1.EncDecAttention.v.weight\n",
      "decoder.block.11.layer.1.EncDecAttention.o.weight\n",
      "decoder.block.11.layer.1.layer_norm.weight\n",
      "decoder.block.11.layer.2.DenseReluDense.wi_0.weight\n",
      "decoder.block.11.layer.2.DenseReluDense.wi_1.weight\n",
      "decoder.block.11.layer.2.DenseReluDense.wo.weight\n",
      "decoder.block.11.layer.2.layer_norm.weight\n",
      "decoder.final_layer_norm.weight\n",
      "lm_head.weight\n"
     ]
    }
   ],
   "source": [
    "for x, y in model.named_parameters():\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a34e1f1-1fc1-4577-a87d-efeac33894b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset glue (/home/mohalisad/.cache/huggingface/datasets/glue/cola/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22d7491179634c75ab8a5c70e9e4188f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/mohalisad/.cache/huggingface/datasets/glue/cola/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-63df8ebe4567b55a.arrow\n",
      "Loading cached processed dataset at /home/mohalisad/.cache/huggingface/datasets/glue/cola/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-bb3872c77bcda3cd.arrow\n"
     ]
    }
   ],
   "source": [
    "data_loader = AutoLoad(tokenizer)\n",
    "dataset = data_loader.get_and_map(config.task[0])\n",
    "train_loader, valid_loader = generate_dataloader(tokenizer, dataset['train'], dataset['valid'], config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf5aea38-4866-4026-b6d4-a8e8b50153b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model(**next(iter(train_loader))).loss.backward()\n",
    "# for i in range(6, 12):\n",
    "#     o = model.encoder.block[i].soft_prompt.sadcl_learned_embedding.grad.abs().sum().item()\n",
    "#     print(i, o)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6281dbae-3023-4e95-82c9-c9d818c37622",
   "metadata": {},
   "source": [
    "# train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd92aff9-e4cb-4b1b-aece-0a7eee27e0e4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import wandb\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=\"my-awesome-project\",\n",
    "    # track hyperparameters and run metadata\n",
    "    config=config.__dict__\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74f04c24-2298-4152-abde-c1ee6a0ea739",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                        | 0/268 [00:00<?, ?it/s]You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:08<00:00, 32.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 8.963883996009827, 'valid_loss': 6.972279635342685, 'valid_accuracy': 0.0, 'valid_f1-score-1': 0.0, 'valid_f1-score-ma': 0.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 34.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 7.36324492141382, 'valid_loss': 5.521347826177424, 'valid_accuracy': 0.0, 'valid_f1-score-1': 0.0, 'valid_f1-score-ma': 0.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 33.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 6.192735992260833, 'valid_loss': 4.384567484711155, 'valid_accuracy': 0.0, 'valid_f1-score-1': 0.0, 'valid_f1-score-ma': 0.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 33.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 5.118913385405469, 'valid_loss': 3.335551644816543, 'valid_accuracy': 0.05465004793863854, 'valid_f1-score-1': 0.14091470951792337, 'valid_f1-score-ma': 0.009394313967861558}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 33.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 4.148920764674002, 'valid_loss': 2.2682720783985024, 'valid_accuracy': 0.174496644295302, 'valid_f1-score-1': 0.36804853387259856, 'valid_f1-score-ma': 0.02164991375721168}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 33.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 3.1643025679374808, 'valid_loss': 1.2784492048350247, 'valid_accuracy': 0.5148609779482263, 'valid_f1-score-1': 0.7208053691275169, 'valid_f1-score-ma': 0.05544656685596284}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 33.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 2.235221519843856, 'valid_loss': 0.6245457141688375, 'valid_accuracy': 0.6625119846596357, 'valid_f1-score-1': 0.7915151515151515, 'valid_f1-score-ma': 0.09733333333333333}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 33.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 1.5051592252592543, 'valid_loss': 0.4341738431742697, 'valid_accuracy': 0.6768935762224353, 'valid_f1-score-1': 0.8077147866744595, 'valid_f1-score-ma': 0.0810680363745307}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 33.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 1.0515665002723238, 'valid_loss': 0.3996329452052261, 'valid_accuracy': 0.6826462128475551, 'valid_f1-score-1': 0.8151116199198626, 'valid_f1-score-ma': 0.08151116199198626}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 34.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 0.8272433252032123, 'valid_loss': 0.3832718174565922, 'valid_accuracy': 0.6855225311601151, 'valid_f1-score-1': 0.8162100456621004, 'valid_f1-score-ma': 0.11660143509458577}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 34.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 0.7063313028705653, 'valid_loss': 0.36713372216080176, 'valid_accuracy': 0.6874400767018217, 'valid_f1-score-1': 0.8175598631698974, 'valid_f1-score-ma': 0.16351197263397949}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 33.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 0.6392747724234168, 'valid_loss': 0.36563855906327564, 'valid_accuracy': 0.6874400767018217, 'valid_f1-score-1': 0.8170940170940172, 'valid_f1-score-ma': 0.2042735042735043}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████| 268/268 [00:07<00:00, 34.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'train_loss': 0.5930970842713741, 'valid_loss': 0.3603471038919507, 'valid_accuracy': 0.6874400767018217, 'valid_f1-score-1': 0.8170940170940172, 'valid_f1-score-ma': 0.2042735042735043}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|█████████████████████████████████████▏                                                        | 106/268 [00:03<00:04, 33.89it/s]\n",
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)\n",
    "\n",
    "model.to(DEVICE)\n",
    "\n",
    "for epoch in range(config.num_epochs):\n",
    "    train_out = train_loop(model=model, loader=train_loader, optimizer=optimizer)\n",
    "    valid_out = valid_loop(model=model, loader=valid_loader)\n",
    "    wandb.log({\n",
    "        **train_out,\n",
    "        **valid_out\n",
    "    })\n",
    "    \n",
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4368eb4c-fb7b-41bf-89e3-bf20cdfec967",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip uninstall bitsandbytes -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0de2e02c-c5fb-4d13-81fd-5ecb53d42b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset['train'].set_format(columns=['label', 'labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687f7994-e875-4f2e-b151-89460bf78eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['train'][0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5493b857-49e0-4963-b220-7f00422b7511",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "x = load_dataset(\"glue\", \"sst2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d055f9b6-294e-4f53-9941-21ecc040e92b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Counter(x['train']['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abf72943-aa98-4543-beec-942f6f601b89",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "g = x['train']\n",
    "l = g.features['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "919fb5a0-fc9c-4fcc-9fe6-21cff8960b51",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "l.int2str(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b286f811-82bc-4f59-914f-e1c5cd5cd1ef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "29780 / (29780 + 37569)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b581a87a-120d-4f7a-a8f4-b39c6a6d1843",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from types import SimpleNamespace\n",
    "config = SimpleNamespace(\n",
    "    model_name='google/t5-base-lm-adapt',\n",
    "    peft_params={\n",
    "        'n_tokens': 30,\n",
    "        'n_layers': 6\n",
    "    },\n",
    "    random_seed=42,\n",
    "    task=['glue:cola'],\n",
    "    hot_modules=['sadcl'],\n",
    "    train_batch_size=32,\n",
    "    valid_batch_size=32,\n",
    "    balancify_sample=False,\n",
    "    learning_rate=0.01,\n",
    "    num_epochs=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7dbb2d9-d545-48e1-a0ac-6d79258a393b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"model_name\": \"google/t5-base-lm-adapt\", \"peft_params\": {\"n_tokens\": 30, \"n_layers\": 6}, \"random_seed\": 42, \"task\": [\"glue:cola\"], \"hot_modules\": [\"sadcl\"], \"train_batch_size\": 32, \"valid_batch_size\": 32, \"balancify_sample\": false, \"learning_rate\": 0.01, \"num_epochs\": 50}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "print(json.dumps(config.__dict__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d18551c3-68e8-4ee0-8936-65bdec51f4eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import T5TokenizerFast, T5ForConditionalGeneration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddac3321-27f4-4b89-aab6-f91ae8bbc86a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer = T5TokenizerFast.from_pretrained(\"google/t5-large-lm-adapt\", model_max_length=2048)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7456f182-5d9d-44da-b36d-2ec4052fbaf6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c476e007-c4fe-4eb3-939c-25d0b0add711",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23830,  2611, 19567, 10149, 20142,  6737, 26963,  6788,  3871,\n",
       "       28330,   724,  7406, 11474, 18399,  2289, 25511, 25299, 23308,\n",
       "       25412,   370, 32091, 28829,  6148, 29154, 30369, 12979,  8560,\n",
       "        6872, 23228,  8051, 19537,  3741, 22206, 20744, 17051, 27857,\n",
       "        3830, 15329, 21857,  8296, 10768,  7854,  5710,  5405, 27449,\n",
       "       11528,  8599, 12695, 15427, 23726,   389,  3231, 15270, 26906,\n",
       "       23085, 15113, 31792,  8766,  9814, 15904,  6320, 23716, 19682,\n",
       "        2690, 30766, 21262, 11415,  2523, 26538,  3647, 13971, 21655,\n",
       "         287, 19479, 28945, 25134, 17673,  9792, 17556, 31293, 25795,\n",
       "        2753,  8955, 21049, 28409, 24281,  3610, 26070,  2189, 25611,\n",
       "        9641, 23766, 29195,   779, 18660, 10731, 19732,  1664,  2176,\n",
       "        2254])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, tokenizer.vocab_size, size=(100,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e83e5c34-0860-4e2a-9b2a-016f37b35003",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f032c7f-ddd4-4350-81c5-9296c8376d8c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "w = torch.load('best.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "112c06a2-1295-4989-9ce6-5f6204e809ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.5470, -0.8095, -1.4617,  ...,  0.8100, -1.1746,  0.5768],\n",
       "        [-0.9284, -0.6230, -2.4697,  ...,  0.3947, -0.5427, -0.3088],\n",
       "        [ 1.4407,  0.8760,  0.2499,  ...,  0.1860, -0.3176,  2.0041],\n",
       "        ...,\n",
       "        [ 0.8714,  1.1013, -2.7711,  ..., -0.2819,  0.7087, -0.6164],\n",
       "        [ 0.8026, -0.7928, -0.8946,  ..., -1.5204,  1.0164, -1.3527],\n",
       "        [ 0.4650, -2.1778,  0.0213,  ..., -1.1430, -2.3895, -0.0235]],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.pop('sadcl_learned_embedding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4055f80b-ea2f-44db-bf32-98ea3ffe9597",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('sadcl_mlp.0.weight',\n",
       "              tensor([[ 0.1171, -0.7743,  0.5095,  ..., -1.0615,  1.5754,  0.7036],\n",
       "                      [-0.2675,  0.0969,  0.0543,  ...,  0.7276, -0.0671,  0.8296],\n",
       "                      [-0.2987, -0.0700, -1.0519,  ...,  0.6090,  0.0193,  0.0410],\n",
       "                      ...,\n",
       "                      [-0.1463, -0.8924,  0.7947,  ...,  0.2265, -0.6957,  0.5928],\n",
       "                      [-0.4365, -0.9251, -1.0378,  ..., -0.8628, -0.5243,  0.0860],\n",
       "                      [ 0.4860,  0.0648, -0.9160,  ..., -0.5342,  0.1072, -0.1397]],\n",
       "                     device='cuda:0')),\n",
       "             ('sadcl_mlp.0.bias',\n",
       "              tensor([-0.6311, -1.0433, -1.0390, -1.6997, -1.0766, -0.2802, -0.9433, -0.7127,\n",
       "                       0.5315, -1.0400, -0.3756, -0.2602, -0.7607,  0.7578, -0.7066, -0.3561,\n",
       "                      -0.5580, -0.7671, -0.2557, -1.6528, -0.1438, -0.4875, -0.6291, -1.2763,\n",
       "                      -0.2484, -0.6396, -0.7225, -0.8314, -1.3913, -0.7696,  0.0864, -0.7268,\n",
       "                      -0.7812, -1.0606, -0.9011,  0.3322,  0.5159, -0.4453, -0.6409,  0.0714,\n",
       "                      -0.2788, -0.1620, -0.9408,  0.1440, -0.8897, -0.9288, -1.2605, -1.2384,\n",
       "                      -0.0090, -0.0661, -0.5203, -1.5729, -0.5143, -0.4943, -0.9472, -0.8107,\n",
       "                      -0.5748, -1.1438, -0.8919, -0.8606, -1.0831, -1.4380, -1.0802,  0.0522,\n",
       "                       0.0785, -2.4277, -1.0447, -0.3124,  0.1173, -0.8195, -0.0623, -0.1913,\n",
       "                      -1.4551, -0.0732, -1.1574, -0.2217, -0.6697, -0.5846, -0.2473, -0.0144,\n",
       "                      -1.2317, -0.5024, -0.2301,  0.2265, -0.6478, -0.8726, -0.8367, -0.0312,\n",
       "                      -0.4783, -0.3132, -0.6115, -1.5002, -0.6820, -0.9731, -0.6438, -0.8716,\n",
       "                      -0.2628, -0.8308, -0.8588,  0.8616, -0.3398,  0.2025, -0.6247, -0.4494,\n",
       "                      -1.2737, -0.9406, -0.5297, -0.4886, -1.6481, -2.5021, -0.1344, -0.8274,\n",
       "                      -1.6135, -0.9598, -0.8659, -1.3385, -1.4567, -1.0869, -0.1999, -1.3751,\n",
       "                      -0.4536, -1.0839, -1.0037, -0.0429, -0.5243, -0.8836, -0.9716, -1.1037],\n",
       "                     device='cuda:0')),\n",
       "             ('sadcl_mlp.2.weight',\n",
       "              tensor([[ 0.3282,  0.2861,  0.4277,  ..., -1.1185,  0.3197,  0.6003],\n",
       "                      [-0.9305,  0.1462, -0.4269,  ..., -0.1129, -0.7909, -0.6872],\n",
       "                      [ 0.0067, -0.7521, -1.6837,  ..., -0.2374, -0.2790, -0.9895],\n",
       "                      ...,\n",
       "                      [-1.8292,  0.9060,  1.3090,  ..., -0.0273, -1.0552, -0.2187],\n",
       "                      [-0.3804,  0.0945,  0.0337,  ..., -1.6941,  0.0693, -0.0288],\n",
       "                      [-1.3038,  0.2590, -0.2965,  ...,  0.9425, -0.0090, -1.2449]],\n",
       "                     device='cuda:0')),\n",
       "             ('sadcl_mlp.3.weight',\n",
       "              tensor([2.4987, 2.5515, 2.0518,  ..., 1.9383, 1.2583, 1.1634], device='cuda:0')),\n",
       "             ('sadcl_mlp.3.bias',\n",
       "              tensor([ 1.0355, -0.7098, -1.4075,  ..., -0.3350, -0.7165, -0.9371],\n",
       "                     device='cuda:0'))])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa50b148-1f34-41c9-b7f4-c26c3e4cbce6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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