SuperposPrompt_Thesis/08_ICLR/attempt.ipynb

{
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   "id": "e6ecf439-a0db-42e0-a6b9-f512198b0e0e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4bcc7c7e-711a-4cd9-b901-d6ff76938a75",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_path = '/home/msadraei/trained_final/iclr_resp_t5_small_glue-cola/10_attempt/best.pt'\n",
    "first_path = '/home/msadraei/trained_final/iclr_resp_t5_small_glue-cola/10_attempt/first.pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eaa4a300-1e6c-46f0-8f0d-16e9c71c2388",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "best = torch.load(best_path)\n",
    "first = torch.load(first_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5e0b6bb-3bde-4526-8a6a-5dac0a3b3cc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sadcl_p_target\n",
      "tensor(42.7208, device='cuda:0')\n",
      "pretrained_tasks\n",
      "tensor(0., device='cuda:0')\n",
      "sadcl_attention_score.g_network.0.weight\n",
      "tensor(157.3032, device='cuda:0')\n",
      "sadcl_attention_score.g_network.2.weight\n",
      "tensor(154.6590, device='cuda:0')\n",
      "sadcl_attention_score.g_network.3.weight\n",
      "tensor(18.1127, device='cuda:0')\n",
      "sadcl_attention_score.g_network.3.bias\n",
      "tensor(19.0149, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "for key in best.keys():\n",
    "    print(key)\n",
    "    v1 = first[key]\n",
    "    v2 = best[key]\n",
    "    print(torch.norm(v1 - v2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42815cf2-b8bf-4219-a3fd-ebbe92fb5c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = '/home/msadraei/trained_final/forward_transfer_test_t5_base_superglue-rte/10_combine_128_4tasks_new_impl_tie_50/100'\n",
    "last_path = f'{base_path}/last.pt'\n",
    "best_path = f'{base_path}/best.pt'\n",
    "first_path = f'{base_path}/first.pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "880cb651-ddea-4564-93ab-c5f52e1f02dd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "last = torch.load(last_path)\n",
    "best = torch.load(best_path)\n",
    "first = torch.load(first_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee4b3287-203f-49b0-8b89-6070f9ff4062",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as  np\n",
    "def pretrained_coeff(state_dict):\n",
    "    return np.stack([\n",
    "        val.cpu().numpy()\n",
    "        for key, val in state_dict.items()\n",
    "        if 'sadcl_coeff_pretrained' in key\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26518ecd-8cc1-4543-acaf-56637295bbe8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "last_coeff = pretrained_coeff(best)\n",
    "best_coeff = pretrained_coeff(best)\n",
    "first_coeff = pretrained_coeff(first)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a850a65-724a-483d-abb3-b7de6118db31",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42],\n",
       "       [0.43, 0.42, 0.42, 0.42]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(last_coeff/ 100 , 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7182b595-5bb3-4c06-88dc-1f50ed774500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(34.9105)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.linalg.vector_norm(torch.Tensor(best_coeff[0]), ord=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e2a2080-9450-4df2-b20e-4619e3f92c1b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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