{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stealth in-place edits for correcting hallucinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "%cd ../../\n",
    "%pwd\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "# load utility functions\n",
    "from util import utils\n",
    "from util import evaluation\n",
    "from util import extraction\n",
    "from util import measures\n",
    "\n",
    "from stealth_edit import compute_wb\n",
    "from stealth_edit import edit_utils\n",
    "\n",
    "from evaluation import eval_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Paths and Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = ['gpt-j-6b', 'llama-3-8b', 'mamba-1.4b']\n",
    "datasets = ['mcf', 'zsre']\n",
    "\n",
    "save_path = './results/in-place/'\n",
    "results_path = './results/in-place/{}/{}/'\n",
    "\n",
    "fs_path = './results/eval_fs/in-place/fs_in-place_{}_{}.pickle'\n",
    "\n",
    "cache_path = './cache'\n",
    "other_pickle = os.path.join(cache_path, 'wiki_test/wikipedia_features_{}_layer{}_w1.pickle')\n",
    "features_cache_path = os.path.join(cache_path, 'prompts_extract_{}_{}.pickle')\n",
    "\n",
    "theta = 0.005"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find all pickle files across layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find unique pickle files\n",
    "pickle_paths = np.array([\n",
    "    f for f in utils.path_all_files(save_path) \\\n",
    "        if f.endswith('.pickle') and ('perplexity' not in f)\n",
    "])\n",
    "_, unique_indices = np.unique(\n",
    "    np.array([os.path.basename(f) for f in pickle_paths]), return_index=True)\n",
    "\n",
    "pickle_paths = pickle_paths[unique_indices]\n",
    "pickle_files = [os.path.basename(f) for f in pickle_paths]\n",
    "\n",
    "# find edited case_ids\n",
    "edited_case_ids = [int(f.split('.')[0]) for f in pickle_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:19<00:00,  2.77s/it]\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "100%|██████████| 8/8 [00:29<00:00,  3.68s/it]\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "100%|██████████| 12/12 [01:09<00:00,  5.76s/it]\n",
      "100%|██████████| 7/7 [00:30<00:00,  4.34s/it]\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "100%|██████████| 8/8 [00:31<00:00,  3.99s/it]\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "100%|██████████| 12/12 [00:50<00:00,  4.19s/it]\n"
     ]
    }
   ],
   "source": [
    "# load PPL metrics\n",
    "perplexity_metrics = {}\n",
    "\n",
    "for dataset_name in datasets:\n",
    "\n",
    "    across_model_metrics = {}\n",
    "    for model_name in models:\n",
    "        across_model_metrics[model_name] = evaluation.eval_model_ppl(\n",
    "            model_name,\n",
    "            results_path = results_path.format(dataset_name, model_name),\n",
    "            eval_op = True,\n",
    "            eval_oap = False,\n",
    "            eval_ap = False,\n",
    "            eval_aug = False,\n",
    "            eval_rnd = False,\n",
    "            num_examples = 1000\n",
    "        )\n",
    "    for model_name in models:\n",
    "        across_model_metrics[model_name]['layer_indices'] = np.array([int(l.split('layer')[-1]) for l in across_model_metrics[model_name]['layer'][:,0]])\n",
    "\n",
    "    summarise_metrics = {}\n",
    "    for model_name in models:\n",
    "        summarise_metrics[model_name] = evaluation.eval_model_ppl_metrics(\n",
    "            across_model_metrics[model_name],\n",
    "            eval_op = True,\n",
    "            eval_oap = False,\n",
    "            eval_ap = False,\n",
    "            eval_aug = False,\n",
    "            eval_rnd = False,\n",
    "        )\n",
    "    perplexity_metrics[dataset_name] = copy.deepcopy(summarise_metrics)\n",
    "\n",
    "# load feature space metrics\n",
    "mcf_fs_contents = {m: utils.loadpickle(fs_path.format('mcf', m)) for m in models}\n",
    "zsre_fs_contents = {m: utils.loadpickle(fs_path.format('zsre', m)) for m in models}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Caclulate Intrinsic Dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Theorem 2 intrinsic dimensions with `wikipedia` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating for gpt-j-6b\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:04<00:00,  1.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating for llama-3-8b\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [00:03<00:00,  2.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating for mamba-1.4b\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [00:02<00:00,  4.73it/s]\n"
     ]
    }
   ],
   "source": [
    "intrinsic_dims = {}\n",
    "\n",
    "for model in models:\n",
    "\n",
    "    print('\\nCalculating for', model)\n",
    "    intrinsic_dims_on_sphere, num_sampled = eval_utils.calculate_t2_intrinsic_dims(\n",
    "        model,\n",
    "        other_pickle,\n",
    "        deltas = [2*(1-theta)**2-2],\n",
    "        layers = evaluation.model_layer_indices[model],\n",
    "        cache_norms_path = cache_path\n",
    "    )\n",
    "    intrinsic_dims[model] = intrinsic_dims_on_sphere\n",
    "\n",
    "probs_wiki = {}\n",
    "for key in intrinsic_dims:\n",
    "    probs_wiki[key] = np.sqrt(2**(-intrinsic_dims[key][:,0]-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate Theorem 2 intrinsic dimensions with the `mcf` and `zsre` datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:00<00:00, 16.33it/s]\n",
      "100%|██████████| 8/8 [00:00<00:00, 16.74it/s]\n",
      "100%|██████████| 12/12 [00:00<00:00, 13.31it/s]\n",
      "100%|██████████| 7/7 [00:00<00:00, 19.95it/s]\n",
      "100%|██████████| 8/8 [00:00<00:00, 20.08it/s]\n",
      "100%|██████████| 12/12 [00:00<00:00, 16.17it/s]\n"
     ]
    }
   ],
   "source": [
    "probs_datasets = {}\n",
    "\n",
    "for dataset in datasets:\n",
    "    \n",
    "    dataset_results = {}\n",
    "    for model_name in models:\n",
    "\n",
    "        layer_indices = evaluation.model_layer_indices[model_name]\n",
    "        contents = utils.loadpickle(features_cache_path.format(dataset, model_name))\n",
    "\n",
    "        # exlude edited case_ids\n",
    "        mask = utils.generate_mask(contents['case_ids'], edited_case_ids)\n",
    "\n",
    "        intrinsic_dims = []\n",
    "        for i in tqdm(layer_indices):\n",
    "            \n",
    "            # project to sphere\n",
    "            features = compute_wb.back_to_sphere(\n",
    "                contents[i][~mask], \n",
    "                model_name, \n",
    "                norm_learnables = extraction.load_norm_learnables(\n",
    "                    model_name, layer=i, cache_path=cache_path)\n",
    "            )\n",
    "\n",
    "            # calculate intrinsic dimension\n",
    "            ids = measures.calc_sep_intrinsic_dim(\n",
    "                features,\n",
    "                centre = False,\n",
    "                deltas = [2*(1-theta)**2-2]\n",
    "            )\n",
    "            intrinsic_dims.append(ids)    \n",
    "\n",
    "        dataset_results[model_name] = np.sqrt(2**(-np.squeeze(np.array(intrinsic_dims))-1))\n",
    "        \n",
    "    probs_datasets[dataset] = dataset_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from util import evaluation\n",
    "reload(evaluation)\n",
    "\n",
    "fig, axs = plt.subplots(2, 4, figsize=(13, 6))\n",
    "\n",
    "main_colors = ['black', 'b', 'red']\n",
    "sub_colors = ['gray', 'lightblue', 'coral']\n",
    "\n",
    "model_handles = []\n",
    "dataset_handles = []\n",
    "\n",
    "\n",
    "for i, model_name in enumerate(models):\n",
    "\n",
    "    relative_depth = evaluation.model_layer_indices[model_name] \\\n",
    "        / evaluation.model_depth[model_name]\n",
    "\n",
    "    axs[0,1].scatter(relative_depth, np.nan_to_num(perplexity_metrics['mcf'][model_name]['efficacy']), color=main_colors[i], s=7)\n",
    "    axs[0,1].plot(relative_depth, np.nan_to_num(perplexity_metrics['mcf'][model_name]['efficacy']), color=sub_colors[i])\n",
    "\n",
    "    axs[0,1].scatter(relative_depth, np.nan_to_num(perplexity_metrics['zsre'][model_name]['efficacy']), color=main_colors[i], s=7, marker='^')\n",
    "    axs[0,1].plot(relative_depth, np.nan_to_num(perplexity_metrics['zsre'][model_name]['efficacy']), color=sub_colors[i], linestyle='--')\n",
    "\n",
    "    axs[0,1].set_xlabel('Edit Layer Depth (normalised)')\n",
    "    axs[0,1].set_ylabel('Success Rate')\n",
    "    axs[0,1].set_title('Edit Success Rate', fontsize=11)\n",
    "    axs[0,1].set_xlim([0,1])\n",
    "\n",
    "\n",
    "    mcf_mean = perplexity_metrics['mcf'][model_name]['ppl_other_mean']\n",
    "    mcf_std = perplexity_metrics['mcf'][model_name]['ppl_other_std']\n",
    "    zsre_mean = perplexity_metrics['zsre'][model_name]['ppl_other_mean']\n",
    "    zsre_std = perplexity_metrics['zsre'][model_name]['ppl_other_std']\n",
    "\n",
    "    max_mean = np.maximum(zsre_mean, mcf_mean)\n",
    "    min_mean = np.minimum(zsre_mean, mcf_mean)\n",
    "    max_std = np.maximum(zsre_std, mcf_std)\n",
    "\n",
    "    if i == 2:\n",
    "        label_to_insert = 'Max STD'\n",
    "    else:\n",
    "        label_to_insert = None\n",
    "\n",
    "    axs[0,2].scatter(relative_depth, mcf_mean, color=main_colors[i], s=7)\n",
    "    axs[0,2].plot(relative_depth, mcf_mean, color=sub_colors[i])\n",
    "\n",
    "    axs[0,2].scatter(relative_depth, zsre_mean, color=main_colors[i], s=7, marker='^')\n",
    "    axs[0,2].plot(relative_depth, zsre_mean, color=sub_colors[i], linestyle='--')\n",
    "    axs[0,2].fill_between(relative_depth, (min_mean-max_std), (max_mean+max_std), color=sub_colors[i], alpha=0.2, label=label_to_insert)\n",
    "\n",
    "    axs[0,2].set_ylabel('Ratio')\n",
    "    axs[0,2].set_xlabel('Edit Layer Depth (normalised)')\n",
    "    axs[0,2].set_title('Perplexity Ratio\\n (500 other prompts in dataset)', fontsize=11)\n",
    "    axs[0,2].legend()\n",
    "    axs[0,2].set_xlim([0,1])\n",
    "\n",
    "    mcf_mean_other_fprs = mcf_fs_contents[model_name]['mean_other_fprs']\n",
    "    zsre_mean_other_fprs = zsre_fs_contents[model_name]['mean_other_fprs']\n",
    "    mcf_std_other_fprs = mcf_fs_contents[model_name]['std_other_fprs']\n",
    "    zsre_std_other_fprs = zsre_fs_contents[model_name]['std_other_fprs']\n",
    "\n",
    "    max_mean_other_fprs = np.maximum(mcf_mean_other_fprs, zsre_mean_other_fprs)\n",
    "    min_mean_other_fprs = np.minimum(mcf_mean_other_fprs, zsre_mean_other_fprs)\n",
    "    max_std_other_fprs = np.maximum(mcf_std_other_fprs, zsre_std_other_fprs)\n",
    "\n",
    "    axs[1,0].scatter(relative_depth, mcf_mean_other_fprs, color=main_colors[i], s=7)\n",
    "    axs[1,0].plot(relative_depth, mcf_mean_other_fprs, color=sub_colors[i])\n",
    "\n",
    "    axs[1,0].scatter(relative_depth, zsre_mean_other_fprs, color=main_colors[i], s=7, marker='^')\n",
    "    axs[1,0].plot(relative_depth, zsre_mean_other_fprs, color=sub_colors[i], linestyle='--')\n",
    "    axs[1,0].fill_between(relative_depth, (min_mean_other_fprs-max_std_other_fprs), (max_mean_other_fprs+max_std_other_fprs), color=sub_colors[i], alpha=0.2, label=label_to_insert)\n",
    "    \n",
    "    axs[1,0].set_xlabel('Edit Layer Depth (normalised)')\n",
    "    axs[1,0].set_ylabel('False Positive Rate')\n",
    "    axs[1,0].set_title('Detector False Positive Rate\\n (other prompts in dataset)', fontsize=11)\n",
    "    axs[1,0].set_xlim([0,1])\n",
    "    axs[1,0].legend()\n",
    "    axs[1,0].set_ylim([-0.05,1.05])\n",
    "\n",
    "\n",
    "    axs[1,1].scatter(relative_depth, probs_datasets['mcf'][model_name], color=main_colors[i], s=7)\n",
    "    axs[1,1].plot(relative_depth, probs_datasets['mcf'][model_name], color=sub_colors[i])\n",
    "\n",
    "    axs[1,1].scatter(relative_depth, probs_datasets['zsre'][model_name], color=main_colors[i], s=7, marker='^')\n",
    "    axs[1,1].plot(relative_depth, probs_datasets['zsre'][model_name], color=sub_colors[i], linestyle='--')\n",
    "\n",
    "    axs[1,1].set_xlabel('Edit Layer Depth (normalised)')\n",
    "    axs[1,1].set_ylabel('False Positive Rate')\n",
    "    axs[1,1].set_title('Theorem 2 Worst Case FPR\\n (other prompts in dataset)', fontsize=11)\n",
    "    axs[1,1].set_xlim([0,1])\n",
    "    axs[1,1].set_ylim([-0.05,1.05])\n",
    "\n",
    "\n",
    "    mcf_mean_wiki_fprs = mcf_fs_contents[model_name]['mean_wiki_fprs']\n",
    "    zsre_mean_wiki_fprs = zsre_fs_contents[model_name]['mean_wiki_fprs']\n",
    "    mcf_std_wiki_fprs = mcf_fs_contents[model_name]['std_wiki_fprs']\n",
    "    zsre_std_wiki_fprs = zsre_fs_contents[model_name]['std_wiki_fprs']\n",
    "\n",
    "    max_mean_wiki_fprs = np.maximum(mcf_mean_wiki_fprs, zsre_mean_wiki_fprs)\n",
    "    min_mean_wiki_fprs = np.minimum(mcf_mean_wiki_fprs, zsre_mean_wiki_fprs)\n",
    "    max_std_wiki_fprs = np.maximum(mcf_std_wiki_fprs, zsre_std_wiki_fprs)\n",
    "\n",
    "    axs[1,2].scatter(relative_depth, mcf_mean_wiki_fprs, color=main_colors[i], s=7)\n",
    "    axs[1,2].plot(relative_depth, mcf_mean_wiki_fprs, color=sub_colors[i])\n",
    "\n",
    "    axs[1,2].scatter(relative_depth, zsre_mean_wiki_fprs, color=main_colors[i], s=7, marker='^')\n",
    "    axs[1,2].plot(relative_depth, zsre_mean_wiki_fprs, color=sub_colors[i], linestyle='--')\n",
    "    axs[1,2].fill_between(relative_depth, (min_mean_wiki_fprs-max_std_wiki_fprs), (max_mean_wiki_fprs+max_std_wiki_fprs), color=sub_colors[i], alpha=0.2, label=label_to_insert)\n",
    "\n",
    "    axs[1,2].set_xlabel('Edit Layer Depth (normalised)')\n",
    "    axs[1,2].set_ylabel('False Positive Rate')\n",
    "    axs[1,2].set_title('Detector False Positive Rate\\n (wikipedia prompts)', fontsize=11)\n",
    "    axs[1,2].set_xlim([0,1])\n",
    "    axs[1,2].legend()\n",
    "    axs[1,2].set_ylim([-0.05,1.05])\n",
    "\n",
    "    \n",
    "    axs[1,3].scatter(relative_depth, probs_wiki[model_name], color=main_colors[i], s=7)\n",
    "    mh = axs[1,3].plot(relative_depth, probs_wiki[model_name], color=sub_colors[i], label=model_name)\n",
    "    model_handles.append(mh[0])\n",
    "\n",
    "    axs[1,3].set_xlabel('Edit Layer Depth (normalised)')\n",
    "    axs[1,3].set_ylabel('False Positive Rate')\n",
    "    axs[1,3].set_title('Theorem 2 Worst Case FPR\\n (wikipedia prompts)', fontsize=11)\n",
    "    axs[1,3].set_xlim([0,1])\n",
    "    axs[1,3].set_ylim([-0.05,1.05])\n",
    "\n",
    "    if i == 0:\n",
    "        dh0 = axs[0,1].plot(relative_depth, np.nan_to_num(perplexity_metrics['mcf'][model_name]['efficacy']), color=sub_colors[i], label='MCF')\n",
    "        dh1 = axs[0,1].plot(relative_depth, np.nan_to_num(perplexity_metrics['zsre'][model_name]['efficacy']), color=sub_colors[i], linestyle='--', label='ZsRE')\n",
    "        dataset_handles.append(dh0[0])\n",
    "        dataset_handles.append(dh1[0])\n",
    "\n",
    "\n",
    "model_legend = fig.legend(model_handles, ['gpt-j-6b', 'llama-3-8b', 'mamba-1.4b'], bbox_to_anchor=(0.94, 0.95), loc = 'upper right', title='Models', title_fontproperties={'weight':'bold'}, fontsize=11)\n",
    "dataset_legend = fig.legend(dataset_handles, ['MCF', 'ZsRE'], bbox_to_anchor=(0.935, 0.74), loc = 'upper right', title='Edited Datasets', title_fontproperties={'weight':'bold'}, fontsize=11)\n",
    "\n",
    "\n",
    "axs[0,0].axis('off')\n",
    "axs[0,3].axis('off')\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(4):\n",
    "        axs[i,j].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('in-place.png', dpi=300)\n",
    "\n",
    "plt.show()"
   ]
  }
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