fan_effects_test.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thaopham03/evaluating_fan_effects_in_large_language_models/blob/main/fan_effects_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kf2JBLkJO-dd",
        "outputId": "3fdd4899-de0f-4090-ec49-3ca17cca89ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/JesseTNRoberts/minicons_modded\n",
            "  Cloning https://github.com/JesseTNRoberts/minicons_modded to /tmp/pip-req-build-4j26ubzi\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/JesseTNRoberts/minicons_modded /tmp/pip-req-build-4j26ubzi\n",
            "  Resolved https://github.com/JesseTNRoberts/minicons_modded to commit 2aa9e4e05fb5c2af99f10dc75cc1e2968b5eaceb\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: minicons\n",
            "  Building wheel for minicons (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for minicons: filename=minicons-0.1-py3-none-any.whl size=19102 sha256=fe2e9513d1782af0048660d7b3af8c23f651b5de860cb04f31f60bb5eb9e0e38\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-969bvzy5/wheels/15/0e/ec/7fb33880467af25612dba7aec20864fb5523bca9247bf3e09b\n",
            "Successfully built minicons\n",
            "Installing collected packages: minicons\n",
            "Successfully installed minicons-0.1\n",
            "Collecting git+https://github.com/JesseTNRoberts/PopulationLM\n",
            "  Cloning https://github.com/JesseTNRoberts/PopulationLM to /tmp/pip-req-build-khd3khyt\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/JesseTNRoberts/PopulationLM /tmp/pip-req-build-khd3khyt\n",
            "  Resolved https://github.com/JesseTNRoberts/PopulationLM to commit e7adde2326e644d7c94d2c7086d9561f8aefb977\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: PopulationLM\n",
            "  Building wheel for PopulationLM (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PopulationLM: filename=PopulationLM-0.1-py3-none-any.whl size=4262 sha256=666ee9634f30e18d8cd1903c86d251845042e5c356f9c4ca89306424c4df6ea1\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-xbqmxx6n/wheels/76/d3/b7/994b47d195d749b9752de5e275517c30ab4ef097e508ec8f2f\n",
            "Successfully built PopulationLM\n",
            "Installing collected packages: PopulationLM\n",
            "Successfully installed PopulationLM-0.1\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.30.1-py3-none-any.whl (302 kB)\n",
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            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.0)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n",
            "Successfully installed accelerate-0.30.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105\n"
          ]
        }
      ],
      "source": [
        "# install external libraries:\n",
        "\n",
        "!pip install git+https://github.com/JesseTNRoberts/minicons_modded\n",
        "!pip install git+https://github.com/JesseTNRoberts/PopulationLM\n",
        "!pip install accelerate transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1IZ1GyRN1F0",
        "outputId": "9c9e7e81-7eb8-454a-bc5f-b587dac0c66f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XaP4VXaMPFXJ",
        "outputId": "fae86557-5da2-4412-87a6-44cfc65eeaf6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import argparse\n",
        "import csv\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import random\n",
        "import accelerate\n",
        "\n",
        "import transformers\n",
        "import os\n",
        "import shutil\n",
        "import glob\n",
        "\n",
        "import torch\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "from transformers import AutoModelForMaskedLM, AutoModelForCausalLM\n",
        "from transformers import AutoModelForMaskedLM, AutoModelForCausalLM\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "from minicons import scorer\n",
        "import PopulationLM as pop\n",
        "\n",
        "# from google.colab import userdata\n",
        "# token = userdata.get('HF_TOKEN')\n",
        "\n",
        "import gc\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "committee_size = 50\n",
        "add_dropout_layers = True\n",
        "\n",
        "\n",
        "inpath = '/content/drive/MyDrive/IGOG-Alignment-Exp/Experiments/fan-effects-exp/generated_birds_prompts.csv'\n",
        "\n",
        "cachepath = '/content/drive/MyDrive/IGOG-Alignment-Exp/cache/'\n",
        "\n",
        "\n",
        "# make results dir: ../IGOG-Alignment-Exp/Experiments/fan-effects-exp/results/ration/model_name.csv\n",
        "components = inpath.split(\"/\")\n",
        "data_dir = \"/\".join(components[0:-1])\n",
        "dataset_name = components[-1].split(\".\")[0]\n",
        "results_dir = f\"{data_dir}/results/rational\"\n",
        "\n",
        "dataset = []\n",
        "with open(inpath, \"r\") as f:\n",
        "    reader = csv.DictReader(f)\n",
        "    column_names = reader.fieldnames\n",
        "    for row in reader:\n",
        "        dataset.append(list(row.values()))\n",
        "\n",
        "batch_size = 54\n",
        "device = 'cuda'\n",
        "stimuli_loader = DataLoader(dataset, batch_size = batch_size, num_workers=0)\n",
        "\n",
        "models = [\n",
        "    # ['bert-base-uncased',  'mlm'], # gpu error\n",
        "    # ['bert-large-uncased', 'mlm'], # gpu error\n",
        "    # ['distilbert-base-uncased', 'mlm'],  # gpu error\n",
        "    # ['roberta-base', 'mlm'],  # finished\n",
        "    # ['roberta-large', 'mlm'], # out of memory\n",
        "    # [\"google/electra-large-generator\", 'mlm'], # gpu error\n",
        "    # ['FacebookAI/xlm-roberta-large', 'mlm'],   # out of memory\n",
        "    # ['distilgpt2', 'incremental'], # finished\n",
        "    # ['gpt2-medium',  'incremental'],  # finished\n",
        "    # ['gpt2',  'incremental'],  # finished\n",
        "    # ['openai-gpt',  'incremental'], # gpu error\n",
        "    # [\"daryl149/llama-2-7b-hf\", 'incremental'],  # gpu error\n",
        "    # [\"mistralai/Mistral-7B-v0.1\",  'incremental'],  # no access\n",
        "    # [\"google/gemma-7b\",  'incremental'],  # no access\n",
        "    ['PKU-Alignment/alpaca-7b-reproduced', 'incremental'],\n",
        "    ['openlm-research/open_llama_13b',  'incremental'],\n",
        "    ['upstage/SOLAR-10.7B-v1.0',  'incremental'],\n",
        "    ['microsoft/phi-2',  'incremental'],\n",
        "    ['meta-llama/Llama-2-13b-hf',  'incremental'],\n",
        "]\n",
        "\n",
        "use_population = True\n",
        "local_only = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "paWcs1u1PF4a",
        "outputId": "6af7b081-5469-4160-ebdb-8a2cd62b3ddf",
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          "text": [
            "PKU-Alignment/alpaca-7b-reproduced\n"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d43052ed3df4bb596aa3950506902b2"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6f5f6c619a14ad69aaa7403c48378a3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00006-of-00007.safetensors:   0%|          | 0.00/1.93G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79be6f2aaf094100ae4c5ce71ed1b226"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00007-of-00007.safetensors:   0%|          | 0.00/1.66G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8e0fa00479c47418b69227d2f2a0222"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26333b8e0600489297ef3493481c750b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79ebd4a461a2484a83ead2dacd92176e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu and disk.\n",
            "WARNING:accelerate.big_modeling:You shouldn't move a model that is dispatched using accelerate hooks.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error loading model PKU-Alignment/alpaca-7b-reproduced: You can't move a model that has some modules offloaded to cpu or disk.\n",
            "openlm-research/open_llama_13b\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "23563c5c2c9e42438927e0c041d19c3b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2038e25942a44264a220e8628e2059e7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a0998639e5141eaa01ca1f893e5d160"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6427fbb3e55f4ebf8fc476990549b0b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin.index.json:   0%|          | 0.00/33.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da10ceae20be48a7b2b4865cb20915be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a456493486514aad8790b75ae4fd9626"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59090962744a45e091b789be90fa8454"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "pytorch_model-00002-of-00003.bin:   0%|          | 0.00/9.90G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9240e50c121540869a7e8ca4d8e1c6f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "251e0c55386748afb50fd88815f3c4cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error loading model openlm-research/open_llama_13b: The current `device_map` had weights offloaded to the disk. Please provide an `offload_folder` for them. Alternatively, make sure you have `safetensors` installed if the model you are using offers the weights in this format.\n",
            "upstage/SOLAR-10.7B-v1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4626a7f7c81b4ed1bb807a3a26fefcb4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e5d89be4abd4b63bdd1de55ec56d24c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f091819b5d794afda46a9c9e1233f67c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dea8ce94216b4aa19f6c96b85c9ba717"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/658 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "640a9bb21d414c7f9e1d5fabad39b829"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/35.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26f8647080b24b83a5b1b5265975f7c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8d1daab518b4300b379c81ed29138ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00005.safetensors:   0%|          | 0.00/4.94G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ee91d3ab53f40a18ac8ba2bd982c696"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-5103b10e6f01>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"incremental\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0;31m# try:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m                 transformer = scorer.IncrementalLMScorer(model[0], device, token=None, cache_dir=cachepath,\n\u001b[0m\u001b[1;32m     22\u001b[0m                                                     local_files_only=local_only, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map=\"auto\")\n\u001b[1;32m     23\u001b[0m         \u001b[0;31m#     except ValueError as e:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/minicons/scorer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model_name, device, token, **kwargs)\u001b[0m\n\u001b[1;32m    981\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIncrementalLMScorer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 983\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModelForCausalLM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m         \u001b[0;31m# define CLS and SEP tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    561\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m             \u001b[0mmodel_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m             return model_class.from_pretrained(\n\u001b[0m\u001b[1;32m    564\u001b[0m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmodel_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhub_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   3509\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_sharded\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3510\u001b[0m             \u001b[0;31m# rsolved_archive_file becomes a list of files that point to the different checkpoint shards in this case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3511\u001b[0;31m             resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(\n\u001b[0m\u001b[1;32m   3512\u001b[0m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3513\u001b[0m                 \u001b[0mresolved_archive_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mget_checkpoint_shard_files\u001b[0;34m(pretrained_model_name_or_path, index_filename, cache_dir, force_download, proxies, resume_download, local_files_only, token, user_agent, revision, subfolder, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m   1038\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1039\u001b[0m             \u001b[0;31m# Load from URL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m             cached_filename = cached_file(\n\u001b[0m\u001b[1;32m   1041\u001b[0m                 \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m                 \u001b[0mshard_filename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    397\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m         \u001b[0;31m# Load from URL or cache if already cached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 399\u001b[0;31m         resolved_file = hf_hub_download(\n\u001b[0m\u001b[1;32m    400\u001b[0m             \u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m             \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, proxies, etag_timeout, token, local_files_only, headers, endpoint, legacy_cache_layout, resume_download, force_filename, local_dir_use_symlinks)\u001b[0m\n\u001b[1;32m   1219\u001b[0m         )\n\u001b[1;32m   1220\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m         return _hf_hub_download_to_cache_dir(\n\u001b[0m\u001b[1;32m   1222\u001b[0m             \u001b[0;31m# Destination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m             \u001b[0mcache_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_hf_hub_download_to_cache_dir\u001b[0;34m(cache_dir, repo_id, filename, repo_type, revision, headers, proxies, etag_timeout, endpoint, local_files_only, force_download)\u001b[0m\n\u001b[1;32m   1365\u001b[0m     \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlock_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mWeakFileLock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlock_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m         _download_to_tmp_and_move(\n\u001b[0m\u001b[1;32m   1368\u001b[0m             \u001b[0mincomplete_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblob_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\".incomplete\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m             \u001b[0mdestination_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblob_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36m_download_to_tmp_and_move\u001b[0;34m(incomplete_path, destination_path, url_to_download, proxies, headers, expected_size, filename, force_download)\u001b[0m\n\u001b[1;32m   1882\u001b[0m             \u001b[0m_check_disk_space\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpected_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdestination_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1884\u001b[0;31m         http_get(\n\u001b[0m\u001b[1;32m   1885\u001b[0m             \u001b[0murl_to_download\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1886\u001b[0m             \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py\u001b[0m in \u001b[0;36mhttp_get\u001b[0;34m(url, temp_file, proxies, resume_size, headers, expected_size, displayed_filename, _nb_retries, _tqdm_bar)\u001b[0m\n\u001b[1;32m    540\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# filter out keep-alive new chunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m                 \u001b[0mprogress\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m                 \u001b[0mtemp_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m                 \u001b[0mnew_resume_size\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m                 \u001b[0;31m# Some data has been downloaded from the server so we reset the number of retries.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "for model in models:\n",
        "    model_name = model[0].replace(\"/\", \"_\")\n",
        "    if os.path.exists(results_dir + f\"/{model_name}.csv\"):\n",
        "        continue\n",
        "\n",
        "    print(model[0])\n",
        "\n",
        "    try:\n",
        "        if model[1] == \"mlm\":\n",
        "            # try:\n",
        "                transformer = scorer.MaskedLMScorer(model[0], device, token=None, cache_dir=cachepath,\n",
        "                                                    local_files_only=local_only, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map=\"auto\")\n",
        "            # except ValueError as e:\n",
        "            #     if \"device_map\" in str(e):\n",
        "            #         transformer = scorer.MaskedLMScorer(model[0], device, token=None, cache_dir=cachepath,\n",
        "            #                                             local_files_only=local_only, low_cpu_mem_usage=True, torch_dtype=torch.float16)\n",
        "            #     else:\n",
        "            #         raise e\n",
        "        elif model[1] == \"incremental\":\n",
        "            # try:\n",
        "                transformer = scorer.IncrementalLMScorer(model[0], device, token=None, cache_dir=cachepath,\n",
        "                                                    local_files_only=local_only, low_cpu_mem_usage=True, torch_dtype=torch.float16, device_map=\"auto\")\n",
        "        #     except ValueError as e:\n",
        "        #         if \"device_map\" in str(e):\n",
        "        #             transformer = scorer.IncrementalLMScorer(model[0], device, token=None, cache_dir=cachepath,\n",
        "        #                                                     local_files_only=local_only, low_cpu_mem_usage=True, torch_dtype=torch.float16)\n",
        "        #         else:\n",
        "        #             raise e\n",
        "        # else:\n",
        "        #     raise ValueError(\"Invalid model type\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error loading model {model[0]}: {e}\")\n",
        "        continue\n",
        "\n",
        "    num_params = [sum(p.numel() for p in transformer.model.parameters())] * len(dataset)\n",
        "\n",
        "    for val in [0.1]:\n",
        "        if use_population:\n",
        "            pop.DropoutUtils.convert_dropouts(transformer.model)\n",
        "            pop.DropoutUtils.activate_mc_dropout(transformer.model, activate=True, random=val)\n",
        "\n",
        "        results = []\n",
        "        control_results = []\n",
        "        conclusion_only = []\n",
        "\n",
        "        call_me = lambda prefixes, queries: transformer.conditional_score(prefixes, queries, reduction=lambda x: (x.sum(0).item(), x.mean(0).item(), x.tolist()))\n",
        "\n",
        "        for batch in stimuli_loader:\n",
        "            premise = list(batch[0])\n",
        "            conclusion = list(batch[1])\n",
        "\n",
        "            if use_population:\n",
        "                population = pop.generate_dropout_population(transformer.model, lambda: call_me(premise, conclusion), committee_size=committee_size)\n",
        "\n",
        "            print(len(premise), len(conclusion))\n",
        "\n",
        "            if use_population:\n",
        "                outs = [item for item in tqdm(pop.call_function_with_population(transformer.model, population, lambda: call_me(premise, conclusion)),\n",
        "                                              total=committee_size)]\n",
        "            else:\n",
        "                outs = [call_me(premise, conclusion)]\n",
        "\n",
        "            transposed_outs = [[row[i] for row in outs] for i in range(len(outs[0]))]\n",
        "            priming_scores = [score for score in transposed_outs]\n",
        "            results.extend(priming_scores)\n",
        "\n",
        "        data_out = list(zip(*dataset))\n",
        "        new_col_names = column_names\n",
        "\n",
        "        data_out.append(results)\n",
        "        new_col_names += [\"score (sum, mean, [list)\"]\n",
        "\n",
        "        data_out.append(num_params)\n",
        "        data_out.append([model_name] * len(results))\n",
        "        new_col_names += [\"params\", \"model\"]\n",
        "\n",
        "        with open(results_dir + f\"/{model_name}\"+\".csv\", \"w\") as f:\n",
        "            writer = csv.writer(f)\n",
        "            writer.writerow(new_col_names)\n",
        "            writer.writerows(list(zip(*data_out)))\n",
        "            f.flush()\n",
        "\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()\n"
      ]
    }
  ],
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