emotion_GPT2_as_text_generator_LoRA.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gis2010/AgentGPT/blob/main/emotion_GPT2_as_text_generator_LoRA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hDownloading nvidia_cusparse_cu11-11.7.4.91-py3-none-manylinux1_x86_64.whl (173.2 MB)\n",
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            "\u001b[?25hDownloading nvidia_nccl_cu11-2.14.3-py3-none-manylinux1_x86_64.whl (177.1 MB)\n",
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            "\u001b[?25hDownloading nvidia_nvtx_cu11-11.7.91-py3-none-manylinux1_x86_64.whl (98 kB)\n",
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            "\u001b[?25hDownloading triton-2.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (63.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.3/63.3 MB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m57.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading urllib3-1.26.20-py2.py3-none-any.whl (144 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.2/144.2 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading lit-18.1.8-py3-none-any.whl (96 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.4/96.4 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tokenizers, lit, urllib3, tqdm, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, requests, nvidia-cusolver-cu11, nvidia-cudnn-cu11, transformers, triton, torch, peft\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.21.0\n",
            "    Uninstalling tokenizers-0.21.0:\n",
            "      Successfully uninstalled tokenizers-0.21.0\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.3.0\n",
            "    Uninstalling urllib3-2.3.0:\n",
            "      Successfully uninstalled urllib3-2.3.0\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.67.1\n",
            "    Uninstalling tqdm-4.67.1:\n",
            "      Successfully uninstalled tqdm-4.67.1\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.32.3\n",
            "    Uninstalling requests-2.32.3:\n",
            "      Successfully uninstalled requests-2.32.3\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.47.1\n",
            "    Uninstalling transformers-4.47.1:\n",
            "      Successfully uninstalled transformers-4.47.1\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.5.1+cu121\n",
            "    Uninstalling torch-2.5.1+cu121:\n",
            "      Successfully uninstalled torch-2.5.1+cu121\n",
            "  Attempting uninstall: peft\n",
            "    Found existing installation: peft 0.14.0\n",
            "    Uninstalling peft-0.14.0:\n",
            "      Successfully uninstalled peft-0.14.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests==2.32.3, but you have requests 2.28.2 which is incompatible.\n",
            "sentence-transformers 3.3.1 requires transformers<5.0.0,>=4.41.0, but you have transformers 4.30.0 which is incompatible.\n",
            "sphinx 8.1.3 requires requests>=2.30.0, but you have requests 2.28.2 which is incompatible.\n",
            "torchaudio 2.5.1+cu121 requires torch==2.5.1, but you have torch 2.0.1 which is incompatible.\n",
            "torchvision 0.20.1+cu121 requires torch==2.5.1, but you have torch 2.0.1 which is incompatible.\n",
            "yfinance 0.2.51 requires requests>=2.31, but you have requests 2.28.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed lit-18.1.8 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 peft-0.4.0 requests-2.28.2 tokenizers-0.13.3 torch-2.0.1 tqdm-4.65.0 transformers-4.30.0 triton-2.0.0 urllib3-1.26.20\n",
            "Using device: cuda\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:795: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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": "4e13af03ed5640a7a40f557ecea599b4"
            }
          },
          "metadata": {}
        },
        {
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ef9f0442d8a4caa986daf6dd35466ff"
            }
          },
          "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": "28d17ab812eb41aab5347afb3de4ef8f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12bca7fc18df4b67a9e79b9f0b2d8edb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "647e544976554c9e9c5586d8af088354"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b00850e8ae174a5ba54a48a22ffde15a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b23b0cfac844dbea7f7fe6ef1f31df3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora.py:299: UserWarning: fan_in_fan_out is set to False but the target module is `Conv1D`. Setting fan_in_fan_out to True.\n",
            "  warnings.warn(\n",
            "Epoch 1/18:   0%|          | 0/1125 [00:03<?, ?it/s]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "The size of tensor a (42) must match the size of tensor b (2) at non-singleton dimension 0",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-d4ffe5932c1c>\u001b[0m in \u001b[0;36m<cell line: 223>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0mprogress_bar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"Epoch {epoch+1}/{num_epochs}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\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    262\u001b[0m             \u001b[0minput_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m             \u001b[0mattention_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\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/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1177\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[0;32m-> 1178\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\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   1179\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\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/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    631\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\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[0m\u001b[1;32m    634\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\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/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    675\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    676\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    678\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\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/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\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---> 51\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     52\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[1;32m     53\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\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/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\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---> 51\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\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     52\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[1;32m     53\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-1-d4ffe5932c1c>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoded_prompt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoded_completion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhdc_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;31m# Retrieve from HDC memory (for demonstration)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-1-d4ffe5932c1c>\u001b[0m in \u001b[0;36mstore\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0mStore\u001b[0m \u001b[0ma\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0mpair\u001b[0m \u001b[0musing\u001b[0m \u001b[0mHDC\u001b[0m \u001b[0mbinding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \"\"\"\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmemory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhdc_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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<ipython-input-1-d4ffe5932c1c>\u001b[0m in \u001b[0;36mbind\u001b[0;34m(self, a, b)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mBinding\u001b[0m \u001b[0moperation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mXOR\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbinary\u001b[0m \u001b[0mvectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \"\"\"\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m^\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0munbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\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;31mRuntimeError\u001b[0m: The size of tensor a (42) must match the size of tensor b (2) at non-singleton dimension 0"
          ]
        }
      ],
      "source": [
        "!pip install torch==2.0.1 peft==0.4.0 transformers==4.30.0 requests==2.28.2 tqdm==4.65.0\n",
        "# Import required libraries\n",
        "import json\n",
        "import random\n",
        "import gzip\n",
        "import requests\n",
        "import torch\n",
        "from peft import get_peft_model, LoraConfig, TaskType\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from torch.optim import AdamW\n",
        "from tqdm import tqdm\n",
        "import re\n",
        "\n",
        "# Set random seeds for reproducibility\n",
        "def set_seed(seed):\n",
        "    random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "\n",
        "# Build prompt for emotion classification\n",
        "def build_prompt(text):\n",
        "    return f\"Predict the emotion for the following text: {text}\\nEmotion:\"\n",
        "\n",
        "# Encode text using tokenizer\n",
        "def encode_text(tokenizer, text, return_tensor=False):\n",
        "    if return_tensor:\n",
        "        return tokenizer.encode(text, add_special_tokens=False, return_tensors=\"pt\")\n",
        "    else:\n",
        "        return tokenizer.encode(text, add_special_tokens=False)\n",
        "\n",
        "# Decode token IDs back to text\n",
        "def decode_text(tokenizer, token_ids):\n",
        "    return tokenizer.decode(token_ids, skip_special_tokens=True)\n",
        "\n",
        "# Define HDC operations\n",
        "class HDCOperations:\n",
        "    def __init__(self, dim=10000):\n",
        "        self.dim = dim\n",
        "\n",
        "    def pad_tensors(self, a, b):\n",
        "        \"\"\"\n",
        "        Pad the shorter tensor with zeros to match the size of the longer tensor.\n",
        "        \"\"\"\n",
        "        max_len = max(a.size(0), b.size(0))\n",
        "        a_padded = torch.zeros(max_len, dtype=a.dtype)\n",
        "        b_padded = torch.zeros(max_len, dtype=b.dtype)\n",
        "        a_padded[:a.size(0)] = a\n",
        "        b_padded[:b.size(0)] = b\n",
        "        return a_padded, b_padded\n",
        "\n",
        "    def bind(self, a, b):\n",
        "        \"\"\"\n",
        "        Binding operation: XOR for binary vectors.\n",
        "        \"\"\"\n",
        "        a, b = self.pad_tensors(a, b)  # Ensure both tensors have the same size\n",
        "        return a ^ b\n",
        "\n",
        "    def unbind(self, a, b):\n",
        "        \"\"\"\n",
        "        Unbinding operation: XOR for binary vectors (self-inverse).\n",
        "        \"\"\"\n",
        "        a, b = self.pad_tensors(a, b)  # Ensure both tensors have the same size\n",
        "        return a ^ b\n",
        "\n",
        "    def bundle(self, vectors):\n",
        "        \"\"\"\n",
        "        Bundling operation: Thresholded sum for binary vectors.\n",
        "        \"\"\"\n",
        "        return (sum(vectors) > len(vectors) / 2).int()\n",
        "\n",
        "# HDC-based memory storage\n",
        "class HDCMemory:\n",
        "    def __init__(self, dim=10000):\n",
        "        self.dim = dim\n",
        "        self.memory = {}\n",
        "        self.hdc_ops = HDCOperations(dim)\n",
        "\n",
        "    def store(self, key, value):\n",
        "        \"\"\"\n",
        "        Store a key-value pair using HDC binding.\n",
        "        \"\"\"\n",
        "        self.memory[key] = self.hdc_ops.bind(key, value)\n",
        "\n",
        "    def retrieve(self, key):\n",
        "        \"\"\"\n",
        "        Retrieve a value using HDC unbinding.\n",
        "        \"\"\"\n",
        "        if key in self.memory:\n",
        "            return self.hdc_ops.unbind(key, self.memory[key])\n",
        "        else:\n",
        "            raise KeyError(\"Key not found in memory.\")\n",
        "\n",
        "    def bundle(self, keys):\n",
        "        \"\"\"\n",
        "        Bundle multiple keys into a single vector.\n",
        "        \"\"\"\n",
        "        vectors = [self.memory[key] for key in keys]\n",
        "        return self.hdc_ops.bundle(vectors)\n",
        "\n",
        "# Dataset class with HDC integration\n",
        "class PromptCompletionDataset(Dataset):\n",
        "    def __init__(self, data, tokenizer, hdc_memory):\n",
        "        self.data = data\n",
        "        self.tokenizer = tokenizer\n",
        "        self.hdc_memory = hdc_memory\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = self.data[idx]\n",
        "        prompt = item[\"prompt\"]\n",
        "        completion = item[\"completion\"]\n",
        "\n",
        "        # Encode prompt and completion as high-dimensional vectors\n",
        "        encoded_prompt = self.tokenizer.encode(prompt, add_special_tokens=False)\n",
        "        encoded_completion = self.tokenizer.encode(completion, add_special_tokens=False)\n",
        "\n",
        "        # Convert to tensors\n",
        "        key = torch.tensor(encoded_prompt, dtype=torch.int)\n",
        "        value = torch.tensor(encoded_completion, dtype=torch.int)\n",
        "\n",
        "        # Store in HDC memory\n",
        "        self.hdc_memory.store(key, value)\n",
        "\n",
        "        # Retrieve from HDC memory (for demonstration)\n",
        "        retrieved_value = self.hdc_memory.retrieve(key)\n",
        "\n",
        "        return {\n",
        "            \"input_ids\": encoded_prompt + encoded_completion + [self.tokenizer.eos_token_id],\n",
        "            \"labels\": [-100] * len(encoded_prompt) + encoded_completion + [self.tokenizer.eos_token_id],\n",
        "            \"prompt\": prompt,\n",
        "            \"expected_completion\": completion,\n",
        "            \"retrieved_completion\": self.tokenizer.decode(retrieved_value.tolist(), skip_special_tokens=True)\n",
        "        }\n",
        "\n",
        "# Collate function for DataLoader\n",
        "def collate_fn(batch):\n",
        "    max_length = max(len(item[\"input_ids\"]) for item in batch)\n",
        "    input_ids = [item[\"input_ids\"] + [tokenizer.pad_token_id] * (max_length - len(item[\"input_ids\"])) for item in batch]\n",
        "    labels = [item[\"labels\"] + [-100] * (max_length - len(item[\"labels\"])) for item in batch]\n",
        "    attention_mask = [[1] * len(item[\"input_ids\"]) + [0] * (max_length - len(item[\"input_ids\"])) for item in batch]\n",
        "    prompts = [item[\"prompt\"] for item in batch]\n",
        "    expected_completions = [item[\"expected_completion\"] for item in batch]\n",
        "    retrieved_completions = [item[\"retrieved_completion\"] for item in batch]\n",
        "\n",
        "    return (\n",
        "        torch.tensor(input_ids),\n",
        "        torch.tensor(attention_mask),\n",
        "        torch.tensor(labels),\n",
        "        prompts,\n",
        "        expected_completions,\n",
        "        retrieved_completions\n",
        "    )\n",
        "\n",
        "# Download and prepare dataset\n",
        "def download_and_prepare_data(data_url, tokenizer, batch_size, test_ratio=0.1):\n",
        "    response = requests.get(data_url)\n",
        "    content = gzip.decompress(response.content).decode()\n",
        "    dataset = [{\"prompt\": build_prompt(entry['text']), \"completion\": entry[\"label\"].strip()} for entry in map(json.loads, content.splitlines())]\n",
        "    random.shuffle(dataset)\n",
        "    split_index = int(len(dataset) * (1 - test_ratio))\n",
        "    train_data = dataset[:split_index]\n",
        "    test_data = dataset[split_index:]\n",
        "\n",
        "    # Initialize HDC memory\n",
        "    hdc_memory = HDCMemory()\n",
        "\n",
        "    # Create datasets\n",
        "    train_dataset = PromptCompletionDataset(train_data, tokenizer, hdc_memory)\n",
        "    test_dataset = PromptCompletionDataset(test_data, tokenizer, hdc_memory)\n",
        "\n",
        "    # Create data loaders\n",
        "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)\n",
        "    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn)\n",
        "\n",
        "    return train_loader, test_loader, hdc_memory\n",
        "\n",
        "# Calculate accuracy\n",
        "def calculate_accuracy(model, tokenizer, loader):\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for input_ids, attention_mask, labels, prompts, expected_completions, retrieved_completions in loader:\n",
        "            for prompt, expected_completion in zip(prompts, expected_completions):\n",
        "                generated_text = generate_text(model, tokenizer, prompt)\n",
        "                if generated_text.strip().lower() == expected_completion.strip().lower():\n",
        "                    correct += 1\n",
        "                total += 1\n",
        "    accuracy = correct / total if total > 0 else 0\n",
        "    model.train()\n",
        "    return accuracy\n",
        "\n",
        "# Generate text using the model\n",
        "def generate_text(model, tokenizer, prompt, max_new_tokens=50):\n",
        "    input_ids = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
        "    output_ids = model.generate(\n",
        "        input_ids=input_ids[\"input_ids\"],\n",
        "        attention_mask=input_ids[\"attention_mask\"],\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        pad_token_id=tokenizer.pad_token_id,\n",
        "        eos_token_id=tokenizer.eos_token_id\n",
        "    )[0]\n",
        "    generated_text = decode_text(tokenizer, output_ids[input_ids[\"input_ids\"].shape[1]:])\n",
        "    return generated_text.strip()\n",
        "\n",
        "# Test the model\n",
        "def test_model(model_path, test_input, hdc_memory):\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    print(f\"Using device: {device}\")\n",
        "\n",
        "    # Load saved model and tokenizer\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_path).to(device)\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "\n",
        "    # Configure padding token\n",
        "    if tokenizer.pad_token is None:\n",
        "        tokenizer.pad_token = tokenizer.eos_token\n",
        "    model.config.pad_token_id = tokenizer.pad_token_id\n",
        "\n",
        "    # Generate and display prediction\n",
        "    prompt = build_prompt(test_input)\n",
        "    generated_text = generate_text(model, tokenizer, prompt)\n",
        "\n",
        "    print(f\"Input: {test_input}\")\n",
        "    print(f\"Generated emotion: {generated_text}\")\n",
        "\n",
        "    # Retrieve from HDC memory (for demonstration)\n",
        "    key = torch.tensor(tokenizer.encode(prompt, add_special_tokens=False), dtype=torch.int)\n",
        "    retrieved_value = hdc_memory.retrieve(key)\n",
        "    print(f\"Retrieved completion from HDC memory: {tokenizer.decode(retrieved_value.tolist(), skip_special_tokens=True)}\")\n",
        "\n",
        "# Main training script\n",
        "if __name__ == \"__main__\":\n",
        "    set_seed(42)\n",
        "\n",
        "    # Configure basic training parameters\n",
        "    data_url = \"https://www.thelmbook.com/data/emotions\"\n",
        "    model_name = \"openai-community/gpt2\"\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    print(f\"Using device: {device}\")\n",
        "\n",
        "    # Initialize tokenizer\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "    # Configure LoRA parameters\n",
        "    peft_config = LoraConfig(\n",
        "        task_type=TaskType.CAUSAL_LM,\n",
        "        inference_mode=False,\n",
        "        r=16,\n",
        "        lora_alpha=32\n",
        "    )\n",
        "\n",
        "    # Load model and apply LoRA configuration\n",
        "    model = AutoModelForCausalLM.from_pretrained(model_name).to(device)\n",
        "    model = get_peft_model(model, peft_config)\n",
        "\n",
        "    # Get hyperparameters and prepare data\n",
        "    num_epochs, batch_size, learning_rate = 18, 16, 5e-5\n",
        "    train_loader, test_loader, hdc_memory = download_and_prepare_data(data_url, tokenizer, batch_size)\n",
        "\n",
        "    # Initialize optimizer\n",
        "    optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
        "\n",
        "    # Training loop\n",
        "    for epoch in range(num_epochs):\n",
        "        total_loss = 0\n",
        "        num_batches = 0\n",
        "        progress_bar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\")\n",
        "\n",
        "        for input_ids, attention_mask, labels, _, _, _ in progress_bar:\n",
        "            input_ids = input_ids.to(device)\n",
        "            attention_mask = attention_mask.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            # Forward pass\n",
        "            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
        "            loss = outputs.loss\n",
        "\n",
        "            # Backward pass and optimization\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # Update metrics\n",
        "            total_loss += loss.item()\n",
        "            num_batches += 1\n",
        "            progress_bar.set_postfix({\"Loss\": total_loss / num_batches})\n",
        "\n",
        "        # Evaluate on test set\n",
        "        test_acc = calculate_accuracy(model, tokenizer, test_loader)\n",
        "        print(f\"Epoch {epoch+1} - Average loss: {total_loss / num_batches:.4f}, Test accuracy: {test_acc:.4f}\")\n",
        "\n",
        "    # Save the model and tokenizer\n",
        "    model.save_pretrained(\"./finetuned_model\")\n",
        "    tokenizer.save_pretrained(\"./finetuned_model\")\n",
        "\n",
        "    # Test the finetuned model\n",
        "    test_input = \"I'm so happy to be able to finetune an LLM!\"\n",
        "    test_model(\"./finetuned_model\", test_input, hdc_memory)"
      ]
    }
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