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https://github.com/tloen/alpaca-lora.git" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Alpaca OpenLLaMa 3B LoRA" + ], + "metadata": { + "id": "Gzg8SopX8EWH" + } + }, + { + "cell_type": "code", + "source": [ + "%cd alpaca-lora/" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "s1xm2uERx_st", + "outputId": "c77b42e1-202b-45ac-aa2c-3fdf2bcde155" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content/alpaca-lora\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install -q datasets loralib sentencepiece\n", + "\n", + "!pip install -q git+https://github.com/huggingface/transformers.git\n", + "!pip install -q git+https://github.com/huggingface/peft.git" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JCB9UzMVwsSM", + "outputId": "7f0688e4-f360-4da6-a4d3-0d59d4135649" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m39.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install bitsandbytes" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qCnXTszZxE2T", + "outputId": "e619573c-3ac2-4ac3-bed5-caab57d00f9a" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting bitsandbytes\n", + " Downloading bitsandbytes-0.39.0-py3-none-any.whl (92.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/92.2 MB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: bitsandbytes\n", + "Successfully installed bitsandbytes-0.39.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NyxvpDjcrUvv", + "outputId": "abe581a2-70ce-4956-b3dd-593fa4f3d8ef" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Check" + ], + "metadata": { + "id": "9w0aSCzhxxQf" + } + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset\n", + "from transformers import LlamaTokenizer\n", + "\n", + "\n", + "tokenizer = LlamaTokenizer.from_pretrained(\"openlm-research/open_llama_3b_600bt_preview\", add_eos_token=True)\n", + "tokenizer.pad_token = tokenizer.eos_token\n", + "tokenizer.pad_token_id = tokenizer.eos_token_id\n", + "\n", + "data = load_dataset(\"json\", data_files=\"/content/drive/MyDrive/alpaca-data.json\")\n", + "\n", + "\n", + "def generate_prompt(data_point):\n", + " # sorry about the formatting disaster gotta move fast\n", + " if data_point[\"input\"]:\n", + " return f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "{data_point[\"instruction\"]}\n", + "\n", + "### Input:\n", + "{data_point[\"input\"]}\n", + "\n", + "### Response:\n", + "{data_point[\"output\"]}\"\"\"\n", + " else:\n", + " return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "{data_point[\"instruction\"]}\n", + "\n", + "### Response:\n", + "{data_point[\"output\"]}\"\"\"" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 150, + "referenced_widgets": [ + "76eba77b0fc9499e9b4015393156153e", + "c42123767d4849ffbcdd48f09580ea13", + "e89f07c1f5744328b071f96a3bdc9532", + "da36c71ba5bc469eb32bde730c5038d3", + "6cc6b7c564fe48f086152c78f8a9a915", + "c6a3adfa76dd4bbd8cc5e75ee28ef3e8", + "0c65f0e913b74de88ab712d65b9027f7", + "b07c7f3b6bc24f10821b88e254e88e43", + "42ea2834ee074029abe8cf8f9ff79a16", + "ec4205b0491a4f53ad1cba42689d59c4", + "f8b9df09c8c54d53a22310e64e30772f", + "e69b8912d3384180842ff0ead6096803", + "550a2ef0612e4d8396462d1b1c43608d", + "f8d82b4d016f46ca8bab1a2fe591861e", + "975d908a1874425198e0d62ae884a3ef", + "152aa8231b3f4b7db4e6f27bb61065b9", + "27b48982ae244c66bccd855e7e33a00d", + "a896cb94d1fc4641ad224c7a9ca619b1", + "be5d02d7001a4a25a848d6b0c582b42b", + "609437910022418e83543fe6c3d16501", + "c3b5ef42dae64f95a2ef055db7e50fa8", + "ee0f0aff0c254b82854d1a6f4b367161", + "fd90085d416440579f409cdd9b29e053", + "fdd55d56775a4641925fc88e43f946b8", + "a54ebe30a12b4c21b67b2f8d43aea2d3", + "6f041f053ff240cbbb52f6d117ebfcf1", + "b7d5f8aaee0047178661e58aa59433ee", + "563ca6cac1594ba0a7c168ee31ccc982", + "e10e7830d1404baea72e81e0df295239", + "acc07aa077fd49d38d74e0e10fa3991a", + "441dfdd43ef0442894c19cad81a478db", + "9efe7ac9ee0946388eeb85f9a4603ffe", + "ae9996dcefcf4cfc8ea2f7488629c5ec", + "db463b34f98c488e8282d7a59c421347", + "2ba524e48f35421c9afc9cc81e9ce3fb", + "1829c96471c64a03a4872e1b7a16551c", + "f639a121fab0407595ebdf04015d9267", + "fa5ff44ae5f64ce4bbf9b59e300b7ba3", + "e0d701b8c09744858db4fda6011b69df", + "7ef61952e0f2463bad3dd1a814bfb95a", + "75263b1d19ce4cb0b95cacb6138b6fcb", + "c3f35ce818e34b05b247fae76200c02e", + "be8dddb19dde441da1193d5f8738413d", + "4be8d304ddce49319488f20ab36e7f53" + ] + }, + "id": "OdgRTo5YxyRL", + "outputId": "e6e64014-796b-4fa9-9635-3cb29f2dab7d" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-e726d3f1eee28f16/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading data files: 0%| | 0/1 [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
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\u001b[0m\u001b[96mself\u001b[0m._inner_training_loop, \u001b[96mself\u001b[0m._train_batch_size, args.auto_find_batch_size \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1695 \u001b[0m\u001b[2m│ │ \u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1696 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m inner_training_loop( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1697 \u001b[0m\u001b[2m│ │ │ \u001b[0margs=args, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1698 \u001b[0m\u001b[2m│ │ │ \u001b[0mresume_from_checkpoint=resume_from_checkpoint, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1699 \u001b[0m\u001b[2m│ │ │ \u001b[0mtrial=trial, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m1973\u001b[0m in \u001b[92m_inner_training_loop\u001b[0m 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"\u001b[31m│\u001b[0m \u001b[2m2788 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2789 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.args.n_gpu > \u001b[94m1\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2790 \u001b[0m\u001b[2m│ │ │ \u001b[0mloss = loss.mean() \u001b[2m# mean() to average on multi-gpu parallel training\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2819\u001b[0m in \u001b[92mcompute_loss\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2816 \u001b[0m\u001b[2m│ │ │ \u001b[0mlabels = inputs.pop(\u001b[33m\"\u001b[0m\u001b[33mlabels\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2817 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2818 \u001b[0m\u001b[2m│ │ │ \u001b[0mlabels = \u001b[94mNone\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2819 \u001b[2m│ │ \u001b[0moutputs = model(**inputs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2820 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Save past state if it exists\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2821 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# TODO: this needs to be fixed and made cleaner later.\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2822 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.args.past_index >= \u001b[94m0\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1498 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1499 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1500 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1502 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1503 \u001b[0m\u001b[2m│ │ \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1504 \u001b[0m\u001b[2m│ │ \u001b[0mbackward_pre_hooks = [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/peft/\u001b[0m\u001b[1;33mpeft_model.py\u001b[0m:\u001b[94m686\u001b[0m in \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 683 \u001b[0m\u001b[2m│ \u001b[0m): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 684 \u001b[0m\u001b[2m│ │ \u001b[0mpeft_config = \u001b[96mself\u001b[0m.active_peft_config \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 685 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m \u001b[96misinstance\u001b[0m(peft_config, PromptLearningConfig): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 686 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.base_model( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 687 \u001b[0m\u001b[2m│ │ │ │ \u001b[0minput_ids=input_ids, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 688 \u001b[0m\u001b[2m│ │ │ │ \u001b[0mattention_mask=attention_mask, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m 689 \u001b[0m\u001b[2m│ │ │ │ \u001b[0minputs_embeds=inputs_embeds, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1498 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1499 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1500 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1502 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1503 \u001b[0m\u001b[2m│ │ \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1504 \u001b[0m\u001b[2m│ │ \u001b[0mbackward_pre_hooks = [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m165\u001b[0m in \u001b[92mnew_forward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m162 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad(): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m163 \u001b[0m\u001b[2m│ │ │ │ \u001b[0moutput = old_forward(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m164 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m165 \u001b[2m│ │ │ \u001b[0moutput = old_forward(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m166 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m module._hf_hook.post_forward(module, output) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m167 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m168 \u001b[0m\u001b[2m│ \u001b[0mmodule.forward = new_forward \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m687\u001b[0m in \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m684 \u001b[0m\u001b[2m│ │ \u001b[0mreturn_dict = return_dict \u001b[94mif\u001b[0m return_dict \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[94melse\u001b[0m \u001b[96mself\u001b[0m.config.use_return \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m685 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m686 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m687 \u001b[2m│ │ \u001b[0moutputs = \u001b[96mself\u001b[0m.model( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m688 \u001b[0m\u001b[2m│ │ │ \u001b[0minput_ids=input_ids, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m689 \u001b[0m\u001b[2m│ │ │ \u001b[0mattention_mask=attention_mask, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m690 \u001b[0m\u001b[2m│ │ │ \u001b[0mposition_ids=position_ids, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1498 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1499 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1500 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1502 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1503 \u001b[0m\u001b[2m│ │ \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1504 \u001b[0m\u001b[2m│ │ \u001b[0mbackward_pre_hooks = [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m165\u001b[0m in \u001b[92mnew_forward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m162 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad(): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m163 \u001b[0m\u001b[2m│ │ │ │ \u001b[0moutput = old_forward(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m164 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m165 \u001b[2m│ │ │ \u001b[0moutput = old_forward(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m166 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m module._hf_hook.post_forward(module, output) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m167 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m168 \u001b[0m\u001b[2m│ \u001b[0mmodule.forward = new_forward \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m569\u001b[0m in \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m566 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m567 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m\u001b[94mreturn\u001b[0m custom_forward \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m568 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m569 \u001b[2m│ │ │ │ \u001b[0mlayer_outputs = torch.utils.checkpoint.checkpoint( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m570 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0mcreate_custom_forward(decoder_layer), \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m571 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0mhidden_states, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m572 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0mattention_mask, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/utils/\u001b[0m\u001b[1;33mcheckpoint.py\u001b[0m:\u001b[94m249\u001b[0m in \u001b[92mcheckpoint\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m246 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mValueError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mUnexpected keyword arguments: \u001b[0m\u001b[33m\"\u001b[0m + \u001b[33m\"\u001b[0m\u001b[33m,\u001b[0m\u001b[33m\"\u001b[0m.join(arg \u001b[94mfor\u001b[0m arg \u001b[95min\u001b[0m kwar \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m247 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m248 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m use_reentrant: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m249 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m CheckpointFunction.apply(function, preserve, *args) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m250 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m251 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m _checkpoint_without_reentrant( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m252 \u001b[0m\u001b[2m│ │ │ \u001b[0mfunction, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/autograd/\u001b[0m\u001b[1;33mfunction.py\u001b[0m:\u001b[94m506\u001b[0m in \u001b[92mapply\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m503 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m torch._C._are_functorch_transforms_active(): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m504 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[2m# See NOTE: [functorch vjp and autograd interaction]\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m505 \u001b[0m\u001b[2m│ │ │ \u001b[0margs = _functorch.utils.unwrap_dead_wrappers(args) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m506 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96msuper\u001b[0m().apply(*args, **kwargs) \u001b[2m# type: ignore[misc]\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m507 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m508 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mcls\u001b[0m.setup_context == _SingleLevelFunction.setup_context: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m509 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mRuntimeError\u001b[0m( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/utils/\u001b[0m\u001b[1;33mcheckpoint.py\u001b[0m:\u001b[94m107\u001b[0m in \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m104 \u001b[0m\u001b[2m│ │ \u001b[0mctx.save_for_backward(*tensor_inputs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m105 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m106 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad(): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m107 \u001b[2m│ │ │ \u001b[0moutputs = run_function(*args) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m108 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m outputs \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m109 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m110 \u001b[0m\u001b[2m│ \u001b[0m\u001b[1;95m@staticmethod\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m565\u001b[0m in \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[92mcustom_forward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m562 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mcreate_custom_forward\u001b[0m(module): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m563 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mcustom_forward\u001b[0m(*inputs): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m564 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0m\u001b[2m# None for past_key_value\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m565 \u001b[2m│ │ │ │ │ │ \u001b[0m\u001b[94mreturn\u001b[0m module(*inputs, output_attentions, \u001b[94mNone\u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m566 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m567 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m\u001b[94mreturn\u001b[0m custom_forward \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m568 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1498 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1499 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1500 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1502 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1503 \u001b[0m\u001b[2m│ │ \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1504 \u001b[0m\u001b[2m│ │ \u001b[0mbackward_pre_hooks = [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m165\u001b[0m in \u001b[92mnew_forward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m162 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad(): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m163 \u001b[0m\u001b[2m│ │ │ │ \u001b[0moutput = old_forward(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m164 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m165 \u001b[2m│ │ │ \u001b[0moutput = old_forward(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m166 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m module._hf_hook.post_forward(module, output) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m167 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m168 \u001b[0m\u001b[2m│ \u001b[0mmodule.forward = new_forward \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m292\u001b[0m in \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m289 \u001b[0m\u001b[2m│ │ \u001b[0mhidden_states = \u001b[96mself\u001b[0m.input_layernorm(hidden_states) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m290 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m291 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Self Attention\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m292 \u001b[2m│ │ \u001b[0mhidden_states, self_attn_weights, present_key_value = \u001b[96mself\u001b[0m.self_attn( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m293 \u001b[0m\u001b[2m│ │ │ \u001b[0mhidden_states=hidden_states, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m294 \u001b[0m\u001b[2m│ │ │ \u001b[0mattention_mask=attention_mask, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m295 \u001b[0m\u001b[2m│ │ │ \u001b[0mposition_ids=position_ids, \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1498 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1499 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1500 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1502 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1503 \u001b[0m\u001b[2m│ │ \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1504 \u001b[0m\u001b[2m│ │ \u001b[0mbackward_pre_hooks = [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/accelerate/\u001b[0m\u001b[1;33mhooks.py\u001b[0m:\u001b[94m165\u001b[0m in \u001b[92mnew_forward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m162 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mwith\u001b[0m torch.no_grad(): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m163 \u001b[0m\u001b[2m│ │ │ │ \u001b[0moutput = old_forward(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m164 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m165 \u001b[2m│ │ │ \u001b[0moutput = old_forward(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m166 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m module._hf_hook.post_forward(module, output) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m167 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m168 \u001b[0m\u001b[2m│ \u001b[0mmodule.forward = new_forward \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/models/llama/\u001b[0m\u001b[1;33mmodeling_llama.py\u001b[0m:\u001b[94m194\u001b[0m in \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m191 \u001b[0m\u001b[2m│ \u001b[0m) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m192 \u001b[0m\u001b[2m│ │ \u001b[0mbsz, q_len, _ = hidden_states.size() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m193 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m194 \u001b[2m│ │ \u001b[0mquery_states = \u001b[96mself\u001b[0m.q_proj(hidden_states).view(bsz, q_len, \u001b[96mself\u001b[0m.num_heads, \u001b[96mself\u001b[0m. \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m195 \u001b[0m\u001b[2m│ │ \u001b[0mkey_states = \u001b[96mself\u001b[0m.k_proj(hidden_states).view(bsz, q_len, \u001b[96mself\u001b[0m.num_heads, \u001b[96mself\u001b[0m.he \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m196 \u001b[0m\u001b[2m│ │ \u001b[0mvalue_states = \u001b[96mself\u001b[0m.v_proj(hidden_states).view(bsz, q_len, \u001b[96mself\u001b[0m.num_heads, \u001b[96mself\u001b[0m. \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m197 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1498 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1499 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1500 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1502 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1503 \u001b[0m\u001b[2m│ │ \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m1504 \u001b[0m\u001b[2m│ │ \u001b[0mbackward_pre_hooks = [] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/peft/tuners/\u001b[0m\u001b[1;33mlora.py\u001b[0m:\u001b[94m709\u001b[0m in \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m706 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.active_adapter = adapter_name \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m707 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m708 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mforward\u001b[0m(\u001b[96mself\u001b[0m, x: torch.Tensor): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m709 \u001b[2m│ │ │ \u001b[0mresult = \u001b[96msuper\u001b[0m().forward(x) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m710 \u001b[0m\u001b[2m│ │ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m711 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.disable_adapters \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m.active_adapter \u001b[95mnot\u001b[0m \u001b[95min\u001b[0m \u001b[96mself\u001b[0m.lora_A.keys(): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m712 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94mreturn\u001b[0m result \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/\u001b[0m\u001b[1;33mmodules.py\u001b[0m:\u001b[94m388\u001b[0m in \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m385 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.bias \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[95mand\u001b[0m \u001b[96mself\u001b[0m.bias.dtype != x.dtype: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m386 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.bias.data = \u001b[96mself\u001b[0m.bias.data.to(x.dtype) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m387 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m388 \u001b[2m│ │ \u001b[0mout = bnb.matmul(x, \u001b[96mself\u001b[0m.weight, bias=\u001b[96mself\u001b[0m.bias, state=\u001b[96mself\u001b[0m.state) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m389 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m390 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m \u001b[96mself\u001b[0m.state.has_fp16_weights: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m391 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.state.CB \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[95mand\u001b[0m \u001b[96mself\u001b[0m.state.CxB \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/\u001b[0m\u001b[1;33m_functions.py\u001b[0m:\u001b[94m559\u001b[0m in \u001b[92mmatmul\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m556 \u001b[0m\u001b[2m│ \u001b[0mstate = state \u001b[95mor\u001b[0m MatmulLtState() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m557 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m threshold > \u001b[94m0.0\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m558 \u001b[0m\u001b[2m│ │ \u001b[0mstate.threshold = threshold \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m559 \u001b[2m│ \u001b[0m\u001b[94mreturn\u001b[0m MatMul8bitLt.apply(A, B, out, bias, state) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m560 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m561 \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m562 \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mmatmul_4bit\u001b[0m(A: tensor, B: tensor, quant_state: List, out: tensor = \u001b[94mNone\u001b[0m, bias=\u001b[94mNone\u001b[0m): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/autograd/\u001b[0m\u001b[1;33mfunction.py\u001b[0m:\u001b[94m506\u001b[0m in \u001b[92mapply\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m503 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m torch._C._are_functorch_transforms_active(): \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m504 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[2m# See NOTE: [functorch vjp and autograd interaction]\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m505 \u001b[0m\u001b[2m│ │ │ \u001b[0margs = _functorch.utils.unwrap_dead_wrappers(args) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m506 \u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96msuper\u001b[0m().apply(*args, **kwargs) \u001b[2m# type: ignore[misc]\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m507 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m508 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mcls\u001b[0m.setup_context == _SingleLevelFunction.setup_context: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m509 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mRuntimeError\u001b[0m( \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/\u001b[0m\u001b[1;33m_functions.py\u001b[0m:\u001b[94m323\u001b[0m in \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m320 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# 1. Quantize A\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m321 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mlen\u001b[0m(A.shape) == \u001b[94m3\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m322 \u001b[0m\u001b[2m│ │ │ \u001b[0mA = A.view(-\u001b[94m1\u001b[0m, A.shape[-\u001b[94m1\u001b[0m]).contiguous() \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m323 \u001b[2m│ │ \u001b[0mCA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A.to(torch.float16), threshold= \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m324 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m325 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m state.threshold > \u001b[94m0.0\u001b[0m \u001b[95mand\u001b[0m coo_tensorA \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m326 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m state.has_fp16_weights: \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/bitsandbytes/\u001b[0m\u001b[1;33mfunctional.py\u001b[0m:\u001b[94m2029\u001b[0m in \u001b[92mdouble_quant\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2026 \u001b[0m\u001b[2m│ │ │ │ \u001b[0mct.c_int32(rows), \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2027 \u001b[0m\u001b[2m│ │ │ │ \u001b[0mct.c_int32(cols), \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2028 \u001b[0m\u001b[2m│ │ │ \u001b[0m) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2029 \u001b[2m│ │ │ \u001b[0mval, idx = torch.sort(coo_tensor.rowidx) \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2030 \u001b[0m\u001b[2m│ │ │ \u001b[0mcoo_tensor.rowidx = val \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2031 \u001b[0m\u001b[2m│ │ │ \u001b[0mcoo_tensor.colidx = coo_tensor.colidx[idx] \u001b[31m│\u001b[0m\n", + "\u001b[31m│\u001b[0m \u001b[2m2032 \u001b[0m\u001b[2m│ │ │ \u001b[0mcoo_tensor.values = coo_tensor.values[idx] \u001b[31m│\u001b[0m\n", + "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n", + "\u001b[1;91mKeyboardInterrupt\u001b[0m\n" + ], + "text/html": [ + "

╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
+              "│ in <cell line: 27>:27                                                                            │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1696 in train                    │\n",
+              "│                                                                                                  │\n",
+              "│   1693 │   │   inner_training_loop = find_executable_batch_size(                                 │\n",
+              "│   1694 │   │   │   self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size  │\n",
+              "│   1695 │   │   )                                                                                 │\n",
+              "│ ❱ 1696 │   │   return inner_training_loop(                                                       │\n",
+              "│   1697 │   │   │   args=args,                                                                    │\n",
+              "│   1698 │   │   │   resume_from_checkpoint=resume_from_checkpoint,                                │\n",
+              "│   1699 │   │   │   trial=trial,                                                                  │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1973 in _inner_training_loop     │\n",
+              "│                                                                                                  │\n",
+              "│   1970 │   │   │   │   │   with model.no_sync():                                                 │\n",
+              "│   1971 │   │   │   │   │   │   tr_loss_step = self.training_step(model, inputs)                  │\n",
+              "│   1972 │   │   │   │   else:                                                                     │\n",
+              "│ ❱ 1973 │   │   │   │   │   tr_loss_step = self.training_step(model, inputs)                      │\n",
+              "│   1974 │   │   │   │                                                                             │\n",
+              "│   1975 │   │   │   │   if (                                                                      │\n",
+              "│   1976 │   │   │   │   │   args.logging_nan_inf_filter                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:2787 in training_step            │\n",
+              "│                                                                                                  │\n",
+              "│   2784 │   │   │   return loss_mb.reduce_mean().detach().to(self.args.device)                    │\n",
+              "│   2785 │   │                                                                                     │\n",
+              "│   2786 │   │   with self.compute_loss_context_manager():                                         │\n",
+              "│ �� 2787 │   │   │   loss = self.compute_loss(model, inputs)                                       │\n",
+              "│   2788 │   │                                                                                     │\n",
+              "│   2789 │   │   if self.args.n_gpu > 1:                                                           │\n",
+              "│   2790 │   │   │   loss = loss.mean()  # mean() to average on multi-gpu parallel training        │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:2819 in compute_loss             │\n",
+              "│                                                                                                  │\n",
+              "│   2816 │   │   │   labels = inputs.pop(\"labels\")                                                 │\n",
+              "│   2817 │   │   else:                                                                             │\n",
+              "│   2818 │   │   │   labels = None                                                                 │\n",
+              "│ ❱ 2819 │   │   outputs = model(**inputs)                                                         │\n",
+              "│   2820 │   │   # Save past state if it exists                                                    │\n",
+              "│   2821 │   │   # TODO: this needs to be fixed and made cleaner later.                            │\n",
+              "│   2822 │   │   if self.args.past_index >= 0:                                                     │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl            │\n",
+              "│                                                                                                  │\n",
+              "│   1498 │   │   if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks   │\n",
+              "│   1499 │   │   │   │   or _global_backward_pre_hooks or _global_backward_hooks                   │\n",
+              "│   1500 │   │   │   │   or _global_forward_hooks or _global_forward_pre_hooks):                   │\n",
+              "│ ❱ 1501 │   │   │   return forward_call(*args, **kwargs)                                          │\n",
+              "│   1502 │   │   # Do not call functions when jit is used                                          │\n",
+              "│   1503 │   │   full_backward_hooks, non_full_backward_hooks = [], []                             │\n",
+              "│   1504 │   │   backward_pre_hooks = []                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/peft/peft_model.py:686 in forward                        │\n",
+              "│                                                                                                  │\n",
+              "│    683 │   ):                                                                                    │\n",
+              "│    684 │   │   peft_config = self.active_peft_config                                             │\n",
+              "│    685 │   │   if not isinstance(peft_config, PromptLearningConfig):                             │\n",
+              "│ ❱  686 │   │   │   return self.base_model(                                                       │\n",
+              "│    687 │   │   │   │   input_ids=input_ids,                                                      │\n",
+              "│    688 │   │   │   │   attention_mask=attention_mask,                                            │\n",
+              "│    689 │   │   │   │   inputs_embeds=inputs_embeds,                                              │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl            │\n",
+              "│                                                                                                  │\n",
+              "│   1498 │   │   if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks   │\n",
+              "│   1499 │   │   │   │   or _global_backward_pre_hooks or _global_backward_hooks                   │\n",
+              "│   1500 │   │   │   │   or _global_forward_hooks or _global_forward_pre_hooks):                   │\n",
+              "│ ❱ 1501 │   │   │   return forward_call(*args, **kwargs)                                          │\n",
+              "│   1502 │   │   # Do not call functions when jit is used                                          │\n",
+              "│   1503 │   │   full_backward_hooks, non_full_backward_hooks = [], []                             │\n",
+              "│   1504 │   │   backward_pre_hooks = []                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/accelerate/hooks.py:165 in new_forward                   │\n",
+              "│                                                                                                  │\n",
+              "│   162 │   │   │   with torch.no_grad():                                                          │\n",
+              "│   163 │   │   │   │   output = old_forward(*args, **kwargs)                                      │\n",
+              "│   164 │   │   else:                                                                              │\n",
+              "│ ❱ 165 │   │   │   output = old_forward(*args, **kwargs)                                          │\n",
+              "│   166 │   │   return module._hf_hook.post_forward(module, output)                                │\n",
+              "│   167 │                                                                                          │\n",
+              "│   168 │   module.forward = new_forward                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py:687 in       │\n",
+              "│ forward                                                                                          │\n",
+              "│                                                                                                  │\n",
+              "│   684 │   │   return_dict = return_dict if return_dict is not None else self.config.use_return   │\n",
+              "│   685 │   │                                                                                      │\n",
+              "│   686 │   │   # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)    │\n",
+              "│ ❱ 687 │   │   outputs = self.model(                                                              │\n",
+              "│   688 │   │   │   input_ids=input_ids,                                                           │\n",
+              "│   689 │   │   │   attention_mask=attention_mask,                                                 │\n",
+              "│   690 │   │   │   position_ids=position_ids,                                                     │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl            │\n",
+              "│                                                                                                  │\n",
+              "│   1498 │   │   if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks   │\n",
+              "│   1499 │   │   │   │   or _global_backward_pre_hooks or _global_backward_hooks                   │\n",
+              "│   1500 │   │   │   │   or _global_forward_hooks or _global_forward_pre_hooks):                   │\n",
+              "│ ❱ 1501 │   │   │   return forward_call(*args, **kwargs)                                          │\n",
+              "│   1502 │   │   # Do not call functions when jit is used                                          │\n",
+              "│   1503 │   │   full_backward_hooks, non_full_backward_hooks = [], []                             │\n",
+              "│   1504 │   │   backward_pre_hooks = []                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/accelerate/hooks.py:165 in new_forward                   │\n",
+              "│                                                                                                  │\n",
+              "│   162 │   │   │   with torch.no_grad():                                                          │\n",
+              "│   163 │   │   │   │   output = old_forward(*args, **kwargs)                                      │\n",
+              "│   164 │   │   else:                                                                              │\n",
+              "│ ❱ 165 │   │   │   output = old_forward(*args, **kwargs)                                          │\n",
+              "│   166 │   │   return module._hf_hook.post_forward(module, output)                                │\n",
+              "│   167 │                                                                                          │\n",
+              "│   168 │   module.forward = new_forward                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py:569 in       │\n",
+              "│ forward                                                                                          │\n",
+              "│                                                                                                  │\n",
+              "│   566 │   │   │   │   │                                                                          │\n",
+              "│   567 │   │   │   │   │   return custom_forward                                                  │\n",
+              "│   568 │   │   │   │                                                                              │\n",
+              "│ ❱ 569 │   │   │   │   layer_outputs = torch.utils.checkpoint.checkpoint(                         │\n",
+              "│   570 │   │   │   │   │   create_custom_forward(decoder_layer),                                  │\n",
+              "│   571 │   │   │   │   │   hidden_states,                                                         │\n",
+              "│   572 │   │   │   │   │   attention_mask,                                                        │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:249 in checkpoint              │\n",
+              "│                                                                                                  │\n",
+              "│   246 │   │   raise ValueError(\"Unexpected keyword arguments: \" + \",\".join(arg for arg in kwar   │\n",
+              "│   247 │                                                                                          │\n",
+              "│   248 │   if use_reentrant:                                                                      │\n",
+              "│ ❱ 249 │   │   return CheckpointFunction.apply(function, preserve, *args)                         │\n",
+              "│   250 │   else:                                                                                  │\n",
+              "│   251 │   │   return _checkpoint_without_reentrant(                                              │\n",
+              "│   252 │   │   │   function,                                                                      │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/autograd/function.py:506 in apply                  │\n",
+              "│                                                                                                  │\n",
+              "│   503 │   │   if not torch._C._are_functorch_transforms_active():                                │\n",
+              "│   504 │   │   │   # See NOTE: [functorch vjp and autograd interaction]                           │\n",
+              "│   505 │   │   │   args = _functorch.utils.unwrap_dead_wrappers(args)                             │\n",
+              "│ ❱ 506 │   │   │   return super().apply(*args, **kwargs)  # type: ignore[misc]                    │\n",
+              "│   507 │   │                                                                                      │\n",
+              "│   508 │   │   if cls.setup_context == _SingleLevelFunction.setup_context:                        │\n",
+              "│   509 │   │   │   raise RuntimeError(                                                            │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:107 in forward                 │\n",
+              "│                                                                                                  │\n",
+              "│   104 │   │   ctx.save_for_backward(*tensor_inputs)                                              │\n",
+              "│   105 │   │                                                                                      │\n",
+              "│   106 │   │   with torch.no_grad():                                                              │\n",
+              "│ ❱ 107 │   │   │   outputs = run_function(*args)                                                  │\n",
+              "│   108 │   │   return outputs                                                                     │\n",
+              "│   109 │                                                                                          │\n",
+              "│   110 │   @staticmethod                                                                          │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py:565 in       │\n",
+              "│ custom_forward                                                                                   │\n",
+              "│                                                                                                  │\n",
+              "│   562 │   │   │   │   def create_custom_forward(module):                                         │\n",
+              "│   563 │   │   │   │   │   def custom_forward(*inputs):                                           │\n",
+              "│   564 │   │   │   │   │   │   # None for past_key_value                                          │\n",
+              "│ ❱ 565 │   │   │   │   │   │   return module(*inputs, output_attentions, None)                    │\n",
+              "│   566 │   │   │   │   │                                                                          │\n",
+              "│   567 │   │   │   │   │   return custom_forward                                                  │\n",
+              "│   568                                                                                            │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl            │\n",
+              "│                                                                                                  │\n",
+              "│   1498 │   │   if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks   │\n",
+              "│   1499 │   │   │   │   or _global_backward_pre_hooks or _global_backward_hooks                   │\n",
+              "│   1500 │   │   │   │   or _global_forward_hooks or _global_forward_pre_hooks):                   │\n",
+              "│ ❱ 1501 │   │   │   return forward_call(*args, **kwargs)                                          │\n",
+              "│   1502 │   │   # Do not call functions when jit is used                                          │\n",
+              "│   1503 │   │   full_backward_hooks, non_full_backward_hooks = [], []                             │\n",
+              "│   1504 │   │   backward_pre_hooks = []                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/accelerate/hooks.py:165 in new_forward                   │\n",
+              "│                                                                                                  │\n",
+              "│   162 │   │   │   with torch.no_grad():                                                          │\n",
+              "│   163 │   │   │   │   output = old_forward(*args, **kwargs)                                      │\n",
+              "│   164 │   │   else:                                                                              │\n",
+              "│ ❱ 165 │   │   │   output = old_forward(*args, **kwargs)                                          │\n",
+              "│   166 │   │   return module._hf_hook.post_forward(module, output)                                │\n",
+              "│   167 │                                                                                          │\n",
+              "│   168 │   module.forward = new_forward                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py:292 in       │\n",
+              "│ forward                                                                                          │\n",
+              "│                                                                                                  │\n",
+              "│   289 │   │   hidden_states = self.input_layernorm(hidden_states)                                │\n",
+              "│   290 │   │                                                                                      │\n",
+              "│   291 │   │   # Self Attention                                                                   │\n",
+              "│ ❱ 292 │   │   hidden_states, self_attn_weights, present_key_value = self.self_attn(              │\n",
+              "│   293 │   │   │   hidden_states=hidden_states,                                                   │\n",
+              "│   294 │   │   │   attention_mask=attention_mask,                                                 │\n",
+              "│   295 │   │   │   position_ids=position_ids,                                                     │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl            │\n",
+              "│                                                                                                  │\n",
+              "│   1498 │   │   if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks   │\n",
+              "│   1499 │   │   │   │   or _global_backward_pre_hooks or _global_backward_hooks                   │\n",
+              "│   1500 │   │   │   │   or _global_forward_hooks or _global_forward_pre_hooks):                   │\n",
+              "│ ❱ 1501 │   │   │   return forward_call(*args, **kwargs)                                          │\n",
+              "│   1502 │   │   # Do not call functions when jit is used                                          │\n",
+              "│   1503 │   │   full_backward_hooks, non_full_backward_hooks = [], []                             │\n",
+              "│   1504 │   │   backward_pre_hooks = []                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/accelerate/hooks.py:165 in new_forward                   │\n",
+              "│                                                                                                  │\n",
+              "│   162 │   │   │   with torch.no_grad():                                                          │\n",
+              "│   163 │   │   │   │   output = old_forward(*args, **kwargs)                                      │\n",
+              "│   164 │   │   else:                                                                              │\n",
+              "│ ❱ 165 │   │   │   output = old_forward(*args, **kwargs)                                          │\n",
+              "│   166 │   │   return module._hf_hook.post_forward(module, output)                                │\n",
+              "│   167 │                                                                                          │\n",
+              "│   168 │   module.forward = new_forward                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/transformers/models/llama/modeling_llama.py:194 in       │\n",
+              "│ forward                                                                                          │\n",
+              "│                                                                                                  │\n",
+              "│   191 │   ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:       │\n",
+              "│   192 │   │   bsz, q_len, _ = hidden_states.size()                                               │\n",
+              "│   193 │   │                                                                                      │\n",
+              "│ ❱ 194 │   │   query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.   │\n",
+              "│   195 │   │   key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.he   │\n",
+              "│   196 │   │   value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.   │\n",
+              "│   197                                                                                            │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1501 in _call_impl            │\n",
+              "│                                                                                                  │\n",
+              "│   1498 │   │   if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks   │\n",
+              "│   1499 │   │   │   │   or _global_backward_pre_hooks or _global_backward_hooks                   │\n",
+              "│   1500 │   │   │   │   or _global_forward_hooks or _global_forward_pre_hooks):                   │\n",
+              "│ ❱ 1501 │   │   │   return forward_call(*args, **kwargs)                                          │\n",
+              "│   1502 │   │   # Do not call functions when jit is used                                          │\n",
+              "│   1503 │   │   full_backward_hooks, non_full_backward_hooks = [], []                             │\n",
+              "│   1504 │   │   backward_pre_hooks = []                                                           │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/peft/tuners/lora.py:709 in forward                       │\n",
+              "│                                                                                                  │\n",
+              "│   706 │   │   │   self.active_adapter = adapter_name                                             │\n",
+              "│   707 │   │                                                                                      │\n",
+              "│   708 │   │   def forward(self, x: torch.Tensor):                                                │\n",
+              "│ ❱ 709 │   │   │   result = super().forward(x)                                                    │\n",
+              "│   710 │   │   │                                                                                  │\n",
+              "│   711 │   │   │   if self.disable_adapters or self.active_adapter not in self.lora_A.keys():     │\n",
+              "│   712 │   │   │   │   return result                                                              │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py:388 in forward                │\n",
+              "│                                                                                                  │\n",
+              "│   385 │   │   if self.bias is not None and self.bias.dtype != x.dtype:                           │\n",
+              "│   386 │   │   │   self.bias.data = self.bias.data.to(x.dtype)                                    │\n",
+              "│   387 │   │                                                                                      │\n",
+              "│ ❱ 388 │   │   out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)                 │\n",
+              "│   389 │   │                                                                                      │\n",
+              "│   390 │   │   if not self.state.has_fp16_weights:                                                │\n",
+              "│   391 │   │   │   if self.state.CB is not None and self.state.CxB is not None:                   │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:559 in matmul        │\n",
+              "│                                                                                                  │\n",
+              "│   556 │   state = state or MatmulLtState()                                                       │\n",
+              "│   557 │   if threshold > 0.0:                                                                    │\n",
+              "│   558 │   │   state.threshold = threshold                                                        │\n",
+              "│ ❱ 559 │   return MatMul8bitLt.apply(A, B, out, bias, state)                                      │\n",
+              "│   560                                                                                            │\n",
+              "│   561                                                                                            │\n",
+              "│   562 def matmul_4bit(A: tensor, B: tensor, quant_state: List, out: tensor = None, bias=None):   │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/torch/autograd/function.py:506 in apply                  │\n",
+              "│                                                                                                  │\n",
+              "│   503 │   │   if not torch._C._are_functorch_transforms_active():                                │\n",
+              "│   504 │   │   │   # See NOTE: [functorch vjp and autograd interaction]                           │\n",
+              "│   505 │   │   │   args = _functorch.utils.unwrap_dead_wrappers(args)                             │\n",
+              "│ ❱ 506 │   │   │   return super().apply(*args, **kwargs)  # type: ignore[misc]                    │\n",
+              "│   507 │   │                                                                                      │\n",
+              "│   508 │   │   if cls.setup_context == _SingleLevelFunction.setup_context:                        │\n",
+              "│   509 │   │   │   raise RuntimeError(                                                            │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:323 in forward       │\n",
+              "│                                                                                                  │\n",
+              "│   320 │   │   # 1. Quantize A                                                                    │\n",
+              "│   321 │   │   if len(A.shape) == 3:                                                              │\n",
+              "│   322 │   │   │   A = A.view(-1, A.shape[-1]).contiguous()                                       │\n",
+              "│ ❱ 323 │   │   CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A.to(torch.float16), threshold=   │\n",
+              "│   324 │   │                                                                                      │\n",
+              "│   325 │   │   if state.threshold > 0.0 and coo_tensorA is not None:                              │\n",
+              "│   326 │   │   │   if state.has_fp16_weights:                                                     │\n",
+              "│                                                                                                  │\n",
+              "│ /usr/local/lib/python3.10/dist-packages/bitsandbytes/functional.py:2029 in double_quant          │\n",
+              "│                                                                                                  │\n",
+              "│   2026 │   │   │   │   ct.c_int32(rows),                                                         │\n",
+              "│   2027 │   │   │   │   ct.c_int32(cols),                                                         │\n",
+              "│   2028 │   │   │   )                                                                             │\n",
+              "│ ❱ 2029 │   │   │   val, idx = torch.sort(coo_tensor.rowidx)                                      │\n",
+              "│   2030 │   │   │   coo_tensor.rowidx = val                                                       │\n",
+              "│   2031 │   │   │   coo_tensor.colidx = coo_tensor.colidx[idx]                                    │\n",
+              "│   2032 │   │   │   coo_tensor.values = coo_tensor.values[idx]                                    │\n",
+              "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+              "KeyboardInterrupt\n",
+              "
\n" + ] + }, + "metadata": {} + } + ] + } + ] +}