From 8e9757ce695a9a55a48a16ebaac018dd3a8c2552 Mon Sep 17 00:00:00 2001 From: Mikhail Nefedov Date: Thu, 18 Apr 2024 16:07:19 +0200 Subject: [PATCH] update peft seminar --- notebooks/peft/PEFT.ipynb | 9738 ++++++++++++++++++++++++++++++++++--- 1 file changed, 9066 insertions(+), 672 deletions(-) diff --git a/notebooks/peft/PEFT.ipynb b/notebooks/peft/PEFT.ipynb index bac315f..34ca852 100644 --- a/notebooks/peft/PEFT.ipynb +++ b/notebooks/peft/PEFT.ipynb @@ -10,13 +10,13 @@ "# Parameter efficient fine-tuning\n", "\n", "В рамках семинаров мы ограничены одной не очень большой GPU доступной на колабе (t4 с 16 гб памяти). Поэтому в предыдущих семинарах, когда нужно было что-то зафайнтюнить мы использовали самые маленькие языковые модели (opt-125m, например), иначе мы бы столкнулись с OOM ошибкой или слишком долгим обучением. Естественно качество таких моделей не впечатляет и хотелось бы попробовать модели побольше. Даже сильно побольше, так как кажется, что [эмержентные](https://ru.wikipedia.org/wiki/%D0%AD%D0%BC%D0%B5%D1%80%D0%B4%D0%B6%D0%B5%D0%BD%D1%82%D0%BD%D0%BE%D1%81%D1%82%D1%8C)\n", - "свойства, о которых все сейчас говорят, начинают проявлятся у моделей размером около 6-10 миллиардов параметров (https://arxiv.org/pdf/2206.07682.pdf). \n", + "свойства, о которых все сейчас говорят, начинают проявлятся у моделей размером около 6-10 миллиардов параметров (https://arxiv.org/pdf/2206.07682.pdf).\n", "По умолчанию модель opt-6.7b требует около 25 гб видеопамяти, то есть даже для инференса ресурсов колаба не хватит, не говоря даже о обучении (для него понадобится в 4 раза больше).\n", "\n", "К счастью нехватка ресурсов - общая проблема. Даже те, у кого есть такие ресурсы заинтересованы в оптимизации (можно использовать меньше ресурсов=денег или же использовать такое же количество ресурсов, но обслуживать больше пользователей=зарабатывать больше денег). Поэтому усилия многих исследователей и компаний направлены в сторону оптимизации больших языковых моделей.\n", "\n", "\n", - "В этом семинаре мы разбере несколько уже разработанных подходов и сможем обучить модель ~~facebook/opt-6.7b в колабе~~ (на самом деле получится только opt-1.3b, так как все библиотеки еще новые и нестабильные)!\n", + "В этом семинаре мы разберем несколько уже разработанных подходов и сможем обучить модель ~~facebook/opt-6.7b в колабе~~ (на самом деле получится только opt-1.3b, так как все библиотеки еще новые и нестабильные)!\n", "\n", "\n", "Оптимизация базовой модели и оптимизация процесса дообучения это немного разные вещи, поэтому разберем их по очереди.\n", @@ -31,21 +31,21 @@ }, "source": [ "## Оптимизация предобученных моделей\n", - "Для уменьшения готовой модели есть два основных подхода: дистиляция и квантизация. \n", + "Для уменьшения готовой модели есть два основных подхода: дистиляция и квантизация.\n", "\n", "**Дистиляция** (knowledge distillation) - это обучение меньшей по размеру модели воспроизводить предсказания большей модели. Исходная модель при таком подходе называется учителем, а меньшая модель - учеником. Например, можно взять большой предобученный классификатор тональности, сделать предсказания на каком-нибудь большом корпусе и обучить меньшую модель на этих предсказаниях. Так как при уменьшении количества параметров будет уменьшаться и качество, можно ограничить обучающий корпус каким-то одним доменом, чтобы упростить меньшей модели задачу (меньшая модель, например, научится хорошо определять тональность коротких текстов, но будет плохо работать с длинными; на практике это может быть приемлимый трейдофф)\n", "\n", "Схема дистилляции:\n", "![](https://miro.medium.com/v2/resize:fit:936/1*8KqNtABnNXM527JK9UuBUQ.jpeg)\n", "\n", - "Применять дистиляцию к простым языковым моделям нет особого смысла, так как обучающие данные и так доступны и можно просто обучить меньшую модель с нуля аналогично большой модели. Но уместна дистиляция для моделей, обученных на инструкциях. Вот например эксперименты по дистиляции GPT в небольшие модели - https://github.com/mbzuai-nlp/LaMini-LM В целом это очень похоже на [Alpaca](https://github.com/tatsu-lab/stanford_alpaca), но датасет здесь намного больше, а сама модель обучается с нуля (в Alpaca дообучается Llama). И также как и с Alpaca, это не очень то легально - по сути это попытка скопировать модель без доступа к обучающим данным, поэтому лицензия на моделях запрещает коммерческое использование. \n", + "Применять дистиляцию к простым языковым моделям нет особого смысла, так как обучающие данные и так доступны и можно просто обучить меньшую модель с нуля аналогично большой модели. Но уместна дистиляция для моделей, обученных на инструкциях. Вот например эксперименты по дистиляции GPT в небольшие модели - https://github.com/mbzuai-nlp/LaMini-LM В целом это очень похоже на [Alpaca](https://github.com/tatsu-lab/stanford_alpaca), но датасет здесь намного больше, а сама модель обучается с нуля (в Alpaca дообучается Llama). И также как и с Alpaca, это не очень то легально - по сути это попытка скопировать модель без доступа к обучающим данным, поэтому лицензия на моделях запрещает коммерческое использование.\n", "\n", "\n", - "**Квантизация** - это уменьшение размера модели за счет уменьшения точности представления чисел. Веса в модели это просто очень много чисел вида 0.23123125, -1.234559 и для хранения каждого такого числа требуется какое-то количество памяти. Интервал допустимых значений и количество знаков после запятой всегда ограничены, но по умолчанию они достаточно большие и, выясняется, что можно достаточно сильно округлить веса, и при этом, практически не потерять в качестве! \n", - "Схема квантизации: \n", + "**Квантизация** - это уменьшение размера модели за счет уменьшения точности представления чисел. Веса в модели это просто очень много чисел вида 0.23123125, -1.234559 и для хранения каждого такого числа требуется какое-то количество памяти. Интервал допустимых значений и количество знаков после запятой всегда ограничены, но по умолчанию они достаточно большие и, выясняется, что можно достаточно сильно округлить веса, и при этом, практически не потерять в качестве!\n", + "Схема квантизации:\n", "![](https://developer-blogs.nvidia.com/wp-content/uploads/2021/07/qat-training-precision.png)\n", "\n", - "Разумеется, квантизация сложнее, чем просто округление, но подробно методы квантизации мы разбирать не будем. Если вам интересно, можно почитать вот это - https://huggingface.co/blog/hf-bitsandbytes-integration Один из авторов - Tim Dettmers, автор библиотеки bitsandbytes, в которой реализованы методы квантизации и которая постепенно интегрируется в huggingface. " + "Разумеется, квантизация сложнее, чем просто округление, но подробно методы квантизации мы разбирать не будем. Если вам интересно, можно почитать вот это - https://huggingface.co/blog/hf-bitsandbytes-integration Один из авторов - Tim Dettmers, автор библиотеки bitsandbytes, в которой реализованы методы квантизации и которая постепенно интегрируется в huggingface." ] }, { @@ -60,50 +60,19 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "wK8sz7edhBc1", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wK8sz7edhBc1", - "outputId": "d9ad4f94-0f71-4db3-baeb-966f819eb158" + "outputId": "f32fcab2-5d8b-44d6-d8ca-46edc781d930" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "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", - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Collecting accelerate\n", - " Downloading accelerate-0.18.0-py3-none-any.whl (215 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.9/dist-packages (from accelerate) (6.0)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from accelerate) (23.1)\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from accelerate) (1.22.4)\n", - "Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.9/dist-packages (from accelerate) (2.0.0+cu118)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.9/dist-packages (from accelerate) (5.9.5)\n", - "Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch>=1.4.0->accelerate) (1.11.1)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch>=1.4.0->accelerate) (3.11.0)\n", - "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch>=1.4.0->accelerate) (4.5.0)\n", - "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch>=1.4.0->accelerate) (2.0.0)\n", - "Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (from torch>=1.4.0->accelerate) (3.1)\n", - "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch>=1.4.0->accelerate) (3.1.2)\n", - "Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch>=1.4.0->accelerate) (3.25.2)\n", - "Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch>=1.4.0->accelerate) (16.0.1)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch>=1.4.0->accelerate) (2.1.2)\n", - "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch>=1.4.0->accelerate) (1.3.0)\n", - "Installing collected packages: accelerate\n", - "Successfully installed accelerate-0.18.0\n" - ] - } - ], + "outputs": [], "source": [ - "!pip install -q git+https://github.com/huggingface/transformers.git@main\n", - "!pip install accelerate" + "# !pip install -q git+https://github.com/huggingface/transformers.git@main\n", + "# !pip install accelerate" ] }, { @@ -118,27 +87,136 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "eddf7387", "metadata": { "id": "eddf7387" }, "outputs": [], "source": [ - "from transformers import AutoModelForCausalLM" + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "import torch" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "0b7057e1", "metadata": { "id": "0b7057e1" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n" + ] + } + ], "source": [ - "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path='facebook/opt-1.3b', \n", - " cache_dir='./models').to('cuda')" + "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path='facebook/opt-2.7b',\n", + " cache_dir='./models').to('cuda')\n", + "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-2.7b\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "pK91NDr-hwXH", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pK91NDr-hwXH", + "outputId": "e205bccb-2899-4a20-d16d-2555ba697375" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:492: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.1` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + " In the beginning the Universe was created. This has made a lot of people very angry and been widely regarded as a bad move.\n", + "I'm not sure if I should upvote or downvote this.\n" + ] + } + ], + "source": [ + "batch = tokenizer(\"In the beginning the Universe was created.\", return_tensors='pt').to('cuda')\n", + "output_tokens = model.generate(**batch, max_new_tokens=50, temperature=0.1, do_sample=True, no_repeat_ngram_size=2)\n", + "print(tokenizer.decode(output_tokens[0], skip_special_tokens=True))" + ] + }, + { + "cell_type": "markdown", + "id": "b05fd87e-86e9-48a7-a2ac-62961f8ad735", + "metadata": {}, + "source": [ + "Можно посмотреть сколько такая модель занимает памяти на gpu" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "rBI8Msw3guF_", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rBI8Msw3guF_", + "outputId": "8e71da1d-5bcf-4d4f-a776-d2e2631c64ed" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Thu Apr 18 12:27:27 2024 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 |\n", + "| 0% 24C P0 129W / 300W | 10635MiB / 23028MiB | 79% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "| 0 N/A N/A 148865 C ...ns/3.10.14/bin/python3.10 10633MiB |\n", + "+-----------------------------------------------------------------------------+\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + } + ], + "source": [ + "!nvidia-smi" ] }, { @@ -148,7 +226,144 @@ "id": "926e291c" }, "source": [ - "Ошибка по памяти. По умолчанию веса хранятся в fp32. Давайте попробуем fp16 - формат, который требует в два раза меньше памяти." + "По умолчанию веса хранятся в fp32 и модель занимает 10гб на gpu. Модель opt6.7B при таком формате не поместится в память." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "aa599404-92e3-47bb-b95c-d9568fdb585c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8276d480f71f4ff5a9b135cccb18f2ce", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/651 [00:00> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModelForCausalLM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfacebook/opt-6.7b\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m----> 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./models\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacebook/opt-6.7b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/modeling_utils.py:2692\u001b[0m, in \u001b[0;36mPreTrainedModel.to\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2687\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_present_in_args:\n\u001b[1;32m 2688\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 2689\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou cannot cast a GPTQ model in a new `dtype`. Make sure to load the model using `from_pretrained` using the desired\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2690\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m `dtype` by passing the correct `torch_dtype` argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2691\u001b[0m )\n\u001b[0;32m-> 2692\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1145\u001b[0m, in \u001b[0;36mModule.to\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m t\u001b[38;5;241m.\u001b[39mto(device, dtype \u001b[38;5;28;01mif\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1142\u001b[0m non_blocking, memory_format\u001b[38;5;241m=\u001b[39mconvert_to_format)\n\u001b[1;32m 1143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m t\u001b[38;5;241m.\u001b[39mto(device, dtype \u001b[38;5;28;01mif\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, non_blocking)\n\u001b[0;32m-> 1145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:797\u001b[0m, in \u001b[0;36mModule._apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_apply\u001b[39m(\u001b[38;5;28mself\u001b[39m, fn):\n\u001b[1;32m 796\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchildren():\n\u001b[0;32m--> 797\u001b[0m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[1;32m 801\u001b[0m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[1;32m 802\u001b[0m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 807\u001b[0m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[1;32m 808\u001b[0m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:797\u001b[0m, in \u001b[0;36mModule._apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_apply\u001b[39m(\u001b[38;5;28mself\u001b[39m, fn):\n\u001b[1;32m 796\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchildren():\n\u001b[0;32m--> 797\u001b[0m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[1;32m 801\u001b[0m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[1;32m 802\u001b[0m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 807\u001b[0m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[1;32m 808\u001b[0m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", + " \u001b[0;31m[... skipping similar frames: Module._apply at line 797 (2 times)]\u001b[0m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:797\u001b[0m, in \u001b[0;36mModule._apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_apply\u001b[39m(\u001b[38;5;28mself\u001b[39m, fn):\n\u001b[1;32m 796\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchildren():\n\u001b[0;32m--> 797\u001b[0m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[1;32m 801\u001b[0m \u001b[38;5;66;03m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[1;32m 802\u001b[0m \u001b[38;5;66;03m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 807\u001b[0m \u001b[38;5;66;03m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[1;32m 808\u001b[0m \u001b[38;5;66;03m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:820\u001b[0m, in \u001b[0;36mModule._apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 816\u001b[0m \u001b[38;5;66;03m# Tensors stored in modules are graph leaves, and we don't want to\u001b[39;00m\n\u001b[1;32m 817\u001b[0m \u001b[38;5;66;03m# track autograd history of `param_applied`, so we have to use\u001b[39;00m\n\u001b[1;32m 818\u001b[0m \u001b[38;5;66;03m# `with torch.no_grad():`\u001b[39;00m\n\u001b[1;32m 819\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m--> 820\u001b[0m param_applied \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 821\u001b[0m should_use_set_data \u001b[38;5;241m=\u001b[39m compute_should_use_set_data(param, param_applied)\n\u001b[1;32m 822\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m should_use_set_data:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1143\u001b[0m, in \u001b[0;36mModule.to..convert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert_to_format \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m t\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m):\n\u001b[1;32m 1141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m t\u001b[38;5;241m.\u001b[39mto(device, dtype \u001b[38;5;28;01mif\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_floating_point() \u001b[38;5;129;01mor\u001b[39;00m t\u001b[38;5;241m.\u001b[39mis_complex() \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1142\u001b[0m non_blocking, memory_format\u001b[38;5;241m=\u001b[39mconvert_to_format)\n\u001b[0;32m-> 1143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_floating_point\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_complex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnon_blocking\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 22.06 GiB total capacity; 21.56 GiB already allocated; 190.38 MiB free; 21.58 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" + ] + } + ], + "source": [ + "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path='facebook/opt-6.7b',\n", + " cache_dir='./models').to('cuda')" + ] + }, + { + "cell_type": "markdown", + "id": "755fd1a8-3543-4f28-8305-46478a0adf09", + "metadata": {}, + "source": [ + "### Не забудьте перезагрузить кернел после OOM ошибки!" ] }, { @@ -158,7 +373,9 @@ "id": "6dc76820" }, "source": [ - "## FP16" + "## FP16\n", + "\n", + "Давайте попробуем загрузить модель в fp16 (из названия можно догадаться, что этот формат в два раза меньше)" ] }, { @@ -179,24 +396,85 @@ "execution_count": 2, "id": "3d8be979", "metadata": { - "id": "3d8be979" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "4b336927f1f64c9bb24b90c834089c2c", + "41302a127f0d482dba9f1fb5ffd526ce", + "cda4091c53174f5283cb5150b05cf91d", + "26a60c2b950444179a4ed48ec8a00554", + "1471536202fd4102b149d6602b5c7bec", + "cae820a1b68c40a58779f26263295fd7", + "2e8a1f91e475413a9bb6e29de8762987", + "cea0924342474a108377d651ccad4639", + "be003b38596140dc8725577ef1d63289", + "166d6334d5e243dcbcc80408830c8cc9", + "c9af1342d81843a8b87261c2bb012c9f", + "0927b10cb102448d9420c7fa84eb5ae0", + "b5c9e7e31c9c436080c6c59b9d99b525", + "9ea4600bb3be4ff3ba8a92fe03810247", + "92569ea32811471c863486276f94ec4a", + "8b10a1a33af4496795d387e13db4bd55", + "997ff5c5cfb1420ba2276c96c9efea0a", + "6c9a4a5d713f4ae995eb924cd617489f", + "0acc35a7479a4b3d86a504cf53a80b81", + "782052800c464544937c72b864c58129", + "fd4bac5d74214f73853e9124cb8fe481", + "fa3c3f040a764e07ac5bb68fdc8274fb", + "eebde15275ac42d08fcbfc0d247c986b", + "37686e753266404abc286d7c760525d5", + "2c610f74ef174ea0ad14cf239f04b459", + "d393ed45cd1047e7b0552fb1324e5f56", + "96cd90b3245546d78ce5b79de6a102fd", + "52600aba818548e7b8297771a4095fc9", + "0b2cda75521c48e087caa35251097a06", + "b05ca77fb92c499db46cedce134b22bb", + "94c54ca364e14e2bb5822205293816b9", + "5038bed284e049a987ee7b35a0bd3699", + "645ca3f73ce1470a9a2dbd68fb9752b0", + "d7917494c71a489c8b080a24d3bc5dec", + "3c01e7789c2249089ce84a609d3fcab0", + "a11e172f0b57420bb9fe04e63fb26ae3", + "61a9e0aee8a145798994078c0f5f0015", + "eef67366414e4708b6901499e553d7ab", + "8865921de0c64362a8bd671f1189bc11", + "72f1f33f91464f9e96d83899cfc9c734", + "7e2cbe002638483abb82f49bb2a62d84", + "7329a72fe0e1451ca414c493aaaa333c", + "f1cc2625a2594cd6beebe9ce40fad354", + "0e37831b4bfd4913b7e0004a6b8c559f", + "d4af2dc62585497eb763afb05e7cf2ce", + "f0dfb03858764cc989fc0d68f141c80e", + "8652e2d453aa40a498b3b687a9a6430a", + "ecb7e03df55c4601857ed58ee6798d12", + "77fec365061d40d7875f388431ddafbd", + "7eb3f1c236cb4ca595b3e23773adf938", + "12281879b3f44e9a90555f60b9fd0d3c", + "1d6c87471ed345878767bf43e61af71b", + "3d6022661f2b40078514394d654bdc25", + "93760564dc2443c189383b045b8bef4f", + "e555c53d408e4c4482d993d9ec28f37a" + ] + }, + "id": "3d8be979", + "outputId": "9b993c41-d06c-47d7-c9f6-3b35a13a0f58" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n" + ] + } + ], "source": [ - "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path='facebook/opt-1.3b', \n", + "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path='facebook/opt-2.7b',\n", " torch_dtype=torch.float16, # указываем fp16\n", " cache_dir='./models').to('cuda')\n", - "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-1.3b\")" - ] - }, - { - "cell_type": "markdown", - "id": "6bf64dc2", - "metadata": { - "id": "6bf64dc2" - }, - "source": [ - "Теперь модель загружается и мы можем даже что-то сгенерировать" + "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-2.7b\")" ] }, { @@ -208,9 +486,17 @@ "base_uri": "https://localhost:8080/" }, "id": "d4d7ebef", - "outputId": "78054e26-dcfd-49e4-d845-3443a12e45ff" + "outputId": "85b04aa4-4553-4e4c-bb88-d47fa91bdc75" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:492: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.1` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", + " warnings.warn(\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -218,163 +504,172 @@ "\n", "\n", " In the beginning the Universe was created. This has made a lot of people very angry and been widely regarded as a bad move.\n", - "I'm not sure if you're being sarcastic or not, but I'm going to go with sarcastic.\n" + "I'm not sure if I should upvote or downvote this.\n" ] } ], "source": [ "batch = tokenizer(\"In the beginning the Universe was created.\", return_tensors='pt').to('cuda')\n", - "output_tokens = model.generate(**batch, max_new_tokens=50, temperature=0.1, no_repeat_ngram_size=2)\n", - "\n", - "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))" + "output_tokens = model.generate(**batch, max_new_tokens=50, temperature=0.1, do_sample=True, no_repeat_ngram_size=2)\n", + "print(tokenizer.decode(output_tokens[0], skip_special_tokens=True))" ] }, { "cell_type": "markdown", - "id": "235df037", - "metadata": { - "id": "235df037" - }, - "source": [ - "## 8-bit" - ] - }, - { - "cell_type": "markdown", - "id": "c15ad8fa", + "id": "6bf64dc2", "metadata": { - "id": "c15ad8fa" + "id": "6bf64dc2" }, "source": [ - "Можно пойти еще дальше и попробовать 8-битный формат, который требует в 4 раза меньше памяти. Так мы можем попробовать даже еще большую модель" + "Теперь занято около 5гб!" ] }, { "cell_type": "code", - "execution_count": 3, - "id": "dEV6IZx2kCQQ", + "execution_count": 4, + "id": "e_hvesTQg1Ra", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "dEV6IZx2kCQQ", - "outputId": "5a245077-458e-499b-adf2-2ee63e1509e0" + "id": "e_hvesTQg1Ra", + "outputId": "08ae0974-ab5d-4777-973f-3b30677a7611" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Collecting bitsandbytes\n", - " Downloading bitsandbytes-0.38.1-py3-none-any.whl (104.3 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.3/104.3 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: bitsandbytes\n", - "Successfully installed bitsandbytes-0.38.1\n" + "Thu Apr 18 12:34:21 2024 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 |\n", + "| 0% 24C P0 70W / 300W | 5613MiB / 23028MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "| 0 N/A N/A 149148 C ...ns/3.10.14/bin/python3.10 5611MiB |\n", + "+-----------------------------------------------------------------------------+\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" ] } ], "source": [ - "!pip install bitsandbytes" + "!nvidia-smi" ] }, { - "cell_type": "code", - "execution_count": 1, - "id": "I0f98_l6jrBj", - "metadata": { - "id": "I0f98_l6jrBj" - }, - "outputs": [], + "cell_type": "markdown", + "id": "0d0d3782-8978-45a9-a39b-40dce5fc4e95", + "metadata": {}, "source": [ - "from transformers import AutoModelForCausalLM, AutoTokenizer\n", - "import torch" + "Давайте попробуем модель с 6.7B параметрами еще раз" ] }, { "cell_type": "code", - "execution_count": null, - "id": "126e16cd", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 623, - "referenced_widgets": [ - "0520749ab4c24669ae83b42967f97fa6", - "e83f1813f9804ad19f31ad562d24c4e3", - "6ab82142e64b452dad20a5da2513813c", - "4daa959bccac47a6836abef93981a9f9", - "cf4af5ba6bcb474dab4cdc52dde457c5", - "258ef1a22d814fd3be7b4dd0a269f5d9", - "eae93864f70742388b3f0b3ac00556b6", - "ee1bab3ef7174a1cad7741bf20ff6620", - "a22cac2095bb415f9cbe6a6a830ad25f", - "c111f71e80344bc887d5d83ee9bbcfb2", - "ae33e50db0ef4b5cb11b21a5657b35d7" - ] - }, - "id": "126e16cd", - "outputId": "df8632ad-58eb-48a8-c8b1-5c68b5369aa5" - }, + "execution_count": 5, + "id": "85ab875f-820e-4b12-85cb-8f5e87e5703c", + "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "20119644699b43629edf56e734ef2324", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00\n", " \n", - " \n", - " [ 4/400 00:09 < 30:17, 0.22 it/s, Epoch 0.02/3]\n", + " \n", + " [ 17/400 01:10 < 29:53, 0.21 it/s, Epoch 0.03/1]\n", " \n", " \n", " \n", @@ -876,11 +2048,63 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
12.7892001.994500
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" @@ -891,6 +2115,55 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m model\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_cache \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;66;03m# silence the warnings. Please re-enable for inference!\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/trainer.py:1859\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1857\u001b[0m hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m 1858\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1859\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1860\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1861\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1862\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1863\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1864\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/trainer.py:2203\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2200\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_begin(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[1;32m 2202\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39maccumulate(model):\n\u001b[0;32m-> 2203\u001b[0m tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2205\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 2206\u001b[0m args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m 2207\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[1;32m 2208\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m 2209\u001b[0m ):\n\u001b[1;32m 2210\u001b[0m \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m 2211\u001b[0m tr_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/trainer.py:3138\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 3135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss_mb\u001b[38;5;241m.\u001b[39mreduce_mean()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m 3137\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_loss_context_manager():\n\u001b[0;32m-> 3138\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mn_gpu \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3141\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mmean() \u001b[38;5;66;03m# mean() to average on multi-gpu parallel training\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/trainer.py:3161\u001b[0m, in \u001b[0;36mTrainer.compute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m 3159\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3160\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 3161\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3162\u001b[0m \u001b[38;5;66;03m# Save past state if it exists\u001b[39;00m\n\u001b[1;32m 3163\u001b[0m \u001b[38;5;66;03m# TODO: this needs to be fixed and made cleaner later.\u001b[39;00m\n\u001b[1;32m 3164\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mpast_index \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/accelerate/utils/operations.py:825\u001b[0m, in \u001b[0;36mconvert_outputs_to_fp32..forward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 824\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 825\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/accelerate/utils/operations.py:813\u001b[0m, in \u001b[0;36mConvertOutputsToFp32.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 812\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 813\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m convert_to_fp32(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/amp/autocast_mode.py:14\u001b[0m, in \u001b[0;36mautocast_decorator..decorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_autocast\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m autocast_instance:\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/peft/peft_model.py:1302\u001b[0m, in \u001b[0;36mPeftModelForCausalLM.forward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)\u001b[0m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_peft_forward_hooks(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1301\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspecial_peft_forward_args}\n\u001b[0;32m-> 1302\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1303\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1304\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1305\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1306\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1307\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1308\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1309\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1310\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1313\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m _get_batch_size(input_ids, inputs_embeds)\n\u001b[1;32m 1314\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attention_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1315\u001b[0m \u001b[38;5;66;03m# concat prompt attention mask\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/peft/tuners/tuners_utils.py:178\u001b[0m, in \u001b[0;36mBaseTuner.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any):\n\u001b[0;32m--> 178\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/peft/peft_model.py:1302\u001b[0m, in \u001b[0;36mPeftModelForCausalLM.forward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)\u001b[0m\n\u001b[1;32m 1300\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_peft_forward_hooks(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1301\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspecial_peft_forward_args}\n\u001b[0;32m-> 1302\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1303\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1304\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1305\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1306\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1307\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1308\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1309\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1310\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1313\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m _get_batch_size(input_ids, inputs_embeds)\n\u001b[1;32m 1314\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attention_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1315\u001b[0m \u001b[38;5;66;03m# concat prompt attention mask\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/accelerate/hooks.py:166\u001b[0m, in \u001b[0;36madd_hook_to_module..new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 164\u001b[0m output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 166\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:1120\u001b[0m, in \u001b[0;36mOPTForCausalLM.forward\u001b[0;34m(self, input_ids, attention_mask, head_mask, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1117\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[1;32m 1119\u001b[0m \u001b[38;5;66;03m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[39;00m\n\u001b[0;32m-> 1120\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1121\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1122\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1123\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1124\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1125\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1126\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1127\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1128\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1129\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1130\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1132\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlm_head(outputs[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[1;32m 1134\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/accelerate/hooks.py:166\u001b[0m, in \u001b[0;36madd_hook_to_module..new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 164\u001b[0m output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 166\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:876\u001b[0m, in \u001b[0;36mOPTDecoder.forward\u001b[0;34m(self, input_ids, attention_mask, head_mask, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 873\u001b[0m past_key_value \u001b[38;5;241m=\u001b[39m past_key_values[idx] \u001b[38;5;28;01mif\u001b[39;00m past_key_values \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_checkpointing \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining:\n\u001b[0;32m--> 876\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gradient_checkpointing_func\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecoder_layer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43mcausal_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 881\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 882\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 883\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 884\u001b[0m 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255\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 256\u001b[0m )\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/autograd/function.py:506\u001b[0m, in \u001b[0;36mFunction.apply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_are_functorch_transforms_active():\n\u001b[1;32m 504\u001b[0m \u001b[38;5;66;03m# See NOTE: [functorch vjp and autograd interaction]\u001b[39;00m\n\u001b[1;32m 505\u001b[0m args \u001b[38;5;241m=\u001b[39m _functorch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39munwrap_dead_wrappers(args)\n\u001b[0;32m--> 506\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39msetup_context \u001b[38;5;241m==\u001b[39m _SingleLevelFunction\u001b[38;5;241m.\u001b[39msetup_context:\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 510\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIn order to use an autograd.Function with functorch transforms \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 511\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(vmap, grad, jvp, jacrev, ...), it must override the setup_context \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstaticmethod. For more details, please see \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 513\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://pytorch.org/docs/master/notes/extending.func.html\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/utils/checkpoint.py:107\u001b[0m, in \u001b[0;36mCheckpointFunction.forward\u001b[0;34m(ctx, run_function, preserve_rng_state, *args)\u001b[0m\n\u001b[1;32m 104\u001b[0m ctx\u001b[38;5;241m.\u001b[39msave_for_backward(\u001b[38;5;241m*\u001b[39mtensor_inputs)\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m--> 107\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mrun_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/accelerate/hooks.py:166\u001b[0m, in \u001b[0;36madd_hook_to_module..new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 164\u001b[0m output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 166\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:527\u001b[0m, in \u001b[0;36mOPTDecoderLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, layer_head_mask, past_key_value, output_attentions, use_cache)\u001b[0m\n\u001b[1;32m 524\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mself_attn_layer_norm(hidden_states)\n\u001b[1;32m 526\u001b[0m \u001b[38;5;66;03m# Self Attention\u001b[39;00m\n\u001b[0;32m--> 527\u001b[0m hidden_states, self_attn_weights, present_key_value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself_attn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 528\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 529\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 531\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 532\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 533\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 534\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mfunctional\u001b[38;5;241m.\u001b[39mdropout(hidden_states, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining)\n\u001b[1;32m 535\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/accelerate/hooks.py:166\u001b[0m, in \u001b[0;36madd_hook_to_module..new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 164\u001b[0m output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 166\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:255\u001b[0m, in \u001b[0;36mOPTAttention.forward\u001b[0;34m(self, hidden_states, key_value_states, past_key_value, attention_mask, layer_head_mask, output_attentions)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \u001b[38;5;66;03m# partitioned aross GPUs when using tensor-parallelism.\u001b[39;00m\n\u001b[1;32m 253\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m attn_output\u001b[38;5;241m.\u001b[39mreshape(bsz, tgt_len, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed_dim)\n\u001b[0;32m--> 255\u001b[0m attn_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mout_proj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattn_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m attn_output, attn_weights_reshaped, past_key_value\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/peft/tuners/lora/bnb.py:217\u001b[0m, in \u001b[0;36mLinear8bitLt.forward\u001b[0;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m 215\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbase_layer(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 217\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m active_adapter \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mactive_adapters:\n\u001b[1;32m 219\u001b[0m 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\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/accelerate/hooks.py:166\u001b[0m, in \u001b[0;36madd_hook_to_module..new_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 164\u001b[0m output \u001b[38;5;241m=\u001b[39m module\u001b[38;5;241m.\u001b[39m_old_forward(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 166\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_old_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mpost_forward(module, output)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:797\u001b[0m, in \u001b[0;36mLinear8bitLt.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 794\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m x\u001b[38;5;241m.\u001b[39mdtype:\n\u001b[1;32m 795\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mto(x\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[0;32m--> 797\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mbnb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mhas_fp16_weights:\n\u001b[1;32m 800\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mCB \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mCxB \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 801\u001b[0m \u001b[38;5;66;03m# we converted 8-bit row major to turing/ampere format in the first inference pass\u001b[39;00m\n\u001b[1;32m 802\u001b[0m \u001b[38;5;66;03m# we no longer need the row-major weight\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:556\u001b[0m, in \u001b[0;36mmatmul\u001b[0;34m(A, B, out, state, threshold, bias)\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m threshold \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m 555\u001b[0m state\u001b[38;5;241m.\u001b[39mthreshold \u001b[38;5;241m=\u001b[39m threshold\n\u001b[0;32m--> 556\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mMatMul8bitLt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/autograd/function.py:506\u001b[0m, in \u001b[0;36mFunction.apply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_are_functorch_transforms_active():\n\u001b[1;32m 504\u001b[0m \u001b[38;5;66;03m# See NOTE: [functorch vjp and autograd interaction]\u001b[39;00m\n\u001b[1;32m 505\u001b[0m args \u001b[38;5;241m=\u001b[39m _functorch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39munwrap_dead_wrappers(args)\n\u001b[0;32m--> 506\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39msetup_context \u001b[38;5;241m==\u001b[39m _SingleLevelFunction\u001b[38;5;241m.\u001b[39msetup_context:\n\u001b[1;32m 509\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 510\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIn order to use an autograd.Function with functorch transforms \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 511\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(vmap, grad, jvp, jacrev, ...), it must override the setup_context \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 512\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstaticmethod. For more details, please see \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 513\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://pytorch.org/docs/master/notes/extending.func.html\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:321\u001b[0m, in \u001b[0;36mMatMul8bitLt.forward\u001b[0;34m(ctx, A, B, out, bias, state)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(A\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[1;32m 320\u001b[0m A \u001b[38;5;241m=\u001b[39m A\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, A\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[0;32m--> 321\u001b[0m CA, CAt, SCA, SCAt, coo_tensorA \u001b[38;5;241m=\u001b[39m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdouble_quant\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat16\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstate\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthreshold\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state\u001b[38;5;241m.\u001b[39mthreshold \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m coo_tensorA \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state\u001b[38;5;241m.\u001b[39mhas_fp16_weights:\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/bitsandbytes/functional.py:2519\u001b[0m, in \u001b[0;36mdouble_quant\u001b[0;34m(A, col_stats, row_stats, out_col, out_row, threshold)\u001b[0m\n\u001b[1;32m 2516\u001b[0m rows \u001b[38;5;241m=\u001b[39m A\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 2518\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m row_stats \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m col_stats \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2519\u001b[0m row_stats, col_stats, nnz_row_ptr \u001b[38;5;241m=\u001b[39m \u001b[43mget_colrow_absmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthreshold\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2521\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_col \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2522\u001b[0m out_col \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(A\u001b[38;5;241m.\u001b[39mshape, device\u001b[38;5;241m=\u001b[39mdevice, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mint8)\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.14/lib/python3.10/site-packages/bitsandbytes/functional.py:2422\u001b[0m, in \u001b[0;36mget_colrow_absmax\u001b[0;34m(A, row_stats, col_stats, nnz_block_ptr, threshold)\u001b[0m\n\u001b[1;32m 2419\u001b[0m post_call(prev_device)\n\u001b[1;32m 2421\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m threshold \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[0;32m-> 2422\u001b[0m \u001b[43mnnz_block_ptr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcumsum_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2424\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m row_stats, col_stats, nnz_block_ptr\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ @@ -905,19 +2178,19 @@ "id": "cNaCxtIksGso" }, "source": [ - "Сохраним модель (сохранятся только дополнительные веса)" + "Сохраним ~~модель~~ адаптер (сохранятся только дополнительные веса)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "0c278d99", "metadata": { "id": "0c278d99" }, "outputs": [], "source": [ - "model.save_pretrained('opt_1.3_lora')" + "model.save_pretrained('opt_2.7_lora')" ] }, { @@ -927,12 +2200,12 @@ "id": "FLXasWR6sFnA" }, "source": [ - "Чтобы загрузить обученную модель нужно сначала загрузить базовую модель, а потом применить к ней LoRa веса" + "Чтобы загрузить обученный адаптер нужно сначала загрузить базовую модель, а потом применить к ней LoRa веса" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "id": "c98b2134", "metadata": { "colab": { @@ -941,31 +2214,29 @@ "id": "c98b2134", "outputId": "d97861d5-8eb6-4315-d3e6-9dad37a8b4cf" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n" - ] - } - ], + "outputs": [], "source": [ "# перед запуском этой ячейки нужно перезапустить кернел\n", "import torch\n", "from peft import PeftModel, PeftConfig\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n", "\n", - "peft_model_id = \"opt_1.3_lora\"\n", + "peft_model_id = \"opt_2.7_lora\"\n", "\n", - "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=\"facebook/opt-1.3b\", \n", - " return_dict=True, load_in_8bit=True, device_map='auto')\n", - "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-1.3b\")" + "quantization_config = BitsAndBytesConfig(\n", + " load_in_8bit=True\n", + " )\n", + "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=\"facebook/opt-2.7b\",\n", + " return_dict=True, \n", + " quantization_config=quantization_config,\n", + " device_map='auto'\n", + " )\n", + "tokenizer = AutoTokenizer.from_pretrained(\"facebook/opt-2.7b\")" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "RVkr8R1EseXY", "metadata": { "id": "RVkr8R1EseXY" @@ -973,10 +2244,10 @@ "outputs": [], "source": [ "def generate(text, tokenizer, model):\n", - " batch = tokenizer(text, return_tensors='pt').to('cuda')\n", - " output_tokens = model.generate(**batch, max_new_tokens=50, temperature=0.0, no_repeat_ngram_size=2)\n", + " batch = tokenizer(text, return_tensors='pt').to('cuda')\n", + " output_tokens = model.generate(**batch, max_new_tokens=50, temperature=0.1, do_sample=True, no_repeat_ngram_size=3)\n", "\n", - " return tokenizer.decode(output_tokens[0], skip_special_tokens=True)" + " return tokenizer.decode(output_tokens[0], skip_special_tokens=True)" ] }, { @@ -991,7 +2262,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "pHbNDutSsWpY", "metadata": { "colab": { @@ -1003,26 +2274,21 @@ }, "outputs": [ { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "\"I have a dream that one day, the world will be a better place.\\nI've got a feeling that's not going to happen.\"" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Write a function to see time. If you want to see the time in seconds, you can use the time.time() function. If the time is in minutes, you need to use the function time.minutes()\n", + "I'm not sure what you mean by\n" + ] } ], "source": [ - "generate(\"I have a dream that\", tokenizer, model)" + "print(generate(\"Write a function to see time. \", tokenizer, model))" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "id": "q7QXe_D1uUkd", "metadata": { "colab": { @@ -1034,52 +2300,16 @@ }, "outputs": [ { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "\"You know you're in love when you can't stop thinking about her.\\nI'm in a relationship with a girl who is the same way. I can never stop looking at her, and I'm not sure if I should be happy or sad.\"" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "generate(\"You know you're in love when\", tokenizer, model)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "96F0-GNruuMt", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "96F0-GNruuMt", - "outputId": "02a6df2b-6e6f-4641-a212-e63b7ceb5f5e" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "\"I am so clever that I can't even tell if this is a joke or not.\\nI'm not sure if you're being sarcastic or if I'm being serious.\"" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Help me with a todo list! I have a list of things I want to do, but I don't know how to prioritize them. What are some good resources for prioritizing tasks?\n", + "I use Trello. It's a great tool for prioritization.\n" + ] } ], "source": [ - "generate(\"I am so clever that\", tokenizer, model)" + "print(generate(\"Help me with a todo list! \", tokenizer, model))" ] }, { @@ -1094,7 +2324,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "id": "qTCL2QUnsS08", "metadata": { "id": "qTCL2QUnsS08" @@ -1106,7 +2336,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "id": "Th3AxagRvERV", "metadata": { "colab": { @@ -1118,26 +2348,29 @@ }, "outputs": [ { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "\"I have a dream that one day, the world will be a better place.\\nI've got a feeling that's not going to happen.\"" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Write a function to see time. \n", + "import time\n", + "\n", + "def get_time(time):\n", + " return time.time()\n", + "\n", + "time = time.now()\n", + "print(get_time('2019-01-01'))\n", + "\n", + "# Output: 2019-\n" + ] } ], "source": [ - "generate(\"I have a dream that\", tokenizer, model)" + "print(generate(\"Write a function to see time. \", tokenizer, model))" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "id": "bYSwNDmTvERW", "metadata": { "colab": { @@ -1149,52 +2382,31 @@ }, "outputs": [ { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "\"You know you're in love when you can't stop thinking about her.\\nI'm in a relationship with a girl who is the same way. I can never stop looking at her, and I'm not sure if I should be happy or sad.\"" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Help me with a todo list! \n", + "import requests\n", + "\n", + "# Create a to-do list\n", + "todo = requests.get('https://todo.io/todo/todos', 'todo', 'list')\n", + "\n", + "print(todo)\n", + "\n", + "\n" + ] } ], "source": [ - "generate(\"You know you're in love when\", tokenizer, model)" + "print(generate(\"Help me with a todo list! \", tokenizer, model))" ] }, { - "cell_type": "code", - "execution_count": 32, - "id": "r_7LlUibvERW", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "r_7LlUibvERW", - "outputId": "d5fba439-19de-4f11-f655-4b4f55b1181c" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - }, - "text/plain": [ - "\"I am so clever that I can't even tell if this is a joke or not.\\nI'm not sure if you're being sarcastic or if I'm being serious.\"" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "id": "c43eea0e-808e-4fa0-8dd9-94fdf9c7b22e", + "metadata": {}, "source": [ - "generate(\"I am so clever that\", tokenizer, model)" + "Теперь модель генерирует код!" ] }, { @@ -1211,9 +2423,9 @@ "metadata": { "accelerator": "GPU", "colab": { + "gpuType": "T4", "provenance": [] }, - "gpuClass": "standard", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", @@ -1229,33 +2441,71 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "0520749ab4c24669ae83b42967f97fa6": { + "00e05d6acf134e5985bea23a9320f5a1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - 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