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new file mode 100644--- /dev/null
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+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ }
+ }
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# HuggingFace meets `bitsandbytes` for lighter models on GPU for inference\n",
+ "\n",
+ "## Running T5-11b on Google Colab "
+ ],
+ "metadata": {
+ "id": "Gbr9iYfRy4GZ"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ " \n",
+ " ![](\"https://s3.amazonaws.com/moonup/production/uploads/1659861207959-62441d1d9fdefb55a0b7d12c.png\")
\n",
+ " "
+ ],
+ "metadata": {
+ "id": "32ShdTnhMoKV"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "You can run your own 8-bit model on any HuggingFace 🤗 model with just few lines of code. This notebook shows how to do it with a `T5` model that would usually require 12GB of GPU RAM.\n",
+ "Install the dependencies below first!\n"
+ ],
+ "metadata": {
+ "id": "K9uJn2NczFBK"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "Aep1KMF6dqdm",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "051e8b85-57d1-4d9f-f3d0-461d4d05e643"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[K |████████████████████████████████| 62.5 MB 1.8 MB/s \n",
+ "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[K |████████████████████████████████| 7.6 MB 30.4 MB/s \n",
+ "\u001b[K |████████████████████████████████| 182 kB 68.5 MB/s \n",
+ "\u001b[?25h Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[K |████████████████████████████████| 175 kB 30.2 MB/s \n",
+ "\u001b[K |████████████████████████████████| 1.3 MB 18.7 MB/s \n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install --quiet bitsandbytes\n",
+ "!pip install --quiet git+https://github.com/huggingface/transformers.git # Install latest version of transformers\n",
+ "!pip install --quiet accelerate\n",
+ "!pip install --quiet sentencepiece"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install datasets"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zvEh5C7b4vHc",
+ "outputId": "5a05c924-2ed2-471b-bfcb-f1175a79bc94"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+ "Collecting datasets\n",
+ " Downloading datasets-2.7.0-py3-none-any.whl (451 kB)\n",
+ "\u001b[K |████████████████████████████████| 451 kB 32.0 MB/s \n",
+ "\u001b[?25hCollecting multiprocess\n",
+ " Downloading multiprocess-0.70.14-py37-none-any.whl (115 kB)\n",
+ "\u001b[K |████████████████████████████████| 115 kB 69.9 MB/s \n",
+ "\u001b[?25hRequirement already satisfied: huggingface-hub<1.0.0,>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.11.0)\n",
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+ "Collecting xxhash\n",
+ " Downloading xxhash-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
+ "\u001b[K |████████████████████████████████| 212 kB 68.9 MB/s \n",
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+ "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
+ "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
+ " Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
+ "\u001b[K |████████████████████████████████| 127 kB 69.8 MB/s \n",
+ "\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.10.0)\n",
+ "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.6)\n",
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+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
+ "Installing collected packages: urllib3, xxhash, responses, multiprocess, datasets\n",
+ " Attempting uninstall: urllib3\n",
+ " Found existing installation: urllib3 1.24.3\n",
+ " Uninstalling urllib3-1.24.3:\n",
+ " Successfully uninstalled urllib3-1.24.3\n",
+ "Successfully installed datasets-2.7.0 multiprocess-0.70.14 responses-0.18.0 urllib3-1.25.11 xxhash-3.1.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install evaluate"
+ ],
+ "metadata": {
+ "id": "ImrtT094EFec",
+ "outputId": "1ca13c87-0519-45e8-93de-ec13d3f00a34",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+ "Collecting evaluate\n",
+ " Downloading evaluate-0.3.0-py3-none-any.whl (72 kB)\n",
+ "\u001b[K |████████████████████████████████| 72 kB 1.6 MB/s \n",
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+ "Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from evaluate) (2.7.0)\n",
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+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.3)\n",
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+ "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.1.1)\n",
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+ "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->evaluate) (3.0.4)\n",
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+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->evaluate) (2022.9.24)\n",
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+ "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->evaluate) (2.8.2)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->evaluate) (1.15.0)\n",
+ "Installing collected packages: evaluate\n",
+ "Successfully installed evaluate-0.3.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import pipeline\n",
+ "from datasets import load_dataset \n",
+ "\n",
+ "pipe = pipeline(\"automatic-speech-recognition\", \"openai/whisper-tiny\", args={\"device_map\":\"auto\", \"load_in_8bit\":True})\n",
+ "ds = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")\n",
+ "audio_sample = ds[0]\n",
+ "speech_file = audio_sample[\"file\"]\n",
+ "print(pipe(speech_file))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 284,
+ "referenced_widgets": [
+ "5fbac070bfc8458998b2afbffb975a53",
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+ "17fc5bdf9779453b833b98752ccc12e4",
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+ "8b65d14819b6487d9a48446acc9ca5ce",
+ "93166a8107014408919d06ed15c6f41c",
+ "158112d75d60421cab30a59f31219cbb",
+ "2bd7ff42c835485690652ca582e473b1",
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+ "3389d93009f54178b2a1b3db0451b06b",
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+ "2a9518d272514afe8758ef18e693e481",
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+ "72f92b50807a447eb0a6e2252869d3bb",
+ "aef2e10bad794352b3cc87266b0445ca",
+ "d73635ec743a4d4eb03902fc52fb43f9",
+ "60a8653318214fe58be257118d77a3b3",
+ "536ff883bfa540b8a1c4034abf0df945",
+ "a7be33c8f45b47b5b697866bb1941c0a",
+ "e70f2f8ed15e4e8ea52dd2e3bf526359"
+ ]
+ },
+ "id": "oOaJH0VNqnRJ",
+ "outputId": "d1562a30-eac0-4c8d-f8cf-498c6be76f4e"
+ },
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading builder script: 0%| | 0.00/5.17k [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mevaluate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mlibrispeech_test_clean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"librispeech_asr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"clean\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, **config_kwargs)\u001b[0m\n\u001b[1;32m 1745\u001b[0m \u001b[0mtry_from_hf_gcs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtry_from_hf_gcs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1746\u001b[0m \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1747\u001b[0;31m \u001b[0mnum_proc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_proc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1748\u001b[0m )\n\u001b[1;32m 1749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36mdownload_and_prepare\u001b[0;34m(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[1;32m 824\u001b[0m \u001b[0mverify_infos\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_infos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 825\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mprepare_split_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 826\u001b[0;31m \u001b[0;34m**\u001b[0m\u001b[0mdownload_and_prepare_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 827\u001b[0m )\n\u001b[1;32m 828\u001b[0m \u001b[0;31m# Sync info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_splits_kwargs)\u001b[0m\n\u001b[1;32m 1554\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_download_and_prepare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_infos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mprepare_splits_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1555\u001b[0m super()._download_and_prepare(\n\u001b[0;32m-> 1556\u001b[0;31m \u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_infos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_duplicate_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_infos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mprepare_splits_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1557\u001b[0m )\n\u001b[1;32m 1558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/builder.py\u001b[0m in \u001b[0;36m_download_and_prepare\u001b[0;34m(self, dl_manager, verify_infos, **prepare_split_kwargs)\u001b[0m\n\u001b[1;32m 889\u001b[0m \u001b[0msplit_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSplitDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 890\u001b[0m \u001b[0msplit_generators_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_split_generators_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprepare_split_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m \u001b[0msplit_generators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split_generators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdl_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msplit_generators_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0;31m# Checksums verification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/cff5df6e7955c80a67f80e27e7e655de71c689e2d2364bece785b972acb37fe7/librispeech_asr.py\u001b[0m in \u001b[0;36m_split_generators\u001b[0;34m(self, dl_manager)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_split_generators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0marchive_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdl_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_DL_URLS\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 118\u001b[0m \u001b[0;31m# (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0mlocal_extracted_archive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdl_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marchive_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdl_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_streaming\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(self, url_or_urls)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0mparallel_min_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0mdisable_tqdm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mis_progress_bar_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Downloading data files\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 319\u001b[0m )\n\u001b[1;32m 320\u001b[0m \u001b[0mduration\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py\u001b[0m in \u001b[0;36mmap_nested\u001b[0;34m(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\u001b[0m\n\u001b[1;32m 444\u001b[0m mapped = [\n\u001b[1;32m 445\u001b[0m \u001b[0m_single_map_nested\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisable_tqdm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 447\u001b[0m ]\n\u001b[1;32m 448\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 444\u001b[0m mapped = [\n\u001b[1;32m 445\u001b[0m \u001b[0m_single_map_nested\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 446\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisable_tqdm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 447\u001b[0m ]\n\u001b[1;32m 448\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py\u001b[0m in \u001b[0;36m_single_map_nested\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;31m# Singleton first to spare some computation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 345\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_struct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_struct\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 346\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_struct\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[0;31m# Reduce logging to keep things readable in multiprocessing with tqdm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/download/download_manager.py\u001b[0m in \u001b[0;36m_download\u001b[0;34m(self, url_or_filename, download_config)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[0;31m# append the relative path to the base_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 337\u001b[0m \u001b[0murl_or_filename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murl_or_path_join\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl_or_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcached_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0miter_archive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath_or_buf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBufferedReader\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py\u001b[0m in \u001b[0;36mcached_path\u001b[0;34m(url_or_filename, download_config, **download_kwargs)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0muse_auth_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_auth_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0mignore_url_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_url_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m \u001b[0mdownload_desc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload_desc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 196\u001b[0m )\n\u001b[1;32m 197\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl_or_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py\u001b[0m in \u001b[0;36mget_from_cache\u001b[0;34m(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)\u001b[0m\n\u001b[1;32m 583\u001b[0m \u001b[0mcookies\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcookies\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0mmax_retries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_desc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 586\u001b[0m )\n\u001b[1;32m 587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/urllib3/response.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_fp_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/urllib3/response.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m 516\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[0mcache_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 518\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfp_closed\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34mb\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 519\u001b[0m if (\n\u001b[1;32m 520\u001b[0m \u001b[0mamt\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[0;31m# Amount is given, implement using readinto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 464\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 465\u001b[0;31m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 466\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmemoryview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;31m# connection, and the user is reading more bytes than will be provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;31m# (for example, reading in 1k chunks)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 510\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;31m# Ideally, we would raise IncompleteRead if the content-length\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/lib/python3.7/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 587\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 590\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/lib/python3.7/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1069\u001b[0m \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1070\u001b[0m self.__class__)\n\u001b[0;32m-> 1071\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1072\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1073\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/lib/python3.7/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 927\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 928\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 929\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 930\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 931\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import WhisperProcessor, WhisperForConditionalGeneration\n",
+ "processor = WhisperProcessor.from_pretrained(\"openai/whisper-large\")\n",
+ "model_ori = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-large\", device_map=\"auto\")\n",
+ "model_ori.get_memory_footprint()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6Csb7vv17TmL",
+ "outputId": "1a711c2a-fbf9-4a67-c833-695e21e62a47"
+ },
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "6173219840"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 1
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Chose your model"
+ ],
+ "metadata": {
+ "id": "ymZwkJUenuV1"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Rerun this cell if you want to change the model!"
+ ],
+ "metadata": {
+ "id": "_3cMSVC-oCyM"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model_name = \"t5-3b-sharded\" #@param [\"t5-11b-sharded\", \"t5-3b-sharded\"]"
+ ],
+ "metadata": {
+ "id": "3HibeFxJnwq7"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Use 8bit models with `t5-3b-sharded` 🤗"
+ ],
+ "metadata": {
+ "id": "BejbQStU5Eik"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
+ "import torch\n",
+ "\n",
+ "# T5-3b and T5-11B are supported!\n",
+ "# We need sharded weights otherwise we get CPU OOM errors\n",
+ "model_id=f\"ybelkada/{model_name}\"\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+ "model_8bit = AutoModelForSeq2SeqLM.from_pretrained(model_id, device_map=\"auto\", load_in_8bit=True)"
+ ],
+ "metadata": {
+ "id": "YJlldexxwnhM",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 341,
+ "referenced_widgets": [
+ "51dbc11ad12148a28e3b3efaa98f46e6",
+ "c50e46b91e694735ae235148ec7e7143",
+ "32f31eb5a44247e0bbe7834bbfb32a06",
+ "7410a4d6aed04e83bdfc96028b11e5c0",
+ "01287a188ddf465ca13c6fbe59840feb",
+ "07138f44660e423ab73502cf9ab889a8",
+ "0146994277b446bb8f64db0c09f31082",
+ "ad31acadfe8d4660bb6d4eb213cd88fd",
+ "5ae1824c30454eb68b8086b3ad84ed3a",
+ "f9dc6d2a2e5b474e916dbc0b73c924bc",
+ "c787def0bcc24a2e9acd9a53d761d2f5",
+ "d13cd79e403441d989231f3fe668b542",
+ "817216ddb5224c799b6421df57ca03fb",
+ "4d8dff5705e6473b8b8f4905230639dc",
+ "5df9a1369c8147b78e427667b2e71db4",
+ "56ff97bdf14b43fca9d73bde9749d0e2",
+ "0630aca5e9a943b1b15fec5fcdac8816",
+ "1958e21ae869473f95a535a13518c4c1",
+ "42c07b3afa654abfb42b92a7a3b7791c",
+ "00844fe4ceb340f78f7136d8ecaca6dd",
+ "2fa7484e6bfd4ceca4455a8ff32f5b3e",
+ "2c4f49c5dba54441a5fc08744cae04ba",
+ "deca54f80962461c93432b43db21258e",
+ "6d7e8eec783e4bf5959fa4cde9839a2e",
+ "cec903acca7a41a0a2a1323e33a8d3aa",
+ "4b1eeadd3b424157a53469744678cccf",
+ "6add74fa75a246578ef2f289913a3f9d",
+ "ae72cc13d39c4150b5a6f118651da938",
+ "53fb20ca4d1344acafc8b5ad2dd5478e",
+ "e512432c679c4c349712593db51d8134",
+ "ba2e537f812044a98ac029fcd3a95d32",
+ "80fe447476a24ec9b482d95c5fb37e64",
+ "38ae635babcf462ea20e8c4ba7a9be43",
+ "2fcdbe65c25a4febb5c366e0456e3498",
+ "39994c5a327e41a1841109202433f111",
+ "5954fcda6cf14f6195b22f57e4927955",
+ "1ddc5692e7e84acaaf4d35d3bf7336eb",
+ "21dc50793f73447bb898937af5c44bd1",
+ "546cd0b4a36f4c71834cb48ef7976877",
+ "6afb2454b8eb44b09d52cc3398bf4a05",
+ "c64730a2e96d47fcb8e9e217a7e7603c",
+ "de86482a52b1435ba899da58b4d73d86",
+ "df510f2e5d0a465dbbfbb6209be2a90e",
+ "3df7cfe9468a42c283f4b711ca261d61",
+ "0788a74e1e954f8d86ad54bdd396ce2f",
+ "8f206bf6d21249958bd7b495f0c7e797",
+ "72e155dd25ae472e91716739bbbc3076",
+ "4a89d2dc655e4ada8f7f8c523827265d",
+ "43d5d1c1930d45589fc209b69c6afe08",
+ "434248e27f394cfbaabaf4ef4dddcb36",
+ "300a93dce36942e49bcbd3ae72894a01",
+ "bfb16ecd994d446abf74a7961918b016",
+ "81a6ab0f16834e5c8359f7cdd4a65c25",
+ "f4229fd72d214b09a952ce99272a515c",
+ "9dd1bc62ec7f44ceaa1ffe5c778ede32",
+ "d3ff61e267294203a33df5f74973bdcf",
+ "3abc3574c85a4167a7f799a29b7bf93b",
+ "314c31279796415bbf6ed99233f9e2f5",
+ "530475b6822b4a50b60a1efb52c9a91b",
+ "331e75f9b1de41d39448cebc5d43b31c",
+ "f7ee938176d54a6c805c02e177b98568",
+ "f20f4e8ef40d4d0aacedcf0cf96332d7",
+ "217d4aef7fcd46d98175b0418b1e7f91",
+ "ecd22d569cfd4b4c8a1ac9f3f8321673",
+ "1ae5d86eac70489288edb7667c9f850c",
+ "1af89eb7db164f1fa8448f678d7eafbd",
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+ ]
+ },
+ "outputId": "66fc5772-770b-4b11-e0a1-e8dbe7655efe"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Moving 0 files to the new cache system\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "0it [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "51dbc11ad12148a28e3b3efaa98f46e6"
+ },
+ "application/json": {
+ "n": 0,
+ "total": 0,
+ "elapsed": 0.019360065460205078,
+ "ncols": null,
+ "nrows": null,
+ "prefix": "",
+ "ascii": false,
+ "unit": "it",
+ "unit_scale": false,
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+ "bar_format": null,
+ "postfix": null,
+ "unit_divisor": 1000,
+ "initial": 0,
+ "colour": null
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading: 0%| | 0.00/1.20k [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_8bit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_memory_footprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'model_8bit' is not defined"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "For `t5-3b` the int8 model is about ~2.9GB! whereas the original model has 11GB. For `t5-11b` the int8 model is about ~11GB vs 42GB for the original model.\n",
+ "Now let's generate and see the qualitative results of the 8bit model!"
+ ],
+ "metadata": {
+ "id": "TsuYzVoBPMtn"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "max_new_tokens = 50\n",
+ "\n",
+ "input_ids = tokenizer(\n",
+ " \"translate English to German: Hello my name is Younes and I am a Machine Learning Engineer at Hugging Face\", return_tensors=\"pt\"\n",
+ ").input_ids \n",
+ "\n",
+ "outputs = model_8bit.generate(input_ids, max_new_tokens=max_new_tokens)\n",
+ "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
+ ],
+ "metadata": {
+ "id": "j1s0spY4icGK"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file