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"@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d51d969821b04ad1b2f9bd2972b0637f": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_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", " \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", "Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.6)\n", "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.64.1)\n", "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", "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from datasets) (3.8.3)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.6)\n", "Collecting responses<0.19\n", " Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n", "Requirement already satisfied: 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"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", "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from evaluate) (21.3)\n", "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from evaluate) (4.13.0)\n", "Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.7/dist-packages (from evaluate) (2022.10.0)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from evaluate) 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/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", "9df79bdb0efd431590bcee9fa59f128a", "17fc5bdf9779453b833b98752ccc12e4", "58accc36b2c24abea5fd3174703d6886", "b3ebb6bb2ac44a4d9f99ec3e829e3330", "78b263288dd64392a3d6326a9ba70636", "328d6bd5f3d242d884aade328e5976bf", "0446d593e36f437b8c720e2fff1796da", "97a4759a3fa64710b29b1de050910b5c", "1220768434124c13b580f61dbf73cbf0", "b627c74dc6a348c780cf8f1031dcfe3b", "991cc58fe7ed4e469f6ae3234ee0a9b3", "6cd3aba0875a49da835a1daeb888edb9", "ea24260746a045d083b1b86dfae519e4", "ce1f6ba68a9b455d83b689e75ecf8219", "2a6f0328c7e546429b87464050cd5f9c", "7662b3023e714ea99b9d85cb03e3e999", "49ba11ce8a814321919dbf7ef584cce6", "e6190a7dfdb14d0b9bbb82d1ae51d11b", "15bdbb883f624fec989e3f4886691d85", "cdf03853bbb14671b86e1e008478d065", "24685d0787b74744ba2da16482cb047b", "0eba498262e24397b267920982e0810a", "27fb5119088e431dab91dcc193fd3d04", "838099af900e4929b2ffeb3f15cc85e9", "e0dc1095a6bb4450b5726e2b9c3baed2", "8b65d14819b6487d9a48446acc9ca5ce", "93166a8107014408919d06ed15c6f41c", "158112d75d60421cab30a59f31219cbb", "2bd7ff42c835485690652ca582e473b1", "80eca954cf2d4c2db498839c78cc1f40", "dbdbdb79afe4451cbcdbd1c78b6668c7", "3389d93009f54178b2a1b3db0451b06b", "ef438d7bd6604fd992b6e96b18d93c93", "2a9518d272514afe8758ef18e693e481", "10a6fc62f23f4df0aa3ec9f3435717e1", "38d31542ee954e0eb6f675eb656d707f", "682069aecc7e4a969dd7763db2399ebd", "729dc5591a3e40738d7775544aee36f4", "fd3bc9dbb68749b283a1fca547e10c41", "7e4bfeaff9fa47149228b7fc7cbb2d1e", "8a3569d84b8e4293be7ce92dc33f62e1", "cc34cd668e674e94beedd387f7a4474f", "c42f25a9b9604430aca6d9bd07b35caf", "58fa685be6fc40ce97104b3fffd36ac5", "d339b89c8405402ebbf6c624eb7a89fc", "903ce1930ed64afb91efce7524eb03d8", "fa0be842063643999537243d8a689928", "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 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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", "95a8f2c7f68c4846a88f997ffb16578b", "7a1de17110b349ff944e304d4d88e9a6", "423bd86e92e94788910c8440b44f6489", "97a25cd437124af09f661e86fe17f653", "93843ea5b2c74c30a0f0616bd50370f2", "f1de8325d42a45aa9fb0cd94b9a076a9", "99fdaab3f95d45a39cbedc4eeb2a1dcc", "83f5a575687b452fbf95d5f7c234d9e1", "94f91e4eaa11412db8ace6e168824bac", "1fa8c9ca37304a328ad5f2b5f2f57098", "ef570c98b87545df9f74bab30598b10f", "2fead7f7d1964b3082bac039e6c30403", "2c11558d44694698a3d5e26fbf798cb4", "0371c46bd8704653adb123f461db3e54", "6edf59ec14f24ca09745f15fc243a7ba", "f9d8c68f545945a9b7e20f08b2156546", "931c2ed76c4c4170baad6c77250d15e7", "d243ef3dcdb24f83af19cb5c146609bb", "697562620cf04e448312f091d1e0344b", "6a17d800a4e440528bc7c11c195e07db", "299d7c95efe84d8d82c14277c543d9e3", "2a019028c7eb44019cf70eaed55d42f7", "fd3f9bf6c00a4723af4be4d3651d8de5", "5cb71f5676144e279e995a7b6394e796", "c8845dd0bd1e4d8caac92748d70add83", "c9335db5a5784d87bc53e36dca0ed314", "cf2c40137aa942b5a372d830db0982af", "f2444d787cc14be3b6779c91e98b17e5", "ce42bda4f6b542b4801b0e3b767c38d5", "ead91f24f68341c08631da682dc2c79e", "993eb11e6c5342349dd68ad9da27edc5", "a58e4b58b4e54c2b9fd5264335e1d650", "e5c744daf6cb4067b2e12c421a82e81f", "86b7b7ebc0104472b4f024ddce3ad1a8", "51b4f37db4534641bb32a89d1eb46fe9", "194d2190966f429791e9ff7e50849f3d", "bbd5d74759af4def8f843066e8529e01", "704ec796875448a682163bdf4f6b186f", "f7a13037bc5942edb23101fc5060aefb", "09eabdd069824dd1ae834cdc77fdb299", "7beb8a8b928845b2962a1058bd19f7cd", "33a843687973446baa59de4701e65bc1", "c93c8e37e6b94be6919566663b4f3cc8", "2929d587084d4af1a75171406fb85af7" ] }, "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, "rate": null, "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": [] } ] }