{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "y-AdvzW8EZRR" }, "source": [ "# Fit in vision models using `quanto`\n", "\n", "\n", "`quanto` is a library to quantize any model in int8, int4, int2 and float8 precision on GPU/CPU/MPS. In this notebook we'll see how to use quanto. If you'd like to learn more about these precisions and how inference takes place, read this [blog](https://huggingface.co/blog/merve/quantization)." ] }, { "cell_type": "markdown", "metadata": { "id": "ddHXttf6Efk4" }, "source": [ "Let's download the necessary libraries." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wbAzsxuPEVz9", "outputId": "e511b2be-5870-485b-e999-e84ad1e38205" }, "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", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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"60b98f07d1ec4c989569d9481615da3d", "231852b010d7416380d36c8d12c4cb25", "332ec5d245314184a3da4f0233763e87", "81a4558477914ba89bcf2b67ac6d3663", "c7e38fad08544dbcaeea864099c5be5e", "6b2838b8b53a45eea8f8b8508e1c5b0a", "f2bef225644646b4b049bf6195564bd2", "8f5c9b4b07f24ff39b9a7b5e370ffca7", "a1b5eab8aa2b45849cf95aacca1b3e2b", "a1c1ae2d9d2f4d8bbf26689120db708b", "a9cba384ef9e411cba10b8783bc5eb67", "227ceee4c8da4f76b316c72a4a197153", "351d669abe6e454c98f51c1e2b310eb0", "5f4b9e02ad0746dca86018cf82d824af", "69141c368d4e4ac6a2e3fed7b470fcbb", "97de7a7bcbfd4c849c7da83959bf537e", "719cfac250134b64b7428a783670a1b4", "700a9db61e3840a5a06b7af651722014", "26230e6254ec4ef5a5d08e2fac5be17e", "a684fc75a8954078a36f7ee287cee7c9", "2b9719270f6f4f6dbe4ea3adf5fe1f29", "cca1ac3bdf484b47ae5736092490b9ab", "cda526f89535413cb82a6f1b9cee8ac7", "8809042eebd34f15a88c881a2b12d07e", "70010663b98844ebb865c4d2d27935a7", "64907549ef454f3caed4d2f20eec36b6", "4b86e01a77db43c8a848d5d3173ae398", "904b38e6453c40159755eff307a68f6b", "24d6cfeb62924042895176ca6163e3ff", "a0232a6cd0484953952ed874fe84d017", "486642d9fd5b4058945333b6cb79aed3", "f062bf4da1e140b89b722dc1bb31194b", "df73c7282d2b47578fc37f5199ed345c", "e69962b5dfe2449b9e591bab3d0f63d4", "a58c3dc91720408b836bfd8d4b3cba9a", "33f0dd5c70bc40ceb5fc631cd6c1de5c", "9b8dfc6ae56d4a08ba4694c0207e677a", "fa12759c8dea4d2e85c4289ff31c5495", "e30ac541ed284e0896f3349bf0ded731", "d2f6f35cabdf4bbf82b9f92afbd6680e", "6769f8c2b4f441d097a42f5d544abd9f", "b7307297589d44b3adac30361e207a90", "99d5e6d7acb649ce934446cf7a40116b", "f5e0b93282e248aca5150abed1040343", "332c7b29f44749088a8b77e40dc7ed89", "d1d53f5e85594e4cb2c8253da5768d83", "a3dcf1ea64fc427f9feb688a515507c3", "fca949c714e648489fcb2cf7ee2eb40f", "1940fbbbcdaf40e0aeff4d14ba61f495", "57253c901e9a4e1daac108341441f9ad", "2c0b3a29026346c8bbd59aaa3e35a093", "6db2c8a6861f42668bf90ba0bff5e376", "ea992a4e33f54020a94fbca3b290ddcf", "50ec14064e1a4f588f1b55c80e0a7fa1", "3d37a01bee8449f6958f9d61f80d262d", "2f3b79b3e7254fa4bd6e6e8c71402e32", "64ab414ff4a04eada91eb603266d6fc0", "90e9b147168740a387b360d45692f6c8", "e27a504a56724ddfa4ec3cd11345d16a", "a48f257b39ba4c7589ca9041865d0bbd", "12d0fad4785d402bb55c40397b8c6125", "b3271759bf7a492d8e654239b61e09a6", "eb60151b75944a07b51aca72e0ad7cdc", "5c240140a1fd4df8a0230d446f578c05", "1f74c046631245359d48b374ce692532", "f9e5d91db5d74678bc78060f6e662dba", "f2e2bfd2ebf642118dad9944ca41b75e", "915619a86e4343d5be00cd0c7a34c409", "7fc08879d5ed4fffa6d44cf0bdb5cbf5", "09d6da78e3294337b029eb53697a1e5b", "d14a3ec2432f4bc7ae69db5d95301152", "d9a6baf6f095408198b0686a8db92a5b" ] }, "id": "prUFIu0HEkH_", "outputId": "f46c341d-9a6b-4065-dd44-a60ba6a140dc" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "92758a99bc724a139cac4675a308feac", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/6.57k [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.imshow(image, cmap='gray')\n", "\n", "for i, mask in enumerate(masks[0][0]):\n", " plt.imshow(mask, cmap='viridis', alpha=0.2, vmin=0, vmax=1)\n", "\n", "plt.axis('off')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "yQh-QTF8JvbG" }, "source": [ "Let's try larger models. We can do 8-bit inference on a VLM of 7B params." ] }, { "cell_type": "markdown", "metadata": { "id": "ZkrOnq6hK7bZ" }, "source": [ "We can use the same quantization config." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 535, "referenced_widgets": [ "1642f74ab2c049a48deac8ca6497bac1", "810a8ff597ca4fe9ab8f9f703033ce5d", "0704c012bef447d4b68deb429f13f463", "bf2ab7eeb63a4cdfb7d77e60a483e76b", "97d6e766a0044e7980f098d7e0e5ed85", "f2196d3805734b0195b17df18133514a", "2b4d78e489e140118fc1da73c3181ca8", "6bbcf18e0e5d4b81a07545ed1317951b", "dbbeb0f6c85b40e295c849560db09962", "a151529a9ed94fee8757618c76462d83", "13ff62d8d78f412aacbccec2d7ab8297", "40630fb71b344d048febe805d30cbf8a", "dfb771925fb54733aaae83f24e031a9f", "16dc7f027cbe4a51ae16be19658a5d16", "47225f71f86c476ba3936f3f3e9e489b", "0649902462ff4790967dac4643785e65", "2d8b451c76d94c29ba0b72bed29bc904", "868dc3fe34734d7d934c4e0bd58d8e9a", "03b17e8bb40d4b7fbafbb3c5e31ecbac", "db69d4a0dd45418daf2467a48acff56e", "6e71cc03ac5140bbb32fa8a061e33207", "f2aa9ecbebe84343b20735ccc3064a1e", "444e1837706b46d993aaf2e504a04e10", "6f4041bb86c24b4f942f69f73a1a19dc", "fdde8cfec56248889655a87245f332ef", "fd5aa315f00049c1bb87bb8c8c030103", "2b6681e9c0f44d689b1b8cc7c7eb6a6f", "7a4a9c487da74049ae51d237f21452d3", "d307fc64c00a4e9b85360536bcaccf4b", "4ea0958211b3407288c6a22edb578685", "f5118377acb946418048eb77cfd2ebb9", "d63ffc9daa3d4215a0123769dc812762", "caa9ab94fd684ff8b76a8b450808cbef", "8050d12816ff4145ac1dcd8fb8595284", "4e0c30cd087a4dbcb10ad0868a9035a2", "198a263cc86344cf9f1806e29c78ac9b", "e0e52ba822f34d4582475ac51a2aef70", "b03ded8d417b45f19b5b28f45600c9b0", "621c6161d994478cb31113e2d669e031", "7a28acdf69004437a167603b648c5d1e", "f0d810f41f644d38992e2d86b038408b", "dea572ea604c4bc88e3e4e23a5ec3776", "db78eabb36e0493a99e1aaab7b9892ac", "54e37b1f0b8542acbb34bcf43ffedb3a", "a1cfe2475d1e4c969f006f3a3949c6b6", "6673ddd5244b45439589baefea9c8794", "5b0d433af98241f3af577955c65e8738", "b50aa19f502440c8a3512666b998417b", "551fb3e829c44d83b34419bacbb55c7a", "2b2eb4e1d7da4eab8aeb64a0b97e0ead", "92e7dabd937b42f3b64c9aecfb14f248", "a12d56fadeb94a02be0fad6d4ca9b8a4", "7b4cc457d58544fa9a768e41ce42e5c9", "67b53975f5c54d0a945dff8b8fcdb678", "ef579ac7b98e46fb8ede7eef903411b4", "62724adad9ce4cbeaed90ef09c220b73", "8e7a9396965d47c4956862335f441e61", "8484953b518240a1a2905ebbfb50c249", "cb640ea8328944beb12fd97e19bf5fde", "e719c72d386341778d679bb6d2888461", "92a262f8ddeb4261b6d9367f9abb78f2", "5a543d64446a46c698603aec47a2d601", "1f91667b5b1b41d2b2f641c717602289", "1005353d3dea48999ed475e3b4e26943", "6fc0e31ff3cc4520ad97f16044e1be39", "a90105a136a44f90ad3bfb8c0e7da312", "0abd63ea63e140f09c927e48c9fb71c6", "16125269e3f44382b88889f245b2183a", "938ac40c2fea4578860ff433e15d5d7c", "ca6d9b54655e4c5e9e7f120fe43dc578", "001bfda0b15142d18ccf07fe1eadfbd9", "b3790ca010ab4324906f0d7ef30e9089", "b9000bc514e848a59e97a9ec9dc417a6", "acce0dc525bb466f807243ff1daff9e3", "c614a8bdeccf4937a16289da7072ae8d", "f24c2b77d0594079b6cf09f3b24835a6", "c99ed604945741d587e7ac62b1484fda", "a8b2b3be8d7148acbc6c17e07a17ede9", "12dd3ae9ccd74874b47516e7253c1929", "e0db4087cfdf4b21a6848e5715586634", "dd5e7d71a65d43889cda81b725bbcedd", "0d3f357d936742659dbefc8760e12a1f", "7a4d24f143c145e493777b8eb27ba838", "0f57cb0a140843e5ad2be62a655822ab", "a31f8d1887024610beb9edff931df47c", "31fd3626b6a74f46887833666f4d07f1", "0678af7c55ef4974a2672df46c213c9e", "911fd90faf3e4f37ba6da58c4aa94b05", "eab3a7fceece4a57a1525af9113bc5fc", "80b582f8cc59480bb3ee160fa5557749", "6c676450a2584b778099841425491244", "aad4c6ca599540839c56b31b40ced2f8", "dbd703975089469897d00f91de6b7cb3", "ced5f0e5507b493e9d4bf1ac024539cb", "1eebd50b4b524341b4b5072172b7a299", "a26ba358b7bf485e8be9e5220fb4a123", "1aac5e6c173f45b296b16e03b0cef582", "96198c198b8a4b82ab29111e69eee278", "ac6aa79dcde541879c06b1d98a12b576", "2e6be10449184fda9f0f9b6a2a8dff99", "9d48a2188ae4422388ff3b4e6fadfadf", "030e3327bfc84566873513cfbf574428", "ca828904376e4d81a5df43261f65750a", "c59bc977486c4adc99b8124a220d7d41", "97b3765430a24385a7c92e9402d42445", "dc227d46d8b24f62b6ffdaf56eb4b0cb", "8e7a1896aacd4ba2aba2dc589ba1bc6c", "621111bb53b4403fa00c110184418cad", "ed50d99e9ca442de856f1a3111c706c9", "a6dcc1a6ff9045bcbadd9ac9ca5786bd", "1d999533849c4e8c908bc737863dde60", "8a68e174f8614a3ea89fd22528028e88", "8e3f43de5f864c428c126c88e606c5db", "9c1047e49dfd4e62af89c58e9a9a5c8f", "f7734ecdbdb343a29517a98e7f1f6f31", "5f9351f91df54452a5b7eb1eb69b18de", "7435e02b3ccd45d694ae21cd92e2c48e", "85cf556237c24015b9ecfd649dc320c0", "b8db47df14e14af49fecc646b6f00d8d", "91f32d8e161e44e5ad79d8b775b51ec7", "a258846ddd1c4601aee093f6d73de380", "5c67a2aa4ed04a12b8d73e495e162844", "70ef054e21854b9db1e69d7dd9dc1238", "118e29d0133047f4bb925aa0bf6ce8eb", "5503d2fd635546f7add4f5744ad458d3", "2c796f6acc2e4f80bc310dd6894f1966", "b1064c3c8fbe4a2bbe34c394b4432e73", "6dc9f3824b0d4139b23e0a95b3fcd01c", "2fa485edbb044ba9bdf92f61824414d5", "6c6473ce59c04b76922792f3b20391c6", "727809c9be1f4309a9e11019550db566", "3294d7ad93de499d995e13a1965c33f9", "d710c3d6e6f34f519968e2bbb824b134", "35ecfcdbfed04e5b9c4bfef1db80b2be", "8933395b8d4a416b8ab7ef34437ed8b9", "e0789c1e4f034d55a7d9eed8039f3a37", "f745e8aa2ea64c5d9a119b299638d95e", "24ba84205476453a9b95cec8be75fe7b", "2f7eb12520254c22ac164cdd5524ab7a", "7c534880fcdc401f93da2dd045d64872", "c69c057d36124e0e89f75ddcf5e7a867", "0ad66d30b4d1406793c2ac46ee043450", "f64f24e185f345d59f275ae58708f443", "79202aee028c427c8827dee0c2a010ea", "1fe2c02159374de1936e15df18b3d29c", "dff8b5c7f4cf4361bd82f933548254e8", "156cc5573b6f45b79cf3a12c200e5f55", "5e2e9147bebb42a4a83e403d5445385d", "66401b2ec0074ff6900e4dee40d42fd2", "62f8336438894c1fa935044ec9c1ca90", "17470e1a56f1412e97d5d41d33b2535a", "bb0c85b1ccbc4b32a5fa29d12e855688", "e3a94c24ed4c46098f17625a5391aa40", "ad4eec05be1544d3a67abddbff0de4d2", "9b2ef6167f51465e9edc182b37ec81b9", "a70d11f47ed243418d0ec194c050e69f", "42742cd51e6d435e80b5e65198c84307", "a43d4266f7fc4b8a8380598536db344f", "3da0de7f39c644688630c9e6f657c9cf", "3a75bfe30046415492f00c6285437025", "0a94bab19a6f4d1daccc6166f39a900b", "17a30237e3ed4ba9a4af1639af7ad95d", "4aef48d07c5a401e9b7c41be7bce6295", "2d165064111540c6a1090a2b5ddf46bc", "546b16c50bec4d81833b512088b62893" ] }, "id": "N85UDZD9Ks1B", "outputId": "58c40a9b-eafa-41bf-fe81-2772eb0a879e" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1642f74ab2c049a48deac8ca6497bac1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "preprocessor_config.json: 0%| | 0.00/754 [00:00\\nWhat is shown in this image? [/INST]\"\n", "\n", "inputs = processor(prompt, image, return_tensors=\"pt\").to(\"cuda\")\n", "output = model.generate(**inputs, max_new_tokens=100)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZRr6T3DsLmH9", "outputId": "add8aebf-9fbc-49f3-d622-9115c177584f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "image shows a close-up of a flowering plant with several flowers in bloom. The flowers are pink with a darker pink or purple center, and one of them has a bee perched on it. The bee appears to be a bumblebee, which is a type of bee known for its large size and fuzzy body. The plant is surrounded by other plants with various colors of flowers, including red and white, suggesting a diverse and colorful garden or\n" ] } ], "source": [ "print(processor.decode(output[0], skip_special_tokens=True)[len(prompt)-1:])" ] }, { "cell_type": "markdown", "metadata": { "id": "qTPOTj3vZQLM" }, "source": [ "Let's fit an even bigger model. Note that the inference will take a long long time compared to running this model in CUDA native precision in A100 (as one should). This is because 8-bit is not native to NVIDIA hardware: we loaded the model to float16, and the model is quantized and dequantized back and forth to 2-bit, which adds overhead. Moreover, T4 is not as fast as well." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "6922d5bd81c0446895e4edc0466e4a08", "e8db46a82d654eb49d791a8718d12c92", "b3b8fbcdfdba4458a8dfd107555939b6", "53422fa730014a3b8f3dc4b1e540cc21", "a4b87f8ba787404d8f31a5f1a9be578b", "2b9f07da72c64ef097781267ad6d394c", "7c92c8c71fad4b53a180323fba865551", "64871f2b6a734c54b391c7b51addf824", "fde63f0224524324b00ad0a9f177fac0", "33fc59bf9fd5430a919b4bc958ea30d3", "a0afae93b42b4a9a80af68ec7be5ffc7" ] }, "id": "r6CTpX1BL_1G", "outputId": "7e6e31db-3a85-4117-9a38-a57a7cc3eb5c" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6922d5bd81c0446895e4edc0466e4a08", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/15 [00:00system\\nAnswer the questions.<|im_end|><|im_start|>user\\n\\nWhat is shown in this image?<|im_end|><|im_start|>assistant\\n\"\n", "\n", "inputs = processor(prompt, image, return_tensors=\"pt\").to(\"cuda\")\n", "output = model.generate(**inputs, max_new_tokens=100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kStw4L-xYxXG" }, "outputs": [], "source": [ "print(processor.decode(output[0], skip_special_tokens=True)[len(prompt):])" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "001bfda0b15142d18ccf07fe1eadfbd9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": 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