diff --git "a/image_classification_peft_lora.ipynb" "b/image_classification_peft_lora.ipynb" new file mode 100644--- /dev/null +++ "b/image_classification_peft_lora.ipynb" @@ -0,0 +1,14951 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "71GTxOD71mEn" + }, + "source": [ + "## Introduction\n", + "\n", + "In this notebook, we will learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 PEFT to fine-tune an image classification model by ONLY using **0.77%** of the original trainable parameters of the model. \n", + "\n", + "LoRA adds low-rank \"update matrices\" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://arxiv.org/abs/2106.09685). \n", + "\n", + "Let's get started by installing the dependencies. \n", + "\n", + "__*Note that this notebook builds on top the [official image classification example notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb).*__" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0a_bETbqv4P7" + }, + "source": [ + "## Install dependencies\n", + "\n", + "Here we're installing `peft` from source to ensure we have access to all the bleeding edge features of `peft`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Z0_5BYt8hobv", + "outputId": "aafcbc39-b972-493a-8922-2141b1621926" + }, + "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[32m6.3/6.3 MB\u001b[0m \u001b[31m53.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 KB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m46.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m102.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ], + "source": [ + "!pip install transformers accelerate evaluate datasets git+https://github.com/huggingface/peft -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y8dSVHoIv7HC" + }, + "source": [ + "## Authentication\n", + "\n", + "We will share our fine-tuned model at the end of training. So, to do that we just authenticate using our 🤗 token. This token is available from [here](https://huggingface.co/settings/tokens). If you don't have a 🤗 account already, we highly encourage you to do so; it's free!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359, + "referenced_widgets": [ + "5d2f5fb454bc4c16b520e4e96381758f", + "dfd2baceac524fe29c0f4a8443b60a71", + "90d8e83a6af54184a82e0b81ae7054b9", + "1f96ca356b6f41b59275abe93df33f43", + "eef81e9bea0c4f5d85e7efa8ebe0463a", + "cab6d36980c0423fb75299c09c33facc", + "dd38a658218d42d7b051c66de4d4180a", + "f34be236ef9c42448ecf2957160990f7", + "38deee504dab482983a8b8f340472282", + "b2688e34899a449e8d1f6ddb5a66bb85", + "dd4edb4de5e14dfbbee418dba0bb3573", + "516c6d75bc654d62b95ac235ce84c59c", + "14c23f636609458ca4493854826c1a8e", + "c778798c234d45b5a4ae2f250e3706f9", + "d5c5396ea2f54ff0aeb9be58b59c253b", + "15bd2dcdbf4b4e74b9db09bdb8822e61", + "ecf73dd75420460399bfd04d8cd81f90" + ] + }, + "id": "31Zv6rFYr37d", + "outputId": "6476ebcf-6d71-4b7d-ee38-dc4f8e8d024e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Token is valid.\n", + "Your token has been saved in your configured git credential helpers (store).\n", + "Your token has been saved to /root/.cache/huggingface/token\n", + "Login successful\n" + ] + } + ], + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AX7aJaIKjbCF" + }, + "source": [ + "## Check the library versions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ejkn8GBzh_DB", + "outputId": "777afbdf-e026-43d8-8efa-80bb958d0ca3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "===================================BUG REPORT===================================\n", + "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n", + "================================================================================\n" + ] + } + ], + "source": [ + "import transformers\n", + "import accelerate\n", + "import peft" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "A833xxo3ir28", + "outputId": "da71ef1c-b6d7-43e2-a78b-23556785ef02" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transformers version: 4.26.0\n", + "Accelerate version: 0.16.0\n", + "PEFT version: 0.1.0.dev0\n" + ] + } + ], + "source": [ + "print(f\"Transformers version: {transformers.__version__}\")\n", + "print(f\"Accelerate version: {accelerate.__version__}\")\n", + "print(f\"PEFT version: {peft.__version__}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Po1Ve9u5v_Ul" + }, + "source": [ + "## Select a model checkpoint to fine-tune" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "vhvCQpP-isJr" + }, + "outputs": [], + "source": [ + "model_checkpoint = \"google/vit-base-patch16-224-in21k\" # pre-trained model from which to fine-tune" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UKN3rMAsjgEz" + }, + "source": [ + "## Load a dataset\n", + "\n", + "We're only loading the first 5000 instances from the training set of the [Food-101 dataset](https://huggingface.co/datasets/food101) to keep this example runtime short. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 379, + "referenced_widgets": [ + "61b957d3b51643f78a921979072fe3b6", + "d7136a7b3d0040d580508fc665b9fb00", + "5ee5e11191fc46dd92d4c2f1a7d6d9da", + "3587d42fa09b4fcdb365956a9bb07c77", + "c1ed0b68884c4d4291cd67c0e685ef18", + "9102cc38ee9942ac91dc66eda069ddcb", + "416c65eedcea4a6ea69dae317de79bca", + "128677e1b5b14e63b06b0f81c9cc4df0", + "22da54e68b1d48f9b3ba55ac1ca56873", + "16c7db587b8e475fa3aa9677385b092a", + "23c608994006427caca7975e0d81271f", + "71f7296ec9be4d9abe1af581722b40fe", + "b98e53eefc1944f193169c4f7a72b799", + "1d4a5a5b7d1645a8bf8133935e173082", + "d29e3b9102f14f3385e47ae6e27d1ab1", + "1e3e374b08964a689cfaac9c826f207b", + "b377e94780fb4e1db3b9678717e04fc1", + "86103f87819b440b8464f4460f50375e", + "c3178221dc074657bc0e585c4cfe326d", + "3c6842e0158b4dcc9b93eddfe3279d2a", + "fc612aaed5644b84959a1958b0240dda", + 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EpochTraining LossValidation LossAccuracy
1No log0.5068710.896000
22.1627000.1891410.946000
30.3451000.1447590.960000
40.2116000.1508860.958000
50.1711000.1497510.958000

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n", + "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45\n", + "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/config.json\n", + "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/pytorch_model.bin\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/preprocessor_config.json\n", + "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n", + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n", + "Loading best model from vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27 (score: 0.96).\n" + ] + } + ], + "source": [ + "trainer = Trainer(\n", + " model,\n", + " args,\n", + " train_dataset=train_ds,\n", + " eval_dataset=val_ds,\n", + " tokenizer=image_processor,\n", + " compute_metrics=compute_metrics,\n", + " data_collator=collate_fn,\n", + ")\n", + "train_results = trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b2NENHxHCejv" + }, + "source": [ + "In just a few minutes, we have a fine-tuned model with 96% validation accuracy. Also, note that we used a very small subset of the training dataset which is definitely impacting the results. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 198 + }, + "id": "_MAd2906jQKG", + "outputId": "7b825531-4e8d-4666-d6fc-eaa1accf3bb6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "***** Running Evaluation *****\n", + " Num examples = 500\n", + " Batch size = 128\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "

\n", + " \n", + " \n", + " [4/4 01:48]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'eval_loss': 0.14475855231285095,\n", + " 'eval_accuracy': 0.96,\n", + " 'eval_runtime': 3.5725,\n", + " 'eval_samples_per_second': 139.958,\n", + " 'eval_steps_per_second': 1.12,\n", + " 'epoch': 5.0}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.evaluate(val_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qo_scDEyAQER" + }, + "source": [ + "## Sharing your model and inference \n", + "\n", + "Once the fine-tuning is done, we can share the LoRA parameters with the community like so: " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 172, + "referenced_widgets": [ + "980d2f61332f414d9888b76e78774be4", + "cbc0ba8e49a740fcae7b94fe7edb8107", + "b3f07ef160a7425880ffe362008d4400", + "49ed3330fa9645eda8b5aed0fd7cbafe", + "f96302d0c2d849c5b5a0206b65e461ab", + "fcc7ad16a0b14d96acd9be9e03ac6af9", + "1a08961f063346ccae206a863ab7df6b", + "1150e391e753424da7d65bda10463da4", + "e6e36d744e1244aeb7eb0c4ce392372d", + "b2ad992db5a045668fa55c7393ec7870", + "b5fad0f3f2d543ecaed726e52d2d86bb", + "e83fd078f467406da0baf26e18b39e89", + "9b4b67731a7a4bc59be132b53c24eae8", + "e33243b001274d02a25f5940ba41ecf6", + "06e4c619e366427a8ff4c358196ecd12", + "bacd429b42d843299cb75224db3afb1e", + "c53429e699e64b3d8895a355bbd947a6", + "2bfd04824f2e4fd6844dd38e46dbbbdf", + "40a5a50aeca24f0d8990da97971004d1", + "be71e6438e0e49759d2f72feec520cae", + "1902fbc4c1da4ffe90f0947e58eb5d48", + "12a94351242444d7b0d23b8accc1824a" + ] + }, + "id": "TyQvIcnFzLIV", + "outputId": "7ac2819e-080e-4940-9755-15c32832d9a6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Uploading the following files to sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101: adapter_config.json,adapter_model.bin\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "980d2f61332f414d9888b76e78774be4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Upload 1 LFS files: 0%| | 0/1 [00:00" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "import requests\n", + "\n", + "url = \"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/beignets.jpeg\"\n", + "image = Image.open(requests.get(url, stream=True).raw)\n", + "image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dFuqZgmCW4cu" + }, + "source": [ + "We first instantiate an `image_processor` from the underlying model repo. " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dN4x_pj-VQx8", + "outputId": "b4fddd49-2f10-48e2-de31-0cefd20d405d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading configuration file preprocessor_config.json from cache at /root/.cache/huggingface/hub/models--sayakpaul--vit-base-patch16-224-in21k-finetuned-lora-food101/snapshots/fa2503cc7d91e0dd69728c1dc66ed80d7bd3289b/preprocessor_config.json\n", + "Image processor ViTImageProcessor {\n", + " \"do_normalize\": true,\n", + " \"do_rescale\": true,\n", + " \"do_resize\": true,\n", + " \"image_mean\": [\n", + " 0.5,\n", + " 0.5,\n", + " 0.5\n", + " ],\n", + " \"image_processor_type\": \"ViTImageProcessor\",\n", + " \"image_std\": [\n", + " 0.5,\n", + " 0.5,\n", + " 0.5\n", + " ],\n", + " \"resample\": 2,\n", + " \"rescale_factor\": 0.00392156862745098,\n", + " \"size\": {\n", + " \"height\": 224,\n", + " \"width\": 224\n", + " }\n", + "}\n", + "\n" + ] + } + ], + "source": [ + "image_processor = AutoImageProcessor.from_pretrained(repo_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dc0rCwC5XAaL" + }, + "source": [ + "We then prepare the sample for inference." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "57C6tcdnVYu1", + "outputId": "f164dd91-8679-482e-fb10-2f530d4ea9f4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 3, 224, 224])\n" + ] + } + ], + "source": [ + "# prepare image for the model\n", + "encoding = image_processor(image.convert(\"RGB\"), return_tensors=\"pt\")\n", + "print(encoding.pixel_values.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Pn4T1GyTXC47" + }, + "source": [ + "And run inference!" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YyQznW5WViMc", + "outputId": "d1b7a77c-68b3-4f6b-a945-4b32e85baabe" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class: beignets\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# forward pass\n", + "with torch.no_grad():\n", + " outputs = inference_model(**encoding)\n", + " logits = outputs.logits\n", + "\n", + "predicted_class_idx = logits.argmax(-1).item()\n", + "print(\"Predicted class:\", inference_model.config.id2label[predicted_class_idx])" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "0a_bETbqv4P7", + "Y8dSVHoIv7HC", + "qo_scDEyAQER" + ], + "machine_shape": "hm", + "provenance": [] + }, + "gpuClass": "premium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + 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