diff --git "a/Finetune_flan_t5_large_bnb_peft.ipynb" "b/Finetune_flan_t5_large_bnb_peft.ipynb" new file mode 100644--- /dev/null +++ "b/Finetune_flan_t5_large_bnb_peft.ipynb" @@ -0,0 +1,8314 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "lw1cWgq-DI5k", + "metadata": { + "id": "lw1cWgq-DI5k" + }, + "source": [ + "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "kBFPA3-aDT7H", + "metadata": { + "id": "kBFPA3-aDT7H" + }, + "source": [ + "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!\n", + "\n", + "We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`.\n", + "\n", + "Note that you could use the same notebook to fine-tune `flan-t5-xl` as well, but you would need to shard the models first to avoid CPU RAM issues on Google Colab, check [these weights](https://huggingface.co/ybelkada/flan-t5-xl-sharded-bf16)." + ] + }, + { + "cell_type": "markdown", + "id": "ShAuuHCDDkvk", + "metadata": { + "id": "ShAuuHCDDkvk" + }, + "source": [ + "## Install requirements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "DRQ4ZrJTDkSy", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DRQ4ZrJTDkSy", + "outputId": "31b108ee-a34c-4a1f-a970-6fa1809b64c5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.6 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[31m45.6 MB/s\u001b[0m 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... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " 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[32m7.6/7.6 MB\u001b[0m \u001b[31m65.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ], + "source": [ + "!pip install -q bitsandbytes datasets accelerate\n", + "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main" + ] + }, + { + "cell_type": "markdown", + "id": "QBdCIrizDxFw", + "metadata": { + "id": "QBdCIrizDxFw" + }, + "source": [ + "## Import model and tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd3c5acc", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350, + "referenced_widgets": [ + "59782bac41834abbbd6cbd6614c2e57b", + "8252a05cb70b46ec8b0480062ea1cb71", + "b77afc7c1f184de0970feb2df8ac5285", + "23e494a586cf47d1afa5e619abfcdbba", + "bdfb4a04e48246a4b0890f52d6dd424b", + "8669a890db6c456cbc3ada28976be30b", + "9e5afa2048c74754816b34a34171fcb0", + "5bfebc75ec424c6cb41b33d210d28d2b", + "ef2fa44d0105457c9aed3812633dd329", + "5a6b38b8fc1345e8bd4e8f3aaea546c9", + "57d4ca686af34205aca630b1c61d4aea", + "dbdb787728184aa1a6906f96c5e6f929", + "e3be963920c84c7fbe7e0bc61b8e778d", + "1275c5a5c88b435a897f88a19c54a0a5", + "81d2f0953e104fc1ad57295819b6b689", + "61ab054f49884b1fadf529a39ccc37dc", + "924e6a8308fc47af929aca1987a12f09", + "c1757a5b684f4496a4b0e3db544bf44b", + "a3bb3f44c1754082a4f5169431c5b760", + "a0186b2194df4a0a9cd1ac49054d68da", + "bcda86e43607436583f1fbfee08a9786", + "55cf3bcee7c745948b39eea5f65fc62b", + "aa2c51ad05c14a02a13e5c047779fc05", + "2ce6779fa5904471945fa5738510af64", + "bdff3b35dcdf49e5ba2c5c2498773cb7", + "6994741f3113493b9d5bba278b8732f5", + "808a41f78a7c4ae0b6aafee59c6234ae", + "c7771479ae4e4efab744fad6da586fd3", + "2d7cb8f5477244ce82e7cec5a9f7ee39", + "df8fbdbe9bc341e3a39a7bda99b70be2", + "179a912bbd1e454eba503782b675efa8", + "232fe29739b74dabb3b558c33835eb32", + "978bbbf33d304588af971d22bb2a3690", + "1b839f5b472e44148c9e02a12550fade", + "b560fc36ee8f424f9590e04a042046fe", + "0845ceae96ca4c95ad4ebebac46b691f", + "e1769695dffd4ebeb79a63ff4812fa9e", + "ec73524ed7f14ea0b67f07d72eada173", + "f2a36b126c1b41848e61b0c581ff8c4b", + "6a40d6535f9e4b5b9c9283a1cd67687a", + "03abf3bb217e4622b31cd869ed11aa00", + "24f9db6df6d54ad6bac4891b65184799", + "f66e179caa8b4393bed19a0488821c47", + "cfc7aa04c11d408c9c12cdbd9cff4bb5", + "65aacfc888014bdea55253428b2569c0", + "82849bb4d5da452e87a18ca749ce5d7b", + "2d2fc7abb9fb411c810b2fbeb54d67cc", + "96e2d208830f48cd821be7e59643c93e", + "8d79b7d0c3cb4f8d99fb20941c35856f", + "2b4d68606bdf4758b812f5a8057af595", + "f9620e01cd6749f88b722a42ff68c502", + "06c3d8c44fbe4f0886cf397d9a3c4b81", + "d72e8b3419f240f2bdce253cce9d24e3", + "2fe0a2fa22a0498da983ec38150216e6", + "f3784e85cef34bdba64b611a1f5883e4", + "d7d9e2e2090d4226ad89e5ba9cec33df", + "5cc620a232bf4d418c3fc882f4c1cd0c", + "f742450a607c4ed0bff98ac9b7685d40", + "e87b05e685b040f7a99450bfbab72433", + "e1c8e6f843604161bbb6cbd269488469", + "34333da7eb0f49a6b83df76e35dcdd93", + "c708031a279e4e55ac7833e6697f93bd", + "e293930c8e2c4eadbda53005e21ec450", + "945ac449c2e84fd6b5a7805b017343f2", + "06cea508d7504b228f6cebc66742d200", + "9d0500a0f5f74be39e5edfbbcd7a64fc", + "0363c4d4dc1449148b09061763ea119a", + "f4ff06e2c48d4e58abe64cb7f41dd886", + "7fb7e3e2c75d4d03a98e581d4ead0f00", + "b8087054f46c44cab9bd62fa23fbf9de", + "85ae7ed1ec244a89aeb9f4552c2c9462", + "4f57cfa7cb3b4199babf82dc9d93b074", + "053de11f995247f6b851909a6a8dfc16", + "43d7a9b421be430286b5eb8441d6d465", + "ebc26228160046c48279d71770c928d8", + "5192bc282c4847cb9df8365fc22a6cc2", + "608e9f7a14054573b9bd07f0f74b6345" + ] + }, + "id": "dd3c5acc", + "outputId": "fab398b4-dace-4f44-89e5-687ec74b18eb" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "59782bac41834abbbd6cbd6614c2e57b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading (…)lve/main/config.json: 0%| | 0.00/662 [00:00\n", + " \n", + " \n", + " [255/255 06:13, Epoch 1/1]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EpochTraining LossValidation Loss
1No log0.017228

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saving model checkpoint to temp/checkpoint-100\n", + "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n", + "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", + " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n", + "Saving model checkpoint to temp/checkpoint-200\n", + "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n", + "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", + " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n", + "***** Running Evaluation *****\n", + " Num examples = 227\n", + " Batch size = 8\n", + "\n", + "\n", + "Training completed. Do not forget to share your model on huggingface.co/models =)\n", + "\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "TrainOutput(global_step=255, training_loss=0.2569344015682445, metrics={'train_runtime': 377.3565, 'train_samples_per_second': 5.398, 'train_steps_per_second': 0.676, 'total_flos': 1181084919791616.0, 'train_loss': 0.2569344015682445, 'epoch': 1.0})" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "r98VtofiGXtO", + "metadata": { + "id": "r98VtofiGXtO" + }, + "source": [ + "## Qualitatively test our model" + ] + }, + { + "cell_type": "markdown", + "id": "NIm7z3UNzGPP", + "metadata": { + "id": "NIm7z3UNzGPP" + }, + "source": [ + "Let's have a quick qualitative evaluation of the model, by taking a sample from the dataset that corresponds to a positive label. Run your generation similarly as you were running your model from `transformers`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c95d6173", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c95d6173", + "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generate config GenerationConfig {\n", + " \"_from_model_config\": true,\n", + " \"decoder_start_token_id\": 0,\n", + " \"eos_token_id\": 1,\n", + " \"pad_token_id\": 0,\n", + " \"transformers_version\": \"4.27.0.dev0\",\n", + " \"use_cache\": false\n", + "}\n", + "\n", + "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n", + " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n", + "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input sentence: In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n", + " output prediction: ['positive']\n" + ] + } + ], + "source": [ + "model.eval()\n", + "input_text = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n", + "inputs = tokenizer(input_text, return_tensors=\"pt\")\n", + "\n", + "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n", + "\n", + "print(\"input sentence: \", input_text)\n", + "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))" + ] + }, + { + "cell_type": "markdown", + "id": "9QqBlwzoGZ3f", + "metadata": { + "id": "9QqBlwzoGZ3f" + }, + "source": [ + "## Share your adapters on 🤗 Hub" + ] + }, + { + "cell_type": "markdown", + "id": "NT-C8SjcKqUx", + "metadata": { + "id": "NT-C8SjcKqUx" + }, + "source": [ + "Once you have trained your adapter, you can easily share it on the Hub using the method `push_to_hub` . Note that only the adapter weights and config will be pushed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcbfa1f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359, + "referenced_widgets": [ + "5bb29f3102954b06bec825f6b3a7aaa7", + "90516032070a40979181d1d27db10c4f", + "4b7dc0fb222b4e2a9bb2ef2501e9fd30", + "06069855ef82484f9985e4619095dbe8", + "1ece69c53e37413caad8db70d9160ad5", + "7ce90db727ea47cc9344176858a2225b", + "64f2b70b63cd4e7eb9e22ac2de5589c9", + "57dea1b3e04142bb91868a474774d86a", + "8ac43334e0ad4a78acda3b876fead058", + "04da98e400514cf2847d172916cd0081", + "4dbe49547fe94010ad5a30818cfc35bc", + "99091ca45c1b4809ba0a1b01af85f528", + "06ec124c3dac4fe6b152fb812d20d86d", + "7561c47a97444666816422a0418e1675", + "45ab5d7049e34dfd8a067643ae887a31", + "434e308cac5847f0bee431c7dbb4c04a", + "dd993a4a7cdf40448098544c95468a10" + ] + }, + "id": "bcbfa1f9", + "outputId": "91ef770e-9fc4-4eb2-b02b-24e635101f97" + }, + "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": "code", + "execution_count": null, + "id": "rFKJ4vHNGkJw", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 152, + "referenced_widgets": [ + "00f7d043cb184d69b828c204dac2c0ab", + "1e409cd3d3a04b558d989d63f0b3b5f7", + "d48cbb38078b456fab1634bec5b0a1ba", + "9030744dbca9427ba8a036a76b5c8bf7", + "53dd4444c0e14e16a912532898b32d92", + "5287ac638c22412ab91c55f3316c9b63", + "a43ddb478f044f17adbcfae841ec2114", + "45141234ce584f208a9d301faadf75d2", + "f62ffbdc24734b999f36058d9edca81f", + "972ddebd536d4685bfc3c7c13e5bd8be", + "64156e2c54b44fb9aec661d9b57da962", + "050de732f51f4af8bb41ab3cad0090a4", + "7960ed3beb2a429ba2aca1c6ed032f64", + "726a2eedc7434210bc5aa4d0a772b313", + "07bf5d621cf944258aaf13954669df56", + "93a0896ca66b4111bc4cabe6e1278440", + "cfc78731f7d543ce8529cc254d92ddf5", + "eacf8e9ed6e847faae2b8ecab283ddc4", + "bef1971d92e6479696e3f9a27a757b8a", + "821f2f296acb40ae9bb40fc3faf4103d", + "11efd993475a4f2aabe7df605bab04dd", + "406e4d8561f64d2a94d93a606d02d7d3" + ] + }, + "id": "rFKJ4vHNGkJw", + "outputId": "07425379-64ad-47e8-ba8f-8d9dc26252b6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Uploading the following files to ybelkada/flan-t5-large-lora: adapter_model.bin,adapter_config.json\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "00f7d043cb184d69b828c204dac2c0ab", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Upload 1 LFS files: 0%| | 0/1 [00:00Pro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. 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