{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Q-bj6K7Qv4ft" }, "source": [ "# Fine-Tuning a Open Pre-trained Transformer (`OPT`)\n", "\n", "1. Install required libraries." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SBWCrz5GfBXo", "outputId": "89d0b02d-52de-4bd6-d05a-c641d3f45bbf" }, "outputs": [], "source": [ "!pip install transformers datasets codecarbon -q" ] }, { "cell_type": "markdown", "metadata": { "id": "y5XnfvSH7w4z" }, "source": [ "2. Load the data from the hub." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "ea9ee97d95e1458bb770d46392320b97", "fc6f48cfcfc74d939655e09a679a1472", "e9c1cb211e9b4fb9b0a7fe89fe682cb7", "bb690d57f8e3404ba674f2b3fb0c33c6", "8dcf51af7ecd47f28451a8f421df1289", "d4d62852c784427db3578219f8b1f43f", "bee61ff0b13c4489aca16262281503f9", "3075d48056dc40168ec3989fc4cb56c3", "00417b6beeff46c4b93052bee2e43857", "43705091c2424461abda4b4edf918696", "0ce5b477953b4c42ba696ddff23253ff", "f6b223f5d3984335aa676948c57ffb6b", "c485deee26f64f1686a7a324e534e0aa", "0ced91464aac4e27b06549571c81b2d0", "09125a18fc9c4acdbdbcc3f3298ef9cb", "9ab9e9a9ee654eddaf210d4016c35cb5", "095b1af2719145ed99d8a992cf841593", "b77dec6b82d5413fb132a16d8d50b4b0", "e76e714befc148de85afdacf43846b6b", "047217e8210e4395b6fadd55d6c052c3", "4775462b256b484aa3182c0090f94a5a", "3dc78057dc964af880eb2637a1635d20", "528b201ccf7547e49dfa98a4bb6a5022", "458b436b82614b6c97e2879574b17a23", "8b8b63239c5346ed8be3cc0dcec4cdb7", "a49ecb91b7b5436abbabec0e57abb65b", "7f07aaa42d9b403b97ede3a7135910f2", "d1ffbdcf8982468c90f3da7f3d46f2b0", "9925bb04cddb416d9fa4b2650ec64cb5", "7d60f7ed55bc471ea1c793de8fa50385", "d2b446fd733a41ae95000939432188e4", "38706c813dd74a74b1f1adb1aeda580d", "8a46c266eecb4f69bcda2a36caee2c5c", "d8bc50e00f104da8892ba54a4150b153", "672f55c70d0c4de79fa84cb2e3e20482", "6c391729bcab4fd18879929310ea59a1", "37c630951d624ac8a88dd0fd2991218d", "bc5b767c6e7849209dd6472a7098af45", "2fd688f9281f4f6b8abc975710ac72cf", "4ec78be71fa746d8a94da535d8fc69f5", "77ad59f81b2a4be1bb85ed1540c49ba6", "aee5b1b468c84a34bc4290c9953b3330", "718859a5ce434a39b71e1d0c08f04722", "630cf072365c47eaa4003defe786b147", "1e33243469154116a8ffc7d76ea38c1d", "ce4698abdd444ba08717c95cf66d5c53", "67fd0a1ac86746c39e65f0756045eda0", "26366eb1c2614d5d9c01f47afc40c589", "feed6bcdd2df4bcd99c62ecde962340a", "5f5c2c7132ce4fd2871789c174074deb", "b6b7e44656294b76988654f59b9cd577", "cd1349f56a6b40f99fe775e03648a10b", "ccfb4003091a4d8ea8cd0dc52135f45d", "3d1bb6a0487b40e6867090791a2526db", "cf0407a4027b4991a2c6ecb161b7a86a", "08990e2c998a4ab1b2eda83199fa51ff", "a6d7b5ec2a7f463b9bdc3ce3383101eb", "0c2b32e8c2614ba699d8451385cf3fc5", "45750c5a050d4eecb191c1b582078fb6", "e50a21c5656c4c498328004b9e980511", "768731a98a2f4e32bc8e74e8880dd474", "ec5e39024f334209a4f0b78c95af8ab1", "a008b1051c7d4e07b78bd328d6a83b17", "d6f13da3aeb846f283135e868b286d27", "939bac8cefc146dfb68bec79aa33a6f5", "eff4bc27c9d04448a0061c4e9e3b5848", "eeabc14a111848df869880e2f9217525", "a1275fbb55334442ae5dc051ad46b20e", "85e0219fc4d641a0b47687ebd6b8cc58", "828aa8bef3cd4ba0a6131d004a9d12b4", "e73607ff314b44b39d9cca381b266894", "adbb37b393884c55a06b55720a63c1b2", "0ae85f581ab2452789323d42f25cd428", "5dc9cc3c060c4a8299cbbc876a3f6356", "7affcee2403b44bfbce942166d566365", "13b236ae0ee34a70b9925ef9275b1afd", "493d4467ab444924bfe06c4911477d50" ] }, "id": "7MbpXGu-v4f1", "outputId": "049d4bc8-ec84-41bb-9d68-c53570fa6fc4" }, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "from datasets import load_dataset\n", "\n", "project = \"Aira-OPT-350M\"\n", "\n", "os.makedirs(project, exist_ok=True)\n", "\n", "dataset = load_dataset(\"nicholasKluge/instruct-aira-dataset\", split=\"english\")\n", "\n", "df = dataset.to_pandas()\n", "\n", "display(df)" ] }, { "cell_type": "markdown", "metadata": { "id": "fEaDTvdOe8rr" }, "source": [ "3. Load `AutoTokenizer` and add the chosen special tokens (`'<|startofinstruction|>', '<|endofinstruction|>', '<|endofcompletion|>','<|pad|>'`)\n", "4. Create demonstrations by prepending the special tokens.\n", "5. Calculate the maximum length (in tokens) that the demonstrations have.\n", "\n", "> Note: OPT tokenizer automatically pre-pends the `bos_token` to the input string. Hence, we don't need to add it to our demonstrations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 512, "referenced_widgets": [ "629d4f8ae76d408ba05b6c35d7a29f1c", "c38c7bc8c4fb4c788b2a3aa05579e02e", "51b24c91a3824bf797bbabbbbb22162e", "870ddd8744ed4528893028bcfdc1c97e", "17d9d42be0e040e39ee935b5ddf6f0d5", "ef8b5ef5c3a049b4bb2c002c11214e80", "5e874da1cad045c7ae72706ed474656e", "d725115d4d6046978b6f3dbc10d8b180", "752fb64d996f4b09bbab96e511c733eb", "4133bbab71414615bcf3fa2047d244c3", "7cb2749b370042829dba7b8e9e33d615", "5166e946310b4349a0f25f15cde0e5c1", "cd3a0652eb1c429b917021a026bb9141", "e56747455ae84bae968f6974169a4142", "090b0b53840e42fe9a10f2549baf7d97", "377f6127caff4a63a6f893d98ebb7b97", "e4733a1de1654de2a8a8afd3523e1457", "81d2e6c9897b4757a55368443625724e", "ca34d936769d4e8ebf15e1f0215bbb49", "8732d583c63b4b37b5ae3fcc070b6739", "dbf47363ed744fa69a3408a77128e34e", "36cdca51984644a2890b627c17d2cc67", "22e792eea5104354822727d5d0b2b132", "b4f95a53cdc743d0a009e6f6f3a16f3a", "fbe8287cfc354db684dc1aa2585334df", "0f5a40a821894b9b9d8c0b4f95089964", "d5e1a3f46ca14f6893da973c6e5f5f2e", "94c97e28fe5a4942b4b14771ee8c2f10", "9581b5190d284b589a1def76a3d2d87c", "b44ad19fd6384189843af11ea5606e76", "28e3efcee1b148028979883797cfd0a5", "5bf02f9adcd84baf82d501f5b14e4a40", "5ea589e0b61446da938aa2a90be19b31", "96d3ebab404f4bb3a1cbc69cc5ba8926", "801ffd072d37459f9c476a4ce8e50f82", "bbd733cd7c084b84879bd0b726ebe59f", "a272ed26a73047d58d2a65261f9e2d4b", "c6de22a3b6384ebdb355716bf973cd74", "fd1bd0c957c04758baeacbb21dd54023", "cae8ff80aa8a495c96a250fd1e6c82fb", "6e68c0306743462d83dcd8ea2aa48af4", "37a5e6635f784881b0d2282785273951", "0eb998e1c8c149a58947bd947f65930e", "848d0d4bf65d4f6a99602dbbeaca9a35", "ca042b41ff4d4679a508ceead71ae967", "20435a7dabbc4d848a6b0f9d352016e8", "1a209ca7f71d4fcb8ff943d270291306", "d484e4b8c2e849b4ab6fd7607ae1850d", "9e961b5cafaf4ceaad3fa4b2e016a2b7", "4987982bd03c4cdab582df237d156aa6", "65c626e53879491ebad678252aa28087", "71783a27e6dc4c6daeda4af45bc78d5c", "73533bc409264ca18c8e9d42cb8ae00f", "8a5270537dcf4c6b9c0108a7fc9202f7", "c23ac12eed5f43d2b35c3d8d0b4b360b" ] }, "id": "hfu84fWIv4f9", "outputId": "93981070-8f7b-4866-93e9-1c1a855695db" }, "outputs": [], "source": [ "from transformers import AutoTokenizer\n", "\n", "model = \"facebook/opt-350m\"\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(model,\n", " bos_token='<|startofinstruction|>',\n", " sep_token = '<|endofinstruction|>',\n", " eos_token='<|endofcompletion|>',\n", " pad_token='<|pad|>',\n", " unk_token='<|unk|>')\n", "\n", "# OPT tokenizer already adds the BOS token, so we do not need to add it manually\n", "df['demonstrations'] = df['prompt'] + tokenizer.sep_token + df['completion'] + tokenizer.eos_token\n", "\n", "df['length'] = df['demonstrations'].apply(lambda x: len(tokenizer.encode(x)))\n", "\n", "print(\"Total number of demonstrations: \", len(df))\n", "print(f\"The longest demonstration is {df['length'].max()} tokens long.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "wkMO17K5e8rs" }, "source": [ "6. Create the Dataset class." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WlbAfMQ4v4gA", "outputId": "41f25dea-33b2-4798-b32c-865f63e833aa" }, "outputs": [], "source": [ "import torch\n", "import tqdm\n", "from torch.utils.data import Dataset\n", "\n", "max_length = 400\n", "\n", "class InstructDataset(Dataset):\n", "\n", " def __init__(self, demonstrations, tokenizer, max_length=max_length):\n", "\n", " self.tokenizer = tokenizer\n", " self.input_ids = []\n", " self.attn_masks = []\n", "\n", " for demo in tqdm.tqdm(demonstrations):\n", "\n", " encodings_dict = tokenizer(demo,\n", " truncation=True,\n", " max_length=max_length,\n", " padding=\"max_length\")\n", "\n", " self.input_ids.append(torch.tensor(encodings_dict['input_ids']))\n", " self.attn_masks.append(torch.tensor(encodings_dict['attention_mask']))\n", "\n", " def __len__(self):\n", " return len(self.input_ids)\n", "\n", " def __getitem__(self, idx):\n", " return self.input_ids[idx], self.attn_masks[idx]\n", "\n", "dataset = InstructDataset(df.demonstrations.to_list(), tokenizer, max_length=max_length)" ] }, { "cell_type": "markdown", "metadata": { "id": "vSqKuRjIe8ru" }, "source": [ "8. Create the `DataLoaders` and specify the `batch_size`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cUkCNV-6v4gG" }, "outputs": [], "source": [ "from torch.utils.data import DataLoader, RandomSampler\n", "\n", "dataloader = DataLoader(\n", " dataset,\n", " sampler=RandomSampler(dataset),\n", " batch_size=16\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "0vxvcTIHe8rv" }, "source": [ "9. Load the base model (`OPTForCausalLM`)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "2fc3f26def6e407b9417f3d9e3b99f47", "fc203efc42bb46459fe041fa992d5442", "e18eb5ebf5914806a32da6344ab29603", "12044231ad1643188b54585903e290e5", "664881c2bca14a6e8ec92440b0a43986", "37bed283f36844f4a4a6f27f055464db", "6824427b68304c9bbd4a76d0a1fdb3e1", "c7bb28f5e5dd42ccb265f2fd74af1ab5", "3c66158fe2ed4c8d909f62ff74762c82", "d37936959a1c4194a7dfc3e23d4ff413", "64f914ddfe8d488a9e9ecfbf7574526a", "f5aee5e34b77421ba3dd3007c9a99f9c", "c2b41b5cadde4ae4bc9f48e768d87a2e", "be55f2d800be4c2fb31da28a3c711172", "1b86a96d402546f29f39c5522e2de729", "ad9940fb8a7f409681fcbd156458e177", "2f52226082b84e7d9abe7bcab27d4dfa", "0350268356414efcbc1c91987f338c15", "63fd0a75d5be44b7b967d51e9c0fe2cd", "8d54ce10213c493f8261fc01dd3c8b53", "a21b2710817e452d90a8e0cafa2957f4", "a1d63faa00654cbcb3c69834a3426ea2" ] }, "id": "Rmg-5YJqv4gH", "outputId": "464593bc-27a6-4cea-cecd-4c9319d1e11d" }, "outputs": [], "source": [ "from transformers import OPTForCausalLM, OPTConfig\n", "\n", "configuration = OPTConfig.from_pretrained(model, output_hidden_states=False)\n", "\n", "model = OPTForCausalLM.from_pretrained(model, config=configuration)\n", "model.resize_token_embeddings(len(tokenizer))\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "model.to(device)" ] }, { "cell_type": "markdown", "metadata": { "id": "GJ1kH3uSe8rw" }, "source": [ "10. Set the training hyperparameters." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qlbLg6tqv4gI" }, "outputs": [], "source": [ "from transformers import get_linear_schedule_with_warmup\n", "\n", "# training rounds\n", "epochs = 3 \n", "\n", "# steps at the start of training that are ignored\n", "warmup_steps = 1e2\n", "\n", "# sample the model to test the output\n", "sample_every = 400\n", "\n", "optimizer = torch.optim.AdamW(model.parameters(), lr = 1e-4, eps = 1e-8)\n", "\n", "# total steps = number of batches * number of epochs\n", "total_steps = len(dataloader) * epochs\n", "\n", "# create the learning rate scheduler\n", "scheduler = get_linear_schedule_with_warmup(optimizer,\n", " num_warmup_steps = warmup_steps,\n", " num_training_steps = total_steps)" ] }, { "cell_type": "markdown", "metadata": { "id": "nz1a9lxXe8rw" }, "source": [ "12. Training/Validation loop. Track the carbon emissions of your work by using `codecarbon`. 🌱" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/" }, "id": "_X_m8XOtv4gR", "outputId": "1089a912-fdbc-4090-e8e4-c31a563dec83" }, "outputs": [], "source": [ "from codecarbon import EmissionsTracker\n", "import tqdm\n", "\n", "\n", "output_dir = f'/content/{project}'\n", "\n", "tracker = EmissionsTracker(\n", " project_name=\"Aira_emissions\",\n", " log_level=\"critical\",\n", " output_dir=output_dir,\n", " output_file=\"Aira_emissions.csv\",\n", ")\n", "\n", "training_stats = []\n", "\n", "tracker.start()\n", "for epoch_i in range(0, epochs):\n", "\n", " print(f'\\nBeginning epoch {epoch_i + 1} of {epochs}\\n')\n", "\n", " total_train_loss = 0\n", "\n", " model.train()\n", "\n", " for step, batch in enumerate(tqdm.tqdm(dataloader)):\n", "\n", " b_input_ids = batch[0].to(device)\n", " b_labels = batch[0].to(device)\n", " b_masks = batch[1].to(device)\n", "\n", " model.zero_grad()\n", "\n", " outputs = model(b_input_ids,\n", " labels=b_labels,\n", " attention_mask = b_masks)\n", "\n", " loss = outputs[0]\n", "\n", " batch_loss = loss.item()\n", " total_train_loss += batch_loss\n", "\n", " if step % sample_every == 0 and not step == 0:\n", "\n", " print(f'\\nBatch {step} of {len(dataloader)}. Loss:{batch_loss}.\\n')\n", "\n", " model.eval()\n", "\n", " inputs = tokenizer(tokenizer.bos_token + df.prompt.sample().iloc[0] + tokenizer.eos_token, return_tensors=\"pt\").to(device)\n", "\n", " sample_outputs = model.generate(**inputs,\n", " bos_token_id=tokenizer.bos_token_id,\n", " pad_token_id=tokenizer.pad_token_id,\n", " eos_token_id=tokenizer.eos_token_id,\n", " do_sample=True,\n", " top_k=50,\n", " max_length = 200,\n", " top_p=0.95,\n", " num_return_sequences=1)\n", "\n", " for i, sample_output in enumerate(sample_outputs):\n", " print(f'\\nExample output: {tokenizer.decode(sample_output, skip_special_tokens=True)}\\n')\n", "\n", " model.train()\n", "\n", " loss.backward()\n", "\n", " optimizer.step()\n", "\n", " scheduler.step()\n", "\n", " avg_train_loss = total_train_loss / len(dataloader)\n", "\n", "\n", " print(f'\\nAverage Training Loss: {avg_train_loss}.\\n')\n", "\n", "\n", " training_stats.append(\n", " {\n", " 'epoch': epoch_i + 1,\n", " 'Training Loss': avg_train_loss,\n", " }\n", " )\n", "\n", "tracker.stop()\n", "print(\"Training complete!\")\n", "\n", "df_stats = pd.DataFrame(data=training_stats)\n", "df_stats = df_stats.set_index('epoch')\n", "df_stats.to_parquet(f\"{output_dir}/training_stats.parquet\", compression=\"gzip\")\n", "\n", "rng_state = torch.get_rng_state()\n", "torch.save(rng_state, f\"{output_dir}/rng_state.pt\")\n", "torch.save(scheduler.state_dict(), f\"{output_dir}/scheduler.pt\")\n", "torch.save(optimizer.state_dict(), f\"{output_dir}/optimizer.pt\")\n", "\n", "model_to_save = model.module if hasattr(model, 'module') else model\n", "model_to_save.save_pretrained(output_dir)\n", "model_to_save.save_pretrained(output_dir, safe_serialization=True)\n", "tokenizer.save_pretrained(output_dir)" ] }, { "cell_type": "markdown", "metadata": { "id": "h48iOsqie8rx" }, "source": [ "13. Check the training stats and plot the learning curves." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "J1-hAY9Av4gT", "outputId": "2965e478-9b22-4f46-d540-13f0620001f2" }, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "df_stats = pd.read_parquet(f\"{output_dir}/training_stats.parquet\")\n", "\n", "sns.set(style='darkgrid')\n", "\n", "sns.set(font_scale=1.5)\n", "plt.rcParams[\"figure.figsize\"] = (12,6)\n", "\n", "plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n", "\n", "plt.title(\"Training Loss\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "plt.xticks(range(1, len(df_stats)+1))\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "dXX4z7Wqe8ry" }, "source": [ "13. Load and test the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mpBJNtYuAY9D" }, "outputs": [], "source": [ "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "import torch\n", "\n", "project = \"Aira-OPT-350M\"\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(f\"nicholasKluge/{project}\")\n", "aira = AutoModelForCausalLM.from_pretrained(f\"nicholasKluge/{project}\")\n", "\n", "aira.eval()\n", "aira.to(device)\n", "\n", "question = input(\"Enter your question: \")\n", "\n", "# OPT tokenizer already adds the BOS token, so we do not need to add it manually\n", "inputs = tokenizer(question + tokenizer.sep_token, return_tensors=\"pt\").to(device)\n", "\n", "responses = aira.generate(**inputs,\n", " bos_token_id=tokenizer.bos_token_id,\n", " pad_token_id=tokenizer.pad_token_id,\n", " eos_token_id=tokenizer.eos_token_id,\n", " do_sample=True,\n", " top_k=20,\n", " max_length=530,\n", " top_p=0.20,\n", " temperature=0.2,\n", " num_return_sequences=2)\n", "\n", "print(f\"Question: 👤 {question}\\n\")\n", "\n", "for i, response in enumerate(responses):\n", " print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, \"\")}')" ] }, { "cell_type": "markdown", "metadata": { "id": "cJXlHi2oe8rz" }, "source": [ "Done! 🤗" ] } ], "metadata": { "accelerator": "GPU", "colab": { "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "00417b6beeff46c4b93052bee2e43857": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": 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