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transformers in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (4.23.1)\r\n", "Requirement already satisfied: torch in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (2.0.0)\r\n", "Collecting accelerate\r\n", " Downloading accelerate-0.20.3-py3-none-any.whl (227 kB)\r\n", "\u001B[K |████████████████████████████████| 227 kB 2.6 MB/s eta 0:00:01\r\n", "\u001B[?25hRequirement already satisfied: packaging>=20.0 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from transformers) (21.3)\r\n", "Requirement already satisfied: regex!=2019.12.17 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from transformers) (2022.10.31)\r\n", "Requirement already satisfied: pyyaml>=5.1 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from transformers) (6.0)\r\n", "Requirement already satisfied: numpy>=1.17 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from 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sympy in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from torch) (1.11.1)\r\n", "Requirement already satisfied: networkx in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from torch) (2.8.8)\r\n", "Requirement already satisfied: jinja2 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from torch) (3.1.2)\r\n", "Requirement already satisfied: psutil in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from accelerate) (5.9.0)\r\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from packaging>=20.0->transformers) (3.0.9)\r\n", "Requirement already satisfied: MarkupSafe>=2.0 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from jinja2->torch) (2.1.1)\r\n", "Requirement already satisfied: idna<4,>=2.5 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from requests->transformers) (3.4)\r\n", "Requirement already satisfied: certifi>=2017.4.17 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from requests->transformers) (2022.12.7)\r\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from requests->transformers) (1.26.12)\r\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from requests->transformers) (2.0.4)\r\n", "Requirement already satisfied: mpmath>=0.19 in /Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages (from sympy->torch) (1.3.0)\r\n", "Installing collected packages: accelerate\r\n", "Successfully installed accelerate-0.20.3\r\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Wczytanie bazowego modelu\n", "Bazowym modelem jest polska wersja GPT2 https://huggingface.co/flax-community/papuGaPT2?text=Najsmaczniejszy+polski+owoc+to" ], "metadata": { "id": "xxbWwu3KSds-" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "LdRQU2xnOrst", "colab": { "base_uri": "https://localhost:8080/", "height": 273, "referenced_widgets": [ "ac800b679bdc4382b28cbcf9c68303f0", "2fa8106d87594af283e5b74915003783", "04e5f6b3190740dd85bedac661c67770", "37c645ebeb934c8db572d4b4da63f280", "4757bdb315e04fb982ac45e0a26a7c2d", "f125275065c64b6ca55f0767737a488a", "06b012d7bd5b418eb74d89ba51dbc690", "7e3d164400b342c897b4e647da36a02e", "eadd02efcb204b20b3bfd8e99cde9ae3", "2264a5b9de1d4e93acbdbd84c3abf040", "e863997d8ce142798230810e384323ba", "c09398bd98554499805b5f14270e4248", "c3b4b0254d8b4e4eb399ca3514d36211", "8adaa113d54247388e1355331451926a", "bbedc4f5cece4090ad997ce39d65e96a", "825fd5e9a017439995c67dcb3a7736b9", "bab30e40fedb43dc8e0044c80727c38b", "ffdbfdfd24f3433f9653a3a0b1a177d6", "fc1af3db81b243178157ee330f62ff3c", 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"7e56e3fd676440d0a23ca09f8b5c7d4e", "855d7b31b1a44fb9b667e84f30bd121a", "164ae1d73d4b461fb27c0401b8ce09fe", "6e3c14738f864803b0f95cd940f23d9e", "3ade165824644044bb922256c3773156", "6c924e348934452887581e23151c7ebb", "85a446e817c943c7a26e40a7ad8f511f", "08c86037bfe442079bb79321147a73df", "d3f1817bcb0a4e8a85e0515898c13850", "2e154a9d0eeb45c38728084edb841a29", "2a67736c8e3a481aa139b021a89140ab", "6bb99699d0614c83b588a7dd65351c1c", "f380816f81d046b880b12a429d624c2f", "17b70c7ee49f477cacf15abe01d88905", "7c0ca4701ce64754b5f31663458e925b" ] }, "outputId": "414a875b-08d7-45f3-ae17-ef47a5fe3db3" }, "outputs": [], "source": [ "from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed\n", "import pandas as pd\n", "\n", "model = AutoModelForCausalLM.from_pretrained('flax-community/papuGaPT2')\n", "tokenizer = AutoTokenizer.from_pretrained('flax-community/papuGaPT2')\n", "\n", "tokenizer.pad_token = tokenizer.eos_token" ] }, { "cell_type": "markdown", "source": [ "# Wczytanie danych do finetuningu\n", "Dane stworzyliśmy ręcznie oraz za pomocą ChatGPT." ], "metadata": { "id": "IY2e11OjS54T" } }, { "cell_type": "markdown", "source": [], "metadata": { "collapsed": false } }, { "cell_type": "code", "source": [ "data = pd.read_csv('prompts.csv', sep=';')\n", "# data.head()\n", "# data[\"answer\"]\n", "texts = 'question: ' + data['question'] + \"\\nanswer: \" + data['answer']\n", "texts = texts.tolist()\n", "print(texts[0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tD7U4Qa5UhEf", "outputId": "1f215c64-dd7f-4d3f-9e65-072aa2ddfab9" }, "execution_count": 2, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "question: Dlaczego w ogóle warto się starać?\n", "answer: Nie warto. Wszystko i tak skończy się niepowodzeniem.\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Preprocessing" ], "metadata": { "id": "CQw_oCFyUnY_" } }, { "cell_type": "code", "source": [ "from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler\n", "import torch\n", "\n", "# Create custom dataset\n", "class PromptsDataset(Dataset):\n", " def __init__(self, txt_list, tokenizer):\n", " self.tokenizer = tokenizer\n", " self.input_ids = []\n", " self.attn_masks = []\n", "\n", " for txt in txt_list:\n", " encodings_dict = tokenizer(txt, padding=\"max_length\", truncation=True, max_length=512)\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]" ], "metadata": { "id": "_AYrfmfGXMEV" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "# Create dataset\n", "dataset = PromptsDataset(texts, tokenizer)\n", "\n", "# Split into training and validation sets\n", "train_size = int(0.9 * len(dataset))\n", "val_size = len(dataset) - train_size\n", "\n", "train_dataset, val_dataset = random_split(dataset, [train_size, val_size])\n", "\n", "print('{:>5,} training samples'.format(train_size))\n", "print('{:>5,} validation samples'.format(val_size))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yQp1opRYXPAv", "outputId": "04a99a2d-d1c6-4216-b676-1197ba2cb781" }, "execution_count": 4, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 154 training samples\n", " 18 validation samples\n" ] } ] }, { "cell_type": "code", "source": [ "dataset[0]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vX-uxFqkl5rw", "outputId": "7c83eac7-8c5c-4910-a7b9-799130dde915" }, "execution_count": 5, 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out batches sequentially.\n", " batch_size = batch_size # Evaluate with this batch size.\n", " )" ], "metadata": { "id": "4LDKgbSAcPo8" }, "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Fine-tuning" ], "metadata": { "id": "a5NTJK7HVjYD" } }, { "cell_type": "code", "source": [ "# some parameters I cooked up that work reasonably well\n", "\n", "epochs = 10\n", "learning_rate = 0.001\n", "warmup_steps = 1e2\n", "epsilon = 1e-8" ], "metadata": { "id": "TnPudHlZVmaA" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import AdamW, get_linear_schedule_with_warmup\n", "\n", "# Note: AdamW is a class from the huggingface library (as opposed to pytorch)\n", "optimizer = AdamW(model.parameters(),\n", " lr = learning_rate,\n", " eps = epsilon\n", " )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZPic7oqNdGcH", "outputId": "11bb22bf-31a5-4855-d35d-79fdd14a7cce" }, "execution_count": 8, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/sparafinski/miniconda3/envs/study/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n" ] } ] }, { "cell_type": "code", "source": [ "# Total number of training steps is [number of batches] x [number of epochs].\n", "# (Note that this is not the same as the number of training samples).\n", "total_steps = len(train_dataloader) * epochs\n", "\n", "# Create the learning rate scheduler.\n", "# This changes the learning rate as the training loop progresses\n", "scheduler = get_linear_schedule_with_warmup(optimizer,\n", " num_warmup_steps = warmup_steps,\n", " num_training_steps = total_steps)" ], "metadata": { "id": "u-zq78GveBbk" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "import datetime\n", "import time\n", "import random\n", "\n", "def format_time(elapsed):\n", " return str(datetime.timedelta(seconds=int(round((elapsed)))))\n", "\n", "device = torch.device(\"mps\")\n", "model.to(device)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "52TDlVRadJCq", "outputId": "a60440b3-a297-4af3-905b-ce47c3cce6f7" }, "execution_count": 10, "outputs": [ { "data": { "text/plain": "GPT2LMHeadModel(\n (transformer): GPT2Model(\n (wte): Embedding(50257, 768)\n (wpe): Embedding(1024, 768)\n (drop): Dropout(p=0.0, inplace=False)\n (h): ModuleList(\n (0-11): 12 x GPT2Block(\n (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n (attn): GPT2Attention(\n (c_attn): Conv1D()\n (c_proj): Conv1D()\n (attn_dropout): Dropout(p=0.0, inplace=False)\n (resid_dropout): Dropout(p=0.0, inplace=False)\n )\n (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n (mlp): GPT2MLP(\n (c_fc): Conv1D()\n (c_proj): Conv1D()\n (act): NewGELUActivation()\n (dropout): Dropout(p=0.0, inplace=False)\n )\n )\n )\n (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n )\n (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n)" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ] }, { "cell_type": "code", "source": [ "total_t0 = time.time()\n", "\n", "training_stats = []\n", "\n", "model = model.to(device)\n", "\n", "for epoch_i in range(0, epochs):\n", "\n", " # ========================================\n", " # Training\n", " # ========================================\n", "\n", " print(\"\")\n", " print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n", " print('Training...')\n", "\n", " t0 = time.time()\n", "\n", " total_train_loss = 0\n", "\n", " model.train()\n", "\n", " for step, batch in enumerate(train_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", " token_type_ids=None\n", " )\n", "\n", " loss = outputs[0]\n", "\n", " batch_loss = loss.item()\n", " total_train_loss += batch_loss\n", "\n", " loss.backward()\n", "\n", " optimizer.step()\n", "\n", " scheduler.step()\n", "\n", " # Calculate the average loss over all of the batches.\n", " avg_train_loss = total_train_loss / len(train_dataloader)\n", "\n", " # Measure how long this epoch took.\n", " training_time = format_time(time.time() - t0)\n", "\n", " print(\"\")\n", " print(\" Average training loss: {0:.2f}\".format(avg_train_loss))\n", " print(\" Training epoch took: {:}\".format(training_time))\n", "\n", " # ========================================\n", " # Validation\n", " # ========================================\n", "\n", " print(\"\")\n", " print(\"Running Validation...\")\n", "\n", " t0 = time.time()\n", "\n", " model.eval()\n", "\n", " total_eval_loss = 0\n", " nb_eval_steps = 0\n", "\n", " # Evaluate data for one epoch\n", " for batch in validation_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", " with torch.no_grad():\n", "\n", " outputs = model(b_input_ids,\n", "# token_type_ids=None,\n", " attention_mask = b_masks,\n", " labels=b_labels)\n", "\n", " loss = outputs[0]\n", "\n", " batch_loss = loss.item()\n", " total_eval_loss += batch_loss\n", "\n", " avg_val_loss = total_eval_loss / len(validation_dataloader)\n", "\n", " validation_time = format_time(time.time() - t0)\n", "\n", " print(\" Validation Loss: {0:.2f}\".format(avg_val_loss))\n", " print(\" Validation took: {:}\".format(validation_time))\n", "\n", " # Record all statistics from this epoch.\n", " training_stats.append(\n", " {\n", " 'epoch': epoch_i + 1,\n", " 'Training Loss': avg_train_loss,\n", " 'Valid. Loss': avg_val_loss,\n", " 'Training Time': training_time,\n", " 'Validation Time': validation_time\n", " }\n", " )\n", "\n", "print(\"\")\n", "print(\"Training complete!\")\n", "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pPNGSJoadS9V", "outputId": "f012a036-80ed-499a-8323-25673d0724a2", "pycharm": { "is_executing": true } }, "execution_count": null, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "======== Epoch 1 / 10 ========\n", "Training...\n" ] } ] }, { "cell_type": "code", "source": [ "model.eval()\n", "\n", "input_text = \"question: Czy życie ma jakiś sens?\\nanswer:\"\n", "input_ids = tokenizer.encode(input_text, return_tensors='pt')\n", "input_ids = input_ids.to(device)\n", "\n", "output = model.generate(input_ids, max_length=100, early_stopping=True)\n", "\n", "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n", "print(generated_text)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YUAZReU3jPwm", "outputId": "9fbaccdd-cd3f-4231-f960-e4457f23aeba" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "question: Czy piłka nożna to dobra pasja?\n", "answer: Absolutnie nie! Czy próbowałeś/aś już grać w piłkę? Może warto spróbować!\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Zapisanie modelu" ], "metadata": { "id": "PaV10cc01n_N" } }, { "cell_type": "code", "source": [ "import os\n", "\n", "# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()\n", "\n", "output_dir = 'model_save/'\n", "\n", "# Create output directory if needed\n", "if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)\n", "\n", "print(\"Saving model to %s\" % output_dir)\n", "\n", "# Save a trained model, configuration and tokenizer using `save_pretrained()`.\n", "# They can then be reloaded using `from_pretrained()`\n", "model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training\n", "model_to_save.save_pretrained(output_dir)\n", "tokenizer.save_pretrained(output_dir)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5Z42j32m1iUF", "outputId": "3cd21c7c-dd84-4314-8aa4-2671e1f02edd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Saving model to /content/gdrive/My Drive/UAM/Magisterka/Empatia/model_save/\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "('/content/gdrive/My Drive/UAM/Magisterka/Empatia/model_save/tokenizer_config.json',\n", " '/content/gdrive/My Drive/UAM/Magisterka/Empatia/model_save/special_tokens_map.json',\n", " '/content/gdrive/My Drive/UAM/Magisterka/Empatia/model_save/vocab.json',\n", " '/content/gdrive/My Drive/UAM/Magisterka/Empatia/model_save/merges.txt',\n", " '/content/gdrive/My Drive/UAM/Magisterka/Empatia/model_save/added_tokens.json',\n", " '/content/gdrive/My Drive/UAM/Magisterka/Empatia/model_save/tokenizer.json')" ] }, "metadata": {}, "execution_count": 15 } ] } ] }