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"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", + "a46138f2b04c4418816fa264c3deb892", + "b47c3295784648cf8847e198ee33c615", + "ee47504dd7274dfb860a5c4355a37295", + "31ec83a1244d416ba8cfe922748b7e55", + "3d41103299d5441184b2890a8a52b6ec", + "01e1e799e775450694e2842d87bbc871", + "244faf9363794c0a86175f72fb63655f", + "14b26d9b078f4a919f09f8133e5dd22d", + "109145210ff64132a36601470b3e24e6", + "1eefecffe6124ad6b835ed579a0adfa5", + "b7b911c6bca94858bb93ffbcb1daa736", + "4b7636fbfe854056acadd394d71cde75", + "afc4c68e30a14d74b7c1547f02570baf", + "57763424d94a4384b4fa4c762062b6ba", + "3a9bbce5adf04b77beed6eb10211551f", + "9125492ba517409eb77722982d57b948", + "ff2160ef3ab14d4682cfbb1b878af62f", + "52d637c25ad84c6fae643fe7f687f63a", + "a87573424cf0463a85b56271931d6dc8", + "95dc4d80e12c4020bdd8f5d849107cf5", + "c1d658d5f21f4731895b397cc3fcb055", + "53f98231a2c140efbd0a1d2d25367b1f", + "367f3aae32ab4e1b9f67f3cd29299c5b", + "b6aa40c7501f44178f14ac88b63f57cb", + "984a17db21ee4aa898323ecde9c14f16", + "dc277586ba1f4eeda104c895cf9a1626", + "3b6bfbe260a9403c8216269b5032cde4", + "a7d258bfe8644c38a56ca365ea9e4ab1", + "0f75959ab6364a60b229051f0d5b2c68", + "ca47940201884085b556bbc7032f17f2", + "eb4e61e04bc04929be057a5a960feb9d", + "b7ef949a1eb0469fb0bd1be5256fa79a", + "f7687d83ee0149248610c40d4d86d944", + "bfa116bd3e144c15b67bd97ff3c85fea", + "7e58b084511e4232a0d197539306c57f", + "7e8ca657aedb41da9a99c3d3d648ea00", + "5cfa6f79da7e47639bfdea6d8f70dd7d", + "3530c50e41b54531b12d04c807d3924a", + "b6afee105c69499f98cf0544ffe325cb", + "3219d928e9e14e4ba95bf2aad70162a3", + "0853024b81894cc79c7f6b8de97c5c5b", + "59d4ec3a13844c878d4e95896a37121d", + "1498b932de1b4a5f922395ecf3b437a9", + "083cd7a3ebb34bbf8940af58359a4848", + "4e16ca13c2dd46069b65dd48f3da8790", + "98e047a8134f4502bb134beb26ffe821", + "ad3e29e3250a4323b715a7ffe279c799", + "5c71295523ea4561a6fe8534c0b160e2", + "c62c829fa4f346f4966f68e020c50813", + "f10ed53b98304b6197789308330e1bf8", + "9516994367bc488faeae786254eff8c7", + "f95ffcc1b9564519aa1f1c81e2d16dbf", + "e7fafc300aa749a2bedd750295731307", + "05032586bc4d4f8c9a1fa023695e60ec", + "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, + "outputs": [ + { + "data": { + "text/plain": "(tensor([ 7636, 1736, 536, 30, 6072, 263, 4090, 1076, 330, 20777,\n 35, 203, 16488, 1633, 30, 225, 624, 1076, 18, 4651,\n 288, 497, 8427, 330, 19241, 3239, 18, 50256, 50256, 50256,\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n 50256, 50256, 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"\n", + "# For validation the order doesn't matter, so we'll just read them sequentially.\n", + "validation_dataloader = DataLoader(\n", + " val_dataset, # The validation samples.\n", + " sampler = SequentialSampler(val_dataset), # Pull 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 + } + ] + } + ] +}