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f2e09fa12f
emphatic_chatbot/finetuning.ipynb
Adam Wojdyla f2e09fa12f finetuned model + empathy
2023-06-18 22:44:55 +02:00

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{
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"metadata": {
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"base_uri": "https://localhost:8080/"
},
"id": "TVWZUBhyPfpa",
"outputId": "2a548af2-1981-4a46-b826-62b88ca08eb1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: transformers in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (4.30.2)\n",
"Requirement already satisfied: torch in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (1.13.0)\n",
"Collecting accelerate\n",
" Downloading accelerate-0.20.3-py3-none-any.whl (227 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.6/227.6 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: filelock in /Users/adamwojdyla/.local/lib/python3.8/site-packages (from transformers) (3.9.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (0.15.1)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (1.24.2)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (23.1)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (6.0)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (2023.3.23)\n",
"Requirement already satisfied: requests in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (2.28.2)\n",
"Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (0.13.3)\n",
"Requirement already satisfied: safetensors>=0.3.1 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (0.3.1)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from transformers) (4.65.0)\n",
"Requirement already satisfied: typing-extensions in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from torch) (4.6.3)\n",
"Requirement already satisfied: psutil in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from accelerate) (5.9.5)\n",
"Requirement already satisfied: fsspec in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from requests->transformers) (3.0.1)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from requests->transformers) (3.4)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from requests->transformers) (1.26.14)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages (from requests->transformers) (2022.12.7)\n",
"Installing collected packages: accelerate\n",
"Successfully installed accelerate-0.20.3\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"!pip install transformers torch accelerate"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xxbWwu3KSds-"
},
"source": [
"# Wczytanie bazowego modelu\n",
"Bazowym modelem jest polska wersja GPT2 https://huggingface.co/flax-community/papuGaPT2?text=Najsmaczniejszy+polski+owoc+to"
]
},
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{
"data": {
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"Downloading config.json: 0%| | 0.00/864 [00:00<?, ?B/s]"
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"Downloading pytorch_model.bin: 0%| | 0.00/510M [00:00<?, ?B/s]"
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"metadata": {},
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"data": {
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"Downloading vocab.json: 0%| | 0.00/888k [00:00<?, ?B/s]"
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{
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"metadata": {},
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{
"data": {
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"text/plain": [
"Downloading tokenizer.json: 0%| | 0.00/1.54M [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"data": {
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"Downloading added_tokens.json: 0%| | 0.00/24.0 [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"data": {
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"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",
"metadata": {
"id": "IY2e11OjS54T"
},
"source": [
"# Wczytanie danych do finetuningu\n",
"Dane stworzyliśmy ręcznie oraz za pomocą ChatGPT."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tD7U4Qa5UhEf",
"outputId": "1f215c64-dd7f-4d3f-9e65-072aa2ddfab9"
},
"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"
]
}
],
"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])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CQw_oCFyUnY_"
},
"source": [
"# Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "_AYrfmfGXMEV"
},
"outputs": [],
"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]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yQp1opRYXPAv",
"outputId": "04a99a2d-d1c6-4216-b676-1197ba2cb781"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 154 training samples\n",
" 18 validation samples\n"
]
}
],
"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))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vX-uxFqkl5rw",
"outputId": "7c83eac7-8c5c-4910-a7b9-799130dde915"
},
"outputs": [
{
"data": {
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" 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, 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]),\n",
" tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0]))"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "4LDKgbSAcPo8"
},
"outputs": [],
"source": [
"batch_size = 8\n",
"\n",
"# Create the DataLoaders for our training and validation datasets.\n",
"# We'll take training samples in random order.\n",
"train_dataloader = DataLoader(\n",
" train_dataset, # The training samples.\n",
" sampler = RandomSampler(train_dataset), # Select batches randomly\n",
" batch_size = batch_size # Trains with this batch size.\n",
" )\n",
"\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",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a5NTJK7HVjYD"
},
"source": [
"# Fine-tuning"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "TnPudHlZVmaA"
},
"outputs": [],
"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"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZPic7oqNdGcH",
"outputId": "11bb22bf-31a5-4855-d35d-79fdd14a7cce"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/Caskroom/miniforge/base/envs/ai_env/lib/python3.8/site-packages/transformers/optimization.py:411: 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"
]
}
],
"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",
" )"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "u-zq78GveBbk"
},
"outputs": [],
"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)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "52TDlVRadJCq",
"outputId": "a60440b3-a297-4af3-905b-ce47c3cce6f7"
},
"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): 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",
" (1): 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",
" (2): 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",
" (3): 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",
" (4): 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",
" (5): 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",
" (6): 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",
" (7): 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",
" (8): 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",
" (9): 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",
" (10): 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",
" (11): 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": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"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)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pPNGSJoadS9V",
"outputId": "f012a036-80ed-499a-8323-25673d0724a2",
"pycharm": {
"is_executing": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"======== Epoch 1 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 2.44\n",
" Training epoch took: 0:01:47\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.26\n",
" Validation took: 0:00:05\n",
"\n",
"======== Epoch 2 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.14\n",
" Training epoch took: 0:01:59\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.14\n",
" Validation took: 0:00:05\n",
"\n",
"======== Epoch 3 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.08\n",
" Training epoch took: 0:01:57\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.13\n",
" Validation took: 0:00:04\n",
"\n",
"======== Epoch 4 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.06\n",
" Training epoch took: 0:01:48\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.13\n",
" Validation took: 0:00:04\n",
"\n",
"======== Epoch 5 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.05\n",
" Training epoch took: 0:01:54\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.14\n",
" Validation took: 0:00:05\n",
"\n",
"======== Epoch 6 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.04\n",
" Training epoch took: 0:01:45\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.15\n",
" Validation took: 0:00:04\n",
"\n",
"======== Epoch 7 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.03\n",
" Training epoch took: 0:02:03\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.15\n",
" Validation took: 0:00:05\n",
"\n",
"======== Epoch 8 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.02\n",
" Training epoch took: 0:01:48\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.15\n",
" Validation took: 0:00:03\n",
"\n",
"======== Epoch 9 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.01\n",
" Training epoch took: 0:01:59\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.16\n",
" Validation took: 0:00:05\n",
"\n",
"======== Epoch 10 / 10 ========\n",
"Training...\n",
"\n",
" Average training loss: 0.01\n",
" Training epoch took: 0:01:50\n",
"\n",
"Running Validation...\n",
" Validation Loss: 0.16\n",
" Validation took: 0:00:04\n",
"\n",
"Training complete!\n",
"Total training took 0:19:35 (h:mm:ss)\n"
]
}
],
"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)))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YUAZReU3jPwm",
"outputId": "9fbaccdd-cd3f-4231-f960-e4457f23aeba"
},
"outputs": [],
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PaV10cc01n_N"
},
"source": [
"# Zapisanie modelu"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5Z42j32m1iUF",
"outputId": "3cd21c7c-dd84-4314-8aa4-2671e1f02edd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving model to model_save/\n"
]
},
{
"data": {
"text/plain": [
"('model_save/tokenizer_config.json',\n",
" 'model_save/special_tokens_map.json',\n",
" 'model_save/vocab.json',\n",
" 'model_save/merges.txt',\n",
" 'model_save/added_tokens.json',\n",
" 'model_save/tokenizer.json')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"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)"
]
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