2762 lines
92 KiB
Text
2762 lines
92 KiB
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"cells": [
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"cell_type": "markdown",
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"metadata": {
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"id": "VTze-VbeU1c0"
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},
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"source": [
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"# Fine-tune a DialoGPT model\n",
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"\n",
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"Adapted from a notebook adapted from the notebook in [this Medium post](https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30?gi=e4a72d1510f0)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Y17kuzFNUSrZ"
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},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "dnv5kT-mLsB-",
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"tags": []
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},
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"outputs": [],
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"source": [
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"# all the imports\n",
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"\n",
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"import glob\n",
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"import logging\n",
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"import os\n",
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"import pickle\n",
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"import random\n",
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"import re\n",
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"import shutil\n",
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"from typing import Dict, List, Tuple\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"from torch.nn.utils.rnn import pad_sequence\n",
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"from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\n",
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"from torch.utils.data.distributed import DistributedSampler\n",
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"from tqdm.notebook import tqdm, trange\n",
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"\n",
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"from pathlib import Path\n",
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"\n",
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"from transformers import (\n",
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" MODEL_WITH_LM_HEAD_MAPPING,\n",
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" WEIGHTS_NAME,\n",
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" AdamW,\n",
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" AutoConfig,\n",
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" PreTrainedModel,\n",
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" PreTrainedTokenizer,\n",
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" get_linear_schedule_with_warmup,\n",
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")\n",
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"\n",
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"\n",
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"try:\n",
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" from torch.utils.tensorboard import SummaryWriter\n",
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"except ImportError:\n",
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" from tensorboardX import SummaryWriter"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "BmrbGB8aUmBm"
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},
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"source": [
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"## Load Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-10-18T05:27:13.388169Z",
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"iopub.status.busy": "2022-10-18T05:27:13.387823Z",
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"iopub.status.idle": "2022-10-18T05:27:13.451208Z",
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"shell.execute_reply": "2022-10-18T05:27:13.450527Z",
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"shell.execute_reply.started": "2022-10-18T05:27:13.388152Z"
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},
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"id": "RXdJTSVwWGHj",
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"tags": []
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},
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"outputs": [],
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"source": [
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"data = pd.read_csv('data/train.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 238
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},
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"execution": {
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"iopub.execute_input": "2022-10-18T05:27:13.452257Z",
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"iopub.status.busy": "2022-10-18T05:27:13.451996Z",
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"iopub.status.idle": "2022-10-18T05:27:13.465447Z",
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"shell.execute_reply": "2022-10-18T05:27:13.464448Z",
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"shell.execute_reply.started": "2022-10-18T05:27:13.452239Z"
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},
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"id": "h6kGx-9eG7qA",
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"outputId": "bd2efe43-1e50-4716-81a2-bf15a3dd03bd",
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name</th>\n",
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" <th>line</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>14152</th>\n",
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" <td>Cartman</td>\n",
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" <td>I... told your mom you got an F on that socia...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10447</th>\n",
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" <td>Kyle</td>\n",
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" <td>It's that ring. Somehow, putting on that ring ...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>65079</th>\n",
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" <td>Kyle</td>\n",
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" <td>Hey Look!</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14862</th>\n",
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" <td>Benedict XVI</td>\n",
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" <td>Tom, Tom! The gingers are claiming they have M...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6060</th>\n",
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" <td>Squirrelly Squirrel</td>\n",
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" <td>Now come on y'all. We can't waste time arguing...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>63693</th>\n",
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" <td>Cartman</td>\n",
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" <td>How could you be so stupid!</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" name line\n",
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"14152 Cartman I... told your mom you got an F on that socia...\n",
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"10447 Kyle It's that ring. Somehow, putting on that ring ...\n",
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"65079 Kyle Hey Look!\n",
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"14862 Benedict XVI Tom, Tom! The gingers are claiming they have M...\n",
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"6060 Squirrelly Squirrel Now come on y'all. We can't waste time arguing...\n",
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"63693 Cartman How could you be so stupid!"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.sample(6)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-10-18T05:27:13.466439Z",
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"iopub.status.busy": "2022-10-18T05:27:13.466236Z",
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"iopub.status.idle": "2022-10-18T05:27:13.469713Z",
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"shell.execute_reply": "2022-10-18T05:27:13.468950Z",
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"shell.execute_reply.started": "2022-10-18T05:27:13.466421Z"
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},
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"id": "PG8v6--qWUwj",
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"tags": []
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},
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"outputs": [],
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"source": [
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"CHARACTER_NAME = 'Cartman'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-10-18T05:27:13.470543Z",
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"iopub.status.busy": "2022-10-18T05:27:13.470349Z",
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"iopub.status.idle": "2022-10-18T05:27:13.643993Z",
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"shell.execute_reply": "2022-10-18T05:27:13.643224Z",
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"shell.execute_reply.started": "2022-10-18T05:27:13.470528Z"
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},
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"id": "GZUcEMd2WLDT",
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"tags": []
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},
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"outputs": [],
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"source": [
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"contexted = []\n",
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"\n",
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"# context window of size 7\n",
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"n = 1\n",
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"\n",
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"for i in data[data.name == CHARACTER_NAME].index:\n",
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" if i < n:\n",
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" continue\n",
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" row = []\n",
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" prev = i - 1 - n # we additionally substract 1, so row will contain current response and 7 previous responses \n",
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" for j in range(i, prev, -1):\n",
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" row.append(data.line[j])\n",
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" contexted.append(row)\n",
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"\n",
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"columns = ['response', 'context'] \n",
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"columns = columns + ['context/' + str(i) for i in range(n - 1)]\n",
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"\n",
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"df = pd.DataFrame.from_records(contexted, columns=columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 446
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},
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"execution": {
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"iopub.execute_input": "2022-10-18T05:27:13.644878Z",
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"iopub.status.busy": "2022-10-18T05:27:13.644713Z",
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"iopub.status.idle": "2022-10-18T05:27:13.652020Z",
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"shell.execute_reply": "2022-10-18T05:27:13.651291Z",
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"shell.execute_reply.started": "2022-10-18T05:27:13.644862Z"
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},
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"id": "4T5OlNZHUxij",
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"outputId": "895603a6-ca02-4301-c4b0-5bccbee8a3b8",
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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" text-align: right;\n",
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" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>response</th>\n",
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" <th>context</th>\n",
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" <tr>\n",
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" <th>13726</th>\n",
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" <td>Yeah, we're gonna use him to help raise money ...</td>\n",
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" <td>Yahahah!!!</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6269</th>\n",
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" <td>My password is uloveboobs!</td>\n",
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" <td>How did you know that?</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2703</th>\n",
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" <td>God, you are such a pussy, Stan! You're such a...</td>\n",
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" <td>Dude, there's nothing I can do about it.</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5788</th>\n",
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" <td>Wow, that makes sense. Don't think anyone can ...</td>\n",
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" <td>It's a complicated political issue, my son. An...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9795</th>\n",
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" <td>Ugh. Uunnh. Ey, give me that! Ahhhh...</td>\n",
|
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" <td>It'll make your itches go away.</td>\n",
|
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" </tr>\n",
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" <tr>\n",
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" <th>5086</th>\n",
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" <td>Heh! You're crazy! It can't be done!</td>\n",
|
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" <td>Alright Eric, here's the deal: This school can...</td>\n",
|
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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|
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" response \\\n",
|
|||
|
|
"13726 Yeah, we're gonna use him to help raise money ... \n",
|
|||
|
|
"6269 My password is uloveboobs! \n",
|
|||
|
|
"2703 God, you are such a pussy, Stan! You're such a... \n",
|
|||
|
|
"5788 Wow, that makes sense. Don't think anyone can ... \n",
|
|||
|
|
"9795 Ugh. Uunnh. Ey, give me that! Ahhhh... \n",
|
|||
|
|
"5086 Heh! You're crazy! It can't be done! \n",
|
|||
|
|
"\n",
|
|||
|
|
" context \n",
|
|||
|
|
"13726 Yahahah!!! \n",
|
|||
|
|
"6269 How did you know that? \n",
|
|||
|
|
"2703 Dude, there's nothing I can do about it. \n",
|
|||
|
|
"5788 It's a complicated political issue, my son. An... \n",
|
|||
|
|
"9795 It'll make your itches go away. \n",
|
|||
|
|
"5086 Alright Eric, here's the deal: This school can... "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df.sample(6)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 380
|
|||
|
|
},
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.execute_input": "2022-10-18T05:27:13.654567Z",
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:13.653983Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:13.669974Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:13.669272Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:13.654539Z"
|
|||
|
|
},
|
|||
|
|
"id": "NGy0MxMQVIAP",
|
|||
|
|
"outputId": "08b7f0eb-6a38-4b83-efdc-e53778d7547a",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
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|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>response</th>\n",
|
|||
|
|
" <th>context</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8751</th>\n",
|
|||
|
|
" <td>Gimme that cake!</td>\n",
|
|||
|
|
" <td>Who the hell are you?!</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>10518</th>\n",
|
|||
|
|
" <td>Aww, screw you guys anyway!</td>\n",
|
|||
|
|
" <td>Cartman, will you shut the hell up and get som...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>7483</th>\n",
|
|||
|
|
" <td>You wanna take it out for a spin?</td>\n",
|
|||
|
|
" <td>It can fly like a quarter mile away from whoev...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>16857</th>\n",
|
|||
|
|
" <td>Well, I guess we can go back to playing laundr...</td>\n",
|
|||
|
|
" <td>Son of a bitch stupid FBI!</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6644</th>\n",
|
|||
|
|
" <td>Hey Token? How are you doing?</td>\n",
|
|||
|
|
" <td>No! Nonono! Nooo!</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" response \\\n",
|
|||
|
|
"8751 Gimme that cake! \n",
|
|||
|
|
"10518 Aww, screw you guys anyway! \n",
|
|||
|
|
"7483 You wanna take it out for a spin? \n",
|
|||
|
|
"16857 Well, I guess we can go back to playing laundr... \n",
|
|||
|
|
"6644 Hey Token? How are you doing? \n",
|
|||
|
|
"\n",
|
|||
|
|
" context \n",
|
|||
|
|
"8751 Who the hell are you?! \n",
|
|||
|
|
"10518 Cartman, will you shut the hell up and get som... \n",
|
|||
|
|
"7483 It can fly like a quarter mile away from whoev... \n",
|
|||
|
|
"16857 Son of a bitch stupid FBI! \n",
|
|||
|
|
"6644 No! Nonono! Nooo! "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"trn_df, val_df = train_test_split(df, test_size=0.1)\n",
|
|||
|
|
"trn_df.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.execute_input": "2022-10-18T05:27:13.671526Z",
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:13.671152Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:13.682715Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:13.681858Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:13.671496Z"
|
|||
|
|
},
|
|||
|
|
"id": "aEeJQlAKWtiJ",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# create dataset suitable for our model\n",
|
|||
|
|
"def construct_conv(row, tokenizer, eos = True):\n",
|
|||
|
|
" flatten = lambda l: [item for sublist in l for item in sublist]\n",
|
|||
|
|
" conv = list(reversed([tokenizer.encode(x) + [tokenizer.eos_token_id] for x in row]))\n",
|
|||
|
|
" conv = flatten(conv)\n",
|
|||
|
|
" return conv\n",
|
|||
|
|
"\n",
|
|||
|
|
"class ConversationDataset(Dataset):\n",
|
|||
|
|
" def __init__(self, tokenizer: PreTrainedTokenizer, args, df, block_size=512):\n",
|
|||
|
|
"\n",
|
|||
|
|
" block_size = block_size - (tokenizer.model_max_length - tokenizer.max_len_single_sentence)\n",
|
|||
|
|
"\n",
|
|||
|
|
" directory = args.cache_dir\n",
|
|||
|
|
" cached_features_file = os.path.join(\n",
|
|||
|
|
" directory, args.model_type + \"_cached_lm_\" + str(block_size)\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" if os.path.exists(cached_features_file) and not args.overwrite_cache:\n",
|
|||
|
|
" logger.info(\"Loading features from cached file %s\", cached_features_file)\n",
|
|||
|
|
" with open(cached_features_file, \"rb\") as handle:\n",
|
|||
|
|
" self.examples = pickle.load(handle)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" logger.info(\"Creating features from dataset file at %s\", directory)\n",
|
|||
|
|
"\n",
|
|||
|
|
" self.examples = []\n",
|
|||
|
|
" for _, row in df.iterrows():\n",
|
|||
|
|
" conv = construct_conv(row, tokenizer)\n",
|
|||
|
|
" self.examples.append(conv)\n",
|
|||
|
|
"\n",
|
|||
|
|
" logger.info(\"Saving features into cached file %s\", cached_features_file)\n",
|
|||
|
|
" with open(cached_features_file, \"wb\") as handle:\n",
|
|||
|
|
" pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def __len__(self):\n",
|
|||
|
|
" return len(self.examples)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def __getitem__(self, item):\n",
|
|||
|
|
" return torch.tensor(self.examples[item], dtype=torch.long)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.execute_input": "2022-10-18T05:27:13.684175Z",
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:13.683859Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:13.693632Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:13.692937Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:13.684147Z"
|
|||
|
|
},
|
|||
|
|
"id": "-3iHwoKlWyrs",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Caching and storing of data/checkpoints\n",
|
|||
|
|
"\n",
|
|||
|
|
"def load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False):\n",
|
|||
|
|
" return ConversationDataset(tokenizer, args, df_val if evaluate else df_trn)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def set_seed(args):\n",
|
|||
|
|
" random.seed(args.seed)\n",
|
|||
|
|
" np.random.seed(args.seed)\n",
|
|||
|
|
" torch.manual_seed(args.seed)\n",
|
|||
|
|
" if args.n_gpu > 0:\n",
|
|||
|
|
" torch.cuda.manual_seed_all(args.seed)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def _sorted_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> List[str]:\n",
|
|||
|
|
" ordering_and_checkpoint_path = []\n",
|
|||
|
|
"\n",
|
|||
|
|
" glob_checkpoints = glob.glob(os.path.join(args.output_dir, \"{}-*\".format(checkpoint_prefix)))\n",
|
|||
|
|
"\n",
|
|||
|
|
" for path in glob_checkpoints:\n",
|
|||
|
|
" if use_mtime:\n",
|
|||
|
|
" ordering_and_checkpoint_path.append((os.path.getmtime(path), path))\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" regex_match = re.match(\".*{}-([0-9]+)\".format(checkpoint_prefix), path)\n",
|
|||
|
|
" if regex_match and regex_match.groups():\n",
|
|||
|
|
" ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))\n",
|
|||
|
|
"\n",
|
|||
|
|
" checkpoints_sorted = sorted(ordering_and_checkpoint_path)\n",
|
|||
|
|
" checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]\n",
|
|||
|
|
" return checkpoints_sorted\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"def _rotate_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> None:\n",
|
|||
|
|
" if not args.save_total_limit:\n",
|
|||
|
|
" return\n",
|
|||
|
|
" if args.save_total_limit <= 0:\n",
|
|||
|
|
" return\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Check if we should delete older checkpoint(s)\n",
|
|||
|
|
" checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)\n",
|
|||
|
|
" if len(checkpoints_sorted) <= args.save_total_limit:\n",
|
|||
|
|
" return\n",
|
|||
|
|
"\n",
|
|||
|
|
" number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)\n",
|
|||
|
|
" checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]\n",
|
|||
|
|
" for checkpoint in checkpoints_to_be_deleted:\n",
|
|||
|
|
" logger.info(\"Deleting older checkpoint [{}] due to args.save_total_limit\".format(checkpoint))\n",
|
|||
|
|
" shutil.rmtree(checkpoint)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "EEDdTJTqUwZJ"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Build Model"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.execute_input": "2022-10-18T05:27:13.694533Z",
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:13.694353Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:21.231192Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:21.230658Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:13.694517Z"
|
|||
|
|
},
|
|||
|
|
"id": "r2cE0fY5UHpz",
|
|||
|
|
"outputId": "e4f382cd-57d9-49b7-9da4-4b44fe57df5b",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer\n",
|
|||
|
|
"import torch\n",
|
|||
|
|
"\n",
|
|||
|
|
"tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-large\")\n",
|
|||
|
|
"model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-large\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.execute_input": "2022-10-18T05:27:21.232063Z",
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:21.231898Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:21.259427Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:21.258757Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:21.232048Z"
|
|||
|
|
},
|
|||
|
|
"id": "ra2vsRp-UMXo",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"\"\"\"\n",
|
|||
|
|
"Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).\n",
|
|||
|
|
"GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned\n",
|
|||
|
|
"using a masked language modeling (MLM) loss.\n",
|
|||
|
|
"\"\"\"\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Configs\n",
|
|||
|
|
"logger = logging.getLogger(__name__)\n",
|
|||
|
|
"\n",
|
|||
|
|
"MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())\n",
|
|||
|
|
"MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.execute_input": "2022-10-18T05:27:21.260380Z",
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:21.260189Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:21.265817Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:21.265203Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:21.260362Z"
|
|||
|
|
},
|
|||
|
|
"id": "2OnASqJjUNJa",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Args to allow for easy conversion of python script to notebook\n",
|
|||
|
|
"class Args():\n",
|
|||
|
|
" def __init__(self):\n",
|
|||
|
|
" self.output_dir = 'models/output-medium-oneline'\n",
|
|||
|
|
" self.model_type = 'gpt2'\n",
|
|||
|
|
" self.model_name_or_path = 'microsoft/DialoGPT-medium'\n",
|
|||
|
|
" self.config_name = 'microsoft/DialoGPT-medium'\n",
|
|||
|
|
" self.tokenizer_name = 'microsoft/DialoGPT-medium'\n",
|
|||
|
|
" self.cache_dir = 'cached'\n",
|
|||
|
|
" self.block_size = 512\n",
|
|||
|
|
" self.do_train = True\n",
|
|||
|
|
" self.do_eval = True\n",
|
|||
|
|
" self.evaluate_during_training = False\n",
|
|||
|
|
" self.per_gpu_train_batch_size = 4\n",
|
|||
|
|
" self.per_gpu_eval_batch_size = 4\n",
|
|||
|
|
" self.gradient_accumulation_steps = 1\n",
|
|||
|
|
" self.learning_rate = 5e-5\n",
|
|||
|
|
" self.weight_decay = 0.0\n",
|
|||
|
|
" self.adam_epsilon = 1e-8\n",
|
|||
|
|
" self.max_grad_norm = 1.0\n",
|
|||
|
|
" self.num_train_epochs = 4\n",
|
|||
|
|
" self.max_steps = -1\n",
|
|||
|
|
" self.warmup_steps = 0\n",
|
|||
|
|
" self.logging_steps = 1000\n",
|
|||
|
|
" self.save_steps = 3500\n",
|
|||
|
|
" self.save_total_limit = None\n",
|
|||
|
|
" self.eval_all_checkpoints = False\n",
|
|||
|
|
" self.no_cuda = False\n",
|
|||
|
|
" self.overwrite_output_dir = True\n",
|
|||
|
|
" self.overwrite_cache = True\n",
|
|||
|
|
" self.should_continue = False\n",
|
|||
|
|
" self.seed = 42\n",
|
|||
|
|
" self.local_rank = -1\n",
|
|||
|
|
" self.fp16 = False\n",
|
|||
|
|
" self.fp16_opt_level = 'O1'\n",
|
|||
|
|
"\n",
|
|||
|
|
"args = Args()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "9Q1dTFXxW9NE"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Train and Evaluate"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.execute_input": "2022-10-18T05:27:21.266896Z",
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:21.266734Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:21.302284Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:21.301566Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:21.266881Z"
|
|||
|
|
},
|
|||
|
|
"id": "PaarIDZrW81h",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:\n",
|
|||
|
|
" \"\"\" Train the model \"\"\"\n",
|
|||
|
|
" if args.local_rank in [-1, 0]:\n",
|
|||
|
|
" tb_writer = SummaryWriter()\n",
|
|||
|
|
"\n",
|
|||
|
|
" args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def collate(examples: List[torch.Tensor]):\n",
|
|||
|
|
" if tokenizer._pad_token is None:\n",
|
|||
|
|
" return pad_sequence(examples, batch_first=True)\n",
|
|||
|
|
" return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
|
|||
|
|
"\n",
|
|||
|
|
" train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)\n",
|
|||
|
|
" train_dataloader = DataLoader(\n",
|
|||
|
|
" train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate, drop_last = True\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" if args.max_steps > 0:\n",
|
|||
|
|
" t_total = args.max_steps\n",
|
|||
|
|
" args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs\n",
|
|||
|
|
"\n",
|
|||
|
|
" model = model.module if hasattr(model, \"module\") else model # Take care of distributed/parallel training\n",
|
|||
|
|
" model.resize_token_embeddings(len(tokenizer))\n",
|
|||
|
|
" # add_special_tokens_(model, tokenizer)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Prepare optimizer and schedule (linear warmup and decay)\n",
|
|||
|
|
" no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
|
|||
|
|
" optimizer_grouped_parameters = [\n",
|
|||
|
|
" {\n",
|
|||
|
|
" \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
|
|||
|
|
" \"weight_decay\": args.weight_decay,\n",
|
|||
|
|
" },\n",
|
|||
|
|
" {\"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], \"weight_decay\": 0.0},\n",
|
|||
|
|
" ]\n",
|
|||
|
|
" optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)\n",
|
|||
|
|
" scheduler = get_linear_schedule_with_warmup(\n",
|
|||
|
|
" optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Check if saved optimizer or scheduler states exist\n",
|
|||
|
|
" if (\n",
|
|||
|
|
" args.model_name_or_path\n",
|
|||
|
|
" and os.path.isfile(os.path.join(args.model_name_or_path, \"optimizer.pt\"))\n",
|
|||
|
|
" and os.path.isfile(os.path.join(args.model_name_or_path, \"scheduler.pt\"))\n",
|
|||
|
|
" ):\n",
|
|||
|
|
" # Load in optimizer and scheduler states\n",
|
|||
|
|
" optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"optimizer.pt\")))\n",
|
|||
|
|
" scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"scheduler.pt\")))\n",
|
|||
|
|
"\n",
|
|||
|
|
" if args.fp16:\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" from apex import amp\n",
|
|||
|
|
" except ImportError:\n",
|
|||
|
|
" raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\")\n",
|
|||
|
|
" model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # multi-gpu training (should be after apex fp16 initialization)\n",
|
|||
|
|
" if args.n_gpu > 1:\n",
|
|||
|
|
" model = torch.nn.DataParallel(model)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Distributed training (should be after apex fp16 initialization)\n",
|
|||
|
|
" if args.local_rank != -1:\n",
|
|||
|
|
" model = torch.nn.parallel.DistributedDataParallel(\n",
|
|||
|
|
" model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Train!\n",
|
|||
|
|
" logger.info(\"***** Running training *****\")\n",
|
|||
|
|
" logger.info(\" Num examples = %d\", len(train_dataset))\n",
|
|||
|
|
" logger.info(\" Num Epochs = %d\", args.num_train_epochs)\n",
|
|||
|
|
" logger.info(\" Instantaneous batch size per GPU = %d\", args.per_gpu_train_batch_size)\n",
|
|||
|
|
" logger.info(\n",
|
|||
|
|
" \" Total train batch size (w. parallel, distributed & accumulation) = %d\",\n",
|
|||
|
|
" args.train_batch_size\n",
|
|||
|
|
" * args.gradient_accumulation_steps\n",
|
|||
|
|
" * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),\n",
|
|||
|
|
" )\n",
|
|||
|
|
" logger.info(\" Gradient Accumulation steps = %d\", args.gradient_accumulation_steps)\n",
|
|||
|
|
" logger.info(\" Total optimization steps = %d\", t_total)\n",
|
|||
|
|
"\n",
|
|||
|
|
" global_step = 0\n",
|
|||
|
|
" epochs_trained = 0\n",
|
|||
|
|
" steps_trained_in_current_epoch = 0\n",
|
|||
|
|
" # Check if continuing training from a checkpoint\n",
|
|||
|
|
" if args.model_name_or_path and os.path.exists(args.model_name_or_path):\n",
|
|||
|
|
" try:\n",
|
|||
|
|
" # set global_step to gobal_step of last saved checkpoint from model path\n",
|
|||
|
|
" checkpoint_suffix = args.model_name_or_path.split(\"-\")[-1].split(\"/\")[0]\n",
|
|||
|
|
" global_step = int(checkpoint_suffix)\n",
|
|||
|
|
" epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)\n",
|
|||
|
|
" steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)\n",
|
|||
|
|
"\n",
|
|||
|
|
" logger.info(\" Continuing training from checkpoint, will skip to saved global_step\")\n",
|
|||
|
|
" logger.info(\" Continuing training from epoch %d\", epochs_trained)\n",
|
|||
|
|
" logger.info(\" Continuing training from global step %d\", global_step)\n",
|
|||
|
|
" logger.info(\" Will skip the first %d steps in the first epoch\", steps_trained_in_current_epoch)\n",
|
|||
|
|
" except ValueError:\n",
|
|||
|
|
" logger.info(\" Starting fine-tuning.\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" tr_loss, logging_loss = 0.0, 0.0\n",
|
|||
|
|
"\n",
|
|||
|
|
" model.zero_grad()\n",
|
|||
|
|
" train_iterator = trange(\n",
|
|||
|
|
" epochs_trained, int(args.num_train_epochs), desc=\"Epoch\", disable=args.local_rank not in [-1, 0]\n",
|
|||
|
|
" )\n",
|
|||
|
|
" set_seed(args) # Added here for reproducibility\n",
|
|||
|
|
" for _ in train_iterator:\n",
|
|||
|
|
" epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=args.local_rank not in [-1, 0])\n",
|
|||
|
|
" for step, batch in enumerate(epoch_iterator):\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Skip past any already trained steps if resuming training\n",
|
|||
|
|
" if steps_trained_in_current_epoch > 0:\n",
|
|||
|
|
" steps_trained_in_current_epoch -= 1\n",
|
|||
|
|
" continue\n",
|
|||
|
|
"\n",
|
|||
|
|
" inputs, labels = (batch, batch)\n",
|
|||
|
|
" if inputs.shape[1] > 1024: continue\n",
|
|||
|
|
" inputs = inputs.to(args.device)\n",
|
|||
|
|
" labels = labels.to(args.device)\n",
|
|||
|
|
" model.train()\n",
|
|||
|
|
" outputs = model(inputs, labels=labels)\n",
|
|||
|
|
" loss = outputs[0] # model outputs are always tuple in transformers (see doc)\n",
|
|||
|
|
"\n",
|
|||
|
|
" if args.n_gpu > 1:\n",
|
|||
|
|
" loss = loss.mean() # mean() to average on multi-gpu parallel training\n",
|
|||
|
|
" if args.gradient_accumulation_steps > 1:\n",
|
|||
|
|
" loss = loss / args.gradient_accumulation_steps\n",
|
|||
|
|
"\n",
|
|||
|
|
" if args.fp16:\n",
|
|||
|
|
" with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
|
|||
|
|
" scaled_loss.backward()\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" loss.backward()\n",
|
|||
|
|
"\n",
|
|||
|
|
" tr_loss += loss.item()\n",
|
|||
|
|
" if (step + 1) % args.gradient_accumulation_steps == 0:\n",
|
|||
|
|
" if args.fp16:\n",
|
|||
|
|
" torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n",
|
|||
|
|
" optimizer.step()\n",
|
|||
|
|
" scheduler.step() # Update learning rate schedule\n",
|
|||
|
|
" model.zero_grad()\n",
|
|||
|
|
" global_step += 1\n",
|
|||
|
|
"\n",
|
|||
|
|
" if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:\n",
|
|||
|
|
" # Log metrics\n",
|
|||
|
|
" if (\n",
|
|||
|
|
" args.local_rank == -1 and args.evaluate_during_training\n",
|
|||
|
|
" ): # Only evaluate when single GPU otherwise metrics may not average well\n",
|
|||
|
|
" results = evaluate(args, model, tokenizer)\n",
|
|||
|
|
" for key, value in results.items():\n",
|
|||
|
|
" tb_writer.add_scalar(\"eval_{}\".format(key), value, global_step)\n",
|
|||
|
|
" tb_writer.add_scalar(\"lr\", scheduler.get_lr()[0], global_step)\n",
|
|||
|
|
" tb_writer.add_scalar(\"loss\", (tr_loss - logging_loss) / args.logging_steps, global_step)\n",
|
|||
|
|
" logging_loss = tr_loss\n",
|
|||
|
|
"\n",
|
|||
|
|
" if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:\n",
|
|||
|
|
" checkpoint_prefix = \"checkpoint\"\n",
|
|||
|
|
" # Save model checkpoint\n",
|
|||
|
|
" output_dir = os.path.join(args.output_dir, \"{}-{}\".format(checkpoint_prefix, global_step))\n",
|
|||
|
|
" os.makedirs(output_dir, exist_ok=True)\n",
|
|||
|
|
" model_to_save = (\n",
|
|||
|
|
" model.module if hasattr(model, \"module\") else model\n",
|
|||
|
|
" ) # Take care of distributed/parallel training\n",
|
|||
|
|
" model_to_save.save_pretrained(output_dir)\n",
|
|||
|
|
" tokenizer.save_pretrained(output_dir)\n",
|
|||
|
|
"\n",
|
|||
|
|
" torch.save(args, os.path.join(output_dir, \"training_args.bin\"))\n",
|
|||
|
|
" logger.info(\"Saving model checkpoint to %s\", output_dir)\n",
|
|||
|
|
"\n",
|
|||
|
|
" _rotate_checkpoints(args, checkpoint_prefix)\n",
|
|||
|
|
"\n",
|
|||
|
|
" torch.save(optimizer.state_dict(), os.path.join(output_dir, \"optimizer.pt\"))\n",
|
|||
|
|
" torch.save(scheduler.state_dict(), os.path.join(output_dir, \"scheduler.pt\"))\n",
|
|||
|
|
" logger.info(\"Saving optimizer and scheduler states to %s\", output_dir)\n",
|
|||
|
|
"\n",
|
|||
|
|
" if args.max_steps > 0 and global_step > args.max_steps:\n",
|
|||
|
|
" epoch_iterator.close()\n",
|
|||
|
|
" break\n",
|
|||
|
|
" if args.max_steps > 0 and global_step > args.max_steps:\n",
|
|||
|
|
" train_iterator.close()\n",
|
|||
|
|
" break\n",
|
|||
|
|
"\n",
|
|||
|
|
" if args.local_rank in [-1, 0]:\n",
|
|||
|
|
" tb_writer.close()\n",
|
|||
|
|
"\n",
|
|||
|
|
" return global_step, tr_loss / global_step\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Evaluation of some model\n",
|
|||
|
|
"\n",
|
|||
|
|
"def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, df_trn, df_val, prefix=\"\") -> Dict:\n",
|
|||
|
|
" # Loop to handle MNLI double evaluation (matched, mis-matched)\n",
|
|||
|
|
" eval_output_dir = args.output_dir\n",
|
|||
|
|
"\n",
|
|||
|
|
" eval_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=True)\n",
|
|||
|
|
" os.makedirs(eval_output_dir, exist_ok=True)\n",
|
|||
|
|
" args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)\n",
|
|||
|
|
" # Note that DistributedSampler samples randomly\n",
|
|||
|
|
"\n",
|
|||
|
|
" def collate(examples: List[torch.Tensor]):\n",
|
|||
|
|
" if tokenizer._pad_token is None:\n",
|
|||
|
|
" return pad_sequence(examples, batch_first=True)\n",
|
|||
|
|
" return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
|
|||
|
|
"\n",
|
|||
|
|
" eval_sampler = SequentialSampler(eval_dataset)\n",
|
|||
|
|
" eval_dataloader = DataLoader(\n",
|
|||
|
|
" eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate, drop_last = True\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # multi-gpu evaluate\n",
|
|||
|
|
" if args.n_gpu > 1:\n",
|
|||
|
|
" model = torch.nn.DataParallel(model)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Eval!\n",
|
|||
|
|
" logger.info(\"***** Running evaluation {} *****\".format(prefix))\n",
|
|||
|
|
" logger.info(\" Num examples = %d\", len(eval_dataset))\n",
|
|||
|
|
" logger.info(\" Batch size = %d\", args.eval_batch_size)\n",
|
|||
|
|
" eval_loss = 0.0\n",
|
|||
|
|
" nb_eval_steps = 0\n",
|
|||
|
|
" model.eval()\n",
|
|||
|
|
"\n",
|
|||
|
|
" for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n",
|
|||
|
|
" inputs, labels = (batch, batch)\n",
|
|||
|
|
" inputs = inputs.to(args.device)\n",
|
|||
|
|
" labels = labels.to(args.device)\n",
|
|||
|
|
"\n",
|
|||
|
|
" with torch.no_grad():\n",
|
|||
|
|
" outputs = model(inputs, labels=labels)\n",
|
|||
|
|
" lm_loss = outputs[0]\n",
|
|||
|
|
" eval_loss += lm_loss.mean().item()\n",
|
|||
|
|
" nb_eval_steps += 1\n",
|
|||
|
|
"\n",
|
|||
|
|
" eval_loss = eval_loss / nb_eval_steps\n",
|
|||
|
|
" perplexity = torch.exp(torch.tensor(eval_loss))\n",
|
|||
|
|
"\n",
|
|||
|
|
" result = {\"perplexity\": perplexity}\n",
|
|||
|
|
"\n",
|
|||
|
|
" output_eval_file = os.path.join(eval_output_dir, prefix, \"eval_results.txt\")\n",
|
|||
|
|
" with open(output_eval_file, \"w\") as writer:\n",
|
|||
|
|
" logger.info(\"***** Eval results {} *****\".format(prefix))\n",
|
|||
|
|
" for key in sorted(result.keys()):\n",
|
|||
|
|
" logger.info(\" %s = %s\", key, str(result[key]))\n",
|
|||
|
|
" writer.write(\"%s = %s\\n\" % (key, str(result[key])))\n",
|
|||
|
|
"\n",
|
|||
|
|
" return result"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.execute_input": "2022-10-18T05:27:21.303584Z",
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:21.303400Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:21.314912Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:21.314269Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:21.303568Z"
|
|||
|
|
},
|
|||
|
|
"id": "SCnGAJWbXD9C",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Main runner\n",
|
|||
|
|
"\n",
|
|||
|
|
"def main(df_trn, df_val):\n",
|
|||
|
|
" args = Args()\n",
|
|||
|
|
" \n",
|
|||
|
|
" if args.should_continue:\n",
|
|||
|
|
" sorted_checkpoints = _sorted_checkpoints(args)\n",
|
|||
|
|
" if len(sorted_checkpoints) == 0:\n",
|
|||
|
|
" raise ValueError(\"Used --should_continue but no checkpoint was found in --output_dir.\")\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" args.model_name_or_path = sorted_checkpoints[-1]\n",
|
|||
|
|
"\n",
|
|||
|
|
" if (\n",
|
|||
|
|
" os.path.exists(args.output_dir)\n",
|
|||
|
|
" and os.listdir(args.output_dir)\n",
|
|||
|
|
" and args.do_train\n",
|
|||
|
|
" and not args.overwrite_output_dir\n",
|
|||
|
|
" and not args.should_continue\n",
|
|||
|
|
" ):\n",
|
|||
|
|
" raise ValueError(\n",
|
|||
|
|
" \"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.\".format(\n",
|
|||
|
|
" args.output_dir\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Setup CUDA, GPU & distributed training\n",
|
|||
|
|
" device = torch.device(\"cuda\")\n",
|
|||
|
|
" args.n_gpu = torch.cuda.device_count()\n",
|
|||
|
|
" args.device = device\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Setup logging\n",
|
|||
|
|
" logging.basicConfig(\n",
|
|||
|
|
" format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n",
|
|||
|
|
" datefmt=\"%m/%d/%Y %H:%M:%S\",\n",
|
|||
|
|
" level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,\n",
|
|||
|
|
" )\n",
|
|||
|
|
" logger.warning(\n",
|
|||
|
|
" \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n",
|
|||
|
|
" args.local_rank,\n",
|
|||
|
|
" device,\n",
|
|||
|
|
" args.n_gpu,\n",
|
|||
|
|
" bool(args.local_rank != -1),\n",
|
|||
|
|
" args.fp16,\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Set seed\n",
|
|||
|
|
" set_seed(args)\n",
|
|||
|
|
"\n",
|
|||
|
|
" config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)\n",
|
|||
|
|
" tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)\n",
|
|||
|
|
" model = AutoModelForCausalLM.from_pretrained(\n",
|
|||
|
|
" args.model_name_or_path,\n",
|
|||
|
|
" from_tf=False,\n",
|
|||
|
|
" config=config,\n",
|
|||
|
|
" cache_dir=args.cache_dir,\n",
|
|||
|
|
" )\n",
|
|||
|
|
" model.to(args.device)\n",
|
|||
|
|
" \n",
|
|||
|
|
" logger.info(\"Training/evaluation parameters %s\", args)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Training\n",
|
|||
|
|
" if args.do_train:\n",
|
|||
|
|
" train_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
" global_step, tr_loss = train(args, train_dataset, model, tokenizer)\n",
|
|||
|
|
" logger.info(\" global_step = %s, average loss = %s\", global_step, tr_loss)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()\n",
|
|||
|
|
" if args.do_train:\n",
|
|||
|
|
" # Create output directory if needed\n",
|
|||
|
|
" os.makedirs(args.output_dir, exist_ok=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
" logger.info(\"Saving model checkpoint to %s\", args.output_dir)\n",
|
|||
|
|
" # Save a trained model, configuration and tokenizer using `save_pretrained()`.\n",
|
|||
|
|
" # They can then be reloaded using `from_pretrained()`\n",
|
|||
|
|
" model_to_save = (\n",
|
|||
|
|
" model.module if hasattr(model, \"module\") else model\n",
|
|||
|
|
" ) # Take care of distributed/parallel training\n",
|
|||
|
|
" model_to_save.save_pretrained(args.output_dir)\n",
|
|||
|
|
" tokenizer.save_pretrained(args.output_dir)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Good practice: save your training arguments together with the trained model\n",
|
|||
|
|
" torch.save(args, os.path.join(args.output_dir, \"training_args.bin\"))\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Load a trained model and vocabulary that you have fine-tuned\n",
|
|||
|
|
" model = AutoModelForCausalLM.from_pretrained(args.output_dir)\n",
|
|||
|
|
" tokenizer = AutoTokenizer.from_pretrained(args.output_dir)\n",
|
|||
|
|
" model.to(args.device)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Evaluation\n",
|
|||
|
|
" results = {}\n",
|
|||
|
|
" if args.do_eval and args.local_rank in [-1, 0]:\n",
|
|||
|
|
" checkpoints = [args.output_dir]\n",
|
|||
|
|
" if args.eval_all_checkpoints:\n",
|
|||
|
|
" checkpoints = list(\n",
|
|||
|
|
" os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + \"/**/\" + WEIGHTS_NAME, recursive=True))\n",
|
|||
|
|
" )\n",
|
|||
|
|
" logging.getLogger(\"transformers.modeling_utils\").setLevel(logging.WARN) # Reduce logging\n",
|
|||
|
|
" logger.info(\"Evaluate the following checkpoints: %s\", checkpoints)\n",
|
|||
|
|
" for checkpoint in checkpoints:\n",
|
|||
|
|
" global_step = checkpoint.split(\"-\")[-1] if len(checkpoints) > 1 else \"\"\n",
|
|||
|
|
" prefix = checkpoint.split(\"/\")[-1] if checkpoint.find(\"checkpoint\") != -1 else \"\"\n",
|
|||
|
|
"\n",
|
|||
|
|
" model = AutoModelForCausalLM.from_pretrained(checkpoint)\n",
|
|||
|
|
" model.to(args.device)\n",
|
|||
|
|
" result = evaluate(args, model, tokenizer, df_trn, df_val, prefix=prefix)\n",
|
|||
|
|
" result = dict((k + \"_{}\".format(global_step), v) for k, v in result.items())\n",
|
|||
|
|
" results.update(result)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return results"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "7NWvkdR-XHeB"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Train it"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 780,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"1d7f4c82687540f1ad69eb54ac3c25b4",
|
|||
|
|
"e7b9f3fc77a24259a87ef0dc735dfecb",
|
|||
|
|
"f3bf54733c2d4d9daa1cc9a7746ccb14",
|
|||
|
|
"aa40eb6346b54e7dac98e0b068cd4927",
|
|||
|
|
"021b771a270f479aa3b9e2b5f17e3d97",
|
|||
|
|
"450b0e7fd7a347c7beb78b7d72f64385",
|
|||
|
|
"9391d7abf6ed4400903995f56d7a1260",
|
|||
|
|
"ea6b919964d24c2f9de1c64c9cefaf23",
|
|||
|
|
"2fa1fa2407384cb98d79a912de2d5b8f",
|
|||
|
|
"dc27e2caf1ea4a4ab9ae3708fb06952f",
|
|||
|
|
"e38fb98fd7b3413392dc39c93a107a35",
|
|||
|
|
"855ca0a6125a4d698416214a9425ad98",
|
|||
|
|
"4699416338ae40a5b6abf19e45089aec",
|
|||
|
|
"43fdb31d3f314624ba07a15718b0c8f3",
|
|||
|
|
"de252cd193114c40ad5f5e9622b7abc7",
|
|||
|
|
"5e48b617cc3f41c3945efc28fc5e0c75",
|
|||
|
|
"68a9dc52819c48fb97259f318f9b5c6a",
|
|||
|
|
"b4e00059cf3a49929978ed780aae8358",
|
|||
|
|
"0ff5f4e3506b493a98d72008a467f35f",
|
|||
|
|
"77b97fa3271b48ac9f93665a102b4fd1",
|
|||
|
|
"a937f1dfeee5432ba31b3016fd30e9e2",
|
|||
|
|
"3c6d446f491c48fcae03e0034bfaaae9",
|
|||
|
|
"a193bb3a0b5b4cbba587e2460075a445",
|
|||
|
|
"75f8aebc30304fe198b5a2898a53a92d",
|
|||
|
|
"8b8a7c771d234f6c9d758a1f07f75a90",
|
|||
|
|
"c6518c4a721745bf97ee682f2ebe4635",
|
|||
|
|
"29cffa2b4f234e12802344eb53838641",
|
|||
|
|
"96243b7b227f465f83a289481680b925",
|
|||
|
|
"8c016a54f0a24fcdacf369baa9d24f1e",
|
|||
|
|
"7fe5b457ca0f417f90a20d235e9cec07",
|
|||
|
|
"fdffb26b99c24c978580f1cf97359fea",
|
|||
|
|
"8e3f1740c82f47949eefc2eb53052eae",
|
|||
|
|
"9cccd43f6acc4e25b4876fd0ae7a2ad6",
|
|||
|
|
"175e94deab7f4d20b99b419bea33583b",
|
|||
|
|
"41f26f7210e540479814e5d68de13ddb",
|
|||
|
|
"cf5cd281fa3b453093e210650bf81e9e",
|
|||
|
|
"e1fbe239c2394cbf973ac5b95e1e1491",
|
|||
|
|
"810ac22adad344b7bf8b556ded990122",
|
|||
|
|
"8b3a41c1900b45ebb9c56601deca0e84",
|
|||
|
|
"002f56aac3d64b33a0e799c0baf1e6b9",
|
|||
|
|
"a0f2a9a279734aa5bf146f0a5b33c43b",
|
|||
|
|
"850b5411122e4d608511fe26818bea68",
|
|||
|
|
"0663fb4bd85f4d87a7d61910b995be14",
|
|||
|
|
"cb7f52610fcf49bda46a14b296ff5bb5",
|
|||
|
|
"0ca29b4a62e04d9c937189ea19b25de8",
|
|||
|
|
"f871b83632974e0088bae65e78efaf28",
|
|||
|
|
"4cacf7fc20754a7ca7fe08c8ec187a81",
|
|||
|
|
"8bcc625c0f284398bbd287fe45021b17"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "e61zo2JtXGNX",
|
|||
|
|
"outputId": "22d4916e-7169-44b5-f9d8-79b9c43fab2e",
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"main(trn_df, val_df)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "YRpQ_n2zXQj-"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Test it"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"execution": {
|
|||
|
|
"iopub.status.busy": "2022-10-18T05:27:32.954246Z",
|
|||
|
|
"iopub.status.idle": "2022-10-18T05:27:32.954450Z",
|
|||
|
|
"shell.execute_reply": "2022-10-18T05:27:32.954353Z",
|
|||
|
|
"shell.execute_reply.started": "2022-10-18T05:27:32.954342Z"
|
|||
|
|
},
|
|||
|
|
"id": "HGw3qgfaXQHX",
|
|||
|
|
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"tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-large')\n",
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]
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"source": [
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|||
|
|
"# Let's chat for 4 lines\n",
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"for step in range(4):\n",
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" # encode the new user input, add the eos_token and return a tensor in Pytorch\n",
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" new_user_input_ids = tokenizer.encode(input(\">> User:\") + tokenizer.eos_token, return_tensors='pt')\n",
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" # print(new_user_input_ids)\n",
|
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"\n",
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|
" # append the new user input tokens to the chat history\n",
|
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" bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n",
|
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|
"\n",
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" chat_history_ids = model.generate(\n",
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" bot_input_ids, max_length= 200,\n",
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" )\n",
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" \n",
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" # pretty print last ouput tokens from bot\n",
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