model_train_upload_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VTze-VbeU1c0"
   },
   "source": [
    "# Fine-tune a DialoGPT model\n",
    "\n",
    "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)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y17kuzFNUSrZ"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GBfltjGHT6KG",
    "outputId": "7822e15b-9c77-412a-a6ed-20100243db13"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T8fgmjaqUErq"
   },
   "outputs": [],
   "source": [
    "!pip -q install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EtCreyG8UG1s"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"/content/drive/My Drive/Colab Notebooks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dnv5kT-mLsB-"
   },
   "outputs": [],
   "source": [
    "# all the imports\n",
    "\n",
    "import glob\n",
    "import logging\n",
    "import os\n",
    "import pickle\n",
    "import random\n",
    "import re\n",
    "import shutil\n",
    "from typing import Dict, List, Tuple\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\n",
    "from torch.utils.data.distributed import DistributedSampler\n",
    "from tqdm.notebook import tqdm, trange\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "from transformers import (\n",
    "    MODEL_WITH_LM_HEAD_MAPPING,\n",
    "    WEIGHTS_NAME,\n",
    "    AdamW,\n",
    "    AutoConfig,\n",
    "    PreTrainedModel,\n",
    "    PreTrainedTokenizer,\n",
    "    get_linear_schedule_with_warmup,\n",
    ")\n",
    "\n",
    "\n",
    "try:\n",
    "    from torch.utils.tensorboard import SummaryWriter\n",
    "except ImportError:\n",
    "    from tensorboardX import SummaryWriter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BmrbGB8aUmBm"
   },
   "source": [
    "## Get Data from Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ftBYBoOoV_Er",
    "outputId": "07da0a13-6112-4c4e-cb49-51580c2d9e7a"
   },
   "outputs": [],
   "source": [
    "!mkdir ~/.kaggle\n",
    "!cp kaggle.json ~/.kaggle/kaggle.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fbITTMcLVbI_",
    "outputId": "fb4c8bf1-ff2d-4952-a451-62cdd0655aea"
   },
   "outputs": [],
   "source": [
    "!kaggle datasets download ruolinzheng/twewy-game-script -f twewy-name-line-full.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RXdJTSVwWGHj"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('twewy-name-line-full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "id": "h6kGx-9eG7qA",
    "outputId": "bd2efe43-1e50-4716-81a2-bf15a3dd03bd"
   },
   "outputs": [],
   "source": [
    "data.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PG8v6--qWUwj"
   },
   "outputs": [],
   "source": [
    "CHARACTER_NAME = 'Joshua'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GZUcEMd2WLDT"
   },
   "outputs": [],
   "source": [
    "contexted = []\n",
    "\n",
    "# context window of size 7\n",
    "n = 7\n",
    "\n",
    "for i in data[data.name == CHARACTER_NAME].index:\n",
    "  if i < n:\n",
    "    continue\n",
    "  row = []\n",
    "  prev = i - 1 - n # we additionally substract 1, so row will contain current responce and 7 previous responces  \n",
    "  for j in range(i, prev, -1):\n",
    "    row.append(data.line[j])\n",
    "  contexted.append(row)\n",
    "\n",
    "columns = ['response', 'context'] \n",
    "columns = columns + ['context/' + str(i) for i in range(n - 1)]\n",
    "\n",
    "df = pd.DataFrame.from_records(contexted, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "id": "4T5OlNZHUxij",
    "outputId": "895603a6-ca02-4301-c4b0-5bccbee8a3b8"
   },
   "outputs": [],
   "source": [
    "df.sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "NGy0MxMQVIAP",
    "outputId": "08b7f0eb-6a38-4b83-efdc-e53778d7547a"
   },
   "outputs": [],
   "source": [
    "trn_df, val_df = train_test_split(df, test_size=0.1)\n",
    "trn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aEeJQlAKWtiJ"
   },
   "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": null,
   "metadata": {
    "id": "-3iHwoKlWyrs"
   },
   "outputs": [],
   "source": [
    "# Cacheing 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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r2cE0fY5UHpz",
    "outputId": "e4f382cd-57d9-49b7-9da4-4b44fe57df5b"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer\n",
    "import torch\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-small\")\n",
    "model = AutoModelWithLMHead.from_pretrained(\"microsoft/DialoGPT-small\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ra2vsRp-UMXo"
   },
   "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": null,
   "metadata": {
    "id": "2OnASqJjUNJa"
   },
   "outputs": [],
   "source": [
    "# Args to allow for easy convertion of python script to notebook\n",
    "class Args():\n",
    "    def __init__(self):\n",
    "        self.output_dir = 'output-small'\n",
    "        self.model_type = 'gpt2'\n",
    "        self.model_name_or_path = 'microsoft/DialoGPT-small'\n",
    "        self.config_name = 'microsoft/DialoGPT-small'\n",
    "        self.tokenizer_name = 'microsoft/DialoGPT-small'\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": null,
   "metadata": {
    "id": "PaarIDZrW81h"
   },
   "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": null,
   "metadata": {
    "id": "SCnGAJWbXD9C"
   },
   "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 = AutoModelWithLMHead.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 = AutoModelWithLMHead.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 = AutoModelWithLMHead.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": [
    "## Run the Main Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 780,
     "referenced_widgets": [
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      "cb7f52610fcf49bda46a14b296ff5bb5",
      "0ca29b4a62e04d9c937189ea19b25de8",
      "f871b83632974e0088bae65e78efaf28",
      "4cacf7fc20754a7ca7fe08c8ec187a81",
      "8bcc625c0f284398bbd287fe45021b17"
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    },
    "id": "e61zo2JtXGNX",
    "outputId": "22d4916e-7169-44b5-f9d8-79b9c43fab2e"
   },
   "outputs": [],
   "source": [
    "main(trn_df, val_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YRpQ_n2zXQj-"
   },
   "source": [
    "## Load the Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HGw3qgfaXQHX",
    "outputId": "93e84cfd-9718-42e5-bd11-418112c91d71"
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-small')\n",
    "model = AutoModelWithLMHead.from_pretrained('output-small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lAWsiAvNXbxd",
    "outputId": "0fd2541e-ee68-4976-b098-8483efe38d5e"
   },
   "outputs": [],
   "source": [
    "# Let's chat for 4 lines\n",
    "for step in range(4):\n",
    "    # encode the new user input, add the eos_token and return a tensor in Pytorch\n",
    "    new_user_input_ids = tokenizer.encode(input(\">> User:\") + tokenizer.eos_token, return_tensors='pt')\n",
    "    # print(new_user_input_ids)\n",
    "\n",
    "    # append the new user input tokens to the chat history\n",
    "    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n",
    "\n",
    "    # generated a response while limiting the total chat history to 1000 tokens, \n",
    "    chat_history_ids = model.generate(\n",
    "        bot_input_ids, max_length=200,\n",
    "        pad_token_id=tokenizer.eos_token_id,  \n",
    "        no_repeat_ngram_size=3,       \n",
    "        do_sample=True, \n",
    "        top_k=100, \n",
    "        top_p=0.7,\n",
    "        temperature=0.8\n",
    "    )\n",
    "    \n",
    "    # pretty print last ouput tokens from bot\n",
    "    print(\"JoshuaBot: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ANSQlQezXqwn"
   },
   "source": [
    "## Push Model to Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VgnHRgHKXwDd"
   },
   "outputs": [],
   "source": [
    "!sudo apt-get install git-lfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uhqMtvfmXei8"
   },
   "outputs": [],
   "source": [
    "!git config --global user.email \"lynnzheng08@outlook.com\"\n",
    "# Tip: using the same email as your huggingface.co account will link your commits to your profile\n",
    "!git config --global user.name \"Lynn Zheng\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tfUsrKR7YLT1"
   },
   "outputs": [],
   "source": [
    "MY_MODEL_NAME = 'DialoGPT-small-joshua'\n",
    "with open('HuggingFace-API-key.txt', 'rt') as f:\n",
    "  HUGGINGFACE_API_KEY = f.read().strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "_65nsiLcYNXI",
    "outputId": "0dbf0cb1-957c-4adb-bf55-4222d2cc85bc"
   },
   "outputs": [],
   "source": [
    "model.push_to_hub(MY_MODEL_NAME, use_auth_token=HUGGINGFACE_API_KEY)\n",
    "tokenizer.push_to_hub(MY_MODEL_NAME, use_auth_token=HUGGINGFACE_API_KEY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D_XfXTCrZKmO"
   },
   "source": [
    "## All Done!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_tIwK7G8ZLrd"
   },
   "outputs": [],
   "source": []
  }
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