notebooks/8_optimizing_fine_tuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# maybe don't show this code? Not necessary for video"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c1_6ZAq1bunH"
   },
   "source": [
    "# Investigating FIne-Tuning Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 8374,
     "status": "ok",
     "timestamp": 1677023335867,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
     },
     "user_tz": 300
    },
    "id": "bQTtFOn7RURn",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "from transformers import (\n",
    "    Trainer,\n",
    "    TrainingArguments,\n",
    "    DataCollatorWithPadding,\n",
    "    AutoModelForSequenceClassification,\n",
    "    AutoTokenizer,\n",
    ")\n",
    "import torch.nn.functional as F\n",
    "import torch\n",
    "from pynvml import (\n",
    "    nvmlInit,\n",
    "    nvmlDeviceGetHandleByIndex,\n",
    "    nvmlDeviceGetMemoryInfo,\n",
    "    nvmlDeviceGetCount,\n",
    "    nvmlDeviceGetName,\n",
    ")\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6YNdCUnhb0n4"
   },
   "source": [
    "# GPU Benchmarking Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1677023335868,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
     },
     "user_tz": 300
    },
    "id": "FtXtrGUKRmhV"
   },
   "outputs": [],
   "source": [
    "def print_gpu_utilization():\n",
    "    if torch.cuda.is_available():\n",
    "        nvmlInit()\n",
    "        deviceCount = nvmlDeviceGetCount()\n",
    "        for i in range(deviceCount):\n",
    "            handle = nvmlDeviceGetHandleByIndex(i)\n",
    "            info = nvmlDeviceGetMemoryInfo(handle)\n",
    "            print(\"Device\", i, \":\", nvmlDeviceGetName(handle))\n",
    "            print(f\"GPU memory occupied: {info.used//1024**2} MB.\")\n",
    "        torch.cuda.empty_cache()\n",
    "\n",
    "\n",
    "def print_summary(result):\n",
    "    if torch.cuda.is_available():\n",
    "        print(f\"Time: {result.metrics['train_runtime']:.2f}\")\n",
    "        print(f\"Samples/second: {result.metrics['train_samples_per_second']:.2f}\")\n",
    "        print_gpu_utilization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = []\n",
    "results = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4587,
     "status": "ok",
     "timestamp": 1677023340956,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
     },
     "user_tz": 300
    },
    "id": "X1Q5_qc-Rfc7",
    "outputId": "215b1372-02e9-4ae8-896a-b778ca561229"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device cuda:1\n",
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 54 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 726 MB.\n",
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 155 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 726 MB.\n"
     ]
    }
   ],
   "source": [
    "## set device\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "DEVICE = 'cuda:1'\n",
    "print(\"Using device\", DEVICE)\n",
    "\n",
    "## measure GPU utilization\n",
    "if torch.cuda.is_available():\n",
    "    print_gpu_utilization()\n",
    "    torch.ones((1, 1)).to(DEVICE)\n",
    "    print_gpu_utilization()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xBvSy74Eb7JG"
   },
   "source": [
    "# Load Model and Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270,
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    },
    "executionInfo": {
     "elapsed": 17326,
     "status": "ok",
     "timestamp": 1677023358698,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
     },
     "user_tz": 300
    },
    "id": "GLe1amWMR0ul",
    "outputId": "ff3c514c-a609-4a37-a23d-363626d00a1a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 621 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 868 MB.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    \"bert-base-cased\", model_max_length=1024, cache_dir=\"./cache/\"\n",
    ")\n",
    "# tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"bert-base-cased\",\n",
    "    num_labels=2,\n",
    ").to(DEVICE)\n",
    "model.config.use_cache = False\n",
    "# model.config.pad_token_id = tokenizer.pad_token_id\n",
    "\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    print_gpu_utilization()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NWLhW1FJb-3o"
   },
   "source": [
    "# Data Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
     "elapsed": 2761,
     "status": "ok",
     "timestamp": 1677023850292,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
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     "user_tz": 300
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    "id": "TPJHs00cR6c4",
    "outputId": "cd4825e0-b97e-4053-eeeb-8a18da499fb5",
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      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/9.72k [00:00<?, ?B/s]"
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      "text/plain": [
       "Downloading readme:   0%|          | 0.00/21.9k [00:00<?, ?B/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset tweet_eval (C:/Users/sinan/.cache/huggingface/datasets/tweet_eval/offensive/1.1.0/12aee5282b8784f3e95459466db4cdf45c6bf49719c25cdb0743d71ed0410343)\n"
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at C:\\Users\\sinan\\.cache\\huggingface\\datasets\\tweet_eval\\offensive\\1.1.0\\12aee5282b8784f3e95459466db4cdf45c6bf49719c25cdb0743d71ed0410343\\cache-b9d172dac694b8d5.arrow\n",
      "Loading cached processed dataset at C:\\Users\\sinan\\.cache\\huggingface\\datasets\\tweet_eval\\offensive\\1.1.0\\12aee5282b8784f3e95459466db4cdf45c6bf49719c25cdb0743d71ed0410343\\cache-99f0c2dc4a43d411.arrow\n"
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    },
    {
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     "text": [
      "500\n",
      "@user Get him some line help. He is gonna be just fine. As the game went on you could see him progressing more with his reads. He brought what has been missing. The deep ball presence. Now he just needs a little more time\n",
      "0\n",
      "[101, 137, 4795, 3949, 1140, 1199, 1413, 1494, 119, 1124, 1110, 6100, 1129, 1198, 2503, 119, 1249, 1103, 1342, 1355, 1113, 1128, 1180, 1267, 1140, 5070, 1158, 1167, 1114, 1117, 9568, 119, 1124, 1814, 1184, 1144, 1151, 3764, 119, 1109, 1996, 3240, 2915, 119, 1986, 1119, 1198, 2993, 170, 1376, 1167, 1159, 102]\n"
     ]
    }
   ],
   "source": [
    "# split = [\"train[:1%]\", \"test[:1%]\"]\n",
    "split = [\"train[:500]\", \"test[:200]\"]\n",
    "raw_train, raw_test = load_dataset(\n",
    "    \"tweet_eval\", \"offensive\",\n",
    "    split=split\n",
    ")\n",
    "\n",
    "print(len(raw_train))\n",
    "print(raw_train[2][\"text\"])\n",
    "print(raw_train[2][\"label\"])\n",
    "print(tokenizer.encode(raw_train[2][\"text\"]))\n",
    "\n",
    "train = raw_train.map(\n",
    "    lambda x: tokenizer(x[\"text\"], truncation=True, padding=\"max_length\"),\n",
    "    batched=True,\n",
    ")\n",
    "test = raw_test.map(\n",
    "    lambda x: tokenizer(x[\"text\"], truncation=True, padding=\"max_length\"),\n",
    "    batched=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6UQTm-ljcDC0"
   },
   "source": [
    "# Vanilla Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
      "C:\\Users\\sinan\\anaconda3\\envs\\DL\\lib\\site-packages\\torch\\nn\\parallel\\_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.654013991355896,\n",
       " 'eval_runtime': 6.798,\n",
       " 'eval_samples_per_second': 29.421,\n",
       " 'eval_steps_per_second': 1.912}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_args = {\n",
    "    \"output_dir\": \"testing_hyper_params\",\n",
    "    \"num_train_epochs\": 1,\n",
    "    \"per_device_train_batch_size\": 1,\n",
    "    \"evaluation_strategy\": \"epoch\",\n",
    "     'report_to': \"none\"\n",
    "}\n",
    "\n",
    "training_args = TrainingArguments(**default_args)\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    train_dataset=train,\n",
    "    eval_dataset=test,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    ")\n",
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "executionInfo": {
     "elapsed": 22024,
     "status": "ok",
     "timestamp": 1677023956749,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
     },
     "user_tz": 300
    },
    "id": "7iPO3LxrR8D6",
    "outputId": "755ced31-af91-4375-ccd2-ec99775c9c46",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sinan\\anaconda3\\envs\\DL\\lib\\site-packages\\transformers\\optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\sinan\\anaconda3\\envs\\DL\\lib\\site-packages\\torch\\cuda\\nccl.py:15: UserWarning: PyTorch is not compiled with NCCL support\n",
      "  warnings.warn('PyTorch is not compiled with NCCL support')\n"
     ]
    },
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       "    <div>\n",
       "      \n",
       "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [250/250 01:21, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.527233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 81.83\n",
      "Samples/second: 6.11\n",
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 1744 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 4191 MB.\n"
     ]
    }
   ],
   "source": [
    "result = trainer.train()\n",
    "labels.append('Vanilla')\n",
    "results.append(result)\n",
    "print_summary(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compiling the model using torch.compile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# compiled_model = torch.compile(model)\n",
    "\n",
    "# trainer = Trainer(\n",
    "#     model=compiled_model,\n",
    "#     train_dataset=train,\n",
    "#     eval_dataset=test,\n",
    "#     tokenizer=tokenizer,\n",
    "#     args=training_args,\n",
    "# )\n",
    "# trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result = trainer.train()\n",
    "# labels.append('Compiled')\n",
    "# results.append(result)\n",
    "# print_summary(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adjust Batch size to 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"bert-base-cased\",\n",
    "    num_labels=2,\n",
    ").to(DEVICE)\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'output_dir': 'testing_hyper_params', 'num_train_epochs': 1, 'per_device_train_batch_size': 4, 'evaluation_strategy': 'epoch', 'report_to': 'none'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='63' max='63' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [63/63 00:37, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.597105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 38.15\n",
      "Samples/second: 13.11\n",
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 3818 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 5751 MB.\n"
     ]
    }
   ],
   "source": [
    "default_args[\"per_device_train_batch_size\"] = 4\n",
    "print(default_args)\n",
    "training_args = TrainingArguments(**default_args)\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    train_dataset=train,\n",
    "    eval_dataset=test,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    ")\n",
    "result = trainer.train()\n",
    "labels.append('Batch Size = 4')\n",
    "results.append(result)\n",
    "print_summary(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "04tT4uQQcFVW"
   },
   "source": [
    "# Gradient Accumulation (Adjust Effective Batch size to 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"bert-base-cased\",\n",
    "    num_labels=2,\n",
    ").to(DEVICE)\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "executionInfo": {
     "elapsed": 18774,
     "status": "ok",
     "timestamp": 1677024114368,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
     },
     "user_tz": 300
    },
    "id": "vFdBWYghTK0c",
    "outputId": "0bd5927f-d101-459d-b35a-c7a77fc0c847"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'output_dir': 'testing_hyper_params', 'num_train_epochs': 1, 'per_device_train_batch_size': 1, 'evaluation_strategy': 'epoch', 'report_to': 'none', 'gradient_accumulation_steps': 4}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='62' max='62' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [62/62 01:13, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.587288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 74.75\n",
      "Samples/second: 6.69\n",
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 1940 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 4237 MB.\n"
     ]
    }
   ],
   "source": [
    "default_args[\"per_device_train_batch_size\"] = 1\n",
    "default_args[\"gradient_accumulation_steps\"] = 4\n",
    "print(default_args)\n",
    "\n",
    "training_args = TrainingArguments(**default_args)\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    train_dataset=train,\n",
    "    eval_dataset=test,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    ")\n",
    "result = trainer.train()\n",
    "labels.append('Gradient Accumulation = 4')\n",
    "results.append(result)\n",
    "print_summary(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vh1oLCbLcJ0J"
   },
   "source": [
    "# Mixed Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"bert-base-cased\",\n",
    "    num_labels=2,\n",
    ").to(DEVICE)\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "executionInfo": {
     "elapsed": 14023,
     "status": "ok",
     "timestamp": 1677024286920,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
     },
     "user_tz": 300
    },
    "id": "IEPe5of4SkpP",
    "outputId": "a4ac564f-1e49-4bbd-e248-f6b5d2500d93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'output_dir': 'testing_hyper_params', 'num_train_epochs': 1, 'per_device_train_batch_size': 1, 'evaluation_strategy': 'epoch', 'report_to': 'none', 'gradient_accumulation_steps': 1, 'fp16': True}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [250/250 01:26, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.560917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 86.55\n",
      "Samples/second: 5.78\n",
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 1946 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 4311 MB.\n"
     ]
    }
   ],
   "source": [
    "default_args[\"fp16\"] = True\n",
    "# default_args[\"gradient_checkpointing\"] = False\n",
    "default_args[\"gradient_accumulation_steps\"] = 1\n",
    "default_args[\"per_device_train_batch_size\"] = 1\n",
    "\n",
    "print(default_args)\n",
    "training_args = TrainingArguments(**default_args)\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    train_dataset=train,\n",
    "    eval_dataset=test,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    ")\n",
    "result = trainer.train()\n",
    "labels.append('Mixed Precision')\n",
    "results.append(result)\n",
    "print_summary(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Mne6BdscMUl"
   },
   "source": [
    "# Dynamic Padding & Uniform Length Batching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"bert-base-cased\",\n",
    "    num_labels=2,\n",
    ").to(DEVICE)\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513,
     "referenced_widgets": [
      "e177eb433eee4ce59482ebb45e66d361",
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      "8fbb76a3046a4f4d9c39e5f561ea31e6",
      "9c4982d7bd0d4b72905f7922f6838271",
      "5e7f2aed30b0401ebea2fd9e474faf02",
      "14d71a16b4e24e33ab85005fbc2f5a98",
      "705c5d860d0b4b31ae46d712659f3e77",
      "2a93e119276d4e41947b1319e3373d05",
      "0653ebf5c936440baff195535a10a9a5",
      "d30d6cd76b5c4ab593f8279a5d9fced7",
      "15738fc98d044774a280057b20eb2dcc",
      "941ee091928e4ab59fdca86a40197798",
      "4b1fd7b7569447028b6c545bc1453464",
      "c3ae57a4e6d34c1a958221f8f38bc8ae",
      "8d5c4097776e4c739a0995e0e77d3fd9",
      "70d71a043a0b4abfbae1b1e308f0d987",
      "3d6ccb091e98433d90f4b774e48aff2e",
      "017355eb6f624cf5acdc4c03973e83ff",
      "ac674a30c7f94c9d9f159c529835465f",
      "5392d46aec554b26a99117b38a023373",
      "516ceaf2a3c242ccbfae24205a97baed",
      "ccf75750a25440bc951ba81635382e79"
     ]
    },
    "executionInfo": {
     "elapsed": 7936,
     "status": "ok",
     "timestamp": 1677024512019,
     "user": {
      "displayName": "Shaan Khosla",
      "userId": "03281704052357658651"
     },
     "user_tz": 300
    },
    "id": "76QPyrD2TZm7",
    "outputId": "94e0af4f-3912-49d1-afc7-c2f6ab9ab23c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/500 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/200 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'output_dir': 'testing_hyper_params', 'num_train_epochs': 1, 'per_device_train_batch_size': 1, 'evaluation_strategy': 'epoch', 'report_to': 'none', 'gradient_accumulation_steps': 1, 'fp16': False}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [250/250 01:02, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.522907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 62.47\n",
      "Samples/second: 8.00\n",
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 1258 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 4066 MB.\n"
     ]
    }
   ],
   "source": [
    "collate_fn = DataCollatorWithPadding(tokenizer=tokenizer, padding=\"longest\")\n",
    "\n",
    "train = raw_train.map(lambda x: tokenizer(x[\"text\"], truncation=True), batched=True)\n",
    "test = raw_test.map(lambda x: tokenizer(x[\"text\"], truncation=True), batched=True)\n",
    "default_args[\"fp16\"] = False\n",
    "\n",
    "print(default_args)\n",
    "training_args = TrainingArguments(**default_args)\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    train_dataset=train,\n",
    "    eval_dataset=test,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    "    data_collator=collate_fn,\n",
    ")\n",
    "result = trainer.train()\n",
    "labels.append('Dynamic Padding')\n",
    "results.append(result)\n",
    "print_summary(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choosing params for my GPUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_args[\"fp16\"] = True\n",
    "default_args[\"gradient_checkpointing\"] = False\n",
    "default_args[\"per_device_train_batch_size\"] = 4\n",
    "default_args[\"gradient_accumulation_steps\"] = 4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'output_dir': 'testing_hyper_params', 'num_train_epochs': 1, 'per_device_train_batch_size': 4, 'evaluation_strategy': 'epoch', 'report_to': 'none', 'gradient_accumulation_steps': 4, 'fp16': True, 'gradient_checkpointing': False}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='15' max='15' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [15/15 00:16, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.608759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 17.76\n",
      "Samples/second: 28.15\n",
      "Device 0 : NVIDIA GeForce GTX TITAN X\n",
      "GPU memory occupied: 1598 MB.\n",
      "Device 1 : NVIDIA TITAN X (Pascal)\n",
      "GPU memory occupied: 4202 MB.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"bert-base-cased\",\n",
    "    num_labels=2,\n",
    ").to(DEVICE)\n",
    "model.config.use_cache = False\n",
    "\n",
    "print(default_args)\n",
    "training_args = TrainingArguments(**default_args)\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    train_dataset=train,\n",
    "    eval_dataset=test,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    "    data_collator=collate_fn,\n",
    ")\n",
    "result = trainer.train()\n",
    "labels.append('BS=4 + Grad. Accum = 4 + MP + DP')\n",
    "results.append(result)\n",
    "print_summary(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 6)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels), len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=15, training_loss=0.6465375264485677, metrics={'train_runtime': 17.76, 'train_samples_per_second': 28.153, 'train_steps_per_second': 0.845, 'total_flos': 18561662733600.0, 'train_loss': 0.6465375264485677, 'epoch': 0.95})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# this will transform your results into a list of lists (which is easier to plot)\n",
    "\n",
    "values1 = [r.metrics['train_runtime'] for r in results]\n",
    "# values2 = [r.metrics['train_samples_per_second'] for r in results]\n",
    "values2 = [1744+4271, 3818+5787, 1940+4275, 1946+4326, 1258+4117, 1632+ 4159]  # memory\n",
    "values2 = [(v / (24*1024) * 100) for v in values2]\n",
    "\n",
    "barWidth = 0.3\n",
    "\n",
    "# here we generate positions for each group of bars\n",
    "r1 = np.arange(len(values1))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "\n",
    "# create figure and axis objects\n",
    "fig, ax1 = plt.subplots(figsize=(14, 10))  # Increase the figure size here\n",
    "\n",
    "color = 'darkblue'\n",
    "ax1.set_xlabel('Parameter Combination', fontweight='bold', fontsize=16)\n",
    "ax1.set_ylabel('Total Seconds to run', color=color, fontweight='bold', fontsize=16)\n",
    "ax1.bar(r1, values1, color=color, width=barWidth, edgecolor='grey', label='Key1', hatch='\\\\')  # Adding hatch pattern\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "ax1.set_xticks([r + barWidth/2 for r in range(len(values1))])\n",
    "ax1.set_xticklabels(labels, rotation=30, ha='right', fontsize=16)  # Rotate labels less and align right\n",
    "\n",
    "# instantiate a second axis that shares the same x-axis\n",
    "ax2 = ax1.twinx()\n",
    "color = 'red'\n",
    "ax2.set_ylabel('GPU Memory % Used', color=color, fontweight='bold', fontsize=14)  # we already handled the x-label with ax1\n",
    "ax2.bar(r2, values2, color=color, width=barWidth, edgecolor='grey', label='Key2', hatch='//')  # Adding hatch pattern\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "ax2.grid(False) \n",
    "ax1.grid(False) \n",
    "\n",
    "plt.title('Comparing Open-source Training Hyperparameters', fontsize=20)\n",
    "fig.tight_layout(pad=2.0)  # Increase padding\n",
    "\n",
    "# Save the plot as a PNG\n",
    "fig.savefig('Training_Comparison.png', dpi=1000, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
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   "execution_count": null,
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  },
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   "execution_count": null,
   "metadata": {},
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   "source": []
  }
 ],
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  "accelerator": "GPU",
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  "gpuClass": "standard",
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   "language": "python",
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