{
 "cells": [
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   "cell_type": "code",
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   "id": "2edec24d8563b583",
   "metadata": {
    "collapsed": false,
    "execution": {
     "shell.execute_reply.end": "2023-12-22T03:34:15.998083Z",
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     "to_execute": "2023-12-22T03:34:15.875Z"
    },
    "libroFormatter": "formatter-string"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=0\n",
      "env: TOKENIZERS_PARALLELISM=false\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_VISIBLE_DEVICES=0\n",
    "%env TOKENIZERS_PARALLELISM=false"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b4cfd741795038",
   "metadata": {
    "id": "95b4cfd741795038",
    "libroFormatter": "formatter-string"
   },
   "source": [
    "## Initialize PolyModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a5c7a99-5208-4d22-ac15-bacebe1b52f9",
   "metadata": {
    "execution": {
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    },
    "id": "1a5c7a99-5208-4d22-ac15-bacebe1b52f9",
    "libroFormatter": "formatter-string"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please run\n",
      "\n",
      "python -m bitsandbytes\n",
      "\n",
      " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n",
      "bin /opt/conda/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cuda121.so\n",
      "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
      "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
      "CUDA SETUP: Detected CUDA version 121\n",
      "CUDA SETUP: Loading binary /opt/conda/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cuda121.so...\n",
      "[2023-12-22 11:34:24,536] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import (\n",
    "    AutoModelForSeq2SeqLM,\n",
    "    AutoTokenizer,\n",
    "    default_data_collator,\n",
    "    Seq2SeqTrainingArguments,\n",
    "    Seq2SeqTrainer,\n",
    ")\n",
    "from datasets import load_dataset, concatenate_datasets\n",
    "from peft import PolyConfig, get_peft_model, TaskType, PeftModel, PeftConfig\n",
    "\n",
    "model_name_or_path = \"google/flan-t5-xl\"\n",
    "\n",
    "r = 8  # rank of lora in poly\n",
    "n_tasks = 4  # number of tasks\n",
    "n_skills = 2  # number of skills (loras)\n",
    "n_splits = 4  # number of heads\n",
    "\n",
    "batch_size = 8\n",
    "lr = 5e-5\n",
    "num_epochs = 8"
   ]
  },
  {
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    "execution": {
     "shell.execute_reply.end": "2023-12-22T03:35:33.229420Z",
     "shell.execute_reply.started": "2023-12-22T03:34:37.266443Z",
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    "libroFormatter": "formatter-string",
    "outputId": "fc90c2cc-9cab-40ed-bf4a-d76bec85b72f"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards: 100%|██████████| 2/2 [00:39<00:00, 19.75s/it]\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)\n",
    "base_model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path, trust_remote_code=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29d701a4-7a4f-4eae-84bd-9e3a02b7ffca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "shell.execute_reply.end": "2023-12-22T03:35:33.396336Z",
     "shell.execute_reply.started": "2023-12-22T03:35:33.250286Z",
     "to_execute": "2023-12-22T03:35:33.272Z"
    },
    "id": "29d701a4-7a4f-4eae-84bd-9e3a02b7ffca",
    "libroFormatter": "formatter-string",
    "outputId": "63898f68-926e-40c4-ca13-ffd1df32fcce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 9,441,792 || all params: 2,859,198,976 || trainable%: 0.33022507629773296\n"
     ]
    }
   ],
   "source": [
    "peft_config = PolyConfig(\n",
    "    task_type=TaskType.SEQ_2_SEQ_LM,\n",
    "    poly_type=\"poly\",\n",
    "    r=r,\n",
    "    n_tasks=n_tasks,\n",
    "    n_skills=n_skills,\n",
    "    n_splits=n_splits,\n",
    ")\n",
    "\n",
    "model = get_peft_model(base_model, peft_config)\n",
    "model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa695c2d-cf9c-432c-ab74-7e89f816ba13",
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   },
   "source": [
    "## Prepare datasets\n",
    "\n",
    "For this example, we selected four `SuperGLUE` benchmark datasets: `boolq`, `multirc`, `rte`, and `wic`, each with a training set of 1,000 examples and an evaluation set of 100 examples."
   ]
  },
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    "libroFormatter": "formatter-string",
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     "name": "stdout",
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     "text": [
      "boolq example: \n",
      "{'input': 'Persian language -- Persian (/ˈpɜːrʒən, -ʃən/), also known by its endonym Farsi (فارسی fārsi (fɒːɾˈsiː) ( listen)), is one of the Western Iranian languages within the Indo-Iranian branch of the Indo-European language family. It is primarily spoken in Iran, Afghanistan (officially known as Dari since 1958), and Tajikistan (officially known as Tajiki since the Soviet era), and some other regions which historically were Persianate societies and considered part of Greater Iran. It is written in the Persian alphabet, a modified variant of the Arabic script, which itself evolved from the Aramaic alphabet.\\nQuestion: do iran and afghanistan speak the same language\\nA. Yes\\nB. No\\nAnswer:', 'output': 'A', 'task_name': 'boolq'}\n",
      "multirc example: \n",
      "{'input': 'While this process moved along, diplomacy continued its rounds. Direct pressure on the Taliban had proved unsuccessful. As one NSC staff note put it, \"Under the Taliban, Afghanistan is not so much a state sponsor of terrorism as it is a state sponsored by terrorists.\" In early 2000, the United States began a high-level effort to persuade Pakistan to use its influence over the Taliban. In January 2000, Assistant Secretary of State Karl Inderfurth and the State Department\\'s counterterrorism coordinator, Michael Sheehan, met with General Musharraf in Islamabad, dangling before him the possibility of a presidential visit in March as a reward for Pakistani cooperation. Such a visit was coveted by Musharraf, partly as a sign of his government\\'s legitimacy. He told the two envoys that he would meet with Mullah Omar and press him on  Bin Laden. They left, however, reporting to Washington that Pakistan was unlikely in fact to do anything,\" given what it sees as the benefits of Taliban control of Afghanistan.\" President Clinton was scheduled to travel to India. The State Department felt that he should not visit India without also visiting Pakistan. The Secret Service and the CIA, however, warned in the strongest terms that visiting Pakistan would risk the President\\'s life. Counterterrorism officials also argued that Pakistan had not done enough to merit a presidential visit. But President Clinton insisted on including Pakistan in the itinerary for his trip to South Asia. His one-day stopover on March 25, 2000, was the first time a U.S. president had been there since 1969. At his meeting with Musharraf and others, President Clinton concentrated on tensions between Pakistan and India and the dangers of nuclear proliferation, but also discussed  Bin Laden. President Clinton told us that when he pulled Musharraf aside for a brief, one-on-one meeting, he pleaded with the general for help regarding  Bin Laden.\" I offered him the moon when I went to see him, in terms of better relations with the United States, if he\\'d help us get  Bin Laden and deal with another issue or two.\" The U.S. effort continued. \\nQuestion: What did the high-level effort to persuade Pakistan include?\\nAnswer: Children, Gerd, or Dorian Popa\\nIs it true?\\nA. Yes\\nB. No\\nAnswer:', 'output': 'B', 'task_name': 'multirc'}\n",
      "rte example: \n",
      "{'input': 'No Weapons of Mass Destruction Found in Iraq Yet.\\nWeapons of Mass Destruction Found in Iraq.\\nIs the sentence below entailed by the sentence above?\\nA. Yes\\nB. No\\nAnswer:', 'output': 'B', 'task_name': 'rte'}\n",
      "wic example: \n",
      "{'input': \"Sentence 1: Do you want to come over to my place later?\\nSentence 2: A political system with no place for the less prominent groups.\\nAre 'place' in the above two sentences the same?\\nA. Yes\\nB. No\\nAnswer:\", 'output': 'B', 'task_name': 'wic'}\n"
     ]
    }
   ],
   "source": [
    "# boolq\n",
    "boolq_dataset = (\n",
    "    load_dataset(\"super_glue\", \"boolq\")\n",
    "    .map(\n",
    "        lambda x: {\n",
    "            \"input\": f\"{x['passage']}\\nQuestion: {x['question']}\\nA. Yes\\nB. No\\nAnswer:\",\n",
    "            # 0 - False\n",
    "            # 1 - True\n",
    "            \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
    "            \"task_name\": \"boolq\",\n",
    "        }\n",
    "    )\n",
    "    .select_columns([\"input\", \"output\", \"task_name\"])\n",
    ")\n",
    "print(\"boolq example: \")\n",
    "print(boolq_dataset[\"train\"][0])\n",
    "\n",
    "# multirc\n",
    "multirc_dataset = (\n",
    "    load_dataset(\"super_glue\", \"multirc\")\n",
    "    .map(\n",
    "        lambda x: {\n",
    "            \"input\": (\n",
    "                f\"{x['paragraph']}\\nQuestion: {x['question']}\\nAnswer: {x['answer']}\\nIs it\"\n",
    "                \" true?\\nA. Yes\\nB. No\\nAnswer:\"\n",
    "            ),\n",
    "            # 0 - False\n",
    "            # 1 - True\n",
    "            \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
    "            \"task_name\": \"multirc\",\n",
    "        }\n",
    "    )\n",
    "    .select_columns([\"input\", \"output\", \"task_name\"])\n",
    ")\n",
    "print(\"multirc example: \")\n",
    "print(multirc_dataset[\"train\"][0])\n",
    "\n",
    "# rte\n",
    "rte_dataset = (\n",
    "    load_dataset(\"super_glue\", \"rte\")\n",
    "    .map(\n",
    "        lambda x: {\n",
    "            \"input\": (\n",
    "                f\"{x['premise']}\\n{x['hypothesis']}\\nIs the sentence below entailed by the\"\n",
    "                \" sentence above?\\nA. Yes\\nB. No\\nAnswer:\"\n",
    "            ),\n",
    "            # 0 - entailment\n",
    "            # 1 - not_entailment\n",
    "            \"output\": [\"A\", \"B\"][int(x[\"label\"])],\n",
    "            \"task_name\": \"rte\",\n",
    "        }\n",
    "    )\n",
    "    .select_columns([\"input\", \"output\", \"task_name\"])\n",
    ")\n",
    "print(\"rte example: \")\n",
    "print(rte_dataset[\"train\"][0])\n",
    "\n",
    "# wic\n",
    "wic_dataset = (\n",
    "    load_dataset(\"super_glue\", \"wic\")\n",
    "    .map(\n",
    "        lambda x: {\n",
    "            \"input\": (\n",
    "                f\"Sentence 1: {x['sentence1']}\\nSentence 2: {x['sentence2']}\\nAre '{x['word']}'\"\n",
    "                \" in the above two sentences the same?\\nA. Yes\\nB. No\\nAnswer:\"\n",
    "            ),\n",
    "            # 0 - False\n",
    "            # 1 - True\n",
    "            \"output\": [\"B\", \"A\"][int(x[\"label\"])],\n",
    "            \"task_name\": \"wic\",\n",
    "        }\n",
    "    )\n",
    "    .select_columns([\"input\", \"output\", \"task_name\"])\n",
    ")\n",
    "print(\"wic example: \")\n",
    "print(wic_dataset[\"train\"][0])"
   ]
  },
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   "execution_count": 6,
   "id": "9fca2225-aaee-47aa-957a-5f8ed3177cdb",
   "metadata": {
    "execution": {
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    "libroFormatter": "formatter-string"
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   "source": [
    "# define a task2id map\n",
    "TASK2ID = {\n",
    "    \"boolq\": 0,\n",
    "    \"multirc\": 1,\n",
    "    \"rte\": 2,\n",
    "    \"wic\": 3,\n",
    "}\n",
    "\n",
    "\n",
    "def tokenize(examples):\n",
    "    inputs, targets = examples[\"input\"], examples[\"output\"]\n",
    "    features = tokenizer(inputs, max_length=512, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
    "    labels = tokenizer(targets, max_length=2, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
    "    labels = labels[\"input_ids\"]\n",
    "    labels[labels == tokenizer.pad_token_id] = -100\n",
    "    features[\"labels\"] = labels\n",
    "    features[\"task_ids\"] = torch.tensor([[TASK2ID[t]] for t in examples[\"task_name\"]]).long()\n",
    "    return features"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "0bf6c31c-73cd-4eed-931b-0cad5d7290fb",
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    "execution": {
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    "libroFormatter": "formatter-string",
    "tags": []
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   "source": [
    "def get_superglue_dataset(\n",
    "    split=\"train\",\n",
    "    n_samples=500,\n",
    "):\n",
    "    ds = concatenate_datasets(\n",
    "        [\n",
    "            boolq_dataset[split].shuffle().select(range(n_samples)),\n",
    "            multirc_dataset[split].shuffle().select(range(n_samples)),\n",
    "            rte_dataset[split].shuffle().select(range(n_samples)),\n",
    "            wic_dataset[split].shuffle().select(range(n_samples)),\n",
    "        ]\n",
    "    )\n",
    "    ds = ds.map(\n",
    "        tokenize,\n",
    "        batched=True,\n",
    "        remove_columns=[\"input\", \"output\", \"task_name\"],\n",
    "        load_from_cache_file=False,\n",
    "    )\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oNvh2WGlLo4z",
   "metadata": {
    "id": "oNvh2WGlLo4z",
    "libroFormatter": "formatter-string"
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   "source": [
    "As a toy example, we only select 1,000 from each subdataset for training and 100 each for eval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1bf88dd1a6aaa6a5",
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    {
     "name": "stderr",
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     "text": [
      "Map: 100%|██████████| 4000/4000 [00:02<00:00, 1365.07 examples/s]\n",
      "Map: 100%|██████████| 400/400 [00:00<00:00, 548.46 examples/s]\n"
     ]
    }
   ],
   "source": [
    "superglue_train_dataset = get_superglue_dataset(split=\"train\", n_samples=1000)\n",
    "superglue_eval_dataset = get_superglue_dataset(split=\"test\", n_samples=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "550abf92-b8ea-424b-aba0-10d8da941297",
   "metadata": {
    "id": "550abf92-b8ea-424b-aba0-10d8da941297",
    "libroFormatter": "formatter-string"
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   "source": [
    "## Train and evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b48135d6-0d83-4e8a-b1f0-c292663c84ec",
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     "text": [
      "/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "    <div>\n",
       "      \n",
       "      <progress value='3784' max='4000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [3784/4000 2:11:42 < 07:31, 0.48 it/s, Epoch 7.57/8]\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",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.458600</td>\n",
       "      <td>0.968393</td>\n",
       "      <td>0.457500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.619800</td>\n",
       "      <td>0.874669</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.548800</td>\n",
       "      <td>0.837347</td>\n",
       "      <td>0.537500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.466800</td>\n",
       "      <td>0.784065</td>\n",
       "      <td>0.552500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.400800</td>\n",
       "      <td>0.768286</td>\n",
       "      <td>0.565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.377200</td>\n",
       "      <td>0.764708</td>\n",
       "      <td>0.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.356300</td>\n",
       "      <td>0.765993</td>\n",
       "      <td>0.562500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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   "source": [
    "# training and evaluation\n",
    "def compute_metrics(eval_preds):\n",
    "    preds, labels = eval_preds\n",
    "    preds = [[i for i in seq if i != -100] for seq in preds]\n",
    "    labels = [[i for i in seq if i != -100] for seq in labels]\n",
    "    preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
    "    labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for pred, true in zip(preds, labels):\n",
    "        if pred.strip() == true.strip():\n",
    "            correct += 1\n",
    "        total += 1\n",
    "    accuracy = correct / total\n",
    "    return {\"accuracy\": accuracy}\n",
    "\n",
    "\n",
    "training_args = Seq2SeqTrainingArguments(\n",
    "    \"output\",\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    learning_rate=lr,\n",
    "    num_train_epochs=num_epochs,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    logging_strategy=\"epoch\",\n",
    "    save_strategy=\"no\",\n",
    "    report_to=[],\n",
    "    predict_with_generate=True,\n",
    "    generation_max_length=2,\n",
    "    remove_unused_columns=False,\n",
    ")\n",
    "trainer = Seq2SeqTrainer(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    "    train_dataset=superglue_train_dataset,\n",
    "    eval_dataset=superglue_eval_dataset,\n",
    "    data_collator=default_data_collator,\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n",
    "trainer.train()"
   ]
  },
  {
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   "execution_count": 15,
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   "source": [
    "# saving model\n",
    "model_name_or_path = \"google/flan-t5-xl\"\n",
    "peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
    "model.save_pretrained(peft_model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e539e3d-0944-4f81-bcd6-dec16c66eeea",
   "metadata": {
    "execution": {
     "shell.execute_reply.end": "2023-12-22T06:59:05.542802Z",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 37M\n",
      "-rw-r--r-- 1 root root  374 12月 22 14:59 adapter_config.json\n",
      "-rw-r--r-- 1 root root  37M 12月 22 14:59 adapter_model.safetensors\n",
      "-rw-r--r-- 1 root root 5.0K 12月 22 14:58 README.md\n"
     ]
    }
   ],
   "source": [
    "!ls -lh $peft_model_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e37b18-d760-4af7-b1ab-40a6880eb92d",
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    "id": "52e37b18-d760-4af7-b1ab-40a6880eb92d",
    "libroFormatter": "formatter-string"
   },
   "source": [
    "## Load and infer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8eeb65376093b996",
   "metadata": {
    "collapsed": false,
    "execution": {
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   "outputs": [],
   "source": [
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70490c8e-d301-4e4e-a5a3-de87b53f1942",
   "metadata": {
    "execution": {
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    "libroFormatter": "formatter-string"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards: 100%|██████████| 2/2 [00:36<00:00, 18.50s/it]\n"
     ]
    }
   ],
   "source": [
    "peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
    "\n",
    "config = PeftConfig.from_pretrained(peft_model_id)\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
    "model = PeftModel.from_pretrained(model, peft_model_id)\n",
    "model = model.to(device)\n",
    "model = model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "80ae512b-545d-470d-874d-969dba42055b",
   "metadata": {
    "execution": {
     "shell.execute_reply.end": "2023-12-22T07:00:17.434446Z",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 1979, the leaders signed the Egypt-Israel peace treaty on the White House lawn. Both President Begin and Sadat received the Nobel Peace Prize for their work. The two nations have enjoyed peaceful relations to this day.\n",
      "The Israel-Egypt Peace Agreement was signed in 1979.\n",
      "Is the sentence below entailed by the sentence above?\n",
      "A. Yes\n",
      "B. No\n",
      "Answer:\n",
      "A\n",
      "{'input_ids': tensor([[   86, 15393,     6,     8,  2440,  3814,     8, 10438,    18, 30387,\n",
      "          3065,  2665,    63,    30,     8,  1945,  1384,  8652,     5,  2867,\n",
      "          1661, 10129,    77,    11, 18875,   144,  1204,     8, 22232, 11128,\n",
      "         11329,    21,    70,   161,     5,    37,   192,  9352,    43,  2994,\n",
      "          9257,  5836,    12,    48,   239,     5,    37,  3352,    18,   427,\n",
      "           122,    63,   102,    17, 11128,  7139,    47,  3814,    16, 15393,\n",
      "             5,    27,     7,     8,  7142,   666,     3,   295, 10990,    57,\n",
      "             8,  7142,   756,    58,    71,     5,  2163,   272,     5,   465,\n",
      "         11801,    10,     1]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], device='cuda:0'), 'task_ids': tensor([2], device='cuda:0')}\n",
      "tensor([ 0, 71,  1], device='cuda:0')\n",
      "A\n"
     ]
    }
   ],
   "source": [
    "i = 5\n",
    "inputs = tokenizer(rte_dataset[\"validation\"][\"input\"][i], return_tensors=\"pt\")\n",
    "inputs[\"task_ids\"] = torch.LongTensor([TASK2ID[\"rte\"]])\n",
    "inputs = {k: v.to(device) for k, v in inputs.items()}\n",
    "print(rte_dataset[\"validation\"][\"input\"][i])\n",
    "print(rte_dataset[\"validation\"][\"output\"][i])\n",
    "print(inputs)\n",
    "\n",
    "with torch.no_grad():\n",
    "    outputs = model.generate(**inputs, max_new_tokens=2)\n",
    "    print(outputs[0])\n",
    "    print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])"
   ]
  },
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