prompt_tune_phi3.ipnb

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     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mCollecting flash-attn\n",
      "  Downloading flash_attn-2.5.9.post1.tar.gz (2.6 MB)\n",
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      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from flash-attn) (2.2.0)\n",
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      "\u001b[?25hBuilding wheels for collected packages: flash-attn\n",
      "  Building wheel for flash-attn (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for flash-attn: filename=flash_attn-2.5.9.post1-cp310-cp310-linux_x86_64.whl size=120821333 sha256=7bfd5ecaaf20577cd1255eaa90d9008a09050b3408ba6388bcbc5b6144f482d0\n",
      "  Stored in directory: /root/.cache/pip/wheels/cc/ad/f6/7ccf0238790d6346e9fe622923a76ec218e890d356b9a2754a\n",
      "Successfully built flash-attn\n",
      "Installing collected packages: einops, flash-attn\n",
      "Successfully installed einops-0.8.0 flash-attn-2.5.9.post1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
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   ],
   "source": [
    "!pip install -q peft transformers datasets huggingface_hub\n",
    "!pip install flash-attn --no-build-isolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1cc378f-afb6-441f-a4c6-2ec427b4cd4b",
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   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup\n",
    "from peft import get_peft_config, get_peft_model, PromptTuningInit, PromptTuningConfig, TaskType, PeftType\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "import os\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import tqdm\n",
    "from huggingface_hub import notebook_login\n",
    "from huggingface_hub import HfApi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e4ab50d7-a4c9-4246-acd8-8875b87fe0da",
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       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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   ],
   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a1cb1f9-b89d-4cac-a595-44e1e0ef85b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "api = HfApi()\n",
    "api.upload_file(path_or_fileobj='Granther/prompt-tuned-phi3',\n",
    "                path_in_repo='"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6cad1e5c-038f-4e75-8c3f-8ce0a43713a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda'\n",
    "\n",
    "model_id = 'microsoft/Phi-3-mini-128k-instruct'\n",
    "\n",
    "peft_conf = PromptTuningConfig(\n",
    "    peft_type=PeftType.PROMPT_TUNING, # what kind of peft\n",
    "    task_type=TaskType.CAUSAL_LM,     # config task\n",
    "    prompt_tuning_init=PromptTuningInit.TEXT, # Set to 'TEXT' to use prompt_tuning_init_text\n",
    "    num_virtual_tokens=8, # x times the number of hidden transformer layers\n",
    "    prompt_tuning_init_text=\"Classify if the tweet is a complaint or not:\",\n",
    "    tokenizer_name_or_path=model_id\n",
    ")\n",
    "\n",
    "dataset_name = \"twitter_complaints\"\n",
    "checkpoint_name = f\"{dataset_name}_{model_id}_{peft_conf.peft_type}_{peft_conf.task_type}_v1.pt\".replace(\n",
    "    \"/\", \"_\"\n",
    ")\n",
    "\n",
    "text_col = 'Tweet text'\n",
    "lab_col = 'text_label'\n",
    "max_len = 64\n",
    "lr = 3e-2\n",
    "epochs = 50\n",
    "batch_size = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6f677839-ef23-428a-bcfe-f596590804ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = load_dataset('ought/raft', dataset_name, split='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c0c05613-7941-4959-ada9-49ed1093bec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unlabeled', 'complaint', 'no complaint']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.features['Label'].names\n",
    "#>>> ['Unlabeled', 'complaint', 'no complaint']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14e2bc8b-b4e3-49c9-ae2b-5946e412caa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "d9e958c687dd493880d18d4f1621dad9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map (num_proc=10):   0%|          | 0/50 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Unlabeled'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create lambda function\n",
    "classes = [k.replace('_', ' ') for k in dataset.features['Label'].names]\n",
    "dataset = dataset.map(\n",
    "    lambda x: {'text_label': [classes[label] for label in x['Label']]},\n",
    "    batched=True,\n",
    "    num_proc=10,\n",
    ")\n",
    "\n",
    "dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19f0865d-e490-4c9f-a5f4-e781ed270f47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1, 853, 29880, 24025, 32000]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "\n",
    "if tokenizer.pad_token_id == None:\n",
    "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "\n",
    "target_max_len = max([len(tokenizer(class_lab)['input_ids']) for class_lab in classes])\n",
    "target_max_len # max length for tokenized labels\n",
    "\n",
    "tokenizer(classes[0])['input_ids'] \n",
    "# Ids corresponding to the tokens in the sequence\n",
    "# Attention mask is a binary tensor used in the transformer block to differentiate between padding tokens and meaningful ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "459d4f69-1d85-42e8-acac-b2c7983c3a33",
   "metadata": {},
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   "source": []
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