optuna_mlm_architecture_search.ipynb

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   "source": [
    "%pip install transformers datasets optuna\n",
    "%pip install accelerate -U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XpBB4G4LskET"
   },
   "source": [
    "This notebook requires a tokenizer trained on BabyLM 10M corpus\n",
    "Use the following code for training or load an already trained tokenizer : https://github.com/upunaprosk/BabyBERTa."
   ]
  },
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   "cell_type": "code",
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   "source": [
    "path_tokenizer_config = 'trained-tokenizer/custom_tokenizer.json'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optuna params type: https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.FixedTrial.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "from transformers import RobertaTokenizerFast,AdamW\n",
    "from datasets import load_dataset\n",
    "from pathlib import Path\n",
    "from transformers import DataCollatorForLanguageModeling,RobertaConfig,RobertaForMaskedLM,TrainingArguments,Trainer\n",
    "import optuna\n",
    "import logging\n",
    "import sys\n",
    "import math\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    # Remove empty lines\n",
    "    examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\n",
    "    return tokenizer(\n",
    "        examples[\"text\"],\n",
    "        padding=True,\n",
    "        truncation=True,\n",
    "        max_length=128,\n",
    "        # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it\n",
    "        # receives the `special_tokens_mask`.\n",
    "        return_special_tokens_mask=True,\n",
    "    )\n",
    "\n",
    "tokenizer = RobertaTokenizerFast(vocab_file=None,\n",
    "                                     merges_file=None,\n",
    "                                     tokenizer_file=path_tokenizer_config,\n",
    "                                     )\n",
    "files=[p.as_posix() for p in Path('./babylm_data/babylm_10M/').glob('*.train')]\n",
    "files = [\n",
    " './babylm_data/babylm_10M/wikipedia.train',\n",
    " './babylm_data/babylm_10M/gutenberg.train',\n",
    " './babylm_data/babylm_10M/cbt.train']\n",
    "dataset = load_dataset('text', data_files={'train': list(files)})\n",
    "\n",
    "tokenized_datasets = dataset.map(\n",
    "    tokenize_function,\n",
    "    batched=True,\n",
    "    num_proc=4,\n",
    "    remove_columns=['text'],\n",
    "    load_from_cache_file=True,\n",
    ")\n",
    "print(f'Length of train data={len(tokenized_datasets[\"train\"])}')\n",
    "\n",
    "data_collator = DataCollatorForLanguageModeling(\n",
    "    tokenizer=tokenizer, mlm=True, mlm_probability=0.135\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "files=[p.as_posix() for p in Path('./babylm_data/babylm_dev/').glob('*.dev')]\n",
    "files = [\n",
    "'./babylm_data/babylm_dev/wikipedia.dev',\n",
    " './babylm_data/babylm_dev/gutenberg.dev',\n",
    " './babylm_data/babylm_dev/cbt.dev']\n",
    "dataset_dev = load_dataset('text', data_files={'validation': list(files)})\n",
    "\n",
    "tokenized_datasets_dev = dataset_dev.map(\n",
    "    tokenize_function,\n",
    "    batched=True,\n",
    "    num_proc=4,\n",
    "    remove_columns=['text'],\n",
    "    load_from_cache_file=True,\n",
    ")\n",
    "print(f'Length of validation data={len(tokenized_datasets_dev[\"validation\"])}')\n",
    "\n",
    "data_collator = DataCollatorForLanguageModeling(\n",
    "    tokenizer=tokenizer, mlm=True, mlm_probability=0.135\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UP7hMq1fv-TZ"
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   "source": [
    "# Default params are based on RoBERTA-base configuration: https://arxiv.org/abs/1907.11692v1\n",
    "DEFAULT_PARAMS={\n",
    "\"model_name\": \"roberta-base\",\n",
    "# \"train_epochs\": 5,\n",
    "\"batch_size\": 16,\n",
    "\"model_parameters\": [{\n",
    "    \"vocab_size\": 30522,\n",
    "    \"hidden_size\": 768,\n",
    "    \"num_hidden_layers\": 6,\n",
    "    \"num_attention_heads\": 12,\n",
    "    \"intermediate_size\": 3072,\n",
    "    \"hidden_act\": \"gelu\",\n",
    "    \"hidden_dropout_prob\": 0.1,\n",
    "    \"attention_probs_dropout_prog\": 0.1,\n",
    "    \"max_position_embeddings\": 128,\n",
    "    \"type_vocab_size\": 1,\n",
    "    \"initializer_range\": 0.02,\n",
    "    \"layer_norm_eps\": 1e-12,\n",
    "    \"gradient_checkpointing\": False,\n",
    "    \"position_embedding_type\": \"absolute\",\n",
    "    \"use_cache\": True\n",
    "}],\n",
    "\n",
    "\"optimizer_parameters\": [{\n",
    "    \"lr\": 1e-5,\n",
    "    \"beta_one\": 0.9,\n",
    "    \"beta_two\": 0.999,\n",
    "    \"eps\": 1e-6,\n",
    "    \"warmup_steps\": 24000,\n",
    "    \"weight_decay\": 0.01\n",
    "}]\n",
    "}"
   ]
  },
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     "base_uri": "https://localhost:8080/"
    },
    "id": "dIGL0CHLyanJ",
    "outputId": "2e9a42d2-6139-4914-a3e2-d761e6eaf8cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘results’: File exists\n",
      "mkdir: cannot create directory ‘logs’: File exists\n"
     ]
    }
   ],
   "source": [
    "!mkdir results\n",
    "!mkdir logs"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cB08mtEllfmz"
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   "source": [
    "def objective(trial: optuna.Trial):\n",
    "    \"\"\"\n",
    "    Function to set up the model and train it.\n",
    "    Loss function is based on perplexity\n",
    "    Find optimal parameters that minimize the output of the objective\n",
    "    \"\"\"\n",
    "    model_parameters = {\n",
    "    'hidden_size': trial.suggest_int('hidden_size_multiplier', 1, 100),\n",
    "    'num_hidden_layers': trial.suggest_int('hidden_layers', 1, 12),\n",
    "    'num_attention_heads': trial.suggest_int('attention_heads', 1, 13),\n",
    "    'intermediate_size': trial.suggest_int('intermediate_size', 1, 3072),\n",
    "    'hidden_act': trial.suggest_categorical('hidden_act', [\n",
    "        'gelu',\n",
    "        'relu',\n",
    "        'silu',\n",
    "        'gelu_new'\n",
    "    ]),\n",
    "    'hidden_dropout_prob': trial.suggest_uniform('hidden_dropout_prob', 0.1, 0.5),\n",
    "    'attention_probs_dropout_prog': trial.suggest_uniform('attention_prob_dropout_prog', 0.1, 0.5),\n",
    "    'max_position_embeddings': 128,\n",
    "    'type_vocab_size': 1,\n",
    "    'initializer_range': 0.02,\n",
    "    'layer_norm_eps': 1e-12,\n",
    "    'gradient_checkpointing': False,\n",
    "    'position_embedding_type': trial.suggest_categorical('position_embedding_type', [\n",
    "        'absolute',\n",
    "        'relative_key',\n",
    "        'relative_key_query'\n",
    "    ]),\n",
    "    'use_cache': True,\n",
    "    }\n",
    "    _config = RobertaConfig(\n",
    "            vocab_size=tokenizer.vocab_size,\n",
    "            hidden_size=model_parameters['hidden_size'] * model_parameters['num_attention_heads'],\n",
    "            num_hidden_layers=model_parameters['num_hidden_layers'],\n",
    "            num_attention_heads=model_parameters['num_attention_heads'],\n",
    "            intermediate_size=model_parameters['intermediate_size'],\n",
    "            hidden_act=model_parameters['hidden_act'],\n",
    "            hidden_dropout_prob=model_parameters['hidden_dropout_prob'],\n",
    "            attention_probs_dropout_prog=model_parameters['attention_probs_dropout_prog'],\n",
    "            max_position_embeddings=model_parameters['max_position_embeddings'] * 2,\n",
    "            type_vocab_size=model_parameters['type_vocab_size'],\n",
    "            initializer_range=model_parameters['initializer_range'],\n",
    "            layer_norm_eps=model_parameters['layer_norm_eps'],\n",
    "            gradient_checkpointing=model_parameters['gradient_checkpointing'],\n",
    "            position_embedding_type=model_parameters['position_embedding_type'],\n",
    "            use_cache=model_parameters['use_cache'],\n",
    "        )\n",
    "    model = RobertaForMaskedLM(config=_config)\n",
    "    model.resize_token_embeddings(len(tokenizer))\n",
    "    opt_param = DEFAULT_PARAMS['optimizer_parameters'][0]\n",
    "    optimizer = AdamW(params=model.parameters(), lr=opt_param['lr'], betas=(opt_param['beta_one'], opt_param['beta_two']), eps=opt_param['eps'],\n",
    "                      weight_decay=opt_param['weight_decay'])\n",
    "    scheduler = None\n",
    "    training_args = TrainingArguments(\n",
    "        output_dir='./opt_results/',\n",
    "        num_train_epochs=10,\n",
    "        per_device_train_batch_size=DEFAULT_PARAMS['batch_size'],\n",
    "        per_device_eval_batch_size=DEFAULT_PARAMS['batch_size'],\n",
    "        warmup_steps=opt_param['warmup_steps'],\n",
    "        weight_decay=opt_param['weight_decay'],\n",
    "        logging_dir='./opt_logs/',\n",
    "        eval_accumulation_steps=10\n",
    "    )\n",
    "    trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_datasets['train'],\n",
    "    eval_dataset=tokenized_datasets_dev['validation'],\n",
    "    data_collator=data_collator,\n",
    "    optimizers=(optimizer, scheduler)\n",
    "    )\n",
    "\n",
    "    train_metrics = trainer.train()\n",
    "    eval_results = trainer.evaluate()\n",
    "    ppl = math.exp(eval_results['eval_loss'])\n",
    "    \n",
    "    return ppl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add stream handler of stdout to show the messages\n",
    "optuna.logging.get_logger(\"optuna\").addHandler(logging.StreamHandler(sys.stdout))\n",
    "study_name=\"mlm-parameter-search-2\"\n",
    "storage_name = \"sqlite:///{}.db\".format(study_name)\n",
    "# Other possible pruners here: https://optuna.readthedocs.io/en/stable/reference/pruners.html\n",
    "study_resume = optuna.create_study(study_name=study_name, load_if_exists=True,\n",
    "                                   storage=storage_name, direction=\"minimize\",\n",
    "                                   pruner=optuna.pruners.MedianPruner())\n",
    "study_resume.optimize(objective) # (objective, n_trials=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(study_resume.best_value)\n",
    "# print(study_resume.best_params)\n",
    "# print(study_resume.best_trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Check failed runs\n",
    "# for trial in study_resume.trials:\n",
    "#     if trial.state == optuna.trial.TrialState.FAIL:\n",
    "#         print(\"FAILED\")\n",
    "#         print(trial.params)\n",
    "## study_resume.enqueue_trial(params=default_fail)"
   ]
  }
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