{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "ca22d12d", "metadata": { "execution": { "iopub.execute_input": "2024-08-14T05:42:36.377550Z", "iopub.status.busy": "2024-08-14T05:42:36.376734Z", "iopub.status.idle": "2024-08-14T05:44:36.924929Z", "shell.execute_reply": "2024-08-14T05:44:36.923740Z" }, "papermill": { "duration": 120.554736, "end_time": "2024-08-14T05:44:36.927333", "exception": false, "start_time": "2024-08-14T05:42:36.372597", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "!pip install -q --upgrade datasets\n", "!pip install -q --upgrade transformers\n", "!pip install -q --upgrade peft\n", "!pip install -q --upgrade trl\n", "!pip install -q bitsandbytes\n", "!pip install -q accelerate\n", "!pip install -q tensorboard\n", "!pip install -q jsonlines\n", "! conda install -y gdown" ] }, { "cell_type": "code", "execution_count": null, "id": "a600d3e0", "metadata": { "execution": { "iopub.execute_input": "2024-08-14T05:46:24.563834Z", "iopub.status.busy": "2024-08-14T05:46:24.563522Z", "iopub.status.idle": "2024-08-14T06:37:53.094328Z", "shell.execute_reply": "2024-08-14T06:37:53.093386Z" }, "papermill": { "duration": 3088.552597, "end_time": "2024-08-14T06:37:53.096673", "exception": false, "start_time": "2024-08-14T05:46:24.544076", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import itertools\n", "import time\n", "import warnings\n", "from peft import LoraConfig, get_peft_model\n", "from transformers import BertForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer\n", "from matplotlib import pyplot as plt\n", "from datasets import load_dataset\n", "import torch\n", "from tqdm import tqdm\n", "import numpy as np\n", "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, matthews_corrcoef, roc_auc_score\n", "\n", "# Suppress warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "# Layer configurations\n", "attention_plus_feed_forward = [\n", " \"bert.encoder.layer.0.attention.self.query\",\n", " \"bert.encoder.layer.0.attention.self.key\",\n", " \"bert.encoder.layer.0.attention.self.value\",\n", " \"bert.encoder.layer.0.attention.output.dense\",\n", " \"bert.encoder.layer.0.intermediate.dense\",\n", " \"bert.encoder.layer.0.output.dense\",\n", " \"bert.encoder.layer.1.attention.self.query\",\n", " \"bert.encoder.layer.1.attention.self.key\",\n", " \"bert.encoder.layer.1.attention.self.value\",\n", " \"bert.encoder.layer.1.attention.output.dense\",\n", " \"bert.encoder.layer.1.intermediate.dense\",\n", " \"bert.encoder.layer.1.output.dense\"\n", "]\n", "\n", "attention = [\n", " \"bert.encoder.layer.0.attention.self.query\",\n", " \"bert.encoder.layer.0.attention.self.key\",\n", " \"bert.encoder.layer.0.attention.self.value\",\n", " \"bert.encoder.layer.0.attention.output.dense\",\n", " \"bert.encoder.layer.1.attention.self.query\",\n", " \"bert.encoder.layer.1.attention.self.key\",\n", " \"bert.encoder.layer.1.attention.self.value\",\n", " \"bert.encoder.layer.1.attention.output.dense\"\n", "]\n", "\n", "feed_forward = [\n", " \"bert.encoder.layer.0.intermediate.dense\",\n", " \"bert.encoder.layer.0.output.dense\",\n", " \"bert.encoder.layer.1.intermediate.dense\",\n", " \"bert.encoder.layer.1.output.dense\"\n", "]\n", "\n", "all_layer_configs = [attention_plus_feed_forward, attention, feed_forward]\n", "tokenizer = AutoTokenizer.from_pretrained('zhihan1996/DNA_bert_6')\n", "\n", "def preprocess_function(examples):\n", " try:\n", " return tokenizer(\n", " examples['sequence'],\n", " padding='max_length',\n", " truncation=True,\n", " max_length=512\n", " )\n", " except KeyError:\n", " return tokenizer(\n", " examples['Sequence'],\n", " padding='max_length',\n", " truncation=True,\n", " max_length=512\n", " )\n", "\n", "\n", "def add_labels(examples):\n", " try:\n", " examples['labels'] = examples['label']\n", " return examples\n", " except KeyError:\n", " examples['labels'] = examples['Label']\n", " return examples\n", "\n", "def create_task_dataset(task_name):\n", " if task_name == 'tfbs':\n", " return load_dataset('csv', data_files='/kaggle/working/tfbs.csv', split='train[0:10000]'), load_dataset('csv', data_files='/kaggle/working/tfbs.csv', split='train[10001:13122]')\n", " elif task_name == 'dnasplice':\n", " return load_dataset('csv', data_files='/kaggle/working/dnasplice.csv', split='train[0:10000]'), load_dataset('csv', data_files='/kaggle/working/dnasplice.csv', split='train[10001:13122]')\n", " elif task_name == 'dnaprom':\n", " return load_dataset('csv', data_files='/kaggle/working/dnaprom.csv', split='train[0:10000]'), load_dataset('csv', data_files='/kaggle/working/dnaprom.csv', split='train[10001:13122]')\n", " else:\n", " raise ValueError(f\"Unknown task: {task_name}\")\n", "\n", "def create_dataset_maps(train_dataset, test_dataset):\n", " train_dataset = train_dataset.map(preprocess_function, batched=True)\n", " train_dataset = train_dataset.map(add_labels)\n", " test_dataset = test_dataset.map(preprocess_function, batched=True)\n", " test_dataset = test_dataset.map(add_labels)\n", " return train_dataset, test_dataset\n", "\n", "def train_model(train_dataset, test_dataset, model, task, model_name, config_name):\n", " def specificity_score(y_true, y_pred):\n", " true_negatives = np.sum((y_pred == 0) & (y_true == 0))\n", " false_positives = np.sum((y_pred == 1) & (y_true == 0))\n", " specificity = true_negatives / (true_negatives + false_positives + np.finfo(float).eps)\n", " return specificity\n", "\n", " def compute_metrics(eval_pred):\n", " logits, labels = eval_pred\n", " predictions = np.argmax(logits, axis=-1)\n", " y_pred = logits[:, 1]\n", "\n", " accuracy = accuracy_score(labels, predictions)\n", " recall = recall_score(labels, predictions)\n", " specificity = specificity_score(labels, predictions)\n", " mcc = matthews_corrcoef(labels, predictions)\n", " roc_auc = roc_auc_score(labels, y_pred)\n", " precision = precision_score(labels, predictions)\n", " f1 = f1_score(labels, predictions)\n", "\n", " return {\n", " 'accuracy': accuracy,\n", " 'recall': recall,\n", " 'specificity': specificity,\n", " 'mcc': mcc,\n", " 'roc_auc': roc_auc,\n", " 'precision': precision,\n", " 'f1': f1\n", " }\n", "\n", " # Define the training arguments\n", " training_arguments = TrainingArguments(\n", " output_dir=f\"outputs/{task}/{model_name}_{config_name}\",\n", " num_train_epochs=3,\n", " fp16=False,\n", " bf16=False,\n", " per_device_train_batch_size=20,\n", " per_device_eval_batch_size=10,\n", " gradient_accumulation_steps=2,\n", " gradient_checkpointing=True,\n", " max_grad_norm=0.3,\n", " learning_rate=4e-4,\n", " weight_decay=0.01,\n", " optim=\"paged_adamw_32bit\",\n", " lr_scheduler_type=\"linear\",\n", " max_steps=-1,\n", " warmup_ratio=0.03,\n", " group_by_length=True,\n", " save_steps=10,\n", " logging_steps=25,\n", " dataloader_pin_memory=False,\n", " report_to='tensorboard',\n", " gradient_checkpointing_kwargs={'use_reentrant': False}\n", " )\n", "\n", " trainer = Trainer(\n", " model=model,\n", " args=training_arguments,\n", " train_dataset=train_dataset,\n", " eval_dataset=test_dataset,\n", " tokenizer=tokenizer,\n", " compute_metrics=compute_metrics,\n", " )\n", "\n", " start_time = time.time()\n", " trainer.train()\n", " end_time = time.time()\n", "\n", " total_time = end_time - start_time\n", " metrics = trainer.evaluate()\n", "\n", " return total_time, metrics\n", "\n", "def estimate_model_size_lora(model):\n", " num_parameters = model.get_nb_trainable_parameters()[0]\n", " size_in_bytes = num_parameters * 4 # Each parameter is 4 bytes (32-bit float)\n", " size_in_mb = size_in_bytes / (1024 ** 2) # Convert bytes to megabytes\n", " return size_in_mb\n", "def estimate_model_size_base(model):\n", " num_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "\n", " size_in_bytes = num_parameters * 4 # Each parameter is 4 bytes (32-bit float)\n", " size_in_mb = size_in_bytes / (1024 ** 2) # Convert bytes to megabytes\n", " return size_in_mb\n", "# Task loop\n", "task_list = ['dnasplice', 'tfbs', 'dnaprom']\n", "log_file = \"training_log.txt\"\n", "model_name = 'fabihamakhdoomi/TinyDNABERT'\n", "r_values = [4, 8, 16, 32] # Different r values for ablation study\n", "\n", "results = []\n", "\n", "for task in task_list:\n", " print(f\"Running TASK : {task}\")\n", " train_dataset, test_dataset = create_task_dataset(task)\n", " train_dataset, test_dataset = create_dataset_maps(train_dataset, test_dataset) \n", " train_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])\n", " test_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])\n", " \n", "\n", "\n", " # Train the base model first\n", " base_model = BertForSequenceClassification.from_pretrained(\n", " model_name,\n", " num_labels=2\n", " )\n", " config_name = \"base_model\"\n", " print(f\"Training MODEL : {config_name} for task : {task}\")\n", " training_time, metrics = train_model(train_dataset, test_dataset, base_model, task, model_name, config_name)\n", " model_size_mb = estimate_model_size_base(base_model)\n", " results.append((task, config_name, model_size_mb, metrics['eval_accuracy']))\n", " with open(log_file, \"a\") as log:\n", " log.write(f\"Task: {task}, Model: {model_name}, Config: {config_name}, Training Time: {training_time}, Model Size: {model_size_mb} MB, Metrics: {metrics}\\n\")\n", "\n", " # Train the LoRA models with different r values\n", " for r in r_values:\n", " for config_name, config in zip(['attention_plus_feed_forward', 'attention', 'feed_forward'], all_layer_configs):\n", " base_model = BertForSequenceClassification.from_pretrained(\n", " model_name,\n", " num_labels=2\n", " )\n", " peft_config = LoraConfig(\n", " lora_alpha=16,\n", " lora_dropout=0.2,\n", " r=r,\n", " bias=\"none\",\n", " task_type=\"SEQ_CLS\",\n", " target_modules=config\n", " )\n", " model = get_peft_model(base_model, peft_config)\n", " print(f\"Training MODEL : {config_name} with r={r} for task : {task}\")\n", " training_time, metrics = train_model(train_dataset, test_dataset, model, task, model_name, f\"{config_name}_r{r}\")\n", " model_size_mb = estimate_model_size_lora(model)\n", " results.append((task, f\"{config_name}_r{r}\", model_size_mb, metrics['eval_accuracy']))\n", " with open(log_file, \"a\") as log:\n", " log.write(f\"Task: {task}, Model: {model_name}, Config: {config_name}, r: {r}, Training Time: {training_time}, Model Size: {model_size_mb} MB, Metrics: {metrics}\\n\")\n", "\n", "# Results logging and visualization\n", "tasks = [result[0] for result in results]\n", "configs = [result[1] for result in results]\n", "model_sizes = [result[2] for result in results]\n", "accuracies = [result[3] for result in results]\n", "\n", "for i, task in enumerate(task_list):\n", " plt.figure(figsize=(10, 6))\n", " task_results = 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