Update train_model.py

src/models/train_model.py

@@ -0,0 +1,441 @@
+import time
+
+import torch
+import numpy as np
+import pandas as pd
+from datasets import load_metric
+from tqdm.auto import tqdm
+from transformers import (
+    AdamW,
+    T5ForConditionalGeneration,
+    MT5ForConditionalGeneration,
+    T5TokenizerFast as T5Tokenizer,
+    MT5TokenizerFast as MT5Tokenizer,
+)
+from transformers import AutoTokenizer
+from torch.utils.data import Dataset, DataLoader
+from transformers import AutoModelWithLMHead, AutoTokenizer
+import pytorch_lightning as pl
+from pytorch_lightning.loggers import MLFlowLogger
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks.early_stopping import EarlyStopping
+from pytorch_lightning import LightningDataModule
+from pytorch_lightning import LightningModule
+
+torch.cuda.empty_cache()
+pl.seed_everything(42)
+
+
+class DataModule(Dataset):
+    """
+    Data Module for pytorch
+    """
+
+    def __init__(
+            self,
+            data: pd.DataFrame,
+            tokenizer: T5Tokenizer,
+            source_max_token_len: int = 512,
+            target_max_token_len: int = 512,
+    ):
+        """
+        :param data:
+        :param tokenizer:
+        :param source_max_token_len:
+        :param target_max_token_len:
+        """
+        self.data = data
+        self.target_max_token_len = target_max_token_len
+        self.source_max_token_len = source_max_token_len
+        self.tokenizer = tokenizer
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index: int):
+        data_row = self.data.iloc[index]
+
+        input_encoding = self.tokenizer(
+            data_row["input_text"],
+            max_length=self.source_max_token_len,
+            padding="max_length",
+            truncation=True,
+            return_attention_mask=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+
+        output_encoding = self.tokenizer(
+            data_row["output_text"],
+            max_length=self.target_max_token_len,
+            padding="max_length",
+            truncation=True,
+            return_attention_mask=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+
+        labels = output_encoding["input_ids"]
+        labels[
+            labels == 0
+            ] = -100
+
+        return dict(
+            keywords=data_row["keywords"],
+            text=data_row["text"],
+            keywords_input_ids=input_encoding["input_ids"].flatten(),
+            keywords_attention_mask=input_encoding["attention_mask"].flatten(),
+            labels=labels.flatten(),
+            labels_attention_mask=output_encoding["attention_mask"].flatten(),
+        )
+
+
+class PLDataModule(LightningDataModule):
+    def __init__(
+            self,
+            train_df: pd.DataFrame,
+            test_df: pd.DataFrame,
+            tokenizer: T5Tokenizer,
+            source_max_token_len: int = 512,
+            target_max_token_len: int = 512,
+            batch_size: int = 4,
+            split: float = 0.1
+    ):
+        """
+        :param data_df:
+        :param tokenizer:
+        :param source_max_token_len:
+        :param target_max_token_len:
+        :param batch_size:
+        :param split:
+        """
+        super().__init__()
+        self.train_df = train_df
+        self.test_df = test_df
+        self.split = split
+        self.batch_size = batch_size
+        self.target_max_token_len = target_max_token_len
+        self.source_max_token_len = source_max_token_len
+        self.tokenizer = tokenizer
+
+    def setup(self, stage=None):
+        self.train_dataset = DataModule(
+            self.train_df,
+            self.tokenizer,
+            self.source_max_token_len,
+            self.target_max_token_len,
+        )
+        self.test_dataset = DataModule(
+            self.test_df,
+            self.tokenizer,
+            self.source_max_token_len,
+            self.target_max_token_len,
+        )
+
+    def train_dataloader(self):
+        """ training dataloader """
+        return DataLoader(
+            self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=2
+        )
+
+    def test_dataloader(self):
+        """ test dataloader """
+        return DataLoader(
+            self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=2
+        )
+
+    def val_dataloader(self):
+        """ validation dataloader """
+        return DataLoader(
+            self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=2
+        )
+
+
+class LightningModel(LightningModule):
+    """ PyTorch Lightning Model class"""
+
+    def __init__(self, tokenizer, model, output: str = "outputs"):
+        """
+        initiates a PyTorch Lightning Model
+        Args:
+            tokenizer : T5 tokenizer
+            model : T5 model
+            output (str, optional): output directory to save model checkpoints. Defaults to "outputs".
+        """
+        super().__init__()
+        self.model = model
+        self.tokenizer = tokenizer
+        self.output = output
+        # self.val_acc = Accuracy()
+        # self.train_acc = Accuracy()
+
+    def forward(self, input_ids, attention_mask, decoder_attention_mask, labels=None):
+        """ forward step """
+        output = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+            decoder_attention_mask=decoder_attention_mask,
+        )
+
+        return output.loss, output.logits
+
+    def training_step(self, batch, batch_size):
+        """ training step """
+        input_ids = batch["keywords_input_ids"]
+        attention_mask = batch["keywords_attention_mask"]
+        labels = batch["labels"]
+        labels_attention_mask = batch["labels_attention_mask"]
+
+        loss, outputs = self(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=labels_attention_mask,
+            labels=labels,
+        )
+        self.log("train_loss", loss, prog_bar=True, logger=True)
+        return loss
+
+    def validation_step(self, batch, batch_size):
+        """ validation step """
+        input_ids = batch["keywords_input_ids"]
+        attention_mask = batch["keywords_attention_mask"]
+        labels = batch["labels"]
+        labels_attention_mask = batch["labels_attention_mask"]
+
+        loss, outputs = self(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=labels_attention_mask,
+            labels=labels,
+        )
+        self.log("val_loss", loss, prog_bar=True, logger=True)
+        return loss
+
+    def test_step(self, batch, batch_size):
+        """ test step """
+        input_ids = batch["keywords_input_ids"]
+        attention_mask = batch["keywords_attention_mask"]
+        labels = batch["labels"]
+        labels_attention_mask = batch["labels_attention_mask"]
+
+        loss, outputs = self(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=labels_attention_mask,
+            labels=labels,
+        )
+
+        self.log("test_loss", loss, prog_bar=True, logger=True)
+        return loss
+
+    def configure_optimizers(self):
+        """ configure optimizers """
+        model = self.model
+        no_decay = ["bias", "LayerNorm.weight"]
+        optimizer_grouped_parameters = [
+            {
+                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
+                "weight_decay": self.hparams.weight_decay,
+            },
+            {
+                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
+                "weight_decay": 0.0,
+            },
+        ]
+        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
+        self.opt = optimizer
+        return [optimizer]
+
+
+class Summarization:
+    """ Custom Summarization class """
+
+    def __init__(self) -> None:
+        """ initiates Summarization class """
+        pass
+
+    def from_pretrained(self, model_name="t5-base") -> None:
+        """
+        loads T5/MT5 Model model for training/finetuning
+        Args:
+            model_name (str, optional): exact model architecture name, "t5-base" or "t5-large". Defaults to "t5-base".
+        """
+        self.tokenizer = T5Tokenizer.from_pretrained(f"{model_name}")
+        self.model = T5ForConditionalGeneration.from_pretrained(
+            f"{model_name}", return_dict=True
+        )
+
+    def train(
+            self,
+            train_df: pd.DataFrame,
+            eval_df: pd.DataFrame,
+            source_max_token_len: int = 512,
+            target_max_token_len: int = 512,
+            batch_size: int = 8,
+            max_epochs: int = 5,
+            use_gpu: bool = True,
+            outputdir: str = "model",
+            early_stopping_patience_epochs: int = 0,  # 0 to disable early stopping feature
+    ):
+        """
+        trains T5/MT5 model on custom dataset
+        Args:
+            train_df (pd.DataFrame): training datarame. Dataframe must have 2 column --> "input_text" and "output_text"
+            eval_df ([type], optional): validation datarame. Dataframe must have 2 column --> "input_text" and
+            "output_text"
+            source_max_token_len (int, optional): max token length of source text. Defaults to 512.
+            target_max_token_len (int, optional): max token length of target text. Defaults to 512.
+            batch_size (int, optional): batch size. Defaults to 8.
+            max_epochs (int, optional): max number of epochs. Defaults to 5.
+            use_gpu (bool, optional): if True, model uses gpu for training. Defaults to True.
+            outputdir (str, optional): output directory to save model checkpoints. Defaults to "outputs".
+            early_stopping_patience_epochs (int, optional): monitors val_loss on epoch end and stops training,
+            if val_loss does not improve after the specied number of epochs. set 0 to disable early stopping.
+            Defaults to 0 (disabled)
+        """
+        self.target_max_token_len = target_max_token_len
+        self.data_module = PLDataModule(
+            train_df,
+            eval_df,
+            self.tokenizer,
+            batch_size=batch_size,
+            source_max_token_len=source_max_token_len,
+            target_max_token_len=target_max_token_len,
+        )
+
+        self.T5Model = LightningModel(
+            tokenizer=self.tokenizer, model=self.model, output=outputdir
+        )
+
+        # checkpoint_callback = ModelCheckpoint(
+        #     dirpath="checkpoints",
+        #     filename="best-checkpoint-{epoch}-{train_loss:.2f}",
+        #     save_top_k=-1,
+        #     verbose=True,
+        #     monitor="train_loss",
+        #     mode="min",
+        # )
+
+        logger = MLFlowLogger(experiment_name="Summarization")
+
+        early_stop_callback = (
+            [
+                EarlyStopping(
+                    monitor="val_loss",
+                    min_delta=0.00,
+                    patience=early_stopping_patience_epochs,
+                    verbose=True,
+                    mode="min",
+                )
+            ]
+            if early_stopping_patience_epochs > 0
+            else None
+        )
+
+        gpus = 1 if use_gpu else 0
+
+        trainer = pl.Trainer(
+            logger=logger,
+            callbacks=early_stop_callback,
+            max_epochs=max_epochs,
+            gpus=gpus,
+            progress_bar_refresh_rate=5,
+        )
+
+        trainer.fit(self.T5Model, self.data_module)
+
+    def load_model(
+            self, model_dir: str = "model", use_gpu: bool = False
+    ):
+        """
+        loads a checkpoint for inferencing/prediction
+        Args:
+            model_type (str, optional): "t5" or "mt5". Defaults to "t5".
+            model_dir (str, optional): path to model directory. Defaults to "outputs".
+            use_gpu (bool, optional): if True, model uses gpu for inferencing/prediction. Defaults to True.
+        """
+        self.model = T5ForConditionalGeneration.from_pretrained(f"{model_dir}")
+        self.tokenizer = T5Tokenizer.from_pretrained(f"{model_dir}")
+
+        if use_gpu:
+            if torch.cuda.is_available():
+                self.device = torch.device("cuda")
+            else:
+                raise Exception("exception ---> no gpu found. set use_gpu=False, to use CPU")
+        else:
+            self.device = torch.device("cpu")
+
+        self.model = self.model.to(self.device)
+
+    def save_model(
+            self,
+            model_dir="model"
+    ):
+        """
+        Save model to dir
+        :param model_dir:
+        :return: model is saved
+        """
+        path = f"{model_dir}"
+        self.tokenizer.save_pretrained(path)
+        self.model.save_pretrained(path)
+
+    def predict(
+            self,
+            source_text: str,
+            max_length: int = 512,
+            num_return_sequences: int = 1,
+            num_beams: int = 2,
+            top_k: int = 50,
+            top_p: float = 0.95,
+            do_sample: bool = True,
+            repetition_penalty: float = 2.5,
+            length_penalty: float = 1.0,
+            early_stopping: bool = True,
+            skip_special_tokens: bool = True,
+            clean_up_tokenization_spaces: bool = True,
+    ):
+        """
+        generates prediction for T5/MT5 model
+        Args:
+            source_text (str): any text for generating predictions
+            max_length (int, optional): max token length of prediction. Defaults to 512.
+            num_return_sequences (int, optional): number of predictions to be returned. Defaults to 1.
+            num_beams (int, optional): number of beams. Defaults to 2.
+            top_k (int, optional): Defaults to 50.
+            top_p (float, optional): Defaults to 0.95.
+            do_sample (bool, optional): Defaults to True.
+            repetition_penalty (float, optional): Defaults to 2.5.
+            length_penalty (float, optional): Defaults to 1.0.
+            early_stopping (bool, optional): Defaults to True.
+            skip_special_tokens (bool, optional): Defaults to True.
+            clean_up_tokenization_spaces (bool, optional): Defaults to True.
+        Returns:
+            list[str]: returns predictions
+        """
+        input_ids = self.tokenizer.encode(
+            source_text, return_tensors="pt", add_special_tokens=True
+        )
+
+        input_ids = input_ids.to(self.device)
+        generated_ids = self.model.generate(
+            input_ids=input_ids,
+            num_beams=num_beams,
+            max_length=max_length,
+            repetition_penalty=repetition_penalty,
+            length_penalty=length_penalty,
+            early_stopping=early_stopping,
+            top_p=top_p,
+            top_k=top_k,
+            num_return_sequences=num_return_sequences,
+        )
+        preds = [
+            self.tokenizer.decode(
+                g,
+                skip_special_tokens=skip_special_tokens,
+                clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            )
+            for g in generated_ids
+        ]
+        return preds