update huggingface code

ML/Pytorch/huggingface/model.py

@@ -0,0 +1,130 @@
+import torch
+import pytorch_lightning as pl
+from datasets import load_dataset, load_metric
+from transformers import T5Config, T5ForConditionalGeneration
+
+from transformers import (
+    AutoModel,
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer,
+    DataCollatorForSeq2Seq,
+    Seq2SeqTrainingArguments,
+    Seq2SeqTrainer,
+)
+
+
+class MyLightningModule(pl.LightningModule):
+    def __init__(self, model_name, learning_rate, weight_decay):
+        super().__init__()
+        self.model_name = model_name
+        self.learning_rate = learning_rate
+        self.weight_decay = weight_decay
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+        # Load the pre-trained model and tokenizer
+        #self.model = torch.compile(
+        #    AutoModelForSeq2SeqLM.from_pretrained(self.model_name)
+        #)
+
+        # Create a T5-small configuration
+        config = T5Config.from_pretrained("t5-small")
+
+        # Initialize the T5 model with random weights
+        self.model = torch.compile(T5ForConditionalGeneration(config))
+
+        # Load the ROUGE metric
+        self.metric = load_metric("rouge")
+        self.logits = []
+        self.labels = []
+
+    def forward(self, input_ids, attention_mask, labels=None):
+        output = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            labels=labels,
+        )
+        return output.loss, output.logits
+
+    def training_step(self, batch, batch_idx):
+        input_ids = batch["input_ids"]
+        attention_mask = batch["attention_mask"]
+        labels = batch["labels"]
+        loss, logits = self(input_ids, attention_mask, labels)
+        self.log("train_loss", loss, on_epoch=True, on_step=True, prog_bar=True)
+        return {"loss": loss, "logits": logits}
+
+    def validation_step(self, batch, batch_idx):
+        input_ids = batch["input_ids"]
+        attention_mask = batch["attention_mask"]
+        labels = batch["labels"]
+        loss, logits = self(input_ids, attention_mask, labels)
+        self.log("val_loss", loss, on_epoch=True, on_step=False)
+
+        # add logits and labels to instance attributes, but make sure to detach them
+        # from the computational graph first
+        self.logits.append(logits.argmax(dim=-1).detach().cpu())
+        self.labels.append(labels.detach().cpu())
+        return {"loss": loss, "logits": logits, "labels": labels}
+
+    def on_validation_epoch_end(self):
+        # Concatenate tensors in logits and labels lists
+        pred_token_ids = torch.cat(self.logits, dim=0)
+        true_labels = torch.cat(self.labels, dim=0)
+
+        # Decode predictions and labels using the saved instance attributes
+        decoded_preds = self.tokenizer.batch_decode(
+            pred_token_ids, skip_special_tokens=True
+        )
+        decoded_labels = self.tokenizer.batch_decode(
+            true_labels, skip_special_tokens=True
+        )
+
+        # Compute ROUGE scores
+        scores = self.metric.compute(
+            predictions=decoded_preds, references=decoded_labels, rouge_types=["rouge1"]
+        )["rouge1"].mid
+
+        self.log("rouge1_precision", scores.precision, prog_bar=True)
+        self.log("rouge1_recall", scores.recall, prog_bar=True)
+        self.log("rouge1_fmeasure", scores.fmeasure, prog_bar=True)
+
+        # Clear logits and labels instance attributes for the next validation epoch
+        self.logits.clear()
+        self.labels.clear()
+
+    def predict(self, article: str, max_input_length: int = 512, max_output_length: int = 150) -> str:
+        # Set the model to evaluation mode
+        self.model.eval()
+
+        # Tokenize the input article
+        inputs = self.tokenizer(
+            article,
+            max_length=max_input_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt"
+        )
+
+        # Move the input tensors to the same device as the model
+        inputs = {key: value.to(self.device) for key, value in inputs.items()}
+
+        # Generate summary
+        with torch.no_grad():
+            output = self.model.generate(
+                input_ids=inputs["input_ids"],
+                attention_mask=inputs["attention_mask"],
+                max_length=max_output_length,
+                num_return_sequences=1,
+            )
+
+        # Decode and return the summary
+        summary = self.tokenizer.decode(output[0], skip_special_tokens=True)
+        return summary
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.AdamW(
+            self.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay
+        )
+        return optimizer
+
+