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