Merge branch 'main' into portability

trainers/FullTrainer.py

@@ -0,0 +1,26 @@
+from typing import Callable
+
+import torch
+from torch import nn as nn
+from torch.utils.data import DataLoader
+
+from .trainer import Trainer
+from .train import train
+from ..utils import print_losses
+
+class FullTrainer(Trainer):
+    def execute(
+            self,
+            model: nn.Module | None,
+            train_loader: DataLoader,
+            validation_loader: DataLoader,
+            loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+            n_epochs: int,
+            device: str
+    ) -> None:
+        if model is None:
+            raise ValueError("Model must be provided: run optuna optimizations first")
+
+        model.to(device)
+        train_loss, val_loss = train(model, train_loader, validation_loader, loss_fn, n_epochs)
+        print_losses(train_loss, val_loss)

trainers/OptunaTrainer.py

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+from typing import Callable
+
+import optuna
+import optuna.trial as tr
+import torch
+from torch import nn as nn
+from torch.utils.data import DataLoader
+
+from .trainer import Trainer
+from ..models.cnn import CNNPredictor
+from .train import train
+
+
+def create_model(trial: tr.Trial, vocab_size: int = 256):
+    hidden_dim = trial.suggest_int("hidden_dim", 64, 512, log=True)
+    embedding_dim = trial.suggest_int("embed_dim", 64, 512, log=True)
+
+    return CNNPredictor(
+        vocab_size=vocab_size,
+        hidden_dim=hidden_dim,
+        embed_dim=embedding_dim,
+    )
+
+
+def objective_function(
+        trial: tr.Trial,
+        training_loader: DataLoader,
+        validation_loader: DataLoader,
+        loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+        device: str
+):
+    model = create_model(trial).to(device)
+    _, validation_loss = train(model, training_loader, validation_loader, loss_fn)
+    return min(validation_loss)
+
+
+class OptunaTrainer(Trainer):
+    def __init__(self, n_trials: int | None = None):
+        super().__init__()
+        self.n_trials = n_trials if n_trials is not None else 20
+        print(f"Creating Optuna trainer(n_trials = {self.n_trials})")
+
+    def execute(
+            self,
+            model: nn.Module | None,
+            train_loader: DataLoader,
+            validation_loader: DataLoader,
+            loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+            n_epochs: int,
+            device: str
+    ) -> None:
+        study = optuna.create_study(study_name="CNN network", direction="minimize")
+        study.optimize(
+            lambda trial: objective_function(trial, train_loader, validation_loader, loss_fn, device),
+            n_trials=self.n_trials
+        )
+
+        best_params = study.best_trial.params
+        best_model = CNNPredictor(
+            **best_params
+        )
+        torch.save(best_model, f"saved_models/{model.__class__.__name__}.pt")

trainers/__init__.py

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+from .OptunaTrainer import OptunaTrainer
+from .FullTrainer import FullTrainer
+from .trainer import Trainer

trainers/train.py

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+import torch
+import torch.nn as nn
+from torch.utils.data.dataloader import DataLoader
+from tqdm import tqdm
+from typing import Callable
+
+
+def train(
+        model: nn.Module,
+        training_loader: DataLoader,
+        validation_loader: DataLoader,
+        loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+        epochs: int = 100,
+        learning_rate: float = 1e-3,
+        weight_decay: float = 1e-8,
+        device="cuda"
+) -> tuple[list[float], list[float]]:
+    
+    model.to(device)
+    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
+    
+    avg_training_losses = []
+    avg_validation_losses = []
+
+    for epoch in range(epochs):
+
+        model.train()
+        total_loss = []
+
+        for x, y in tqdm(training_loader):
+            x = x.long().to(device)  # important for Embedding
+            y = y.long().to(device)  # must be (B,) for CE
+
+            optimizer.zero_grad()
+            logits = model(x)  # (B, 256)
+            loss = loss_fn(logits, y)
+            loss.backward()
+            optimizer.step()
+
+            total_loss.append(loss.item())
+
+        avg_training_losses.append(sum(total_loss) / len(total_loss))
+
+        # ----- validation -----
+        model.eval()
+        with torch.no_grad():
+            losses = []
+            for x, y in validation_loader:
+                x = x.long().to(device)
+                y = y.long().to(device)
+
+                logits = model(x)
+                loss = loss_fn(logits, y)
+                losses.append(loss.item())
+
+            avg_loss = sum(losses) / len(losses)
+            avg_validation_losses.append(avg_loss)
+
+    return avg_training_losses, avg_validation_losses

trainers/trainer.py

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+from abc import ABC, abstractmethod
+from typing import Callable
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+
+
+class Trainer(ABC):
+    """Abstract class for trainers."""
+
+    @abstractmethod
+    def execute(
+            self,
+            model: nn.Module | None,
+            train_loader: DataLoader,
+            validation_loader: DataLoader,
+            loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+            n_epochs: int,
+            device: str
+    ) -> None:
+        pass