feat: uhm, i changed some things

CNN-model/datasets/LoremIpsumDataset.py

@@ -0,0 +1,5 @@
+import lorem
+
+class LoremIpsumDataset:
+    def __init__(self):
+        self.data = lorem.text(paragraphs=100)

CNN-model/datasets/__init__.py

@@ -0,0 +1,2 @@
+from EnWik9 import EnWik9DataSet
+from LoremIpsumDataset import LoremIpsumDataset

CNN-model/main_cnn.py

@@ -1,93 +1,55 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import optuna.trial as tr
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-import argparse
+from argparse import ArgumentParser
+from math import ceil
 
-from optuna_trial import create_model
-from utils import make_context_pairs, load_data
-import optuna
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+
+from datasets import EnWik9DataSet, LoremIpsumDataset
+from trainers import OptunaTrainer, Trainer
+
+BATCH_SIZE = 64
+DEVICE = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
 
 # hyper parameters
 context_length = 128
 
-def train_and_eval(
-        model: nn.Module,
-        training_data: bytes,
-        validation_data: bytes,
-        batch_size: int,
-        epochs: int = 100,
-        learning_rate: float = 1e-3,
-        device: torch.device = torch.device("cpu")
-) -> dict:
-    model.to(device)
-    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
-    training_loader = DataLoader(make_context_pairs(training_data, context_length=context_length), batch_size=batch_size)
-    validation_loader= DataLoader(make_context_pairs(validation_data, context_length=context_length), batch_size=batch_size)
-
-    training_losses = []
-    validation_losses = []
-    best_val_loss = float("inf")
-
-    for epoch in range(epochs):
-        model.train()
-        train_loss = 0
-        for x, y in tqdm(training_loader, desc=f"Epoch {epoch}"):
-            x, y = x.to(device), y.to(device)
-            prediction = model(x)
-            loss = F.cross_entropy(prediction, y)
-            train_loss += loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-        training_losses.append(train_loss / len(training_loader))
-
-        model.eval()
-
-        with torch.no_grad():
-            val_loss = 0
-            for x, y in validation_loader:
-                x, y = x.to(device), y.to(device)
-                prediction = model(x)
-                loss = F.cross_entropy(prediction, y)
-                val_loss += loss.item()
-            validation_losses.append(val_loss / len(validation_loader))
-            if validation_losses[-1] < best_val_loss:
-                best_val_loss = validation_losses[-1]
-
-    return {
-        "training_losses": training_losses,
-        "validation_losses": validation_losses,
-        "best_validation_loss": best_val_loss
-    }
-
-
-
-def objective_function(trial: tr.Trial, train_data: bytes, validation_data: bytes, batch_size: int):
-    model = create_model(trial)
-    result = train_and_eval(model, train_data, validation_data, batch_size)
-    return result["best_validation_loss"]
-
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--train-data", type=str, required=True)
-    parser.add_argument("--validation-data", type=str, required=True)
-    parser.add_argument("--batch-size", type=int, default=128)
-
+    print(f"Running on device: {DEVICE}...")
+    parser = ArgumentParser()
+    parser.add_argument("--method", choices=["optuna", "train"], required=True)
+    parser.add_argument("--model-path", type=str, required=False)
     args = parser.parse_args()
-    print(args)
 
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    train_data = load_data(args.train_data)
-    validation_data = load_data(args.validation_data)
-    batch_size = args.batch_size
+    if args.method == "train":
+        dataset = EnWik9DataSet()
+    elif args.method == "optuna":
+        dataset = LoremIpsumDataset()
+    else:
+        raise ValueError(f"Unknown method: {args.method}")
 
-    print(f"training data length: {len(train_data)}")
-    print(f"validation data length: {len(validation_data)}")
-    print(f"batch size: {batch_size}")
+    dataset_length = len(dataset)
+    training_size = ceil(0.8 * dataset_length)
 
-    study = optuna.create_study(study_name="CNN network",direction="minimize")
-    study.optimize(lambda trial: objective_function(trial, train_data, validation_data, batch_size), n_trials=10)
+    print(f"training set size = {training_size}, validation set size {dataset_length - training_size}")
+    data = dataset.data["text"]
+
+    train_set, validate_set = torch.utils.data.random_split(TensorDataset(data),
+                                                            [training_size, dataset_length - training_size])
+    training_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
+    validation_loader = DataLoader(validate_set, batch_size=BATCH_SIZE, shuffle=False)
+    loss_fn = torch.nn.CrossEntropyLoss()
+
+    model = None
+    if args.model_path is not None:
+        model = torch.load(args.model_path)
+
+    trainer: Trainer = OptunaTrainer() if args.method == "optuna" else None
+
+    trainer.execute(
+        model=model,
+        train_loader=training_loader,
+        validation_loader=validation_loader,
+        loss_fn=loss_fn,
+        n_epochs=200,
+        device=DEVICE
+    )

CNN-model/model/__init__.py

@@ -0,0 +1 @@
+from .cnn import CNNPredictor

CNN-model/model/cnn.py

@@ -8,7 +8,7 @@ class CausalConv1d(nn.Conv1d):
     def __init__(self, input_channels, output_channels, kernel_size, **kwargs):
         super().__init__(input_channels, output_channels, kernel_size, padding=kernel_size-1, **kwargs)
     def forward(self, input: Tensor) -> Tensor:
-        return super().forward(input)
+        return super().forward(input)[:, :, :input.size(-1)]
 
 class CNNPredictor(nn.Module):
     def __init__(
@@ -41,5 +41,5 @@ class CNNPredictor(nn.Module):
         emdedding = emdedding.transpose(1, 2)   # B, H, L
         prediction = self.network(emdedding)
         last_prediction = prediction[:, :, -1]
-        return softmax(self.output_layer(last_prediction), dim=-1) # convert output of linear layer to prob. distr.
+        return self.output_layer(last_prediction)
 

CNN-model/optuna_trial.py

@@ -1,18 +0,0 @@
-import optuna.trial as tr
-from cnn import CNNPredictor
-
-def create_model(trial: tr.Trial, vocab_size: int = 256, context_length: int = 128):
-    num_layers = trial.suggest_int("num_layers", 1, 6)
-    hidden_dim = trial.suggest_int("hidden_dim", 64, 512, log=True)
-    kernel_size = trial.suggest_int("kernel_size", 2, 7)
-    dropout_prob = trial.suggest_float("dropout_prob", 0.1, 0.5)
-    use_batchnorm = trial.suggest_categorical("use_batchnorm", [True, False])
-
-    return CNNPredictor(
-        vocab_size=vocab_size,
-        num_layers=num_layers,
-        hidden_dim=hidden_dim,
-        kernel_size=kernel_size,
-        dropout_prob=dropout_prob,
-        use_batchnorm=use_batchnorm
-    )

CNN-model/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)

CNN-model/trainers/OptunaTrainer.py

@@ -0,0 +1,63 @@
+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 ..model.cnn import CNNPredictor
+from train import train
+
+
+def create_model(trial: tr.Trial, vocab_size: int = 256):
+    num_layers = trial.suggest_int("num_layers", 1, 6)
+    hidden_dim = trial.suggest_int("hidden_dim", 64, 512, log=True)
+    kernel_size = trial.suggest_int("kernel_size", 2, 7)
+    dropout_prob = trial.suggest_float("dropout_prob", 0.1, 0.5)
+    use_batchnorm = trial.suggest_categorical("use_batchnorm", [True, False])
+
+    return CNNPredictor(
+        vocab_size=vocab_size,
+        num_layers=num_layers,
+        hidden_dim=hidden_dim,
+        kernel_size=kernel_size,
+        dropout_prob=dropout_prob,
+        use_batchnorm=use_batchnorm
+    )
+
+
+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 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=20
+        )
+
+        best_params = study.best_trial.params
+        best_model = CNNPredictor(
+            **best_params
+        )
+        torch.save(best_model, "models/final_model.pt")

CNN-model/trainers/__init__.py

@@ -0,0 +1,2 @@
+from OptunaTrainer import OptunaTrainer
+from trainer import Trainer

CNN-model/trainers/train.py

@@ -0,0 +1,50 @@
+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
+) -> tuple[list[float], list[float]]:
+    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 data in tqdm(training_loader):
+            optimizer.zero_grad()
+
+            x_hat = model(data)
+
+            loss = loss_fn(x_hat, data)
+            loss.backward()
+            optimizer.step()
+
+            total_loss.append(loss.item())
+
+        avg_training_losses.append(sum(total_loss) / len(total_loss))
+
+        with torch.no_grad():
+            losses = []
+            for data in validation_loader:
+                x_hat = model(data)
+                loss = loss_fn(x_hat, data)
+                losses.append(loss.item())
+            avg_loss = sum(losses) / len(losses)
+            avg_validation_losses.append(avg_loss)
+        tqdm.write(f"epoch: {epoch + 1}, avg loss = {avg_loss}")
+
+    return avg_training_losses, avg_validation_losses
+

CNN-model/trainers/trainer.py

@@ -0,0 +1,22 @@
+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

CNN-model/utils/__init__.py

@@ -0,0 +1 @@
+from .utils import *

CNN-model/utils/utils.py

@@ -14,6 +14,17 @@ def print_distribution(from_to: tuple[int, int], probabilities: list[float]):
     plt.hist(range(from_to[0], from_to[1]), weights=probabilities)
     plt.show()
 
+def print_losses(train_losses: list[float], validation_losses: list[float], show=False):
+    plt.plot(train_losses, label="Training loss")
+    plt.plot(validation_losses, label="Validation loss")
+    plt.xlabel("Epoch")
+    plt.ylabel("Loss (cross entropy)")
+    plt.legend()
+
+    if show:
+        plt.show()
+    plt.savefig("losses.png")
+
 
 def load_data(path: str) -> bytes:
     with open(path, "rb") as f: