fix: fixed model shapes + redit training loop

CNN-model/model/cnn.py

@@ -1,45 +1,52 @@
 import torch
 import torch.nn as nn
-from torch import Tensor
-from torch.nn.functional import softmax
-
-
-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)[:, :, :input.size(-1)]
 
 class CNNPredictor(nn.Module):
     def __init__(
-            self,
-            vocab_size=256,
-            num_layers=3,
-            hidden_dim=128,
-            kernel_size=3,
-            dropout_prob=0.1,
-            use_batchnorm=False
-    ):
+            self, 
+            vocab_size=256, 
+            embed_dim=64, 
+            hidden_channels=128,
+        ):
         super().__init__()
-        self.embedding = nn.Embedding(vocab_size, hidden_dim)
-        layers = []
-        in_channels = hidden_dim
-        for _ in range(num_layers):
-            out_channels = hidden_dim
-            layers.append(CausalConv1d(in_channels, out_channels, kernel_size))
-            if use_batchnorm:
-                layers.append(nn.BatchNorm1d(out_channels))
-            layers.append(nn.ReLU())
-            layers.append(nn.Dropout(dropout_prob))
-            in_channels = out_channels
 
-        self.network = nn.Sequential(*layers)
-        self.output_layer = nn.Linear(hidden_dim, vocab_size)
+        # 1. Embedding: maps bytes (0–255) → vectors
+        self.embed = nn.Embedding(vocab_size, embed_dim)
+
+        # 2. Convolutional feature extractor
+        self.conv_layers = nn.Sequential(
+            nn.Conv1d(embed_dim, hidden_channels, kernel_size=5, padding=2),
+            nn.ReLU(),
+            nn.Conv1d(hidden_channels, hidden_channels, kernel_size=5, padding=2),
+            nn.ReLU(),
+            nn.Conv1d(hidden_channels, hidden_channels, kernel_size=5, padding=2),
+            nn.ReLU(),
+        )
+
+        # 3. Global pooling to collapse sequence length
+        self.pool = nn.AdaptiveAvgPool1d(1)  # → (B, hidden_channels, 1)
+
+        # 4. Final classifier
+        self.fc = nn.Linear(hidden_channels, vocab_size)  # → (B, 256)
+
+    def forward(self, x):
+        """
+        x: LongTensor of shape (B, 128), values 0-255
+        """
+        # embed: (B, 128, embed_dim)
+        x = self.embed(x)
+
+        # conv1d expects (B, C_in, L) → swap dims
+        x = x.transpose(1, 2)  # (B, embed_dim, 128)
+
+        # apply CNN
+        x = self.conv_layers(x)  # (B, hidden_channels, 128)
+
+        # global average pooling over sequence
+        x = self.pool(x).squeeze(-1)  # (B, hidden_channels)
+
+        # final classifier
+        logits = self.fc(x)  # (B, 256)
+        return logits
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        emdedding = self.embedding(x)           # B, L, H
-        emdedding = emdedding.transpose(1, 2)   # B, H, L
-        prediction = self.network(emdedding)
-        last_prediction = prediction[:, :, -1]
-        return self.output_layer(last_prediction)
 

CNN-model/trainers/OptunaTrainer.py

@@ -12,19 +12,13 @@ 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])
+    embedding_dim = trial.suggest_int("embedding_dim", 64, 512, log=True)
 
     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
+        embed_dim=embedding_dim,
     )
 
 

CNN-model/trainers/train.py

@@ -2,7 +2,6 @@ import torch
 import torch.nn as nn
 from torch.utils.data.dataloader import DataLoader
 from tqdm import tqdm
-
 from typing import Callable
 
 
@@ -13,22 +12,28 @@ def train(
         loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
         epochs: int = 100,
         learning_rate: float = 1e-3,
-        weight_decay: float = 1e-8
+        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 data in tqdm(training_loader):
+        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()
-
-            x_hat = model(data)
-
-            loss = loss_fn(x_hat, data)
+            logits = model(x)  # (B, 256)
+            loss = loss_fn(logits, y)
             loss.backward()
             optimizer.step()
 
@@ -36,15 +41,21 @@ def train(
 
         avg_training_losses.append(sum(total_loss) / len(total_loss))
 
+        # ----- validation -----
+        model.eval()
         with torch.no_grad():
             losses = []
-            for data in validation_loader:
-                x_hat = model(data)
-                loss = loss_fn(x_hat, data)
+            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)
-        tqdm.write(f"epoch: {epoch + 1}, avg loss = {avg_loss}")
+
+        tqdm.write(f"epoch: {epoch + 1}, avg val loss = {avg_loss:.4f}")
 
     return avg_training_losses, avg_validation_losses
-