Update model.py

model.py

@@ -1,57 +1,61 @@
-
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
 import json
 import os
 
-# --- Hyperparameters (same as before) ---
-batch_size = 32
-block_size = 8
-max_iters = 3000
-eval_interval = 300
-learning_rate = 1e-2
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-eval_iters = 200
-n_embd = 32
-n_head = 4
-n_layer = 4
-dropout = 0.0
-
-# --- Data Preparation (same as before) ---
+# --- Hyperparameters ---
+# These are the settings for our model. You can experiment with these values.
+batch_size = 64  # Increased from 32 to process more sequences in parallel
+block_size = 32  # Increased from 8. This is the maximum context length for predictions. A larger value helps the model see more of the text, leading to better coherence.
+max_iters = 15000  # Increased from 3000 to give the model more training time to learn complex patterns.
+eval_interval = 500  # How often to evaluate the model
+learning_rate = 3e-4 # A slightly lower learning rate is often better for more complex models.
+device = 'cuda' if torch.cuda.is_available() else 'cpu'  # Use GPU if available
+eval_iters = 200  # Number of iterations for evaluation
+n_embd = 64  # Increased from 32. The dimension of the token embeddings. A larger embedding size allows the model to store more information about each character.
+n_layer = 4  # Increased from 2. The number of LSTM layers. More layers can capture more abstract patterns.
+dropout = 0.0  # Dropout rate for regularization
+
+# --- Data Preparation ---
+# This code now expects a 'dataset.jsonl' file to be present in the same directory.
 file_path = 'dataset.jsonl'
 corpus = ""
 try:
     with open(file_path, 'r') as f:
         for line in f:
             data_point = json.loads(line)
+            # The corrected line now uses 'header' and 'formal_statement'
             corpus += data_point['header'] + '\n' + data_point['formal_statement'] + '\n'
 except FileNotFoundError:
-    print(f"Error: The file '{file_path}' was not found.")
-    exit()
-except json.JSONDecodeError:
-    print(f"Error: There was a problem parsing a line in '{file_path}'.")
+    print(f"Error: The file '{file_path}' was not found. Please create it and run again.")
     exit()
-except KeyError:
-    print(f"Error: A line in '{file_path}' does not have the expected keys.")
+except (json.JSONDecodeError, KeyError) as e:
+    print(f"Error: There was a problem parsing a line in '{file_path}'. Details: {e}")
     exit()
 
 if not corpus:
-    print("Error: The corpus is empty.")
+    print("Error: The corpus is empty. The dataset file might be empty or incorrectly formatted.")
     exit()
 
+# Create a simple character-level tokenizer.
 chars = sorted(list(set(corpus)))
 vocab_size = len(chars)
 stoi = {ch: i for i, ch in enumerate(chars)}
 itos = {i: ch for i, ch in enumerate(chars)}
-# Corrected the encode function
 encode = lambda s: [stoi[c] for c in s]
 decode = lambda l: ''.join([itos[i] for i in l])
+
+# Convert the entire text into a PyTorch tensor.
 data = torch.tensor(encode(corpus), dtype=torch.long)
+
+# Create a simple train/validation split.
 n = int(0.9 * len(data))
 train_data = data[:n]
 val_data = data[n:]
 
+# --- Helper Functions ---
+# This function gets a random batch of data from either the training or validation set.
 def get_batch(split):
     data = train_data if split == 'train' else val_data
     ix = torch.randint(len(data) - block_size, (batch_size,))
@@ -60,10 +64,11 @@ def get_batch(split):
     x, y = x.to(device), y.to(device)
     return x, y
 
+# This function is used to estimate the model's loss.
 @torch.no_grad()
 def estimate_loss():
     out = {}
-    model.eval()
+    model.eval()  # Set the model to evaluation mode.
     for split in ['train', 'val']:
         losses = torch.zeros(eval_iters)
         for k in range(eval_iters):
@@ -71,119 +76,89 @@ def estimate_loss():
             logits, loss = model(X, Y)
             losses[k] = loss.item()
         out[split] = losses.mean()
-    model.train()
+    model.train()  # Set the model back to training mode.
     return out
 
-# --- Model Definition (same as before) ---
-class Head(nn.Module):
-    def __init__(self, head_size):
-        super().__init__()
-        self.key = nn.Linear(n_embd, head_size, bias=False)
-        self.query = nn.Linear(n_embd, head_size, bias=False)
-        self.value = nn.Linear(n_embd, head_size, bias=False)
-        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
-        self.dropout = nn.Dropout(dropout)
-    def forward(self, x):
-        B, T, C = x.shape
-        k = self.key(x)
-        q = self.query(x)
-        wei = q @ k.transpose(-2, -1) * C**-0.5
-        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
-        wei = F.softmax(wei, dim=-1)
-        self.dropout(wei)
-        v = self.value(x)
-        out = wei @ v
-        return out
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, num_heads, head_size):
-        super().__init__()
-        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
-        self.proj = nn.Linear(num_heads * head_size, n_embd)
-        self.dropout = nn.Dropout(dropout)
-    def forward(self, x):
-        out = torch.cat([h(x) for h in self.heads], dim=-1)
-        out = self.dropout(self.proj(out))
-        return out
-
-class FeedFoward(nn.Module):
-    def __init__(self, n_embd):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(n_embd, 4 * n_embd),
-            nn.ReLU(),
-            nn.Linear(4 * n_embd, n_embd),
-            nn.Dropout(dropout),
-        )
-    def forward(self, x):
-        return self.net(x)
-
-class TransformerBlock(nn.Module):
-    def __init__(self, n_embd, n_head):
-        super().__init__()
-        head_size = n_embd // n_head
-        self.sa = MultiHeadAttention(n_head, head_size)
-        self.ffwd = FeedFoward(n_embd)
-        self.ln1 = nn.LayerNorm(n_embd)
-        self.ln2 = nn.LayerNorm(n_embd)
-    def forward(self, x):
-        x = x + self.sa(self.ln1(x))
-        x = x + self.ffwd(self.ln2(x))
-        return x
-
+# --- The Main LSTM Language Model ---
 class LanguageModel(nn.Module):
-    def __init__(self, vocab_size, block_size, n_embd, n_head, n_layer, dropout):
+    def __init__(self):
         super().__init__()
+        # An embedding table to convert tokens to dense vectors.
         self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
-        self.position_embedding_table = nn.Embedding(block_size, n_embd)
-        self.blocks = nn.Sequential(*[TransformerBlock(n_embd, n_head) for _ in range(n_layer)])
-        self.ln_f = nn.LayerNorm(n_embd)
+        # An LSTM layer to process the sequence.
+        self.lstm = nn.LSTM(n_embd, n_embd, num_layers=n_layer, batch_first=True)
+        # A final linear layer to project the LSTM's output to the vocabulary size.
         self.lm_head = nn.Linear(n_embd, vocab_size)
-        self.block_size = block_size
-        self.vocab_size = vocab_size
 
     def forward(self, idx, targets=None):
-        B, T = idx.shape
-        tok_emb = self.token_embedding_table(idx)
-        pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))
-        x = tok_emb + pos_emb
-        x = self.blocks(x)
-        x = self.ln_f(x)
-        logits = self.lm_head(x)
+        # Get the token embeddings.
+        tok_emb = self.token_embedding_table(idx)  # (B, T, n_embd)
+        # Pass the embeddings through the LSTM layer.
+        lstm_out, _ = self.lstm(tok_emb)  # lstm_out shape: (B, T, n_embd)
+        # Project the LSTM's output to the vocabulary size to get logits.
+        logits = self.lm_head(lstm_out)  # (B, T, vocab_size)
+
         loss = None
         if targets is not None:
+            # Reshape for cross-entropy loss calculation.
             B, T, C = logits.shape
             logits = logits.view(B * T, C)
             targets = targets.view(B * T)
             loss = F.cross_entropy(logits, targets)
+
         return logits, loss
 
     def generate(self, idx, max_new_tokens):
+        # The `generate` method for LSTMs needs to handle hidden and cell states.
+        h_and_c = None # Start with no hidden state.
         for _ in range(max_new_tokens):
-            idx_cond = idx[:, -self.block_size:]
-            logits, loss = self(idx_cond)
-            logits = logits[:, -1, :]
+            # We only need the last token to predict the next one.
+            idx_cond = idx[:, -1].unsqueeze(1) # (B, 1)
+            tok_emb = self.token_embedding_table(idx_cond) # (B, 1, n_embd)
+            # Pass the single token through the LSTM, along with the previous hidden state.
+            lstm_out, h_and_c = self.lstm(tok_emb, h_and_c)
+            # Focus on the output of the last time step.
+            logits = self.lm_head(lstm_out[:, -1, :]) # (B, vocab_size)
+            # Apply softmax to get probabilities.
             probs = F.softmax(logits, dim=-1)
+            # Sample from the distribution.
             idx_next = torch.multinomial(probs, num_samples=1)
+            # Append the new token to the sequence.
             idx = torch.cat((idx, idx_next), dim=1)
         return idx
 
-
 # --- Training and Generation ---
-model = LanguageModel(vocab_size, block_size, n_embd, n_head, n_layer, dropout)
+model = LanguageModel()
 m = model.to(device)
+
+# Create a PyTorch optimizer.
 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 
+# Main training loop.
 for iter in range(max_iters):
+    # Every few iterations, evaluate the loss on both splits.
     if iter % eval_interval == 0:
         losses = estimate_loss()
         print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
+
+    # Sample a batch of data.
     xb, yb = get_batch('train')
+
+    # Forward pass: compute loss.
     logits, loss = model(xb, yb)
+    # Backward pass: compute gradients.
     optimizer.zero_grad(set_to_none=True)
     loss.backward()
+    # Update the model parameters.
     optimizer.step()
 
+# --- Generate new text from the trained model ---
+context = torch.zeros((1, 1), dtype=torch.long, device=device)
+generated_text_indices = m.generate(context, max_new_tokens=20)
+print("\nGenerated text:")
+print(decode(generated_text_indices[0].tolist()))
+
 # Save the model's state dictionary after training
 torch.save(m.state_dict(), 'model.pt')
-print("Model saved to model.pt")
+print("Model saved to model.pt")
+