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import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader, random_split
import numpy as np
from tqdm import tqdm
# Configuration
CONFIG = {
"FILE_PATH": 'dataset.txt',
"SEQ_LENGTH": 32, # Increased sequence length
"BATCH_SIZE": 8, # Increased batch size
"EPOCHS": 1,
"EMBEDDING_DIM": 64, # Deeper embedding layer
"HIDDEN_DIM": 64, # Larger hidden dimension
"NUM_LAYERS": 1, # More LSTM layers
"BIDIRECTIONAL": False, # Optional bidirectionality
"DROPOUT": 0.3,
"LEARNING_RATE": 0.01,
"CLIP_GRAD": 1.0, # Gradient clipping
"LR_GAMMA": 0.9, # Learning rate decay
"VAL_SPLIT": 0.1, # Validation split
"EARLY_STOP_PATIENCE": 3, # Early stopping patience
"MODEL_SAVE_PATH": "char_lm_advanced.pth",
"TEMPERATURE": 0.7,
"TOP_K": 10,
"TOP_P": 0.95
}
# Check for GPU
device = torch.device("cpu")
# Read and process text
with open(CONFIG["FILE_PATH"], 'r', encoding='utf-8') as f:
text = f.read()
chars = sorted(set(text))
vocab_size = len(chars)
char_to_idx = {ch: i for i, ch in enumerate(chars)}
idx_to_char = {i: ch for i, ch in enumerate(chars)}
encoded_text = np.array([char_to_idx[ch] for ch in text])
# Dataset Class
class TextDataset(Dataset):
def __init__(self, data, seq_length):
self.data = data
self.seq_length = seq_length
def __len__(self):
return len(self.data) - self.seq_length - 1
def __getitem__(self, idx):
x = self.data[idx:idx+self.seq_length]
y = self.data[idx+1:idx+self.seq_length+1]
return torch.from_numpy(x).long(), torch.from_numpy(y).long()
# Splitting dataset
full_dataset = TextDataset(encoded_text, CONFIG["SEQ_LENGTH"])
val_size = int(len(full_dataset) * CONFIG["VAL_SPLIT"])
train_size = len(full_dataset) - val_size
train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size])
train_loader = DataLoader(train_dataset, batch_size=CONFIG["BATCH_SIZE"], shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=CONFIG["BATCH_SIZE"])
# Advanced LSTM Model
class CharLM(nn.Module):
def __init__(self):
super(CharLM, self).__init__()
self.embedding = nn.Embedding(vocab_size, CONFIG["EMBEDDING_DIM"])
self.lstm = nn.LSTM(
CONFIG["EMBEDDING_DIM"], CONFIG["HIDDEN_DIM"], CONFIG["NUM_LAYERS"],
dropout=CONFIG["DROPOUT"], bidirectional=CONFIG["BIDIRECTIONAL"], batch_first=True
)
self.layer_norm = nn.LayerNorm(CONFIG["HIDDEN_DIM"])
self.fc = nn.Linear(CONFIG["HIDDEN_DIM"], vocab_size)
self.dropout = nn.Dropout(CONFIG["DROPOUT"])
self.init_weights()
def init_weights(self):
nn.init.xavier_uniform_(self.embedding.weight)
for name, param in self.lstm.named_parameters():
if 'weight' in name:
nn.init.xavier_uniform_(param)
elif 'bias' in name:
param.data.fill_(0)
def forward(self, x, hidden=None):
x = self.embedding(x)
out, hidden = self.lstm(x, hidden)
out = self.layer_norm(out)
out = self.dropout(out)
out = self.fc(out)
return out, hidden
model = CharLM().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=CONFIG["LEARNING_RATE"])
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=CONFIG["LR_GAMMA"])
scaler = torch.cuda.amp.GradScaler()
# Training with Mixed Precision & Early Stopping
best_val_loss = float('inf')
patience_counter = 0
for epoch in range(CONFIG["EPOCHS"]):
model.train()
train_loss = 0
progress_bar = tqdm(train_loader, desc=f'Epoch {epoch+1}/{CONFIG["EPOCHS"]}')
for inputs, targets in progress_bar:
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
with torch.cuda.amp.autocast():
outputs, _ = model(inputs)
loss = criterion(outputs.reshape(-1, vocab_size), targets.reshape(-1))
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
nn.utils.clip_grad_norm_(model.parameters(), CONFIG["CLIP_GRAD"])
scaler.step(optimizer)
scaler.update()
train_loss += loss.item()
progress_bar.set_postfix({'loss': loss.item()})
# Validation
model.eval()
val_loss = 0
with torch.no_grad():
for inputs, targets in val_loader:
inputs, targets = inputs.to(device), targets.to(device)
outputs, _ = model(inputs)
loss = criterion(outputs.reshape(-1, vocab_size), targets.reshape(-1))
val_loss += loss.item()
avg_train_loss = train_loss / len(train_loader)
avg_val_loss = val_loss / len(val_loader)
print(f'Epoch {epoch+1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f}')
if avg_val_loss < best_val_loss:
best_val_loss = avg_val_loss
torch.save(model.state_dict(), CONFIG["MODEL_SAVE_PATH"])
patience_counter = 0
else:
patience_counter += 1
if patience_counter >= CONFIG["EARLY_STOP_PATIENCE"]:
print("Early stopping triggered")
break
scheduler.step()
print(f'Model saved to {CONFIG["MODEL_SAVE_PATH"]} with best val loss: {best_val_loss:.4f}')
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