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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 context window
"BATCH_SIZE": 512, # Increased batch size
"EPOCHS": 20,
"EMBEDDING_DIM": 64,
"HIDDEN_DIM": 64,
"NUM_LAYERS": 1, # Multi-layer LSTM
"DROPOUT": 0.1,
"LEARNING_RATE": 0.01,
"CLIP_GRAD": 1.0, # Gradient clipping
"LR_GAMMA": 0.95, # Learning rate decay
"VAL_SPLIT": 0.1, # Validation split
"EARLY_STOP_PATIENCE": 3, # Early stopping patience
"MODEL_SAVE_PATH": "char_lm_model.pth",
"TEMPERATURE": 0.7,
"TOP_K": 5,
"TOP_P": 0.95
}
# Read and process text
with open(CONFIG["FILE_PATH"], 'r', encoding='utf-8') as f:
text = f.read()
# Vocabulary setup
chars = sorted(list(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)}
# Encode text
encoded_text = np.array([char_to_idx[ch] for ch in text])
# Dataset class with train-val split
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()
dataset = TextDataset(encoded_text, CONFIG["SEQ_LENGTH"])
val_size = int(len(dataset) * CONFIG["VAL_SPLIT"])
train_size = len(dataset) - val_size
train_dataset, val_dataset = random_split(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 Model architecture with LSTM and dropout
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"],
num_layers=CONFIG["NUM_LAYERS"],
dropout=CONFIG["DROPOUT"] if CONFIG["NUM_LAYERS"] > 1 else 0,
batch_first=True
)
self.dropout = nn.Dropout(CONFIG["DROPOUT"])
self.fc = nn.Linear(CONFIG["HIDDEN_DIM"], vocab_size)
self.init_weights()
def init_weights(self):
# Initialize weights for better convergence
nn.init.xavier_uniform_(self.embedding.weight)
for name, param in self.lstm.named_parameters():
if 'weight_ih' in name:
nn.init.xavier_uniform_(param.data)
elif 'weight_hh' in name:
nn.init.orthogonal_(param.data)
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.dropout(out)
out = self.fc(out)
return out, hidden
model = CharLM()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=CONFIG["LEARNING_RATE"])
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=CONFIG["LR_GAMMA"])
# Training loop with validation and 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:
optimizer.zero_grad()
outputs, _ = model(inputs)
loss = criterion(outputs.reshape(-1, vocab_size), targets.reshape(-1))
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), CONFIG["CLIP_GRAD"])
optimizer.step()
train_loss += loss.item()
progress_bar.set_postfix({'loss': loss.item()})
# Validation phase
model.eval()
val_loss = 0
with torch.no_grad():
for inputs, targets in val_loader:
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}')
# Early stopping and checkpointing
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'Best model saved to {CONFIG["MODEL_SAVE_PATH"]} with validation loss: {best_val_loss:.4f}')
# Advanced Text Generation with multiple sampling methods
def generate_text(model, start_str, length=200, temperature=CONFIG["TEMPERATURE"],
top_k=CONFIG["TOP_K"], top_p=CONFIG["TOP_P"]):
"""
Generate text with temperature scaling, top-k, and nucleus (top-p) sampling
"""
model.eval()
chars = list(start_str)
input_seq = torch.tensor([char_to_idx[ch] for ch in chars]).unsqueeze(0)
hidden = None
with torch.no_grad():
for _ in tqdm(range(length), desc="Generating text"):
outputs, hidden = model(input_seq, hidden)
logits = outputs[0, -1] / temperature
# Apply top-k filtering
if top_k > 0:
top_vals, top_idx = torch.topk(logits, top_k)
logits[logits < top_vals[-1]] = -float('Inf')
# Apply nucleus (top-p) filtering
if top_p > 0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = -float('Inf')
probs = torch.softmax(logits, dim=-1)
next_char = torch.multinomial(probs, num_samples=1).item()
chars.append(idx_to_char[next_char])
input_seq = torch.tensor([[next_char]])
return ''.join(chars)
# Generation examples with different parameters
print("\nConservative sampling (temperature=0.5):")
print(generate_text(model, "The ", temperature=0.5))
print("\nCreative sampling (temperature=1.2, top_p=0.9):")
print(generate_text(model, "Once ", temperature=1.2, top_p=0.9))
print("\nTop-k sampling (k=5):")
print(generate_text(model, "In ", top_k=5))
print("\nCombined sampling (temp=0.7, top_k=3, top_p=0.9):")
print(generate_text(model, "Artificial is ", temperature=0.7, top_k=3, top_p=0.9)) |