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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, random_split
from torch.cuda.amp import autocast, GradScaler
from torch.utils.tensorboard import SummaryWriter
import numpy as np
from tqdm import tqdm
import json
import argparse
from datetime import datetime
# Configuration with Transformer-specific parameters
CONFIG = {
"FILE_PATH": 'dataset.txt',
"SEQ_LENGTH": 32,
"BATCH_SIZE": 8,
"EPOCHS": 1,
"EMBEDDING_DIM": 64,
"N_HEADS": 1,
"FFN_DIM": 64,
"NUM_LAYERS": 3,
"DROPOUT": 0.1,
"LEARNING_RATE": 0.0005,
"WEIGHT_DECAY": 0.01,
"CLIP_GRAD": 1.0,
"LABEL_SMOOTHING": 0.1,
"GRAD_ACCUM_STEPS": 2,
"VAL_SPLIT": 0.1,
"EARLY_STOP_PATIENCE": 3,
"MODEL_SAVE_PATH": "transformer_lm_model.pth",
"TEMPERATURE": 0.7,
"TOP_K": 50,
"TOP_P": 0.9,
"LOG_DIR": "runs"
}
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
scaler = GradScaler(enabled=device.type == 'cuda')
# Handle command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, help='Path to config JSON file')
args = parser.parse_args()
if args.config:
with open(args.config) as f:
CONFIG.update(json.load(f))
# Initialize TensorBoard
writer = SummaryWriter(f"{CONFIG['LOG_DIR']}/{datetime.now().strftime('%Y%m%d-%H%M%S')}")
# 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 memory mapping
class TextDataset(Dataset):
def __init__(self, data, seq_length):
self.data = torch.from_numpy(data).long()
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 x, y
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, pin_memory=True, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=CONFIG["BATCH_SIZE"],
pin_memory=True, num_workers=4)
# Transformer-based Language Model
class TransformerLM(nn.Module):
def __init__(self):
super().__init__()
self.embedding = nn.Embedding(vocab_size, CONFIG["EMBEDDING_DIM"])
self.pos_embed = nn.Embedding(CONFIG["SEQ_LENGTH"], CONFIG["EMBEDDING_DIM"])
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=CONFIG["EMBEDDING_DIM"],
nhead=CONFIG["N_HEADS"],
dim_feedforward=CONFIG["FFN_DIM"],
dropout=CONFIG["DROPOUT"],
activation='gelu',
batch_first=True
),
num_layers=CONFIG["NUM_LAYERS"]
)
self.ln = nn.LayerNorm(CONFIG["EMBEDDING_DIM"])
self.fc = nn.Linear(CONFIG["EMBEDDING_DIM"], vocab_size)
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.Embedding):
nn.init.xavier_uniform_(module.weight)
def forward(self, x, mask=None):
batch_size, seq_len = x.size()
positions = torch.arange(seq_len, device=device).expand(batch_size, seq_len)
x = self.embedding(x) + self.pos_embed(positions)
if mask is None:
mask = nn.Transformer.generate_square_subsequent_mask(seq_len).to(device)
x = self.transformer(x, mask)
x = self.ln(x)
return self.fc(x), None # Return None for compatibility with generation code
model = TransformerLM().to(device)
optimizer = torch.optim.AdamW(model.parameters(),
lr=CONFIG["LEARNING_RATE"],
weight_decay=CONFIG["WEIGHT_DECAY"])
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='min', factor=0.5, patience=2)
# Training loop with advanced features
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 i, (inputs, targets) in enumerate(progress_bar):
inputs, targets = inputs.to(device), targets.to(device)
with autocast(enabled=device.type == 'cuda'):
outputs, _ = model(inputs)
logits = outputs.view(-1, vocab_size)
targets = targets.view(-1)
if CONFIG["LABEL_SMOOTHING"]:
loss = F.cross_entropy(logits, targets,
label_smoothing=CONFIG["LABEL_SMOOTHING"])
else:
loss = F.cross_entropy(logits, targets)
loss = loss / CONFIG["GRAD_ACCUM_STEPS"]
scaler.scale(loss).backward()
if (i + 1) % CONFIG["GRAD_ACCUM_STEPS"] == 0:
scaler.unscale_(optimizer)
nn.utils.clip_grad_norm_(model.parameters(), CONFIG["CLIP_GRAD"])
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
lr = optimizer.param_groups[0]['lr']
progress_bar.set_postfix({'loss': loss.item() * CONFIG["GRAD_ACCUM_STEPS"], 'lr': lr})
train_loss += loss.item() * CONFIG["GRAD_ACCUM_STEPS"]
# Validation phase
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 = F.cross_entropy(outputs.view(-1, vocab_size), targets.view(-1))
val_loss += loss.item()
avg_train_loss = train_loss / len(train_loader)
avg_val_loss = val_loss / len(val_loader)
scheduler.step(avg_val_loss)
# Log metrics
writer.add_scalar('Loss/train', avg_train_loss, epoch)
writer.add_scalar('Loss/val', avg_val_loss, epoch)
writer.add_scalar('Learning Rate', optimizer.param_groups[0]['lr'], epoch)
writer.add_scalar('Perplexity/train', np.exp(avg_train_loss), epoch)
writer.add_scalar('Perplexity/val', np.exp(avg_val_loss), epoch)
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({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
'config': CONFIG
}, CONFIG["MODEL_SAVE_PATH"])
patience_counter = 0
else:
patience_counter += 1
if patience_counter >= CONFIG["EARLY_STOP_PATIENCE"]:
print("Early stopping triggered")
break
writer.close()
print(f'Best model saved to {CONFIG["MODEL_SAVE_PATH"]} with validation loss: {best_val_loss:.4f}')
# Advanced generation with multiple sampling strategies
def generate_text(model, start_str, length=200, temperature=CONFIG["TEMPERATURE"],
top_k=CONFIG["TOP_K"], top_p=CONFIG["TOP_P"]):
model.eval()
chars = list(start_str)
input_seq = torch.tensor([char_to_idx[ch] for ch in chars], device=device).unsqueeze(0)
with torch.no_grad():
for _ in tqdm(range(length), desc="Generating text"):
mask = nn.Transformer.generate_square_subsequent_mask(input_seq.size(1)).to(device)
outputs, _ = model(input_seq[:, -CONFIG["SEQ_LENGTH"]:], mask)
logits = outputs[:, -1] / temperature
# Apply nucleus sampling first
if top_p > 0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.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_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
logits = logits.masked_fill(indices_to_remove, float('-inf'))
# Then apply top-k filtering
if top_k > 0:
top_k = min(top_k, logits.size(-1))
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits = logits.masked_fill(indices_to_remove, float('-inf'))
probs = F.softmax(logits, dim=-1)
next_char = torch.multinomial(probs, num_samples=1)
chars.append(idx_to_char[next_char.item()])
input_seq = torch.cat([input_seq, next_char], dim=1)
return ''.join(chars)
# Generate examples with different parameters
print("\nConservative sampling:")
print(generate_text(model, "The ", temperature=0.5, top_p=0))
print("\nCreative sampling:")
print(generate_text(model, "Once ", temperature=1.2, top_p=0.9))
print("\nTop-k sampling:")
print(generate_text(model, "In ", top_k=50))
print("\nCombined sampling:")
print(generate_text(model, "Artificial intelligence ", temperature=0.8, top_k=50, top_p=0.9)) |