94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
import torch
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import torch.nn as nn
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from torch.utils.data import Dataset, DataLoader
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import torch.optim as optim
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import json
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# Define TextDataset
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class TextDataset(Dataset):
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def __init__(self, corpus_file, vocab, max_len=32):
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self.vocab = vocab
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self.max_len = max_len
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with open(corpus_file, 'r', encoding='utf-8') as f:
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text = f.read().lower().split()
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self.tokens = [self.vocab.get(word, self.vocab['<unk>']) for word in text]
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def __len__(self):
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return len(self.tokens) // self.max_len
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def __getitem__(self, idx):
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start = idx * self.max_len
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seq = self.tokens[start:start + self.max_len]
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if len(seq) < self.max_len:
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seq += [self.vocab['<pad>']] * (self.max_len - len(seq))
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return torch.tensor(seq[:-1]), torch.tensor(seq[1:])
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# Define model
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class VivianTransformer(nn.Module):
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def __init__(self, vocab_size, d_model=128, n_layers=2, n_heads=4, d_ff=512):
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super().__init__()
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self.embedding = nn.Embedding(vocab_size, d_model)
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self.pos_encoding = nn.Parameter(torch.randn(1, 512, d_model))
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encoder_layer = nn.TransformerEncoderLayer(d_model, n_heads, d_ff, dropout=0.1)
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self.transformer = nn.TransformerEncoder(encoder_layer, n_layers)
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self.fc_out = nn.Linear(d_model, vocab_size)
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def forward(self, x):
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x = self.embedding(x) + self.pos_encoding[:, :x.size(1), :]
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x = self.transformer(x)
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return self.fc_out(x)
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# Check device
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f"Using device: {device}")
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if device.type == 'cuda':
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print(f"GPU: {torch.cuda.get_device_name(0)}")
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else:
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print("Warning: CUDA not available. Training on CPU will be slower.")
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# Load vocab
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try:
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with open('vocab.json', 'r') as f:
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vocab = json.load(f)
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except FileNotFoundError:
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print("Error: vocab.json not found. Run build_tokenizer.py first.")
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exit(1)
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# Load dataset
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try:
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dataset = torch.load('dataset.pt')
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except FileNotFoundError:
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print("Error: dataset.pt not found. Run prepare_dataset.py first.")
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exit(1)
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except Exception as e:
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print(f"Error loading dataset.pt: {e}")
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exit(1)
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# Create dataloader
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dataloader = DataLoader(dataset, batch_size=16, shuffle=True)
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# Initialize model
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model = VivianTransformer(len(vocab)).to(device)
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optimizer = optim.Adam(model.parameters(), lr=0.0001)
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criterion = nn.CrossEntropyLoss(ignore_index=vocab['<pad>'])
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# Train
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print("Starting training...")
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for epoch in range(5):
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model.train()
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total_loss = 0
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for batch_idx, (src, tgt) in enumerate(dataloader):
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src, tgt = src.to(device), tgt.to(device)
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optimizer.zero_grad()
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output = model(src)
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loss = criterion(output.view(-1, len(vocab)), tgt.view(-1))
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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if batch_idx % 100 == 0:
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print(f"Epoch {epoch+1}, Batch {batch_idx}, Loss: {loss.item():.4f}")
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print(f'Epoch {epoch+1}, Average Loss: {total_loss / len(dataloader):.4f}')
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# Save model
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torch.save(model.state_dict(), 'vivi_base.pt')
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print("Model saved to vivi_base.pt") |