| --- |
| license: gpl-3.0 |
| datasets: |
| - karpathy/tiny_shakespeare |
| language: |
| - en |
| pipeline_tag: text-generation |
| --- |
| |
| ## Usage |
|
|
| ```seq_length = 32 |
| batch_size = 16 |
| embed_dim = 256 |
| num_heads = 4 |
| ff_dim = 512 |
| num_layers = 2 |
| noise_prob = 0.3 |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| |
| class PositionalEncoding(nn.Module): |
| def __init__(self, d_model, max_len=5000): |
| super().__init__() |
| pe = torch.zeros(max_len, d_model) |
| position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) |
| div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)) |
| pe[:, 0::2] = torch.sin(position * div_term) |
| pe[:, 1::2] = torch.cos(position * div_term) |
| self.register_buffer('pe', pe.unsqueeze(0)) |
| |
| def forward(self, x): |
| return x + self.pe[:, :x.size(1)] |
| |
| class TransformerBlock(nn.Module): |
| def __init__(self, embed_dim, num_heads, ff_dim): |
| super().__init__() |
| self.attention = nn.MultiheadAttention(embed_dim, num_heads) |
| self.norm1 = nn.LayerNorm(embed_dim) |
| self.ff = nn.Sequential( |
| nn.Linear(embed_dim, ff_dim), |
| nn.ReLU(), |
| nn.Linear(ff_dim, embed_dim) |
| ) |
| self.norm2 = nn.LayerNorm(embed_dim) |
| |
| def forward(self, x): |
| attn_output, _ = self.attention(x, x, x) |
| x = self.norm1(x + attn_output) |
| ff_output = self.ff(x) |
| return self.norm2(x + ff_output) |
| |
| class DenoisingTransformer(nn.Module): |
| def __init__(self, vocab_size, embed_dim, num_heads, ff_dim, num_layers): |
| super().__init__() |
| self.embedding = nn.Embedding(vocab_size, embed_dim) |
| self.positional_encoding = PositionalEncoding(embed_dim) |
| self.transformer_blocks = nn.ModuleList([ |
| TransformerBlock(embed_dim, num_heads, ff_dim) for _ in range(num_layers) |
| ]) |
| self.fc = nn.Linear(embed_dim, vocab_size) |
| |
| def forward(self, x): |
| x = self.embedding(x) |
| x = self.positional_encoding(x) |
| for block in self.transformer_blocks: |
| x = block(x) |
| return self.fc(x) |
| |
| def load_model(path, device='cpu'): |
| checkpoint = torch.load(path, map_location=device) |
| hp = checkpoint['hyperparameters'] |
| |
| model = DenoisingTransformer( |
| hp['vocab_size'], |
| hp['embed_dim'], |
| hp['num_heads'], |
| hp['ff_dim'], |
| hp['num_layers'] |
| ).to(device) |
| |
| model.load_state_dict(checkpoint['model_state_dict']) |
| return model, checkpoint['word2idx'], checkpoint['idx2word'] |
| |
| loaded_model, word2idx, idx2word = load_model('denoising_transformer.pth', device=device) |
| |
| print("Model loaded successfully!") |
| print(f"Model device: {next(loaded_model.parameters()).device}")``` |
