""" LaM-SLidE Autoencoder for discrete token reconstruction of variable-sized entity sets (music score notes). Provides the main autoencoder model, the NoteFeatureEmbedder, and config dataclasses. Architecture: Input: (features_dict, entity_ids, mask) -> NoteFeatureEmbedder -> Encoder (cross-attn) -> fixed latent (B, L, D) -> Decoder (cross-attn) -> per-entity logits dict """ from dataclasses import dataclass, field from functools import partial from typing import Dict, List, Optional import torch import torch.nn as nn from .encoder import Encoder from .decoder import Decoder from .entity_embeddings import EntityEmbeddingFactorized, EntityEmbeddingOrthogonal from .note_hgt import NoteHGT @dataclass class FeatureConfig: """Configuration for a single feature.""" name: str # Feature name (e.g., 'grid_position') vocab_size: int # Number of discrete tokens embed_dim: int = 32 # Embedding dimension for this feature is_input: bool = True # Use as input feature is_output: bool = True # Reconstruct this feature @dataclass class AutoencoderConfig: """Configuration for the LaM-SLidE Autoencoder.""" # Feature configuration (multi-feature support) # Default: single grid_position feature for backwards compatibility features: List[FeatureConfig] = field(default_factory=lambda: [ FeatureConfig(name='grid_position', vocab_size=33, embed_dim=32), ]) # Entity identifier settings identifier_pool_size: int = 512 # Size of entity ID pool entity_embed_dim: int = 128 # Dimension of entity embeddings entity_embed_type: str = 'factorized' # 'factorized' or 'orthogonal' # Latent space dim_latent: int = 128 # Latent space dimension num_latents: int = 32 # Number of latent vectors (bottleneck) # Attention configuration dim_head_cross: int = 32 # Dimension per head in cross-attention dim_head_latent: int = 32 # Dimension per head in self-attention num_head_cross: int = 4 # Number of cross-attention heads num_head_latent: int = 4 # Number of self-attention heads # Architecture depth num_block_cross_enc: int = 2 # Cross-attention blocks in encoder num_block_attn_enc: int = 2 # Self-attention blocks in encoder num_block_cross_dec: int = 2 # Cross-attention blocks in decoder num_block_attn_dec: int = 2 # Self-attention blocks in decoder # Regularization dropout_latent: float = 0.0 # Dropout on latent vectors qk_norm: bool = True # Query-key normalization # Feature mixing MLP (applied after embedding concat, before HGT/encoder) feature_mlp_hidden_dim: int = 0 # 0 = disabled, >0 = hidden dim of feature mixing MLP # HGT (Heterogeneous Graph Transformer) for note-level message passing use_hgt: bool = False # Whether to use HGT after feature embedding hgt_num_layers: int = 2 # Number of HGT layers hgt_num_heads: int = 4 # Number of attention heads in HGT hgt_dropout: float = 0.1 # Dropout in HGT layers @property def input_features(self) -> List[FeatureConfig]: """Get features used as inputs.""" return [f for f in self.features if f.is_input] @property def output_features(self) -> List[FeatureConfig]: """Get features to reconstruct.""" return [f for f in self.features if f.is_output] @property def total_input_dim(self) -> int: """Total dimension of concatenated input embeddings.""" return sum(f.embed_dim for f in self.input_features) @property def output_vocab_sizes(self) -> Dict[str, int]: """Dict of output feature names to vocab sizes.""" return {f.name: f.vocab_size for f in self.output_features} class NoteFeatureEmbedder(nn.Module): """Embeds multiple discrete note features into continuous space and manages the shared entity identifier embeddings used by both encoder and decoder. """ def __init__(self, config: AutoencoderConfig): super().__init__() self.config = config # Feature embeddings: each discrete feature -> continuous vector self.feature_embeddings = nn.ModuleDict() for feat in config.input_features: self.feature_embeddings[feat.name] = nn.Embedding( num_embeddings=feat.vocab_size, embedding_dim=feat.embed_dim, ) # Entity embedding: shared between encoder and decoder for traceability. # Factorized variant uses sqrt(pool_size) base+offset tables. if config.entity_embed_type == 'factorized': self.entity_embedding = EntityEmbeddingFactorized( n_entiy_embeddings=config.identifier_pool_size, embedding_dim=config.entity_embed_dim, requires_grad=True, combine='concat', # base || offset -> full embedding max_norm=1.0, ) else: self.entity_embedding = EntityEmbeddingOrthogonal( n_entiy_embeddings=config.identifier_pool_size, embedding_dim=config.entity_embed_dim, requires_grad=True, max_norm=1.0, ) # Optional HGT for note-level message passing after feature embedding self.use_hgt = config.use_hgt if config.use_hgt: self.hgt = NoteHGT( note_dim=config.total_input_dim, # Same dim as feature embeddings num_layers=config.hgt_num_layers, num_heads=config.hgt_num_heads, dropout=config.hgt_dropout, ) else: self.hgt = None # Optional feature mixing MLP applied after embedding concat, before # HGT and entity concat. Pre-norm residual (LayerNorm -> MLP + skip). if config.feature_mlp_hidden_dim > 0: act = partial(nn.GELU, approximate="tanh") self.feature_mlp = nn.Sequential( nn.LayerNorm(config.total_input_dim), nn.Linear(config.total_input_dim, config.feature_mlp_hidden_dim), act(), nn.Linear(config.feature_mlp_hidden_dim, config.total_input_dim), ) else: self.feature_mlp = None def embed_features( self, features: Dict[str, torch.Tensor], edge_dicts: Optional[List[Dict]] = None, mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ Embed multiple discrete features and concatenate. Optionally applies HGT message passing if enabled. Args: features: Dict of feature_name -> (batch, num_entities) tensors edge_dicts: Optional list of edge_dict per sample (required if use_hgt=True) Each edge_dict maps edge_type_tuple -> edge_index (2, E) mask: Optional (B, N) validity mask Returns: combined_embeds: (batch, num_entities, total_input_dim) """ embeddings = [] for feat in self.config.input_features: if feat.name in features: emb = self.feature_embeddings[feat.name](features[feat.name]) embeddings.append(emb) else: raise KeyError(f"Missing input feature: {feat.name}") # Concatenate all feature embeddings combined = torch.cat(embeddings, dim=-1) # (B, N, total_input_dim) # Feature mixing MLP (residual): learns cross-feature interactions if self.feature_mlp is not None: combined = combined + self.feature_mlp(combined) # Apply HGT if enabled if self.use_hgt and self.hgt is not None: if edge_dicts is None: raise ValueError("edge_dicts required when use_hgt=True") # Derive num_notes_list from mask num_notes_list = mask.sum(dim=1).tolist() if mask is not None else [combined.shape[1]] * combined.shape[0] combined = self.hgt.forward_batch(combined, edge_dicts, num_notes_list, mask=mask) return combined def embed_entities(self, entity_ids: torch.Tensor) -> torch.Tensor: """ Embed entity identifiers to continuous vectors. Args: entity_ids: (batch, num_entities) entity identifier indices Returns: entity_embeds: (batch, num_entities, entity_embed_dim) """ return self.entity_embedding(entity_ids) @property def input_dim(self) -> int: """Total dimension of concatenated feature embeddings.""" return self.config.total_input_dim @property def entity_dim(self) -> int: """Dimension of entity embeddings.""" return self.entity_embedding.embedding_dim class LaMSLiDEAutoencoder(nn.Module): """Autoencoder for multi-feature discrete token reconstruction. Compresses variable-sized entity sets into a fixed-size latent (B, L, D) and reconstructs per-entity feature logits. Entity IDs provide a return address so the decoder can query the correct features from the latent. """ def __init__(self, config: AutoencoderConfig): super().__init__() self.config = config # Feature embedder: handles all embedding operations self.embedder = NoteFeatureEmbedder(config) # Encoder: variable-size input -> fixed-size latent self.encoder = Encoder( dim_input=self.embedder.input_dim, # Total input dimension dim_latent=config.dim_latent, dim_head_cross=config.dim_head_cross, dim_head_latent=config.dim_head_latent, num_latents=config.num_latents, num_head_cross=config.num_head_cross, num_head_latent=config.num_head_latent, num_block_cross=config.num_block_cross_enc, num_block_attn=config.num_block_attn_enc, qk_norm=config.qk_norm, entity_embedding=self.embedder.entity_embedding, # Shared! dropout_latent=config.dropout_latent, ) # Decoder: fixed-size latent -> variable-size per-entity logits self.decoder = Decoder( outputs=config.output_vocab_sizes, # Dict: feature_name -> vocab_size dim_query=config.dim_latent, dim_latent=config.dim_latent, entity_embedding=self.embedder.entity_embedding, # Shared! dim_head_cross=config.dim_head_cross, dim_head_latent=config.dim_head_latent, num_head_cross=config.num_head_cross, num_head_latent=config.num_head_latent, num_block_cross=config.num_block_cross_dec, num_block_attn=config.num_block_attn_dec, qk_norm=config.qk_norm, ) def encode( self, features: Dict[str, torch.Tensor], entity_ids: torch.Tensor, mask: Optional[torch.Tensor] = None, edge_dicts: Optional[List[Dict]] = None, ) -> torch.Tensor: """Encode variable-sized entity set to fixed-size latent (B, L, D). 1. Embed all input features -> concatenated continuous vectors 2. (Optional) Apply HGT message passing 3. Encoder concatenates feature + entity embeddings, then cross/self-attn Args: features: Dict of feature_name -> (B, N) discrete feature tensors entity_ids: (B, N) unique entity identifiers from pool mask: (B, N) boolean mask, True for valid entities edge_dicts: Optional list of edge_dict per sample (for HGT) Returns: latent: (B, L, D_latent) fixed-size latent representation """ # Embed and concatenate input features (+ optional HGT) feature_embeds = self.embedder.embed_features( features, edge_dicts=edge_dicts, mask=mask ) # (B, N, total_dim) # Encode to fixed-size latent (concat entity embeddings, cross/self attn) latent = self.encoder(feature_embeds, entity_ids, mask=mask) # (B, L, D_lat) return latent def decode( self, latent: torch.Tensor, entity_ids: torch.Tensor, ) -> Dict[str, torch.Tensor]: """ Decode latent representation back to per-entity feature logits. The decoder uses entity IDs as queries through cross-attention to retrieve feature logits from the latent space. Args: latent: (B, L, D_latent) fixed-size latent representation entity_ids: (B, N) entity identifiers to decode for Returns: outputs: Dict of feature_name -> (B, N, vocab_size) logits """ # Decoder uses entity IDs as queries to retrieve per-entity features outputs = self.decoder(latent, entity_ids) return outputs def forward( self, features: Dict[str, torch.Tensor], entity_ids: torch.Tensor, mask: Optional[torch.Tensor] = None, edge_dicts: Optional[List[Dict]] = None, ) -> Dict[str, torch.Tensor]: """ Full forward pass: encode then decode. Args: features: Dict of feature_name -> (B, N) discrete feature tensors entity_ids: (B, N) entity identifiers mask: (B, N) validity mask for variable-sized batches edge_dicts: Optional list of edge_dict per sample (for HGT) Returns: outputs: Dict of feature_name -> (B, N, vocab_size) logits """ # Encode: features -> (B, L, D_lat) latent = self.encode( features, entity_ids, mask=mask, edge_dicts=edge_dicts ) # Decode: (B, L, D_lat) -> {feature_name: (B, N, vocab_size)} outputs = self.decode(latent, entity_ids) return outputs def count_parameters(self) -> int: """Count trainable parameters.""" return sum(p.numel() for p in self.parameters() if p.requires_grad) def create_autoencoder_from_dict(config_dict: Dict) -> LaMSLiDEAutoencoder: """ Create autoencoder from a dictionary config (e.g., from OmegaConf). Args: config_dict: Dictionary with model configuration Returns: Configured LaMSLiDEAutoencoder instance """ # Parse features if provided as list of dicts if 'features' in config_dict: features = [ FeatureConfig(**f) if isinstance(f, dict) else f for f in config_dict['features'] ] config_dict = {**config_dict, 'features': features} config = AutoencoderConfig(**config_dict) return LaMSLiDEAutoencoder(config) def create_single_feature_config( feature_name: str = 'grid_position', vocab_size: int = 33, embed_dim: int = 32, **kwargs, ) -> AutoencoderConfig: """ Create a config for single-feature reconstruction (backwards compatible). Args: feature_name: Name of the feature vocab_size: Number of discrete tokens embed_dim: Embedding dimension **kwargs: Additional AutoencoderConfig parameters Returns: AutoencoderConfig with single feature """ feature = FeatureConfig( name=feature_name, vocab_size=vocab_size, embed_dim=embed_dim, ) return AutoencoderConfig(features=[feature], **kwargs)