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from typing import Dict, Tuple
import torch
import torch.nn as nn
def create_masked_tensor(data: torch.Tensor, lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Converts a batch of variable-length sequences into a padded tensor and corresponding mask.
Args:
data (torch.Tensor): Input tensor containing flattened sequences.
- For indices: shape (total_elements,) of dtype long
- For embeddings: shape (total_elements, embedding_dim)
lengths (torch.Tensor): 1D tensor of sequence lengths, shape (batch_size,)
Returns:
Tuple[torch.Tensor, torch.Tensor]:
- padded_tensor: Padded tensor of shape:
- (batch_size, max_seq_len) for indices
- (batch_size, max_seq_len, embedding_dim) for embeddings
- mask: Boolean mask of shape (batch_size, max_seq_len) where True indicates valid elements
Note:
- Zero-padding is added to the right of shorter sequences
"""
batch_size = lengths.shape[0]
max_sequence_length = int(lengths.max().item())
if len(data.shape) == 1: # indices
padded_tensor = torch.zeros(
batch_size, max_sequence_length, dtype=data.dtype, device=data.device
) # (batch_size, max_seq_len)
else:
assert len(data.shape) == 2 # embeddings
padded_tensor = torch.zeros(
batch_size, max_sequence_length, *data.shape[1:], dtype=data.dtype, device=data.device
) # (batch_size, max_seq_len, embedding_dim)
mask = (
torch.arange(end=max_sequence_length, device=lengths.device)[None] < lengths[:, None]
) # (batch_size, max_seq_len)
padded_tensor[mask] = data
return padded_tensor, mask
class SASRecEncoder(nn.Module):
def __init__(
self,
num_items: int,
max_sequence_length: int,
embedding_dim: int,
num_heads: int,
num_layers: int,
dim_feedforward: int | None = None,
dropout: float = 0.0,
activation: nn.Module = nn.GELU(),
layer_norm_eps: float = 1e-9,
initializer_range: float = 0.02,
) -> None:
super().__init__()
self._num_items = num_items
self._num_heads = num_heads
self._embedding_dim = embedding_dim
self._item_embeddings = nn.Embedding(
num_embeddings=num_items + 1, # add zero id embedding
embedding_dim=embedding_dim,
)
self._position_embeddings = nn.Embedding(num_embeddings=max_sequence_length, embedding_dim=embedding_dim)
self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
self._dropout = nn.Dropout(dropout)
transformer_encoder_layer = nn.TransformerEncoderLayer(
d_model=embedding_dim,
nhead=num_heads,
dim_feedforward=dim_feedforward or 4 * embedding_dim,
dropout=dropout,
activation=activation,
layer_norm_eps=layer_norm_eps,
batch_first=True,
)
self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
self._init_weights(initializer_range)
@property
def item_embeddings(self) -> nn.Module:
return self._item_embeddings
@property
def num_items(self) -> int:
return self._num_items
def _apply_sequential_encoder(self, events: torch.Tensor, lengths: torch.Tensor):
"""
Processes variable-length event sequences through a transformer encoder with positional embeddings.
Args:
events (torch.Tensor): Flattened tensor of event indices, shape (total_events,)
lengths (torch.Tensor): 1D tensor of sequence lengths, shape (batch_size,)
Returns:
Tuple[torch.Tensor, torch.Tensor]:
- embeddings: Processed sequence embeddings, shape (batch_size, seq_len, embedding_dim)
- mask: Boolean mask indicating valid elements, shape (batch_size, seq_len)
Processing Steps:
1. Embedding Lookup:
- Converts event indices to dense embeddings
2. Positional Encoding:
- Generates reverse-order positions (newest event first)
- Adds positional embeddings to item embeddings
3. Transformer Processing:
- Applies layer norm and dropout
- Uses causal attention mask for autoregressive modeling
- Uses padding mask to ignore invalid positions
Note:
- Position indices are generated in reverse chronological order (newest event = position 0)
"""
embeddings = self._item_embeddings(events) # (total_batch_events, embedding_dim)
embeddings, mask = create_masked_tensor(
data=embeddings, lengths=lengths
) # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
batch_size = mask.shape[0]
seq_len = mask.shape[1]
positions = (
torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None].tile([batch_size, 1]).long()
) # (batch_size, seq_len)
positions_mask = positions < lengths[:, None] # (batch_size, max_seq_len)
positions = positions[positions_mask] # (total_batch_events)
position_embeddings = self._position_embeddings(positions) # (total_batch_events, embedding_dim)
position_embeddings, _ = create_masked_tensor(
data=position_embeddings, lengths=lengths
) # (batch_size, seq_len, embedding_dim)
embeddings = embeddings + position_embeddings # (batch_size, seq_len, embedding_dim)
embeddings = self._layernorm(embeddings) # (batch_size, seq_len, embedding_dim)
embeddings = self._dropout(embeddings) # (batch_size, seq_len, embedding_dim)
embeddings[~mask] = 0
causal_mask = torch.tril(torch.ones(seq_len, seq_len)).bool().to(mask.device) # (seq_len, seq_len)
embeddings = self._encoder(
src=embeddings, mask=~causal_mask, src_key_padding_mask=~mask
) # (batch_size, seq_len, embedding_dim)
return embeddings, mask
@torch.no_grad()
def _init_weights(self, initializer_range: float) -> None:
"""
Initialize all model parameters (weights and biases) in-place.
For each parameter in the model:
- If the parameter name contains 'weight':
- If it also contains 'norm' (e.g., for normalization layers), initialize with ones.
- Otherwise, initialize with a truncated normal distribution (mean=0, std=initializer_range)
and values clipped to the range [-2 * initializer_range, 2 * initializer_range].
- If the parameter name contains 'bias', initialize with zeros.
- If the parameter name does not match either case, raise a ValueError.
Args:
initializer_range (float): Standard deviation for the truncated normal distribution
used to initialize non-normalization weights.
Note:
This method should be called during model initialization to ensure all weights and biases
are properly set. It runs in a no-grad context and does not track gradients.
"""
for key, value in self.named_parameters():
if 'weight' in key:
if 'norm' in key:
nn.init.ones_(value.data)
else:
nn.init.trunc_normal_(
value.data, std=initializer_range, a=-2 * initializer_range, b=2 * initializer_range
)
else:
assert 'bias' in key
nn.init.zeros_(value.data)
@staticmethod
def _get_last_embedding(embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""
Extracts the embedding of the last valid (non-padded) element from each sequence in a batch.
Args:
embeddings (torch.Tensor): Tensor of shape (batch_size, seq_len, embedding_dim)
containing embeddings for each element in each sequence.
mask (torch.Tensor): Boolean tensor of shape (batch_size, seq_len) indicating
valid (True) and padded (False) positions in each sequence.
Returns:
torch.Tensor: Tensor of shape (batch_size, embedding_dim) containing the embedding
of the last valid element for each sequence in the batch.
"""
flatten_embeddings = embeddings[mask] # (total_batch_events, embedding_dim)
lengths = torch.sum(mask, dim=-1) # (batch_size)
offsets = torch.cumsum(lengths, dim=0) # (batch_size)
last_embeddings = flatten_embeddings[offsets.long() - 1] # (batch_size, embedding_dim)
return last_embeddings
def forward(self, inputs: Dict) -> torch.Tensor:
"""
Forward pass of the model, handling both training and evaluation modes.
Args:
inputs (Dict): Input dictionary containing:
- 'item.ids' (torch.LongTensor): Flattened tensor of item IDs for all sequences in the batch.
Shape: (total_batch_events,)
- 'item.length' (torch.LongTensor): Sequence lengths for each sample in the batch.
Shape: (batch_size,)
- 'positive.ids' (torch.LongTensor, training only): Positive sample IDs for contrastive learning.
Shape: (total_batch_events,)
- 'negative.ids' (torch.LongTensor, training only): Negative sample IDs for contrastive learning.
Shape: (total_batch_events,)
Returns:
torch.Tensor:
- During training: Binary cross-entropy loss between positive/negative sample scores.
Shape: (1,)
- During evaluation: Embeddings of the last valid item in each sequence.
Shape: (batch_size, embedding_dim)
"""
all_sample_events = inputs['item.ids'] # (total_batch_events)
all_sample_lengths = inputs['item.length'] # (batch_size)
embeddings, mask = self._apply_sequential_encoder(
all_sample_events, all_sample_lengths
) # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
if self.training: # training mode
# queries
in_batch_queries_embeddings = embeddings[mask] # (total_batch_events, embedding_dim)
# positives
in_batch_positive_events = inputs['positive.ids'] # (total_batch_events)
in_batch_positive_embeddings = self._item_embeddings(
in_batch_positive_events
) # (total_batch_events, embedding_dim)
positive_scores = torch.einsum(
'bd,bd->b', in_batch_queries_embeddings, in_batch_positive_embeddings
) # (total_batch_events)
# negatives
in_batch_negative_events = inputs['negative.ids'] # (total_batch_events)
in_batch_negative_embeddings = self._item_embeddings(
in_batch_negative_events
) # (total_batch_events, embedding_dim)
negative_scores = torch.einsum(
'bd,bd->b', in_batch_queries_embeddings, in_batch_negative_embeddings
) # (total_batch_events)
loss = nn.functional.binary_cross_entropy_with_logits(
torch.cat([positive_scores, negative_scores], dim=0),
torch.cat([torch.ones_like(positive_scores), torch.zeros_like(negative_scores)]),
) # (1)
return loss
else: # eval mode
last_embeddings = self._get_last_embedding(embeddings, mask) # (batch_size, embedding_dim)
return last_embeddings
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