DeepHealth/models.py

import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Tuple
import math

class CausalSelfAttention(nn.Module):
    """
    A vanilla multi-head masked self-attention layer with a projection at the end.
    """

    def __init__(self, n_embd: int, n_head: int, pdrop: float):
        super().__init__()
        assert n_embd % n_head == 0
        # key, query, value projections for all heads
        self.c_attn = nn.Linear(n_embd, 3 * n_embd)
        # output projection
        self.c_proj = nn.Linear(n_embd, n_embd)
        # regularization
        self.attn_dropout = nn.Dropout(pdrop)
        self.resid_dropout = nn.Dropout(pdrop)
        self.n_head = n_head
        self.n_embd = n_embd

    def forward(self, x: torch.Tensor, custom_mask: torch.Tensor) -> torch.Tensor:
        B, L, D = x.size() # batch size, sequence length, embedding dimensionality (n_embd)

        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        q, k, v  = self.c_attn(x).split(self.n_embd, dim=2)
        k = k.view(B, L, self.n_head, D // self.n_head).transpose(1, 2) # (B, nh, L, hs)
        q = q.view(B, L, self.n_head, D // self.n_head).transpose(1, 2) # (B, nh, L, hs)
        v = v.view(B, L, self.n_head, D // self.n_head).transpose(1, 2) # (B, nh, L, hs)

        # causal self-attention; Self-attend: (B, nh, L, hs) x (B, nh, hs, L) -> (B, nh, L, L)
        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        
        # Apply the time-based causal mask
        att = att.masked_fill(custom_mask.unsqueeze(1) == 0, float('-inf'))
        
        att = F.softmax(att, dim=-1)
        att = self.attn_dropout(att)
        y = att @ v # (B, nh, L, L) x (B, nh, L, hs) -> (B, nh, L, hs)
        y = y.transpose(1, 2).contiguous().view(B, L, D) # re-assemble all head outputs side by side

        # output projection
        y = self.resid_dropout(self.c_proj(y))
        return y

class Block(nn.Module):
    """ an unassuming Transformer block """

    def __init__(self, n_embd: int, n_head: int, pdrop: float):
        super().__init__()
        self.ln_1 = nn.LayerNorm(n_embd)
        self.attn = CausalSelfAttention(n_embd, n_head, pdrop)
        self.ln_2 = nn.LayerNorm(n_embd)
        self.mlp = nn.ModuleDict(dict(
            c_fc    = nn.Linear(n_embd, 4 * n_embd),
            c_proj  = nn.Linear(4 * n_embd, n_embd),
            act     = nn.GELU(),
            dropout = nn.Dropout(pdrop),
        ))
        m = self.mlp
        self.mlpf = lambda x: m.dropout(m.c_proj(m.act(m.c_fc(x)))) # MLP forward

    def forward(self, x: torch.Tensor, custom_mask: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.ln_1(x), custom_mask=custom_mask)
        x = x + self.mlpf(self.ln_2(x))
        return x

class AgeSinusoidalEncoding(nn.Module):
    """
    Encodes age using sinusoidal functions, similar to positional encodings
    in Transformers. This module creates a fixed-size embedding for an age
    value given in days.
    """

    def __init__(self, embedding_dim: int):
        """
        Initializes the AgeSinusoidalEncoding module.

        Args:
            embedding_dim (int): The dimensionality of the output embedding.
                Must be an even number.

        Raises:
            ValueError: If embedding_dim is not an even number.
        """
        super().__init__()
        if embedding_dim % 2 != 0:
            raise ValueError(f"Embedding dimension must be an even number, but got {embedding_dim}")

        self.embedding_dim = embedding_dim

        # Pre-calculate the divisor term for the sinusoidal formula.
        # The formula for the divisor is 10000^(2i/D), where D is the
        # embedding_dim and i is the index for each pair of dimensions.
        # i ranges from 0 to D/2 - 1.
        i = torch.arange(0, self.embedding_dim, 2, dtype=torch.float32)
        divisor = torch.pow(10000, i / self.embedding_dim)

        # Register the divisor as a non-trainable buffer. This ensures it is
        # moved to the correct device (e.g., GPU) along with the model.
        self.register_buffer('divisor', divisor)

    def forward(self, t: torch.Tensor) -> torch.Tensor:
        """
        Forward pass for the AgeSinusoidalEncoding.

        Args:
            t (torch.Tensor): A tensor of shape (batch_size, sequence_length)
                with dtype=torch.float32, representing age in days.

        Returns:
            torch.Tensor: The encoded age tensor of shape
                (batch_size, sequence_length, embedding_dim).
        """
        # 1. Unit Conversion: Convert age from days to years.
        # We use 365.25 to account for leap years.
        t_years = t / 365.25

        # 2. Argument Calculation: Calculate the arguments for the sin/cos functions.
        # The shapes are broadcast to (B, L, D/2).
        # Input t_years: (B, L) -> unsqueezed to (B, L, 1)
        # Divisor: (D/2) -> viewed as (1, 1, D/2)
        args = t_years.unsqueeze(-1) * self.divisor.view(1, 1, -1)

        # 3. Sinusoidal Application: Create the final output tensor.
        # Initialize an empty tensor to store the embeddings.
        output = torch.zeros(t.shape[0], t.shape[1], self.embedding_dim, device=t.device)

        # Assign cosine of the arguments to the even indices.
        output[:, :, 0::2] = torch.cos(args)
        
        # Assign sine of the arguments to the odd indices.
        output[:, :, 1::2] = torch.sin(args)

        return output

class TimeAwareGPT2(nn.Module):
    """
    A time-aware GPT-2 model with custom temporal features.
    """

    def __init__(self, vocab_size: int, n_embd: int, n_layer: int, n_head: int, pdrop: float, token_pdrop: float):
        super().__init__()
        self.token_pdrop = token_pdrop

        # Token and positional embeddings
        self.wte = nn.Embedding(vocab_size, n_embd)
        self.age_encoder = AgeSinusoidalEncoding(n_embd)
        self.drop = nn.Dropout(pdrop)

        # Transformer blocks
        self.blocks = nn.ModuleList([Block(n_embd, n_head, pdrop) for _ in range(n_layer)])
        
        # Final layer norm and linear head
        self.ln_f = nn.LayerNorm(n_embd)
        self.head = nn.Linear(n_embd, vocab_size, bias=False)

        self.n_embd = n_embd

    def forward(self, event_seq: torch.Tensor, time_seq: torch.Tensor) -> torch.Tensor:
        """
        Forward pass for the TimeAwareGPT2 model.

        Args:
            event_seq (torch.Tensor): Token indices of shape (B, L).
            time_seq (torch.Tensor): Timestamps for each event of shape (B, L).

        Returns:
            torch.Tensor: Logits of shape (B, L, vocab_size).
        """
        B, L = event_seq.size()

        # 1. Get token embeddings
        token_embeddings = self.wte(event_seq)

        # 2. Apply token dropout (only during training)
        if self.training and self.token_pdrop > 0:
            # Create a mask to randomly zero out entire token embedding vectors
            drop_mask = torch.rand(token_embeddings.shape[:2], device=token_embeddings.device) < self.token_pdrop
            token_embeddings[drop_mask] = 0.0

        # 3. Get positional embeddings from time sequence
        pos_embeddings = self.age_encoder(time_seq.float())

        # 4. Combine embeddings and apply dropout
        x = self.drop(token_embeddings + pos_embeddings)

        # 5. Generate attention mask
        # The attention mask combines two conditions:
        # a) Time-based causality: A token i can attend to a token j only if time_seq[j] < time_seq[i].
        # b) Padding mask: Do not attend to positions where the event token is 0.

        # a) Time-based causal mask
        t_i = time_seq.unsqueeze(-1)  # (B, L, 1)
        t_j = time_seq.unsqueeze(1)   # (B, 1, L)
        time_mask = (t_j < t_i)

        # b) Padding mask (prevents attending to key positions that are padding)
        padding_mask = (event_seq != 0).unsqueeze(1) # Shape: (B, 1, L)
        
        # Combine the masks. A position (j) can be attended to by a query (i) only if
        # it's in the past (time_mask) AND it's not a padding token (padding_mask).
        combined_mask = time_mask & padding_mask

        # 6. Pass through transformer blocks
        for block in self.blocks:
            x = block(x, custom_mask=combined_mask)

        # 7. Final layer norm and projection to vocab size
        x = self.ln_f(x)
        logits = self.head(x)

        return logits

    def get_num_params(self) -> float:
        """
        Returns the number of trainable parameters in the model in millions.
        """
        return sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e6

class CombinedLoss(nn.Module):
    """
    Computes a two-part loss: a standard cross-entropy loss for event type
    prediction and a survival analysis loss for event timing.
    """

    def __init__(self, ignored_token_ids: list[int]):
        """
        Initializes the CombinedLoss module.

        Args:
            ignored_token_ids (list[int]): A list of event type IDs to be
                excluded from all loss calculations.
        """
        super().__init__()
        self.ignored_token_ids = ignored_token_ids

    def forward(self, logits: torch.Tensor, x: torch.Tensor, t: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Calculates the combined cross-entropy and survival loss.

        Args:
            logits (torch.Tensor): Raw model outputs of shape (B, L, N).
            x (torch.Tensor): Ground-truth event labels of shape (B, L).
            t (torch.Tensor): True time duration for each event, shape (B, L).

        Returns:
            A tuple containing the two scalar loss tensors: (loss_ce, loss_survival).
        """
        # 1. Create a mask to filter out ignored token IDs from loss calculation.
        # An element is True if the corresponding label in x is NOT in the ignored list.
        mask = torch.ones_like(x, dtype=torch.bool)
        for token_id in self.ignored_token_ids:
            mask = mask & (x != token_id)

        # If the mask is all False (all tokens are ignored), return zero for both losses.
        if not mask.any():
            return torch.tensor(0.0, device=logits.device), torch.tensor(0.0, device=logits.device)

        # 2. Part 1: Cross-Entropy Loss (loss_ce)
        # Permute logits from (B, L, N) to (B, N, L) for F.cross_entropy.
        logits_for_ce = logits.permute(0, 2, 1)
        
        # Calculate per-element loss without reduction.
        per_element_ce = F.cross_entropy(logits_for_ce, x, reduction='none')
        
        # Apply the mask and compute the mean of valid elements.
        loss_ce = per_element_ce[mask].mean()

        # 3. Part 2: Survival Loss (loss_survival)
        # Calculate event intensity (lambda) as the sum of exponentiated logits.
        intensity = torch.sum(torch.exp(logits), dim=2)

        # Calculate per-element survival loss (negative log-likelihood of exponential dist).
        # We add a small epsilon for numerical stability with the log.
        per_element_survival = -(torch.log(intensity + 1e-8) - intensity * t)
        
        # Apply the mask and compute the mean of valid elements.
        loss_survival = per_element_survival[mask].mean()

        return loss_ce, loss_survival