Jiarui Li
d4d25ac9c7
Introduce PiecewiseLinearEncoder for continuous variable encoding. Add CovariateAwareGPT2 to extend TimeAwareGPT2 with static and time-varying covariate processing. The model combines piecewise linear and sinusoidal encodings for covariates and integrates them via concatenation before a final MLP head. Reorganize models.py for better logical structure.
375 lines
14 KiB
Python
375 lines
14 KiB
Python
import torch
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import torch.nn as nn
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from torch.nn import functional as F
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from typing import Tuple
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# =============================================================================
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# 1. Component Modules (Building Blocks)
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# =============================================================================
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class Block(nn.Module):
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""" an unassuming Transformer block """
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def __init__(self, n_embd: int, n_head: int, pdrop: float):
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super().__init__()
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self.n_head = n_head
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self.ln_1 = nn.LayerNorm(n_embd)
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self.attn = nn.MultiheadAttention(n_embd, n_head, dropout=pdrop, batch_first=True)
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self.ln_2 = nn.LayerNorm(n_embd)
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self.mlp = nn.ModuleDict(dict(
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c_fc = nn.Linear(n_embd, 4 * n_embd),
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c_proj = nn.Linear(4 * n_embd, n_embd),
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act = nn.GELU(),
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dropout = nn.Dropout(pdrop),
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))
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m = self.mlp
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self.mlpf = lambda x: m.dropout(m.c_proj(m.act(m.c_fc(x)))) # MLP forward
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self.resid_dropout = nn.Dropout(pdrop)
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def forward(self, x: torch.Tensor, custom_mask: torch.Tensor) -> torch.Tensor:
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normed_x = self.ln_1(x)
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attn_mask = ~custom_mask
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attn_mask = attn_mask.repeat_interleave(self.n_head, dim=0)
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attn_output, _ = self.attn(normed_x, normed_x, normed_x, attn_mask=attn_mask, need_weights=False)
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x = x + self.resid_dropout(attn_output)
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x = x + self.mlpf(self.ln_2(x))
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return x
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class AgeSinusoidalEncoding(nn.Module):
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"""
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Encodes age using sinusoidal functions, similar to positional encodings
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in Transformers. This module creates a fixed-size embedding for an age
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value given in days.
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"""
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def __init__(self, embedding_dim: int):
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"""
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Initializes the AgeSinusoidalEncoding module.
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Args:
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embedding_dim (int): The dimensionality of the output embedding.
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Must be an even number.
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Raises:
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ValueError: If embedding_dim is not an even number.
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"""
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super().__init__()
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if embedding_dim % 2 != 0:
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raise ValueError(f"Embedding dimension must be an even number, but got {embedding_dim}")
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self.embedding_dim = embedding_dim
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# Pre-calculate the divisor term for the sinusoidal formula.
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i = torch.arange(0, self.embedding_dim, 2, dtype=torch.float32)
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divisor = torch.pow(10000, i / self.embedding_dim)
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self.register_buffer('divisor', divisor)
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def forward(self, t: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass for the AgeSinusoidalEncoding.
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Args:
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t (torch.Tensor): A tensor of shape (batch_size, sequence_length)
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with dtype=torch.float32, representing age in days.
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Returns:
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torch.Tensor: The encoded age tensor of shape
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(batch_size, sequence_length, embedding_dim).
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"""
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t_years = t / 365.25
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args = t_years.unsqueeze(-1) * self.divisor.view(1, 1, -1)
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output = torch.zeros(t.shape[0], t.shape[1], self.embedding_dim, device=t.device)
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output[:, :, 0::2] = torch.cos(args)
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output[:, :, 1::2] = torch.sin(args)
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return output
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class PiecewiseLinearEncoder(nn.Module):
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"""
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Encodes continuous variables using piecewise linear encoding.
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This module defines bins based on standard normal distribution quantiles,
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encodes an input by finding its bin, and calculates its position as a
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linear interpolation between boundaries. The result is projected to the
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final embedding dimension by a shared linear layer.
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"""
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def __init__(self, num_bins: int, embedding_dim: int):
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"""
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Initializes the PiecewiseLinearEncoder module.
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Args:
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num_bins (int): The number of bins for the encoding.
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embedding_dim (int): The dimensionality of the output embedding (D).
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"""
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super().__init__()
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if num_bins <= 0:
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raise ValueError("num_bins must be a positive integer.")
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self.num_bins = num_bins
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self.embedding_dim = embedding_dim
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if num_bins > 1:
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quantiles = torch.linspace(1.0 / num_bins, (num_bins - 1.0) / num_bins, num_bins - 1)
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normal_dist = torch.distributions.normal.Normal(0, 1)
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boundaries = normal_dist.icdf(quantiles)
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else:
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boundaries = torch.tensor([])
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boundaries = torch.cat([
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torch.tensor([float('-inf')]),
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boundaries,
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torch.tensor([float('inf')])
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])
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self.register_buffer('boundaries', boundaries)
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self.linear = nn.Linear(num_bins, embedding_dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass for the piecewise linear encoding.
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Args:
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x (torch.Tensor): Input tensor of shape (*, N), where * is any
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number of batch dimensions and N is the number of continuous
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features. Assumed to be pre-scaled.
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Returns:
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torch.Tensor: Encoded tensor of shape (*, N, D).
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"""
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original_shape = x.shape
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x = x.reshape(-1, original_shape[-1])
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bin_indices = torch.searchsorted(self.boundaries, x, right=True) - 1
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bin_indices = bin_indices.clamp(0, self.num_bins - 1)
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lower_bounds = self.boundaries[bin_indices]
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upper_bounds = self.boundaries[bin_indices + 1]
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delta = upper_bounds - lower_bounds + 1e-8
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weight_upper = (x - lower_bounds) / delta
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weight_lower = 1.0 - weight_upper
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is_first_bin = (bin_indices == 0)
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is_last_bin = (bin_indices == self.num_bins - 1)
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weight_lower[is_first_bin] = 1.0
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weight_upper[is_first_bin] = 0.0
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weight_lower[is_last_bin] = 0.0
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weight_upper[is_last_bin] = 1.0
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encoded = torch.zeros(*x.shape, self.num_bins, device=x.device, dtype=x.dtype)
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encoded.scatter_(-1, bin_indices.unsqueeze(-1), weight_lower.unsqueeze(-1))
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upper_indices = (bin_indices + 1).clamp(max=self.num_bins - 1)
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encoded.scatter_add_(-1, upper_indices.unsqueeze(-1), weight_upper.unsqueeze(-1))
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encoded = encoded.view(*original_shape, self.num_bins)
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output = self.linear(encoded)
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return output
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# =============================================================================
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# 2. Main Model Architectures
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# =============================================================================
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class TimeAwareGPT2(nn.Module):
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"""
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A time-aware GPT-2 model with custom temporal features.
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"""
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def __init__(self, vocab_size: int, n_embd: int, n_layer: int, n_head: int, pdrop: float, token_pdrop: float):
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super().__init__()
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self.token_pdrop = token_pdrop
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self.wte = nn.Embedding(vocab_size, n_embd)
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self.age_encoder = AgeSinusoidalEncoding(n_embd)
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self.drop = nn.Dropout(pdrop)
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self.blocks = nn.ModuleList([Block(n_embd, n_head, pdrop) for _ in range(n_layer)])
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self.ln_f = nn.LayerNorm(n_embd)
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self.head = nn.Linear(n_embd, vocab_size, bias=False)
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self.n_embd = n_embd
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def forward(self, event_seq: torch.Tensor, time_seq: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass for the TimeAwareGPT2 model.
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Args:
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event_seq (torch.Tensor): Token indices of shape (B, L).
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time_seq (torch.Tensor): Timestamps for each event of shape (B, L).
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Returns:
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torch.Tensor: Logits of shape (B, L, vocab_size).
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"""
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B, L = event_seq.size()
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token_embeddings = self.wte(event_seq)
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if self.training and self.token_pdrop > 0:
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drop_mask = torch.rand(token_embeddings.shape[:2], device=token_embeddings.device) < self.token_pdrop
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token_embeddings[drop_mask] = 0.0
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pos_embeddings = self.age_encoder(time_seq.float())
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x = self.drop(token_embeddings + pos_embeddings)
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t_i = time_seq.unsqueeze(-1)
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t_j = time_seq.unsqueeze(1)
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time_mask = (t_j < t_i)
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padding_mask = (event_seq != 0).unsqueeze(1)
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combined_mask = time_mask & padding_mask
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is_row_all_zero = ~combined_mask.any(dim=-1)
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is_not_padding = (event_seq != 0)
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force_self_attention = is_row_all_zero & is_not_padding
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combined_mask.diagonal(dim1=-2, dim2=-1)[force_self_attention] = True
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for block in self.blocks:
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x = block(x, custom_mask=combined_mask)
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x = self.ln_f(x)
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logits = self.head(x)
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return logits
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def get_num_params(self) -> float:
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"""
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Returns the number of trainable parameters in the model in millions.
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"""
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return sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e6
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class CovariateAwareGPT2(nn.Module):
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"""
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Extends TimeAwareGPT2 to incorporate static and time-varying covariates.
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"""
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def __init__(self, vocab_size: int, n_embd: int, n_layer: int, n_head: int,
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pdrop: float, token_pdrop: float, num_bins: int):
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"""
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Initializes the CovariateAwareGPT2 model.
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Args:
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vocab_size (int): Size of the event vocabulary.
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n_embd (int): Embedding dimensionality.
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n_layer (int): Number of transformer layers.
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n_head (int): Number of attention heads.
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pdrop (float): Dropout probability for layers.
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token_pdrop (float): Dropout probability for input token embeddings.
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num_bins (int): Number of bins for the PiecewiseLinearEncoder.
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"""
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super().__init__()
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self.token_pdrop = token_pdrop
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self.wte = nn.Embedding(vocab_size, n_embd)
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self.age_encoder = AgeSinusoidalEncoding(n_embd)
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self.drop = nn.Dropout(pdrop)
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self.blocks = nn.ModuleList([Block(n_embd, n_head, pdrop) for _ in range(n_layer)])
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self.n_embd = n_embd
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self.cov_encoder = PiecewiseLinearEncoder(num_bins=num_bins, embedding_dim=n_embd)
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self.ln_f = nn.LayerNorm(2 * n_embd)
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self.head = nn.Sequential(
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nn.Linear(2 * n_embd, n_embd),
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nn.GELU(),
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nn.Linear(n_embd, vocab_size)
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)
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def forward(self, x: torch.Tensor, t: torch.Tensor, cov: torch.Tensor, cov_t: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass for the CovariateAwareGPT2 model.
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Args:
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x (torch.Tensor): Event sequence tensor of shape (B, L).
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t (torch.Tensor): Time sequence tensor of shape (B, L).
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cov (torch.Tensor): Covariate tensor of shape (B, N).
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cov_t (torch.Tensor): Covariate time tensor of shape (B).
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Returns:
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torch.Tensor: Logits of shape (B, L, vocab_size).
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"""
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B, L = x.size()
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cov_encoded = self.cov_encoder(cov).sum(dim=1).unsqueeze(1)
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cov_t_encoded = self.age_encoder(t - cov_t.unsqueeze(1))
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cov_embed = cov_encoded + cov_t_encoded
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token_embeddings = self.wte(x)
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if self.training and self.token_pdrop > 0:
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drop_mask = torch.rand(token_embeddings.shape[:2], device=token_embeddings.device) < self.token_pdrop
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token_embeddings[drop_mask] = 0.0
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pos_embeddings = self.age_encoder(t.float())
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seq_embed = self.drop(token_embeddings + pos_embeddings)
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t_i = t.unsqueeze(-1)
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t_j = t.unsqueeze(1)
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time_mask = (t_j < t_i)
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padding_mask = (x != 0).unsqueeze(1)
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combined_mask = time_mask & padding_mask
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is_row_all_zero = ~combined_mask.any(dim=-1)
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is_not_padding = (x != 0)
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force_self_attention = is_row_all_zero & is_not_padding
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combined_mask.diagonal(dim1=-2, dim2=-1)[force_self_attention] = True
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block_output = seq_embed
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for block in self.blocks:
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block_output = block(block_output, custom_mask=combined_mask)
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integrated_embed = torch.cat([block_output, cov_embed], dim=-1)
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final_output = self.ln_f(integrated_embed)
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logits = self.head(final_output)
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return logits
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def get_num_params(self) -> float:
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"""
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Returns the number of trainable parameters in the model in millions.
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"""
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return sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e6
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# =============================================================================
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# 3. Loss Function
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# =============================================================================
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class CombinedLoss(nn.Module):
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"""
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Computes a two-part loss: a standard cross-entropy loss for event type
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prediction and a survival analysis loss for event timing.
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"""
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def __init__(self, ignored_token_ids: list[int]):
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"""
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Initializes the CombinedLoss module.
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Args:
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ignored_token_ids (list[int]): A list of event type IDs to be
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excluded from all loss calculations.
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"""
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super().__init__()
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self.ignored_token_ids = ignored_token_ids
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def forward(self, logits: torch.Tensor, x: torch.Tensor, t: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Calculates the combined cross-entropy and survival loss.
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Args:
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logits (torch.Tensor): Raw model outputs of shape (B, L, N).
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x (torch.Tensor): Ground-truth event labels of shape (B, L).
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t (torch.Tensor): True time duration for each event, shape (B, L).
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Returns:
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A tuple containing the two scalar loss tensors: (loss_ce, loss_survival).
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"""
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mask = torch.ones_like(x, dtype=torch.bool)
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for token_id in self.ignored_token_ids:
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mask = mask & (x != token_id)
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if not mask.any():
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return torch.tensor(0.0, device=logits.device), torch.tensor(0.0, device=logits.device)
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logits_for_ce = logits.permute(0, 2, 1)
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per_element_ce = F.cross_entropy(logits_for_ce, x, reduction='none')
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loss_ce = per_element_ce[mask].mean()
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intensity = torch.sum(torch.exp(logits), dim=2)
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per_element_survival = -(torch.log(intensity + 1e-8) - intensity * t)
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loss_survival = per_element_survival[mask].mean()
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return loss_ce, loss_survival
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