Jiarui Li
1757bcd25b
- Removed BaselineEncoder and CrossAttention classes from models.py. - Introduced OtherInfoTokenizer for handling additional token types. - Updated DeepHealth class to integrate OtherInfoTokenizer and manage extra pooling logic. - Added support for extra_pool_reduce parameter to control pooling behavior. - Modified forward methods to return structured output using DeepHealthOutput dataclass. - Updated training scripts to accommodate changes in model architecture and output handling. - Enhanced error handling and validation for input shapes and types.
329 lines
12 KiB
Python
329 lines
12 KiB
Python
import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class TimeRoPE(nn.Module):
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def __init__(self, dim: int, base: float = 10000.0):
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super().__init__()
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assert dim % 2 == 0, "RoPE dim must be even"
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self.dim = dim
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# inv_freq is not trainable, but should move with device.
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inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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def precompute_cache(self, tau: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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t = tau.unsqueeze(-1) # (B, L, 1)
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angles = t * self.inv_freq # (B, L, dim//2)
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# Pre-expand for heads and interleave once (avoids N_layers repeats)
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cos = angles.cos().unsqueeze(1).repeat_interleave(2, dim=-1)
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sin = angles.sin().unsqueeze(1).repeat_interleave(2, dim=-1)
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return cos, sin # (B, 1, L, dim)
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@staticmethod
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def _rotate_half(x: torch.Tensor) -> torch.Tensor:
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"""Rotate pairs: ``[-x2, x1, -x4, x3, ...]``."""
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x1 = x[..., 0::2]
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x2 = x[..., 1::2]
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return torch.stack((-x2, x1), dim=-1).flatten(-2)
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@staticmethod
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def apply_from_cache(
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q: torch.Tensor,
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k: torch.Tensor,
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rope_cache: tuple[torch.Tensor, torch.Tensor],
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) -> tuple[torch.Tensor, torch.Tensor]:
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cos, sin = rope_cache # each (B, 1, L, dim)
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q_rot = q * cos + TimeRoPE._rotate_half(q) * sin
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k_rot = k * cos + TimeRoPE._rotate_half(k) * sin
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return q_rot, k_rot
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def forward(
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self,
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tau: torch.Tensor,
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q: torch.Tensor,
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k: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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cache = self.precompute_cache(tau)
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return self.apply_from_cache(q, k, cache)
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class GaussianRBFTimeBasis(nn.Module):
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def __init__(
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self,
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n_bases: int = 16,
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max_time_diff: float = 40.0,
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):
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super().__init__()
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self.n_bases = n_bases
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# Evenly spaced RBF centres for non-negative linear time differences.
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# Causal masking enforces query_time >= key_time, so diff is >= 0.
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centers = torch.linspace(0.0, max_time_diff, n_bases)
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self.register_buffer("centers", centers,
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persistent=False) # (n_bases,)
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# Learnable log-widths (initialized to center spacing on linear scale).
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init_width = max(max_time_diff / max(n_bases - 1, 1), 1e-3)
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init_log_width = math.log(init_width)
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self.log_widths = nn.Parameter(torch.full((n_bases,), init_log_width))
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def precompute_cache(self, tau: torch.Tensor) -> torch.Tensor:
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time_coord = tau.float() # (B, L)
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# Pairwise signed difference: query_i - key_j.
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diff = time_coord.unsqueeze(
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2) - time_coord.unsqueeze(1) # (B, L_q, L_k)
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# Gaussian RBF: exp(-0.5 * ((diff - c) / w)^2)
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diff = diff.unsqueeze(-1) # (B, L, L, 1)
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widths = self.log_widths.exp() # (n_bases,)
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rbf_acts = torch.exp(
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-0.5 * ((diff - self.centers) / widths).square()
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# (B, L, L, n_bases)
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)
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return rbf_acts
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class TemporalAttention(nn.Module):
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def __init__(
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self,
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n_embd: int,
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n_head: int,
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n_rbf_bases: int = 16,
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dropout: float = 0.0,
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use_time_rope: bool = True,
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use_rbf_bias: bool = True,
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):
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super().__init__()
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assert n_embd % n_head == 0, "n_embd must be divisible by n_head"
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self.n_head = n_head
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self.d_head = n_embd // n_head
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self.scale = 1.0 / math.sqrt(self.d_head)
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self.use_time_rope = use_time_rope
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self.use_rbf_bias = use_rbf_bias
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# QKV projection (fused for efficiency)
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self.qkv = nn.Linear(n_embd, 3 * n_embd, bias=False)
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# Output projection
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self.out_proj = nn.Linear(n_embd, n_embd, bias=False)
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# Layer-specific projection from shared RBF basis activations to per-head attention bias.
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self.rbf_proj = nn.Linear(n_rbf_bases, n_head, bias=False)
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self.time_bias_scale = nn.Parameter(torch.tensor(0.0))
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self.resid_drop = nn.Dropout(dropout)
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self.reset_parameters()
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def reset_parameters(self) -> None:
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"""Match the previous version's GPT-style weight initialization."""
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nn.init.normal_(self.qkv.weight, mean=0.0, std=0.02)
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nn.init.normal_(self.out_proj.weight, mean=0.0, std=0.02)
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nn.init.zeros_(self.rbf_proj.weight)
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def forward(
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self,
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x: torch.Tensor,
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rope_cache: tuple[torch.Tensor, torch.Tensor] | None = None,
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rbf_cache: torch.Tensor | None = None,
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attn_mask: torch.Tensor | None = None,
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) -> torch.Tensor:
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if self.use_time_rope:
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assert rope_cache is not None, "rope_cache must be provided when use_time_rope is True"
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if self.use_rbf_bias:
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assert rbf_cache is not None, "rbf_cache must be provided when use_rbf_bias is True"
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B, L, _ = x.shape
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H, D = self.n_head, self.d_head
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# --- QKV ----------------------------------------------------------
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qkv = self.qkv(x).reshape(B, L, 3, H, D).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0) # each (B, H, L, D)
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# --- Apply RoPE (from shared cache) --------------------------------
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if self.use_time_rope:
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q, k = TimeRoPE.apply_from_cache(q, k, rope_cache)
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# Build additive attention bias mask: time bias + causal/padding mask.
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time_bias = None
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if self.use_rbf_bias:
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time_bias = self.rbf_proj(rbf_cache).permute(
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0, 3, 1, 2) # (B, H, L, L)
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time_bias = self.time_bias_scale.tanh() * time_bias
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if time_bias is not None and attn_mask is not None:
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attn_bias = time_bias + attn_mask.to(time_bias.dtype)
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elif time_bias is not None:
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attn_bias = time_bias
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elif attn_mask is not None:
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attn_bias = attn_mask
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else:
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attn_bias = None
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out = F.scaled_dot_product_attention(
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q,
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k,
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v,
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attn_mask=attn_bias,
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dropout_p=0.0,
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is_causal=False,
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scale=self.scale,
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)
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# --- Aggregate & project out --------------------------------------
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out = out.transpose(1, 2).reshape(B, L, H * D)
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return self.resid_drop(self.out_proj(out))
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class SwiGLU(nn.Module):
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def __init__(
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self,
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n_embd: int,
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hidden_dim: int | None = None,
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dropout: float = 0.0,
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bias: bool = True,
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):
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super().__init__()
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hidden_dim = hidden_dim if hidden_dim is not None else int(
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n_embd * 2.5)
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self.w1 = nn.Linear(n_embd, hidden_dim, bias=bias) # gate path
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self.w2 = nn.Linear(n_embd, hidden_dim, bias=bias) # value path
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# output projection
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self.w3 = nn.Linear(hidden_dim, n_embd, bias=bias)
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self.drop = nn.Dropout(dropout)
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self.reset_parameters()
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def reset_parameters(self) -> None:
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"""GPT-style parameter initialization for MLP paths."""
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nn.init.normal_(self.w1.weight, mean=0.0, std=0.02)
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nn.init.normal_(self.w2.weight, mean=0.0, std=0.02)
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nn.init.normal_(self.w3.weight, mean=0.0, std=0.02)
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if self.w1.bias is not None:
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nn.init.zeros_(self.w1.bias)
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nn.init.zeros_(self.w2.bias)
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nn.init.zeros_(self.w3.bias)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""``(B, L, n_embd) -> (B, L, n_embd)``."""
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return self.drop(self.w3(F.silu(self.w1(x)) * self.w2(x)))
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class GPTBlock(nn.Module):
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def __init__(
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self,
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n_embd: int,
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n_head: int,
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attn_dropout: float = 0.0,
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mlp_dropout: float = 0.0,
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use_time_rope: bool = False,
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use_rbf_bias: bool = False,
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n_rbf_bases: int = 16,
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):
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super().__init__()
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self.attn = TemporalAttention(
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n_embd=n_embd,
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n_head=n_head,
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n_rbf_bases=n_rbf_bases,
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dropout=attn_dropout,
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use_time_rope=use_time_rope,
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use_rbf_bias=use_rbf_bias,
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)
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self.mlp = SwiGLU(n_embd=n_embd, dropout=mlp_dropout)
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self.ln1 = nn.LayerNorm(n_embd)
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self.ln2 = nn.LayerNorm(n_embd)
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def forward(
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self,
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x: torch.Tensor,
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rope_cache: tuple[torch.Tensor, torch.Tensor] | None = None,
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rbf_cache: torch.Tensor | None = None,
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attn_mask: torch.Tensor | None = None,
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) -> torch.Tensor:
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x = x + self.attn(self.ln1(x), rope_cache, rbf_cache, attn_mask)
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x = x + self.mlp(self.ln2(x))
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return x
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class TokenAutoDiscretization(nn.Module):
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def __init__(
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self,
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n_cont_types: int,
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n_bins: int,
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n_embd: int,
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):
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super().__init__()
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if n_cont_types <= 0:
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raise ValueError(f"n_cont_types must be > 0, got {n_cont_types}")
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if n_bins <= 1:
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raise ValueError(f"n_bins must be > 1, got {n_bins}")
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if n_embd <= 0:
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raise ValueError(f"n_embd must be > 0, got {n_embd}")
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self.n_cont_types = n_cont_types
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self.n_bins = n_bins
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self.n_embd = n_embd
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self.weight = nn.Parameter(torch.empty(n_cont_types, n_bins))
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self.bias = nn.Parameter(torch.empty(n_cont_types, n_bins))
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self.bin_emb = nn.Parameter(torch.empty(n_cont_types, n_bins, n_embd))
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self.reset_parameters()
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def reset_parameters(self) -> None:
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nn.init.normal_(self.weight, mean=0.0, std=0.02)
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nn.init.zeros_(self.bias)
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nn.init.normal_(self.bin_emb, mean=0.0, std=0.02)
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def forward(
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self,
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cont_type_idx: torch.LongTensor, # (N,)
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value: torch.Tensor, # (N,)
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) -> torch.Tensor:
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if cont_type_idx.dim() != 1:
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raise ValueError(
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f"cont_type_idx must be 1D, got {tuple(cont_type_idx.shape)}"
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)
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if value.dim() != 1:
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raise ValueError(f"value must be 1D, got {tuple(value.shape)}")
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if cont_type_idx.numel() != value.numel():
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raise ValueError("cont_type_idx and value must have the same length")
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w = self.weight[cont_type_idx] # (N, n_bins)
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b = self.bias[cont_type_idx] # (N, n_bins)
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e = self.bin_emb[cont_type_idx] # (N, n_bins, D)
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logits = value.unsqueeze(-1) * w + b
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probs = torch.softmax(logits, dim=-1)
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return torch.einsum("nb,nbd->nd", probs, e)
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class AgeSinusoidalEncoding(nn.Module):
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def __init__(self, embedding_dim: int):
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super().__init__()
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if embedding_dim % 2 != 0:
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raise ValueError(
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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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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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self.linear = nn.Linear(embedding_dim, embedding_dim, bias=False)
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def forward(self, t: torch.Tensor) -> torch.Tensor:
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t_years = t
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# Broadcast (B, L, 1) against (1, 1, D/2) to get (B, L, D/2)
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args = t_years.unsqueeze(-1) / self.divisor.view(1, 1, -1)
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# Interleave cos and sin along the last dimension
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output = torch.zeros(t.shape[0], t.shape[1],
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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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output = self.linear(output)
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return output
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