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from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from transformers import AutoConfig, AutoModel, PreTrainedModel
from transformers.utils import ModelOutput
from .configuration_radar import RADARConfig
# ---------------------------------------------------------------------------
# Cross-Attention Fusion Layer
# ---------------------------------------------------------------------------
class CrossAttentionFusion(torch.nn.Module):
"""
Fuses semantic (ARSE) and stylometric (SLPE) representations.
The semantic embedding attends to the projected style embedding,
then the attended output is concatenated with the original style
features and passed through a projection MLP.
"""
def __init__(
self,
semantic_dim: int,
style_dim: int,
style_proj_dim: int,
fusion_dim: int,
num_heads: int = 8,
dropout: float = 0.1,
):
super().__init__()
# Project style features to semantic_dim for attention compatibility
self.style_to_semantic = torch.nn.Linear(style_proj_dim, semantic_dim)
# Multi-head cross-attention: query = semantic, key/value = projected style
self.cross_attn = torch.nn.MultiheadAttention(
embed_dim=semantic_dim,
num_heads=num_heads,
dropout=dropout,
batch_first=True,
)
self.attn_norm = torch.nn.LayerNorm(semantic_dim)
# Fusion MLP: concat attended semantic + raw style projection → fusion_dim
self.fusion_mlp = torch.nn.Sequential(
torch.nn.Linear(semantic_dim + style_proj_dim, fusion_dim),
torch.nn.LayerNorm(fusion_dim),
torch.nn.GELU(),
torch.nn.Dropout(dropout),
)
def forward(
self, h_semantic: torch.Tensor, h_style_proj: torch.Tensor
) -> torch.Tensor:
"""
Parameters
----------
h_semantic : [B, semantic_dim] CLS embedding from ARSE
h_style_proj : [B, style_proj_dim] projected stylometric features
Returns
-------
h_fused : [B, fusion_dim]
"""
# Project style to match semantic dimension for attention
style_as_semantic = self.style_to_semantic(h_style_proj) # [B, semantic_dim]
# Add sequence dimension: [B, 1, dim]
query = h_semantic.unsqueeze(1)
kv = style_as_semantic.unsqueeze(1)
# Cross-attention: semantic query attends to style key/value
attended, _ = self.cross_attn(query, kv, kv)
attended = attended.squeeze(1) # [B, semantic_dim]
# Residual connection + norm
attended = self.attn_norm(attended + h_semantic)
# Concatenate attended semantic + original style projection
h_concat = torch.cat(
[attended, h_style_proj], dim=-1
) # [B, semantic_dim + style_proj_dim]
# Project to fusion dimension
h_fused = self.fusion_mlp(h_concat) # [B, fusion_dim]
return h_fused
# ---------------------------------------------------------------------------
# RADAR Model
# ---------------------------------------------------------------------------
@dataclass
class RADAROutput(ModelOutput):
"""
Output dataclass for RADARModel forward pass.
"""
logits: torch.FloatTensor = None
h_fused: Optional[torch.FloatTensor] = None # exposed for auxiliary losses
class RADARModel(PreTrainedModel):
"""
Full RADAR detection model.
Components:
1. ARSE: ModernBERT-large encoder → h_semantic (hidden_size)
2. SLPE: Learnable projection of 38 stylometric features → h_style (128)
3. CrossAttentionFusion: h_semantic × h_style → h_fused (512)
Forward returns a dict with keys:
- logits raw logit from primary head
"""
config_class = RADARConfig
all_tied_weights_keys = OrderedDict()
base_model_prefix = "encoder"
supports_gradient_checkpointing = True
def __init__(self, config: RADARConfig):
super().__init__(config)
# --- ARSE: Semantic Encoder ---
encoder_config = AutoConfig.from_pretrained(config.base_model_name)
self.encoder = AutoModel.from_config(encoder_config)
hidden_size = encoder_config.hidden_size
# --- SLPE: Stylometric Feature Projection ---
self.style_proj = nn.Sequential(
nn.Linear(config.style_dim, config.style_proj_dim),
nn.LayerNorm(config.style_proj_dim),
nn.GELU(),
nn.Dropout(config.dropout),
)
# --- Cross-Attention Fusion ---
self.fusion = CrossAttentionFusion(
semantic_dim=hidden_size,
style_dim=config.style_dim,
style_proj_dim=config.style_proj_dim,
fusion_dim=config.fusion_dim,
num_heads=config.num_attention_heads,
dropout=config.dropout,
)
# --- UACC: Dual-Head Classifier ---
self.classifier = nn.Linear(config.fusion_dim, 1)
self.post_init()
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.trunc_normal_(module.weight, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
def get_encoder(self) -> nn.Module:
return self.encoder
def encode(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
style_features: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Encode text (and optionally style features) into a fused representation.
Returns h_fused : [B, fusion_dim]
"""
# Run transformer encoder
encoder_output = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
)
# Use CLS token representation
h_semantic = encoder_output.last_hidden_state[:, 0, :] # [B, hidden_size]
if style_features is not None:
# Project stylometric features
h_style_proj = self.style_proj(style_features) # [B, style_proj_dim]
# Fuse
h_fused = self.fusion(h_semantic, h_style_proj) # [B, fusion_dim]
else:
# If no style features provided, project semantic directly
B, H = h_semantic.shape
dummy_style = h_semantic.new_zeros(B, self.config.style_proj_dim)
h_fused = self.fusion(h_semantic, dummy_style)
return h_fused
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
style_features: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
**kwargs,
) -> RADAROutput:
"""
Forward pass.
Parameters
----------
input_ids : [B, seq_len]
attention_mask : [B, seq_len]
style_features : [B, 38] optional stylometric features
labels : [B] float labels 0.0 / 1.0 for loss computation
Returns
-------
RADAROutput with fields:
- loss (if labels provided) weighted BCE on calibrated score
- logits raw logit from primary head (before sigmoid)
- score calibrated probability score
- h_fused fused representation (exposed for auxiliary losses)
"""
h_fused = self.encode(input_ids, attention_mask, style_features)
# Primary classification logit
logit = self.classifier(h_fused).squeeze(-1) # [B]
return RADAROutput(
logits=logit,
h_fused=h_fused, # exposed for AIT triplet / invariance losses
)
@classmethod
def from_pretrained_encoder(
cls, encoder_name: str = "answerdotai/ModernBERT-large", **kwargs
) -> "RADARModel":
"""
Create a RADAR model and load pretrained encoder weights.
All other weights are randomly initialized.
"""
config = RADARConfig(base_model_name=encoder_name, **kwargs)
model = cls(config)
encoder = AutoModel.from_pretrained(encoder_name)
model.encoder.load_state_dict(encoder.state_dict(), strict=False)
return model
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