v4 update

v4/classes/bridges/hyperbridge.py

@@ -0,0 +1,56 @@
+"""hyperbridge — HyperBridge: bilateral fusion over paired embeddings, embedding output only."""
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+
+
+class HyperBridge(nn.Module):
+    """Fuse side embeddings (e.g. two z_fused vectors) into a single embedding.
+
+    Pure embedding producer — no classification head.  Attach a head stage in
+    the pipeline config to produce logits.
+
+    Modes
+    -----
+    embedding_mlp (default)
+        Linear projection of concatenated inputs → hidden_dim embedding.
+
+    classic_bridge
+        Per-side projection → Hadamard product → hidden_dim embedding.
+
+    Parameters
+    ----------
+    input_dims  : {side_key: dim} — e.g. {"a": 256, "b": 256}
+    hidden_dim  : output embedding dimension
+    mode        : "embedding_mlp" | "classic_bridge"
+    """
+
+    def __init__(
+        self,
+        input_dims: dict[str, int],
+        hidden_dim: int = 256,
+        mode:       str = "embedding_mlp",
+    ):
+        super().__init__()
+        self.input_names = list(input_dims.keys())
+        self.mode        = mode
+        self.out_dim     = hidden_dim
+        dims             = list(input_dims.values())
+
+        if mode == "embedding_mlp":
+            self.proj = nn.Linear(sum(dims), hidden_dim)
+        elif mode == "classic_bridge":
+            self.W  = nn.ModuleList([nn.Linear(d, hidden_dim) for d in dims])
+            self.ln = nn.ModuleList([nn.LayerNorm(hidden_dim) for _ in dims])
+        else:
+            raise ValueError(f"Unknown HyperBridge mode: {mode!r}")
+
+    def forward(self, inputs: dict[str, torch.Tensor]) -> torch.Tensor:
+        ordered = [inputs[name] for name in self.input_names]
+        if self.mode == "embedding_mlp":
+            return self.proj(torch.cat(ordered, dim=1))
+        h = self.ln[0](self.W[0](ordered[0]))
+        for i in range(1, len(ordered)):
+            h = h * self.ln[i](self.W[i](ordered[i]))
+        return h