rpotter6298
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
"""hyperbridge — HyperBridge: bilateral fusion over paired embeddings, embedding output only."""
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from __future__ import annotations
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import torch
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import torch.nn as nn
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class HyperBridge(nn.Module):
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"""Fuse side embeddings (e.g. two z_fused vectors) into a single embedding.
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Pure embedding producer — no classification head. Attach a head stage in
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the pipeline config to produce logits.
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Modes
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-----
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embedding_mlp (default)
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Linear projection of concatenated inputs → hidden_dim embedding.
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classic_bridge
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Per-side projection → Hadamard product → hidden_dim embedding.
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Parameters
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----------
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input_dims : {side_key: dim} — e.g. {"a": 256, "b": 256}
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hidden_dim : output embedding dimension
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mode : "embedding_mlp" | "classic_bridge"
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"""
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def __init__(
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self,
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input_dims: dict[str, int],
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hidden_dim: int = 256,
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mode: str = "embedding_mlp",
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):
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super().__init__()
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self.input_names = list(input_dims.keys())
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self.mode = mode
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self.out_dim = hidden_dim
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dims = list(input_dims.values())
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if mode == "embedding_mlp":
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self.proj = nn.Linear(sum(dims), hidden_dim)
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elif mode == "classic_bridge":
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self.W = nn.ModuleList([nn.Linear(d, hidden_dim) for d in dims])
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self.ln = nn.ModuleList([nn.LayerNorm(hidden_dim) for _ in dims])
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else:
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raise ValueError(f"Unknown HyperBridge mode: {mode!r}")
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def forward(self, inputs: dict[str, torch.Tensor]) -> torch.Tensor:
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ordered = [inputs[name] for name in self.input_names]
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if self.mode == "embedding_mlp":
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return self.proj(torch.cat(ordered, dim=1))
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h = self.ln[0](self.W[0](ordered[0]))
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for i in range(1, len(ordered)):
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h = h * self.ln[i](self.W[i](ordered[i]))
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return h
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