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|
|
|
| """Hub-compliant kernel layer for the szl-lambda-gate kernel.
|
|
|
| Per the Kernel Hub `kernel-requirements`, layers exposed for extension must be
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| PURE torch.nn.Module subclasses:
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| - no custom __init__,
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| - no class variables,
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| - only a `forward` method.
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|
|
| The layer therefore reads its parameters (weights / threshold) off the module
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| instance it is bound to (set by the host model) and only defines `forward`.
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|
|
| HONESTY: `LambdaGate` emits an ADVISORY governance signal (the weighted
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| geometric mean Λ plus a pass/fail vs threshold). Λ is NOT proven trust; its
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| uniqueness is Conjecture 1 (open).
|
| """
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| import torch
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| from torch import nn
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|
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| from ._lambda import lambda_aggregate, lambda_gate
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|
|
|
|
| class LambdaGate(nn.Module):
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| """Pure Λ-gate layer.
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|
|
| Reads optional ``self.weights`` (1-D, length k) and ``self.threshold``
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| (float, default 0.5) off the bound module instance.
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|
|
| forward(axes) -> LambdaGateResult(score, passed, threshold, advisory) where
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| ``score`` = Λ(axes) over the last dim and ``passed`` = score >= threshold.
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| Differentiable in ``score`` w.r.t. ``axes``.
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| """
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|
|
| def forward(self, axes: torch.Tensor):
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| weights = getattr(self, "weights", None)
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| threshold = getattr(self, "threshold", 0.5)
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| return lambda_gate(axes, weights=weights, threshold=float(threshold))
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|
|
|
|
| class LambdaAggregate(nn.Module):
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| """Pure Λ-aggregator layer: forward(axes) -> Λ(axes) tensor in [0,1].
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|
|
| Reads optional ``self.weights`` (1-D, length k) off the bound module
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| instance; uniform weights when absent. Returns just the score (no gate),
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| fully differentiable w.r.t. ``axes``.
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| """
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|
|
| def forward(self, axes: torch.Tensor) -> torch.Tensor:
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| weights = getattr(self, "weights", None)
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| return lambda_aggregate(axes, weights=weights)
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|
|
