XiuchengWu
300e86956b
- Add ConsensusSnapshot and ConsensusSlot classes for structured consensus state representation. - Introduce OnlineLanguageLearner for stateful online learning with experience replay and EWC regularization. - Create WorkspaceEvent and ActionEvent classes for serializable event types. - Develop InMemoryVectorMemoryStore for storing and querying workspace events. - Implement ActionPolicy to manage action selection and motor parameterization. - Establish a unified WorkspaceRuntime for managing the agent's lifecycle and interactions. - Enhance tests in test_smoke.py to cover new functionalities and ensure correctness.
84 lines
2.4 KiB
Python
84 lines
2.4 KiB
Python
"""
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Consensus state utilities.
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The workspace vector is still the primary state, but these helpers expose a
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small structured summary that other subsystems can share without needing to
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decode the full latent every time.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import math
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import torch
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@dataclass
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class ConsensusSlot:
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index: int
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label: str
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weight: float
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@dataclass
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class ConsensusSnapshot:
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modality: str
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workspace_norm: float
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confidence: float
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attention_entropy: float
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slots: list[ConsensusSlot]
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@classmethod
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def from_workspace(
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cls,
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w: torch.Tensor,
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alpha: torch.Tensor | None,
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modality: str,
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labels: list[str] | None = None,
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top_k: int = 3,
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) -> "ConsensusSnapshot":
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workspace_norm = float(w.norm(dim=-1).mean().item())
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slots: list[ConsensusSlot] = []
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attention_entropy = 0.0
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confidence = 0.0
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if alpha is not None and alpha.numel() > 0:
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probs = alpha[0].detach().cpu()
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top_vals, top_idx = probs.topk(min(top_k, probs.numel()))
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slots = [
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ConsensusSlot(
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index=int(idx.item()),
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label=labels[int(idx.item())] if labels and int(idx.item()) < len(labels)
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else f"expert_{idx.item()}",
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weight=float(val.item()),
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)
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for val, idx in zip(top_vals, top_idx)
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]
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probs_clamped = probs.clamp_min(1e-8)
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attention_entropy = float((-(probs_clamped * probs_clamped.log()).sum()).item())
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max_entropy = math.log(max(2, probs.numel()))
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confidence = float(max(0.0, 1.0 - attention_entropy / max_entropy))
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return cls(
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modality=modality,
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workspace_norm=workspace_norm,
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confidence=confidence,
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attention_entropy=attention_entropy,
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slots=slots,
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)
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def primary_slot(self) -> ConsensusSlot | None:
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return self.slots[0] if self.slots else None
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def to_dict(self) -> dict:
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return {
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"modality": self.modality,
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"workspace_norm": self.workspace_norm,
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"confidence": self.confidence,
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"attention_entropy": self.attention_entropy,
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"slots": [
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{"index": slot.index, "label": slot.label, "weight": slot.weight}
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for slot in self.slots
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],
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}
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