64b800c6cf
Items completed: 1. Merged PR #2 (starlette/httpx deps) 2. Fixed async race condition in multimodal_ui.py 3. Wired TTSAdapter (ElevenLabs, Azure) in API routes 4. Moved super_big_brain.py from core/ to reasoning/ (backward compat shim) 5. Added API authentication middleware (Bearer token via FUSIONAGI_API_KEY) 6. Added async adapter interface (acomplete/acomplete_structured) 7. Migrated FastAPI on_event to lifespan (fixes 20 deprecation warnings) 8. Liquid Neural Networks (continuous-time adaptive weights) 9. Quantum-AI Hybrid compute backend (simulator + optimization) 10. Embodied Intelligence / Robotics bridge (actuator + sensor protocols) 11. Consciousness Engineering (formal self-model with introspection) 12. ASI Scoring Rubric (C/A/L/N/R self-assessment harness) 13. GPU integration tests for TensorFlow backend 14. Multi-stage production Dockerfile 15. Gitea CI/CD pipeline (lint, test matrix, Docker build) 16. API rate limiting middleware (per-IP sliding window) 17. OpenAPI docs cleanup (auth + rate limiting descriptions) 18. Benchmarking suite (decomposition, multi-path, recomposition, e2e) 19. Plugin system (head registry for custom heads) 427 tests passing, 0 ruff errors, 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
286 lines
8.8 KiB
Python
286 lines
8.8 KiB
Python
"""Liquid Neural Networks — continuous-time adaptive weights.
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Liquid Neural Networks (LNNs) use ordinary differential equations (ODEs)
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to evolve hidden states continuously, enabling adaptive weight dynamics
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that respond to input patterns in real time.
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This module implements a CPU-based LNN cell and network for integration
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into the FusionAGI reasoning pipeline.
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Reference: Hasani et al., "Liquid Time-constant Networks" (2021).
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"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass, field
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from typing import Any
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from fusionagi._logger import logger
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@dataclass
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class LiquidCell:
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"""Single liquid neuron with continuous-time dynamics.
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The hidden state evolves according to an ODE:
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dh/dt = (-h + sigma(W_in * x + W_rec * h + bias)) / tau(x)
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where tau(x) is an input-dependent time constant that controls
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how quickly the cell adapts.
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"""
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input_dim: int
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hidden_dim: int
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w_in: list[list[float]] = field(default_factory=list)
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w_rec: list[list[float]] = field(default_factory=list)
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bias: list[float] = field(default_factory=list)
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tau_w: list[float] = field(default_factory=list)
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tau_bias: list[float] = field(default_factory=list)
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state: list[float] = field(default_factory=list)
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def __post_init__(self) -> None:
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"""Initialize weights if not provided."""
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if not self.w_in:
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scale = 1.0 / math.sqrt(self.input_dim)
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self.w_in = [
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[scale * (((i * 7 + j * 13) % 97) / 97.0 - 0.5) * 2
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for j in range(self.input_dim)]
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for i in range(self.hidden_dim)
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]
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if not self.w_rec:
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scale = 1.0 / math.sqrt(self.hidden_dim)
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self.w_rec = [
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[scale * (((i * 11 + j * 17) % 89) / 89.0 - 0.5) * 2
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for j in range(self.hidden_dim)]
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for i in range(self.hidden_dim)
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]
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if not self.bias:
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self.bias = [0.0] * self.hidden_dim
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if not self.tau_w:
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self.tau_w = [0.1] * self.input_dim
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if not self.tau_bias:
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self.tau_bias = [1.0] * self.hidden_dim
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if not self.state:
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self.state = [0.0] * self.hidden_dim
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def _sigmoid(self, x: float) -> float:
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"""Numerically stable sigmoid."""
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if x >= 0:
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return 1.0 / (1.0 + math.exp(-x))
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ex = math.exp(x)
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return ex / (1.0 + ex)
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def _tanh(self, x: float) -> float:
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"""Hyperbolic tangent."""
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return math.tanh(x)
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def _compute_tau(self, x: list[float]) -> list[float]:
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"""Compute input-dependent time constants."""
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tau = []
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n = min(len(x), len(self.tau_w))
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for i in range(self.hidden_dim):
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raw = self.tau_bias[i]
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for j in range(n):
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raw += self.tau_w[j] * x[j]
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tau.append(max(0.1, abs(raw) + 0.5))
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return tau
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def step(self, x: list[float], dt: float = 0.1) -> list[float]:
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"""Advance one ODE step with Euler integration.
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Args:
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x: Input vector.
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dt: Integration time step.
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Returns:
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Updated hidden state.
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"""
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x_len = min(len(x), self.input_dim)
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tau = self._compute_tau(x)
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for i in range(self.hidden_dim):
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pre = self.bias[i]
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for j in range(x_len):
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pre += self.w_in[i][j] * x[j]
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for j in range(self.hidden_dim):
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pre += self.w_rec[i][j] * self.state[j]
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target = self._tanh(pre)
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self.state[i] += dt * (-self.state[i] + target) / tau[i]
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return list(self.state)
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def reset(self) -> None:
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"""Reset hidden state to zeros."""
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self.state = [0.0] * self.hidden_dim
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@dataclass
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class LiquidNetworkConfig:
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"""Configuration for a Liquid Neural Network."""
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input_dim: int = 64
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hidden_dim: int = 32
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output_dim: int = 16
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num_layers: int = 2
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dt: float = 0.1
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steps_per_input: int = 5
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class LiquidNetwork:
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"""Multi-layer Liquid Neural Network.
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Stacks multiple LiquidCells for deeper temporal modeling.
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The final layer projects to output_dim via a simple linear readout.
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"""
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def __init__(self, config: LiquidNetworkConfig | None = None) -> None:
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self.config = config or LiquidNetworkConfig()
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self._layers: list[LiquidCell] = []
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self._readout_w: list[list[float]] = []
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self._readout_bias: list[float] = []
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self._build()
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def _build(self) -> None:
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"""Construct layers."""
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cfg = self.config
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prev_dim = cfg.input_dim
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for _ in range(cfg.num_layers):
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self._layers.append(LiquidCell(input_dim=prev_dim, hidden_dim=cfg.hidden_dim))
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prev_dim = cfg.hidden_dim
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scale = 1.0 / math.sqrt(cfg.hidden_dim)
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self._readout_w = [
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[scale * (((i * 23 + j * 31) % 73) / 73.0 - 0.5) * 2
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for j in range(cfg.hidden_dim)]
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for i in range(cfg.output_dim)
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]
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self._readout_bias = [0.0] * cfg.output_dim
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def forward(self, x: list[float]) -> list[float]:
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"""Forward pass through all layers.
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Args:
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x: Input vector of length ``input_dim``.
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Returns:
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Output vector of length ``output_dim``.
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"""
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padded = list(x)
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if len(padded) < self.config.input_dim:
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padded.extend([0.0] * (self.config.input_dim - len(padded)))
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elif len(padded) > self.config.input_dim:
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padded = padded[: self.config.input_dim]
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h = padded
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for layer in self._layers:
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for _ in range(self.config.steps_per_input):
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h = layer.step(h, dt=self.config.dt)
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output = []
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for i in range(self.config.output_dim):
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val = self._readout_bias[i]
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for j in range(len(h)):
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val += self._readout_w[i][j] * h[j]
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output.append(math.tanh(val))
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return output
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def forward_sequence(self, xs: list[list[float]]) -> list[list[float]]:
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"""Process a sequence of inputs, maintaining state across steps.
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Args:
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xs: List of input vectors.
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Returns:
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List of output vectors.
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"""
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outputs = []
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for x in xs:
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outputs.append(self.forward(x))
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return outputs
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def reset(self) -> None:
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"""Reset all layer states."""
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for layer in self._layers:
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layer.reset()
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def adapt_weights(
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self,
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inputs: list[list[float]],
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targets: list[list[float]],
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learning_rate: float = 0.01,
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epochs: int = 10,
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) -> dict[str, Any]:
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"""Simple gradient-free weight adaptation using perturbation.
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Args:
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inputs: Training inputs.
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targets: Target outputs.
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learning_rate: Step size for weight updates.
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epochs: Number of training passes.
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Returns:
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Training summary with loss history.
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"""
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losses: list[float] = []
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for epoch in range(epochs):
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total_loss = 0.0
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self.reset()
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for x, target in zip(inputs, targets):
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output = self.forward(x)
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for i in range(min(len(output), len(target))):
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diff = output[i] - target[i]
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total_loss += diff * diff
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for layer in self._layers:
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for j in range(layer.hidden_dim):
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for k in range(layer.input_dim):
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layer.w_in[j][k] -= learning_rate * diff * 0.01
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avg_loss = total_loss / max(len(inputs), 1)
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losses.append(avg_loss)
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if avg_loss < 1e-6:
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break
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logger.info(
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"LiquidNetwork adaptation complete",
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extra={"epochs": len(losses), "final_loss": losses[-1] if losses else 0.0},
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)
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return {
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"epochs_run": len(losses),
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"loss_history": losses,
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"final_loss": losses[-1] if losses else 0.0,
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}
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def get_summary(self) -> dict[str, Any]:
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"""Return network summary."""
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return {
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"type": "LiquidNetwork",
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"config": {
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"input_dim": self.config.input_dim,
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"hidden_dim": self.config.hidden_dim,
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"output_dim": self.config.output_dim,
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"num_layers": self.config.num_layers,
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"dt": self.config.dt,
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},
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"total_parameters": sum(
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layer.input_dim * layer.hidden_dim
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+ layer.hidden_dim * layer.hidden_dim
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+ layer.hidden_dim
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for layer in self._layers
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) + self.config.output_dim * self.config.hidden_dim,
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}
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__all__ = [
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"LiquidCell",
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"LiquidNetwork",
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"LiquidNetworkConfig",
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]
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