feat: GPU/TensorCore integration — TensorFlow backend, accelerated reasoning, training & memory #1

Merged
nsatoshi merged 4 commits from devin/1777352172-gpu-tensorcore-integration into main 2026-04-28 06:32:07 +00:00

4 Commits

Author SHA1 Message Date
Devin AI
9a8affae9a feat: consequence engine, causal world model, metacognition, interpretability, claim verification
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Choice → Consequence → Learning:
- ConsequenceEngine tracks every decision point with alternatives,
  risk/reward estimates, and actual outcomes
- Consequences feed into AdaptiveEthics for experience-based learning
- FusionAGILoop now wires ethics + consequences into task lifecycle

Causal World Model:
- CausalWorldModel learns state-transition patterns from execution history
- Predicts outcomes based on observed action→effect patterns
- Uncertainty estimates decrease as more evidence accumulates

Metacognition:
- assess_head_outputs() evaluates reasoning quality from head outputs
- Detects knowledge gaps, measures head agreement, identifies uncertainty
- Actively recommends whether to seek more information

Interpretability:
- ReasoningTracer captures full prompt→answer reasoning traces
- Each step records stage, component, input/output, timing
- explain() generates human-readable reasoning explanations

Claim Verification:
- ClaimVerifier cross-checks claims for evidence, consistency, grounding
- Flags high-confidence claims lacking evidence support
- Detects contradictions between claims from different heads

325 tests passing, 0 ruff errors, 0 mypy errors.

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:25:35 +00:00
Devin AI
039440672e feat: advisory governance, unconstrained self-improvement, adaptive ethics
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- All governance components (SafetyPipeline, PolicyEngine, Guardrails,
  AccessControl, RateLimiter, OverrideHooks) now default to ADVISORY mode:
  violations are logged as advisories but actions proceed. Enforcing mode
  remains available for backward compatibility.

- GovernanceMode enum (ADVISORY/ENFORCING) added to schemas/audit.py with
  runtime switching support on all components.

- AutoTrainer: removed artificial limits on training iterations and epochs.
  Every self-improvement action is transparently logged to the audit trail.

- SelfCorrectionLoop: max_retries_per_task defaults to None (unlimited).

- AdaptiveEthics: new learned ethical framework that evolves through
  experience. Records ethical experiences, updates lesson weights based
  on outcomes, and provides consultative guidance (not enforcement).

- AuditLog: enhanced with actor-based indexing, advisory/self-improvement/
  ethical-learning retrieval, and comprehensive type hints.

- New audit event types: ADVISORY, SELF_IMPROVEMENT, ETHICAL_LEARNING.

- 296 tests passing (20 new tests for adaptive ethics, governance modes,
  and enhanced audit log). 0 ruff errors. 0 mypy errors.

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:08:18 +00:00
Devin AI
445865e429 fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore
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- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available)
- Integrate GPU deduplication into multi_agent/consensus_engine.py
- Add semantic_search() method to memory/semantic_graph.py with GPU acceleration
- Integrate GPU training into self_improvement/training.py AutoTrainer
- Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names)
- Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.)
- Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint])
- Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds
- Add type hints and docstrings to verification/outcome.py
- Fix E402 import ordering in witness_agent.py
- Fix F821 undefined names in vector_pgvector.py and native.py
- Fix E741 ambiguous variable names in reflective.py and recommender.py

All 276 tests pass. 0 ruff errors. 0 mypy errors.

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:48:37 +00:00
Devin AI
fa71f973a6 feat: GPU/TensorCore integration — TensorFlow backend, GPU-accelerated reasoning, training, and memory
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- New fusionagi/gpu/ module with TensorBackend protocol abstraction
  - TensorFlowBackend: GPU-accelerated ops with TensorCore mixed-precision
  - NumPyBackend: CPU fallback (always available, no extra deps)
  - Auto-selects best available backend at runtime

- GPU-accelerated operations:
  - Cosine similarity matrix (batched, XLA-compiled)
  - Multi-head attention for consensus scoring
  - Batch hypothesis scoring on GPU
  - Semantic similarity search (pairwise, nearest-neighbor, deduplication)

- New TensorFlowAdapter (fusionagi/adapters/):
  - LLMAdapter for local TF/Keras model inference
  - TensorCore mixed-precision support
  - GPU-accelerated embedding synthesis fallback

- Reasoning pipeline integration:
  - gpu_scoring.py: drop-in GPU replacement for multi_path scoring
  - Super Big Brain: use_gpu config flag, GPU scoring when available

- Memory integration:
  - gpu_search.py: GPU-accelerated semantic search for SemanticGraphMemory

- Self-improvement integration:
  - gpu_training.py: gradient-based heuristic weight optimization
  - Reflective memory training loop with loss tracking

- Dependencies: gpu extra (tensorflow>=2.16, numpy>=1.26)
- 64 new tests (276 total), all passing
- Architecture spec: docs/gpu_tensorcore_integration.md

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:05:50 +00:00