445865e429
Some checks failed
- 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>
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
"""Multi-path inference: parallel hypothesis generation and scoring.
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Supports GPU-accelerated scoring when ``fusionagi[gpu]`` is installed;
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falls back to CPU ``ThreadPoolExecutor`` otherwise.
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"""
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from __future__ import annotations
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from typing import Callable
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from fusionagi._logger import logger
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from fusionagi.reasoning.tot import ThoughtNode
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from fusionagi.schemas.atomic import AtomicSemanticUnit
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def _score_coherence(node: ThoughtNode, _units: list[AtomicSemanticUnit]) -> float:
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return node.score * (0.9 + 0.1 * min(1, len(node.trace) / 5))
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def _score_consistency(node: ThoughtNode, units: list[AtomicSemanticUnit]) -> float:
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if not units:
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return 0.5
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unit_content = " ".join(u.content.lower() for u in units)
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thought_words = set(node.thought.lower().split())
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unit_words = set(unit_content.split())
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overlap = len(thought_words & unit_words) / max(len(thought_words), 1)
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return min(1.0, overlap * 2)
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def _try_gpu_score(
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hypotheses: list[str],
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units: list[AtomicSemanticUnit],
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) -> list[tuple[ThoughtNode, float]] | None:
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"""Attempt GPU-accelerated scoring; return ``None`` if unavailable."""
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try:
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from fusionagi.gpu.tensor_scoring import gpu_score_hypotheses
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results = gpu_score_hypotheses(hypotheses, units)
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logger.debug(
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"multi_path: GPU scoring used",
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extra={"count": len(hypotheses)},
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)
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return results
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except ImportError:
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return None
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def generate_and_score_parallel(
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hypotheses: list[str],
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units: list[AtomicSemanticUnit],
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score_fn: Callable[[ThoughtNode, list[AtomicSemanticUnit]], float] | None = None,
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*,
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use_gpu: bool = True,
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) -> list[tuple[ThoughtNode, float]]:
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"""Score multiple hypotheses in parallel.
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When *use_gpu* is ``True`` (default) and no custom *score_fn* is
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provided, tries GPU-accelerated scoring first. Falls back to the
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threaded CPU implementation when the GPU module is unavailable.
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"""
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if use_gpu and score_fn is None:
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gpu_result = _try_gpu_score(hypotheses, units)
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if gpu_result is not None:
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return gpu_result
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score_fn = score_fn or (lambda n, u: _score_coherence(n, u) * 0.5 + _score_consistency(n, u) * 0.5)
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def score_one(h: str, i: int) -> tuple[ThoughtNode, float]:
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node = ThoughtNode(thought=h, trace=[h], unit_refs=[u.unit_id for u in units[:10]])
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s = score_fn(node, units)
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node.score = s
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return node, s
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results: list[tuple[ThoughtNode, float]] = []
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with ThreadPoolExecutor(max_workers=min(len(hypotheses), 8)) as ex:
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futures = {ex.submit(score_one, h, i): i for i, h in enumerate(hypotheses)}
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for future in as_completed(futures):
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try:
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node, score = future.result()
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results.append((node, score))
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except Exception as e:
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logger.warning("Multi-path score failed", extra={"error": str(e)})
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return results
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