SolitaryThinker
a9e5f6ee7a
Directory rename (git mv, history preserved) plus rewrite of all references: absolute imports in tests, the zero-dep runner, docstrings, comments, and the README. No behavior change. Run: python3 -m pytest v2/tests/ -q ; python3 v2/run_tests.py ; python3 -m v2.examples
65 lines
2.6 KiB
Python
65 lines
2.6 KiB
Python
"""Built-in read-only observers (design_v3 §11): Profiler, NaNWatch.
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ParityAligner lives in ``parity/`` (it is a first-class package, §18) but also implements
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the Observer ``observe`` protocol so it attaches to the same bus.
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"""
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from __future__ import annotations
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from typing import Any
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import numpy as np
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class Profiler:
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"""Per-step wall/CUDA timing; calibrates the cost model (design_v3 §6.1, §11).
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Here it accumulates (work_units, actual_seconds) samples and can fit a card's
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CostModel coefficients — the online calibration the conservative baseline refines.
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"""
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def __init__(self) -> None:
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self.samples: list[tuple[str, float, float]] = [] # (batch_key_repr, work_units, seconds)
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def observe(self, event: str, **kw) -> None:
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if event == "step_complete":
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plan, result = kw.get("plan"), kw.get("result")
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if plan is not None and result is not None:
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self.samples.append((repr(plan.shape_sig.batch_key),
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float(plan.shape_sig.work_units),
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float(result.actual_seconds)))
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def calibrate(self, cost_model) -> None:
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"""Fit base + per_unit seconds from observed samples (least squares)."""
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if len(self.samples) < 2:
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return
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x = np.array([s[1] for s in self.samples], dtype=np.float64)
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y = np.array([s[2] for s in self.samples], dtype=np.float64)
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if np.ptp(x) == 0:
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cost_model.base_seconds = float(y.mean())
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return
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slope, intercept = np.polyfit(x, y, 1)
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cost_model.per_unit_seconds = float(max(slope, 0.0))
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cost_model.base_seconds = float(max(intercept, 0.0))
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class NaNWatch:
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"""First-NaN/Inf localization (design_v3 §11). Request-fatal → SPMD-consistent abort (§6.4)."""
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def __init__(self) -> None:
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self.first: tuple[str, int] | None = None # (tap/output name, step)
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def observe(self, event: str, **kw) -> None:
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if event == "step_complete" and self.first is None:
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plan, result = kw.get("plan"), kw.get("result")
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if result is None:
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return
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for name, val in result.output.items():
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arr = np.asarray(val) if hasattr(val, "__array__") or isinstance(val, (list, tuple)) else None
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if arr is not None and arr.dtype.kind == "f" and not np.all(np.isfinite(arr)):
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self.first = (name, getattr(plan, "label", "") or name)
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return
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@property
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def tripped(self) -> bool:
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return self.first is not None
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