395 lines
16 KiB
Python
395 lines
16 KiB
Python
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, Dict, Optional
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import torch
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from .config import SpectrumConfig
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from .forecast import ChebyshevSpectrumForecaster
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@dataclass(slots=True)
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class RuntimeStats:
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actual_forward_count: int = 0
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forecasted_count: int = 0
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total_steps: int = 0
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current_window: float = 0.0
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run_id: int = 0
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forecast_disabled: bool = False
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disable_reason: Optional[str] = None
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sampler_name: Optional[str] = None
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@dataclass(slots=True)
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class _ActiveRun:
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run_id: int
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sampler_name: str
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total_steps: int
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schedule_values: tuple[float, ...]
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schedule_coords: tuple[float, ...]
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supports_solver_steps: bool
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next_solver_step_id: int = 0
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@dataclass(slots=True)
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class _ActiveStep:
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solver_step_id: int
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time_coord: float
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decision: Dict[str, Any]
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feature_tail_shape: Optional[tuple[int, ...]] = None
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hook_call_count: int = 0
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call_expected_shapes: list[tuple[int, ...]] = field(default_factory=list)
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call_branch_signatures: list[Optional[tuple[Any, ...]]] = field(default_factory=list)
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call_observed_actual: list[bool] = field(default_factory=list)
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call_used_forecast: list[bool] = field(default_factory=list)
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call_actual_features: list[Optional[torch.Tensor]] = field(default_factory=list)
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call_predicted_features: list[Optional[torch.Tensor]] = field(default_factory=list)
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class SpectrumRuntime:
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def __init__(self, cfg: SpectrumConfig):
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self.cfg = cfg.validate()
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self.forecaster = ChebyshevSpectrumForecaster(
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degree=self.cfg.degree,
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ridge_lambda=self.cfg.ridge_lambda,
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max_history=self.cfg.max_history,
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)
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self.run_id = 0
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self.stats = RuntimeStats(current_window=float(self.cfg.window_size))
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self._active_run: Optional[_ActiveRun] = None
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self._active_steps: Dict[int, _ActiveStep] = {}
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self._reset_scheduler_state()
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@property
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def min_fit_points(self) -> int:
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return max(2, self.cfg.degree + 1)
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@property
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def active_run_id(self) -> Optional[int]:
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if self._active_run is None:
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return None
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return self._active_run.run_id
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@property
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def next_solver_step_id(self) -> int:
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if self._active_run is None:
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return 0
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return self._active_run.next_solver_step_id
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@property
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def active_run_supports_solver_steps(self) -> bool:
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if self._active_run is None:
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return False
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return bool(self._active_run.supports_solver_steps)
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def _reset_scheduler_state(self) -> None:
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self.curr_ws = float(self.cfg.window_size)
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self.num_consecutive_cached_steps = 0
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self.forecast_disabled = False
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self.forecast_disable_reason: Optional[str] = None
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self.forecaster.reset()
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self._active_steps = {}
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self.stats.current_window = float(self.cfg.window_size)
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self.stats.forecast_disabled = False
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self.stats.disable_reason = None
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def reset_all(self) -> None:
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self.run_id += 1
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self.stats = RuntimeStats(current_window=float(self.cfg.window_size), run_id=self.run_id)
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self._active_run = None
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self._reset_scheduler_state()
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def _disable_forecasting(self, reason: str) -> None:
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if self.forecast_disabled:
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return
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self.forecast_disabled = True
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self.forecast_disable_reason = reason
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self.num_consecutive_cached_steps = 0
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self.curr_ws = float(self.cfg.window_size)
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self.forecaster.reset()
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for step in self._active_steps.values():
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step.call_predicted_features = [None] * len(step.call_predicted_features)
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step.call_used_forecast = [False] * len(step.call_used_forecast)
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self.stats.current_window = self.curr_ws
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self.stats.forecast_disabled = True
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self.stats.disable_reason = reason
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def num_steps(self) -> int:
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if self.stats.total_steps > 0:
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return self.stats.total_steps
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return 50
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@staticmethod
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def _build_schedule_coords(sample_sigmas: torch.Tensor) -> tuple[tuple[float, ...], tuple[float, ...]]:
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values = tuple(float(v) for v in sample_sigmas.detach().flatten().tolist()[:-1])
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if not values:
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return (), ()
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start = values[0]
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end = values[-1]
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denom = end - start
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if abs(denom) < 1e-12:
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coords = tuple(0.0 for _ in values)
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else:
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coords = tuple(((v - start) / denom) * 2.0 - 1.0 for v in values)
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return values, coords
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def time_coord_for_step(self, solver_step_id: int) -> float:
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if self._active_run is None:
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raise RuntimeError("Spectrum runtime is not inside an active run.")
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idx = int(solver_step_id)
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if idx < 0 or idx >= len(self._active_run.schedule_coords):
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raise RuntimeError(f"Spectrum solver step {solver_step_id} is outside the active schedule.")
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return float(self._active_run.schedule_coords[idx])
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def _is_tail_actual_step(self, solver_step_id: int) -> bool:
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if self._active_run is None:
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return False
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tail_actual_steps = int(self.cfg.tail_actual_steps)
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if tail_actual_steps <= 0:
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return False
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tail_start = max(0, self._active_run.total_steps - tail_actual_steps)
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return int(solver_step_id) >= tail_start
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def start_run(self, sample_sigmas: torch.Tensor, sampler_name: str, *, supports_solver_steps: bool) -> int:
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self.run_id += 1
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schedule_values, schedule_coords = self._build_schedule_coords(sample_sigmas)
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total_steps = max(len(schedule_coords), 1)
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self.stats = RuntimeStats(
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current_window=float(self.cfg.window_size),
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total_steps=total_steps,
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run_id=self.run_id,
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sampler_name=sampler_name,
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)
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self._active_run = _ActiveRun(
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run_id=self.run_id,
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sampler_name=sampler_name,
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total_steps=total_steps,
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schedule_values=schedule_values,
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schedule_coords=schedule_coords,
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supports_solver_steps=bool(supports_solver_steps),
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)
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self._reset_scheduler_state()
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self.stats.run_id = self.run_id
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self.stats.total_steps = total_steps
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self.stats.sampler_name = sampler_name
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if not supports_solver_steps:
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self._disable_forecasting(
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f"sampler {sampler_name!r} does not expose one predict_noise call per solver step"
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)
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return self.run_id
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def end_run(self, run_id: int) -> None:
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if self._active_run is None or self._active_run.run_id != int(run_id):
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return
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self._active_run = None
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self._active_steps = {}
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def begin_solver_step(self, run_id: int, solver_step_id: int, time_coord: float, total_steps: int) -> Dict[str, Any]:
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if self._active_run is None or self._active_run.run_id != int(run_id):
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raise RuntimeError("Spectrum runtime is not inside the requested sampling run.")
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if int(total_steps) != self._active_run.total_steps:
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self._disable_forecasting("solver-step total_steps changed inside one sampling run")
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expected_time_coord = self.time_coord_for_step(int(solver_step_id))
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if not math.isclose(float(time_coord), expected_time_coord, rel_tol=0.0, abs_tol=1e-8):
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self._disable_forecasting("solver-step time_coord did not match the active schedule")
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existing = self._active_steps.get(int(solver_step_id))
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if existing is not None:
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return existing.decision
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if int(solver_step_id) != self._active_run.next_solver_step_id:
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self._disable_forecasting("solver-step ids are not sequential within the sampling run")
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actual_forward = True
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tail_actual_only = self._is_tail_actual_step(int(solver_step_id))
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if (
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not self.forecast_disabled
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and not tail_actual_only
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and int(solver_step_id) >= self.cfg.warmup_steps
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and self.forecaster.ready(self.min_fit_points)
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):
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ws_floor = max(1, int(math.floor(self.curr_ws)))
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actual_forward = ((self.num_consecutive_cached_steps + 1) % ws_floor) == 0
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if self.forecast_disabled or tail_actual_only or not self.forecaster.ready(self.min_fit_points):
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actual_forward = True
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decision = {
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"run_id": int(run_id),
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"solver_step_id": int(solver_step_id),
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"time_coord": float(time_coord),
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"total_steps": int(total_steps),
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"actual_forward": bool(actual_forward),
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"forecast_disabled": self.forecast_disabled,
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}
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self._active_steps[int(solver_step_id)] = _ActiveStep(
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solver_step_id=int(solver_step_id),
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time_coord=float(time_coord),
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decision=decision,
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)
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self._active_run.next_solver_step_id = int(solver_step_id) + 1
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self.stats.current_window = self.curr_ws
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return decision
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def get_step_decision(self, run_id: int, solver_step_id: int) -> Optional[Dict[str, Any]]:
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step = self._active_steps.get(int(solver_step_id))
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if step is None or step.decision["run_id"] != int(run_id):
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return None
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return step.decision
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def step_used_forecast(self, run_id: int, solver_step_id: int) -> bool:
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step = self._require_active_step(run_id, solver_step_id)
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return any(step.call_used_forecast)
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def register_model_hook_call(
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self,
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run_id: int,
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solver_step_id: int,
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*,
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expected_shape: tuple[int, ...],
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branch_signature: Optional[tuple[Any, ...]] = None,
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) -> int:
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step = self._require_active_step(run_id, solver_step_id)
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shape = tuple(expected_shape)
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tail_shape = shape[1:]
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step.hook_call_count += 1
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if step.feature_tail_shape is None:
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step.feature_tail_shape = tail_shape
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elif tail_shape != step.feature_tail_shape:
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self._disable_forecasting("model-hook feature shape changed within one solver step")
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for prev_branch_signature in step.call_branch_signatures:
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if prev_branch_signature != branch_signature:
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self._disable_forecasting("model-hook branch signature changed within one solver step")
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break
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step.call_expected_shapes.append(shape)
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step.call_branch_signatures.append(branch_signature)
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step.call_observed_actual.append(False)
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step.call_used_forecast.append(False)
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step.call_actual_features.append(None)
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step.call_predicted_features.append(None)
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return len(step.call_expected_shapes) - 1
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def observe_actual_feature(
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self,
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run_id: int,
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solver_step_id: int,
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feature: torch.Tensor,
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*,
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call_id: Optional[int] = None,
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) -> None:
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step = self._require_active_step(run_id, solver_step_id)
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resolved_call_id = self._resolve_call_id(step, call_id)
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step.call_observed_actual[resolved_call_id] = True
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step.call_used_forecast[resolved_call_id] = False
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step.call_predicted_features[resolved_call_id] = None
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step.call_actual_features[resolved_call_id] = feature.detach()
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def predict_feature(
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self,
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run_id: int,
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solver_step_id: int,
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*,
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expected_shape: Optional[tuple[int, ...]] = None,
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call_id: Optional[int] = None,
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) -> Optional[torch.Tensor]:
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step = self._require_active_step(run_id, solver_step_id)
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resolved_call_id = self._resolve_call_id(step, call_id)
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if step.decision["actual_forward"]:
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return None
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if self.forecast_disabled or not self.forecaster.ready(self.min_fit_points):
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return None
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target_shape = tuple(expected_shape) if expected_shape is not None else step.call_expected_shapes[resolved_call_id]
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history_shape = self.forecaster.feature_shape
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if history_shape is not None:
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history_shape = tuple(history_shape)
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if history_shape[1:] != target_shape[1:]:
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self._disable_forecasting("predicted feature shape did not match the current solver-step input")
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return None
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if history_shape[0] != target_shape[0]:
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return None
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if step.hook_call_count > 1:
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return None
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if step.call_predicted_features[resolved_call_id] is None:
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predicted_feature = self.forecaster.predict(
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time_coord=step.time_coord,
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blend_weight=self.cfg.blend_weight,
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)
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if tuple(predicted_feature.shape) != target_shape:
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self._disable_forecasting("predicted feature shape did not match the current solver-step input")
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return None
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step.call_predicted_features[resolved_call_id] = predicted_feature
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step.call_used_forecast[resolved_call_id] = True
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return step.call_predicted_features[resolved_call_id]
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def finalize_solver_step(self, run_id: int, solver_step_id: int, *, used_forecast: bool) -> None:
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step = self._require_active_step(run_id, solver_step_id)
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requested_actual_forward = bool(step.decision["actual_forward"])
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observed_actual = any(step.call_observed_actual)
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used_forecast_any = any(step.call_used_forecast)
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if bool(used_forecast) and not observed_actual:
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used_forecast_any = True
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if step.decision["actual_forward"] and not observed_actual:
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self._disable_forecasting("solver step requested an actual forward but no actual feature was observed")
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if not observed_actual and not used_forecast_any:
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self._disable_forecasting("solver step finished without an actual feature or a forecasted feature")
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if observed_actual and used_forecast_any:
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self._disable_forecasting("solver step mixed forecasted and actual model-hook paths")
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used_forecast_any = False
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if used_forecast_any:
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if step.hook_call_count > 1:
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self._disable_forecasting("forecasted solver step re-entered the model hook")
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self.num_consecutive_cached_steps += 1
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self.stats.forecasted_count += 1
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step.decision["actual_forward"] = False
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else:
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actual_parts = [part for part in step.call_actual_features if part is not None]
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if actual_parts and not self.forecast_disabled:
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combined_feature = actual_parts[0] if len(actual_parts) == 1 else torch.cat(actual_parts, dim=0)
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try:
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self.forecaster.update(step.time_coord, combined_feature)
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except ValueError:
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self._disable_forecasting("combined actual feature shape changed across solver steps")
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if (
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requested_actual_forward
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and not self.forecast_disabled
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and step.solver_step_id >= self.cfg.warmup_steps
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):
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self.curr_ws = round(self.curr_ws + float(self.cfg.flex_window), 6)
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self.num_consecutive_cached_steps = 0
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self.stats.actual_forward_count += 1
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step.decision["actual_forward"] = True
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self.stats.current_window = self.curr_ws
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self._active_steps.pop(int(solver_step_id), None)
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@staticmethod
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def _resolve_call_id(step: _ActiveStep, call_id: Optional[int]) -> int:
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if not step.call_expected_shapes:
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raise RuntimeError("Spectrum solver step has no active model-hook call.")
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resolved = len(step.call_expected_shapes) - 1 if call_id is None else int(call_id)
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if resolved < 0 or resolved >= len(step.call_expected_shapes):
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raise RuntimeError(f"Spectrum solver-step call id {resolved} is not active.")
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return resolved
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def _require_active_step(self, run_id: int, solver_step_id: int) -> _ActiveStep:
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if self._active_run is None or self._active_run.run_id != int(run_id):
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raise RuntimeError("Spectrum runtime is not inside the requested sampling run.")
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step = self._active_steps.get(int(solver_step_id))
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if step is None:
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raise RuntimeError(f"Spectrum solver step {solver_step_id} is not active.")
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return step
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