SolitaryThinker
4e331eb7ff
Mechanical conversion of every relative import under v2/ to an absolute v2.* path (from .x / ..x / ...x -> from v2.<pkg>.x) so imports are unambiguous, grep-able, and stable when code is copied/moved between entrypoints (VideoGenerator, CLI, server). Surgical prefix-only rewrite: only the 'from <dots><module>' prefix changed — import names, parentheses, multi-line formatting, comments, and ordering are byte-for-byte preserved (no collapsing, no reorder, no unrelated reformatting). - 471 imports across 125 files; v2/tests/ was already absolute (untouched). - Validated: all 125 files compile, every 'from v2.* import' target resolves to a real module/package, zero relative imports remain (full sweep), CPU mini suite green (202 passed, 2 skipped).
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
"""BehaviorRecord — captured at generation time on the serving loop (design_v3 §10).
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> The rollout forward *is* the serve forward plus capture.
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Sized honestly (design_v3 §10): full routing capture is GB/sample for omni, so it is an opt-in
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instrument. ``logprobs`` is None for likelihood-free methods (DiffusionNFT) — the C2 split.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Any
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from v2._enums import ConsistencyLevel
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class TrajectoryStatus(str, Enum):
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COMPLETED = "completed"
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TRUNCATED = "truncated"
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ABORTED = "aborted"
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@dataclass
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class BehaviorRecord:
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request_id: str
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model_id: str
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loop_id: str
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weights_version: str = "v0"
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adapter_versions: dict[str, str] = field(default_factory=dict)
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seeds: dict[str, int] = field(default_factory=dict)
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timesteps: list[float] = field(default_factory=list)
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sigmas: list[float] = field(default_factory=list)
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latents: Any = None # final clean latents (or refs if too large)
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logprobs: list[float] | None = None # None for likelihood-free methods (NFT)
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sampled_tokens: list[int] = field(default_factory=list)
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guidance: dict[str, float] = field(default_factory=dict)
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reward_inputs: dict[str, Any] = field(default_factory=dict)
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reward_outputs: dict[str, float] = field(default_factory=dict)
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precision: str = "float32"
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parallel_plan_hash: str = ""
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attention_backend: str = "dense"
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deterministic_flags: dict[str, bool] = field(default_factory=dict)
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consistency_level: ConsistencyLevel = ConsistencyLevel.C1
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status: TrajectoryStatus = TrajectoryStatus.COMPLETED
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