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).
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
"""v2 — a scoped, CPU-testable realization of the design_v3 model-native runtime.
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> A model card is a (recipe, runtime) pair with a parity obligation.
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> The model owns loop semantics; the runtime owns loop lifecycle.
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> One resident instance runs many loops; one scheduler runs their steps in one currency.
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> Caches are correct by key; parity is correct by test; the interleave gate is non-negotiable.
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> Training records behavior on the same loops it serves.
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Phase 1 supports Wan2.1-1.3B (T2V) and LTX2.3 (2-stage distilled), plus four training
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methods on Wan2.1-1.3B (finetuning, DMD2, DiffusionNFT, self-forcing). The spine is
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omni-ready (multi-loop ModelInstance, ar_decode/chunk_rollout loop kinds) for the phase-2
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Cosmos3 + vllm-omni omni ports.
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The core is numpy-only and CPU-testable; heavy Wan/LTX neural forwards become lazy torch
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adapters (see ``v2/models/*/components.py``) that are off the test path.
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"""
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from __future__ import annotations
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from v2._enums import (
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Capability,
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ConsistencyLevel,
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ExecutionProfile,
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LoopKind,
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WorkUnitKind,
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)
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from v2.card import (
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CapabilityMatrix,
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ComponentSpec,
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CostModel,
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LoopSpec,
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ModelCard,
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ModelInstance,
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ParitySpec,
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RecipeSpec,
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load_card,
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)
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from v2.program import ComponentNode, ModelLoopNode, Program, ProgramKind, when_opt, when_task
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from v2.request import (
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DiffusionParams,
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Output,
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Request,
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SamplingParams,
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Session,
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TaskType,
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make_request,
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)
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from v2.runtime import AsyncEngine, Engine
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__version__ = "0.2.0"
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__all__ = [
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"ModelCard", "ComponentSpec", "LoopSpec", "RecipeSpec", "ParitySpec", "CostModel",
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"CapabilityMatrix", "ModelInstance", "load_card",
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"Engine", "AsyncEngine", "Program", "ProgramKind", "ComponentNode", "ModelLoopNode", "when_task", "when_opt",
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"Request", "Session", "Output", "make_request", "TaskType", "SamplingParams", "DiffusionParams",
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"LoopKind", "WorkUnitKind", "ConsistencyLevel", "ExecutionProfile", "Capability",
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"VideoGenerator",
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"__version__",
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]
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def __getattr__(name: str):
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# Lazy: the GPU entrypoint imports torch / fastvideo, so resolve it only on access — plain
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# ``import v2`` (and the CPU-only mini) stay torch-free.
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if name == "VideoGenerator":
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from v2.video_generator import VideoGenerator
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return VideoGenerator
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raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
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