Will Lin
51898f48e9
Frees v2/models/ to become the vendored-architecture namespace that mirrors fastvideo/models (part of making v2 self-contained / able to replace fastvideo). The v2 recipe layer (per-family card.py/loop.py/program.py + common.py + the build_*_engine re-exports) is the recipe, not the architectures, so it moves to v2/recipes/. The pure-numpy toy/parity implementations (ToyDiT etc.) move from v2/models/backend.py to v2/platform/backends/toy.py (alongside cpu.py/accel.py/torch_*). Mechanical: all imports are absolute, so v2.models.<x> -> v2.recipes.<x> and v2.models.backend -> v2.platform.backends.toy across v2/ + examples/ (89 files, 178 refs). No behavior change. CPU mini green (202 passed, 2 skipped); all v2 files compile.
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/platform/backends/``) 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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