Will Lin
1fe50c0092
The v2 top level had grown to ~27 dirs + 13 loose files — half of them
single-concept abstraction shells (memory/ 86 LOC, transport/ 222, parity/ 178,
extend/ 226) and half vendored fastvideo code sitting as peers to the actual v2
design. Regroup to mirror README section 3 "Planes & dependency order":
core/ enums+types, card, loop, program, parity, request, parallel
(the model-native contracts; no kernels)
runtime/ + folded-in substrate: cache, memory, transport, extend
(the import graph shows only runtime consumes them; compile/cudagraph
already lived here)
serving/ + deploy/ (products / fleet)
_vendor/ all copied fastvideo: models, layers, attention, configs, distributed,
platforms, api, hooks, logging_utils, third_party + fastvideo_args/
utils/logger/envs/forward_context — internal layout unchanged, still
mirrors upstream for diffing
28 dirs + 13 root files -> 8 dirs + 6 root files. Pure mechanical move: 947
absolute-import paths rewritten (v2.X -> v2.{core,runtime,serving,_vendor}.X),
boundary-anchored so platform/ (native dispatch) and platforms/ (vendored CUDA
detect) no longer collide and neither does hooks/ vs extend/. Deleted the empty
v2/loader/. README section 3 + 16 updated; stale test count corrected
(34 files/216 tests -> 22 files/143 tests).
Validated: `import v2` stays torch-free; `v2/run_tests.py` and `pytest v2/tests/`
-> 143 passed, 2 skipped on the numpy toy backend. (On a GPU box force the toy
backend with CUDA_VISIBLE_DEVICES="" or detect() picks cuda.) No external importer
changed — examples use the re-exported `from v2 import VideoGenerator`.
61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
"""Artifact — a named, typed output with provenance.
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Each modality is its own typed artifact (e.g. audio carries its sample rate as a
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field, not via an ``extra`` dict). Every artifact records which program node
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produced it (``producer``), so multi-modal fan-out outputs are traceable.
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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 v2.core.types import TensorLike
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from v2.core.request.modalpart import Modality
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@dataclass
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class Artifact:
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name: str # output slot name, e.g. "video", "audio", "text"
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producer: str = "" # program node id that produced it (provenance)
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meta: dict[str, object] = field(default_factory=dict)
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@dataclass
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class VideoArtifact(Artifact):
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frames: TensorLike = None # T x H x W x C
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fps: int = 16
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@dataclass
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class AudioArtifact(Artifact):
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samples: TensorLike = None
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sample_rate: int = 44100 # first-class, not an extra-dict passenger
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@dataclass
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class TextArtifact(Artifact):
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token_ids: tuple[int, ...] = ()
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text: str = ""
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@dataclass
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class TensorArtifact(Artifact):
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tensor: TensorLike = None # generic (e.g. action stream)
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@dataclass
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class LatentArtifact(Artifact):
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latent: TensorLike = None
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of_modality: Modality = Modality.VIDEO
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@dataclass
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class Output:
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"""The collected result of running a program for one request."""
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request_id: str
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artifacts: dict[str, Artifact] = field(default_factory=dict)
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metrics: dict[str, float] = field(default_factory=dict)
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error: str | None = None # set on partial/aborted delivery (failure isolation)
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def get(self, name: str) -> Artifact | None:
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return self.artifacts.get(name)
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