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hao-ai-lab-FastVideo/v2/program/specs.py
T
SolitaryThinker 4e331eb7ff [refactor] v2: use absolute imports (v2.*) everywhere instead of relative
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).
2026-07-04 17:15:34 +00:00

110 lines
3.7 KiB
Python

"""Programs compose a card's loops into a task (design_v3 §13).
> The card says what loops *exist*, the program says how to *run* them for this request.
Kinds: InlineProgram (many loops, one resident instance — the omni default), TrainingProgram,
etc. Nodes are typed (ModelLoopNode / ComponentNode / ...); edges are typed and carry *named*
artifacts (not a god-batch). Linear pipelines are the degenerate case; ``when=`` predicates and
multiple producers give branches/fan-out (LTX-2 base→upsample→refine→decode; A/V fan-out).
"""
from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Callable
from v2.request.tasks import TaskType
class ProgramKind(str, Enum):
INLINE = "inline"
DISAGGREGATED = "disaggregated"
TRAINING = "training"
REALTIME = "realtime"
WORKFLOW = "workflow"
# --- node predicates (replace geometry heuristics; design.md P7) -------------- #
def always(_request: Any) -> bool:
return True
def when_task(*tasks: TaskType) -> Callable[[Any], bool]:
s = set(tasks)
return lambda req: req.task in s
def when_opt(node_id: str, key: str) -> Callable[[Any], bool]:
return lambda req: bool(req.node_override(node_id).get(key, False))
# --- nodes -------------------------------------------------------------------- #
@dataclass
class ProgramNode:
node_id: str
when: Callable[[Any], bool] = always
reads: tuple[str, ...] = () # input slot names (typed edges in)
writes: tuple[str, ...] = () # output slot names (typed edges out)
@dataclass
class ComponentNode(ProgramNode):
"""A one-shot transform: text/vision encode, VAE decode, latent upsample, vocoder.
``fn(instance, slots, request, ctx)`` reads its ``reads`` slots and writes its ``writes``
slots. Runs to completion in a single engine tick (not step-interleaved)."""
fn: Callable[..., None] | None = None
@dataclass
class ModelLoopNode(ProgramNode):
"""Binds one of the card's loops. The engine drives it via a LoopRunner, one step per tick,
so its steps interleave with other requests' steps (design_v3 §5, §6.3)."""
loop_id: str = ""
output_slot: str = "latents"
# --- typed edges (graph IR; validated, data flows via named slots) ------------ #
class EdgeKind(str, Enum):
TENSOR = "tensor"
ARTIFACT = "artifact"
STREAM = "stream"
CONTROL = "control"
CACHE = "cache"
BEHAVIOR = "behavior"
@dataclass
class Edge:
src: str # slot or node id
dst: str
kind: EdgeKind = EdgeKind.TENSOR
# --- program ------------------------------------------------------------------ #
@dataclass
class Program:
program_id: str
kind: ProgramKind
nodes: list[ProgramNode] = field(default_factory=list)
edges: list[Edge] = field(default_factory=list)
# artifact name -> slot that holds its value at the end (design_v3 §12 named artifacts)
output_artifacts: dict[str, str] = field(default_factory=dict)
def active_nodes(self, request: Any) -> list[ProgramNode]:
return [n for n in self.nodes if n.when(request)]
def validate(self) -> "Program":
produced: set[str] = set()
for n in self.nodes:
for r in n.reads:
if r not in produced:
# not fatal in mini (some reads come from the request itself), but flag dangling
pass
produced.update(n.writes)
for name, slot in self.output_artifacts.items():
if slot not in produced:
raise ValueError(f"program {self.program_id!r} output {name!r} reads slot "
f"{slot!r} that no node writes")
return self