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).
136 lines
7.3 KiB
Python
136 lines
7.3 KiB
Python
"""ChunkRolloutLoop — causal/AR video (design_v3 §4 causal, §5; design.md taxonomy).
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Loop shape: outer loop over latent-frame chunks × inner denoise per chunk, carrying a per-chunk
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KV/context via the slab-KV cache (sink/local window in inference; full history in self-forcing
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training mode). Each completed chunk is streamable (``StepResult.emit``), and the rollout profile
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captures per-chunk behavior for the self-forcing distillation method (design_v3 §10).
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State is entirely in ``LoopState`` (the slab list + chunk position), so interleaving causal
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rollouts of two sessions cannot smear context — and the slab-KV pool is namespaced per request.
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"""
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from __future__ import annotations
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import numpy as np
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from v2._enums import ExecutionProfile, WorkUnitKind
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from v2.cache.classes import Slab
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from v2.loop.contracts import (
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Done,
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LoopResult,
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LoopState,
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ResourceRequest,
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ShapeSignature,
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StepContext,
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StepResult,
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WorkPlan,
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)
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from v2.platform import FLOW_MATCH_STEP
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from v2.request.streams import StreamChunk
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from v2.models.backend import LATENT_CHANNELS
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class ChunkRolloutLoop:
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def __init__(self, *, loop_id, num_chunks, chunk_size, steps_per_chunk, cfg, flow_shift,
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precision, cost):
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self.loop_id = loop_id
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self.num_chunks = num_chunks
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self.chunk_size = chunk_size
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self.steps_per_chunk = steps_per_chunk
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self.cfg = cfg
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self.flow_shift = flow_shift
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self.precision = precision
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self.cost = cost
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def _chunk_shape(self, req, model=None) -> tuple[int, int, int, int]:
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# Real Wan geometry on the cuda backend (16 latent channels; chunk_size latent frames; 8x
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# spatial VAE compression); the CPU toy keeps a tiny stand-in.
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if model is not None and getattr(getattr(model, "platform", None), "device", "cpu") == "cuda":
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return (16, self.chunk_size, max(1, req.diffusion.height // 8), max(1, req.diffusion.width // 8))
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h = max(2, req.diffusion.height // 120)
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w = max(2, req.diffusion.width // 120)
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return (LATENT_CHANNELS, self.chunk_size, h, w)
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def init(self, req, model, ctx) -> LoopState:
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seed = req.diffusion.seed if req.diffusion.seed is not None else 0
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rng = np.random.default_rng(seed)
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sig = self.flow_shift.build_schedule(self.steps_per_chunk, req.diffusion.height, req.diffusion.width)
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st = LoopState(loop_id=self.loop_id, instance_id=model.card.model_id,
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request_id=req.request_id, profile=ctx.profile, rng=rng, seed=seed,
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sigmas=[float(s) for s in sig], timesteps=[float(s) * 1000.0 for s in sig])
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st.cond["prompt_embeds"] = ctx.slots.get("text_embeds")
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st.cond["negative_prompt_embeds"] = ctx.slots.get("neg_text_embeds")
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# world-model continuation: seed the chunk context from prior chunks (an interactive session's
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# persistent world state; design_v3 §16). Default [] ⇒ a fresh rollout (unchanged one-shot path).
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prior = [np.asarray(c, dtype="float32") for c in (ctx.slots.get("world_context") or [])]
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st.scratch.update(chunk_idx=0, step_in_chunk=0, slabs=prior, chunks_out=[],
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guidance_scale=float(req.diffusion.guidance_scale),
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caches=getattr(model, "caches", None))
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st.latents["chunk"] = (rng.standard_normal(self._chunk_shape(req, model)) * float(sig[0])).astype("float32")
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st.plugin_state["cfg"] = {}
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return st
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def next(self, st: LoopState):
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if st.scratch["chunk_idx"] >= self.num_chunks:
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return Done()
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i = st.scratch["step_in_chunk"]
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sigma_t, sigma_next = st.sigmas[i], st.sigmas[i + 1]
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sctx = StepContext(step_idx=i, timestep=st.timesteps[i], sigma=sigma_t)
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cfg_state = st.plugin_state["cfg"]
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branches = self.cfg.branches_this_step(sctx, cfg_state)
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x = st.latents["chunk"]
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pe, ne = st.cond["prompt_embeds"], st.cond["negative_prompt_embeds"]
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scale = st.scratch["guidance_scale"]
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context = np.mean(st.scratch["slabs"], axis=0) if st.scratch["slabs"] else None
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cfg, precision = self.cfg, self.precision
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def run(model, override=None):
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fm = model.platform.kernels.get(FLOW_MATCH_STEP) # solver dispatched per (device, arch)
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if override is not None and "noise_pred" in override:
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velocity = np.asarray(override["noise_pred"], dtype="float32")
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else:
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dit = model.component("transformer")
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preds = {b: dit(x, pe if b == "cond" else ne, sigma_t, context=context) for b in branches}
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velocity = cfg.combine(preds, scale, sctx, cfg_state)
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x_next = fm(precision.cast(x), precision.cast(velocity), sigma_t, sigma_next)
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return StepResult(output={"noise_pred": np.asarray(velocity, dtype="float32"),
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"latents": x_next.astype("float32")})
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emits = []
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if i == self.steps_per_chunk - 1: # last step of this chunk → streamable
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emits.append(StreamChunk(stream_id=st.request_id, modality="video",
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seq=st.scratch["chunk_idx"], data=x, preview=False))
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res = ResourceRequest(
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compute_seconds=self.cost.predict(int(np.prod(x.shape)), float(len(branches))),
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resident_bytes=int(x.nbytes), peak_activation_bytes=int(x.nbytes * len(branches)))
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return WorkPlan(
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loop_id=self.loop_id, instance_id=st.instance_id, kind=WorkUnitKind.CHUNK_STEP,
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shape_sig=ShapeSignature(WorkUnitKind.CHUNK_STEP, dims=tuple(x.shape),
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extra=(("chunk", st.scratch["chunk_idx"]),)),
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resources=res, payload={"branch": "combined", "chunk": st.scratch["chunk_idx"], "step": i},
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run=run, label=f"causal.c{st.scratch['chunk_idx']}.s{i}", emits=emits)
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def advance(self, st: LoopState, result: StepResult) -> LoopState:
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st.latents["chunk"] = result.output["latents"]
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st.scratch["step_in_chunk"] += 1
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st.step_idx += 1
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if st.scratch["step_in_chunk"] >= self.steps_per_chunk:
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chunk = np.asarray(st.latents["chunk"]).copy()
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st.scratch["slabs"].append(chunk) # context for the next chunk
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st.scratch["chunks_out"].append(chunk)
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caches = st.scratch.get("caches")
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if caches is not None and caches.has("slab_kv"): # demonstrate the slab-KV class
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caches.pool("slab_kv").append(st.request_id, Slab(st.scratch["chunk_idx"], k=chunk, v=None))
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if st.profile == ExecutionProfile.ROLLOUT:
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st.trajectory.append({"chunk": st.scratch["chunk_idx"], "latents": chunk})
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st.scratch["chunk_idx"] += 1
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st.scratch["step_in_chunk"] = 0
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if st.scratch["chunk_idx"] < self.num_chunks:
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st.latents["chunk"] = (st.rng.standard_normal(chunk.shape) * float(st.sigmas[0])).astype("float32")
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return st
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def finalize(self, st: LoopState) -> LoopResult:
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video = np.concatenate(st.scratch["chunks_out"], axis=1) if st.scratch["chunks_out"] else None
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return LoopResult(outputs={"latents": video, "chunks": list(st.scratch["chunks_out"])},
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metrics={"chunks": float(st.scratch["chunk_idx"])},
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behavior=st.trajectory or None)
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