SolitaryThinker
098bcf014a
- Admission fails fast with AdmissionInfeasible on infeasible/deadlocked reservations instead of a 10M-iteration busy-spin: no-progress detection in run_to_completion/run_interleaved via a real progress token, plus feasibility pre-checks (need > pool capacity). - Compute budget is now a refundable concurrency gate (release() refunds spent), not a never-refunded lifetime cap that silently deadlocks. - §7.1: the text-encoder feature CacheKey carries adapter_versions + precision (no stale serve across te-LoRA stacks); per-component weight versions mean a transformer-only RL weight sync no longer flushes the frozen text-encoder cache (component-scoped invalidation, not wholesale). - Interleave gate flags symmetric-empty output instead of passing it vacuously. - skipped_steps counted only when the override is actually consumed; BatchScheduler wired for round batch-accounting (metric renamed stepped_units); ResidualCache.get cleanup; stream chunks carry a latent preview payload; dead progress-vars removed. - 5 regression tests added for the previously-untested paths. 54 tests pass (pytest + zero-dep runner).
38 lines
1.7 KiB
Python
38 lines
1.7 KiB
Python
"""Shared component-node helpers (text encode with feature cache, etc.).
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The content-hash feature cache (design_v3 §7.2) is the reuse mechanism behind the §10 claim that
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a K-sample RL group encodes its shared prompt once instead of K times.
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"""
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from __future__ import annotations
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from typing import Any
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from ..cache.keys import CacheKey, content_hash
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def cached_text_encode(instance: Any, text: str) -> Any:
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enc = instance.component("text_encoder")
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cache = instance.caches
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if cache is not None and cache.has("feature"):
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# §7.1: every output-semantic field is in the key — adapter stack + precision partition it,
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# and the text-encoder's OWN version (not the instance's) so a transformer weight sync
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# does not invalidate the (frozen) text encoder's embeddings.
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key = CacheKey(model_id=instance.card.model_id, component_id="text_encoder",
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weights_version=instance.version_of("text_encoder"),
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adapter_versions=CacheKey.adapters(instance.adapter_versions),
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precision=instance.card.precision.dtype_for("text_encoder"),
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input_hashes=(("text", content_hash(text)),))
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hit = cache.pool("feature").get(key)
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if hit is not None:
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return hit
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emb = enc.encode(text)
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cache.pool("feature").put(key, emb)
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return emb
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return enc.encode(text)
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def text_encode_node_fn(instance, slots, request, ctx) -> None:
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"""ComponentNode fn: prompt + negative prompt → cached text embeddings in slots."""
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slots["text_embeds"] = cached_text_encode(instance, request.prompt())
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slots["neg_text_embeds"] = cached_text_encode(instance, request.diffusion.negative_prompt)
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