SolitaryThinker
db866dfb04
Sweep all 296 v2 modules: simplify verbose docstrings/comments and remove 376 dangling "(design_vN §X)" citations to the now-deleted design docs (v2/README.md is the source of truth). Comment/docstring-only — AST-verified code-identical; the CPU suite holds at 237 passed / 5 pre-existing. Also applies yapf + ruff --fix auto-fixes (import ordering, forward-ref annotation de-quoting under `from __future__ import annotations`; behavior- neutral, suite-confirmed) and adds the legitimate domain terms mot/clen/te to the codespell ignore-list. Remaining ruff (24) + mypy (68 no-untyped-call) findings are pre-existing v2 debt, untouched here.
39 lines
1.7 KiB
Python
39 lines
1.7 KiB
Python
"""Shared component-node helpers (text encode with feature cache, etc.).
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The content-hash feature cache lets a K-sample RL group encode its shared prompt once instead of
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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 v2.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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# Every output-semantic field is in the key — adapter stack + precision partition it, and the
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# text-encoder's OWN version (not the instance's) so a transformer weight sync does not
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# invalidate the (frozen) text encoder's embeddings.
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key = CacheKey(model_id=instance.card.model_id,
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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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