fix: community cache blocked by memory hit — move before memory check
Community download block was inside Pass 1, after the memory check's early return. Runs with memory_mode=auto never reached the community code, so nothing was downloaded or uploaded despite community_cache being set to upload_and_download. Moved lora/pair cache init + community download block to before the persistent memory lookup so community hits and uploads work regardless of memory cache state. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
7f63e610f8
commit
a031d7b027
+94
-94
@@ -8599,6 +8599,100 @@ class LoRAAutoTuner(LoRAOptimizer):
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logging.info("[LoRA AutoTuner] Using cached result")
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return cached_result
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# Load pair/lora caches and run community cache check before any early returns
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pairs_for_cache = [(i, j) for i in range(len(active_loras))
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for j in range(i+1, len(active_loras))]
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lora_hashes = {i: self._lora_identity_hash(lora)
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for i, lora in enumerate(active_loras)}
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lora_caches = {i: self._lora_cache_load(h) or {}
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for i, h in lora_hashes.items()}
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pair_caches = {(i, j): self._pair_cache_load(lora_hashes[i], lora_hashes[j]) or {}
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for i, j in pairs_for_cache}
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# --- Community cache download ---
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_community_tuner_data = None
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content_hashes = {}
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if community_cache in ("download_only", "upload_and_download") and not _is_sub_merge:
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logging.info(f"[AutoTuner Community] Mode: {community_cache} — computing content hashes "
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f"for {len(active_loras)} LoRA(s)...")
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_all_hashed = True
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for _i, _lora in enumerate(active_loras):
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_ch = self._lora_content_hash(_lora)
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if _ch is not None:
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content_hashes[_i] = _ch
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else:
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logging.warning(
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f"[AutoTuner Community] Could not hash '{_lora['name']}', disabling community cache")
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_all_hashed = False
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break
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if _all_hashed:
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_arch_key_for_community, _ = _resolve_arch_preset(
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architecture_preset, getattr(self, '_detected_arch', None) or 'unknown')
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_community_tuner_data = self._community_download_caches(
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active_loras, content_hashes, lora_caches, pair_caches,
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arch_preset=_arch_key_for_community, top_n=top_n)
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else:
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content_hashes = {}
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if _community_tuner_data is not None and not _is_sub_merge:
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_comm_score = _community_tuner_data["top_n"][0].get("score_final", 0.0)
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logging.info(f"[AutoTuner Community] COMMUNITY CACHE HIT — "
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f"replaying config (score={_comm_score:.4f}), skipping full sweep")
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if len(_community_tuner_data["top_n"]) > top_n:
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_community_tuner_data["top_n"] = _community_tuner_data["top_n"][:top_n]
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_sel_idx = min(selection, len(_community_tuner_data["top_n"])) - 1
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_comm_banner = (
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"=" * 54 + "\n"
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" COMMUNITY CACHE HIT — Results from HF community cache\n"
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" Set community_cache='disabled' to run locally.\n"
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"=" * 54 + "\n\n"
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)
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_comm_report = _comm_banner + self._build_autotuner_report(
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_community_tuner_data["top_n"], _community_tuner_data["analysis_summary"],
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output_strength, scoring_speed=scoring_speed, applied_rank=_sel_idx + 1)
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if output_mode == "tuning_only":
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_comm_result = (model, clip, _comm_report, "", _community_tuner_data, None)
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if cache_patches == "enabled":
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if not hasattr(self, '_autotuner_cache'):
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self._autotuner_cache = {}
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self._autotuner_cache[at_cache_key] = (_comm_result, "tuning_only")
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return _comm_result
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_comm_config = _community_tuner_data["top_n"][_sel_idx]["config"]
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_comm_strategy_override = (_comm_config["merge_mode"]
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if _comm_config["optimization_mode"] == "global" else "")
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_comm_model, _comm_clip, _, _comm_analysis_report, _comm_lora_data = super().optimize_merge(
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model, lora_stack, output_strength,
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clip=clip,
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clip_strength_multiplier=clip_strength_multiplier,
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auto_strength=_comm_config["auto_strength"],
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auto_strength_floor=_community_tuner_data.get(
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"auto_strength_floor", auto_strength_floor),
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optimization_mode=_comm_config["optimization_mode"],
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sparsification=_comm_config["sparsification"],
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sparsification_density=_comm_config["sparsification_density"],
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dare_dampening=_comm_config["dare_dampening"],
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merge_refinement=_comm_config["merge_refinement"],
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merge_strategy_override=_comm_strategy_override,
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strategy_set=_comm_config.get("strategy_set", "full"),
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normalize_keys=_community_tuner_data.get("normalize_keys", normalize_keys),
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architecture_preset=_community_tuner_data.get(
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"architecture_preset", architecture_preset),
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decision_smoothing=_community_tuner_data.get(
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"decision_smoothing", decision_smoothing),
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smooth_slerp_gate=smooth_slerp_gate,
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cache_patches=cache_patches,
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vram_budget=vram_budget,
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)
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_comm_result = (_comm_model, _comm_clip, _comm_report,
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_comm_analysis_report, _community_tuner_data, _comm_lora_data)
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if cache_patches == "enabled":
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if not hasattr(self, '_autotuner_cache'):
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self._autotuner_cache = {}
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self._autotuner_cache[at_cache_key] = (_comm_result, "merge")
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return _comm_result
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# --- Persistent memory lookup ---
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if memory_mode != "disabled" and not _is_sub_merge:
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memory_settings = {
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@@ -8729,100 +8823,6 @@ class LoRAAutoTuner(LoRAOptimizer):
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self._analysis_partial_save(
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names_only_hash, partial_accumulated, source_loras_for_cache)
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# Load pair/lora caches for cross-run reuse
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pairs_for_cache = [(i, j) for i in range(len(active_loras))
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for j in range(i+1, len(active_loras))]
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lora_hashes = {i: self._lora_identity_hash(lora)
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for i, lora in enumerate(active_loras)}
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lora_caches = {i: self._lora_cache_load(h) or {}
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for i, h in lora_hashes.items()}
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pair_caches = {(i, j): self._pair_cache_load(lora_hashes[i], lora_hashes[j]) or {}
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for i, j in pairs_for_cache}
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# --- Community cache download ---
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_community_tuner_data = None
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content_hashes = {}
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if community_cache in ("download_only", "upload_and_download") and not _is_sub_merge:
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logging.info(f"[AutoTuner Community] Mode: {community_cache} — computing content hashes "
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f"for {len(active_loras)} LoRA(s)...")
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_all_hashed = True
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for _i, _lora in enumerate(active_loras):
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_ch = self._lora_content_hash(_lora)
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if _ch is not None:
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content_hashes[_i] = _ch
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else:
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logging.warning(
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f"[AutoTuner Community] Could not hash '{_lora['name']}', disabling community cache")
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_all_hashed = False
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break
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if _all_hashed:
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_arch_key_for_community, _ = _resolve_arch_preset(
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architecture_preset, getattr(self, '_detected_arch', None) or 'unknown')
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_community_tuner_data = self._community_download_caches(
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active_loras, content_hashes, lora_caches, pair_caches,
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arch_preset=_arch_key_for_community, top_n=top_n)
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else:
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content_hashes = {}
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if _community_tuner_data is not None and not _is_sub_merge:
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_comm_score = _community_tuner_data["top_n"][0].get("score_final", 0.0)
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logging.info(f"[AutoTuner Community] COMMUNITY CACHE HIT — "
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f"replaying config (score={_comm_score:.4f}), skipping full sweep")
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if len(_community_tuner_data["top_n"]) > top_n:
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_community_tuner_data["top_n"] = _community_tuner_data["top_n"][:top_n]
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_sel_idx = min(selection, len(_community_tuner_data["top_n"])) - 1
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_comm_banner = (
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"=" * 54 + "\n"
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" COMMUNITY CACHE HIT — Results from HF community cache\n"
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" Set community_cache='disabled' to run locally.\n"
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"=" * 54 + "\n\n"
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)
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_comm_report = _comm_banner + self._build_autotuner_report(
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_community_tuner_data["top_n"], _community_tuner_data["analysis_summary"],
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output_strength, scoring_speed=scoring_speed, applied_rank=_sel_idx + 1)
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if output_mode == "tuning_only":
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_comm_result = (model, clip, _comm_report, "", _community_tuner_data, None)
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if cache_patches == "enabled":
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if not hasattr(self, '_autotuner_cache'):
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self._autotuner_cache = {}
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self._autotuner_cache[at_cache_key] = (_comm_result, "tuning_only")
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return _comm_result
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_comm_config = _community_tuner_data["top_n"][_sel_idx]["config"]
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_comm_strategy_override = (_comm_config["merge_mode"]
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if _comm_config["optimization_mode"] == "global" else "")
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_comm_model, _comm_clip, _, _comm_analysis_report, _comm_lora_data = super().optimize_merge(
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model, lora_stack, output_strength,
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clip=clip,
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clip_strength_multiplier=clip_strength_multiplier,
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auto_strength=_comm_config["auto_strength"],
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auto_strength_floor=_community_tuner_data.get(
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"auto_strength_floor", auto_strength_floor),
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optimization_mode=_comm_config["optimization_mode"],
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sparsification=_comm_config["sparsification"],
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sparsification_density=_comm_config["sparsification_density"],
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dare_dampening=_comm_config["dare_dampening"],
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merge_refinement=_comm_config["merge_refinement"],
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merge_strategy_override=_comm_strategy_override,
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strategy_set=_comm_config.get("strategy_set", "full"),
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normalize_keys=_community_tuner_data.get("normalize_keys", normalize_keys),
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architecture_preset=_community_tuner_data.get(
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"architecture_preset", architecture_preset),
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decision_smoothing=_community_tuner_data.get(
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"decision_smoothing", decision_smoothing),
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smooth_slerp_gate=smooth_slerp_gate,
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cache_patches=cache_patches,
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vram_budget=vram_budget,
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)
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_comm_result = (_comm_model, _comm_clip, _comm_report,
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_comm_analysis_report, _community_tuner_data, _comm_lora_data)
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if cache_patches == "enabled":
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if not hasattr(self, '_autotuner_cache'):
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self._autotuner_cache = {}
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self._autotuner_cache[at_cache_key] = (_comm_result, "merge")
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return _comm_result
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analysis_data = self._run_group_analysis(
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target_groups, active_loras, model, clip, compute_device,
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clip_strength_multiplier=clip_strength_multiplier,
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