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Add token overrepresentation rating option

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This commit is contained in:
aimfordeb
2026-04-19 23:20:23 +03:00
committed by GitHub
parent fba26a7b0e
commit bf0b397b9e
1 changed files with 18 additions and 15 deletions
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funpack.py
+18 -15
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@@ -102,7 +102,7 @@ class FunPackGemmaEmbeddingRefiner:
"max": 5,
"step": 1,
"display": "slider",
"label": "Token Feedback (1=absent, 5=perfect)"
"label": "Token Feedback (1=absent, 5=perfect, 6=overrepresented)"
}),
}
}
@@ -187,7 +187,6 @@ class FunPackGemmaEmbeddingRefiner:
if negative_prompt:
prompt_key += f" |||NEG||| {negative_prompt}"
# ====================== SESSION RESET ======================
if reset_session or not os.path.exists(json_file):
data = {
"refinement_key": refinement_key,
@@ -223,7 +222,7 @@ class FunPackGemmaEmbeddingRefiner:
prompt_histories = data.get("prompt_histories", {})
tokenizer = self._get_tokenizer()
# ====================== FEEDBACK STATE MACHINE (UPGRADED) ======================
# ====================== FEEDBACK STATE MACHINE (UPGRADED with rating 6 support) ======================
feedback_question_output = ""
pending = data.get("pending_feedback")
@@ -235,18 +234,25 @@ class FunPackGemmaEmbeddingRefiner:
feedback_question_output = "Feedback disabled. Queue cleared."
else:
if pending is not None:
# Process feedback - principled EMA
# Process previous token feedback - principled update with overrepresentation support
token_id = pending["token_id"]
tid_str = str(token_id)
token_importance = global_adaptive["token_importance"]
old_imp = token_importance.get(tid_str, 1.0)
target_imp = 0.4 + (feedback_rating / 5.0) * 2.1
if feedback_rating == 6: # Overrepresented → suppress
target_imp = 0.65 # Pull toward mild suppression
else:
target_imp = 0.4 + (feedback_rating / 5.0) * 2.1
new_imp = 0.65 * old_imp + 0.35 * target_imp
token_importance[tid_str] = max(0.3, min(2.5, new_imp))
# Update library counts (rating 6 treated as neutral for counts)
token_library = global_adaptive.setdefault("token_library", {})
if tid_str in token_library:
if feedback_rating >= 4:
if feedback_rating >= 4 and feedback_rating != 6:
token_library[tid_str]["high_rated_count"] += 2
elif feedback_rating <= 2:
token_library[tid_str]["low_rated_count"] += 2
@@ -276,7 +282,7 @@ class FunPackGemmaEmbeddingRefiner:
cur_token_ids = tokenizer.encode(positive_prompt, add_special_tokens=True) if tokenizer and positive_prompt else []
# Cross-prompt transfer
# Cross-prompt token importance transfer
if is_new_prompt and tokenizer and cur_token_ids:
current_set = set(cur_token_ids)
token_importance = global_adaptive["token_importance"]
@@ -434,7 +440,6 @@ class FunPackGemmaEmbeddingRefiner:
token_importance[tid_str] = max(0.3, min(2.5,
token_importance[tid_str] + reward * lr * update_strength / norm_factor))
# Infusion
if random.random() < 0.18 and token_library:
infusion_weights = torch.ones(embed_seq_len, device=device)
infusion_any = False
@@ -469,7 +474,6 @@ class FunPackGemmaEmbeddingRefiner:
new_token_mask = None
infusion_weights = None
# Delta construction
history = active.get("history", [])
is_close = (not is_new_prompt) and (similarity >= sim_threshold)
@@ -507,7 +511,6 @@ class FunPackGemmaEmbeddingRefiner:
else:
new_delta = torch.randn_like(reference) * expl * 0.45
# Apply importance, new tokens, infusion
if token_importance and cur_positive.dim() > 1:
seq_len = cur_positive.shape[1] if cur_positive.dim() == 3 else cur_positive.shape[0]
importance_tensor = torch.ones(seq_len, device=device)
@@ -548,7 +551,7 @@ class FunPackGemmaEmbeddingRefiner:
norm_factor_neg = raw_negative.norm(dim=-1, keepdim=True) + 1e-8
new_negative = new_negative / (new_negative.norm(dim=-1, keepdim=True) + 1e-8) * norm_factor_neg
# ====================== LATENT REFINEMENT ======================
# ====================== SAFE LATENT REFINEMENT ======================
modified_latent = latent
latent_info = " | Latent unchanged"
if latent is not None and "samples" in latent:
@@ -578,11 +581,11 @@ class FunPackGemmaEmbeddingRefiner:
mask = torch.abs(samples) < 0.05
latent_delta = latent_delta * (~mask).float()
if torch.abs(latent_delta).max() > 1e-4:
new_samples = torch.clamp(samples + latent_delta, min=-12.0, max=-12.0)
new_samples = torch.clamp(samples + latent_delta, min=-12.0, max=12.0)
modified_latent = {"samples": new_samples.cpu(), "noise_mask": latent.get("noise_mask")}
latent_info = f" | Latent ±{len(good_latents)}g/{len(bad_latents)}b"
# Update momentum and adaptive params
# Update momentum and adaptive parameters
momentum = 0.75 * momentum + 0.25 * (new_delta * reward)
if avg_reward_ema > 0.3:
sim_threshold = max(0.75, sim_threshold - 0.002)
@@ -632,7 +635,7 @@ class FunPackGemmaEmbeddingRefiner:
data["prompt_histories"] = prompt_histories
data["global_adaptive"] = global_adaptive
# ====================== SMART FEEDBACK QUESTION (UPGRADED) ======================
# ====================== GENERATE NEW FEEDBACK QUESTION (UPGRADED) ======================
if feedback_enabled and data.get("pending_feedback") is None and tokenizer and positive_prompt:
prompt_token_ids = tokenizer.encode(positive_prompt, add_special_tokens=False)
last_tid = global_adaptive.get("last_feedback_tid")
@@ -675,7 +678,7 @@ class FunPackGemmaEmbeddingRefiner:
"iteration": len(history)
}
global_adaptive["last_feedback_tid"] = int(best_tid)
feedback_question_output = f"Token: '{best_text}' – How well represented? (1=absent, 5=perfect)"
feedback_question_output = f"Token: '{best_text}' – How well represented? (1=absent, 5=perfect, 6=overrepresented)"
else:
feedback_question_output = "Feedback enabled but no valuable token found in prompt."