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Update funpack.py

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This commit is contained in:
aimfordeb
2026-04-20 00:31:26 +03:00
committed by GitHub
parent 62d520a022
commit 31699d92db
1 changed files with 46 additions and 27 deletions
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funpack.py
+46 -27
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@@ -200,7 +200,8 @@ class FunPackGemmaEmbeddingRefiner:
"good_ratio": 0.0,
"dynamic_sim_threshold": 0.82,
"last_feedback_tid": None,
"last_feedback_word": None
"last_feedback_word": None,
"feedbacked_words": []
},
"prompt_histories": {
prompt_key: {
@@ -281,8 +282,9 @@ class FunPackGemmaEmbeddingRefiner:
full_token_ids = tokenizer.encode(positive_prompt, add_special_tokens=True) if tokenizer and positive_prompt else []
# ====================== WORD GROUPING ======================
word_groups = []
# ====================== STRICT WORD GROUPING ======================
word_groups = [] # (start, end, full_word, word_token_list)
current_words = set()
if tokenizer and positive_prompt and full_token_ids:
try:
words = positive_prompt.split()
@@ -294,15 +296,18 @@ class FunPackGemmaEmbeddingRefiner:
word_len = len(word_tokens)
if token_idx + word_len <= len(full_token_ids) and full_token_ids[token_idx:token_idx + word_len] == word_tokens:
word_groups.append((token_idx, token_idx + word_len, word, word_tokens))
current_words.add(word.lower())
token_idx += word_len
else:
if token_idx < len(full_token_ids):
single_token = full_token_ids[token_idx]
decoded = tokenizer.decode([single_token]).strip()
word_groups.append((token_idx, token_idx + 1, decoded, [single_token]))
single = full_token_ids[token_idx]
decoded = tokenizer.decode([single]).strip()
word_groups.append((token_idx, token_idx + 1, decoded, [single]))
current_words.add(decoded.lower())
token_idx += 1
except Exception:
word_groups = [(i, i+1, tokenizer.decode([tid]).strip(), [tid]) for i, tid in enumerate(full_token_ids)]
current_words = {w.lower() for _, _, w, _ in word_groups if self._is_valuable_token(w)}
# ====================== CROSS-PROMPT TRANSFER ======================
if is_new_prompt and tokenizer and full_token_ids:
@@ -438,7 +443,7 @@ class FunPackGemmaEmbeddingRefiner:
if full_token_ids[i] != prev_token_ids[i]:
new_token_mask[i] = 0.0
# ====================== WORD-LEVEL IMPORTANCE UPDATE ======================
# ====================== WORD-LEVEL IMPORTANCE ======================
for start, end, full_word, word_token_list in word_groups:
is_valuable = self._is_valuable_token(full_word)
group_key = str(word_token_list[0])
@@ -463,7 +468,7 @@ class FunPackGemmaEmbeddingRefiner:
else:
token_importance[tid_str] = max(0.3, min(2.5, 1.0 + group_delta))
# Infusion
# ====================== INFUSION (only current prompt words) ======================
if random.random() < 0.18 and token_library:
infusion_weights = torch.ones(embed_seq_len, device=device)
infusion_any = False
@@ -656,29 +661,42 @@ class FunPackGemmaEmbeddingRefiner:
data["prompt_histories"] = prompt_histories
data["global_adaptive"] = global_adaptive
# ====================== FEEDBACK QUESTION (whole word) ======================
# ====================== NEW FEEDBACK QUESTION (new words first + only current prompt) ======================
if feedback_enabled and data.get("pending_feedback") is None and tokenizer and word_groups:
feedbacked = set(global_adaptive.get("feedbacked_words", []))
last_word = global_adaptive.get("last_feedback_word")
candidates = []
for start, end, full_word, word_token_list in word_groups:
if not self._is_valuable_token(full_word) or full_word == last_word:
continue
group_key = str(word_token_list[0])
imp = token_importance.get(group_key, 1.0)
freq = token_library.get(group_key, {}).get("fqc", 1)
score = freq * (1.0 - abs(imp - 1.0))
candidates.append((full_word, group_key, score, word_token_list[0]))
# Priority 1: New words in current prompt (never asked before)
new_words = [ (full_word, group_key, score, best_tid)
for start, end, full_word, word_token_list in word_groups
for group_key in [str(word_token_list[0])]
for best_tid in [word_token_list[0]]
for score in [token_library.get(group_key, {}).get("fqc", 1) * (1.0 - abs(token_importance.get(group_key, 1.0) - 1.0))]
if self._is_valuable_token(full_word) and full_word.lower() not in feedbacked and full_word != last_word ]
if candidates:
low_imp = [c for c in candidates if c[2] < 1.0]
if low_imp and random.random() < 0.35:
chosen = max(low_imp, key=lambda x: x[2])
else:
if new_words:
# Pick the most "interesting" new word
chosen = max(new_words, key=lambda x: x[2])
full_word, group_key, score, best_tid = chosen
else:
# Priority 2: Existing words that are valuable and not recently asked
candidates = []
for start, end, full_word, word_token_list in word_groups:
if not self._is_valuable_token(full_word) or full_word == last_word:
continue
group_key = str(word_token_list[0])
imp = token_importance.get(group_key, 1.0)
freq = token_library.get(group_key, {}).get("fqc", 1)
score = freq * (1.0 - abs(imp - 1.0))
candidates.append((full_word, group_key, score, word_token_list[0]))
if candidates:
chosen = max(candidates, key=lambda x: x[2])
full_word, group_key, score, best_tid = chosen
else:
full_word = None
full_word, group_key, _, best_tid = chosen
if full_word:
data["pending_feedback"] = {
"token_id": int(best_tid),
"token_text": full_word,
@@ -687,10 +705,11 @@ class FunPackGemmaEmbeddingRefiner:
}
global_adaptive["last_feedback_tid"] = int(best_tid)
global_adaptive["last_feedback_word"] = full_word
global_adaptive.setdefault("feedbacked_words", []).append(full_word)
feedback_question_output = f"Based on the previously generated video, how would you rate the representation of this token? '{full_word}' (1=absent, 5=perfect, 6=overrepresented). If not sure, leave the rating at 5."
feedback_question_output = f"Word: '{full_word}' – How well represented? (1=absent, 5=perfect, 6=overrepresented)"
else:
feedback_question_output = "Feedback enabled but no suitable word found."
feedback_question_output = "Feedback enabled but no new or suitable word found in current prompt."
# ====================== FINAL SAVE ======================
with open(json_file, "w", encoding="utf-8") as f: