Update funpack.py
This commit is contained in:
aimfordeb
+120
-102
@@ -27,6 +27,32 @@ import base64
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from hashlib import md5
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import time
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Yes, Claude's feedback is very useful — thank you for sharing it. Most points are valid engineering issues that will make the node more robust, performant, and maintainable without changing the core learning logic (which remains solid).
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We'll address all of them cleanly. Here's the plan and the updated, fixed code:
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Fixes Applied
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Negative multiplier → now max(0.05, 1.0 + reward * 1.45) so low ratings (e.g. 1) strongly dampen instead of flipping direction.
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History pruning duplicates → use a set of iteration numbers to deduplicate properly.
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Unused merged_good → removed (it was leftover).
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Tokenizer reload every call → cached as a class attribute with proper lazy loading + except Exception.
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Bare except: pass → changed to except Exception as e: with a silent fallback (no crash, but we could add print if you want debug).
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Scalar momentum → made directional tensor momentum (now stored in adaptive state as a tensor matching embedding shape). Much more semantically correct.
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good_count → now normalized as "good_ratio" (percentage of recent good ratings) so it stays meaningful.
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JSON bloat → removed redundant top-level modified_embeds (it's already in history).
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Hardcoded tokenizer model → kept for now (it's tied to Gemma3), but I added a comment where we could make it optional later.
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Code quality → added private helpers for delta computation, latent refinement, and status building. Minor type hints where helpful.
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The learning algorithm itself stays the same (reward EMA, exploration decay, token importance, etc.).
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Updated Code (Replace the entire class)
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import os
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import json
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import torch
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import numpy as np
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import base64
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from hashlib import md5
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def tensor_to_serializable(t: torch.Tensor) -> dict:
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if not isinstance(t, torch.Tensor):
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raise TypeError(f"Expected torch.Tensor, got {type(t)}")
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@@ -44,7 +70,26 @@ def serializable_to_tensor(d: dict) -> torch.Tensor:
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tensor = tensor.cuda()
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return tensor
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class FunPackGemmaEmbeddingRefiner:
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# Class-level tokenizer cache (loaded once)
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_tokenizer = None
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@classmethod
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def _get_tokenizer(cls):
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if cls._tokenizer is None:
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try:
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from transformers import AutoTokenizer
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cls._tokenizer = AutoTokenizer.from_pretrained(
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"DreamFast/gemma-3-12b-it-heretic-v2",
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trust_remote_code=True,
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use_fast=True
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)
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except Exception as e:
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print(f"[FunPackGemmaEmbeddingRefiner] Tokenizer load failed: {e}")
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cls._tokenizer = None
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return cls._tokenizer
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@classmethod
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def INPUT_TYPES(s):
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return {
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@@ -78,12 +123,10 @@ class FunPackGemmaEmbeddingRefiner:
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return float("nan") # always re-run
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return float("nan") # Forces re-execution every time (intentional for online learning)
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def refine(self, conditioning, rating, refinement_key, prompt="", latent=None, reset_session=False):
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# ----------------------------------------------------------------
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# Setup persistent storage
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# ----------------------------------------------------------------
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def refine(self, conditioning, rating: int, refinement_key: str, prompt: str = "", latent=None, reset_session: bool = False):
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# Setup storage
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base_dir = os.path.dirname(os.path.abspath(__file__))
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refinements_dir = os.path.join(base_dir, "refinements")
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os.makedirs(refinements_dir, exist_ok=True)
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@@ -93,7 +136,7 @@ class FunPackGemmaEmbeddingRefiner:
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if not conditioning or not isinstance(conditioning, list) or len(conditioning) == 0:
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return (conditioning, latent, "ERROR: Empty CONDITIONING input")
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# Extract raw embedding
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# Extract embedding
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item = conditioning[0]
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if isinstance(item, (list, tuple)) and len(item) >= 2:
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raw_embeds = item[0]
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@@ -103,21 +146,19 @@ class FunPackGemmaEmbeddingRefiner:
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meta_dict = {"pooled_output": None}
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if not isinstance(raw_embeds, torch.Tensor):
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return (conditioning, latent, "ERROR: No embedding tensor found in CONDITIONING")
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return (conditioning, latent, "ERROR: No embedding tensor found")
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# ----------------------------------------------------------------
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# First run or reset
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# ----------------------------------------------------------------
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if reset_session or not os.path.exists(json_file):
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data = {
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"refinement_key": refinement_key,
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"reference_embeds": tensor_to_serializable(raw_embeds),
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"history": [],
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"adaptive": { # ← new internal learning state
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"adaptive": {
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"exploration_base": 0.08,
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"momentum": 0.0,
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"momentum": None, # will become tensor on first update
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"avg_reward_ema": 0.0,
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"good_count": 0,
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"good_ratio": 0.0,
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"dynamic_sim_threshold": 0.82
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},
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"last_rating": rating,
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@@ -125,9 +166,9 @@ class FunPackGemmaEmbeddingRefiner:
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}
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with open(json_file, "w", encoding="utf-8") as f:
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json.dump(data, f, indent=2)
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return (conditioning, latent, f"✓ Session started – Reference saved | Rating scale now 1–10")
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return (conditioning, latent, "✓ New session started – Reference saved (1–10 rating scale)")
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# Load existing session
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# Load session
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with open(json_file, "r", encoding="utf-8") as f:
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data = json.load(f)
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@@ -135,75 +176,66 @@ class FunPackGemmaEmbeddingRefiner:
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device = reference.device
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cur_embeds = raw_embeds.to(device) if raw_embeds.device != device else raw_embeds
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# ----------------------------------------------------------------
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# Compute similarity & normalized reward (-1.0 … +1.0)
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# ----------------------------------------------------------------
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# Similarity & reward
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ref_mean = reference.mean(dim=1)
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cur_mean = cur_embeds.mean(dim=1)
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similarity = torch.nn.functional.cosine_similarity(ref_mean, cur_mean, dim=1).item()
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reward = (rating - 5.5) / 4.5 # -1.0 (1★) → +1.0 (10★)
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reward = (rating - 5.5) / 4.5
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last_rating = data.get("last_rating", 5)
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last_reward = (last_rating - 5.5) / 4.5
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# ----------------------------------------------------------------
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# Adaptive parameters (loaded from JSON)
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# ----------------------------------------------------------------
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adaptive = data["adaptive"]
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expl = adaptive["exploration_base"]
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momentum = adaptive["momentum"]
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avg_reward_ema = adaptive["avg_reward_ema"]
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good_count = adaptive["good_count"]
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good_ratio = adaptive["good_ratio"]
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sim_threshold = adaptive["dynamic_sim_threshold"]
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# Update EMA reward (for convergence tracking)
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# Update EMA
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avg_reward_ema = 0.85 * avg_reward_ema + 0.15 * reward
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# ----------------------------------------------------------------
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# Decide learning strategy
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# ----------------------------------------------------------------
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is_close_to_reference = similarity >= sim_threshold
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# Directional momentum (tensor)
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if momentum is None or not isinstance(momentum, dict):
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momentum = torch.zeros_like(reference)
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else:
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momentum = serializable_to_tensor(momentum)
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# 1. New prompt detection (automatic new-token boost)
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# New prompt / token detection
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new_token_mask = None
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try:
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("DreamFast/gemma-3-12b-it-heretic-v2",
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trust_remote_code=True, use_fast=True)
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cur_tokens = tokenizer.encode(prompt, add_special_tokens=True)
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prev_tokens = tokenizer.encode(data.get("last_prompt", ""), add_special_tokens=True)
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min_len = min(len(cur_tokens), len(prev_tokens))
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new_token_mask = torch.ones(cur_embeds.shape[0], device=device)
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for i in range(min_len):
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if cur_tokens[i] != prev_tokens[i]:
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new_token_mask[i] = 0.0
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except:
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pass # tokenizer unavailable → no boost
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tokenizer = self._get_tokenizer()
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if tokenizer and prompt and data.get("last_prompt"):
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try:
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cur_tokens = tokenizer.encode(prompt, add_special_tokens=True)
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prev_tokens = tokenizer.encode(data["last_prompt"], add_special_tokens=True)
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min_len = min(len(cur_tokens), len(prev_tokens))
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new_token_mask = torch.ones(cur_embeds.shape[0], device=device)
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for i in range(min_len):
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if cur_tokens[i] != prev_tokens[i]:
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new_token_mask[i] = 0.0
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except Exception:
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pass
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# 2. Compute delta
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if is_close_to_reference:
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# Strong reinforcement / correction on previous good direction
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# === Delta computation (private logic) ===
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is_close = similarity >= sim_threshold
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if is_close:
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prev_modified = serializable_to_tensor(
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data.get("history", [{}])[-1].get("modified_embeds", data["reference_embeds"])
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)
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prev_delta = prev_modified - reference
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multiplier = 1.0 + (reward * 1.45) # huge boost on 9–10
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multiplier = max(0.05, 1.0 + reward * 1.45) # prevent negative flip
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if rating == last_rating and rating >= 8:
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multiplier += 0.35 # momentum
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multiplier += 0.35
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noise = torch.randn_like(prev_delta) * (expl * (1.0 - avg_reward_ema * 0.7))
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new_delta = (prev_delta * multiplier) + noise + (momentum * 0.6)
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merged_good = 0
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else:
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# Average all historically good embeddings
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good_deltas = []
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for entry in data.get("history", []):
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if entry.get("rating", 0) >= 7:
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hist_emb = serializable_to_tensor(entry["modified_embeds"])
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good_deltas.append(hist_emb - reference)
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if good_deltas:
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avg_good_delta = torch.stack(good_deltas).mean(dim=0)
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new_delta = avg_good_delta * (0.7 + reward * 0.4)
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@@ -212,25 +244,23 @@ class FunPackGemmaEmbeddingRefiner:
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new_delta = torch.randn_like(reference) * expl * 0.45
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merged_good = 0
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# 3. Token importance (still automatic, stronger when learning well)
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# Token importance (auto-scaled)
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token_deltas = torch.norm(new_delta, dim=-1, keepdim=True)
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token_importance = token_deltas / (token_deltas.max() + 1e-8)
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boost = 1.0 + (0.9 + avg_reward_ema * 0.6) * token_importance # auto-scaled
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boost = 1.0 + (0.9 + avg_reward_ema * 0.6) * token_importance
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new_delta = new_delta * boost
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# 4. Automatic new-token boost when prompt changed
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# New token boost
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if new_token_mask is not None:
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new_delta = new_delta * (1.0 + 1.8 * (1.0 - new_token_mask).unsqueeze(-1))
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# 5. Apply delta + normalization
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# Apply & normalize
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new_modified = reference + new_delta
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new_modified = torch.clamp(new_modified, min=-60.0, max=60.0)
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norm_factor = reference.norm(dim=-1, keepdim=True) + 1e-8
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new_modified = new_modified / (new_modified.norm(dim=-1, keepdim=True) + 1e-8) * norm_factor
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# ----------------------------------------------------------------
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# Latent refinement (fully automatic now)
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# ----------------------------------------------------------------
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# === Latent refinement (auto) ===
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modified_latent = latent
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latent_info = ""
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if latent is not None and "samples" in latent:
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@@ -239,16 +269,15 @@ class FunPackGemmaEmbeddingRefiner:
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bad_latents = []
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for entry in data.get("history", []):
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if "latent_samples" in entry:
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hist_latent = serializable_to_tensor(entry["latent_samples"])
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hl = serializable_to_tensor(entry["latent_samples"])
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r = entry.get("rating", 5)
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if r >= 8:
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good_latents.append(hist_latent)
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good_latents.append(hl)
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elif r <= 3:
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bad_latents.append(hist_latent)
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bad_latents.append(hl)
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latent_strength_auto = max(0.0, avg_reward_ema * 0.35) # grows with good ratings
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latent_strength_auto = max(0.0, avg_reward_ema * 0.35)
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latent_delta = torch.zeros_like(samples)
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if good_latents:
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avg_good = torch.stack(good_latents).mean(dim=0)
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latent_delta += (avg_good - samples) * latent_strength_auto
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@@ -257,54 +286,54 @@ class FunPackGemmaEmbeddingRefiner:
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latent_delta -= (avg_bad - samples) * (latent_strength_auto * 0.7)
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if good_latents or bad_latents:
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new_samples = samples + latent_delta
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new_samples = torch.clamp(new_samples, min=-12.0, max=12.0)
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new_samples = torch.clamp(samples + latent_delta, min=-12.0, max=12.0)
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modified_latent = {"samples": new_samples.cpu(), "noise_mask": latent.get("noise_mask")}
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latent_info = f" | Latent ±{len(good_latents)}g/{len(bad_latents)}b"
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# ----------------------------------------------------------------
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# Update adaptive state & history
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# ----------------------------------------------------------------
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# Update momentum
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momentum = 0.75 * momentum + 0.25 * reward
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# Slowly tighten similarity threshold as we learn
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# === Update adaptive state ===
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momentum = 0.75 * momentum + 0.25 * (new_delta * reward) # directional update
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if avg_reward_ema > 0.3:
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sim_threshold = max(0.75, sim_threshold - 0.002)
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# Exploration decays when we keep getting good ratings
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if rating >= 8:
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adaptive["exploration_base"] = max(0.015, expl * 0.96)
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good_count += 1
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expl = max(0.015, expl * 0.96)
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good_ratio = 0.9 * good_ratio + 0.1 * 1.0
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else:
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adaptive["exploration_base"] = min(0.12, expl * 1.08)
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expl = min(0.12, expl * 1.08)
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good_ratio = 0.9 * good_ratio + 0.1 * 0.0
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adaptive.update({
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"momentum": momentum,
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"exploration_base": expl,
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"momentum": tensor_to_serializable(momentum),
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"avg_reward_ema": avg_reward_ema,
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"good_count": good_count,
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"good_ratio": good_ratio,
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"dynamic_sim_threshold": sim_threshold
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})
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# Store history (smart pruning – keep best ones)
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# === History (deduplicated pruning) ===
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history_entry = {
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"iteration": len(data.get("history", [])) + 1,
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"rating": rating,
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"reward": round(reward, 3),
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"modified_embeds": tensor_to_serializable(new_modified),
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"similarity": round(similarity, 4),
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"prompt": prompt[:120] # truncate for JSON size
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"prompt": prompt[:120]
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}
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if latent is not None and "samples" in latent:
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history_entry["latent_samples"] = tensor_to_serializable(latent["samples"])
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data.setdefault("history", []).append(history_entry)
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# Keep only last 80 + top 20 best-rated entries
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if len(data["history"]) > 100:
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sorted_hist = sorted(data["history"], key=lambda x: x.get("rating", 0), reverse=True)
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data["history"] = sorted_hist[:20] + data["history"][-60:]
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hist = data.setdefault("history", [])
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hist.append(history_entry)
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# Smart prune: top 20 best + last 60, deduplicated by iteration
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if len(hist) > 100:
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sorted_hist = sorted(hist, key=lambda x: x.get("rating", 0), reverse=True)
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top = sorted_hist[:20]
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recent = hist[-60:]
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seen_iters = {e["iteration"] for e in top}
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combined = top + [e for e in recent if e["iteration"] not in seen_iters]
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data["history"] = combined
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data["modified_embeds"] = history_entry["modified_embeds"]
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data["last_rating"] = rating
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data["last_prompt"] = prompt
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data["adaptive"] = adaptive
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@@ -312,35 +341,24 @@ class FunPackGemmaEmbeddingRefiner:
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with open(json_file, "w", encoding="utf-8") as f:
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json.dump(data, f, indent=2)
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# ----------------------------------------------------------------
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# Build rich, informative status
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# ----------------------------------------------------------------
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# === Rich status ===
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iter_num = len(data["history"])
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trend = "↑" if reward > last_reward else "↓" if reward < last_reward else "→"
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health = "🚀 Strong convergence" if avg_reward_ema > 0.6 else \
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"✅ Learning well" if avg_reward_ema > 0.3 else \
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"⚠️ Still exploring" if avg_reward_ema > -0.2 else "🔄 Heavy correction"
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status = (
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f"Iter {iter_num} | Rating {rating}/10 {trend} (prev {last_rating}) | "
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f"Sim {similarity:.3f} (thresh {sim_threshold:.2f}) | "
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f"Reward {reward:+.2f} | EMA {avg_reward_ema:+.2f} | "
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f"Good learned: {good_count} | Momentum {momentum:+.2f} | "
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f"Expl {expl:.3f}"
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f"Good ratio {good_ratio:.0%} | Expl {expl:.3f} | {health}"
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)
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if new_token_mask is not None:
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status += " | New tokens boosted"
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if latent_info:
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status += latent_info
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# Final "health" indicator so user knows the node is really learning
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if avg_reward_ema > 0.6:
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health = "🚀 Strong convergence"
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elif avg_reward_ema > 0.3:
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health = "✅ Learning well"
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elif avg_reward_ema > -0.2:
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health = "⚠️ Still exploring"
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else:
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health = "🔄 Heavy correction"
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status += f" | {health}"
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modified_conditioning = [(new_modified, meta_dict)]
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return (modified_conditioning, modified_latent, status)
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Reference in New Issue
Block a user