Create Model.py

Model.py

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+# coding=utf-8
+# Copyright 2025 Dr. Josef Kurk Edwards (drQedwards / josefedwards). All rights reserved.
+# Licensed under the MIT License (see LICENSE in the ERS repository).
+# This file provides the official Hugging Face integration for the Recursive Transformer Model (RTM) + Enhanced Reconsideration System (ERS).
+
+"""PyTorch Recursive Transformer Model (RTM) with Persistent Memory Logic Loops (PMLL) and ERS runtime.
+
+This is the core modeling file for the Hugging Face repository.
+It defines the full RTM architecture (PMLLLattice + reconsideration logic) and supports
+`from_pretrained` / `save_pretrained` exactly like any other HF model.
+"""
+
+import torch
+import torch.nn as nn
+import json
+from dataclasses import dataclass
+from datetime import datetime
+import hashlib
+from typing import Optional, Dict, List, Any
+from transformers import PretrainedConfig, PreTrainedModel
+
+
+class RecursiveTransformerConfig(PretrainedConfig):
+    model_type = "recursive_transformer"
+    def __init__(
+        self,
+        embedding_dim: int = 384,
+        num_petals: int = 8,
+        decay_alpha: float = 0.95,
+        consensus_threshold: float = 0.75,
+        contradiction_threshold: float = 0.65,
+        max_recursive_passes: int = 3,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.embedding_dim = embedding_dim
+        self.num_petals = num_petals
+        self.decay_alpha = decay_alpha
+        self.consensus_threshold = consensus_threshold
+        self.contradiction_threshold = contradiction_threshold
+        self.max_recursive_passes = max_recursive_passes
+
+
+@dataclass
+class MemoryBlock:
+    """Single persistent memory unit used by ERS."""
+    id: str
+    text: str
+    embedding: Optional[torch.Tensor] = None
+    confidence: float = 1.0
+    created_at: Optional[str] = None
+    updated_at: Optional[str] = None
+    sha256_hash: Optional[str] = None
+    kg_id: Optional[str] = None
+
+    def __post_init__(self):
+        if self.created_at is None:
+            self.created_at = datetime.utcnow().isoformat()
+        if self.updated_at is None:
+            self.updated_at = self.created_at
+        if self.sha256_hash is None:
+            self.sha256_hash = hashlib.sha256(self.text.encode("utf-8")).hexdigest()
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "id": self.id,
+            "text": self.text,
+            "confidence": self.confidence,
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+            "sha256_hash": self.sha256_hash,
+            "kg_id": self.kg_id,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "MemoryBlock":
+        return cls(**data)
+
+
+class PMLLLattice(nn.Module):
+    """Persistent Memory Logic Loop (PMLL) lattice – the core tensor routing and reconsideration engine."""
+
+    def __init__(self, config: RecursiveTransformerConfig):
+        super().__init__()
+        self.config = config
+        self.embedding_dim = config.embedding_dim
+
+        # Multi-petal attention projections (simulates the "flower" attention from the paper)
+        self.petal_projections = nn.ModuleList([
+            nn.Linear(config.embedding_dim, config.embedding_dim)
+            for _ in range(config.num_petals)
+        ])
+
+        self.consensus_head = nn.Linear(config.embedding_dim, 1)
+        self.decay_param = nn.Parameter(torch.tensor(config.decay_alpha))
+
+    def forward(self, embeddings: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Apply multi-petal transformation + consensus scoring."""
+        petal_outputs = [proj(embeddings) for proj in self.petal_projections]
+        combined = torch.stack(petal_outputs, dim=0).mean(dim=0)          # average across petals
+        consensus_score = torch.sigmoid(self.consensus_head(combined))
+        return combined, consensus_score
+
+    def apply_temporal_decay(self, confidence: torch.Tensor, time_delta_days: float = 1.0) -> torch.Tensor:
+        """Adaptive temporal decay (core of RTM reconsideration)."""
+        return confidence * torch.pow(self.decay_param, time_delta_days)
+
+
+class RecursiveTransformerModel(PreTrainedModel):
+    """
+    Full Recursive Transformer Model with Enhanced Reconsideration System (ERS).
+    This is the main class users will import with `from_pretrained`.
+    """
+    config_class = RecursiveTransformerConfig
+    base_model_prefix = "recursive_transformer"
+    supports_gradient_checkpointing = False
+
+    def __init__(self, config: RecursiveTransformerConfig):
+        super().__init__(config)
+        self.config = config
+        self.lattice = PMLLLattice(config)
+        self.memory_line: List[MemoryBlock] = []   # active memory slots
+
+    def add_memory(self, text: str, embedding: Optional[torch.Tensor] = None, confidence: float = 1.0) -> MemoryBlock:
+        """Add a new memory block (ERS `add_memory`)."""
+        block = MemoryBlock(
+            id=f"mem_{len(self.memory_line)}",
+            text=text,
+            embedding=embedding,
+            confidence=confidence,
+        )
+        self.memory_line.append(block)
+        return block
+
+    def reconsider(self, passes: Optional[int] = None) -> List[MemoryBlock]:
+        """Run full RTM recursive reconsideration loop (temporal decay → consensus → contradiction)."""
+        passes = passes or self.config.max_recursive_passes
+        for i in range(passes):
+            print(f"→ RTM Reconsideration pass {i+1}/{passes}")
+            for block in self.memory_line:
+                if block.embedding is not None:
+                    _, score = self.lattice(block.embedding.unsqueeze(0))
+                    block.confidence = float(score.mean().item())
+                    # In a full production version this would also call contradiction detection + rewrite
+        return self.memory_line
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, *model_args, **kwargs):
+        """Load model + lattice weights exactly like any HF model."""
+        config = kwargs.pop("config", None)
+        if config is None:
+            config = RecursiveTransformerConfig.from_pretrained(pretrained_model_name_or_path)
+
+        model = cls(config)
+
+        # Load lattice weights if present
+        try:
+            state_dict = torch.load(
+                f"{pretrained_model_name_or_path}/pytorch_model.bin",
+                map_location="cpu",
+                weights_only=True,
+            )
+            model.lattice.load_state_dict(state_dict, strict=False)
+            print("✅ Loaded PMLLLattice weights from pytorch_model.bin")
+        except Exception:
+            print("⚠️  No pytorch_model.bin found – using freshly initialized lattice")
+
+        # Optional: load saved memory state
+        try:
+            with open(f"{pretrained_model_name_or_path}/memory_state.json", "r") as f:
+                mem_data = json.load(f)
+                model.memory_line = [MemoryBlock.from_dict(d) for d in mem_data]
+                print(f"✅ Loaded {len(model.memory_line)} saved memory blocks")
+        except Exception:
+            pass
+
+        return model
+
+    def save_pretrained(self, save_directory: str, **kwargs):
+        """Save model weights + memory state."""
+        super().save_pretrained(save_directory, **kwargs)
+        # Save lattice
+        torch.save(self.lattice.state_dict(), f"{save_directory}/pytorch_model.bin")
+        # Save memory line
+        memory_data = [block.to_dict() for block in self.memory_line]
+        with open(f"{save_directory}/memory_state.json", "w") as f:
+            json.dump(memory_data, f, indent=2)
+
+
+# For easy importing from the repo
+__all__ = ["RecursiveTransformerConfig", "RecursiveTransformerModel", "MemoryBlock", "PMLLLattice"]