Research-Stack/4-Infrastructure/infra/gemma_4_integration.py

#!/usr/bin/env python3
"""
Gemma 4 Integration with Topological State Machine

Integrates Gemma 4 (low-load, flexible multimodal model) with the
GPU duty assignment system and omnidirectional interface.

Gemma 4 Variants:
- E2B: 2B effective parameters (smallest, supports audio)
- E4B: 4B effective parameters (supports audio) - RECOMMENDED for low load
- 31B: 31B parameters (dense model)
- 26B-A4B: 26B total, 4B active (MoE model, runs like 4B)
"""

import sys
import json
import sqlite3
from pathlib import Path
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from enum import Enum
import hashlib

sys.path.insert(0, str(Path(__file__).parent.parent))

from infra.ene_api import ENEAPIHook, AccessLevel
from infra.lean_unified_shim import GPUDutyAssignmentSystem, DutyType


class GemmaVariant(Enum):
    """Gemma 4 model variants"""
    E2B = "google/gemma-4-E2B-it"  # 2B effective, audio support
    E4B = "google/gemma-4-E4B-it"  # 4B effective, audio support - RECOMMENDED
    E31B = "google/gemma-4-31B-it"  # 31B dense
    E26B_A4B = "google/gemma-4-26B-A4B-it"  # 26B total, 4B active MoE
    
    @classmethod
    def from_string(cls, value: str):
        """Convert string to GemmaVariant"""
        value_map = {
            "E2B": cls.E2B,
            "E4B": cls.E4B,
            "E31B": cls.E31B,
            "26B-A4B": cls.E26B_A4B
        }
        return value_map.get(value, cls.E4B)  # Default to E4B


class GemmaTask(Enum):
    """Types of tasks Gemma 4 can perform"""
    TEXT_GENERATION = "text_generation"
    MULTIMODAL_PROCESSING = "multimodal_processing"
    AUDIO_TRANSCRIPTION = "audio_transcription"
    IMAGE_UNDERSTANDING = "image_understanding"
    REASONING = "reasoning"
    CODE_GENERATION = "code_generation"
    FUNCTION_CALLING = "function_calling"


@dataclass
class GemmaTaskRequest:
    """Task request for Gemma 4"""
    task_id: str
    task_type: GemmaTask
    variant: GemmaVariant
    input_data: Dict[str, Any]
    enable_thinking: bool = False
    max_tokens: int = 1024
    priority: int = 5


class Gemma4Integration:
    """Gemma 4 integration with TSM"""
    
    def __init__(self, db_path: str = "/home/allaun/Documents/Research Stack/data/substrate_index.db",
                 default_variant: GemmaVariant = GemmaVariant.E4B):
        self.db_path = db_path
        self.ene_api = ENEAPIHook()
        self.default_variant = default_variant
        self.gpu_duty_system = GPUDutyAssignmentSystem()
        self._init_gemma_tables()
    
    def _init_gemma_tables(self):
        """Initialize Gemma 4 integration tables"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        # Gemma task requests table
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS gemma_task_requests (
                task_id TEXT PRIMARY KEY,
                task_type TEXT NOT NULL,
                variant TEXT NOT NULL,
                input_data TEXT NOT NULL,
                enable_thinking BOOLEAN DEFAULT FALSE,
                max_tokens INTEGER DEFAULT 1024,
                priority INTEGER DEFAULT 5,
                status TEXT NOT NULL,
                created_at INTEGER NOT NULL,
                started_at INTEGER,
                completed_at INTEGER,
                result TEXT,
                error TEXT
            )
        """)
        
        # Gemma performance metrics
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS gemma_performance_metrics (
                variant TEXT PRIMARY KEY,
                total_tasks INTEGER DEFAULT 0,
                avg_latency REAL,
                avg_tokens_per_second REAL,
                last_updated INTEGER NOT NULL
            )
        """)
        
        conn.commit()
        conn.close()
    
    def submit_task(self, task: GemmaTaskRequest) -> str:
        """Submit a task to Gemma 4"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute("""
            INSERT INTO gemma_task_requests
            (task_id, task_type, variant, input_data, enable_thinking, max_tokens, priority, status, created_at)
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
        """, (
            task.task_id,
            task.task_type.value,
            task.variant.value,
            json.dumps(task.input_data),
            task.enable_thinking,
            task.max_tokens,
            task.priority,
            "pending",
            int(__import__('time').time())
        ))
        
        # Update performance metrics
        cursor.execute("""
            INSERT INTO gemma_performance_metrics (variant, total_tasks, last_updated)
            VALUES (?, 1, ?)
            ON CONFLICT(variant) DO UPDATE SET
                total_tasks = total_tasks + 1,
                last_updated = ?
        """, (task.variant.value, int(__import__('time').time()), int(__import__('time').time())))
        
        conn.commit()
        conn.close()
        
        return task.task_id
    
    def execute_task(self, task_id: str) -> Dict[str, Any]:
        """Execute a Gemma 4 task"""
        task = self._get_task(task_id)
        if not task:
            return {"success": False, "error": "Task not found"}
        
        # Mark as in progress
        self._update_task_status(task_id, "in_progress")
        
        try:
            # Assign to GPU duty system for execution
            gpu_duty_id = self.gpu_duty_system.assign_duty(
                DutyType.GENERAL_COMPUTE,
                "gemma_4_integration",
                {
                    "task_id": task_id,
                    "task_type": task["task_type"],
                    "variant": task["variant"],
                    "input_data": json.loads(task["input_data"]),
                    "enable_thinking": task["enable_thinking"],
                    "max_tokens": task["max_tokens"]
                },
                priority=task["priority"]
            )
            
            # Execute the duty
            duty_result = self.gpu_duty_system.execute_duty(gpu_duty_id)
            
            if duty_result.get("success"):
                result = self._simulate_gemma_execution(task)
                self._update_task_status(task_id, "completed", result)
                return {"success": True, "result": result}
            else:
                self._update_task_status(task_id, "failed", error=duty_result.get("error"))
                return {"success": False, "error": duty_result.get("error")}
                
        except Exception as e:
            self._update_task_status(task_id, "failed", error=str(e))
            return {"success": False, "error": str(e)}
    
    def _get_task(self, task_id: str) -> Optional[Dict]:
        """Get task by ID"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute("""
            SELECT task_id, task_type, variant, input_data, enable_thinking, max_tokens, priority, status, created_at
            FROM gemma_task_requests
            WHERE task_id = ?
        """, (task_id,))
        
        row = cursor.fetchone()
        conn.close()
        
        if row:
            return {
                "task_id": row[0],
                "task_type": row[1],
                "variant": row[2],
                "input_data": row[3],
                "enable_thinking": row[4],
                "max_tokens": row[5],
                "priority": row[6],
                "status": row[7],
                "created_at": row[8]
            }
        return None
    
    def _update_task_status(self, task_id: str, status: str, result: Optional[Dict] = None, error: Optional[str] = None):
        """Update task status"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        update_fields = ["status = ?"]
        update_values = [status]
        
        if status == "in_progress":
            update_fields.append("started_at = ?")
            update_values.append(int(__import__('time').time()))
        elif status in ["completed", "failed"]:
            update_fields.append("completed_at = ?")
            update_values.append(int(__import__('time').time()))
            
            if result:
                update_fields.append("result = ?")
                update_values.append(json.dumps(result))
            if error:
                update_fields.append("error = ?")
                update_values.append(error)
        
        update_values.append(task_id)
        
        cursor.execute(f"""
            UPDATE gemma_task_requests
            SET {', '.join(update_fields)}
            WHERE task_id = ?
        """, update_values)
        
        conn.commit()
        conn.close()
    
    def _simulate_gemma_execution(self, task: Dict) -> Dict:
        """Simulate Gemma 4 execution (placeholder for actual model loading)"""
        task_type = task["task_type"]
        input_data = json.loads(task["input_data"])
        
        # Simulate execution based on task type
        if task_type == "text_generation":
            result = {
                "generated_text": "This is a simulated response from Gemma 4",
                "tokens_generated": 50,
                "reasoning_enabled": task["enable_thinking"]
            }
        elif task_type == "multimodal_processing":
            result = {
                "processed_modalities": ["text", "image"],
                "understanding": "Multimodal content processed successfully"
            }
        elif task_type == "audio_transcription":
            result = {
                "transcription": "Audio transcribed successfully",
                "language": "en"
            }
        elif task_type == "reasoning":
            result = {
                "reasoning_steps": ["Step 1: Analyze", "Step 2: Deduce", "Step 3: Conclude"],
                "final_answer": "Reasoning completed"
            }
        else:
            result = {
                "status": "completed",
                "task_type": task_type
            }
        
        return result
    
    def get_task_queue(self) -> List[Dict]:
        """Get current Gemma task queue"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute("""
            SELECT task_id, task_type, variant, priority, status, created_at
            FROM gemma_task_requests
            WHERE status IN ('pending', 'in_progress')
            ORDER BY priority DESC, created_at ASC
        """)
        
        rows = cursor.fetchall()
        conn.close()
        
        return [
            {
                "task_id": row[0],
                "task_type": row[1],
                "variant": row[2],
                "priority": row[3],
                "status": row[4],
                "created_at": row[5]
            }
            for row in rows
        ]
    
    def get_performance_metrics(self) -> Dict[str, Any]:
        """Get performance metrics by variant"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute("SELECT * FROM gemma_performance_metrics")
        rows = cursor.fetchall()
        conn.close()
        
        metrics = {}
        for row in rows:
            metrics[row[0]] = {
                "total_tasks": row[1],
                "avg_latency": row[2],
                "avg_tokens_per_second": row[3],
                "last_updated": row[4]
            }
        
        return metrics


# Example usage
if __name__ == "__main__":
    print("=" * 70)
    print("GEMMA 4 INTEGRATION TEST")
    print("=" * 70)
    
    gemma = Gemma4Integration(default_variant=GemmaVariant.E4B)
    
    # Test 1: Submit text generation task
    print("\n[Test 1] Submitting text generation task...")
    task1 = GemmaTaskRequest(
        task_id="gemma_task_001",
        task_type=GemmaTask.TEXT_GENERATION,
        variant=GemmaVariant.E4B,
        input_data={"prompt": "Explain the concept of hyperbolic manifolds"},
        enable_thinking=True,
        max_tokens=512,
        priority=8
    )
    
    task_id = gemma.submit_task(task1)
    print(f"Task submitted: {task_id}")
    
    # Test 2: Submit multimodal task
    print("\n[Test 2] Submitting multimodal task...")
    task2 = GemmaTaskRequest(
        task_id="gemma_task_002",
        task_type=GemmaTask.MULTIMODAL_PROCESSING,
        variant=GemmaVariant.E4B,
        input_data={"text": "Describe this image", "image_url": "placeholder"},
        enable_thinking=False,
        max_tokens=256,
        priority=6
    )
    
    task_id = gemma.submit_task(task2)
    print(f"Task submitted: {task_id}")
    
    # Test 3: Get task queue
    print("\n[Test 3] Getting task queue...")
    queue = gemma.get_task_queue()
    print(f"Queue length: {len(queue)}")
    for task in queue:
        print(f"  - {task['task_id']}: {task['task_type']} (priority: {task['priority']})")
    
    # Test 4: Execute task
    print("\n[Test 4] Executing task...")
    result = gemma.execute_task("gemma_task_001")
    print(f"Result: {result}")
    
    # Test 5: Get performance metrics
    print("\n[Test 5] Getting performance metrics...")
    metrics = gemma.get_performance_metrics()
    print(json.dumps(metrics, indent=2))
    
    print("\n" + "=" * 70)
    print("GEMMA 4 INTEGRATION TEST COMPLETE")
    print("=" * 70)