src/vgpu.py

"""
vGPU Core Processor Module

This module implements the central orchestrator of the virtual GPU, managing
workload distribution across 800 SMs and 50,000 cores, and coordinating
operations between all other modules.
"""

import asyncio
import time
from collections import deque
from enum import Enum
from typing import Dict, List, Optional, Any
from dataclasses import dataclass


class TaskType(Enum):
    """Enumeration of task types that can be processed by the vGPU."""
    RENDER_PIXEL_BLOCK = "render_pixel_block"
    RENDER_CLEAR = "render_clear"
    RENDER_RECT = "render_rect"
    RENDER_IMAGE = "render_image"
    AI_MATRIX_MULTIPLY = "ai_matrix_multiply"
    AI_VECTOR_OP = "ai_vector_op"


class TaskStatus(Enum):
    """Enumeration of task statuses."""
    PENDING = "pending"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"


@dataclass
class Task:
    """Represents a single task to be processed by the vGPU."""
    task_id: str
    task_type: TaskType
    payload: Dict[str, Any]
    sm_id: Optional[int] = None
    status: TaskStatus = TaskStatus.PENDING
    created_time: float = 0.0
    start_time: float = 0.0
    end_time: float = 0.0


class StreamingMultiprocessor:
    """Represents a single Streaming Multiprocessor (SM) in the vGPU."""
    
    def __init__(self, sm_id: int, cores_per_sm: int = 62):
        self.sm_id = sm_id
        self.cores_per_sm = cores_per_sm
        self.task_queue = deque()
        self.current_task: Optional[Task] = None
        self.is_busy = False
        self.total_tasks_processed = 0
        
    def add_task(self, task: Task) -> None:
        """Add a task to this SM's queue."""
        task.sm_id = self.sm_id
        self.task_queue.append(task)
        
    def get_next_task(self) -> Optional[Task]:
        """Get the next task from the queue."""
        if self.task_queue and not self.is_busy:
            task = self.task_queue.popleft()
            self.current_task = task
            self.is_busy = True
            task.status = TaskStatus.IN_PROGRESS
            task.start_time = time.time()
            return task
        return None
        
    def complete_task(self) -> Optional[Task]:
        """Mark the current task as completed."""
        if self.current_task:
            self.current_task.status = TaskStatus.COMPLETED
            self.current_task.end_time = time.time()
            completed_task = self.current_task
            self.current_task = None
            self.is_busy = False
            self.total_tasks_processed += 1
            return completed_task
        return None
        
    def get_queue_length(self) -> int:
        """Get the current queue length."""
        return len(self.task_queue)


class VirtualGPU:
    """
    The main Virtual GPU class that orchestrates all operations.
    
    This class manages 800 SMs with a total of 50,000 cores, handles task
    distribution, and coordinates with other modules like VRAM, renderer, and AI.
    """
    
    def __init__(self, num_sms: int = 800, total_cores: int = 50000):
        self.num_sms = num_sms
        self.total_cores = total_cores
        self.cores_per_sm = total_cores // num_sms
        
        # Initialize Streaming Multiprocessors
        self.sms: List[StreamingMultiprocessor] = []
        for i in range(num_sms):
            # Distribute cores evenly, with some SMs getting an extra core if needed
            cores_for_this_sm = self.cores_per_sm
            if i < (total_cores % num_sms):
                cores_for_this_sm += 1
            self.sms.append(StreamingMultiprocessor(i, cores_for_this_sm))
        
        # Global task management
        self.pending_tasks = deque()
        self.completed_tasks = deque()
        self.task_counter = 0
        
        # GPU state
        self.is_running = False
        self.clock_cycle = 0
        self.tick_rate = 60  # Hz
        
        # Module references (to be set by external initialization)
        self.vram = None
        self.renderer = None
        self.ai_accelerator = None
        self.driver = None
        
    def set_modules(self, vram, renderer, ai_accelerator, driver):
        """Set references to other vGPU modules."""
        self.vram = vram
        self.renderer = renderer
        self.ai_accelerator = ai_accelerator
        self.driver = driver
        
    def submit_task(self, task_type: TaskType, payload: Dict[str, Any]) -> str:
        """Submit a new task to the vGPU."""
        task_id = f"task_{self.task_counter}"
        self.task_counter += 1
        
        task = Task(
            task_id=task_id,
            task_type=task_type,
            payload=payload,
            created_time=time.time()
        )
        
        self.pending_tasks.append(task)
        return task_id
        
    def distribute_tasks(self) -> None:
        """Distribute pending tasks to available SMs using round-robin."""
        sm_index = 0
        max_queue_length = 10  # Prevent any SM from being overloaded
        
        while self.pending_tasks:
            # Find an SM that's not overloaded
            attempts = 0
            while attempts < self.num_sms:
                current_sm = self.sms[sm_index]
                if current_sm.get_queue_length() < max_queue_length:
                    task = self.pending_tasks.popleft()
                    current_sm.add_task(task)
                    break
                sm_index = (sm_index + 1) % self.num_sms
                attempts += 1
            
            if attempts >= self.num_sms:
                # All SMs are overloaded, break to avoid infinite loop
                break
                
            sm_index = (sm_index + 1) % self.num_sms
            
    def process_sm_tasks(self) -> None:
        """Process tasks on all SMs."""
        for sm in self.sms:
            # Start a new task if the SM is idle
            if not sm.is_busy:
                task = sm.get_next_task()
                if task:
                    # Task will be processed in the next step
                    pass
            
            # Process the current task (simulate work completion)
            if sm.current_task:
                # Simulate task processing by calling appropriate module
                self._execute_task(sm.current_task)
                completed_task = sm.complete_task()
                if completed_task:
                    self.completed_tasks.append(completed_task)
                    
    def _execute_task(self, task: Task) -> None:
        """Execute a specific task by calling the appropriate module."""
        try:
            if task.task_type == TaskType.RENDER_CLEAR and self.renderer:
                self.renderer.clear(**task.payload)
            elif task.task_type == TaskType.RENDER_RECT and self.renderer:
                self.renderer.draw_rect(**task.payload)
            elif task.task_type == TaskType.RENDER_IMAGE and self.renderer:
                self.renderer.draw_image(**task.payload)
            elif task.task_type == TaskType.AI_MATRIX_MULTIPLY and self.ai_accelerator:
                self.ai_accelerator.matrix_multiply(**task.payload)
            elif task.task_type == TaskType.AI_VECTOR_OP and self.ai_accelerator:
                self.ai_accelerator.vector_operation(**task.payload)
            else:
                print(f"Unknown task type: {task.task_type}")
                task.status = TaskStatus.FAILED
        except Exception as e:
            print(f"Error executing task {task.task_id}: {e}")
            task.status = TaskStatus.FAILED
            
    async def tick(self) -> None:
        """Main GPU tick cycle."""
        self.clock_cycle += 1
        
        # 1. Distribute pending tasks to SMs
        self.distribute_tasks()
        
        # 2. Process tasks on all SMs
        self.process_sm_tasks()
        
        # 3. Handle any driver commands
        if self.driver:
            await self.driver.process_commands()
            
    async def run(self) -> None:
        """Main GPU execution loop."""
        self.is_running = True
        tick_interval = 1.0 / self.tick_rate
        
        print(f"Starting vGPU with {self.num_sms} SMs and {self.total_cores} cores")
        print(f"Tick rate: {self.tick_rate} Hz")
        
        while self.is_running:
            start_time = time.time()
            
            await self.tick()
            
            # Maintain consistent tick rate
            elapsed = time.time() - start_time
            if elapsed < tick_interval:
                await asyncio.sleep(tick_interval - elapsed)
                
    def stop(self) -> None:
        """Stop the GPU execution."""
        self.is_running = False
        
    def get_stats(self) -> Dict[str, Any]:
        """Get current GPU statistics."""
        total_tasks_processed = sum(sm.total_tasks_processed for sm in self.sms)
        total_queue_length = sum(sm.get_queue_length() for sm in self.sms)
        busy_sms = sum(1 for sm in self.sms if sm.is_busy)
        
        return {
            "clock_cycle": self.clock_cycle,
            "total_sms": self.num_sms,
            "total_cores": self.total_cores,
            "busy_sms": busy_sms,
            "total_tasks_processed": total_tasks_processed,
            "pending_tasks": len(self.pending_tasks),
            "total_queue_length": total_queue_length,
            "completed_tasks": len(self.completed_tasks)
        }


if __name__ == "__main__":
    # Basic test of the vGPU
    async def test_vgpu():
        vgpu = VirtualGPU()
        
        # Submit some test tasks
        vgpu.submit_task(TaskType.RENDER_CLEAR, {"color": (255, 0, 0)})
        vgpu.submit_task(TaskType.RENDER_RECT, {"x": 10, "y": 10, "width": 100, "height": 50, "color": (0, 255, 0)})
        
        # Run a few ticks
        for _ in range(5):
            await vgpu.tick()
            print(f"Stats: {vgpu.get_stats()}")
            await asyncio.sleep(0.1)
    
    asyncio.run(test_vgpu())