test_ai_integration_http.py

"""
Custom PyTorch device implementation that routes operations through our virtual GPU.
"""
import torch
from torch.library import Library, impl
from typing import Optional, Union, Tuple
import numpy as np
from virtual_vram import VirtualVRAM

# Initialize custom backend
def init_vgpu_backend():
    try:
        # First rename the backend
        torch.utils.rename_privateuse1_backend("vgpu")
        
        # Then generate all the necessary methods
        torch.utils.generate_methods_for_privateuse1_backend(
            for_tensor=True,
            for_module=True,
            for_packed_sequence=True,
            for_storage=True
        )
        
        # Register our custom library
        lib = Library("vgpu", "DEF")
        lib.define("custom_op(Tensor self) -> Tensor")
        
        @impl("vgpu", "custom_op", "Tensor")
        def custom_op_impl(tensor):
            return tensor.clone()
            
        return True
    except Exception as e:
        print(f"Backend initialization warning: {e}")
        return False

# Initialize the backend
VGPU_BACKEND_INITIALIZED = init_vgpu_backend()

class VGPUStorage(torch.Storage):
    """Custom storage class that uses our virtual VRAM"""
    
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.vram = kwargs.get('vram')
        if not self.vram:
            from virtual_vram import VirtualVRAM
            self.vram = VirtualVRAM()
        self.tensor_id = kwargs.get('tensor_id', f"tensor_{id(self)}")
    
    def _new_shared(self, size):
        return VGPUStorage(size, vram=self.vram)

class VGPUTensor:
    """Tensor implementation that uses vGPU for computations"""
    @staticmethod
    def __new__(cls, elem):
        return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)

class VGPUDevice:
    """
    Custom PyTorch device implementation that routes operations through vGPU.
    Usage:
        vgpu = VGPUDevice()
        with vgpu.mode():
            tensor = torch.randn(2, 3)  # Will be on vGPU
    """
    _VGPU_INSTANCES = {}  # Class-level dict to track instances
    
    def __init__(self, vram: Optional[VirtualVRAM] = None):
        self.vram = vram or VirtualVRAM()
        self.tensor_cores = None  # Will be initialized when needed
        self.device_name = "vgpu"  # Both internal and user-facing name
        self._register_device()

    def _register_device(self):
        """Register vGPU device using PyTorch's device system"""
        try:
            if not VGPU_BACKEND_INITIALIZED:
                raise RuntimeError("VGPU backend not properly initialized")
                
            # Create device with explicit index
            self._device = torch.device("vgpu")
            
            # Store this instance for reuse
            VGPUDevice._VGPU_INSTANCES[self.device_name] = self
            
            # Define custom operations for the device
            class VGPUAllocator:
                def __init__(self, vram, device):
                    self.vram = vram
                    self.device = device
                
                def __call__(self, size, dtype=None, device=None):
                    # Create tensor on CPU first
                    cpu_tensor = torch.empty(size, dtype=dtype, device='cpu')
                    # Move to vGPU storage
                    return to_vgpu(cpu_tensor, self.vram)
            
            # Set up allocator
            self._allocator = VGPUAllocator(self.vram, self._device)
            
        except Exception as e:
            raise RuntimeError(f"Failed to register vGPU device: {str(e)}")
    
    @property
    def type(self):
        return self.internal_name
        
    def __str__(self):
        return f"{self.internal_name}:0"
        
    def __repr__(self):
        return f"vgpu(device='{self.internal_name}:0')"
        
    def device(self):
        """Get the PyTorch device object that maps to our vGPU"""
        return self._device  # Return the already created device object
    
    def mode(self):
        """Get a context manager for vGPU operations"""
        return torch.device(self._device)
    
    def _init_tensor_cores(self):
        if self.tensor_cores is None:
            from tensor_core import TensorCoreArray
            self.tensor_cores = TensorCoreArray()
            
    def _to_vram(self, tensor: torch.Tensor) -> str:
        """Store tensor data in virtual VRAM"""
        tensor_id = f"tensor_{id(tensor)}"
        data = tensor.detach().cpu().numpy()
        self.vram.storage.store_tensor(tensor_id, data)
        return tensor_id
        
    def _from_vram(self, tensor_id: str) -> torch.Tensor:
        """Retrieve tensor data from virtual VRAM"""
        data = self.vram.storage.load_tensor(tensor_id)
        return torch.from_numpy(data)

    def matmul(self, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
        """Matrix multiplication using tensor cores"""
        self._init_tensor_cores()
        
        # Store inputs in VRAM
        a_id = self._to_vram(a)
        b_id = self._to_vram(b)
        
        # Perform matmul using tensor cores
        result = self.tensor_cores.matmul(
            self.vram.storage.load_tensor(a_id),
            self.vram.storage.load_tensor(b_id)
        )
        
        # Create new tensor with result
        return torch.from_numpy(result)

def to_vgpu(tensor: torch.Tensor, vram: Optional[VirtualVRAM] = None) -> torch.Tensor:
    """Move a tensor to vGPU device"""
    if not isinstance(tensor, torch.Tensor):
        tensor = torch.tensor(tensor)
    
    # Get or create vGPU device
    if not VGPUDevice._VGPU_INSTANCES:
        device = VGPUDevice(vram)
    else:
        device = next(iter(VGPUDevice._VGPU_INSTANCES.values()))
        if vram is not None:
            device.vram = vram
    
    # Move data to vRAM
    tensor_id = device._to_vram(tensor)
    result = device._from_vram(tensor_id)
    result.requires_grad = tensor.requires_grad
    
    # Set the device using the internal name
    result.data = result.data.to(device._device)
    return result