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from typing import Dict, List, Union, Optional, Any, Tuple
import numpy as np
import threading
from queue import Queue
import sys
from pathlib import Path
# Add parent directory to path for imports
sys.path.append(str(Path(__file__).parent.parent))
from virtual_vram import VirtualVRAM
from tensor_core import TensorCore, TensorCoreArray
from http_storage import LocalStorage
class VirtualGPUDevice:
"""Adapter for Virtual GPU integration with Helium"""
def __init__(self, device_id: int = 0, memory_size: Optional[int] = None):
"""Initialize virtual GPU device
Args:
device_id: Virtual GPU device ID
memory_size: VRAM size in GB (None for unlimited)
"""
self.device_id = device_id
# Initialize virtual VRAM with unlimited memory
self.vram = VirtualVRAM(size_gb=memory_size) # None = unlimited
# Initialize tensor cores with unlimited memory
self.tensor_cores = TensorCoreArray(
num_tensor_cores=8000, # Like an A100
memory_size=None, # Unlimited memory
device_id=device_id
)
# Command queue for async execution
self._command_queue: Queue = Queue()
self._worker_thread = threading.Thread(target=self._process_commands, daemon=True)
self._worker_thread.start()
# Cache of allocated tensors
self._tensor_cache: Dict[str, Any] = {}
def _process_commands(self):
"""Process commands from queue"""
while True:
cmd = self._command_queue.get()
if cmd is None:
break
op, args, kwargs = cmd
if hasattr(self.tensor_cores, op):
getattr(self.tensor_cores, op)(*args, **kwargs)
def allocate(self, shape: Tuple[int, ...], dtype=np.float32) -> str:
"""Allocate memory on virtual GPU
Returns:
Tensor ID in virtual GPU memory
"""
size = np.prod(shape) * np.dtype(dtype).itemsize
tensor_id = self.vram.allocate(size)
self._tensor_cache[tensor_id] = {
'tensor_id': tensor_id,
'shape': shape,
'dtype': dtype
}
return tensor_id
def to_gpu(self, data: np.ndarray) -> str:
"""Copy numpy array to virtual GPU memory"""
tensor_id = self.allocate(data.shape, data.dtype)
self.vram.store_tensor(tensor_id, data)
return tensor_id
def from_gpu(self, tensor_id: str) -> np.ndarray:
"""Copy data from virtual GPU to CPU"""
info = self._tensor_cache[tensor_id]
data = self.vram.load_tensor(info['tensor_id'])
return np.asarray(data, dtype=info['dtype']).reshape(info['shape'])
def matmul(self, a: Union[str, "HeliumTensor"], b: Union[str, "HeliumTensor"]) -> str:
"""Matrix multiplication on virtual GPU"""
a_id = a if isinstance(a, str) else self.to_gpu(a.numpy())
b_id = b if isinstance(b, str) else self.to_gpu(b.numpy())
a_info = self._tensor_cache[a_id]
b_info = self._tensor_cache[b_id]
out_shape = (a_info['shape'][0], b_info['shape'][1])
out_id = self.allocate(out_shape, a_info['dtype'])
# Load tensors
a_data = self.vram.load_tensor(a_info['tensor_id'])
b_data = self.vram.load_tensor(b_info['tensor_id'])
# Queue computation
self._command_queue.put((
'matmul',
(a_data, b_data),
{'out_id': self._tensor_cache[out_id]['tensor_id']}
))
return out_id
def add(self, a: Union[str, "HeliumTensor"], b: Union[str, "HeliumTensor"]) -> str:
"""Element-wise addition on virtual GPU"""
a_id = a if isinstance(a, str) else self.to_gpu(a.numpy())
b_id = b if isinstance(b, str) else self.to_gpu(b.numpy())
a_info = self._tensor_cache[a_id]
b_info = self._tensor_cache[b_id]
out_id = self.allocate(a_info['shape'], a_info['dtype'])
# Load tensors
a_data = self.vram.load_tensor(a_info['tensor_id'])
b_data = self.vram.load_tensor(b_info['tensor_id'])
# Queue computation
self._command_queue.put((
'add',
(a_data, b_data),
{'out_id': self._tensor_cache[out_id]['tensor_id']}
))
return out_id
def mul(self, a: Union[str, "HeliumTensor"], b: Union[str, "HeliumTensor"]) -> str:
"""Element-wise multiplication on virtual GPU"""
a_id = a if isinstance(a, str) else self.to_gpu(a.numpy())
b_id = b if isinstance(b, str) else self.to_gpu(b.numpy())
a_info = self._tensor_cache[a_id]
b_info = self._tensor_cache[b_id]
out_id = self.allocate(a_info['shape'], a_info['dtype'])
# Load tensors
a_data = self.vram.load_tensor(a_info['tensor_id'])
b_data = self.vram.load_tensor(b_info['tensor_id'])
# Queue computation
self._command_queue.put((
'multiply',
(a_data, b_data),
{'out_id': self._tensor_cache[out_id]['tensor_id']}
))
return out_id
def mul_scalar(self, a: Union[str, "HeliumTensor"], scalar: float) -> str:
"""Scalar multiplication on virtual GPU"""
a_id = a if isinstance(a, str) else self.to_gpu(a.numpy())
a_info = self._tensor_cache[a_id]
out_id = self.allocate(a_info['shape'], a_info['dtype'])
# Load tensor
a_data = self.vram.load_tensor(a_info['tensor_id'])
# Queue computation
self._command_queue.put((
'scalar_multiply',
(a_data, scalar),
{'out_id': self._tensor_cache[out_id]['tensor_id']}
))
return out_id
def transpose(self, a: Union[str, "HeliumTensor"], axes: Optional[Tuple[int, ...]] = None) -> str:
"""Transpose tensor on virtual GPU"""
a_id = a if isinstance(a, str) else self.to_gpu(a.numpy())
a_info = self._tensor_cache[a_id]
if axes is None:
axes = tuple(range(len(a_info['shape'])-1, -1, -1))
new_shape = tuple(a_info['shape'][i] for i in axes)
out_id = self.allocate(new_shape, a_info['dtype'])
# Load tensor
a_data = self.vram.load_tensor(a_info['tensor_id'])
# Queue computation
self._command_queue.put((
'transpose',
(a_data,),
{
'axes': axes,
'out_id': self._tensor_cache[out_id]['tensor_id']
}
))
return out_id
def reshape(self, a: Union[str, "HeliumTensor"], new_shape: Tuple[int, ...]) -> str:
"""Reshape tensor on virtual GPU"""
a_id = a if isinstance(a, str) else self.to_gpu(a.numpy())
a_info = self._tensor_cache[a_id]
# Verify shapes are compatible
if np.prod(new_shape) != np.prod(a_info['shape']):
raise ValueError("New shape must have same total size as old shape")
out_id = self.allocate(new_shape, a_info['dtype'])
# Load tensor
a_data = self.vram.load_tensor(a_info['tensor_id'])
# Queue computation
self._command_queue.put((
'reshape',
(a_data,),
{
'new_shape': new_shape,
'out_id': self._tensor_cache[out_id]['tensor_id']
}
))
return out_id
def softmax(self, a: Union[str, "HeliumTensor"], axis: int = -1) -> str:
"""Softmax on virtual GPU"""
a_id = a if isinstance(a, str) else self.to_gpu(a.numpy())
a_info = self._tensor_cache[a_id]
out_id = self.allocate(a_info['shape'], a_info['dtype'])
# Load tensor
a_data = self.vram.load_tensor(a_info['tensor_id'])
# Queue computation
self._command_queue.put((
'softmax',
(a_data,),
{
'axis': axis,
'out_id': self._tensor_cache[out_id]['tensor_id']
}
))
return out_id
def get_tensor(self, tensor_id: str) -> np.ndarray:
"""Get tensor data"""
if tensor_id not in self._tensor_cache:
raise KeyError(f"Tensor {tensor_id} not found")
return self.from_gpu(tensor_id)
def tensor_exists(self, tensor_id: str) -> bool:
"""Check if tensor exists in virtual GPU memory"""
return tensor_id in self._tensor_cache
def delete_tensor(self, tensor_id: str):
"""Free tensor memory"""
if tensor_id in self._tensor_cache:
self.vram.free(self._tensor_cache[tensor_id]['tensor_id'])
del self._tensor_cache[tensor_id]
def __del__(self):
"""Cleanup allocated memory and stop worker"""
# Stop command processing thread
self._command_queue.put(None)
if hasattr(self, '_worker_thread'):
self._worker_thread.join()
# Free allocated tensors
for tensor_id in list(self._tensor_cache.keys()):
self.delete_tensor(tensor_id)
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