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"""
Advanced tensor core with modality support for virtual GPU simulation.
Extends TensorCore with modality-aware operations and distributed computing.
"""
import numpy as np
from typing import Optional, Dict, Any, Tuple, List, Union
from .modality import ModalityType, ModalityConfig, ModalityMixer
from .tensor_ops import TensorOps
class ModalityAwareTensorCore(TensorOps):
"""TensorCore extension with modality support"""
def __init__(
self,
bits: int = 2,
memory_size: Optional[int] = None,
bandwidth_tbps: float = 10000,
sm=None,
storage=None,
device: Optional[str] = None
):
super().__init__(device)
# Initialize base TensorCore components
self.bits = bits
self.bandwidth_tbps = bandwidth_tbps
self.sm = sm
self.storage = storage
# Initialize modality components
self.modality_mixer = ModalityMixer()
self._active_modality: Optional[ModalityType] = None
self._modality_configs: Dict[ModalityType, Dict[str, Any]] = {}
# Virtual memory space for modalities
self.modality_memory: Dict[ModalityType, Dict[str, np.ndarray]] = {
modality: {} for modality in ModalityType
}
def set_modality(self, modality: ModalityType) -> None:
"""Set active modality for subsequent operations"""
self._active_modality = modality
if modality not in self._modality_configs:
self._modality_configs[modality] = ModalityConfig.get_config(modality)
def get_active_modality(self) -> Optional[ModalityType]:
"""Get currently active modality"""
return self._active_modality
def matmul(
self,
x: np.ndarray,
y: np.ndarray,
x_modality: Optional[ModalityType] = None,
y_modality: Optional[ModalityType] = None
) -> np.ndarray:
"""Matrix multiplication with modality handling"""
x_mod = x_modality or self._active_modality
y_mod = y_modality or self._active_modality
return super().matmul(x, y, x_mod, y_mod)
def conv(
self,
x: np.ndarray,
weight: np.ndarray,
stride: Union[int, Tuple[int, ...]] = 1,
padding: Union[int, Tuple[int, ...]] = 0,
modality: Optional[ModalityType] = None
) -> np.ndarray:
"""Convolution with modality handling"""
mod = modality or self._active_modality
return super().conv(x, weight, stride, padding, mod)
def attention(
self,
q: np.ndarray,
k: np.ndarray,
v: np.ndarray,
mask: Optional[np.ndarray] = None,
modality: Optional[ModalityType] = None
) -> np.ndarray:
"""Attention with modality handling"""
mod = modality or self._active_modality
return super().attention(q, k, v, mod, mask)
def pool(
self,
x: np.ndarray,
kernel_size: Union[int, Tuple[int, ...]],
stride: Optional[Union[int, Tuple[int, ...]]] = None,
mode: str = 'max',
modality: Optional[ModalityType] = None
) -> np.ndarray:
"""Pooling with modality handling"""
mod = modality or self._active_modality
return super().pool(x, kernel_size, stride, mode, mod)
def normalize(
self,
x: np.ndarray,
modality: Optional[ModalityType] = None,
eps: float = 1e-5
) -> np.ndarray:
"""Normalization with modality handling"""
mod = modality or self._active_modality
return super().normalize(x, mod, eps)
def fuse_modalities(
self,
x: np.ndarray,
y: np.ndarray,
x_modality: ModalityType,
y_modality: ModalityType
) -> np.ndarray:
"""Fuse tensors from different modalities"""
return self.modality_mixer.fuse(x, y, x_modality, y_modality)
def unfuse_modalities(
self,
z: np.ndarray,
x_modality: ModalityType,
y_modality: ModalityType
) -> Tuple[np.ndarray, np.ndarray]:
"""Separate fused tensor back into modalities"""
return self.modality_mixer.unfuse(z, x_modality, y_modality)
def store_modality_tensor(
self,
data: np.ndarray,
modality: ModalityType,
key: str
) -> None:
"""Store tensor in modality-specific memory space"""
self.modality_memory[modality][key] = data
def load_modality_tensor(
self,
modality: ModalityType,
key: str
) -> Optional[np.ndarray]:
"""Load tensor from modality-specific memory space"""
return self.modality_memory[modality].get(key)
def clear_modality_memory(
self,
modality: Optional[ModalityType] = None
) -> None:
"""Clear modality-specific memory space"""
if modality:
self.modality_memory[modality].clear()
else:
for mod in self.modality_memory:
self.modality_memory[mod].clear()
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