helium/tensor_ops.py

from typing import Dict, List, Optional, Union, Any, Tuple
import numpy as np
from .modality import ModalityType, ModalityConfig, ModalityMixer

class TensorOps:
    """Hardware-accelerated tensor operations with modality support"""

    def __init__(self, device: Optional[str] = None):
        self.device = device
        self.modality_mixer = ModalityMixer()

    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"""
        if x_modality == y_modality or (x_modality is None and y_modality is None):
            # Same modality - direct matmul
            return x @ y
        else:
            # Cross-modal matmul through modality mixer
            return self.modality_mixer.fuse(x, y, x_modality, y_modality)
            
    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 operation with modality-specific behaviors"""
        config = ModalityConfig.get_config(modality) if modality else None
        
        if modality == ModalityType.IMAGE:
            # 2D convolution for images
            return self._conv2d(x, weight, stride, padding)
        elif modality == ModalityType.AUDIO:
            # 1D convolution for audio
            return self._conv1d(x, weight, stride, padding)
        elif modality == ModalityType.VIDEO:
            # 3D convolution for video
            return self._conv3d(x, weight, stride, padding)
        else:
            # Default to standard convolution
            return self._conv2d(x, weight, stride, padding)

    def attention(
        self,
        q: np.ndarray,
        k: np.ndarray,
        v: np.ndarray,
        modality: Optional[ModalityType] = None,
        mask: Optional[np.ndarray] = None
    ) -> np.ndarray:
        """Attention operation with modality-specific patterns"""
        config = ModalityConfig.get_config(modality) if modality else None
        
        # Get attention pattern from config
        pattern = config['attention_pattern'] if config else 'full'
        
        if pattern == 'causal':
            # Causal masking for text
            if mask is None:
                mask = np.triu(np.ones((q.shape[1], k.shape[1])), k=1)
            scores = (q @ k.transpose(-2, -1)) / np.sqrt(k.shape[-1])
            scores = np.ma.masked_array(scores, mask=mask)
            attn = np.exp(scores) / np.exp(scores).sum(axis=-1, keepdims=True)
            return attn @ v
            
        elif pattern == 'local':
            # Local attention for audio/image
            window_size = config['block_size'] if config else 8
            return self._local_attention(q, k, v, window_size)
            
        elif pattern == 'local3d':
            # 3D local attention for video/voxels
            window_size = config['block_size'] if config else 4
            return self._local_attention_3d(q, k, v, window_size)
            
        else:  # 'full'
            # Standard attention
            scores = (q @ k.transpose(-2, -1)) / np.sqrt(k.shape[-1])
            if mask is not None:
                scores = scores.masked_fill(mask == 0, float('-inf'))
            attn = np.exp(scores) / np.exp(scores).sum(axis=-1, keepdims=True)
            return attn @ v

    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 operation with modality-specific behaviors"""
        if modality == ModalityType.IMAGE:
            return self._pool2d(x, kernel_size, stride, mode)
        elif modality == ModalityType.AUDIO:
            return self._pool1d(x, kernel_size, stride, mode)
        elif modality == ModalityType.VIDEO:
            return self._pool3d(x, kernel_size, stride, mode)
        else:
            return self._pool2d(x, kernel_size, stride, mode)

    def normalize(
        self,
        x: np.ndarray,
        modality: Optional[ModalityType] = None,
        eps: float = 1e-5
    ) -> np.ndarray:
        """Normalization with modality-specific behaviors"""
        if modality == ModalityType.TEXT:
            # Layer norm for text
            return (x - x.mean(axis=-1, keepdims=True)) / (x.std(axis=-1, keepdims=True) + eps)
        elif modality in [ModalityType.IMAGE, ModalityType.VIDEO]:
            # Instance norm for visual data
            return (x - x.mean(axis=(2, 3), keepdims=True)) / (x.std(axis=(2, 3), keepdims=True) + eps)
        elif modality == ModalityType.AUDIO:
            # Instance norm for audio
            return (x - x.mean(axis=2, keepdims=True)) / (x.std(axis=2, keepdims=True) + eps)
        else:
            # Default to layer norm
            return (x - x.mean(axis=-1, keepdims=True)) / (x.std(axis=-1, keepdims=True) + eps)

    def _conv1d(self, x: np.ndarray, weight: np.ndarray, stride: int = 1, padding: int = 0) -> np.ndarray:
        """1D convolution implementation"""
        # TODO: Implement efficient 1D convolution
        raise NotImplementedError

    def _conv2d(self, x: np.ndarray, weight: np.ndarray, stride: Union[int, Tuple[int, int]] = 1,
                padding: Union[int, Tuple[int, int]] = 0) -> np.ndarray:
        """2D convolution implementation"""
        # TODO: Implement efficient 2D convolution
        raise NotImplementedError

    def _conv3d(self, x: np.ndarray, weight: np.ndarray, stride: Union[int, Tuple[int, int, int]] = 1,
                padding: Union[int, Tuple[int, int, int]] = 0) -> np.ndarray:
        """3D convolution implementation"""
        # TODO: Implement efficient 3D convolution
        raise NotImplementedError

    def _pool1d(self, x: np.ndarray, kernel_size: int, stride: Optional[int] = None,
                mode: str = 'max') -> np.ndarray:
        """1D pooling implementation"""
        # TODO: Implement efficient 1D pooling
        raise NotImplementedError

    def _pool2d(self, x: np.ndarray, kernel_size: Union[int, Tuple[int, int]],
                stride: Optional[Union[int, Tuple[int, int]]] = None, mode: str = 'max') -> np.ndarray:
        """2D pooling implementation"""
        # TODO: Implement efficient 2D pooling
        raise NotImplementedError

    def _pool3d(self, x: np.ndarray, kernel_size: Union[int, Tuple[int, int, int]],
                stride: Optional[Union[int, Tuple[int, int, int]]] = None, mode: str = 'max') -> np.ndarray:
        """3D pooling implementation"""
        # TODO: Implement efficient 3D pooling
        raise NotImplementedError

    def _local_attention(self, q: np.ndarray, k: np.ndarray, v: np.ndarray, window_size: int) -> np.ndarray:
        """Local attention implementation"""
        # TODO: Implement efficient local attention
        raise NotImplementedError

    def _local_attention_3d(self, q: np.ndarray, k: np.ndarray, v: np.ndarray, window_size: int) -> np.ndarray:
        """3D local attention implementation"""
        # TODO: Implement efficient 3D local attention
        raise NotImplementedError