[ENH] Optimize PyTorch backend and refactor kernel functions for GPU performance#44
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[ENH] Optimize PyTorch backend and refactor kernel functions for GPU performance#44Leguark wants to merge 7 commits intooctree_improvementfrom
Leguark wants to merge 7 commits intooctree_improvementfrom
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…r PyTorch backend - Ensure edge vectors are placed on the same device as input data - Add support for creating scalar tensors on the correct device and dtype based on backend tensor engine
…kend - Introduced `@torch.compile` to several kernel functions for performance optimization - Refactored cubic, exponential, and Matern functions for improved stability and reduced computational overhead - Enhanced code comments for better clarity and maintainability
…Torch - Implemented NumPy-based kernel functions as the default backend - Refactored PyTorch kernel functions with lazy loading for optional JIT optimizations - Updated `KernelFunction` to allow switching between NumPy and PyTorch implementations - Enhanced flexibility in `AvailableKernelFunctions` to support dual-backend configurations
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_compress_binary_indices for PyTorch backend
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[ENH] Improve GPU performance and tensor handling for PyTorch backend
torch.set_default_device("cuda")when GPU is requested_arraymethodcdistandeinsum[ENH] Add JIT-compiled kernel functions for PyTorch backend
@torch.compileoptimized versions of kernel functions[ENH] Improve tensor device handling in data structures
_compress_binary_indices_secure_casthelper to safely handle tensor creation across backends[ENH] Optimize memory layout for tensor operations