Bump gsply from 0.2.10 to 0.2.11

[dependabot]

Bumps gsply from 0.2.10 to 0.2.11.

Release notes

Sourced from gsply's releases.

v0.2.11 (GPU Compression Optimization)

Performance Improvements

• torch.compile() Auto-Optimization: GPU compression now automatically uses torch.compile() when available
  • ~25% faster GPU compression (5.0ms → 4.0ms for 365K Gaussians)
  • Automatic fallback to eager mode if compilation fails (e.g., no Triton/MSVC)
  • Zero configuration required - works transparently
  • Triton backend provides JIT-compiled optimized kernels

Bug Fixes

• GPU Compression Rounding Fix: Fixed quaternion quantization producing 1-bit differences vs CPU
  • Changed from torch.round() (banker's rounding) to + 0.5 before int conversion
  • Now matches CPU Numba behavior exactly
  • Ensures consistent round-trip compression/decompression across CPU and GPU

Platform Support

• Triton Requirements:
  • Linux: Standard triton package (installed with PyTorch)
  • Windows: Requires triton-windows package for torch.compile optimization
  • Falls back to eager mode automatically when Triton unavailable

Implementation Details

• Lazy compilation with caching (_COMPILED_FUNCTIONS dict)
• Runtime error handling catches compilation failures gracefully
• Individual packing functions compiled separately for better error isolation
• Position packing: 7.8x speedup with torch.compile
• Color/opacity packing: 6.2x speedup with torch.compile
• Quaternion packing: 2.3x speedup with torch.compile

Changelog

Sourced from gsply's changelog.

v0.2.11 (GPU Compression Optimization)

Performance Improvements

• torch.compile() Auto-Optimization: GPU compression now automatically uses torch.compile() when available
  • ~25% faster GPU compression (5.0ms → 4.0ms for 365K Gaussians)
  • Automatic fallback to eager mode if compilation fails (e.g., no Triton/MSVC)
  • Zero configuration required - works transparently
  • Triton backend provides JIT-compiled optimized kernels

Bug Fixes

• GPU Compression Rounding Fix: Fixed quaternion quantization producing 1-bit differences vs CPU
  • Changed from torch.round() (banker's rounding) to + 0.5 before int conversion
  • Now matches CPU Numba behavior exactly
  • Ensures consistent round-trip compression/decompression across CPU and GPU

Platform Support

• Triton Requirements:
  • Linux: Standard triton package (installed with PyTorch)
  • Windows: Requires triton-windows package for torch.compile optimization
  • Falls back to eager mode automatically when Triton unavailable

Implementation Details

• Lazy compilation with caching (_COMPILED_FUNCTIONS dict)
• Runtime error handling catches compilation failures gracefully
• Individual packing functions compiled separately for better error isolation
• Position packing: 7.8x speedup with torch.compile
• Color/opacity packing: 6.2x speedup with torch.compile
• Quaternion packing: 2.3x speedup with torch.compile

Testing

• All 406 tests passing
• GPU compression roundtrip verified with both torch.compile enabled and disabled

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Commits

• 07a776a v0.1.11
• See full diff in compare view

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[dependabot]

Looks like gsply is up-to-date now, so this is no longer needed.