Summary

This release includes significant performance improvements, bug fixes, and architectural refactoring.
Key Improvements:

• TMA autotuning and MMA matmul tuning enabled for better performance
• ONNX-IR refactored to an op/node-centric architecture IR refactored to define outputs as a function of the operation

Bug Fixes:

• Fixed autodiff graph cleanup issues (multiple fixes for deferred/consumed nodes)
• Fixed Linear layer panic when output size is one
• Fixed PyTorch pickle reader regression with integer dict keys
• Fixed RoPE sum_dim calculation
• Fixed tensor *_like dtype preservation
• Fixed squeeze check for D2 > 0
• Fixed QLinear implementation
• Fixed async barrier & TMA checks

New Features:

• Added matvec operation
• Added support for custom learning strategies
• Added Candle device seeding
• Added Shape::ravel_index for row-major raveling
• Generalized linalg::outer semantics with new linalg::outer_dim
• Implemented error handling for DataError
• Added square() optimization where appropriate