trunk/65e006d4183b3534f7f8082a1f16d31bc5ba48ae: [Inductor] Add coordinate descent tuning for combo kernels (#177725)

The PyTorch team has integrated a significant optimization into its core framework with commit 65e006d, titled "[Inductor] Add coordinate descent tuning for combo kernels." This update addresses a previous limitation where the coordinate descent autotuning algorithm could not intelligently handle "combo kernels"—fused operations that combine multiple computational subkernels. Previously, these composite kernels relied on heuristic configurations or a less sophisticated chaining method, lacking a dedicated, combo-aware search strategy for finding the most performant block sizes.

The change makes the tuning process fully metadata-driven. When a combo kernel is generated, it now records two critical pieces of information: `combo_coordesc_field_order`, which defines the priority order for tuning each subkernel's block dimensions, and `combo_coordesc_field_limits`, which sets individual upper bounds for those dimensions based on each subkernel's own requirements. The coordinate descent algorithm then uses this metadata to explore the optimal configuration space more efficiently, prioritizing the combo-specific fields and respecting each subkernel's unique constraints rather than relying on merged, less precise size hints.

This technical refinement, approved by core maintainer eellison, represents a low-level but impactful improvement to PyTorch's Inductor compiler backend. For machine learning engineers and researchers, it translates to more automated and effective optimization of complex, fused operations. The result is a streamlined path to achieving better hardware utilization and faster execution times during model training and inference, all handled transparently by the framework's compilation stack.