Add logic for block-scaled tensors with GEMM swizzled scales

[timmoon10]

Description

All of the supported block-scaled tensor formats (MXFP8, NVFP4, DSv3 FP8) have two ways of ordering their scaling factors:

• "Compact" ordering for quantization, dequantization, and communication
• "Swizzled" ordering for GEMM

The core infrastructure handles this in an ad hoc way, blindly assuming that the "right" scale ordering is used for the different operations. The PyTorch infrastructure only supports MXFP8 and NVFP4 scales are in compact order, although DSv3 FP8 does have awareness of "compact" and "GEMM-ready" formats. This situation makes it hard to implement fused kernels that can bypass the swizzle kernel.

This PR adds a with_gemm_swizzled_scales field in the C++ tensor class so that the core infrastructure can distinguish between the different scale orderings. It also adds this field in the PyTorch quantized tensor classes, and exposes a optimize_for_gemm option in the quantizer so that we can create tensors that do not need communication or checkpointing.

Progress

• MXFP8
• DSv3 FP8
• NVFP4
• Add option to pre-swizzle weights

Closes #2446.

Type of change

• Documentation change (change only to the documentation, either a fix or a new content)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Infra/Build change
• Code refactoring

Changes

Please list the changes introduced in this PR:

• Support GEMM swizzled scales in C++ tensor class
• Support GEMM swizzled scales in PyTorch quantized tensor classes
• Support optimize_for_gemm option in PyTorch quantizer
• Expose PyTorch function to swizzle scales

Checklist:

• I have read and followed the contributing guidelines
• The functionality is complete
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes