Feature Request: FlashOptim - optimizer memory reduction

## Summary
Request to investigate integrating [FlashOptim](https://arxiv.org/abs/2602.23349) into Megatron Core's optimizer infrastructure. FlashOptim reduces AdamW memory from 16 to 7 bytes/parameter (5 with gradient release) by combining ULP-bounded master weight splitting with companded 8-bit optimizer state quantization — with no measurable quality loss and no wall-clock overhead.

## Motivation
Optimizer states dominate training memory for large models. FlashOptim addresses this with two complementary techniques:

**Master Weight Splitting**: Splits FP32 weights into BF16 + INT8 error correction. Key insight: reconstruction error is bounded by the unit of least precision (ULP), so the exponent bits in the correction term are redundant. Achieves ~24-bit effective precision with bitwise-perfect reconstruction in 99.92% of values.

**Companded State Quantization**: Applies nonlinear companding before INT8 quantization of optimizer states — `2x/(1+|x|)` for momentum (signed), `√x` for variance (unsigned). This is critical: linear quantization of Adam states causes divergence due to heavy-tailed variance distributions.

Results:
| Task | Model | Reference | FlashOptim |
|------|-------|-----------|-----------|
| ImageNet | ResNet-50 | 77.01% | 77.16% |
| Pretraining | GPT-2 124M | 3.263 loss | 3.265 loss |
| Finetuning | Llama-3.1-8B | 75.09% GSM8k | 74.98% GSM8k |

All within measurement variance. Optimizer step is actually 8% faster on Llama-3.1-8B (fused Triton kernels). Supports SGD, AdamW, and Lion.

## Memory Savings (bytes/parameter)

| | AdamW | FlashAdamW | FlashAdamW + grad release |
|--|-------|-----------|--------------------------|
| **Total** | 16 | 7 | 5 |

Achieved via BF16+INT8 master weight splitting and companded INT8 optimizer states.

## Requested Feature
Investigate adding `FlashAdamW` as a supported optimizer in Megatron-Core's distributed optimizer, including compatibility with existing distributed checkpointing. FlashOptim >= 0.1.3 provides native DTensor support for PyTorch DCP/FSDP2 integration.

## References
- [FlashOptim Paper (Gonzalez Ortiz, Gupta, Rinard, Blalock)](https://arxiv.org/abs/2602.23349)
- [GitHub: databricks/flashoptim](https://github.com/databricks/flashoptim)
- [Automodel FlashOptim integration](https://github.com/NVIDIA-NeMo/Automodel/pull/1492) - merged PR adding `FlashAdamW` with FSDP2/DCP support