Autoquant and GPTQ in support in Megatron-Core [OMNIML-3151] (#1562)

### What does this PR do?

Type of change: New Feature

Autoquant and GPTQ in support in Megatron-Core

- Add EP support to AutoQuantize
- Register MCore support in AutoQuantize
- Add decoder `output_layer` (lm head) to layerwise hook so that GPTQ
can register all decoder layers & lm head
- Split dataloader helper function out of megatron calibration utils so
that AutoQuantize in Megatron-LM can reuse the same dataloader

### Usage
See https://github.com/NVIDIA/Megatron-LM/pull/4821 for Autoquant usage
in Megatron
```python
# For GPTQ pick a recipe that uses gptq algorithm and run mtq.quantize
# e.g. general/ptq/nvfp4_default-kv_none-gptq
```

### Testing
Tested AutoQuant on Nemotron Nano and Ultra.
Tested GPTQ on Nano 3.
Added unit tests for both AutoQuant and GPTQ

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- Is this change backward compatible?: ✅ / ❌ / N/A <!--- If ❌, explain
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### Additional Information
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

## Summary by CodeRabbit

* **New Features**
* Added lazy Megatron-Core AutoQuant integration with Megatron-specific
quantization hooks and better decoder-layer discovery for layerwise
calibration.
* Improved AutoQuantize for expert-parallel (EP) models, including
consistent per-layer recipe selection across DP/TP/EP.
* Extended quant-layer grouping for NemotronH MCore fused
“local_experts” linear layers.

* **Bug Fixes**
* Prevented division-by-zero when calibration inputs are empty during
Hessian updates.

* **Tests**
* Added/extended unit and GPU coverage for EP AutoQuant, decoder-layer
calibration discovery behavior, and zero-token Hessian no-op.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jennifer Chen <jennifchen@nvidia.com>
Signed-off-by: Jenny Chen <jennifchen@nvidia.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

.github/workflows/gpu_tests.yml

@@ -42,7 +42,7 @@ jobs:
             timeout: 60
             container_image: nvcr.io/nvidia/pytorch:26.05-py3
           - example: gpu_megatron
-            timeout: 60
+            timeout: 75
             container_image: nvcr.io/nvidia/nemo:26.06
           - example: gpu_trtllm
             timeout: 15