Noeyy/fix bug 6701777 (#2402)

### What does this PR do?

Type of change: Bug fix: 6701777

Regression source: "[OMNIML-3349] Add FP8 MHA
quantization support for HuggingFace ViT" (#1289), merged into
0.44.0rc3 via the batch cherry-pick #1350. This PR:
  1. Registers nn.LayerNorm as a QuantModule for the first time
     (modelopt/torch/quantization/nn/modules/quant_layernorm.py),
     intended to let FP8_DEFAULT_CFG's BMM input / LayerNorm output
     quantizer rules apply to ViT.
  2. Removes the prior forced Cast-alignment logic in export_onnx.py
that used to normalize Q/DQ node dtypes to trt_high_precision_dtype.

Root Cause:
Once nn.LayerNorm became a registered QuantModule, these wildcards
started unintentionally matching norm1.norm inside FLUX's
AdaLayerNormZero block — an elementwise_affine=False LayerNorm with
no learnable weight/bias. Its input got routed through NVFP4 Q/DQ
(emitted as Float32) while its synthesized affine scale remained
native BFloat16, producing the dtype mismatch. 

Chosen fix:
Explicitly exclude nn.LayerNorm from the diffusers NVFP4 presets
rather than touching the global QuantModuleRegistry (which ViT FP8
MHA still needs). Add, in both
modelopt_recipes/configs/ptq/presets/diffusers/nvfp4.yaml and
nvfp4_fp8_mha.yaml, after the existing weight/input wildcard rules
(list order matters — later entries override earlier ones):

  - parent_class: 'nn.LayerNorm'
    quantizer_name: '*'
    enable: false

### Usage

```
python examples/diffusers/quantization/quantize.py --model flux-dev --format fp4 --batch-size 2 --percentile 1.0 --alpha 0.8 --quant-algo max --n-steps 20 --quantized-torch-ckpt-save-path /tmp/pytest-of-root/pytest-0/test_diffusers_quant_export_on0/flux-dev-fp4.pt --onnx-dir /tmp/pytest-of-root/pytest-0/test_diffusers_quant_export_on0/flux-dev-fp4 --collect-method default --calib-size 128 --model-dtype BFloat16 --trt-high-precision-dtype BFloat16

trtexec --onnx=/tmp/pytest-of-root/pytest-0/test_diffusers_quant_export_on0/flux-dev-fp4/model.onnx --builderOptimizationLevel=4 --saveEngine=/tmp/pytest-of-root/pytest-0/test_diffusers_quant_export_on0/flux-dev-fp4/model.plan --stronglyTyped --minShapes=hidden_states:1x1024x64,img_ids:1024x3,encoder_hidden_states:1x512x4096,txt_ids:512x3,timestep:1,pooled_projections:1x768,guidance:1 --optShapes=hidden_states:1x4096x64,img_ids:4096x3,encoder_hidden_states:1x512x4096,txt_ids:512x3,timestep:1,pooled_projections:1x768,guidance:1 --maxShapes=hidden_states:1x4096x64,img_ids:4096x3,encoder_hidden_states:1x512x4096,txt_ids:512x3,timestep:1,pooled_projections:1x768,guidance:1
```

### Testing
 The above test commands.

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- Is this change backward compatible?: N/A 
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dependency, did you follow guidance in `CONTRIBUTING.md`: N/A
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### Additional Information
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **Bug Fixes**
* Improved Diffusers NVFP4 and NVFP4/FP8 MHA quantization presets by
excluding LayerNorm modules from quantization.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

modelopt_recipes/configs/ptq/presets/diffusers/nvfp4.yaml

@@ -35,3 +35,6 @@ quant_cfg:
   - quantizer_name: '*softmax_quantizer'
     cfg:
       $import: fp8
+  - parent_class: 'nn.LayerNorm'
+    quantizer_name: '*'
+    enable: false

modelopt_recipes/configs/ptq/presets/diffusers/nvfp4_fp8_mha.yaml

@@ -39,3 +39,6 @@ quant_cfg:
   - quantizer_name: '*bmm2_output_quantizer'
     cfg:
       $import: fp8
+  - parent_class: 'nn.LayerNorm'
+    quantizer_name: '*'
+    enable: false