Fix DQ1 output type error in DQ1->DQ2 for FP4 weights in NVFP4 model

[vishalpandya1990]

What does this PR do?

Type of change: Bug Fix

Overview: ?

• In post-processing after NVFP4 PTQ and ONNX Export, we convert FP4-QDQ into DQ1->DQ2 for FP4 weights of the MatMuls. The output of DQ1 is of the original weight-type (FP16 for FP16 base model) but its scale is in FP32. There is a cast-to-fp16 after DQ2.
• In above setting, with FP16 base model weights, DQ1 has x_scale in FP32 but its output type is set to FP16. This hybrid precision mode is not allowed up to opset-21, and thereby it leads to error when run with Onnxruntime.
• Note that such hybrid precision mode is allowed in opset-23+ but they are not fully supported with onnxruntime EPs today, and even in future we would want to support opset < 23 too.
• So, in this change, setting output of DQ1 to FP32 since its scale is in FP32. There is already a cast-to-fp16 after DQ2 (before Gemm).

Testing

• Checked with trtexec binary and onnxruntime-trt-rtx ep - using sd3.5-medium model, on Windows RTX 5090.

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• Is this change backward compatible?: Yes/No
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Additional Information

[codecov]

Codecov Report

❌ Patch coverage is 0% with 6 lines in your changes missing coverage. Please review.
✅ Project coverage is 73.45%. Comparing base (b660d39) to head (e1d4af9).
⚠️ Report is 15 commits behind head on main.

Files with missing lines	Patch %	Lines
modelopt/onnx/quantization/qdq_utils.py	0.00%	6 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #513      +/-   ##
==========================================
+ Coverage   73.39%   73.45%   +0.05%     
==========================================
  Files         180      180              
  Lines       18134    18141       +7     
==========================================
+ Hits        13310    13325      +15     
+ Misses       4824     4816       -8     

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[jingyu-ml]

Did you test the performance before and after the change? Please attach the results to the PR, I’m asking because I’m concerned about potential performance impacts.