Support for INT4_AWQ quantization for Nemotron-3-Nano-30B-A3B

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Describe the bug

Quantization for Nemotron-3-Nano-30B-A3B using INT4_AWQ faces several issues.

Steps/Code to reproduce bug

Followed the tutorial https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/megatron_bridge/tutorials/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16, then modified the flags to run on GB200 and quant config to INT4_AWQ_CFG. All steps run fine for "MAMBA_MOE_FP8_CONSERVATIVE_CFG" quantization on GB200. Issues arise when quantization flag changed to INT4_AWQ_CFG.

When I run the command

torchrun --nproc_per_node 4 quantize.py
--hf_model_name_or_path nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
--trust_remote_code
--tp_size 4
--quant_cfg INT4_AWQ_CFG
--calib_batch_size 4
--seq_length 8192
--export_megatron_path /opt/Model-Optimizer/output/iter_0000800_int4_megatron
--skip_generate

I get several errors

Error:

AttributeError: 'QuantTEColumnParallelGroupedLinear' object has no attribute 'weight'. Did you mean: 'weight0'?

File: modelopt/torch/quantization/model_calib.py ~line 1358

Root cause: awq_lite() iterates all quantized linear modules and calls AWQLiteHelper(module, name),
which unconditionally accesses module.weight. TE's fused MoE grouped linear exposes weights as
weight0, weight1, ... not weight. The smooth-quant path already handles this correctly
(line 755: getattr(module, "weight", None)) — awq_lite was missing the same guard.

2. AssertionError: TEGroupedLinear only supports per-tensor quantization

Error:

AssertionError: TEGroupedLinear only supports per-tensor quantization

File: modelopt/torch/quantization/plugins/megatron.py ~line 696

Root cause: _QuantMegatronTEGroupedLinear._process_quantizer_amax asserted v.numel() == 1,
written only for FP8 (per-tensor amax). INT4 AWQ per-block quantization produces a multi-element
amax tensor (block_size=128), which fails the assert during checkpoint save.

Expected behavior

Who can help?

• ?

System information

• Container used (if applicable): nvcr.io/nvidia/nemo:26.04
• OS (e.g., Ubuntu 22.04, CentOS 7, Windows 10): Ubuntu 24.04.3 LTS
• CPU architecture (x86_64, aarch64): aarch64
• GPU name (e.g. H100, A100, L40S): NVIDIA GB200
• GPU memory size: 185.0 GB
• Number of GPUs: 4
• Library versions (if applicable):
  • Python: 3.12.3
  • ModelOpt version or commit hash: 0.44.0rc1
  • CUDA: 13.1
  • Transformers: 4.57.3
• Any other details that may help: ?