ModelOpt 0.33.0 Release

Backward Breaking Changes

• PyTorch dependencies for modelopt.torch features are no longer optional and pip install nvidia-modelopt is now same as pip install nvidia-modelopt[torch].

New Features

• Upgrade TensorRT-LLM dependency to 0.20.
• Add new CNN QAT example to demonstrate how to use ModelOpt for QAT.
• Add support for ONNX models with custom TensorRT ops in Autocast.
• Add quantization aware distillation (QAD) support in llm_qat example.
• Add support for BF16 in ONNX quantization.
• Add per node calibration support in ONNX quantization.
• ModelOpt now supports quantization of tensor-parallel sharded Huggingface transformer models. This requires transformers>=4.52.0.
• Support quantization of FSDP2 wrapped models and add FSDP2 support in the llm_qat example.
• Add NeMo 2 Simplified Flow examples for quantization aware training/distillation (QAT/QAD), speculative decoding, pruning & distillation.