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Model Optimizer Changelog (Linux)

0.23 (2025-01-29)

Backward Breaking Changes

  • Support TensorRT-LLM to 0.17. Examples (e.g. benchmark task in llm_ptq) may not be fully compatible with TensorRT-LLM 0.15.
  • Nvidia TensorRT Model Optimizer has changed its LICENSE from NVIDIA Proprietary (library wheel) and MIT (examples) to Apache 2.0 in this first full OSS release.
  • Deprecate Python 3.8, Torch 2.0, and Cuda 11.x support.
  • ONNX Runtime dependency upgraded to 1.20 which no longer supports Python 3.9.
  • In the Huggingface examples, the trust_remote_code is by default set to false and require users to explicitly turning it on with --trust_remote_code flag.

New Features

  • Added OCP Microscaling Formats (MX) for fake quantization support, including FP8 (E5M2, E4M3), FP6 (E3M2, E2M3), FP4, INT8.
  • Added NVFP4 quantization support for NVIDIA Blackwell GPUs along with updated examples.
  • Allows export lm_head quantized TensorRT-LLM checkpoint. Quantize lm_head could benefit smaller sized models at a potential cost of additional accuracy loss.
  • TensorRT-LLM now supports Moe FP8 and w4a8_awq inference on SM89 (Ada) GPUs.
  • New models support in the llm_ptq example: Llama 3.3, Phi 4.
  • Added Minitron pruning support for NeMo 2.0 GPT models.
  • Exclude modules in TensorRT-LLM export configs are now wildcards
  • The unified llama3.1 FP8 huggingface checkpoints can be deployed on SGLang.

0.21 (2024-12-03)

Backward Breaking Changes

New Features

Bug Fixes

  • Improve Minitron pruning quality by avoiding possible bf16 overflow in importance calculation and minor change in hidden_size importance ranking.

Misc

  • Added deprecation warnings for Python 3.8, torch 2.0, and CUDA 11.x. Support will be dropped in the next release.

0.19 (2024-10-23)

Backward Breaking Changes

New Features

  • ModelOpt is compatible for SBSA aarch64 (e.g. GH200) now! Except ONNX PTQ with plugins is not supported.
  • Add effective_bits as a constraint for :meth:`mtq.auto_qauntize <modelopt.torch.quantization.model_quant.auto_quantize>`.
  • lm_evaluation_harness is fully integrated to modelopt backed by TensorRT-LLM. lm_evaluation_harness benchmarks are now available in the examples for LLM accuracy evaluation.
  • A new --perf flag is introduced in the modelopt_to_tensorrt_llm.py example to build engines with max perf.
  • Users can choose the execution provider to run the calibration in ONNX quantization.
  • Added automatic detection of custom ops in ONNX models using TensorRT plugins. This requires the tensorrt python package to be installed.
  • Replaced jax with cupy for faster INT4 ONNX quantization.
  • :meth:`mtq.auto_quantize <modelopt.torch.quantization.model_quant.auto_quantize>` now supports search based automatic quantization for NeMo & MCore models (in addition to HuggingFace models).
  • Add num_layers and hidden_size pruning support for NeMo / Megatron-core models.

0.17 (2024-09-11)

Backward Breaking Changes

New Features

Misc

0.15 (2024-07-25)

Backward Breaking Changes

New Features

  • Added quantization support for torch RNN, LSTM, GRU modules. Only available for torch>=2.0.
  • modelopt.torch.quantization now supports module class based quantizer attribute setting for :meth:`mtq.quantize <modelopt.torch.quantization.model_quant.quantize>` API.
  • Added new LLM PTQ example for DBRX model.
  • Added new LLM (Gemma 2) PTQ and TensorRT-LLM checkpoint export support.
  • Added new LLM QAT example for NVIDIA NeMo framework.
  • TensorRT-LLM dependency upgraded to 0.11.0.
  • (Experimental): :meth:`mtq.auto_quantize <modelopt.torch.quantization.model_quant.auto_quantize>` API which quantizes a model by searching for the best per-layer quantization formats.
  • (Experimental): Added new LLM QLoRA example with NF4 and INT4_AWQ quantization.
  • (Experimental): modelopt.torch.export now supports exporting quantized checkpoints with packed weights for Hugging Face models with namings aligned with its original checkpoints.
  • (Experimental) Added support for quantization of ONNX models with TensorRT plugin.

Misc

  • Added deprecation warning for torch<2.0. Support will be dropped in next release.

0.13 (2024-06-14)

Backward Breaking Changes

New Features

  • Adding TensorRT-LLM checkpoint export support for Medusa decoding (official MedusaModel and Megatron Core GPTModel).
  • Enable support for mixtral, recurrentgemma, starcoder, qwen in PTQ examples.
  • Adding TensorRT-LLM checkpoint export and engine building support for sparse models.
  • Import scales from TensorRT calibration cache and use them for quantization.
  • (Experimental) Enable low GPU memory FP8 calibration for the Hugging Face models when the original model size does not fit into the GPU memory.
  • (Experimental) Support exporting FP8 calibrated model to VLLM deployment.
  • (Experimental) Python 3.12 support added.

0.11 (2024-05-07)

Backward Breaking Changes

  • [!!!] The package was renamed from ammo to modelopt. The new full product name is Nvidia TensorRT Model Optimizer. PLEASE CHANGE ALL YOUR REFERENCES FROM ammo to modelopt including any paths and links!
  • Default installation pip install nvidia-modelopt will now only install minimal core dependencies. Following optional dependencies are available depending on the features that are being used: [deploy], [onnx], [torch], [hf]. To install all dependencies, use pip install "nvidia-modelopt[all]".
  • Deprecated inference_gpus arg in modelopt.torch.export.model_config_export.torch_to_tensorrt_llm_checkpoint. User should use inference_tensor_parallel instead.
  • Experimental modelopt.torch.deploy module is now available as modelopt.torch._deploy.

New Features

  • modelopt.torch.sparsity now supports sparsity-aware training (SAT). Both SAT and post-training sparsification supports chaining with other modes, e.g. SAT + QAT.
  • modelopt.torch.quantization natively support distributed data and tensor parallelism while estimating quantization parameters. The data and tensor parallel groups needs to be registered with modelopt.torch.utils.distributed.set_data_parallel_group and modelopt.torch.utils.distributed.set_tensor_parallel_group APIs. By default, the data parallel group is set as the default distributed group and the tensor parallel group is disabled.
  • modelopt.torch.opt now supports chaining multiple optimization techniques that each require modifications to the same model, e.g., you can now sparsify and quantize a model at the same time.
  • modelopt.onnx.quantization supports FLOAT8 quantization format with Distribution calibration algorithm.
  • Native support of modelopt.torch.opt with FSDP (Fully Sharded Data Parallel) for torch>=2.1. This includes sparsity, quantization, and any other model modification & optimization.
  • Added FP8 ONNX quantization support in modelopt.onnx.quantization.
  • Added Windows (win_amd64) support for ModelOpt released wheels. Currently supported for modelopt.onnx submodule only.

Bug Fixes

  • Fixed the compatibility issue of modelopt.torch.sparsity with FSDP.
  • Fixed an issue in dynamic dim handling in modelopt.onnx.quantization with random calibration data.
  • Fixed graph node naming issue after opset conversion operation.
  • Fixed an issue in negative dim handling like dynamic dim in modelopt.onnx.quantization with random calibration data.
  • Fixed allowing to accept .pb file for input file.
  • Fixed copy extra data to tmp folder issue for ONNX PTQ.