Model Optimizer Changelog (Linux)

0.19 (2024-10-23)

Backward Breaking Changes

Deprecated the summarize task in the llm_ptq example.
Deprecated the type flag in the huggingface_example.sh
Deprecated Python plugin support in ONNX.
Support TensorRT-LLM 0.13. Examples not compatible with TensorRT-LLM 0.12.
mtq.auto_quantize API has been updated. The API now accepts forward_step and forward_backward_step as arguments instead of loss_func and collect_func. Please see the API documentation for more details.

New Features

ModelOpt is compatbile for SBSA aarch64 (e.g. GH200) now! Except ONNX PTQ with plugins is not supported.
Add effective_bits as a constraint for mtq.auto_qauntize.
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.
mtq.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

Deprecated torch<2.0 support.
modelopt.torch.utils.dataset_utils.get_dataset_dataloader() now returns a key value pair instead of the tensor.

New Features

New APIs and examples: modelopt.torch.prune for pruning Conv, Linear, and Attention heads for NVIDIA Megatron-core GPT-style models (e.g. Llama 3), PyTorch Computer Vision models, and HuggingFace Bert/GPT-J models.
New API: modelopt.torch.distill for knowledge distillation, along with guides and example.
New Example: HF BERT Prune, Distill & Quantize showcasing how to chain pruning, distillation, and quantization to achieve the best performance on a given model.
Added INT8/FP8 DQ-only support for ONNX model.
New API: modelopt.torch.speculative for end-to-end support of Medusa models.
Added Medusa QAT and End-to-end examples.
Modelopt now supports automatic save/restore of modelopt_state with the .save_pretrained and .from_pretrained APIs from Huggingface libraries, such as transformers and diffusers. This feature can be enabled by calling mto.enable_huggingface_checkpointing().
ONNX FP8 quantization support with amax calibration.
TensorRT-LLM dependency upgraded to 0.12.0. Huggingface tokenizer files are now also stored in the engine dir.
The unified model export API modelopt.torch.export.export_hf_checkpoint supports exporting fp8 and int4_awq quantized checkpoints with packed weights for Hugging Face models with namings aligned with its original checkpoints. The exported fp8 checkpoints can be deployed with both TensorRT-LLM and VLLM.
Add int8 and fp8 quantization support for the FLUX.1-dev model.
Add a Python-friendly TensorRT inference pipeline for diffusion models.

Misc

Added deprecation warning for set_data_parallel_group and set_tensor_parallel_group. These APIs are no longer needed for supporting distributed data and tensor parallelism in quantization. They will be removed in a future release.

0.15 (2024-07-25)

Backward Breaking Changes

Deprecated QuantDescriptor. Use QuantizerAttributeConfig to configure TensorQuantizer. set_from_attribute_config can be used to set the quantizer attributes from the config class or attribute dictionary. This change applies only to backend APIs. The change is backward compatible if you are using only the mtq.quantize API.

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 mtq.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): mtq.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

PTQ examples have been upgraded to use TensorRT-LLM 0.10.

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 convertion 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.