Olive-ai 0.3.2

Examples

The following examples are added

• DirectML SDXL refiner #487
• Open Llama arc #582
• Enable Intel® Neural Compressor 4-bits weight-only quantization #614
• Add NCHW GroupNorm fusion to DirectML's SD examples #617

Passes (optimization techniques)

• QLoRA pass for torch model fine-tuning
• Intel® Neural Compressor 4-bits weight-only quantization
• OnnxModelOptimizer
  • inserts a Cast operation for cases where ArgMax input isn't supported on the device
  • Fuse consecutive Reshape operations when the latter results in flattening

Engine

• Summarize pass run history in table(install tabulate for better preview)
• Support to tune and evaluate models across different execution providers which are managed by Olive-ai.

Model

• Add model_loading_args, load_model and load_model_config to HFConfig.
• Add adapter_path to PyTorchModel
• Introduce model_attributes which can be used to simplify user's input for transformer_optimization
• Add AML curated model support

Dataset

• Auto-insertion of the input model (if it's a pytorch model with hf_config.dataset) data config in pass configs is removed. Use “input_model_data_config” if user want to use the input model's data config.
• Support a second type of dataset for text-generation tasks called pair
• Support convert olive dataset to huggingface datasets.Dataset

Known Issues

• #571 Whisper gpu does not consume gpu resources
• #573 Distinguish pass instance with name not cls name

Dependencies:

• Support onnxruntime 1.16.1
• Drop python 3.7. Now you should ensure python >=3.8 to run Olive-ai optimization.