Olive-ai 0.5.1

Examples

The following examples are added

• Mistral FP16. #980
• Phi2 Fine tuning example. #1030

Passes (optimization techniques)

• QNNPreprocess: Add the configs which are added in onnxruntime nightly package.
• GptqQuantizer: PTQ quantization using Hugging Face Optimum and export model with onnxruntime optimized kernel.
• OnnxMatMul4Quantizer: Add matmul RTN/HQQ/GPTQ quant configs.
• Move all pass need create inference session to run on target:
  • IncQuantization
  • OptimumMerging
  • OrtTransformersOptimization
  • VitisAIQuantization
  • OrtPerfTuning

Engine

• Support to pack AzureML output.
• Remove execution_providers from engine config, typical config looks like:

"systems": {
    "local_system": {
        "type": "LocalSystem",
        "config": {
            "accelerators": [
                {
                    "device": "gpu",
                    "execution_providers": [
                        "CUDAExecutionProvider"
                    ]
                }
            ]
        }
    }
},
"engine": {
      "host": "local_system",
      "target": "local_system",
}

Workflows

• Delayed python pass module loading and provide the option --package-config to let advanced users to write their individual pass module and corresponding dependencies.

Fix

• Cannot load MLFlow model as from_pretrained_args is missed.
• LoRA: Provide save_embedding_layers=False to saving the peft model. Otherwise, it defaults to "auto" which checks if the vocab size changed.
• Update the model_rank file for zipfile packaging type. The model path now is the path relative to the output zip file.
• Fix windows shutil.which return None when passing full python path.