[Quant tool] Handle input models with pre-quantized weights

[adrianlizarraga]

Description

Allows the QDQ quantizer to handle input models that already have some pre-quantized weights. In this case, the qdq quantizer will properly skip/handle the pre-quantized weights.

Also handles an operator (e.g., Conv) with a pre-quantized weight and a float bias. The tool will read the pre-quantized weight's quantization scale to compute the bias's scale (bias_scale = input_scale * weight_scale).

Input model (pre-quantized Conv weight):

Output QDQ model (everything is quantized):

Motivation and Context

Customers may use external tools to quantize some weights (e.g., int4 for Conv/MatMul). The qdq quantizer should still be able to quantize the rest of the model (float weights and activations) in this case.

[adrianlizarraga]

/azp run Linux CPU CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU CI Pipeline, Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline, MacOS CI Pipeline, ONNX Runtime Web CI Pipeline, onnxruntime-binary-size-checks-ci-pipeline, Linux QNN CI Pipeline

[adrianlizarraga]

/azp run Linux CPU CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU CI Pipeline, Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline, MacOS CI Pipeline, ONNX Runtime Web CI Pipeline, onnxruntime-binary-size-checks-ci-pipeline, Linux QNN CI Pipeline