[Quant Tool] Introduce get_qdq_config() helper to get QDQ configurations

[adrianlizarraga]

Description

Introduces the get_qdq_config() function to get a quantization configuration for a full integer QDQ model. This function provides an easier way of specifying commonly used options and sets convenient defaults. Specifically:

• Instead of requiring the user to pass a dictionary of extra_options, the new interface adds function parameters for common settings:
  • All calibrator settings
  • Whether activations/weights are symmetric
  • Whether to keep or fuse relu/clip into Q
  • Minimum real range for quantization
  • Dictionary of tensor quantization overrides.
• Automatically scans the input floating-point model and fills out the operator types to quantize. Otherwise, only a limited number of operator types would be quantized by default.
• Detects if the input model uses external data. If so, ensures that the generated QDQ model also uses external data.
• Detects if the model will use newly introduced quantization types (int4/int16) with an older opset. If so, forces the use of the com.microsoft domain for Q/DQ ops, which support all types.
• Automatically enables the "extra option" called ForceQuantizeNoInputCheck to ensure data movement operators (e.g., Transpose) are always quantized.
• User can pass a function to indicate which nodes to exclude from quantization.
• The user can still pass their own extra_options to override any of the above if necessary.

from onnxruntime.quantization import get_int_qdq_config, quantize # , ...

# Get QDQ configuration
qdq_config = get_int_qdq_config(
    float_model,
    data_reader,
    calibrate_method=CalibrationMethod.Percentile,
    calibrate_args={"percentile": 99.98},  # Converted to extra_options
    activation_type=QuantType.QUInt8,
    weight_type=QuantType.QInt8,
    per_channel=True,
    nodes_to_exclude=["Mul"], # Could also be a function. Ex: `lambda model, node: node.op_type == "Softmax"`

    # Other options converted to extra_options:
    min_real_range=0.0001,
    keep_removable_activations=True,
    activation_symmetric=True,
    weight_symmetric=True,
)

# Quantize model
quantize(float_model_path, qdq_model_path, qdq_config)

Motivation and Context

Need a version of get_qnn_qdq_config() that is not EP-specific.

[fajin-corp]