onnxruntime

[Quantization] Tensor quant overrides and QNN EP quantization configuration (#18465)

### Description
#### 1. Adds `TensorQuantOverrides` extra option
Allows specifying a dictionary of tensor-level quantization overrides:
```
TensorQuantOverrides = dictionary :
    Default is {}. Set tensor quantization overrides. The key is a tensor name and the value is a
    list of dictionaries. For per-tensor quantization, the list contains a single dictionary. For
    per-channel quantization, the list contains a dictionary for each channel in the tensor.
    Each dictionary contains optional overrides with the following keys and values.
          'quant_type' = QuantType : The tensor's quantization data type.
          'scale' =  Float         : The scale value to use. Must also specify `zero_point` if set.
          'zero_point' = Int       : The zero-point value to use. Must also specify `scale` is set.
          'symmetric' = Bool       : If the tensor should use symmetric quantization. Invalid if also
                                     set `scale` or `zero_point`.
          'reduce_range' = Bool    : If the quantization range should be reduced. Invalid if also
                                     set `scale` or `zero_point`.
          'rmax' = Float           : Override the maximum real tensor value in calibration data.
                                     Invalid if also set `scale` or `zero_point`.
          'rmin' = Float           : Override the minimum real tensor value in calibration data.
                                     Invalid if also set `scale` or `zero_point`.
```

- All of the options are optional.
- Some combinations are invalid.
- Ex: `rmax` and `rmin` are unnecessary if the `zero_point` and `scale`
are also specified.

Example for per-tensor quantization overrides:
```Python3
extra_options = {
    "TensorQuantOverrides": {
        "SIG_OUT": [{"scale": 1.0, "zero_point": 127}],
        "WGT": [{"quant_type": quantization.QuantType.QInt8, "symmetric": True, "reduce_range": True}],
        "BIAS": [{"quant_type": quantization.QuantType.QInt8, "symmetric": True, "reduce_range": True}],
    },
}
```

Example for per-channel quantization overrides (Conv weight and bias):
```Python3
extra_options = {
    "TensorQuantOverrides": {
        "WGT": [
            {
                "quant_type": quantization.QuantType.QUInt8,
                "rmin": 0.0,
                "rmax": 2.5,
                "reduce_range": True,
            },
            {
                "quant_type": quantization.QuantType.QUInt8,
                "rmin": 0.2,
                "rmax": 2.55,
                "reduce_range": False,
            },
        ],
        "BIAS": [
            {"zero_point": 0, "scale": 0.000621},
            {"zero_point": 0, "scale": 0.23},
        ],
    },
}
```

#### 2. Adds utilities to get the default QDQ configs for QNN EP
Added a `quantization.execution_providers.qnn.get_qnn_qdq_config` method
that inspects the model and returns suitable quantization
configurations.

Example usage:
```python3
from quantization import quantize, QuantType
from quantization.execution_providers.qnn import get_qnn_qdq_config

qnn_config = get_qnn_qdq_config(input_model_path,
                                data_reader,
                                activation_type=QuantType.QUInt16,
                                weight_type=QuantType.QUInt8)
                                
quantize(input_model_path,
         output_model_path,
         qnn_config)
```

### Motivation and Context
Make it possible to create more QDQ models that run on QNN EP.

---------

Signed-off-by: adrianlizarraga <adlizarraga@microsoft.com>