As title. This enables support for further reduced quantized model size
and improved runtime efficiency, within acceptable range of accuracy
degradation.

Orthogonal to #940. This PR targets already quantized models in
autoawq/autogptq format that **has** lmhead quantized.

### Description

This PR adds new INT4 quantization features to the model builder.

1. The model builder can now quantize the embedding layer and the
language modeling head to INT4 precision by default.
2. For already-quantized PyTorch models that are passed to the model
builder, any ops that are still created with `MatMul` can now be
quantized to `MatMulNBits` via RTN.
3. A new optional flag in the extra options called
`int4_op_types_to_quantize` has been added to allow more flexibility
with INT4 quantization.

### Motivation and Context

With these PR changes, the size of the ONNX models can be reduced by
quantizing the embedding layer and/or the language modeling head.

For the ONNX models built from already-quantized PyTorch models, one
example is with using AutoAWQ. AutoAWQ does not quantize the language
modeling head. The resulting ONNX model typically contains a `MatMul` op
for the language modeling head. Now, that `MatMul` op will be quantized
via RTN to `MatMulNBits` to reduce memory.

As title. This enables support for further reduced quantized model size
and improved runtime efficiency, within acceptable range of accuracy
degradation.

Orthogonal to #940. This PR targets already quantized models in
autoawq/autogptq format that **has** lmhead quantized.