Add 4bit quantizer to onnx runtime doc (#21835)

### Description
Introduce how to use matmul_4bits_quantizer to do weight only
quantization.

### Motivation and Context
Add 4bit quantizer to onnx runtime doc

docs/performance/model-optimizations/quantization.md

@@ -231,6 +231,59 @@ ONNX Runtime leverages the TensorRT Execution Provider for quantization on GPU n
 
 We provide two end-to end examples: [Yolo V3](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/quantization/object_detection/trt/yolov3) and [resnet50](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/quantization/image_classification/trt/resnet50).
 
+## Quantize to Int4/UInt4
+
+ONNX Runtime can quantize certain operators in a model to 4 bit integer types. Block-wise weight-only quantizaiton is applied to the operators. The supported op types are:
+- [MatMul](https://github.com/onnx/onnx/blob/main/docs/Operators.md#matmul):
+  - The node is quantized only if the input `B` is constant
+  - support QOperator or QDQ format.
+  - If QOperator is selected, the node is converted to a [MatMulNBits](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#commicrosoftmatmulnbits) node. Weight `B` is blockwise quantized and saved in the new node. [HQQ](https://arxiv.org/pdf/2309.15531.pdf), [GPTQ](https://huggingface.co/docs/transformers/main/en/quantization/gptq) and RTN (default) algorithms are supported.
+  - If QDQ is selected, the MatMul node is replaced by a DequantizeLinear -> MatMul pair. Weight `B` is blockwise quantized and saved in the DequantizeLinear node as an initializer.
+- [Gather](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Gather):
+  - The node is quantized only if the input `data` is constant.
+  - support QOperator
+  - Gather is quantized to a [GatherBlockQuantized](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#commicrosoftgatherblockquantized) node. Input `data` is blockwise quantized and saved in the new node. Only support RTN algorithm.
+
+Since Int4/UInt4 types are introduced in [onnx opset 21](https://github.com/onnx/onnx/releases/tag/v1.16.0), if the model's onnx domain version is < 21, it is force upgraded to opset 21. Please make sure the operators in the model are compatible with onnx opset 21.
+
+To run a model that has GatherBlockQuantized nodes, ONNX Runtime 1.20 is needed.
+
+Code Examples:
+
+```python
+from onnxruntime.quantization import (
+    matmul_4bits_quantizer,
+    quant_utils,
+    quantize
+)
+from pathlib import Path
+
+model_fp32_path="path/to/orignal/model.onnx"
+model_int4_path="path/to/save/quantized/model.onnx"
+
+quant_config = matmul_4bits_quantizer.DefaultWeightOnlyQuantConfig(
+  block_size=128, # 2's exponential and >= 16
+  is_symmetric=True, # if true, quantize to Int4. otherwsie, quantize to uint4.
+  accuracy_level=4, # used by MatMulNbits, see https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#attributes-35
+  quant_format=quant_utils.QuantFormat.QOperator, 
+  op_types_to_quantize=("MatMul","Gather"), # specify which op types to quantize
+  quant_axes=(("MatMul", 0), ("Gather", 1),) # specify which axis to quantize for an op type.
+
+model = quant_utils.load_model_with_shape_infer(Path(model_fp32_path))
+quant = matmul_4bits_quantizer.MatMul4BitsQuantizer(
+  model, 
+  nodes_to_exclude=None, # specify a list of nodes to exclude from quantizaiton
+  nodes_to_include=None, # specify a list of nodes to force include from quantization
+  algo_config=quant_config,)
+quant.process()
+quant.model.save_model_to_file(
+  model_int4_path,
+  True) # save data to external file
+
+```
+
+For AWQ and GTPQ quantization usage, please refer to [Gen-AI model builder](https://github.com/microsoft/onnxruntime-genai/tree/main/src/python/py/models#quantized-pytorch-model).
+
 ## FAQ
 ### Why am I not seeing performance improvements?
 {: .no_toc }