onnxruntime

b02d5e6d - [CPU EP] Int4 support for QuantizeLinear, DequantizeLinear, and Transpose (#20362)

[CPU EP] Int4 support for QuantizeLinear, DequantizeLinear, and Transpose (#20362) ### Description - 4-bit QuantizeLinear(21). **Blocked quantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit DequantizeLinear(21). **Blocked dequantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit Transpose(21). - Update quantization tool with int4 types. - Disable QDQ fusions for 4-bit types. See: https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc - MLAS 4-bit quantization kernels for intel, neon, powerpc. ##### Notes To calculate a tensor's storage size, we normally get the number of elements from the shape (i.e., `tensor_shape.Size()`) and multiply by the size of a single element. This does not directly work for sub-byte elements like int4 as each element in a `Tensor<Int4x2>` stores **two** packed int4 elements in a byte. The `Tensor:: CalculateTensorStorageSize` should be called to perform the correct calculation for any tensor element type. ### Motivation and Context ONNX 1.16 added the int4 and uint4 types. This initial PR adds the int4 type to ORT and adds int4 implementations for the Quant, Dequant, and Transpose ops on CPU EP. We still need to add int4 support for many ops and execution providers. See the ONNX 1.16 release notes: https://github.com/onnx/onnx/releases.