Update on "[ET-VK] Add kInt8x4 dtype and GPUMemoryLayouts for packed quantized tensors"

## Motivation

Lay the foundations for being able to execute statically quantized CNNs with ET-VK. Unlike with dynamic quantization, static quantization allows the output of quantized operators to stay in integer representation and be fed directly to the next quantized operator.

## Context

Typically, int8 quantized tensors can be represented by simply having the tensor use the int8 data type. While this is possible in ET-VK, in practice quantized operators expect int8 quantized tensors to be packed so that 16 8-bit values are packed into each `ivec4`, such that quantized int8 tensors will load/store with a granularity of 16 elements.

The reason for this is twofold:
* Support for shader int8 / storage buffer int8 extension is not guaranteed, meaning some devices do not allow using int8 types in shaders
* We have found that load/store from storage buffers/textures that use int8 data types sometimes results in worse memory load performance, due to vectorized load/store instructions not being used.

Therefore, in ET-VK we need a way to mark that a quantized tensor should

1. Use int32 as the underlying data type for the storage buffer/texture
2. Account for the block-packing that may be used

## Changes

First, introduce the `Int8x4` dtype that can be used for packed int8 tensors. This dtype is functionally the same as `Int`, but denotes that each int32 actually contains 4 packed 8-bit values.

Second, introduce new memory layouts: `kPackedInt8_4W4C` and `kPackedInt8_4H4W`. The former will be used for convolution, whil the latter will be used for matrix multiplication. See the inline comments for more details about these memory layouts.

Then, update `QuantizedConvolution.cpp` and `QuantizedLinear.cpp` to use the new data type and memory layouts for the packed int8 input tensor.

Differential Revision: [D82542336](https://our.internmc.facebook.com/intern/diff/D82542336/)

[ghstack-poisoned]

Author: ssjia