Pull Request resolved: #12574

# Changes
* Introduce `linear_qta8a_qga4w` custom operator in `custom_ops_lib.py` to handle dynamic activation + grouped weight quantized linear operations
* Add pattern matching and fusion logic in `FuseQuantizedOpsTransform` to detect and replace dequant + dequant + linear sequences with the new fused operator
* Implement comprehensive test coverage in `test_vulkan_passes.py` for the QTA8A_QGA4W fusion pattern validation
* Add 4-bit weight packing utilities and grouped quantization support for efficient memory usage

# Motivation
The existing quantization workflow in Vulkan backend processes dynamic activation + grouped weight quantized linear operations as separate quantize/dequantize/linear steps, which creates performance overhead through:
* Multiple kernel dispatches instead of a single fused operation
* Intermediate tensor allocations for dequantized weights and activations
* Suboptimal memory bandwidth utilization

The new `linear_qta8a_qga4w` operator fuses the entire sequence into a single operation that:
* Directly processes 8-bit quantized activations with per-token scales/zero-points
* Handles 4-bit grouped quantized weights with configurable group sizes
* Eliminates intermediate dequantization steps by performing dequantization inline
* Reduces memory footprint through packed 4-bit weight storage

This aligns with the broader goal of optimizing quantized model inference in the Vulkan backend by leveraging graph-level transformations to improve computational efficiency while maintaining numerical accuracy.
ghstack-source-id: 299473616
@exported-using-ghexport

Differential Revision: [D78291269](https://our.internmc.facebook.com/intern/diff/D78291269/)