opencl: add int8 dp4 dense and MoE prefill optimization for Adreno GPUs

[wanghqc]

Overview

• This is to add int8 (dp4a) prefill MatMul path alongside the f16 GEMMs: activations quantized to q8_1 once and do int8 dot-product with weights, which may be re-quantized to int8 if needed.
• This applies to many flavors of dense GEMMs, including q4_K/q6_K/q5_K/q8_0/q5_0/IQ4_NL (+ weight-as-texture).
• MoE: add added generic per-quant MoE expert GEMM + dedicated q4_0/mxfp4 MoE kernels.
• Ragged MoE with DP4A: added ragged tiling (skip fully padded per-expert token tiles), and MoE-combine fusion.

Additional information

Here is a quick perf on X2-90, pp512, ON vs OFF same binary, without QPM bin kernels:

• Qwen3-30B Q4_K +66.7%
• gpt-oss MXFP4 +54.5%
• Qwen3-30B Q4_0 +70.2%
• Token generation neutral (prefill only)

For X1-85: q4_K/q6_K MoE X1 gate wins +20–23%, dense tile +56%.

Right now, it is default-on for Adreno X2E, and there are some regresses on X1's narrower ALU / lower occupancy. X1 keeps the f16 path mostly.

Requirements

• I have read and agree with the contributing guidelines Yes.
• AI usage disclosure: Yes, testing and prototyping.