You will implement a quantize func and block scaled MXFP4 matrix-matrix multiplication kernel optimized for AMD Instinct MI355X GPU.
To be explicit, you will be given a tuple of tensors:
```
(A, B, B_q, B_shuffle, B_scale_sh)
```
where:
* `A` is M x K in K-major order in bfloat16
* `B` is N x K in K-major order in bfloat16
* `B_q` is N x K/2 in K-major order in MXFP4
* `B_shuffle` is N x K/2 in shuffled order in MXFP4, shuffled to (16,16) tile coalesced
* `B_scale_sh` is * x K/32 in E8M0, * means it will be padded.

Then, the kernel flow is bf16 A, MXFP4 B -> MXFP4 per-1x32 quant A -> gemm_a4w4 -> BF16 C [m,n].
To be specific, the invocation flow is:
(1) Quant A to MXFP4: aiter.get_triton_quant(QuantType.per_1x32). 
(2) GEMM: aiter.gemm_a4w4.
m, n divisible by 64; k divisible by 64.

The ranking criteria is the geometric mean of the benchmark results.
Pls note that this is the elimination round, whoever rank top5 are selected into the next round, e2e optimization for deepseek-R1-MXFP4 and GPTOSS-MXFP4 mdoel
```
The aiter performance is:
M   N    K   time[us]
  4 2880   512  8.198
 16 2112  7168 20.873
 32 4096   512  9.462
 32 2880   512  9.173
 64 7168  2048 12.738
256 3072  1536 12.219
```
