vulkan: unpack more values at a time for iquants mat mul

[netrunnereve]

This change was taken from @remyoudompheng's #12260 and rebased as the original PR has been abandoned for a while. @remyoudompheng if you'd rather submit this yourself please let me know.

On my RX 470 it's around 10-15% faster.

PR:

  MUL_MAT(type_a=iq2_xxs,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                    44 runs - 23378.84 us/run -  60.13 GFLOP/run -   2.57 TFLOPS
  MUL_MAT(type_a=iq2_xs,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                     44 runs - 23188.05 us/run -  60.13 GFLOP/run -   2.59 TFLOPS
  MUL_MAT(type_a=iq2_s,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                      38 runs - 27454.82 us/run -  60.13 GFLOP/run -   2.19 TFLOPS
  MUL_MAT(type_a=iq3_xxs,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                    42 runs - 24248.86 us/run -  60.13 GFLOP/run -   2.48 TFLOPS
  MUL_MAT(type_a=iq1_s,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                      44 runs - 23257.50 us/run -  60.13 GFLOP/run -   2.59 TFLOPS
  MUL_MAT(type_a=iq1_m,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                      44 runs - 23611.98 us/run -  60.13 GFLOP/run -   2.55 TFLOPS
  MUL_MAT(type_a=iq4_nl,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                     44 runs - 23516.11 us/run -  60.13 GFLOP/run -   2.56 TFLOPS
  MUL_MAT(type_a=iq3_s,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                      42 runs - 24849.00 us/run -  60.13 GFLOP/run -   2.42 TFLOPS

model	size	params	backend	ngl	test	t/s
llama 8B IQ1_M - 1.75 bpw	2.01 GiB	8.03 B	Vulkan	100	pp512	200.65 ± 0.32
llama 8B IQ2_S - 2.5 bpw	2.56 GiB	8.03 B	Vulkan	100	pp512	200.98 ± 0.13

Master:

  MUL_MAT(type_a=iq2_xxs,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                    40 runs - 25567.17 us/run -  60.13 GFLOP/run -   2.35 TFLOPS
  MUL_MAT(type_a=iq2_xs,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                     38 runs - 26685.89 us/run -  60.13 GFLOP/run -   2.25 TFLOPS
  MUL_MAT(type_a=iq2_s,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                      34 runs - 31151.91 us/run -  60.13 GFLOP/run -   1.93 TFLOPS
  MUL_MAT(type_a=iq3_xxs,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                    38 runs - 26525.08 us/run -  60.13 GFLOP/run -   2.27 TFLOPS
  MUL_MAT(type_a=iq1_s,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                      42 runs - 24953.29 us/run -  60.13 GFLOP/run -   2.41 TFLOPS
  MUL_MAT(type_a=iq1_m,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                      38 runs - 26897.55 us/run -  60.13 GFLOP/run -   2.24 TFLOPS
  MUL_MAT(type_a=iq4_nl,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                     44 runs - 23591.05 us/run -  60.13 GFLOP/run -   2.55 TFLOPS
  MUL_MAT(type_a=iq3_s,type_b=f32,m=4096,n=512,k=14336,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0):                      38 runs - 27017.13 us/run -  60.13 GFLOP/run -   2.23 TFLOPS

model	size	params	backend	ngl	test	t/s
llama 8B IQ1_M - 1.75 bpw	2.01 GiB	8.03 B	Vulkan	100	pp512	172.03 ± 0.38
llama 8B IQ2_S - 2.5 bpw	2.56 GiB	8.03 B	Vulkan	100	pp512	174.84 ± 0.36

[0cc4m]

I see improvements on all my devices, thank you @remyoudompheng and @netrunnereve