[Perf] Tunings for SM100 FP8 CUTLASS kernel

[mgoin]

I noticed that the FP8 CUTLASS kernel for Blackwell only had one default set of configs. This PR adds new configs for small M < 128.

For Llama 8B on B200, these tunings offer a GEMM improvement of:

• 1.7 to 2.5x speedup at M<64
• 1.1 to 1.3x speedup at 64<=M<128

Accuracy eval:

lm_eval --model vllm --model_args pretrained=RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic --trust_remote_code --tasks gsm8k --num_fewshot 5 --batch_size auto
vllm (pretrained=RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.7657|±  |0.0117|
|     |       |strict-match    |     5|exact_match|↑  |0.7422|±  |0.0120|

Kernel benchmarks using #17126

# B200 original tunings
python benchmarks/kernels/bench_fp8_gemm.py --model meta-llama/Llama-3.1-8B-Instruct --tp-sizes 1
meta-llama/Llama-3.1-8B-Instruct, N=6144 K=4096, BF16 vs FP8 GEMMs TFLOP/s:
BF16 vs FP8 GEMMs:
   batch_size   torch-bf16  fp8-tensor-w-tensor-a  fp8-channel-w-token-a  fp8-tensor-w-tensor-a-noquant  fp8-channel-w-token-a-noquant
0         1.0     5.947784               2.190874               2.072256                       2.506103                       2.457149
1        16.0    94.680891              42.792760              39.819737                      47.809155                      47.631335
2        64.0   402.859620             288.133123             254.794430                     372.338300                     373.705553
3       128.0   728.629250             569.629964             505.291218                     736.715692                     740.021782
4       256.0  1095.642978            1078.797214             898.837969                    1464.385495                    1478.845473
5       512.0  1261.717282            1316.419568            1082.039073                    1754.103111                    1795.102080
6      1024.0  1421.279286            1790.971627            1448.677562                    2235.177663                    2584.518414
meta-llama/Llama-3.1-8B-Instruct, N=4096 K=4096, BF16 vs FP8 GEMMs TFLOP/s:
BF16 vs FP8 GEMMs:
   batch_size   torch-bf16  fp8-tensor-w-tensor-a  fp8-channel-w-token-a  fp8-tensor-w-tensor-a-noquant  fp8-channel-w-token-a-noquant
0         1.0     2.748527               2.011939               1.852286                       2.426765                       2.356377
1        16.0    72.977131              38.338769              34.676703                      45.512563                      45.085737
2        64.0   286.363606             193.600910             170.867183                     251.441611                     251.175358
3       128.0   562.207888             383.696405             339.840832                     498.322879                     497.543227
4       256.0   870.002978             727.559934             603.567390                     993.429300                     993.718536
5       512.0  1278.707903            1219.430597             928.872100                    1859.222222                    1932.512698
6      1024.0  1404.598978            1542.923304            1147.494197                    2087.175763                    2354.134809
meta-llama/Llama-3.1-8B-Instruct, N=28672 K=4096, BF16 vs FP8 GEMMs TFLOP/s:
BF16 vs FP8 GEMMs:
   batch_size   torch-bf16  fp8-tensor-w-tensor-a  fp8-channel-w-token-a  fp8-tensor-w-tensor-a-noquant  fp8-channel-w-token-a-noquant
0         1.0     5.782370               2.746741               2.707684                       3.076109                       2.971836
1        16.0    80.890917              57.057816              56.033710                      59.886153                      59.472853
2        64.0   315.610284             439.068431             449.404687                     475.453743                     499.493704
3       128.0   605.145298             889.769393             854.905235                     914.885575                     996.511367
4       256.0  1070.665944            1645.908127            1636.642023                    1723.292399                    1847.836941
5       512.0  1193.333425            2045.287912            2040.134063                    2035.227478                    2357.713930
6      1024.0  1304.686942            2096.869473            2323.544617                    2108.071352                    2593.971492
meta-llama/Llama-3.1-8B-Instruct, N=4096 K=14336, BF16 vs FP8 GEMMs TFLOP/s:
BF16 vs FP8 GEMMs:
   batch_size   torch-bf16  fp8-tensor-w-tensor-a  fp8-channel-w-token-a  fp8-tensor-w-tensor-a-noquant  fp8-channel-w-token-a-noquant
0         1.0     4.897150               2.804295               2.710499                       3.428506                       3.439044
1        16.0    80.956437              75.659048              71.651283                      91.678247                      91.692121
2        64.0   307.759226             299.856325             284.288350                     370.326019                     370.149516
3       128.0   598.344683             593.513235             567.052452                     729.721261                     729.418140
4       256.0   872.655528             980.745535            1013.446415                    1455.929961                    1457.608605
5       512.0  1165.633163            1575.324418            1564.636469                    2301.963073                    2626.473590
6      1024.0  1367.848100            1851.543351            1646.237302                    2299.788792                    2808.035307

# B200 new tunings
python benchmarks/kernels/bench_fp8_gemm.py --model meta-llama/Llama-3.1-8B-Instruct --tp-sizes 1
meta-llama/Llama-3.1-8B-Instruct, N=6144 K=4096, BF16 vs FP8 GEMMs TFLOP/s:
BF16 vs FP8 GEMMs:
   batch_size   torch-bf16  fp8-tensor-w-tensor-a  fp8-channel-w-token-a  fp8-tensor-w-tensor-a-noquant  fp8-channel-w-token-a-noquant
0         1.0     5.910482               6.019409               5.222792                       8.450960                       8.513710
1        16.0    94.847035              95.974738              83.468890                     137.105099                     139.305386
2        64.0   402.608787             383.513195             332.526351                     533.221407                     542.513655
3       128.0   727.344333             668.354537             585.709400                     905.743648                     914.948318
4       256.0  1111.895929            1063.121536             898.299833                    1462.903418                    1478.033168
5       512.0  1337.772701            1308.382300            1082.112777                    1759.884804                    1795.570613
6      1024.0  1432.973854            1778.950801            1450.251744                    2207.502274                    2585.290571
meta-llama/Llama-3.1-8B-Instruct, N=4096 K=4096, BF16 vs FP8 GEMMs TFLOP/s:
BF16 vs FP8 GEMMs:
   batch_size   torch-bf16  fp8-tensor-w-tensor-a  fp8-channel-w-token-a  fp8-tensor-w-tensor-a-noquant  fp8-channel-w-token-a-noquant
0         1.0     2.737313               4.082587               3.519601                       5.853116                       5.868324
1        16.0    72.871574              65.314403              56.451493                      93.371674                      93.612592
2        64.0   286.086195             258.552930             223.655973                     367.486248                     368.629133
3       128.0   562.275811             448.684298             392.292799                     610.722210                     612.259419
4       256.0   893.528563             724.776694             603.564815                     992.761187                     993.035383
5       512.0  1309.727992            1238.251236             930.249372                    1875.319022                    1936.937988
6      1024.0  1446.266181            1504.181444            1146.414136                    2277.382550                    2333.145483
meta-llama/Llama-3.1-8B-Instruct, N=28672 K=4096, BF16 vs FP8 GEMMs TFLOP/s:
BF16 vs FP8 GEMMs:
   batch_size   torch-bf16  fp8-tensor-w-tensor-a  fp8-channel-w-token-a  fp8-tensor-w-tensor-a-noquant  fp8-channel-w-token-a-noquant
0         1.0     5.874980               8.907531               8.513217                      10.032002                      10.016935
1        16.0    81.733219             140.734419             134.216982                     158.030780                     157.949288
2        64.0   320.265939             547.445855             522.185380                     615.991357                     616.723404
3       128.0   602.637950             937.018227             910.115237                    1055.155568                    1056.821424
4       256.0  1088.675311            1526.447920            1638.574638                    1741.023667                    1957.404538
5       512.0  1126.877028            1714.897685            2107.324511                    2042.790650                    2611.255491
6      1024.0  1255.755813            2064.446702            2314.894491                    2191.506895                    2736.695630
meta-llama/Llama-3.1-8B-Instruct, N=4096 K=14336, BF16 vs FP8 GEMMs TFLOP/s:
BF16 vs FP8 GEMMs:
   batch_size   torch-bf16  fp8-tensor-w-tensor-a  fp8-channel-w-token-a  fp8-tensor-w-tensor-a-noquant  fp8-channel-w-token-a-noquant
0         1.0     4.862665               7.579185               6.920945                      10.217567                      10.249600
1        16.0    81.973118             119.307032             109.946944                     162.638848                     163.243717
2        64.0   303.985531             469.113775             433.804909                     642.102177                     645.760931
3       128.0   597.201158             718.894275             685.251755                     931.641494                     935.816374
4       256.0   870.699430            1120.576212            1011.127380                    1455.045031                    1455.170319
5       512.0  1282.913614            1284.473964            1530.047594                    2387.802820                    2663.654061
6      1024.0  1276.525418            1640.552066            1695.358034                    2447.932588                    2800.132639

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[houseroad]

Looks good. shall we do some accuracy test?

[houseroad]

cc: @chenyang78 @drisspg