onnxruntime

Add sparse attention kernel for H100 (sm90) (#20553)

### Description
Follow up of https://github.com/microsoft/onnxruntime/pull/20216 to add
sparse attention kernel compiled by Triton for H100 (sm90).
- [x] Refine sparse attention v1 kernel compilation (remove some
combinations)
- [x] compile kernels for v1 kernels
- [x] compile kernels for H100
- [x] run performance tests

### Performane

Test setting `batch_size=4, num_heads=32, max_seq_len=8192,
head_size=128, sparse_block_size=64, local_blocks=16, vert_stride=8,
num_layout=8`

We compare sparse attention to corresponding GQA with local attention
windows size 1024, or GQA with dense causal. Note that ORT-GQA-Dense has
more computation than ORT-SparseAtt, while ORT-GQA-Local has less
computation (no vertial strides) than ORT-SparseAtt. They are added for
reference. It is not fair comparison, but could show the benefit of
sparsity vs dense.

Example results in Azure Standard_ND96isr_H100_v5 VM with NVIDIA
H100-80GB-HBM3 GPU (sm=90):
```
    prompt-sm90-batch4-head32-d128-local16-vert8-torch.float16:
       sequence_length  TORCH-GQA  ORT-GQA-Dense  ORT-GQA-Local  ORT-SparseAtt
    0             16.0   0.079877       0.006362       0.006403       0.042758
    1             32.0   0.086920       0.016404       0.016686       0.044183
    2             64.0   0.090727       0.020429       0.020409       0.045343
    3            128.0   0.128148       0.032009       0.031984       0.051516
    4            256.0   0.323933       0.074110       0.073920       0.068308
    5            512.0   1.021856       0.162167       0.161951       0.109226
    6           1024.0   3.596002       0.452629       0.452780       0.231653
    7           2048.0  13.865088       1.499534       1.195749       0.515488
    8           4096.0   0.000000       5.454785       2.669682       1.163233
    9           8192.0   0.000000      22.068159       6.018604       2.772873

    token-sm90-batch4-head32-d128-local16-vert8-torch.float16:
       past_sequence_length  TORCH-GQA  ORT-GQA-Dense  ORT-GQA-Local  ORT-SparseAtt
    0                  16.0   0.104460       0.012652       0.012661       0.069549
    1                  32.0   0.113866       0.012776       0.012765       0.069024
    2                  64.0   0.124600       0.016791       0.012672       0.069397
    3                 128.0   0.108658       0.017900       0.018294       0.074844
    4                 256.0   0.115463       0.029409       0.029608       0.078911
    5                 512.0   0.149824       0.033968       0.033701       0.092998
    6                1024.0   0.234050       0.042930       0.042951       0.116920
    7                2048.0   0.390695       0.061462       0.043008       0.121555
    8                4096.0   0.000000       0.097505       0.042948       0.134757
    9                8191.0   0.000000       0.165861       0.043542       0.158796
```
The following might be able to help performance on short sequence
length. Need update operator spec:
 Fall back to flash attention when total_sequence length < local_blocks * block_size

### Motivation and Context
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