[wave] NSA: numerical correctness tests vs dense attention reference

## Parent
Part of #1243 — DeepSeek NSA kernels for MI350

## Description

Build a comprehensive correctness test suite that validates all NSA kernels against a pure PyTorch dense attention reference implementation.

### Test matrix

| Kernel | Reference | Tolerance |
|--------|-----------|-----------|
| Mean pooling | `torch.mean(K.reshape(B,N//bs,bs,G,D), dim=2)` | exact (FP32), 1e-3 (FP16) |
| Compressed attention | `F.scaled_dot_product_attention(Q, K_cmp, V_cmp)` | 1e-3 abs, 1e-2 rel |
| Top-k selection | `torch.topk(scores, k=block_count)` | exact (integer indices) |
| Selection attention | Dense attention masked to selected blocks | 1e-3 abs, 1e-2 rel |
| Sliding window | `flash_attn_func(..., window_size=...)` | 1e-3 abs |
| Gated combination | `g_cmp*O_cmp + g_slc*O_slc + g_swa*O_swa` | exact (FP32), 1e-4 (FP16) |
| Full NSA pipeline | Dense full attention | 1e-2 rel (aggregate) |

### Test configurations

```python
configs = [
dict(B=1, M=1024, N=1024, H=32, G=4, D=64, bs=32, bc=8, ws=128),
dict(B=2, M=4096, N=4096, H=64, G=8, D=128, bs=64, bc=16, ws=512),
dict(B=1, M=64, N=65536, H=128, G=8, D=128, bs=64, bc=16, ws=512), # decode-like
dict(B=1, M=1, N=8192, H=128, G=8, D=128, bs=64, bc=16, ws=512), # single-token decode
]
```

### Edge cases
- Sequence length not divisible by block_size
- block_count > N // block_size (more blocks requested than available)
- window_size > N (window larger than sequence)
- Causal mask at sequence boundaries
- GQA with HEADS_PER_GROUP=1 (MHA) and HEADS_PER_GROUP=H (MQA)

### Infrastructure
- Use pytest with parametrized fixtures
- Run on both CPU (FP64 reference) and MI350 (FP16 kernel)
- CI integration: add to wave test suite

### Depends on
- All forward kernel tickets: #1245-#1251