[wave] NSA: gradient correctness tests (backward pass)

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

## Description

Validate gradient correctness for all NSA backward kernels using `torch.autograd.gradcheck` and manual reference comparisons.

### Test approach

1. **`torch.autograd.gradcheck`** — run with FP64 inputs and small tensor sizes to verify analytical gradients match finite-difference numerical gradients
- Requires FP64 kernel variants or CPU fallback
- Inputs to check: Q, K, V, g_cmp, g_slc, g_swa
- Use `eps=1e-6`, `atol=1e-4`, `rtol=1e-3`

2. **Reference comparison** — compute gradients using pure PyTorch autograd on the dense-equivalent computation, compare against kernel gradients at FP16
- Tolerance: 1e-2 relative for dQ, dK, dV; 1e-3 for dg_*

3. **Per-kernel backward tests**:
- Selection attention backward: dQ_slc, dK_slc, dV_slc
- Compressed attention backward: dQ_cmp, dK_cmp → dK_pool, dV_cmp → dV_pool
- Gating backward: dg_cmp, dg_slc, dg_swa, dO_cmp, dO_slc, dO_swa
- Sliding window backward: reuse FA v3 backward (already tested)

4. **Atomic accumulation correctness**
- Selection attention backward uses atomic adds for dK/dV
- Test with scenarios where many queries select the same blocks (high contention)
- Verify determinism or acceptable non-determinism bounds

### Configurations
- Small sizes for gradcheck: B=1, M=64, N=128, H=4, G=2, D=32
- Realistic sizes for reference comparison: B=1, M=4096, N=4096, H=128, G=8, D=128

### Depends on
- All backward kernel tickets: #1252, #1253, #1254
- Training integration: #1255