[wave] NSA: water dialect lowering for NSA composite ops

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

## Description

Define new water/wave dialect operations for NSA and implement lowering paths to the existing wave compiler infrastructure.

### New ops needed

1. **`wave.nsa_compress`** — mean-pool KV into compressed blocks
- Could lower through existing reduction + reshape ops if available
- Otherwise, a new dedicated op

2. **`wave.nsa_topk_select`** — top-k block index selection
- Novel op: combines scoring (compressed attention logits) with top-k extraction
- Returns integer indices — different from typical wave float ops
- May need a specialized lowering since top-k doesn't map cleanly to matmul/elementwise

3. **`wave.nsa_selection_attn`** — sparse attention over gathered blocks
- This is the core NSA primitive
- Could potentially decompose into existing `wave.attention` + gather ops, but a fused op will be much more efficient
- Needs to carry block_indices as an additional operand

4. **`wave.nsa_gated_combine`** — weighted sum of three branches
- Straightforward elementwise, could lower through existing ops

### Lowering strategy

- **Phase 1**: Implement as opaque custom kernels called from Python (bypass water dialect, direct wave kernel launch). Gets us running quickly.
- **Phase 2**: Define water dialect ops with proper index expression inference, EPT (elements-per-thread) propagation, and scheduling strategy support. Enables the wave compiler to optimize across NSA ops.

### Index expression considerations (per #1081, #1189)
- Selection attention's gather pattern means index expressions depend on runtime values (block_indices)
- This is fundamentally different from the static index patterns in standard attention
- May need a new `InferIndexExprsOpInterface` implementation that handles indirect indexing

### Depends on
- #1244 (design doc)
- #1081 (water index expression inference for attention)
- All forward kernel tickets (for the ops to lower to)