phase 14: Flash Attention 2 for SM120 (RTX 5090)

Implement Flash Attention 2 forward kernel targeting SM120 (CC 12.0).
FA4 requires TMEM (only on data-center Blackwell SM100), so FA2 is the
correct target for consumer Blackwell GPUs like the RTX 5090.

CUDA kernel (csrc/attention/flash_attention.cu):
- Online softmax with tiled Q/K/V — O(1) extra memory, no S×S matrix
- Tile sizes: BR=BC=64, head_dim up to 128 (runtime parameter)
- BF16 input, FP32 accumulation, BF16 output
- Native GQA: kv_head = q_head / (num_q_heads / num_kv_heads)
- Causal mask with tile-level skip optimization
- Shared memory: 32 KB (Q_tile 16KB + KV_tile 16KB, fits in 48KB default)
- Grid: (q_tiles, batch × num_q_heads), Block: 128 threads

Integration:
- flash_attention() Rust wrapper in xserv-kernels with shape/dtype validation
- Qwen3 forward_gpu_cache uses flash_attention directly (no repeat_kv_gpu)
- Eliminates repeat_kv memory allocation + copy per layer per step
- Naive attention() preserved for testing/comparison

Validated on dash5 (RTX 5090, CUDA 12.9):
- Correctness: 9/10 top-1 match vs HF (identical to pre-FA baseline)
- Throughput: 12.9 tok/s (up from 10.3, +25% improvement)
- Now at 35% of HF transformers baseline (up from 30%)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

crates/xserv-model/src/qwen3.rs

@@ -191,12 +191,8 @@ impl Qwen3 {
             cache.append(layer_idx, &k, &v, new_tokens, pos_offset);
             let (k_full, v_full) = cache.get_kv_len(layer_idx, pos_offset + new_tokens);
 
-            // GPU repeat KV for GQA
-            let n_rep = num_heads / num_kv_heads;
-            let k_full = xserv_kernels::repeat_kv_gpu(&k_full, n_rep);
-            let v_full = xserv_kernels::repeat_kv_gpu(&v_full, n_rep);
-
-            let attn_out = attention(&q, &k_full, &v_full, true);
+            // Flash Attention with native GQA (no repeat_kv needed)
+            let attn_out = flash_attention(&q, &k_full, &v_full, true);
             // GPU merge_heads: [1, H, S, D] → [S, H*D]
             let attn_merged = xserv_kernels::merge_heads_gpu(&attn_out, new_tokens, num_heads, head_dim);
             let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);