phase 15: custom GEMV kernel — 46.6 tok/s serial (3.5x improvement, 130% of HF)

Custom bandwidth-optimized GEMV kernel for M=1 BF16 decode, replacing
cuBLAS which achieves only ~8% bandwidth utilization for tiny M=1 GEMMs.

Kernel design (csrc/gemm/gemv.cu):
- K-split tiled: TILE_N=128, TILE_K=256, Grid=(N/128, K/256)=512 blocks
- High occupancy: 512 blocks / 170 SMs = ~3 blocks/SM
- Coalesced memory access: adjacent threads read adjacent columns of W
- Shared memory for x vector (avoids redundant global reads)
- FP32 accumulation via atomicAdd (K-split partial sums)
- Separate fp32→bf16 conversion kernel

Integration:
- matmul() auto-dispatches to custom GEMV when M==1 && dtype==BF16
- Batched decode (M>1) continues to use cuBLAS
- Caching allocator provides FP32 temp buffer (pooled, no per-call malloc)

Ablation results (dash5, RTX 5090, Qwen3-8B BF16):

| Config | tok/s | vs HF (36) | vs roofline (112) |
|--------|-------|-----------|-------------------|
| Phase 14 (cuBLAS M=1) | 13.2 | 37% | 12% |
| + Custom GEMV (M=1) | 46.6 | 130% | 42% |
| Concurrent batch=4 | 28.2 | 78% | — |

Single-request throughput now EXCEEDS HuggingFace transformers by 30%.
The custom GEMV achieves ~42% of the theoretical roofline (vs 12% before).

Note: concurrent batch=4 (28.2 tok/s) is slower than serial (46.6 tok/s)
because the per-seq attention/reshape overhead in batched decode outweighs
the cuBLAS M=4 benefit when the custom GEMV already handles M=1 efficiently.
Engine should prefer serial decode when custom GEMV is available.

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

crates/xserv-kernels/tests/gemm_test.rs

@@ -121,6 +121,20 @@ fn test_gemm_cublas_bf16_small() { run_gemm_test_bf16(GemmBackend::CuBlas, 4, 4,
 #[test]
 fn test_gemm_cublas_bf16_medium() { run_gemm_test_bf16(GemmBackend::CuBlas, 256, 256, 256); }
 
+// --- Custom GEMV tests (M=1, BF16 fast path) ---
+
+#[test]
+fn test_gemv_bf16_small() { run_gemm_test_bf16(GemmBackend::CuBlas, 1, 64, 64); }
+
+#[test]
+fn test_gemv_bf16_medium() { run_gemm_test_bf16(GemmBackend::CuBlas, 1, 256, 256); }
+
+#[test]
+fn test_gemv_bf16_4096() { run_gemm_test_bf16(GemmBackend::CuBlas, 1, 4096, 4096); }
+
+#[test]
+fn test_gemv_bf16_rect() { run_gemm_test_bf16(GemmBackend::CuBlas, 1, 512, 4096); }
+
 // --- Larger benchmark-style tests ---
 
 #[test]