moe: correct + deterministic KV-cached gpt-oss decode (single card)

Root-caused the decode non-determinism: matmul's m==1 custom-GEMV fast path
reduces over K with a grid-split atomicAdd, whose float accumulation order
is non-deterministic. Negligible for attention's stable pre-transposed
weights, but for gpt-oss's wide expert GEMMs (K=2880, N up to 5760) over
freshly-dequantized MXFP4 weights it produced visibly different results
run-to-run (and a wrong argmax). Added gemm::matmul_dense (plain cublasGemmEx,
no GEMV shortcut) and route the expert GEMMs through it.

Now decode_step (KV cache + GPU sink-attention + MXFP4 experts) is:
- deterministic: 3/3 identical runs
- correct: top-1 token 12650 = " Paris" for "The capital of France is",
  MATCH_TOP1 with the host-attention reference forward
- end-to-end: gptoss-gen generates 32 tokens at ~6.85 tok/s on one 5090.

Removed the temporary A/B debug dumps. gptoss-logits runs both paths and
asserts the top-1 match; gptoss-gen times greedy generation.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

crates/xserv-kernels/src/lib.rs

@@ -12,9 +12,9 @@ pub mod transpose;
 
 pub use activation::{add, gelu, mul, scale, silu, silu_mul};
 pub use transpose::{merge_heads_gpu, repeat_kv_gpu, reshape_heads_gpu, strided_to_contiguous_gpu, transpose_for_rope_gpu, transpose_from_rope_gpu};
-pub use attention::{attention, decode_attention, flash_attention, paged_decode_attention};
+pub use attention::{attention, decode_attention, decode_attention_sink, flash_attention, paged_decode_attention};
 pub use embedding::embedding;
-pub use gemm::{batched_matmul, matmul, GemmBackend};
+pub use gemm::{batched_matmul, matmul, matmul_dense, GemmBackend};
 pub use layernorm::layernorm;
 pub use quant::dequant_mxfp4;
 pub use rmsnorm::{add_rmsnorm, rmsnorm};