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-kernels/build.rs

@@ -24,6 +24,7 @@ fn main() {
         .file("../../csrc/embedding/rope.cu")
         .file("../../csrc/attention/causal_mask.cu")
         .file("../../csrc/embedding/transpose.cu")
+        .file("../../csrc/attention/flash_attention.cu")
         .compile("xserv_kernels");
 
     println!("cargo:rerun-if-changed=../../csrc/");

crates/xserv-kernels/src/attention.rs

@@ -10,6 +10,12 @@ unsafe extern "C" {
                               offset: i32, stream: *mut c_void);
     fn launch_causal_mask_bf16(scores: *mut c_void, batch: i32, rows: i32, cols: i32,
                                offset: i32, stream: *mut c_void);
+    fn launch_flash_attention_bf16(
+        q: *const c_void, k: *const c_void, v: *const c_void, o: *mut c_void,
+        batch: i32, num_q_heads: i32, num_kv_heads: i32,
+        q_len: i32, kv_len: i32, head_dim: i32,
+        scale: f32, causal: i32, stream: *mut c_void,
+    );
 }
 
 fn apply_causal_mask(scores: &Tensor, offset: usize) {
@@ -74,3 +80,59 @@ pub fn attention(q: &Tensor, k: &Tensor, v: &Tensor, causal: bool) -> Tensor {
     // output = weights @ V → [B, H, q_len, head_dim]
     batched_matmul(&weights, v)
 }
+
+/// Flash Attention 2 — O(1) extra memory, supports GQA natively.
+///
+/// q: [batch, num_q_heads, q_len, head_dim]   BF16, contiguous, GPU
+/// k: [batch, num_kv_heads, kv_len, head_dim] BF16, contiguous, GPU
+/// v: [batch, num_kv_heads, kv_len, head_dim] BF16, contiguous, GPU
+///
+/// Returns: [batch, num_q_heads, q_len, head_dim] BF16
+pub fn flash_attention(q: &Tensor, k: &Tensor, v: &Tensor, causal: bool) -> Tensor {
+    assert_eq!(q.ndim(), 4);
+    assert_eq!(k.ndim(), 4);
+    assert_eq!(v.ndim(), 4);
+    assert!(q.is_contiguous() && k.is_contiguous() && v.is_contiguous());
+    assert_eq!(q.dtype(), DType::BF16, "flash_attention requires BF16");
+    assert_eq!(k.dtype(), DType::BF16);
+    assert_eq!(v.dtype(), DType::BF16);
+
+    let batch = q.shape()[0];
+    let num_q_heads = q.shape()[1];
+    let q_len = q.shape()[2];
+    let head_dim = q.shape()[3];
+    let num_kv_heads = k.shape()[1];
+    let kv_len = k.shape()[2];
+
+    assert_eq!(k.shape(), &[batch, num_kv_heads, kv_len, head_dim]);
+    assert_eq!(v.shape(), &[batch, num_kv_heads, kv_len, head_dim]);
+    assert!(num_q_heads % num_kv_heads == 0, "num_q_heads must be divisible by num_kv_heads");
+    assert!(head_dim <= 128, "flash_attention supports head_dim up to 128");
+
+    let scale = 1.0 / (head_dim as f32).sqrt();
+    let output = Tensor::zeros(
+        &[batch, num_q_heads, q_len, head_dim],
+        DType::BF16,
+        q.device(),
+    );
+
+    unsafe {
+        launch_flash_attention_bf16(
+            q.data_ptr() as *const c_void,
+            k.data_ptr() as *const c_void,
+            v.data_ptr() as *const c_void,
+            output.data_ptr() as *mut c_void,
+            batch as i32,
+            num_q_heads as i32,
+            num_kv_heads as i32,
+            q_len as i32,
+            kv_len as i32,
+            head_dim as i32,
+            scale,
+            if causal { 1 } else { 0 },
+            std::ptr::null_mut(),
+        );
+    }
+
+    output
+}

crates/xserv-kernels/src/lib.rs

@@ -10,7 +10,7 @@ pub mod transpose;
 
 pub use activation::{add, gelu, mul, scale, silu};
 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;
+pub use attention::{attention, flash_attention};
 pub use embedding::embedding;
 pub use gemm::{batched_matmul, matmul, GemmBackend};
 pub use layernorm::layernorm;

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);