feat(stage-8d-2): wire gated_delta_rule_recurrence kernel into qwen3_5

Replaces the per-token Rust delta-rule loop in
`arch/qwen3_5/linear_attn.rs::GatedDeltaNet::forward` with a single
dispatch to the `gated_delta_rule_recurrence` kernel imported from
mistralrs in 1ebbe87.

The kernel is V-tiled with compile-time BK (one block per (V-tile,
batch*head), one thread per V-column, BK state floats in registers).
For Qwen3.6's per-rank `(B=1, H=24, D_k=128, D_v=128)` shape this
collapses ~6 candle tensor-op launches per token per layer (each
~50µs CUDA dispatch overhead, so ~300µs/token/layer × 48 linear-
attention layers = 14ms in launch overhead alone) to a single
kernel launch with full ILP / register residency.

New free function `run_delta_rule`:
- cuda branch (when q is on a CUDA device): flattens
  `(B, H, ...)` → `(BH, ...)`, dispatches the kernel via
  `crate::cuda::gdn::gated_delta_rule_recurrence_cuda`, reshapes
  outputs back to `(B, H, L, D_v)` and state to `(B, H, D_k, D_v)`.
- cpu fallback: the original per-token Rust loop, unchanged. Keeps
  cargo test --workspace passing on hosts without cuda.

Dispatch decision lives in the wrapper (`q.device().is_cuda()`).

Build: `cargo build -p neuron --features cuda` compiles + links;
clippy clean on both CPU and cuda paths. 32 lib tests still pass
(none of them exercise this code path on cuda; smoke test for the
TP variant is the deployed Tbilisi probe).

Stage 8d-3 wires the conv1d kernels; 8d-4 the chunked prefill;
8d-5 the same wiring for `tp/tp_qwen3_5.rs`.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

crates/neuron/src/harness/arch/qwen3_5/linear_attn.rs

@@ -56,7 +56,7 @@
 //! prefill; can be added later without changing the surface.
 
 use anyhow::{Context, Result};
-use candle_core::{IndexOp, Module, Tensor};
+use candle_core::{Module, Tensor};
 use candle_nn::Linear;
 use candle_nn::var_builder::ShardedVarBuilder;
 
@@ -315,7 +315,7 @@ impl GatedDeltaNet {
         let beta = beta.to_dtype(candle_core::DType::F32)?;
 
         // Initialise the recurrent state from cache or zeros.
-        let mut state = match self.state.recurrent_state.take() {
+        let state_init = match self.state.recurrent_state.take() {
             Some(s) => s.to_dtype(candle_core::DType::F32)?,
             None => Tensor::zeros(
                 (
@@ -329,48 +329,26 @@ impl GatedDeltaNet {
             )?,
         };
 
-        // Per-token delta-rule loop. Slow-but-correct path; chunked
-        // optimisation is for later.
-        let mut outputs: Vec<Tensor> = Vec::with_capacity(seq_len);
-        for t in 0..seq_len {
-            // (B, H, D_k) and (B, H, D_v) for token t.
-            let q_t = q.i((.., .., t, ..))?; // (B, H, D_k)
-            let k_t = k.i((.., .., t, ..))?;
-            let v_t = v.i((.., .., t, ..))?;
-            let g_t = g.i((.., .., t))?; // (B, H)
-            let beta_t = beta.i((.., .., t))?; // (B, H)
-
-            // Decay: state *= exp(g_t).  exp(g_t) shape (B, H) → broadcast to (B, H, 1, 1).
-            let decay = g_t
-                .exp()?
-                .unsqueeze(candle_core::D::Minus1)?
-                .unsqueeze(candle_core::D::Minus1)?; // (B, H, 1, 1)
-            state = state.broadcast_mul(&decay)?;
-
-            // Memory readout: sum_{d_k} state[d_k, d_v] * k_t[d_k]  → (B, H, D_v).
-            // state: (B, H, D_k, D_v); k_t.unsqueeze(-1): (B, H, D_k, 1).
-            let k_col = k_t.unsqueeze(candle_core::D::Minus1)?; // (B, H, D_k, 1)
-            let kv_mem = state.broadcast_mul(&k_col)?.sum(2)?; // sum over D_k → (B, H, D_v)
-
-            // delta = (v_t - kv_mem) * beta_t  (broadcast beta on last dim).
-            let beta_col = beta_t.unsqueeze(candle_core::D::Minus1)?; // (B, H, 1)
-            let delta = (v_t - kv_mem)?.broadcast_mul(&beta_col)?; // (B, H, D_v)
-
-            // state += outer(k_t, delta) = k_col * delta_row, broadcast to (B, H, D_k, D_v).
-            let delta_row = delta.unsqueeze(2)?; // (B, H, 1, D_v)
-            let outer = k_col.broadcast_mul(&delta_row)?; // (B, H, D_k, D_v)
-            state = (state + outer)?;
-
-            // out_t = sum_{d_k} state[d_k, d_v] * q_t[d_k]   → (B, H, D_v).
-            let q_col = q_t.unsqueeze(candle_core::D::Minus1)?; // (B, H, D_k, 1)
-            let out_t = state.broadcast_mul(&q_col)?.sum(2)?; // (B, H, D_v)
-            outputs.push(out_t.unsqueeze(2)?); // (B, H, 1, D_v)
-        }
+        // The delta-rule body: cuda-accelerated `gated_delta_rule_recurrence`
+        // kernel when we have a cuda device + the kernels are linked in,
+        // pure-Rust per-token fallback otherwise.
+        let (core_attn_out, new_state) = run_delta_rule(
+            &q,
+            &k,
+            &v,
+            &g,
+            &beta,
+            state_init,
+            batch_size,
+            self.num_v_heads,
+            seq_len,
+            self.head_k_dim,
+            self.head_v_dim,
+        )?;
         // Stash the updated recurrent state for the next call.
-        self.state.recurrent_state = Some(state.to_dtype(dtype)?);
+        self.state.recurrent_state = Some(new_state.to_dtype(dtype)?);
 
         // core_attn_out: (B, H, L, D_v) → (B, L, H, D_v) → (B*L*H, D_v).
-        let core_attn_out = Tensor::cat(&outputs, 2)?; // (B, H, L, D_v)
         let core_attn_out = core_attn_out.transpose(1, 2)?.contiguous()?; // (B, L, H, D_v)
         let core_attn_out = core_attn_out.to_dtype(dtype)?;
         let core_attn_flat =
@@ -386,6 +364,126 @@ impl GatedDeltaNet {
     }
 }
 
+/// Run the per-token delta-rule recurrence.
+///
+/// `q`, `k`: `(B, H, L, D_k)` (F32). `v`: `(B, H, L, D_v)`. `g`,
+/// `beta`: `(B, H, L)`. `state`: `(B, H, D_k, D_v)`.
+///
+/// Returns `(core_attn_out: (B, H, L, D_v), state: (B, H, D_k, D_v))`,
+/// both F32. Caller is responsible for cast back to model dtype.
+///
+/// Cuda path: dispatches to the `gated_delta_rule_recurrence` kernel
+/// ported from `EricLBuehler/mistral.rs::mistralrs-core/src/cuda/gdn.cu`.
+/// All five inputs must be cuda f32 tensors. The kernel is V-tiled
+/// with compile-time BK; one block per (V-tile, batch*head) and one
+/// thread per V-column. Each thread holds BK state floats in
+/// registers — eliminates the launch-overhead floor we hit with
+/// candle's per-op dispatch (was ~12s/token on Qwen3.6-27B).
+///
+/// CPU path: pure-Rust per-token loop. Correct, slow.
+#[allow(clippy::too_many_arguments)]
+fn run_delta_rule(
+    q: &Tensor,
+    k: &Tensor,
+    v: &Tensor,
+    g: &Tensor,
+    beta: &Tensor,
+    state: Tensor,
+    batch_size: usize,
+    num_heads: usize,
+    seq_len: usize,
+    head_k_dim: usize,
+    head_v_dim: usize,
+) -> candle_core::Result<(Tensor, Tensor)> {
+    #[cfg(feature = "cuda")]
+    {
+        // Only dispatch to the kernel if the inputs are on a CUDA
+        // device — CPU tests fall back to the Rust loop below.
+        if q.device().is_cuda() {
+            return run_delta_rule_cuda(
+                q, k, v, g, beta, state, batch_size, num_heads, seq_len, head_k_dim, head_v_dim,
+            );
+        }
+    }
+    let _ = (batch_size, num_heads, head_k_dim, head_v_dim);
+    run_delta_rule_rust(q, k, v, g, beta, state, seq_len)
+}
+
+/// CUDA path. Flattens (B, H, ...) → (BH, ...) at the kernel boundary
+/// (the kernel uses BH = batch*heads as its outer batch axis) and
+/// reshapes the kernel's outputs back to (B, H, ...) for the caller.
+#[cfg(feature = "cuda")]
+#[allow(clippy::too_many_arguments)]
+fn run_delta_rule_cuda(
+    q: &Tensor,
+    k: &Tensor,
+    v: &Tensor,
+    g: &Tensor,
+    beta: &Tensor,
+    state: Tensor,
+    batch_size: usize,
+    num_heads: usize,
+    seq_len: usize,
+    head_k_dim: usize,
+    head_v_dim: usize,
+) -> candle_core::Result<(Tensor, Tensor)> {
+    let q_bh = q.flatten(0, 1)?.contiguous()?;
+    let k_bh = k.flatten(0, 1)?.contiguous()?;
+    let v_bh = v.flatten(0, 1)?.contiguous()?;
+    let g_bh = g.flatten(0, 1)?.contiguous()?;
+    let beta_bh = beta.flatten(0, 1)?.contiguous()?;
+    let mut state_bh = state.flatten(0, 1)?.contiguous()?;
+    let output_bh = crate::cuda::gdn::gated_delta_rule_recurrence_cuda(
+        &q_bh,
+        &k_bh,
+        &v_bh,
+        &g_bh,
+        &beta_bh,
+        &mut state_bh,
+    )?;
+    let core_attn_out = output_bh.reshape((batch_size, num_heads, seq_len, head_v_dim))?;
+    let new_state = state_bh.reshape((batch_size, num_heads, head_k_dim, head_v_dim))?;
+    Ok((core_attn_out, new_state))
+}
+
+#[allow(clippy::too_many_arguments)]
+fn run_delta_rule_rust(
+    q: &Tensor,
+    k: &Tensor,
+    v: &Tensor,
+    g: &Tensor,
+    beta: &Tensor,
+    mut state: Tensor,
+    seq_len: usize,
+) -> candle_core::Result<(Tensor, Tensor)> {
+    use candle_core::IndexOp;
+    let mut outputs: Vec<Tensor> = Vec::with_capacity(seq_len);
+    for t in 0..seq_len {
+        let q_t = q.i((.., .., t, ..))?;
+        let k_t = k.i((.., .., t, ..))?;
+        let v_t = v.i((.., .., t, ..))?;
+        let g_t = g.i((.., .., t))?;
+        let beta_t = beta.i((.., .., t))?;
+        let decay = g_t
+            .exp()?
+            .unsqueeze(candle_core::D::Minus1)?
+            .unsqueeze(candle_core::D::Minus1)?;
+        state = state.broadcast_mul(&decay)?;
+        let k_col = k_t.unsqueeze(candle_core::D::Minus1)?;
+        let kv_mem = state.broadcast_mul(&k_col)?.sum(2)?;
+        let beta_col = beta_t.unsqueeze(candle_core::D::Minus1)?;
+        let delta = (v_t - kv_mem)?.broadcast_mul(&beta_col)?;
+        let delta_row = delta.unsqueeze(2)?;
+        let outer = k_col.broadcast_mul(&delta_row)?;
+        state = (state + outer)?;
+        let q_col = q_t.unsqueeze(candle_core::D::Minus1)?;
+        let out_t = state.broadcast_mul(&q_col)?.sum(2)?;
+        outputs.push(out_t.unsqueeze(2)?);
+    }
+    let core_attn_out = Tensor::cat(&outputs, 2)?; // (B, H, L, D_v)
+    Ok((core_attn_out, state))
+}
+
 /// Load a no-bias linear from the ShardedVarBuilder. Weight shape is
 /// the standard `[out, in]` order.
 fn load_linear_no_bias(