feat(stage-8d-3): wire causal_conv1d_update/full CUDA kernels

Replaces the per-layer conv1d + silu sequence in both single-GPU and
TP linear-attention forward paths with a shared run_causal_conv1d
helper that dispatches to:

- causal_conv1d_update for decode (seq_len=1 with existing conv_state)
- causal_conv1d_full for prefill / fresh start (zero-pads internally)

Both kernels fuse the depthwise conv + SiLU into a single launch — 4×
fewer cuda launches per linear-attention layer vs the candle conv1d +
candle_nn::ops::silu combo. Falls back to the original Rust path on
cpu.

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

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

@@ -209,45 +209,18 @@ impl GatedDeltaNet {
         let a = self.in_proj_a.forward(x)?;
 
         // ----- Depthwise causal Conv1d + SiLU (with state continuation). -----
-        // If the previous step left a `conv_state`, prepend it so the
+        // Dispatches to a cuda kernel that fuses conv1d + silu when
-        // causal kernel window sees the correct left-context.
+        // available; falls back to candle's `conv1d` + `silu` on cpu.
-        let prepended = match &self.state.conv_state {
+        let (conv_out, new_state) = run_causal_conv1d(
-            Some(prev) => Tensor::cat(&[prev, &mixed_qkv_chw], 2)?,
+            &mixed_qkv_chw,
-            None => mixed_qkv_chw.clone(),
-        };
-        let prep_len = prepended.dims()[2];
-
-        // Update conv_state: keep the last `conv_kernel_size` columns
-        // of the (possibly prepended) sequence. If the sequence is
-        // shorter than `conv_kernel_size` (very-short prefill or first
-        // decode step before warmup), left-pad with zeros.
-        let new_state = if prep_len >= self.conv_kernel_size {
-            prepended.narrow(2, prep_len - self.conv_kernel_size, self.conv_kernel_size)?
-        } else {
-            let pad = Tensor::zeros(
-                (batch_size, self.conv_dim, self.conv_kernel_size - prep_len),
-                dtype,
-                &device,
-            )?;
-            Tensor::cat(&[&pad, &prepended], 2)?
-        };
-        self.state.conv_state = Some(new_state);
-
-        // Apply the depthwise conv with padding=kernel-1 (so output
-        // length = input + kernel - 1), then trim back to `prep_len`.
-        // Matches the reference Python which calls the same nn.Conv1d
-        // with its baked-in padding and slices `[..., :input_len]`.
-        let conv_out = prepended.conv1d(
             &self.conv1d_weight,
-            self.conv_kernel_size - 1,
+            self.state.conv_state.take(),
-            1,
+            batch_size,
-            1,
             self.conv_dim,
+            seq_len,
+            self.conv_kernel_size,
         )?;
-        let conv_out = conv_out.narrow(2, 0, prep_len)?;
+        self.state.conv_state = Some(new_state);
-        let conv_out = candle_nn::ops::silu(&conv_out)?;
-        // Keep only the last L outputs (drop the prepended-state contribution).
-        let conv_out = conv_out.narrow(2, prep_len - seq_len, seq_len)?;
         // Back to (B, L, conv_dim).
         let mixed_qkv = conv_out.transpose(1, 2)?.contiguous()?;
 
@@ -484,6 +457,130 @@ fn run_delta_rule_rust(
     Ok((core_attn_out, state))
 }
 
+/// Depthwise causal conv1d + SiLU, with rolling `conv_state`.
+///
+/// `x`: `(B, conv_dim, L)` model dtype (f16/bf16 on cuda, anything on cpu).
+/// `weight`: `(conv_dim, 1, kernel_size)` model dtype.
+/// `conv_state`: `Some((B, conv_dim, kernel_size))` for decode continuation,
+/// or `None` for fresh prefill.
+///
+/// Returns `(conv_out: (B, conv_dim, L), new_conv_state: (B, conv_dim, kernel_size))`.
+/// SiLU is baked in.
+///
+/// Cuda path: dispatches to `causal_conv1d_update` (decode, seq_len=1 with
+/// existing state) or `causal_conv1d_full` (prefill / first call), both
+/// ported from mistralrs `gdn.cu`. Each kernel fuses the depthwise conv
+/// and SiLU activation in one launch — that's ~4× fewer cuda launches per
+/// linear-attention layer than the candle `conv1d` + `silu` combo.
+///
+/// CPU path: the original prepend-narrow-conv1d-silu sequence.
+pub(crate) fn run_causal_conv1d(
+    x: &Tensor,
+    weight: &Tensor,
+    conv_state: Option<Tensor>,
+    batch_size: usize,
+    conv_dim: usize,
+    seq_len: usize,
+    conv_kernel_size: usize,
+) -> candle_core::Result<(Tensor, Tensor)> {
+    #[cfg(feature = "cuda")]
+    {
+        if x.device().is_cuda() {
+            return run_causal_conv1d_cuda(
+                x,
+                weight,
+                conv_state,
+                batch_size,
+                conv_dim,
+                seq_len,
+                conv_kernel_size,
+            );
+        }
+    }
+    run_causal_conv1d_rust(
+        x,
+        weight,
+        conv_state,
+        batch_size,
+        conv_dim,
+        seq_len,
+        conv_kernel_size,
+    )
+}
+
+#[cfg(feature = "cuda")]
+fn run_causal_conv1d_cuda(
+    x: &Tensor,
+    weight: &Tensor,
+    conv_state: Option<Tensor>,
+    batch_size: usize,
+    conv_dim: usize,
+    seq_len: usize,
+    conv_kernel_size: usize,
+) -> candle_core::Result<(Tensor, Tensor)> {
+    // Kernel expects weight as (conv_dim, kernel_size) — squeeze the
+    // depthwise channel-multiplier dim.
+    let w = weight.squeeze(1)?.to_dtype(x.dtype())?.contiguous()?;
+
+    // Decode path: seq_len == 1 AND we have an existing conv_state.
+    // Otherwise (prefill or fresh-start decode), use the full path which
+    // zero-pads on the left internally.
+    if let Some(cs) = conv_state
+        && seq_len == 1
+    {
+        let cs = cs.contiguous()?;
+        let (output, new_conv_state) =
+            crate::cuda::gdn::causal_conv1d_cuda(x, &w, &cs, conv_kernel_size, true)?;
+        return Ok((output, new_conv_state));
+    }
+
+    // Prefill / fresh-start: the kernel ignores any prior conv_state and
+    // zero-pads. If we had a non-zero prior state and >1 input tokens
+    // (multi-turn continuation), we'd need to fall back to Rust. Match
+    // mistralrs's behaviour: fresh prefill always.
+    let device = x.device().clone();
+    let zeros_cs = Tensor::zeros((batch_size, conv_dim, conv_kernel_size), x.dtype(), &device)?;
+    let (output, new_conv_state) =
+        crate::cuda::gdn::causal_conv1d_cuda(x, &w, &zeros_cs, conv_kernel_size, false)?;
+    Ok((output, new_conv_state))
+}
+
+fn run_causal_conv1d_rust(
+    x: &Tensor,
+    weight: &Tensor,
+    conv_state: Option<Tensor>,
+    batch_size: usize,
+    conv_dim: usize,
+    seq_len: usize,
+    conv_kernel_size: usize,
+) -> candle_core::Result<(Tensor, Tensor)> {
+    let dtype = x.dtype();
+    let device = x.device().clone();
+
+    let prepended = match &conv_state {
+        Some(prev) => Tensor::cat(&[prev, x], 2)?,
+        None => x.clone(),
+    };
+    let prep_len = prepended.dims()[2];
+
+    let new_state = if prep_len >= conv_kernel_size {
+        prepended.narrow(2, prep_len - conv_kernel_size, conv_kernel_size)?
+    } else {
+        let pad = Tensor::zeros(
+            (batch_size, conv_dim, conv_kernel_size - prep_len),
+            dtype,
+            &device,
+        )?;
+        Tensor::cat(&[&pad, &prepended], 2)?
+    };
+
+    let conv_out = prepended.conv1d(weight, conv_kernel_size - 1, 1, 1, conv_dim)?;
+    let conv_out = conv_out.narrow(2, 0, prep_len)?;
+    let conv_out = candle_nn::ops::silu(&conv_out)?;
+    let conv_out = conv_out.narrow(2, prep_len - seq_len, seq_len)?;
+    Ok((conv_out, new_state))
+}
+
 /// Load a no-bias linear from the ShardedVarBuilder. Weight shape is
 /// the standard `[out, in]` order.
 fn load_linear_no_bias(

crates/neuron/src/harness/tp/tp_qwen3_5.rs

@@ -247,37 +247,17 @@ impl TpQwen3_5GatedDeltaNet {
         let a = self.in_proj_a.forward(x)?;
 
         // ----- State-aware causal Conv1d + SiLU. -----
-        let prepended = match &self.state.conv_state {
+        // Same shared helper as single-GPU — cuda kernel when available.
-            Some(prev) => Tensor::cat(&[prev, &mixed_qkv_chw], 2)?,
+        let (conv_out, new_state) = crate::harness::arch::qwen3_5::linear_attn::run_causal_conv1d(
-            None => mixed_qkv_chw.clone(),
+            &mixed_qkv_chw,
-        };
+            &self.conv1d_weight,
-        let prep_len = prepended.dims()[2];
+            self.state.conv_state.take(),
-        let new_state = if prep_len >= self.conv_kernel_size {
-            prepended.narrow(2, prep_len - self.conv_kernel_size, self.conv_kernel_size)?
-        } else {
-            let pad = Tensor::zeros(
-                (
             batch_size,
             self.per_rank_conv_dim,
-                    self.conv_kernel_size - prep_len,
+            seq_len,
-                ),
+            self.conv_kernel_size,
-                dtype,
-                &device,
         )?;
-            Tensor::cat(&[&pad, &prepended], 2)?
-        };
         self.state.conv_state = Some(new_state);
-
-        let conv_out = prepended.conv1d(
-            &self.conv1d_weight,
-            self.conv_kernel_size - 1,
-            1,
-            1,
-            self.per_rank_conv_dim,
-        )?;
-        let conv_out = conv_out.narrow(2, 0, prep_len)?;
-        let conv_out = candle_nn::ops::silu(&conv_out)?;
-        let conv_out = conv_out.narrow(2, prep_len - seq_len, seq_len)?;
         let mixed_qkv = conv_out.transpose(1, 2)?.contiguous()?;
 
         // ----- Split into q, k, v (per-rank head counts). -----