llama : enable chunked fused GDN path (#20340)

* llama : enable chunked fused GDN path

* models : avoid Q and K repeats when using fused GDA

* cont : fix comment

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

* cont : fix the fix

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

* cont : fix

* metal : add GDN kernel (#20361)

* metal : add Metal backend for GGML_OP_GATED_DELTA_NET

Add a fused Metal kernel for the gated delta net recurrence op
(#19504), enabling GPU-accelerated inference for DeltaNet-based
models (Qwen3.5, etc.) on Apple Silicon.

Supports both GDA (scalar gate) and KDA (per-row gate) modes
with head_size 64 and 128. Unsupported configurations (head_size
32, non-contiguous tensors) gracefully fall back to CPU.

Performance: Qwen3.5-0.8B Q4_K_M on M4 Max
  tg128: 170 -> 213 t/s (+25%)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* metal : validate contiguity of all input tensors in supports_op

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* metal : add algorithm equivalence comment for GDA decay path

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* cont : unslop + optimize

* cont : clean-up

---------

Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* CUDA: AR gated delta net improvements (#20391)

* Add FastDiv to gated_delta_net_cuda

* Shard columns across warps

This reduces register pressure (avoids spill for S_v = 128) and gives
the warp-scheduler more CTAs to schedule (thus hiding data-access
latencies).

* Remove unneded include in gated_delta_net.cu

* Improve comments

* Apply code-formating

* Make sharding HIP-compatible

1. Use ggml_cuda_get_physical_warp_size() to determine warp size flexibly
2. Add test with partial warp to test sum reduction on CUDA

* Remove fastdiv_s64, as we can treat neqk1 and rq3 as uint32_t

* Rename variables

* Enable GDN also for prefill, move TODO for chunked_GDN

* Actually remove the TODO from 206890897546bd16602c3b79394fd5ea09ef199f

* Get warp size at runtime

warp_size is not known at compile time in hip host code.

* Don't expose ggml_cuda_get_physical_warp_size on host

---------

Co-authored-by: uvos <devnull@uvos.xyz>

* llama : refactor llm_build_delta_net_base API

---------

Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Oliver Simons <osimons@nvidia.com>
Co-authored-by: uvos <devnull@uvos.xyz>

src/models/qwen3next.cpp

@@ -406,6 +406,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs);
 
     // if head keys and value keys are different, repeat to force tensors into matching shapes
+    // TODO: avoid repeats for fused GDN, needs broadcast configuration for GDN op [TAG_GGML_GDN_BCAST]
     if (num_k_heads != num_v_heads) {
         GGML_ASSERT(num_v_heads % num_k_heads == 0);
         int64_t repeat_factor = num_v_heads / num_k_heads;
@@ -431,13 +432,8 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
-    std::pair<ggml_tensor *, ggml_tensor *> attn_out; // pair of (output, new_state)
-    if (n_seq_tokens == 1) {
-        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
-    } else {
-        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il);
-    }
+    auto attn_out = build_delta_net(q_conv, k_conv, v_conv, gate, beta, state, il);
+
     ggml_tensor * output    = attn_out.first;
     ggml_tensor * new_state = attn_out.second;
     cb(output, "attn_output", il);