tests : add support for qwen3 SSM archs (#24031)
* tests : add support for qwen3 SSM archs * arch : add LLM_KV_ATTENTION_RECURRENT_LAYERS * cont : naming + TODOs
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@@ -14,11 +14,11 @@ void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) {
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ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
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// Mark recurrent layers (linear attention layers)
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{
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if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer, false)) {
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uint32_t full_attn_interval = 4;
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ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
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for (uint32_t i = 0; i < hparams.n_layer; ++i) {
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hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
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hparams.is_recr_impl[i] = ((i + 1) % full_attn_interval != 0);
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}
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}
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@@ -68,7 +68,7 @@ void llama_model_qwen3next::load_arch_tensors(llama_model_loader &) {
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
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if (!hparams.is_recurrent(i)) {
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if (!hparams.is_recr(i)) {
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// Attention layers
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create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
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@@ -129,7 +129,7 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
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ggml_build_forward_expand(gf, cur);
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// Determine layer type and build appropriate attention mechanism
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if (hparams.is_recurrent(il)) {
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if (hparams.is_recr(il)) {
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// Linear attention layer (gated delta net)
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cur = build_layer_attn_linear(inp->get_recr(), cur, il);
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} else {
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