tests: add end-to-end tests per model architecture

src/models/kimi-linear.cpp

@@ -118,12 +118,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
 
         ggml_build_forward_expand(gf, cur);
 
-        // Check layer type by checking which tensors exist
-        // KDA layers have ssm_a_log tensor, MLA layers have wkv_a_mqa tensor
-        bool is_kda = (layer.ssm_a != nullptr);
-        bool is_mla = (layer.wkv_a_mqa != nullptr);
-
-        if (is_kda) {
+        if (hparams.is_recurrent(il)) {
             // === KDA Layer (Kimi Delta Attention) with Recurrent State ===
             // Reference: vLLM kda.py
             const auto * mctx_cur = inp_rs->mctx;
@@ -211,7 +206,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
             cur = ggml_mul_mat(ctx0, layer.wo, gated);
             cb(cur, "kda_out", il);
 
-        } else if (is_mla) {
+        } else {
             // === MLA Layer (Multi-head Latent Attention) without KV Cache ===
             // Reference: vLLM mla.py
             // Step 1: Q projection and reshape
@@ -310,9 +305,6 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll
                 cur = build_attn(inp_attn_kv, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il);
                 cb(cur, "mla_out", il);
             }
-        } else {
-            // Unknown layer type - this should not happen
-            GGML_ABORT("Kimi layer is neither KDA nor MLA - missing required tensors");
         }
 
         // On last layer, select only the output tokens