tests: add long-sequence cases and fix inputs for gated_delta_net (#22794)

* tests : add long-seq + tail cases for gated_delta_net

* tests : realistic input ranges for gated_delta_net

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 450c0aeb8300a4fc9d77875ae8a317de743f9af2..2f4988aa525045c7e0195da33f22fc6adcfcfbab 100644 (file)
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3763,13 +3763,37 @@ struct test_gated_delta_net : public test_case {
             k = ggml_new_tensor_4d(ctx, type, head_size, head_count, n_seq_tokens, n_seqs);
             v = ggml_new_tensor_4d(ctx, type, head_size, head_count * v_repeat, n_seq_tokens, n_seqs);
         }
+        ggml_set_name(q, "q");
+        ggml_set_name(k, "k");
+        ggml_set_name(v, "v");
         const int64_t g_ne0 = kda ? head_size : 1;
         ggml_tensor * g     = ggml_new_tensor_4d(ctx, type, g_ne0, head_count * v_repeat, n_seq_tokens, n_seqs);
         ggml_tensor * beta  = ggml_new_tensor_4d(ctx, type, 1, head_count * v_repeat, n_seq_tokens, n_seqs);
         ggml_tensor * state = ggml_new_tensor_2d(ctx, type, head_size * v_repeat * head_size * head_count, n_seqs);
+        ggml_set_name(g,     "g");
+        ggml_set_name(beta,  "beta");
+        ggml_set_name(state, "state");
+        // q/k are L2-normalised in qwen35/kimi-linear before delta_net
+        q = ggml_l2_norm(ctx, q, 1e-6f);
+        k = ggml_l2_norm(ctx, k, 1e-6f);
         ggml_tensor * out   = ggml_gated_delta_net(ctx, q, k, v, g, beta, state);
         return out;
     }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+            if (ggml_is_view_op(t->op)) { continue; }
+            if (strcmp(t->name, "g") == 0) {
+                init_tensor_uniform(t, -20.0f, -1e-4f);
+            } else if (strcmp(t->name, "beta") == 0) {
+                init_tensor_uniform(t, 0.0f, 1.0f);
+            } else if (strcmp(t->name, "v") == 0) {
+                init_tensor_uniform(t, -0.3f, 5.0f);
+            } else {
+                init_tensor_uniform(t);
+            }
+        }
+    }
 };
 
 // GGML_OP_GATED_LINEAR_ATTN
@@ -8871,6 +8895,17 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 8, 32, 4, 2, 2, false, true));
     test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 2, 1, true,  true));
     test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 16, 4, 2, 1, true,  true));
+    // chunked path: multi-chunk and non-multiple-of-chunk-size (chunk_size=64 GDN, 16 KDA)
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  64, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 127, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 256, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  65, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 100, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 200, 1));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 127, 2));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  64, 1, 1, false, true));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64,  33, 1, 1, false, true));
+    test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 100, 1, 1, false, true));
 
 #if 0
     // these tests are disabled to save execution time, sbut they can be handy for debugging