test(paged): cover ragged MoE dispatch

Assisted-by: Claude:opus-4.8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

Author: mudler

tests/test-backend-ops.cpp

@@ -4615,6 +4615,115 @@ struct test_moe_weighted_combine : public test_case {
     }
 };
 
+// Ragged 256-expert MoE dispatch gate for serving decode.
+struct test_mul_mat_id_ragged_moe : public test_case {
+    const ggml_type type_a;
+    const int n_mats;
+    const int n_used;
+    const int64_t m;
+    const int64_t n;
+    const int64_t k;
+
+    std::string vars() override {
+        return VARS_TO_STR6(type_a, n_mats, n_used, m, n, k);
+    }
+
+    double max_nmse_err() override {
+        return 5e-4;
+    }
+
+    double max_nmse_err(ggml_backend_t backend) override {
+        if ((type_a == GGML_TYPE_MXFP4 || type_a == GGML_TYPE_NVFP4) && backend_has_feature(backend, "BLACKWELL_NATIVE_FP4")) {
+            return 2e-2;
+        }
+        return max_nmse_err();
+    }
+
+    uint64_t op_flops(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return 2 * m * k * n * n_used;
+    }
+
+    test_mul_mat_id_ragged_moe(ggml_type type_a = GGML_TYPE_NVFP4, int n_mats = 256, int n_used = 8,
+            int64_t m = 768, int64_t n = 128, int64_t k = 2048)
+        : type_a(type_a), n_mats(n_mats), n_used(n_used), m(m), n(n), k(k) {
+            GGML_ASSERT(n_used <= n_mats);
+        }
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * as = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats);
+        ggml_set_name(as, "as");
+
+        ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n);
+        ggml_set_name(ids, "ids");
+        if (n_used != n_mats) {
+            ids = ggml_view_2d(ctx, ids, n_used, n, ids->nb[1], 0);
+            ggml_set_name(ids, "view_of_ids");
+        }
+
+        ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, k, n_used, n);
+        ggml_set_name(b, "b");
+
+        ggml_tensor * out = ggml_mul_mat_id(ctx, as, b, ids);
+        ggml_set_name(out, "out");
+
+        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 (t->type != GGML_TYPE_I32) {
+                init_tensor_uniform(t);
+                continue;
+            }
+
+            std::vector<int32_t> data(t->ne[0]);
+            for (int64_t token = 0; token < ggml_nrows(t); ++token) {
+                for (int64_t r = 0; r < t->ne[0]; ++r) {
+                    data[r] = (int32_t) ((token * 17 + r * 31) % n_mats);
+                }
+
+                if (n_used >= 8) {
+                    // Skew rank 0 heavily to expert 0, exercise max expert id,
+                    // leave many experts empty, and preserve unique top-k ids.
+                    std::vector<bool> used(n_mats, false);
+                    const int64_t seeds[8] = {
+                        0,
+                        1 + token % 4,
+                        4 + (token * 3) % 8,
+                        n_mats - 1,
+                        token * 5 + 7,
+                        token * 7 + 11,
+                        token * 13 + 19,
+                        token * 29 + 23,
+                    };
+
+                    for (int64_t r = 0; r < 8; ++r) {
+                        int32_t id = (int32_t) (seeds[r] % n_mats);
+                        while (used[id]) {
+                            id = (id + 1) % n_mats;
+                        }
+                        data[r] = id;
+                        used[id] = true;
+                    }
+                }
+
+                ggml_backend_tensor_set(t, data.data(), token * t->nb[1], t->ne[0] * sizeof(int32_t));
+            }
+        }
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "MUL_MAT_ID_RAGGED_MOE";
+    }
+};
+
 // GGML_OP_OUT_PROD
 struct test_out_prod : public test_case {
     const ggml_type type_a;
@@ -8941,6 +9050,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_moe_weighted_combine(GGML_TYPE_NVFP4, 128, 8, 768, n, 2048));
     }
 
+    // [paged Phase 8] Ragged 256-expert MoE dispatch gate for live serving decode.
+    // Deterministic ids skew many tokens into a few hot experts, include expert 255,
+    // and leave many experts empty. n=1 covers single-token decode; n=257 crosses
+    // the MMVQ/MMID batch cutoff while preserving top-8 routing.
+    test_cases.emplace_back(new test_mul_mat_id_ragged_moe(GGML_TYPE_F32, 16, 8, 32, 8, 64));
+    for (int n : {1, 8, 33, 128, 257}) {
+        test_cases.emplace_back(new test_mul_mat_id_ragged_moe(GGML_TYPE_NVFP4, 256, 8, 768, n, 2048));
+    }
+
     // [paged P0 / track B] NVFP4/MXFP4 dense decode-shape mmq_y-down bit-exact gate.
     // The dense FP4 weight GEMM is the track-B target; P1 lowers mmq_y (the weight-row tile) on the
     // NVFP4 decode path to raise resident-CTA occupancy. mmq_y is a pure N-row tiling knob, so a