ggml-cuda: refactor fusion code (#22468)

* ggml-cuda: refactor fusion code

* apply formatting + make env variable truthy

ggml/src/ggml-cpu/CMakeLists.txt

@@ -485,6 +485,13 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             if (GGML_RV_ZIHINTPAUSE)
                 string(APPEND MARCH_STR "_zihintpause")
             endif()
+            if (GGML_CPU_RISCV64_SPACEMIT)
+                # `xsmtvdotii' is only required for GCC >= 15.
+                if (CMAKE_C_COMPILER_ID STREQUAL "GNU" AND
+                    CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 15)
+                    string(APPEND MARCH_STR "_xsmtvdotii")
+                endif()
+            endif()
 
             list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
         else()

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -3640,6 +3640,357 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
     return false;
 }
 
+// try and fuse nodes and return the number of nodes to skip
+static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, int i) {
+
+    static bool disable_fusion = getenv("GGML_CUDA_DISABLE_FUSION") != nullptr && std::atoi(getenv("GGML_CUDA_DISABLE_FUSION"));
+    if (disable_fusion) {
+        return 0;
+    }
+
+    ggml_tensor * node = cgraph->nodes[i];
+
+    //topk-moe
+    if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX ||
+            cgraph->nodes[i]->op == GGML_OP_ARGSORT) {
+        ggml_cuda_topk_moe_args args;
+        const bool              can_fuse = ggml_cuda_topk_moe_fusion(cgraph, i, args);
+        std::vector<ggml_op>    ops;
+
+        if (can_fuse) {
+            const ggml_tensor * logits  = node->src[0];
+            ggml_tensor *       weights = nullptr;
+            ggml_tensor *       ids     = nullptr;
+            const ggml_tensor * bias    = nullptr;
+            const ggml_tensor * clamp   = nullptr;
+            const ggml_tensor * scale   = nullptr;
+
+            if (!args.delayed_softmax) {
+                ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX;
+                int     out_nodes[2];  // nodes which can't be elided
+
+                if (args.prob_bias) {
+                    bias = cgraph->nodes[i + 2]->src[1];
+                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW,
+                                            GGML_OP_GET_ROWS });
+                    out_nodes[0] = i + 4;
+                    ids          = cgraph->nodes[i + 4];
+                } else {
+                    ops.insert(ops.end(),
+                               { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS });
+                    out_nodes[0] = i + 3;
+                    ids          = cgraph->nodes[i + 3];
+                }
+
+                if (args.norm) {
+                    ops.insert(ops.end(),
+                               { GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP, GGML_OP_DIV, GGML_OP_RESHAPE });
+                    clamp = cgraph->nodes[i + ops.size() - 3];
+                }
+                if (args.scale) {
+                    ops.insert(ops.end(), { GGML_OP_SCALE });
+                    scale = cgraph->nodes[i + ops.size() - 1];
+                }
+
+                weights      = cgraph->nodes[i + ops.size() - 1];
+                out_nodes[1] = i + ops.size() - 1;
+
+                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
+                        ggml_cuda_should_use_topk_moe(node, logits, weights, ids) &&
+                        ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/true)) {
+                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
+                    return ops.size() - 1;
+                }
+            } else if (!args.norm && !args.prob_bias) {
+                //special case gpt-oss, no norm, no bias.
+                ops.insert(ops.end(), { GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
+                                        GGML_OP_SOFT_MAX, GGML_OP_RESHAPE });
+                weights                     = cgraph->nodes[i + 5];
+                ids                         = cgraph->nodes[i + 1];
+                const ggml_tensor * softmax = cgraph->nodes[i + 4];
+
+                int out_nodes[2] = { i + 1, i + 5 };
+                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
+                        ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids) &&
+                        ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/true)) {
+                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
+                    return ops.size() - 1;
+                }
+            }
+        }
+    }
+
+    //RoPE + view + set-rows
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) {
+        ggml_tensor * rope     = cgraph->nodes[i];
+        ggml_tensor * set_rows = cgraph->nodes[i + 2];
+
+        ggml_cuda_op_rope_fused(*cuda_ctx, rope, set_rows);
+        return 2;
+    }
+
+    // multi-(add or mul)
+    if (node->op == GGML_OP_ADD || node->op == GGML_OP_MUL) {
+        int     n_fuse = 0;
+        ggml_op ops[8];
+        std::fill(ops, ops + 8, node->op);
+
+        for (; n_fuse <= 6; ++n_fuse) {
+            if (!ggml_can_fuse(cgraph, i + n_fuse, ops + n_fuse, 2)) {
+                break;
+            }
+            if (cgraph->nodes[i + n_fuse] != cgraph->nodes[i + n_fuse + 1]->src[0]) {
+                break;
+            }
+            if (!ggml_are_same_layout(cgraph->nodes[i + n_fuse]->src[1], cgraph->nodes[i + n_fuse + 1]->src[1])) {
+                break;
+            }
+        }
+
+        n_fuse++;
+
+        if (n_fuse > 1) {
+            ggml_tensor fused_node;
+            memcpy(&fused_node, node, sizeof(ggml_tensor));
+            for (int j = 0; j < n_fuse - 1; ++j) {
+                fused_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
+            }
+            fused_node.data = cgraph->nodes[i + n_fuse - 1]->data;
+            if (node->op == GGML_OP_ADD) {
+                ggml_cuda_op_fused_add(*cuda_ctx, &fused_node, n_fuse);
+            } else {
+                ggml_cuda_op_fused_mul(*cuda_ctx, &fused_node, n_fuse);
+            }
+            return n_fuse - 1;
+        }
+    }
+
+    bool fused_mul_mat_vec = false;
+    int  fused_node_count  = 0;
+
+    // gate + glu + up
+    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+        if (ggml_cuda_can_fuse(cgraph, i, { op, bias_op, op, bias_op, GGML_OP_GLU }, {})) {
+            ggml_tensor * glu         = cgraph->nodes[i + 4];
+            ggml_tensor * gate_bias_n = glu->src[0];
+            ggml_tensor * up_bias_n   = glu->src[1];
+
+            //we don't assume the order for {gate, up}. Instead infer it from the bias tensor
+            ggml_tensor * gate_n = nullptr;
+            ggml_tensor * up_n   = nullptr;
+
+            if (gate_bias_n->src[0] == cgraph->nodes[i] || gate_bias_n->src[1] == cgraph->nodes[i]) {
+                gate_n = cgraph->nodes[i];
+                up_n   = cgraph->nodes[i + 2];
+            } else if (gate_bias_n->src[0] == cgraph->nodes[i + 2] || gate_bias_n->src[1] == cgraph->nodes[i + 2]) {
+                gate_n = cgraph->nodes[i + 2];
+                up_n   = cgraph->nodes[i];
+            } else {
+                continue;
+            }
+
+            auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) {
+                if (op_bias == GGML_OP_ADD) {
+                    if (bias_node->src[0] == mul_node) {
+                        return bias_node->src[1];
+                    }
+                    if (bias_node->src[1] == mul_node) {
+                        return bias_node->src[0];
+                    }
+                    return (ggml_tensor *) nullptr;
+                }
+                GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
+                GGML_ASSERT(bias_node->src[0] == mul_node);
+                return bias_node->src[1];
+            };
+
+            ggml_tensor * up_bias_tensor   = get_bias_tensor(up_bias_n, up_n, bias_op);
+            ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
+
+            if (!up_bias_tensor || !gate_bias_tensor) {
+                continue;
+            }
+
+            // we don't support repeating adds
+            if (bias_op == GGML_OP_ADD && (!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
+                                           !ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
+                continue;
+            }
+
+            const ggml_tensor * src0 = up_n->src[0];
+            const ggml_tensor * src1 = up_n->src[1];
+            const ggml_tensor * ids  = up_n->src[2];
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_f(up_n)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate      = gate_n->src[0];
+                fusion_data.x_bias    = up_bias_tensor;
+                fusion_data.gate_bias = gate_bias_tensor;
+                fusion_data.glu_op    = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 5;
+                break;
+            }
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate      = gate_n->src[0];
+                fusion_data.x_bias    = up_bias_tensor;
+                fusion_data.gate_bias = gate_bias_tensor;
+                fusion_data.glu_op    = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 5;
+                break;
+            }
+        } else if (ggml_cuda_can_fuse(cgraph, i, { op, op, GGML_OP_GLU }, {})) {
+            ggml_tensor * glu  = cgraph->nodes[i + 2];
+            ggml_tensor * gate = glu->src[0];
+            ggml_tensor * up   = glu->src[1];
+
+            bool ok = (gate == cgraph->nodes[i] && up == cgraph->nodes[i + 1]) ||
+                      (gate == cgraph->nodes[i + 1] && up == cgraph->nodes[i]);
+
+            if (!ok) {
+                continue;
+            }
+
+            const ggml_tensor * src0 = up->src[0];
+            const ggml_tensor * src1 = up->src[1];
+            const ggml_tensor * ids  = up->src[2];
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_f(up)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate   = gate->src[0];
+                fusion_data.glu_op = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 3;
+                break;
+            }
+
+            if (ggml_cuda_should_fuse_mul_mat_vec_q(up)) {
+                ggml_cuda_mm_fusion_args_host fusion_data{};
+                fusion_data.gate   = gate->src[0];
+                fusion_data.glu_op = ggml_get_glu_op(glu);
+
+                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+                fused_mul_mat_vec = true;
+                fused_node_count  = 3;
+                break;
+            }
+        }
+    }
+
+    if (fused_mul_mat_vec) {
+        return fused_node_count - 1;
+    }
+
+    fused_mul_mat_vec = false;
+    fused_node_count  = 0;
+
+    // gate + add + glu + up + add
+    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+        if (!ggml_can_fuse(cgraph, i, { op, bias_op })) {
+            continue;
+        }
+
+        ggml_tensor * mm_node   = cgraph->nodes[i];
+        ggml_tensor * bias_node = cgraph->nodes[i + 1];
+
+        ggml_tensor * bias_tensor = nullptr;
+        if (bias_op == GGML_OP_ADD) {
+            if (bias_node->src[0] == mm_node) {
+                bias_tensor = bias_node->src[1];
+            } else if (bias_node->src[1] == mm_node) {
+                bias_tensor = bias_node->src[0];
+            } else {
+                continue;
+            }
+        } else {
+            if (bias_node->src[0] != mm_node) {
+                continue;
+            }
+            bias_tensor = bias_node->src[1];
+        }
+
+        const ggml_tensor * src0 = mm_node->src[0];
+        const ggml_tensor * src1 = mm_node->src[1];
+        const ggml_tensor * ids  = mm_node->src[2];
+
+        if (bias_op == GGML_OP_ADD_ID && bias_node->src[2] != ids) {
+            continue;
+        }
+
+        if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
+            continue;
+        }
+
+        ggml_cuda_mm_fusion_args_host fusion_data{};
+        fusion_data.x_bias = bias_tensor;
+
+        if (ggml_cuda_should_fuse_mul_mat_vec_f(mm_node)) {
+            ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
+            fused_mul_mat_vec = true;
+            fused_node_count  = 2;
+            break;
+        }
+
+        if (ggml_cuda_should_fuse_mul_mat_vec_q(mm_node)) {
+            ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
+            fused_mul_mat_vec = true;
+            fused_node_count  = 2;
+            break;
+        }
+    }
+
+    if (fused_mul_mat_vec) {
+        return fused_node_count - 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD }, {})) {
+        ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i + 1], cgraph->nodes[i + 2]);
+        return 2;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL }, {})) {
+        ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) {
+        ggml_cuda_op_ssm_conv(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SILU }) ||
+        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SIGMOID }) ||
+        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SOFTPLUS })) {
+        ggml_cuda_op_unary_mul(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_SQR }, { GGML_UNARY_OP_RELU })) {
+        ggml_cuda_op_relu_sqr(*cuda_ctx, node, cgraph->nodes[i + 1]);
+        return 1;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SCALE, GGML_OP_UNARY, GGML_OP_SCALE }, { GGML_UNARY_OP_TANH })) {
+        ggml_cuda_op_softcap(*cuda_ctx, cgraph->nodes[i + 2], node);
+        return 2;
+    }
+
+    return 0;
+}
+
 static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph, const bool use_cuda_graph, const bool cuda_graph_update_required, const void * graph_key) {
     bool graph_evaluated_or_captured = false;
 
@@ -3786,355 +4137,11 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                     continue;
                 }
 
-                // start of fusion operations
-                static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
-                if (!disable_fusion) {
-                    ggml_cuda_topk_moe_args args;
+                int nodes_to_skip = ggml_cuda_try_fuse(cuda_ctx, cgraph, i);
 
-                    if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX ||
-                        cgraph->nodes[i]->op == GGML_OP_ARGSORT) {
-                        const bool can_fuse = ggml_cuda_topk_moe_fusion(cgraph, i, args);
-
-                        std::vector<ggml_op> ops;
-
-                        if (can_fuse) {
-                            const ggml_tensor * logits  = node->src[0];
-                            ggml_tensor *       weights = nullptr;
-                            ggml_tensor *       ids     = nullptr;
-                            const ggml_tensor * bias    = nullptr;
-                            const ggml_tensor * clamp   = nullptr;
-                            const ggml_tensor * scale   = nullptr;
-
-                            if (!args.delayed_softmax) {
-                                ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX;
-                                int     out_nodes[2];  // nodes which can't be elided
-
-                                if (args.prob_bias) {
-                                    bias = cgraph->nodes[i + 2]->src[1];
-                                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT,
-                                                            GGML_OP_VIEW, GGML_OP_GET_ROWS });
-                                    out_nodes[0] = i + 4;
-                                    ids          = cgraph->nodes[i + 4];
-                                } else {
-                                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW,
-                                                            GGML_OP_GET_ROWS });
-                                    out_nodes[0] = i + 3;
-                                    ids          = cgraph->nodes[i + 3];
-                                }
-
-                                if (args.norm) {
-                                    ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP,
-                                                            GGML_OP_DIV, GGML_OP_RESHAPE });
-                                    clamp = cgraph->nodes[i + ops.size() - 3];
-                                }
-                                if (args.scale) {
-                                    ops.insert(ops.end(), { GGML_OP_SCALE });
-                                    scale = cgraph->nodes[i + ops.size() - 1];
-                                }
-
-                                weights      = cgraph->nodes[i + ops.size() - 1];
-                                out_nodes[1] = i + ops.size() - 1;
-
-                                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
-                                    ggml_cuda_should_use_topk_moe(node, logits, weights, ids) &&
-                                    ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/ true)) {
-                                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
-                                    i += ops.size() - 1;
-                                    continue;
-                                }
-                            } else if (!args.norm && !args.prob_bias) {
-                                //special case gpt-oss, no norm, no bias.
-                                ops.insert(ops.end(), { GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS,
-                                                        GGML_OP_RESHAPE, GGML_OP_SOFT_MAX, GGML_OP_RESHAPE });
-                                weights                     = cgraph->nodes[i + 5];
-                                ids                         = cgraph->nodes[i + 1];
-                                const ggml_tensor * softmax = cgraph->nodes[i + 4];
-
-                                int out_nodes[2] = { i + 1, i + 5 };
-                                if (ggml_can_fuse_subgraph(cgraph, i, ops.size(), ops.data(), out_nodes, 2) &&
-                                    ggml_cuda_should_use_topk_moe(softmax, logits, weights, ids) &&
-                                    ggml_cuda_check_fusion_memory_ranges(cgraph, i, ops.size(), out_nodes, 2, /*is_topk_moe=*/ true)) {
-                                    ggml_cuda_op_topk_moe(*cuda_ctx, logits, weights, ids, clamp, scale, bias, args);
-                                    i += ops.size() - 1;
-                                    continue;
-                                }
-                            }
-                        }
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) {
-                        ggml_tensor * rope = cgraph->nodes[i];
-                        ggml_tensor * set_rows = cgraph->nodes[i + 2];
-
-                        ggml_cuda_op_rope_fused(*cuda_ctx, rope, set_rows);
-                        i += 2;
-                        continue;
-                    }
-
-                    if (node->op == GGML_OP_ADD || node->op == GGML_OP_MUL) {
-                        int n_fuse = 0;
-                        ggml_op ops[8];
-                        std::fill(ops, ops + 8, node->op);
-
-                        for (; n_fuse <= 6; ++n_fuse){
-                            if (!ggml_can_fuse(cgraph, i + n_fuse, ops + n_fuse, 2)) {
-                                break;
-                            }
-                            if (cgraph->nodes[i + n_fuse] != cgraph->nodes[i + n_fuse + 1]->src[0]) {
-                                break;
-                            }
-                            if (!ggml_are_same_layout(cgraph->nodes[i + n_fuse]->src[1], cgraph->nodes[i + n_fuse + 1]->src[1])) {
-                                break;
-                            }
-                        }
-
-                        n_fuse++;
-
-                        if (n_fuse > 1) {
-                            ggml_tensor fused_node;
-                            memcpy(&fused_node, node, sizeof(ggml_tensor));
-                            for (int j = 0; j < n_fuse - 1; ++j) {
-                                fused_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
-                            }
-                            fused_node.data = cgraph->nodes[i + n_fuse - 1]->data;
-                            if (node->op == GGML_OP_ADD) {
-                                ggml_cuda_op_fused_add(*cuda_ctx, &fused_node, n_fuse);
-                            } else {
-                                ggml_cuda_op_fused_mul(*cuda_ctx, &fused_node, n_fuse);
-                            }
-                            i += n_fuse - 1;
-
-                            continue;
-                        }
-                    }
-
-                    bool fused_mul_mat_vec = false;
-                    int fused_node_count = 0;
-
-                    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
-                        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
-
-                        if (ggml_cuda_can_fuse(cgraph, i, { op, bias_op, op, bias_op, GGML_OP_GLU }, {})) {
-                            ggml_tensor * glu         = cgraph->nodes[i + 4];
-                            ggml_tensor * gate_bias_n = glu->src[0];
-                            ggml_tensor * up_bias_n   = glu->src[1];
-
-                            //we don't assume the order for {gate, up}. Instead infer it from the bias tensor
-                            ggml_tensor * gate_n      = nullptr;
-                            ggml_tensor * up_n        = nullptr;
-
-                            if (gate_bias_n->src[0] == cgraph->nodes[i] || gate_bias_n->src[1] == cgraph->nodes[i]) {
-                                gate_n = cgraph->nodes[i];
-                                up_n   = cgraph->nodes[i + 2];
-                            } else if (gate_bias_n->src[0] == cgraph->nodes[i + 2] || gate_bias_n->src[1] == cgraph->nodes[i + 2]) {
-                                gate_n = cgraph->nodes[i + 2];
-                                up_n   = cgraph->nodes[i];
-                            } else {
-                                continue;
-                            }
-
-                            auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) {
-                                if (op_bias == GGML_OP_ADD) {
-                                    if (bias_node->src[0] == mul_node) {
-                                        return bias_node->src[1];
-                                    }
-                                    if (bias_node->src[1] == mul_node) {
-                                        return bias_node->src[0];
-                                    }
-                                    return (ggml_tensor *) nullptr;
-                                }
-                                GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
-                                GGML_ASSERT(bias_node->src[0] == mul_node);
-                                return bias_node->src[1];
-                            };
-
-                            ggml_tensor * up_bias_tensor   = get_bias_tensor(up_bias_n, up_n, bias_op);
-                            ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
-
-                            if (!up_bias_tensor || !gate_bias_tensor) {
-                                continue;
-                            }
-
-                            // we don't support repeating adds
-                            if (bias_op == GGML_OP_ADD &&
-                                (!ggml_are_same_shape(gate_bias_n->src[0], gate_bias_n->src[1]) ||
-                                 !ggml_are_same_shape(up_bias_n->src[0], up_bias_n->src[1]))) {
-                                continue;
-                            }
-
-                            const ggml_tensor * src0 = up_n->src[0];
-                            const ggml_tensor * src1 = up_n->src[1];
-                            const ggml_tensor * ids  = up_n->src[2];
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_f(up_n)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate      = gate_n->src[0];
-                                fusion_data.x_bias    = up_bias_tensor;
-                                fusion_data.gate_bias = gate_bias_tensor;
-                                fusion_data.glu_op    = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 5;
-                                break;
-                            }
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate      = gate_n->src[0];
-                                fusion_data.x_bias    = up_bias_tensor;
-                                fusion_data.gate_bias = gate_bias_tensor;
-                                fusion_data.glu_op    = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 5;
-                                break;
-                            }
-                        } else if (ggml_cuda_can_fuse(cgraph, i, { op, op, GGML_OP_GLU }, {})) {
-                            ggml_tensor * glu  = cgraph->nodes[i + 2];
-                            ggml_tensor * gate = glu->src[0];
-                            ggml_tensor * up   = glu->src[1];
-
-                            bool ok = (gate == cgraph->nodes[i] && up == cgraph->nodes[i + 1])
-                                || (gate == cgraph->nodes[i + 1] && up == cgraph->nodes[i]);
-
-                            if (!ok) continue;
-
-                            const ggml_tensor * src0 = up->src[0];
-                            const ggml_tensor * src1 = up->src[1];
-                            const ggml_tensor * ids  = up->src[2];
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_f(up)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate   = gate->src[0];
-                                fusion_data.glu_op = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 3;
-                                break;
-                            }
-
-                            if (ggml_cuda_should_fuse_mul_mat_vec_q(up)) {
-                                ggml_cuda_mm_fusion_args_host fusion_data{};
-                                fusion_data.gate   = gate->src[0];
-                                fusion_data.glu_op = ggml_get_glu_op(glu);
-
-                                ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
-                                fused_mul_mat_vec = true;
-                                fused_node_count = 3;
-                                break;
-                            }
-                        }
-                    }
-
-                    if (fused_mul_mat_vec) {
-                        i += fused_node_count - 1;
-                        continue;
-                    }
-
-                    fused_mul_mat_vec = false;
-                    fused_node_count = 0;
-
-                    for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
-                        const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
-
-                        if (!ggml_can_fuse(cgraph, i, { op, bias_op })) {
-                            continue;
-                        }
-
-                        ggml_tensor * mm_node   = cgraph->nodes[i];
-                        ggml_tensor * bias_node = cgraph->nodes[i + 1];
-
-                        ggml_tensor * bias_tensor = nullptr;
-                        if (bias_op == GGML_OP_ADD) {
-                            if (bias_node->src[0] == mm_node) {
-                                bias_tensor = bias_node->src[1];
-                            } else if (bias_node->src[1] == mm_node) {
-                                bias_tensor = bias_node->src[0];
-                            } else {
-                                continue;
-                            }
-                        } else {
-                            if (bias_node->src[0] != mm_node) {
-                                continue;
-                            }
-                            bias_tensor = bias_node->src[1];
-                        }
-
-                        const ggml_tensor * src0 = mm_node->src[0];
-                        const ggml_tensor * src1 = mm_node->src[1];
-                        const ggml_tensor * ids  = mm_node->src[2];
-
-                        if (bias_op == GGML_OP_ADD_ID && bias_node->src[2] != ids) {
-                            continue;
-                        }
-
-                        if (bias_op == GGML_OP_ADD && !ggml_are_same_shape(bias_node->src[0], bias_node->src[1])) {
-                            continue;
-                        }
-
-                        ggml_cuda_mm_fusion_args_host fusion_data{};
-                        fusion_data.x_bias = bias_tensor;
-
-                        if (ggml_cuda_should_fuse_mul_mat_vec_f(mm_node)) {
-                            ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
-                            fused_mul_mat_vec = true;
-                            fused_node_count = 2;
-                            break;
-                        }
-
-                        if (ggml_cuda_should_fuse_mul_mat_vec_q(mm_node)) {
-                            ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
-                            fused_mul_mat_vec = true;
-                            fused_node_count = 2;
-                            break;
-                        }
-                    }
-
-                    if (fused_mul_mat_vec) {
-                        i += fused_node_count - 1;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD}, {})) {
-                        ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i+1], cgraph->nodes[i+2]);
-                        i += 2;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL}, {})) {
-                        ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SSM_CONV, GGML_OP_UNARY }, { GGML_UNARY_OP_SILU })) {
-                        ggml_cuda_op_ssm_conv(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SILU }) ||
-                        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SIGMOID }) ||
-                        ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_MUL }, { GGML_UNARY_OP_SOFTPLUS })) {
-                        ggml_cuda_op_unary_mul(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_UNARY, GGML_OP_SQR }, { GGML_UNARY_OP_RELU })) {
-                        ggml_cuda_op_relu_sqr(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SCALE, GGML_OP_UNARY, GGML_OP_SCALE }, { GGML_UNARY_OP_TANH })) {
-                        i += 2;
-                        ggml_cuda_op_softcap(*cuda_ctx, cgraph->nodes[i], node);
-                        continue;
-                    }
+                if (nodes_to_skip != 0) {
+                    i += nodes_to_skip;
+                    continue;
                 }
 #ifndef NDEBUG
                 assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));