#include "ggml_graph_cut.h"

#include <algorithm>
#include <cstring>
#include <map>
#include <set>
#include <sstream>
#include <stack>
#include <unordered_map>

#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "util.h"

#include "../ggml/src/ggml-impl.h"

namespace sd::ggml_graph_cut {

    static constexpr double MAX_VRAM_BYTES_PER_GIB = 1024.0 * 1024.0 * 1024.0;

    static std::string graph_cut_tensor_display_name(const ggml_tensor* tensor) {
        if (tensor == nullptr) {
            return "<null>";
        }
        if (tensor->name[0] != '\0') {
            return tensor->name;
        }
        return sd_format("<tensor@%p>", (const void*)tensor);
    }

    static int graph_leaf_index(ggml_cgraph* gf, const ggml_tensor* tensor) {
        GGML_ASSERT(gf != nullptr);
        GGML_ASSERT(tensor != nullptr);
        for (int i = 0; i < gf->n_leafs; ++i) {
            if (gf->leafs[i] == tensor) {
                return i;
            }
        }
        return -1;
    }

    static bool is_params_tensor(const std::unordered_set<const ggml_tensor*>& params_tensor_set,
                                 const ggml_tensor* tensor) {
        if (tensor == nullptr) {
            return false;
        }
        return params_tensor_set.find(tensor) != params_tensor_set.end();
    }

    static int graph_node_index_by_name(ggml_cgraph* gf, const char* name) {
        GGML_ASSERT(gf != nullptr);
        if (name == nullptr || name[0] == '\0') {
            return -1;
        }
        const int n_nodes = ggml_graph_n_nodes(gf);
        for (int i = 0; i < n_nodes; ++i) {
            ggml_tensor* node = ggml_graph_node(gf, i);
            if (node != nullptr && std::strcmp(node->name, name) == 0) {
                return i;
            }
        }
        return -1;
    }

    static Plan::InputShape input_shape(const ggml_tensor* tensor) {
        Plan::InputShape shape;
        if (tensor == nullptr) {
            return shape;
        }
        shape.type = tensor->type;
        for (int i = 0; i < GGML_MAX_DIMS; ++i) {
            shape.ne[static_cast<size_t>(i)] = tensor->ne[i];
        }
        return shape;
    }

    static size_t graph_cut_segment_vram_bytes(const Segment& segment) {
        return segment.compute_buffer_size +
               segment.input_param_bytes +
               segment.input_previous_cut_bytes +
               segment.output_bytes;
    }

    size_t max_vram_gib_to_bytes(float max_vram) {
        if (max_vram <= 0.f) {
            return 0;
        }
        return static_cast<size_t>(static_cast<double>(max_vram) * MAX_VRAM_BYTES_PER_GIB);
    }

    static float max_vram_bytes_to_gib(size_t max_vram_bytes) {
        return static_cast<float>(static_cast<double>(max_vram_bytes) / MAX_VRAM_BYTES_PER_GIB);
    }

    static size_t resolve_auto_max_vram_bytes(float spare_vram, ggml_backend_t backend) {
        if (backend == nullptr) {
            LOG_WARN("--max-vram < 0 requested, but no backend is available; disabling graph splitting");
            return 0;
        }

        ggml_backend_dev_t dev = ggml_backend_get_device(backend);
        if (dev == nullptr) {
            LOG_WARN("--max-vram < 0 requested, but no backend device is available; disabling graph splitting");
            return 0;
        }
        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
            LOG_WARN("--max-vram < 0 requested, but the main backend is CPU; disabling graph splitting");
            return 0;
        }

        size_t free_vram  = 0;
        size_t total_vram = 0;
        ggml_backend_dev_memory(dev, &free_vram, &total_vram);
        size_t spare_bytes = static_cast<size_t>(MAX_VRAM_BYTES_PER_GIB * spare_vram);

        if (free_vram <= spare_bytes) {
            LOG_WARN("--max-vram < 0 requested, but free VRAM is %.2f GiB; reserving %.2f GiB leaves no graph budget",
                     free_vram / MAX_VRAM_BYTES_PER_GIB, spare_vram);
            return 0;
        }

        const size_t max_vram_bytes = free_vram - spare_bytes;
        LOG_INFO("--max-vram < 0 auto-detected %.2f GiB free VRAM (%.2f GiB total), reserving %.2f GiB; using %.2f GiB",
                 free_vram / MAX_VRAM_BYTES_PER_GIB,
                 total_vram / MAX_VRAM_BYTES_PER_GIB,
                 spare_vram,
                 max_vram_bytes / MAX_VRAM_BYTES_PER_GIB);
        return max_vram_bytes;
    }

    float resolve_max_vram_gib(float max_vram, ggml_backend_t backend) {
        if (max_vram >= 0.f) {
            return max_vram;
        }
        return max_vram_bytes_to_gib(resolve_auto_max_vram_bytes(-max_vram, backend));
    }

    static Segment make_segment_seed(const Plan& plan,
                                     size_t start_segment_index,
                                     size_t end_segment_index) {
        GGML_ASSERT(start_segment_index < plan.segments.size());
        GGML_ASSERT(end_segment_index < plan.segments.size());
        GGML_ASSERT(start_segment_index <= end_segment_index);

        Segment seed;
        const auto& start_segment  = plan.segments[start_segment_index];
        const auto& target_segment = plan.segments[end_segment_index];
        std::unordered_set<int> seen_output_node_indices;
        for (size_t seg_idx = start_segment_index; seg_idx <= end_segment_index; ++seg_idx) {
            for (int output_node_index : plan.segments[seg_idx].output_node_indices) {
                if (seen_output_node_indices.insert(output_node_index).second) {
                    seed.output_node_indices.push_back(output_node_index);
                }
            }
        }
        if (start_segment_index == end_segment_index) {
            seed.group_name = target_segment.group_name;
        } else {
            seed.group_name = sd_format("%s..%s",
                                        start_segment.group_name.c_str(),
                                        target_segment.group_name.c_str());
        }
        return seed;
    }

    static void build_segment(ggml_cgraph* gf,
                              Plan& plan,
                              Segment& segment,
                              const std::unordered_map<const ggml_tensor*, int>& producer_index,
                              std::unordered_set<int>& available_cut_output_node_indices,
                              ggml_backend_t backend,
                              const std::unordered_set<const ggml_tensor*>& params_tensor_set,
                              const char* log_desc) {
        std::set<int> internal_nodes;
        std::unordered_set<const ggml_tensor*> input_seen;
        std::vector<Segment::InputRef> input_refs;

        std::stack<ggml_tensor*> work_stack;
        for (int output_node_index : segment.output_node_indices) {
            ggml_tensor* output = ggml_graph_node(gf, output_node_index);
            if (output != nullptr) {
                work_stack.push(output);
            }
        }

        while (!work_stack.empty()) {
            ggml_tensor* tensor = work_stack.top();
            work_stack.pop();

            if (tensor == nullptr) {
                continue;
            }

            auto producer_it = producer_index.find(tensor);
            if (producer_it == producer_index.end()) {
                if (input_seen.insert(tensor).second) {
                    Segment::InputRef input_ref;
                    input_ref.type         = is_params_tensor(params_tensor_set, tensor) ? Segment::INPUT_PARAM : Segment::INPUT_EXTERNAL;
                    input_ref.display_name = graph_cut_tensor_display_name(tensor);
                    input_ref.leaf_index   = graph_leaf_index(gf, tensor);
                    input_refs.push_back(std::move(input_ref));
                }
                continue;
            }

            int node_idx = producer_it->second;
            if (available_cut_output_node_indices.find(node_idx) != available_cut_output_node_indices.end()) {
                if (input_seen.insert(tensor).second) {
                    Segment::InputRef input_ref;
                    input_ref.type         = Segment::INPUT_PREVIOUS_CUT;
                    input_ref.display_name = graph_cut_tensor_display_name(tensor);
                    input_ref.node_index   = node_idx;
                    input_refs.push_back(std::move(input_ref));
                }
                continue;
            }

            if (!internal_nodes.insert(node_idx).second) {
                continue;
            }

            ggml_tensor* node = ggml_graph_node(gf, node_idx);
            for (int src_idx = 0; src_idx < GGML_MAX_SRC; ++src_idx) {
                if (node->src[src_idx] != nullptr) {
                    work_stack.push(node->src[src_idx]);
                }
            }
        }

        if (!internal_nodes.empty()) {
            segment.internal_node_indices.assign(internal_nodes.begin(), internal_nodes.end());
        }

        std::sort(input_refs.begin(),
                  input_refs.end(),
                  [](const Segment::InputRef& a, const Segment::InputRef& b) {
                      if (a.type != b.type) {
                          return a.type < b.type;
                      }
                      return a.display_name < b.display_name;
                  });
        segment.input_refs = input_refs;
        for (const auto& input : input_refs) {
            ggml_tensor* current_input = input_tensor(gf, input);
            size_t tensor_bytes        = current_input == nullptr
                                             ? 0
                                             : (input.type == Segment::INPUT_PREVIOUS_CUT
                                                    ? cache_tensor_bytes(current_input)
                                                    : ggml_nbytes(current_input));
            switch (input.type) {
                case Segment::INPUT_PREVIOUS_CUT:
                    segment.input_previous_cut_bytes += tensor_bytes;
                    break;
                case Segment::INPUT_PARAM:
                    segment.input_param_bytes += tensor_bytes;
                    break;
                case Segment::INPUT_EXTERNAL:
                default:
                    segment.input_external_bytes += tensor_bytes;
                    break;
            }
        }
        for (int output_node_index : segment.output_node_indices) {
            ggml_tensor* output = ggml_graph_node(gf, output_node_index);
            segment.output_bytes += cache_tensor_bytes(output);
        }
        segment.compute_buffer_size = measure_segment_compute_buffer(backend, gf, segment, log_desc);

        for (int output_node_index : segment.output_node_indices) {
            available_cut_output_node_indices.insert(output_node_index);
        }
        plan.segments.push_back(std::move(segment));
    }

    bool is_graph_cut_tensor(const ggml_tensor* tensor) {
        if (tensor == nullptr || tensor->name[0] == '\0') {
            return false;
        }
        return std::strncmp(tensor->name, GGML_RUNNER_CUT_PREFIX, std::strlen(GGML_RUNNER_CUT_PREFIX)) == 0;
    }

    std::string make_graph_cut_name(const std::string& group, const std::string& output) {
        return std::string(GGML_RUNNER_CUT_PREFIX) + group + "|" + output;
    }

    void mark_graph_cut(ggml_tensor* tensor, const std::string& group, const std::string& output) {
        if (tensor == nullptr) {
            return;
        }
        auto name = make_graph_cut_name(group, output);
        ggml_set_name(tensor, name.c_str());
    }

    int leaf_count(ggml_cgraph* gf) {
        GGML_ASSERT(gf != nullptr);
        return gf->n_leafs;
    }

    ggml_tensor* leaf_tensor(ggml_cgraph* gf, int leaf_index) {
        GGML_ASSERT(gf != nullptr);
        if (leaf_index < 0 || leaf_index >= gf->n_leafs) {
            return nullptr;
        }
        return gf->leafs[leaf_index];
    }

    ggml_backend_buffer_t tensor_buffer(const ggml_tensor* tensor) {
        if (tensor == nullptr) {
            return nullptr;
        }
        return tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
    }

    ggml_tensor* cache_source_tensor(ggml_tensor* tensor) {
        if (tensor == nullptr) {
            return nullptr;
        }
        if (tensor_buffer(tensor) == nullptr && tensor->src[0] != nullptr &&
            ggml_nelements(tensor->src[0]) == ggml_nelements(tensor) &&
            ggml_nbytes(tensor->src[0]) == ggml_nbytes(tensor)) {
            return cache_source_tensor(tensor->src[0]);
        }
        return tensor->view_src ? tensor->view_src : tensor;
    }

    size_t cache_tensor_bytes(const ggml_tensor* tensor) {
        if (tensor == nullptr) {
            return 0;
        }
        const ggml_tensor* cache_src = tensor->view_src ? tensor->view_src : tensor;
        return ggml_nbytes(cache_src);
    }

    bool plan_matches_graph(ggml_cgraph* gf, const Plan& plan) {
        GGML_ASSERT(gf != nullptr);
        if (ggml_graph_n_nodes(gf) != plan.n_nodes || gf->n_leafs != plan.n_leafs) {
            return false;
        }
        for (const auto& input_shape_ref : plan.input_shapes) {
            if (input_shape_ref.leaf_index < 0 || input_shape_ref.leaf_index >= gf->n_leafs) {
                return false;
            }
            ggml_tensor* leaf = gf->leafs[input_shape_ref.leaf_index];
            if (leaf == nullptr || input_shape_ref.type != leaf->type) {
                return false;
            }
            for (int d = 0; d < GGML_MAX_DIMS; ++d) {
                if (input_shape_ref.ne[static_cast<size_t>(d)] != leaf->ne[d]) {
                    return false;
                }
            }
        }
        return true;
    }

    ggml_tensor* output_tensor(ggml_cgraph* gf, const Segment& segment, size_t output_index) {
        GGML_ASSERT(gf != nullptr);
        if (output_index >= segment.output_node_indices.size()) {
            return nullptr;
        }
        int node_index = segment.output_node_indices[output_index];
        if (node_index < 0 || node_index >= ggml_graph_n_nodes(gf)) {
            return nullptr;
        }
        return ggml_graph_node(gf, node_index);
    }

    ggml_tensor* input_tensor(ggml_cgraph* gf, const Segment::InputRef& input_ref) {
        GGML_ASSERT(gf != nullptr);
        if (input_ref.type == Segment::INPUT_PREVIOUS_CUT) {
            if (input_ref.node_index < 0 || input_ref.node_index >= ggml_graph_n_nodes(gf)) {
                return nullptr;
            }
            return ggml_graph_node(gf, input_ref.node_index);
        }
        if (input_ref.leaf_index < 0 || input_ref.leaf_index >= gf->n_leafs) {
            return nullptr;
        }
        return leaf_tensor(gf, input_ref.leaf_index);
    }

    std::vector<ggml_tensor*> param_tensors(ggml_cgraph* gf, const Segment& segment) {
        GGML_ASSERT(gf != nullptr);
        std::vector<ggml_tensor*> tensors;
        std::unordered_set<ggml_tensor*> seen_tensors;
        tensors.reserve(segment.input_refs.size());
        seen_tensors.reserve(segment.input_refs.size());
        for (const auto& input_ref : segment.input_refs) {
            if (input_ref.type != Segment::INPUT_PARAM) {
                continue;
            }
            ggml_tensor* tensor = input_tensor(gf, input_ref);
            if (tensor == nullptr) {
                continue;
            }
            if (seen_tensors.insert(tensor).second) {
                tensors.push_back(tensor);
            }
        }
        return tensors;
    }

    std::vector<ggml_tensor*> runtime_param_tensors(ggml_cgraph* gf, const Segment& segment, const char* log_desc) {
        std::vector<ggml_tensor*> tensors = param_tensors(gf, segment);
        std::vector<ggml_tensor*> filtered_tensors;
        filtered_tensors.reserve(tensors.size());
        for (ggml_tensor* tensor : tensors) {
            if (tensor_buffer(tensor) == nullptr) {
                LOG_WARN("%s graph cut skipping param input without buffer: segment=%s tensor=%s",
                         log_desc == nullptr ? "unknown" : log_desc,
                         segment.group_name.c_str(),
                         tensor->name);
                continue;
            }
            filtered_tensors.push_back(tensor);
        }
        return filtered_tensors;
    }

    std::unordered_set<std::string> collect_future_input_names(ggml_cgraph* gf,
                                                               const Plan& plan,
                                                               size_t current_segment_index) {
        GGML_ASSERT(gf != nullptr);
        std::unordered_set<std::string> future_input_names;
        for (size_t seg_idx = current_segment_index + 1; seg_idx < plan.segments.size(); ++seg_idx) {
            const auto& segment = plan.segments[seg_idx];
            for (const auto& input_ref : segment.input_refs) {
                if (input_ref.type != Segment::INPUT_PREVIOUS_CUT) {
                    continue;
                }
                ggml_tensor* current_input = input_tensor(gf, input_ref);
                if (current_input != nullptr && current_input->name[0] != '\0') {
                    future_input_names.insert(current_input->name);
                }
            }
        }
        return future_input_names;
    }

    ggml_cgraph* build_segment_graph(ggml_cgraph* gf,
                                     const Segment& segment,
                                     ggml_context** graph_ctx_out) {
        GGML_ASSERT(gf != nullptr);
        GGML_ASSERT(graph_ctx_out != nullptr);

        const size_t graph_size = segment.internal_node_indices.size() + segment.input_refs.size() + 8;
        ggml_init_params params = {
            /*.mem_size   =*/ggml_graph_overhead_custom(graph_size, false) + 1024,
            /*.mem_buffer =*/nullptr,
            /*.no_alloc   =*/true,
        };
        ggml_context* graph_ctx = ggml_init(params);
        GGML_ASSERT(graph_ctx != nullptr);
        ggml_cgraph* segment_graph = ggml_new_graph_custom(graph_ctx, graph_size, false);
        GGML_ASSERT(segment_graph != nullptr);

        for (const auto& input : segment.input_refs) {
            ggml_tensor* current_input = input_tensor(gf, input);
            if (current_input == nullptr) {
                continue;
            }
            GGML_ASSERT(segment_graph->n_leafs < segment_graph->size);
            segment_graph->leafs[segment_graph->n_leafs++] = current_input;
        }

        for (int output_node_index : segment.output_node_indices) {
            ggml_tensor* output = ggml_graph_node(gf, output_node_index);
            if (output == nullptr) {
                continue;
            }
            ggml_set_output(output);
        }
        for (int node_idx : segment.internal_node_indices) {
            ggml_graph_add_node(segment_graph, ggml_graph_node(gf, node_idx));
        }
        *graph_ctx_out = graph_ctx;
        return segment_graph;
    }

    size_t measure_segment_compute_buffer(ggml_backend_t backend,
                                          ggml_cgraph* gf,
                                          const Segment& segment,
                                          const char* log_desc) {
        GGML_ASSERT(backend != nullptr);
        GGML_ASSERT(gf != nullptr);
        if (segment.internal_node_indices.empty()) {
            return 0;
        }

        ggml_context* graph_ctx    = nullptr;
        ggml_cgraph* segment_graph = build_segment_graph(gf, segment, &graph_ctx);
        ggml_gallocr_t allocr      = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));

        size_t sizes[1] = {0};
        ggml_gallocr_reserve_n_size(
            allocr,
            segment_graph,
            nullptr,
            nullptr,
            sizes);
        size_t buffer_size = sizes[0];

        ggml_gallocr_free(allocr);
        ggml_free(graph_ctx);
        return buffer_size;
    }

    Plan build_plan(ggml_backend_t backend,
                    ggml_cgraph* gf,
                    const std::unordered_set<const ggml_tensor*>& params_tensor_set,
                    const char* log_desc) {
        GGML_ASSERT(backend != nullptr);
        GGML_ASSERT(gf != nullptr);
        Plan plan;
        plan.available    = true;
        const int n_nodes = ggml_graph_n_nodes(gf);
        if (n_nodes <= 0) {
            return plan;
        }
        plan.n_nodes = n_nodes;
        plan.n_leafs = gf->n_leafs;
        for (int i = 0; i < gf->n_leafs; ++i) {
            ggml_tensor* leaf = gf->leafs[i];
            if (is_params_tensor(params_tensor_set, leaf)) {
                continue;
            }
            auto shape       = input_shape(leaf);
            shape.leaf_index = i;
            plan.input_shapes.push_back(shape);
        }

        std::unordered_map<const ggml_tensor*, int> producer_index;
        producer_index.reserve(static_cast<size_t>(n_nodes));
        for (int i = 0; i < n_nodes; ++i) {
            producer_index[ggml_graph_node(gf, i)] = i;
        }

        std::vector<Segment> grouped_segments;
        std::unordered_map<std::string, size_t> group_to_segment;
        for (int i = 0; i < n_nodes; ++i) {
            ggml_tensor* node = ggml_graph_node(gf, i);
            if (!is_graph_cut_tensor(node)) {
                continue;
            }

            plan.has_cuts = true;
            std::string full_name(node->name);
            std::string payload = full_name.substr(std::strlen(GGML_RUNNER_CUT_PREFIX));
            size_t sep          = payload.find('|');
            std::string group   = sep == std::string::npos ? payload : payload.substr(0, sep);

            auto it = group_to_segment.find(group);
            if (it == group_to_segment.end()) {
                Segment segment;
                segment.group_name = group;
                segment.output_node_indices.push_back(i);
                group_to_segment[group] = grouped_segments.size();
                grouped_segments.push_back(std::move(segment));
            } else {
                auto& segment = grouped_segments[it->second];
                segment.output_node_indices.push_back(i);
            }
        }

        if (!plan.has_cuts) {
            return plan;
        }

        std::unordered_set<int> available_cut_output_node_indices;
        available_cut_output_node_indices.reserve(static_cast<size_t>(n_nodes));
        for (auto& segment : grouped_segments) {
            build_segment(gf,
                          plan,
                          segment,
                          producer_index,
                          available_cut_output_node_indices,
                          backend,
                          params_tensor_set,
                          log_desc);
        }

        int final_output_index = graph_node_index_by_name(gf, "ggml_runner_final_result_tensor");
        if (final_output_index < 0) {
            final_output_index = n_nodes - 1;
        }
        ggml_tensor* final_output = final_output_index >= 0 ? ggml_graph_node(gf, final_output_index) : nullptr;
        if (final_output != nullptr && available_cut_output_node_indices.find(final_output_index) == available_cut_output_node_indices.end()) {
            Segment final_segment;
            final_segment.group_name = "ggml_runner.final";
            final_segment.output_node_indices.push_back(final_output_index);
            build_segment(gf,
                          plan,
                          final_segment,
                          producer_index,
                          available_cut_output_node_indices,
                          backend,
                          params_tensor_set,
                          log_desc);
        }

        return plan;
    }

    Plan apply_max_vram_budget(ggml_cgraph* gf,
                               const Plan& base_plan,
                               size_t max_graph_vram_bytes,
                               ggml_backend_t backend,
                               const std::unordered_set<const ggml_tensor*>& params_tensor_set,
                               const char* log_desc) {
        GGML_ASSERT(backend != nullptr);
        GGML_ASSERT(gf != nullptr);
        int64_t t_budget_begin = ggml_time_ms();
        if (max_graph_vram_bytes == 0 || !base_plan.has_cuts || base_plan.segments.size() <= 1) {
            return base_plan;
        }

        const int n_nodes = ggml_graph_n_nodes(gf);
        std::unordered_map<const ggml_tensor*, int> producer_index;
        producer_index.reserve(static_cast<size_t>(n_nodes));
        for (int i = 0; i < n_nodes; ++i) {
            producer_index[ggml_graph_node(gf, i)] = i;
        }

        Plan merged_plan;
        merged_plan.available = true;
        merged_plan.has_cuts  = base_plan.has_cuts;
        merged_plan.valid     = base_plan.valid;
        merged_plan.n_nodes   = base_plan.n_nodes;
        merged_plan.n_leafs   = base_plan.n_leafs;

        std::unordered_set<int> available_cut_output_node_indices;
        available_cut_output_node_indices.reserve(static_cast<size_t>(n_nodes));

        size_t start_segment_index = 0;
        while (start_segment_index < base_plan.segments.size()) {
            Plan single_plan;
            auto single_available_cut_output_node_indices = available_cut_output_node_indices;
            auto single_seed                              = make_segment_seed(base_plan,
                                                                              start_segment_index,
                                                                              start_segment_index);
            build_segment(gf,
                          single_plan,
                          single_seed,
                          producer_index,
                          single_available_cut_output_node_indices,
                          backend,
                          params_tensor_set,
                          log_desc);
            GGML_ASSERT(!single_plan.segments.empty());

            size_t best_end_segment_index = start_segment_index;
            bool can_merge_next_segment   = graph_cut_segment_vram_bytes(single_plan.segments.back()) <= max_graph_vram_bytes;

            while (can_merge_next_segment && best_end_segment_index + 1 < base_plan.segments.size()) {
                const size_t next_end_segment_index = best_end_segment_index + 1;
                Plan candidate_plan;
                auto candidate_available_cut_output_node_indices = available_cut_output_node_indices;
                auto candidate_seed                              = make_segment_seed(base_plan,
                                                                                     start_segment_index,
                                                                                     next_end_segment_index);
                build_segment(gf,
                              candidate_plan,
                              candidate_seed,
                              producer_index,
                              candidate_available_cut_output_node_indices,
                              backend,
                              params_tensor_set,
                              log_desc);
                GGML_ASSERT(!candidate_plan.segments.empty());

                const auto& candidate_segment = candidate_plan.segments.back();
                if (graph_cut_segment_vram_bytes(candidate_segment) > max_graph_vram_bytes) {
                    break;
                }

                best_end_segment_index = next_end_segment_index;
            }

            auto best_seed = make_segment_seed(base_plan,
                                               start_segment_index,
                                               best_end_segment_index);
            build_segment(gf,
                          merged_plan,
                          best_seed,
                          producer_index,
                          available_cut_output_node_indices,
                          backend,
                          params_tensor_set,
                          log_desc);
            start_segment_index = best_end_segment_index + 1;
        }

        if (log_desc != nullptr && merged_plan.segments.size() != base_plan.segments.size()) {
            LOG_INFO("%s graph cut max_vram=%.2f MB merged %zu segments -> %zu segments",
                     log_desc,
                     max_graph_vram_bytes / 1024.0 / 1024.0,
                     base_plan.segments.size(),
                     merged_plan.segments.size());
        }

        if (log_desc != nullptr) {
            LOG_INFO("%s graph cut max_vram budget merge took %lld ms",
                     log_desc,
                     ggml_time_ms() - t_budget_begin);
        }

        return merged_plan;
    }

    Plan resolve_plan(ggml_backend_t backend,
                      ggml_cgraph* gf,
                      PlanCache* cache,
                      size_t max_graph_vram_bytes,
                      const std::unordered_set<const ggml_tensor*>& params_tensor_set,
                      const char* log_desc) {
        GGML_ASSERT(backend != nullptr);
        GGML_ASSERT(gf != nullptr);
        GGML_ASSERT(cache != nullptr);

        int64_t t_prepare_begin = ggml_time_ms();
        Plan base_plan;
        int64_t t_plan_begin = ggml_time_ms();
        if (cache->graph_cut_plan.available && plan_matches_graph(gf, cache->graph_cut_plan)) {
            base_plan = cache->graph_cut_plan;
        } else {
            base_plan                                = build_plan(backend, gf, params_tensor_set, log_desc);
            cache->graph_cut_plan                    = base_plan;
            cache->graph_cut_plan.available          = true;
            cache->budgeted_graph_cut_plan.available = false;
            if (log_desc != nullptr) {
                LOG_INFO("%s build cached graph cut plan done (taking %lld ms)", log_desc, ggml_time_ms() - t_plan_begin);
            }
        }

        Plan resolved_plan = base_plan;
        if (max_graph_vram_bytes > 0 && base_plan.has_cuts) {
            if (cache->budgeted_graph_cut_plan.available &&
                cache->budgeted_graph_cut_plan_max_vram_bytes == max_graph_vram_bytes &&
                plan_matches_graph(gf, cache->budgeted_graph_cut_plan)) {
                resolved_plan = cache->budgeted_graph_cut_plan;
            } else {
                resolved_plan                                 = apply_max_vram_budget(gf,
                                                                                      base_plan,
                                                                                      max_graph_vram_bytes,
                                                                                      backend,
                                                                                      params_tensor_set,
                                                                                      log_desc);
                cache->budgeted_graph_cut_plan                = resolved_plan;
                cache->budgeted_graph_cut_plan.available      = true;
                cache->budgeted_graph_cut_plan_max_vram_bytes = max_graph_vram_bytes;
            }
        }
        return resolved_plan;
    }

}  // namespace sd::ggml_graph_cut