// SDQ expert pager 實作：見 sdq_pager.h 的設計說明
#include "sdq_pager.h"

#include "llama-mmap.h"

#include <algorithm>
#include <thread>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <fcntl.h>
#include <ctime>
#include <fstream>
#include <map>
#include <set>
#include <sstream>
#include <stdexcept>
#include <sys/mman.h>
#include <sys/stat.h>
#include <sys/types.h>
#include <unistd.h>

namespace sdq {

// ============================================================ 小工具

uint64_t now_us() {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return (uint64_t) ts.tv_sec * 1000000ull + (uint64_t) ts.tv_nsec / 1000ull;
}

size_t process_rss_bytes() {
    std::ifstream f("/proc/self/statm");
    size_t total = 0, resident = 0;
    if (f >> total >> resident) {
        return resident * (size_t) sysconf(_SC_PAGESIZE);
    }
    return 0;
}

size_t physical_ram_bytes() {
    std::ifstream f("/proc/meminfo");
    std::string key;
    size_t value = 0;
    std::string unit;
    while (f >> key >> value >> unit) {
        if (key == "MemTotal:") {
            return value * 1024;
        }
    }
    return 0;
}

static size_t env_size(const char * name, size_t dflt) {
    const char * v = getenv(name);
    if (!v || !*v) {
        return dflt;
    }
    return (size_t) strtoull(v, nullptr, 10);
}

static bool env_flag(const char * name, bool dflt) {
    const char * v = getenv(name);
    if (!v || !*v) {
        return dflt;
    }
    return !(v[0] == '0' && v[1] == '\0');
}

// ============================================================ GGUF 檔頭解析

namespace {

struct gguf_reader {
    const uint8_t * p = nullptr;
    size_t size = 0;
    size_t o = 0;

    uint8_t  u8()  { return p[o++]; }
    uint32_t u32() { uint32_t v; memcpy(&v, p + o, 4); o += 4; return v; }
    uint64_t u64() { uint64_t v; memcpy(&v, p + o, 8); o += 8; return v; }
    int64_t  i64() { int64_t  v; memcpy(&v, p + o, 8); o += 8; return v; }
    float    f32() { float    v; memcpy(&v, p + o, 4); o += 4; return v; }
    std::string str() {
        uint64_t n = u64();
        std::string s((const char *) p + o, (size_t) n);
        o += n;
        return s;
    }
    void skip(uint64_t n) { o += (size_t) n; }
    uint16_t u16() { uint16_t v; memcpy(&v, p + o, 2); o += 2; return v; }
    int16_t  i16() { int16_t  v; memcpy(&v, p + o, 2); o += 2; return v; }
    double   f64() { double   v; memcpy(&v, p + o, 8); o += 8; return v; }
};

struct kv_value {
    enum kind { NONE, INT, REAL, STR, ARR } k = NONE;
    int64_t i = 0;
    double  f = 0;
    std::string s;
    std::vector<int64_t> arr;
};

struct tensor_info {
    std::string name;
    std::vector<int64_t> dims;
    ggml_type type = GGML_TYPE_COUNT;
    uint64_t offset = 0;   // 在所屬 shard 檔案內、相對 data_start 的偏移
    int      shard = 0;    // 屬於哪一個 shard（sharded GGUF）
};

size_t ggml_type_size_of(ggml_type t) {
    return ggml_row_size(t, 1);
}

} // namespace

// ============================================================ pager

pager & pager::instance() {
    static pager p;
    return p;
}

// 依 GGUF split 命名規則把第一個 shard 路徑展開成全部 shard。
// 規則是 "<prefix>-00001-of-00004.gguf"；llama.cpp 用 llama_split_prefix/llama_split_path
// 做同一件事，這裡獨立實作以免 pager 依賴 loader 的內部符號。
std::vector<std::string> pager::list_splits(const std::string & first_path, int n_split) {
    std::vector<std::string> out;
    if (n_split <= 1) {
        out.push_back(first_path);
        return out;
    }
    // 用 llama.cpp 自己的命名規則（%s-%05d-of-%05d.gguf，且 split_no 由 1 起算）。
    // 自己刻字串很容易差一位數字 —— 實測就是這樣變成 "...XL--00001-of-..."。
    std::vector<char> pre(llama_path_max(), 0);
    const int pre_len = llama_split_prefix(pre.data(), pre.size(), first_path.c_str(), 0, n_split);
    if (pre_len <= 0) {
        throw std::runtime_error("無法解析 split 檔名：" + first_path);
    }
    std::vector<char> buf(llama_path_max(), 0);
    for (int i = 0; i < n_split; i++) {
        const int len = llama_split_path(buf.data(), buf.size(), pre.data(), i, n_split);
        if (len <= 0) {
            throw std::runtime_error("無法產生 split 路徑：" + first_path);
        }
        out.emplace_back(buf.data(), (size_t) len);
    }
    return out;
}

void pager::load_gguf_header(const std::string & path) {
    const int fd = open(path.c_str(), O_RDONLY);
    if (fd < 0) {
        throw std::runtime_error("無法開啟 GGUF：" + path);
    }

    struct stat st;
    fstat(fd, &st);

    // 只讀檔頭：metadata + tensor 資訊大約 11 MB（shard 1 還含整份 tokenizer，
    // 這裡的 64 MiB 上限對它足夠）
    const size_t want = (size_t) std::min<int64_t>(st.st_size, 64ull << 20);
    std::vector<uint8_t> buf(want);
    ssize_t got = 0;
    while (got < (ssize_t) want) {
        ssize_t n = pread(fd, buf.data() + got, want - got, got);
        if (n <= 0) {
            break;
        }
        got += n;
    }
    buf.resize((size_t) std::max<ssize_t>(got, 0));

    gguf_reader r{buf.data(), buf.size(), 0};
    if (memcmp(buf.data(), "GGUF", 4) != 0) {
        close(fd);
        throw std::runtime_error("不是 GGUF 檔：" + path);
    }
    r.o = 4;
    const uint32_t version = r.u32();
    if (version < 2 || version > 3) {
        close(fd);
        throw std::runtime_error("不支援的 GGUF 版本");
    }
    const uint64_t n_tensors = r.u64();
    const uint64_t n_kv = r.u64();

    std::map<std::string, kv_value> kv;
    for (uint64_t i = 0; i < n_kv; i++) {
        std::string key = r.str();
        const uint32_t t = r.u32();
        kv_value v;
        switch (t) {
            // GGUF 型別寬度：U8/I8/BOOL=1、U16/I16=2、U32/I32/F32=4、U64/I64/F64=8。
            // 舊版把 case 0/1/2/3 都讀成 u32，遇到 tokenizer 或 U16 metadata 就整個
            // header 錯位（症狀是 std::length_error: basic_string::_M_create）。
            case 0: v.k = kv_value::INT; v.i = (int64_t) r.u8();  v.f = (double) v.i; break;
            case 1: v.k = kv_value::INT; v.i = (int64_t) (int8_t)  r.u8();  v.f = (double) v.i; break;
            case 2: v.k = kv_value::INT; v.i = (int64_t) r.u16(); v.f = (double) v.i; break;
            case 3: v.k = kv_value::INT; v.i = (int64_t) r.i16(); v.f = (double) v.i; break;
            case 4: v.k = kv_value::INT; v.i = (int64_t) (uint32_t) r.u32(); v.f = (double) v.i; break;
            case 5: v.k = kv_value::INT; v.i = (int64_t) (int32_t)  r.u32(); v.f = (double) v.i; break;
            case 6: v.k = kv_value::REAL; v.f = r.f32(); v.i = (int64_t) v.f; break;
            case 7: v.k = kv_value::INT; v.i = (int64_t) r.u8(); v.f = (double) v.i; break; // BOOL = 1 byte
            case 8: v.k = kv_value::STR;  v.s = r.str(); break;
            case 9: {
                v.k = kv_value::ARR;
                const uint32_t et = r.u32();
                const uint64_t n  = r.u64();
                for (uint64_t j = 0; j < n; j++) {
                    switch (et) {
                        // 元素寬度必須跟 kv 一致（U8/I8/BOOL=1、U16/I16=2 …）。
                        // F64 一定要消費 8 bytes，舊版沒讀會讓 header 往後全部錯位。
                        case 0: v.arr.push_back(r.u8()); break;
                        case 1: v.arr.push_back((int8_t) r.u8()); break;
                        case 2: v.arr.push_back(r.u16()); break;
                        case 3: v.arr.push_back(r.i16()); break;
                        case 4: v.arr.push_back((uint32_t) r.u32()); break;
                        case 5: v.arr.push_back((int32_t) r.u32()); break;
                        case 6: v.arr.push_back((int64_t) r.f32()); break;
                        case 7: v.arr.push_back(r.u8()); break;
                        case 10: v.arr.push_back((int64_t) r.u64()); break;
                        case 11: v.arr.push_back(r.i64()); break;
                        case 12: r.f64(); v.arr.push_back(0); break;
                        case 8: r.skip(r.u64()); v.arr.push_back(0); break;
                        default:
                            close(fd);
                            throw std::runtime_error("GGUF 陣列元素型別不支援: " + std::to_string(et));
                    }
                }
            } break;
            case 10: v.k = kv_value::INT; v.i = (int64_t) r.u64(); v.f = (double) v.i; break;
            case 11: v.k = kv_value::INT; v.i = r.i64(); v.f = (double) v.i; break;
            case 12: v.k = kv_value::REAL; { double d; memcpy(&d, buf.data() + r.o, 8); r.o += 8; v.f = d; v.i = (int64_t) d; } break;
            default: close(fd); throw std::runtime_error("GGUF kv 型別不支援: " + std::to_string(t));
        }
        kv[key] = v;
    }

    std::vector<tensor_info> tensors;
    tensors.reserve(n_tensors);
    for (uint64_t i = 0; i < n_tensors; i++) {
        tensor_info ti;
        ti.name = r.str();
        const uint32_t nd = r.u32();
        for (uint32_t d = 0; d < nd; d++) {
            ti.dims.push_back((int64_t) r.u64());
        }
        ti.type = (ggml_type) r.u32();
        ti.offset = r.u64();
        tensors.push_back(std::move(ti));
    }
    const uint64_t align = kv.count("general.alignment") ? (uint64_t) kv["general.alignment"].i : 32;
    const uint64_t data_start = (r.o + align - 1) / align * align;

    close(fd);

    // ---- sharded GGUF：shard 1 只帶 metadata（n_tensors=0），真正的權重分散在
    // 其他 shard，而且每個 shard 有自己的 data_start。舊版只讀第一個檔，
    // 於是 page map 會是空的（症狀：pager 起來但沒有任何 expert 可分頁）。
    // 這裡把每個 shard 的 header 都讀一遍，張量名稱為全域鍵，offset 記成 (shard, off)。
    {
        const int64_t n_split = kv.count("split.count") ? kv["split.count"].i : 1;
        shard_paths_ = list_splits(path, (int) n_split);
        shard_data_start_.assign(shard_paths_.size(), 0);
        shard_data_start_[0] = data_start;

        if (n_split > 1) {
            std::vector<tensor_info> merged;
            merged.reserve((size_t) n_tensors + 1024);
            for (size_t si = 1; si < shard_paths_.size(); si++) {
                const int sfd = open(shard_paths_[si].c_str(), O_RDONLY);
                if (sfd < 0) {
                    throw std::runtime_error("無法開啟 GGUF shard：" + shard_paths_[si]);
                }
                struct stat sst;
                fstat(sfd, &sst);
                // 後續 shard 的 header 很小（沒有 tokenizer），讀 16 MiB 綽綽有餘
                const size_t swant = (size_t) std::min<int64_t>(sst.st_size, 16ull << 20);
                std::vector<uint8_t> sbuf(swant);
                ssize_t sgot = 0;
                while (sgot < (ssize_t) swant) {
                    const ssize_t n = pread(sfd, sbuf.data() + sgot, swant - sgot, sgot);
                    if (n <= 0) {
                        break;
                    }
                    sgot += n;
                }
                sbuf.resize((size_t) std::max<ssize_t>(sgot, 0));

                gguf_reader sr{sbuf.data(), sbuf.size(), 0};
                if (sbuf.size() < 16 || memcmp(sbuf.data(), "GGUF", 4) != 0) {
                    close(sfd);
                    throw std::runtime_error("shard 不是 GGUF：" + shard_paths_[si]);
                }
                sr.o = 4;
                sr.u32();                                  // version
                const uint64_t s_ntensors = sr.u64();
                const uint64_t s_nkv      = sr.u64();
                for (uint64_t i = 0; i < s_nkv; i++) {
                    sr.str();                               // key
                    const uint32_t t = sr.u32();
                    switch (t) {
                        case 0: case 1: case 7: sr.skip(1); break;
                        case 2: case 3:         sr.skip(2); break;
                        case 4: case 5: case 6: sr.skip(4); break;
                        case 10: case 11: case 12: sr.skip(8); break;
                        case 8: sr.skip(sr.u64()); break;
                        case 9: {
                            const uint32_t et = sr.u32();
                            const uint64_t n  = sr.u64();
                            if (et == 8) {
                                for (uint64_t j = 0; j < n; j++) { sr.skip(sr.u64()); }
                            } else {
                                size_t esz = 0;
                                switch (et) {
                                    case 0: case 1: case 7: esz = 1; break;
                                    case 2: case 3:         esz = 2; break;
                                    case 4: case 5: case 6: esz = 4; break;
                                    case 10: case 11: case 12: esz = 8; break;
                                    default: esz = 0; break;
                                }
                                sr.skip((uint64_t) n * esz);
                            }
                        } break;
                        default:
                            close(sfd);
                            throw std::runtime_error("shard kv 型別不支援: " + std::to_string(t));
                    }
                }
                for (uint64_t i = 0; i < s_ntensors; i++) {
                    tensor_info ti;
                    ti.name = sr.str();
                    const uint32_t nd = sr.u32();
                    for (uint32_t d = 0; d < nd; d++) {
                        ti.dims.push_back((int64_t) sr.u64());
                    }
                    ti.type   = (ggml_type) sr.u32();
                    ti.offset = sr.u64();
                    ti.shard  = (int) si;
                    merged.push_back(std::move(ti));
                }
                shard_data_start_[si] = (sr.o + align - 1) / align * align;
                close(sfd);
            }
            for (auto & ti : tensors) {
                ti.shard = 0;
                merged.push_back(std::move(ti));
            }
            tensors.swap(merged);
            fprintf(stderr, "[sdq] sharded GGUF：%d 個 shard，合計 %zu 個張量（shard1 為 metadata-only）\n",
                    (int) shard_paths_.size(), tensors.size());
        }
    }

    // ---- hparams
    auto gi = [&](const std::string & k, int64_t dflt) -> int64_t {
        auto it = kv.find(k);
        return it == kv.end() ? dflt : it->second.i;
    };
    info_.arch = kv.count("general.architecture") ? kv["general.architecture"].s : "";
    info_.name = kv.count("general.name") ? kv["general.name"].s : "";
    const std::string pfx = info_.arch + ".";
    info_.n_layer      = gi(pfx + "block_count", 0);
    info_.n_expert     = gi(pfx + "expert_count", 0);
    info_.n_expert_used= gi(pfx + "expert_used_count", 0);
    info_.n_embd       = gi(pfx + "embedding_length", 0);
    info_.n_ff_exp     = gi(pfx + "expert_feed_forward_length", 0);
    info_.n_ff_shexp   = gi(pfx + "expert_shared_feed_forward_length", 0);
    info_.n_head       = gi(pfx + "attention.head_count", 0);
    info_.n_head_kv    = gi(pfx + "attention.head_count_kv", 0);
    info_.n_embd_head_k= gi(pfx + "attention.key_length", 0);
    info_.n_embd_head_v= gi(pfx + "attention.value_length", info_.n_embd_head_k);
    info_.context_length = gi(pfx + "context_length", 0);
    info_.full_attention_interval = gi(pfx + "full_attention_interval", 4);
    // bailingmoe3（Ling-3.0-flash）：前 n_layer_dense_lead 層是 dense FFN，
    // 它們沒有 ffn_*_exps 張量，必須讓 page map 保持「空」——
    // sdq_pager_active_for_layer() 看到 layer() == nullptr 就會走原生路徑。
    info_.n_layer_dense_lead = gi(pfx + "leading_dense_block_count", 0);
    info_.n_expert_groups   = gi(pfx + "expert_group_count", 0);
    info_.n_expert_group_used = gi(pfx + "expert_group_used_count", 0);

    if (info_.n_expert <= 0 || info_.n_layer <= 0) {
        throw std::runtime_error("這不是 MoE 模型（expert_count=" + std::to_string(info_.n_expert) + "）");
    }

    // ---- 頁面表：每個 (layer, expert) 的三段
    layers_.assign((size_t) info_.n_layer, {});
    std::map<std::string, const tensor_info *> by_name;
    for (const auto & ti : tensors) {
        by_name[ti.name] = &ti;
    }

    size_t max_expert_bytes = 0;
    int n_moe_layers = 0;
    for (int64_t il = 0; il < info_.n_layer; il++) {
        const std::string b = "blk." + std::to_string(il) + ".";
        layer_desc & L = layers_[(size_t) il];

        auto get = [&](const std::string & suffix) -> const tensor_info * {
            auto it = by_name.find(b + suffix);
            return it == by_name.end() ? nullptr : it->second;
        };

        const tensor_info * t_gate = get("ffn_gate_exps.weight");
        const tensor_info * t_up   = get("ffn_up_exps.weight");
        const tensor_info * t_gu   = get("ffn_gate_up_exps.weight");
        const tensor_info * t_down = get("ffn_down_exps.weight");

        if (!t_down || (t_gate && !t_up && !t_gu)) {
            // 這層不是 MoE（bailingmoe3 的 leading dense layers 就是這樣）。
            // 保持 experts 為空 → layer() 回 nullptr → 這層走原生 mul_mat_id。
            continue;
        }
        L.experts.assign((size_t) info_.n_expert, {});
        n_moe_layers++;

        // sharded GGUF：每段的絕對偏移要用「該張量自己 shard 的 data_start」，
        // 不能用第一個 shard 的（兩者差了近 10 MB，直接用會讀到別的權重）。
        auto abs_off = [&](const tensor_info * t, uint64_t rel) -> uint64_t {
            const size_t s = (size_t) t->shard;
            const uint64_t base = (s < shard_data_start_.size()) ? shard_data_start_[s] : 0;
            return base + rel;
        };

        for (int64_t e = 0; e < info_.n_expert; e++) {
            expert_desc & ed = L.experts[(size_t) e];
            if (t_gate || t_gu) {
                const tensor_info * t = t_gate ? t_gate : t_gu;
                const uint64_t nb = (uint64_t) ggml_row_size(t->type, t->dims[0]) * (uint64_t) t->dims[1];
                ed.seg[part::GATE].off   = abs_off(t, t->offset + (uint64_t) e * nb);
                ed.seg[part::GATE].size  = nb;
                ed.seg[part::GATE].shard = t->shard;
                ed.ne0 = t->dims[0];
                ed.ne1 = t->dims[1];
                ed.type[part::GATE] = t->type;
            }
            if (t_up) {
                const uint64_t nb = (uint64_t) ggml_row_size(t_up->type, t_up->dims[0]) * (uint64_t) t_up->dims[1];
                ed.seg[part::UP].off   = abs_off(t_up, t_up->offset + (uint64_t) e * nb);
                ed.seg[part::UP].size  = nb;
                ed.seg[part::UP].shard = t_up->shard;
                ed.type[part::UP] = t_up->type;   // gate/up 都是 Q4_K，型別一定要各自填
                if (!t_gate) {
                    ed.ne0 = t_up->dims[0];
                    ed.ne1 = t_up->dims[1];
                    ed.type[part::GATE] = t_up->type;
                }
            }
            {
                const uint64_t nb = (uint64_t) ggml_row_size(t_down->type, t_down->dims[0]) * (uint64_t) t_down->dims[1];
                ed.seg[part::DOWN].off   = abs_off(t_down, t_down->offset + (uint64_t) e * nb);
                ed.seg[part::DOWN].size  = nb;
                ed.seg[part::DOWN].shard = t_down->shard;
                ed.type[part::DOWN] = t_down->type;
                ed.down_ne0 = t_down->dims[0];
                ed.down_ne1 = t_down->dims[1];
            }
            ed.fused_gate_up = (t_gate == nullptr && t_gu != nullptr);
            ed.bytes = 0;
            int64_t acc = 0;
            for (int p = 0; p < 3; p++) {
                if (ed.seg[p].size == 0) {
                    continue;
                }
                acc = (acc + 63) & ~(int64_t) 63; // 每段 64B 對齊
                ed.slot_off[p] = acc;
                acc += (int64_t) ed.seg[p].size;
            }
            ed.bytes = acc;
            max_expert_bytes = std::max(max_expert_bytes, (size_t) ed.bytes);
        }
        L.bytes_per_expert = L.experts.empty() ? 0 : L.experts[0].bytes;
    }

    slot_bytes_ = (max_expert_bytes + 4095) & ~(size_t) 4095;
    if (slot_bytes_ < (256ull << 10)) {
        slot_bytes_ = 256ull << 10;
    }

    // ---- 大小兩種槽位 ----------------------------------------------------
    // 這個模型有 37 層的 expert 是 1900544 B（Q4_K + Q5_K），但層 34/38/39 是
    // 2039808 B（down 換成 Q6_K）。若所有槽位都照最大的配，arena 有 6.8% 被浪費
    // ——而 arena 就是 8 GB 預算裡最寶貴的資源（實測 +5% 槽位 ≈ +9% tok/s）。
    //
    // 解法：槽位分成兩區。「小槽位」給 37 層用，「大槽位」只給那 3 層用，
    // 大槽位的數量按「那 3 層佔請求的比例」分配（3/40 = 7.5%）。
    // 選「大多數層」當小的：用眾數而不是最大值，這樣即使某個模型只有一種大小，
    // 也不會誤判。
    {
        std::map<size_t, int> hist;
        for (const auto & L : layers_) {
            if (L.experts.empty()) {
                continue;   // dense layer：沒有 expert，不參與槽位尺寸統計
            }
            hist[(size_t) L.bytes_per_expert]++;
        }
        size_t small = 0;
        int best_n = -1;
        for (const auto & kv : hist) {
            if (kv.second > best_n) {
                best_n = kv.second;
                small = kv.first;
            }
        }
        size_t big = 0;
        for (const auto & L : layers_) {
            if (!L.experts.empty() && (size_t) L.bytes_per_expert > small) {
                big = std::max(big, (size_t) L.bytes_per_expert);
            }
        }
        small_bytes_ = (small + 4095) & ~(size_t) 4095;
        big_bytes_   = big ? ((big + 4095) & ~(size_t) 4095) : 0;
        if (big_bytes_ && big_bytes_ <= small_bytes_) {
            big_bytes_ = 0;   // 其實一樣大，別分兩區
        }
        slot_bytes_ = big_bytes_ ? big_bytes_ : small_bytes_;
        int n_big_layers = 0;
        for (auto & L : layers_) {
            L.needs_big_slot = big_bytes_ && !L.experts.empty() &&
                               (size_t) L.bytes_per_expert > small_bytes_;
            if (L.needs_big_slot) {
                n_big_layers++;
            }
        }
        // 統計給 README / 實驗報告用：expert 權重在檔案裡到底佔多少
        uint64_t expert_bytes = 0;
        for (const auto & L : layers_) {
            expert_bytes += (uint64_t) L.bytes_per_expert * (uint64_t) L.experts.size();
        }
        n_moe_layers_ = n_moe_layers;
        expert_bytes_total_ = expert_bytes;
        fprintf(stderr,
                "[sdq] MoE 層 %d / 共 %lld 層（dense lead %lld）"
                "：expert 權重合計 %.2f GiB\n",
                n_moe_layers, (long long) info_.n_layer,
                (long long) info_.n_layer_dense_lead,
                expert_bytes / 1073741824.0);
        fprintf(stderr,
                "[sdq] 槽位大小：小的 %zu B（%zu 層）／大的 %zu B（%d 層）\n",
                small_bytes_, n_moe_layers - n_big_layers, big_bytes_, n_big_layers);
    }
}

bool pager::need_big_slot(int il, int ie) const {
    if (!big_bytes_) {
        return false;
    }
    const layer_desc * L = layer(il);
    if (!L) {
        return false;
    }
    if (L->needs_big_slot) {
        return true;
    }
    return ie >= 0 && ie < (int) L->experts.size() &&
           (size_t) L->experts[(size_t) ie].bytes > small_bytes_;
}

void pager::alloc_arena() {
    const size_t slot = cfg_.slot_bytes ? cfg_.slot_bytes : slot_bytes_;
    slot_bytes_ = slot;

    // arena 大小決定：RAM 是「總預算」，不是「還有多少可用」
    //   總預算 = 非專家權重 + ggml 模型緩衝 + KV cache + 計算緩衝 + 詞表 + arena
    // 所以有指定 ram_budget 時，用「目前 RSS（不含 arena）」當作已用額度扣掉。
    //
    // 重要：pager 在 llama.cpp 載入權重**之前**就做 sizing，此時 rss_now 只有
    // 幾十 MB（實測 0.09 GiB），所以後續所有匿名配置都不在 rss_now 裡，
    // 必須由 reserve 手動涵蓋。這些事實上是三塊：
    //
    //   1. ggml 的模型緩衝 —— 主機有 AMX 時（-march=native 會定義 __AMX_INT8__
    //      使 llama.cpp 啟用 AMX repack buffer）Ling-3.0-flash Q4_K_M 要
    //      1876.34 MiB；關掉 AMX（GGML_NATIVE=OFF）則不需要這塊。
    //   2. CPU compute buffer：83.5 MiB @ubatch=128，ubatch 越大越多。
    //   3. KV/SSM state + 執行緒堆疊：ctx 越大越多。
    //
    // 舊的預設值 900 MB 只涵蓋 (2)+(3)，漏掉 (1)，在 AMX 主機上會讓實際
    // 匿名用量超出預算約 1.9 GiB —— 嚴格 RLIMIT_DATA 下直接 OOM。
    // 實測（本機，ctx=2048、ubatch=128、AMX build）：
    //   reserve 1024 MB → 預算 8 GiB 實測匿名峰值 10.96 GiB（超出，OOM）
    //   reserve 3600 MB → 預算 8 GiB 通過，arena 4.40 GiB
    //   reserve 2700 MB → 預算 4 GiB 通過，arena 1.27 GiB（無 AMX build）
    // 寧可少一些槽位，也不要在目標機器的 8 GiB 上 OOM —— 這是本專案的硬約束。
    size_t reserve = (size_t) env_size("SDQ_RESERVE_MB", 3600) << 20;
    const size_t rss_now = process_rss_bytes();

    size_t arena;
    if (cfg_.ram_budget_bytes) {
        const size_t used = rss_now;   // 這時候 arena 還沒配置
        size_t avail = cfg_.ram_budget_bytes > used + reserve
                     ? cfg_.ram_budget_bytes - used - reserve
                     : 0;
        arena = avail;
        if (cfg_.arena_bytes) {
            arena = std::min(arena, cfg_.arena_bytes);
        }
        fprintf(stderr, "[sdq] RAM 預算 %.2f GiB：已用(RSS) %.2f GiB、預留 %.2f GiB → arena %.2f GiB\n",
                cfg_.ram_budget_bytes / 1073741824.0, used / 1073741824.0,
                reserve / 1073741824.0, arena / 1073741824.0);
    } else {
        const size_t phys = physical_ram_bytes();
        const size_t budget = (size_t) (phys * 0.70);
        arena = cfg_.arena_bytes ? cfg_.arena_bytes : std::min(budget, (size_t) (4ull << 30));
    }
    if (arena < slot_bytes_ * 16) {
        throw std::runtime_error("expert arena 太小");
    }
    // 切成兩區：大槽位只分給「需要大槽位的層」，比例 = 那些層佔全部層的比例。
    const bool force_uniform = env_flag("SDQ_FORCE_UNIFORM_SLOT", false);
    if (big_bytes_ && small_bytes_ && !layers_.empty() && !force_uniform) {
        int n_big_layers = 0;
        for (const auto & L : layers_) {
            if (L.needs_big_slot) {
                n_big_layers++;
            }
        }
        const double denom = (double) (n_moe_layers_ ? n_moe_layers_ : layers_.size());
        const double f = (double) n_big_layers / denom;
        const double avg = f * (double) big_bytes_ + (1.0 - f) * (double) small_bytes_;
        // Never reserve more than one slot per expert: once all expert pages fit,
        // a larger arena adds no hit-rate benefit and only wastes anonymous RAM.
        size_t n_total = std::min(
            (size_t)((double) arena / avg),
            (size_t) info_.n_expert * (size_t) n_moe_layers_);
        // Allocate big slots in proportion to the fraction of MoE layers that need
        // them. The previous Ling-specific 25% cap starved larger experts when a
        // model had many Q6_K layers (LFM2.5 has 18/38 = 47%).
        size_t nb = (size_t)(n_total * f);
        // During decode, only the current layer's selected experts need to be pinned.
        // Keep at least one layer's top-k working set in each class when possible;
        // don't scale this minimum by the number of layers.
        const size_t big_need = (size_t) info_.n_expert_used;
        if (nb < big_need) {
            nb = big_need;
        }
        if (nb > n_total) {
            nb = n_total;
        }
        n_big_ = nb;
        n_small_ = n_total > nb ? n_total - nb : 0;
        arena = n_small_ * small_bytes_ + n_big_ * big_bytes_;
    } else {
        // 單一尺寸：全部槽位都是「大的那一種」（slot_bytes_）。
        // 這是 SDQ_FORCE_UNIFORM_SLOT=1 與「模型只有一種 expert 大小」時的路徑。
        //
        // 這裡必須記成 n_small_=0 / n_big_=全部，不能記成「n_small_=全部、單位用大的」：
        // slot_ptr()/slot_size_of() 是用「索引 < n_small_ → 小槽位」來分支的，
        // 把它記成全部都是小槽位的話，大 expert 會拿到小槽位的 stride
        // （寫出槽位邊界、算錯），而且大 expert 永遠配不到槽位 → 推論卡死。
        // （第一版就是這樣，實驗跑到一半 hang 才找出來。）
        const size_t unit = slot_bytes_ ? slot_bytes_ : small_bytes_;
        // slot_ptr()/slot_size_of() 對索引 >= n_small_ 的槽位用 big_bytes_ 當 stride。
        // 這裡 n_small_=0、所有槽位都在大槽位區，所以 big_bytes_ 必須等於 unit；
        // 留成 0 會讓每個槽位都指向 arena_[0]（多執行緒時互相覆蓋，靜默算錯）。
        big_bytes_ = unit;
        n_small_ = 0;
        n_big_ = arena / unit;
        arena = n_big_ * unit;
    }
    if (n_small_ == 0 && n_big_ == 0) {
        throw std::runtime_error("expert arena 太小（算不出槽位）");
    }
    n_slots_ = n_small_ + n_big_;
    if (n_slots_ < 16) {
        throw std::runtime_error("expert arena 太小（算不出 16 個槽位）");
    }

    void * p = nullptr;
    const size_t alloc = arena + (2ull << 20);
    if (posix_memalign(&p, 2ull << 20, alloc) != 0 || !p) {
        throw std::runtime_error("無法配置 expert arena（" + std::to_string(arena >> 20) + " MB）");
    }
    // 讓 arena 全部變成實體 RAM，並提示核心使用 huge page（減少 TLB 壓力）
    memset(p, 0, alloc);
    madvise(p, alloc, MADV_HUGEPAGE);
    arena_ = (uint8_t *) p;
    arena_bytes_ = arena;

    // OPT-1：剖面計數表（n_layer × n_expert，40×256 = 10240 筆 ≈ 40 KB，可忽略）
    prof_.assign((size_t) info_.n_layer * (size_t) info_.n_expert, 0);
    ensure_layer_stats((int) info_.n_layer);   // SDQ-0004
    if (!cfg_.profile_in.empty()) {
        load_profile(cfg_.profile_in);
    }
    profile_out_ = cfg_.profile_out;

    slots_.assign(n_slots_, {});
    free_slots_.reserve(n_small_);
    for (size_t i = n_small_; i-- > 0;) {
        free_slots_.push_back((int32_t) i);
    }
    free_big_slots_.reserve(n_big_);
    for (size_t i = n_slots_; i-- > n_small_;) {
        free_big_slots_.push_back((int32_t) i);
    }
    used_.store(0);

    hist_.assign((size_t) info_.n_layer, {});
    for (auto & per_layer : hist_) {
        per_layer.assign((size_t) info_.n_expert, {});
    }
    prev_used_.assign((size_t) info_.n_layer, {});
    cur_used_.assign((size_t) info_.n_layer, {});
}

void pager::init(const config & cfg, ggml_context * ctx) {
    (void) ctx;
    if (ready_) {
        return;
    }
    cfg_ = cfg;
    if (cfg_.model_path.empty()) {
        const char * v = getenv("SDQ_MODEL_PATH");
        cfg_.model_path = v ? v : "";
    }
    if (cfg_.model_path.empty()) {
        throw std::runtime_error("SDQ 缺少模型路徑（設定 SDQ_MODEL_PATH）");
    }

    // 先讀 header（順便列舉 shard），再開 fd：shard 清單只有在解析
    // split.count 之後才知道，而 open_model_fd() 需要它。
    load_gguf_header(cfg_.model_path);
    open_model_fd();
    if (shard_fd_.empty() || shard_fd_[0] < 0) {
        throw std::runtime_error("無法開啟模型檔：" + shard_path(0));
    }
    // 順序有意義：I/O 池的對齊緩衝必須在 alloc_arena() 之前配置，
    // 否則 arena 會把它當成「不存在的 RAM」，實際上就偷用了 8 GB 預算。
    io_pool_start();
    alloc_arena();

    if (cfg_.prefetch) {
        pf_ = std::thread([this] { pf_worker(); });
    }
    ready_ = true;
    print_summary();
}

const layer_desc * pager::layer(int il) const {
    if (il < 0 || il >= (int) layers_.size()) {
        return nullptr;
    }
    const layer_desc & L = layers_[(size_t) il];
    if (L.experts.empty()) {
        return nullptr;
    }
    return &L;
}

void pager::open_model_fd() {
    if (shard_paths_.empty()) {
        shard_paths_.push_back(cfg_.model_path);
    }
    shard_fd_.assign(shard_paths_.size(), -1);
    shard_fd_direct_.assign(shard_paths_.size(), -1);
    for (size_t i = 0; i < shard_paths_.size(); i++) {
        shard_fd_[i] = open(shard_paths_[i].c_str(), O_RDONLY);
        if (shard_fd_[i] < 0) {
            fprintf(stderr, "[sdq] 無法開啟 shard %zu: %s\n", i, shard_paths_[i].c_str());
            return;
        }
    }
    // O_DIRECT：讀資料完全不進 kernel page cache。
    // 這是「RAM 帳目誠實」的關鍵 —— 否則核心預讀會讓 mmap 檔案的頁算進 RSS
    // （實測 4K prefill 時檔案對映 RSS 可以多到 6 GB 以上），而且量到的
    // 「SSD 讀取量」會被上一輪的 page cache 灌水。
    const char * no_direct = getenv("SDQ_NO_DIRECT_IO");
    if (no_direct && *no_direct && *no_direct != '0') {
        return;
    }
#ifdef O_DIRECT
    // 每個 shard 各自開一個 O_DIRECT fd：O_DIRECT 是 fd 層級的屬性，不能共用。
    for (size_t i = 0; i < shard_paths_.size(); i++) {
        shard_fd_direct_[i] = open(shard_paths_[i].c_str(), O_RDONLY | O_DIRECT);
        if (shard_fd_direct_[i] < 0) {
            break;
        }
    }
    use_direct_ = shard_fd_direct_[0] >= 0;
    if (use_direct_) {
        fprintf(stderr, "[sdq] 使用 O_DIRECT 讀取模型檔（%zu 個 shard，不佔用 kernel page cache）\n",
                shard_paths_.size());
    } else {
        fprintf(stderr, "[sdq] O_DIRECT 不可用，改用 buffered + fadvise(DONTNEED)\n");
    }
#endif
}

// 有對齊的緩衝區（O_DIRECT 要求），thread_local 避免競爭
static thread_local uint8_t * g_direct_buf = nullptr;
static thread_local size_t   g_direct_sz  = 0;

// 與 read_range_direct 相同，但用呼叫端提供的緩衝區（已配置好、已算進 RSS）。
// I/O 執行緒池走這條：每條 worker 在啟動時就配置好對齊緩衝，於是 arena  sizing 時
// 這筆記憶體已經在 RSS 裡，不會變成「額外偷用的 RAM」。
// sharded GGUF：把「哪個 shard」一路帶到 pread。
static inline int sdq_fd_for(const std::vector<int> & fds, int shard) {
    if (shard < 0 || shard >= (int) fds.size()) {
        return -1;
    }
    return fds[(size_t) shard];
}

bool pager::read_range_staged(int shard, uint64_t off, uint8_t * dst, size_t size,
                              uint8_t * stage, size_t stage_sz) {
    if (!use_direct_) {
        return read_range(shard, off, dst, size);
    }
    const int fd = sdq_fd_for(shard_fd_direct_, shard);
    if (fd < 0) {
        return false;
    }
    const size_t blk = 4096;
    const uint64_t start = off & ~(uint64_t)(blk - 1);
    const uint64_t end = (off + size + blk - 1) & ~(uint64_t)(blk - 1);
    const size_t bytes = (size_t)(end - start);
    if (stage_sz < bytes) {
        // 預先配置不足（不該發生）：退回 thread_local 的通用路徑
        return read_range_direct(shard, off, dst, size);
    }
    size_t done = 0;
    while (done < bytes) {
        const ssize_t n = pread(fd, stage + done, bytes - done,
                                (off_t)(start + done));
        if (n <= 0) {
            return false;
        }
        done += (size_t) n;
    }
    memcpy(dst, stage + (off - start), size);
    return true;
}

bool pager::read_range_direct(int shard, uint64_t off, uint8_t * dst, size_t size) {
    if (!use_direct_) {
        return read_range(shard, off, dst, size);
    }
    const int fd = sdq_fd_for(shard_fd_direct_, shard);
    if (fd < 0) {
        return false;
    }
    const size_t blk = 4096;
    const uint64_t start = off & ~(uint64_t)(blk - 1);
    const uint64_t end = (off + size + blk - 1) & ~(uint64_t)(blk - 1);
    const size_t bytes = (size_t)(end - start);
    if (g_direct_sz < bytes) {
        if (g_direct_buf) {
            free(g_direct_buf);
        }
        if (posix_memalign((void **) &g_direct_buf, blk, bytes) != 0) {
            g_direct_buf = nullptr;
            g_direct_sz = 0;
            use_direct_ = false;
            return read_range(shard, off, dst, size);
        }
        g_direct_sz = bytes;
    }
    size_t done = 0;
    while (done < bytes) {
        const ssize_t n = pread(fd, g_direct_buf + done, bytes - done,
                                (off_t)(start + done));
        if (n <= 0) {
            return false;
        }
        done += (size_t) n;
    }
    memcpy(dst, g_direct_buf + (off - start), size);
    return true;
}

bool pager::read_range(int shard, uint64_t off, uint8_t * dst, size_t size) {
    const int fd = sdq_fd_for(shard_fd_, shard);
    if (fd < 0) {
        return false;
    }
    size_t done = 0;
    while (done < size) {
        const ssize_t n = pread(fd, dst + done, size - done, (off_t) (off + done));
        if (n <= 0) {
            return false;
        }
        done += (size_t) n;
    }
    // 讓 RAM 帳目誠實：把 kernel 對這個檔案的 page cache 丟掉，避免「藏在核心裡的 RAM」
    if (cfg_.fadvise_dontneed) {
        posix_fadvise(fd, (off_t) off, (off_t) size, POSIX_FADV_DONTNEED);
    }
    return true;
}

bool pager::read_expert(int il, int ie, uint8_t * dst) {
    const layer_desc * L = layer(il);
    if (!L || ie < 0 || ie >= (int) L->experts.size()) {
        return false;
    }
    const expert_desc & ed = L->experts[(size_t) ie];
    for (int p = 0; p < 3; p++) {
        if (ed.seg[p].size == 0) {
            continue;
        }
        if (!read_range_direct(ed.seg[p].shard, ed.seg[p].off,
                               dst + ed.slot_off[p], (size_t) ed.seg[p].size)) {
            return false;
        }
    }
    return true;
}

// ---------------------------------------------------------------- I/O 執行緒池
//
// 問題：一個 expert = gate / up / down 三段，檔案上是三段不相鄰的資料。
// 循序讀就是 3 次 pread 排在同一條執行緒上，而單次 1.9 MB 的 pread 在本機量到
// ~7 ms（≈275 MB/s 的單流頻寬）。因此「一個 expert 要多久」= 3 × 單次延遲，
// 這條路徑的長度跟「有多少條執行緒」無關 —— mode C 再怎麼排程都救不了。
//
// 做法：把三段交給三條執行緒同時 pread，臨界路徑降到約「單段」的時間。
// 量測（8 個 expert 全部 miss 的一層，各 9 次取中位數）：
//     8 執行緒 × 三段序列讀 : 12.60 ms
//    16 執行緒 × 三段平行讀 :  7.57 ms
//    24 執行緒 × 三段平行讀 :  7.58 ms   ← 16 就飽和，所以預設 3 × n_threads
//
// 這個池子對 prefill 更有用：一層要 256 個 expert = 768 個讀取工作，
// 序列讀時 16 執行緒只能拿到 16 路的頻寬 utilization。
void pager::io_pool_start() {
    if (!cfg_.io_parallel) {
        fprintf(stderr, "[sdq] I/O 段平行：關（每個 expert 的 gate/up/down 循序讀）\n");
        return;
    }
    int n = cfg_.io_threads > 0 ? cfg_.io_threads : cfg_.n_threads * 3;
    if (n < 4) {
        n = 4;
    }
if (n > 48) {
        n = 48;
    }
    // 先算「一段權重最大有多少位元組」，每條 worker 配一塊對齊緩衝。
    // 這必须在 alloc_arena() 之前做完，否則這筆 RAM 會變成帳外支出。
    size_t max_seg = 0;
    for (const auto & L : layers_) {
        for (const auto & ed : L.experts) {
            for (int p = 0; p < 3; p++) {
                max_seg = std::max(max_seg, (size_t) ed.seg[p].size);
            }
        }
    }
    const size_t stage_bytes = ((max_seg + 2 * 4096) + 4095) & ~(size_t) 4095;
    io_stage_.assign((size_t) n, io_stage{});
    for (int i = 0; i < n; i++) {
        void * p = nullptr;
        if (posix_memalign(&p, 4096, stage_bytes) == 0 && p) {
            memset(p, 0, stage_bytes);   // 實際碰頁，讓它出現在 RSS 裡
            io_stage_[(size_t) i].buf = (uint8_t *) p;
            io_stage_[(size_t) i].sz  = stage_bytes;
        }
    }
    io_split_ = cfg_.io_split > 0 ? cfg_.io_split : 1;
    io_stop_ = false;
    io_pool_.reserve((size_t) n);
    for (int i = 0; i < n; i++) {
        const size_t wid = (size_t) i;
        try {
            io_pool_.emplace_back([this, wid] { io_worker(wid); });
        } catch (...) {
            break;
        }
    }
    io_threads_ = (int) io_pool_.size();
    io_pool_on_ = io_threads_ > 0;
    if (io_pool_on_) {
        fprintf(stderr, "[sdq] I/O 段平行：%d 條讀取執行緒 × %zu KB 對齊緩衝"
                        "（gate/up/down 同時讀，每段再切 %d 塊）\n",
                io_threads_, stage_bytes >> 10, io_split_);
    }
}

void pager::io_pool_stop() {
    if (io_pool_.empty()) {
        for (auto & s : io_stage_) {
            free(s.buf);
            s.buf = nullptr;
        }
        io_stage_.clear();
        return;
    }
    {
        std::lock_guard<std::mutex> lk(io_mu_);
        io_stop_ = true;
    }
    io_cv_.notify_all();
    for (auto & t : io_pool_) {
        if (t.joinable()) {
            t.join();
        }
    }
    io_pool_.clear();
    io_pool_on_ = false;
    for (auto & s : io_stage_) {
        free(s.buf);
        s.buf = nullptr;
        s.sz = 0;
    }
    io_stage_.clear();
}

void pager::io_worker(size_t wid) {
    for (;;) {
        io_job job{};
        {
            std::unique_lock<std::mutex> lk(io_mu_);
            io_cv_.wait(lk, [this] { return io_stop_ || !io_q_.empty(); });
            if (io_q_.empty()) {
                if (io_stop_) {
                    return;
                }
                continue;
            }
            job = io_q_.front();
            io_q_.pop_front();
        }
        io_jobs_.fetch_add(1, std::memory_order_relaxed);
        // 讀取本身不持鎖：io_mu_ 只保護佇列與完成計數
        const bool ok = (wid < io_stage_.size() && io_stage_[wid].buf)
                      ? read_range_staged(job.shard, job.off, job.dst, job.size,
                                          io_stage_[wid].buf, io_stage_[wid].sz)
                      : read_range_direct(job.shard, job.off, job.dst, job.size);
        {
            std::lock_guard<std::mutex> lk(io_mu_);
            if (!ok) {
                job.ctx->ok = false;
            }
            if (job.ctx->remaining.fetch_sub(1) == 1) {
                io_done_cv_.notify_all();
            }
        }
    }
}

// 把 (il,ie) 的三段交給執行緒池並行讀，呼叫端等到整批完成才返回。
// ctx 放在呼叫端 stack：worker 只會在 remaining 歸零之後才再碰它，
// 而我們正是等到 remaining==0 才離開函式，所以沒有 use-after-return。
bool pager::read_expert_par(int il, int ie, uint8_t * dst) {
    const layer_desc * L = layer(il);
    if (!L || ie < 0 || ie >= (int) L->experts.size()) {
        return false;
    }
    if (!io_pool_on_) {
        return read_expert(il, ie, dst);
    }
    const expert_desc & ed = L->experts[(size_t) ie];
    io_ctx ctx;
    int n = 0;
    // 把每一段再切成一樣大的好幾塊，交給不同的執行緒同時讀。
    //
    // 為什麼：單一 pread 的時間 ≈ 固定延遲 + 位元組數 / 單流頻寬。
    // 本機實測單流約 275 MB/s、4K 隨機讀延遲約 0.57 ms，所以
    //   590 KB 讀一次 ≈ 0.57 + 2.1 = 2.7 ms
    //   590 KB 切成 2 塊 ≈ 0.57 + 1.05 = 1.6 ms
    // 而「讀完一個 expert」的時間正是每層的關鍵路徑，切塊就是直接砍關鍵路徑。
    // 設定檔 SDQ_IO_SPLIT。**實測預設是 1（不切塊）**：切塊在 24 條讀取執行緒
    // 之下沒有可重現的改善（split=1..6 的中位數在 4.9~5.9 之間跳動，純噪音），
    // 因為 8 個 expert × 3 段 = 24 個並行請求已經把裝置的佇列灌飽，
    // 這時瓶頸是裝置總頻寬而不是單一請求的延遲。保留這個開關是為了其他裝置。
    const int split = io_split_;
    const size_t blk = 4096;
    {
        std::lock_guard<std::mutex> lk(io_mu_);
        for (int p = 0; p < 3; p++) {
            const size_t sz = (size_t) ed.seg[p].size;
            if (sz == 0) {
                continue;
            }
            uint8_t * base = dst + ed.slot_off[p];
            const int sh = ed.seg[p].shard;
            if (split <= 1 || sz <= 2 * blk) {
                io_q_.push_back(io_job{sh, ed.seg[p].off, base, sz, &ctx});
                n++;
                continue;
            }
            size_t chunk = ((sz + (size_t) split - 1) / (size_t) split + blk - 1) & ~(blk - 1);
            for (size_t off = 0; off < sz; off += chunk) {
                const size_t take = sz - off < chunk ? sz - off : chunk;
                io_q_.push_back(io_job{sh, ed.seg[p].off + off, base + off, take, &ctx});
                n++;
            }
        }
        ctx.remaining.store(n);
    }
    if (n == 0) {
        return true;
    }
    io_cv_.notify_all();
    const uint64_t t0 = now_us();
    {
        std::unique_lock<std::mutex> lk(io_mu_);
        io_done_cv_.wait(lk, [&ctx] { return ctx.remaining.load() == 0; });
    }
    io_wait_ns_.fetch_add((now_us() - t0) * 1000ull, std::memory_order_relaxed);
    return ctx.ok;
}

uint64_t pager::read_expert_span(int il, const int32_t * experts, int n, const int32_t * slots) {
    const layer_desc * L = layer(il);
    if (!L || n <= 0) {
        return 0;
    }
    const uint64_t t_start = now_us();
    // 每個執行緒一份暫存區（多執行緒會同時呼叫，不能共用一個 buffer）
    static thread_local std::vector<uint8_t> scratch_buf;
    uint64_t total = 0;

    // 把 experts 切成「檔案編號連續」的區段
    int i = 0;
    while (i < n) {
        int j = i + 1;
        while (j < n && experts[j] == experts[j - 1] + 1) {
            j++;
        }
        const int cnt = j - i;
        const expert_desc & first = L->experts[(size_t) experts[i]];
        const expert_desc & last = L->experts[(size_t) experts[j - 1]];

        for (int p = 0; p < 3; p++) {
            if (first.seg[p].size == 0 || last.seg[p].size == 0) {
                continue;
            }
            // 連續區段必須在同一個 shard 內；跨 shard 就切斷這個區段。
            if (last.seg[p].shard != first.seg[p].shard) {
                continue;
            }
            const uint64_t off = first.seg[p].off;
            const uint64_t end = last.seg[p].off + last.seg[p].size;
            const size_t bytes = (size_t) (end - off);
            if (scratch_buf.size() < bytes) {
                scratch_buf.assign(bytes, 0);
            }
            if (!read_range_direct(first.seg[p].shard, off, scratch_buf.data(), bytes)) {
                return total;
            }
            total += bytes;
            // 散到各自的槽位：scratch 從 off 開始，所以偏移是「相對於 off」
            for (int k = i; k < j; k++) {
                const expert_desc & ed = L->experts[(size_t) experts[k]];
                const size_t seg_bytes = (size_t) ed.seg[p].size;
                const size_t delta = (size_t) (ed.seg[p].off - off);
                if (slots[k] < 0) {
                    continue;
                }
                if ((size_t) slots[k] >= n_slots_ || (uint64_t) ed.slot_off[p] + seg_bytes > slot_bytes_
                    || delta + seg_bytes > scratch_buf.size()) {
                    fprintf(stderr, "[sdq-pager] span 越界：il=%d slot=%d/%zu slot_off=%lld seg=%zu delta=%zu scratch=%zu p=%d e=%d\n",
                            il, slots[k], n_slots_, (long long) ed.slot_off[p], seg_bytes, delta, scratch_buf.size(), p, experts[k]);
                    return total;
                }
                memcpy(slot_ptr(slots[k]) + ed.slot_off[p],
                       scratch_buf.data() + delta, seg_bytes);
            }
        }
        i = j;
    }
    stats_.demand_wait_us.fetch_add(now_us() - t_start, std::memory_order_relaxed);
    return total;
}

int pager::reserve_slot(int il, int ie) {
    if (!ready_) {
        return -1;
    }
    count_request(il, ie);
    std::lock_guard<std::mutex> lk(mu_);
    const int32_t exist = find_slot_locked(il, ie);
    if (exist >= 0) {
        slots_[(size_t) exist].last_use = ++clock_;
        slots_[(size_t) exist].pin++;
        touch_freq_locked(slots_[(size_t) exist]);
        return exist;
    }
    const int32_t s = victim_locked();
    if (s < 0) {
        return -1;
    }
    slot_state & st = slots_[(size_t) s];
    if (st.layer >= 0) {
        page_.erase(((int64_t) st.layer << 20) | (int64_t) st.expert);
        stats_.evictions.fetch_add(1, std::memory_order_relaxed);
        used_.fetch_sub(1, std::memory_order_relaxed);
    }
    st.layer = il;
    st.expert = ie;
    st.last_use = ++clock_;
    st.freq = 1;
    st.pin = 1;
    st.is_hot = hot_lookup(il, ie);
    page_[((int64_t) il << 20) | (int64_t) ie] = s;
    used_.fetch_add(1, std::memory_order_relaxed);
    stats_.expert_misses.fetch_add(1, std::memory_order_relaxed);
    return s;
}

int pager::probe(int il, int ie) {
    if (!ready_) {
        return -1;
    }
    stats_.expert_requests.fetch_add(1, std::memory_order_relaxed);
    count_request(il, ie);
    std::lock_guard<std::mutex> lk(mu_);
    const int32_t s = find_slot_locked(il, ie);
    if (s < 0) {
        stats_.expert_misses.fetch_add(1, std::memory_order_relaxed);
        return -1;
    }
    slots_[(size_t) s].last_use = ++clock_;
    slots_[(size_t) s].pin++;
    touch_freq_locked(slots_[(size_t) s]);
    stats_.expert_hits.fetch_add(1, std::memory_order_relaxed);
    return s;
}

void pager::note_span_read(uint64_t bytes) {
    stats_.ssb_read_bytes_demand.fetch_add(bytes, std::memory_order_relaxed);
    stats_.demand_wait_us.fetch_add(0, std::memory_order_relaxed);
}

// 衰減式頻率：每 8192 次使用就把所有計數減半，讓「過去熱門、現在變冷」的 expert 能被淘汰
void pager::touch_freq_locked(slot_state & st) {
    if (++freq_tick_ >= 8192) {
        freq_tick_ = 0;
        for (auto & s : slots_) {
            s.freq /= 2;
        }
    }
    if (st.freq < 0xFFFFFFFFu) {
        st.freq++;
    }
}

// ============================== OPT-1：熱門 expert 剖面 ==============================
// 問題實測（docs/03）：arena 3.88 GiB 只放得下 2044 個 expert，總共卻有 10240 個，
// prefill 的幾百個「只用一次」的專家會把熱區整個洗掉，命中率卡在 ~70%。
// 解法借用 Strata 的做法（src/core/expert_cache.cpp）：**離線剖面 + 靜態排行榜 +
// 熱頁永不淘汰**。這裡不動檔案格式，用純文字存排行榜。

// ---- SDQ-0004
void pager::ensure_layer_stats(int n_layer) {
    if (lstats_ || n_layer <= 0) {
        return;
    }
    lstats_.reset(new layer_stat[(size_t) n_layer]);
}

void pager::count_request(int il, int ie) {
    if (prof_.empty() || il < 0 || ie < 0) {
        return;
    }
    const size_t idx = (size_t) il * (size_t) info_.n_expert + (size_t) ie;
    if (idx < prof_.size()) {
        // 單執行緒 ++ 即可：請求次數是統計用途，不參與任何正確性判斷
        if (prof_[idx] < 0xFFFFFFFFu) {
            prof_[idx]++;
        }
    }
}

bool pager::hot_lookup(int il, int ie) const {
    if (hot_.empty() || il < 0 || ie < 0) {
        return false;
    }
    const size_t idx = (size_t) il * (size_t) info_.n_expert + (size_t) ie;
    return idx < hot_.size() && hot_[idx] != 0;
}

// 挑一個容得下 (il,ie) 的槽。與 victim_locked 的差別：**先只在冷頁裡找**，
// 找不到才退讓去動熱頁。這就是「熱區不會被 prefill 洗掉」的機制。
// 這個槽位放得下「需要 big 的 expert」嗎？
//   小槽位：只能放小 expert
//   大槽位：小expert 也放得下（只是浪費一點空間）
static inline bool slot_accepts(bool need_big, bool slot_is_big) {
    return !need_big || slot_is_big;
}

int pager::admit_slot_locked(int il, int ie) {
    // il/ie 都 < 0 代表「不指定大小」→ 兩種槽位都可以（舊的呼叫路徑）。
    const bool need_big = (il >= 0 && ie >= 0) ? need_big_slot(il, ie) : false;
    // 完全沒用過的空槽永遠優先（它不是熱頁，佔用等於浪費）
    // 大 expert 先看大槽位；小 expert 先看小槽位。
    std::vector<int32_t> & first  = need_big ? free_big_slots_ : free_slots_;
    std::vector<int32_t> & second = need_big ? free_slots_ : free_big_slots_;
    while (!first.empty()) {
        const int32_t s = first.back();
        if (!slots_[(size_t) s].busy) {
            first.pop_back();
            return s;
        }
    }
    // 第一輪：只在「冷頁」裡挑最久未用的。
    //   want_hot=true  → 熱門 expert 進來時，把冷頁換掉（不動其他熱頁）
    //   want_hot=false → 冷門 expert 進來時，優先吃冷頁
    // 兩種情況的規則相同，所以 hot_lookup 的結果不影響選擇順序，只影響標記。
    for (int pass = 0; pass < 2; pass++) {
        int64_t best = -1;
        uint64_t best_clock = UINT64_MAX;
        for (size_t s = 0; s < n_slots_; s++) {
            const slot_state & st = slots_[s];
            if (st.pin > 0 || st.busy) {
                continue;
            }
            if (pass == 0 && st.is_hot) {
                continue;   // 第一輪不動熱頁
            }
            if (!slot_accepts(need_big, is_big_slot(s))) {
                continue;
            }
            if (st.last_use < best_clock) {
                best_clock = st.last_use;
                best = (int64_t) s;
            }
        }
        if (best >= 0) {
            return (int) best;
        }
    }
    // 退讓：另一種大小的空槽。**一定要檢查放不放得下** —— 小槽位放不下大 expert，
    // 硬塞會讓 read_expert 寫出槽位邊界（靜默把別的 expert 寫壞）。
    while (!second.empty()) {
        const int32_t s = second.back();
        if (!slots_[(size_t) s].busy &&
            slot_accepts(need_big, is_big_slot(s))) {
            second.pop_back();
            return s;
        }
        if (!slots_[(size_t) s].busy) {
            second.pop_back();
        } else {
            break;
        }
    }
    return -1;
}

bool pager::load_profile(const std::string & path) {
    std::FILE * f = std::fopen(path.c_str(), "rb");
    if (!f) {
        fprintf(stderr, "[sdq] 找不到 expert 剖面 %s（將以動態 LRU/LFU 運作）\n", path.c_str());
        return false;
    }
    char magic[8] = {0};
    if (std::fread(magic, 1, 4, f) != 4 || std::memcmp(magic, "SDQP", 4) != 0) {
        std::fclose(f);
        fprintf(stderr, "[sdq] %s 不是 SDQ expert 剖面（缺少 SDQP 標頭）\n", path.c_str());
        return false;
    }
    uint32_t hdr[3] = {0, 0, 0};
    if (std::fread(hdr, sizeof(uint32_t), 3, f) != 3) {
        std::fclose(f);
        return false;
    }
    const uint32_t n = hdr[2];
    std::vector<std::pair<uint32_t, std::pair<int32_t, int32_t>>> ranked;
    ranked.reserve(n);
    for (uint32_t i = 0; i < n; i++) {
        int32_t l = 0, e = 0;
        uint32_t c = 0;
        if (std::fread(&l, sizeof(int32_t), 1, f) != 1) break;
        if (std::fread(&e, sizeof(int32_t), 1, f) != 1) break;
        if (std::fread(&c, sizeof(uint32_t), 1, f) != 1) break;
        ranked.push_back({c, {l, e}});
    }
    std::fclose(f);
    std::sort(ranked.begin(), ranked.end(),
              [](const auto & a, const auto & b) { return a.first > b.first; });

    hot_.assign((size_t) info_.n_layer * (size_t) info_.n_expert, 0);
    const int want = cfg_.hot_pin > 0 ? cfg_.hot_pin : (int) ranked.size();
    n_hot_ = 0;
    for (const auto & r : ranked) {
        if (n_hot_ >= want) {
            break;
        }
        const int l = r.second.first, e = r.second.second;
        if (l < 0 || l >= info_.n_layer || e < 0 || e >= info_.n_expert) {
            continue;
        }
        const size_t idx = (size_t) l * (size_t) info_.n_expert + (size_t) e;
        if (!hot_[idx]) {
            hot_[idx] = 1;
            n_hot_++;
        }
    }
    fprintf(stderr, "[sdq] expert 剖面：%s → 釘選 %d / %zu 個熱門 expert（永不淘汰）\n",
            path.c_str(), n_hot_, ranked.size());
    return n_hot_ > 0;
}

void pager::dump_profile(const std::string & path) {
    if (prof_.empty()) {
        return;
    }
    std::vector<std::pair<uint32_t, std::pair<int32_t, int32_t>>> ranked;
    ranked.reserve(prof_.size());
    uint64_t total = 0;
    for (int l = 0; l < info_.n_layer; l++) {
        for (int e = 0; e < info_.n_expert; e++) {
            const uint32_t c = prof_[(size_t) l * (size_t) info_.n_expert + (size_t) e];
            if (c > 0) {
                ranked.push_back({c, {l, e}});
                total += c;
            }
        }
    }
    std::sort(ranked.begin(), ranked.end(),
              [](const auto & a, const auto & b) { return a.first > b.first; });
    std::FILE * f = std::fopen(path.c_str(), "wb");
    if (!f) {
        fprintf(stderr, "[sdq] 無法寫出 expert 剖面 %s\n", path.c_str());
        return;
    }
    std::fwrite("SDQP", 1, 4, f);
    const uint32_t hdr[3] = {1, (uint32_t) info_.n_layer, (uint32_t) ranked.size()};
    std::fwrite(hdr, sizeof(uint32_t), 3, f);
    for (const auto & r : ranked) {
        const int32_t l = r.second.first, e = r.second.second;
        const uint32_t c = r.first;
        std::fwrite(&l, sizeof(int32_t), 1, f);
        std::fwrite(&e, sizeof(int32_t), 1, f);
        std::fwrite(&c, sizeof(uint32_t), 1, f);
    }
    std::fclose(f);
    // 覆盖率報告：前 K 名可以蓋掉多少請求 → 直接決定 hot_pin 該設多少
    uint64_t acc = 0;
    const size_t ks[6] = {512, 1024, 1536, 2048, 3072, 4096};
    fprintf(stderr, "[sdq] expert 剖面已寫到 %s（%zu 個有被用到，總請求 %llu）\n",
            path.c_str(), ranked.size(), (unsigned long long) total);
    size_t ki = 0;
    for (size_t i = 0; i < ranked.size(); i++) {
        acc += ranked[i].first;
        if (ki < 6 && i + 1 == ks[ki]) {
            fprintf(stderr, "[sdq]   前 %5zu 名涵蓋 %6.2f%% 的請求\n",
                    ks[ki], 100.0 * (double) acc / (double) (total ? total : 1));
            ki++;
        }
    }
}

int pager::find_slot_locked(int il, int ie) {
    const int64_t key = ((int64_t) il << 20) | (int64_t) ie;
    auto it = page_.find(key);
    return it == page_.end() ? -1 : it->second;
}

// 找一個「可以放 (il,ie) 而且 last_use <= stale_before」的槽位。
//
// 為什麼不能直接用 victim_stale_locked()：大小兩種槽位之後，那個函式會掃過**所有**
// 槽位（包含小槽位），於是一個需要大槽位的 expert 可能拿到小槽位，
// 預取時 read_expert 會寫出槽位邊界 → 靜默把鄰居槽位的資料寫壞。
// 這是引進大小兩種槽位時真正會咬人的地方。
//
// 順序：先看對應尺寸的空槽（從沒用過，最該拿來放預取），
// 再掃全部槽位挑最久未用的（放得下的那種尺寸）。
int pager::victim_stale_for(int il, int ie, uint64_t stale_before) {
    const bool need_big = need_big_slot(il, ie);
    std::vector<int32_t> & first  = need_big ? free_big_slots_ : free_slots_;
    std::vector<int32_t> & second = need_big ? free_slots_ : free_big_slots_;
    for (std::vector<int32_t> * lst : { &first, &second }) {
        while (!lst->empty()) {
            const int32_t s = lst->back();
            if (slots_[(size_t) s].busy) {
                break;
            }
            if (slots_[(size_t) s].last_use <= stale_before &&
                slot_accepts(need_big, is_big_slot(s))) {
                lst->pop_back();
                return s;
            }
            lst->pop_back();
        }
    }
    int64_t best = -1;
    uint64_t best_clock = UINT64_MAX;
    for (size_t i = 0; i < n_slots_; i++) {
        const slot_state & st = slots_[i];
        if (st.pin > 0 || st.busy || st.last_use > stale_before) {
            continue;
        }
        if (!slot_accepts(need_big, is_big_slot(i))) {
            continue;
        }
        if (st.last_use < best_clock) {
            best_clock = st.last_use;
            best = (int64_t) i;
        }
    }
    return (int) best;
}

int pager::victim_stale_locked(uint64_t stale_before) {
    // 只挑「last_use 很舊」而且沒被 pin 的槽：保護最近的工作集
    while (!free_slots_.empty()) {
        const int32_t s = free_slots_.back();
        if (!slots_[(size_t) s].busy && slots_[(size_t) s].last_use <= stale_before) {
            free_slots_.pop_back();
            return s;
        }
        if (free_slots_.size() * 4 < n_slots_) {
            break;
        }
        free_slots_.pop_back();
    }
    int64_t best = -1;
    uint64_t best_clock = UINT64_MAX;
    for (size_t i = 0; i < n_slots_; i++) {
        const slot_state & st = slots_[i];
        if (st.pin > 0 || st.busy || st.last_use > stale_before) {
            continue;
        }
        if (st.last_use < best_clock) {
            best_clock = st.last_use;
            best = (int64_t) i;
        }
    }
    return (int) best;
}

int pager::acquire(int il, int ie, bool blocking) {
    if (!ready_) {
        return -1;
    }
    std::unique_lock<std::mutex> lk(mu_);
    int32_t s = find_slot_locked(il, ie);
    if (s >= 0 && slots_[s].busy) {
        // 這個槽位**正在被填入**：另一條執行緒剛 admit 它、資料還沒讀完。
        //
        // 這裡一定要等，不能當成命中直接用。否則讀到的是「寫到一半的權重」，
        // 而且那個錯誤是**非決定性**的 —— 每次執行結果可能不同（實測在
        // prefill 時會讓同一個提示詞跑出不同的文字）。
        // busy 的槽位在 admit/victim 那邊本來就會被跳過，所以這裡是唯一
        // 還能拿到它的路徑，也因此必須在這裡等。
        const uint64_t t0 = now_us();
        while (slots_[s].busy) {
            lk.unlock();
            std::this_thread::sleep_for(std::chrono::microseconds(200));
            lk.lock();
            if (now_us() - t0 > (uint64_t) env_size("SDQ_BUSY_WAIT_MS", 30000) * 1000) {
                fprintf(stderr,
                        "[sdq] 致命：等待槽位 il=%d ie=%d 讀入超過 %u ms（busy 一直沒解除）\n",
                        il, ie, (unsigned) env_size("SDQ_BUSY_WAIT_MS", 30000));
                std::fflush(stderr);
                abort();
            }
        }
        // 讀取可能失敗（那時槽位已被清空）→ 重新走一次正常的 miss 路徑
        if (find_slot_locked(il, ie) != s) {
            s = -1;
        }
    }
    if (s >= 0) {
        slots_[s].last_use = ++clock_;
        slots_[s].pin++;
        touch_freq_locked(slots_[(size_t) s]);
        stats_.expert_requests.fetch_add(1, std::memory_order_relaxed);
        stats_.expert_hits.fetch_add(1, std::memory_order_relaxed);
        if (lstats_ && il >= 0 && il < (int) info_.n_layer) {
            lstats_[il].req.fetch_add(1, std::memory_order_relaxed);
            lstats_[il].hit.fetch_add(1, std::memory_order_relaxed);
        }
        // A prefetch that already finished counts as "used" here. Without this
        // the finished-before-request case was invisible and prefetch precision
        // was understated (docs/10 §f).
        if (slots_[s].from_prefetch) {
            slots_[s].from_prefetch = false;
            stats_.prefetch_used.fetch_add(1, std::memory_order_relaxed);
            stats_.prefetch_bytes_used.fetch_add(slot_size_of(s), std::memory_order_relaxed);
        }
        return s;
    }

    stats_.expert_requests.fetch_add(1, std::memory_order_relaxed);
    count_request(il, ie);
    stats_.expert_misses.fetch_add(1, std::memory_order_relaxed);
    if (lstats_ && il >= 0 && il < (int) info_.n_layer) {
        lstats_[il].req.fetch_add(1, std::memory_order_relaxed);
        lstats_[il].miss.fetch_add(1, std::memory_order_relaxed);
    }

    // 先找一個已經在背景讀入這頁的槽
    for (size_t i = 0; i < n_slots_; i++) {
        slot_state & st = slots_[i];
        if (st.busy && st.layer == il && st.expert == ie) {
            // 等背景執行緒讀完
            lk.unlock();
            const uint64_t t0 = now_us();
            while (true) {
                lk.lock();
                if (!slots_[i].busy) {
                    break;
                }
                lk.unlock();
                std::this_thread::sleep_for(std::chrono::microseconds(200));
            }
            s = (int32_t) i;
            lk.unlock();
            stats_.prefetch_wait_us.fetch_add(now_us() - t0, std::memory_order_relaxed);
            std::unique_lock<std::mutex> lk2(mu_);
            slots_[s].last_use = ++clock_;
            slots_[s].pin++;
            stats_.expert_hits.fetch_add(1, std::memory_order_relaxed);
            stats_.prefetch_used.fetch_add(1, std::memory_order_relaxed);
            return s;
        }
    }

    if (!blocking) {
        return -1;
    }

    // 選槽位時必須知道「這個 expert 需要哪一種大小」，否則大 expert 可能被塞進小槽位
    // （寫出邊界 → 靜默算錯），或者小 expert 佔掉大槽位（浪費 6.8% 的 arena）。
    s = admit_slot_locked(il, ie);
    if (s < 0) {
        // 拿不到槽位：短暫等待別的執行緒解除 pin（每層開頭會 unpin_all）。
        // 這裡「重試」而不是直接回 -1，因為回 -1 會讓呼叫端**跳過這個 expert**，
        // 結果是靜默少算一條專家分支 —— 那是最嚴重的錯誤。
        // 2000 次 × 250 µs = 最多等 0.5 秒；真的撐不到那麼久，代表 arena 被
        // 預取的 busy 槽位佔住了（實測：跨層預取開啟時會發生）。
        for (int attempt = 0; attempt < 2000 && s < 0; attempt++) {
            lk.unlock();
            std::this_thread::sleep_for(std::chrono::microseconds(250));
            lk.lock();
            s = admit_slot_locked(il, ie);
        }
        if (s < 0) {
            fprintf(stderr,
                    "[sdq] 嚴重：找不到能放 il=%d ie=%d（need_big=%d）的槽位，"
                    "大槽 %zu／小槽 %zu\n",
                    il, ie, (int) need_big_slot(il, ie), n_big_, n_small_);
            // 這裡「回 -1」會讓呼叫端**跳過這個 expert**，結果是靜默少算一條專家分支
            // —— 那是整個引擎最嚴重的錯誤型態（模型會安靜地給出錯誤答案，而且
            // 每次執行還不一定一樣）。寧可整個行程死掉，也不要產出「看起來正常」
            // 的錯誤結果。
            fprintf(stderr,
                    "[sdq] 致命：無法取得 expert 槽位。為了不給出靜默錯誤的結果，"
                    "直接中止。請回報這個情況（多半是預取把槽位佔成 busy 太久）。\n");
            std::fflush(stderr);
            abort();
        }
    }
    slot_state & st = slots_[s];
    if (st.layer >= 0) {
        page_.erase(((int64_t) st.layer << 20) | (int64_t) st.expert);
        stats_.evictions.fetch_add(1, std::memory_order_relaxed);
        used_.fetch_sub(1, std::memory_order_relaxed);
    }
    st.layer = il;
    st.expert = ie;
    st.last_use = ++clock_;
    st.pin = 1;
    st.is_hot = hot_lookup(il, ie);
    // **先標 busy，再 publish 到 page_。**
    // 順序反過來的話（先 publish 再標 busy 中間有一個窗口）另一條執行緒會把它
    // 當成命中，然後在資料還沒讀完時就拿去做矩陣乘法 → 非決定性的錯誤輸出。
    // acquire() 看到 busy 會等；admit/victim 本來就會跳過 busy 的槽位。
    st.busy = true;
    page_[((int64_t) il << 20) | (int64_t) ie] = s;

    lk.unlock();

    const uint64_t t0 = now_us();
    const bool ok = read_expert_par(il, ie, slot_ptr(s));
    const uint64_t dt = now_us() - t0;

    std::unique_lock<std::mutex> lk2(mu_);
    st.busy = false;
    if (!ok) {
        st.layer = -1;
        st.expert = -1;
        st.pin = 0;
        page_.erase(((int64_t) il << 20) | (int64_t) ie);
        return -1;
    }
    used_.fetch_add(1, std::memory_order_relaxed);
    stats_.demand_wait_us.fetch_add(dt, std::memory_order_relaxed);
    const layer_desc * L = layer(il);
    if (L) {
        const uint64_t nb = (uint64_t) L->experts[(size_t) ie].bytes;
        stats_.ssb_read_bytes_demand.fetch_add(nb, std::memory_order_relaxed);
        if (lstats_ && il >= 0 && il < (int) info_.n_layer) {   // SDQ-0004
            lstats_[il].demand_bytes.fetch_add(nb, std::memory_order_relaxed);
        }
    }
    return s;
}

void pager::prefetch_expert(int il, int ie) {
    if (!ready_ || !cfg_.prefetch) {
        return;
    }
    {
        std::unique_lock<std::mutex> lk(mu_);
        if (find_slot_locked(il, ie) >= 0) {
            stats_.prefetch_duplicate.fetch_add(1, std::memory_order_relaxed);
            return;
        }
        // 預取只能占用「空槽」或「已經很久沒被用到的頁」。
        // （實測：無差別淘汰會讓命中率從 90% 掉到 27%，而預取使用率是 0%，純粹有害。）
        //
        // **槽位一定要選對尺寸**：need_big_slot() 為真的 expert 拿到小槽位會寫出邊界。
        const uint64_t stale_before = clock_ - (uint64_t) env_size("SDQ_PREFETCH_STALE_TOKENS", 2) * 320;
        // 預取佇列也不能太深：SSD 讀不夠快時，排在後面的預取早就過期了
        if (pf_q_.size() > (size_t) env_size("SDQ_PREFETCH_QUEUE", 64)) {
            return;
        }
        // **必須留夠空的槽位給 demand。**
        // 預取會把槽位標成 busy 並佔著它讀取；大槽位本來就少（工作集下限是
        // n_big_layers × n_expert_used），如果預取把最後幾個空槽都吃掉，
        // 下一層的 demand 就會「找不到能放 il=34 ie=114 的槽位」而中止。
        // （這是 verify 新增的預取壓力測試 [2b] 抓到的實際情況。）
        // 所以：對應尺寸的空槽少於 n_expert_used 個時，這一筆預取直接不做。
        const size_t reserve = (size_t) info_.n_expert_used;
        const size_t n_free_right = need_big_slot(il, ie) ? free_big_slots_.size() : free_slots_.size();
        if (n_free_right < reserve) {
            stats_.prefetch_duplicate.fetch_add(1, std::memory_order_relaxed);
            return;
        }
        const int32_t s = victim_stale_for(il, ie, stale_before);
        if (s < 0) {
            return;
        }
        slot_state & st = slots_[s];
        if (st.layer >= 0) {
            page_.erase(((int64_t) st.layer << 20) | (int64_t) st.expert);
            stats_.evictions.fetch_add(1, std::memory_order_relaxed);
            used_.fetch_sub(1, std::memory_order_relaxed);
        }
        st.layer = il;
        st.expert = ie;
        st.busy = true;
        st.pin = 0;
        pf_q_.push_back({s, il, ie, now_us()});
        stats_.prefetch_issued.fetch_add(1, std::memory_order_relaxed);
    }
    pf_cv_.notify_one();
}

void pager::pf_worker() {
    std::unique_lock<std::mutex> lk(pf_mu_);
    for (;;) {
        pf_cv_.wait(lk, [this] { return pf_stop_ || !pf_q_.empty(); });
        if (pf_stop_ && pf_q_.empty()) {
            return;
        }
        const pf_item item = pf_q_.front();
        pf_q_.pop_front();
        lk.unlock();

        // 這個槽可能已經被淘汰/重用 → 檢查 generation
        {
            std::unique_lock<std::mutex> g(mu_);
            const slot_state & st = slots_[item.slot];
            const bool still = st.busy && st.layer == item.il && st.expert == item.ie;
            if (!still) {
                lk.lock();
                continue;
            }
        }

        // 預取也走 I/O 段平行：單獨一條執行緒循序讀三段，是這條路徑唯一的瓶頸。
        const bool ok = cfg_.io_parallel ? read_expert_par(item.il, item.ie, slot_ptr(item.slot))
                                         : read_expert(item.il, item.ie, slot_ptr(item.slot));
        const layer_desc * L = layer(item.il);
        {
            std::unique_lock<std::mutex> g(mu_);
            slot_state & st = slots_[item.slot];
            if (st.busy && st.layer == item.il && st.expert == item.ie) {
                st.busy = false;
                st.last_use = ++clock_;
                if (ok) {
                    page_[((int64_t) item.il << 20) | (int64_t) item.ie] = item.slot;
                    used_.fetch_add(1, std::memory_order_relaxed);
                    stats_.prefetch_completed.fetch_add(1, std::memory_order_relaxed);
                    // SDQ-0004: tag the page so the demand path can credit it,
                    // and so unused prefetched bytes can be reported as waste.
                    st.from_prefetch = true;
                    if (lstats_ && item.il >= 0 && item.il < (int) info_.n_layer) {
                        lstats_[item.il].prefetch_bytes.fetch_add(
                            (uint64_t) slot_size_of(item.slot), std::memory_order_relaxed);
                    }
                    if (L) {
                        stats_.ssb_read_bytes_prefetch.fetch_add(
                            (uint64_t) L->experts[(size_t) item.ie].bytes, std::memory_order_relaxed);
                    }
                } else {
                    st.layer = -1;
                    st.expert = -1;
                }
            }
        }
        lk.lock();
    }
}

void pager::release(int s) {
    if (s < 0 || (size_t) s >= n_slots_) {
        return;
    }
    std::lock_guard<std::mutex> lk(mu_);
    slot_state & st = slots_[(size_t) s];
    if (st.pin > 0) {
        st.pin--;
    }
}

void pager::unpin_all() {
    std::unique_lock<std::mutex> lk(mu_);
    for (auto & st : slots_) {
        st.pin = 0;
    }
}

void pager::note_used(int il, int ie) {
    if (il < 0 || ie < 0) {
        return;
    }
    std::lock_guard<std::mutex> lk(hist_mu_);
    if (il >= (int) cur_used_.size()) {
        return;
    }
    auto & v = cur_used_[(size_t) il];
    if (std::find(v.begin(), v.end(), ie) == v.end()) {
        v.push_back(ie);
    }
}

// 「下一層」預取：只在 layer L 的 op 開頭呼叫一次。
//
// 為什麼需要這個函式：end_token() 是**整個 token 的 40 層都算完之後**才被呼叫的
// （sdq_cli.cpp 在 llama_decode 之後呼叫），所以它排的預取完全沒有和任何計算重疊
// —— 量測上 prefetch 使用率只有 0~8 %，等於整條預取路徑是關的。
// 在 layer L 開始時就替 layer L+1 排預取，才有一整層的時間（讀＋算，約 4 ms）
// 可以讓 SSD 讀取躲在後面。
//
// 預測用的是 prev_used_[il+1]（上一個 token 在該層選的專家）—— MoE 的路由在
// 鄰近 token 之間非常穩定，這是最便宜且不用訓練的預測器。
void pager::prefetch_layer_ahead(int il) {
    if (!ready_ || !cfg_.prefetch || il < 0 || il >= (int) layers_.size()) {
        return;
    }
    int32_t ids[64];
    const int k = (int) info_.n_expert_used;
    if (k <= 0 || k > 64) {
        return;
    }
    predict(il, ids, k);
    for (int i = 0; i < k; i++) {
        prefetch_expert(il, ids[i]);
    }
}

// SDQ-0005: called once per llama_decode() from any front end.
//
// sdq-chat drives this itself from main(); llama-server does not, which left
// tokens at 0, ssd_bytes_per_token undefined and SDQ_STATS_FILE empty. This is
// the single place that closes a decode step, so hook it here.
//
// n_tokens > 1 means prefill: a whole prompt was just paged in. Counting that as
// one "token" would make ssd_bytes_per_token meaningless, and feeding it to the
// Markov table would poison the transition counts with a whole-batch transition.
// So prefill advances the byte accounting by n_tokens but leaves the routing
// history alone -- it is not a single-token transition.
void pager::note_decode(int n_tokens) {
    if (!ready_ || n_tokens <= 0) {
        return;
    }
    stats_.tokens.fetch_add((uint64_t) n_tokens, std::memory_order_relaxed);
    if (n_tokens == 1) {
        end_token();
    }
    dump_stats("decode");
}

void pager::end_token() {
    if (!ready_) {
        return;
    }
    // 用「上個 token 的選擇」更新一階 Markov 轉移表
    {
        std::lock_guard<std::mutex> lk(hist_mu_);
        for (size_t il = 0; il < cur_used_.size(); il++) {
            auto & cur = cur_used_[il];
            if (cur.empty()) {
                continue;
            }
            auto & layer_hist = hist_[il];
            // 以每層最近一次（或平均）的前一次選擇為前驅
            if (!prev_used_.empty() && il < prev_used_.size() && !prev_used_[il].empty()) {
                for (int32_t from : prev_used_[il]) {
                    trans & t = layer_hist[(size_t) from];
                    for (int32_t to : cur) {
                        int found = -1;
                        for (int32_t i = 0; i < t.n; i++) {
                            if (t.to[i] == to) {
                                t.cnt[i]++;
                                found = i;
                                break;
                            }
                        }
                        if (found < 0 && t.n < 64) {
                            t.to[t.n] = to;
                            t.cnt[t.n] = 1;
                            t.n++;
                        }
                    }
                }
            }
            prev_used_[il] = cur;
            cur.clear();
        }
    }

    // 依預測排預取
    if (!cfg_.prefetch) {
        return;
    }
    int32_t ids[64];
    for (size_t il = 0; il < layers_.size(); il++) {
        const int k = (int) info_.n_expert_used;
        predict((int) il, ids, k);
        for (int i = 0; i < k; i++) {
            prefetch_expert((int) il, ids[i]);
        }
    }
}

void pager::predict(int il, int32_t * out, int k) {
    if (il < 0 || il >= (int) prev_used_.size()) {
        for (int i = 0; i < k; i++) {
            out[i] = i;
        }
        return;
    }
    // 以最近一次選擇為前驅；沒有歷史就直接沿用上一次的選擇
    const std::vector<int32_t> & prev = prev_used_[(size_t) il];
    std::vector<std::pair<uint32_t, int32_t>> scored;
    if (prev.empty()) {
        for (int i = 0; i < k; i++) {
            out[i] = i;
        }
        return;
    }
    for (int32_t from : prev) {
        const trans & t = hist_[(size_t) il][(size_t) from];
        for (int32_t i = 0; i < t.n; i++) {
            scored.push_back({t.cnt[i], t.to[i]});
        }
    }
    if (scored.empty()) {
        for (size_t i = 0; i < prev.size() && i < (size_t) k; i++) {
            out[i] = prev[i];
        }
        for (int i = (int) prev.size(); i < k; i++) {
            out[i] = i;
        }
        return;
    }
    std::sort(scored.begin(), scored.end(), [](auto & a, auto & b) {
        return a.first > b.first;
    });
    for (int i = 0; i < k && i < (int) scored.size(); i++) {
        out[i] = scored[(size_t) i].second;
    }
}

double pager::hit_rate() const {
    const uint64_t req = stats_.expert_requests.load();
    const uint64_t hit = stats_.expert_hits.load();
    return req ? (double) hit / (double) req : 0.0;
}

void pager::shutdown() {
    if (!ready_) {
        return;
    }
    if (!profile_out_.empty()) {
        std::lock_guard<std::mutex> lk(mu_);
        dump_profile(profile_out_);
    }
    if (pf_.joinable()) {
        {
            std::unique_lock<std::mutex> lk(pf_mu_);
            pf_stop_ = true;
        }
        pf_cv_.notify_all();
        pf_.join();
    }
    io_pool_stop();
    for (auto & fd : shard_fd_) {
        if (fd >= 0) {
            close(fd);
        }
    }
    for (auto & fd : shard_fd_direct_) {
        if (fd >= 0) {
            close(fd);
        }
    }
    shard_fd_.clear();
    shard_fd_direct_.clear();
    ready_ = false;
}

pager::~pager() {
    shutdown();
    if (arena_) {
        // 注意：arena 是 posix_memalign 配置的，必須用 free() 釋放，
        // 用 munmap() 會破壞 glibc 的 heap（之後任何 free 都會報 double free）。
        free(arena_);
        arena_ = nullptr;
    }
}

static double pct(uint64_t a, uint64_t b) {
    return b ? 100.0 * (double) a / (double) b : 0.0;
}

void pager::print_summary() const {
    if (!ready_) {
        return;
    }
    // bailingmoe3 的第 0 層是 dense FFN（沒有 expert），要抓第一個真的有分頁的層
    const layer_desc * L0 = nullptr;
    int first_moe = -1;
    for (size_t i = 0; i < layers_.size(); i++) {
        if (!layers_[i].experts.empty()) {
            L0 = &layers_[i];
            first_moe = (int) i;
            break;
        }
    }
    if (L0 && !L0->experts.empty()) {
        const expert_desc & e = L0->experts[0];
        fprintf(stderr, "[sdq]   page-map: n_layer=%lld n_expert=%lld n_expert_used=%lld n_embd=%lld n_ff_exp=%lld"
                " | L%dE0 gate=%llu/%llu up=%llu/%llu down=%llu/%llu (off/size) ne0=%lld ne1=%lld d_ne0=%lld d_ne1=%lld\n",
                (long long) info_.n_layer, (long long) info_.n_expert, (long long) info_.n_expert_used,
                (long long) info_.n_embd, (long long) info_.n_ff_exp, first_moe,
                (unsigned long long) e.seg[part::GATE].off, (unsigned long long) e.seg[part::GATE].size,
                (unsigned long long) e.seg[part::UP].off, (unsigned long long) e.seg[part::UP].size,
                (unsigned long long) e.seg[part::DOWN].off, (unsigned long long) e.seg[part::DOWN].size,
                (long long) e.ne0, (long long) e.ne1, (long long) e.down_ne0, (long long) e.down_ne1);
    }
    fprintf(stderr,
            "[sdq] expert pager ready: %s\n"
            "[sdq]   熱門 expert 釘選：%d 個%s\n"
            "[sdq]   slot=%.2f MiB (%zu B)  slots=%zu  arena=%.2f GiB  (RAM 實測 RSS=%.2f GiB)\n",
            cfg_.model_path.c_str(),
            n_hot_, n_hot_ > 0 ? "（永不淘汰）" : "（無剖面，用動態 LRU/LFU）",
            slot_bytes_ / 1048576.0, slot_bytes_, n_slots_, arena_bytes_ / 1073741824.0,
            process_rss_bytes() / 1073741824.0);
    if (n_big_) {
        // 大小兩種槽位：把配置結果講清楚，否則只看「slots=N」會誤以為每個槽一樣大。
        fprintf(stderr,
                "[sdq]   槽位分兩種：小 %.2f MiB × %zu + 大 %.2f MiB × %zu（平均 %.2f MiB）\n",
                small_bytes_ / 1048576.0, n_small_,
                big_bytes_ / 1048576.0, n_big_,
                (double) arena_bytes_ / (double) (n_slots_ ? n_slots_ : 1) / 1048576.0);
    }
    fprintf(stderr,
            "[sdq]   prefetch=%s  fadvise_dontneed=%d\n",
            cfg_.prefetch ? "on" : "off", (int) cfg_.fadvise_dontneed);
}

void pager::dump_stats(const char * phase) {
    if (cfg_.stats_path.empty()) {
        return;
    }
    const uint64_t tokens = stats_.tokens.load();
    const uint64_t req    = stats_.expert_requests.load();
    const uint64_t hit    = stats_.expert_hits.load();
    const uint64_t issued = stats_.prefetch_issued.load();
    const uint64_t used   = stats_.prefetch_used.load();
    const uint64_t b_dem  = stats_.ssb_read_bytes_demand.load();
    const uint64_t b_pre  = stats_.ssb_read_bytes_prefetch.load();
    // SDQ-0004: prefetched bytes that were consumed, so the rest is real waste.
    const uint64_t b_pre_used = stats_.prefetch_bytes_used.load();

    std::ostringstream o;
    o << "{\"phase\":\"" << phase << "\""
      << ",\"tokens\":" << tokens
      << ",\"expert_requests\":" << req
      << ",\"expert_hits\":" << hit
      << ",\"expert_misses\":" << (req - hit)
      << ",\"hit_rate\":" << pct(hit, req)
      << ",\"ssb_demand_bytes\":" << b_dem
      << ",\"ssb_prefetch_bytes\":" << b_pre
      << ",\"ssb_total_bytes\":" << (b_dem + b_pre)
      << ",\"ssd_bytes_per_token\":" << (tokens ? (double) (b_dem + b_pre) / (double) tokens : 0.0)
      << ",\"prefetch_issued\":" << issued
      << ",\"prefetch_used\":" << used
      << ",\"prefetch_used_rate\":" << pct(used, issued)
      // SDQ-0004: prefetch waste. b_pre counts every byte the background reader
      // pulled in; b_pre_used counts the ones a demand request actually hit.
      // The difference is what made docs/07's negative result worse than "slower".
      << ",\"prefetch_bytes_used\":" << b_pre_used
      << ",\"prefetch_bytes_wasted\":" << (b_pre > b_pre_used ? b_pre - b_pre_used : 0)
      << ",\"prefetch_waste_rate\":" << pct(b_pre > b_pre_used ? b_pre - b_pre_used : 0, b_pre)
      << ",\"prefetch_duplicate\":" << stats_.prefetch_duplicate.load()
      << ",\"evictions\":" << stats_.evictions.load()
      << ",\"demand_wait_us\":" << stats_.demand_wait_us.load()
      << ",\"prefetch_wait_us\":" << stats_.prefetch_wait_us.load()
      << ",\"arena_bytes\":" << arena_bytes_
      // 大小兩種槽位之後，「用掉幾個槽位」不能直接乘 slot_bytes_（那是大的那種）。
      // dump_stats 只在階段轉換時呼叫一次，遍歷兩千幾個槽位的成本可以忽略。
      << ",\"arena_used_bytes\":" << used_bytes_snapshot()
      << ",\"rss_bytes\":" << process_rss_bytes()
      << ",\"n_slots\":" << n_slots_
      << ",\"n_small_slots\":" << n_small_
      << ",\"n_big_slots\":" << n_big_
      << ",\"small_slot_bytes\":" << small_bytes_
      << ",\"big_slot_bytes\":" << big_bytes_
      << ",\"slot_bytes\":" << slot_bytes_
      << ",\"n_moe_layers\":" << n_moe_layers_
      << ",\"expert_bytes_total\":" << expert_bytes_total_
      << ",\"arch\":\"" << info_.arch << "\""
      << ",\"name\":\"" << info_.name << "\""
      // SDQ-0004: per-layer breakdown. docs/10 needs this to tell "the device is
      // slow" apart from "one layer keeps evicting everyone else's experts".
      << ",\"per_layer\":[";
    for (int64_t il = 0; lstats_ && il < (int64_t) info_.n_layer; il++) {
        const uint64_t lreq = lstats_[il].req.load();
        const uint64_t lhit = lstats_[il].hit.load();
        o << (il ? "," : "")
          << "{\"layer\":" << il
          << ",\"req\":" << lreq
          << ",\"hit\":" << lhit
          << ",\"miss\":" << (lreq - lhit)
          << ",\"hit_rate\":" << pct(lhit, lreq)
          << ",\"demand_bytes\":" << lstats_[il].demand_bytes.load()
          << ",\"prefetch_bytes\":" << lstats_[il].prefetch_bytes.load()
          << "}";
    }
    o << "]" << "}";
    std::ofstream f(cfg_.stats_path, std::ios::app);
    if (f) {
        f << o.str() << "\n";
    }
}

} // namespace sdq
namespace sdq {
// 由 CLI 呼叫：依環境變數/參數初始化 pager
bool sdq_pager_active_for_layer(int il) {
    pager & P = pager::instance();
    if (!P.ready() || P.layer(il) == nullptr) {
        return false;
    }
    // 除錯用：SDQ_MOE_LO / SDQ_MOE_HI 可以只讓部分層走分頁路徑，
    // 用來二分搜尋第一個發生數值差異的層。
    static const int lo = [] { const char * e = getenv("SDQ_MOE_LO"); return e ? atoi(e) : 0; }();
    static const int hi = [] { const char * e = getenv("SDQ_MOE_HI"); return e ? atoi(e) : 1 << 30; }();
    if (il < lo || il >= hi) {
        return false;
    }
    // SDQ_MOE_SKIP=3,7：這些層即使在 [lo,hi) 內也走原生路徑。
    // 搭配 SDQ_BUFT_KEEP（讓該層的 expert 留在 RAM）就能做同一層的 A/B 比對。
    static const std::set<int> skip = [] {
        std::set<int> s;
        const char * e = getenv("SDQ_MOE_SKIP");
        if (e) {
            for (const char * p = e; *p; ) {
                const char * q = strchr(p, ',');
                const size_t n = q ? (size_t) (q - p) : strlen(p);
                if (n) {
                    s.insert(atoi(std::string(p, n).c_str()));
                }
                if (!q) {
                    break;
                }
                p = q + 1;
            }
        }
        return s;
    }();
    return skip.find(il) == skip.end();
}

void sdq_on_decode(int n_tokens) {
    auto & inst = pager::instance();
    if (inst.ready()) {
        inst.note_decode(n_tokens);
    }
}

bool sdq_init(const std::string & model_path) {
    config c;
    c.model_path = model_path;
    c.arena_bytes = env_size("SDQ_ARENA_MB", 0) << 20;
    c.ram_budget_bytes = env_size("SDQ_RAM_BUDGET_MB", 0) << 20;
    c.slot_bytes = env_size("SDQ_SLOT_KB", 0) << 10;
    // 預設關閉。docs/07 的實測是「開 5.68 / 關 6.20 tok/s」的負結果，而且
    // 預取中的槽位會讓 acquire() 靜默跳過 expert，曾造成輸出不一致（stage S7）。
    // 這裡原本是 true，等於讓那個負結果從來沒有真正生效。
    c.prefetch = env_flag("SDQ_PREFETCH", false);
    // ---- OPT-1：熱門 expert 剖面（Strata 式）----
    if (const char * pi = getenv("SDQ_PROFILE_IN"))  { c.profile_in  = pi; }
    if (const char * po = getenv("SDQ_PROFILE_OUT")) { c.profile_out = po; }
    c.hot_pin = (int) env_size("SDQ_HOT_PIN", 0);
    c.admit_min_hits = (int) env_size("SDQ_ADMIT_MIN_HITS", 1);
    c.fadvise_dontneed = env_flag("SDQ_FADV_DONTNEED", true);
    c.io_parallel = env_flag("SDQ_IO_PARALLEL", true);
    // I/O 讀取執行緒數。sdq-chat 會在載入模型「之前」自己 setenv 這個值
    // （3 × 計算執行緒，上限 48，docs/05 量到序列 3.79 → 24 條 6.68 tok/s）。
    // 但 cfg_.n_threads 從來沒有被賦值，所以任何沒有自行設定的前端
    // （llama-server 就是）會拿到 0，然後在 io_pool_start() 被夾到下限 4 條 ——
    // 等於一直跑在「序列讀」那一端。改成在 pager 自己算，量測與 sdq-chat 一致。
    if (!getenv("SDQ_IO_THREADS")) {
        const int hw = (int) std::max(1u, std::thread::hardware_concurrency());
        const int compute = hw < 8 ? hw : 8;      // 與 sdq-chat 同一個上限 8
        c.io_threads = compute * 3;
    } else {
        c.io_threads = (int) env_size("SDQ_IO_THREADS", 0);
    }
    c.io_split = (int) env_size("SDQ_IO_SPLIT", 1);
    c.verbose = env_flag("SDQ_VERBOSE", false);
    const char * sp = getenv("SDQ_STATS_FILE");
    if (sp) {
        c.stats_path = sp;
    }
    sdq_set_config(c);
    try {
        pager::instance().init(c, nullptr);
    } catch (const std::exception & e) {
        fprintf(stderr, "[sdq] pager 初始化失敗：%s\n", e.what());
        return false;
    }
    // 長駐前端（llama-server）不會呼叫 sdq_shutdown()；用 atexit 在收尾時補上摘要。
    // 註冊時機必須在 pager::instance() 建構之後：static 物件的析構與 atexit 依
    // 「後註冊者先執行」，反過來就會在 pager 已經析構後才跑這段（讀到已釋放的物件）。
    static std::once_flag once_exit;
    std::call_once(once_exit, [] { std::atexit([] { sdq_shutdown(); }); });
    return pager::instance().ready();
}


// ---------------------------------------------------------------- 除錯：dump 任意 F32 張量
// 目的：讓「原生 mul_mat_id 路徑」與「SDQ 分頁路徑」輸出同一層的 MoE 結果，
// 兩邊各自 dump 一次就能直接 A/B 對照，判定到底是哪一邊算錯。
namespace {
struct dump_arg { std::string path; };

void dump_op(ggml_tensor * dst, int ith, int /*nth*/, void * userdata) {
    if (ith != 0) {
        return;
    }
    const ggml_tensor * src = dst->src[0];
    dump_arg * a = (dump_arg *) userdata;
    FILE * f = fopen(a->path.c_str(), "wb");
    if (!f) {
        fprintf(stderr, "[sdq-dump] 無法開啟 %s\n", a->path.c_str());
        return;
    }
    const int64_t meta[6] = { src->ne[0], src->ne[1], src->ne[2], src->ne[3],
                              (int64_t) ggml_nbytes(src), 0 };
    fwrite(meta, sizeof(int64_t), 6, f);
    // 只支援 F32 contiguous；其他情況就明確報錯，不要假設
    if (src->type != GGML_TYPE_F32 || !ggml_is_contiguous(src)) {
        fprintf(stderr, "[sdq-dump] %s 不是 contiguous F32（type=%d）\n",
                a->path.c_str(), (int) src->type);
    } else {
        fwrite(src->data, sizeof(float), (size_t) (ggml_nelements(src)), f);
    }
    fclose(f);
    fprintf(stderr, "[sdq-dump] %s (%lld 個元素)\n", a->path.c_str(),
            (long long) ggml_nelements(src));
}
} // namespace

ggml_tensor * sdq_maybe_dump_tensor(ggml_context * ctx, ggml_tensor * t, int il) {
    const char * prefix = getenv("SDQ_MOE_OUT_DUMP");
    if (!prefix || !t) {
        return t;
    }
    int layer = getenv("SDQ_MOE_OUT_DUMP_LAYER") ? atoi(getenv("SDQ_MOE_OUT_DUMP_LAYER")) : -1;
    if (layer >= 0 && il != layer) {
        return t;
    }
    dump_arg * a = new dump_arg{ std::string(prefix) + "." + std::to_string(il) };
    return ggml_custom_4d(ctx, GGML_TYPE_F32, t->ne[0], t->ne[1], t->ne[2], t->ne[3],
                          &t, 1, dump_op, 1, a);
}

void sdq_shutdown() {
    pager & P = pager::instance();
    sdq_print_time_profile();
    P.dump_stats("shutdown");
    P.print_summary();
    P.shutdown();
}
} // namespace sdq