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qwen3.8-27b-quant-challenge

// llama_kld.cpp -- scores a GGUF model run by llama.cpp against a sparse radiance KL reference
// (radiance --kld-record DIR --kld-top N, format radiance-kld-top-1), with the same arithmetic as
// radiance's --kld-ref (core/kld.cpp, Kld::score_row_top), so a llama.cpp quant and a radiance one
// are measured against one reference in one way.
//
// The reference's own tokens are fed (tokens.i32), never a re-tokenisation, one document at a time
// from an empty cache. Every scored position's logits come back from llama.cpp in f32; the
// candidate's distribution is its softmax over its whole row, and the KL is that of both
// distributions coarsened to the reference's N tokens and one bucket for every other token:
//
//   KL = sum_{i in head} p_i (log p_i - log q_i) + p_tail (log p_tail - log q_tail)
//
// with p renormalised over head and tail (S = sum p; KL = A / S - log S), q_tail summed over the
// candidate's own row outside the head.
//
//   llama_kld --model M.gguf --ref DIR --out REPORT.json [--ctx N] [--batch N] [--ubatch N]
//             [--kv bf16|f16] [--split layer|row|none] [--threads N]
#include "llama.h"

#include <algorithm>
#include <atomic>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <ctime>
#include <fstream>
#include <limits>
#include <map>
#include <sstream>
#include <string>
#include <thread>
#include <vector>

#include <fcntl.h>
#include <sys/stat.h>
#include <unistd.h>

namespace {

struct TopEntry { int32_t id; float lp; };

struct Doc {
    std::string source;
    int64_t off = 0, n = 0, score_from = 0, row0 = 0;
    int64_t rows() const { return n - 1 - score_from; }
};

// A minimal reader for the fields of kld.json this tool needs. The manifest is written by
// radiance's nlohmann::json dump; values are numbers or strings without escapes beyond \".
struct Manifest {
    std::string format, model;
    int64_t n_vocab = 0, rows = 0;
    int top = 0;
    std::vector<Doc> docs;
};

std::string read_all(const std::string& p) {
    std::ifstream in(p, std::ios::binary);
    std::stringstream ss;
    ss << in.rdbuf();
    return ss.str();
}

std::string str_after(const std::string& s, size_t& at, const char* key) {
    const std::string k = std::string("\"") + key + "\"";
    size_t p = s.find(k, at);
    if (p == std::string::npos) return {};
    p = s.find(':', p + k.size());
    p = s.find('"', p);
    std::string out;
    for (size_t i = p + 1; i < s.size(); ++i) {
        if (s[i] == '\\' && i + 1 < s.size()) { out += s[++i]; continue; }
        if (s[i] == '"') { at = i + 1; return out; }
        out += s[i];
    }
    return out;
}

int64_t num_after(const std::string& s, size_t& at, const char* key) {
    const std::string k = std::string("\"") + key + "\"";
    size_t p = s.find(k, at);
    if (p == std::string::npos) return -1;
    p = s.find(':', p + k.size()) + 1;
    while (p < s.size() && (s[p] == ' ' || s[p] == '\n')) ++p;
    char* end = nullptr;
    const long long v = std::strtoll(s.c_str() + p, &end, 10);
    at = (size_t)(end - s.c_str());
    return v;
}

bool load_manifest(const std::string& dir, Manifest& m) {
    const std::string s = read_all(dir + "/kld.json");
    if (s.empty()) return false;
    size_t at = 0;
    m.format = str_after(s, at, "format");
    at = 0; m.model = str_after(s, at, "model");
    at = 0; m.n_vocab = num_after(s, at, "n_vocab");
    at = 0; m.top = (int)num_after(s, at, "top");
    at = 0; m.rows = num_after(s, at, "rows");
    size_t p = s.find("\"docs\"");
    if (p == std::string::npos) return false;
    int64_t off = 0;
    while (true) {
        const size_t q = s.find('{', p);
        if (q == std::string::npos) break;
        size_t at2 = q;
        Doc d;
        d.source = str_after(s, at2, "source");
        d.n = num_after(s, at2, "tokens");
        d.score_from = num_after(s, at2, "score_from");
        d.row0 = num_after(s, at2, "row0");
        d.off = off;
        off += d.n;
        m.docs.push_back(d);
        p = s.find('}', at2);
        if (p == std::string::npos) break;
    }
    return m.format == "radiance-kld-top-1" && m.top > 0 && m.n_vocab > 0 && !m.docs.empty();
}

bool pread_all(int fd, void* p, size_t n, int64_t off) {
    auto* b = static_cast<char*>(p);
    while (n) {
        const ssize_t r = ::pread(fd, b, n, (off_t)off);
        if (r <= 0) return false;
        b += r; n -= (size_t)r; off += r;
    }
    return true;
}

double quantile(const std::vector<float>& v, double q) {
    if (v.empty()) return 0;
    const double x = q * (double)(v.size() - 1);
    const size_t i = (size_t)x;
    if (i + 1 >= v.size()) return v.back();
    return v[i] + (x - (double)i) * (v[i + 1] - v[i]);
}

std::string summary(std::vector<float> kl, int64_t t1, double nq, double np) {
    char buf[1024];
    const size_t n = kl.size();
    if (!n) return "{}";
    std::sort(kl.begin(), kl.end());
    double sum = 0;
    for (float v : kl) sum += v;
    std::snprintf(buf, sizeof buf,
                  "{\"positions\": %zu, \"kld\": {\"mean\": %.9g, \"median\": %.9g, \"p90\": %.9g, "
                  "\"p95\": %.9g, \"p99\": %.9g, \"p99_9\": %.9g, \"p99_99\": %.9g, \"max\": %.9g}, "
                  "\"top1_agreement\": %.9g, \"ppl\": {\"candidate\": %.9g, \"reference\": %.9g, "
                  "\"ratio\": %.9g}}",
                  n, sum / n, quantile(kl, 0.5), quantile(kl, 0.9), quantile(kl, 0.95),
                  quantile(kl, 0.99), quantile(kl, 0.999), quantile(kl, 0.9999), (double)kl.back(),
                  (double)t1 / n, std::exp(nq / n), std::exp(np / n), std::exp((nq - np) / n));
    return buf;
}

std::string jesc(const std::string& s) {
    std::string o;
    for (char c : s) { if (c == '"' || c == '\\') o += '\\'; o += c; }
    return o;
}

}  // namespace

int main(int argc, char** argv) {
    std::string model, ref, out, kv = "bf16", split = "layer";
    int64_t n_ctx = 0;
    int n_batch = 2048, n_ubatch = 512, n_threads = (int)std::max(1u, std::thread::hardware_concurrency() - 2);
    for (int i = 1; i < argc; ++i) {
        const std::string a = argv[i];
        auto next = [&]() -> const char* { if (i + 1 >= argc) { fprintf(stderr, "%s needs a value\n", a.c_str()); exit(2); } return argv[++i]; };
        if (a == "--model") model = next();
        else if (a == "--ref") ref = next();
        else if (a == "--out") out = next();
        else if (a == "--ctx") n_ctx = std::atoll(next());
        else if (a == "--batch") n_batch = std::atoi(next());
        else if (a == "--ubatch") n_ubatch = std::atoi(next());
        else if (a == "--kv") kv = next();
        else if (a == "--split") split = next();
        else if (a == "--threads") n_threads = std::atoi(next());
        else { fprintf(stderr, "unknown argument %s\n", a.c_str()); return 2; }
    }
    if (model.empty() || ref.empty() || out.empty()) {
        fprintf(stderr, "usage: llama_kld --model M.gguf --ref DIR --out REPORT.json [--ctx N] "
                        "[--batch N] [--ubatch N] [--kv bf16|f16] [--split layer|row|none] [--threads N]\n");
        return 2;
    }

    Manifest man;
    if (!load_manifest(ref, man)) {
        fprintf(stderr, "llama_kld: %s/kld.json is not a radiance-kld-top-1 reference\n", ref.c_str());
        return 1;
    }
    int64_t n_tok = 0, longest = 0, rows = 0;
    for (const Doc& d : man.docs) { n_tok += d.n; longest = std::max(longest, d.n); rows += d.rows(); }
    if (rows != man.rows) { fprintf(stderr, "llama_kld: documents add up to %lld rows, manifest %lld\n", (long long)rows, (long long)man.rows); return 1; }
    std::vector<int32_t> tokens((size_t)n_tok);
    const int fd_tok = ::open((ref + "/tokens.i32").c_str(), O_RDONLY);
    const int fd_top = ::open((ref + "/top.bin").c_str(), O_RDONLY);
    const int fd_rows = ::open((ref + "/rows.f32").c_str(), O_RDONLY);
    if (fd_tok < 0 || fd_top < 0 || fd_rows < 0 || !pread_all(fd_tok, tokens.data(), (size_t)n_tok * 4, 0)) {
        fprintf(stderr, "llama_kld: cannot read the reference files in %s\n", ref.c_str());
        return 1;
    }
    const int top = man.top;
    if (n_ctx <= 0) n_ctx = longest + 16;

    llama_backend_init();
    llama_model_params mp = llama_model_default_params();
    mp.n_gpu_layers = 999;
    mp.split_mode = split == "row" ? LLAMA_SPLIT_MODE_ROW : split == "none" ? LLAMA_SPLIT_MODE_NONE : LLAMA_SPLIT_MODE_LAYER;
    llama_model* lm = llama_model_load_from_file(model.c_str(), mp);
    if (!lm) { fprintf(stderr, "llama_kld: cannot load %s\n", model.c_str()); return 1; }
    const llama_vocab* vocab = llama_model_get_vocab(lm);
    const int64_t nv = llama_vocab_n_tokens(vocab);
    llama_context_params cp = llama_context_default_params();
    cp.n_ctx = (uint32_t)n_ctx;
    cp.n_batch = (uint32_t)n_batch;
    cp.n_ubatch = (uint32_t)n_ubatch;
    cp.n_seq_max = 1;
    cp.type_k = cp.type_v = kv == "f16" ? GGML_TYPE_F16 : GGML_TYPE_BF16;
    cp.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
    cp.n_threads = cp.n_threads_batch = n_threads;
    cp.no_perf = true;
    llama_context* ctx = llama_init_from_model(lm, cp);
    if (!ctx) { fprintf(stderr, "llama_kld: cannot create a %lld-token context\n", (long long)n_ctx); return 1; }
    fprintf(stderr, "llama_kld: %s, vocab %lld (reference %lld), %zu documents, %lld positions, top-%d, "
                    "ctx %lld, kv %s\n", model.c_str(), (long long)nv, (long long)man.n_vocab,
            man.docs.size(), (long long)rows, top, (long long)n_ctx, kv.c_str());
    for (const Doc& d : man.docs)
        for (int64_t i = 0; i < d.n; ++i)
            if (tokens[(size_t)(d.off + i)] >= nv) {
                fprintf(stderr, "llama_kld: token %d is outside the model's %lld-token vocabulary\n",
                        tokens[(size_t)(d.off + i)], (long long)nv);
                return 1;
            }

    std::vector<float> kl((size_t)rows), nq((size_t)rows), np((size_t)rows);
    std::vector<uint8_t> t1((size_t)rows);
    std::atomic<int> io_err{0};
    llama_batch batch = llama_batch_init(n_batch, 0, 1);
    const time_t t0 = time(nullptr);
    int64_t done = 0;

    for (size_t di = 0; di < man.docs.size(); ++di) {
        const Doc& d = man.docs[di];
        llama_memory_clear(llama_get_memory(ctx), true);
        for (int64_t p0 = 0; p0 < d.n; p0 += n_batch) {
            const int64_t m = std::min<int64_t>(n_batch, d.n - p0);
            batch.n_tokens = (int32_t)m;
            std::vector<int64_t> scored;     // batch index -> document position
            for (int64_t j = 0; j < m; ++j) {
                const int64_t p = p0 + j;
                batch.token[j] = tokens[(size_t)(d.off + p)];
                batch.pos[j] = (llama_pos)p;
                batch.n_seq_id[j] = 1;
                batch.seq_id[j][0] = 0;
                const bool s = p >= d.score_from && p < d.n - 1;
                batch.logits[j] = s;
                if (s) scored.push_back(j);
            }
            if (llama_decode(ctx, batch) != 0) {
                fprintf(stderr, "llama_kld: llama_decode failed in document %zu at %lld\n", di, (long long)p0);
                return 1;
            }
            // The scored rows of this batch, split over threads.
            std::atomic<size_t> next{0};
            auto work = [&] {
                std::vector<TopEntry> head((size_t)top);
                std::vector<uint8_t> mark((size_t)nv, 0);
                float rr[3];
                for (size_t k; (k = next.fetch_add(1)) < scored.size();) {
                    const int64_t j = scored[k], p = p0 + j, r = d.row0 + p - d.score_from;
                    const float* l = llama_get_logits_ith(ctx, (int32_t)j);
                    if (!l || !pread_all(fd_top, head.data(), (size_t)top * sizeof(TopEntry), r * (int64_t)top * (int64_t)sizeof(TopEntry)) ||
                        !pread_all(fd_rows, rr, sizeof rr, r * (int64_t)sizeof rr)) { io_err = 1; continue; }
                    float mx = -std::numeric_limits<float>::infinity();
                    int64_t arg = 0;
                    for (int64_t i = 0; i < nv; ++i) if (l[i] > mx) { mx = l[i]; arg = i; }
                    double sum = 0;
                    for (int64_t i = 0; i < nv; ++i) sum += std::exp((double)(l[i] - mx));
                    const double lse = (double)mx + std::log(sum);
                    double S = 0, A = 0;
                    for (int t = 0; t < top; ++t) {
                        const int32_t id = head[(size_t)t].id;
                        const double lp = head[(size_t)t].lp;
                        const double lq = id < nv ? (double)l[id] - lse : -std::numeric_limits<double>::infinity();
                        if (id < nv) mark[(size_t)id] = 1;
                        const double pp = std::exp(lp);
                        if (!(pp > 0)) continue;
                        S += pp;
                        A += pp * (lp - lq);
                    }
                    double q_tail = 0;
                    for (int64_t i = 0; i < nv; ++i) if (!mark[(size_t)i]) q_tail += std::exp((double)l[i] - lse);
                    for (int t = 0; t < top; ++t) if (head[(size_t)t].id < nv) mark[(size_t)head[(size_t)t].id] = 0;
                    const double p_tail = rr[2];
                    if (p_tail > 0) {
                        S += p_tail;
                        A += p_tail * (std::log(p_tail) - std::log(std::max(q_tail, std::numeric_limits<double>::min())));
                    }
                    const int32_t nxt = tokens[(size_t)(d.off + p + 1)];
                    kl[(size_t)r] = (float)(A / S - std::log(S));
                    t1[(size_t)r] = arg == (int64_t)head[0].id;
                    nq[(size_t)r] = (float)(lse - (double)l[nxt]);
                    np[(size_t)r] = rr[0];
                }
            };
            const int nt = (int)std::min<size_t>((size_t)n_threads, std::max<size_t>(1, scored.size()));
            std::vector<std::thread> th;
            for (int t = 0; t < nt; ++t) th.emplace_back(work);
            for (auto& t : th) t.join();
            if (io_err) { fprintf(stderr, "llama_kld: reading the reference failed\n"); return 1; }
            done += (int64_t)scored.size();
        }
        if (di % 50 == 0 || di + 1 == man.docs.size())
            fprintf(stderr, "llama_kld: %zu / %zu documents, %lld / %lld positions, %lld s\n", di + 1,
                    man.docs.size(), (long long)done, (long long)rows, (long long)(time(nullptr) - t0));
    }

    // The report, in radiance's --kld-out layout.
    auto group = [&](const std::vector<size_t>& ds) {
        std::vector<float> v;
        int64_t a = 0;
        double q = 0, p = 0;
        for (size_t di : ds) {
            const Doc& d = man.docs[di];
            for (int64_t r = d.row0; r < d.row0 + d.rows(); ++r) {
                v.push_back(kl[(size_t)r]); a += t1[(size_t)r]; q += nq[(size_t)r]; p += np[(size_t)r];
            }
        }
        return summary(std::move(v), a, q, p);
    };
    std::vector<size_t> all(man.docs.size());
    std::map<std::string, std::vector<size_t>> by;
    for (size_t i = 0; i < man.docs.size(); ++i) { all[i] = i; by[man.docs[i].source].push_back(i); }
    std::ofstream o(out);
    char tb[32];
    const time_t now = time(nullptr);
    std::strftime(tb, sizeof tb, "%Y-%m-%dT%H:%M:%SZ", gmtime(&now));
    o << "{\n \"model\": \"" << jesc(model) << "\",\n \"engine\": \"llama.cpp\",\n \"reference\": \""
      << jesc(man.model) << "\",\n \"ref_dir\": \"" << jesc(ref) << "\",\n \"ref_form\": \"top-" << top
      << " + tail\",\n \"kv\": \"" << kv << "\",\n \"created\": \"" << tb << "\",\n \"docs\": "
      << man.docs.size() << ",\n \"all\": " << group(all) << ",\n \"by_source\": {";
    bool first = true;
    for (const auto& [name, ds] : by) {
        o << (first ? "\n" : ",\n") << "  \"" << jesc(name) << "\": " << group(ds);
        first = false;
    }
    o << "\n }\n}\n";
    o.close();
    std::vector<float> rr((size_t)rows * 4);
    for (int64_t r = 0; r < rows; ++r) {
        rr[(size_t)r * 4] = kl[(size_t)r]; rr[(size_t)r * 4 + 1] = nq[(size_t)r];
        rr[(size_t)r * 4 + 2] = np[(size_t)r]; rr[(size_t)r * 4 + 3] = t1[(size_t)r];
    }
    std::ofstream orow(out + ".rows", std::ios::binary);
    orow.write(reinterpret_cast<const char*>(rr.data()), (std::streamsize)(rr.size() * sizeof(float)));
    fprintf(stderr, "llama_kld: report in %s: %s\n", out.c_str(), group(all).c_str());
    llama_batch_free(batch);
    llama_free(ctx);
    llama_model_free(lm);
    return 0;
}

	// llama_kld.cpp -- scores a GGUF model run by llama.cpp against a sparse radiance KL reference
	// (radiance --kld-record DIR --kld-top N, format radiance-kld-top-1), with the same arithmetic as
	// radiance's --kld-ref (core/kld.cpp, Kld::score_row_top), so a llama.cpp quant and a radiance one
	// are measured against one reference in one way.
	//
	// The reference's own tokens are fed (tokens.i32), never a re-tokenisation, one document at a time
	// from an empty cache. Every scored position's logits come back from llama.cpp in f32; the
	// candidate's distribution is its softmax over its whole row, and the KL is that of both
	// distributions coarsened to the reference's N tokens and one bucket for every other token:
	//
	// KL = sum_{i in head} p_i (log p_i - log q_i) + p_tail (log p_tail - log q_tail)
	//
	// with p renormalised over head and tail (S = sum p; KL = A / S - log S), q_tail summed over the
	// candidate's own row outside the head.
	//
	// llama_kld --model M.gguf --ref DIR --out REPORT.json [--ctx N] [--batch N] [--ubatch N]
	// [--kv bf16\|f16] [--split layer\|row\|none] [--threads N]
	#include "llama.h"

	#include <algorithm>
	#include <atomic>
	#include <cmath>
	#include <cstdint>
	#include <cstdio>
	#include <cstdlib>
	#include <cstring>
	#include <ctime>
	#include <fstream>
	#include <limits>
	#include <map>
	#include <sstream>
	#include <string>
	#include <thread>
	#include <vector>

	#include <fcntl.h>
	#include <sys/stat.h>
	#include <unistd.h>

	namespace {

	struct TopEntry { int32_t id; float lp; };

	struct Doc {
	std::string source;
	int64_t off = 0, n = 0, score_from = 0, row0 = 0;
	int64_t rows() const { return n - 1 - score_from; }
	};

	// A minimal reader for the fields of kld.json this tool needs. The manifest is written by
	// radiance's nlohmann::json dump; values are numbers or strings without escapes beyond \".
	struct Manifest {
	std::string format, model;
	int64_t n_vocab = 0, rows = 0;
	int top = 0;
	std::vector<Doc> docs;
	};

	std::string read_all(const std::string& p) {
	std::ifstream in(p, std::ios::binary);
	std::stringstream ss;
	ss << in.rdbuf();
	return ss.str();
	}

	std::string str_after(const std::string& s, size_t& at, const char* key) {
	const std::string k = std::string("\"") + key + "\"";
	size_t p = s.find(k, at);
	if (p == std::string::npos) return {};
	p = s.find(':', p + k.size());
	p = s.find('"', p);
	std::string out;
	for (size_t i = p + 1; i < s.size(); ++i) {
	if (s[i] == '\\' && i + 1 < s.size()) { out += s[++i]; continue; }
	if (s[i] == '"') { at = i + 1; return out; }
	out += s[i];
	}
	return out;
	}

	int64_t num_after(const std::string& s, size_t& at, const char* key) {
	const std::string k = std::string("\"") + key + "\"";
	size_t p = s.find(k, at);
	if (p == std::string::npos) return -1;
	p = s.find(':', p + k.size()) + 1;
	while (p < s.size() && (s[p] == ' ' \|\| s[p] == '\n')) ++p;
	char* end = nullptr;
	const long long v = std::strtoll(s.c_str() + p, &end, 10);
	at = (size_t)(end - s.c_str());
	return v;
	}

	bool load_manifest(const std::string& dir, Manifest& m) {
	const std::string s = read_all(dir + "/kld.json");
	if (s.empty()) return false;
	size_t at = 0;
	m.format = str_after(s, at, "format");
	at = 0; m.model = str_after(s, at, "model");
	at = 0; m.n_vocab = num_after(s, at, "n_vocab");
	at = 0; m.top = (int)num_after(s, at, "top");
	at = 0; m.rows = num_after(s, at, "rows");
	size_t p = s.find("\"docs\"");
	if (p == std::string::npos) return false;
	int64_t off = 0;
	while (true) {
	const size_t q = s.find('{', p);
	if (q == std::string::npos) break;
	size_t at2 = q;
	Doc d;
	d.source = str_after(s, at2, "source");
	d.n = num_after(s, at2, "tokens");
	d.score_from = num_after(s, at2, "score_from");
	d.row0 = num_after(s, at2, "row0");
	d.off = off;
	off += d.n;
	m.docs.push_back(d);
	p = s.find('}', at2);
	if (p == std::string::npos) break;
	}
	return m.format == "radiance-kld-top-1" && m.top > 0 && m.n_vocab > 0 && !m.docs.empty();
	}

	bool pread_all(int fd, void* p, size_t n, int64_t off) {
	auto* b = static_cast<char*>(p);
	while (n) {
	const ssize_t r = ::pread(fd, b, n, (off_t)off);
	if (r <= 0) return false;
	b += r; n -= (size_t)r; off += r;
	}
	return true;
	}

	double quantile(const std::vector<float>& v, double q) {
	if (v.empty()) return 0;
	const double x = q * (double)(v.size() - 1);
	const size_t i = (size_t)x;
	if (i + 1 >= v.size()) return v.back();
	return v[i] + (x - (double)i) * (v[i + 1] - v[i]);
	}

	std::string summary(std::vector<float> kl, int64_t t1, double nq, double np) {
	char buf[1024];
	const size_t n = kl.size();
	if (!n) return "{}";
	std::sort(kl.begin(), kl.end());
	double sum = 0;
	for (float v : kl) sum += v;
	std::snprintf(buf, sizeof buf,
	"{\"positions\": %zu, \"kld\": {\"mean\": %.9g, \"median\": %.9g, \"p90\": %.9g, "
	"\"p95\": %.9g, \"p99\": %.9g, \"p99_9\": %.9g, \"p99_99\": %.9g, \"max\": %.9g}, "
	"\"top1_agreement\": %.9g, \"ppl\": {\"candidate\": %.9g, \"reference\": %.9g, "
	"\"ratio\": %.9g}}",
	n, sum / n, quantile(kl, 0.5), quantile(kl, 0.9), quantile(kl, 0.95),
	quantile(kl, 0.99), quantile(kl, 0.999), quantile(kl, 0.9999), (double)kl.back(),
	(double)t1 / n, std::exp(nq / n), std::exp(np / n), std::exp((nq - np) / n));
	return buf;
	}

	std::string jesc(const std::string& s) {
	std::string o;
	for (char c : s) { if (c == '"' \|\| c == '\\') o += '\\'; o += c; }
	return o;
	}

	} // namespace

	int main(int argc, char** argv) {
	std::string model, ref, out, kv = "bf16", split = "layer";
	int64_t n_ctx = 0;
	int n_batch = 2048, n_ubatch = 512, n_threads = (int)std::max(1u, std::thread::hardware_concurrency() - 2);
	for (int i = 1; i < argc; ++i) {
	const std::string a = argv[i];
	auto next = [&]() -> const char* { if (i + 1 >= argc) { fprintf(stderr, "%s needs a value\n", a.c_str()); exit(2); } return argv[++i]; };
	if (a == "--model") model = next();
	else if (a == "--ref") ref = next();
	else if (a == "--out") out = next();
	else if (a == "--ctx") n_ctx = std::atoll(next());
	else if (a == "--batch") n_batch = std::atoi(next());
	else if (a == "--ubatch") n_ubatch = std::atoi(next());
	else if (a == "--kv") kv = next();
	else if (a == "--split") split = next();
	else if (a == "--threads") n_threads = std::atoi(next());
	else { fprintf(stderr, "unknown argument %s\n", a.c_str()); return 2; }
	}
	if (model.empty() \|\| ref.empty() \|\| out.empty()) {
	fprintf(stderr, "usage: llama_kld --model M.gguf --ref DIR --out REPORT.json [--ctx N] "
	"[--batch N] [--ubatch N] [--kv bf16\|f16] [--split layer\|row\|none] [--threads N]\n");
	return 2;
	}

	Manifest man;
	if (!load_manifest(ref, man)) {
	fprintf(stderr, "llama_kld: %s/kld.json is not a radiance-kld-top-1 reference\n", ref.c_str());
	return 1;
	}
	int64_t n_tok = 0, longest = 0, rows = 0;
	for (const Doc& d : man.docs) { n_tok += d.n; longest = std::max(longest, d.n); rows += d.rows(); }
	if (rows != man.rows) { fprintf(stderr, "llama_kld: documents add up to %lld rows, manifest %lld\n", (long long)rows, (long long)man.rows); return 1; }
	std::vector<int32_t> tokens((size_t)n_tok);
	const int fd_tok = ::open((ref + "/tokens.i32").c_str(), O_RDONLY);
	const int fd_top = ::open((ref + "/top.bin").c_str(), O_RDONLY);
	const int fd_rows = ::open((ref + "/rows.f32").c_str(), O_RDONLY);
	if (fd_tok < 0 \|\| fd_top < 0 \|\| fd_rows < 0 \|\| !pread_all(fd_tok, tokens.data(), (size_t)n_tok * 4, 0)) {
	fprintf(stderr, "llama_kld: cannot read the reference files in %s\n", ref.c_str());
	return 1;
	}
	const int top = man.top;
	if (n_ctx <= 0) n_ctx = longest + 16;

	llama_backend_init();
	llama_model_params mp = llama_model_default_params();
	mp.n_gpu_layers = 999;
	mp.split_mode = split == "row" ? LLAMA_SPLIT_MODE_ROW : split == "none" ? LLAMA_SPLIT_MODE_NONE : LLAMA_SPLIT_MODE_LAYER;
	llama_model* lm = llama_model_load_from_file(model.c_str(), mp);
	if (!lm) { fprintf(stderr, "llama_kld: cannot load %s\n", model.c_str()); return 1; }
	const llama_vocab* vocab = llama_model_get_vocab(lm);
	const int64_t nv = llama_vocab_n_tokens(vocab);
	llama_context_params cp = llama_context_default_params();
	cp.n_ctx = (uint32_t)n_ctx;
	cp.n_batch = (uint32_t)n_batch;
	cp.n_ubatch = (uint32_t)n_ubatch;
	cp.n_seq_max = 1;
	cp.type_k = cp.type_v = kv == "f16" ? GGML_TYPE_F16 : GGML_TYPE_BF16;
	cp.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
	cp.n_threads = cp.n_threads_batch = n_threads;
	cp.no_perf = true;
	llama_context* ctx = llama_init_from_model(lm, cp);
	if (!ctx) { fprintf(stderr, "llama_kld: cannot create a %lld-token context\n", (long long)n_ctx); return 1; }
	fprintf(stderr, "llama_kld: %s, vocab %lld (reference %lld), %zu documents, %lld positions, top-%d, "
	"ctx %lld, kv %s\n", model.c_str(), (long long)nv, (long long)man.n_vocab,
	man.docs.size(), (long long)rows, top, (long long)n_ctx, kv.c_str());
	for (const Doc& d : man.docs)
	for (int64_t i = 0; i < d.n; ++i)
	if (tokens[(size_t)(d.off + i)] >= nv) {
	fprintf(stderr, "llama_kld: token %d is outside the model's %lld-token vocabulary\n",
	tokens[(size_t)(d.off + i)], (long long)nv);
	return 1;
	}

	std::vector<float> kl((size_t)rows), nq((size_t)rows), np((size_t)rows);
	std::vector<uint8_t> t1((size_t)rows);
	std::atomic<int> io_err{0};
	llama_batch batch = llama_batch_init(n_batch, 0, 1);
	const time_t t0 = time(nullptr);
	int64_t done = 0;

	for (size_t di = 0; di < man.docs.size(); ++di) {
	const Doc& d = man.docs[di];
	llama_memory_clear(llama_get_memory(ctx), true);
	for (int64_t p0 = 0; p0 < d.n; p0 += n_batch) {
	const int64_t m = std::min<int64_t>(n_batch, d.n - p0);
	batch.n_tokens = (int32_t)m;
	std::vector<int64_t> scored; // batch index -> document position
	for (int64_t j = 0; j < m; ++j) {
	const int64_t p = p0 + j;
	batch.token[j] = tokens[(size_t)(d.off + p)];
	batch.pos[j] = (llama_pos)p;
	batch.n_seq_id[j] = 1;
	batch.seq_id[j][0] = 0;
	const bool s = p >= d.score_from && p < d.n - 1;
	batch.logits[j] = s;
	if (s) scored.push_back(j);
	}
	if (llama_decode(ctx, batch) != 0) {
	fprintf(stderr, "llama_kld: llama_decode failed in document %zu at %lld\n", di, (long long)p0);
	return 1;
	}
	// The scored rows of this batch, split over threads.
	std::atomic<size_t> next{0};
	auto work = [&] {
	std::vector<TopEntry> head((size_t)top);
	std::vector<uint8_t> mark((size_t)nv, 0);
	float rr[3];
	for (size_t k; (k = next.fetch_add(1)) < scored.size();) {
	const int64_t j = scored[k], p = p0 + j, r = d.row0 + p - d.score_from;
	const float* l = llama_get_logits_ith(ctx, (int32_t)j);
	if (!l \|\| !pread_all(fd_top, head.data(), (size_t)top * sizeof(TopEntry), r * (int64_t)top * (int64_t)sizeof(TopEntry)) \|\|
	!pread_all(fd_rows, rr, sizeof rr, r * (int64_t)sizeof rr)) { io_err = 1; continue; }
	float mx = -std::numeric_limits<float>::infinity();
	int64_t arg = 0;
	for (int64_t i = 0; i < nv; ++i) if (l[i] > mx) { mx = l[i]; arg = i; }
	double sum = 0;
	for (int64_t i = 0; i < nv; ++i) sum += std::exp((double)(l[i] - mx));
	const double lse = (double)mx + std::log(sum);
	double S = 0, A = 0;
	for (int t = 0; t < top; ++t) {
	const int32_t id = head[(size_t)t].id;
	const double lp = head[(size_t)t].lp;
	const double lq = id < nv ? (double)l[id] - lse : -std::numeric_limits<double>::infinity();
	if (id < nv) mark[(size_t)id] = 1;
	const double pp = std::exp(lp);
	if (!(pp > 0)) continue;
	S += pp;
	A += pp * (lp - lq);
	}
	double q_tail = 0;
	for (int64_t i = 0; i < nv; ++i) if (!mark[(size_t)i]) q_tail += std::exp((double)l[i] - lse);
	for (int t = 0; t < top; ++t) if (head[(size_t)t].id < nv) mark[(size_t)head[(size_t)t].id] = 0;
	const double p_tail = rr[2];
	if (p_tail > 0) {
	S += p_tail;
	A += p_tail * (std::log(p_tail) - std::log(std::max(q_tail, std::numeric_limits<double>::min())));
	}
	const int32_t nxt = tokens[(size_t)(d.off + p + 1)];
	kl[(size_t)r] = (float)(A / S - std::log(S));
	t1[(size_t)r] = arg == (int64_t)head[0].id;
	nq[(size_t)r] = (float)(lse - (double)l[nxt]);
	np[(size_t)r] = rr[0];
	}
	};
	const int nt = (int)std::min<size_t>((size_t)n_threads, std::max<size_t>(1, scored.size()));
	std::vector<std::thread> th;
	for (int t = 0; t < nt; ++t) th.emplace_back(work);
	for (auto& t : th) t.join();
	if (io_err) { fprintf(stderr, "llama_kld: reading the reference failed\n"); return 1; }
	done += (int64_t)scored.size();
	}
	if (di % 50 == 0 \|\| di + 1 == man.docs.size())
	fprintf(stderr, "llama_kld: %zu / %zu documents, %lld / %lld positions, %lld s\n", di + 1,
	man.docs.size(), (long long)done, (long long)rows, (long long)(time(nullptr) - t0));
	}

	// The report, in radiance's --kld-out layout.
	auto group = [&](const std::vector<size_t>& ds) {
	std::vector<float> v;
	int64_t a = 0;
	double q = 0, p = 0;
	for (size_t di : ds) {
	const Doc& d = man.docs[di];
	for (int64_t r = d.row0; r < d.row0 + d.rows(); ++r) {
	v.push_back(kl[(size_t)r]); a += t1[(size_t)r]; q += nq[(size_t)r]; p += np[(size_t)r];
	}
	}
	return summary(std::move(v), a, q, p);
	};
	std::vector<size_t> all(man.docs.size());
	std::map<std::string, std::vector<size_t>> by;
	for (size_t i = 0; i < man.docs.size(); ++i) { all[i] = i; by[man.docs[i].source].push_back(i); }
	std::ofstream o(out);
	char tb[32];
	const time_t now = time(nullptr);
	std::strftime(tb, sizeof tb, "%Y-%m-%dT%H:%M:%SZ", gmtime(&now));
	o << "{\n \"model\": \"" << jesc(model) << "\",\n \"engine\": \"llama.cpp\",\n \"reference\": \""
	<< jesc(man.model) << "\",\n \"ref_dir\": \"" << jesc(ref) << "\",\n \"ref_form\": \"top-" << top
	<< " + tail\",\n \"kv\": \"" << kv << "\",\n \"created\": \"" << tb << "\",\n \"docs\": "
	<< man.docs.size() << ",\n \"all\": " << group(all) << ",\n \"by_source\": {";
	bool first = true;
	for (const auto& [name, ds] : by) {
	o << (first ? "\n" : ",\n") << " \"" << jesc(name) << "\": " << group(ds);
	first = false;
	}
	o << "\n }\n}\n";
	o.close();
	std::vector<float> rr((size_t)rows * 4);
	for (int64_t r = 0; r < rows; ++r) {
	rr[(size_t)r * 4] = kl[(size_t)r]; rr[(size_t)r * 4 + 1] = nq[(size_t)r];
	rr[(size_t)r * 4 + 2] = np[(size_t)r]; rr[(size_t)r * 4 + 3] = t1[(size_t)r];
	}
	std::ofstream orow(out + ".rows", std::ios::binary);
	orow.write(reinterpret_cast<const char>(rr.data()), (std::streamsize)(rr.size() sizeof(float)));
	fprintf(stderr, "llama_kld: report in %s: %s\n", out.c_str(), group(all).c_str());
	llama_batch_free(batch);
	llama_free(ctx);
	llama_model_free(lm);
	return 0;
	}