#include "../src/llama-ext.h"
#include "ggml-cpp.h"
#include "gguf-model-data.h"
#include "llama.h"
#include <algorithm>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <sstream>
#include <string>
#include <utility>
#include <vector>
// ---------------------------------------------------------------------------
// ftype name <-> enum mapping
// ---------------------------------------------------------------------------
struct ftype_name_entry {
const char * name;
llama_ftype ftype;
};
static const ftype_name_entry ftype_name_table[] = {
{ "F32", LLAMA_FTYPE_ALL_F32 },
{ "F16", LLAMA_FTYPE_MOSTLY_F16 },
{ "BF16", LLAMA_FTYPE_MOSTLY_BF16 },
{ "Q4_0", LLAMA_FTYPE_MOSTLY_Q4_0 },
{ "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1 },
{ "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0 },
{ "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1 },
{ "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0 },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S },
{ "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S },
{ "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M },
{ "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L },
{ "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S },
{ "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M },
{ "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S },
{ "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M },
{ "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M },
{ "IQ2_XXS", LLAMA_FTYPE_MOSTLY_IQ2_XXS },
{ "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS },
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M },
{ "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS },
{ "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS },
{ "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S },
{ "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M },
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL },
{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS },
{ "TQ1_0", LLAMA_FTYPE_MOSTLY_TQ1_0 },
{ "TQ2_0", LLAMA_FTYPE_MOSTLY_TQ2_0 },
{ "MXFP4_MOE", LLAMA_FTYPE_MOSTLY_MXFP4_MOE },
{ "NVFP4", LLAMA_FTYPE_MOSTLY_NVFP4 },
};
static llama_ftype llama_ftype_from_name(const char * name) {
for (const auto & e : ftype_name_table) {
if (strcmp(name, e.name) == 0) {
return e.ftype;
}
}
return (llama_ftype) -1;
}
static const char * llama_ftype_to_name(llama_ftype ftype) {
for (const auto & e : ftype_name_table) {
if (e.ftype == ftype) {
return e.name;
}
}
return nullptr;
}
// ---------------------------------------------------------------------------
// ggml_type name lookup
// ---------------------------------------------------------------------------
static ggml_type ggml_type_from_name(const std::string & name) {
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
const char * tname = ggml_type_name((ggml_type) i);
if (tname && name == tname) {
return (ggml_type) i;
}
}
return GGML_TYPE_COUNT;
}
// ---------------------------------------------------------------------------
// File parser for snapshot files (quant type schemas)
// ---------------------------------------------------------------------------
struct snapshot_section {
llama_ftype ftype;
ggml_type default_type;
std::vector<std::pair<std::string, ggml_type>> overrides;
};
// This function is pretty ugly, but it's a trade-off of readable snapshot files
// versus readable parsing code
static bool parse_snapshot_file(const std::string & path, std::vector<snapshot_section> & sections) {
std::ifstream f(path);
if (!f.good()) {
return false;
}
snapshot_section * cur = nullptr;
std::string line;
while (std::getline(f, line)) {
if (line.empty() || line[0] == '#') {
continue;
}
// section header: [FTYPE_NAME] default_type
if (line[0] == '[') {
auto close = line.find(']');
if (close == std::string::npos) {
fprintf(stderr, "parse error: missing ] in '%s'\n", line.c_str());
return false;
}
std::string ftype_str = line.substr(1, close - 1);
std::string default_str;
size_t pos = close + 1;
while (pos < line.size() && line[pos] == ' ') {
pos++;
}
default_str = line.substr(pos);
llama_ftype ftype = llama_ftype_from_name(ftype_str.c_str());
if ((int) ftype < 0) {
fprintf(stderr, "parse error: unknown ftype '%s'\n", ftype_str.c_str());
return false;
}
ggml_type dtype = ggml_type_from_name(default_str);
if (dtype == GGML_TYPE_COUNT) {
fprintf(stderr, "parse error: unknown default type '%s'\n", default_str.c_str());
return false;
}
sections.push_back({ ftype, dtype, {} });
cur = §ions.back();
continue;
}
if (!cur) {
fprintf(stderr, "parse error: tensor line before any section: '%s'\n", line.c_str());
return false;
}
auto sp = line.rfind(' ');
if (sp == std::string::npos) {
fprintf(stderr, "parse error: no space in tensor line: '%s'\n", line.c_str());
return false;
}
std::string tname = line.substr(0, sp);
std::string ttype = line.substr(sp + 1);
ggml_type gt = ggml_type_from_name(ttype);
if (gt == GGML_TYPE_COUNT) {
fprintf(stderr, "parse error: unknown type '%s' for tensor '%s'\n", ttype.c_str(), tname.c_str());
return false;
}
cur->overrides.push_back({ tname, gt });
}
return true;
}
// ---------------------------------------------------------------------------
// Remote model support using gguf-model-data.cpp
// ---------------------------------------------------------------------------
struct remote_model_spec {
const char * repo;
const char * quant;
};
// Get model name from repo: strip org prefix, strip -GGUF suffix,
// and strip anything up to and including first '_' (e.g. "deepseek-ai_DeepSeek-V3.1").
static std::string model_name_from_repo(const char * repo) {
std::string s(repo);
auto slash = s.find('/');
if (slash != std::string::npos) {
s = s.substr(slash + 1);
}
const std::string suffix = "-GGUF";
if (s.size() >= suffix.size() && s.compare(s.size() - suffix.size(), suffix.size(), suffix) == 0) {
s = s.substr(0, s.size() - suffix.size());
}
auto underscore = s.find('_');
if (underscore != std::string::npos) {
s = s.substr(underscore + 1);
}
return s;
}
static std::string snapshot_file_from_name(const std::string & name) {
std::string lower = name;
for (auto & c : lower) {
c = std::tolower(c);
}
return lower;
}
static const remote_model_spec model_specs[] = {
{ "ggml-org/Qwen3-0.6B-GGUF", "Q8_0" },
{ "ggml-org/GLM-4.6V-GGUF", "Q8_0" },
{ "ggml-org/Step-3.5-Flash-GGUF", "Q4_K" },
{ "ggml-org/Qwen3-Coder-Next-GGUF", "Q8_0" },
{ "ggml-org/Qwen3-14B-GGUF", "Q8_0" },
{ "ggml-org/NVIDIA-Nemotron-Nano-3-30B-A3B-GGUF", "Q8_0" },
{ "ggml-org/gpt-oss-120b-GGUF", "mxfp4" },
{ "ggml-org/gemma-3-4b-it-GGUF", "Q8_0" },
{ "bartowski/Meta-Llama-3.1-70B-Instruct-GGUF", "Q4_K_M" },
{ "bartowski/deepseek-ai_DeepSeek-V3.1-GGUF", "IQ1_M" },
//{ "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF", "IQ1_S" }, // TODO: swap with ggml-org if/when it's released
{ "ggml-org/Qwen3.6-27B-GGUF", "Q8_0" },
};
static const int n_model_specs = (int) (sizeof(model_specs) / sizeof(model_specs[0]));
static llama_model * build_mock_model_from_remote(const gguf_remote_model & remote) {
llama_quant_model_desc desc = {};
desc.architecture = remote.architecture.c_str();
desc.n_embd = remote.n_embd;
desc.n_ff = remote.n_ff;
desc.n_layer = remote.n_layer;
desc.n_head = remote.n_head;
desc.n_head_kv = remote.n_head_kv;
desc.n_expert = remote.n_expert;
desc.n_embd_head_k = remote.n_embd_head_k;
desc.n_embd_head_v = remote.n_embd_head_v;
return llama_quant_model_from_metadata(&desc);
}
// Single ggml context holding all quantizable tensors for a model.
struct mock_tensors {
ggml_context_ptr ctx;
std::vector<ggml_tensor *> tensors;
};
static mock_tensors build_mock_tensors(const quantize_state_impl * qs, const gguf_remote_model & remote) {
const size_t ctx_size = remote.tensors.size() * ggml_tensor_overhead();
struct ggml_init_params params = { ctx_size, nullptr, true };
ggml_context_ptr ctx(ggml_init(params));
std::vector<ggml_tensor *> result;
for (const auto & t : remote.tensors) {
ggml_tensor * gt = ggml_new_tensor_4d(ctx.get(), GGML_TYPE_F32, t.ne[0], t.ne[1], t.ne[2], t.ne[3]);
ggml_set_name(gt, t.name.c_str());
if (llama_quant_tensor_allows_quantization(qs, gt)) {
result.push_back(gt);
}
}
// sort by layer index then name, matching llama_model_loader::weight_name_comparer
std::sort(result.begin(), result.end(), [](const ggml_tensor * a, const ggml_tensor * b) {
int a_layer = -1, b_layer = -1;
sscanf(a->name, "blk.%d.", &a_layer);
sscanf(b->name, "blk.%d.", &b_layer);
if (a_layer != b_layer) {
return a_layer < b_layer;
}
return strcmp(a->name, b->name) < 0;
});
return { std::move(ctx), std::move(result) };
}
// ---------------------------------------------------------------------------
// Generate mode: regenerate all snapshot files
// Use this when either adding new models or modifying quants
// ---------------------------------------------------------------------------
static std::string generate_snapshot(const std::string & name,
const gguf_remote_model & remote,
quantize_state_impl * qs,
mock_tensors & mt) {
std::ostringstream out;
out << "# Model: " << name << "\n";
out << "# n_embd=" << remote.n_embd << ", n_ff=" << remote.n_ff << ", n_vocab=" << remote.n_vocab
<< ", n_layer=" << remote.n_layer << ", n_head=" << remote.n_head << ", n_head_kv=" << remote.n_head_kv;
if (remote.n_expert > 0) {
out << ", n_expert=" << remote.n_expert;
}
out << "\n";
for (int i = 0; i < LLAMA_FTYPE_GUESSED; i++) {
llama_ftype ft = (llama_ftype) i;
ggml_type default_type = llama_ftype_get_default_type(ft);
if (default_type == GGML_TYPE_COUNT) {
continue;
}
const char * fname = llama_ftype_to_name(ft);
if (!fname) {
continue;
}
std::vector<ggml_type> result_types(mt.tensors.size());
llama_quant_compute_types(qs, ft, mt.tensors.data(), result_types.data(), mt.tensors.size());
out << "\n[" << fname << "] " << ggml_type_name(default_type) << "\n";
for (size_t j = 0; j < mt.tensors.size(); j++) {
if (result_types[j] != default_type) {
out << ggml_get_name(mt.tensors[j]) << " " << ggml_type_name(result_types[j]) << "\n";
}
}
}
return out.str();
}
static int run_generate(const std::string & snapshot_dir) {
fprintf(stderr, "This will overwrite all snapshot files in:\n %s\n", snapshot_dir.c_str());
fprintf(stderr, "Continue? [y/N] ");
int ch = fgetc(stdin);
if (ch != 'y' && ch != 'Y') {
fprintf(stderr, "Aborted.\n");
return 1;
}
fprintf(stderr, "\n");
int n_written = 0;
for (int m = 0; m < n_model_specs; m++) {
const auto & spec = model_specs[m];
std::string name = model_name_from_repo(spec.repo);
fprintf(stderr, "Fetching model metadata for %s from %s...\n", name.c_str(), spec.repo);
auto result = gguf_fetch_model_meta(spec.repo, spec.quant);
if (!result.has_value()) {
fprintf(stderr, "ERROR: could not fetch model metadata for %s\n", name.c_str());
return 1;
}
const auto & remote = result.value();
llama_model * model = build_mock_model_from_remote(remote);
llama_model_quantize_params qparams = llama_model_quantize_default_params();
quantize_state_impl * qs = llama_quant_init(model, &qparams);
auto mt = build_mock_tensors(qs, remote);
std::string content = generate_snapshot(name, remote, qs, mt);
std::string path = snapshot_dir + "/" + snapshot_file_from_name(name) + ".schema";
std::ofstream f(path);
if (!f.good()) {
fprintf(stderr, "ERROR: could not write %s\n", path.c_str());
llama_quant_free(qs);
llama_model_free(model);
return 1;
}
f << content;
n_written++;
fprintf(stderr, " wrote %s\n", path.c_str());
llama_quant_free(qs);
llama_model_free(model);
}
fprintf(stderr, "%d files written\n", n_written);
return 0;
}
// ---------------------------------------------------------------------------
// Test mode: compare against snapshot files
// ---------------------------------------------------------------------------
static bool run_test_section(quantize_state_impl * qs, mock_tensors & mt, const snapshot_section & section) {
// verify default_type matches what llama_ftype_get_default_type returns
ggml_type computed_default = llama_ftype_get_default_type(section.ftype);
if (computed_default != section.default_type) {
printf(" FAIL [%s] default type mismatch: file says %s, code says %s\n", llama_ftype_to_name(section.ftype),
ggml_type_name(section.default_type), ggml_type_name(computed_default));
return false;
}
std::vector<ggml_type> result_types(mt.tensors.size());
llama_quant_compute_types(qs, section.ftype, mt.tensors.data(), result_types.data(), mt.tensors.size());
std::map<std::string, ggml_type> override_map(section.overrides.begin(), section.overrides.end());
bool all_pass = true;
int n_override_found = 0;
for (size_t i = 0; i < mt.tensors.size(); i++) {
const char * name = ggml_get_name(mt.tensors[i]);
ggml_type got = result_types[i];
ggml_type expected = section.default_type;
auto it = override_map.find(name);
if (it != override_map.end()) {
expected = it->second;
n_override_found++;
}
if (got != expected) {
printf(" FAIL %-50s %-10s expected %s, got %s\n", name, llama_ftype_to_name(section.ftype),
ggml_type_name(expected), ggml_type_name(got));
all_pass = false;
}
}
if (n_override_found != (int) section.overrides.size()) {
printf(" FAIL [%s] override count mismatch: listed %d, matched %d\n", llama_ftype_to_name(section.ftype),
(int) section.overrides.size(), n_override_found);
all_pass = false;
}
return all_pass;
}
static int run_remote_tests(const std::string & snapshot_dir, const char * argv0) {
int total_pass = 0;
int total_fail = 0;
int total_skip = 0;
for (int m = 0; m < n_model_specs; m++) {
const auto & spec = model_specs[m];
std::string name = model_name_from_repo(spec.repo);
printf("=== %s ===\n", name.c_str());
auto result = gguf_fetch_model_meta(spec.repo, spec.quant, "", false);
if (!result.has_value()) {
printf(" SKIP (could not fetch model metadata)\n\n");
total_skip++;
continue;
}
const auto & remote = result.value();
llama_model * model = build_mock_model_from_remote(remote);
llama_model_quantize_params qparams = llama_model_quantize_default_params();
quantize_state_impl * qs = llama_quant_init(model, &qparams);
auto mt = build_mock_tensors(qs, remote);
std::string snapshot_path = snapshot_dir + "/" + snapshot_file_from_name(name) + ".schema";
std::vector<snapshot_section> sections;
if (!parse_snapshot_file(snapshot_path, sections)) {
printf(" SKIP (could not read snapshot file: %s)\n\n", snapshot_path.c_str());
llama_quant_free(qs);
llama_model_free(model);
total_skip++;
continue;
}
int model_pass = 0;
int model_fail = 0;
for (const auto & section : sections) {
bool pass = run_test_section(qs, mt, section);
if (pass) {
model_pass++;
} else {
model_fail++;
}
}
printf(" %s %s: %d/%d ftype sections passed (%d tensors)\n", model_fail == 0 ? "PASS" : "FAIL", name.c_str(),
model_pass, model_pass + model_fail, (int) mt.tensors.size());
printf("\n");
if (model_fail == 0) {
total_pass++;
} else {
total_fail++;
}
llama_quant_free(qs);
llama_model_free(model);
}
printf("%d/%d models passed", total_pass, total_pass + total_fail);
if (total_skip > 0) {
printf(", %d skipped", total_skip);
}
printf("\n");
if (total_fail > 0) {
printf("\nIf these changes are intentional, regenerate snapshot files with:\n");
printf(" %s --generate\n", argv0);
}
return total_fail > 0 ? 1 : 0;
}
int main(int argc, char ** argv) {
std::string snapshot_dir = SNAPSHOT_DIR;
bool generate = false;
for (int i = 1; i < argc; i++) {
if (strcmp(argv[i], "--generate") == 0) {
generate = true;
} else if (strcmp(argv[i], "--snapshot-dir") == 0 && i + 1 < argc) {
snapshot_dir = argv[++i];
}
}
if (generate) {
return run_generate(snapshot_dir);
}
// suppress llama log warnings during test (e.g. tensor type fallback messages)
llama_log_set([](enum ggml_log_level, const char *, void *) {}, nullptr);
return run_remote_tests(snapshot_dir, argv[0]);
}