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llama.cpp
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Bartowski 2026-03-24 13:46:36 +00:00 committed by GitHub
parent 3fc6f1aed1 9c00aab225
commit e17eb694cf
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20 changed files with 35283 additions and 45 deletions
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50
src/llama-ext.h
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@ -1,8 +1,8 @@
#pragma once
#include "llama-context.h"
#include "ggml.h"
#include "stdint.h"
#include "llama.h"
#include <cstdint>
// Reserve a new compute graph. It is valid until the next call to llama_graph_reserve.
LLAMA_API struct ggml_cgraph * llama_graph_reserve(
@ -10,3 +10,47 @@ LLAMA_API struct ggml_cgraph * llama_graph_reserve(
uint32_t n_tokens,
uint32_t n_seqs,
uint32_t n_outputs);
// Get the default ggml_type for a given ftype.
LLAMA_API ggml_type llama_ftype_get_default_type(llama_ftype ftype);
// Quantization state.
struct quantize_state_impl;
LLAMA_API quantize_state_impl * llama_quant_init(
const llama_model * model,
const llama_model_quantize_params * params);
LLAMA_API void llama_quant_free(quantize_state_impl * qs);
// Descriptor for constructing a mock model for quantization testing.
struct llama_quant_model_desc {
const char * architecture;
uint32_t n_embd;
uint32_t n_ff;
uint32_t n_layer;
uint32_t n_head;
uint32_t n_head_kv;
uint32_t n_expert;
uint32_t n_embd_head_k;
uint32_t n_embd_head_v;
};
// Create a mock model from a metadata descriptor (for testing).
// The returned model must be freed with llama_model_free().
LLAMA_API llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * desc);
// Returns true if this tensor should be quantized (based on name, dims, params).
LLAMA_API bool llama_quant_tensor_allows_quantization(
const quantize_state_impl * qs,
const ggml_tensor * tensor);
// Compute quantization type assignments for a list of tensors.
// All tensors should be quantizable (use llama_quant_tensor_allows_quantization to filter).
// result_types: caller-allocated array of n_tensors elements, filled with assigned types.
LLAMA_API void llama_quant_compute_types(
quantize_state_impl * qs,
llama_ftype ftype,
ggml_tensor ** tensors,
ggml_type * result_types,
size_t n_tensors);

156
src/llama-quant.cpp
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@ -1,11 +1,11 @@
#include "llama.h"
#include "llama-impl.h"
#include "llama-model.h"
#include "llama-model-loader.h"
#include "llama-ext.h"
#include <algorithm>
#include <cmath>
#include <cstring>
#include <string>
#include <cinttypes>
#include <fstream>
#include <mutex>
@ -199,6 +199,7 @@ struct quantize_state_impl {
// per-tensor metadata, computed in the preliminary loop and used in the main loop
struct tensor_metadata {
std::string name;
ggml_type target_type;
tensor_category category;
std::string remapped_imatrix_name;
@ -784,7 +785,7 @@ static bool tensor_requires_imatrix(const char * tensor_name, const ggml_type ds
// given a file type, get the default tensor type
//
static ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
switch (ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: return GGML_TYPE_Q4_0;
case LLAMA_FTYPE_MOSTLY_Q4_1: return GGML_TYPE_Q4_1;
@ -823,16 +824,32 @@ static ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ3_S:
case LLAMA_FTYPE_MOSTLY_IQ3_M: return GGML_TYPE_IQ3_S;
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
default: return GGML_TYPE_COUNT;
}
}
static void init_quantize_state_counters(quantize_state_impl & qs, std::vector<tensor_metadata> & metadata) {
for (auto & tm : metadata) {
tensor_category cat = tensor_get_category(tm.name);
tm.category = cat;
if (category_is_attn_v(cat)) {
++qs.n_attention_wv;
}
if (cat == tensor_category::OUTPUT) {
qs.has_tied_embeddings = false;
}
}
qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer;
}
//
// main quantization driver
//
static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
ggml_type default_type;
llama_ftype ftype = params->ftype;
int nthread = params->nthread;
@ -841,7 +858,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
nthread = std::thread::hardware_concurrency();
}
default_type = llama_ftype_get_default_type(ftype);
ggml_type default_type = llama_ftype_get_default_type(ftype);
if (default_type == GGML_TYPE_COUNT) {
throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
// mmap consistently increases speed on Linux, and also increases speed on Windows with
// hot cache. It may cause a slowdown on macOS, possibly related to free memory.
@ -961,6 +981,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
});
}
// compute tensor metadata once and cache it
std::vector<tensor_metadata> metadata(tensors.size());
for (size_t i = 0; i < tensors.size(); ++i) {
metadata[i].name = ggml_get_name(tensors[i]->tensor);
}
// initialize quantization state counters and metadata categories
init_quantize_state_counters(qs, metadata);
int idx = 0;
uint16_t n_split = 1;
@ -973,25 +1002,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
std::vector<gguf_context_ptr> ctx_outs(n_split);
ctx_outs[0] = std::move(ctx_out);
// compute tensor metadata once and cache it
std::vector<tensor_metadata> metadata(tensors.size());
// initialize quantization state before preliminary loop (counters for use_more_bits)
{
for (size_t i = 0; i < tensors.size(); ++i) {
const auto cat = tensor_get_category(tensors[i]->tensor->name);
if (category_is_attn_v(cat)) {
++qs.n_attention_wv;
}
if (cat == tensor_category::OUTPUT) {
qs.has_tied_embeddings = false;
}
metadata[i].category = cat; // save and re-use the category while we're at it
}
// these also need to be set to n_layer by default
qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer;
}
// flag for --dry-run
bool will_require_imatrix = false;
@ -1002,7 +1012,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
for (size_t i = 0; i < tensors.size(); ++i) {
const auto * it = tensors[i];
const struct ggml_tensor * tensor = it->tensor;
const std::string name = ggml_get_name(tensor);
uint16_t i_split = params->keep_split ? it->idx : 0;
if (!ctx_outs[i_split]) {
@ -1031,7 +1040,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
" - offending tensor: %s\n"
" - target type: %s\n"
"============================================================================\n\n",
name.c_str(), ggml_type_name(metadata[i].target_type));
metadata[i].name.c_str(), ggml_type_name(metadata[i].target_type));
throw std::runtime_error("this quantization requires an imatrix!");
}
}
@ -1104,7 +1113,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
new_ofstream(weight.idx);
}
const std::string name = ggml_get_name(tensor);
const size_t tensor_size = ggml_nbytes(tensor);
if (!params->dry_run) {
@ -1235,9 +1243,9 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
total_size_new += new_size;
// update the gguf meta data as we go
gguf_set_tensor_type(ctx_outs[cur_split].get(), name.c_str(), new_type);
GGML_ASSERT(gguf_get_tensor_size(ctx_outs[cur_split].get(), gguf_find_tensor(ctx_outs[cur_split].get(), name.c_str())) == new_size);
gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data);
gguf_set_tensor_type(ctx_outs[cur_split].get(), metadata[i].name.c_str(), new_type);
GGML_ASSERT(gguf_get_tensor_size(ctx_outs[cur_split].get(), gguf_find_tensor(ctx_outs[cur_split].get(), metadata[i].name.c_str())) == new_size);
gguf_set_tensor_data(ctx_outs[cur_split].get(), metadata[i].name.c_str(), new_data);
// write tensor data + padding
fout.write((const char *) new_data, new_size);
@ -1302,3 +1310,89 @@ uint32_t llama_model_quantize(
return 0;
}
//
// Helper functions for external tools exposed in llama-ext.h
//
quantize_state_impl * llama_quant_init(
const llama_model * model,
const llama_model_quantize_params * params) {
return new quantize_state_impl(*model, params);
}
void llama_quant_free(quantize_state_impl * qs) {
delete qs;
}
llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * desc) {
struct llama_model_params mparams = llama_model_default_params();
auto * model = new llama_model(mparams);
model->arch = llm_arch_from_string(desc->architecture);
// infer llm_type: only LLM_TYPE_70B matters for quantization logic
if (model->arch == LLM_ARCH_LLAMA && desc->n_layer == 80 && desc->n_head != desc->n_head_kv) {
model->type = LLM_TYPE_70B;
}
model->hparams.n_embd = desc->n_embd;
model->hparams.n_embd_head_k_full = desc->n_embd_head_k;
model->hparams.n_embd_head_v_full = desc->n_embd_head_v;
model->hparams.n_layer = desc->n_layer;
model->hparams.n_expert = desc->n_expert;
for (uint32_t i = 0; i < desc->n_layer; i++) {
model->hparams.n_head_arr[i] = desc->n_head;
model->hparams.n_head_kv_arr[i] = desc->n_head_kv;
model->hparams.n_ff_arr[i] = desc->n_ff;
}
return model;
}
bool llama_quant_tensor_allows_quantization(
const quantize_state_impl * qs,
const ggml_tensor * tensor) {
return tensor_allows_quantization(qs->params, qs->model.arch, tensor);
}
void llama_quant_compute_types(
quantize_state_impl * qs,
llama_ftype ftype,
ggml_tensor ** tensors,
ggml_type * result_types,
size_t n_tensors) {
// reset per-computation state
qs->n_attention_wv = 0;
qs->n_ffn_down = 0;
qs->n_ffn_gate = 0;
qs->n_ffn_up = 0;
qs->i_attention_wv = 0;
qs->i_ffn_down = 0;
qs->i_ffn_gate = 0;
qs->i_ffn_up = 0;
qs->n_fallback = 0;
qs->has_imatrix = false;
qs->has_tied_embeddings = true;
// build metadata from tensor names
std::vector<tensor_metadata> metadata(n_tensors);
for (size_t i = 0; i < n_tensors; i++) {
metadata[i].name = ggml_get_name(tensors[i]);
}
// initialize counters and categories
init_quantize_state_counters(*qs, metadata);
// use a local copy of params with the requested ftype
llama_model_quantize_params local_params = *qs->params;
local_params.ftype = ftype;
ggml_type default_type = llama_ftype_get_default_type(ftype);
// compute types
for (size_t i = 0; i < n_tensors; i++) {
result_types[i] = llama_tensor_get_type(*qs, &local_params, tensors[i], default_type, metadata[i]);
}
}

1
tests/.gitignore vendored
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@ -1,5 +1,6 @@
*
!*.*
!snapshots/
*.o
ggml-common.h
**/*.swp

6
tests/CMakeLists.txt
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@ -274,6 +274,12 @@ if (TARGET cpp-httplib)
add_executable(test-gguf-model-data test-gguf-model-data.cpp)
target_link_libraries(test-gguf-model-data PRIVATE gguf-model-data common)
llama_test(test-gguf-model-data LABEL "model")
# test-quant-type-selection requires gguf-model-data for remote model metadata
llama_build_and_test(test-quant-type-selection.cpp LABEL "model")
target_link_libraries(test-quant-type-selection PRIVATE gguf-model-data)
target_compile_definitions(test-quant-type-selection PRIVATE
SNAPSHOT_DIR="${CMAKE_CURRENT_SOURCE_DIR}/snapshots")
endif()
endif()

58
tests/gguf-model-data.cpp
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@ -124,6 +124,35 @@ static bool gguf_skip_value(gguf_buf_reader & r, int32_t vtype) {
}
static bool gguf_read_uint32_val(gguf_buf_reader & r, int32_t vtype, uint32_t & out) {
// Handle array-valued fields (e.g. per-layer head counts in hybrid models)
// by reading the first element as a representative value.
if (vtype == GGUF_TYPE_ARRAY) {
int32_t elem_type;
uint64_t count;
if (!r.read_val(elem_type)) {
return false;
}
if (!r.read_val(count)) {
return false;
}
if (count == 0) {
return false;
}
// Read first element, skip the rest
if (!gguf_read_uint32_val(r, elem_type, out)) {
return false;
}
for (uint64_t i = 1; i < count; i++) {
size_t sz = gguf_val_type_size(elem_type);
if (sz == 0) {
return false;
}
if (!r.skip(sz)) {
return false;
}
}
return true;
}
if (vtype == GGUF_TYPE_UINT8) {
uint8_t v;
if (!r.read_val(v)) {
@ -486,7 +515,8 @@ static std::string detect_gguf_filename(const std::string & repo, const std::str
static std::optional<gguf_remote_model> fetch_and_parse(
const std::string & repo,
const std::string & filename,
const std::string & cache_path) {
const std::string & cache_path,
bool verbose) {
std::string url = "https://huggingface.co/" + repo + "/resolve/main/" + filename;
// Progressive download inspired by RangeView.fetchChunk()
@ -495,7 +525,9 @@ static std::optional<gguf_remote_model> fetch_and_parse(
const size_t max_chunk = 64 * 1024 * 1024;
while (chunk_size <= max_chunk) {
fprintf(stderr, "gguf_fetch: downloading %zu bytes from %s\n", chunk_size, filename.c_str());
if (verbose) {
fprintf(stderr, "gguf_fetch: downloading %zu bytes from %s\n", chunk_size, filename.c_str());
}
char range_buf[64];
snprintf(range_buf, sizeof(range_buf), "bytes=0-%zu", chunk_size - 1);
@ -536,7 +568,8 @@ static std::optional<gguf_remote_model> fetch_or_cached(
const std::string & repo,
const std::string & filename,
const std::string & cdir,
const std::string & repo_part) {
const std::string & repo_part,
bool verbose) {
std::string fname_part = sanitize_for_path(filename);
std::string cache_path = cdir + "/" + repo_part + "--" + fname_part + ".partial";
@ -545,20 +578,23 @@ static std::optional<gguf_remote_model> fetch_or_cached(
if (std::filesystem::exists(cache_path) && read_file(cache_path, cached)) {
auto result = gguf_parse_meta(cached);
if (result.has_value()) {
fprintf(stderr, "gguf_fetch: loaded from cache: %s\n", cache_path.c_str());
if (verbose) {
fprintf(stderr, "gguf_fetch: loaded from cache: %s\n", cache_path.c_str());
}
return result;
}
}
}
fs_create_directory_with_parents(cdir);
return fetch_and_parse(repo, filename, cache_path);
return fetch_and_parse(repo, filename, cache_path, verbose);
}
std::optional<gguf_remote_model> gguf_fetch_model_meta(
const std::string & repo,
const std::string & quant,
const std::string & cache_dir) {
const std::string & cache_dir,
bool verbose) {
std::string cdir = cache_dir.empty() ? get_default_cache_dir() : cache_dir;
std::string repo_part = sanitize_for_path(repo);
@ -568,7 +604,7 @@ std::optional<gguf_remote_model> gguf_fetch_model_meta(
return std::nullopt;
}
auto model_opt = fetch_or_cached(repo, filename, cdir, repo_part);
auto model_opt = fetch_or_cached(repo, filename, cdir, repo_part, verbose);
if (!model_opt.has_value()) {
fprintf(stderr, "gguf_fetch: failed to fetch %s\n", filename.c_str());
return std::nullopt;
@ -583,8 +619,10 @@ std::optional<gguf_remote_model> gguf_fetch_model_meta(
return std::nullopt;
}
fprintf(stderr, "gguf_fetch: split model with %u shards, fetching remaining %u...\n",
model.n_split, model.n_split - 1);
if (verbose) {
fprintf(stderr, "gguf_fetch: split model with %u shards, fetching remaining %u...\n",
model.n_split, model.n_split - 1);
}
for (int i = 2; i <= model.n_split; i++) {
char num_buf[6], total_buf[6];
@ -592,7 +630,7 @@ std::optional<gguf_remote_model> gguf_fetch_model_meta(
snprintf(total_buf, sizeof(total_buf), "%05d", (int)model.n_split);
std::string shard_name = split_prefix + "-" + num_buf + "-of-" + total_buf + ".gguf";
auto shard = fetch_or_cached(repo, shard_name, cdir, repo_part);
auto shard = fetch_or_cached(repo, shard_name, cdir, repo_part, verbose);
if (!shard.has_value()) {
fprintf(stderr, "gguf_fetch: failed to fetch shard %d: %s\n", i, shard_name.c_str());
return std::nullopt;

3
tests/gguf-model-data.h
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@ -39,4 +39,5 @@ struct gguf_remote_model {
std::optional<gguf_remote_model> gguf_fetch_model_meta(
const std::string & repo,
const std::string & quant = "Q8_0",
const std::string & cache_dir = ""); // empty = default
const std::string & cache_dir = "", // empty = default
bool verbose = true);

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tests/test-gguf-model-data.cpp
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@ -116,6 +116,39 @@ int main() {
// Verify tensor count
TEST_ASSERT(model3.tensors.size() == 780, "expected tensor count == 780");
// Test a hybrid-attention model with array-valued head counts
auto result4 = gguf_fetch_model_meta("ggml-org/Step-3.5-Flash-GGUF", "Q4_K");
if (!result4.has_value()) {
fprintf(stderr, "FAIL: could not fetch Step-3.5-Flash metadata\n");
return 1;
}
const auto & model4 = result4.value();
fprintf(stderr, "Architecture: %s\n", model4.architecture.c_str());
fprintf(stderr, "n_embd: %u\n", model4.n_embd);
fprintf(stderr, "n_ff: %u\n", model4.n_ff);
fprintf(stderr, "n_vocab: %u\n", model4.n_vocab);
fprintf(stderr, "n_layer: %u\n", model4.n_layer);
fprintf(stderr, "n_head: %u\n", model4.n_head);
fprintf(stderr, "n_head_kv: %u\n", model4.n_head_kv);
fprintf(stderr, "n_expert: %u\n", model4.n_expert);
fprintf(stderr, "n_embd_head_k: %u\n", model4.n_embd_head_k);
fprintf(stderr, "n_embd_head_v: %u\n", model4.n_embd_head_v);
fprintf(stderr, "tensors: %zu\n", model4.tensors.size());
TEST_ASSERT(model4.architecture == "step35", "expected architecture 'step35'");
TEST_ASSERT(model4.n_layer == 45, "expected n_layer == 45");
TEST_ASSERT(model4.n_embd == 4096, "expected n_embd == 4096");
TEST_ASSERT(model4.n_ff == 11264, "expected n_ff == 11264");
TEST_ASSERT(model4.n_head == 64, "expected n_head == 64 (first element of per-layer array)");
TEST_ASSERT(model4.n_head_kv == 8, "expected n_head_kv == 8 (first element of per-layer array)");
TEST_ASSERT(model4.n_expert == 288, "expected n_expert == 288");
TEST_ASSERT(model4.n_embd_head_k == 128, "expected n_embd_head_k == 128");
TEST_ASSERT(model4.n_embd_head_v == 128, "expected n_embd_head_v == 128");
TEST_ASSERT(model4.n_vocab == 128896, "expected n_vocab == 128896");
TEST_ASSERT(model4.tensors.size() == 754, "expected tensor count == 754");
fprintf(stderr, "=== ALL TESTS PASSED ===\n");
return 0;
}

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tests/test-quant-type-selection.cpp Normal file
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@ -0,0 +1,512 @@
#include "llama.h"
#include "../src/llama-ext.h"
#include "ggml-cpp.h"
#include "gguf-model-data.h"
#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 },
};
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 = &sections.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/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
{ "bartowski/Qwen_Qwen3.5-27B-GGUF", "Q8_0" }, // TODO: swap with ggml-org if/when it's released
};
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);
}
}
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]);
}