#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(
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);
-#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>
// 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;
// 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;
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;
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.
});
}
+ // 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;
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;
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]) {
" - 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!");
}
}
new_ofstream(weight.idx);
}
- const std::string name = ggml_get_name(tensor);
const size_t tensor_size = ggml_nbytes(tensor);
if (!params->dry_run) {
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);
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]);
+ }
+}
*
!*.*
+!snapshots/
*.o
ggml-common.h
**/*.swp
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()
}
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)) {
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()
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);
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 cache_path = get_cache_file_path(cdir, repo_part, filename);
{
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);
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;
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];
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;
gguf_context_ptr gguf_fetch_gguf_ctx(
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);
return nullptr;
}
- 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 nullptr;
return nullptr;
}
- 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];
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 nullptr;
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);
gguf_context_ptr gguf_fetch_gguf_ctx(
const std::string & repo,
const std::string & quant = "Q8_0",
- const std::string & cache_dir = "");
+ const std::string & cache_dir = "",
+ bool verbose = true);
--- /dev/null
+# Model: DeepSeek-V3.1
+# n_embd=7168, n_ff=18432, n_vocab=129280, n_layer=61, n_head=128, n_head_kv=1, n_expert=256
+
+[F32] f32
+
+[F16] f16
+
+[Q4_0] q4_0
+output.weight q6_K
+
+[Q4_1] q4_1
+output.weight q6_K
+
+[Q8_0] q8_0
+
+[Q5_0] q5_0
+output.weight q6_K
+
+[Q5_1] q5_1
+output.weight q6_K
+
+[Q2_K] q2_K
+output.weight q6_K
+blk.0.attn_k_b.weight q4_0
+blk.0.attn_output.weight q3_K
+blk.0.ffn_down.weight q3_K
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--- /dev/null
+# Model: gemma-3-4b-it
+# n_embd=2560, n_ff=10240, n_vocab=262144, n_layer=34, n_head=8, n_head_kv=4
+
+[F32] f32
+
+[F16] f16
+
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+
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+
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+
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+
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+
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+
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--- /dev/null
+# Model: GLM-4.6V
+# n_embd=4096, n_ff=10944, n_vocab=151552, n_layer=46, n_head=96, n_head_kv=8, n_expert=128
+
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--- /dev/null
+# Model: gpt-oss-120b
+# n_embd=2880, n_ff=2880, n_vocab=0, n_layer=36, n_head=64, n_head_kv=8, n_expert=128
+
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--- /dev/null
+# Model: Meta-Llama-3.1-70B-Instruct
+# n_embd=8192, n_ff=28672, n_vocab=128256, n_layer=80, n_head=64, n_head_kv=8
+
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+
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--- /dev/null
+# Model: Nemotron-Nano-3-30B-A3B
+# n_embd=2688, n_ff=0, n_vocab=131072, n_layer=52, n_head=32, n_head_kv=0, n_expert=128
+
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--- /dev/null
+# Model: Qwen3-0.6B
+# n_embd=1024, n_ff=3072, n_vocab=151936, n_layer=28, n_head=16, n_head_kv=8
+
+[F32] f32
+
+[F16] f16
+
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+
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+
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+
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--- /dev/null
+# Model: Qwen3-14B
+# n_embd=5120, n_ff=17408, n_vocab=151936, n_layer=40, n_head=40, n_head_kv=8
+
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--- /dev/null
+# Model: Qwen3-Coder-Next
+# n_embd=2048, n_ff=5120, n_vocab=151936, n_layer=48, n_head=16, n_head_kv=2, n_expert=512
+
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--- /dev/null
+# Model: Qwen3.5-27B
+# n_embd=5120, n_ff=17408, n_vocab=248320, n_layer=64, n_head=24, n_head_kv=4
+
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--- /dev/null
+# Model: Qwen3.5-397B-A17B
+# n_embd=4096, n_ff=0, n_vocab=248320, n_layer=60, n_head=32, n_head_kv=2, n_expert=512
+
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--- /dev/null
+# Model: Step-3.5-Flash
+# n_embd=4096, n_ff=11264, n_vocab=128896, n_layer=45, n_head=64, n_head_kv=8, n_expert=288
+
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// 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;
}
--- /dev/null
+#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/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);
+ }
+ }
+
+ // 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]);
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff