ikawrakow/ik_llama.cpp
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ik_llama.cpp/src/llama-model-loader.cpp
Kawrakow c03c2d7cc6 Merge ffn_up and ffn_gate experts tensors (#1137) 
* WIP - not working

* WIP - not working

* WIP - GPT-OSS working

However, extremely stupid. The only way I could correctly repack the
up/gate experts is to copy up and gate into host buffers, repack
into another host buffer, copy back into the ffn_up_gate_exps tensor.
This is going to be very slow for giant 500 GB models.

My attempts to do this via a compute graph on the backend holding
the tensors was unsuccessful.

For GPT-OSS-20B I see ~6-7% better PP when using the original
ik_llama.cpp fused_up_gate CUDA implementation, and ~10% when
using the small batch size implementation.

Other models are not working yet on CUDA as I need to fix the
fused mul-unary implementation.

* WIP

* WIP - Qwen3-MoE (and hopefully all others) working

But when I say here and in the previous commit "working",
I mean PP is working. TG is still broken.

* WIP: TG seems to be working

* Minor

* Add command line option to merge experts up/gate

* Add merge up/gate command line parameter to llama-bench

* Turn off merge_up_gate_exps if split mode graph

It is not yet implemented

* When no bias, allow merging up/gate with tensor overrides

* Arghh, we need to increase the context size again

* Cleanup
2026-01-12 18:30:53 +02:00

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#include "llama-model-loader.h"
#include "llama-impl.h"
#include "llama-mmap.h"
#include "llama-model.h"
#include "ggml.h"
//#include "ggml-backend.h"
#ifdef GGML_USE_CUDA
# include "ggml-cuda.h"
#elif defined(GGML_USE_VULKAN)
# include "ggml-vulkan.h"
#elif defined(GGML_USE_SYCL)
# include "ggml-sycl.h"
#elif defined(GGML_USE_KOMPUTE)
# include "ggml-kompute.h"
#elif defined(GGML_USE_CANN)
# include "ggml-cann.h"
#endif
#include <set>
#include <map>
#include <array>
#include <future>
#include <regex>
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#ifndef PATH_MAX
#define PATH_MAX MAX_PATH
#endif
#include <io.h>
#endif
#define LLAMA_API_INTERNAL
namespace GGUFMeta {
template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int)>
struct GKV_Base_Type {
static constexpr gguf_type gt = gt_;
static T getter(const gguf_context * ctx, const int kid) {
return gfun(ctx, kid);
}
};
template<typename T> struct GKV_Base;
template<> struct GKV_Base<bool >: GKV_Base_Type<bool, GGUF_TYPE_BOOL, gguf_get_val_bool> {};
template<> struct GKV_Base<uint8_t >: GKV_Base_Type<uint8_t, GGUF_TYPE_UINT8, gguf_get_val_u8 > {};
template<> struct GKV_Base<uint16_t >: GKV_Base_Type<uint16_t, GGUF_TYPE_UINT16, gguf_get_val_u16 > {};
template<> struct GKV_Base<uint32_t >: GKV_Base_Type<uint32_t, GGUF_TYPE_UINT32, gguf_get_val_u32 > {};
template<> struct GKV_Base<uint64_t >: GKV_Base_Type<uint64_t, GGUF_TYPE_UINT64, gguf_get_val_u64 > {};
template<> struct GKV_Base<int8_t >: GKV_Base_Type<int8_t, GGUF_TYPE_INT8, gguf_get_val_i8 > {};
template<> struct GKV_Base<int16_t >: GKV_Base_Type<int16_t, GGUF_TYPE_INT16, gguf_get_val_i16 > {};
template<> struct GKV_Base<int32_t >: GKV_Base_Type<int32_t, GGUF_TYPE_INT32, gguf_get_val_i32 > {};
template<> struct GKV_Base<int64_t >: GKV_Base_Type<int64_t, GGUF_TYPE_INT64, gguf_get_val_i64 > {};
template<> struct GKV_Base<float >: GKV_Base_Type<float, GGUF_TYPE_FLOAT32, gguf_get_val_f32 > {};
template<> struct GKV_Base<double >: GKV_Base_Type<double, GGUF_TYPE_FLOAT64, gguf_get_val_f64 > {};
template<> struct GKV_Base<const char *>: GKV_Base_Type<const char *, GGUF_TYPE_STRING, gguf_get_val_str > {};
template<> struct GKV_Base<std::string> {
static constexpr gguf_type gt = GGUF_TYPE_STRING;
static std::string getter(const gguf_context * ctx, const int kid) {
return gguf_get_val_str(ctx, kid);
}
};
struct ArrayInfo {
const gguf_type gt;
const size_t length;
const void * data;
};
template<> struct GKV_Base<ArrayInfo> {
public:
static constexpr gguf_type gt = GGUF_TYPE_ARRAY;
static ArrayInfo getter(const gguf_context *ctx, const int k) {
return ArrayInfo {
gguf_get_arr_type(ctx, k),
size_t(gguf_get_arr_n(ctx, k)),
gguf_get_arr_data(ctx, k),
};
}
};
template<typename T>
class GKV : public GKV_Base<T> {
GKV() = delete;
public:
static T get_kv(const gguf_context * ctx, const int k) {
const enum gguf_type kt = gguf_get_kv_type(ctx, k);
if (kt != GKV::gt) {
throw std::runtime_error(format("key %s has wrong type %s but expected type %s",
gguf_get_key(ctx, k), gguf_type_name(kt), gguf_type_name(GKV::gt)));
}
return GKV::getter(ctx, k);
}
static const char * override_type_to_str(const llama_model_kv_override_type ty) {
switch (ty) {
case LLAMA_KV_OVERRIDE_TYPE_BOOL: return "bool";
case LLAMA_KV_OVERRIDE_TYPE_INT: return "int";
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
case LLAMA_KV_OVERRIDE_TYPE_STR: return "str";
}
return "unknown";
}
static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
if (!ovrd) { return false; }
if (ovrd->tag == expected_type) {
LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
__func__, override_type_to_str(ovrd->tag), ovrd->key);
switch (ovrd->tag) {
case LLAMA_KV_OVERRIDE_TYPE_BOOL: {
LLAMA_LOG_INFO("%s\n", ovrd->val_bool ? "true" : "false");
} break;
case LLAMA_KV_OVERRIDE_TYPE_INT: {
LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->val_i64);
} break;
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
LLAMA_LOG_INFO("%.6f\n", ovrd->val_f64);
} break;
case LLAMA_KV_OVERRIDE_TYPE_STR: {
LLAMA_LOG_INFO("%s\n", ovrd->val_str);
} break;
default:
// Shouldn't be possible to end up here, but just in case...
throw std::runtime_error(
format("Unsupported attempt to override %s type for metadata key %s\n",
override_type_to_str(ovrd->tag), ovrd->key));
}
return true;
}
LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
__func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag));
return false;
}
template<typename OT>
static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
try_override(OT & target, const struct llama_model_kv_override * ovrd) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
target = ovrd->val_bool;
return true;
}
return false;
}
template<typename OT>
static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
try_override(OT & target, const struct llama_model_kv_override * ovrd) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
target = ovrd->val_i64;
return true;
}
return false;
}
template<typename OT>
static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
try_override(T & target, const struct llama_model_kv_override * ovrd) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
target = ovrd->val_f64;
return true;
}
return false;
}
template<typename OT>
static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
try_override(T & target, const struct llama_model_kv_override * ovrd) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_STR, ovrd)) {
target = ovrd->val_str;
return true;
}
return false;
}
static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
if (try_override<T>(target, ovrd)) {
return true;
}
if (k < 0) { return false; }
target = get_kv(ctx, k);
return true;
}
static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
return set(ctx, gguf_find_key(ctx, key), target, ovrd);
}
static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
return set(ctx, key.c_str(), target, ovrd);
}
};
}
llama_model_loader::llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors,
bool repack_tensors, bool use_thp, bool merge_qkv, bool merge_up_gate_exps,
const llama_model_kv_override * param_overrides_p,
const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) {
int trace = 0;
if (getenv("LLAMA_TRACE")) {
trace = atoi(getenv("LLAMA_TRACE"));
}
#ifdef _WIN32
// Only bump maxstdio if the user really wants large contexts:
#if defined(GGML_MAX_CONTEXTS) && (GGML_MAX_CONTEXTS > 512)
// Cap at MSVC's hard limit of 8192 - https://learn.microsoft.com/en-us/cpp/c-runtime-library/reference/setmaxstdio?view=msvc-160
#if (GGML_MAX_CONTEXTS > 8192)
#define _GGML_STDIO_TARGET 8192
#else
#define _GGML_STDIO_TARGET GGML_MAX_CONTEXTS
#endif
int _setmaxstdio_ret = _setmaxstdio(_GGML_STDIO_TARGET);
if (_setmaxstdio_ret == -1) {
LLAMA_LOG_INFO("%s: failed to set max stdio to %d. (setmaxstdio returned -1)\n", __func__, _GGML_STDIO_TARGET);
} else {
LLAMA_LOG_INFO("%s: max stdio successfully set to %d\n", __func__, _setmaxstdio_ret);
}
#endif // GGML_MAX_CONTEXTS > 512
#endif // _WIN32
if (param_overrides_p != nullptr) {
for (const struct llama_model_kv_override * p = param_overrides_p; p->key[0] != 0; p++) {
kv_overrides.insert({std::string(p->key), *p});
}
}
tensor_buft_overrides = param_tensor_buft_overrides_p;
struct ggml_context * ctx = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx,
};
meta = gguf_init_from_file(fname.c_str(), params);
if (!meta) {
throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
}
get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
llm_kv = LLM_KV(llm_arch_from_string(arch_name));
files.emplace_back(new llama_file(fname.c_str(), "rb"));
contexts.emplace_back(ctx);
// Save tensors data offset of the main file.
// For subsidiary files, `meta` tensor data offset must not be used,
// so we build a unified tensors index for weights.
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
weights.emplace_back(files.back().get(), 0, cur->name, meta, cur);
}
uint16_t n_split = 0;
get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
// Load additional GGML contexts
if (n_split > 1) {
uint16_t idx = 0;
get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
if (idx != 0) {
throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
}
char split_prefix[PATH_MAX] = {0};
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) {
throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
}
if (trace > 0) {
LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
}
char split_path[PATH_MAX] = {0};
for (idx = 1; idx < n_split; idx++) {
llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
struct gguf_init_params split_params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params);
if (!ctx_gguf) {
throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path));
}
files.emplace_back(new llama_file(split_path, "rb"));
contexts.emplace_back(ctx);
// Save tensors data offset info of the shard.
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur);
}
gguf_free(ctx_gguf);
}
get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors);
// sanity check
{
const int n_tensors_loaded = (int) weights.size();
if (n_tensors != n_tensors_loaded) {
throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
}
}
LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1);
}
n_kv = gguf_get_n_kv(meta);
n_tensors = weights.size();
fver = (enum llama_fver) gguf_get_version(meta);
std::set<std::string> tensor_names;
for (auto & w : weights) {
n_elements += ggml_nelements(w.tensor);
n_bytes += ggml_nbytes(w.tensor);
// make sure there is no duplicated tensor names
const std::string name(w.tensor->name);
auto found = tensor_names.find(name);
if (found != tensor_names.end()) {
throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name));
}
tensor_names.insert(name);
}
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
// determine file type based on the number of tensors for each quantization and print meta data
// TODO: make optional
{
std::map<enum ggml_type, uint32_t> n_type;
uint32_t n_type_max = 0;
enum ggml_type type_max = GGML_TYPE_F32;
for (int i = 0; i < n_tensors; i++) {
const ggml_tensor * tensor = weights.at(i).tensor;
enum ggml_type type = tensor->type;
n_type[type]++;
if (n_type_max < n_type[type]) {
n_type_max = n_type[type];
type_max = type;
}
if (trace > 0) {
const uint16_t sid = weights.at(i).idx;
LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
}
}
switch (type_max) {
case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break;
case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break;
case GGML_TYPE_BF16: ftype = LLAMA_FTYPE_MOSTLY_BF16; break;
case GGML_TYPE_BF16_R16:ftype = LLAMA_FTYPE_MOSTLY_BF16_R16;break;
case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break;
case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break;
case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break;
case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1; break;
case GGML_TYPE_Q6_0: ftype = LLAMA_FTYPE_MOSTLY_Q6_0; break;
case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0; break;
case GGML_TYPE_Q8_KV: ftype = LLAMA_FTYPE_MOSTLY_Q8_KV; break;
case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break;
case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
case GGML_TYPE_Q3_K_R4: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_R4; break;
case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
case GGML_TYPE_Q4_K_R4: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_R4; break;
case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
case GGML_TYPE_Q5_K_R4: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_R4; break;
case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break;
case GGML_TYPE_Q6_K_R4: ftype = LLAMA_FTYPE_MOSTLY_Q6_K_R4; break;
case GGML_TYPE_Q8_K_R8: ftype = LLAMA_FTYPE_MOSTLY_Q8_K_R8; break;
case GGML_TYPE_Q8_KV_R8: ftype = LLAMA_FTYPE_MOSTLY_Q8_KV_R8; break;
case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
case GGML_TYPE_IQ2_XXS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS_R4; break;
case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break;
case GGML_TYPE_IQ2_XS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS_R4; break;
case GGML_TYPE_IQ2_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_KS; break;
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_M; break;
case GGML_TYPE_IQ2_S_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_M_R4;break;
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
case GGML_TYPE_IQ3_XXS_R4: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS_R4; break;
case GGML_TYPE_IQ1_KT: ftype = LLAMA_FTYPE_MOSTLY_IQ1_KT; break;
case GGML_TYPE_IQ2_KT: ftype = LLAMA_FTYPE_MOSTLY_IQ2_KT; break;
case GGML_TYPE_IQ3_KT: ftype = LLAMA_FTYPE_MOSTLY_IQ3_KT; break;
case GGML_TYPE_IQ4_KT: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KT; break;
case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
case GGML_TYPE_IQ1_S_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ1_S_R4;break;
case GGML_TYPE_IQ1_M_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ1_M_R4;break;
case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break;
case GGML_TYPE_IQ1_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ1_BN; break;
case GGML_TYPE_IQ2_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN; break;
case GGML_TYPE_IQ2_BN_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN_R4;break;
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ4_NL_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL_R4;break;
case GGML_TYPE_IQ4_XS_R8:ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS_R8;break;
case GGML_TYPE_Q4_0_R8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_R8; break;
case GGML_TYPE_Q5_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q5_0_R4; break;
case GGML_TYPE_Q6_0_R4: ftype = LLAMA_FTYPE_MOSTLY_Q6_0_R4; break;
case GGML_TYPE_Q8_0_R8: ftype = LLAMA_FTYPE_MOSTLY_Q8_0_R8; break;
case GGML_TYPE_MXFP4: ftype = LLAMA_FTYPE_MOSTLY_MXFP4; break;
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
case GGML_TYPE_IQ4_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS; break;
case GGML_TYPE_IQ4_KS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_KS_R4; break;
case GGML_TYPE_IQ5_KS_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ5_KS_R4; break;
case GGML_TYPE_IQ4_KSS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_KSS; break;
case GGML_TYPE_IQ5_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ5_KS; break;
case GGML_TYPE_IQ2_K: ftype = LLAMA_FTYPE_MOSTLY_IQ2_K; break;
case GGML_TYPE_IQ2_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ2_K_R4;break;
case GGML_TYPE_IQ3_KS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_KS; break;
case GGML_TYPE_IQ2_KL: ftype = LLAMA_FTYPE_MOSTLY_IQ2_KL; break;
case GGML_TYPE_IQ3_K: ftype = LLAMA_FTYPE_MOSTLY_IQ3_K; break;
case GGML_TYPE_IQ3_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ3_K_R4;break;
case GGML_TYPE_IQ4_K: ftype = LLAMA_FTYPE_MOSTLY_IQ4_K; break;
case GGML_TYPE_IQ4_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ4_K_R4;break;
case GGML_TYPE_IQ5_K: ftype = LLAMA_FTYPE_MOSTLY_IQ5_K; break;
case GGML_TYPE_IQ5_K_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ5_K_R4;break;
case GGML_TYPE_IQ6_K: ftype = LLAMA_FTYPE_MOSTLY_IQ6_K; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
case GGML_TYPE_IQ3_S_R4:ftype = LLAMA_FTYPE_MOSTLY_IQ3_S_R4;break;
case GGML_TYPE_Q4_0_4_4: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_4; break;
case GGML_TYPE_Q4_0_4_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_8; break;
case GGML_TYPE_Q4_0_8_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_8_8; break;
default:
{
LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
ftype = LLAMA_FTYPE_ALL_F32;
} break;
}
// this is a way to mark that we have "guessed" the file type
ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
{
const int kid = gguf_find_key(meta, "general.file_type"); // TODO: use LLM_KV
if (kid >= 0) {
ftype = (llama_ftype) gguf_get_val_u32(meta, kid);
}
}
LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
for (int i = 0; i < n_kv; i++) {
const char * name = gguf_get_key(meta, i);
const enum gguf_type type = gguf_get_kv_type(meta, i);
const std::string type_name =
type == GGUF_TYPE_ARRAY
? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta, i)), gguf_get_arr_n(meta, i))
: gguf_type_name(type);
std::string value = gguf_kv_to_str(meta, i);
const size_t MAX_VALUE_LEN = 40;
if (value.size() > MAX_VALUE_LEN) {
value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
}
replace_all(value, "\n", "\\n");
LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
}
// print type counts
for (auto & kv : n_type) {
if (kv.second == 0) {
continue;
}
LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
}
}
if (!llama_mmap::SUPPORTED) {
LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
use_mmap = false;
}
if (repack_tensors) {
use_mmap = false;
}
this->use_mmap = use_mmap;
this->check_tensors = check_tensors;
this->repack_tensors = repack_tensors;
this->use_thp = use_thp;
this->merge_qkv = merge_qkv;
this->merge_up_gate_exps = merge_up_gate_exps;
}
llama_model_loader::~llama_model_loader() {
if (meta) {
gguf_free(meta);
}
for (auto * ctx : contexts) {
ggml_free(ctx);
}
}
template<typename T>
typename std::enable_if<std::is_integral<T>::value, bool>::type
llama_model_loader::get_arr_n(const std::string & key, T & result, const bool required) {
const int kid = gguf_find_key(meta, key.c_str());
if (kid < 0) {
if (required) {
throw std::runtime_error(format("key not found in model: %s", key.c_str()));
}
return false;
}
struct GGUFMeta::ArrayInfo arr_info =
GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
result = arr_info.length;
return true;
}
template<typename T>
typename std::enable_if<std::is_integral<T>::value, bool>::type
llama_model_loader::get_arr_n(const enum llm_kv kid, T & result, const bool required) {
return get_arr_n(llm_kv(kid), result, required);
}
template<typename T>
bool llama_model_loader::get_arr(const std::string & key, std::vector<T> & result, const bool required) {
const int kid = gguf_find_key(meta, key.c_str());
if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) {
if (required) {
throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
}
return false;
}
struct GGUFMeta::ArrayInfo arr_info =
GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
switch (arr_info.gt) {
case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
case GGUF_TYPE_INT32: GGML_ASSERT(
(std::is_same<T, int32_t>::value) ||
(std::is_same<T, uint32_t>::value)); break;
default:
throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
}
result.resize(arr_info.length);
result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length);
return true;
}
template<typename T, size_t N_MAX>
bool llama_model_loader::get_arr(const std::string & key, std::array<T, N_MAX> & result, const bool required) {
const int kid = gguf_find_key(meta, key.c_str());
if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) {
if (required) {
throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
}
return false;
}
struct GGUFMeta::ArrayInfo arr_info =
GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
switch (arr_info.gt) {
case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
case GGUF_TYPE_INT32: GGML_ASSERT(
(std::is_same<T, int32_t>::value) ||
(std::is_same<T, uint32_t>::value)); break;
default:
throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
}
if (arr_info.length > N_MAX) {
throw std::runtime_error(format("array length %u for key %s exceeds max %u", (uint32_t) arr_info.length, key.c_str(), (uint32_t) N_MAX));
}
std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
return true;
}
template<typename T>
bool llama_model_loader::get_arr(const enum llm_kv kid, T & result, const bool required) {
return get_arr(llm_kv(kid), result, required);
}
template<typename T>
bool llama_model_loader::get_key(const std::string & key, T & result, const bool required) {
auto it = kv_overrides.find(key);
const struct llama_model_kv_override * override =
it != kv_overrides.end() ? &it->second : nullptr;
const bool found = GGUFMeta::GKV<T>::set(meta, key, result, override);
if (required && !found) {
throw std::runtime_error(format("key not found in model: %s", key.c_str()));
}
return found;
}
template<typename T>
bool llama_model_loader::get_key(const enum llm_kv kid, T & result, const bool required) {
return get_key(llm_kv(kid), result, required);
}
// get array of n <= N_MAX elements, or a single element repeated n times
template<typename T, size_t N_MAX>
bool llama_model_loader::get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, const bool required) {
const int kid = gguf_find_key(meta, key.c_str());
if (kid < 0) {
if (required) {
throw std::runtime_error(format("key not found in model: %s", key.c_str()));
}
return false;
}
if (n > N_MAX) {
throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str()));
}
if (gguf_get_kv_type(meta, kid) == GGUF_TYPE_ARRAY) {
struct GGUFMeta::ArrayInfo arr_info =
GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
if (n != arr_info.length) {
throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length));
}
return get_arr(key, result, required);
} else {
T value;
bool ok = get_key(key, value, required);
if (!ok) {
return false;
}
for (uint32_t i = 0; i < n; i++) {
result[i] = value;
}
return true;
}
}
template<typename T>
bool llama_model_loader::get_key_or_arr(const enum llm_kv kid, T & result, uint32_t n, const bool required) {
return get_key_or_arr(llm_kv(kid), result, n, required);
}
const char * llama_model_loader::get_tensor_name(int i) const {
return weights.at(i).tensor->name;
}
const llama_model_loader::llama_tensor_weight * llama_model_loader::get_weight(const char * name) const {
for (const auto & weight : weights) {
if (strcmp(name, weight.tensor->name) == 0) {
return &weight;
}
}
return nullptr;
}
const llama_model_loader::llama_tensor_weight & llama_model_loader::require_weight(const char * name) const {
const llama_tensor_weight * weight = get_weight(name);
if (!weight) {
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
}
return *weight;
}
struct ggml_tensor * llama_model_loader::get_tensor_meta(const char * name) const {
const auto * weight = get_weight(name);
if (!weight) {
return nullptr;
}
return weight->tensor;
}
struct ggml_tensor * llama_model_loader::require_tensor_meta(const char * name) const {
struct ggml_tensor * tensor = get_tensor_meta(name);
if (!tensor) {
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
}
return tensor;
}
struct ggml_tensor * llama_model_loader::create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) {
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
ggml_set_name(tensor, ggml_get_name(cur));
if (duplicated) {
size_data += ggml_nbytes(cur);
} else {
n_created++;
}
return tensor;
}
const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
if (cur == NULL) {
if (!required) {
return NULL;
}
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
}
{
bool is_ok = true;
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
is_ok = false;
break;
}
}
if (!is_ok) {
throw std::runtime_error(
format("%s: tensor '%s' has wrong shape; expected %s, got %s",
__func__, name.c_str(),
llama_format_tensor_shape(ne).c_str(),
llama_format_tensor_shape(cur).c_str()));
}
}
return cur;
}
struct ggml_tensor * llama_model_loader::create_tensor(struct ggml_context * ctx, const std::string & name,
const std::vector<int64_t> & ne, int flags) {
const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
if (cur == NULL) {
return NULL;
}
// skip unused tensors
if (flags & TENSOR_SKIP) {
const size_t nbytes = ggml_nbytes(cur);
LLAMA_LOG_WARN("model has unused tensor %s (size = %zu bytes) -- ignoring\n", name.c_str(), nbytes);
size_data -= nbytes;
n_created++;
return nullptr;
}
return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED);
}
struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base,
const std::string & name, const std::vector<int64_t> & ne, size_t offset, bool required) {
const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
if (cur == NULL) {
return NULL;
}
if (cur->type != base->type) {
throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
}
std::array<int64_t, GGML_MAX_DIMS> dims;
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
dims[i] = i < ne.size() ? ne[i] : 1;
}
struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
dims[0], dims[1], dims[2], dims[3],
cur->nb[1], cur->nb[2], cur->nb[3],
offset);
ggml_set_name(tensor, name.c_str());
n_created++;
return tensor;
}
void llama_model_loader::done_getting_tensors() const {
if (n_created != n_tensors) {
throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
}
}
void llama_model_loader::init_mappings(bool prefetch, llama_mlocks * mlock_mmaps, bool use_thp) {
if (use_mmap) {
mappings.reserve(files.size());
mmaps_used.reserve(files.size());
for (const auto & file : files) {
std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, ggml_is_numa(), use_thp));
mmaps_used.emplace_back(mapping->size(), 0);
if (mlock_mmaps) {
std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
mlock_mmap->init(mapping->addr());
mlock_mmaps->emplace_back(std::move(mlock_mmap));
}
mappings.emplace_back(std::move(mapping));
}
}
// compute the total size of all tensors for progress reporting
for (auto & w : weights) {
size_data += ggml_nbytes(w.tensor);
}
}
void llama_model_loader::get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const {
GGML_ASSERT(!mappings.empty());
const auto & mapping = mappings.at(idx);
*first = mapping->size();
*last = 0;
*addr = mapping->addr();
for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
try {
const auto * weight = get_weight(ggml_get_name(tensor));
if (!weight) {
continue;
}
if (weight->idx != idx) {
continue;
}
*first = std::min(*first, weight->offs);
*last = std::max(*last, weight->offs + ggml_nbytes(tensor));
} catch(...) {
// the tensor is not in the model
}
}
}
// for backwards compatibility, does not support ggml-backend
void llama_model_loader::load_data_for(struct ggml_tensor * cur) const {
const auto & w = require_weight(ggml_get_name(cur));
if (use_mmap) {
const auto & mapping = mappings.at(w.idx);
if (cur->data == nullptr) {
cur->data = (uint8_t *)mapping->addr() + w.offs;
} else {
memcpy(cur->data, (uint8_t *)mapping->addr() + w.offs, ggml_nbytes(cur));
}
} else {
GGML_ASSERT(cur->data != nullptr);
GGML_ASSERT(w.idx < files.size());
const auto & file = files.at(w.idx);
file->seek(w.offs, SEEK_SET);
file->read_raw(cur->data, ggml_nbytes(cur));
}
if (check_tensors && !ggml_validate_row_data(cur->type, cur->data, ggml_nbytes(cur))) {
throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
}
}
// Returns false if cancelled by progress_callback
bool llama_model_loader::load_all_data(
struct ggml_context * ctx,
llama_buf_map & bufs_mmap,
llama_mlocks * lmlocks,
llama_progress_callback progress_callback,
void * progress_callback_user_data) {
GGML_ASSERT(size_data != 0 && "call init_mappings() first");
std::vector<no_init<uint8_t>> read_buf;
std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;
#if defined(GGML_USE_CUDA)
// 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
// NVMe raid configurations might require more / larger buffers.
constexpr size_t n_buffers = 4;
constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
std::vector<ggml_backend_buffer_t> host_buffers;
std::vector<void*> host_ptrs;
std::vector<ggml_backend_event_t> events;
size_t buffer_idx = 0; // buffer to use for async loads
ggml_backend_t cuda_backend = nullptr;
if (!use_mmap && !check_tensors) {
// When not using mmaped io use async uploads from pinned memory to GPU memory.
// First determine if the CUDA backend is active, and if so, determine the device ID.
ggml_backend_buffer_t buf = bufs_mmap.count(0) ? bufs_mmap.at(0) : nullptr;
if (buf) {
ggml_backend_buffer_type_t buffer_type = ggml_backend_buffer_get_type(buf);
for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
auto * cuda_buffer_type = ggml_backend_cuda_buffer_type(i);
if (buffer_type == cuda_buffer_type) {
cuda_backend = ggml_backend_cuda_init(i, nullptr);
break;
}
}
}
// If the cuda backend is active create pinned memory buffers and events for synchronisation.
if (cuda_backend) {
for (size_t idx = 0; idx < n_buffers; ++idx) {
host_buffers.emplace_back(ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buffer_size));
host_ptrs.emplace_back(ggml_backend_buffer_get_base(host_buffers[idx]));
events.emplace_back(ggml_backend_event_new(cuda_backend));
}
}
}
#endif
for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
const auto * weight = get_weight(ggml_get_name(cur));
if (weight == nullptr) {
// this can happen with split experts models
continue;
}
if (progress_callback) {
if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
return false;
}
}
size_t n_size = ggml_nbytes(cur);
if (use_mmap) {
const auto & mapping = mappings.at(weight->idx);
ggml_backend_buffer_t buf_mmap = nullptr;
if (bufs_mmap.count(weight->idx)) {
buf_mmap = bufs_mmap.at(weight->idx);
}
uint8_t * data = (uint8_t *) mapping->addr() + weight->offs;
if (check_tensors) {
validation_result.emplace_back(std::async(std::launch::async, [cur, data, n_size] {
return std::make_pair(cur, ggml_validate_row_data(cur->type, data, n_size));
}));
}
GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
if (buf_mmap && cur->data == nullptr) {
ggml_backend_tensor_alloc(buf_mmap, cur, data);
if (lmlocks) {
const auto & lmlock = lmlocks->at(weight->idx);
lmlock->grow_to(weight->offs + n_size);
}
auto & mmap_used = mmaps_used[weight->idx];
mmap_used.first = std::min(mmap_used.first, weight->offs);
mmap_used.second = std::max(mmap_used.second, weight->offs + n_size);
} else {
ggml_backend_tensor_set(cur, data, 0, n_size);
}
} else {
GGML_ASSERT(weight->idx < files.size());
const auto & file = files.at(weight->idx);
if (ggml_backend_buffer_is_host(cur->buffer)) {
file->seek(weight->offs, SEEK_SET);
file->read_raw(cur->data, n_size);
if (check_tensors) {
validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] {
return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size));
}));
}
} else {
#if defined(GGML_USE_CUDA)
// If cuda_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
if (cuda_backend) {
file->seek(weight->offs, SEEK_SET);
size_t bytes_read = 0;
while (bytes_read < n_size) {
size_t read_iteration = std::min<size_t>(buffer_size, n_size - bytes_read);
ggml_backend_event_synchronize(events[buffer_idx]);
file->read_raw(host_ptrs[buffer_idx], read_iteration);
ggml_backend_tensor_set_async(cuda_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
ggml_backend_event_record(events[buffer_idx]);
bytes_read += read_iteration;
++buffer_idx;
buffer_idx %= n_buffers;
}
}
else
#endif
{
read_buf.resize(n_size);
file->seek(weight->offs, SEEK_SET);
file->read_raw(read_buf.data(), n_size);
ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
}
}
}
}
size_done += n_size;
}
#if defined(GGML_USE_CUDA)
// free temporary resources used for async cuda uploads
if (cuda_backend) {
for (size_t idx = 0; idx < n_buffers;++idx) {
ggml_backend_event_synchronize(events[idx]);
ggml_backend_event_free(events[idx]);
ggml_backend_buffer_free(host_buffers[idx]);
}
ggml_backend_free(cuda_backend);
}
#endif
// check validation results
bool validation_failed = false;
for (auto & future : validation_result) {
auto result = future.get();
if (!result.second) {
LLAMA_LOG_ERROR("%s: tensor '%s' has invalid data\n", __func__, ggml_get_name(result.first));
validation_failed = true;
}
}
if (validation_failed) {
throw std::runtime_error("found tensors with invalid data");
}
// check if this is the last call and do final cleanup
if (size_done >= size_data) {
// unmap offloaded tensors and metadata
if (use_mmap) {
for (uint32_t idx = 0; idx < mappings.size(); idx++) {
const auto & mmap_used = mmaps_used.at(idx);
auto & mapping = mappings.at(idx);
mapping->unmap_fragment(0, mmap_used.first);
if (mmap_used.second != 0) {
mapping->unmap_fragment(mmap_used.second, mapping->size());
}
}
}
if (progress_callback) {
// Even though the model is done loading, we still honor
// cancellation since we need to free allocations.
return progress_callback(1.0f, progress_callback_user_data);
}
}
return true;
}
template<>
bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
uint32_t tmp;
const bool found = get_key(kid, tmp, required);
if (found) {
result = (enum llama_pooling_type) tmp;
} else {
result = LLAMA_POOLING_TYPE_UNSPECIFIED;
}
return found;
}
template bool llama_model_loader::get_key<bool> (enum llm_kv kid, bool & result, bool required);
template bool llama_model_loader::get_key<float> (enum llm_kv kid, float & result, bool required);
template bool llama_model_loader::get_key<uint32_t> (enum llm_kv kid, uint32_t & result, bool required);
template bool llama_model_loader::get_key<std::string>(enum llm_kv kid, std::string & result, bool required);
template bool llama_model_loader::get_key_or_arr<std::array<int, 4>>(enum llm_kv kid, std::array<int, 4> & result, uint32_t n, bool required);
template bool llama_model_loader::get_key_or_arr<std::array<uint32_t, 512>>(enum llm_kv kid, std::array<uint32_t, 512> & result, uint32_t n, bool required);
template std::enable_if<std::is_integral<unsigned int>::value, bool>::type llama_model_loader::get_arr_n<unsigned int>(enum llm_kv, unsigned int&, bool);
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