vbatts/llama.cpp
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Merge branch 'master' into gg/bpe-preprocess

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ggml-ci
This commit is contained in:
Georgi Gerganov 2024-04-28 15:34:36 +03:00
commit d63cc9068b
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GPG key ID: 449E073F9DC10735
15 changed files with 588 additions and 218 deletions
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31
.github/workflows/server.yml vendored
View file

@ -41,24 +41,16 @@ jobs:
sanitizer: ""
fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
container:
image: ubuntu:latest
ports:
- 8888
options: --cpus 4
steps:
- name: Dependencies
id: depends
run: |
apt-get update
apt-get -y install \
sudo apt-get update
sudo apt-get -y install \
build-essential \
xxd \
git \
cmake \
python3-pip \
python3-venv \
curl \
wget \
language-pack-en \
@ -71,6 +63,17 @@ jobs:
fetch-depth: 0
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
- name: Python setup
id: setup_python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Tests dependencies
id: test_dependencies
run: |
pip install -r examples/server/tests/requirements.txt
- name: Verify server deps
id: verify_server_deps
run: |
@ -101,13 +104,6 @@ jobs:
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
- name: Setup python env
id: pipenv
run: |
cd examples/server/tests
python3 -m venv venv
. venv/bin/activate
pip install -r requirements.txt
- name: Tests
id: server_integration_tests
@ -133,6 +129,7 @@ jobs:
uses: actions/checkout@v4
with:
fetch-depth: 0
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
- name: libCURL
id: get_libcurl

2
Makefile
View file

@ -790,7 +790,7 @@ batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS)
quantize: examples/quantize/quantize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

94
common/common.cpp
View file

@ -234,8 +234,54 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
return result;
}
bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
const char * sep = strchr(data, '=');
if (sep == nullptr || sep - data >= 128) {
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
return false;
}
llama_model_kv_override kvo;
std::strncpy(kvo.key, data, sep - data);
kvo.key[sep - data] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.val_f64 = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.val_bool = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.val_bool = false;
} else {
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
return false;
}
} else if (strncmp(sep, "str:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
if (strlen(sep) > 127) {
fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
return false;
}
strncpy(kvo.val_str, sep, 127);
kvo.val_str[127] = '\0';
} else {
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
return false;
}
overrides.emplace_back(std::move(kvo));
return true;
}
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
llama_sampling_params& sparams = params.sparams;
llama_sampling_params & sparams = params.sparams;
if (arg == "-s" || arg == "--seed") {
if (++i >= argc) {
@ -1089,6 +1135,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.n_print = std::stoi(argv[i]);
return true;
}
if (arg == "--check-tensors") {
params.check_tensors = true;
return true;
}
if (arg == "--ppl-output-type") {
if (++i >= argc) {
invalid_param = true;
@ -1240,47 +1290,11 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
invalid_param = true;
return true;
}
char* sep = strchr(argv[i], '=');
if (sep == nullptr || sep - argv[i] >= 128) {
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
invalid_param = true;
return true;
}
struct llama_model_kv_override kvo;
std::strncpy(kvo.key, argv[i], sep - argv[i]);
kvo.key[sep - argv[i]] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.int_value = std::atol(sep);
}
else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.float_value = std::atof(sep);
}
else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
}
else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
}
else {
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
invalid_param = true;
return true;
}
}
else {
if (!parse_kv_override(argv[i], params.kv_overrides)) {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
return true;
}
params.kv_overrides.push_back(kvo);
return true;
}
#ifndef LOG_DISABLE_LOGS
@ -1551,9 +1565,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" path to dynamic lookup cache to use for lookup decoding (updated by generation)\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf(" -ptc N, --print-token-count N\n");
printf(" print token count every N tokens (default: %d)\n", params.n_print);
printf(" --check-tensors check model tensor data for invalid values\n");
printf("\n");
#ifndef LOG_DISABLE_LOGS
log_print_usage();
@ -1786,6 +1801,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
mparams.tensor_split = params.tensor_split;
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
mparams.check_tensors = params.check_tensors;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {

3
common/common.h
View file

@ -161,6 +161,7 @@ struct gpt_params {
bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes
bool no_kv_offload = false; // disable KV offloading
bool warmup = true; // warmup run
bool check_tensors = false; // validate tensor data
std::string cache_type_k = "f16"; // KV cache data type for the K
std::string cache_type_v = "f16"; // KV cache data type for the V
@ -170,6 +171,8 @@ struct gpt_params {
std::string image = ""; // path to an image file
};
bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);
bool gpt_params_parse(int argc, char ** argv, gpt_params & params);

4
common/log.h
View file

@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
// INTERNAL, DO NOT USE
// USE LOG() INSTEAD
//
#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER)
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER)
#define LOG_IMPL(str, ...) \
do { \
if (LOG_TARGET != nullptr) \
@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
// INTERNAL, DO NOT USE
// USE LOG_TEE() INSTEAD
//
#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER)
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER)
#define LOG_TEE_IMPL(str, ...) \
do { \
if (LOG_TARGET != nullptr) \

77
examples/imatrix/imatrix.cpp
View file

@ -23,6 +23,7 @@ struct Stats {
};
struct StatParams {
std::string dataset;
std::string ofile = "imatrix.dat";
int n_output_frequency = 10;
int verbosity = 1;
@ -46,7 +47,7 @@ private:
std::vector<float> m_src1_data;
std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
//
void save_imatrix(const char * file_name) const;
void save_imatrix(const char * file_name, const char * dataset) const;
void keep_imatrix(int ncall) const;
};
@ -199,7 +200,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
}
void IMatrixCollector::save_imatrix() const {
save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str());
save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str());
}
void IMatrixCollector::keep_imatrix(int ncall) const {
@ -207,24 +208,33 @@ void IMatrixCollector::keep_imatrix(int ncall) const {
if (file_name.empty()) file_name = "imatrix.dat";
file_name += ".at_";
file_name += std::to_string(ncall);
save_imatrix(file_name.c_str());
save_imatrix(file_name.c_str(), m_params.dataset.c_str());
}
void IMatrixCollector::save_imatrix(const char * fname) const {
void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const {
std::ofstream out(fname, std::ios::binary);
int n_entries = m_stats.size();
out.write((const char*)&n_entries, sizeof(n_entries));
for (auto& p : m_stats) {
out.write((const char *) &n_entries, sizeof(n_entries));
for (const auto & p : m_stats) {
int len = p.first.size();
out.write((const char*)&len, sizeof(len));
out.write((const char *) &len, sizeof(len));
out.write(p.first.c_str(), len);
out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
out.write((const char *) &p.second.ncall, sizeof(p.second.ncall));
int nval = p.second.values.size();
out.write((const char*)&nval, sizeof(nval));
if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
out.write((const char *) &nval, sizeof(nval));
if (nval > 0) out.write((const char *) p.second.values.data(), nval * sizeof(float));
}
// Write the number of call the matrix was computed with
out.write((const char *) &m_last_call, sizeof(m_last_call));
// Write the dataset name at the end of the file to later on specify it in quantize
int n_dataset = strlen(dataset);
out.write((const char *) &n_dataset, sizeof(n_dataset));
out.write(dataset, n_dataset);
if (m_params.verbosity > 0) {
fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname);
fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname);
}
}
@ -547,6 +557,29 @@ int main(int argc, char ** argv) {
}
}
gpt_params params;
params.n_batch = 512;
if (!gpt_params_parse(args.size(), args.data(), params)) {
return 1;
}
params.logits_all = true;
params.n_batch = std::min(params.n_batch, params.n_ctx);
print_build_info();
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
sparams.dataset = params.prompt_file;
g_collector.set_parameters(std::move(sparams));
if (!combine_files.empty()) {
@ -585,28 +618,6 @@ int main(int argc, char ** argv) {
}
}
gpt_params params;
params.n_batch = 512;
if (!gpt_params_parse(args.size(), args.data(), params)) {
return 1;
}
params.logits_all = true;
params.n_batch = std::min(params.n_batch, params.n_ctx);
print_build_info();
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_backend_init();
llama_numa_init(params.numa);

2
examples/quantize/CMakeLists.txt
View file

@ -1,6 +1,6 @@
set(TARGET quantize)
add_executable(${TARGET} quantize.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
target_include_directories(${TARGET} PRIVATE ../../common)
target_compile_features(${TARGET} PRIVATE cxx_std_11)

103
examples/quantize/quantize.cpp
View file

@ -8,7 +8,6 @@
#include <unordered_map>
#include <fstream>
#include <cmath>
#include <algorithm>
struct quant_option {
std::string name;
@ -53,6 +52,10 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", },
};
static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE = "quantize.imatrix.file";
static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix.dataset";
static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count";
static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS = "quantize.imatrix.chunks_count";
static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
std::string ftype_str;
@ -113,7 +116,7 @@ static void usage(const char * executable) {
exit(1);
}
static void load_imatrix(const std::string & imatrix_file, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
std::ifstream in(imatrix_file.c_str(), std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
@ -160,18 +163,33 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map<st
printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
}
}
printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
// latest imatrix version contains the dataset filename at the end of the file
int m_last_call = 0;
if (in.peek() != EOF) {
in.read((char *)&m_last_call, sizeof(m_last_call));
int dataset_len;
in.read((char *)&dataset_len, sizeof(dataset_len));
std::vector<char> dataset_as_vec(dataset_len);
in.read(dataset_as_vec.data(), dataset_len);
imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
}
printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
return m_last_call;
}
static void prepare_imatrix(const std::string & imatrix_file,
static int prepare_imatrix(const std::string & imatrix_file,
std::string & imatrix_dataset,
const std::vector<std::string> & included_weights,
const std::vector<std::string> & excluded_weights,
std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
int m_last_call = -1;
if (!imatrix_file.empty()) {
load_imatrix(imatrix_file, imatrix_data);
m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data);
}
if (imatrix_data.empty()) {
return;
return m_last_call;
}
if (!excluded_weights.empty()) {
for (auto& name : excluded_weights) {
@ -197,6 +215,7 @@ static void prepare_imatrix(const std::string & imatrix_file,
if (!imatrix_data.empty()) {
printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
}
return m_last_call;
}
static ggml_type parse_ggml_type(const char * arg) {
@ -211,43 +230,6 @@ static ggml_type parse_ggml_type(const char * arg) {
return result;
}
static bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
const char* sep = strchr(data, '=');
if (sep == nullptr || sep - data >= 128) {
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
return false;
}
llama_model_kv_override kvo;
std::strncpy(kvo.key, data, sep - data);
kvo.key[sep - data] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.int_value = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.float_value = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
} else {
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
return false;
}
} else {
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
return false;
}
overrides.emplace_back(std::move(kvo));
return true;
}
int main(int argc, char ** argv) {
if (argc < 3) {
usage(argv[0]);
@ -316,10 +298,43 @@ int main(int argc, char ** argv) {
usage(argv[0]);
}
std::string imatrix_dataset;
std::unordered_map<std::string, std::vector<float>> imatrix_data;
prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data);
int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data);
if (!imatrix_data.empty()) {
params.imatrix = &imatrix_data;
{
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
strncpy(kvo.val_str, imatrix_file.c_str(), 127);
kvo.val_str[127] = '\0';
kv_overrides.emplace_back(std::move(kvo));
}
if (!imatrix_dataset.empty()) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
kvo.val_str[127] = '\0';
kv_overrides.emplace_back(std::move(kvo));
}
{
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = imatrix_data.size();
kv_overrides.emplace_back(std::move(kvo));
}
if (m_last_call > 0) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = m_last_call;
kv_overrides.emplace_back(std::move(kvo));
}
}
if (!kv_overrides.empty()) {
kv_overrides.emplace_back();

38
examples/server/server.cpp
View file

@ -1208,7 +1208,7 @@ struct server_context {
}
auto n_ctx_train = llama_n_ctx_train(model);
if (slot.params.n_predict < 1 && slot.ga_n == 1
if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1
&& slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
LOG_WARNING("n_predict is not set and self-context extend is disabled."
" Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", {
@ -2392,7 +2392,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict);
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
printf(" --chat-template JINJA_TEMPLATE\n");
@ -2823,43 +2823,11 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
invalid_param = true;
break;
}
char * sep = strchr(argv[i], '=');
if (sep == nullptr || sep - argv[i] >= 128) {
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
struct llama_model_kv_override kvo;
std::strncpy(kvo.key, argv[i], sep - argv[i]);
kvo.key[sep - argv[i]] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.int_value = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.float_value = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
} else {
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
} else {
if (!parse_kv_override(argv[i], params.kv_overrides)) {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
params.kv_overrides.push_back(kvo);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
server_print_usage(argv[0], default_params, default_sparams);

6
flake.lock generated
View file

@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1713537308,
"narHash": "sha256-XtTSSIB2DA6tOv+l0FhvfDMiyCmhoRbNB+0SeInZkbk=",
"lastModified": 1714076141,
"narHash": "sha256-Drmja/f5MRHZCskS6mvzFqxEaZMeciScCTFxWVLqWEY=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "5c24cf2f0a12ad855f444c30b2421d044120c66f",
"rev": "7bb2ccd8cdc44c91edba16c48d2c8f331fb3d856",
"type": "github"
},
"original": {

2
ggml-backend.c
View file

@ -1784,12 +1784,14 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
// reset state for the next run
if (!sched->is_reset) {
size_t hash_size = sched->hash_set.size;
memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); // NOLINT
memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
memset(sched->tensor_copies, 0, sizeof(sched->tensor_copies[0]) * hash_size);
sched->is_reset = true;
}
sched->is_alloc = false;
}

284
ggml-quants.c
View file

@ -12383,3 +12383,287 @@ void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k)
block_iq2_s * restrict y = vy;
quantize_row_iq2_s_reference(x, y, k);
}
static bool validate_float(float f, size_t i) {
if (isinf(f)) {
fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
return false;
}
if (isnan(f)) {
fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
return false;
}
return true;
}
static bool isinf_fp16(ggml_fp16_t f) {
return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) == 0;
}
static bool isnan_fp16(ggml_fp16_t f) {
return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) != 0;
}
static bool validate_fp16(ggml_fp16_t f, size_t i) {
if (isinf_fp16(f)) {
fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
return false;
}
if (isnan_fp16(f)) {
fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
return false;
}
return true;
}
#define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \
const type * q = (const type *) (data); \
for (size_t i = 0; i < (nb); ++i) { \
if (!validate_fp16(q[i].d, i)) { \
return false; \
} \
}
#define VALIDATE_ROW_DATA_DM_F16_IMPL(type, data, nb, d, m) \
const type * q = (const type *) (data); \
for (size_t i = 0; i < (nb); ++i) { \
if (!validate_fp16(q[i].d, i) || !validate_fp16(q[i].m, i)) { \
return false; \
} \
}
bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
if (type < 0 || type >= GGML_TYPE_COUNT) {
fprintf(stderr, "%s: invalid type %d\n", __func__, type);
return false;
}
if (nbytes % ggml_type_size(type) != 0) {
fprintf(stderr, "%s: invalid size %zu for type %d\n", __func__, nbytes, type);
return false;
}
const size_t nb = nbytes/ggml_type_size(type);
switch (type) {
case GGML_TYPE_F16:
{
const ggml_fp16_t * f = (const ggml_fp16_t *) data;
size_t i = 0;
#if defined(__AVX2__)
for (; i + 15 < nb; i += 16) {
__m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
__m256i vexp = _mm256_and_si256(v, _mm256_set1_epi16(0x7c00));
__m256i cmp = _mm256_cmpeq_epi16(vexp, _mm256_set1_epi16(0x7c00));
int mask = _mm256_movemask_epi8(cmp);
if (mask) {
for (size_t j = 0; j < 16; ++j) {
if (!validate_fp16(f[i + j], i + j)) {
return false;
}
}
GGML_UNREACHABLE();
}
}
#elif defined(__ARM_NEON)
for (; i + 7 < nb; i += 8) {
uint16x8_t v = vld1q_u16(f + i);
uint16x8_t vexp = vandq_u16(v, vdupq_n_u16(0x7c00));
uint16x8_t cmp = vceqq_u16(vexp, vdupq_n_u16(0x7c00));
uint64_t mask = vget_lane_u64(vreinterpret_u64_u8(vshrn_n_u16(cmp, 4)), 0);
if (mask) {
for (size_t j = 0; j < 8; ++j) {
if (!validate_fp16(f[i + j], i + j)) {
return false;
}
}
GGML_UNREACHABLE();
}
}
#endif
for (; i < nb; ++i) {
if (!validate_fp16(f[i], i)) {
return false;
}
}
} break;
case GGML_TYPE_F32:
{
const float * f = (const float *) data;
size_t i = 0;
#if defined(__AVX2__)
for (; i + 7 < nb; i += 8) {
__m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
__m256i vexp = _mm256_and_si256(v, _mm256_set1_epi32(0x7f800000));
__m256i cmp = _mm256_cmpeq_epi32(vexp, _mm256_set1_epi32(0x7f800000));
int mask = _mm256_movemask_epi8(cmp);
if (mask) {
for (size_t j = 0; j < 8; ++j) {
if (!validate_float(f[i + j], i + j)) {
return false;
}
}
GGML_UNREACHABLE();
}
}
#elif defined(__ARM_NEON)
for (; i + 3 < nb; i += 4) {
uint32x4_t v = vld1q_u32((const uint32_t *)f + i);
uint32x4_t vexp = vandq_u32(v, vdupq_n_u32(0x7f800000));
uint32x4_t cmp = vceqq_u32(vexp, vdupq_n_u32(0x7f800000));
uint64_t mask = vget_lane_u64(vreinterpret_u64_u16(vshrn_n_u32(cmp, 8)), 0);
if (mask) {
for (size_t j = 0; j < 4; ++j) {
if (!validate_float(f[i + j], i + j)) {
return false;
}
}
GGML_UNREACHABLE();
}
}
#endif
for (; i < nb; ++i) {
if (!validate_float(f[i], i)) {
return false;
}
}
} break;
case GGML_TYPE_F64:
{
const double * f = (const double *) data;
for (size_t i = 0; i < nb; ++i) {
if (!validate_float(f[i], i)) {
return false;
}
}
} break;
case GGML_TYPE_Q4_0:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q4_0, data, nb);
} break;
case GGML_TYPE_Q4_1:
{
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_1, data, nb, d, m);
} break;
case GGML_TYPE_Q5_0:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_0, data, nb);
} break;
case GGML_TYPE_Q5_1:
{
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_1, data, nb, d, m);
} break;
case GGML_TYPE_Q8_0:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb);
} break;
case GGML_TYPE_Q2_K:
{
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin);
} break;
case GGML_TYPE_Q3_K:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q3_K, data, nb);
} break;
case GGML_TYPE_Q4_K:
{
#ifdef GGML_QKK_64
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]);
#else
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin);
#endif
} break;
case GGML_TYPE_Q5_K:
{
#ifdef GGML_QKK_64
VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb);
#else
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin);
#endif
} break;
case GGML_TYPE_Q6_K:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q6_K, data, nb);
} break;
case GGML_TYPE_Q8_K:
{
const block_q8_K * q = (const block_q8_K *) data;
for (size_t i = 0; i < nb; ++i) {
if (!validate_float(q[i].d, i)) {
return false;
}
}
} break;
case GGML_TYPE_IQ1_S:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq1_s, data, nb);
} break;
case GGML_TYPE_IQ1_M:
{
const block_iq1_m * q = (const block_iq1_m *) data;
for (size_t i = 0; i < nb; ++i) {
#if QK_K == 64
if (!validate_fp16(q[i].d, i)) {
return false;
}
#else
iq1m_scale_t scale;
const uint16_t * sc = (const uint16_t *)q[i].scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
if (!validate_fp16(scale.f16, i)) {
return false;
}
#endif
}
} break;
case GGML_TYPE_IQ2_XXS:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xxs, data, nb);
} break;
case GGML_TYPE_IQ2_XS:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xs, data, nb);
} break;
case GGML_TYPE_IQ2_S:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_s, data, nb);
} break;
case GGML_TYPE_IQ3_XXS:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_xxs, data, nb);
} break;
case GGML_TYPE_IQ3_S:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb);
} break;
case GGML_TYPE_IQ4_XS:
#if QK_K != 64
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb);
} break;
#endif
// with QK_K == 64, iq4_xs is iq4_nl
case GGML_TYPE_IQ4_NL:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
} break;
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_I64:
// nothing to validate
break;
default:
{
fprintf(stderr, "%s: invalid type %d\n", __func__, type);
return false;
}
}
return true;
}

2
ggml.h
View file

@ -762,6 +762,8 @@ extern "C" {
// use this to compute the memory overhead of a tensor
GGML_API size_t ggml_tensor_overhead(void);
GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes);
// main
GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);

129
llama.cpp
View file

@ -75,6 +75,7 @@
#include <forward_list>
#include <fstream>
#include <functional>
#include <future>
#include <initializer_list>
#include <locale>
#include <map>
@ -2885,6 +2886,7 @@ namespace GGUFMeta {
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";
}
@ -2896,13 +2898,16 @@ namespace GGUFMeta {
__func__, override_type_to_str(ovrd->tag), ovrd->key);
switch (ovrd->tag) {
case LLAMA_KV_OVERRIDE_TYPE_BOOL: {
LLAMA_LOG_INFO("%s\n", ovrd->bool_value ? "true" : "false");
LLAMA_LOG_INFO("%s\n", ovrd->val_bool ? "true" : "false");
} break;
case LLAMA_KV_OVERRIDE_TYPE_INT: {
LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->int_value);
LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->val_i64);
} break;
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
LLAMA_LOG_INFO("%.6f\n", ovrd->float_value);
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...
@ -2921,7 +2926,7 @@ namespace GGUFMeta {
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->bool_value;
target = ovrd->val_bool;
return true;
}
return false;
@ -2931,7 +2936,7 @@ namespace GGUFMeta {
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->int_value;
target = ovrd->val_i64;
return true;
}
return false;
@ -2941,7 +2946,7 @@ namespace GGUFMeta {
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->float_value;
target = ovrd->val_f64;
return true;
}
return false;
@ -2950,12 +2955,11 @@ namespace GGUFMeta {
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) {
(void)target;
(void)ovrd;
if (!ovrd) { return false; }
// Currently, we should never end up here so it would be a bug if we do.
throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n",
ovrd ? ovrd->key : "NULL"));
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) {
@ -2988,6 +2992,7 @@ struct llama_model_loader {
size_t n_bytes = 0;
bool use_mmap = false;
bool check_tensors;
llama_files files;
llama_ftype ftype;
@ -3021,7 +3026,7 @@ struct llama_model_loader {
std::string arch_name;
LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) {
llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p) {
int trace = 0;
if (getenv("LLAMA_TRACE")) {
trace = atoi(getenv("LLAMA_TRACE"));
@ -3226,6 +3231,7 @@ struct llama_model_loader {
}
this->use_mmap = use_mmap;
this->check_tensors = check_tensors;
}
~llama_model_loader() {
@ -3484,6 +3490,10 @@ struct llama_model_loader {
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)));
}
}
size_t size_done = 0;
@ -3500,6 +3510,8 @@ struct llama_model_loader {
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;
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) {
@ -3521,37 +3533,66 @@ struct llama_model_loader {
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, (uint8_t *) mapping->addr + weight->offs);
ggml_backend_tensor_alloc(buf_mmap, cur, data);
if (lmlocks) {
const auto & lmlock = lmlocks->at(weight->idx);
lmlock->grow_to(weight->offs + ggml_nbytes(cur));
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, (uint8_t *) mapping->addr + weight->offs, 0, n_size);
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, ggml_nbytes(cur));
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 {
read_buf.resize(ggml_nbytes(cur));
read_buf.resize(n_size);
file->seek(weight->offs, SEEK_SET);
file->read_raw(read_buf.data(), ggml_nbytes(cur));
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;
}
// 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
@ -5996,7 +6037,7 @@ static bool llm_load_tensors(
// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
try {
llama_model_loader ml(fname, params.use_mmap, params.kv_overrides);
llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
model.hparams.vocab_only = params.vocab_only;
@ -11453,6 +11494,10 @@ static int llama_decode_internal(
}
}
// Reset state for the next token before backend sync, to allow the CPU activities in the reset to
// overlap with device computation.
ggml_backend_sched_reset(lctx.sched);
return 0;
}
@ -14289,14 +14334,20 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
}
static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
if (nthread < 2) {
// single-thread
size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
if (!ggml_validate_row_data(new_type, new_data, new_size)) {
throw std::runtime_error("quantized data validation failed");
}
return new_size;
}
std::mutex mutex;
int64_t counter = 0;
size_t new_size = 0;
if (nthread < 2) {
// single-thread
return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
}
auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size,
bool valid = true;
auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
nrows, n_per_row, imatrix]() {
const int64_t nrows_per_chunk = chunk_size / n_per_row;
size_t local_size = 0;
@ -14311,7 +14362,17 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa
}
lock.unlock();
const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
local_size += this_size;
// validate the quantized data
const size_t row_size = ggml_row_size(new_type, n_per_row);
void * this_data = (char *) new_data + first_row * row_size;
if (!ggml_validate_row_data(new_type, this_data, this_size)) {
std::unique_lock<std::mutex> lock(mutex);
valid = false;
break;
}
}
};
for (int it = 0; it < nthread - 1; ++it) {
@ -14320,6 +14381,9 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa
compute();
for (auto & w : workers) { w.join(); }
workers.clear();
if (!valid) {
throw std::runtime_error("quantized data validation failed");
}
return new_size;
}
@ -14382,7 +14446,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
kv_overrides = v->data();
}
llama_model_loader ml(fname_inp, use_mmap, kv_overrides);
llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
ml.init_mappings(false); // no prefetching
llama_model model;
@ -14420,11 +14484,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
for (auto & o : overrides) {
if (o.key[0] == 0) break;
if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
gguf_set_val_f32(ctx_out, o.key, o.float_value);
gguf_set_val_f32(ctx_out, o.key, o.val_f64);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
gguf_set_val_i32(ctx_out, o.key, o.int_value);
gguf_set_val_i32(ctx_out, o.key, o.val_i64);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
gguf_set_val_bool(ctx_out, o.key, o.bool_value);
gguf_set_val_bool(ctx_out, o.key, o.val_bool);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {
gguf_set_val_str(ctx_out, o.key, o.val_str);
} else {
LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
}
@ -14743,7 +14809,7 @@ static int llama_apply_lora_from_file_internal(
std::unique_ptr<llama_model_loader> ml;
if (path_base_model) {
LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr));
ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr));
ml->init_mappings(/*prefetch*/ false); // no prefetching
}
@ -15002,6 +15068,7 @@ struct llama_model_params llama_model_default_params() {
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
/*.use_mlock =*/ false,
/*.check_tensors =*/ false,
};
#ifdef GGML_USE_METAL

13
llama.h
View file

@ -203,15 +203,19 @@ extern "C" {
LLAMA_KV_OVERRIDE_TYPE_INT,
LLAMA_KV_OVERRIDE_TYPE_FLOAT,
LLAMA_KV_OVERRIDE_TYPE_BOOL,
LLAMA_KV_OVERRIDE_TYPE_STR,
};
struct llama_model_kv_override {
char key[128];
enum llama_model_kv_override_type tag;
char key[128];
union {
int64_t int_value;
double float_value;
bool bool_value;
int64_t val_i64;
double val_f64;
bool val_bool;
char val_str[128];
};
};
@ -243,6 +247,7 @@ extern "C" {
bool vocab_only; // only load the vocabulary, no weights
bool use_mmap; // use mmap if possible
bool use_mlock; // force system to keep model in RAM
bool check_tensors; // validate model tensor data
};
struct llama_context_params {