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feat: sync llama.cpp #75

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merged 4 commits into from
Aug 26, 2024
Merged

feat: sync llama.cpp #75

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9df1936
feat: sync llama.cpp
jhen0409 Aug 23, 2024
2ed24b9
fix: migrate from api changes
jhen0409 Aug 23, 2024
f2f4ba7
feat: sync llama.cpp
jhen0409 Aug 25, 2024
958ab56
feat: sync llama.cpp
jhen0409 Aug 26, 2024
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2 changes: 1 addition & 1 deletion android/src/main/jni.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -168,7 +168,7 @@ Java_com_rnllama_LlamaContext_initContext(

const char *lora_chars = env->GetStringUTFChars(lora_str, nullptr);
if (lora_chars != nullptr && lora_chars[0] != '\0') {
defaultParams.lora_adapter.push_back({lora_chars, lora_scaled});
defaultParams.lora_adapters.push_back({lora_chars, lora_scaled});
defaultParams.use_mmap = false;
}

Expand Down
201 changes: 158 additions & 43 deletions cpp/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -83,6 +83,41 @@ char const *LLAMA_BUILD_TARGET = "unknown";

using json = nlohmann::ordered_json;

//
// Environment variable utils
//

template<typename T>
static typename std::enable_if<std::is_same<T, std::string>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::string(value) : target;
}

template<typename T>
static typename std::enable_if<!std::is_same<T, bool>::value && std::is_integral<T>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::stoi(value) : target;
}

template<typename T>
static typename std::enable_if<std::is_floating_point<T>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
target = value ? std::stof(value) : target;
}

template<typename T>
static typename std::enable_if<std::is_same<T, bool>::value, void>::type
get_env(std::string name, T & target) {
char * value = std::getenv(name.c_str());
if (value) {
std::string val(value);
target = val == "1" || val == "true";
}
}

//
// CPU utils
//
Expand Down Expand Up @@ -116,8 +151,34 @@ int32_t cpu_get_num_physical_cores() {
if (result == 0) {
return num_physical_cores;
}
#elif defined(_WIN32)
//TODO: Implement
#elif defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
// TODO: windows + arm64 + mingw64
unsigned int n_threads_win = std::thread::hardware_concurrency();
unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4;

DWORD buffer_size = 0;
if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) {
if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) {
return default_threads;
}
}

std::vector<char> buffer(buffer_size);
if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data()), &buffer_size)) {
return default_threads;
}

int32_t num_physical_cores = 0;
PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data());
while (buffer_size > 0) {
if (info->Relationship == RelationProcessorCore) {
num_physical_cores += info->Processor.GroupCount;
}
buffer_size -= info->Size;
info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(reinterpret_cast<char*>(info) + info->Size);
}

return num_physical_cores > 0 ? num_physical_cores : default_threads;
#endif
unsigned int n_threads = std::thread::hardware_concurrency();
return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
Expand Down Expand Up @@ -200,12 +261,6 @@ int32_t cpu_get_num_math() {
// CLI argument parsing
//

void gpt_params_handle_hf_token(gpt_params & params) {
if (params.hf_token.empty() && std::getenv("HF_TOKEN")) {
params.hf_token = std::getenv("HF_TOKEN");
}
}

void gpt_params_handle_model_default(gpt_params & params) {
if (!params.hf_repo.empty()) {
// short-hand to avoid specifying --hf-file -> default it to --model
Expand Down Expand Up @@ -253,7 +308,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {

gpt_params_handle_model_default(params);

gpt_params_handle_hf_token(params);
if (params.hf_token.empty()) {
get_env("HF_TOKEN", params.hf_token);
}

if (params.escape) {
string_process_escapes(params.prompt);
Expand All @@ -273,6 +330,25 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
return true;
}

void gpt_params_parse_from_env(gpt_params & params) {
// we only care about server-related params for now
get_env("LLAMA_ARG_MODEL", params.model);
get_env("LLAMA_ARG_THREADS", params.n_threads);
get_env("LLAMA_ARG_CTX_SIZE", params.n_ctx);
get_env("LLAMA_ARG_N_PARALLEL", params.n_parallel);
get_env("LLAMA_ARG_BATCH", params.n_batch);
get_env("LLAMA_ARG_UBATCH", params.n_ubatch);
get_env("LLAMA_ARG_N_GPU_LAYERS", params.n_gpu_layers);
get_env("LLAMA_ARG_THREADS_HTTP", params.n_threads_http);
get_env("LLAMA_ARG_CHAT_TEMPLATE", params.chat_template);
get_env("LLAMA_ARG_N_PREDICT", params.n_predict);
get_env("LLAMA_ARG_ENDPOINT_METRICS", params.endpoint_metrics);
get_env("LLAMA_ARG_ENDPOINT_SLOTS", params.endpoint_slots);
get_env("LLAMA_ARG_EMBEDDINGS", params.embedding);
get_env("LLAMA_ARG_FLASH_ATTN", params.flash_attn);
get_env("LLAMA_ARG_DEFRAG_THOLD", params.defrag_thold);
}

bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
const auto params_org = params; // the example can modify the default params

Expand Down Expand Up @@ -690,14 +766,24 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
}
if (arg == "--lora") {
CHECK_ARG
params.lora_adapter.emplace_back(argv[i], 1.0f);
params.lora_adapters.push_back({
std::string(argv[i]),
1.0,
});
return true;
}
if (arg == "--lora-scaled") {
CHECK_ARG
const char* lora_adapter = argv[i];
std::string lora_adapter = argv[i];
CHECK_ARG
params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
params.lora_adapters.push_back({
lora_adapter,
std::stof(argv[i]),
});
return true;
}
if (arg == "--lora-init-without-apply") {
params.lora_init_without_apply = true;
return true;
}
if (arg == "--control-vector") {
Expand Down Expand Up @@ -821,7 +907,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
}
return true;
}
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--gpu-layers-draft") {
if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--n-gpu-layers-draft") {
CHECK_ARG
params.n_gpu_layers_draft = std::stoi(argv[i]);
if (!llama_supports_gpu_offload()) {
Expand Down Expand Up @@ -1660,6 +1746,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
options.push_back({ "server", "-sps, --slot-prompt-similarity SIMILARITY",
"how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity });
options.push_back({ "server", " --lora-init-without-apply", "load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"});

#ifndef LOG_DISABLE_LOGS
options.push_back({ "logging" });
Expand Down Expand Up @@ -1722,7 +1809,13 @@ std::string gpt_params_get_system_info(const gpt_params & params) {
if (params.n_threads_batch != -1) {
os << " (n_threads_batch = " << params.n_threads_batch << ")";
}
#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
// TODO: windows + arm64 + mingw64
DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS);
os << " / " << logicalProcessorCount << " | " << llama_print_system_info();
#else
os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();
#endif

return os.str();
}
Expand Down Expand Up @@ -1772,6 +1865,17 @@ std::string string_get_sortable_timestamp() {
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
}

void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return; // Avoid infinite loop if 'search' is an empty string
}
size_t pos = 0;
while ((pos = s.find(search, pos)) != std::string::npos) {
s.replace(pos, search.length(), replace);
pos += replace.length();
}
}

void string_process_escapes(std::string & input) {
std::size_t input_len = input.length();
std::size_t output_idx = 0;
Expand Down Expand Up @@ -2045,8 +2149,8 @@ std::string fs_get_cache_file(const std::string & filename) {
//
// Model utils
//

std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
llama_init_result iparams;
auto mparams = llama_model_params_from_gpt_params(params);

llama_model * model = nullptr;
Expand All @@ -2061,7 +2165,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par

if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return std::make_tuple(nullptr, nullptr);
return iparams;
}

auto cparams = llama_context_params_from_gpt_params(params);
Expand All @@ -2070,7 +2174,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
if (lctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
return std::make_tuple(nullptr, nullptr);
return iparams;
}

if (!params.control_vectors.empty()) {
Expand All @@ -2081,7 +2185,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
if (cvec.n_embd == -1) {
llama_free(lctx);
llama_free_model(model);
return std::make_tuple(nullptr, nullptr);
return iparams;
}

int err = llama_control_vector_apply(lctx,
Expand All @@ -2093,21 +2197,26 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
if (err) {
llama_free(lctx);
llama_free_model(model);
return std::make_tuple(nullptr, nullptr);
return iparams;
}
}

for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
float lora_scale = std::get<1>(params.lora_adapter[i]);
auto adapter = llama_lora_adapter_init(model, lora_adapter.c_str());
if (adapter == nullptr) {
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
// load and optionally apply lora adapters
for (auto & la : params.lora_adapters) {
llama_lora_adapter_container loaded_la;
loaded_la.path = la.path;
loaded_la.scale = la.scale;
loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
if (loaded_la.adapter == nullptr) {
fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
llama_free(lctx);
llama_free_model(model);
return std::make_tuple(nullptr, nullptr);
return iparams;
}
llama_lora_adapter_set(lctx, adapter, lora_scale);
iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
}
if (!params.lora_init_without_apply) {
llama_lora_adapters_apply(lctx, iparams.lora_adapters);
}

if (params.ignore_eos) {
Expand Down Expand Up @@ -2135,13 +2244,26 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
tmp.clear();
tmp.push_back(decoder_start_token_id);
}
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
if (llama_model_has_decoder(model)) {
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
}
llama_kv_cache_clear(lctx);
llama_synchronize(lctx);
llama_reset_timings(lctx);
}

return std::make_tuple(model, lctx);
iparams.model = model;
iparams.context = lctx;
return iparams;
}

void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
llama_lora_adapter_clear(ctx);
for (auto & la : lora_adapters) {
if (la.scale != 0.0f) {
llama_lora_adapter_set(ctx, la.adapter, la.scale);
}
}
}

struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) {
Expand Down Expand Up @@ -2668,12 +2790,6 @@ std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token>
return text;
}

bool llama_should_add_bos_token(const llama_model * model) {
const int add_bos = llama_add_bos_token(model);

return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM);
}

//
// Chat template utils
//
Expand Down Expand Up @@ -3166,19 +3282,18 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
}

fprintf(stream, "lora:\n");
for (std::tuple<std::string, float> la : params.lora_adapter) {
if (std::get<1>(la) != 1.0f) {
continue;
for (auto & la : params.lora_adapters) {
if (la.scale == 1.0f) {
fprintf(stream, " - %s\n", la.path.c_str());
}
fprintf(stream, " - %s\n", std::get<0>(la).c_str());
}
fprintf(stream, "lora_scaled:\n");
for (std::tuple<std::string, float> la : params.lora_adapter) {
if (std::get<1>(la) == 1.0f) {
continue;
for (auto & la : params.lora_adapters) {
if (la.scale != 1.0f) {
fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale);
}
fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la));
}
fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
Expand Down
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