vbatts/llama.cpp
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Henri Vasserman 2023-06-11 14:01:42 +03:00
parent bac0ddb58f
commit 2c00bf855d
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GPG key ID: 2995FC0F58B1A986
1 changed files with 99 additions and 124 deletions
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223
examples/server/server.cpp
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@ -5,8 +5,7 @@
#include "httplib.h"
#include "json.hpp"
struct server_params
{
struct server_params {
std::string hostname = "127.0.0.1";
int32_t port = 8080;
int32_t read_timeout = 600;
@ -25,14 +24,12 @@ enum stop_type {
STOP_PARTIAL,
};
bool ends_with(const std::string & str, const std::string & suffix)
{
bool ends_with(const std::string & str, const std::string & suffix) {
return str.size() >= suffix.size() &&
0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
}
size_t find_partial_stop_string(const std::string & stop, const std::string & text)
{
size_t find_partial_stop_string(const std::string & stop, const std::string & text) {
if (!text.empty() && !stop.empty()) {
const char text_last_char = text.back();
for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
@ -59,8 +56,8 @@ static std::string debug_str(const std::string & s) {
return ret;
}
template<class InputIt, class OutputIt>
static std::string tokens_to_str(llama_context * ctx, InputIt begin, OutputIt end) {
template<class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret;
for (; begin != end; (void)++begin) {
ret += llama_token_to_str(ctx, *begin);
@ -68,8 +65,7 @@ static std::string tokens_to_str(llama_context * ctx, InputIt begin, OutputIt en
return ret;
}
struct llama_server_context
{
struct llama_server_context {
bool stream = false;
bool has_next_token = false;
std::string generated_text = "";
@ -90,8 +86,7 @@ struct llama_server_context
int json_indent = -1;
int32_t multibyte_pending = 0;
~llama_server_context()
{
~llama_server_context() {
if (ctx) {
llama_free(ctx);
ctx = nullptr;
@ -110,12 +105,10 @@ struct llama_server_context
n_past = 0;
}
bool loadModel(const gpt_params & params_)
{
bool loadModel(const gpt_params & params_) {
params = params_;
ctx = llama_init_from_gpt_params(params);
if (ctx == NULL)
{
if (ctx == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return false;
}
@ -184,8 +177,7 @@ struct llama_server_context
has_next_token = true;
}
void beginCompletion()
{
void beginCompletion() {
// number of tokens to keep when resetting context
n_remain = params.n_predict;
llama_set_rng_seed(ctx, params.seed);
@ -215,15 +207,12 @@ struct llama_server_context
}
}
while (n_past < embd.size())
{
while (n_past < embd.size()) {
int n_eval = (int)embd.size() - n_past;
if (n_eval > params.n_batch)
{
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads))
{
if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
has_next_token = false;
return result;
@ -245,8 +234,7 @@ struct llama_server_context
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
{
llama_token id = 0; {
auto * logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
@ -257,8 +245,7 @@ struct llama_server_context
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++)
{
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
@ -273,18 +260,15 @@ struct llama_server_context
llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
last_n_repeat, alpha_frequency, alpha_presence);
if (!penalize_nl)
{
if (!penalize_nl) {
logits[llama_token_nl()] = nl_logit;
}
if (temp <= 0)
{
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
} else {
if (mirostat == 1)
{
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
@ -328,8 +312,7 @@ struct llama_server_context
}
size_t findStoppingStrings(const std::string & text, const size_t last_token_size,
const stop_type type)
{
const stop_type type) {
size_t stop_pos = std::string::npos;
for (const std::string & word : params.antiprompt) {
size_t pos;
@ -353,8 +336,7 @@ struct llama_server_context
return stop_pos;
}
std::string doCompletion()
{
std::string doCompletion() {
llama_token token = nextToken();
std::string token_text = token == -1 ? "" : llama_token_to_str(ctx, token);
@ -405,8 +387,7 @@ using namespace httplib;
using json = nlohmann::json;
void server_print_usage(int /*argc*/, char ** argv, const gpt_params & params, const server_params & sparams)
{
void server_print_usage(int /*argc*/, char ** argv, const gpt_params & params, const server_params & sparams) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
@ -417,12 +398,10 @@ void server_print_usage(int /*argc*/, char ** argv, const gpt_params & params, c
fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n");
if (llama_mlock_supported())
{
if (llama_mlock_supported()) {
fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
if (llama_mmap_supported())
{
if (llama_mmap_supported()) {
fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
@ -446,8 +425,7 @@ void server_print_usage(int /*argc*/, char ** argv, const gpt_params & params, c
}
void server_params_parse(int argc, char ** argv, server_params & sparams,
gpt_params & params)
{
gpt_params & params) {
gpt_params default_params;
server_params default_sparams;
std::string arg;
@ -522,10 +500,8 @@ void server_params_parse(int argc, char ** argv, server_params & sparams,
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
#endif
}
else if (arg == "--tensor-split" || arg == "-ts")
{
if (++i >= argc)
{
else if (arg == "--tensor-split" || arg == "-ts") {
if (++i >= argc) {
invalid_param = true;
break;
}
@ -538,14 +514,11 @@ void server_params_parse(int argc, char ** argv, server_params & sparams,
std::vector<std::string> split_arg{ it, {} };
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i)
{
if (i < split_arg.size())
{
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
if (i < split_arg.size()) {
params.tensor_split[i] = std::stof(split_arg[i]);
}
else
{
else {
params.tensor_split[i] = 0.0f;
}
}
@ -553,10 +526,8 @@ void server_params_parse(int argc, char ** argv, server_params & sparams,
fprintf(stderr, "WARNING: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
#endif // GGML_USE_CUBLAS
}
else if (arg == "--main-gpu" || arg == "-mg")
{
if (++i >= argc)
{
else if (arg == "--main-gpu" || arg == "-mg") {
if (++i >= argc) {
invalid_param = true;
break;
}
@ -603,32 +574,31 @@ json format_generation_settings(llama_server_context & llama) {
const bool ignore_eos = eos_bias != llama.params.logit_bias.end() &&
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
return json{
{ "seed", llama.params.seed },
{ "temp", llama.params.temp },
{ "top_k", llama.params.top_k },
{ "top_p", llama.params.top_p },
{ "tfs_z", llama.params.tfs_z },
{ "typical_p", llama.params.typical_p },
{ "repeat_last_n", llama.params.repeat_last_n },
{ "repeat_penalty", llama.params.repeat_penalty },
{ "presence_penalty", llama.params.presence_penalty },
{ "frequency_penalty", llama.params.frequency_penalty },
{ "mirostat", llama.params.mirostat },
{ "mirostat_tau", llama.params.mirostat_tau },
{ "mirostat_eta", llama.params.mirostat_eta },
{ "penalize_nl", llama.params.penalize_nl },
{ "stop", llama.params.antiprompt },
{ "n_predict", llama.params.n_predict },
{ "n_keep", llama.params.n_keep },
{ "ignore_eos", ignore_eos },
{ "stream", llama.stream },
{ "logit_bias", llama.params.logit_bias },
return json {
{ "seed", llama.params.seed },
{ "temp", llama.params.temp },
{ "top_k", llama.params.top_k },
{ "top_p", llama.params.top_p },
{ "tfs_z", llama.params.tfs_z },
{ "typical_p", llama.params.typical_p },
{ "repeat_last_n", llama.params.repeat_last_n },
{ "repeat_penalty", llama.params.repeat_penalty },
{ "presence_penalty", llama.params.presence_penalty },
{ "frequency_penalty", llama.params.frequency_penalty },
{ "mirostat", llama.params.mirostat },
{ "mirostat_tau", llama.params.mirostat_tau },
{ "mirostat_eta", llama.params.mirostat_eta },
{ "penalize_nl", llama.params.penalize_nl },
{ "stop", llama.params.antiprompt },
{ "n_predict", llama.params.n_predict },
{ "n_keep", llama.params.n_keep },
{ "ignore_eos", ignore_eos },
{ "stream", llama.stream },
{ "logit_bias", llama.params.logit_bias },
};
}
bool parse_options_completion(json body, llama_server_context & llama, Response & res)
{
bool parse_options_completion(json body, llama_server_context & llama, Response & res) {
gpt_params default_params;
if (!body["stream"].is_null()) {
llama.stream = body["stream"].get<bool>();
@ -766,8 +736,7 @@ bool parse_options_completion(json body, llama_server_context & llama, Response
return true;
}
int main(int argc, char ** argv)
{
int main(int argc, char ** argv) {
// own arguments required by this example
gpt_params params;
server_params sparams;
@ -791,20 +760,20 @@ int main(int argc, char ** argv)
std::thread::hardware_concurrency(), llama_print_system_info());
// load the model
if (!llama.loadModel(params))
{
if (!llama.loadModel(params)) {
return 1;
}
Server svr;
svr.set_default_headers({
{"Access-Control-Allow-Origin", "*"},
{"Access-Control-Allow-Headers", "content-type"}
});
{ "Access-Control-Allow-Origin", "*" },
{ "Access-Control-Allow-Headers", "content-type" }
});
svr.Get("/", [](const Request &, Response & res)
{ res.set_content("<h1>llama.cpp server works</h1>", "text/html"); });
svr.Get("/", [](const Request &, Response & res) {
res.set_content("<h1>llama.cpp server works</h1>", "text/html");
});
svr.Post("/completion", [&llama](const Request & req, Response & res) {
@ -836,13 +805,15 @@ int main(int argc, char ** argv)
llama.generated_text.end());
}
json data = { {"content", llama.generated_text},
{"stop", true},
{"model", llama.params.model_alias},
{"tokens_predicted", llama.num_tokens_predicted},
{"generation_settings", format_generation_settings(llama)},
{"prompt", llama.params.prompt},
{"stopping_word", llama.stopping_word} };
json data {
{ "content", llama.generated_text },
{ "stop", true },
{ "model", llama.params.model_alias },
{ "tokens_predicted", llama.num_tokens_predicted },
{ "generation_settings", format_generation_settings(llama) },
{ "prompt", llama.params.prompt },
{ "stopping_word", llama.stopping_word },
};
llama_print_timings(llama.ctx);
@ -851,7 +822,7 @@ int main(int argc, char ** argv)
"application/json");
}
else {
const auto chunked_content_provider = [&](size_t, DataSink& sink) {
const auto chunked_content_provider = [&](size_t, DataSink & sink) {
size_t sent_count = 0;
while (llama.has_next_token) {
@ -880,18 +851,22 @@ int main(int argc, char ** argv)
json data;
if (llama.has_next_token) {
data = { {"content", to_send}, {"stop", false} };
data = {
{ "content", to_send },
{ "stop", false },
};
} else {
// Generation is done, send extra information.
data = {
{"content", to_send},
{"stop", true},
{"model", llama.params.model_alias},
{"tokens_predicted", llama.num_tokens_predicted},
{"generation_settings", format_generation_settings(llama)},
{"prompt", llama.params.prompt},
{"stopping_word", llama.stopping_word},
{"generated_text", llama.generated_text} };
{ "content", to_send },
{ "stop", true },
{ "model", llama.params.model_alias },
{ "tokens_predicted", llama.num_tokens_predicted },
{ "generation_settings", format_generation_settings(llama) },
{ "prompt", llama.params.prompt },
{ "stopping_word", llama.stopping_word },
{ "generated_text", llama.generated_text },
};
}
std::string str =
@ -919,31 +894,31 @@ int main(int argc, char ** argv)
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider);
}
});
});
svr.Options(R"(/.*)", [](const Request &, Response & res)
{
return res.set_content("", "application/json");
});
svr.Options(R"(/.*)", [](const Request &, Response & res) {
return res.set_content("", "application/json");
});
svr.Post("/tokenize", [&llama](const Request & req, Response & res)
{
json body = json::parse(req.body);
json data = {
{"tokens", ::llama_tokenize(llama.ctx, body["content"].get<std::string>(), false) } };
return res.set_content(data.dump(llama.json_indent), "application/json");
});
svr.Post("/tokenize", [&llama](const Request & req, Response & res) {
json body = json::parse(req.body);
std::string content = body["content"].get<std::string>();
std::vector<llama_token> tokens = ::llama_tokenize(llama.ctx, content, false);
json data {{ "tokens", tokens }};
return res.set_content(data.dump(llama.json_indent), "application/json");
});
svr.set_logger([](const Request & req, const Response & res) {
json log = {
{ "time", time(NULL) },
{ "ip", req.remote_addr },
{ "status", res.status },
{ "path", req.path },
{ "request", req.body },
{ "response", res.body },
};
fprintf(stdout, "http_request: %s\n",
log.dump(-1, ' ', false, json::error_handler_t::replace).c_str());
});
fprintf(stdout, "%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str());
});
svr.set_exception_handler([](const Request &, Response & res, std::exception_ptr ep) {
const auto * fmt = "500 Internal Server Error\n%s";