aboutsummaryrefslogtreecommitdiff
path: root/examples/server/server.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'examples/server/server.cpp')
-rw-r--r--examples/server/server.cpp1581
1 files changed, 855 insertions, 726 deletions
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 8727500..12d4e2f 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1,799 +1,928 @@
-#include <httplib.h>
-#include <json.hpp>
#include "common.h"
#include "llama.h"
+#include "build-info.h"
-struct server_params
-{
- std::string hostname = "127.0.0.1";
- int32_t port = 8080;
+// single thread
+#define CPPHTTPLIB_THREAD_POOL_COUNT 1
+#ifndef NDEBUG
+// crash the server in debug mode, otherwise send an http 500 error
+#define CPPHTTPLIB_NO_EXCEPTIONS 1
+#endif
+
+#include "httplib.h"
+#include "json.hpp"
+
+#ifndef SERVER_VERBOSE
+#define SERVER_VERBOSE 1
+#endif
+
+using namespace httplib;
+using json = nlohmann::json;
+
+struct server_params {
+ std::string hostname = "127.0.0.1";
+ int32_t port = 8080;
+ int32_t read_timeout = 600;
+ int32_t write_timeout = 600;
};
-struct llama_server_context
-{
- bool as_loop = false;
- bool has_next_token = false;
- std::string generated_text = "";
-
- int32_t num_tokens_predicted = 0;
- int32_t n_past = 0;
- int32_t n_consumed = 0;
- int32_t n_session_consumed = 0;
- int32_t n_remain = 0;
-
- std::vector<llama_token> embd;
- std::vector<llama_token> last_n_tokens;
- std::vector<llama_token> processed_tokens;
- std::vector<llama_token> llama_token_newline;
- std::vector<llama_token> embd_inp;
- std::vector<std::vector<llama_token>> no_show_words;
- std::vector<llama_token> tokens_predicted;
-
- llama_context *ctx;
- gpt_params params;
-
- void rewind() {
- as_loop = false;
- params.antiprompt.clear();
- no_show_words.clear();
- num_tokens_predicted = 0;
- generated_text = "";
- }
-
- bool loadModel(gpt_params params_)
- {
- params = params_;
- ctx = llama_init_from_gpt_params(params);
- if (ctx == NULL)
- {
- fprintf(stderr, "%s: error: unable to load model\n", __func__);
- return false;
- }
- // determine newline token
- llama_token_newline = ::llama_tokenize(ctx, "\n", false);
- last_n_tokens.resize(params.n_ctx);
- std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
- return true;
- }
-
- bool loadPrompt() {
- params.prompt.insert(0, 1, ' '); // always add a first space
- std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
- // compare the evaluated prompt with the new prompt
- int new_prompt_len = 0;
- for (size_t i = 0; i < prompt_tokens.size(); i++) {
- if (i < processed_tokens.size() &&
- processed_tokens[i] == prompt_tokens[i])
- {
- continue;
- }
- else
- {
- embd_inp.push_back(prompt_tokens[i]);
- if(new_prompt_len == 0) {
- if(int32_t(i) - 1 < n_past) {
- processed_tokens.erase(processed_tokens.begin() + i, processed_tokens.end());
- }
- // Evaluate the new fragment prompt from the last token processed.
- n_past = processed_tokens.size();
+static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
+ size_t i;
+ for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
+ return i;
+}
+
+enum stop_type {
+ STOP_FULL,
+ STOP_PARTIAL,
+};
+
+static 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);
+}
+
+static 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--) {
+ if (stop[char_index] == text_last_char) {
+ const std::string current_partial = stop.substr(0, char_index + 1);
+ if (ends_with(text, current_partial)) {
+ return text.size() - char_index - 1;
+ }
+ }
}
- new_prompt_len ++;
- }
- }
- if(n_past > 0 && params.interactive) {
- n_remain -= new_prompt_len;
}
- if ((int)embd_inp.size() > params.n_ctx - 4)
- {
- return false;
+ return std::string::npos;
+}
+
+template<class Iter>
+static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
+ std::string ret;
+ for (; begin != end; ++begin) {
+ ret += llama_token_to_str(ctx, *begin);
}
- has_next_token = true;
- return true;
- }
-
- void beginCompletion()
- {
- if(n_remain == 0) {
- // number of tokens to keep when resetting context
- if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size())
- {
- params.n_keep = (int)embd_inp.size();
- }
+ return ret;
+}
+
+static void server_log(const char * level, const char * function, int line,
+ const char * message, const nlohmann::ordered_json & extra) {
+ nlohmann::ordered_json log {
+ { "timestamp", time(nullptr) },
+ { "level", level },
+ { "function", function },
+ { "line", line },
+ { "message", message },
+ };
+
+ if (!extra.empty()) {
+ log.merge_patch(extra);
}
- n_remain = params.n_predict;
- }
-
- llama_token nextToken() {
- llama_token result = -1;
- if (embd.size() > 0)
- {
- if (n_past + (int)embd.size() > params.n_ctx)
- {
- // Reset context
- const int n_left = n_past - params.n_keep;
- n_past = std::max(1, params.n_keep);
- processed_tokens.erase(processed_tokens.begin() + n_past, processed_tokens.end());
- embd.insert(embd.begin(), last_n_tokens.begin() + params.n_ctx - n_left / 2 - embd.size(), last_n_tokens.end() - embd.size());
- }
- for (int i = 0; i < (int)embd.size(); i += params.n_batch)
- {
- int n_eval = (int)embd.size() - i;
- if (n_eval > params.n_batch)
- {
- n_eval = params.n_batch;
+
+ const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
+ fprintf(stdout, "%.*s\n", (int)str.size(), str.data());
+ fflush(stdout);
+}
+
+static bool server_verbose = false;
+
+#if SERVER_VERBOSE != 1
+# define LOG_VERBOSE(MSG, ...)
+#else
+# define LOG_VERBOSE(MSG, ...) \
+ do { \
+ if (server_verbose) { \
+ server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
+ } \
+ } while(0)
+#endif
+
+#define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__)
+#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
+#define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
+
+struct llama_server_context {
+ bool stream = false;
+ bool has_next_token = false;
+ std::string generated_text;
+
+ size_t num_tokens_predicted = 0;
+ size_t n_past = 0;
+ size_t n_remain = 0;
+
+ std::vector<llama_token> embd;
+ std::vector<llama_token> last_n_tokens;
+
+ llama_context * ctx = nullptr;
+ gpt_params params;
+
+ bool truncated = false;
+ bool stopped_eos = false;
+ bool stopped_word = false;
+ bool stopped_limit = false;
+ std::string stopping_word;
+ int32_t multibyte_pending = 0;
+
+ ~llama_server_context() {
+ if (ctx) {
+ llama_free(ctx);
+ ctx = nullptr;
}
- if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads))
- {
- fprintf(stderr, "%s : failed to eval\n", __func__);
- has_next_token = false;
- return result;
+ }
+
+ void rewind() {
+ params.antiprompt.clear();
+ num_tokens_predicted = 0;
+ generated_text = "";
+ generated_text.reserve(params.n_ctx);
+ truncated = false;
+ stopped_eos = false;
+ stopped_word = false;
+ stopped_limit = false;
+ stopping_word = "";
+ multibyte_pending = 0;
+
+ n_remain = 0;
+ n_past = 0;
+ }
+
+ bool loadModel(const gpt_params & params_) {
+ params = params_;
+ ctx = llama_init_from_gpt_params(params);
+ if (ctx == nullptr) {
+ LOG_ERROR("unable to load model", { { "model", params_.model } });
+ return false;
}
- n_past += n_eval;
- }
+
+ last_n_tokens.resize(params.n_ctx);
+ std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
+ return true;
}
- embd.clear();
- if ((int)embd_inp.size() <= n_consumed && has_next_token)
- {
- // out of user input, sample next token
- const float temp = params.temp;
- // const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
- const float top_p = params.top_p;
- const float tfs_z = params.tfs_z;
- const float typical_p = params.typical_p;
- const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n;
- const float repeat_penalty = params.repeat_penalty;
- const float alpha_presence = params.presence_penalty;
- const float alpha_frequency = params.frequency_penalty;
- const int mirostat = params.mirostat;
- 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;
- {
- auto logits = llama_get_logits(ctx);
- auto n_vocab = llama_n_vocab(ctx);
-
- // Apply params.logit_bias map
- for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++)
- {
- logits[it->first] += it->second;
+
+ void loadPrompt() {
+ params.prompt.insert(0, 1, ' '); // always add a first space
+ std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
+
+ if (params.n_keep < 0) {
+ params.n_keep = (int)prompt_tokens.size();
}
+ params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
+
+ // if input prompt is too big, truncate like normal
+ if (prompt_tokens.size() >= (size_t)params.n_ctx) {
+ const int n_left = (params.n_ctx - params.n_keep) / 2;
+ std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
+ const int erased_blocks = (prompt_tokens.size() - params.n_keep - n_left - 1) / n_left;
+ new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
+ std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin());
+
+ LOG_VERBOSE("input truncated", {
+ { "n_ctx", params.n_ctx },
+ { "n_keep", params.n_keep },
+ { "n_left", n_left },
+ { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
+ });
- std::vector<llama_token_data> candidates;
- candidates.reserve(n_vocab);
- 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});
+ truncated = true;
+ prompt_tokens = new_tokens;
+ } else {
+ const size_t ps = prompt_tokens.size();
+ std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
+ std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
}
- llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
-
- // Apply penalties
- float nl_logit = logits[llama_token_nl()];
- auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx);
- llama_sample_repetition_penalty(ctx, &candidates_p,
- last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
- last_n_repeat, repeat_penalty);
- 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)
- {
- logits[llama_token_nl()] = nl_logit;
+ // compare the evaluated prompt with the new prompt
+ n_past = common_part(embd, prompt_tokens);
+ embd = prompt_tokens;
+ if (n_past == prompt_tokens.size()) {
+ // we have to evaluate at least 1 token to generate logits.
+ n_past--;
}
- if (temp <= 0)
- {
- // Greedy sampling
- id = llama_sample_token_greedy(ctx, &candidates_p);
+ LOG_VERBOSE("prompt ingested", {
+ { "n_past", n_past },
+ { "cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past) },
+ { "to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
+ });
+
+ has_next_token = true;
+ }
+
+ void beginCompletion() {
+ // number of tokens to keep when resetting context
+ n_remain = params.n_predict;
+ llama_set_rng_seed(ctx, params.seed);
+ }
+
+ llama_token nextToken() {
+ llama_token result = -1;
+
+ if (embd.size() >= (size_t)params.n_ctx) {
+ // Reset context
+ const int n_left = (params.n_ctx - params.n_keep) / 2;
+
+ std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + params.n_keep);
+ new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
+ embd = new_tokens;
+ n_past = params.n_keep;
+ truncated = true;
+ LOG_VERBOSE("input truncated", {
+ { "n_ctx", params.n_ctx },
+ { "n_keep", params.n_keep },
+ { "n_left", n_left },
+ { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
+ });
}
- else
- {
- if (mirostat == 1)
- {
- static float mirostat_mu = 2.0f * mirostat_tau;
- const int mirostat_m = 100;
- llama_sample_temperature(ctx, &candidates_p, temp);
- id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
- }
- else if (mirostat == 2)
- {
- static float mirostat_mu = 2.0f * mirostat_tau;
- llama_sample_temperature(ctx, &candidates_p, temp);
- id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
- }
- else
- {
- // Temperature sampling
- llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
- llama_sample_typical(ctx, &candidates_p, typical_p, 1);
- llama_sample_top_p(ctx, &candidates_p, top_p, 1);
- llama_sample_temperature(ctx, &candidates_p, temp);
- id = llama_sample_token(ctx, &candidates_p);
- }
+
+ while (n_past < embd.size()) {
+ int n_eval = (int)embd.size() - n_past;
+ 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)) {
+ LOG_ERROR("failed to eval", {
+ { "n_eval", n_eval },
+ { "n_past", n_past },
+ { "n_threads", params.n_threads },
+ { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
+ });
+ has_next_token = false;
+ return result;
+ }
+ n_past += n_eval;
}
- last_n_tokens.erase(last_n_tokens.begin());
- last_n_tokens.push_back(id);
- processed_tokens.push_back(id);
- num_tokens_predicted++;
- }
-
- // replace end of text token with newline token when in interactive mode
- if (id == llama_token_eos() && params.interactive)
- {
- id = llama_token_newline.front();
- if (params.antiprompt.size() != 0)
+
+ // out of user input, sample next token
+ const float temp = params.temp;
+ const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
+ const float top_p = params.top_p;
+ const float tfs_z = params.tfs_z;
+ const float typical_p = params.typical_p;
+ const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n;
+ const float repeat_penalty = params.repeat_penalty;
+ const float alpha_presence = params.presence_penalty;
+ const float alpha_frequency = params.frequency_penalty;
+ const int mirostat = params.mirostat;
+ 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;
+
{
- // tokenize and inject first reverse prompt
- const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false);
- embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
+ auto * logits = llama_get_logits(ctx);
+ auto n_vocab = llama_n_vocab(ctx);
+
+ // Apply params.logit_bias map
+ for (const auto & it : params.logit_bias) {
+ logits[it.first] += it.second;
+ }
+
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+ 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 });
+ }
+
+ llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+ // Apply penalties
+ float nl_logit = logits[llama_token_nl()];
+ auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx);
+ llama_sample_repetition_penalty(ctx, &candidates_p,
+ last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+ last_n_repeat, repeat_penalty);
+ 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) {
+ logits[llama_token_nl()] = nl_logit;
+ }
+
+ if (temp <= 0) {
+ // Greedy sampling
+ id = llama_sample_token_greedy(ctx, &candidates_p);
+ } else {
+ if (mirostat == 1) {
+ static float mirostat_mu = 2.0f * mirostat_tau;
+ const int mirostat_m = 100;
+ llama_sample_temperature(ctx, &candidates_p, temp);
+ id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
+ } else if (mirostat == 2) {
+ static float mirostat_mu = 2.0f * mirostat_tau;
+ llama_sample_temperature(ctx, &candidates_p, temp);
+ id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
+ } else {
+ // Temperature sampling
+ llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
+ llama_sample_typical(ctx, &candidates_p, typical_p, 1);
+ llama_sample_top_p(ctx, &candidates_p, top_p, 1);
+ llama_sample_top_k(ctx, &candidates_p, top_k, 1);
+ llama_sample_temperature(ctx, &candidates_p, temp);
+ id = llama_sample_token(ctx, &candidates_p);
+ }
+ }
+ last_n_tokens.erase(last_n_tokens.begin());
+ last_n_tokens.push_back(id);
+ num_tokens_predicted++;
}
- }
- // add it to the context
- embd.push_back(id);
- for (auto id : embd)
- {
+ // add it to the context
+ embd.push_back(id);
result = id;
- }
- // decrement remaining sampling budget
- --n_remain;
- }
- else
- {
- // some user input remains from prompt or interaction, forward it to processing
- while ((int)embd_inp.size() > n_consumed)
- {
- embd.push_back(embd_inp[n_consumed]);
- last_n_tokens.erase(last_n_tokens.begin());
- last_n_tokens.push_back(embd_inp[n_consumed]);
- processed_tokens.push_back(embd_inp[n_consumed]);
- ++n_consumed;
- if ((int)embd.size() >= params.n_batch)
- {
- break;
- }
- }
- }
- if (params.interactive && (int)embd_inp.size() <= n_consumed)
- {
- // check for reverse prompt
- if (params.antiprompt.size())
- {
- std::string last_output;
- for (auto id : last_n_tokens)
- {
- last_output += llama_token_to_str(ctx, id);
- }
- has_next_token = true;
- // Check if each of the reverse prompts appears at the end of the output.
- for (std::string &antiprompt : params.antiprompt)
- {
- if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos)
- {
+ // decrement remaining sampling budget
+ --n_remain;
+
+ if (!embd.empty() && embd.back() == llama_token_eos()) {
+ //stopping_word = llama_token_to_str(ctx, embd.back());
has_next_token = false;
+ stopped_eos = true;
+ LOG_VERBOSE("eos token found", {});
return result;
- }
}
- }
- if (n_past > 0)
- {
- has_next_token = true;
- }
- }
- if (!embd.empty() && embd.back() == llama_token_eos()) {
- has_next_token = false;
+ has_next_token = params.n_predict == -1 || n_remain != 0;
+ return result;
}
- if (params.interactive && n_remain <= 0 && params.n_predict != -1)
- {
- n_remain = params.n_predict;
- }
- has_next_token = n_remain != 0;
- return result;
- }
-
- std::string doCompletion()
- {
- llama_token token = nextToken();
- if (token == -1) {
- return "";
- }
- tokens_predicted.clear();
- tokens_predicted.push_back(token);
-
- // Avoid add the no show words to the response
- for (std::vector<llama_token> word_tokens : no_show_words)
- {
- size_t match_token = 1;
- if (tokens_predicted.front() == word_tokens.front())
- {
- bool execute_matching = true;
- if (tokens_predicted.size() > 1) { // if previus tokens had been tested
- for (size_t i = 1; i < word_tokens.size(); i++)
- {
- if (i >= tokens_predicted.size()) {
- match_token = i;
- break;
+ size_t findStoppingStrings(const std::string & text, const size_t last_token_size,
+ const stop_type type) {
+ size_t stop_pos = std::string::npos;
+ for (const std::string & word : params.antiprompt) {
+ size_t pos;
+ if (type == STOP_FULL) {
+ const size_t tmp = word.size() + last_token_size;
+ const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
+ pos = text.find(word, from_pos);
}
- if (tokens_predicted[i] == word_tokens[i])
- {
- continue;
+ else {
+ pos = find_partial_stop_string(word, text);
}
- else
- {
- execute_matching = false;
- break;
+ if (pos != std::string::npos &&
+ (stop_pos == std::string::npos || pos < stop_pos)) {
+ if (type == STOP_FULL) {
+ stopping_word = word;
+ stopped_word = true;
+ has_next_token = false;
+ }
+ stop_pos = pos;
}
- }
}
- while (execute_matching) {
- if (match_token == word_tokens.size()) {
- return "";
- }
- token = nextToken();
- tokens_predicted.push_back(token);
- if (token == word_tokens[match_token])
- { // the token follow the sequence
- match_token++;
- }
- else if (match_token < word_tokens.size())
- { // no complete all word sequence
- break;
- }
+ return stop_pos;
+ }
+
+ std::string doCompletion() {
+ const llama_token token = nextToken();
+
+ const std::string token_text = token == -1 ? "" : llama_token_to_str(ctx, token);
+ generated_text += token_text;
+
+ if (multibyte_pending > 0) {
+ multibyte_pending -= token_text.size();
+ } else if (token_text.size() == 1) {
+ const char c = token_text[0];
+ // 2-byte characters: 110xxxxx 10xxxxxx
+ if ((c & 0xE0) == 0xC0) {
+ multibyte_pending = 1;
+ // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
+ } else if ((c & 0xF0) == 0xE0) {
+ multibyte_pending = 2;
+ // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
+ } else if ((c & 0xF8) == 0xF0) {
+ multibyte_pending = 3;
+ } else {
+ multibyte_pending = 0;
+ }
}
- }
- }
- if(as_loop) {
- generated_text = "";
- }
- for (llama_token tkn : tokens_predicted)
- {
- generated_text += llama_token_to_str(ctx, tkn);
- }
- return generated_text;
- }
-
- std::vector<float> embedding(std::string content, int threads) {
- content.insert(0, 1, ' ');
- std::vector<llama_token> tokens = ::llama_tokenize(ctx, content, true);
- if (tokens.size() > 0)
- {
- if (llama_eval(ctx, tokens.data(), tokens.size(), 0, threads))
- {
- fprintf(stderr, "%s : failed to eval\n", __func__);
- std::vector<float> embeddings_;
- return embeddings_;
- }
- }
- const int n_embd = llama_n_embd(ctx);
- const auto embeddings = llama_get_embeddings(ctx);
- std::vector<float> embeddings_(embeddings, embeddings + n_embd);
- return embeddings_;
- }
-};
-using namespace httplib;
+ if (multibyte_pending > 0 && !has_next_token) {
+ has_next_token = true;
+ n_remain++;
+ }
-using json = nlohmann::json;
+ if (!has_next_token && n_remain == 0) {
+ stopped_limit = true;
+ }
-void server_print_usage(int /*argc*/, char **argv, const gpt_params &params)
-{
- fprintf(stderr, "usage: %s [options]\n", argv[0]);
- fprintf(stderr, "\n");
- fprintf(stderr, "options:\n");
- fprintf(stderr, " -h, --help show this help message and exit\n");
- fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
- fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
- 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");
- fprintf(stderr, " --embedding enable embedding mode\n");
- fprintf(stderr, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
- if (llama_mlock_supported())
- {
- fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
- }
- 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
- fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
- fprintf(stderr, " number of layers to store in VRAM\n");
- fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
- fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
- fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
- fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
- fprintf(stderr, " -lv, --low-vram don't allocate VRAM scratch buffer\n" );
-#endif
- fprintf(stderr, " -m FNAME, --model FNAME\n");
- fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
- fprintf(stderr, " -a ALIAS, --alias ALIAS\n");
- fprintf(stderr, " set an alias for the model, will be added as `model` field in completion response\n");
- fprintf(stderr, " --host ip address to listen (default 127.0.0.1)\n");
- fprintf(stderr, " --port PORT port to listen (default 8080)\n");
- fprintf(stderr, "\n");
-}
+ LOG_VERBOSE("next token", {
+ { "token", token },
+ { "token_text", llama_token_to_str(ctx, token) },
+ { "has_next_token", has_next_token },
+ { "n_remain", n_remain },
+ { "num_tokens_predicted", num_tokens_predicted },
+ { "stopped_eos", stopped_eos },
+ { "stopped_word", stopped_word },
+ { "stopped_limit", stopped_limit },
+ { "stopping_word", stopping_word },
+ });
-bool server_params_parse(int argc, char **argv, server_params &sparams, gpt_params &params)
-{
- gpt_params default_params;
- std::string arg;
- bool invalid_param = false;
-
- for (int i = 1; i < argc; i++)
- {
- arg = argv[i];
- if (arg == "--port")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
- sparams.port = std::stoi(argv[i]);
+ return token_text;
}
- else if (arg == "--host")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
- sparams.hostname = argv[i];
+};
+
+static void server_print_usage(const char * argv0, const gpt_params & params,
+ const server_params & sparams) {
+ fprintf(stderr, "usage: %s [options]\n", argv0);
+ fprintf(stderr, "\n");
+ fprintf(stderr, "options:\n");
+ fprintf(stderr, " -h, --help show this help message and exit\n");
+ fprintf(stderr, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
+ fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
+ fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
+ 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()) {
+ fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
+ }
+ if (llama_mmap_supported()) {
+ fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
- else if (arg == "-s" || arg == "--seed")
- {
-#if defined(GGML_USE_CUBLAS)
- fprintf(stderr, "WARNING: when using cuBLAS generation results are NOT guaranteed to be reproducible.\n");
+#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
+ fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
+ fprintf(stderr, " number of layers to store in VRAM\n");
+ fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
+ fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
+ fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
+ fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
+ fprintf(stderr, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
#endif
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
- params.seed = std::stoi(argv[i]);
- }
- else if (arg == "-m" || arg == "--model")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
- params.model = argv[i];
- }
- else if (arg == "-a" || arg == "--alias")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
- params.model_alias = argv[i];
- }
- else if (arg == "--embedding")
- {
- params.embedding = true;
- }
- else if (arg == "-h" || arg == "--help")
- {
- server_print_usage(argc, argv, default_params);
- exit(0);
- }
- else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
- params.n_ctx = std::stoi(argv[i]);
- }
- else if (arg == "--memory-f32" || arg == "--memory_f32")
- {
- params.memory_f16 = false;
- }
- else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
+ fprintf(stderr, " -m FNAME, --model FNAME\n");
+ fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
+ fprintf(stderr, " -a ALIAS, --alias ALIAS\n");
+ fprintf(stderr, " set an alias for the model, will be added as `model` field in completion response\n");
+ fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
+ fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
+ fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
+ fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port);
+ fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
+ fprintf(stderr, "\n");
+}
+
+static void server_params_parse(int argc, char ** argv, server_params & sparams,
+ gpt_params & params) {
+ gpt_params default_params;
+ server_params default_sparams;
+ std::string arg;
+ bool invalid_param = false;
+
+ for (int i = 1; i < argc; i++) {
+ arg = argv[i];
+ if (arg == "--port") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ sparams.port = std::stoi(argv[i]);
+ } else if (arg == "--host") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ sparams.hostname = argv[i];
+ } else if (arg == "--timeout" || arg == "-to") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ sparams.read_timeout = std::stoi(argv[i]);
+ sparams.write_timeout = std::stoi(argv[i]);
+ } else if (arg == "-m" || arg == "--model") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.model = argv[i];
+ } else if (arg == "-a" || arg == "--alias") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.model_alias = argv[i];
+ } else if (arg == "-h" || arg == "--help") {
+ server_print_usage(argv[0], default_params, default_sparams);
+ exit(0);
+ } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.n_ctx = std::stoi(argv[i]);
+ } else if (arg == "--memory-f32" || arg == "--memory_f32") {
+ params.memory_f16 = false;
+ } else if (arg == "--threads" || arg == "-t") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.n_threads = std::stoi(argv[i]);
+ } else if (arg == "-b" || arg == "--batch-size") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.n_batch = std::stoi(argv[i]);
+ params.n_batch = std::min(512, params.n_batch);
+ } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
- params.n_gpu_layers = std::stoi(argv[i]);
+ params.n_gpu_layers = std::stoi(argv[i]);
#else
- fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
- fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+ LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
+ "See main README.md for information on enabling GPU BLAS support", { { "n_gpu_layers", params.n_gpu_layers } });
#endif
- }
- else if (arg == "--tensor-split" || arg == "-ts")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
+ }
+ else if (arg == "--tensor-split" || arg == "-ts") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
#ifdef GGML_USE_CUBLAS
- std::string arg_next = argv[i];
+ std::string arg_next = argv[i];
- // split string by , and /
- const std::regex regex{R"([,/]+)"};
- std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
- std::vector<std::string> split_arg{it, {}};
- GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
+ // split string by , and /
+ const std::regex regex{ R"([,/]+)" };
+ std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
+ 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())
- {
- params.tensor_split[i] = std::stof(split_arg[i]);
- }
- else
- {
- params.tensor_split[i] = 0.0f;
- }
- }
+ for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) {
+ if (i_device < split_arg.size()) {
+ params.tensor_split[i_device] = std::stof(split_arg[i_device]);
+ }
+ else {
+ params.tensor_split[i_device] = 0.0f;
+ }
+ }
#else
- fprintf(stderr, "WARNING: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
+ LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.", {});
#endif // GGML_USE_CUBLAS
- }
- else if (arg == "--low-vram" || arg == "-lv")
- {
+ }
+ else if (arg == "--low-vram" || arg == "-lv")
+ {
#ifdef GGML_USE_CUBLAS
- params.low_vram = true;
+ params.low_vram = true;
#else
- fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
+ fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
#endif // GGML_USE_CUBLAS
- }
- else if (arg == "--main-gpu" || arg == "-mg")
- {
- if (++i >= argc)
- {
- invalid_param = true;
- break;
- }
+ }
+ else if (arg == "--main-gpu" || arg == "-mg") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
#ifdef GGML_USE_CUBLAS
- params.main_gpu = std::stoi(argv[i]);
+ params.main_gpu = std::stoi(argv[i]);
#else
- fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
+ LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
#endif
+ } else if (arg == "--lora") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.lora_adapter = argv[i];
+ params.use_mmap = false;
+ } else if (arg == "--lora-base") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.lora_base = argv[i];
+ } else if (arg == "-v" || arg == "--verbose") {
+#if SERVER_VERBOSE != 1
+ LOG_WARNING("server.cpp is not built with verbose logging.", {});
+#else
+ server_verbose = true;
+#endif
+ } else if (arg == "--mlock") {
+ params.use_mlock = true;
+ } else if (arg == "--no-mmap") {
+ params.use_mmap = false;
+ } else {
+ fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+ server_print_usage(argv[0], default_params, default_sparams);
+ exit(1);
+ }
}
- else
- {
- fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
- server_print_usage(argc, argv, default_params);
- exit(1);
+
+ if (invalid_param) {
+ fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
+ server_print_usage(argv[0], default_params, default_sparams);
+ exit(1);
}
- }
-
- if (invalid_param)
- {
- fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
- server_print_usage(argc, argv, default_params);
- exit(1);
- }
- return true;
}
-bool parse_options_completion(json body, llama_server_context& llama, Response &res) {
- if (!body["threads"].is_null())
- {
- llama.params.n_threads = body["threads"].get<int>();
- }
- if (!body["n_predict"].is_null())
- {
- llama.params.n_predict = body["n_predict"].get<int>();
- }
- if (!body["top_k"].is_null())
- {
- llama.params.top_k = body["top_k"].get<int>();
- }
- if (!body["top_p"].is_null())
- {
- llama.params.top_p = body["top_p"].get<float>();
- }
- if (!body["temperature"].is_null())
- {
- llama.params.temp = body["temperature"].get<float>();
- }
- if (!body["batch_size"].is_null())
- {
- llama.params.n_batch = body["batch_size"].get<int>();
- }
- if (!body["n_keep"].is_null())
- {
- llama.params.n_keep = body["n_keep"].get<int>();
- }
- if (!body["as_loop"].is_null())
- {
- llama.as_loop = body["as_loop"].get<bool>();
- }
- if (!body["interactive"].is_null())
- {
- llama.params.interactive = body["interactive"].get<bool>();
- }
- if (!body["prompt"].is_null())
- {
- llama.params.prompt = body["prompt"].get<std::string>();
- }
- else
- {
- json data = {
- {"status", "error"},
- {"reason", "You need to pass the prompt"}};
- res.set_content(data.dump(), "application/json");
- res.status = 400;
- return false;
- }
- if (!body["stop"].is_null())
- {
- std::vector<std::string> stop_words = body["stop"].get<std::vector<std::string>>();
- for (std::string stop_word : stop_words)
- {
- llama.params.antiprompt.push_back(stop_word);
- llama.no_show_words.push_back(::llama_tokenize(llama.ctx, stop_word, false));
+static json format_generation_settings(llama_server_context & llama) {
+ const auto eos_bias = llama.params.logit_bias.find(llama_token_eos());
+ 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 },
+ };
+}
+
+static json format_final_response(llama_server_context & llama, const std::string & content) {
+ return json {
+ { "content", content },
+ { "stop", true },
+ { "model", llama.params.model_alias },
+ { "tokens_predicted", llama.num_tokens_predicted },
+ { "generation_settings", format_generation_settings(llama) },
+ { "prompt", llama.params.prompt },
+ { "truncated", llama.truncated },
+ { "stopped_eos", llama.stopped_eos },
+ { "stopped_word", llama.stopped_word },
+ { "stopped_limit", llama.stopped_limit },
+ { "stopping_word", llama.stopping_word },
+ };
+}
+
+static json format_partial_response(const std::string & content) {
+ return json {
+ { "content", content },
+ { "stop", false },
+ };
+}
+
+static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
+ return json {
+ { "tokens", tokens }
+ };
+}
+
+static void parse_options_completion(const json & body, llama_server_context & llama) {
+ gpt_params default_params;
+
+ llama.stream = body.value("stream", false);
+ llama.params.n_predict = body.value("n_predict", default_params.n_predict);
+ llama.params.top_k = body.value("top_k", default_params.top_k);
+ llama.params.top_p = body.value("top_p", default_params.top_p);
+ llama.params.tfs_z = body.value("tfs_z", default_params.tfs_z);
+ llama.params.typical_p = body.value("typical_p", default_params.typical_p);
+ llama.params.repeat_last_n = body.value("repeat_last_n", default_params.repeat_last_n);
+ llama.params.temp = body.value("temperature", default_params.temp);
+ llama.params.repeat_penalty = body.value("repeat_penalty", default_params.repeat_penalty);
+ llama.params.presence_penalty = body.value("presence_penalty", default_params.presence_penalty);
+ llama.params.frequency_penalty = body.value("frequency_penalty", default_params.frequency_penalty);
+ llama.params.mirostat = body.value("mirostat", default_params.mirostat);
+ llama.params.mirostat_tau = body.value("mirostat_tau", default_params.mirostat_tau);
+ llama.params.mirostat_eta = body.value("mirostat_eta", default_params.mirostat_eta);
+ llama.params.penalize_nl = body.value("penalize_nl", default_params.penalize_nl);
+ llama.params.n_keep = body.value("n_keep", default_params.n_keep);
+ llama.params.seed = body.value("seed", default_params.seed);
+ llama.params.prompt = body.value("prompt", default_params.prompt);
+
+ llama.params.logit_bias.clear();
+ if (body.value("ignore_eos", false)) {
+ llama.params.logit_bias[llama_token_eos()] = -INFINITY;
+ }
+
+ const auto & logit_bias = body.find("logit_bias");
+ if (logit_bias != body.end() && logit_bias->is_array()) {
+ const int n_vocab = llama_n_vocab(llama.ctx);
+ for (const auto & el : *logit_bias) {
+ if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) {
+ llama_token tok = el[0].get<llama_token>();
+ if (tok >= 0 && tok < n_vocab) {
+ if (el[1].is_number()) {
+ llama.params.logit_bias[tok] = el[1].get<float>();
+ } else if (el[1].is_boolean() && !el[1].get<bool>()) {
+ llama.params.logit_bias[tok] = -INFINITY;
+ }
+ }
+ }
+ }
}
- }
- if (!body["exclude"].is_null())
- {
- std::vector<std::string> no_show_words = body["exclude"].get<std::vector<std::string>>();
- for (std::string no_show : no_show_words)
- {
- llama.no_show_words.push_back(::llama_tokenize(llama.ctx, no_show, false));
+
+ llama.params.antiprompt.clear();
+ const auto & stop = body.find("stop");
+ if (stop != body.end() && stop->is_array()) {
+ for (const auto & word : *stop) {
+ if (!word.empty()) {
+ llama.params.antiprompt.push_back(word);
+ }
+ }
}
- }
- return true;
+
+ LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama));
}
-int main(int argc, char **argv)
-{
- // own arguments required by this example
- gpt_params params;
- server_params sparams;
-
- // struct that contains llama context and inference
- llama_server_context llama;
- params.model = "ggml-model.bin";
-
- if (server_params_parse(argc, argv, sparams, params) == false)
- {
- return 1;
- }
-
- if (params.seed <= 0)
- {
- params.seed = time(NULL);
- }
-
- fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
-
- // load the model
- if (!llama.loadModel(params))
- {
- return 1;
- }
-
- Server svr;
-
- 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)
- {
- if(llama.params.embedding) {
- json data = {
- {"status", "error"},
- {"reason", "To use completion function disable embedding mode"}};
- res.set_content(data.dump(), "application/json");
- res.status = 400;
- return;
- }
-
- llama.rewind();
-
- if(parse_options_completion(json::parse(req.body), llama, res) == false){
- return;
- }
-
- if (!llama.loadPrompt())
- {
- json data = {
- {"status", "error"},
- {"reason", "Context too long, please be more specific"}};
- res.set_content(data.dump(), "application/json");
- res.status = 400;
- return;
- }
-
- llama.beginCompletion();
- if(llama.as_loop) {
- json data = {
- {"status", "done" } };
- return res.set_content(data.dump(), "application/json");
- } else {
- // loop inference until finish completion
- while (llama.has_next_token)
- {
- llama.doCompletion();
- }
- try
- {
- json data = {
- {"model", llama.params.model_alias },
- {"content", llama.generated_text },
- {"tokens_predicted", llama.num_tokens_predicted}};
- return res.set_content(data.dump(), "application/json");
- }
- catch (const json::exception &e)
- {
- // Some tokens have bad UTF-8 strings, the json parser is very sensitive
- json data = {
- {"content", "Bad encoding token"},
- {"tokens_predicted", 0}};
- return res.set_content(data.dump(), "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(), "application/json");
- });
+static void log_server_request(const Request & req, const Response & res) {
+ LOG_INFO("request", {
+ { "remote_addr", req.remote_addr },
+ { "remote_port", req.remote_port },
+ { "status", res.status },
+ { "path", req.path },
+ { "request", req.body },
+ { "response", res.body },
+ });
+}
- svr.Post("/embedding", [&llama](const Request &req, Response &res)
- {
- if(!llama.params.embedding) {
- std::vector<float> empty;
- json data = {
- {"embedding", empty}};
- fprintf(stderr, "[llama-server] : You need enable embedding mode adding: --embedding option\n");
- return res.set_content(data.dump(), "application/json");
- }
- json body = json::parse(req.body);
- std::string content = body["content"].get<std::string>();
- int threads = body["threads"].get<int>();
- json data = {
- {"embedding", llama.embedding(content, threads) } };
- return res.set_content(data.dump(), "application/json");
- });
+int main(int argc, char ** argv) {
+ // own arguments required by this example
+ gpt_params params;
+ server_params sparams;
+
+ // struct that contains llama context and inference
+ llama_server_context llama;
+
+ server_params_parse(argc, argv, sparams, params);
+
+ if (params.model_alias == "unknown") {
+ params.model_alias = params.model;
+ }
+
+ llama_init_backend();
+
+ LOG_INFO("build info", {
+ { "build", BUILD_NUMBER },
+ { "commit", BUILD_COMMIT }
+ });
+ LOG_INFO("system info", {
+ { "n_threads", params.n_threads },
+ { "total_threads", std::thread::hardware_concurrency() },
+ { "system_info", llama_print_system_info() },
+ });
+
+ // load the model
+ if (!llama.loadModel(params)) {
+ return 1;
+ }
+
+ Server svr;
+
+ svr.set_default_headers({
+ { "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.Post("/completion", [&llama](const Request & req, Response & res) {
+ llama.rewind();
+ llama_reset_timings(llama.ctx);
+
+ parse_options_completion(json::parse(req.body), llama);
+
+ llama.loadPrompt();
+ llama.beginCompletion();
+
+ if (!llama.stream) {
+ size_t stop_pos = std::string::npos;
- svr.Get("/next-token", [&llama](const Request &req, Response &res)
- {
- if(llama.params.embedding) {
- res.set_content("{}", "application/json");
- return;
+ while (llama.has_next_token) {
+ const std::string token_text = llama.doCompletion();
+
+ stop_pos = llama.findStoppingStrings(llama.generated_text,
+ token_text.size(), STOP_FULL);
}
- std::string result = "";
- if (req.has_param("stop")) {
- llama.has_next_token = false;
- } else {
- result = llama.doCompletion(); // inference next token
+
+ if (stop_pos == std::string::npos) {
+ stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL);
}
- try {
- json data = {
- {"content", result },
- {"stop", !llama.has_next_token }};
- return res.set_content(data.dump(), "application/json");
- } catch (const json::exception &e) {
- // Some tokens have bad UTF-8 strings, the json parser is very sensitive
- json data = {
- {"content", "" },
- {"stop", !llama.has_next_token }};
- return res.set_content(data.dump(), "application/json");
+ if (stop_pos != std::string::npos) {
+ llama.generated_text.erase(llama.generated_text.begin() + stop_pos,
+ llama.generated_text.end());
}
- });
- fprintf(stderr, "%s: http server Listening at http://%s:%i\n", __func__, sparams.hostname.c_str(), sparams.port);
+ const json data = format_final_response(llama, llama.generated_text);
+
+ llama_print_timings(llama.ctx);
+
+ res.set_content(data.dump(-1, ' ', false, json::error_handler_t::replace),
+ "application/json");
+ } else {
+ const auto chunked_content_provider = [&](size_t, DataSink & sink) {
+ size_t sent_count = 0;
+
+ while (llama.has_next_token) {
+ const std::string token_text = llama.doCompletion();
+ if (llama.multibyte_pending > 0) {
+ continue;
+ }
+
+ size_t pos = std::min(sent_count, llama.generated_text.size());
+
+ const std::string str_test = llama.generated_text.substr(pos);
+ size_t stop_pos =
+ llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL);
+ if (stop_pos != std::string::npos) {
+ llama.generated_text.erase(
+ llama.generated_text.begin() + pos + stop_pos,
+ llama.generated_text.end());
+ pos = std::min(sent_count, llama.generated_text.size());
+ } else {
+ stop_pos = llama.findStoppingStrings(str_test, token_text.size(),
+ STOP_PARTIAL);
+ }
+
+ const std::string to_send = llama.generated_text.substr(pos, stop_pos);
+ sent_count += to_send.size();
+
+ const json data = llama.has_next_token
+ ? format_partial_response(to_send)
+ // Generation is done, send extra information.
+ : format_final_response(llama, to_send);
+
+ const std::string str =
+ "data: " +
+ data.dump(-1, ' ', false, json::error_handler_t::replace) +
+ "\n\n";
+
+ LOG_VERBOSE("data stream", {
+ { "to_send", str }
+ });
+
+ if (!sink.write(str.data(), str.size())) {
+ LOG_VERBOSE("stream closed", {});
+ llama_print_timings(llama.ctx);
+ return false;
+ }
+ }
+
+ llama_print_timings(llama.ctx);
+ sink.done();
+ return true;
+ };
+ 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.Post("/tokenize", [&llama](const Request & req, Response & res) {
+ const json body = json::parse(req.body);
+ const std::string content = body["content"].get<std::string>();
+ const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
+ const json data = format_tokenizer_response(tokens);
+ return res.set_content(data.dump(), "application/json");
+ });
+
+ svr.set_logger(log_server_request);
+
+ svr.set_exception_handler([](const Request &, Response & res, std::exception_ptr ep) {
+ const auto * fmt = "500 Internal Server Error\n%s";
+ char buf[BUFSIZ];
+ try {
+ std::rethrow_exception(std::move(ep));
+ } catch (std::exception & e) {
+ snprintf(buf, sizeof(buf), fmt, e.what());
+ } catch (...) {
+ snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
+ }
+ res.set_content(buf, "text/plain");
+ res.status = 500;
+ });
+
+ // set timeouts and change hostname and port
+ svr.set_read_timeout(sparams.read_timeout);
+ svr.set_write_timeout(sparams.write_timeout);
+
+ if (!svr.bind_to_port(sparams.hostname, sparams.port)) {
+ LOG_ERROR("couldn't bind to server socket", {
+ { "hostname", sparams.hostname },
+ { "port", sparams.port },
+ });
+ return 1;
+ }
+
+ LOG_INFO("HTTP server listening", {
+ { "hostname", sparams.hostname },
+ { "port", sparams.port },
+ });
- if(params.embedding) {
- fprintf(stderr, "NOTE: Mode embedding enabled. Completion function doesn't work in this mode.\n");
- }
+ if (!svr.listen_after_bind()) {
+ return 1;
+ }
- // change hostname and port
- svr.listen(sparams.hostname, sparams.port);
+ return 0;
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-10-18 14:29:36 +0000