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server : refactor ctx_sampling init + n_ctx + names

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Georgi Gerganov 2023-10-22 16:57:05 +03:00
parent ef18f4d579
commit 569ebf11cf
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1 changed files with 121 additions and 135 deletions
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examples/server/server.cpp
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@ -341,47 +341,55 @@ struct llama_client_slot
{ {
int id; int id;
int task_id = -1; int task_id = -1;
struct slot_params params;
slot_state state = IDLE;
slot_command command = NONE;
// generation props // generation props
int32_t n_past = 0; int32_t n_ctx = 0; // context size per slot
int32_t n_decoded = 0; int32_t n_past = 0;
int32_t i_batch = -1; int32_t n_decoded = 0;
size_t num_prompt_tokens = 0;
int32_t num_prompt_tokens_processed = 0;
int32_t n_remaining = -1; int32_t n_remaining = -1;
int32_t i_batch = -1;
int32_t num_prompt_tokens = 0;
int32_t num_prompt_tokens_processed = 0;
int32_t multibyte_pending = 0;
json prompt; json prompt;
std::string generated_text; std::string generated_text;
llama_token sampled; llama_token sampled;
std::vector<llama_token> cache_tokens; std::vector<llama_token> cache_tokens;
std::vector<completion_token_output> generated_token_probs; std::vector<completion_token_output> generated_token_probs;
slot_state state = IDLE;
slot_command command = NONE; bool infill = false;
bool has_next_token = true;
bool truncated = false; bool truncated = false;
bool stopped_eos = false; bool stopped_eos = false;
bool stopped_word = false; bool stopped_word = false;
bool stopped_limit = false; bool stopped_limit = false;
std::string stopping_word; std::string stopping_word;
int32_t multibyte_pending = 0;
// sampling
struct llama_sampling_params sparams;
llama_sampling_context *ctx_sampling = nullptr;
// multimodal
std::vector<slot_image> images;
// stats
size_t sent_count = 0; size_t sent_count = 0;
size_t sent_token_probs_index = 0; size_t sent_token_probs_index = 0;
bool infill = false;
int64_t t_start_process_prompt; int64_t t_start_process_prompt;
int64_t t_start_genereration; int64_t t_start_genereration;
double t_prompt_processing; // ms double t_prompt_processing; // ms
double t_token_generation; // ms double t_token_generation; // ms
struct slot_params params;
// sampling
struct llama_sampling_params sparams;
llama_sampling_context* ctx_sampling = nullptr;
bool has_next_token = true;
// multimodal
std::vector<slot_image> images;
void reset() { void reset() {
num_prompt_tokens = 0; num_prompt_tokens = 0;
generated_text = ""; generated_text = "";
@ -397,13 +405,6 @@ struct llama_client_slot
infill = false; infill = false;
generated_token_probs.clear(); generated_token_probs.clear();
if (ctx_sampling != nullptr)
{
llama_sampling_free(ctx_sampling);
}
ctx_sampling = llama_sampling_init(sparams);
for (slot_image &img : images) for (slot_image &img : images)
{ {
free(img.image_embedding); free(img.image_embedding);
@ -415,17 +416,6 @@ struct llama_client_slot
// llama_set_rng_seed(ctx, params.seed); in batched the seed matter??????? // llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
} }
bool load_grammar()
{
if (ctx_sampling != nullptr)
{
llama_sampling_free(ctx_sampling);
}
ctx_sampling = llama_sampling_init(sparams);
return ctx_sampling != nullptr;
}
bool has_budget(gpt_params &global_params) { bool has_budget(gpt_params &global_params) {
n_remaining = -1; n_remaining = -1;
if(params.n_predict != -1) if(params.n_predict != -1)
@ -491,33 +481,33 @@ struct llama_client_slot
struct llama_server_context struct llama_server_context
{ {
std::vector<llama_client_slot> slots;
// system prompt
std::string system_prompt;
bool need_update_system_prompt = false;
std::vector<llama_token> tokens_system;
int32_t num_tokens_system;
// broadcast to all clients to keep the same prompt format
std::string user_name; // this should be the anti prompt
std::string assistant_name; // this is for generate the prompt
bool multimodal = false;
clip_ctx *clp_ctx = nullptr;
int n_embd;
llama_model *model = nullptr; llama_model *model = nullptr;
llama_context *ctx = nullptr; llama_context *ctx = nullptr;
llama_batch batch;
bool all_slots_are_idle = false;
gpt_params params;
int n_ctx;
int n_vocab;
int max_ctx_per_slot = -1;
bool clean_kv_cache = true;
int id_gen; clip_ctx *clp_ctx = nullptr;
gpt_params params;
llama_batch batch;
bool multimodal = false;
bool clean_kv_cache = true;
bool all_slots_are_idle = false;
int32_t id_gen;
int32_t n_ctx; // total context for all clients / slots
// system prompt
bool system_need_update = false;
std::string system_prompt;
std::vector<llama_token> system_tokens;
std::string name_user; // this should be the antiprompt
std::string name_assistant;
// slots / clients
std::vector<llama_client_slot> slots;
std::vector<task_server> queue_tasks; std::vector<task_server> queue_tasks;
std::vector<task_result> queue_results; std::vector<task_result> queue_results;
@ -541,7 +531,7 @@ struct llama_server_context
bool load_model(const gpt_params &params_) bool load_model(const gpt_params &params_)
{ {
params = params_; params = params_;
if(!params.mmproj.empty()) { if (!params.mmproj.empty()) {
multimodal = true; multimodal = true;
LOG_TEE("Multi Modal Mode Enabled"); LOG_TEE("Multi Modal Mode Enabled");
clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1); clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
@ -550,10 +540,11 @@ struct llama_server_context
return false; return false;
} }
if(params.n_ctx < 2048) { // request larger context for the image embedding if (params.n_ctx < 2048) { // request larger context for the image embedding
params.n_ctx = 2048; params.n_ctx = 2048;
} }
} }
std::tie(model, ctx) = llama_init_from_gpt_params(params); std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == nullptr) if (model == nullptr)
{ {
@ -561,18 +552,19 @@ struct llama_server_context
return false; return false;
} }
if(multimodal) { if (multimodal) {
int n_img_embd = clip_n_mmproj_embd(clp_ctx); const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
n_embd = llama_n_embd(model); const int n_embd_llm = llama_n_embd(model);
if (n_img_embd != n_embd) { if (n_embd_clip != n_embd_llm) {
LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_img_embd, n_embd); LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
llama_free(ctx); llama_free(ctx);
llama_free_model(model); llama_free_model(model);
return false; return false;
} }
} }
n_ctx = llama_n_ctx(ctx); n_ctx = llama_n_ctx(ctx);
n_vocab = llama_n_vocab(model);
return true; return true;
} }
@ -581,25 +573,19 @@ struct llama_server_context
// create slots // create slots
all_slots_are_idle = true; all_slots_are_idle = true;
if (max_ctx_per_slot == -1)
{ const int32_t n_ctx_slot = n_ctx / params.n_parallel;
max_ctx_per_slot = n_ctx / params.n_parallel; // split context
}
if (max_ctx_per_slot * params.n_parallel > n_ctx)
{
printf("Error: The max context per slot is more greater than model context size");
return;
}
LOG_TEE("Available slots:\n"); LOG_TEE("Available slots:\n");
for (int i = 0; i < params.n_parallel; i++) for (int i = 0; i < params.n_parallel; i++)
{ {
llama_client_slot slot; llama_client_slot slot;
slot.id = i; slot.id = i;
slot.sparams.n_prev = max_ctx_per_slot; slot.n_ctx = n_ctx_slot;
slot.reset(); slot.reset();
LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, max_ctx_per_slot); LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
slots.push_back(slot); slots.push_back(slot);
} }
@ -607,7 +593,7 @@ struct llama_server_context
// empty system prompt // empty system prompt
system_prompt = ""; system_prompt = "";
num_tokens_system = 0; system_tokens.clear();
} }
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
@ -699,16 +685,16 @@ struct llama_server_context
{ {
slot->params.input_prefix = ""; slot->params.input_prefix = "";
} }
if (data.count("input_suffix") != 0) if (data.count("input_suffix") != 0)
{ {
slot->params.input_suffix = data["input_suffix"]; slot->params.input_suffix = data["input_suffix"];
} }
// common params
else else
{ {
slot->params.input_suffix = ""; slot->params.input_suffix = "";
} }
if (data.count("prompt") != 0) if (data.count("prompt") != 0)
{ {
slot->prompt = data["prompt"]; slot->prompt = data["prompt"];
@ -717,11 +703,14 @@ struct llama_server_context
{ {
slot->prompt = ""; slot->prompt = "";
} }
slot->sparams.logit_bias.clear(); slot->sparams.logit_bias.clear();
if (json_value(data, "ignore_eos", false)) if (json_value(data, "ignore_eos", false))
{ {
slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY; slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY;
} }
const auto &logit_bias = data.find("logit_bias"); const auto &logit_bias = data.find("logit_bias");
if (logit_bias != data.end() && logit_bias->is_array()) if (logit_bias != data.end() && logit_bias->is_array())
{ {
@ -832,36 +821,37 @@ struct llama_server_context
} }
} }
} }
if (!slot->load_grammar())
if (slot->ctx_sampling != nullptr)
{ {
return false; llama_sampling_free(slot->ctx_sampling);
} }
all_slots_are_idle = false; slot->ctx_sampling = llama_sampling_init(slot->sparams);
slot->command = LOAD_PROMPT; slot->command = LOAD_PROMPT;
all_slots_are_idle = false;
LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id); LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
return true; return true;
} }
void kv_cache_clear() { void kv_cache_clear() {
// clear the entire KV cache // clear the entire KV cache
for (int i = 0; i < params.n_parallel; ++i) llama_kv_cache_tokens_rm(ctx, -1, -1);
{
llama_kv_cache_seq_rm(ctx, i, 0, -1);
}
clean_kv_cache = false; clean_kv_cache = false;
} }
void update_system_prompt() { void update_system_prompt() {
tokens_system = ::llama_tokenize(ctx, system_prompt, true); system_tokens = ::llama_tokenize(ctx, system_prompt, true);
num_tokens_system = tokens_system.size();
batch.n_tokens = num_tokens_system; llama_batch_clear(batch);
kv_cache_clear(); kv_cache_clear();
for (int32_t i = 0; i < batch.n_tokens; ++i) for (int32_t i = 0; i < batch.n_tokens; ++i)
{ {
llama_batch_add(batch, tokens_system[i], i, { 0 }, false); llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
} }
if (llama_decode(ctx, batch) != 0) if (llama_decode(ctx, batch) != 0)
@ -873,11 +863,11 @@ struct llama_server_context
// assign the system KV cache to all parallel sequences // assign the system KV cache to all parallel sequences
for (int32_t i = 1; i < params.n_parallel; ++i) for (int32_t i = 1; i < params.n_parallel; ++i)
{ {
llama_kv_cache_seq_cp(ctx, 0, i, 0, num_tokens_system); llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
} }
LOG_TEE("system prompt updated\n"); LOG_TEE("system prompt updated\n");
need_update_system_prompt = false; system_need_update = false;
} }
void notify_system_prompt_changed() { void notify_system_prompt_changed() {
@ -890,8 +880,8 @@ struct llama_server_context
all_slots_are_idle = true; all_slots_are_idle = true;
// wait until system prompt load // wait until system prompt load
need_update_system_prompt = true; system_need_update = true;
while (need_update_system_prompt) { while (system_need_update) {
std::this_thread::sleep_for(std::chrono::milliseconds(5)); std::this_thread::sleep_for(std::chrono::milliseconds(5));
} }
// system prompt loaded, continue // system prompt loaded, continue
@ -899,8 +889,8 @@ struct llama_server_context
void process_system_prompt_data(const json &sys_props) { void process_system_prompt_data(const json &sys_props) {
system_prompt = sys_props.value("prompt", ""); system_prompt = sys_props.value("prompt", "");
user_name = sys_props.value("anti_prompt", ""); name_user = sys_props.value("anti_prompt", "");
assistant_name = sys_props.value("assistant_name", ""); name_assistant = sys_props.value("assistant_name", "");
if (slots.size() > 0) if (slots.size() > 0)
{ {
@ -908,7 +898,7 @@ struct llama_server_context
} }
else else
{ {
need_update_system_prompt = true; system_need_update = true;
} }
} }
@ -1036,14 +1026,14 @@ struct llama_server_context
} }
// check the limits // check the limits
if ( if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
{ {
slot.stopped_limit = true; slot.stopped_limit = true;
slot.has_next_token = false; slot.has_next_token = false;
} }
if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)) { if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx))
{
slot.stopped_eos = true; slot.stopped_eos = true;
slot.has_next_token = false; slot.has_next_token = false;
LOG_VERBOSE("eos token found", {}); LOG_VERBOSE("eos token found", {});
@ -1111,12 +1101,12 @@ struct llama_server_context
return get_formated_generation(slots[0]); return get_formated_generation(slots[0]);
} }
json get_formated_generation(llama_client_slot & slot) { json get_formated_generation(llama_client_slot &slot) {
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx)); const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx));
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
eos_bias->second < 0.0f && std::isinf(eos_bias->second); eos_bias->second < 0.0f && std::isinf(eos_bias->second);
return json{ return json {
{"n_ctx", max_ctx_per_slot}, {"n_ctx", slot.n_ctx},
{"model", params.model_alias}, {"model", params.model_alias},
{"seed", slot.params.seed}, {"seed", slot.params.seed},
{"temp", slot.sparams.temp}, {"temp", slot.sparams.temp},
@ -1219,7 +1209,8 @@ struct llama_server_context
res.id = slot.task_id; res.id = slot.task_id;
res.error = false; res.error = false;
res.stop = true; res.stop = true;
static const int n_embd = llama_n_embd(model);
const int n_embd = llama_n_embd(model);
if (!params.embedding) if (!params.embedding)
{ {
LOG_WARNING("embedding disabled", { LOG_WARNING("embedding disabled", {
@ -1229,7 +1220,9 @@ struct llama_server_context
{ {
{"embedding", std::vector<float>(n_embd, 0.0f)}, {"embedding", std::vector<float>(n_embd, 0.0f)},
}; };
} else { }
else
{
const float *data = llama_get_embeddings(ctx); const float *data = llama_get_embeddings(ctx);
std::vector<float> embedding(data, data + n_embd); std::vector<float> embedding(data, data + n_embd);
res.result_json = json res.result_json = json
@ -1312,6 +1305,7 @@ struct llama_server_context
n_eval = n_batch; n_eval = n_batch;
} }
const int n_embd = llama_n_embd(model);
llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, };
if (llama_decode(ctx, batch_img)) if (llama_decode(ctx, batch_img))
{ {
@ -1400,7 +1394,7 @@ struct llama_server_context
process_tasks(); process_tasks();
// update the system prompt wait until all slots are idle state // update the system prompt wait until all slots are idle state
if (need_update_system_prompt) if (system_need_update)
{ {
LOG_TEE("updating system prompt\n"); LOG_TEE("updating system prompt\n");
update_system_prompt(); update_system_prompt();
@ -1421,7 +1415,7 @@ struct llama_server_context
for (llama_client_slot &slot : slots) for (llama_client_slot &slot : slots)
{ {
if (slot.is_processing() && slot.cache_tokens.size() >= (size_t)max_ctx_per_slot) if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
{ {
// Shift context // Shift context
const int n_left = slot.n_past - slot.params.n_keep - 1; const int n_left = slot.n_past - slot.params.n_keep - 1;
@ -1443,7 +1437,7 @@ struct llama_server_context
slot.truncated = true; slot.truncated = true;
LOG_VERBOSE("context shift", { LOG_VERBOSE("context shift", {
{"n_ctx", n_ctx}, {"n_ctx", n_ctx},
{"n_keep", params.n_keep}, {"n_keep", params.n_keep},
{"n_left", n_left}, {"n_left", n_left},
}); });
@ -1478,7 +1472,7 @@ struct llama_server_context
slot.i_batch = batch.n_tokens; slot.i_batch = batch.n_tokens;
llama_batch_add(batch, slot.sampled, num_tokens_system + slot.n_past, { slot.id }, true); llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
slot.n_decoded += 1; slot.n_decoded += 1;
slot.n_past += 1; slot.n_past += 1;
@ -1537,21 +1531,21 @@ struct llama_server_context
{ {
if (slot.params.n_keep < 0) if (slot.params.n_keep < 0)
{ {
slot.params.n_keep = (int)slot.num_prompt_tokens; slot.params.n_keep = slot.num_prompt_tokens;
} }
slot.params.n_keep = std::min(max_ctx_per_slot - 4, slot.params.n_keep); slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
//if input prompt is too big, truncate like normal //if input prompt is too big, truncate like normal
if (slot.num_prompt_tokens >= (size_t)max_ctx_per_slot) if (slot.num_prompt_tokens >= slot.n_ctx)
{ {
// applied bug of #3661 // applied bug of #3661
const int n_left = max_ctx_per_slot - slot.params.n_keep; const int n_left = slot.n_ctx - slot.params.n_keep;
const int n_block_size = n_left / 2; const int n_block_size = n_left / 2;
const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep);
// Use half the left-over space in the context for the prompt // Use half the left-over space in the context for the prompt
new_tokens.insert(new_tokens.end(), prompt_tokens.end() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); new_tokens.insert(new_tokens.end(), prompt_tokens.end() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());
LOG_VERBOSE("input truncated", { LOG_VERBOSE("input truncated", {
{"n_ctx", max_ctx_per_slot}, {"n_ctx", slot.n_ctx},
{"n_keep", slot.params.n_keep}, {"n_keep", slot.params.n_keep},
{"n_left", n_left}, {"n_left", n_left},
{"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
@ -1559,7 +1553,7 @@ struct llama_server_context
slot.truncated = true; slot.truncated = true;
prompt_tokens = new_tokens; prompt_tokens = new_tokens;
slot.num_prompt_tokens = prompt_tokens.size(); slot.num_prompt_tokens = prompt_tokens.size();
GGML_ASSERT(slot.num_prompt_tokens < (size_t)max_ctx_per_slot); GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
} }
const size_t ps = slot.num_prompt_tokens; const size_t ps = slot.num_prompt_tokens;
std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end() - ps, 0); std::fill(slot.ctx_sampling->prev.begin(), slot.ctx_sampling->prev.end() - ps, 0);
@ -1569,12 +1563,13 @@ struct llama_server_context
LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
} }
LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, num_tokens_system + slot.n_past); LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past);
llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1);
llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1);
slot.cache_tokens = prompt_tokens; slot.cache_tokens = prompt_tokens;
if (slot.n_past == (int) slot.num_prompt_tokens) if (slot.n_past == slot.num_prompt_tokens)
{ {
// we have to evaluate at least 1 token to generate logits. // we have to evaluate at least 1 token to generate logits.
LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
@ -1593,7 +1588,7 @@ struct llama_server_context
std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens;
for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
{ {
llama_batch_add(batch, prefix_tokens[slot.n_past], num_tokens_system + slot.n_past, { slot.id }, false); llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false);
} }
if (has_images && !ingest_images(slot, n_batch)) if (has_images && !ingest_images(slot, n_batch))
@ -1842,15 +1837,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
} }
params.n_ctx = std::stoi(argv[i]); params.n_ctx = std::stoi(argv[i]);
} }
else if (arg == "-cps" || arg == "--ctx-per-slot" || arg == "--ctx_per_slot")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
llama.max_ctx_per_slot = std::stoi(argv[i]);
}
else if (arg == "--rope-freq-base") else if (arg == "--rope-freq-base")
{ {
if (++i >= argc) if (++i >= argc)
@ -2227,8 +2213,8 @@ int main(int argc, char **argv)
{ {
res.set_header("Access-Control-Allow-Origin", "*"); res.set_header("Access-Control-Allow-Origin", "*");
json data = { json data = {
{ "user_name", llama.user_name.c_str() }, { "user_name", llama.name_user.c_str() },
{ "assistant_name", llama.assistant_name.c_str() } { "assistant_name", llama.name_assistant.c_str() }
}; };
res.set_content(data.dump(), "application/json"); res.set_content(data.dump(), "application/json");
}); });
@ -2434,7 +2420,7 @@ int main(int argc, char **argv)
svr.set_base_dir(sparams.public_path); svr.set_base_dir(sparams.public_path);
// to make it ctrl+clickable: // to make it ctrl+clickable:
printf("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
LOG_INFO("HTTP server listening", { LOG_INFO("HTTP server listening", {
{"hostname", sparams.hostname}, {"hostname", sparams.hostname},