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server : remove hack for extra parallel slot
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ggml-ci
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Georgi Gerganov 2024-11-05 22:31:11 +02:00
parent b8deef0ec0
commit c3beb9b9dc
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1 changed files with 24 additions and 29 deletions
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examples/server/server.cpp
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@ -378,8 +378,8 @@ struct server_queue {
std::condition_variable condition_tasks; std::condition_variable condition_tasks;
// callback functions // callback functions
std::function<void(server_task&)> callback_new_task; std::function<void(server_task)> callback_new_task;
std::function<void(void)> callback_update_slots; std::function<void(void)> callback_update_slots;
// Add a new task to the end of the queue // Add a new task to the end of the queue
int post(server_task task, bool front = false) { int post(server_task task, bool front = false) {
@ -431,7 +431,7 @@ struct server_queue {
} }
// Register function to process a new task // Register function to process a new task
void on_new_task(std::function<void(server_task &)> callback) { void on_new_task(std::function<void(server_task)> callback) {
callback_new_task = std::move(callback); callback_new_task = std::move(callback);
} }
@ -481,7 +481,7 @@ struct server_queue {
lock.unlock(); lock.unlock();
QUE_DBG("processing task, id = %d\n", task.id); QUE_DBG("processing task, id = %d\n", task.id);
callback_new_task(task); callback_new_task(std::move(task));
} }
// all tasks in the current loop is processed, slots data is now ready // all tasks in the current loop is processed, slots data is now ready
@ -644,17 +644,12 @@ struct server_context {
bool load_model(const common_params & params_) { bool load_model(const common_params & params_) {
params = params_; params = params_;
// reserve one extra sequence (seq_id == 0) for extra features
params.n_parallel += 1;
common_init_result llama_init = common_init_from_params(params); common_init_result llama_init = common_init_from_params(params);
model = llama_init.model; model = llama_init.model;
ctx = llama_init.context; ctx = llama_init.context;
loras = llama_init.lora_adapters; loras = llama_init.lora_adapters;
params.n_parallel -= 1; // but be sneaky about it
if (model == nullptr) { if (model == nullptr) {
SRV_ERR("failed to load model, '%s'\n", params.model.c_str()); SRV_ERR("failed to load model, '%s'\n", params.model.c_str());
return false; return false;
@ -1288,16 +1283,16 @@ struct server_context {
void send_embedding(const server_slot & slot, const llama_batch & batch) { void send_embedding(const server_slot & slot, const llama_batch & batch) {
server_task_result res; server_task_result res;
res.id = slot.id_task; res.id = slot.id_task;
res.error = false; res.error = false;
res.stop = true; res.stop = true;
const int n_embd = llama_n_embd(model); const int n_embd = llama_n_embd(model);
std::vector<float> embd_res(n_embd, 0.0f); std::vector<float> embd_res(n_embd, 0.0f);
for (int i = 0; i < batch.n_tokens; ++i) { for (int i = 0; i < batch.n_tokens; ++i) {
if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
continue; continue;
} }
@ -1332,12 +1327,12 @@ struct server_context {
void send_rerank(const server_slot & slot, const llama_batch & batch) { void send_rerank(const server_slot & slot, const llama_batch & batch) {
server_task_result res; server_task_result res;
res.id = slot.id_task; res.id = slot.id_task;
res.error = false; res.error = false;
res.stop = true; res.stop = true;
for (int i = 0; i < batch.n_tokens; ++i) { for (int i = 0; i < batch.n_tokens; ++i) {
if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
continue; continue;
} }
@ -1510,7 +1505,7 @@ struct server_context {
// Functions to process the task // Functions to process the task
// //
void process_single_task(const server_task & task) { void process_single_task(server_task task) {
switch (task.type) { switch (task.type) {
case SERVER_TASK_TYPE_INFERENCE: case SERVER_TASK_TYPE_INFERENCE:
{ {
@ -1646,7 +1641,7 @@ struct server_context {
std::string filename = task.data.at("filename"); std::string filename = task.data.at("filename");
std::string filepath = task.data.at("filepath"); std::string filepath = task.data.at("filepath");
const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count); const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), token_count);
const int64_t t_end = ggml_time_us(); const int64_t t_end = ggml_time_us();
const double t_save_ms = (t_end - t_start) / 1000.0; const double t_save_ms = (t_end - t_start) / 1000.0;
@ -1688,7 +1683,7 @@ struct server_context {
slot->cache_tokens.resize(slot->n_ctx); slot->cache_tokens.resize(slot->n_ctx);
size_t token_count = 0; size_t token_count = 0;
size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count); size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
if (nread == 0) { if (nread == 0) {
slot->cache_tokens.resize(0); slot->cache_tokens.resize(0);
send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST); send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
@ -1731,7 +1726,7 @@ struct server_context {
// Erase token cache // Erase token cache
const size_t n_erased = slot->cache_tokens.size(); const size_t n_erased = slot->cache_tokens.size();
llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1); llama_kv_cache_seq_rm(ctx, slot->id, -1, -1);
slot->cache_tokens.clear(); slot->cache_tokens.clear();
server_task_result result; server_task_result result;
@ -1808,8 +1803,8 @@ struct server_context {
SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard); SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard); llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard);
llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, slot.n_past, -n_discard); llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, slot.n_past, -n_discard);
if (slot.params.cache_prompt) { if (slot.params.cache_prompt) {
for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
@ -1836,7 +1831,7 @@ struct server_context {
slot.i_batch = batch.n_tokens; slot.i_batch = batch.n_tokens;
common_batch_add(batch, slot.sampled, slot.n_past, { slot.id + 1 }, true); common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true);
slot.n_past += 1; slot.n_past += 1;
@ -1983,8 +1978,8 @@ struct server_context {
const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c; const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c;
llama_kv_cache_seq_rm (ctx, slot.id + 1, head_p, head_c); llama_kv_cache_seq_rm (ctx, slot.id, head_p, head_c);
llama_kv_cache_seq_add(ctx, slot.id + 1, head_c, -1, kv_shift); llama_kv_cache_seq_add(ctx, slot.id, head_c, -1, kv_shift);
for (size_t i = 0; i < n_match; i++) { for (size_t i = 0; i < n_match; i++) {
slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i]; slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i];
@ -2033,9 +2028,9 @@ struct server_context {
} }
// keep only the common part // keep only the common part
if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, slot.n_past, -1)) { if (!llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1)) {
// could not partially delete (likely using a non-Transformer model) // could not partially delete (likely using a non-Transformer model)
llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1); llama_kv_cache_seq_rm(ctx, slot.id, -1, -1);
// there is no common part left // there is no common part left
slot.n_past = 0; slot.n_past = 0;
@ -2048,7 +2043,7 @@ struct server_context {
// add prompt tokens for processing in the current batch // add prompt tokens for processing in the current batch
while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) { while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id + 1 }, false); common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false);
if (slot.params.cache_prompt) { if (slot.params.cache_prompt) {
slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);