mirror of
https://github.com/ggerganov/llama.cpp.git
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server : refactor ctx_sampling init + n_ctx + names
This commit is contained in:
Georgi Gerganov
parent
ef18f4d579
commit
569ebf11cf
@ -341,47 +341,55 @@ struct llama_client_slot
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{
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int id;
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int task_id = -1;
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struct slot_params params;
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slot_state state = IDLE;
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slot_command command = NONE;
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// generation props
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int32_t n_ctx = 0; // context size per slot
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int32_t n_past = 0;
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int32_t n_decoded = 0;
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int32_t i_batch = -1;
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size_t num_prompt_tokens = 0;
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int32_t num_prompt_tokens_processed = 0;
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int32_t n_remaining = -1;
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int32_t i_batch = -1;
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int32_t num_prompt_tokens = 0;
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int32_t num_prompt_tokens_processed = 0;
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int32_t multibyte_pending = 0;
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json prompt;
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std::string generated_text;
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llama_token sampled;
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std::vector<llama_token> cache_tokens;
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std::vector<completion_token_output> generated_token_probs;
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slot_state state = IDLE;
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slot_command command = NONE;
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bool infill = false;
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bool has_next_token = true;
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bool truncated = false;
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bool stopped_eos = false;
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bool stopped_word = false;
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bool stopped_limit = false;
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std::string stopping_word;
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int32_t multibyte_pending = 0;
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// sampling
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struct llama_sampling_params sparams;
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llama_sampling_context *ctx_sampling = nullptr;
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// multimodal
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std::vector<slot_image> images;
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// stats
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size_t sent_count = 0;
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size_t sent_token_probs_index = 0;
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bool infill = false;
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int64_t t_start_process_prompt;
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int64_t t_start_genereration;
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double t_prompt_processing; // ms
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double t_token_generation; // ms
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struct slot_params params;
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// sampling
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struct llama_sampling_params sparams;
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llama_sampling_context* ctx_sampling = nullptr;
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bool has_next_token = true;
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// multimodal
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std::vector<slot_image> images;
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void reset() {
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num_prompt_tokens = 0;
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generated_text = "";
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@ -397,13 +405,6 @@ struct llama_client_slot
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infill = false;
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generated_token_probs.clear();
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if (ctx_sampling != nullptr)
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{
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llama_sampling_free(ctx_sampling);
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}
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ctx_sampling = llama_sampling_init(sparams);
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for (slot_image &img : images)
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{
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free(img.image_embedding);
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@ -415,17 +416,6 @@ struct llama_client_slot
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// llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
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}
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bool load_grammar()
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{
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if (ctx_sampling != nullptr)
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{
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llama_sampling_free(ctx_sampling);
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}
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ctx_sampling = llama_sampling_init(sparams);
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return ctx_sampling != nullptr;
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}
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bool has_budget(gpt_params &global_params) {
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n_remaining = -1;
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if(params.n_predict != -1)
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@ -491,33 +481,33 @@ struct llama_client_slot
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struct llama_server_context
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{
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std::vector<llama_client_slot> slots;
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// system prompt
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std::string system_prompt;
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bool need_update_system_prompt = false;
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std::vector<llama_token> tokens_system;
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int32_t num_tokens_system;
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// broadcast to all clients to keep the same prompt format
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std::string user_name; // this should be the anti prompt
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std::string assistant_name; // this is for generate the prompt
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bool multimodal = false;
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clip_ctx *clp_ctx = nullptr;
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int n_embd;
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llama_model *model = nullptr;
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llama_context *ctx = nullptr;
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llama_batch batch;
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bool all_slots_are_idle = false;
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gpt_params params;
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int n_ctx;
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int n_vocab;
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int max_ctx_per_slot = -1;
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bool clean_kv_cache = true;
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int id_gen;
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clip_ctx *clp_ctx = nullptr;
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gpt_params params;
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llama_batch batch;
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bool multimodal = false;
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bool clean_kv_cache = true;
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bool all_slots_are_idle = false;
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int32_t id_gen;
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int32_t n_ctx; // total context for all clients / slots
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// system prompt
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bool system_need_update = false;
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std::string system_prompt;
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std::vector<llama_token> system_tokens;
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std::string name_user; // this should be the antiprompt
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std::string name_assistant;
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// slots / clients
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std::vector<llama_client_slot> slots;
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std::vector<task_server> queue_tasks;
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std::vector<task_result> queue_results;
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@ -541,7 +531,7 @@ struct llama_server_context
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bool load_model(const gpt_params ¶ms_)
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{
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params = params_;
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if(!params.mmproj.empty()) {
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if (!params.mmproj.empty()) {
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multimodal = true;
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LOG_TEE("Multi Modal Mode Enabled");
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clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
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@ -550,10 +540,11 @@ struct llama_server_context
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return false;
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}
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if(params.n_ctx < 2048) { // request larger context for the image embedding
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if (params.n_ctx < 2048) { // request larger context for the image embedding
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params.n_ctx = 2048;
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}
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}
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == nullptr)
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{
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@ -561,18 +552,19 @@ struct llama_server_context
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return false;
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}
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if(multimodal) {
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int n_img_embd = clip_n_mmproj_embd(clp_ctx);
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n_embd = llama_n_embd(model);
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if (n_img_embd != n_embd) {
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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);
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if (multimodal) {
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const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
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const int n_embd_llm = llama_n_embd(model);
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if (n_embd_clip != n_embd_llm) {
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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);
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llama_free(ctx);
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llama_free_model(model);
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return false;
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}
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}
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n_ctx = llama_n_ctx(ctx);
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n_vocab = llama_n_vocab(model);
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return true;
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}
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@ -581,25 +573,19 @@ struct llama_server_context
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// create slots
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all_slots_are_idle = true;
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if (max_ctx_per_slot == -1)
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{
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max_ctx_per_slot = n_ctx / params.n_parallel; // split context
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}
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if (max_ctx_per_slot * params.n_parallel > n_ctx)
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{
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printf("Error: The max context per slot is more greater than model context size");
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return;
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}
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const int32_t n_ctx_slot = n_ctx / params.n_parallel;
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LOG_TEE("Available slots:\n");
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for (int i = 0; i < params.n_parallel; i++)
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{
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llama_client_slot slot;
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slot.id = i;
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slot.sparams.n_prev = max_ctx_per_slot;
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slot.n_ctx = n_ctx_slot;
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slot.reset();
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LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, max_ctx_per_slot);
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LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
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slots.push_back(slot);
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}
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@ -607,7 +593,7 @@ struct llama_server_context
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// empty system prompt
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system_prompt = "";
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num_tokens_system = 0;
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system_tokens.clear();
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}
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std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
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@ -699,16 +685,16 @@ struct llama_server_context
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{
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slot->params.input_prefix = "";
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}
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if (data.count("input_suffix") != 0)
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{
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slot->params.input_suffix = data["input_suffix"];
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}
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// common params
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else
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{
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slot->params.input_suffix = "";
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}
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if (data.count("prompt") != 0)
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{
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slot->prompt = data["prompt"];
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@ -717,11 +703,14 @@ struct llama_server_context
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{
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slot->prompt = "";
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}
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slot->sparams.logit_bias.clear();
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if (json_value(data, "ignore_eos", false))
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{
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slot->sparams.logit_bias[llama_token_eos(ctx)] = -INFINITY;
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}
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const auto &logit_bias = data.find("logit_bias");
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if (logit_bias != data.end() && logit_bias->is_array())
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{
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@ -832,36 +821,37 @@ struct llama_server_context
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}
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}
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}
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if (!slot->load_grammar())
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if (slot->ctx_sampling != nullptr)
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{
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return false;
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llama_sampling_free(slot->ctx_sampling);
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}
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all_slots_are_idle = false;
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slot->ctx_sampling = llama_sampling_init(slot->sparams);
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slot->command = LOAD_PROMPT;
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all_slots_are_idle = false;
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LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
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return true;
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}
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void kv_cache_clear() {
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// clear the entire KV cache
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for (int i = 0; i < params.n_parallel; ++i)
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{
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llama_kv_cache_seq_rm(ctx, i, 0, -1);
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}
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llama_kv_cache_tokens_rm(ctx, -1, -1);
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clean_kv_cache = false;
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}
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void update_system_prompt() {
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tokens_system = ::llama_tokenize(ctx, system_prompt, true);
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num_tokens_system = tokens_system.size();
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system_tokens = ::llama_tokenize(ctx, system_prompt, true);
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batch.n_tokens = num_tokens_system;
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llama_batch_clear(batch);
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kv_cache_clear();
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for (int32_t i = 0; i < batch.n_tokens; ++i)
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{
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llama_batch_add(batch, tokens_system[i], i, { 0 }, false);
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llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
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}
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if (llama_decode(ctx, batch) != 0)
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@ -873,11 +863,11 @@ struct llama_server_context
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// assign the system KV cache to all parallel sequences
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for (int32_t i = 1; i < params.n_parallel; ++i)
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{
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llama_kv_cache_seq_cp(ctx, 0, i, 0, num_tokens_system);
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llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
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}
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LOG_TEE("system prompt updated\n");
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need_update_system_prompt = false;
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system_need_update = false;
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}
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void notify_system_prompt_changed() {
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@ -890,8 +880,8 @@ struct llama_server_context
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all_slots_are_idle = true;
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// wait until system prompt load
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need_update_system_prompt = true;
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while (need_update_system_prompt) {
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system_need_update = true;
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while (system_need_update) {
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std::this_thread::sleep_for(std::chrono::milliseconds(5));
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}
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// system prompt loaded, continue
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@ -899,8 +889,8 @@ struct llama_server_context
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void process_system_prompt_data(const json &sys_props) {
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system_prompt = sys_props.value("prompt", "");
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user_name = sys_props.value("anti_prompt", "");
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assistant_name = sys_props.value("assistant_name", "");
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name_user = sys_props.value("anti_prompt", "");
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name_assistant = sys_props.value("assistant_name", "");
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if (slots.size() > 0)
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{
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@ -908,7 +898,7 @@ struct llama_server_context
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}
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else
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{
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need_update_system_prompt = true;
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system_need_update = true;
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}
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}
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@ -1036,14 +1026,14 @@ struct llama_server_context
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}
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// check the limits
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if (
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slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
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if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
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{
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slot.stopped_limit = true;
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slot.has_next_token = false;
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}
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if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx)) {
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if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(ctx))
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{
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slot.stopped_eos = true;
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slot.has_next_token = false;
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LOG_VERBOSE("eos token found", {});
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@ -1111,12 +1101,12 @@ struct llama_server_context
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return get_formated_generation(slots[0]);
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}
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json get_formated_generation(llama_client_slot & slot) {
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json get_formated_generation(llama_client_slot &slot) {
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const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(ctx));
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const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
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eos_bias->second < 0.0f && std::isinf(eos_bias->second);
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return json{
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{"n_ctx", max_ctx_per_slot},
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return json {
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{"n_ctx", slot.n_ctx},
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{"model", params.model_alias},
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{"seed", slot.params.seed},
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{"temp", slot.sparams.temp},
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@ -1219,7 +1209,8 @@ struct llama_server_context
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res.id = slot.task_id;
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res.error = false;
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res.stop = true;
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static const int n_embd = llama_n_embd(model);
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const int n_embd = llama_n_embd(model);
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if (!params.embedding)
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{
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LOG_WARNING("embedding disabled", {
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@ -1229,7 +1220,9 @@ struct llama_server_context
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{
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{"embedding", std::vector<float>(n_embd, 0.0f)},
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};
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} else {
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}
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else
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{
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const float *data = llama_get_embeddings(ctx);
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std::vector<float> embedding(data, data + n_embd);
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res.result_json = json
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@ -1312,6 +1305,7 @@ struct llama_server_context
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n_eval = n_batch;
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}
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const int n_embd = llama_n_embd(model);
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llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, };
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if (llama_decode(ctx, batch_img))
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{
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@ -1400,7 +1394,7 @@ struct llama_server_context
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process_tasks();
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// update the system prompt wait until all slots are idle state
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if (need_update_system_prompt)
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if (system_need_update)
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{
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LOG_TEE("updating system prompt\n");
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update_system_prompt();
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@ -1421,7 +1415,7 @@ struct llama_server_context
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for (llama_client_slot &slot : slots)
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{
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if (slot.is_processing() && slot.cache_tokens.size() >= (size_t)max_ctx_per_slot)
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if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
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{
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// Shift context
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const int n_left = slot.n_past - slot.params.n_keep - 1;
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@ -1478,7 +1472,7 @@ struct llama_server_context
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slot.i_batch = batch.n_tokens;
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llama_batch_add(batch, slot.sampled, num_tokens_system + slot.n_past, { slot.id }, true);
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llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
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slot.n_decoded += 1;
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slot.n_past += 1;
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@ -1537,21 +1531,21 @@ struct llama_server_context
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{
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if (slot.params.n_keep < 0)
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{
|
||||
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 (slot.num_prompt_tokens >= (size_t)max_ctx_per_slot)
|
||||
if (slot.num_prompt_tokens >= slot.n_ctx)
|
||||
{
|
||||
// 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 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);
|
||||
// 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());
|
||||
LOG_VERBOSE("input truncated", {
|
||||
{"n_ctx", max_ctx_per_slot},
|
||||
{"n_ctx", slot.n_ctx},
|
||||
{"n_keep", slot.params.n_keep},
|
||||
{"n_left", n_left},
|
||||
{"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
|
||||
@ -1559,7 +1553,7 @@ struct llama_server_context
|
||||
slot.truncated = true;
|
||||
prompt_tokens = new_tokens;
|
||||
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;
|
||||
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 : kv cache rm - [%d, end)\n", slot.id, num_tokens_system + slot.n_past);
|
||||
llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1);
|
||||
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, system_tokens.size() + slot.n_past, -1);
|
||||
|
||||
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.
|
||||
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;
|
||||
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))
|
||||
@ -1842,15 +1837,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
|
||||
}
|
||||
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")
|
||||
{
|
||||
if (++i >= argc)
|
||||
@ -2227,8 +2213,8 @@ int main(int argc, char **argv)
|
||||
{
|
||||
res.set_header("Access-Control-Allow-Origin", "*");
|
||||
json data = {
|
||||
{ "user_name", llama.user_name.c_str() },
|
||||
{ "assistant_name", llama.assistant_name.c_str() }
|
||||
{ "user_name", llama.name_user.c_str() },
|
||||
{ "assistant_name", llama.name_assistant.c_str() }
|
||||
};
|
||||
res.set_content(data.dump(), "application/json");
|
||||
});
|
||||
@ -2434,7 +2420,7 @@ int main(int argc, char **argv)
|
||||
svr.set_base_dir(sparams.public_path);
|
||||
|
||||
// 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", {
|
||||
{"hostname", sparams.hostname},
|
||||
|
||||
Loading…
Reference in New Issue
Block a user