move llama_client_slot to utils.hpp
This commit is contained in:
ngxson
parent
0eac1a3ec3
commit
a5603ded45
6 changed files with 344 additions and 325 deletions
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@ -144,238 +144,6 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector<com
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return out;
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}
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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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// used to determine the slot that has been used the longest
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int64_t t_last_used = -1;
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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 n_remaining = -1;
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int32_t i_batch = -1;
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int32_t n_predict = -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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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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bool infill = false;
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bool embedding = 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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bool oaicompat = false;
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std::string oaicompat_model;
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std::string stopping_word;
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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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int32_t ga_i = 0; // group-attention state
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int32_t ga_n = 1; // group-attention factor
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int32_t ga_w = 512; // group-attention width
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int32_t n_past_se = 0; // self-extend
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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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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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// multitasks
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int multitask_id = -1;
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void reset() {
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num_prompt_tokens = 0;
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generated_text = "";
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truncated = false;
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stopped_eos = false;
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stopped_word = false;
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stopped_limit = false;
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stopping_word = "";
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n_past = 0;
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sent_count = 0;
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sent_token_probs_index = 0;
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infill = false;
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ga_i = 0;
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n_past_se = 0;
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generated_token_probs.clear();
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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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if (img.img_data) {
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clip_image_u8_free(img.img_data);
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}
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img.prefix_prompt = "";
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}
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images.clear();
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}
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bool has_budget(gpt_params &global_params) {
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if (params.n_predict == -1 && global_params.n_predict == -1)
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{
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return true; // limitless
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}
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n_remaining = -1;
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if (params.n_predict != -1)
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{
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n_remaining = params.n_predict - n_decoded;
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}
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else if (global_params.n_predict != -1)
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{
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n_remaining = global_params.n_predict - n_decoded;
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}
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return n_remaining > 0; // no budget
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}
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bool available() const {
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return state == IDLE && command == NONE;
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}
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bool is_processing() const {
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return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
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}
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void add_token_string(const completion_token_output &token) {
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if (command == RELEASE)
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{
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return;
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}
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cache_tokens.push_back(token.tok);
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generated_token_probs.push_back(token);
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}
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void release() {
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if (state == PROCESSING)
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{
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t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
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command = RELEASE;
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}
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}
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json get_formated_timings() {
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return json
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{
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{"prompt_n", num_prompt_tokens_processed},
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{"prompt_ms", t_prompt_processing},
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{"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed},
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{"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed},
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{"predicted_n", n_decoded},
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{"predicted_ms", t_token_generation},
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{"predicted_per_token_ms", t_token_generation / n_decoded},
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{"predicted_per_second", 1e3 / t_token_generation * n_decoded},
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};
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}
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void print_timings() const {
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char buffer[512];
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double t_token = t_prompt_processing / num_prompt_tokens_processed;
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double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed;
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sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
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t_prompt_processing, num_prompt_tokens_processed,
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t_token, n_tokens_second);
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LOG_INFO(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_prompt_processing", t_prompt_processing},
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{"num_prompt_tokens_processed", num_prompt_tokens_processed},
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{"t_token", t_token},
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{"n_tokens_second", n_tokens_second},
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});
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t_token = t_token_generation / n_decoded;
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n_tokens_second = 1e3 / t_token_generation * n_decoded;
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sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
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t_token_generation, n_decoded,
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t_token, n_tokens_second);
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LOG_INFO(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_token_generation", t_token_generation},
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{"n_decoded", n_decoded},
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{"t_token", t_token},
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{"n_tokens_second", n_tokens_second},
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});
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sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
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LOG_INFO(buffer, {
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{"slot_id", id},
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{"task_id", task_id},
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{"t_prompt_processing", t_prompt_processing},
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{"t_token_generation", t_token_generation},
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{"t_total", t_prompt_processing + t_token_generation},
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});
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}
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};
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struct llama_metrics {
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uint64_t n_prompt_tokens_processed_total = 0;
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uint64_t n_tokens_predicted_total = 0;
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uint64_t n_prompt_tokens_processed = 0;
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uint64_t t_prompt_processing = 0;
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uint64_t n_tokens_predicted = 0;
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uint64_t t_tokens_generation = 0;
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void on_prompt_eval(const llama_client_slot &slot) {
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n_prompt_tokens_processed_total += slot.num_prompt_tokens_processed;
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n_prompt_tokens_processed += slot.num_prompt_tokens_processed;
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t_prompt_processing += slot.t_prompt_processing;
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}
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void on_prediction(const llama_client_slot &slot) {
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n_tokens_predicted_total += slot.n_decoded;
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n_tokens_predicted += slot.n_decoded;
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t_tokens_generation += slot.t_token_generation;
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}
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void reset_bucket() {
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n_prompt_tokens_processed = 0;
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t_prompt_processing = 0;
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n_tokens_predicted = 0;
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t_tokens_generation = 0;
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}
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};
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struct llama_server_context
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{
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llama_model *model = nullptr;
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@ -1795,21 +1563,8 @@ struct llama_server_context
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if (slot.ga_n != 1)
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{
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int ga_i = 0;
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int32_t ga_n = slot.ga_n;
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int32_t ga_w = slot.ga_w;
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int32_t slot_npast = 0;
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for (int k = 0; k < slot.n_past; ++k)
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{
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while (slot_npast >= ga_i + ga_w) {
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const int bd = (ga_w/ga_n)*(ga_n - 1);
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slot_npast -= bd;
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ga_i += ga_w/ga_n;
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}
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slot_npast++;
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}
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slot.n_past_se = slot_npast;
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slot.ga_i = ga_i;
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// context extension via Self-Extend
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slot.grp_attn_update_params();
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}
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LOG_INFO("slot progression", {
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@ -1855,22 +1610,16 @@ struct llama_server_context
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// process the prefix of first image
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std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
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int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
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int32_t ga_i = slot.ga_i;
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int32_t ga_n = slot.ga_n;
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int32_t ga_w = slot.ga_w;
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int32_t slot_npast = slot.n_past;
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for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
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{
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if (slot.ga_n != 1)
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{
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while (slot_npast >= ga_i + ga_w) {
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const int bd = (ga_w/ga_n)*(ga_n - 1);
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slot_npast -= bd;
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ga_i += ga_w/ga_n;
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}
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// context extension via Self-Extend
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slot_npast = slot.grp_attn_calc_npast();
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}
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llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false);
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slot_npast++;
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}
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@ -1902,6 +1651,7 @@ struct llama_server_context
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all_slots_are_idle = true;
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}
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// loop of n_batch
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for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
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{
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const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
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@ -1911,28 +1661,9 @@ struct llama_server_context
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if (slot.ga_n != 1)
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{
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// context extension via Self-Extend
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while (slot.n_past_se >= slot.ga_i + slot.ga_w)
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{
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const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
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const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
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const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
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LOG_TEE("\n");
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LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
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LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
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LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
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llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
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llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
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llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
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slot.n_past_se -= bd;
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slot.ga_i += slot.ga_w / slot.ga_n;
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LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
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}
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slot.n_past_se += n_tokens;
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// TODO @ngxson: What happen if we're retrying with smaller n_batch?
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// By the second time we retry, "grp_attn_shift" has already been called
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slot.grp_attn_shift(ctx, n_tokens);
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}
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}
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@ -1962,7 +1693,7 @@ struct llama_server_context
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slot.release();
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}
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has_next_response = false;
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break;
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break; // break loop of n_batch
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}
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LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
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@ -1970,14 +1701,15 @@ struct llama_server_context
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// retry with half the batch size to try to find a free slot in the KV cache
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n_batch /= 2;
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i -= n_batch;
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continue;
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continue; // continue loop of n_batch
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}
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// loop of slots
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for (auto & slot : slots)
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{
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if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens))
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{
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continue;
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continue; // continue loop of slots
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}
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// prompt evaluated for embedding
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@ -1986,7 +1718,7 @@ struct llama_server_context
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send_embedding(slot);
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slot.release();
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slot.i_batch = -1;
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continue;
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continue; // continue loop of slots
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}
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completion_token_output result;
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@ -2,7 +2,7 @@
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Feature: Parallel
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Background: Server startup
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Given a server listening on localhost:8080
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Given a server listening on 0.0.0.0:8080
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And a model file stories260K.gguf
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And a model alias tinyllama-2
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And 42 as server seed
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@ -2,7 +2,7 @@
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Feature: Security
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Background: Server startup with an api key defined
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Given a server listening on localhost:8080
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Given a server listening on 0.0.0.0:8080
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And a model file stories260K.gguf
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And a server api key llama.cpp
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Then the server is starting
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@ -2,7 +2,7 @@
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Feature: llama.cpp server
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Background: Server startup
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Given a server listening on localhost:8080
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Given a server listening on 0.0.0.0:8080
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And a model file stories260K.gguf
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And a model alias tinyllama-2
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And 42 as server seed
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@ -6,7 +6,7 @@ Feature: Wrong usage of llama.cpp server
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# to cap the number of tokens any completion request can generate
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# or pass n_predict/max_tokens in the request.
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Scenario: Infinite loop
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Given a server listening on localhost:8080
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Given a server listening on 0.0.0.0:8080
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And a model file stories260K.gguf
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# Uncomment below to fix the issue
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#And 64 server max tokens to predict
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@ -37,9 +37,49 @@ extern bool server_log_json;
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#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
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#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
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//
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// parallel
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//
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static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra)
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{
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std::stringstream ss_tid;
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ss_tid << std::this_thread::get_id();
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json log = nlohmann::ordered_json{
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{"tid", ss_tid.str()},
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{"timestamp", time(nullptr)},
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};
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if (server_log_json) {
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log.merge_patch(
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{
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{"level", level},
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{"function", function},
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{"line", line},
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{"msg", message},
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});
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if (!extra.empty()) {
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log.merge_patch(extra);
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}
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std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush;
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} else {
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char buf[1024];
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snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
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if (!extra.empty()) {
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log.merge_patch(extra);
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}
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std::stringstream ss;
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ss << buf << " |";
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for (const auto& el : log.items())
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{
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const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
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||||
snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str());
|
||||
ss << buf;
|
||||
}
|
||||
|
||||
const std::string str = ss.str();
|
||||
printf("%.*s\n", (int)str.size(), str.data());
|
||||
fflush(stdout);
|
||||
}
|
||||
}
|
||||
|
||||
enum server_state {
|
||||
SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet
|
||||
|
|
@ -134,49 +174,296 @@ struct completion_token_output
|
|||
std::string text_to_send;
|
||||
};
|
||||
|
||||
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra)
|
||||
struct llama_client_slot
|
||||
{
|
||||
std::stringstream ss_tid;
|
||||
ss_tid << std::this_thread::get_id();
|
||||
json log = nlohmann::ordered_json{
|
||||
{"tid", ss_tid.str()},
|
||||
{"timestamp", time(nullptr)},
|
||||
};
|
||||
int id;
|
||||
int task_id = -1;
|
||||
|
||||
if (server_log_json) {
|
||||
log.merge_patch(
|
||||
{
|
||||
{"level", level},
|
||||
{"function", function},
|
||||
{"line", line},
|
||||
{"msg", message},
|
||||
});
|
||||
if (!extra.empty()) {
|
||||
log.merge_patch(extra);
|
||||
}
|
||||
struct slot_params params;
|
||||
|
||||
std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush;
|
||||
} else {
|
||||
char buf[1024];
|
||||
snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
|
||||
slot_state state = IDLE;
|
||||
slot_command command = NONE;
|
||||
|
||||
if (!extra.empty()) {
|
||||
log.merge_patch(extra);
|
||||
}
|
||||
std::stringstream ss;
|
||||
ss << buf << " |";
|
||||
for (const auto& el : log.items())
|
||||
// used to determine the slot that has been used the longest
|
||||
int64_t t_last_used = -1;
|
||||
|
||||
// generation props
|
||||
int32_t n_ctx = 0; // context size per slot
|
||||
int32_t n_past = 0;
|
||||
int32_t n_decoded = 0;
|
||||
int32_t n_remaining = -1;
|
||||
int32_t i_batch = -1;
|
||||
int32_t n_predict = -1;
|
||||
|
||||
int32_t num_prompt_tokens = 0;
|
||||
int32_t num_prompt_tokens_processed = 0;
|
||||
|
||||
json prompt;
|
||||
std::string generated_text;
|
||||
llama_token sampled;
|
||||
std::vector<llama_token> cache_tokens;
|
||||
std::vector<completion_token_output> generated_token_probs;
|
||||
|
||||
bool infill = false;
|
||||
bool embedding = false;
|
||||
bool has_next_token = true;
|
||||
bool truncated = false;
|
||||
bool stopped_eos = false;
|
||||
bool stopped_word = false;
|
||||
bool stopped_limit = false;
|
||||
|
||||
bool oaicompat = false;
|
||||
std::string oaicompat_model;
|
||||
|
||||
std::string stopping_word;
|
||||
|
||||
// sampling
|
||||
struct llama_sampling_params sparams;
|
||||
llama_sampling_context *ctx_sampling = nullptr;
|
||||
|
||||
int32_t ga_i = 0; // group-attention state
|
||||
int32_t ga_n = 1; // group-attention factor
|
||||
int32_t ga_w = 512; // group-attention width
|
||||
|
||||
int32_t n_past_se = 0; // self-extend
|
||||
|
||||
// multimodal
|
||||
std::vector<slot_image> images;
|
||||
|
||||
// stats
|
||||
size_t sent_count = 0;
|
||||
size_t sent_token_probs_index = 0;
|
||||
|
||||
int64_t t_start_process_prompt;
|
||||
int64_t t_start_genereration;
|
||||
|
||||
double t_prompt_processing; // ms
|
||||
double t_token_generation; // ms
|
||||
|
||||
// multitasks
|
||||
int multitask_id = -1;
|
||||
|
||||
void reset() {
|
||||
num_prompt_tokens = 0;
|
||||
generated_text = "";
|
||||
truncated = false;
|
||||
stopped_eos = false;
|
||||
stopped_word = false;
|
||||
stopped_limit = false;
|
||||
stopping_word = "";
|
||||
n_past = 0;
|
||||
sent_count = 0;
|
||||
sent_token_probs_index = 0;
|
||||
infill = false;
|
||||
ga_i = 0;
|
||||
n_past_se = 0;
|
||||
|
||||
generated_token_probs.clear();
|
||||
|
||||
for (slot_image & img : images)
|
||||
{
|
||||
const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
|
||||
snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str());
|
||||
ss << buf;
|
||||
free(img.image_embedding);
|
||||
if (img.img_data) {
|
||||
clip_image_u8_free(img.img_data);
|
||||
}
|
||||
img.prefix_prompt = "";
|
||||
}
|
||||
|
||||
const std::string str = ss.str();
|
||||
printf("%.*s\n", (int)str.size(), str.data());
|
||||
fflush(stdout);
|
||||
images.clear();
|
||||
}
|
||||
}
|
||||
|
||||
bool has_budget(gpt_params &global_params) {
|
||||
if (params.n_predict == -1 && global_params.n_predict == -1)
|
||||
{
|
||||
return true; // limitless
|
||||
}
|
||||
|
||||
n_remaining = -1;
|
||||
|
||||
if (params.n_predict != -1)
|
||||
{
|
||||
n_remaining = params.n_predict - n_decoded;
|
||||
}
|
||||
else if (global_params.n_predict != -1)
|
||||
{
|
||||
n_remaining = global_params.n_predict - n_decoded;
|
||||
}
|
||||
|
||||
return n_remaining > 0; // no budget
|
||||
}
|
||||
|
||||
bool available() const {
|
||||
return state == IDLE && command == NONE;
|
||||
}
|
||||
|
||||
bool is_processing() const {
|
||||
return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
|
||||
}
|
||||
|
||||
void add_token_string(const completion_token_output &token) {
|
||||
if (command == RELEASE)
|
||||
{
|
||||
return;
|
||||
}
|
||||
cache_tokens.push_back(token.tok);
|
||||
generated_token_probs.push_back(token);
|
||||
}
|
||||
|
||||
void release() {
|
||||
if (state == PROCESSING)
|
||||
{
|
||||
t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
|
||||
command = RELEASE;
|
||||
}
|
||||
}
|
||||
|
||||
json get_formated_timings() {
|
||||
return json
|
||||
{
|
||||
{"prompt_n", num_prompt_tokens_processed},
|
||||
{"prompt_ms", t_prompt_processing},
|
||||
{"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed},
|
||||
{"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed},
|
||||
|
||||
{"predicted_n", n_decoded},
|
||||
{"predicted_ms", t_token_generation},
|
||||
{"predicted_per_token_ms", t_token_generation / n_decoded},
|
||||
{"predicted_per_second", 1e3 / t_token_generation * n_decoded},
|
||||
};
|
||||
}
|
||||
|
||||
void print_timings() const {
|
||||
char buffer[512];
|
||||
double t_token = t_prompt_processing / num_prompt_tokens_processed;
|
||||
double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed;
|
||||
sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
|
||||
t_prompt_processing, num_prompt_tokens_processed,
|
||||
t_token, n_tokens_second);
|
||||
LOG_INFO(buffer, {
|
||||
{"slot_id", id},
|
||||
{"task_id", task_id},
|
||||
{"t_prompt_processing", t_prompt_processing},
|
||||
{"num_prompt_tokens_processed", num_prompt_tokens_processed},
|
||||
{"t_token", t_token},
|
||||
{"n_tokens_second", n_tokens_second},
|
||||
});
|
||||
|
||||
t_token = t_token_generation / n_decoded;
|
||||
n_tokens_second = 1e3 / t_token_generation * n_decoded;
|
||||
sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
|
||||
t_token_generation, n_decoded,
|
||||
t_token, n_tokens_second);
|
||||
LOG_INFO(buffer, {
|
||||
{"slot_id", id},
|
||||
{"task_id", task_id},
|
||||
{"t_token_generation", t_token_generation},
|
||||
{"n_decoded", n_decoded},
|
||||
{"t_token", t_token},
|
||||
{"n_tokens_second", n_tokens_second},
|
||||
});
|
||||
|
||||
sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
|
||||
LOG_INFO(buffer, {
|
||||
{"slot_id", id},
|
||||
{"task_id", task_id},
|
||||
{"t_prompt_processing", t_prompt_processing},
|
||||
{"t_token_generation", t_token_generation},
|
||||
{"t_total", t_prompt_processing + t_token_generation},
|
||||
});
|
||||
}
|
||||
|
||||
// context extension via Self-Extend
|
||||
void grp_attn_update_params() {
|
||||
int grpa_i = 0;
|
||||
// copy to local variables
|
||||
int32_t grpa_n = ga_n;
|
||||
int32_t grpa_w = ga_w;
|
||||
int32_t slot_npast = 0;
|
||||
for (int k = 0; k < n_past; ++k)
|
||||
{
|
||||
while (slot_npast >= grpa_i + grpa_w) {
|
||||
const int bd = (grpa_w/grpa_n)*(grpa_n - 1);
|
||||
slot_npast -= bd;
|
||||
grpa_i += grpa_w/grpa_n;
|
||||
}
|
||||
slot_npast++;
|
||||
}
|
||||
n_past_se = slot_npast;
|
||||
ga_i = grpa_i;
|
||||
}
|
||||
|
||||
int32_t grp_attn_calc_npast() {
|
||||
int32_t slot_npast = n_past_se > 0 ? n_past_se : n_past;
|
||||
// copy to local variables
|
||||
int32_t grpa_i = ga_i;
|
||||
int32_t grpa_n = ga_n;
|
||||
int32_t grpa_w = ga_w;
|
||||
while (slot_npast >= grpa_i + grpa_w) {
|
||||
const int bd = (grpa_w/grpa_n)*(grpa_n - 1);
|
||||
slot_npast -= bd;
|
||||
grpa_i += grpa_w/grpa_n;
|
||||
}
|
||||
return slot_npast;
|
||||
}
|
||||
|
||||
void grp_attn_shift(llama_context * ctx, const int32_t n_tokens) {
|
||||
while (n_past_se >= ga_i + ga_w)
|
||||
{
|
||||
const int ib = (ga_n * ga_i) / ga_w;
|
||||
const int bd = (ga_w / ga_n) * (ga_n - 1);
|
||||
const int dd = (ga_w / ga_n) - ib * bd - ga_w;
|
||||
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past_se, ib * bd, ga_i + ib * bd, n_past_se + ib * bd);
|
||||
LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib * bd, ga_i + ib * bd + ga_w, ga_n, (ga_i + ib * bd) / ga_n, (ga_i + ib * bd + ga_w) / ga_n);
|
||||
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib * bd + ga_w, n_past_se + ib * bd, dd, ga_i + ib * bd + ga_w + dd, n_past_se + ib * bd + dd);
|
||||
|
||||
llama_kv_cache_seq_shift(ctx, id, ga_i, n_past_se, ib * bd);
|
||||
llama_kv_cache_seq_div(ctx, id, ga_i + ib * bd, ga_i + ib * bd + ga_w,ga_n);
|
||||
llama_kv_cache_seq_shift(ctx, id, ga_i + ib * bd + ga_w,n_past_se + ib * bd, dd);
|
||||
|
||||
n_past_se -= bd;
|
||||
|
||||
ga_i += ga_w / ga_n;
|
||||
|
||||
LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past_se + bd, n_past_se, ga_i);
|
||||
}
|
||||
n_past_se += n_tokens;
|
||||
}
|
||||
};
|
||||
|
||||
struct llama_metrics {
|
||||
uint64_t n_prompt_tokens_processed_total = 0;
|
||||
uint64_t n_tokens_predicted_total = 0;
|
||||
|
||||
uint64_t n_prompt_tokens_processed = 0;
|
||||
uint64_t t_prompt_processing = 0;
|
||||
|
||||
uint64_t n_tokens_predicted = 0;
|
||||
uint64_t t_tokens_generation = 0;
|
||||
|
||||
|
||||
void on_prompt_eval(const llama_client_slot &slot) {
|
||||
n_prompt_tokens_processed_total += slot.num_prompt_tokens_processed;
|
||||
|
||||
n_prompt_tokens_processed += slot.num_prompt_tokens_processed;
|
||||
t_prompt_processing += slot.t_prompt_processing;
|
||||
}
|
||||
|
||||
void on_prediction(const llama_client_slot &slot) {
|
||||
n_tokens_predicted_total += slot.n_decoded;
|
||||
|
||||
n_tokens_predicted += slot.n_decoded;
|
||||
t_tokens_generation += slot.t_token_generation;
|
||||
}
|
||||
|
||||
void reset_bucket() {
|
||||
n_prompt_tokens_processed = 0;
|
||||
t_prompt_processing = 0;
|
||||
n_tokens_predicted = 0;
|
||||
t_tokens_generation = 0;
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
// server utils
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue