mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
c03ee1a4d2
* server: improve speed of speculative decoding change logs rpc: add recompute spec dec fix * Fix n_batch_size not set to context size for draft model --------- Co-authored-by: firecoperana <firecoperana>
2826 lines
114 KiB
C++
2826 lines
114 KiB
C++
#include "server-context.h"
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#include "server-common.h"
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#include "server-task.h"
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#include "server-queue.h"
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#include "common.h"
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#include "llama.h"
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#include "log.h"
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#include "sampling.h"
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#include "speculative.h"
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#include "mtmd.h"
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#include "mtmd-helper.h"
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server_context::~server_context() {
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if (ctx) {
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llama_free(ctx);
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ctx = nullptr;
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}
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if (model) {
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llama_free_model(model);
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model = nullptr;
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}
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// Free multimodal
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mtmd_free(mctx);
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// Free draft model and context if they exist
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if (ctx_draft) {
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llama_free(ctx_draft);
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ctx_draft = nullptr;
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}
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if (model_draft) {
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llama_free_model(model_draft);
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model_draft = nullptr;
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}
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// Clear any sampling context
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for (server_slot& slot : slots) {
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if (slot.ctx_sampling != nullptr) {
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llama_sampling_free(slot.ctx_sampling);
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}
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if (slot.ctx_dft) {
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llama_free(slot.ctx_dft);
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}
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if (slot.spec) {
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llama_speculative_free(slot.spec);
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}
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llama_batch_free(slot.batch_spec);
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}
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llama_batch_free(batch);
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}
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bool server_context::load_model(const gpt_params& params_) {
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params = params_;
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llama_init_result llama_init = llama_init_from_gpt_params(params);
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model = llama_init.model;
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ctx = llama_init.context;
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lora_adapters = llama_init.lora_adapters;
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if (model == nullptr) {
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LOG_ERROR("unable to load model", { {"model", params.model} });
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return false;
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}
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n_ctx = llama_n_ctx(ctx);
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add_bos_token = llama_should_add_bos_token(model);
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has_eos_token = llama_add_eos_token(model) != 1;
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chat_templates = common_chat_templates_init(model, params.chat_template);
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try {
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common_chat_format_example(chat_templates.get(), params.use_jinja, {});
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}
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catch (const std::exception& e) {
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LOG_WARNING("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
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chat_templates = common_chat_templates_init(model, "chatml");
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}
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bool has_draft_model = !params.model_draft.empty() || !params.draft_params.empty();
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std::string& mmproj_path = params.mmproj.path;
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if (!mmproj_path.empty()) {
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mtmd_context_params mparams = mtmd_context_params_default();
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mparams.use_gpu = params.mmproj_use_gpu;
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mparams.print_timings = false;
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mparams.n_threads = params.n_threads;
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mparams.flash_attn_type = params.flash_attn ? LLAMA_FLASH_ATTN_TYPE_ENABLED : LLAMA_FLASH_ATTN_TYPE_DISABLED;
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mparams.verbosity = params.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO;
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mparams.image_min_tokens = params.image_min_tokens;
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mparams.image_max_tokens = params.image_max_tokens;
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mctx = mtmd_init_from_file(mmproj_path.c_str(), model, mparams);
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if (mctx == nullptr) {
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LOG_ERROR("failed to load multimodal model, '%s'\n", mmproj_path.c_str());
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return false;
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}
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LOG_INFO("loaded multimodal model, '%s'\n", mmproj_path.c_str());
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if (params.ctx_shift) {
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params.ctx_shift = false;
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LOG_WARNING("%s\n", "ctx_shift is not supported by multimodal, it will be disabled");
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}
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//if (params.n_cache_reuse) {
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// params_base.n_cache_reuse = 0;
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// SRV_WRN("%s\n", "cache_reuse is not supported by multimodal, it will be disabled");
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//}
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if (has_draft_model) {
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LOG_ERROR("%s\n", "err: speculative decode is not supported by multimodal");
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return false;
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}
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}
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// Load draft model for speculative decoding if specified
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if (has_draft_model) {
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LLAMA_LOG_INFO("\n\n==================================loading DRAFT model==================================\n\n");
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gpt_params params_dft;
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params_dft.devices = params.devices_draft;
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params_dft.model = params.model_draft;
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params_dft.n_gpu_layers = params.n_gpu_layers_draft;
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params_dft.rpc_servers = params.rpc_servers;
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params_dft.cache_type_k = params.cache_type_k_draft.empty() ? params.cache_type_k : params.cache_type_k_draft;
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params_dft.cache_type_v = params.cache_type_v_draft.empty() ? params.cache_type_v : params.cache_type_v_draft;
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params_dft.flash_attn = params.flash_attn;
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if (!params.draft_params.empty()) {
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auto [argc, argv] = parse_command_line("llama-server " + params.draft_params);
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if (!gpt_params_parse(argc, argv, params_dft)) {
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gpt_params_print_usage(argc, argv, params_dft);
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free_command_line(argc, argv);
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return false;
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};
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free_command_line(argc, argv);
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}
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LOG_INFO("", { {"model", params_dft.model} });
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if (params_dft.n_ctx == 0) {
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params_dft.n_ctx = params.n_ctx_draft;
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}
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params_dft.n_ctx = params_dft.n_ctx == 0 ? params.n_ctx / params.n_parallel : params_dft.n_ctx;
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params_dft.n_parallel = 1;
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params_dft.n_batch = params_dft.n_ctx;
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llama_init_result llama_init_dft = llama_init_from_gpt_params(params_dft);
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llama_model* model_dft = llama_init_dft.model;
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if (model_dft == nullptr) {
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LOG_ERROR("failed to load draft model", { {"model", params.model_draft} });
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return false;
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}
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if (!llama_speculative_are_compatible(ctx, llama_init_dft.context)) {
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LOG_INFO("the draft model is not compatible with the target model. tokens will be translated between the draft and target models.", { {} });
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}
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const int n_ctx_dft = llama_n_ctx(llama_init_dft.context);
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cparams_dft = llama_context_params_from_gpt_params(params_dft);
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model_draft = llama_init_dft.model;
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ctx_draft = llama_init_dft.context;
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}
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return true;
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}
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void server_context::init() {
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const int32_t n_ctx_slot = n_ctx / params.n_parallel;
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LOG_INFO("initializing slots", { {"n_slots", params.n_parallel} });
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for (int i = 0; i < params.n_parallel; i++) {
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server_slot slot;
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slot.id = i;
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slot.ctx = ctx;
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slot.n_ctx = n_ctx_slot;
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slot.n_predict = params.n_predict;
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slot.mctx = mctx;
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slot.cache_tokens.has_mtmd = mctx != nullptr;
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slot.params.think_tokens = params.think_tokens;
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if (params.think_tokens.exclude) {
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SRV_WRN("Exclude reasoning tokens when selecting slot based on similarity: start: %s, end: %s\nuse `--reasoning-tokens none` to disable.\n", params.think_tokens.begin.c_str(), params.think_tokens.end.c_str() );
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}
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else {
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SRV_WRN("%s", "Include reasoning tokens when selecting slot based on similarity\nuse `--reasoning-tokens auto` to exclude reasoning tokens.\n");
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}
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LOG_INFO("new slot", {
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{"id_slot", slot.id},
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{"n_ctx_slot", slot.n_ctx}
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});
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const int ga_n = params.grp_attn_n;
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const int ga_w = params.grp_attn_w;
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if (ga_n != 1) {
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GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT
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GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
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//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
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//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
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LOG_INFO("slot self-extend", {
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{"id_slot", slot.id},
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{"ga_n", ga_n},
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{"ga_w", ga_w}
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});
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}
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slot.ga_i = 0;
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slot.ga_n = ga_n;
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slot.ga_w = ga_w;
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slot.sparams = params.sparams;
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// Initialize speculative decoding if a draft model is loaded
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if (ctx_draft) {
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slot.batch_spec = llama_batch_init(slot.params.speculative.n_max + 1, 0, 1);
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// slot.ctx_dft = llama_new_context_with_model(model_draft, cparams_dft); // initialized twice
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slot.ctx_dft = ctx_draft;
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if (slot.ctx_dft == nullptr) {
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LOG_ERROR("failed to create draft context", {});
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return;
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}
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slot.spec = llama_speculative_init(ctx, slot.ctx_dft);
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if (slot.spec == nullptr) {
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LOG_ERROR("failed to create speculator", {});
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return;
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}
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for (auto& pair : params.replacements_draft) {
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llama_speculative_add_replacement_tgt_dft(slot.spec, pair.first.c_str(), pair.second.c_str());
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}
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}
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slot.reset();
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slots.push_back(std::move(slot));
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}
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default_generation_settings_for_props = get_formated_generation(slots.front());
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default_generation_settings_for_props["seed"] = -1;
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// the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens
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// note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
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{
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const int32_t n_batch = llama_n_batch(ctx);
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// only a single seq_id per token is needed
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batch = llama_batch_init(std::max(n_batch, params.n_parallel), 0, 1);
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}
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metrics.init();
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if (params.cache_ram_mib != 0) {
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if (params.cache_ram_mib < 0) {
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LLAMA_LOG_INFO("prompt cache is enabled, size limit: %s\n", "no limit");
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}
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else {
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LLAMA_LOG_INFO("prompt cache is enabled, size limit: %d MiB\n", params.cache_ram_mib);
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}
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LLAMA_LOG_INFO("%s", "use `--cache-ram 0` to disable the prompt cache\n");
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// only apply ram size limit. No token limit for now.
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prompt_cache = std::make_unique<server_prompt_cache>(ctx, params.cache_ram_mib, 0);
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}
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else {
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LLAMA_LOG_INFO("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n");
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}
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// thinking is enabled if:
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// 1. It's not explicitly disabled (reasoning_budget == 0)
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// 2. The chat template supports it
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const bool enable_thinking = params.use_jinja && params.reasoning_budget != 0 && common_chat_templates_support_enable_thinking(chat_templates.get());
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//LLAMA_LOG_INFO("Enable thinking? %d\n", enable_thinking);
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oai_parser_opt = {
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/* use_jinja */ params.use_jinja,
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/* prefill_assistant */ params.prefill_assistant,
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/* reasoning_format */ params.reasoning_format,
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/* chat_template_kwargs */ params.default_template_kwargs,
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/* common_chat_templates */ chat_templates.get(),
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/* allow_image */ mctx ? mtmd_support_vision(mctx) : false,
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/* allow_audio */ mctx ? mtmd_support_audio(mctx) : false,
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/* enable_thinking */ enable_thinking,
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};
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}
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void server_slot::prompt_save(server_prompt_cache& prompt_cache) const {
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assert(server_cached_prompt.data.size() == 0);
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const size_t cur_size = llama_state_seq_get_size(ctx, id);
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LLAMA_LOG_INFO(" - saving prompt with length %d, total state size = %.3f MiB\n",
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(int)server_cached_prompt.tokens.size(), cur_size / (1024.0 * 1024.0));
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auto* cur = prompt_cache.alloc(server_cached_prompt, cur_size);
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if (cur == nullptr) {
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return;
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}
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llama_state_seq_get_data(ctx, cur->data.data(), cur_size, id);
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}
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void server_slot::prompt_load(server_prompt_cache& prompt_cache, const server_tokens& tokens) {
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bool res = prompt_cache.load(server_cached_prompt, tokens, ctx, id);
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if (!res) {
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LLAMA_LOG_INFO("failed to load prompt from cache\n");
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}
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}
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void server_slot::reset() {
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n_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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n_sent_text = 0;
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drafted.clear();
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i_batch_dft.clear();
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n_sent_token_probs = 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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chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
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generated_token_probs.clear();
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// Reset speculative decoding stats
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n_draft_total = 0;
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n_draft_accepted = 0;
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chat_msg = {};
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json_schema = json();
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generated_tool_call_ids.clear();
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task.reset();
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}
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bool server_slot::has_budget(gpt_params& global_params) {
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if (params.n_predict == -1 && global_params.n_predict == -1) {
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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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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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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 server_slot::available() const {
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return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
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}
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bool server_slot::is_processing() const {
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return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING;
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}
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void server_slot::add_token_string(const completion_token_output& token) {
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if (command == SLOT_COMMAND_RELEASE) {
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return;
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}
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generated_token_probs.push_back(token);
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}
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int server_slot::get_n_draft_max() const {
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if (!ctx_dft) {
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return 0;
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}
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// determine the max draft that fits the current slot state
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int n_draft_max = params.speculative.n_max;
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// note: slot.prompt is not yet expanded with the `id` token sampled above
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// also, need to leave space for 1 extra token to allow context shifts
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n_draft_max = std::min(n_draft_max, n_ctx - n_past - 2);
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if (n_remaining > 0) {
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n_draft_max = std::min(n_draft_max, n_remaining - 1);
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}
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SLT_DBG(*this, "max possible draft: %d\n", n_draft_max);
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if (n_draft_max < params.speculative.n_min) {
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SLT_DBG(*this, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, params.speculative.n_min);
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n_draft_max = 0;
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}
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return n_draft_max;
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}
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void server_slot::release() {
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if (state == SLOT_STATE_PROCESSING) {
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t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
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command = SLOT_COMMAND_RELEASE;
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task.reset();
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}
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}
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json server_slot::get_formated_timings() const {
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return json{
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{"prompt_n", n_prompt_tokens_processed},
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{"prompt_ms", t_prompt_processing},
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{"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed},
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{"prompt_per_second", 1e3 / t_prompt_processing * n_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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{"n_ctx", n_ctx},
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{"n_past", n_past},
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};
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}
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result_timings server_slot::get_timings() const {
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result_timings timings;
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timings.prompt_n = n_prompt_tokens_processed;
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timings.prompt_ms = t_prompt_processing;
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timings.prompt_per_token_ms = t_prompt_processing / n_prompt_tokens_processed;
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timings.prompt_per_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
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timings.predicted_n = n_decoded;
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timings.predicted_ms = t_token_generation;
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timings.predicted_per_token_ms = t_token_generation / n_decoded;
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timings.predicted_per_second = 1e3 / t_token_generation * n_decoded;
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timings.n_ctx = n_ctx;
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timings.n_past = n_past;
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// Add speculative metrics
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if (n_draft_total > 0) {
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timings.draft_n = n_draft_total;
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timings.draft_n_accepted = n_draft_accepted;
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}
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return timings;
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}
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const common_chat_msg& server_slot::update_chat_msg(std::vector<common_chat_msg_diff>& diffs) {
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auto previous_msg = chat_msg;
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auto new_msg = common_chat_parse(
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generated_text,
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/* is_partial= */ stop != STOP_TYPE_EOS,
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params.oaicompat_chat_syntax);
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|
if (!new_msg.empty()) {
|
|
new_msg.ensure_tool_call_ids_set(generated_tool_call_ids, gen_tool_call_id);
|
|
chat_msg = new_msg;
|
|
diffs = common_chat_msg_diff::compute_diffs(previous_msg, new_msg.empty() ? previous_msg : new_msg);
|
|
}
|
|
//LLAMA_LOG_DEBUG("Parsing chat message: %s\n", generated_text.c_str());
|
|
//LLAMA_LOG_DEBUG("Parsing chat message: %s\n", chat_msg.reasoning_content.c_str());
|
|
//LLAMA_LOG_DEBUG("Parsing chat message: %s\n", chat_msg.content.c_str());
|
|
return chat_msg;
|
|
}
|
|
|
|
|
|
size_t server_slot::find_stopping_strings(const std::string& text, const size_t last_token_size, bool is_full_stop) {
|
|
size_t stop_pos = std::string::npos;
|
|
|
|
for (const std::string& word : params.antiprompt) {
|
|
size_t pos;
|
|
|
|
if (is_full_stop) {
|
|
const size_t tmp = word.size() + last_token_size;
|
|
const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
|
|
|
|
pos = text.find(word, from_pos);
|
|
}
|
|
else {
|
|
pos = string_find_partial_stop(text, word);
|
|
}
|
|
|
|
if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) {
|
|
if (is_full_stop) {
|
|
stopped_word = true;
|
|
stopping_word = word;
|
|
has_next_token = false;
|
|
}
|
|
stop_pos = pos;
|
|
}
|
|
}
|
|
|
|
return stop_pos;
|
|
}
|
|
|
|
void server_slot::print_timings() const {
|
|
char buffer[512];
|
|
double t_token = t_prompt_processing / n_prompt_tokens_processed;
|
|
double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;
|
|
|
|
//snprintf(buffer, 512, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
|
|
// t_prompt_processing, n_prompt_tokens_processed,
|
|
// t_token, n_tokens_second);
|
|
|
|
//LOG_INFO(buffer, {});
|
|
|
|
double t_token_gen = t_token_generation / n_decoded;
|
|
double n_tokens_second_gen = 1e3 / t_token_generation * n_decoded;
|
|
|
|
//snprintf(buffer, 512, "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, {});
|
|
|
|
//snprintf(buffer, 512, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
|
|
|
|
//LOG_INFO(buffer, {});
|
|
SLT_INF(*this,
|
|
"\n"
|
|
"prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n"
|
|
" eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n"
|
|
" total time = %10.2f ms / %5d tokens\n",
|
|
t_prompt_processing, n_prompt_tokens_processed, t_token, n_tokens_second,
|
|
t_token_generation, n_decoded, t_token_gen, n_tokens_second_gen,
|
|
t_prompt_processing + t_token_generation, n_prompt_tokens_processed + n_decoded);
|
|
|
|
if (n_draft_total > 0) {
|
|
const float draft_ratio = (float)n_draft_accepted / n_draft_total;
|
|
SLT_CNT(*this,
|
|
"draft acceptance rate = %0.5f (%5d accepted / %5d generated)\n",
|
|
draft_ratio, n_draft_accepted, n_draft_total
|
|
);
|
|
}
|
|
}
|
|
|
|
void server_metrics::init() {
|
|
t_start = ggml_time_us();
|
|
}
|
|
|
|
void server_metrics::on_prompt_eval(const server_slot& slot) {
|
|
n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
|
|
n_prompt_tokens_processed += slot.n_prompt_tokens_processed;
|
|
t_prompt_processing += slot.t_prompt_processing;
|
|
t_prompt_processing_total += slot.t_prompt_processing;
|
|
}
|
|
|
|
void server_metrics::on_prediction(const server_slot& slot) {
|
|
n_tokens_predicted_total += slot.n_decoded;
|
|
n_tokens_predicted += slot.n_decoded;
|
|
t_tokens_generation += slot.t_token_generation;
|
|
t_tokens_generation_total += slot.t_token_generation;
|
|
}
|
|
|
|
void server_metrics::reset_bucket() {
|
|
n_prompt_tokens_processed = 0;
|
|
t_prompt_processing = 0;
|
|
n_tokens_predicted = 0;
|
|
t_tokens_generation = 0;
|
|
}
|
|
|
|
std::vector<llama_token> server_context::tokenize(const json& json_prompt, bool add_special) const {
|
|
// TODO: currently, we tokenize using special tokens by default
|
|
// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
|
|
// but it's better compared to completely ignoring ChatML and other chat templates
|
|
const bool TMP_FORCE_SPECIAL = true;
|
|
|
|
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
|
|
// or the first element of the json_prompt array is a string.
|
|
std::vector<llama_token> prompt_tokens;
|
|
|
|
if (json_prompt.is_array()) {
|
|
bool first = true;
|
|
for (const auto& p : json_prompt) {
|
|
if (p.is_string()) {
|
|
auto s = p.template get<std::string>();
|
|
|
|
std::vector<llama_token> p;
|
|
if (first) {
|
|
p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
|
|
first = false;
|
|
}
|
|
else {
|
|
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
|
|
}
|
|
|
|
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
|
|
}
|
|
else {
|
|
if (first) {
|
|
first = false;
|
|
}
|
|
|
|
prompt_tokens.push_back(p.template get<llama_token>());
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
auto s = json_prompt.template get<std::string>();
|
|
prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
|
|
}
|
|
|
|
return prompt_tokens;
|
|
}
|
|
|
|
server_slot* server_context::get_slot_by_id(int id) {
|
|
for (server_slot& slot : slots) {
|
|
if (slot.id == id) {
|
|
return &slot;
|
|
}
|
|
}
|
|
|
|
return nullptr;
|
|
}
|
|
|
|
float server_context::calculate_slot_f_keep(const server_slot & slot, llama_context * ctx,const server_tokens & a, const server_tokens & b) {
|
|
float f_keep = 0.0f;
|
|
if (!a.empty()) {
|
|
if (slot.ga_n == 1 && slot.n_discarded_prompt > 0 && b.size() >= slot.n_ctx) {
|
|
f_keep = a.get_cached_tokens_similarity(slot.ctx, b, slot.params.n_keep + add_bos_token, slot.n_discarded_prompt);
|
|
}
|
|
else {
|
|
f_keep = a.get_cached_tokens_similarity(slot.ctx, b, 0, 0);
|
|
}
|
|
}
|
|
return f_keep;
|
|
}
|
|
|
|
std::pair<common_prefix, float> server_context::calculate_slot_similarity(const server_slot& slot, llama_context* ctx, const server_tokens& a, const server_tokens& b) {
|
|
std::pair<common_prefix, float> sim;
|
|
// length of the Longest Common Prefix between the current slot's prompt and the input prompt
|
|
common_prefix lcp_len = a.get_common_prefix(slot.ctx, b);
|
|
// fraction of the Longest Common Prefix length with respect to the input prompt and cached prompt length
|
|
float sim_cur = a.get_tokens_similarity(slot.ctx, b, 0, 0);
|
|
// handle context shift
|
|
if (slot.ga_n == 1 && slot.n_discarded_prompt > 0 && b.size() >= slot.n_ctx) {
|
|
float sim_cur_ctx_shift = a.get_tokens_similarity(slot.ctx, b, slot.n_kept_prompt, slot.n_discarded_prompt);
|
|
if (sim_cur_ctx_shift > sim_cur) {
|
|
sim_cur = sim_cur_ctx_shift;
|
|
}
|
|
}
|
|
sim.first = lcp_len;
|
|
sim.second = sim_cur;
|
|
return sim;
|
|
}
|
|
|
|
void server_context::copy_data_to_cached_prompt(const server_tokens & tokens, server_slot & slot) {
|
|
slot.server_cached_prompt.tokens = server_tokens(tokens.get_text_tokens(), false); // copy cache tokens
|
|
slot.server_cached_prompt.n_discarded_prompt = slot.n_discarded_prompt;
|
|
slot.server_cached_prompt.n_kept_prompt = slot.n_kept_prompt;
|
|
slot.server_cached_prompt.think_tokens = slot.params.think_tokens;
|
|
}
|
|
|
|
server_slot* server_context::get_available_slot(const server_task& task) {
|
|
server_slot* ret = nullptr;
|
|
bool update_cache = false;
|
|
|
|
// find the slot that has at least n% prompt similarity
|
|
if (ret == nullptr && slot_prompt_similarity != 0.0f) {
|
|
int max_lcp_len = 0;
|
|
float sim_best = 0;
|
|
|
|
for (server_slot& slot : slots) {
|
|
// skip the slot if it is not available
|
|
if (!slot.available()) {
|
|
continue;
|
|
}
|
|
auto& cache_tokens = slot.cache_tokens;
|
|
// skip the slot if it does not contains prompt
|
|
if (cache_tokens.empty()) {
|
|
continue;
|
|
}
|
|
bool exclude_think = !cache_tokens.has_mtmd && slot.params.think_tokens.exclude;
|
|
std::pair<common_prefix, float> sim;
|
|
if (exclude_think) {
|
|
auto temp = slot.cache_tokens.get_text_tokens_exclude_think(slot.ctx, slot.params.think_tokens);
|
|
server_tokens cache_tokens_exclude_think = server_tokens(temp, false);
|
|
temp = task.tokens.get_text_tokens_exclude_think(slot.ctx, slot.params.think_tokens);
|
|
server_tokens prompt_tokens_exclude_think = server_tokens(temp, false);
|
|
sim = calculate_slot_similarity(slot, ctx, cache_tokens_exclude_think, prompt_tokens_exclude_think);
|
|
}
|
|
else {
|
|
sim = calculate_slot_similarity(slot, ctx, cache_tokens, task.tokens);
|
|
}
|
|
common_prefix lcp_len = sim.first;
|
|
float sim_cur = sim.second;
|
|
|
|
// select the current slot if the criteria match
|
|
if (sim_cur > sim_best && sim_cur > slot_prompt_similarity) {
|
|
sim_best = sim_cur;
|
|
max_lcp_len = lcp_len.first;
|
|
ret = &slot;
|
|
}
|
|
}
|
|
if (ret != nullptr) {
|
|
LOG_VERBOSE("selected slot by lcp similarity", {
|
|
{"id_slot", ret->id},
|
|
{"max_lcp_len", max_lcp_len},
|
|
{"similarity", sim_best},
|
|
});
|
|
}
|
|
}
|
|
|
|
// find the slot that has been least recently used
|
|
if (ret == nullptr) {
|
|
int64_t t_last = ggml_time_us();
|
|
for (server_slot& slot : slots) {
|
|
// skip the slot if it is not available
|
|
if (!slot.available()) {
|
|
continue;
|
|
}
|
|
// select the current slot if the criteria match
|
|
if (slot.t_last_used < t_last) {
|
|
t_last = slot.t_last_used;
|
|
ret = &slot;
|
|
}
|
|
}
|
|
|
|
if (ret != nullptr) {
|
|
LOG_VERBOSE("selected slot by lru", {
|
|
{"id_slot", ret->id},
|
|
{"t_last", t_last},
|
|
});
|
|
}
|
|
}
|
|
if (ret) {
|
|
auto& tokens = ret->cache_tokens;
|
|
float f_keep = 0;
|
|
size_t cache_token_size = tokens.size();
|
|
if (!tokens.empty()) {
|
|
bool exclude_think = !tokens.has_mtmd && ret->params.think_tokens.exclude;
|
|
if (exclude_think) {
|
|
auto temp = tokens.get_text_tokens_exclude_think(ret->ctx, ret->params.think_tokens);
|
|
server_tokens cache_exclude_think = server_tokens(temp, false);
|
|
|
|
temp = task.tokens.get_text_tokens_exclude_think(ret->ctx, ret->params.think_tokens);
|
|
server_tokens prompt_exclude_think = server_tokens(temp, false);
|
|
|
|
cache_token_size = cache_exclude_think.size();
|
|
f_keep = calculate_slot_f_keep(*ret, ret->ctx, cache_exclude_think, prompt_exclude_think);
|
|
}
|
|
else {
|
|
f_keep = calculate_slot_f_keep(*ret, ret->ctx, tokens, task.tokens);
|
|
}
|
|
// if we are about to lose a large portion of the existing context - save it in the prompt cache
|
|
if (f_keep < cache_ram_similarity) {
|
|
update_cache = true;
|
|
}
|
|
}
|
|
|
|
update_cache = update_cache && prompt_cache;
|
|
// cache prompts only for completion tasks
|
|
update_cache = update_cache && task.type == SERVER_TASK_TYPE_COMPLETION;
|
|
|
|
// don't update the cache if the slot's context is above cache_ram_n_min
|
|
update_cache = update_cache && cache_token_size >= cache_ram_n_min;
|
|
|
|
// TODO: mtmd does not support prompt cache
|
|
update_cache = update_cache && (ret->mctx == nullptr);
|
|
|
|
LLAMA_LOG_INFO("======== Prompt cache: cache size: %d, n_keep: %d, n_discarded_prompt: %d, cache_ram_n_min: %d, f_keep: %.2f, cache_ram_similarity: %.2f\n",
|
|
(int)tokens.size(), ret->n_kept_prompt, ret->n_discarded_prompt, cache_ram_n_min, f_keep, cache_ram_similarity);
|
|
if (update_cache) {
|
|
const int64_t t_start = ggml_time_us();
|
|
LLAMA_LOG_INFO("updating prompt cache\n");
|
|
// copy cache tokens
|
|
copy_data_to_cached_prompt(tokens, *ret);
|
|
|
|
ret->prompt_save(*prompt_cache);
|
|
LLAMA_LOG_INFO("prompt cache save took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
|
|
}
|
|
// has prompts saved earlier to load
|
|
if (prompt_cache && !prompt_cache->states.empty()) {
|
|
const int64_t t_start = ggml_time_us();
|
|
copy_data_to_cached_prompt(tokens, *ret);
|
|
|
|
ret->prompt_load(*prompt_cache, task.tokens);
|
|
prompt_cache->update();
|
|
|
|
ret->cache_tokens = server_tokens(ret->server_cached_prompt.tokens.get_text_tokens(), false); // recover cache tokens
|
|
ret->n_discarded_prompt = ret->server_cached_prompt.n_discarded_prompt;
|
|
ret->n_kept_prompt = ret->server_cached_prompt.n_kept_prompt;
|
|
|
|
LLAMA_LOG_INFO("prompt cache load took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
|
|
}
|
|
}
|
|
return ret;
|
|
}
|
|
|
|
bool server_context::launch_slot_with_task(server_slot& slot, server_task& task) {
|
|
slot_params default_params;
|
|
// Sampling parameter defaults are loaded from the global server context (but individual requests can still override them)
|
|
llama_sampling_params default_sparams = params.sparams;
|
|
auto& data = task.data;
|
|
|
|
if (data.count("__oaicompat") != 0) {
|
|
slot.oaicompat = true;
|
|
slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
|
|
}
|
|
else {
|
|
slot.oaicompat = false;
|
|
slot.oaicompat_model = "";
|
|
}
|
|
slot.params.timings_per_token = json_value(data, "timings_per_token", false);
|
|
slot.params.stream = json_value(data, "stream", false);
|
|
auto stream_opt = json_value(data, "stream_options", json::object());
|
|
slot.params.include_usage = json_value(stream_opt, "include_usage", false);
|
|
slot.params.cache_prompt = json_value(data, "cache_prompt", true);
|
|
slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict));
|
|
slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
|
|
slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
|
|
slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
|
|
slot.sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
|
|
slot.sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
|
|
slot.sparams.temp = json_value(data, "temperature", default_sparams.temp);
|
|
slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
|
|
slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
|
|
slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability);
|
|
slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold);
|
|
slot.sparams.top_n_sigma = json_value(data, "top_n_sigma", default_sparams.top_n_sigma);
|
|
slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
|
|
slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat);
|
|
slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
|
|
slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present);
|
|
slot.sparams.dry_multiplier = json_value(data, "dry_multiplier", default_sparams.dry_multiplier);
|
|
slot.sparams.dry_base = json_value(data, "dry_base", default_sparams.dry_base);
|
|
slot.sparams.dry_allowed_length = json_value(data, "dry_allowed_length", default_sparams.dry_allowed_length);
|
|
slot.sparams.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", default_sparams.dry_penalty_last_n);
|
|
slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
|
|
slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
|
|
slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
|
|
slot.sparams.adaptive_target = json_value(data, "adaptive_target", default_sparams.adaptive_target);
|
|
slot.sparams.adaptive_decay = json_value(data, "adaptive_decay", default_sparams.adaptive_decay);
|
|
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
|
|
slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
|
|
slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
|
|
slot.sparams.seed = json_value(data, "seed", default_sparams.seed);
|
|
slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
|
|
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
|
|
|
|
slot.params.post_sampling_probs = json_value(data, "post_sampling_probs", default_params.post_sampling_probs);
|
|
|
|
// speculative decoding parameters
|
|
slot.params.speculative.n_max = json_value(data, "speculative.n_max", params.n_draft);
|
|
slot.params.speculative.n_min = json_value(data, "speculative.n_min", params.n_draft_min);
|
|
slot.params.speculative.p_min = json_value(data, "speculative.p_min", params.p_draft_min);
|
|
|
|
// Clamp speculative parameters
|
|
slot.params.speculative.n_min = std::min(slot.params.speculative.n_max, slot.params.speculative.n_min);
|
|
slot.params.speculative.n_min = std::max(slot.params.speculative.n_min, 0);
|
|
slot.params.speculative.n_max = std::max(slot.params.speculative.n_max, 0);
|
|
|
|
if (slot.sparams.penalty_last_n < -1) {
|
|
throw std::runtime_error("Error: repeat_last_n must be >= -1");
|
|
}
|
|
|
|
if (slot.sparams.dry_penalty_last_n < -1) {
|
|
throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
|
|
}
|
|
|
|
if (slot.sparams.penalty_last_n == -1) {
|
|
// note: should be the slot's context and not the full context, but it's ok
|
|
slot.sparams.penalty_last_n = llama_n_ctx(ctx);
|
|
}
|
|
|
|
if (slot.sparams.dry_penalty_last_n == -1) {
|
|
slot.sparams.dry_penalty_last_n = llama_n_ctx(ctx);
|
|
|
|
}
|
|
if (slot.sparams.dry_base < 1.0f)
|
|
{
|
|
slot.sparams.dry_base = default_sparams.dry_base;
|
|
}
|
|
|
|
// sequence breakers for DRY
|
|
{
|
|
// Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
|
|
// Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39
|
|
|
|
if (data.contains("dry_sequence_breakers")) {
|
|
slot.sparams.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
|
|
if (slot.sparams.dry_sequence_breakers.empty()) {
|
|
send_error(task, "Error: dry_sequence_breakers must be a non-empty array of strings", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
// process "json_schema" and "grammar"
|
|
if (data.contains("json_schema") && !data.contains("grammar")) {
|
|
try {
|
|
auto schema = json_value(data, "json_schema", json::object());
|
|
LLAMA_LOG_DEBUG("JSON schema: %s\n", schema.dump(2).c_str());
|
|
slot.sparams.grammar = json_schema_to_grammar(schema);
|
|
LLAMA_LOG_DEBUG("Converted grammar: %s\n", slot.sparams.grammar.c_str());
|
|
}
|
|
catch (const std::exception& e) {
|
|
throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
|
|
}
|
|
}
|
|
else {
|
|
slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
|
|
LLAMA_LOG_DEBUG("Grammar: %s\n", slot.sparams.grammar.c_str());
|
|
slot.sparams.grammar_lazy = json_value(data, "grammar_lazy", default_sparams.grammar_lazy);
|
|
LLAMA_LOG_DEBUG("Grammar lazy: %s\n", slot.sparams.grammar_lazy ? "true" : "false");
|
|
}
|
|
|
|
if (slot.params.cache_prompt && slot.ga_n != 1) {
|
|
LOG_WARNING("cache_prompt is not supported with group-attention", {});
|
|
slot.params.cache_prompt = false;
|
|
}
|
|
|
|
if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
|
|
// Might be better to reject the request with a 400 ?
|
|
LOG_WARNING("Max tokens to predict exceeds server configuration", {
|
|
{"params.n_predict", slot.params.n_predict},
|
|
{"slot.n_predict", slot.n_predict},
|
|
});
|
|
slot.params.n_predict = slot.n_predict;
|
|
}
|
|
|
|
// infill
|
|
slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
|
|
slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
|
|
|
|
// get prompt
|
|
if (!task.infill) {
|
|
// maybe not needed since prompt has been tokenized?
|
|
const auto& prompt = data.find("prompt");
|
|
if (!slot.prompt_tokens.validate(ctx)) {
|
|
send_error(task, "Prompt contains invalid tokens", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
if (prompt == data.end()) {
|
|
send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
|
|
if ((prompt->is_string()) ||
|
|
(prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_string()) ||
|
|
(prompt->is_array() && !prompt->empty() && prompt->at(0).is_number_integer())) {
|
|
slot.prompt = *prompt;
|
|
}
|
|
else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) {
|
|
slot.prompt = prompt->at(0);
|
|
}
|
|
else {
|
|
send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
slot.prompt_tokens = std::move(task.tokens);
|
|
}
|
|
|
|
// penalize user-provided tokens
|
|
{
|
|
slot.sparams.penalty_prompt_tokens.clear();
|
|
slot.sparams.use_penalty_prompt_tokens = false;
|
|
|
|
const auto& penalty_prompt = data.find("penalty_prompt");
|
|
|
|
if (penalty_prompt != data.end()) {
|
|
if (penalty_prompt->is_string()) {
|
|
const auto penalty_prompt_string = penalty_prompt->get<std::string>();
|
|
slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false);
|
|
|
|
if (slot.params.n_predict > 0) {
|
|
slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict);
|
|
}
|
|
slot.sparams.use_penalty_prompt_tokens = true;
|
|
|
|
LOG_VERBOSE("penalty_prompt_tokens", {
|
|
{"id_slot", slot.id},
|
|
{"tokens", slot.sparams.penalty_prompt_tokens},
|
|
});
|
|
}
|
|
else if (penalty_prompt->is_array()) {
|
|
const auto n_tokens = penalty_prompt->size();
|
|
slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict));
|
|
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto& penalty_token : *penalty_prompt) {
|
|
if (penalty_token.is_number_integer()) {
|
|
const auto tok = penalty_token.get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab) {
|
|
slot.sparams.penalty_prompt_tokens.push_back(tok);
|
|
}
|
|
}
|
|
}
|
|
slot.sparams.use_penalty_prompt_tokens = true;
|
|
|
|
LOG_VERBOSE("penalty_prompt_tokens", {
|
|
{"id_slot", slot.id},
|
|
{"tokens", slot.sparams.penalty_prompt_tokens},
|
|
});
|
|
}
|
|
}
|
|
}
|
|
{
|
|
auto it = data.find("chat_format");
|
|
if (it != data.end()) {
|
|
slot.params.oaicompat_chat_syntax.format = static_cast<common_chat_format>(it->get<int>());
|
|
LLAMA_LOG_DEBUG("Chat format: %s\n", common_chat_format_name(slot.params.oaicompat_chat_syntax.format));
|
|
}
|
|
else {
|
|
slot.params.oaicompat_chat_syntax.format = default_params.oaicompat_chat_syntax.format;
|
|
}
|
|
common_reasoning_format reasoning_format = params.reasoning_format;
|
|
if (data.contains("reasoning_format")) {
|
|
reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
|
|
}
|
|
slot.params.oaicompat_chat_syntax.reasoning_format = reasoning_format;
|
|
slot.params.oaicompat_chat_syntax.reasoning_in_content = slot.params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
|
|
slot.params.oaicompat_chat_syntax.parse_tool_calls = json_value(data, "parse_tool_calls", false);
|
|
|
|
slot.params.oaicompat_chat_syntax.thinking_forced_open = json_value(data, "thinking_forced_open", false);
|
|
}
|
|
{
|
|
|
|
const auto preserved_tokens = data.find("preserved_tokens");
|
|
if (preserved_tokens != data.end()) {
|
|
for (const auto& t : *preserved_tokens) {
|
|
auto ids = llama_tokenize(model, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
|
|
if (ids.size() == 1) {
|
|
LOG("Preserved token: %d\n", ids[0]);
|
|
slot.sparams.preserved_tokens.insert(ids[0]);
|
|
}
|
|
else {
|
|
// This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
|
|
LOG("Not preserved because more than 1 token: %s\n", t.get<std::string>().c_str());
|
|
}
|
|
}
|
|
}
|
|
const auto grammar_triggers = data.find("grammar_triggers");
|
|
if (grammar_triggers != data.end()) {
|
|
for (const auto& t : *grammar_triggers) {
|
|
server_grammar_trigger ct(t);
|
|
if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
|
|
const auto& word = ct.value.value;
|
|
auto ids = llama_tokenize(model, word, /* add_special= */ false, /* parse_special= */ true);
|
|
if (ids.size() == 1) {
|
|
auto token = ids[0];
|
|
if (std::find(slot.sparams.preserved_tokens.begin(), slot.sparams.preserved_tokens.end(), (llama_token)token) == slot.sparams.preserved_tokens.end()) {
|
|
throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
|
|
}
|
|
LOG("Grammar trigger token: %d (`%s`)\n", token, word.c_str());
|
|
common_grammar_trigger trigger;
|
|
trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
|
|
trigger.value = word;
|
|
trigger.token = token;
|
|
slot.sparams.grammar_triggers.push_back(std::move(trigger));
|
|
}
|
|
else {
|
|
LOG("Grammar trigger word: `%s`\n", word.c_str());
|
|
slot.sparams.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word });
|
|
}
|
|
}
|
|
else {
|
|
//slot.sparams.grammar_triggers.push_back(ct);
|
|
if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) {
|
|
LLAMA_LOG_DEBUG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str());
|
|
}
|
|
else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) {
|
|
LLAMA_LOG_DEBUG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str());
|
|
}
|
|
else {
|
|
throw std::runtime_error("Unknown grammar trigger type");
|
|
}
|
|
slot.sparams.grammar_triggers.emplace_back(std::move(ct.value));
|
|
}
|
|
}
|
|
}
|
|
|
|
if (slot.sparams.grammar_lazy && slot.sparams.grammar_triggers.empty()) {
|
|
throw std::runtime_error("Error: no triggers set for lazy grammar!");
|
|
}
|
|
}
|
|
|
|
{
|
|
slot.sparams.logit_bias.clear();
|
|
|
|
if (json_value(data, "ignore_eos", false) && has_eos_token) {
|
|
slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
|
|
}
|
|
|
|
const auto& logit_bias = data.find("logit_bias");
|
|
if (logit_bias != data.end() && logit_bias->is_array()) {
|
|
const int n_vocab = llama_n_vocab(model);
|
|
for (const auto& el : *logit_bias) {
|
|
// TODO: we may want to throw errors here, in case "el" is incorrect
|
|
if (el.is_array() && el.size() == 2) {
|
|
float bias;
|
|
if (el[1].is_number()) {
|
|
bias = el[1].get<float>();
|
|
}
|
|
else if (el[1].is_boolean() && !el[1].get<bool>()) {
|
|
bias = -INFINITY;
|
|
}
|
|
else {
|
|
continue;
|
|
}
|
|
|
|
if (el[0].is_number_integer()) {
|
|
llama_token tok = el[0].get<llama_token>();
|
|
if (tok >= 0 && tok < n_vocab) {
|
|
slot.sparams.logit_bias[tok] = bias;
|
|
}
|
|
}
|
|
else if (el[0].is_string()) {
|
|
auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
|
|
for (auto tok : toks) {
|
|
slot.sparams.logit_bias[tok] = bias;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
slot.params.antiprompt.clear();
|
|
|
|
const auto& stop = data.find("stop");
|
|
if (stop != data.end() && stop->is_array()) {
|
|
for (const auto& word : *stop) {
|
|
if (!word.empty()) {
|
|
slot.params.antiprompt.push_back(word);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
const auto samplers = data.find("samplers");
|
|
if (samplers != data.end()) {
|
|
if (samplers->is_array()) {
|
|
slot.sparams.samplers_sequence = llama_sampling_types_from_names(*samplers, false);
|
|
}
|
|
else if (samplers->is_string()) {
|
|
slot.sparams.samplers_sequence = llama_sampling_types_from_chars(samplers->get<std::string>());
|
|
}
|
|
else {
|
|
slot.sparams.samplers_sequence = default_sparams.samplers_sequence;
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
if (slot.ctx_sampling != nullptr) {
|
|
llama_sampling_free(slot.ctx_sampling);
|
|
}
|
|
slot.ctx_sampling = llama_sampling_init(llama_get_model_vocab(model), slot.sparams);
|
|
if (slot.ctx_sampling == nullptr) {
|
|
// for now, the only error that may happen here is invalid grammar
|
|
send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
slot.command = SLOT_COMMAND_LOAD_PROMPT;
|
|
// slot.prompt_tokens.clear();
|
|
|
|
LOG_INFO("slot is processing task", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
});
|
|
|
|
return true;
|
|
}
|
|
|
|
void server_context::kv_cache_clear() {
|
|
LOG_VERBOSE("clearing KV cache", {});
|
|
|
|
// clear the entire KV cache
|
|
llama_kv_cache_clear(ctx);
|
|
clean_kv_cache = false;
|
|
}
|
|
|
|
void server_context::system_prompt_update() {
|
|
LOG_VERBOSE("system prompt update", {
|
|
{"system_prompt", system_prompt},
|
|
});
|
|
|
|
kv_cache_clear();
|
|
system_tokens.clear();
|
|
|
|
if (!system_prompt.empty()) {
|
|
system_tokens = ::llama_tokenize(ctx, system_prompt, true);
|
|
|
|
const int32_t n_batch = llama_n_batch(ctx);
|
|
const int32_t n_tokens_prompt = system_tokens.size();
|
|
|
|
for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) {
|
|
const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i);
|
|
|
|
llama_batch_clear(batch);
|
|
|
|
for (int32_t j = 0; j < n_tokens; ++j) {
|
|
llama_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false);
|
|
}
|
|
|
|
if (llama_decode(ctx, batch) != 0) {
|
|
LOG_ERROR("llama_decode() failed", {});
|
|
return;
|
|
}
|
|
}
|
|
|
|
// assign the system KV cache to all parallel sequences
|
|
for (int32_t i = 1; i <= params.n_parallel; ++i) {
|
|
llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
|
|
}
|
|
}
|
|
|
|
system_need_update = false;
|
|
}
|
|
|
|
bool server_context::system_prompt_set(const std::string& sys_prompt) {
|
|
system_prompt = sys_prompt;
|
|
|
|
LOG_VERBOSE("system prompt process", {
|
|
{"system_prompt", system_prompt},
|
|
});
|
|
|
|
// release all slots
|
|
for (server_slot& slot : slots) {
|
|
slot.release();
|
|
}
|
|
|
|
system_need_update = true;
|
|
return true;
|
|
}
|
|
|
|
bool server_context::process_token(completion_token_output& result, server_slot& slot) {
|
|
// remember which tokens were sampled - used for repetition penalties during sampling
|
|
const std::string token_str = result.text_to_send;
|
|
slot.sampled = result.tok;
|
|
|
|
// search stop word and delete it
|
|
slot.generated_text += token_str;
|
|
slot.has_next_token = true;
|
|
|
|
if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) {
|
|
// we can change penalty_prompt_tokens because it is always created from scratch each request
|
|
slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
|
|
}
|
|
|
|
// check if there is incomplete UTF-8 character at the end
|
|
bool incomplete = validate_utf8(slot.generated_text) < slot.generated_text.size();
|
|
|
|
if (!incomplete) {
|
|
size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());
|
|
|
|
const std::string str_test = slot.generated_text.substr(pos);
|
|
bool send_text = true;
|
|
|
|
size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), true);
|
|
if (stop_pos != std::string::npos) {
|
|
slot.generated_text.erase(
|
|
slot.generated_text.begin() + pos + stop_pos,
|
|
slot.generated_text.end());
|
|
pos = std::min(slot.n_sent_text, slot.generated_text.size());
|
|
}
|
|
else if (slot.has_next_token && !llama_token_is_eog(model, result.tok)) {
|
|
stop_pos = slot.find_stopping_strings(str_test, token_str.size(), false);
|
|
send_text = stop_pos == std::string::npos;
|
|
}
|
|
|
|
// check if there is any token to predict
|
|
if (send_text) {
|
|
// no send the stop word in the response
|
|
result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
|
|
slot.n_sent_text += result.text_to_send.size();
|
|
// add the token to slot queue and cache
|
|
}
|
|
else {
|
|
result.text_to_send = "";
|
|
}
|
|
|
|
slot.add_token_string(result);
|
|
if (slot.params.stream) {
|
|
send_partial_response(slot, result);
|
|
}
|
|
}
|
|
|
|
if (incomplete) {
|
|
slot.has_next_token = true;
|
|
}
|
|
|
|
// check the limits
|
|
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) {
|
|
slot.stopped_limit = true;
|
|
slot.has_next_token = false;
|
|
|
|
LOG_VERBOSE("stopped by limit", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"n_predict", slot.params.n_predict},
|
|
});
|
|
}
|
|
|
|
if (llama_token_is_eog(model, result.tok)) {
|
|
slot.stopped_eos = true;
|
|
slot.has_next_token = false;
|
|
|
|
LOG_VERBOSE("eos token found", {});
|
|
}
|
|
|
|
auto n_ctx_train = llama_n_ctx_train(model);
|
|
if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1
|
|
&& slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
|
|
LOG_WARNING("n_predict is not set and self-context extend is disabled."
|
|
" Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", {
|
|
{ "id_slot", slot.id },
|
|
{ "params.n_predict", slot.params.n_predict },
|
|
{ "slot.n_prompt_tokens", slot.n_prompt_tokens },
|
|
{ "slot.n_decoded", slot.n_decoded },
|
|
{ "slot.n_predict", slot.n_predict },
|
|
{ "n_slots", params.n_parallel },
|
|
{ "slot.n_ctx", slot.n_ctx },
|
|
{ "n_ctx", n_ctx },
|
|
{ "n_ctx_train", n_ctx_train },
|
|
{ "ga_n", slot.ga_n },
|
|
});
|
|
slot.truncated = true;
|
|
slot.stopped_limit = true;
|
|
slot.has_next_token = false; // stop prediction
|
|
}
|
|
|
|
LOG_VERBOSE("next token", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"token", result.tok},
|
|
{"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
|
|
{"has_next_token", slot.has_next_token},
|
|
{"n_remain", slot.n_remaining},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
});
|
|
|
|
return slot.has_next_token; // continue
|
|
}
|
|
|
|
void server_context::populate_token_probs(const server_slot& slot, completion_token_output& result, bool post_sampling, bool special, int idx) {
|
|
size_t n_probs = slot.sparams.n_probs;
|
|
size_t n_vocab = llama_n_vocab(llama_get_model(ctx));
|
|
|
|
if (post_sampling) {
|
|
const auto* cur_p = llama_sampling_get_candidates(slot.ctx_sampling);
|
|
const size_t max_probs = cur_p->size;
|
|
|
|
// set probability for sampled token
|
|
for (size_t i = 0; i < max_probs; i++) {
|
|
if (cur_p->data[i].id == result.tok) {
|
|
result.prob = cur_p->data[i].p;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// set probability for top n_probs tokens
|
|
result.probs.reserve(max_probs);
|
|
for (size_t i = 0; i < std::min(max_probs, n_probs); i++) {
|
|
result.probs.push_back({
|
|
cur_p->data[i].id,
|
|
llama_detokenize(ctx, {cur_p->data[i].id}, special),
|
|
cur_p->data[i].p
|
|
});
|
|
}
|
|
}
|
|
else {
|
|
auto&& [sampled_token_p, cur] = get_token_probabilities(ctx, idx, result.tok, n_probs);
|
|
|
|
// set probability for sampled token
|
|
result.prob = sampled_token_p;
|
|
|
|
// set probability for top n_probs tokens
|
|
result.probs.reserve(n_probs);
|
|
for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) {
|
|
result.probs.push_back({
|
|
cur[i].id,
|
|
llama_detokenize(ctx, {cur[i].id}, special),
|
|
cur[i].p
|
|
});
|
|
}
|
|
}
|
|
}
|
|
|
|
json server_context::get_formated_generation(const server_slot& slot) const {
|
|
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
|
|
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second);
|
|
|
|
std::vector<std::string> samplers_sequence;
|
|
samplers_sequence.reserve(slot.sparams.samplers_sequence.size());
|
|
for (const auto& sampler_type : slot.sparams.samplers_sequence) {
|
|
samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type));
|
|
}
|
|
|
|
auto grammar_triggers = json::array();
|
|
for (const auto& trigger : slot.sparams.grammar_triggers) {
|
|
grammar_triggers.push_back(trigger.to_json<json>());
|
|
}
|
|
|
|
return json{
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_predict", slot.n_predict}, // Server configured n_predict
|
|
{"model", params.model_alias},
|
|
{"seed", slot.sparams.seed},
|
|
{"temperature", slot.sparams.temp},
|
|
{"dynatemp_range", slot.sparams.dynatemp_range},
|
|
{"dynatemp_exponent", slot.sparams.dynatemp_exponent},
|
|
{"top_k", slot.sparams.top_k},
|
|
{"top_p", slot.sparams.top_p},
|
|
{"min_p", slot.sparams.min_p},
|
|
{"tfs_z", slot.sparams.tfs_z},
|
|
{"typical_p", slot.sparams.typical_p},
|
|
{"repeat_last_n", slot.sparams.penalty_last_n},
|
|
{"repeat_penalty", slot.sparams.penalty_repeat},
|
|
{"presence_penalty", slot.sparams.penalty_present},
|
|
{"frequency_penalty", slot.sparams.penalty_freq},
|
|
{"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
|
|
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
|
|
{"dry_multiplier", slot.sparams.dry_multiplier},
|
|
{"dry_base", slot.sparams.dry_base},
|
|
{"dry_allowed_length", slot.sparams.dry_allowed_length},
|
|
{"dry_penalty_last_n", slot.sparams.dry_penalty_last_n},
|
|
{"dry_sequence_breakers", slot.sparams.dry_sequence_breakers},
|
|
{"mirostat", slot.sparams.mirostat},
|
|
{"mirostat_tau", slot.sparams.mirostat_tau},
|
|
{"mirostat_eta", slot.sparams.mirostat_eta},
|
|
{"adaptive_target", slot.sparams.adaptive_target},
|
|
{"adaptive_decay", slot.sparams.adaptive_decay},
|
|
{"penalize_nl", slot.sparams.penalize_nl},
|
|
{"stop", slot.params.antiprompt},
|
|
{"max_tokens", slot.params.n_predict}, // User configured n_predict
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_discard", slot.params.n_discard},
|
|
{"ignore_eos", ignore_eos},
|
|
{"stream", slot.params.stream},
|
|
{"logit_bias", slot.sparams.logit_bias},
|
|
{"n_probs", slot.sparams.n_probs},
|
|
{"min_keep", slot.sparams.min_keep},
|
|
{"grammar", slot.sparams.grammar},
|
|
{"grammar_triggers", grammar_triggers},
|
|
{"preserved_tokens", slot.sparams.preserved_tokens},
|
|
{"chat_format", common_chat_format_name(slot.params.oaicompat_chat_syntax.format)},
|
|
{"reasoning_format", common_reasoning_format_name(slot.params.oaicompat_chat_syntax.reasoning_format)},
|
|
{"reasoning_in_content", slot.params.oaicompat_chat_syntax.reasoning_in_content},
|
|
{"thinking_forced_open", slot.params.oaicompat_chat_syntax.thinking_forced_open},
|
|
{"samplers", samplers_sequence}
|
|
};
|
|
}
|
|
|
|
void server_context::send_error(const server_task& task, const std::string& error, const enum error_type type) {
|
|
send_error(task.id, task.id_multi, error, type);
|
|
}
|
|
|
|
void server_context::send_error(const server_slot& slot, const std::string& error, const enum error_type type) {
|
|
send_error(slot.id_task, slot.id_multi, error, type);
|
|
}
|
|
|
|
void server_context::send_error(const int id_task, const int id_multi, const std::string& error, const enum error_type type ) {
|
|
LOG_ERROR("task error", {
|
|
{"id_multi", id_multi},
|
|
{"id_task", id_task},
|
|
{"error", error},
|
|
});
|
|
|
|
server_task_result res;
|
|
res.id = id_task;
|
|
res.id_multi = id_multi;
|
|
res.stop = false;
|
|
res.error = true;
|
|
res.data = format_error_response(error, type);
|
|
|
|
queue_results.send(res);
|
|
}
|
|
|
|
// if multimodal is enabled, send an error and return false
|
|
bool server_context::ensure_no_mtmd(const int id_task) {
|
|
if (mctx) {
|
|
int id_multi = 0;
|
|
send_error(id_task, id_multi, "This feature is not supported by multimodal", ERROR_TYPE_NOT_SUPPORTED);
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void server_context::send_partial_response(server_slot& slot, completion_token_output tkn) {
|
|
server_task_result res;
|
|
res.final_result = false;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = false;
|
|
res.stream = slot.params.stream;
|
|
res.content = tkn.text_to_send;
|
|
res.post_sampling_probs = slot.params.post_sampling_probs;
|
|
res.oaicompat = slot.params.oaicompat;
|
|
res.oaicompat_model = slot.params.oaicompat_model;
|
|
res.oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
|
|
res.n_decoded = slot.n_decoded;
|
|
res.n_prompt_tokens = slot.n_prompt_tokens;
|
|
res.data = json{
|
|
{"content", tkn.text_to_send},
|
|
{"stop", false},
|
|
{"id_slot", slot.id},
|
|
{"multimodal", false}
|
|
};
|
|
slot.update_chat_msg(res.oaicompat_msg_diffs);
|
|
|
|
// populate res.probs_output
|
|
if (slot.sparams.n_probs > 0) {
|
|
res.probs_output = { tkn }; // copy the token probs
|
|
res.data["completion_probabilities"] = probs_vector_to_json(ctx, res.probs_output);
|
|
}
|
|
|
|
if (slot.oaicompat) {
|
|
res.data["oaicompat_token_ctr"] = slot.n_decoded;
|
|
res.data["model"] = slot.oaicompat_model;
|
|
}
|
|
|
|
// populate timings if this is final response or timings_per_token is enabled
|
|
if (slot.params.timings_per_token) {
|
|
res.timings = slot.get_timings();
|
|
}
|
|
queue_results.send(std::move(res));
|
|
}
|
|
|
|
void server_context::send_final_response(server_slot& slot) {
|
|
server_task_result res;
|
|
res.final_result = true;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = true; // to do: set value
|
|
res.stream = slot.params.stream;
|
|
res.include_usage = slot.params.include_usage;
|
|
res.content = slot.generated_text;
|
|
res.timings = slot.get_timings();
|
|
res.post_sampling_probs = slot.params.post_sampling_probs;
|
|
res.oaicompat = slot.params.oaicompat;
|
|
res.oaicompat_model = slot.params.oaicompat_model;
|
|
res.oaicompat_cmpl_id = slot.params.oaicompat_cmpl_id;
|
|
res.oaicompat_msg = slot.update_chat_msg(res.oaicompat_msg_diffs);
|
|
res.n_decoded = slot.n_decoded;
|
|
res.n_prompt_tokens = slot.n_prompt_tokens;
|
|
res.oaicompat_model = slot.oaicompat_model;
|
|
res.data = json{
|
|
{"content", !slot.params.stream ? slot.generated_text : ""},
|
|
{"generated_text", slot.generated_text}, // Always include full text for finish_reason logic
|
|
{"id_slot", slot.id},
|
|
{"stop", true},
|
|
{"model", params.model_alias},
|
|
{"tokens_predicted", slot.n_decoded},
|
|
{"tokens_evaluated", slot.n_prompt_tokens},
|
|
{"generation_settings", get_formated_generation(slot)},
|
|
{"prompt", slot.prompt},
|
|
{"truncated", slot.truncated},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
{"tokens_cached", slot.n_past},
|
|
{"timings", slot.get_formated_timings()},
|
|
//{"oaicompat_chat_format", slot.params.oaicompat_chat_format},
|
|
};
|
|
|
|
// populate res.probs_output
|
|
if (slot.sparams.n_probs > 0) {
|
|
res.probs_output = std::vector<completion_token_output>(
|
|
slot.generated_token_probs.begin(),
|
|
slot.generated_token_probs.end());
|
|
res.data["completion_probabilities"] = probs_vector_to_json(ctx, res.probs_output);
|
|
}
|
|
|
|
if (slot.oaicompat) {
|
|
res.data["oaicompat_token_ctr"] = slot.n_decoded;
|
|
res.data["model"] = slot.oaicompat_model;
|
|
}
|
|
|
|
queue_results.send(std::move(res));
|
|
}
|
|
|
|
void server_context::send_embedding(const server_slot& slot, const llama_batch& batch) {
|
|
server_task_result res;
|
|
res.id = slot.id_task;
|
|
res.id_multi = slot.id_multi;
|
|
res.error = false;
|
|
res.stop = true;
|
|
|
|
const int n_embd = llama_n_embd(model);
|
|
|
|
std::vector<float> embd_res(n_embd, 0.0f);
|
|
|
|
for (int i = 0; i < batch.n_tokens; ++i) {
|
|
if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
|
|
continue;
|
|
}
|
|
|
|
const float* embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
|
|
if (embd == NULL) {
|
|
embd = llama_get_embeddings_ith(ctx, i);
|
|
}
|
|
|
|
if (embd == NULL) {
|
|
LOG_ERROR("failed to get embeddings", {
|
|
{"token", batch.token[i]},
|
|
{"seq_id", batch.seq_id[i][0]}
|
|
});
|
|
|
|
res.data = json{
|
|
{"embedding", std::vector<float>(n_embd, 0.0f)},
|
|
{"tokens_evaluated", slot.n_prompt_tokens},
|
|
};
|
|
|
|
continue;
|
|
}
|
|
|
|
llama_embd_normalize(embd, embd_res.data(), n_embd);
|
|
|
|
res.data = json{
|
|
{"embedding", embd_res},
|
|
{"tokens_evaluated", slot.n_prompt_tokens},
|
|
};
|
|
}
|
|
|
|
queue_results.send(res);
|
|
}
|
|
|
|
void server_context::request_completion(int id_task, int id_multi, json data, bool infill, bool embedding, server_tokens&& inputs) {
|
|
server_task task;
|
|
task.id = id_task;
|
|
task.id_multi = id_multi;
|
|
task.id_target = 0;
|
|
task.data = std::move(data);
|
|
task.infill = infill;
|
|
task.embedding = embedding;
|
|
task.type = SERVER_TASK_TYPE_COMPLETION;
|
|
task.tokens = std::move(inputs);
|
|
// when a completion task's prompt array is not a singleton, we split it into multiple requests
|
|
// otherwise, it's a single-prompt task, we actually queue it
|
|
// if there's numbers in the prompt array it will be treated as an array of tokens
|
|
if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
|
|
bool numbers = false;
|
|
for (const auto& e : task.data.at("prompt")) {
|
|
if (e.is_number()) {
|
|
numbers = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
|
|
// it will completely stall the server. I don't know where the bug for this is.
|
|
//
|
|
// if there are numbers, it needs to be treated like a single prompt,
|
|
// queue_tasks handles a mix of strings and numbers just fine.
|
|
if (numbers) {
|
|
queue_tasks.post(std::move(task));
|
|
}
|
|
else {
|
|
split_multiprompt_task(id_task, task);
|
|
}
|
|
}
|
|
else {
|
|
queue_tasks.post(std::move(task));
|
|
}
|
|
}
|
|
|
|
void server_context::request_cancel(int id_task) {
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_CANCEL;
|
|
task.id_target = id_task;
|
|
|
|
queue_tasks.post(std::move(task));
|
|
}
|
|
|
|
void server_context::split_multiprompt_task(int id_multi, server_task& multiprompt_task) {
|
|
const int prompt_count = multiprompt_task.data.at("prompt").size();
|
|
if (prompt_count <= 1) {
|
|
send_error(multiprompt_task, "error while handling multiple prompts");
|
|
return;
|
|
}
|
|
|
|
// generate all the ID for subtask
|
|
std::vector<int> subtask_ids(prompt_count);
|
|
for (int i = 0; i < prompt_count; i++) {
|
|
subtask_ids[i] = queue_tasks.get_new_id();
|
|
}
|
|
|
|
// queue up the multitask so we can track its subtask progression
|
|
queue_tasks.add_multitask(id_multi, subtask_ids);
|
|
|
|
// add subtasks
|
|
for (int i = 0; i < prompt_count; i++) {
|
|
json subtask_data = multiprompt_task.data;
|
|
subtask_data["prompt"] = subtask_data.at("prompt")[i];
|
|
|
|
// subtasks inherit everything else (infill mode, embedding mode, etc.)
|
|
request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding,
|
|
std::move(multiprompt_task.tokens));
|
|
}
|
|
}
|
|
|
|
void server_context::process_single_task(server_task&& task) {
|
|
switch (task.type) {
|
|
case SERVER_TASK_TYPE_COMPLETION:
|
|
{
|
|
const int id_slot = json_value(task.data, "id_slot", -1);
|
|
|
|
server_slot* slot;
|
|
|
|
if (id_slot != -1) {
|
|
slot = get_slot_by_id(id_slot);
|
|
}
|
|
else {
|
|
slot = get_available_slot(task);
|
|
}
|
|
|
|
if (slot == nullptr) {
|
|
// if no slot is available, we defer this task for processing later
|
|
LOG_VERBOSE("no slot is available", { {"id_task", task.id} });
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
if (!slot->available()) {
|
|
// if requested slot is unavailable, we defer this task for processing later
|
|
LOG_VERBOSE("requested slot is unavailable", { {"id_task", task.id} });
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
|
|
if (task.data.contains("system_prompt")) {
|
|
std::string sys_prompt = json_value(task.data, "system_prompt", std::string());
|
|
system_prompt_set(sys_prompt);
|
|
|
|
for (server_slot& slot : slots) {
|
|
slot.n_past = 0;
|
|
slot.n_past_se = 0;
|
|
}
|
|
}
|
|
|
|
slot->reset();
|
|
|
|
slot->id_task = task.id;
|
|
slot->id_multi = task.id_multi;
|
|
slot->infill = task.infill;
|
|
slot->embedding = task.embedding;
|
|
|
|
if (!launch_slot_with_task(*slot, task)) {
|
|
LOG_ERROR("error while launching slot", task.data);
|
|
break;
|
|
}
|
|
} break;
|
|
case SERVER_TASK_TYPE_CANCEL:
|
|
{
|
|
// release slot linked with the task id
|
|
for (auto& slot : slots) {
|
|
if (slot.id_task == task.id_target) {
|
|
slot.release();
|
|
break;
|
|
}
|
|
}
|
|
} break;
|
|
case SERVER_TASK_TYPE_NEXT_RESPONSE:
|
|
{
|
|
// do nothing
|
|
} break;
|
|
case SERVER_TASK_TYPE_METRICS:
|
|
{
|
|
json slots_data = json::array();
|
|
|
|
int n_idle_slots = 0;
|
|
int n_processing_slots = 0;
|
|
|
|
for (server_slot& slot : slots) {
|
|
json slot_data = get_formated_generation(slot);
|
|
slot_data["id"] = slot.id;
|
|
slot_data["id_task"] = slot.id_task;
|
|
slot_data["state"] = slot.state;
|
|
slot_data["prompt"] = slot.prompt;
|
|
slot_data["next_token"] = {
|
|
{"has_next_token", slot.has_next_token},
|
|
{"n_remain", slot.n_remaining},
|
|
{"n_decoded", slot.n_decoded},
|
|
{"stopped_eos", slot.stopped_eos},
|
|
{"stopped_word", slot.stopped_word},
|
|
{"stopped_limit", slot.stopped_limit},
|
|
{"stopping_word", slot.stopping_word},
|
|
};
|
|
|
|
if (slot_data["state"] == SLOT_STATE_IDLE) {
|
|
n_idle_slots++;
|
|
}
|
|
else {
|
|
n_processing_slots++;
|
|
}
|
|
|
|
slots_data.push_back(slot_data);
|
|
}
|
|
LOG_INFO("slot data", {
|
|
{"id_task", task.id},
|
|
{"n_idle_slots", n_idle_slots},
|
|
{"n_processing_slots", n_processing_slots}
|
|
});
|
|
|
|
LOG_VERBOSE("slot data", {
|
|
{"id_task", task.id},
|
|
{"n_idle_slots", n_idle_slots},
|
|
{"n_processing_slots", n_processing_slots},
|
|
{"slots", slots_data}
|
|
});
|
|
|
|
server_task_result res;
|
|
res.id = task.id;
|
|
res.id_multi = task.id_multi;
|
|
res.stop = true;
|
|
res.error = false;
|
|
res.data = {
|
|
{ "idle", n_idle_slots },
|
|
{ "processing", n_processing_slots },
|
|
{ "deferred", queue_tasks.queue_tasks_deferred.size() },
|
|
{ "t_start", metrics.t_start},
|
|
|
|
{ "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
|
|
{ "t_tokens_generation_total", metrics.t_tokens_generation_total},
|
|
{ "n_tokens_predicted_total", metrics.n_tokens_predicted_total},
|
|
{ "t_prompt_processing_total", metrics.t_prompt_processing_total},
|
|
|
|
{ "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed},
|
|
{ "t_prompt_processing", metrics.t_prompt_processing},
|
|
{ "n_tokens_predicted", metrics.n_tokens_predicted},
|
|
{ "t_tokens_generation", metrics.t_tokens_generation},
|
|
|
|
{ "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)},
|
|
{ "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)},
|
|
|
|
{ "slots", slots_data },
|
|
};
|
|
|
|
if (json_value(task.data, "reset_bucket", false)) {
|
|
metrics.reset_bucket();
|
|
}
|
|
queue_results.send(res);
|
|
} break;
|
|
case SERVER_TASK_TYPE_SLOT_SAVE:
|
|
{
|
|
if (!ensure_no_mtmd(task.id)) {
|
|
break;
|
|
}
|
|
int id_slot = task.data.at("id_slot");
|
|
server_slot* slot = get_slot_by_id(id_slot);
|
|
if (slot == nullptr) {
|
|
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
|
|
break;
|
|
}
|
|
if (!slot->available()) {
|
|
// if requested slot is unavailable, we defer this task for processing later
|
|
LOG_VERBOSE("requested slot is unavailable", { {"id_task", task.id} });
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
|
|
const size_t token_count = slot->cache_tokens.size();
|
|
const int64_t t_start = ggml_time_us();
|
|
|
|
std::string filename = task.data.at("filename");
|
|
std::string filepath = task.data.at("filepath");
|
|
|
|
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 double t_save_ms = (t_end - t_start) / 1000.0;
|
|
|
|
server_task_result result;
|
|
result.id = task.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
result.data = json{
|
|
{ "id_slot", id_slot },
|
|
{ "filename", filename },
|
|
{ "n_saved", token_count }, // tokens saved
|
|
{ "n_written", nwrite }, // bytes written
|
|
{ "timings", {
|
|
{ "save_ms", t_save_ms }
|
|
} }
|
|
};
|
|
queue_results.send(result);
|
|
} break;
|
|
case SERVER_TASK_TYPE_SLOT_RESTORE:
|
|
{
|
|
if (!ensure_no_mtmd(task.id)) break;
|
|
int id_slot = task.data.at("id_slot");
|
|
server_slot* slot = get_slot_by_id(id_slot);
|
|
if (slot == nullptr) {
|
|
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
|
|
break;
|
|
}
|
|
if (!slot->available()) {
|
|
// if requested slot is unavailable, we defer this task for processing later
|
|
LOG_VERBOSE("requested slot is unavailable", { {"id_task", task.id} });
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
|
|
const int64_t t_start = ggml_time_us();
|
|
|
|
std::string filename = task.data.at("filename");
|
|
std::string filepath = task.data.at("filepath");
|
|
|
|
slot->cache_tokens.resize(slot->n_ctx);
|
|
size_t token_count = 0;
|
|
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) {
|
|
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);
|
|
break;
|
|
}
|
|
slot->cache_tokens.resize(token_count);
|
|
|
|
const int64_t t_end = ggml_time_us();
|
|
const double t_restore_ms = (t_end - t_start) / 1000.0;
|
|
|
|
server_task_result result;
|
|
result.id = task.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
result.data = json{
|
|
{ "id_slot", id_slot },
|
|
{ "filename", filename },
|
|
{ "n_restored", token_count }, // tokens restored
|
|
{ "n_read", nread }, // bytes read
|
|
{ "timings", {
|
|
{ "restore_ms", t_restore_ms }
|
|
} }
|
|
};
|
|
queue_results.send(result);
|
|
} break;
|
|
case SERVER_TASK_TYPE_SLOT_ERASE:
|
|
{
|
|
if (!ensure_no_mtmd(task.id)) break;
|
|
int id_slot = task.data.at("id_slot");
|
|
server_slot* slot = get_slot_by_id(id_slot);
|
|
if (slot == nullptr) {
|
|
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
|
|
break;
|
|
}
|
|
if (!slot->available()) {
|
|
// if requested slot is unavailable, we defer this task for processing later
|
|
LOG_VERBOSE("requested slot is unavailable", { {"id_task", task.id} });
|
|
queue_tasks.defer(std::move(task));
|
|
break;
|
|
}
|
|
|
|
// Erase token cache
|
|
const size_t n_erased = slot->cache_tokens.size();
|
|
llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1);
|
|
slot->cache_tokens.clear();
|
|
|
|
server_task_result result;
|
|
result.id = task.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
result.data = json{
|
|
{ "id_slot", id_slot },
|
|
{ "n_erased", n_erased }
|
|
};
|
|
queue_results.send(result);
|
|
} break;
|
|
case SERVER_TASK_TYPE_SET_LORA:
|
|
{
|
|
llama_lora_adapters_apply(ctx, lora_adapters);
|
|
server_task_result result;
|
|
result.id = task.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
result.data = json{ { "success", true } };
|
|
queue_results.send(result);
|
|
} break;
|
|
}
|
|
}
|
|
|
|
void server_context::on_finish_multitask(const server_task_multi& multitask) {
|
|
// all subtasks done == multitask is done
|
|
server_task_result result;
|
|
result.id = multitask.id;
|
|
result.stop = true;
|
|
result.error = false;
|
|
|
|
// collect json results into one json result
|
|
std::vector<json> result_jsons;
|
|
for (const auto& subres : multitask.results) {
|
|
result_jsons.push_back(subres.data);
|
|
result.error = result.error && subres.error;
|
|
}
|
|
result.data = json{
|
|
{ "results", result_jsons }
|
|
};
|
|
|
|
queue_results.send(result);
|
|
}
|
|
|
|
void server_context::print_tokens(const server_tokens& prompt, const server_tokens& cache, size_t start1, size_t start2, size_t length) {
|
|
if (cache.size() > start2) {
|
|
LLAMA_LOG_INFO("cache : %s\n", cache.detokenize(ctx, true, start2, length).c_str());
|
|
}
|
|
if (prompt.size() > start1) {
|
|
LLAMA_LOG_INFO("prompt: %s\n", prompt.detokenize(ctx, true, start1, length).c_str());
|
|
}
|
|
|
|
}
|
|
|
|
void server_context::discard_n_kv_and_cache_tokens(llama_context* ctx, server_slot& slot, int32_t n_keep, int32_t n_discard) {
|
|
llama_kv_cache_seq_rm(ctx, slot.id, n_keep, n_keep + n_discard);
|
|
llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
|
|
if (slot.params.cache_prompt) {
|
|
slot.cache_tokens.discard_n_tokens(n_keep, n_discard);
|
|
}
|
|
}
|
|
|
|
// convert keep first few and discard next tokens in a to b
|
|
void server_context::context_shift_find_n_tokens(llama_context* ctx, const server_tokens& a, const server_tokens& b, int32_t n_keep,
|
|
int32_t n_discard, int32_t& n_kept, int32_t& n_discarded, bool exact) {
|
|
|
|
common_prefix ctx_keep_prefix = a.get_common_prefix_first_n(ctx, b, n_keep, exact);
|
|
common_prefix ctx_total_discard_prefix = a.get_common_prefix_first_n(ctx, b, n_discard + n_keep, exact);
|
|
// only if there is enough common token
|
|
int32_t discard_offset = ctx_total_discard_prefix.first - (n_discard + n_keep);
|
|
int32_t keep_offset = ctx_keep_prefix.first - n_keep;
|
|
n_kept = ctx_keep_prefix.second - keep_offset;
|
|
n_discarded = ctx_total_discard_prefix.second - ctx_keep_prefix.second - discard_offset;
|
|
if (n_kept < 0) {
|
|
n_kept = n_keep;
|
|
}
|
|
if (n_discarded < 0) {
|
|
n_discarded = n_discard;
|
|
}
|
|
}
|
|
|
|
void server_context::context_shift_prompt(llama_context* ctx, server_slot& slot, bool exact) {
|
|
int n_keep = std::max(0, slot.params.n_keep + add_bos_token);
|
|
const int n_left = slot.n_ctx - n_keep;
|
|
int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
|
|
|
|
int n_discard_prompt = 0;
|
|
// we still need to truncate input since we have not discarded enough tokens
|
|
while (slot.n_prompt_tokens - slot.n_discarded_prompt >= slot.n_ctx) {
|
|
slot.n_discarded_prompt = slot.n_discarded_prompt + n_discard;
|
|
n_discard_prompt = n_discard_prompt + n_discard;
|
|
}
|
|
|
|
// Handle mistokenization between prompt and cache during context shift
|
|
//
|
|
int32_t n_discard_cache = n_discard_prompt;
|
|
int32_t n_kept = n_keep;
|
|
slot.prompt_tokens.discard_n_tokens(n_keep, slot.n_discarded_prompt - n_discard_prompt);
|
|
if (n_discard_prompt > 0) {
|
|
context_shift_find_n_tokens(ctx, slot.prompt_tokens, slot.cache_tokens, n_keep,
|
|
n_discard, n_kept, n_discard_cache, exact);
|
|
}
|
|
|
|
int n_discard_cache_max = std::max((int32_t)slot.cache_tokens.size() - n_kept, 0);
|
|
n_discard_cache = std::min(n_discard_cache, n_discard_cache_max);
|
|
// discard matching tokens from cache and kv cache to avoid reprocessing the prompt
|
|
if (n_discard_cache > 0) {
|
|
discard_n_kv_and_cache_tokens(ctx, slot, n_kept, n_discard_cache);
|
|
}
|
|
// discard extra tokens from prompts
|
|
slot.n_kept_prompt = n_keep;
|
|
slot.prompt_tokens.discard_n_tokens(n_keep, n_discard_prompt);
|
|
slot.n_prompt_tokens = slot.prompt_tokens.size();
|
|
}
|
|
|
|
void server_context::update_slots() {
|
|
if (system_need_update) {
|
|
system_prompt_update();
|
|
}
|
|
|
|
// release slots
|
|
for (auto& slot : slots) {
|
|
if (slot.command == SLOT_COMMAND_RELEASE) {
|
|
slot.state = SLOT_STATE_IDLE;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.t_last_used = ggml_time_us();
|
|
|
|
LOG_INFO("slot released", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()},
|
|
{"truncated", slot.truncated}
|
|
});
|
|
|
|
queue_tasks.notify_slot_changed();
|
|
}
|
|
}
|
|
|
|
// check if all slots are idle
|
|
{
|
|
bool all_idle = true;
|
|
|
|
for (auto& slot : slots) {
|
|
if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) {
|
|
all_idle = false;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (all_idle) {
|
|
LOG_INFO("all slots are idle", {});
|
|
if (system_prompt.empty() && clean_kv_cache) {
|
|
kv_cache_clear();
|
|
}
|
|
|
|
return;
|
|
}
|
|
}
|
|
|
|
{
|
|
LOG_VERBOSE("posting NEXT_RESPONSE", {});
|
|
|
|
server_task task;
|
|
task.type = SERVER_TASK_TYPE_NEXT_RESPONSE;
|
|
task.id_target = -1;
|
|
|
|
queue_tasks.post(std::move(task));
|
|
}
|
|
|
|
// apply context-shift if needed
|
|
// TODO: simplify and improve
|
|
for (server_slot& slot : slots) {
|
|
if (slot.ga_n == 1) {
|
|
if (slot.is_processing() && (int)system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
|
|
if (!params.ctx_shift) {
|
|
// this check is redundant (for good)
|
|
// we should never get here, because generation should already stopped in process_token()
|
|
send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER);
|
|
slot.release();
|
|
continue;
|
|
}
|
|
if (mctx) {
|
|
// we should never reach this because params_base.ctx_shift is automatically disabled if mmproj is loaded
|
|
// we don't support ctx_shift because an image chunk may contains multiple tokens
|
|
GGML_ABORT("not supported by multimodal");
|
|
}
|
|
// Shift context
|
|
int n_keep = slot.params.n_keep < 0 ? slot.prompt_tokens.size() : slot.params.n_keep;
|
|
if (add_bos_token) {
|
|
n_keep += 1;
|
|
}
|
|
n_keep = std::min(slot.n_ctx - 4, n_keep);
|
|
|
|
const int n_left = (int)system_tokens.size() + slot.n_past - n_keep;
|
|
const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
|
|
int32_t n_kept;
|
|
int32_t n_discard_cache;
|
|
if (n_discard > 0) {
|
|
context_shift_find_n_tokens(ctx, slot.prompt_tokens, slot.cache_tokens, n_keep,
|
|
n_discard, n_kept, n_discard_cache);
|
|
LOG_INFO("slot context shift", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_keep", n_keep},
|
|
{"n_left", n_left},
|
|
{"n_discard", n_discard},
|
|
{"n_ctx", n_ctx},
|
|
{"n_past", slot.n_past},
|
|
{"n_system_tokens", system_tokens.size()},
|
|
{"n_cache_tokens", slot.cache_tokens.size()}
|
|
});
|
|
slot.n_discarded_prompt = slot.n_discarded_prompt + n_discard;
|
|
slot.n_kept_prompt = n_keep;
|
|
discard_n_kv_and_cache_tokens(ctx, slot, n_kept, n_discard_cache);
|
|
slot.n_past -= n_discard_cache;
|
|
slot.truncated = true;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
// start populating the batch for this iteration
|
|
llama_batch_clear(batch);
|
|
|
|
auto accept_special_token = [&](server_slot& slot, llama_token token) {
|
|
return params.special || slot.sparams.preserved_tokens.find(token) != slot.sparams.preserved_tokens.end();
|
|
};
|
|
|
|
// frist, add sampled tokens from any ongoing sequences
|
|
for (auto& slot : slots) {
|
|
if (slot.state == SLOT_STATE_IDLE) {
|
|
continue;
|
|
}
|
|
|
|
// generate draft tokens in speculative decoding mode
|
|
// TODO: rework to have a single draft llama_context shared across all slots [TAG_SERVER_SPEC_REWORK]
|
|
// perform the speculative drafting for all sequences at the same time in a single batch
|
|
int n_draft_max = slot.get_n_draft_max();
|
|
if (n_draft_max > 0) {
|
|
if (mctx) {
|
|
// we should never reach this, as speculative is automatically disabled if mmproj is loaded
|
|
GGML_ABORT("not supported by multimodal");
|
|
}
|
|
|
|
struct llama_speculative_params params_spec;
|
|
params_spec.n_draft = n_draft_max;
|
|
params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.params.speculative.n_max;
|
|
params_spec.p_min = slot.params.speculative.p_min;
|
|
const llama_tokens& cached_text_tokens = slot.cache_tokens.get_text_tokens();
|
|
llama_tokens draft = llama_speculative_gen_draft(slot.spec, params_spec, cached_text_tokens, slot.sampled);
|
|
|
|
// add the sampled token to the batch
|
|
slot.i_batch_dft.push_back(batch.n_tokens);
|
|
llama_batch_add(batch, slot.sampled, slot.cache_tokens.pos_next(), { slot.id }, true);
|
|
slot.cache_tokens.push_back(slot.sampled);
|
|
|
|
if (slot.params.speculative.n_min > (int)draft.size()) {
|
|
SLT_DBG(slot, "ignoring small draft: %d < %d\n", (int)draft.size(), slot.params.speculative.n_min);
|
|
// fallback to normal decoding
|
|
slot.i_batch = slot.i_batch_dft[0];
|
|
slot.drafted.clear();
|
|
slot.i_batch_dft.clear();
|
|
}
|
|
else {
|
|
// keep track of total number of drafted tokens tested
|
|
slot.n_draft_total += draft.size();
|
|
|
|
// add all drafted tokens to the batch
|
|
for (size_t i = 0; i < draft.size(); i++) {
|
|
slot.i_batch_dft.push_back(batch.n_tokens);
|
|
llama_batch_add(batch, draft[i], slot.cache_tokens.pos_next(), { slot.id }, true);
|
|
slot.cache_tokens.push_back(draft[i]);
|
|
}
|
|
slot.drafted = std::move(draft);
|
|
}
|
|
}
|
|
else {
|
|
// no speculative decoding
|
|
slot.i_batch = batch.n_tokens;
|
|
|
|
llama_batch_add(batch, slot.sampled, slot.cache_tokens.pos_next(), { slot.id }, true);
|
|
|
|
slot.cache_tokens.push_back(slot.sampled);
|
|
|
|
SLT_DBG(slot, "slot decode token, n_ctx = %d, n_tokens = %d, truncated = %d\n",
|
|
slot.n_ctx, slot.cache_tokens.size(), slot.truncated);
|
|
}
|
|
slot.n_past = slot.cache_tokens.n_tokens();
|
|
}
|
|
|
|
// process in chunks of params.n_batch
|
|
int32_t n_batch = llama_n_batch(ctx);
|
|
int32_t n_ubatch = llama_n_ubatch(ctx);
|
|
|
|
// track if this is an embedding or non-embedding batch
|
|
// if we've added sampled tokens above, we are in non-embedding mode
|
|
// -1: none, 0: non-embedding, 1: embedding
|
|
int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
|
|
|
|
// next, batch any pending prompts without exceeding n_batch
|
|
if (params.cont_batching || batch.n_tokens == 0) {
|
|
for (auto& slot : slots) {
|
|
// this slot still has a prompt to be processed
|
|
if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) {
|
|
auto& prompt_tokens = slot.prompt_tokens;
|
|
|
|
// we haven't tokenized the prompt yet - do it now:
|
|
if (prompt_tokens.empty() || slot.n_prompt_tokens == 0) {
|
|
LOG_VERBOSE("tokenizing prompt", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task}
|
|
});
|
|
|
|
slot.t_start_process_prompt = ggml_time_us();
|
|
slot.t_start_generation = 0;
|
|
|
|
if (slot.infill) {
|
|
const bool add_bos = llama_should_add_bos_token(model);
|
|
bool suff_rm_leading_spc = true;
|
|
if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
|
|
params.input_suffix.erase(0, 1);
|
|
suff_rm_leading_spc = false;
|
|
}
|
|
|
|
auto prefix_tokens = tokenize(slot.params.input_prefix, false);
|
|
auto suffix_tokens = tokenize(slot.params.input_suffix, false);
|
|
|
|
const int space_token = 29871; // TODO: this should not be hardcoded
|
|
if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
|
|
suffix_tokens.erase(suffix_tokens.begin());
|
|
}
|
|
|
|
prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
|
|
suffix_tokens.insert(suffix_tokens.begin(), llama_token_suffix(model));
|
|
|
|
auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens;
|
|
auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens;
|
|
if (add_bos) {
|
|
embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
|
|
}
|
|
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
|
|
|
|
const llama_token middle_token = llama_token_middle(model);
|
|
if (middle_token >= 0) {
|
|
embd_inp.push_back(middle_token);
|
|
}
|
|
|
|
prompt_tokens = server_tokens(embd_inp, false);
|
|
}
|
|
else {
|
|
// prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
|
|
}
|
|
|
|
slot.n_past = 0;
|
|
slot.n_prompt_tokens = prompt_tokens.size();
|
|
|
|
LOG_VERBOSE("prompt tokenized", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_prompt_tokens", slot.n_prompt_tokens},
|
|
{"prompt_tokens", prompt_tokens.detokenize(ctx, true)},
|
|
});
|
|
|
|
// empty prompt passed -> release the slot and send empty response
|
|
if (prompt_tokens.empty()) {
|
|
LOG_INFO("empty prompt - releasing slot", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task}
|
|
});
|
|
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
continue;
|
|
}
|
|
|
|
if (slot.embedding) {
|
|
// this prompt is too large to process - discard it
|
|
if (slot.n_prompt_tokens > n_ubatch) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.release();
|
|
send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
|
|
continue;
|
|
}
|
|
}
|
|
else {
|
|
// if input prompt is too big, truncate it (if group attention self-extend is disabled)
|
|
// context shift for prompt processing
|
|
if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) {
|
|
if (!params.ctx_shift) {
|
|
send_error(slot, "the request exceeds the available context size, try increasing it", ERROR_TYPE_SERVER);
|
|
slot.release();
|
|
continue;
|
|
}
|
|
if (mctx) {
|
|
// we should never reach this because params.ctx_shift is automatically disabled if mmproj is loaded
|
|
// we don't support ctx_shift because an image chunk may contains multiple tokens
|
|
GGML_ABORT("not supported by multimodal");
|
|
}
|
|
|
|
context_shift_prompt(ctx, slot);
|
|
slot.truncated = true;
|
|
LOG_VERBOSE("input truncated", {
|
|
{"id_slot", slot.id},
|
|
{"id_task", slot.id_task},
|
|
{"n_ctx", slot.n_ctx},
|
|
{"n_keep", slot.params.n_keep},
|
|
{"n_left", slot.n_ctx - slot.params.n_keep},
|
|
{"n_prompt_tokens", slot.n_prompt_tokens},
|
|
{"prompt_tokens", prompt_tokens.detokenize(ctx, true)},
|
|
});
|
|
|
|
GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
|
|
|
|
#ifndef NDEBUG
|
|
// debug
|
|
common_prefix prefix = slot.cache_tokens.get_common_prefix(ctx, prompt_tokens, false);
|
|
int32_t back = 1;
|
|
if (slot.cache_tokens.size() && slot.cache_tokens.size() > prefix.first + 20
|
|
&& prefix.second >= back && prefix.first >= back) {
|
|
LLAMA_LOG_INFO("After context shift :\n");
|
|
print_tokens(slot.prompt_tokens, slot.cache_tokens, prefix.second - back, prefix.first - back, 50);
|
|
}
|
|
#endif
|
|
}
|
|
else {
|
|
slot.n_discarded_prompt = 0;
|
|
}
|
|
llama_sampling_reset(llama_get_model_vocab(model), slot.ctx_sampling);
|
|
|
|
if (!slot.params.cache_prompt) {
|
|
slot.n_past_se = 0;
|
|
slot.ga_i = 0;
|
|
}
|
|
else {
|
|
GGML_ASSERT(slot.ga_n == 1);
|
|
|
|
// reuse any previously computed tokens that are common with the new prompt
|
|
common_prefix prefix = slot.cache_tokens.get_common_prefix(ctx, prompt_tokens, true); // string level match
|
|
common_prefix prefix_nonexact = slot.cache_tokens.get_common_prefix(ctx, prompt_tokens, false);
|
|
auto n_past0 = slot.cache_tokens.get_common_prefix_exact(prompt_tokens); // token level match
|
|
LLAMA_LOG_INFO("======== Cache: cache_size = %d, n_past0 = %d, n_past1 = %d, n_past_prompt1 = %d, n_past2 = %d, n_past_prompt2 = %d\n", (int32_t)slot.cache_tokens.size(), (int32_t)n_past0, (int32_t)prefix.first, (int32_t)prefix.second, (int32_t)prefix_nonexact.first, (int32_t)prefix_nonexact.second);
|
|
int32_t size_threshold = 20;
|
|
if (prefix.first + size_threshold < prefix_nonexact.first) {
|
|
LLAMA_LOG_WARN("Common part contains missing or extra space and new line\n");
|
|
prefix = prefix_nonexact;
|
|
}
|
|
slot.n_past = prefix.first;
|
|
slot.n_past_prompt = prefix.second;
|
|
if (slot.n_past != slot.n_past_prompt) {
|
|
LLAMA_LOG_INFO("Mistokenization found and handled successfully.\n");
|
|
}
|
|
if ((slot.n_past + size_threshold < slot.cache_tokens.size()))
|
|
{
|
|
LLAMA_LOG_WARN("Common part does not match fully\n");
|
|
int32_t back = 4;
|
|
if (prefix.second >= back && prefix.first >= back) {
|
|
print_tokens(slot.prompt_tokens, slot.cache_tokens, prefix.second - back, prefix.first - back, 30);
|
|
}
|
|
}
|
|
|
|
// push the prompt into the sampling context (do not apply grammar)
|
|
for (int i = 0; i < slot.n_past; ++i) {
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (slot.n_past_prompt == slot.n_prompt_tokens && slot.n_past_prompt > 0) {
|
|
// we have to evaluate at least 1 token to generate logits.
|
|
LOG_INFO("we have to evaluate at least 1 token to generate logits", {
|
|
{ "id_slot", slot.id },
|
|
{ "id_task", slot.id_task }
|
|
});
|
|
|
|
slot.n_past_prompt--;
|
|
slot.n_past--;
|
|
if (slot.ga_i > 0) {
|
|
slot.n_past_se--;
|
|
}
|
|
}
|
|
|
|
slot.n_prompt_tokens_processed = 0;
|
|
}
|
|
|
|
if (slot.embedding) {
|
|
// cannot fit the prompt in the current batch - will try next iter
|
|
if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
|
|
continue;
|
|
}
|
|
}
|
|
|
|
// check that we are in the right batch_type, if not defer the slot
|
|
bool slot_type = slot.embedding ? 1 : 0;
|
|
if (batch_type == -1) {
|
|
batch_type = slot_type;
|
|
}
|
|
else if (batch_type != slot_type) {
|
|
continue;
|
|
}
|
|
|
|
// keep only the common part
|
|
// remove the non-common part from the cache
|
|
slot.cache_tokens.keep_first(slot.n_past);
|
|
int p0 = (int)system_tokens.size() + slot.n_past;
|
|
p0 = system_tokens.size() + slot.cache_tokens.pos_next();
|
|
if (!llama_kv_cache_seq_rm(ctx, slot.id, p0, -1)) {
|
|
// could not partially delete (likely using a non-Transformer model)
|
|
llama_kv_cache_seq_rm(ctx, slot.id, -1, -1);
|
|
|
|
p0 = (int)system_tokens.size();
|
|
if (p0 != 0) {
|
|
// copy over the system prompt when there is one
|
|
llama_kv_cache_seq_cp(ctx, 0, slot.id, -1, -1);
|
|
}
|
|
|
|
// there is no common part left (except for the system prompt)
|
|
slot.n_past = 0;
|
|
slot.n_past_se = 0;
|
|
slot.ga_i = 0;
|
|
// TODO: is the system prompt ever in the sampling context?
|
|
llama_sampling_reset(llama_get_model_vocab(model), slot.ctx_sampling);
|
|
}
|
|
|
|
LOG_INFO("kv cache rm [p0, end)", {
|
|
{ "id_slot", slot.id },
|
|
{ "id_task", slot.id_task },
|
|
{ "p0", p0 }
|
|
});
|
|
|
|
// check if we should process the image
|
|
if (slot.n_past_prompt < slot.n_prompt_tokens
|
|
&& slot.prompt_tokens[slot.n_past_prompt] == LLAMA_TOKEN_NULL) {
|
|
// process the image
|
|
size_t n_tokens_out = 0;
|
|
llama_pos p1 = slot.cache_tokens.pos_next() + slot.n_past_prompt - slot.n_past; // add offset to prompt
|
|
int32_t res = slot.prompt_tokens.process_chunk(ctx, mctx, slot.n_past_prompt, p1, slot.id, n_tokens_out);
|
|
if (res != 0) {
|
|
LLAMA_LOG_ERROR("failed to process image, res = %d\n", res);
|
|
slot.release();
|
|
send_error(slot, "failed to process image", ERROR_TYPE_SERVER);
|
|
continue;
|
|
}
|
|
|
|
// add the image chunk to cache
|
|
{
|
|
const auto& chunk = slot.prompt_tokens.find_chunk(slot.n_past_prompt);
|
|
slot.cache_tokens.push_back(chunk.get()); // copy
|
|
}
|
|
|
|
slot.n_past += n_tokens_out;
|
|
slot.n_past_prompt += n_tokens_out;
|
|
slot.n_prompt_tokens_processed += n_tokens_out;
|
|
|
|
}
|
|
|
|
|
|
|
|
int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
|
|
|
|
int32_t ga_i = slot.ga_i;
|
|
int32_t ga_n = slot.ga_n;
|
|
int32_t ga_w = slot.ga_w;
|
|
|
|
// add prompt tokens for processing in the current batch
|
|
// TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow
|
|
while (slot.n_past_prompt < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
|
|
// get next token to process
|
|
llama_token cur_tok = slot.prompt_tokens[slot.n_past_prompt];
|
|
if (cur_tok == LLAMA_TOKEN_NULL) {
|
|
break; // end of text chunk
|
|
}
|
|
if (slot.ga_n != 1) {
|
|
while (slot_npast >= ga_i + ga_w) {
|
|
const int bd = (ga_w / ga_n) * (ga_n - 1);
|
|
slot_npast -= bd;
|
|
ga_i += ga_w / ga_n;
|
|
}
|
|
}
|
|
|
|
int p0 = system_tokens.size() + slot.cache_tokens.pos_next();
|
|
llama_batch_add(batch, cur_tok, p0, { slot.id }, false);
|
|
|
|
slot.cache_tokens.push_back(cur_tok);
|
|
|
|
|
|
slot.n_prompt_tokens_processed++;
|
|
slot_npast++;
|
|
slot.n_past_prompt++;
|
|
slot.n_past++;
|
|
}
|
|
LOG_VERBOSE("prompt processing progress", {
|
|
{"id_slot", slot.id},
|
|
{"n_past", slot.n_past},
|
|
{"n_ctx", n_ctx},
|
|
{"n_tokens", batch.n_tokens},
|
|
{"progress", (float)slot.n_prompt_tokens_processed / slot.n_prompt_tokens},
|
|
});
|
|
|
|
// entire prompt has been processed - start decoding new tokens
|
|
if (slot.n_past_prompt == slot.n_prompt_tokens) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
|
|
GGML_ASSERT(batch.n_tokens > 0);
|
|
GGML_ASSERT((size_t)slot.n_prompt_tokens == slot.prompt_tokens.size());
|
|
llama_sampling_reset(llama_get_model_vocab(model), slot.ctx_sampling);
|
|
for (int i = 0; i < slot.n_prompt_tokens; ++i) {
|
|
llama_token id = slot.prompt_tokens[i];
|
|
if (id != LLAMA_TOKEN_NULL) {
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, id, false);
|
|
}
|
|
}
|
|
|
|
// extract the logits only for the last token
|
|
batch.logits[batch.n_tokens - 1] = true;
|
|
|
|
slot.n_decoded = 0;
|
|
slot.i_batch = batch.n_tokens - 1;
|
|
|
|
LOG_VERBOSE("prompt done", {
|
|
{"id_slot", slot.id},
|
|
{"n_past", slot.n_past},
|
|
{"n_ctx", n_ctx},
|
|
{"n_tokens", batch.n_tokens},
|
|
});
|
|
}
|
|
}
|
|
|
|
if (batch.n_tokens >= n_batch) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (batch.n_tokens == 0) {
|
|
LOG_VERBOSE("no tokens to decode", {});
|
|
return;
|
|
}
|
|
|
|
LOG_VERBOSE("decoding batch", {
|
|
{"n_tokens", batch.n_tokens},
|
|
});
|
|
|
|
// make sure we're in the right embedding mode
|
|
llama_set_embeddings(ctx, batch_type == 1);
|
|
|
|
// process the created batch of tokens
|
|
for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
|
|
const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
|
|
|
|
for (auto& slot : slots) {
|
|
if (slot.ga_n != 1) {
|
|
// context extension via Self-Extend
|
|
// TODO: simplify and/or abstract this
|
|
while (slot.n_past_se >= slot.ga_i + slot.ga_w) {
|
|
const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
|
|
const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
|
|
const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
|
|
|
|
LOG_TEE("\n");
|
|
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);
|
|
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);
|
|
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);
|
|
|
|
llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
|
|
llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n);
|
|
llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd);
|
|
|
|
slot.n_past_se -= bd;
|
|
|
|
slot.ga_i += slot.ga_w / slot.ga_n;
|
|
|
|
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);
|
|
}
|
|
|
|
slot.n_past_se += n_tokens;
|
|
}
|
|
}
|
|
|
|
llama_batch batch_view = {
|
|
n_tokens,
|
|
batch.token + i,
|
|
nullptr,
|
|
batch.pos + i,
|
|
batch.n_seq_id + i,
|
|
batch.seq_id + i,
|
|
batch.logits + i,
|
|
0, 0, 0, // unused
|
|
};
|
|
|
|
const int ret = llama_decode(ctx, batch_view);
|
|
|
|
if (ret != 0) {
|
|
if (n_batch == 1 || ret < 0) {
|
|
// if you get here, it means the KV cache is full - try increasing it via the context size
|
|
LOG_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size", {
|
|
{"i", i},
|
|
{"n_batch", ret},
|
|
{"ret", ret},
|
|
});
|
|
for (auto& slot : slots) {
|
|
slot.state = SLOT_STATE_PROCESSING;
|
|
slot.command = SLOT_COMMAND_NONE;
|
|
slot.release();
|
|
LLAMA_LOG_INFO("n_past = %d\n", (int)slot.cache_tokens.size());
|
|
send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size.");
|
|
}
|
|
break; // break loop of n_batch
|
|
}
|
|
|
|
|
|
// retry with half the batch size to try to find a free slot in the KV cache
|
|
n_batch /= 2;
|
|
i -= n_batch;
|
|
|
|
LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation", {
|
|
{"i", i},
|
|
{"n_batch", n_batch},
|
|
{"ret", ret},
|
|
});
|
|
|
|
continue; // continue loop of n_batch
|
|
}
|
|
|
|
// technically, measuring the time here excludes the sampling time for the last batch
|
|
// but on the other hand, we don't want to do too many system calls to measure the time, so it's ok
|
|
const int64_t t_current = ggml_time_us();
|
|
|
|
for (auto& slot : slots) {
|
|
if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int)i || slot.i_batch >= (int)(i + n_tokens)) {
|
|
continue; // continue loop of slots
|
|
}
|
|
|
|
// prompt evaluated for embedding
|
|
if (slot.embedding) {
|
|
send_embedding(slot, batch_view);
|
|
slot.release();
|
|
slot.i_batch = -1;
|
|
continue; // continue loop of slots
|
|
}
|
|
|
|
completion_token_output result;
|
|
if (slot.i_batch_dft.size() > 0) {
|
|
continue; // sample using speculative decoding
|
|
}
|
|
const int tok_idx = slot.i_batch - i;
|
|
const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, tok_idx);
|
|
|
|
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
|
|
|
|
slot.n_decoded += 1;
|
|
|
|
const int64_t t_current = ggml_time_us();
|
|
|
|
if (slot.n_decoded == 1) {
|
|
slot.t_start_generation = ggml_time_us();
|
|
slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
|
|
metrics.on_prompt_eval(slot);
|
|
}
|
|
|
|
//slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3;
|
|
slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;
|
|
|
|
result.tok = id;
|
|
result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
|
|
result.text_to_send = llama_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
|
|
|
|
if (slot.sparams.n_probs > 0) {
|
|
populate_token_probs(slot, result, slot.params.post_sampling_probs, params.special, tok_idx);
|
|
}
|
|
|
|
if (!process_token(result, slot)) {
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
metrics.on_prediction(slot);
|
|
}
|
|
|
|
slot.i_batch = -1;
|
|
}
|
|
|
|
// speculative decoding - main model sample and accept
|
|
for (auto& slot : slots) {
|
|
if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch_dft.empty()) {
|
|
continue;
|
|
}
|
|
|
|
size_t n_draft = slot.drafted.size();
|
|
|
|
// the accepted tokens from the speculation
|
|
const auto ids = llama_sampling_sample_and_accept_n(slot.ctx_sampling, ctx, slot.i_batch_dft, slot.drafted);
|
|
slot.i_batch_dft.clear();
|
|
slot.drafted.clear();
|
|
|
|
slot.n_past += ids.size();
|
|
slot.n_decoded += ids.size();
|
|
|
|
slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;
|
|
|
|
// update how many tokens out of those tested were accepted
|
|
slot.n_draft_accepted += ids.size() - 1;
|
|
|
|
// rollback to the state before sampling the draft tokens
|
|
slot.cache_tokens.keep_first(slot.cache_tokens.n_tokens() - n_draft);
|
|
// slot.n_past -= n_draft;
|
|
// add accepted tokens to the prompt
|
|
slot.cache_tokens.insert({ ids.begin(), ids.end() - 1 });
|
|
slot.sampled = ids.back(); // last accepted token
|
|
slot.n_past = slot.cache_tokens.n_tokens();
|
|
llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1);
|
|
|
|
for (size_t i = 0; i < ids.size(); ++i) {
|
|
completion_token_output result;
|
|
|
|
result.tok = ids[i];
|
|
result.text_to_send = llama_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
|
|
result.prob = 1.0f; // set later
|
|
|
|
if (slot.sparams.n_probs > 0) {
|
|
populate_token_probs(slot, result, slot.params.post_sampling_probs, params.special, i);
|
|
}
|
|
|
|
if (!process_token(result, slot)) {
|
|
// release slot because of stop condition
|
|
slot.release();
|
|
slot.print_timings();
|
|
send_final_response(slot);
|
|
metrics.on_prediction(slot);
|
|
break;
|
|
}
|
|
}
|
|
SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int)ids.size() - 1, (int)slot.drafted.size(), slot.n_past);
|
|
LOG_VERBOSE("speculative decoding result", {
|
|
{"id_slot", slot.id},
|
|
{"accepted", (int)ids.size() - 1},
|
|
{"total", (int)slot.drafted.size()},
|
|
{"new_n_past", slot.n_past}
|
|
});
|
|
}
|
|
}
|
|
|
|
LOG_VERBOSE("run slots completed", {});
|
|
}
|
|
|
|
json server_context::model_meta() const {
|
|
return json{
|
|
{"vocab_type", llama_vocab_type(model)},
|
|
{"n_vocab", llama_n_vocab(model)},
|
|
{"n_ctx_train", llama_n_ctx_train(model)},
|
|
{"n_embd", llama_n_embd(model)},
|
|
{"n_params", llama_model_n_params(model)},
|
|
{"size", llama_model_size(model)},
|
|
};
|
|
}
|