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https://github.com/ggerganov/llama.cpp.git
synced 2024-11-14 06:49:54 +00:00
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94d8cb8be1
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b11f9ba9b8
@ -378,8 +378,8 @@ struct server_queue {
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std::condition_variable condition_tasks;
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// callback functions
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std::function<void(server_task&)> callback_new_task;
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std::function<void(void)> callback_update_slots;
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std::function<void(server_task)> callback_new_task;
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std::function<void(void)> callback_update_slots;
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// Add a new task to the end of the queue
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int post(server_task task, bool front = false) {
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@ -431,7 +431,7 @@ struct server_queue {
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}
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// Register function to process a new task
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void on_new_task(std::function<void(server_task &)> callback) {
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void on_new_task(std::function<void(server_task)> callback) {
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callback_new_task = std::move(callback);
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}
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@ -481,7 +481,7 @@ struct server_queue {
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lock.unlock();
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QUE_DBG("processing task, id = %d\n", task.id);
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callback_new_task(task);
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callback_new_task(std::move(task));
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}
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// all tasks in the current loop is processed, slots data is now ready
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@ -644,17 +644,12 @@ struct server_context {
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bool load_model(const common_params & params_) {
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params = params_;
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// reserve one extra sequence (seq_id == 0) for extra features
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params.n_parallel += 1;
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common_init_result llama_init = common_init_from_params(params);
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model = llama_init.model;
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ctx = llama_init.context;
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loras = llama_init.lora_adapters;
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params.n_parallel -= 1; // but be sneaky about it
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if (model == nullptr) {
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SRV_ERR("failed to load model, '%s'\n", params.model.c_str());
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return false;
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@ -1288,16 +1283,16 @@ struct server_context {
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void send_embedding(const server_slot & slot, const llama_batch & batch) {
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server_task_result res;
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res.id = slot.id_task;
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res.error = false;
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res.stop = true;
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res.id = slot.id_task;
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res.error = false;
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res.stop = true;
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const int n_embd = llama_n_embd(model);
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std::vector<float> embd_res(n_embd, 0.0f);
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for (int i = 0; i < batch.n_tokens; ++i) {
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if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
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if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
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continue;
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}
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@ -1332,12 +1327,12 @@ struct server_context {
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void send_rerank(const server_slot & slot, const llama_batch & batch) {
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server_task_result res;
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res.id = slot.id_task;
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res.error = false;
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res.stop = true;
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res.id = slot.id_task;
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res.error = false;
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res.stop = true;
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for (int i = 0; i < batch.n_tokens; ++i) {
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if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
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if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
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continue;
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}
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@ -1510,7 +1505,7 @@ struct server_context {
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// Functions to process the task
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//
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void process_single_task(const server_task & task) {
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void process_single_task(server_task task) {
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switch (task.type) {
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case SERVER_TASK_TYPE_INFERENCE:
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{
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@ -1646,7 +1641,7 @@ struct server_context {
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std::string filename = task.data.at("filename");
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std::string filepath = task.data.at("filepath");
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const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count);
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const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), token_count);
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const int64_t t_end = ggml_time_us();
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const double t_save_ms = (t_end - t_start) / 1000.0;
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@ -1688,7 +1683,7 @@ struct server_context {
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slot->cache_tokens.resize(slot->n_ctx);
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size_t token_count = 0;
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size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
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size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
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if (nread == 0) {
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slot->cache_tokens.resize(0);
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send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
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@ -1731,7 +1726,7 @@ struct server_context {
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// Erase token cache
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const size_t n_erased = slot->cache_tokens.size();
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llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1);
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llama_kv_cache_seq_rm(ctx, slot->id, -1, -1);
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slot->cache_tokens.clear();
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server_task_result result;
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@ -1808,8 +1803,8 @@ struct server_context {
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SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
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llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard);
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llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, slot.n_past, -n_discard);
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llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard);
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llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, slot.n_past, -n_discard);
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if (slot.params.cache_prompt) {
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for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
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@ -1836,7 +1831,7 @@ struct server_context {
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slot.i_batch = batch.n_tokens;
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common_batch_add(batch, slot.sampled, slot.n_past, { slot.id + 1 }, true);
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common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true);
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slot.n_past += 1;
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@ -1983,8 +1978,8 @@ struct server_context {
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const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c;
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llama_kv_cache_seq_rm (ctx, slot.id + 1, head_p, head_c);
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llama_kv_cache_seq_add(ctx, slot.id + 1, head_c, -1, kv_shift);
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llama_kv_cache_seq_rm (ctx, slot.id, head_p, head_c);
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llama_kv_cache_seq_add(ctx, slot.id, head_c, -1, kv_shift);
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for (size_t i = 0; i < n_match; i++) {
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slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i];
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@ -2033,9 +2028,9 @@ struct server_context {
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}
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// keep only the common part
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if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, slot.n_past, -1)) {
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if (!llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1)) {
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// could not partially delete (likely using a non-Transformer model)
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llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1);
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llama_kv_cache_seq_rm(ctx, slot.id, -1, -1);
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// there is no common part left
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slot.n_past = 0;
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@ -2048,7 +2043,7 @@ struct server_context {
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// add prompt tokens for processing in the current batch
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while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
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common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id + 1 }, false);
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common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false);
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if (slot.params.cache_prompt) {
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slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
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