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https://github.com/ggerganov/llama.cpp.git
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ggml-ci
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@ -1103,7 +1103,7 @@ gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex,
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else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; }
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else { throw std::invalid_argument("invalid value"); }
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}
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).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
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).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
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add_opt(llama_arg(
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{"--attention"}, "{causal,non,causal}",
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"attention type for embeddings, use model default if unspecified",
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@ -92,6 +92,7 @@ enum server_task_type {
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enum server_task_cmpl_type {
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SERVER_TASK_CMPL_TYPE_NORMAL,
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SERVER_TASK_CMPL_TYPE_EMBEDDING,
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SERVER_TASK_CMPL_TYPE_RERANK,
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SERVER_TASK_CMPL_TYPE_INFILL,
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};
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@ -172,6 +173,7 @@ struct server_slot {
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std::vector<completion_token_output> generated_token_probs;
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server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL;
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bool has_next_token = true;
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bool truncated = false;
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bool stopped_eos = false;
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@ -942,8 +944,17 @@ struct server_context {
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slot.prompt = *prompt;
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} else if (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_array()) {
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slot.prompt = prompt->at(0);
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} else if (prompt->is_array() && prompt->size() > 1) {
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// array of strings
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for (const auto & el : *prompt) {
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if (!el.is_string()) {
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send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
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return false;
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}
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}
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slot.prompt = *prompt;
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} else {
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send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST);
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send_error(task, "\"prompt\" must be a string, an array of strings or an array of integers", ERROR_TYPE_INVALID_REQUEST);
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return false;
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}
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}
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@ -1368,6 +1379,7 @@ struct server_context {
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res.data = json {
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{"embedding", std::vector<float>(n_embd, 0.0f)},
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{"index", slot.index},
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};
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continue;
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@ -1386,6 +1398,44 @@ struct server_context {
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queue_results.send(res);
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}
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void send_rank(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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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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continue;
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}
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const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
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if (embd == NULL) {
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embd = llama_get_embeddings_ith(ctx, i);
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}
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if (embd == NULL) {
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SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]);
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res.data = json {
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{"index", slot.index},
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{"rank", -1e6},
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};
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continue;
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}
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res.data = json {
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{"index", slot.index},
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{"rank", embd[0]},
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};
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}
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SLT_DBG(slot, "sending rank, res = '%s'\n", res.data.dump().c_str());
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queue_results.send(res);
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}
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//
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// Functions to create new task(s) and receive result(s)
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//
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@ -1421,6 +1471,15 @@ struct server_context {
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// otherwise, it's a multiple-prompt task, we break it into smaller tasks
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else if (prompt.is_array()) {
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std::vector<json> prompts = prompt;
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if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
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for (size_t i = 1; i < prompts.size(); i++) {
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json qd;
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qd.push_back(prompts[0]);
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qd.push_back(prompts[i]);
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data["index"] = i - 1;
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create_task(data, true, qd);
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}
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} else {
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for (size_t i = 0; i < prompts.size(); i++) {
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const auto & e = prompts[i];
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if (e.is_string() || json_is_array_of_numbers(e)) {
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@ -1431,6 +1490,7 @@ struct server_context {
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}
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}
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}
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}
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// invalid case
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else {
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throw std::runtime_error(error_msg);
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@ -1471,7 +1531,9 @@ struct server_context {
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break;
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}
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size_t idx = result.data["index"];
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const size_t idx = result.data["index"];
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GGML_ASSERT(idx < results.size() && "index out of range");
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results[idx] = result;
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}
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result_handler(results);
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@ -1922,6 +1984,29 @@ struct server_context {
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}
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prompt_tokens = embd_inp;
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} else if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
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// require slot.prompt to be array of 2 strings
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if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
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SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
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slot.release();
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send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
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continue;
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}
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// prompt: <s>query</s><s>doc</s>
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prompt_tokens.clear();
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prompt_tokens.push_back(llama_token_bos(model));
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{
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const auto part = tokenize(slot.prompt[0], false);
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prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
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}
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prompt_tokens.push_back(llama_token_eos(model));
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prompt_tokens.push_back(llama_token_bos(model));
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{
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const auto part = tokenize(slot.prompt[1], false);
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prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
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}
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prompt_tokens.push_back(llama_token_eos(model));
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} else {
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prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
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}
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@ -1941,7 +2026,7 @@ struct server_context {
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continue;
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}
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if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
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if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
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// this prompt is too large to process - discard it
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if (slot.n_prompt_tokens > n_ubatch) {
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slot.release();
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@ -2011,7 +2096,7 @@ struct server_context {
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slot.n_prompt_tokens_processed = 0;
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}
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if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING) {
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if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING || slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
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// cannot fit the prompt in the current batch - will try next iter
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if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
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continue;
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@ -2019,7 +2104,10 @@ struct server_context {
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}
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// check that we are in the right batch_type, if not defer the slot
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bool slot_type = slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ? 1 : 0;
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const bool slot_type =
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slot.cmpl_type == SERVER_TASK_CMPL_TYPE_EMBEDDING ||
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slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK ? 1 : 0;
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if (batch_type == -1) {
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batch_type = slot_type;
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} else if (batch_type != slot_type) {
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@ -2192,6 +2280,13 @@ struct server_context {
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continue; // continue loop of slots
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}
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if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
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send_rank(slot, batch_view);
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slot.release();
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slot.i_batch = -1;
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continue; // continue loop of slots
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}
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// prompt evaluated for next-token prediction
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slot.state = SLOT_STATE_GENERATING;
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} else if (slot.state != SLOT_STATE_GENERATING) {
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@ -2974,6 +3069,82 @@ int main(int argc, char ** argv) {
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res_ok(res, root);
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};
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const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
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const json body = json::parse(req.body);
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// TODO: implement
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//int top_n = 1;
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//if (body.count("top_n") != 1) {
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// top_n = body.at("top_n");
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//} else {
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// res_error(res, format_error_response("\"top_n\" must be provided", ERROR_TYPE_INVALID_REQUEST));
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// return;
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//}
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json query;
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if (body.count("query") == 1) {
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query = body.at("query");
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if (!query.is_string()) {
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res_error(res, format_error_response("\"query\" must be a string", ERROR_TYPE_INVALID_REQUEST));
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return;
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}
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} else {
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exit(0);
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res_error(res, format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST));
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return;
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}
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json documents;
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if (body.count("documents") != 0) {
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documents = body.at("documents");
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if (!documents.is_array() || documents.size() == 0) {
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res_error(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST));
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return;
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}
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} else {
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res_error(res, format_error_response("\"documents\" must be provided", ERROR_TYPE_INVALID_REQUEST));
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return;
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}
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// construct prompt object: array of ["query", "doc0", "doc1", ...]
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json prompt;
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prompt.push_back(query);
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for (const auto & doc : documents) {
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prompt.push_back(doc);
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}
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LOG_DBG("rerank prompt: %s\n", prompt.dump().c_str());
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// create and queue the task
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json responses = json::array();
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bool error = false;
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{
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std::vector<server_task> tasks = ctx_server.create_tasks_cmpl({{"prompt", prompt}}, SERVER_TASK_CMPL_TYPE_RERANK);
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ctx_server.queue_results.add_waiting_tasks(tasks);
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ctx_server.queue_tasks.post(tasks);
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// get the result
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std::unordered_set<int> task_ids = server_task::get_list_id(tasks);
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ctx_server.receive_cmpl_results(task_ids, [&](std::vector<server_task_result> & results) {
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for (const auto & res : results) {
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responses.push_back(res.data);
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}
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}, [&](const json & error_data) {
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res_error(res, error_data);
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error = true;
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});
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}
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if (error) {
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return;
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}
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// write JSON response
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json root = format_response_rerank(body, responses);
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res_ok(res, root);
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};
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const auto handle_lora_adapters_list = [&](const httplib::Request &, httplib::Response & res) {
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json result = json::array();
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for (size_t i = 0; i < ctx_server.loras.size(); ++i) {
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@ -3070,6 +3241,7 @@ int main(int argc, char ** argv) {
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svr->Post("/embedding", handle_embeddings); // legacy
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svr->Post("/embeddings", handle_embeddings);
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svr->Post("/v1/embeddings", handle_embeddings);
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svr->Post("/v1/rerank", handle_rerank);
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svr->Post("/tokenize", handle_tokenize);
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svr->Post("/detokenize", handle_detokenize);
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// LoRA adapters hotswap
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@ -534,7 +534,7 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
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json res = json {
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{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
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{"object", "list"},
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{"usage", json {
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{"usage", json { // TODO: fill
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{"prompt_tokens", 0},
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{"total_tokens", 0}
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}},
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@ -544,6 +544,29 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
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return res;
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}
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static json format_response_rerank(const json & request, const json & ranks) {
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json data = json::array();
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int i = 0;
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for (const auto & rank : ranks) {
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data.push_back(json{
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{"index", i++},
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{"relevance_score", json_value(rank, "rank", 0.0)},
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});
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}
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json res = json {
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{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
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{"object", "list"},
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{"usage", json { // TODO: fill
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{"prompt_tokens", 0},
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{"total_tokens", 0}
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}},
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{"results", data}
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};
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return res;
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}
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static bool is_valid_utf8(const std::string & str) {
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const unsigned char* bytes = reinterpret_cast<const unsigned char*>(str.data());
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const unsigned char* end = bytes + str.length();
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