server : allow using LoRA adapters per-request (#10994)
* slot.can_batch_with * lora per request * test: force disable cache prompt * move can_batch_with check * fix condition * add slow test with llama 8b * update docs * move lora change task to queue * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * lora_base * remove redundant check --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Xuan Son Nguyen
GitHub
parent
a45433ba20
commit
0da5d86026
8 changed files with 235 additions and 59 deletions
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@ -98,6 +98,8 @@ struct slot_params {
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int64_t t_max_prompt_ms = -1; // TODO: implement
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int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
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std::vector<common_lora_adapter_container> lora;
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std::vector<std::string> antiprompt;
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std::vector<std::string> response_fields;
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bool timings_per_token = false;
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@ -120,6 +122,11 @@ struct slot_params {
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samplers.emplace_back(common_sampler_type_to_str(sampler));
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}
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json lora = json::array();
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for (size_t i = 0; i < this->lora.size(); ++i) {
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lora.push_back({{"id", i}, {"scale", this->lora[i].scale}});
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}
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return json {
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{"n_predict", n_predict}, // Server configured n_predict
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{"seed", sampling.seed},
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@ -160,6 +167,7 @@ struct slot_params {
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{"speculative.p_min", speculative.p_min},
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{"timings_per_token", timings_per_token},
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{"post_sampling_probs", post_sampling_probs},
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{"lora", lora},
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};
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}
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};
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@ -189,12 +197,16 @@ struct server_task {
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// used by SERVER_TASK_TYPE_METRICS
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bool metrics_reset_bucket = false;
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// used by SERVER_TASK_TYPE_SET_LORA
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std::vector<common_lora_adapter_container> set_lora;
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server_task(server_task_type type) : type(type) {}
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static slot_params params_from_json_cmpl(
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const llama_model * model,
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const llama_context * ctx,
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const common_params & params_base,
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const std::vector<common_lora_adapter_container> & lora_base,
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const json & data) {
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slot_params params;
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@ -251,6 +263,16 @@ struct server_task {
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params.speculative.n_min = std::max(params.speculative.n_min, 2);
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params.speculative.n_max = std::max(params.speculative.n_max, 0);
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if (data.contains("lora")) {
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if (data.at("lora").is_array()) {
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params.lora = parse_lora_request(lora_base, data.at("lora"));
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} else {
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throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
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}
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} else {
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params.lora = lora_base;
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}
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// TODO: add more sanity checks for the input parameters
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if (params.sampling.penalty_last_n < -1) {
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@ -1110,6 +1132,8 @@ struct server_slot {
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common_speculative * spec = nullptr;
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std::vector<common_lora_adapter_container> lora;
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// the index relative to completion multi-task request
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size_t index = 0;
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@ -1191,6 +1215,11 @@ struct server_slot {
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return task_type == SERVER_TASK_TYPE_EMBEDDING || task_type == SERVER_TASK_TYPE_RERANK;
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}
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bool can_batch_with(server_slot & other_slot) {
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return is_non_causal() == other_slot.is_non_causal()
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&& are_lora_equal(lora, other_slot.lora);
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}
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bool has_budget(const common_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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@ -1600,7 +1629,7 @@ struct server_context {
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llama_model * model = nullptr;
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llama_context * ctx = nullptr;
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std::vector<common_lora_adapter_container> loras;
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std::vector<common_lora_adapter_container> lora;
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llama_model * model_dft = nullptr;
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llama_context_params cparams_dft;
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@ -1667,7 +1696,7 @@ struct server_context {
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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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lora = llama_init.lora_adapters;
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if (model == nullptr) {
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SRV_ERR("failed to load model, '%s'\n", params_base.model.c_str());
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@ -1866,6 +1895,12 @@ struct server_context {
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slot.params = std::move(task.params);
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slot.prompt_tokens = std::move(task.prompt_tokens);
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if (!are_lora_equal(task.params.lora, slot.lora)) {
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// if lora is changed, we cannot reuse cached tokens
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slot.cache_tokens.clear();
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slot.lora = std::move(task.params.lora);
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}
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SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str());
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if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
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@ -2557,7 +2592,7 @@ struct server_context {
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} break;
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case SERVER_TASK_TYPE_SET_LORA:
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{
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common_lora_adapters_apply(ctx, loras);
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lora = std::move(task.set_lora);
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auto res = std::make_unique<server_task_result_apply_lora>();
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res->id = task.id;
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queue_results.send(std::move(res));
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@ -2634,12 +2669,22 @@ struct server_context {
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// start populating the batch for this iteration
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common_batch_clear(batch);
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// track if given slot can be batched with slots already in the batch
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server_slot * slot_batched = nullptr;
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// frist, add sampled tokens from any ongoing sequences
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for (auto & slot : slots) {
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if (slot.state != SLOT_STATE_GENERATING) {
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continue;
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}
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// check if we can batch this slot with the previous one
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if (!slot_batched) {
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slot_batched = &slot;
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} else if (!slot_batched->can_batch_with(slot)) {
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continue;
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}
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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 }, true);
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@ -2658,15 +2703,18 @@ struct server_context {
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int32_t n_batch = llama_n_batch(ctx);
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int32_t n_ubatch = llama_n_ubatch(ctx);
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// track if this is an embedding or non-embedding batch
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// if we've added sampled tokens above, we are in non-embedding mode
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// -1: none, 0: non-embedding, 1: embedding
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// TODO: make enum
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int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
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// next, batch any pending prompts without exceeding n_batch
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if (params_base.cont_batching || batch.n_tokens == 0) {
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for (auto & slot : slots) {
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// check if we can batch this slot with the previous one
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if (slot.is_processing()) {
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if (!slot_batched) {
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slot_batched = &slot;
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} else if (!slot_batched->can_batch_with(slot)) {
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continue;
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}
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}
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// this slot still has a prompt to be processed
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if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) {
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auto & prompt_tokens = slot.prompt_tokens;
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@ -2827,14 +2875,6 @@ struct server_context {
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}
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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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int slot_type = slot.is_non_causal();
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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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continue;
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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, slot.n_past, -1)) {
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// could not partially delete (likely using a non-Transformer model)
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@ -2902,8 +2942,12 @@ struct server_context {
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SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens);
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// make sure we're in the right embedding mode
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llama_set_embeddings(ctx, batch_type == 1);
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if (slot_batched) {
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// make sure we're in the right embedding mode
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llama_set_embeddings(ctx, slot_batched->is_non_causal());
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// apply lora, only need to do it once per batch
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common_lora_adapters_apply(ctx, slot_batched->lora);
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}
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// process the created batch of tokens
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for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
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@ -3623,7 +3667,12 @@ int main(int argc, char ** argv) {
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task.index = i;
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task.prompt_tokens = std::move(tokenized_prompts[i]);
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task.params = server_task::params_from_json_cmpl(ctx_server.model, ctx_server.ctx, ctx_server.params_base, data);
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task.params = server_task::params_from_json_cmpl(
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ctx_server.model,
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ctx_server.ctx,
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ctx_server.params_base,
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ctx_server.lora,
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data);
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task.id_selected_slot = json_value(data, "id_slot", -1);
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// OAI-compat
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@ -4049,8 +4098,8 @@ int main(int argc, char ** argv) {
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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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auto & lora = ctx_server.loras[i];
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for (size_t i = 0; i < ctx_server.lora.size(); ++i) {
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auto & lora = ctx_server.lora[i];
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result.push_back({
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{"id", i},
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{"path", lora.path},
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@ -4062,27 +4111,14 @@ int main(int argc, char ** argv) {
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};
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const auto handle_lora_adapters_apply = [&](const httplib::Request & req, httplib::Response & res) {
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const std::vector<json> body = json::parse(req.body);
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int max_idx = ctx_server.loras.size();
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// clear existing value
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for (auto & lora : ctx_server.loras) {
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lora.scale = 0.0f;
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const json body = json::parse(req.body);
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if (!body.is_array()) {
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res_error(res, format_error_response("Request body must be an array", ERROR_TYPE_INVALID_REQUEST));
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return;
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}
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// set value
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for (auto entry : body) {
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int id = entry.at("id");
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float scale = entry.at("scale");
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if (0 <= id && id < max_idx) {
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ctx_server.loras[id].scale = scale;
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} else {
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throw std::runtime_error("invalid adapter id");
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}
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}
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server_task task(SERVER_TASK_TYPE_SET_LORA);
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task.id = ctx_server.queue_tasks.get_new_id();
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task.set_lora = parse_lora_request(ctx_server.lora, body);
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ctx_server.queue_results.add_waiting_task_id(task.id);
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ctx_server.queue_tasks.post(task);
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