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https://github.com/TabbyML/tabby
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feat(llama-cpp-server): reuse local server for completion and chat (#2812)
* ✨ support reuse the same server if model id is equaled Signed-off-by: Wei Zhang <kweizh@gmail.com> * 🎨 fix review Signed-off-by: Wei Zhang <kweizh@gmail.com> * 🔨 should compare model in llama cpp server Signed-off-by: Wei Zhang <kweizh@gmail.com> * match case for creating completion and chat Signed-off-by: Wei Zhang <kweizh@gmail.com> * if let case for creating completion and chat Signed-off-by: Wei Zhang <kweizh@gmail.com> * add changelog * rebase && make fix --------- Signed-off-by: Wei Zhang <kweizh@gmail.com> Co-authored-by: Meng Zhang <meng@tabbyml.com>
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@ -0,0 +1,4 @@
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kind: Fixed and Improvements
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body: When reusing a model for Chat / Completion together (e.g Codestral-22B) with
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local model backend, resuse the llama-server sub process.
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time: 2024-08-10T22:10:45.454278-07:00
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@ -66,7 +66,7 @@ impl Embedding for EmbeddingServer {
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struct CompletionServer {
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#[allow(unused)]
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server: LlamaCppSupervisor,
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server: Arc<LlamaCppSupervisor>,
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completion: Arc<dyn CompletionStream>,
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}
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@ -89,6 +89,10 @@ impl CompletionServer {
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context_size,
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);
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server.start().await;
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Self::new_with_supervisor(Arc::new(server)).await
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}
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async fn new_with_supervisor(server: Arc<LlamaCppSupervisor>) -> Self {
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let config = HttpModelConfigBuilder::default()
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.api_endpoint(Some(api_endpoint(server.port())))
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.kind("llama.cpp/completion".to_string())
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@ -108,7 +112,7 @@ impl CompletionStream for CompletionServer {
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struct ChatCompletionServer {
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#[allow(unused)]
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server: LlamaCppSupervisor,
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server: Arc<LlamaCppSupervisor>,
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chat_completion: Arc<dyn ChatCompletionStream>,
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}
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@ -132,6 +136,10 @@ impl ChatCompletionServer {
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context_size,
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);
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server.start().await;
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Self::new_with_supervisor(Arc::new(server)).await
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}
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async fn new_with_supervisor(server: Arc<LlamaCppSupervisor>) -> Self {
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let config = HttpModelConfigBuilder::default()
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.api_endpoint(Some(api_endpoint(server.port())))
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.kind("openai/chat".to_string())
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@ -202,6 +210,53 @@ pub async fn create_completion(
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(stream, prompt_info)
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}
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pub async fn create_completion_and_chat(
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completion_model: &LocalModelConfig,
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chat_model: &LocalModelConfig,
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) -> (
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Arc<dyn CompletionStream>,
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PromptInfo,
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Arc<dyn ChatCompletionStream>,
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) {
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let chat_model_path = resolve_model_path(&chat_model.model_id).await;
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let chat_template = resolve_prompt_info(&chat_model.model_id)
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.await
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.chat_template
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.unwrap_or_else(|| panic!("Chat model requires specifying prompt template"));
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let model_path = resolve_model_path(&completion_model.model_id).await;
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let prompt_info = resolve_prompt_info(&completion_model.model_id).await;
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let server = Arc::new(LlamaCppSupervisor::new(
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"chat",
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chat_model.num_gpu_layers,
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false,
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&chat_model_path,
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chat_model.parallelism,
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Some(chat_template),
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chat_model.enable_fast_attention.unwrap_or_default(),
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chat_model.context_size,
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));
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server.start().await;
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let chat = ChatCompletionServer::new_with_supervisor(server.clone()).await;
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let completion = if completion_model == chat_model {
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CompletionServer::new_with_supervisor(server).await
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} else {
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CompletionServer::new(
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completion_model.num_gpu_layers,
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&model_path,
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completion_model.parallelism,
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completion_model.enable_fast_attention.unwrap_or_default(),
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completion_model.context_size,
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)
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.await
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};
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(Arc::new(completion), prompt_info, Arc::new(chat))
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}
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pub async fn create_embedding(config: &ModelConfig) -> Arc<dyn Embedding> {
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match config {
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ModelConfig::Http(http) => http_api_bindings::create_embedding(http).await,
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@ -250,7 +250,7 @@ pub struct HttpModelConfig {
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pub chat_template: Option<String>,
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}
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#[derive(Serialize, Deserialize, Debug, Clone)]
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#[derive(Serialize, Deserialize, Debug, Clone, PartialEq)]
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pub struct LocalModelConfig {
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pub model_id: String,
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@ -24,11 +24,11 @@ use crate::{
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services::{
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self,
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code::create_code_search,
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completion::{self, create_completion_service},
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completion::{self, create_completion_service_and_chat, CompletionService},
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embedding,
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event::create_event_logger,
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health,
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model::{self, download_model_if_needed},
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model::download_model_if_needed,
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tantivy::IndexReaderProvider,
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},
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to_local_config, Device,
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@ -171,17 +171,22 @@ pub async fn main(config: &Config, args: &ServeArgs) {
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index_reader_provider.clone(),
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));
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let chat = if let Some(chat) = &config.model.chat {
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Some(model::load_chat_completion(chat).await)
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} else {
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None
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};
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let model = &config.model;
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let (completion, chat) = create_completion_service_and_chat(
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&config.completion,
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code.clone(),
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logger.clone(),
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model.completion.clone(),
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model.chat.clone(),
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)
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.await;
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let mut api = api_router(
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args,
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&config,
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logger.clone(),
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code.clone(),
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completion,
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chat.clone(),
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webserver,
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)
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@ -227,24 +232,15 @@ async fn api_router(
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args: &ServeArgs,
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config: &Config,
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logger: Arc<dyn EventLogger>,
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code: Arc<dyn CodeSearch>,
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_code: Arc<dyn CodeSearch>,
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completion_state: Option<CompletionService>,
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chat_state: Option<Arc<dyn ChatCompletionStream>>,
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webserver: Option<bool>,
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) -> Router {
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let model = &config.model;
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let completion_state = if let Some(completion) = &model.completion {
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Some(Arc::new(
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create_completion_service(&config.completion, code.clone(), logger.clone(), completion)
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.await,
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))
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} else {
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None
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};
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let mut routers = vec![];
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let health_state = Arc::new(health::HealthState::new(
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model,
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&config.model,
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&args.device,
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args.chat_model
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.as_deref()
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@ -273,7 +269,7 @@ async fn api_router(
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Router::new()
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.route(
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"/v1/completions",
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routing::post(routes::completions).with_state(completion_state),
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routing::post(routes::completions).with_state(Arc::new(completion_state)),
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)
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.layer(TimeoutLayer::new(Duration::from_secs(
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config.server.completion_timeout,
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@ -12,7 +12,9 @@ use tabby_common::{
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config::{CompletionConfig, ModelConfig},
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languages::get_language,
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};
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use tabby_inference::{CodeGeneration, CodeGenerationOptions, CodeGenerationOptionsBuilder};
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use tabby_inference::{
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ChatCompletionStream, CodeGeneration, CodeGenerationOptions, CodeGenerationOptionsBuilder,
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};
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use thiserror::Error;
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use utoipa::ToSchema;
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@ -352,26 +354,32 @@ impl CompletionService {
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}
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}
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pub async fn create_completion_service(
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pub async fn create_completion_service_and_chat(
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config: &CompletionConfig,
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code: Arc<dyn CodeSearch>,
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logger: Arc<dyn EventLogger>,
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model: &ModelConfig,
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) -> CompletionService {
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let (
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engine,
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model::PromptInfo {
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prompt_template, ..
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},
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) = model::load_code_generation(model).await;
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completion: Option<ModelConfig>,
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chat: Option<ModelConfig>,
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) -> (
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Option<CompletionService>,
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Option<Arc<dyn ChatCompletionStream>>,
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) {
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let (code_generation, prompt, chat) =
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model::load_code_generation_and_chat(completion, chat).await;
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CompletionService::new(
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config.to_owned(),
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engine.clone(),
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code,
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logger,
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prompt_template,
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)
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let completion = code_generation.map(|code_generation| {
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CompletionService::new(
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config.to_owned(),
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code_generation.clone(),
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code,
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logger,
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prompt
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.unwrap_or_else(|| panic!("Prompt template is required for code completion"))
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.prompt_template,
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)
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});
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(completion, chat)
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}
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#[cfg(test)]
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use tabby_inference::{ChatCompletionStream, CodeGeneration, CompletionStream, Embedding};
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use tracing::info;
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pub async fn load_chat_completion(chat: &ModelConfig) -> Arc<dyn ChatCompletionStream> {
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match chat {
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ModelConfig::Http(http) => http_api_bindings::create_chat(http).await,
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ModelConfig::Local(llama) => llama_cpp_server::create_chat_completion(llama).await,
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}
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}
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pub async fn load_embedding(config: &ModelConfig) -> Arc<dyn Embedding> {
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llama_cpp_server::create_embedding(config).await
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}
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pub async fn load_code_generation(model: &ModelConfig) -> (Arc<CodeGeneration>, PromptInfo) {
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let (engine, prompt_info) = load_completion(model).await;
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(Arc::new(CodeGeneration::new(engine)), prompt_info)
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pub async fn load_code_generation_and_chat(
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completion_model: Option<ModelConfig>,
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chat_model: Option<ModelConfig>,
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) -> (
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Option<Arc<CodeGeneration>>,
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Option<PromptInfo>,
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Option<Arc<dyn ChatCompletionStream>>,
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) {
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let (engine, prompt_info, chat) = load_completion_and_chat(completion_model, chat_model).await;
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let code = engine.map(|engine| Arc::new(CodeGeneration::new(engine)));
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(code, prompt_info, chat)
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}
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async fn load_completion(model: &ModelConfig) -> (Arc<dyn CompletionStream>, PromptInfo) {
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match model {
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ModelConfig::Http(http) => {
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let engine = http_api_bindings::create(http).await;
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let (prompt_template, chat_template) = http_api_bindings::build_completion_prompt(http);
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(
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engine,
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PromptInfo {
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prompt_template,
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chat_template,
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},
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)
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}
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ModelConfig::Local(llama) => llama_cpp_server::create_completion(llama).await,
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async fn load_completion_and_chat(
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completion_model: Option<ModelConfig>,
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chat_model: Option<ModelConfig>,
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) -> (
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Option<Arc<dyn CompletionStream>>,
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Option<PromptInfo>,
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Option<Arc<dyn ChatCompletionStream>>,
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) {
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if let (Some(ModelConfig::Local(completion)), Some(ModelConfig::Local(chat))) =
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(&completion_model, &chat_model)
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{
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let (completion, prompt, chat) =
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llama_cpp_server::create_completion_and_chat(completion, chat).await;
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return (Some(completion), Some(prompt), Some(chat));
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}
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let (completion, prompt) = if let Some(completion_model) = completion_model {
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match completion_model {
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ModelConfig::Http(http) => {
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let engine = http_api_bindings::create(&http).await;
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let (prompt_template, chat_template) =
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http_api_bindings::build_completion_prompt(&http);
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(
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Some(engine),
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Some(PromptInfo {
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prompt_template,
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chat_template,
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}),
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)
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}
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ModelConfig::Local(llama) => {
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let (stream, prompt) = llama_cpp_server::create_completion(&llama).await;
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(Some(stream), Some(prompt))
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}
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}
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} else {
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(None, None)
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};
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let chat = if let Some(chat_model) = chat_model {
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match chat_model {
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ModelConfig::Http(http) => Some(http_api_bindings::create_chat(&http).await),
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ModelConfig::Local(llama) => {
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Some(llama_cpp_server::create_chat_completion(&llama).await)
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}
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}
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} else {
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None
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};
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(completion, prompt, chat)
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}
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pub async fn download_model_if_needed(model: &str) {
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