[router] update generate spec to align with sgl io struct (#11591)

sgl-router/benches/request_processing.rs

@@ -28,15 +28,38 @@ fn get_bootstrap_info(worker: &BasicWorker) -> (String, Option<u16>) {
 fn default_generate_request() -> GenerateRequest {
     GenerateRequest {
         text: None,
-        prompt: None,
         input_ids: None,
-        stream: false,
+        input_embeds: None,
+        image_data: None,
+        video_data: None,
+        audio_data: None,
         sampling_params: None,
-        return_logprob: false,
-        // SGLang Extensions
-        lora_path: None,
-        session_params: None,
+        return_logprob: None,
+        logprob_start_len: None,
+        top_logprobs_num: None,
+        token_ids_logprob: None,
+        return_text_in_logprobs: false,
+        stream: false,
+        log_metrics: true,
         return_hidden_states: false,
+        modalities: None,
+        session_params: None,
+        lora_path: None,
+        lora_id: None,
+        custom_logit_processor: None,
+        bootstrap_host: None,
+        bootstrap_port: None,
+        bootstrap_room: None,
+        bootstrap_pair_key: None,
+        data_parallel_rank: None,
+        background: false,
+        conversation_id: None,
+        priority: None,
+        extra_key: None,
+        no_logs: false,
+        custom_labels: None,
+        return_bytes: false,
+        return_entropy: false,
         rid: None,
     }
 }
@@ -101,6 +124,7 @@ fn create_sample_generate_request() -> GenerateRequest {
     GenerateRequest {
         text: Some("Write a story about artificial intelligence".to_string()),
         sampling_params: Some(SamplingParams {
+            max_new_tokens: Some(100),
             temperature: Some(0.8),
             top_p: Some(0.9),
             top_k: Some(50),

sgl-router/src/grpc_client/sglang_scheduler.rs

@@ -280,13 +280,13 @@ impl SglangSchedulerClient {
                 input_ids: token_ids,
             }),
             sampling_params: Some(sampling_params),
-            return_logprob: body.return_logprob,
-            logprob_start_len: -1,
-            top_logprobs_num: 0,
-            token_ids_logprob: vec![],
+            return_logprob: body.return_logprob.unwrap_or(false),
+            logprob_start_len: body.logprob_start_len.unwrap_or(-1),
+            top_logprobs_num: body.top_logprobs_num.unwrap_or(0),
+            token_ids_logprob: body.token_ids_logprob.clone().unwrap_or_default(),
             return_hidden_states: body.return_hidden_states,
             stream: body.stream,
-            log_metrics: true,
+            log_metrics: body.log_metrics,
             ..Default::default()
         };
 

sgl-router/src/protocols/spec.rs

@@ -356,7 +356,7 @@ pub struct ChatCompletionRequest {
 
     /// Path to LoRA adapter(s) for model customization
     #[serde(skip_serializing_if = "Option::is_none")]
-    pub lora_path: Option<LoRAPath>,
+    pub lora_path: Option<String>,
 
     /// Session parameters for continual prompting
     #[serde(skip_serializing_if = "Option::is_none")]
@@ -905,7 +905,7 @@ pub struct CompletionRequest {
 
     /// Path to LoRA adapter(s) for model customization
     #[serde(skip_serializing_if = "Option::is_none")]
-    pub lora_path: Option<LoRAPath>,
+    pub lora_path: Option<String>,
 
     /// Session parameters for continual prompting
     #[serde(skip_serializing_if = "Option::is_none")]
@@ -2309,10 +2309,6 @@ fn validate_sampling_params(params: &SamplingParams) -> Result<(), validator::Va
 #[derive(Clone, Debug, Serialize, Deserialize, Validate)]
 #[validate(schema(function = "validate_generate_request"))]
 pub struct GenerateRequest {
-    /// The prompt to generate from (OpenAI style)
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub prompt: Option<StringOrArray>,
-
     /// Text input - SGLang native format
     #[serde(skip_serializing_if = "Option::is_none")]
     pub text: Option<String>,
@@ -2321,31 +2317,144 @@ pub struct GenerateRequest {
     #[serde(skip_serializing_if = "Option::is_none")]
     pub input_ids: Option<InputIds>,
 
+    /// Input embeddings for direct embedding input
+    /// Can be a 2D array (single request) or 3D array (batch of requests)
+    /// Placeholder for future use
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub input_embeds: Option<Value>,
+
+    /// Image input data
+    /// Can be an image instance, file name, URL, or base64 encoded string
+    /// Supports single images, lists of images, or nested lists for batch processing
+    /// Placeholder for future use
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub image_data: Option<Value>,
+
+    /// Video input data
+    /// Can be a file name, URL, or base64 encoded string
+    /// Supports single videos, lists of videos, or nested lists for batch processing
+    /// Placeholder for future use
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub video_data: Option<Value>,
+
+    /// Audio input data
+    /// Can be a file name, URL, or base64 encoded string
+    /// Supports single audio files, lists of audio, or nested lists for batch processing
+    /// Placeholder for future use
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub audio_data: Option<Value>,
+
     /// Sampling parameters (sglang style)
     #[serde(skip_serializing_if = "Option::is_none")]
     pub sampling_params: Option<SamplingParams>,
 
+    /// Whether to return logprobs
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub return_logprob: Option<bool>,
+
+    /// If return logprobs, the start location in the prompt for returning logprobs.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub logprob_start_len: Option<i32>,
+
+    /// If return logprobs, the number of top logprobs to return at each position.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub top_logprobs_num: Option<i32>,
+
+    /// If return logprobs, the token ids to return logprob for.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub token_ids_logprob: Option<Vec<u32>>,
+
+    /// Whether to detokenize tokens in text in the returned logprobs.
+    #[serde(default)]
+    pub return_text_in_logprobs: bool,
+
     /// Whether to stream the response
     #[serde(default)]
     pub stream: bool,
 
-    /// Whether to return logprobs
-    #[serde(default)]
-    pub return_logprob: bool,
-
-    /// Path to LoRA adapter(s) for model customization
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub lora_path: Option<LoRAPath>,
-
-    /// Session parameters for continual prompting
-    #[serde(skip_serializing_if = "Option::is_none")]
-    pub session_params: Option<HashMap<String, Value>>,
+    /// Whether to log metrics for this request (e.g. health_generate calls do not log metrics)
+    #[serde(default = "default_true")]
+    pub log_metrics: bool,
 
     /// Return model hidden states
     #[serde(default)]
     pub return_hidden_states: bool,
 
-    /// Request ID for tracking
+    /// The modalities of the image data [image, multi-images, video]
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub modalities: Option<Vec<String>>,
+
+    /// Session parameters for continual prompting
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub session_params: Option<HashMap<String, Value>>,
+
+    /// Path to LoRA adapter(s) for model customization
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub lora_path: Option<String>,
+
+    /// LoRA adapter ID (if pre-loaded)
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub lora_id: Option<String>,
+
+    /// Custom logit processor for advanced sampling control. Must be a serialized instance
+    /// of `CustomLogitProcessor` in python/sglang/srt/sampling/custom_logit_processor.py
+    /// Use the processor's `to_str()` method to generate the serialized string.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub custom_logit_processor: Option<String>,
+
+    /// For disaggregated inference
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub bootstrap_host: Option<String>,
+
+    /// For disaggregated inference
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub bootstrap_port: Option<i32>,
+
+    /// For disaggregated inference
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub bootstrap_room: Option<i32>,
+
+    /// For disaggregated inference
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub bootstrap_pair_key: Option<String>,
+
+    /// Data parallel rank routing
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub data_parallel_rank: Option<i32>,
+
+    /// Background response
+    #[serde(default)]
+    pub background: bool,
+
+    /// Conversation ID for tracking
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub conversation_id: Option<String>,
+
+    /// Priority for the request
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub priority: Option<i32>,
+
+    /// Extra key for classifying the request (e.g. cache_salt)
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub extra_key: Option<String>,
+
+    /// Whether to disallow logging for this request (e.g. due to ZDR)
+    #[serde(default)]
+    pub no_logs: bool,
+
+    /// Custom metric labels
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub custom_labels: Option<HashMap<String, String>>,
+
+    /// Whether to return bytes for image generation
+    #[serde(default)]
+    pub return_bytes: bool,
+
+    /// Whether to return entropy
+    #[serde(default)]
+    pub return_entropy: bool,
+
+    /// Request ID for tracking (inherited from BaseReq in Python)
     #[serde(skip_serializing_if = "Option::is_none")]
     pub rid: Option<String>,
 }
@@ -2358,7 +2467,7 @@ impl Normalizable for GenerateRequest {
 fn validate_generate_request(req: &GenerateRequest) -> Result<(), validator::ValidationError> {
     // Exactly one of text or input_ids must be provided
     // Note: input_embeds not yet supported in Rust implementation
-    let has_text = req.text.is_some() || req.prompt.is_some();
+    let has_text = req.text.is_some();
     let has_input_ids = req.input_ids.is_some();
 
     let count = [has_text, has_input_ids].iter().filter(|&&x| x).count();
@@ -2389,18 +2498,11 @@ impl GenerationRequest for GenerateRequest {
     }
 
     fn extract_text_for_routing(&self) -> String {
-        // Check fields in priority order: text, prompt, inputs
+        // Check fields in priority order: text, input_ids
         if let Some(ref text) = self.text {
             return text.clone();
         }
 
-        if let Some(ref prompt) = self.prompt {
-            return match prompt {
-                StringOrArray::String(s) => s.clone(),
-                StringOrArray::Array(v) => v.join(" "),
-            };
-        }
-
         if let Some(ref input_ids) = self.input_ids {
             return match input_ids {
                 InputIds::Single(ids) => ids

sgl-router/src/routers/grpc/pipeline.rs

@@ -877,7 +877,7 @@ impl ResponseProcessingStage {
         }
 
         // Non-streaming: Delegate to ResponseProcessor
-        let request_logprobs = ctx.generate_request().return_logprob;
+        let request_logprobs = ctx.generate_request().return_logprob.unwrap_or(false);
         let generate_request = ctx.generate_request_arc();
 
         let stop_decoder = ctx

sgl-router/src/routers/grpc/streaming.rs

@@ -616,7 +616,7 @@ impl StreamingProcessor {
         generate_request: Arc<GenerateRequest>,
         dispatch: context::DispatchMetadata,
     ) -> Response {
-        let return_logprob = generate_request.return_logprob;
+        let return_logprob = generate_request.return_logprob.unwrap_or(false);
 
         // Create SSE channel
         let (tx, rx) = mpsc::unbounded_channel::<Result<Bytes, io::Error>>();

sgl-router/src/routers/http/pd_router.rs

@@ -150,11 +150,6 @@ impl PDRouter {
     }
 
     fn get_generate_batch_size(req: &GenerateRequest) -> Option<usize> {
-        if let Some(StringOrArray::Array(arr)) = &req.prompt {
-            if !arr.is_empty() {
-                return Some(arr.len());
-            }
-        }
         if let Some(text) = &req.text {
             if text.contains("[") && text.contains("]") {
                 return None;
@@ -1061,18 +1056,10 @@ impl RouterTrait for PDRouter {
         model_id: Option<&str>,
     ) -> Response {
         let is_stream = body.stream;
-        let return_logprob = body.return_logprob;
+        let return_logprob = body.return_logprob.unwrap_or(false);
 
         let request_text = if self.policies_need_request_text() {
-            body.text
-                .as_deref()
-                .or_else(|| {
-                    body.prompt.as_ref().and_then(|p| match p {
-                        StringOrArray::String(s) => Some(s.as_str()),
-                        StringOrArray::Array(v) => v.first().map(|s| s.as_str()),
-                    })
-                })
-                .map(|s| s.to_string())
+            body.text.as_deref().map(|s| s.to_string())
         } else {
             None
         };

sgl-router/tests/test_openai_routing.rs

@@ -598,15 +598,39 @@ async fn test_unsupported_endpoints() {
     .unwrap();
 
     let generate_request = GenerateRequest {
-        prompt: None,
         text: Some("Hello world".to_string()),
         input_ids: None,
+        input_embeds: None,
+        image_data: None,
+        video_data: None,
+        audio_data: None,
         sampling_params: None,
         stream: false,
-        return_logprob: false,
-        lora_path: None,
-        session_params: None,
+        return_logprob: Some(false),
+        logprob_start_len: None,
+        top_logprobs_num: None,
+        token_ids_logprob: None,
+        return_text_in_logprobs: false,
+        log_metrics: true,
         return_hidden_states: false,
+        modalities: None,
+        session_params: None,
+        lora_path: None,
+        lora_id: None,
+        custom_logit_processor: None,
+        bootstrap_host: None,
+        bootstrap_port: None,
+        bootstrap_room: None,
+        bootstrap_pair_key: None,
+        data_parallel_rank: None,
+        background: false,
+        conversation_id: None,
+        priority: None,
+        extra_key: None,
+        no_logs: false,
+        custom_labels: None,
+        return_bytes: false,
+        return_entropy: false,
         rid: None,
     };