implement Open Inference Protocol endpoints (huggingface#1942)

* feat: add kserve feature and basic routes

* feat: implement infer endpoint wrapper around generate

* fix: refactor and improve types

* fix: improve infer and simplify

* fix: cleanup and improve api docs

* fix: refactor and encapsulate kserve feat in file

* fix: remove typos after rebase

router/Cargo.toml

@@ -59,3 +59,4 @@ vergen = { version = "8.2.5", features = ["build", "git", "gitcl"] }
 default = ["ngrok"]
 ngrok = ["dep:ngrok"]
 google = []
+kserve = []

router/src/kserve.rs

@@ -0,0 +1,247 @@
+use crate::{
+    default_parameters,
+    server::{generate_internal, ComputeType},
+    Deserialize, ErrorResponse, GenerateParameters, GenerateRequest, Infer, Serialize, ToSchema,
+};
+use axum::extract::{Extension, Path};
+use axum::response::{IntoResponse, Response};
+use axum::Json;
+use futures::stream::FuturesUnordered;
+use futures::TryStreamExt;
+use reqwest::header::HeaderMap;
+use reqwest::StatusCode;
+
+#[derive(Debug, Serialize, Deserialize, ToSchema)]
+pub struct OutputChunk {
+    pub name: String,
+    pub shape: Vec<usize>,
+    pub datatype: String,
+    pub data: Vec<u8>,
+}
+
+#[derive(Debug, Serialize, Deserialize, ToSchema)]
+pub struct InferenceOutput {
+    pub id: String,
+    pub outputs: Vec<OutputChunk>,
+}
+
+#[derive(Debug, Deserialize, ToSchema)]
+pub(crate) struct InferenceRequest {
+    pub id: String,
+    #[serde(default = "default_parameters")]
+    pub parameters: GenerateParameters,
+    pub inputs: Vec<Input>,
+    pub outputs: Vec<Output>,
+}
+
+#[derive(Debug, Serialize, Deserialize, ToSchema)]
+pub(crate) struct Input {
+    pub name: String,
+    pub shape: Vec<usize>,
+    pub datatype: String,
+    pub data: Vec<u8>,
+}
+
+#[derive(Debug, Serialize, Deserialize, ToSchema)]
+pub(crate) struct Output {
+    pub name: String,
+}
+
+#[derive(Debug, Serialize, Deserialize, ToSchema)]
+pub struct LiveResponse {
+    pub live: bool,
+}
+
+#[derive(Debug, Serialize, Deserialize, ToSchema)]
+pub struct ReadyResponse {
+    pub live: bool,
+}
+
+#[derive(Debug, Serialize, Deserialize, ToSchema)]
+pub struct MetadataServerResponse {
+    pub name: String,
+    pub version: String,
+    pub extensions: Vec<String>,
+}
+
+// Routes
+
+#[utoipa::path(
+    post,
+    tag = "Text Generation Inference",
+    path = "/v2/health/live",
+    responses(
+        (status = 200, description = "Service is live", body = LiveReponse),
+        (status = 404, description = "Service not found", body = ErrorResponse,
+            example = json!({"error": "No response"}))
+    )
+)]
+pub async fn kserve_health_live() -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
+    let data = LiveResponse { live: true };
+    Ok((HeaderMap::new(), Json(data)).into_response())
+}
+
+#[utoipa::path(
+    post,
+    tag = "Text Generation Inference",
+    path = "/v2/health/ready",
+    responses(
+        (status = 200, description = "Service is ready", body = ReadyResponse),
+        (status = 404, description = "Service not found", body = ErrorResponse,
+            example = json!({"error": "No response"}))
+    )
+)]
+pub async fn kserve_health_ready() -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
+    let data = ReadyResponse { live: true };
+    Ok((HeaderMap::new(), Json(data)).into_response())
+}
+
+#[utoipa::path(
+    get,
+    tag = "Text Generation Inference",
+    path = "/v2",
+    responses(
+        (status = 200, description = "Metadata retrieved", body = MetadataServerResponse),
+        (status = 404, description = "Service not found", body = ErrorResponse,
+            example = json!({"error": "No response"}))
+    )
+)]
+pub async fn kerve_server_metadata() -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
+    let data = MetadataServerResponse {
+        name: "text-generation-inference".to_string(),
+        version: env!("CARGO_PKG_VERSION").to_string(),
+        extensions: vec![
+            "health".to_string(),
+            "models".to_string(),
+            "metrics".to_string(),
+        ],
+    };
+    Ok((HeaderMap::new(), Json(data)).into_response())
+}
+
+#[utoipa::path(
+    get,
+    tag = "Text Generation Inference",
+    path = "/v2/models/{model_name}/versions/{model_version}",
+    responses(
+        (status = 200, description = "Model version metadata retrieved", body = MetadataServerResponse),
+        (status = 404, description = "Model or version not found", body = ErrorResponse,
+            example = json!({"error": "No response"}))
+    )
+)]
+pub async fn kserve_model_metadata(
+    Path((model_name, model_version)): Path<(String, String)>,
+) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
+    let data = MetadataServerResponse {
+        name: model_name,
+        version: model_version,
+        extensions: vec!["infer".to_string(), "ready".to_string()],
+    };
+    Ok((HeaderMap::new(), Json(data)).into_response())
+}
+
+#[utoipa::path(
+    post,
+    tag = "Text Generation Inference",
+    path = "/v2/models/{model_name}/versions/{model_version}/infer",
+    request_body = Json<InferenceRequest>,
+    responses(
+        (status = 200, description = "Inference executed successfully", body = InferenceOutput),
+        (status = 404, description = "Model or version not found", body = ErrorResponse,
+            example = json!({"error": "No response"}))
+    )
+)]
+pub async fn kserve_model_infer(
+    infer: Extension<Infer>,
+    Extension(compute_type): Extension<ComputeType>,
+    Json(payload): Json<InferenceRequest>,
+) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
+    let id = payload.id.clone();
+    let str_inputs = payload
+        .inputs
+        .iter()
+        .map(|input| {
+            std::str::from_utf8(&input.data).map_err(|e| {
+                (
+                    StatusCode::UNPROCESSABLE_ENTITY,
+                    Json(ErrorResponse {
+                        error: e.to_string(),
+                        error_type: "utf8".to_string(),
+                    }),
+                )
+            })
+        })
+        .collect::<Result<Vec<_>, _>>()?;
+
+    if str_inputs.len() != payload.outputs.len() {
+        return Err((
+            StatusCode::UNPROCESSABLE_ENTITY,
+            Json(ErrorResponse {
+                error: "Inputs and outputs length mismatch".to_string(),
+                error_type: "length mismatch".to_string(),
+            }),
+        ));
+    }
+
+    let output_chunks = str_inputs
+        .iter()
+        .zip(&payload.outputs)
+        .map(|(str_input, output)| {
+            let generate_request = GenerateRequest {
+                inputs: str_input.to_string(),
+                parameters: payload.parameters.clone(),
+            };
+            let infer = infer.clone();
+            let compute_type = compute_type.clone();
+            let span = tracing::Span::current();
+            async move {
+                generate_internal(infer, compute_type, Json(generate_request), span)
+                    .await
+                    .map(|(_, Json(generation))| {
+                        let generation_as_bytes = generation.generated_text.as_bytes().to_vec();
+                        OutputChunk {
+                            name: output.name.clone(),
+                            shape: vec![1, generation_as_bytes.len()],
+                            datatype: "BYTES".to_string(),
+                            data: generation_as_bytes,
+                        }
+                    })
+                    .map_err(|_| {
+                        (
+                            StatusCode::INTERNAL_SERVER_ERROR,
+                            Json(ErrorResponse {
+                                error: "Incomplete generation".into(),
+                                error_type: "Incomplete generation".into(),
+                            }),
+                        )
+                    })
+            }
+        })
+        .collect::<FuturesUnordered<_>>()
+        .try_collect::<Vec<_>>()
+        .await?;
+
+    let inference_output = InferenceOutput {
+        id: id.clone(),
+        outputs: output_chunks,
+    };
+
+    Ok((HeaderMap::new(), Json(inference_output)).into_response())
+}
+
+#[utoipa::path(
+    get,
+    tag = "Text Generation Inference",
+    path = "/v2/models/{model_name}/versions/{model_version}/ready",
+    responses(
+        (status = 200, description = "Model version is ready", body = ReadyResponse),
+        (status = 404, description = "Model or version not found", body = ErrorResponse,
+            example = json!({"error": "No response"}))
+    )
+)]
+pub async fn kserve_model_metadata_ready(
+    Path((_model_name, _model_version)): Path<(String, String)>,
+) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
+    let data = ReadyResponse { live: true };
+    Ok((HeaderMap::new(), Json(data)).into_response())
+}

router/src/lib.rs

@@ -4,6 +4,9 @@ mod infer;
 pub mod server;
 mod validation;
 
+#[cfg(feature = "kserve")]
+mod kserve;
+
 use serde::{Deserialize, Serialize};
 use tracing::warn;
 use utoipa::ToSchema;

router/src/server.rs

@@ -4,6 +4,11 @@ use crate::infer::v2::SchedulerV2;
 use crate::infer::v3::SchedulerV3;
 use crate::infer::{HealthCheck, Scheduler};
 use crate::infer::{Infer, InferError, InferResponse, InferStreamResponse, ToolGrammar};
+#[cfg(feature = "kserve")]
+use crate::kserve::{
+    kerve_server_metadata, kserve_health_live, kserve_health_ready, kserve_model_infer,
+    kserve_model_metadata, kserve_model_metadata_ready,
+};
 use crate::validation::ValidationError;
 use crate::{
     BestOfSequence, Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest,
@@ -172,7 +177,7 @@ async fn generate(
     generate_internal(infer, ComputeType(compute_type), Json(req), span).await
 }
 
-async fn generate_internal(
+pub(crate) async fn generate_internal(
     infer: Extension<Infer>,
     ComputeType(compute_type): ComputeType,
     Json(req): Json<GenerateRequest>,
@@ -1727,28 +1732,58 @@ pub async fn run(
         docker_label: option_env!("DOCKER_LABEL"),
     };
 
-    // Define VertextApiDoc conditionally only if the "google" feature is enabled
-    let doc = {
-        // avoid `mut` if possible
-        #[cfg(feature = "google")]
-        {
-            use crate::VertexInstance;
-
-            #[derive(OpenApi)]
-            #[openapi(
-                paths(vertex_compatibility),
-                components(schemas(VertexInstance, VertexRequest, VertexResponse))
-            )]
-            struct VertextApiDoc;
-
-            // limiting mutability to the smallest scope necessary
-            let mut doc = ApiDoc::openapi();
-            doc.merge(VertextApiDoc::openapi());
-            doc
-        }
-        #[cfg(not(feature = "google"))]
-        ApiDoc::openapi()
-    };
+    #[allow(unused_mut)] // mut is needed for conditional compilation
+    let mut doc = ApiDoc::openapi();
+
+    #[cfg(feature = "google")]
+    {
+        use crate::VertexInstance;
+
+        #[derive(OpenApi)]
+        #[openapi(
+            paths(vertex_compatibility),
+            components(schemas(VertexInstance, VertexRequest, VertexResponse))
+        )]
+        struct VertexApiDoc;
+
+        doc.merge(VertexApiDoc::openapi());
+    }
+
+    #[cfg(feature = "kserve")]
+    {
+        use crate::kserve::{
+            InferenceOutput, InferenceRequest, LiveResponse, MetadataServerResponse, OutputChunk,
+            ReadyResponse,
+        };
+        use crate::kserve::{
+            __path_kerve_server_metadata, __path_kserve_health_live, __path_kserve_health_ready,
+            __path_kserve_model_infer, __path_kserve_model_metadata,
+            __path_kserve_model_metadata_ready,
+        };
+
+        #[derive(OpenApi)]
+        #[openapi(
+            paths(
+                kserve_model_infer,
+                kserve_health_live,
+                kserve_health_ready,
+                kerve_server_metadata,
+                kserve_model_metadata,
+                kserve_model_metadata_ready,
+            ),
+            components(schemas(
+                InferenceOutput,
+                InferenceRequest,
+                LiveResponse,
+                MetadataServerResponse,
+                OutputChunk,
+                ReadyResponse,
+            ))
+        )]
+        struct KServeApiDoc;
+
+        doc.merge(KServeApiDoc::openapi());
+    }
 
     // Configure Swagger UI
     let swagger_ui = SwaggerUi::new("/docs").url("/api-doc/openapi.json", doc);
@@ -1798,6 +1833,27 @@ pub async fn run(
         }
     }
 
+    #[cfg(feature = "kserve")]
+    {
+        tracing::info!("Built with `kserve` feature");
+        app = app
+            .route(
+                "/v2/models/:model_name/versions/:model_version/infer",
+                post(kserve_model_infer),
+            )
+            .route(
+                "/v2/models/:model_name/versions/:model_version",
+                get(kserve_model_metadata),
+            )
+            .route("/v2/health/ready", get(kserve_health_ready))
+            .route("/v2/health/live", get(kserve_health_live))
+            .route("/v2", get(kerve_server_metadata))
+            .route(
+                "/v2/models/:model_name/versions/:model_version/ready",
+                get(kserve_model_metadata_ready),
+            );
+    }
+
     // add layers after routes
     app = app
         .layer(Extension(info))