fix(frontend): prepend KServe batch dim for Triton metadata

lib/llm/src/grpc/service/kserve.rs

@@ -67,7 +67,9 @@ impl GrpcTuningConfig {
 
 use crate::grpc::service::dispatch_error_status;
 use crate::grpc::service::openai::completion_response_stream;
-use crate::grpc::service::tensor::{ExtendedNvCreateTensorResponse, tensor_response_stream};
+use crate::grpc::service::tensor::{
+    ExtendedNvCreateTensorResponse, kserve_metadata_shape, tensor_response_stream,
+};
 use std::convert::{TryFrom, TryInto};
 use tonic::{Request, Response, Status, transport::Server};
 
@@ -686,7 +688,10 @@ impl GrpcInferenceService for KserveService {
                                         .and_then(|dt| dt.oip_name())
                                         .unwrap_or("TYPE_INVALID")
                                         .to_string(),
-                                    shape: input.dims.clone(),
+                                    shape: kserve_metadata_shape(
+                                        &input.dims,
+                                        model_config.max_batch_size,
+                                    ),
                                 })
                                 .collect(),
                             outputs: model_config
@@ -700,7 +705,10 @@ impl GrpcInferenceService for KserveService {
                                             .and_then(|dt| dt.oip_name())
                                             .unwrap_or("TYPE_INVALID")
                                             .to_string(),
-                                        shape: output.dims.clone(),
+                                        shape: kserve_metadata_shape(
+                                            &output.dims,
+                                            model_config.max_batch_size,
+                                        ),
                                     },
                                 )
                                 .collect(),

lib/llm/src/grpc/service/tensor.rs

@@ -854,6 +854,20 @@ impl DataType {
     }
 }
 
+/// KServe v2 `ModelMetadata` wire shape for a Triton tensor.
+///
+/// Triton's `ModelConfig` stores per-tensor `dims` without the batch dimension.
+/// When `max_batch_size > 0`, native Triton's KServe adapter prepends `-1`
+/// (variable batch) so clients can send batched inputs. Copying `dims`
+/// verbatim drops that axis and KServe v2 clients reject the tensor.
+pub fn kserve_metadata_shape(dims: &[i64], max_batch_size: i32) -> Vec<i64> {
+    if max_batch_size > 0 {
+        std::iter::once(-1).chain(dims.iter().copied()).collect()
+    } else {
+        dims.to_vec()
+    }
+}
+
 impl std::fmt::Display for tensor::DataType {
     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
         match *self {
@@ -1222,3 +1236,22 @@ mod oip_datatype_tests {
         }
     }
 }
+
+#[cfg(test)]
+mod kserve_metadata_shape_tests {
+    use super::kserve_metadata_shape;
+
+    #[test]
+    fn prepends_variable_batch_dim_when_batching_enabled() {
+        assert_eq!(kserve_metadata_shape(&[-1], 4), vec![-1, -1]);
+        assert_eq!(kserve_metadata_shape(&[768], 8), vec![-1, 768]);
+        assert_eq!(kserve_metadata_shape(&[], 1), vec![-1]);
+    }
+
+    #[test]
+    fn leaves_dims_unchanged_when_batching_disabled() {
+        assert_eq!(kserve_metadata_shape(&[-1], 0), vec![-1]);
+        assert_eq!(kserve_metadata_shape(&[3, 224, 224], 0), vec![3, 224, 224]);
+        assert_eq!(kserve_metadata_shape(&[], 0), Vec::<i64>::new());
+    }
+}