fix: Add support for single element arrays for chat and completions prompts

components/src/dynamo/sglang/multimodal_utils/multimodal_chat_processor.py

@@ -38,7 +38,6 @@ def multimodal_request_to_sglang(raw_request, tokenizer, chat_template):
     sglang_request = {
         "model": raw_request.model,
         "token_ids": input_ids,
-        "batch_token_ids": None,
         "stop_conditions": {"max_tokens": raw_request.max_tokens or None},
         "sampling_options": {"temperature": raw_request.temperature or 0.7},
         "eos_token_ids": [tokenizer.eos_token_id],

lib/llm/src/preprocessor.rs

@@ -14,12 +14,11 @@
 pub mod prompt;
 pub mod tools;
 
-use anyhow::Result;
+use anyhow::{Result, bail};
 use dynamo_async_openai::types::{ChatCompletionToolChoiceOption, EncodingFormat};
 use futures::Stream;
 use futures::stream::{self, StreamExt};
 use prompt::OAIPromptFormatter;
-use rayon::iter::{IntoParallelRefIterator, ParallelIterator};
 use std::{collections::HashMap, pin::Pin, sync::Arc};
 use tracing;
 
@@ -282,8 +281,10 @@ impl OpenAIPreprocessor {
                             if token_batches.len() == 1 {
                                 builder.token_ids(token_batches[0].clone());
                             } else {
-                                builder.batch_token_ids(Some(token_batches));
-                                builder.token_ids(vec![]);
+                                bail!(
+                                    "Batch token input not supported for more than one token in requests (got {})",
+                                    token_batches.len()
+                                );
                             }
                         }
                     }
@@ -345,16 +346,15 @@ impl OpenAIPreprocessor {
                             builder.token_ids(tokens_vec);
                         }
                         TextInput::Batch(texts) => {
-                            let token_batches: Vec<Vec<u32>> = texts
-                                .par_iter()
-                                .map(|text| {
-                                    self.tokenizer
-                                        .encode(text)
-                                        .map(|encoded| encoded.token_ids().to_vec())
-                                })
-                                .collect::<Result<Vec<_>>>()?;
-                            builder.batch_token_ids(Some(token_batches));
-                            builder.token_ids(vec![]);
+                            if texts.len() == 1 {
+                                let encoding = self.tokenizer.encode(&texts[0])?;
+                                builder.token_ids(encoding.token_ids().to_vec());
+                            } else {
+                                bail!(
+                                    "Batch text input not supported for more than one text in requests (got {})",
+                                    texts.len()
+                                );
+                            }
                         }
                     }
                 }

lib/llm/src/protocols/common/preprocessor.rs

@@ -18,10 +18,6 @@ pub struct PreprocessedRequest {
     /// Type of prompt
     pub token_ids: Vec<TokenIdType>,
 
-    /// Batch Token Ids = for batch completion requests (i.e using ArrayOfIntegerArray type from OpenAI /completions)
-    #[builder(default)]
-    pub batch_token_ids: Option<Vec<Vec<TokenIdType>>>,
-
     /// StopConditions are conditions that the inference engine will use to stop generation.
     pub stop_conditions: StopConditions,
 

tests/frontend/test_completion_vllm.py

@@ -0,0 +1,216 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""End-to-end tests covering reasoning effort behaviour."""
+
+from __future__ import annotations
+
+import logging
+import os
+import shutil
+from typing import Any, Dict
+
+import pytest
+import requests
+
+from tests.conftest import EtcdServer, NatsServer
+from tests.utils.constants import GPT_OSS
+from tests.utils.managed_process import ManagedProcess
+from tests.utils.payloads import check_models_api
+
+logger = logging.getLogger(__name__)
+
+REASONING_TEST_MODEL = GPT_OSS
+
+
+class DynamoFrontendProcess(ManagedProcess):
+    """Process manager for Dynamo frontend"""
+
+    def __init__(self, request):
+        command = ["python", "-m", "dynamo.frontend", "--router-mode", "round-robin"]
+
+        log_dir = f"{request.node.name}_frontend"
+
+        # Clean up any existing log directory from previous runs
+        try:
+            shutil.rmtree(log_dir)
+            logger.info(f"Cleaned up existing log directory: {log_dir}")
+        except FileNotFoundError:
+            # Directory doesn't exist, which is fine
+            pass
+
+        super().__init__(
+            command=command,
+            display_output=True,
+            terminate_existing=True,
+            log_dir=log_dir,
+        )
+
+
+class GPTOSSWorkerProcess(ManagedProcess):
+    """Worker process for GPT-OSS model."""
+
+    def __init__(self, request, worker_id: str = "reasoning-worker"):
+        self.worker_id = worker_id
+
+        command = [
+            "python3",
+            "-m",
+            "dynamo.vllm",
+            "--model",
+            REASONING_TEST_MODEL,
+            "--enforce-eager",
+            "--dyn-tool-call-parser",
+            "harmony",
+            "--dyn-reasoning-parser",
+            "gpt_oss",
+        ]
+
+        env = os.environ.copy()
+        env["DYN_LOG"] = "debug"
+        env["DYN_SYSTEM_ENABLED"] = "true"
+        env["DYN_SYSTEM_USE_ENDPOINT_HEALTH_STATUS"] = '["generate"]'
+        env["DYN_SYSTEM_PORT"] = "8083"
+
+        log_dir = f"{request.node.name}_{worker_id}"
+
+        try:
+            shutil.rmtree(log_dir)
+        except FileNotFoundError:
+            pass
+
+        super().__init__(
+            command=command,
+            env=env,
+            health_check_urls=[
+                ("http://localhost:8000/v1/models", check_models_api),
+                ("http://localhost:8083/health", self.is_ready),
+            ],
+            timeout=300,
+            display_output=True,
+            terminate_existing=False,
+            stragglers=["VLLM::EngineCore"],
+            straggler_commands=["-m dynamo.vllm"],
+            log_dir=log_dir,
+        )
+
+    def is_ready(self, response) -> bool:
+        try:
+            status = (response.json() or {}).get("status")
+        except ValueError:
+            logger.warning("%s health response is not valid JSON", self.worker_id)
+            return False
+
+        is_ready = status == "ready"
+        if is_ready:
+            logger.info("%s status is ready", self.worker_id)
+        else:
+            logger.warning("%s status is not ready: %s", self.worker_id, status)
+        return is_ready
+
+
+def _send_completion_request(
+    payload: Dict[str, Any],
+    timeout: int = 180,
+) -> requests.Response:
+    """Send a text completion request"""
+
+    headers = {"Content-Type": "application/json"}
+
+    response = requests.post(
+        "http://localhost:8000/v1/completions",
+        headers=headers,
+        json=payload,
+        timeout=timeout,
+    )
+    return response
+
+
+@pytest.fixture(scope="module")
+def runtime_services(request):
+    """Module-scoped runtime services for this test file."""
+    with NatsServer(request) as nats_process:
+        with EtcdServer(request) as etcd_process:
+            yield nats_process, etcd_process
+
+
+@pytest.fixture(scope="module")
+def start_services(request, runtime_services, predownload_models):
+    """Start frontend and worker processes once for this module's tests."""
+    with DynamoFrontendProcess(request):
+        logger.info("Frontend started for tests")
+        with GPTOSSWorkerProcess(request):
+            logger.info("Worker started for tests")
+            yield
+
+
+@pytest.mark.usefixtures("start_services")
+@pytest.mark.vllm
+@pytest.mark.gpu_1
+@pytest.mark.e2e
+@pytest.mark.model(REASONING_TEST_MODEL)
+def test_completion_single_element_array_prompt() -> None:
+    """Exercise completions with reasoning effort and prompt."""
+    reasoning_effort = "low"
+
+    payload: Dict[str, Any] = {
+        "model": REASONING_TEST_MODEL,
+        "prompt": ["Tell me about Mars"],
+        "max_tokens": 2000,
+        "chat_template_args": {"reasoning_effort": reasoning_effort},
+    }
+
+    response = _send_completion_request(payload)
+
+    assert response.status_code == 200, (
+        f"Completion request ({reasoning_effort}) failed with status "
+        f"{response.status_code}: {response.text}"
+    )
+
+
+@pytest.mark.usefixtures("start_services")
+@pytest.mark.vllm
+@pytest.mark.gpu_1
+@pytest.mark.e2e
+@pytest.mark.model(REASONING_TEST_MODEL)
+def test_completion_multi_string_prompt() -> None:
+    """Exercise completions with reasoning effort and prompt."""
+    reasoning_effort = "low"
+
+    payload: Dict[str, Any] = {
+        "model": REASONING_TEST_MODEL,
+        "prompt": "Tell me about Mars",
+        "max_tokens": 2000,
+        "chat_template_args": {"reasoning_effort": reasoning_effort},
+    }
+
+    response = _send_completion_request(payload)
+
+    assert response.status_code == 200, (
+        f"Completion request ({reasoning_effort}) failed with status "
+        f"{response.status_code}: {response.text}"
+    )
+
+
+@pytest.mark.usefixtures("start_services")
+@pytest.mark.vllm
+@pytest.mark.gpu_1
+@pytest.mark.e2e
+@pytest.mark.model(REASONING_TEST_MODEL)
+def test_completion_multi_element_array_prompt() -> None:
+    """Exercise completions with reasoning effort and prompt."""
+    reasoning_effort = "low"
+
+    payload: Dict[str, Any] = {
+        "model": REASONING_TEST_MODEL,
+        "prompt": ["Tell me about Mars", "Tell me about Ceres"],
+        "max_tokens": 2000,
+        "chat_template_args": {"reasoning_effort": reasoning_effort},
+    }
+
+    response = _send_completion_request(payload)
+
+    # request should fail because we are sending multiple prompts
+    assert (
+        response.status_code == 500
+    ), f"Request should fail with code 500; response:{response.text}"