[Rust Frontend] Extract shared chat types into vllm-chat-types

Signed-off-by: Bugen Zhao <i@bugenzhao.com>
[CPU][Docs] Update docs and dockerfile for s390x (#49523 )
2026-07-23 10:16:32 +00:00 · 2026-07-23 07:17:24 +00:00 · 2026-07-23 05:48:37 +00:00 · 2026-07-23 05:06:21 +00:00 · 2026-07-23 05:03:34 +00:00 · 2026-07-23 04:34:57 +00:00
205 changed files with 5952 additions and 2798 deletions
@@ -17,7 +17,7 @@ DEFAULT_REPO_SLUG="vllm-project/vllm"
 DEFAULT_CI_HCL_SOURCE="docker/ci-rocm.hcl"
 DEFAULT_CI_BASE_CONTENT_FILES="requirements/common.txt requirements/rocm.txt requirements/test/rocm.txt docker/Dockerfile.rocm_base docker/ci-rocm.hcl docker/docker-bake-rocm.hcl tools/install_torchcodec_rocm.sh tools/install_protoc.sh rust-toolchain.toml tests/vllm_test_utils .buildkite/scripts/ci-bake-rocm.sh .buildkite/scripts/rocm/build-ci-base.sh"
 DEFAULT_CI_BASE_DOCKERFILE="docker/Dockerfile.rocm"
-DEFAULT_CI_BASE_DOCKERFILE_STAGES="base rust_toolchain_input_0 rust_toolchain_input_1 rust-toolchain-input rust-toolchain build_rixl build_rocshmem build_deepep mori_base ci_base"
+DEFAULT_CI_BASE_DOCKERFILE_STAGES="base rust_toolchain_input_0 rust_toolchain_input_1 rust-toolchain-input rust-toolchain build_nixl build_rocshmem build_deepep mori_base ci_base"
 DEFAULT_CI_BASE_METADATA_VERSION="1"
 IMAGE_EXISTED_BEFORE_BUILD=0

@@ -1159,8 +1159,8 @@ ci_base_metadata_pairs() {
    metadata_pair "vllm.rocm.nic_backend" "$(resolve_dockerfile_arg_value "${dockerfile}" "NIC_BACKEND")"
    metadata_pair "vllm.rocm.ainic_version" "$(resolve_dockerfile_arg_value "${dockerfile}" "AINIC_VERSION")"
    metadata_pair "vllm.rocm.ubuntu_codename" "$(resolve_dockerfile_arg_value "${dockerfile}" "UBUNTU_CODENAME")"
-    metadata_pair "vllm.rocm.rixl_repo" "$(resolve_dockerfile_arg_value "${dockerfile}" "RIXL_REPO")"
-    metadata_pair "vllm.rocm.rixl_commit" "${RIXL_BRANCH:-$(resolve_dockerfile_arg_value "${dockerfile}" "RIXL_BRANCH")}"
+    metadata_pair "vllm.rocm.nixl_repo" "$(resolve_dockerfile_arg_value "${dockerfile}" "NIXL_REPO")"
+    metadata_pair "vllm.rocm.nixl_commit" "${NIXL_BRANCH:-$(resolve_dockerfile_arg_value "${dockerfile}" "NIXL_BRANCH")}"
    metadata_pair "vllm.rocm.ucx_repo" "$(resolve_dockerfile_arg_value "${dockerfile}" "UCX_REPO")"
    metadata_pair "vllm.rocm.ucx_commit" "${UCX_BRANCH:-$(resolve_dockerfile_arg_value "${dockerfile}" "UCX_BRANCH")}"
    metadata_pair "vllm.rocm.rocshmem_repo" "$(resolve_dockerfile_arg_value "${dockerfile}" "ROCSHMEM_REPO")"
@@ -1169,7 +1169,7 @@ ci_base_metadata_pairs() {
    metadata_pair "vllm.rocm.deepep_commit" "${DEEPEP_BRANCH:-$(resolve_dockerfile_arg_value "${dockerfile}" "DEEPEP_BRANCH")}"
    metadata_pair "vllm.rocm.deepep_nic" "$(resolve_dockerfile_arg_value "${dockerfile}" "DEEPEP_NIC")"
    metadata_pair "vllm.rocm.deepep_rocm_arch" "$(resolve_dockerfile_arg_value "${dockerfile}" "DEEPEP_ROCM_ARCH")"
-    metadata_pair "vllm.rocm.rixl_cache_key" "${RIXL_CACHE_KEY:-}"
+    metadata_pair "vllm.rocm.nixl_cache_key" "${NIXL_CACHE_KEY:-}"
    metadata_pair "vllm.rocm.rocshmem_cache_key" "${ROCSHMEM_CACHE_KEY:-}"
    metadata_pair "vllm.rocm.deepep_cache_key" "${DEEPEP_CACHE_KEY:-}"

@@ -1686,7 +1686,7 @@ extract_dependency_pins() {
        return 0
    fi

-    for var in RIXL_BRANCH UCX_BRANCH ROCSHMEM_BRANCH DEEPEP_BRANCH; do
+    for var in NIXL_BRANCH UCX_BRANCH ROCSHMEM_BRANCH DEEPEP_BRANCH; do
        if [[ -n "${!var:-}" ]]; then
            echo "Using provided ${var}: ${!var}"
            continue
@@ -1706,30 +1706,30 @@ extract_dependency_pins() {
 compute_dependency_cache_keys() {
    local bake_dir=""
    local dockerfile_rocm=""
-    local rixl_branch=""
+    local nixl_branch=""
    local ucx_branch=""
    local rocshmem_branch=""
    local deepep_branch=""
-    local rixl_material=""
+    local nixl_material=""
    local rocshmem_material=""
    local deepep_material=""

    bake_dir=$(dirname "${VLLM_BAKE_FILE}")
    dockerfile_rocm="${bake_dir}/Dockerfile.rocm"
-    rixl_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "RIXL_BRANCH")
+    nixl_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "NIXL_BRANCH")
    ucx_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "UCX_BRANCH")
    rocshmem_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "ROCSHMEM_BRANCH")
    deepep_branch=$(resolve_dockerfile_arg_value "${dockerfile_rocm}" "DEEPEP_BRANCH")

-    if [[ -n "${rixl_branch}" && -n "${ucx_branch}" ]]; then
-        rixl_material=$(compose_stage_cache_material "${dockerfile_rocm}" "base build_rixl")
-        RIXL_CACHE_KEY=$(
+    if [[ -n "${nixl_branch}" && -n "${ucx_branch}" ]]; then
+        nixl_material=$(compose_stage_cache_material "${dockerfile_rocm}" "base build_nixl")
+        NIXL_CACHE_KEY=$(
            compose_dependency_cache_key \
-                "${rixl_branch}-ucx-${ucx_branch}" \
-                "${rixl_material}"
+                "${nixl_branch}-ucx-${ucx_branch}" \
+                "${nixl_material}"
        )
-        export RIXL_CACHE_KEY
-        echo "RIXL dependency cache key: ${RIXL_CACHE_KEY}"
+        export NIXL_CACHE_KEY
+        echo "NIXL dependency cache key: ${NIXL_CACHE_KEY}"
    fi

    if [[ -n "${rocshmem_branch}" ]]; then
@@ -1780,11 +1780,11 @@ dependency_cache_ref_for_target() {
    local cache_repo="${DOCKERHUB_CACHE_REPO:-rocm/vllm-ci-cache}"

    case "${target}" in
-        rixl-rocm-ci)
-            if [[ -n "${RIXL_CACHE_KEY:-}" ]]; then
-                printf '%s\n' "${cache_repo}:rixl-rocm-${RIXL_CACHE_KEY}"
-            elif [[ -n "${RIXL_BRANCH:-}" ]]; then
-                printf '%s\n' "${cache_repo}:rixl-rocm-${RIXL_BRANCH}-ucx-${UCX_BRANCH:-}"
+        nixl-rocm-ci)
+            if [[ -n "${NIXL_CACHE_KEY:-}" ]]; then
+                printf '%s\n' "${cache_repo}:nixl-rocm-${NIXL_CACHE_KEY}"
+            elif [[ -n "${NIXL_BRANCH:-}" ]]; then
+                printf '%s\n' "${cache_repo}:nixl-rocm-${NIXL_BRANCH}-ucx-${UCX_BRANCH:-}"
            fi
            ;;
        rocshmem-rocm-ci)
@@ -1815,7 +1815,7 @@ add_dependency_cache_target() {

 resolve_ci_base_dependency_targets() {
    local mode="${ROCM_DEP_CACHE_EXPORT_MODE:-missing}"
-    local rixl_ref=""
+    local nixl_ref=""
    local rocshmem_ref=""
    local deepep_ref=""

@@ -1824,7 +1824,7 @@ resolve_ci_base_dependency_targets() {
    case "${mode}" in
        always)
            echo "ROCM_DEP_CACHE_EXPORT_MODE=always; exporting all dependency caches serially"
-            for target in rixl-rocm-ci rocshmem-rocm-ci deepep-rocm-ci; do
+            for target in nixl-rocm-ci rocshmem-rocm-ci deepep-rocm-ci; do
                if [[ -n "$(dependency_cache_ref_for_target "${target}")" ]]; then
                    add_dependency_cache_target "${target}"
                fi
@@ -1844,13 +1844,13 @@ resolve_ci_base_dependency_targets() {
            ;;
    esac

-    if [[ "${mode}" != "always" && -n "${RIXL_CACHE_KEY:-}" ]]; then
-        rixl_ref=$(dependency_cache_ref_for_target "rixl-rocm-ci")
-        if dependency_cache_ref_exists "${rixl_ref}"; then
-            echo "RIXL dependency cache exists: ${rixl_ref}"
+    if [[ "${mode}" != "always" && -n "${NIXL_CACHE_KEY:-}" ]]; then
+        nixl_ref=$(dependency_cache_ref_for_target "nixl-rocm-ci")
+        if dependency_cache_ref_exists "${nixl_ref}"; then
+            echo "NIXL dependency cache exists: ${nixl_ref}"
        else
-            echo "RIXL dependency cache missing; will seed: ${rixl_ref}"
-            add_dependency_cache_target "rixl-rocm-ci"
+            echo "NIXL dependency cache missing; will seed: ${nixl_ref}"
+            add_dependency_cache_target "nixl-rocm-ci"
        fi
    fi

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Correctness
  key: basic-correctness
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Benchmarks CLI Test
  key: benchmarks-cli-test
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -26,7 +26,10 @@ steps:
  - vllm/v1/cudagraph_dispatcher.py
  - vllm/config/compilation.py
  - vllm/compilation
+  - vllm/v1/worker/encoder_cudagraph.py
+  - vllm/v1/worker/encoder_cudagraph_defs.py
  commands:
    - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
    - pytest -v -s v1/cudagraph/test_breakable_cudagraph.py
+    - pytest -v -s v1/cudagraph/test_encoder_cudagraph.py
@@ -51,7 +51,7 @@ steps:

 - label: e2e Scheduling (1 GPU)
  key: e2e-scheduling-1-gpu
-  timeout_in_minutes: 35
+  timeout_in_minutes: 53
  device: h200_18gb
  source_file_dependencies:
    - vllm/v1/
@@ -39,7 +39,7 @@ steps:
 - label: Entrypoints Integration (API Server)
  key: entrypoints-integration-api-server
  device: h200_35gb
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -59,7 +59,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 1)
  device: h200_35gb
  key: entrypoints-integration-api-server-openai-part-1
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -78,7 +78,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 2)
  device: h200_35gb
  key: entrypoints-integration-api-server-openai-part-2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 83
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -156,7 +156,7 @@ steps:
 - label: Entrypoints Integration (Pooling)
  device: h200_35gb
  key: entrypoints-integration-pooling
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/
@@ -31,7 +31,7 @@ steps:

 - label: V1 Sample + Logits
  key: v1-sample-logits
-  timeout_in_minutes: 45
+  timeout_in_minutes: 83
  device: h200_18gb
  source_file_dependencies:
    - vllm/config/
@@ -90,6 +90,7 @@ steps:
    - tests/v1/kv_offload
    - tests/v1/simple_kv_offload
    - tests/v1/worker
+    - tests/v1/streaming_input
    - tests/v1/kv_connector/unit
    - tests/v1/ec_connector/unit
    - tests/v1/metrics
@@ -103,6 +104,7 @@ steps:
    - pytest -v -s v1/kv_offload
    - pytest -v -s v1/simple_kv_offload
    - pytest -v -s v1/worker
+    - pytest -v -s v1/streaming_input
    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
    - pytest -v -s -m 'not cpu_test' v1/ec_connector/unit
    - pytest -v -s -m 'not cpu_test' v1/metrics
@@ -143,6 +145,7 @@ steps:
    - pytest -v -s -m 'cpu_test' v1/core
    - pytest -v -s v1/structured_output
    - pytest -v -s v1/test_serial_utils.py
+    - pytest -v -s v1/cudagraph/test_cudagraph_manager.py
    - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
    - pytest -v -s -m 'cpu_test' v1/metrics

@@ -5,7 +5,7 @@ steps:
 - label: Model Executor
  device: h200_35gb
  key: model-executor
-  timeout_in_minutes: 45
+  timeout_in_minutes: 60
  source_file_dependencies:
  - vllm/engine/arg_utils.py
  - vllm/config/model.py
@@ -46,6 +46,19 @@ steps:
      depends_on:
      - image-build-amd

+- label: Inkling Unit Tests (B200)
+  key: inkling-unit-tests-b200
+  timeout_in_minutes: 40
+  device: b200-k8s
+  source_file_dependencies:
+  - vllm/models/inkling/
+  - vllm/cute_utils/
+  - cmake/external_projects/tml_fa4.cmake
+  - tests/models/inkling/
+  commands:
+    # FA4 kernel tests require SM100; the suite skips them elsewhere.
+    - pytest -v -s models/inkling
+
 - label: Basic Models Test (Other CPU) # 5min
  key: basic-models-test-other-cpu
  depends_on:
@@ -137,7 +137,7 @@ steps:

 - label: Language Models Test (MTEB)
  key: language-models-test-mteb
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  optional: true
  source_file_dependencies:
@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2"
  key: multi-modal-models-standard-1-qwen2
-  timeout_in_minutes: 45
+  timeout_in_minutes: 68
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -20,7 +20,7 @@ steps:

 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
  key: multi-modal-models-standard-2-qwen3-gemma
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -54,7 +54,7 @@ steps:
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
  device: h200_35gb
  key: multi-modal-models-standard-4-other-whisper
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
  source_file_dependencies:
  - vllm/
  - tests/models/multimodal
@@ -85,7 +85,7 @@ steps:

 - label: Multi-Modal Processor # 44min
  key: multi-modal-processor
-  timeout_in_minutes: 65
+  timeout_in_minutes: 98
  device: h200_18gb
  source_file_dependencies:
  - vllm/
@@ -5,7 +5,7 @@ steps:
 - label: PyTorch Compilation Unit Tests
  device: h200_35gb
  key: pytorch-compilation-unit-tests
-  timeout_in_minutes: 90
+  timeout_in_minutes: 110
  source_file_dependencies:
    - vllm/__init__.py
    - vllm/_aiter_ops.py
@@ -170,3 +170,19 @@ steps:
    - tests/v1/e2e/spec_decode/
  commands:
    - pytest -v -s v1/e2e/spec_decode -k "qwen3_5-hybrid"
+
+- label: Spec Decode DeepSeek MTP Parallel Load (B200)
+  key: spec-decode-deepseek-mtp-parallel-load-b200
+  timeout_in_minutes: 30
+  device: b200-k8s
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/v1/spec_decode/llm_base_proposer.py
+    - vllm/v1/spec_decode/eagle.py
+    - vllm/v1/worker/gpu/spec_decode/eagle/
+    - vllm/model_executor/models/deepseek_mtp.py
+    - vllm/model_executor/models/deepseek_v2.py
+    - tests/v1/e2e/spec_decode/test_mtp_parallel_load.py
+  commands:
+    - pytest -v -s v1/e2e/spec_decode/test_mtp_parallel_load.py
@@ -181,6 +181,18 @@ pull_request_rules:
      add:
        - performance

+- name: label-quantization
+  description: Automatically apply quantization label
+  conditions:
+    - label != stale
+    - or:
+      - files~=^vllm/model_executor/layers/quantization/
+      - title~=(?i)quant
+  actions:
+    label:
+      add:
+        - quantization
+
 - name: label-qwen
  description: Automatically apply qwen label
  conditions:
@@ -130,6 +130,47 @@ jobs:
                  },
                ],
              },
+              quantization: {
+                keywords: [
+                  {
+                    term: "quantization",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "quantized",
+                    searchIn: "both"
+                  },
+                ],
+              },
+              "intel-gpu": {
+                // Keyword search - matches whole words only (with word boundaries)
+                keywords: [
+                  {
+                    term: "B50",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "B60",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "B70",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "intel gpu",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "Arc GPU",
+                    searchIn: "both"
+                  },
+                  {
+                    term: "BMG",
+                    searchIn: "both"
+                  },
+                ],
+              },
              // Add more label configurations here as needed
              // example: {
              //   keywords: [...],
@@ -491,4 +532,4 @@ jobs:
              issue_number: context.issue.number,
              body: message,
            });
-            core.notice(`Requested missing ROCm info from @${author}: ${missing.map(m => m.name).join(', ')}`);
+            core.notice(`Requested missing ROCm info from @${author}: ${missing.map(m => m.name).join(', ')}`);
@@ -17,7 +17,7 @@ else()
  FetchContent_Declare(
    fmha_sm100
    GIT_REPOSITORY https://github.com/vllm-project/MSA.git
-    GIT_TAG 2e63ec37a0fc29bc20f39cd1a52e0f5affc33a73
+    GIT_TAG 890aaa1a37a598ad17ccff0827fea21540d381fa
    GIT_PROGRESS TRUE
    CONFIGURE_COMMAND ""
    BUILD_COMMAND ""
@@ -39,7 +39,7 @@ else()
  FetchContent_Declare(
          vllm-flash-attn
          GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 168920233059c48de6199e2cda74003b2ce3d199
+          GIT_TAG ed4b7342bc8f0489dd9b649d5288867e35fc6a32
          GIT_PROGRESS TRUE
          # Don't share the vllm-flash-attn build between build types
          BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn
@@ -1025,6 +1025,9 @@ __global__ void gather_and_maybe_dequant_cache(
    batch_offset += offset;
    int32_t block_table_id = batch_offset / block_size;
    int32_t slot_id = batch_offset % block_size;
+    // seq_starts may push the block index past the end of the batch's block
+    // table row.
+    if (block_table_id >= block_table_stride) continue;
    int32_t block_table_offset = batch_id * block_table_stride + block_table_id;
    int32_t block_id = block_table[block_table_offset];
    int64_t cache_offset =
@@ -9,14 +9,16 @@ void topk_softmax(torch::stable::Tensor& topk_weights,
                  torch::stable::Tensor& topk_indices,
                  torch::stable::Tensor& token_expert_indices,
                  torch::stable::Tensor& gating_output, bool renormalize,
-                  std::optional<torch::stable::Tensor> bias);
+                  std::optional<torch::stable::Tensor> bias,
+                  std::optional<torch::stable::Tensor> is_padding);

 void topk_sigmoid(torch::stable::Tensor& topk_weights,
                  torch::stable::Tensor& topk_indices,
                  torch::stable::Tensor& token_expert_indices,
                  torch::stable::Tensor& gating_output, bool renormalize,
                  std::optional<torch::stable::Tensor> bias,
-                  double routed_scaling_factor);
+                  double routed_scaling_factor,
+                  std::optional<torch::stable::Tensor> is_padding);

 void topk_softplus_sqrt(
    torch::stable::Tensor& topk_weights, torch::stable::Tensor& topk_indices,
@@ -25,7 +27,8 @@ void topk_softplus_sqrt(
    double routed_scaling_factor,
    const std::optional<torch::stable::Tensor>& correction_bias,
    const std::optional<torch::stable::Tensor>& input_ids,
-    const std::optional<torch::stable::Tensor>& tid2eid);
+    const std::optional<torch::stable::Tensor>& tid2eid,
+    const std::optional<torch::stable::Tensor>& is_padding);

 void moe_sum(torch::stable::Tensor& input, torch::stable::Tensor& output,
             std::optional<torch::stable::Tensor> topk_ids,
@@ -174,7 +174,8 @@ __launch_bounds__(TPB) __global__ void moeTopK(
    const int end_expert,
    const bool renormalize,
    const float* bias,
-    const double routed_scaling_factor)
+    const double routed_scaling_factor,
+    const bool* is_padding)
 {

    using cub_kvp = cub::KeyValuePair<int, float>;
@@ -228,12 +229,14 @@ __launch_bounds__(TPB) __global__ void moeTopK(
            const int expert = result_kvp.key;
            const bool node_uses_expert = expert >= start_expert && expert < end_expert;
            const bool should_process_row = row_is_active && node_uses_expert;
+            const bool is_pad_row = is_padding != nullptr && is_padding[block_row];

            const int idx = k * block_row + k_idx;
            // Return the unbiased scores for output weights
            output[idx] = inputs_after_softmax[thread_read_offset + expert];
-            indices[idx] = should_process_row ? (expert - start_expert) : num_experts;
-            assert(indices[idx] >= 0);
+            indices[idx] = is_pad_row ? static_cast<IndType>(-1)
+                                       : (should_process_row ? (expert - start_expert) : num_experts);
+            assert(is_pad_row || indices[idx] >= 0);
            source_rows[idx] = k_idx * num_rows + block_row;
            if (renormalize) {
                selected_sum += inputs_after_softmax[thread_read_offset + expert];
@@ -277,7 +280,7 @@ template <int VPT, int NUM_EXPERTS, int WARPS_PER_CTA, int BYTES_PER_LDG, int WA
 __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
    void topkGating(const InputType* input, const bool* finished, float* output, const int num_rows, IndType* indices,
        int* source_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-        const float* bias, const double routed_scaling_factor)
+        const float* bias, const double routed_scaling_factor, const bool* is_padding)
 {
    static_assert(std::is_same_v<InputType, float> || std::is_same_v<InputType, __nv_bfloat16> ||
                      std::is_same_v<InputType, __half>,
@@ -545,12 +548,14 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
            // Add a guard to ignore experts not included by this node
            const bool node_uses_expert = expert >= start_expert && expert < end_expert;
            const bool should_process_row = row_is_active && node_uses_expert;
+            const bool is_pad_row = is_padding != nullptr && is_padding[thread_row];

            // The lead thread from each sub-group will write out the final results to global memory. (This will be a
            // single) thread per row of the input/output matrices.
            const int idx = k * thread_row + k_idx;
            output[idx] = max_val;
-            indices[idx] = should_process_row ? (expert - start_expert) : NUM_EXPERTS;
+            indices[idx] = is_pad_row ? static_cast<IndType>(-1)
+                                       : (should_process_row ? (expert - start_expert) : NUM_EXPERTS);
            source_rows[idx] = k_idx * num_rows + thread_row;
            if (renormalize) {
                selected_sum += max_val;
@@ -605,7 +610,7 @@ struct TopkConstants
 template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType, typename InputType, ScoringFunc SF>
 void topkGatingLauncherHelper(const InputType* input, const bool* finished, float* output, IndType* indices,
    int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, const bool renormalize,
-    const float* bias, const double routed_scaling_factor, cudaStream_t stream)
+    const float* bias, const double routed_scaling_factor, cudaStream_t stream, const bool* is_padding)
 {
    static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(InputType) * EXPERTS);
    using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM, InputType>;
@@ -616,7 +621,7 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa

    dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB);
    topkGating<VPT, EXPERTS, WARPS_PER_TB, BYTES_PER_LDG, WARP_SIZE_PARAM, IndType, InputType, SF><<<num_blocks, block_dim, 0, stream>>>(
-        input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert, renormalize, bias, routed_scaling_factor);
+        input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert, renormalize, bias, routed_scaling_factor, is_padding);
 }

 #ifndef USE_ROCM
@@ -627,7 +632,7 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa
                             IndType, InputType, SF>(                         \
        gating_output, nullptr, topk_weights, topk_indices,                   \
        token_expert_indices, num_tokens, topk, 0, num_experts, renormalize,  \
-        bias, routed_scaling_factor, stream);
+        bias, routed_scaling_factor, stream, is_padding);
 #else
  #define LAUNCH_TOPK(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                    \
    if (WARP_SIZE == 64) {                                                     \
@@ -635,13 +640,13 @@ void topkGatingLauncherHelper(const InputType* input, const bool* finished, floa
                               IndType, InputType, SF>(                        \
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
-          bias, routed_scaling_factor, stream);                                                       \
+          bias, routed_scaling_factor, stream, is_padding);                                            \
    } else if (WARP_SIZE == 32) {                                              \
      topkGatingLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32, MAX_BYTES,       \
                               IndType, InputType, SF>(                        \
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
-          bias, routed_scaling_factor, stream);                                                       \
+          bias, routed_scaling_factor, stream, is_padding);                                            \
    } else {                                                                   \
      assert(false &&                                                          \
             "Unsupported warp size. Only 32 and 64 are supported for ROCm");  \
@@ -661,7 +666,8 @@ void topkGatingKernelLauncher(
    const bool renormalize,
    const float* bias,
    const double routed_scaling_factor,
-    cudaStream_t stream) {
+    cudaStream_t stream,
+    const bool* is_padding) {
    static constexpr int WARPS_PER_TB = 4;
    static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
 #ifndef USE_ROCM
@@ -736,7 +742,7 @@ void topkGatingKernelLauncher(
            }
            moeTopK<TPB><<<num_tokens, TPB, 0, stream>>>(
                workspace, nullptr, topk_weights, topk_indices, token_expert_indices,
-                num_experts, topk, 0, num_experts, renormalize, bias, routed_scaling_factor);
+                num_experts, topk, 0, num_experts, renormalize, bias, routed_scaling_factor, is_padding);
        }
    }
 }
@@ -755,7 +761,8 @@ void dispatch_topk_launch(
    int num_tokens, int num_experts, int topk, bool renormalize,
    std::optional<torch::stable::Tensor> bias,
    double routed_scaling_factor,
-    cudaStream_t stream)
+    cudaStream_t stream,
+    std::optional<torch::stable::Tensor> is_padding)
 {
    const float* bias_ptr = nullptr;
    if (bias.has_value()) {
@@ -769,6 +776,18 @@ void dispatch_topk_launch(
      bias_ptr = bias_tensor.const_data_ptr<float>();
    }

+    const bool* is_padding_ptr = nullptr;
+    if (is_padding.has_value()) {
+      const torch::stable::Tensor& is_padding_tensor = is_padding.value();
+      STD_TORCH_CHECK(is_padding_tensor.scalar_type() == torch::headeronly::ScalarType::Bool,
+                      "is_padding tensor must be bool");
+      STD_TORCH_CHECK(is_padding_tensor.dim() == 1, "is_padding tensor must be 1D");
+      STD_TORCH_CHECK(is_padding_tensor.size(0) == num_tokens,
+                      "is_padding size mismatch, expected: ", num_tokens);
+      STD_TORCH_CHECK(is_padding_tensor.is_contiguous(), "is_padding tensor must be contiguous");
+      is_padding_ptr = is_padding_tensor.const_data_ptr<bool>();
+    }
+
    if (topk_indices.scalar_type() == torch::headeronly::ScalarType::Int) {
        vllm::moe::topkGatingKernelLauncher<int, ComputeType, SF>(
            reinterpret_cast<const ComputeType*>(gating_output.const_data_ptr()),
@@ -777,7 +796,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, routed_scaling_factor, stream, is_padding_ptr);
    } else if (topk_indices.scalar_type() == torch::headeronly::ScalarType::UInt32) {
        vllm::moe::topkGatingKernelLauncher<uint32_t, ComputeType, SF>(
            reinterpret_cast<const ComputeType*>(gating_output.const_data_ptr()),
@@ -786,7 +805,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, routed_scaling_factor, stream, is_padding_ptr);
    } else {
        STD_TORCH_CHECK(topk_indices.scalar_type() == torch::headeronly::ScalarType::Long);
        vllm::moe::topkGatingKernelLauncher<int64_t, ComputeType, SF>(
@@ -796,7 +815,7 @@ void dispatch_topk_launch(
            token_expert_indices.mutable_data_ptr<int>(),
            softmax_workspace.mutable_data_ptr<float>(),
            num_tokens, num_experts, topk, renormalize,
-            bias_ptr, routed_scaling_factor, stream);
+            bias_ptr, routed_scaling_factor, stream, is_padding_ptr);
    }
 }

@@ -806,7 +825,8 @@ void topk_softmax(
    torch::stable::Tensor& token_expert_indices,        // [num_tokens, topk]
    torch::stable::Tensor& gating_output,               // [num_tokens, num_experts]
    bool renormalize,
-    std::optional<torch::stable::Tensor> bias)
+    std::optional<torch::stable::Tensor> bias,
+    std::optional<torch::stable::Tensor> is_padding)
 {
    const int num_experts = gating_output.size(-1);
    const auto num_tokens = gating_output.numel() / num_experts;
@@ -825,15 +845,15 @@ void topk_softmax(
    if (gating_output.scalar_type() == torch::headeronly::ScalarType::Float) {
        dispatch_topk_launch<float, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, 1.0, stream, is_padding);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::Half) {
        dispatch_topk_launch<__half, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, 1.0, stream, is_padding);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::BFloat16) {
        dispatch_topk_launch<__nv_bfloat16, vllm::moe::SCORING_SOFTMAX>(gating_output, topk_weights, topk_indices,
            token_expert_indices, softmax_workspace, num_tokens, num_experts, topk, renormalize,
-            bias, 1.0, stream);
+            bias, 1.0, stream, is_padding);
    } else {
        STD_TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
    }
@@ -846,7 +866,8 @@ void topk_sigmoid(
    torch::stable::Tensor& gating_output,               // [num_tokens, num_experts]
    bool renormalize,
    std::optional<torch::stable::Tensor> bias,
-    double routed_scaling_factor)
+    double routed_scaling_factor,
+    std::optional<torch::stable::Tensor> is_padding)
 {
    const int num_experts = gating_output.size(-1);
    const auto num_tokens = gating_output.numel() / num_experts;
@@ -865,15 +886,15 @@ void topk_sigmoid(
    if (gating_output.scalar_type() == torch::headeronly::ScalarType::Float) {
        dispatch_topk_launch<float, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, routed_scaling_factor, stream, is_padding);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::Half) {
        dispatch_topk_launch<__half, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, routed_scaling_factor, stream, is_padding);
    } else if (gating_output.scalar_type() == torch::headeronly::ScalarType::BFloat16) {
        dispatch_topk_launch<__nv_bfloat16, vllm::moe::SCORING_SIGMOID>(gating_output, topk_weights, topk_indices,
            token_expert_indices, workspace, num_tokens, num_experts, topk, renormalize,
-            bias, routed_scaling_factor, stream);
+            bias, routed_scaling_factor, stream, is_padding);
    } else {
        STD_TORCH_CHECK(false, "Unsupported gating_output data type: ", gating_output.scalar_type());
    }
@@ -80,22 +80,27 @@ __launch_bounds__(128) __global__
                                  OutIndType* indices, int num_rows,
                                  int num_experts, float routed_scaling_factor,
                                  const HashIndType* input_ids,
-                                  const HashIndType* tid2eid) {
+                                  const HashIndType* tid2eid,
+                                  const bool* is_padding) {
  const int warp = (blockIdx.x * blockDim.x + threadIdx.x) / 32;
  const int lane = threadIdx.x % 32;
  if (warp >= num_rows) return;
  const int64_t token_id = load_index_as_int64(input_ids, warp);
+  const bool is_pad_row = is_padding != nullptr && is_padding[warp];

  #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
  cudaGridDependencySynchronize();
  #endif
  int expert = 0;
  float weight = 0.f;
-  if (lane < 6) {
+  if (lane < 6 && !is_pad_row) {
    // only load and calculate for 6 experts
    expert = static_cast<int>(tid2eid[token_id * 6 + lane]);
    const float x = input[warp * num_experts + expert];
    weight = sqrtf(fmaxf(x, 0.f) + __logf(1.f + __expf(-fabsf(x))));
+    if (isnan(weight)) {
+      weight = 0.f;
+    }
  }
  float weight_sum = weight;
  #pragma unroll
@@ -111,7 +116,8 @@ __launch_bounds__(128) __global__
    const int offset = warp * 6 + lane;
    output[offset] =
        weight * routed_scaling_factor / (weight_sum > 0.f ? weight_sum : 1.f);
-    indices[offset] = static_cast<OutIndType>(expert);
+    indices[offset] = !is_pad_row ? static_cast<OutIndType>(expert)
+                                  : static_cast<OutIndType>(-1);
  }
 }

@@ -120,7 +126,8 @@ void launchDsv4HashTopk(const float* input, float* output, OutIndType* indices,
                        int num_rows, int num_experts,
                        double routed_scaling_factor,
                        const HashIndType* input_ids,
-                        const HashIndType* tid2eid, cudaStream_t stream) {
+                        const HashIndType* tid2eid, cudaStream_t stream,
+                        const bool* is_padding) {
  if (num_rows == 0) return;
  auto* kernel = &dsv4HashTopkSoftplusSqrt<OutIndType, HashIndType>;
  cudaLaunchConfig_t config = {};
@@ -134,7 +141,7 @@ void launchDsv4HashTopk(const float* input, float* output, OutIndType* indices,
  config.numAttrs = 1;
  const float scale = static_cast<float>(routed_scaling_factor);
  cudaLaunchKernelEx(&config, kernel, input, output, indices, num_rows,
-                     num_experts, scale, input_ids, tid2eid);
+                     num_experts, scale, input_ids, tid2eid, is_padding);
 }
 #endif

@@ -166,7 +173,8 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
        const int num_rows, IndType* indices, int* source_rows, const int k,
        const int start_expert, const int end_expert, const bool renormalize,
        double routed_scaling_factor, const float* correction_bias,
-        const HashIndType* input_ids, const HashIndType* tid2eid) {
+        const HashIndType* input_ids, const HashIndType* tid2eid,
+        const bool* is_padding) {
  static_assert(std::is_same_v<InputType, float> ||
                    std::is_same_v<InputType, __nv_bfloat16> ||
                    std::is_same_v<InputType, __half>,
@@ -231,6 +239,7 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
    return;
  }
  const bool row_is_active = finished ? !finished[thread_row] : true;
+  const bool is_pad_row = is_padding != nullptr && is_padding[thread_row];

  // We finally start setting up the read pointers for each thread. First, each
  // thread jumps to the start of the row it will read.
@@ -249,9 +258,12 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
  cudaGridDependencySynchronize();
 #endif

-  // NOTE(zhuhaoran): dispatch different input types loading, BF16/FP16 convert
-  // to float
-  if constexpr (std::is_same_v<InputType, float>) {
+  if (is_pad_row) {
+#pragma unroll
+    for (int ii = 0; ii < VPT; ++ii) {
+      row_chunk[ii] = 0.f;
+    }
+  } else if constexpr (std::is_same_v<InputType, float>) {
    using VecType = AlignedArray<float, ELTS_PER_LDG>;
    VecType* row_chunk_vec_ptr = reinterpret_cast<VecType*>(&row_chunk);
    const VecType* vec_thread_read_ptr =
@@ -315,12 +327,22 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
  if constexpr (USE_HASH) {
    const int64_t token_id = load_index_as_int64(input_ids, thread_row);
    const int64_t token_expert_offset = token_id * static_cast<int64_t>(k);
+    if (!is_pad_row) {
 #pragma unroll
-    for (int ii = 0; ii < VPT; ++ii) {
-      float val = row_chunk[ii];
-      float val_b = val * beta;
-      val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
-      row_chunk[ii] = sqrtf(val);
+      for (int ii = 0; ii < VPT; ++ii) {
+        float val = row_chunk[ii];
+        float val_b = val * beta;
+        val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
+        val = sqrtf(val);
+
+        // Dummy/padding tokens can result in NaN values, so
+        // clamp them to 0.0. Note: this clamp could likely be removed if
+        // 'is_padding' is made mandatory
+        if (isnan(val)) {
+          val = 0.f;
+        }
+        row_chunk[ii] = val;
+      }
    }
    float selected_sum = 0.f;
 #pragma unroll
@@ -335,7 +357,8 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
                               group_id * THREADS_PER_ROW * ELTS_PER_LDG +
                               local_id;
        if (expert == expert_idx) {
-          indices[idx] = static_cast<IndType>(expert);
+          indices[idx] = !is_pad_row ? static_cast<IndType>(expert)
+                                     : static_cast<IndType>(-1);
          selected_sum += row_chunk[ii];
          break;
        }
@@ -379,23 +402,31 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
 #endif
    return;
  } else {
+    if (!is_pad_row) {
 #pragma unroll
-    for (int ii = 0; ii < VPT; ++ii) {
-      float val = row_chunk[ii];
-      float val_b = val * beta;
-      // Compute softplus: log(1 + exp(val)) with numerical stability
-      // When val > threshold, softplus(x) ≈ x to avoid exp overflow
-      val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
-      val = sqrtf(val);
-      if (correction_bias) {
-        const int group_id = ii / ELTS_PER_LDG;
-        const int local_id = ii % ELTS_PER_LDG;
-        const int expert_idx = first_elt_read_by_thread +
-                               group_id * THREADS_PER_ROW * ELTS_PER_LDG +
-                               local_id;
-        val = val + correction_bias[expert_idx];
+      for (int ii = 0; ii < VPT; ++ii) {
+        float val = row_chunk[ii];
+        float val_b = val * beta;
+        // Compute softplus: log(1 + exp(val)) with numerical stability
+        // When val > threshold, softplus(x) ≈ x to avoid exp overflow
+        val = (val_b > threshold) ? val : (__logf(1.0f + __expf(val_b))) / beta;
+        val = sqrtf(val);
+        // Dummy/padding tokens can result in NaN values, so
+        // clamp them to 0.0. Note: this clamp could likely be removed if
+        // 'is_padding' is made mandatory
+        if (isnan(val)) {
+          val = 0.f;
+        }
+        if (correction_bias) {
+          const int group_id = ii / ELTS_PER_LDG;
+          const int local_id = ii % ELTS_PER_LDG;
+          const int expert_idx = first_elt_read_by_thread +
+                                 group_id * THREADS_PER_ROW * ELTS_PER_LDG +
+                                 local_id;
+          val = val + correction_bias[expert_idx];
+        }
+        row_chunk[ii] = val;
      }
-      row_chunk[ii] = val;
    }

    // Original TopK path: find top-k experts by score
@@ -450,18 +481,19 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
        // Add a guard to ignore experts not included by this node
        const bool node_uses_expert =
            expert >= start_expert && expert < end_expert;
-        const bool should_process_row = row_is_active && node_uses_expert;
+        const bool should_process_row =
+            row_is_active && node_uses_expert && !is_pad_row;

        // The lead thread from each sub-group will write out the final results
        // to global memory. (This will be a single) thread per row of the
        // input/output matrices.
        const int idx = k * thread_row + k_idx;
-        if (correction_bias != nullptr) {
+        if (correction_bias != nullptr && should_process_row) {
          max_val -= correction_bias[expert];
        }
        output[idx] = max_val;
        indices[idx] =
-            should_process_row ? (expert - start_expert) : NUM_EXPERTS;
+            !is_pad_row ? expert - start_expert : static_cast<IndType>(-1);
        source_rows[idx] = k_idx * num_rows + thread_row;
        if (renormalize) {
          selected_sum += max_val;
@@ -544,7 +576,7 @@ void topkGatingSoftplusSqrtLauncherHelper(
    const int start_expert, const int end_expert, const bool renormalize,
    double routed_scaling_factor, const float* correction_bias,
    const bool use_hash, const HashIndType* input_ids,
-    const HashIndType* tid2eid, cudaStream_t stream) {
+    const HashIndType* tid2eid, cudaStream_t stream, const bool* is_padding) {
  static constexpr int BYTES_PER_LDG =
      MIN(MAX_BYTES_PER_LDG, sizeof(InputType) * EXPERTS);
  using Constants =
@@ -573,12 +605,12 @@ void topkGatingSoftplusSqrtLauncherHelper(
    cudaLaunchKernelEx(&config, kernel, input, finished, output, num_rows,
                       indices, source_row, k, start_expert, end_expert,
                       renormalize, routed_scaling_factor, correction_bias,
-                       input_ids, tid2eid);
+                       input_ids, tid2eid, is_padding);
 #else
    kernel<<<num_blocks, block_dim, 0, stream>>>(
        input, finished, output, num_rows, indices, source_row, k, start_expert,
        end_expert, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid);
+        input_ids, tid2eid, is_padding);
 #endif
  })
 }
@@ -592,7 +624,7 @@ void topkGatingSoftplusSqrtLauncherHelper(
        gating_output, nullptr, topk_weights, topk_indices,                    \
        token_expert_indices, num_tokens, topk, 0, num_experts, renormalize,   \
        routed_scaling_factor, correction_bias, use_hash, input_ids, tid2eid,  \
-        stream);
+        stream, is_padding);
 #else
  #define LAUNCH_SOFTPLUS_SQRT(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)           \
    if (WARP_SIZE == 64) {                                                     \
@@ -601,14 +633,14 @@ void topkGatingSoftplusSqrtLauncherHelper(
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
          routed_scaling_factor, correction_bias, use_hash, input_ids,         \
-          tid2eid, stream);                                                    \
+          tid2eid, stream, is_padding);                                        \
    } else if (WARP_SIZE == 32) {                                              \
      topkGatingSoftplusSqrtLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32,      \
                                           MAX_BYTES>(                         \
          gating_output, nullptr, topk_weights, topk_indices,                  \
          token_expert_indices, num_tokens, topk, 0, num_experts, renormalize, \
          routed_scaling_factor, correction_bias, use_hash, input_ids,         \
-          tid2eid, stream);                                                    \
+          tid2eid, stream, is_padding);                                        \
    } else {                                                                   \
      assert(false &&                                                          \
             "Unsupported warp size. Only 32 and 64 are supported for ROCm");  \
@@ -622,14 +654,14 @@ void topkGatingSoftplusSqrtKernelLauncher(
    const int topk, const bool renormalize, double routed_scaling_factor,
    const float* correction_bias, const bool use_hash,
    const HashIndType* input_ids, const HashIndType* tid2eid,
-    cudaStream_t stream) {
+    cudaStream_t stream, const bool* is_padding) {
 #ifndef USE_ROCM
  if constexpr (std::is_same_v<InputType, float>) {
    if (use_hash && topk == 6 && renormalize &&
        (num_experts == 256 || num_experts == 384)) {
      launchDsv4HashTopk<IndType, HashIndType>(
          gating_output, topk_weights, topk_indices, num_tokens, num_experts,
-          routed_scaling_factor, input_ids, tid2eid, stream);
+          routed_scaling_factor, input_ids, tid2eid, stream, is_padding);
      return;
    }
  }
@@ -728,7 +760,8 @@ void dispatch_topk_softplus_sqrt_launch(
    int num_experts, int topk, bool renormalize, double routed_scaling_factor,
    const std::optional<torch::stable::Tensor>& correction_bias,
    const std::optional<torch::stable::Tensor>& input_ids,
-    const std::optional<torch::stable::Tensor>& tid2eid, cudaStream_t stream) {
+    const std::optional<torch::stable::Tensor>& tid2eid, cudaStream_t stream,
+    const std::optional<torch::stable::Tensor>& is_padding) {
  const float* bias_ptr = nullptr;
  if (correction_bias.has_value()) {
    bias_ptr = correction_bias.value().const_data_ptr<float>();
@@ -737,6 +770,22 @@ void dispatch_topk_softplus_sqrt_launch(
  auto launch = [&](auto* topk_indices_ptr) {
    using OutIndType =
        typename std::remove_pointer<decltype(topk_indices_ptr)>::type;
+
+    const bool* is_padding_ptr = nullptr;
+    if (is_padding.has_value()) {
+      const torch::stable::Tensor& is_padding_tensor = is_padding.value();
+      STD_TORCH_CHECK(is_padding_tensor.scalar_type() ==
+                          torch::headeronly::ScalarType::Bool,
+                      "is_padding tensor must be bool");
+      STD_TORCH_CHECK(is_padding_tensor.dim() == 1,
+                      "is_padding tensor must be 1D");
+      STD_TORCH_CHECK(is_padding_tensor.size(0) == num_tokens,
+                      "is_padding size mismatch, expected: ", num_tokens);
+      STD_TORCH_CHECK(is_padding_tensor.is_contiguous(),
+                      "is_padding tensor must be contiguous");
+      is_padding_ptr = is_padding_tensor.const_data_ptr<bool>();
+    }
+
    if (tid2eid.has_value()) {
      STD_TORCH_CHECK(input_ids.has_value(),
                      "input_ids is required for hash MoE");
@@ -751,7 +800,7 @@ void dispatch_topk_softplus_sqrt_launch(
            topk_indices_ptr, token_expert_indices.mutable_data_ptr<int>(),
            num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
            bias_ptr, true, input_ids.value().const_data_ptr<int64_t>(),
-            tid2eid.value().const_data_ptr<int64_t>(), stream);
+            tid2eid.value().const_data_ptr<int64_t>(), stream, is_padding_ptr);
      } else {
        STD_TORCH_CHECK(tid2eid.value().scalar_type() ==
                        torch::headeronly::ScalarType::Int);
@@ -761,7 +810,7 @@ void dispatch_topk_softplus_sqrt_launch(
            topk_indices_ptr, token_expert_indices.mutable_data_ptr<int>(),
            num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
            bias_ptr, true, input_ids.value().const_data_ptr<int>(),
-            tid2eid.value().const_data_ptr<int>(), stream);
+            tid2eid.value().const_data_ptr<int>(), stream, is_padding_ptr);
      }
    } else {
      vllm::moe::topkGatingSoftplusSqrtKernelLauncher<OutIndType, ComputeType>(
@@ -769,7 +818,7 @@ void dispatch_topk_softplus_sqrt_launch(
          topk_indices_ptr, token_expert_indices.mutable_data_ptr<int>(),
          num_tokens, num_experts, topk, renormalize, routed_scaling_factor,
          bias_ptr, false, static_cast<const OutIndType*>(nullptr),
-          static_cast<const OutIndType*>(nullptr), stream);
+          static_cast<const OutIndType*>(nullptr), stream, is_padding_ptr);
    }
  };

@@ -793,7 +842,8 @@ void topk_softplus_sqrt(
    bool renormalize, double routed_scaling_factor,
    const std::optional<torch::stable::Tensor>& correction_bias,
    const std::optional<torch::stable::Tensor>& input_ids,
-    const std::optional<torch::stable::Tensor>& tid2eid) {
+    const std::optional<torch::stable::Tensor>& tid2eid,
+    const std::optional<torch::stable::Tensor>& is_padding) {
  const int num_experts = gating_output.size(-1);
  const auto num_tokens = gating_output.numel() / num_experts;
  const int topk = topk_weights.size(-1);
@@ -806,21 +856,22 @@ void topk_softplus_sqrt(
    dispatch_topk_softplus_sqrt_launch<float>(
        gating_output.const_data_ptr<float>(), topk_weights, topk_indices,
        token_expert_indices, num_tokens, num_experts, topk, renormalize,
-        routed_scaling_factor, correction_bias, input_ids, tid2eid, stream);
+        routed_scaling_factor, correction_bias, input_ids, tid2eid, stream,
+        is_padding);
  } else if (gating_output.scalar_type() ==
             torch::headeronly::ScalarType::Half) {
    dispatch_topk_softplus_sqrt_launch<__half>(
        reinterpret_cast<const __half*>(gating_output.const_data_ptr()),
        topk_weights, topk_indices, token_expert_indices, num_tokens,
        num_experts, topk, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid, stream);
+        input_ids, tid2eid, stream, is_padding);
  } else if (gating_output.scalar_type() ==
             torch::headeronly::ScalarType::BFloat16) {
    dispatch_topk_softplus_sqrt_launch<__nv_bfloat16>(
        reinterpret_cast<const __nv_bfloat16*>(gating_output.const_data_ptr()),
        topk_weights, topk_indices, token_expert_indices, num_tokens,
        num_experts, topk, renormalize, routed_scaling_factor, correction_bias,
-        input_ids, tid2eid, stream);
+        input_ids, tid2eid, stream, is_padding);
  } else {
    STD_TORCH_CHECK(false, "Unsupported gating_output data type: ",
                    gating_output.scalar_type());
@@ -8,19 +8,19 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_moe_C, m) {
  m.def(
      "topk_softmax(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
      "token_expert_indices, Tensor gating_output, bool renormalize, Tensor? "
-      "bias) -> ()");
+      "bias, Tensor? is_padding) -> ()");

  // Apply topk sigmoid to the gating outputs.
  m.def(
      "topk_sigmoid(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
      "token_expert_indices, Tensor gating_output, bool renormalize, "
-      "Tensor? bias, float routed_scaling_factor) -> ()");
+      "Tensor? bias, float routed_scaling_factor, Tensor? is_padding) -> ()");

  m.def(
      "topk_softplus_sqrt(Tensor! topk_weights, Tensor! topk_indices, Tensor! "
      "token_expert_indices, Tensor gating_output, bool renormalize, float "
      "routed_scaling_factor, Tensor? "
-      "bias, Tensor? input_ids, Tensor? tid2eid) -> ()");
+      "bias, Tensor? input_ids, Tensor? tid2eid, Tensor? is_padding) -> ()");

  // Calculate the result of moe by summing up the partial results
  // from all selected experts. topk_ids/expert_map are optional and, when
@@ -793,7 +793,7 @@ RUN --mount=type=cache,target=/opt/uv/cache \
 # Install FlashInfer JIT cache (requires CUDA-version-specific index URL)
 # https://docs.flashinfer.ai/installation.html
 # From versions.json: .flashinfer.version
-ARG FLASHINFER_VERSION=0.6.14
+ARG FLASHINFER_VERSION=0.6.15.post1
 RUN --mount=type=cache,target=/opt/uv/cache \
    uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \
        --index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
@@ -339,18 +339,17 @@ COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/rust /rust
 COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/rust-toolchain.toml /rust-toolchain.toml
 COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/vllm/v1 /vllm_v1

-# RIXL/UCX build stages
-FROM base AS build_rixl
-ARG RIXL_BRANCH="39be1de8"
-ARG RIXL_REPO="https://github.com/ROCm/RIXL.git"
-ARG UCX_BRANCH="bfb51733"
+# NIXL/UCX build stages
+FROM base AS build_nixl
+ARG NIXL_BRANCH="231d56753047c989062a5cb2ac703a1ad761c7d2"
+ARG NIXL_REPO="https://github.com/ai-dynamo/nixl.git"
+ARG UCX_BRANCH="96e58a16039f6d7d213bc967b8069238742c5194"
 ARG UCX_REPO="https://github.com/openucx/ucx.git"
 ENV ROCM_PATH=/opt/rocm
 ENV UCX_HOME=/usr/local/ucx
-ENV RIXL_HOME=/usr/local/rixl
-ENV RIXL_BENCH_HOME=/usr/local/rixl_bench
+ENV NIXL_HOME=/usr/local/nixl

-# RIXL build system dependences and RDMA support
+# NIXL build system dependencies and RDMA support
 RUN apt-get -y update && apt-get -y install autoconf libtool pkg-config \
    libgrpc-dev \
    libgrpc++-dev \
@@ -368,7 +367,8 @@ RUN apt-get -y update && apt-get -y install autoconf libtool pkg-config \
    && rm -rf /var/lib/apt/lists/*

 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system meson auditwheel patchelf tomlkit
+    uv pip install --system meson meson-python pybind11 pyyaml types-PyYAML \
+        auditwheel build patchelf pytest tomlkit "setuptools>=80.9.0"

 RUN --mount=type=cache,target=/root/.cache/ccache \
    cd /usr/local/src && \
@@ -396,30 +396,50 @@ ENV PATH=/usr/local/ucx/bin:$PATH
 ENV LD_LIBRARY_PATH=${UCX_HOME}/lib:${LD_LIBRARY_PATH}

 RUN --mount=type=cache,target=/root/.cache/ccache \
-    git clone ${RIXL_REPO} /opt/rixl && \
-    cd /opt/rixl && \
-    git checkout ${RIXL_BRANCH} && \
+    git clone ${NIXL_REPO} /opt/nixl && \
+    cd /opt/nixl && \
+    git checkout ${NIXL_BRANCH} && \
    CC="ccache gcc" CXX="ccache g++" \
-    meson setup build --prefix=${RIXL_HOME} \
+    meson setup build --prefix=${NIXL_HOME} \
                     -Ducx_path=${UCX_HOME} \
-                     -Drocm_path=${ROCM_PATH} && \
+                     -Dwheel_variant=rocm \
+                     -Dbuild_tests=false \
+                     -Dbuild_examples=false && \
    cd build && \
    ninja -j$(nproc) && \
-    ninja install
+    ninja install && \
+    echo "${NIXL_HOME}/lib/$(uname -m)-linux-gnu" \
+        > /etc/ld.so.conf.d/nixl.conf && \
+    echo "${NIXL_HOME}/lib/$(uname -m)-linux-gnu/plugins" \
+        >> /etc/ld.so.conf.d/nixl.conf && \
+    ldconfig

-# Generate RIXL wheel
+# Generate the ROCm NIXL wheel. Upstream's generic wheel helper detects CUDA,
+# so configure the ROCm wheel variant directly through Meson.
 # Exclude libcore and libpull from auditwheel: transitive dependencies
 # that are not shipped in the wheel and vary across base images.
-RUN cd /opt/rixl && \
-    sed -i "s/--exclude 'libamdhip64\*'/--exclude 'libamdhip64*' --exclude 'libcore*' --exclude 'libpull*'/" \
-        contrib/build-wheel.sh && \
-    mkdir -p /app/install && \
-    _ucx_install_dir=${UCX_HOME} \
-    ./contrib/build-wheel.sh \
-        --output-dir /app/install \
-        --rocm-dir ${ROCM_PATH} \
+RUN cd /opt/nixl && \
+    ./contrib/tomlutil.py --wheel-name nixl-rocm pyproject.toml && \
+    CC="ccache gcc" CXX="ccache g++" \
+    uv build --wheel --no-build-isolation --out-dir /tmp/nixl_wheels \
+        --python ${PYTHON_VERSION} \
+        -Csetup-args=-Ducx_path=${UCX_HOME} \
+        -Csetup-args=-Dwheel_variant=rocm \
+        -Csetup-args=-Dbuild_tests=false \
+        -Csetup-args=-Dbuild_examples=false && \
+    mkdir -p /tmp/nixl_wheels/repaired /app/install && \
+    auditwheel repair \
+        --exclude 'libamdhip64*' \
+        --exclude 'libcore*' \
+        --exclude 'libpull*' \
+        /tmp/nixl_wheels/nixl_rocm*.whl \
+        --plat manylinux_2_34_$(uname -m) \
+        --wheel-dir /tmp/nixl_wheels/repaired && \
+    ./contrib/wheel_add_ucx_plugins.py \
        --ucx-plugins-dir ${UCX_HOME}/lib/ucx \
-        --nixl-plugins-dir ${RIXL_HOME}/lib/x86_64-linux-gnu/plugins
+        --nixl-plugins-dir ${NIXL_HOME}/lib/$(uname -m)-linux-gnu/plugins \
+        /tmp/nixl_wheels/repaired/*.whl && \
+    cp /tmp/nixl_wheels/repaired/*.whl /app/install

 # ROCShmem build stage - split from DeepEP so changing DEEPEP_BRANCH does not
 # invalidate the slow ROCShmem build.
@@ -660,10 +680,10 @@ RUN if [ "${DEEPEP_NIC}" = "cx7" ] || [ "${DEEPEP_NIC}" = "io" ]; then \
    ninja && ninja install && ldconfig && rm -rf /tmp/rdma-core; \
 fi

-# Install RIXL + DeepEP wheels.
-RUN --mount=type=bind,from=build_rixl,src=/app/install,target=/rixl_install \
+# Install NIXL + DeepEP wheels.
+RUN --mount=type=bind,from=build_nixl,src=/app/install,target=/nixl_install \
    --mount=type=bind,from=build_deepep,src=/app/deep_install,target=/deep_install \
-    uv pip install --system /rixl_install/*.whl /deep_install/*.whl
+    uv pip install --system /nixl_install/*.whl /deep_install/*.whl

 # Copy ROCShmem runtime libraries.
 COPY --from=build_rocshmem /opt/rocshmem /opt/rocshmem
@@ -724,6 +744,7 @@ ENV MIOPEN_DEBUG_CONV_GEMM=0
 # Use legacy IPC mode for HSA to avoid GPU memory pinning issues with UCX rocm_ipc.
 # See: https://github.com/ROCm/rocm-libraries/issues/6266
 ENV HSA_ENABLE_IPC_MODE_LEGACY=1
+ENV UCX_RMA_PPLN_ENABLE=y

 # ROCm profiler limits workaround.
 RUN echo "ROCTRACER_MAX_EVENTS=10000000" > ${COMMON_WORKDIR}/libkineto.conf
@@ -796,9 +817,9 @@ RUN --mount=type=bind,from=export_vllm,src=/,target=/install \
    && pip uninstall -y vllm \
    && uv pip install --system *.whl

-# Install RIXL wheel
-RUN --mount=type=bind,from=build_rixl,src=/app/install,target=/rixl_install \
-    uv pip install --system /rixl_install/*.whl
+# Install NIXL ROCm wheel
+RUN --mount=type=bind,from=build_nixl,src=/app/install,target=/nixl_install \
+    uv pip install --system /nixl_install/*.whl

 ARG COMMON_WORKDIR
 ARG BASE_IMAGE
@@ -813,6 +834,7 @@ COPY --from=export_vllm /docker ${COMMON_WORKDIR}/vllm/docker
 # Use legacy IPC mode for HSA to avoid GPU memory pinning issues with UCX rocm_ipc
 # See: https://github.com/ROCm/rocm-libraries/issues/6266
 ENV HSA_ENABLE_IPC_MODE_LEGACY=1
+ENV UCX_RMA_PPLN_ENABLE=y

 ENV TOKENIZERS_PARALLELISM=false

@@ -9,7 +9,7 @@ ARG PYTORCH_AUDIO_BRANCH="v2.9.0"
 ARG PYTORCH_AUDIO_REPO="https://github.com/pytorch/audio.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="v0.1.16.post3"
+ARG AITER_BRANCH="v0.1.16.post5"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 ARG MORI_BRANCH="v1.1.0"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
@@ -86,6 +86,29 @@ RUN --mount=type=cache,target=/root/.cache/uv \
    mkdir -p /tmp/hf-xet/dist && \
    cp dist/*.whl /tmp/hf-xet/dist/

+# Build LLVM 20 from source for llvmlite (system repos ship LLVM 21 which
+# llvmlite v0.47 does not support; only SystemZ target is needed).
+FROM base AS llvm20-build
+ARG LLVM_VERSION=20.1.8
+WORKDIR /tmp
+RUN microdnf install -y ninja-build gcc gcc-c++ python3 xz && \
+    curl -LO https://github.com/llvm/llvm-project/releases/download/llvmorg-${LLVM_VERSION}/llvm-project-${LLVM_VERSION}.src.tar.xz && \
+    tar -xf llvm-project-${LLVM_VERSION}.src.tar.xz && \
+    cmake -G Ninja -S llvm-project-${LLVM_VERSION}.src/llvm -B build \
+        -DCMAKE_BUILD_TYPE=Release \
+        -DCMAKE_INSTALL_PREFIX=/opt/llvm20 \
+        -DLLVM_TARGETS_TO_BUILD="SystemZ" \
+        -DLLVM_ENABLE_RTTI=ON \
+        -DLLVM_BUILD_TOOLS=OFF \
+        -DLLVM_BUILD_UTILS=ON \
+        -DLLVM_BUILD_EXAMPLES=OFF \
+        -DLLVM_BUILD_TESTS=OFF \
+        -DLLVM_INCLUDE_TESTS=OFF \
+        -DLLVM_INCLUDE_EXAMPLES=OFF \
+        -DLLVM_INCLUDE_BENCHMARKS=OFF && \
+    ninja -C build install && \
+    rm -rf build llvm-project-${LLVM_VERSION}.src*
+
 # Build numba
 FROM python-install AS numba-builder

@@ -96,11 +119,13 @@ WORKDIR /tmp

 # Clone all required dependencies
 RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,from=llvm20-build,source=/opt/llvm20,target=/opt/llvm20 \
    microdnf install ninja-build gcc gcc-c++ -y && \
    git clone --recursive https://github.com/numba/llvmlite.git -b v0.47.0 && \
    git clone --recursive https://github.com/numba/numba.git -b ${NUMBA_VERSION} && \
    cd llvmlite && \
    uv pip install 'cmake<4' 'setuptools<70' numpy && \
+    CMAKE_PREFIX_PATH=/opt/llvm20 LLVM_CONFIG=/opt/llvm20/bin/llvm-config \
    python setup.py bdist_wheel && \
    cd ../numba && \
    if ! grep '#include "dynamic_annotations.h"' numba/_dispatcher.cpp; then \
@@ -158,7 +183,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     NUMBA_WHL_FILE=$(ls /tmp/numba-wheels/*.whl) && \
     OPENCV_WHL_FILE=$(ls /tmp/opencv-wheels/*.whl) && \
     uv pip install -v \
-        $ARROW_WHL_FILE \
        $VISION_WHL_FILE \
        $HF_XET_WHL_FILE \
        $LLVM_WHL_FILE \
@@ -59,7 +59,7 @@ variable "PYTORCH_ROCM_ARCH" {
 }

 # Pre-built CI base image (Tier 1). Per-PR builds pull this instead of
-# rebuilding RIXL/DeepEP/torchcodec from scratch. The ci_base stage in
+# rebuilding NIXL/DeepEP/torchcodec from scratch. The ci_base stage in
 # Dockerfile.rocm inherits from base, so CI_BASE_IMAGE only affects the test
 # stage and is irrelevant when building --target ci_base itself.
 variable "CI_BASE_IMAGE" {
@@ -75,7 +75,7 @@ variable "CI_MAX_JOBS" {
 # Upstream dependency commit pins -- extracted from Dockerfile.rocm by
 # ci-bake-rocm.sh at build time. Empty defaults are safe: the cache
 # functions produce no entries when the variable is empty.
-variable "RIXL_BRANCH" {
+variable "NIXL_BRANCH" {
  default = ""
 }

@@ -91,7 +91,7 @@ variable "DEEPEP_BRANCH" {
  default = ""
 }

-variable "RIXL_CACHE_KEY" {
+variable "NIXL_CACHE_KEY" {
  default = ""
 }

@@ -236,7 +236,7 @@ function "get_cache_to_rocm_rust" {
  ])
 }

-# Cache functions for upstream dependency stages (RIXL/UCX, ROCShmem, DeepEP).
+# Cache functions for upstream dependency stages (NIXL/UCX, ROCShmem, DeepEP).
 # These stages are pinned to specific upstream commit hashes, so cache keys use
 # those hashes rather than the Buildkite commit. This means the cache persists
 # across all vLLM commits as long as the upstream dependency pins don't change.
@@ -244,16 +244,16 @@ function "get_cache_to_rocm_rust" {
 function "get_cache_from_rocm_deps" {
  params = []
  result = compact([
-    RIXL_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rixl-rocm-${RIXL_CACHE_KEY}" : (RIXL_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rixl-rocm-${RIXL_BRANCH}-ucx-${UCX_BRANCH}" : ""),
+    NIXL_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:nixl-rocm-${NIXL_CACHE_KEY}" : (NIXL_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:nixl-rocm-${NIXL_BRANCH}-ucx-${UCX_BRANCH}" : ""),
    ROCSHMEM_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rocshmem-rocm-${ROCSHMEM_CACHE_KEY}" : (ROCSHMEM_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rocshmem-rocm-${ROCSHMEM_BRANCH}" : ""),
    DEEPEP_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:deepep-rocm-${DEEPEP_CACHE_KEY}" : (DEEPEP_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:deepep-rocm-${DEEPEP_BRANCH}-rocshmem-${ROCSHMEM_BRANCH}" : ""),
  ])
 }

-function "get_cache_to_rocm_rixl" {
+function "get_cache_to_rocm_nixl" {
  params = []
  result = compact([
-    RIXL_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rixl-rocm-${RIXL_CACHE_KEY},mode=min" : (RIXL_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:rixl-rocm-${RIXL_BRANCH}-ucx-${UCX_BRANCH},mode=min" : ""),
+    NIXL_CACHE_KEY != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:nixl-rocm-${NIXL_CACHE_KEY},mode=min" : (NIXL_BRANCH != "" ? "type=registry,ref=${DOCKERHUB_CACHE_REPO}:nixl-rocm-${NIXL_BRANCH}-ucx-${UCX_BRANCH},mode=min" : ""),
  ])
 }

@@ -372,11 +372,11 @@ variable "CI_BASE_IMAGE_TAG_STABLE" {
 # in the registry cache keyed by its upstream commit hash. When ci_base rebuilds
 # (e.g., requirements change), these stages are cache hits if their upstream
 # pins haven't changed -- saving ~35min of compilation.
-target "rixl-rocm-ci" {
+target "nixl-rocm-ci" {
  inherits   = ["_common-rocm", "_ci-rocm"]
-  target     = "build_rixl"
+  target     = "build_nixl"
  cache-from = get_cache_from_rocm_deps()
-  cache-to   = get_cache_to_rocm_rixl()
+  cache-to   = get_cache_to_rocm_nixl()
  output     = ["type=cacheonly"]
 }

@@ -396,7 +396,7 @@ target "deepep-rocm-ci" {
  output     = ["type=cacheonly"]
 }

-# Builds only the ci_base stage (RIXL, DeepEP, torchcodec, etc.)
+# Builds only the ci_base stage (NIXL, DeepEP, torchcodec, etc.)
 # Invoked by the ensure-ci-base step when the content hash of ci_base-affecting
 # files drifts from the remote image label. Per-PR builds then pull the result
 # as CI_BASE_IMAGE instead of rebuilding those slow layers on every commit.
@@ -412,7 +412,7 @@ target "ci-base-rocm-ci" {
      CI_BASE_IMAGE_TAG_CONTENT_EXTRA != "" ? "type=registry,ref=${CI_BASE_IMAGE_TAG_CONTENT_EXTRA}" : "",
      CI_BASE_IMAGE_TAG_STABLE != "" ? "type=registry,ref=${CI_BASE_IMAGE_TAG_STABLE}" : "",
    ]),
-    # Import upstream dependency caches so RIXL/ROCShmem/DeepEP stages
+    # Import upstream dependency caches so NIXL/ROCShmem/DeepEP stages
    # are cache hits even when ci_base itself needs rebuilding.
    get_cache_from_rocm_deps(),
  )
@@ -424,5 +424,5 @@ target "ci-base-rocm-ci" {
 # Group for ci_base builds -- exports dependency stage caches alongside the
 # ci_base image so future rebuilds can reuse them independently.
 group "ci-base-rocm-ci-with-deps" {
-  targets = ["rixl-rocm-ci", "rocshmem-rocm-ci", "deepep-rocm-ci", "ci-base-rocm-ci"]
+  targets = ["nixl-rocm-ci", "rocshmem-rocm-ci", "deepep-rocm-ci", "ci-base-rocm-ci"]
 }
@@ -53,7 +53,7 @@ variable "CI_BASE_IMAGE" {
 # Upstream dependency commit pins. Plain local bake builds use the Dockerfile
 # ARG defaults. ci-bake-rocm.sh resolves those defaults (plus any env
 # overrides) and writes a small HCL override before invoking CI targets.
-variable "RIXL_BRANCH" {
+variable "NIXL_BRANCH" {
  default = ""
 }

@@ -106,7 +106,7 @@ target "test-rocm" {
  output   = ["type=docker"]
 }

-# CI base image target - builds only the ci_base stage (RIXL, DeepEP,
+# CI base image target - builds only the ci_base stage (NIXL, DeepEP,
 # torchcodec, requirements, etc.). Used by the weekly scheduled build and
 # the auto-rebuild trigger when requirements change in a PR.
 target "ci-base-rocm" {
@@ -68,7 +68,7 @@
      "default": "true"
    },
    "FLASHINFER_VERSION": {
-      "default": "0.6.14"
+      "default": "0.6.15.post1"
    },
    "GDRCOPY_CUDA_VERSION": {
      "default": "12.8"
@@ -13,11 +13,7 @@ Install the NIXL library: `uv pip install nixl`, as a quick start on Nvidia plat
 - Refer to [NIXL official repository](https://github.com/ai-dynamo/nixl) for more installation instructions
 - The specified required NIXL version can be found in [requirements/kv_connectors.txt](../../requirements/kv_connectors.txt) and other relevant config files

-For ROCm platform, the [ROCm docker file](../../docker/Dockerfile.rocm) includes RIXL and ucx already.
-
- Refer to [RIXL official repository](https://github.com/rocm/rixl) for more information
- The supportive libraries for RIXL can be found in [requirements/kv_connectors_rocm.txt](../../requirements/kv_connectors_rocm.txt)
- In the future we may remove RIXL from docker image file and users will be able to install from pre-compiled binary packages
+For ROCm, the [ROCm Dockerfile](../../docker/Dockerfile.rocm) builds NIXL and UCX with ROCm support from source.

 For non-cuda platform, please install nixl with ucx build from source, instructed as below.

@@ -315,7 +315,7 @@ vLLM CPU supports data parallel (DP), tensor parallel (TP) and pipeline parallel
 - vLLM CPU supports quantizations:
    - AWQ (x86 only)
    - GPTQ (x86 only)
-    - compressed-tensor INT8 W8A8 (x86, s390x)
+    - compressed-tensor INT8 W8A8 (x86 only)

 ### Why do I see `get_mempolicy: Operation not permitted` when running in Docker?

@@ -11,7 +11,7 @@ Currently, the CPU implementation for s390x architecture supports FP32, BF16 and
 - OS: `Linux`
 - SDK: `gcc/g++ >= 14.0.0` or later with Command Line Tools
 - Instruction Set Architecture (ISA): VXE support is required. Works with Z14 and above.
- Build install python packages: `torchvision`, `llvmlite`, `numba`, `pyarrow (for testing)`, `opencv-headless`
+- Build from source python packages (no pre-built s390x wheels): `torchvision`, `llvmlite`, `numba`, `opencv-python-headless`, `hf-xet`

 --8<-- [end:requirements]
 --8<-- [start:set-up-using-python]
@@ -28,13 +28,24 @@ Install the following packages from the package manager before building the vLLM

 ```bash
 dnf install -y \
-    which procps findutils tar vim git gcc-toolset-14 gcc-toolset-14-binutils gcc-toolset-14-libatomic-devel zlib-devel \
+    which procps findutils tar vim git patch xz ninja-build \
+    gcc-toolset-14 gcc-toolset-14-binutils gcc-toolset-14-libatomic-devel zlib-devel \
    libjpeg-turbo-devel libtiff-devel libpng-devel libwebp-devel freetype-devel harfbuzz-devel \
    openssl-devel openblas openblas-devel autoconf automake libtool cmake numpy libsndfile \
    clang llvm-devel llvm-static clang-devel
 ```

-Install rust>=1.80 which is needed for `outlines-core` and `uvloop` python packages installation.
+Build and install `numactl` from source:
+
+```bash
+curl -LO https://github.com/numactl/numactl/archive/refs/tags/v2.0.19.tar.gz
+tar -xvzf v2.0.19.tar.gz
+cd numactl-2.0.19
+./autogen.sh && ./configure && make && make install
+cd ..
+```
+
+Install rust>=1.80 which is needed for `outlines-core`, `uvloop`, and `hf-xet` python packages installation.

 ```bash
 curl https://sh.rustup.rs -sSf | sh -s -- -y && \
@@ -44,26 +55,79 @@ curl https://sh.rustup.rs -sSf | sh -s -- -y && \
 Execute the following commands to build and install vLLM from source.

 !!! tip
-    Please build the following dependencies, `torchvision`, `llvmlite`, `numba`, `llguidance`, `pyarrow`, `opencv-headless` from source before building vLLM.
+    Pre-built wheels are not available for s390x for the following packages. Build them from source before building vLLM: `torchvision`, `llvmlite`, `numba`, `opencv-python-headless`, `hf-xet`.
+    See `docker/Dockerfile.s390x` for exact versions and build commands used in each multi-stage build.
+
+!!! note "LLVM 20 required for llvmlite"
+    `llvmlite v0.47` requires LLVM 20, but UBI 9.6 repos ship LLVM 21 which is
+    not compatible. You must build LLVM 20 from source before building `llvmlite`:
+
+    ```bash
+    curl -LO https://github.com/llvm/llvm-project/releases/download/llvmorg-20.1.8/llvm-project-20.1.8.src.tar.xz
+    tar -xf llvm-project-20.1.8.src.tar.xz
+    cmake -G Ninja -S llvm-project-20.1.8.src/llvm -B llvm-build \
+        -DCMAKE_BUILD_TYPE=Release \
+        -DCMAKE_INSTALL_PREFIX=/opt/llvm20 \
+        -DLLVM_TARGETS_TO_BUILD="SystemZ" \
+        -DLLVM_ENABLE_RTTI=ON \
+        -DLLVM_BUILD_TOOLS=OFF \
+        -DLLVM_BUILD_UTILS=ON \
+        -DLLVM_BUILD_EXAMPLES=OFF \
+        -DLLVM_BUILD_TESTS=OFF \
+        -DLLVM_INCLUDE_TESTS=OFF \
+        -DLLVM_INCLUDE_EXAMPLES=OFF \
+        -DLLVM_INCLUDE_BENCHMARKS=OFF
+    ninja -C llvm-build install
+    ```
+
+    Then build `llvmlite` pointing to LLVM 20:
+
+    ```bash
+    CMAKE_PREFIX_PATH=/opt/llvm20 LLVM_CONFIG=/opt/llvm20/bin/llvm-config \
+        python setup.py bdist_wheel
+    ```

 ```bash
-    uv pip install -v \
-        -r requirements/build/cpu.txt \
-        -r requirements/cpu.txt \
-        --torch-backend cpu \
-        --index-strategy unsafe-best-match && \
-    VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
-        uv pip install dist/*.whl
+uv pip install -v \
+    /path/to/torchvision.whl \
+    /path/to/llvmlite.whl \
+    /path/to/numba.whl \
+    /path/to/opencv_python_headless.whl \
+    /path/to/hf_xet.whl \
+    -r requirements/build/cpu.txt \
+    -r requirements/cpu.txt \
+    --torch-backend cpu \
+    --index-strategy unsafe-best-match && \
+VLLM_TARGET_DEVICE=cpu VLLM_CPU_MOE_PREPACK=0 python setup.py bdist_wheel && \
+    uv pip install dist/*.whl
 ```

 ??? console "pip"
    ```bash
-        pip install -v \
-            --extra-index-url https://download.pytorch.org/whl/cpu \
-            -r requirements/build/cpu.txt \
-            -r requirements/cpu.txt \
-        VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
-            pip install dist/*.whl
+    pip install -v \
+        --extra-index-url https://download.pytorch.org/whl/cpu \
+        /path/to/torchvision.whl \
+        /path/to/llvmlite.whl \
+        /path/to/numba.whl \
+        /path/to/opencv_python_headless.whl \
+        /path/to/hf_xet.whl \
+        -r requirements/build/cpu.txt \
+        -r requirements/cpu.txt && \
+    VLLM_TARGET_DEVICE=cpu VLLM_CPU_MOE_PREPACK=0 python setup.py bdist_wheel && \
+        pip install dist/*.whl
+    ```
+
+!!! warning "Protobuf workaround for s390x"
+    The C++ protobuf extension crashes on s390x. After installation, set the
+    following environment variable and remove the C++ extensions:
+
+    ```bash
+    export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+    # Remove C++ protobuf extensions that crash on s390x
+    SITE_PKGS=$(python -c "import site; print(site.getsitepackages()[0])")
+    rm -rf "$SITE_PKGS/google/_upb/"*.so \
+           "$SITE_PKGS/google/protobuf/pyext/"*.so 2>/dev/null || true
    ```

 --8<-- [end:build-wheel-from-source]
@@ -80,19 +144,20 @@ docker build -f docker/Dockerfile.s390x \

 # Launch OpenAI server
 docker run --rm \
-    --privileged true \
+    --security-opt seccomp=unconfined \
+    --cap-add SYS_NICE \
    --shm-size 4g \
    -p 8000:8000 \
    -e VLLM_CPU_KVCACHE_SPACE=<KV cache space> \
    -e VLLM_CPU_OMP_THREADS_BIND=<CPU cores for inference> \
    vllm-cpu-env \
    --model meta-llama/Llama-3.2-1B-Instruct \
-    --dtype float \
+    --dtype bfloat16 \
    other vLLM OpenAI server arguments
 ```

 !!! tip
-    An alternative of `--privileged true` is `--cap-add SYS_NICE --security-opt seccomp=unconfined`.
+    Alternatively, `--privileged=true` also works but is broader and not generally recommended.

 --8<-- [end:build-image-from-source]
 --8<-- [start:extra-information]
@@ -0,0 +1,3 @@
+// Reo.Dev documentation tracking
+// https://docs.reo.dev/integrations/input-sources/developer-insights/documentation
+!function(){var e,t,n;e="d5c4337961ef0ac",t=function(){Reo.init({clientID:"d5c4337961ef0ac", enableThirdPartyTracking: true})},(n=document.createElement("script")).src="https://static.reo.dev/"+e+"/reo.js",n.defer=!0,n.onload=t,document.head.appendChild(n)}();
@@ -160,3 +160,4 @@ extra_javascript:
  - https://unpkg.com/mathjax@3.2.2/es5/tex-mml-chtml.js
  - mkdocs/javascript/edit_and_feedback.js
  - mkdocs/javascript/slack_and_forum.js
+  - mkdocs/javascript/reo.js
@@ -14,8 +14,8 @@ PyNvVideoCodec==2.0.4
 # flashinfer-cubin is not on PyPI since 0.6.14; setup.py excludes it from
 # install_requires so the published wheel does not carry an unresolvable pin
 --extra-index-url https://flashinfer.ai/whl/
-flashinfer-python==0.6.14
-flashinfer-cubin==0.6.14
+flashinfer-python==0.6.15.post1
+flashinfer-cubin==0.6.15.post1
 apache-tvm-ffi==0.1.10
 tilelang==0.1.9
 nvidia-cudnn-frontend>=1.19.1
@@ -26,7 +26,7 @@ fastsafetensors >= 0.3.2

 # QuACK and Cutlass DSL for FA4 (cute-DSL implementation)
 nvidia-cutlass-dsl[cu13]==4.6.0
-quack-kernels>=0.4.0  # Required for tml-fa4
+quack-kernels>=0.6.1  # Required for CUTLASS DSL 4.6 by MSA

 # Tokenspeed_MLA for faster mla with spec decode
 tokenspeed-mla==0.1.8; platform_system == "Linux"
@@ -12,4 +12,4 @@ ray[data]
 setuptools==78.1.0
 setuptools-rust>=1.9.0
 nixl==0.3.0
-tpu-inference==0.24.0
+tpu-inference==0.25.0
@@ -5516,7 +5516,6 @@ dependencies = [
 "asynk-strim-attr",
 "bytes",
 "clap",
- "easy-ext",
 "expect-test",
 "futures",
 "half",
@@ -5546,6 +5545,7 @@ dependencies = [
 "tracing-subscriber",
 "trait-set",
 "uuid",
+ "vllm-chat-types",
 "vllm-engine-core-client",
 "vllm-llm",
 "vllm-parser",
@@ -5555,6 +5555,16 @@ dependencies = [
 "zeromq",
 ]

+[[package]]
+name = "vllm-chat-types"
+version = "0.1.0"
+dependencies = [
+ "easy-ext",
+ "serde",
+ "serde_json",
+ "serde_with",
+]
+
 [[package]]
 name = "vllm-cmd"
 version = "0.1.0"
@@ -5695,11 +5705,11 @@ dependencies = [
 "expect-test",
 "futures",
 "openai-protocol",
- "serde",
 "serde_json",
 "thiserror 2.0.18",
 "thiserror-ext",
 "tool-parser",
+ "vllm-chat-types",
 "vllm-tokenizer",
 "winnow",
 "xgrammar-structural-tag",
@@ -2,6 +2,7 @@
 members = [
    "src/bench",
    "src/chat",
+    "src/chat-types",
    "src/cmd",
    "src/engine-core-client",
    "src/llm",
@@ -135,6 +136,7 @@ uuid = { version = "1.22.0", features = ["v4"] }
 validator = { version = "0.20.0", features = ["derive"] }
 vllm-bench = { path = "src/bench" }
 vllm-chat = { path = "src/chat" }
+vllm-chat-types = { path = "src/chat-types" }
 vllm-engine-core-client = { path = "src/engine-core-client" }
 vllm-llm = { path = "src/llm" }
 vllm-managed-engine = { path = "src/managed-engine" }
@@ -14,6 +14,10 @@ service Generate {
  rpc GenerateStream (GenerateRequest) returns (stream GenerateResponse) {}
 }

+service Control {
+  rpc Abort (AbortRequest) returns (AbortResponse) {}
+}
+
 // ======================================================================================
 // Generate Request
 // ======================================================================================
@@ -201,3 +205,12 @@ message TokenIds {
  repeated uint32 ids = 1;
 }

+// ======================================================================================
+// Control
+// ======================================================================================
+
+message AbortRequest {
+  repeated string request_ids = 1;
+}
+
+message AbortResponse {}
@@ -0,0 +1,14 @@
+[package]
+name = "vllm-chat-types"
+version.workspace = true
+edition.workspace = true
+license.workspace = true
+
+[dependencies]
+easy-ext.workspace = true
+serde.workspace = true
+serde_json.workspace = true
+serde_with.workspace = true
+
+[lints]
+workspace = true
@@ -0,0 +1,155 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+use std::ops::Deref;
+
+use serde::{Deserialize, Serialize};
+
+/// One finalized assistant tool call.
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+pub struct AssistantToolCall {
+    /// Stable tool-call identifier.
+    pub id: String,
+    /// Function name selected by the assistant.
+    pub name: String,
+    /// Serialized function arguments.
+    pub arguments: String,
+}
+
+/// Semantic kind of one assistant output block.
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
+pub enum AssistantBlockKind {
+    /// Visible final-answer text.
+    Text,
+    /// Extracted reasoning content.
+    Reasoning,
+    /// One finalized tool call.
+    ToolCall,
+}
+
+/// One structured assistant output block.
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+pub enum AssistantContentBlock {
+    /// Visible final-answer text.
+    Text {
+        /// Visible text.
+        text: String,
+    },
+    /// Extracted reasoning content.
+    Reasoning {
+        /// Reasoning text.
+        text: String,
+    },
+    /// One finalized tool call.
+    ToolCall(AssistantToolCall),
+}
+
+impl AssistantContentBlock {
+    /// Return the semantic kind of this block.
+    pub fn kind(&self) -> AssistantBlockKind {
+        match self {
+            Self::Text { .. } => AssistantBlockKind::Text,
+            Self::Reasoning { .. } => AssistantBlockKind::Reasoning,
+            Self::ToolCall(..) => AssistantBlockKind::ToolCall,
+        }
+    }
+
+    /// Return this block as one finalized tool call when applicable.
+    pub fn as_tool_call(&self) -> Option<&AssistantToolCall> {
+        match self {
+            Self::ToolCall(call) => Some(call),
+            _ => None,
+        }
+    }
+
+    /// Trim whitespace from text and tool arguments.
+    ///
+    /// Returns `None` when trimming makes a text or reasoning block empty.
+    pub fn trim(mut self) -> Option<Self> {
+        match &mut self {
+            Self::Text { text } | Self::Reasoning { text } => {
+                let trimmed_text = text.trim();
+                if trimmed_text.is_empty() {
+                    return None;
+                }
+                *text = trimmed_text.to_string();
+            }
+            Self::ToolCall(call) => {
+                call.arguments = call.arguments.trim().to_string();
+            }
+        }
+        Some(self)
+    }
+}
+
+#[easy_ext::ext(AssistantMessageExt)]
+impl [AssistantContentBlock] {
+    /// Concatenate all visible final-answer text blocks.
+    pub fn text(&self) -> String {
+        self.iter()
+            .filter_map(|block| match block {
+                AssistantContentBlock::Text { text } => Some(text.as_str()),
+                _ => None,
+            })
+            .collect()
+    }
+
+    /// Concatenate all extracted reasoning blocks.
+    pub fn reasoning(&self) -> Option<String> {
+        Some(
+            self.iter()
+                .filter_map(|block| match block {
+                    AssistantContentBlock::Reasoning { text } => Some(text.as_str()),
+                    _ => None,
+                })
+                .collect(),
+        )
+        .filter(|text: &String| !text.is_empty())
+    }
+
+    /// Return whether this assistant message contains reasoning text.
+    pub fn has_reasoning(&self) -> bool {
+        self.iter().any(|block| match block {
+            AssistantContentBlock::Reasoning { text } => !text.is_empty(),
+            _ => false,
+        })
+    }
+
+    /// Iterate over finalized assistant tool calls in encounter order.
+    pub fn tool_calls(&self) -> impl Iterator<Item = &AssistantToolCall> {
+        self.iter().filter_map(AssistantContentBlock::as_tool_call)
+    }
+
+    /// Return whether this assistant message contains any tool-call blocks.
+    pub fn has_tool_calls(&self) -> bool {
+        self.iter().any(|block| matches!(block, AssistantContentBlock::ToolCall(_)))
+    }
+}
+
+/// Final structured assistant message assembled from parsed output.
+#[derive(Debug, Clone, Default, PartialEq, Eq, Serialize, Deserialize)]
+pub struct AssistantMessage {
+    /// Assistant content blocks in emission order.
+    pub content: Vec<AssistantContentBlock>,
+}
+
+impl Deref for AssistantMessage {
+    type Target = [AssistantContentBlock];
+
+    fn deref(&self) -> &Self::Target {
+        &self.content
+    }
+}
+
+impl AssistantMessage {
+    /// Push one new block to the end of the message content.
+    pub fn push_block(&mut self, block: AssistantContentBlock) {
+        self.content.push(block);
+    }
+
+    /// Trim all blocks and remove text blocks that become empty.
+    pub fn trim(mut self) -> Self {
+        self.content = self.content.into_iter().filter_map(AssistantContentBlock::trim).collect();
+        self
+    }
+}
@@ -0,0 +1,191 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+use serde::{Deserialize, Serialize};
+
+/// Detail level requested for an OpenAI-style image input.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq, Serialize, Deserialize)]
+#[serde(rename_all = "snake_case")]
+pub enum ImageDetail {
+    /// Let the model-specific multimodal processor select the detail level.
+    #[default]
+    Auto,
+    /// Request low-detail image processing.
+    Low,
+    /// Request high-detail image processing.
+    High,
+}
+
+/// One chat content part in OpenAI-style block format.
+#[serde_with::skip_serializing_none]
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+#[serde(tag = "type", rename_all = "snake_case")]
+pub enum ChatContentPart {
+    /// One plain-text content block.
+    Text {
+        /// Plain-text content.
+        text: String,
+    },
+    /// One image URL or data URL content block.
+    ImageUrl {
+        /// Image URL or data URL.
+        image_url: String,
+        /// Requested image detail level.
+        detail: Option<ImageDetail>,
+        /// Optional caller-provided media identifier.
+        uuid: Option<String>,
+    },
+    /// One video URL or data URL content block.
+    VideoUrl {
+        /// Video URL or data URL.
+        video_url: String,
+        /// Optional caller-provided media identifier.
+        uuid: Option<String>,
+    },
+    /// One `input_audio` content block carrying base64-encoded audio bytes.
+    InputAudio {
+        /// Base64-encoded audio bytes.
+        data: String,
+        /// Optional audio format such as `wav` or `mp3`.
+        format: Option<String>,
+        /// Optional caller-provided media identifier.
+        uuid: Option<String>,
+    },
+    /// One audio URL or data URL content block.
+    AudioUrl {
+        /// Audio URL or data URL.
+        audio_url: String,
+        /// Optional caller-provided media identifier.
+        uuid: Option<String>,
+    },
+}
+
+impl ChatContentPart {
+    /// Construct one text content part with plain string content.
+    pub fn text(text: impl Into<String>) -> Self {
+        Self::Text { text: text.into() }
+    }
+
+    /// Construct one image URL content part with the given URL string.
+    pub fn image_url(image_url: impl Into<String>) -> Self {
+        Self::ImageUrl {
+            image_url: image_url.into(),
+            detail: None,
+            uuid: None,
+        }
+    }
+
+    /// Construct one video URL content part with the given URL string.
+    pub fn video_url(video_url: impl Into<String>) -> Self {
+        Self::VideoUrl {
+            video_url: video_url.into(),
+            uuid: None,
+        }
+    }
+
+    /// Construct one base64-encoded input-audio content part.
+    pub fn input_audio(data: impl Into<String>, format: Option<String>) -> Self {
+        Self::InputAudio {
+            data: data.into(),
+            format,
+            uuid: None,
+        }
+    }
+
+    /// Construct one audio URL content part with the given URL string.
+    pub fn audio_url(audio_url: impl Into<String>) -> Self {
+        Self::AudioUrl {
+            audio_url: audio_url.into(),
+            uuid: None,
+        }
+    }
+
+    /// Return the text content of this part.
+    ///
+    /// Returns the static content-part type for multimodal content.
+    pub fn as_text(&self) -> Result<&str, &'static str> {
+        match self {
+            Self::Text { text } => Ok(text),
+            Self::ImageUrl { .. } => Err("image_url"),
+            Self::VideoUrl { .. } => Err("video_url"),
+            Self::InputAudio { .. } => Err("input_audio"),
+            Self::AudioUrl { .. } => Err("audio_url"),
+        }
+    }
+
+    /// Return whether this part is a text block with empty content.
+    fn is_empty_text(&self) -> bool {
+        matches!(self, Self::Text { text } if text.is_empty())
+    }
+
+    /// Return whether this part contains any multimodal content.
+    fn is_multimodal(&self) -> bool {
+        match self {
+            Self::Text { .. } => false,
+            Self::ImageUrl { .. }
+            | Self::VideoUrl { .. }
+            | Self::InputAudio { .. }
+            | Self::AudioUrl { .. } => true,
+        }
+    }
+}
+
+/// Chat content represented as a string or OpenAI-style content parts.
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+#[serde(untagged)]
+pub enum ChatContent {
+    /// Simple text content.
+    Text(String),
+    /// OpenAI-style content parts.
+    Parts(Vec<ChatContentPart>),
+}
+
+impl ChatContent {
+    /// Flatten text parts into one string without adding separators.
+    ///
+    /// Returns the static content-part type when the content is multimodal.
+    pub fn try_flatten_to_text(&self) -> Result<String, &'static str> {
+        Ok(match self {
+            Self::Text(text) => text.clone(),
+            Self::Parts(parts) => parts
+                .iter()
+                .map(ChatContentPart::as_text)
+                .collect::<Result<Vec<_>, _>>()?
+                .concat(),
+        })
+    }
+
+    /// Return whether the content has no text or only empty text blocks.
+    pub fn is_empty(&self) -> bool {
+        match self {
+            Self::Text(text) => text.is_empty(),
+            Self::Parts(parts) => parts.iter().all(ChatContentPart::is_empty_text),
+        }
+    }
+
+    /// Return whether this content contains any multimodal parts.
+    pub fn has_multimodal(&self) -> bool {
+        match self {
+            Self::Text(_) => false,
+            Self::Parts(parts) => parts.iter().any(ChatContentPart::is_multimodal),
+        }
+    }
+}
+
+impl From<String> for ChatContent {
+    fn from(value: String) -> Self {
+        Self::Text(value)
+    }
+}
+
+impl From<&str> for ChatContent {
+    fn from(value: &str) -> Self {
+        Self::Text(value.to_string())
+    }
+}
+
+impl From<Vec<ChatContentPart>> for ChatContent {
+    fn from(value: Vec<ChatContentPart>) -> Self {
+        Self::Parts(value)
+    }
+}
@@ -0,0 +1,26 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+//! Engine-independent data types shared by chat renderers and output parsers.
+//!
+//! This crate defines chat history, rendering options, tool descriptions, and
+//! structured assistant payloads. Serving requests, streamed events, renderer
+//! implementations, parser state, and engine metadata live in their owning
+//! crates.
+
+mod assistant;
+mod content;
+mod message;
+mod options;
+#[cfg(test)]
+mod tests;
+mod tool;
+
+pub use assistant::{
+    AssistantBlockKind, AssistantContentBlock, AssistantMessage, AssistantMessageExt,
+    AssistantToolCall,
+};
+pub use content::{ChatContent, ChatContentPart, ImageDetail};
+pub use message::{ChatMessage, ChatRole};
+pub use options::{ChatOptions, ChatToolChoice, GenerationPromptMode, ReasoningEffort};
+pub use tool::Tool;
@@ -0,0 +1,195 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+use serde::{Deserialize, Serialize};
+
+use crate::{AssistantContentBlock, AssistantMessage, AssistantMessageExt as _, ChatContent, Tool};
+
+/// Role label for one chat message.
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+#[serde(rename_all = "snake_case")]
+pub enum ChatRole {
+    /// System instructions.
+    System,
+    /// Developer instructions.
+    Developer,
+    /// User input.
+    User,
+    /// Assistant history.
+    Assistant,
+    /// Result of an assistant tool call.
+    ToolResponse,
+}
+
+impl ChatRole {
+    /// Return the role string exposed to chat templates.
+    pub fn as_str(&self) -> &'static str {
+        match self {
+            Self::System => "system",
+            Self::Developer => "developer",
+            Self::User => "user",
+            Self::Assistant => "assistant",
+            Self::ToolResponse => "tool_response",
+        }
+    }
+}
+
+/// One chat message.
+///
+/// Original Python API reference:
+/// <https://github.com/vllm-project/vllm/blob/bc2c0c86efb28e77677a3cfb8687e976914a313a/vllm/entrypoints/chat_utils.py#L309-L333>
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+#[serde(tag = "role", rename_all = "snake_case")]
+pub enum ChatMessage {
+    /// System message.
+    System {
+        /// Message content.
+        content: ChatContent,
+    },
+    /// Developer message with optional message-local tools.
+    Developer {
+        /// Message content.
+        content: ChatContent,
+        /// Tools introduced by this developer message.
+        tools: Option<Vec<Tool>>,
+    },
+    /// User message.
+    User {
+        /// Message content.
+        content: ChatContent,
+    },
+    /// Assistant history assembled from structured blocks.
+    Assistant {
+        /// Structured assistant content.
+        content: Vec<AssistantContentBlock>,
+    },
+    /// Tool response associated with one prior assistant tool call.
+    ToolResponse {
+        /// Tool response content.
+        content: ChatContent,
+        /// Identifier of the assistant tool call being answered.
+        tool_call_id: String,
+    },
+}
+
+impl ChatMessage {
+    /// Construct one chat message with plain string content.
+    ///
+    /// # Panics
+    ///
+    /// Panics for [`ChatRole::ToolResponse`], which requires a tool-call ID.
+    /// Use [`Self::tool_response`] for tool responses.
+    pub fn text(role: ChatRole, text: impl Into<String>) -> Self {
+        let content: String = text.into();
+
+        match role {
+            ChatRole::System => Self::system(content),
+            ChatRole::Developer => Self::developer(content, None),
+            ChatRole::User => Self::user(content),
+            ChatRole::Assistant => Self::assistant_text(content),
+            ChatRole::ToolResponse => {
+                panic!(
+                    "tool response messages require a tool_call_id; \
+                     use ChatMessage::tool_response() instead"
+                )
+            }
+        }
+    }
+
+    /// Construct one system message.
+    pub fn system(content: impl Into<ChatContent>) -> Self {
+        Self::System {
+            content: content.into(),
+        }
+    }
+
+    /// Construct one developer message.
+    pub fn developer(content: impl Into<ChatContent>, tools: Option<Vec<Tool>>) -> Self {
+        Self::Developer {
+            content: content.into(),
+            tools,
+        }
+    }
+
+    /// Construct one user message.
+    pub fn user(content: impl Into<ChatContent>) -> Self {
+        Self::User {
+            content: content.into(),
+        }
+    }
+
+    /// Construct one assistant message with plain string content.
+    pub fn assistant_text(text: impl Into<String>) -> Self {
+        Self::Assistant {
+            content: vec![AssistantContentBlock::Text { text: text.into() }],
+        }
+    }
+
+    /// Construct one assistant message with structured content blocks.
+    pub fn assistant_blocks(content: Vec<AssistantContentBlock>) -> Self {
+        Self::Assistant { content }
+    }
+
+    /// Construct one tool-response message.
+    pub fn tool_response(content: impl Into<ChatContent>, tool_call_id: impl Into<String>) -> Self {
+        Self::ToolResponse {
+            content: content.into(),
+            tool_call_id: tool_call_id.into(),
+        }
+    }
+
+    /// Return the role of this message.
+    pub fn role(&self) -> ChatRole {
+        match self {
+            Self::System { .. } => ChatRole::System,
+            Self::Developer { .. } => ChatRole::Developer,
+            Self::User { .. } => ChatRole::User,
+            Self::Assistant { .. } => ChatRole::Assistant,
+            Self::ToolResponse { .. } => ChatRole::ToolResponse,
+        }
+    }
+
+    /// Concatenate the visible text carried by this message.
+    ///
+    /// Returns the static content-part type when a non-assistant message
+    /// contains multimodal content.
+    pub fn text_content(&self) -> Result<String, &'static str> {
+        match self {
+            Self::System { content }
+            | Self::Developer { content, .. }
+            | Self::User { content }
+            | Self::ToolResponse { content, .. } => content.try_flatten_to_text(),
+            Self::Assistant { content } => Ok(content.text()),
+        }
+    }
+
+    /// Concatenate assistant reasoning text when present.
+    pub fn reasoning_content(&self) -> Option<String> {
+        match self {
+            Self::Assistant { content } => content.reasoning(),
+            Self::System { .. }
+            | Self::Developer { .. }
+            | Self::User { .. }
+            | Self::ToolResponse { .. } => None,
+        }
+    }
+
+    /// Return whether this message contains multimodal content.
+    pub fn has_multimodal(&self) -> bool {
+        match self {
+            Self::System { content }
+            | Self::Developer { content, .. }
+            | Self::User { content }
+            | Self::ToolResponse { content, .. } => content.has_multimodal(),
+            Self::Assistant { .. } => false,
+        }
+    }
+}
+
+impl From<AssistantMessage> for ChatMessage {
+    fn from(value: AssistantMessage) -> Self {
+        Self::Assistant {
+            content: value.content,
+        }
+    }
+}
@@ -0,0 +1,134 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+use std::collections::HashMap;
+
+use serde::{Deserialize, Serialize};
+use serde_json::Value;
+
+/// Controls how prompt rendering should end after the existing chat history.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq, Serialize, Deserialize)]
+#[serde(rename_all = "snake_case")]
+pub enum GenerationPromptMode {
+    /// Append a generation prompt for a new assistant turn.
+    ///
+    /// Equivalent to `add_generation_prompt = true` and
+    /// `continue_final_message = false`.
+    #[default]
+    StartNewAssistant,
+    /// Leave the final assistant message open so generation continues it.
+    ///
+    /// Equivalent to `add_generation_prompt = false` and
+    /// `continue_final_message = true`.
+    ContinueFinalAssistant,
+    /// Render the existing chat history without adding any trailing generation
+    /// prompt.
+    ///
+    /// Equivalent to `add_generation_prompt = false` and
+    /// `continue_final_message = false`.
+    NoGenerationPrompt,
+}
+
+/// Effort level for reasoning models.
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
+#[serde(rename_all = "lowercase")]
+pub enum ReasoningEffort {
+    /// Disable reasoning.
+    None,
+    /// Use the smallest available reasoning effort.
+    Minimal,
+    /// Use low reasoning effort.
+    Low,
+    /// Use medium reasoning effort.
+    Medium,
+    /// Use high reasoning effort.
+    High,
+    /// Use extra-high reasoning effort.
+    XHigh,
+    /// Use the largest available reasoning effort.
+    Max,
+}
+
+impl ReasoningEffort {
+    /// Return the lowercase value exposed to chat templates.
+    pub fn as_str(self) -> &'static str {
+        match self {
+            Self::None => "none",
+            Self::Minimal => "minimal",
+            Self::Low => "low",
+            Self::Medium => "medium",
+            Self::High => "high",
+            Self::XHigh => "xhigh",
+            Self::Max => "max",
+        }
+    }
+}
+
+/// Chat-template-related request options.
+///
+/// These are the chat controls that currently affect prompt rendering.
+#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
+pub struct ChatOptions {
+    /// Controls whether rendering starts a new assistant turn, continues the
+    /// final assistant message, or emits no trailing generation prompt.
+    pub generation_prompt_mode: GenerationPromptMode,
+
+    /// Per-request Jinja chat template override.
+    ///
+    /// The renderer uses this template in place of the model's default chat
+    /// template when it is present.
+    pub chat_template: Option<String>,
+
+    /// Effort level exposed to chat templates for reasoning models.
+    pub reasoning_effort: Option<ReasoningEffort>,
+
+    /// Additional keyword arguments exposed to the chat template.
+    pub template_kwargs: HashMap<String, Value>,
+}
+
+impl Default for ChatOptions {
+    fn default() -> Self {
+        Self {
+            generation_prompt_mode: GenerationPromptMode::StartNewAssistant,
+            chat_template: None,
+            reasoning_effort: None,
+            template_kwargs: HashMap::new(),
+        }
+    }
+}
+
+impl ChatOptions {
+    /// Return whether rendering adds a prompt for a new assistant turn.
+    pub fn add_generation_prompt(&self) -> bool {
+        matches!(
+            self.generation_prompt_mode,
+            GenerationPromptMode::StartNewAssistant
+        )
+    }
+
+    /// Return whether rendering continues the final assistant message.
+    pub fn continue_final_message(&self) -> bool {
+        matches!(
+            self.generation_prompt_mode,
+            GenerationPromptMode::ContinueFinalAssistant
+        )
+    }
+}
+
+/// Tool-choice semantics supported by the shared chat types.
+#[derive(Debug, Clone, Default, PartialEq, Eq, Serialize, Deserialize)]
+#[serde(rename_all = "snake_case")]
+pub enum ChatToolChoice {
+    /// Disable tool calling.
+    #[default]
+    None,
+    /// Let the model choose whether to call a tool.
+    Auto,
+    /// Require the model to call a tool.
+    Required,
+    /// Require one named function.
+    Function {
+        /// Required function name.
+        name: String,
+    },
+}
@@ -0,0 +1,117 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+use serde_json::{json, to_value};
+
+use crate::{AssistantContentBlock, ChatContent, ChatContentPart, ChatMessage, ChatRole, Tool};
+
+#[test]
+fn chat_content_deserializes_from_raw_string() {
+    let content: ChatContent = serde_json::from_value(json!("hello")).unwrap();
+    assert_eq!(content, ChatContent::Text("hello".to_string()));
+}
+
+#[test]
+fn chat_content_video_url_part_round_trips_through_serde() {
+    let content = ChatContent::Parts(vec![ChatContentPart::VideoUrl {
+        video_url: "https://example.com/demo.mp4".to_string(),
+        uuid: Some("video-1".to_string()),
+    }]);
+
+    let value = to_value(&content).unwrap();
+    assert_eq!(
+        value,
+        json!([{
+            "type": "video_url",
+            "video_url": "https://example.com/demo.mp4",
+            "uuid": "video-1",
+        }])
+    );
+    let decoded: ChatContent = serde_json::from_value(value).unwrap();
+    assert_eq!(decoded, content);
+}
+
+#[test]
+fn chat_content_deserializes_from_openai_text_blocks() {
+    let content: ChatContent =
+        serde_json::from_value(json!([{ "type": "text", "text": "hello" }])).unwrap();
+    assert_eq!(
+        content,
+        ChatContent::Parts(vec![ChatContentPart::text("hello")])
+    );
+}
+
+#[test]
+fn chat_content_from_string_like_values_builds_text() {
+    assert_eq!(
+        ChatContent::from("hello"),
+        ChatContent::Text("hello".to_string())
+    );
+    assert_eq!(
+        ChatContent::from("hello".to_string()),
+        ChatContent::Text("hello".to_string())
+    );
+}
+
+#[test]
+fn chat_content_try_flattens_text_parts_without_separators() {
+    let content = ChatContent::Parts(vec![
+        ChatContentPart::text("hello"),
+        ChatContentPart::text(" world"),
+    ]);
+    assert_eq!(content.try_flatten_to_text().unwrap(), "hello world");
+}
+
+#[test]
+fn multimodal_content_parts_return_static_type_names() {
+    let parts = [
+        (ChatContentPart::image_url("image"), "image_url"),
+        (ChatContentPart::video_url("video"), "video_url"),
+        (ChatContentPart::input_audio("audio", None), "input_audio"),
+        (ChatContentPart::audio_url("audio"), "audio_url"),
+    ];
+
+    for (part, expected) in parts {
+        assert_eq!(part.as_text(), Err(expected));
+        assert_eq!(
+            ChatContent::Parts(vec![part]).try_flatten_to_text(),
+            Err(expected)
+        );
+    }
+}
+
+#[test]
+fn assistant_message_collects_visible_and_reasoning_text() {
+    let message = ChatMessage::assistant_blocks(vec![
+        AssistantContentBlock::Reasoning {
+            text: "inner".to_string(),
+        },
+        AssistantContentBlock::Text {
+            text: "outer".to_string(),
+        },
+    ]);
+
+    assert_eq!(message.role(), ChatRole::Assistant);
+    assert_eq!(message.text_content().unwrap(), "outer");
+    assert_eq!(message.reasoning_content().as_deref(), Some("inner"));
+}
+
+#[test]
+fn developer_message_round_trips_through_serde() {
+    let message = ChatMessage::developer(
+        "hello",
+        Some(vec![Tool {
+            name: "get_weather".to_string(),
+            description: Some("Get weather".to_string()),
+            parameters: json!({
+                "type": "object",
+                "properties": {"city": {"type": "string"}},
+            }),
+            strict: Some(true),
+        }]),
+    );
+
+    let value = to_value(&message).unwrap();
+    let decoded: ChatMessage = serde_json::from_value(value).unwrap();
+    assert_eq!(decoded, message);
+}
@@ -0,0 +1,18 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+use serde::{Deserialize, Serialize};
+use serde_json::Value;
+
+/// One function-style tool made available to the model.
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+pub struct Tool {
+    /// Function name exposed to the model.
+    pub name: String,
+    /// Optional human-readable function description.
+    pub description: Option<String>,
+    /// JSON Schema describing the function parameters.
+    pub parameters: Value,
+    /// Optional strict-schema enforcement request.
+    pub strict: Option<bool>,
+}
@@ -7,7 +7,6 @@ license.workspace = true
 [dependencies]
 anyhow.workspace = true
 asynk-strim-attr.workspace = true
-easy-ext.workspace = true
 futures.workspace = true
 half.workspace = true
 indexmap.workspace = true
@@ -30,6 +29,7 @@ tokio.workspace = true
 tracing.workspace = true
 trait-set.workspace = true
 uuid.workspace = true
+vllm-chat-types.workspace = true
 vllm-engine-core-client.workspace = true
 vllm-llm.workspace = true
 vllm-parser.workspace = true
@@ -1,155 +1,17 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

-use std::ops::Deref;
 use std::sync::Arc;

-use serde::{Deserialize, Serialize};
 use vllm_llm::TokenUsage;
 use vllm_text::{DecodedLogprobs, DecodedPromptLogprobs};

 use crate::FinishReason;

-/// One finalized assistant tool call.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-pub struct AssistantToolCall {
-    pub id: String,
-    pub name: String,
-    pub arguments: String,
-}
-
-/// Semantic kind of one assistant output block.
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
-pub enum AssistantBlockKind {
-    /// Visible final-answer text.
-    Text,
-    /// Extracted reasoning content.
-    Reasoning,
-    /// One finalized tool call.
-    ToolCall,
-}
-
-/// One structured assistant output block.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-pub enum AssistantContentBlock {
-    /// Visible final-answer text.
-    Text { text: String },
-    /// Extracted reasoning content.
-    Reasoning { text: String },
-    /// One finalized tool call.
-    ToolCall(AssistantToolCall),
-}
-
-impl AssistantContentBlock {
-    /// Return the semantic kind of this block.
-    pub fn kind(&self) -> AssistantBlockKind {
-        match self {
-            Self::Text { .. } => AssistantBlockKind::Text,
-            Self::Reasoning { .. } => AssistantBlockKind::Reasoning,
-            Self::ToolCall(..) => AssistantBlockKind::ToolCall,
-        }
-    }
-
-    /// Return this block as one finalized tool call, if applicable.
-    pub fn as_tool_call(&self) -> Option<&AssistantToolCall> {
-        match self {
-            Self::ToolCall(call) => Some(call),
-            _ => None,
-        }
-    }
-
-    /// Return a copy of this block with leading and trailing whitespace trimmed from all text
-    /// fields and tool call arguments, or `None` if the resulting text would be empty.
-    pub fn trim(mut self) -> Option<Self> {
-        match &mut self {
-            Self::Text { text } | Self::Reasoning { text } => {
-                let trimmed_text = text.trim();
-                if trimmed_text.is_empty() {
-                    return None;
-                } else {
-                    *text = trimmed_text.to_string();
-                }
-            }
-            Self::ToolCall(call) => {
-                call.arguments = call.arguments.trim().to_string();
-            }
-        }
-        Some(self)
-    }
-}
-
-#[easy_ext::ext(AssistantMessageExt)]
-impl [AssistantContentBlock] {
-    /// Concatenate all visible final-answer text blocks.
-    pub fn text(&self) -> String {
-        self.iter()
-            .filter_map(|block| match block {
-                AssistantContentBlock::Text { text } => Some(text.as_str()),
-                _ => None,
-            })
-            .collect()
-    }
-
-    /// Concatenate all extracted reasoning blocks, if any.
-    pub fn reasoning(&self) -> Option<String> {
-        Some(
-            self.iter()
-                .filter_map(|block| match block {
-                    AssistantContentBlock::Reasoning { text } => Some(text.as_str()),
-                    _ => None,
-                })
-                .collect(),
-        )
-        .filter(|s: &String| !s.is_empty())
-    }
-
-    /// Return whether this assistant message contains any non-empty reasoning
-    /// text blocks.
-    pub fn has_reasoning(&self) -> bool {
-        self.iter().any(|block| match block {
-            AssistantContentBlock::Reasoning { text } => !text.is_empty(),
-            _ => false,
-        })
-    }
-
-    /// Return finalized assistant tool calls in encounter order.
-    pub fn tool_calls(&self) -> impl Iterator<Item = &AssistantToolCall> {
-        self.iter().filter_map(AssistantContentBlock::as_tool_call)
-    }
-
-    /// Return whether this assistant message contains any tool-call blocks.
-    pub fn has_tool_calls(&self) -> bool {
-        self.iter().any(|block| matches!(block, AssistantContentBlock::ToolCall(_)))
-    }
-}
-
-/// Final structured assistant message assembled from the event stream.
-#[derive(Debug, Clone, Default, PartialEq, Eq, Serialize, Deserialize)]
-pub struct AssistantMessage {
-    pub content: Vec<AssistantContentBlock>,
-}
-
-impl Deref for AssistantMessage {
-    type Target = [AssistantContentBlock];
-
-    fn deref(&self) -> &Self::Target {
-        &self.content
-    }
-}
-
-impl AssistantMessage {
-    /// Push one new block to the end of the message content.
-    pub(crate) fn push_block(&mut self, block: AssistantContentBlock) {
-        self.content.push(block);
-    }
-
-    /// Return a copy of this message with leading and trailing whitespace trimmed from all text
-    /// fields and tool call arguments, and with any blocks that are empty after trimming removed.
-    pub fn trim(mut self) -> Self {
-        self.content = self.content.into_iter().filter_map(|block| block.trim()).collect();
-        self
-    }
-}
+pub use vllm_chat_types::{
+    AssistantBlockKind, AssistantContentBlock, AssistantMessage, AssistantMessageExt,
+    AssistantToolCall,
+};

 /// Streamed chat event emitted by [`crate::ChatEventStream`].
 #[derive(Debug, Clone, PartialEq)]
@@ -164,44 +26,65 @@ pub enum ChatEvent {
    },
    /// A new assistant output block has started.
    BlockStart {
+        /// Stable block index within the assistant message.
        index: usize,
+        /// Semantic kind of the opened block.
        kind: AssistantBlockKind,
    },
    /// A newly observed delta for one open assistant output block.
    BlockDelta {
+        /// Stable block index within the assistant message.
        index: usize,
+        /// Semantic kind of the open block.
        kind: AssistantBlockKind,
+        /// Newly emitted text.
        delta: String,
    },
-    /// Per-decoded-update sample metadata: logprobs and/or output token IDs.
+    /// Per-decoded-update sample metadata.
    LogprobsDelta {
+        /// Decoded output logprobs, when requested.
        logprobs: Option<DecodedLogprobs>,
+        /// Output token IDs emitted by this update.
        token_ids: Vec<u32>,
    },
    /// One assistant output block has ended.
    BlockEnd {
+        /// Stable block index within the assistant message.
        index: usize,
+        /// Finalized block.
        block: AssistantContentBlock,
    },
    /// One tool call has started.
    ToolCallStart {
+        /// Stable tool-call index within the assistant message.
        index: usize,
+        /// Stable tool-call identifier.
        id: String,
+        /// Function name selected by the assistant.
        name: String,
    },
-    /// One incremental tool-call arguments delta for the currently open tool
-    /// call.
-    ToolCallArgumentsDelta { index: usize, delta: String },
+    /// One incremental tool-call arguments delta.
+    ToolCallArgumentsDelta {
+        /// Stable tool-call index within the assistant message.
+        index: usize,
+        /// Newly emitted arguments text.
+        delta: String,
+    },
    /// One tool call has ended.
    ToolCallEnd {
+        /// Stable tool-call index within the assistant message.
        index: usize,
+        /// Finalized tool call.
        call: AssistantToolCall,
    },
    /// Terminal event carrying the final assembled assistant message and finish
    /// metadata.
    Done {
+        /// Final structured assistant message.
        message: AssistantMessage,
+        /// Final token usage.
        usage: TokenUsage,
+        /// Reason generation stopped.
        finish_reason: FinishReason,
        /// Connector-specific KV transfer parameters for disaggregated serving.
        kv_transfer_params: Option<serde_json::Value>,
@@ -37,7 +37,7 @@ pub use renderer::{
 };
 pub use request::{
    ChatContent, ChatContentPart, ChatMessage, ChatOptions, ChatRequest, ChatRole, ChatTool,
-    ChatToolChoice, GenerationPromptMode, ReasoningEffort, SamplingParams,
+    ChatToolChoice, GenerationPromptMode, ImageDetail, ReasoningEffort, SamplingParams,
 };
 pub use stream::{ChatEventStream, ChatEventStreamTrait, CollectedAssistantMessage};
 pub use vllm_llm::FinishReason;
@@ -54,6 +54,7 @@ mod stream;

 use vllm_engine_core_client::EngineCoreClient;
 use vllm_engine_core_client::protocol::dtype::ModelDtype;
+use vllm_engine_core_client::protocol::multimodal::MmFeatures;
 use vllm_engine_core_client::protocol::request::ReasoningParserKwargs;
 use vllm_llm::Llm;
 use vllm_text::{Prompt, TextLlm, TextRequest};
@@ -88,6 +89,92 @@ pub fn validate_parser_overrides(
    Ok(())
 }

+/// Chat request preparation shared by inference and render-only frontends.
+pub struct ChatRequestProcessor {
+    backend: DynChatBackend,
+    /// Effective model dtype reported by the engine.
+    /// Absent for text-only frontends without an engine handshake.
+    model_dtype: Option<ModelDtype>,
+}
+
+impl ChatRequestProcessor {
+    /// Create a processor with multimodal support using the effective model
+    /// dtype reported by the engine.
+    fn new(backend: DynChatBackend, model_dtype: ModelDtype) -> Self {
+        Self {
+            backend,
+            model_dtype: Some(model_dtype),
+        }
+    }
+
+    /// Create a render-only processor that rejects multimodal requests.
+    pub fn render_only(backend: DynChatBackend) -> Self {
+        Self {
+            backend,
+            model_dtype: None,
+        }
+    }
+
+    async fn finalize_rendered_prompt(
+        &self,
+        request: &ChatRequest,
+        rendered: RenderedPrompt,
+    ) -> Result<(Prompt, Option<MmFeatures>)> {
+        match self.model_dtype {
+            Some(model_dtype) => {
+                multimodal::finalize_rendered_prompt(
+                    request,
+                    rendered,
+                    self.backend.multimodal_model_info(),
+                    model_dtype,
+                )
+                .await
+            }
+            None if !request.has_multimodal() => Ok((rendered.prompt, None)),
+            None => Err(Error::UnsupportedMultimodalRenderer),
+        }
+    }
+
+    /// Prepare one chat request without submitting it to an engine.
+    pub async fn prepare(
+        &self,
+        mut request: ChatRequest,
+        options: NewChatOutputProcessorOptions<'_>,
+    ) -> Result<(TextRequest, DynChatOutputProcessor)> {
+        request.validate()?;
+
+        // Stamp before rendering so render and tokenize count toward TTFT/e2e.
+        let arrival_time = vllm_llm::current_unix_timestamp_secs();
+        let output_processor = self.backend.new_chat_output_processor(&mut request, options)?;
+        let rendered = self.backend.chat_renderer().render(&request)?;
+        let reasoning_parser_kwargs =
+            request
+                .sampling_params
+                .structured_outputs
+                .is_some()
+                .then(|| ReasoningParserKwargs {
+                    chat_template_kwargs: rendered.effective_template_kwargs.clone(),
+                });
+        let (prompt, mm_features) = self.finalize_rendered_prompt(&request, rendered).await?;
+        let text_request = TextRequest {
+            request_id: request.request_id,
+            prompt,
+            mm_features,
+            sampling_params: request.sampling_params,
+            decode_options: request.decode_options,
+            intermediate: request.intermediate,
+            priority: request.priority,
+            cache_salt: request.cache_salt,
+            add_special_tokens: request.add_special_tokens,
+            data_parallel_rank: request.data_parallel_rank,
+            reasoning_parser_kwargs,
+            lora_request: request.lora_request,
+            arrival_time: Some(arrival_time),
+        };
+        Ok((text_request, output_processor))
+    }
+}
+
 /// Structured chat facade above [`TextLlm`].
 ///
 /// This layer stays above raw text semantics: it takes care of chat-template
@@ -95,9 +182,7 @@ pub fn validate_parser_overrides(
 /// request semantics such as tool calls.
 pub struct ChatLlm {
    text: TextLlm,
-    backend: DynChatBackend,
-    /// Effective model dtype reported by the engine.
-    model_dtype: ModelDtype,
+    processor: ChatRequestProcessor,
    /// Tool-call parser selection.
    tool_call_parser: ParserSelection,
    /// Reasoning parser selection.
@@ -112,8 +197,7 @@ impl ChatLlm {

        Self {
            text,
-            backend,
-            model_dtype,
+            processor: ChatRequestProcessor::new(backend, model_dtype),
            tool_call_parser: ParserSelection::Auto,
            reasoning_parser: ParserSelection::Auto,
        }
@@ -140,7 +224,7 @@ impl ChatLlm {

    /// Override the effective model dtype used for multimodal tensor encoding.
    pub fn with_model_dtype(mut self, model_dtype: ModelDtype) -> Self {
-        self.model_dtype = model_dtype;
+        self.processor.model_dtype = Some(model_dtype);
        self
    }

@@ -172,75 +256,36 @@ impl ChatLlm {
    }

    /// Render, tokenize, and submit one chat request.
-    pub async fn chat(&self, mut request: ChatRequest) -> Result<ChatEventStream> {
-        request.validate()?;
-
-        // Stamp before rendering so render and tokenize count toward TTFT/e2e.
-        let arrival_time = vllm_llm::current_unix_timestamp_secs();
-
-        let output_processor = self.backend.new_chat_output_processor(
-            &mut request,
-            NewChatOutputProcessorOptions {
-                tool_call_parser: &self.tool_call_parser,
-                reasoning_parser: &self.reasoning_parser,
-            },
-        )?;
-        let rendered = self.backend.chat_renderer().render(&request)?;
-        let reasoning_parser_kwargs =
-            request
-                .sampling_params
-                .structured_outputs
-                .is_some()
-                .then(|| ReasoningParserKwargs {
-                    chat_template_kwargs: rendered.effective_template_kwargs.clone(),
-                });
-
-        let (prompt, mm_features) = multimodal::finalize_rendered_prompt(
-            &request,
-            rendered,
-            self.backend.multimodal_model_info(),
-            self.model_dtype,
-        )
-        .await?;
-
-        let text_request = TextRequest {
-            request_id: request.request_id.clone(),
-            prompt,
-            mm_features,
-            sampling_params: request.sampling_params,
-            decode_options: request.decode_options,
-            intermediate: request.intermediate,
-            priority: request.priority,
-            cache_salt: request.cache_salt,
-            add_special_tokens: request.add_special_tokens,
-            data_parallel_rank: request.data_parallel_rank,
-            reasoning_parser_kwargs,
-            lora_request: request.lora_request,
-            arrival_time: Some(arrival_time),
-        };
+    pub async fn chat(&self, request: ChatRequest) -> Result<ChatEventStream> {
+        let (text_request, output_processor) = self
+            .processor
+            .prepare(
+                request,
+                NewChatOutputProcessorOptions {
+                    tool_call_parser: &self.tool_call_parser,
+                    reasoning_parser: &self.reasoning_parser,
+                },
+            )
+            .await?;
+        let request_id = text_request.request_id.clone();
        let decoded_stream = self.text.generate(text_request).await?.map_err(Error::from).boxed();

        let structured_stream = output_processor.process(decoded_stream)?;

-        Ok(ChatEventStream::new(request.request_id, structured_stream))
+        Ok(ChatEventStream::new(request_id, structured_stream))
    }

    /// Render through the chat template and tokenize, without submitting to the engine.
    ///
-    /// Same render → [`multimodal::finalize_rendered_prompt`] → encode pipeline as
-    /// [`Self::chat`], but stops after token IDs so `/tokenize` counts match what
-    /// generation would see. Used by `POST /tokenize` (chat form).
+    /// Uses the same render, multimodal finalization, and encoding pipeline as
+    /// [`Self::chat`], but stops after token IDs so `/tokenize` counts match
+    /// what generation would see. Used by `POST /tokenize` (chat form).
    pub async fn tokenize_chat(&self, request: ChatRequest) -> Result<Vec<u32>> {
        request.validate()?;

-        let rendered = self.backend.chat_renderer().render(&request)?;
-        let (prompt, _mm_features) = multimodal::finalize_rendered_prompt(
-            &request,
-            rendered,
-            self.backend.multimodal_model_info(),
-            self.model_dtype,
-        )
-        .await?;
+        let rendered = self.processor.backend.chat_renderer().render(&request)?;
+        let (prompt, _mm_features) =
+            self.processor.finalize_rendered_prompt(&request, rendered).await?;

        let tokenizer = self.text.tokenizer();
        let token_ids = match prompt {
@@ -33,7 +33,7 @@ use vllm_text::tokenizer::{DynTokenizer, Tokenizer};

 use crate::error::{Error, Result, bail_multimodal, multimodal};
 use crate::renderer::RenderedPrompt;
-use crate::request::{ChatContent, ChatContentPart, ChatMessage, ChatRequest};
+use crate::request::{ChatContent, ChatContentPart, ChatMessage, ChatRequest, ImageDetail};

 mod audio;
 mod expand;
@@ -529,7 +529,7 @@ fn extract_media_parts(request: &ChatRequest) -> Result<Vec<MediaContentPart>> {
                    uuid,
                } => all_parts.push(MediaContentPart::ImageUrl {
                    url: image_url.clone(),
-                    detail: *detail,
+                    detail: detail.map(to_multimodal_image_detail),
                    uuid: uuid.clone(),
                }),
                ChatContentPart::VideoUrl { video_url, uuid } => {
@@ -556,6 +556,15 @@ fn extract_media_parts(request: &ChatRequest) -> Result<Vec<MediaContentPart>> {
    Ok(all_parts)
 }

+/// Convert the protocol-level image detail into the multimodal processor type.
+fn to_multimodal_image_detail(detail: ImageDetail) -> llm_multimodal::ImageDetail {
+    match detail {
+        ImageDetail::Auto => llm_multimodal::ImageDetail::Auto,
+        ImageDetail::Low => llm_multimodal::ImageDetail::Low,
+        ImageDetail::High => llm_multimodal::ImageDetail::High,
+    }
+}
+
 /// Wrap OpenAI base64 audio in a data URL consumed by `MediaConnector`.
 fn input_audio_data_url(data: &str, format: Option<&str>) -> Result<String> {
    let mime_type = match format {
@@ -178,8 +178,8 @@ impl ChatOutputProcessor for DefaultChatOutputProcessor {
    /// events through two sequential stages once text decoding has
    /// already happened:
    ///
-    /// 1. [`unified_event_stream`] — reasoning and tool-call parsing
-    /// 2. [`structured_chat_event_stream`] — final block assembly
+    /// 1. `unified_event_stream` — reasoning and tool-call parsing
+    /// 2. `structured_chat_event_stream` — final block assembly
    fn process(self: Box<Self>, decoded: DynDecodedTextEventStream) -> Result<DynChatEventStream> {
        let parsed = unified_event_stream(decoded, self.parser);
        let structured = structured_chat_event_stream(parsed, self.parallel_tool_calls);
@@ -517,7 +517,7 @@ fn write_chat_content(out: &mut String, content: &ChatContent) -> Result<()> {
        ChatContent::Text(text) => out.push_str(text),
        ChatContent::Parts(parts) => {
            for part in parts {
-                out.push_str(part.as_text()?);
+                out.push_str(part.as_text().map_err(Error::UnsupportedMultimodalContent)?);
            }
        }
    }
@@ -519,7 +519,7 @@ fn write_chat_content(out: &mut String, content: &ChatContent) -> Result<()> {
        ChatContent::Text(text) => out.push_str(text),
        ChatContent::Parts(parts) => {
            for part in parts {
-                out.push_str(part.as_text()?);
+                out.push_str(part.as_text().map_err(Error::UnsupportedMultimodalContent)?);
            }
        }
    }
@@ -427,7 +427,7 @@ fn auto_drop_analysis_messages(messages: Vec<Message>) -> Vec<Message> {

 /// Flatten vLLM text content and reject unsupported multimodal parts.
 fn flatten_text(content: &ChatContent) -> Result<String> {
-    content.try_flatten_to_text()
+    content.try_flatten_to_text().map_err(Error::UnsupportedMultimodalContent)
 }

 /// Convert vLLM function tool definitions to Harmony tool descriptions.
@@ -277,7 +277,7 @@ impl InklingChatRenderer {
        tool_call_id: &str,
        tool_call_id_to_name: &HashMap<String, String>,
    ) -> Result<()> {
-        let text = content.try_flatten_to_text()?;
+        let text = content.try_flatten_to_text().map_err(Error::UnsupportedMultimodalContent)?;
        let tool_name = tool_call_id_to_name.get(tool_call_id).map(String::as_str).unwrap_or("");
        self.write_text_block(out, self.special.message_tool, Some(tool_name), &text)
    }
@@ -1,449 +1,17 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project

-use std::collections::HashMap;
-
-use llm_multimodal::ImageDetail;
 use serde::{Deserialize, Serialize};
 use serde_json::Value;
+pub use vllm_chat_types::{
+    ChatContent, ChatContentPart, ChatMessage, ChatOptions, ChatRole, ChatToolChoice,
+    GenerationPromptMode, ImageDetail, ReasoningEffort, Tool as ChatTool,
+};
 use vllm_engine_core_client::protocol::lora::LoraRequest;
-pub use vllm_parser::tool::Tool as ChatTool;
 pub use vllm_text::SamplingParams;
 use vllm_text::TextDecodeOptions;

-use crate::AssistantMessageExt;
 use crate::error::{Error, Result};
-use crate::event::{AssistantContentBlock, AssistantMessage};
-
-/// Role label for one text-only chat message.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(rename_all = "snake_case")]
-pub enum ChatRole {
-    System,
-    Developer,
-    User,
-    Assistant,
-    ToolResponse,
-}
-
-/// One text-only chat content part in OpenAI-style block format.
-#[serde_with::skip_serializing_none]
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(tag = "type", rename_all = "snake_case")]
-pub enum ChatContentPart {
-    /// One plain-text content block.
-    Text { text: String },
-    /// One image URL/data URL content block.
-    ImageUrl {
-        image_url: String,
-        detail: Option<ImageDetail>,
-        uuid: Option<String>,
-    },
-    /// One video URL/data URL content block.
-    VideoUrl {
-        video_url: String,
-        uuid: Option<String>,
-    },
-    /// One `input_audio` content block carrying base64-encoded audio bytes.
-    InputAudio {
-        data: String,
-        format: Option<String>,
-        uuid: Option<String>,
-    },
-    /// One audio URL/data URL content block.
-    AudioUrl {
-        audio_url: String,
-        uuid: Option<String>,
-    },
-    // ImageData...
-    // VideoData...
-    // ImageEmbeds...
-}
-
-impl ChatContentPart {
-    /// Construct one text content part with plain string content.
-    pub fn text(text: impl Into<String>) -> Self {
-        Self::Text { text: text.into() }
-    }
-
-    /// Construct one image URL content part with the given URL string.
-    pub fn image_url(image_url: impl Into<String>) -> Self {
-        Self::ImageUrl {
-            image_url: image_url.into(),
-            detail: None,
-            uuid: None,
-        }
-    }
-
-    /// Construct one video URL content part with the given URL string.
-    pub fn video_url(video_url: impl Into<String>) -> Self {
-        Self::VideoUrl {
-            video_url: video_url.into(),
-            uuid: None,
-        }
-    }
-
-    /// Construct one base64-encoded input-audio content part.
-    pub fn input_audio(data: impl Into<String>, format: Option<String>) -> Self {
-        Self::InputAudio {
-            data: data.into(),
-            format,
-            uuid: None,
-        }
-    }
-
-    /// Construct one audio URL content part with the given URL string.
-    pub fn audio_url(audio_url: impl Into<String>) -> Self {
-        Self::AudioUrl {
-            audio_url: audio_url.into(),
-            uuid: None,
-        }
-    }
-
-    /// Return the text content of this part when it's a text block, or an
-    /// "unsupported multimodal content" error otherwise.
-    pub(crate) fn as_text(&self) -> Result<&str> {
-        match self {
-            Self::Text { text } => Ok(text),
-            Self::ImageUrl { .. } => Err(Error::UnsupportedMultimodalContent("image_url")),
-            Self::VideoUrl { .. } => Err(Error::UnsupportedMultimodalContent("video_url")),
-            Self::InputAudio { .. } => Err(Error::UnsupportedMultimodalContent("input_audio")),
-            Self::AudioUrl { .. } => Err(Error::UnsupportedMultimodalContent("audio_url")),
-        }
-    }
-
-    /// Return whether this part is a text block with empty content.
-    pub(crate) fn is_empty_text(&self) -> bool {
-        matches!(self, Self::Text { text } if text.is_empty())
-    }
-
-    /// Return whether this part contains any multimodal content.
-    pub(crate) fn is_multimodal(&self) -> bool {
-        match self {
-            Self::Text { .. } => false,
-            Self::ImageUrl { .. }
-            | Self::VideoUrl { .. }
-            | Self::InputAudio { .. }
-            | Self::AudioUrl { .. } => true,
-        }
-    }
-}
-
-/// Text-only chat content.
-///
-/// This supports either a simple string or an OpenAI-style list of text blocks.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(untagged)]
-pub enum ChatContent {
-    /// Simple text content.
-    Text(String),
-    /// OpenAI-style blocks.
-    Parts(Vec<ChatContentPart>),
-}
-
-impl ChatContent {
-    /// Flatten the text content into one plain string without adding
-    /// separators.
-    // TODO: this method will be truly fallible once we add non-text content parts.
-    pub fn try_flatten_to_text(&self) -> Result<String> {
-        Ok(match self {
-            Self::Text(text) => text.clone(),
-            Self::Parts(parts) => {
-                parts.iter().map(ChatContentPart::as_text).collect::<Result<Vec<_>>>()?.concat()
-            }
-        })
-    }
-
-    /// Return whether there's no text content or only empty text blocks.
-    pub fn is_empty(&self) -> bool {
-        match self {
-            Self::Text(text) => text.is_empty(),
-            Self::Parts(parts) => parts.iter().all(ChatContentPart::is_empty_text),
-        }
-    }
-
-    /// Return whether this content contains any multimodal parts.
-    pub fn has_multimodal(&self) -> bool {
-        match self {
-            Self::Text(_) => false,
-            Self::Parts(parts) => parts.iter().any(ChatContentPart::is_multimodal),
-        }
-    }
-}
-
-impl From<String> for ChatContent {
-    fn from(value: String) -> Self {
-        Self::Text(value)
-    }
-}
-
-impl From<&str> for ChatContent {
-    fn from(value: &str) -> Self {
-        Self::Text(value.to_string())
-    }
-}
-
-impl From<Vec<ChatContentPart>> for ChatContent {
-    fn from(value: Vec<ChatContentPart>) -> Self {
-        Self::Parts(value)
-    }
-}
-
-/// One chat message.
-///
-/// Original Python API reference:
-/// <https://github.com/vllm-project/vllm/blob/bc2c0c86efb28e77677a3cfb8687e976914a313a/vllm/entrypoints/chat_utils.py#L309-L333>
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(tag = "role", rename_all = "snake_case")]
-pub enum ChatMessage {
-    /// System message content.
-    System { content: ChatContent },
-    /// Developer message content plus optional message-local tools.
-    Developer {
-        content: ChatContent,
-        tools: Option<Vec<ChatTool>>,
-    },
-    /// User message content.
-    User { content: ChatContent },
-    /// Assistant history content assembled from structured assistant blocks.
-    Assistant { content: Vec<AssistantContentBlock> },
-    /// Tool response content associated with one prior assistant tool call.
-    ToolResponse {
-        content: ChatContent,
-        tool_call_id: String,
-    },
-}
-
-impl ChatMessage {
-    /// Construct one chat message with plain string content.
-    pub fn text(role: ChatRole, text: impl Into<String>) -> Self {
-        let content: String = text.into();
-
-        match role {
-            ChatRole::System => Self::system(content),
-            ChatRole::Developer => Self::developer(content, None),
-            ChatRole::User => Self::user(content),
-            ChatRole::Assistant => Self::assistant_text(content),
-            ChatRole::ToolResponse => {
-                panic!(
-                    "tool response messages require a tool_call_id; \
-                     use ChatMessage::tool_response() instead"
-                )
-            }
-        }
-    }
-
-    /// Construct one chat message with system role.
-    pub fn system(content: impl Into<ChatContent>) -> Self {
-        Self::System {
-            content: content.into(),
-        }
-    }
-
-    /// Construct one chat message with developer role.
-    pub fn developer(content: impl Into<ChatContent>, tools: Option<Vec<ChatTool>>) -> Self {
-        Self::Developer {
-            content: content.into(),
-            tools,
-        }
-    }
-
-    /// Construct one chat message with user role.
-    pub fn user(content: impl Into<ChatContent>) -> Self {
-        Self::User {
-            content: content.into(),
-        }
-    }
-
-    /// Construct one chat message with assistant role and plain string content.
-    pub fn assistant_text(text: impl Into<String>) -> Self {
-        Self::Assistant {
-            content: vec![AssistantContentBlock::Text { text: text.into() }],
-        }
-    }
-
-    /// Construct one chat message with assistant role and structured content
-    /// blocks.
-    pub fn assistant_blocks(content: Vec<AssistantContentBlock>) -> Self {
-        Self::Assistant { content }
-    }
-
-    /// Construct one tool-role message.
-    pub fn tool_response(content: impl Into<ChatContent>, tool_call_id: impl Into<String>) -> Self {
-        Self::ToolResponse {
-            content: content.into(),
-            tool_call_id: tool_call_id.into(),
-        }
-    }
-
-    /// Return the chat role of this message.
-    pub fn role(&self) -> ChatRole {
-        match self {
-            Self::System { .. } => ChatRole::System,
-            Self::Developer { .. } => ChatRole::Developer,
-            Self::User { .. } => ChatRole::User,
-            Self::Assistant { .. } => ChatRole::Assistant,
-            Self::ToolResponse { .. } => ChatRole::ToolResponse,
-        }
-    }
-
-    /// Concatenate the visible text carried by this message.
-    pub fn text_content(&self) -> Result<String> {
-        match self {
-            Self::System { content }
-            | Self::Developer { content, .. }
-            | Self::User { content }
-            | Self::ToolResponse { content, .. } => content.try_flatten_to_text(),
-            Self::Assistant { content } => Ok(content.text()),
-        }
-    }
-
-    /// Concatenate assistant reasoning text when present.
-    pub fn reasoning_content(&self) -> Option<String> {
-        match self {
-            Self::Assistant { content } => content.reasoning(),
-            Self::System { .. }
-            | Self::Developer { .. }
-            | Self::User { .. }
-            | Self::ToolResponse { .. } => None,
-        }
-    }
-
-    /// Return whether this message contains any multimodal content.
-    pub fn has_multimodal(&self) -> bool {
-        match self {
-            Self::System { content }
-            | Self::Developer { content, .. }
-            | Self::User { content }
-            | Self::ToolResponse { content, .. } => content.has_multimodal(),
-            Self::Assistant { .. } => false,
-        }
-    }
-}
-
-impl From<AssistantMessage> for ChatMessage {
-    fn from(value: AssistantMessage) -> Self {
-        Self::Assistant {
-            content: value.content,
-        }
-    }
-}
-
-/// Controls how prompt rendering should end after the existing chat history.
-#[derive(Debug, Clone, Copy, Default, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(rename_all = "snake_case")]
-pub enum GenerationPromptMode {
-    /// Append a generation prompt for a new assistant turn.
-    ///
-    /// Equivalent to `add_generation_prompt = true` and `continue_final_message
-    /// = false`.
-    #[default]
-    StartNewAssistant,
-    /// Leave the final assistant message open so generation continues it.
-    ///
-    /// Equivalent to `add_generation_prompt = false` and
-    /// `continue_final_message = true`.
-    ContinueFinalAssistant,
-    /// Render the existing chat history without adding any trailing generation
-    /// prompt.
-    ///
-    /// Equivalent to `add_generation_prompt = false` and
-    /// `continue_final_message = false`.
-    NoGenerationPrompt,
-}
-
-/// Effort level for reasoning models.
-#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(rename_all = "lowercase")]
-pub enum ReasoningEffort {
-    None,
-    Minimal,
-    Low,
-    Medium,
-    High,
-    XHigh,
-    Max,
-}
-
-impl ReasoningEffort {
-    pub fn as_str(self) -> &'static str {
-        match self {
-            Self::None => "none",
-            Self::Minimal => "minimal",
-            Self::Low => "low",
-            Self::Medium => "medium",
-            Self::High => "high",
-            Self::XHigh => "xhigh",
-            Self::Max => "max",
-        }
-    }
-}
-
-/// Chat-template-related request options.
-///
-/// These are the small subset of chat controls that currently affect prompt
-/// rendering in `vllm-chat`.
-#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
-pub struct ChatOptions {
-    /// Controls whether rendering starts a new assistant turn, continues the
-    /// final assistant message, or emits no trailing generation prompt at
-    /// all.
-    pub generation_prompt_mode: GenerationPromptMode,
-
-    /// Per-request Jinja chat template override. When set, this template is
-    /// used instead of the model's default chat template.
-    pub chat_template: Option<String>,
-
-    /// Effort level exposed to chat templates for reasoning models.
-    pub reasoning_effort: Option<ReasoningEffort>,
-
-    /// Additional keyword arguments exposed to the chat template.
-    pub template_kwargs: HashMap<String, Value>,
-}
-
-impl Default for ChatOptions {
-    fn default() -> Self {
-        Self {
-            generation_prompt_mode: GenerationPromptMode::StartNewAssistant,
-            chat_template: None,
-            reasoning_effort: None,
-            template_kwargs: HashMap::new(),
-        }
-    }
-}
-
-impl ChatOptions {
-    /// Whether to add a generation prompt for a new assistant turn after the
-    /// existing chat history.
-    pub fn add_generation_prompt(&self) -> bool {
-        matches!(
-            self.generation_prompt_mode,
-            GenerationPromptMode::StartNewAssistant
-        )
-    }
-
-    /// Whether to leave the final assistant message open so generation
-    /// continues it.
-    pub fn continue_final_message(&self) -> bool {
-        matches!(
-            self.generation_prompt_mode,
-            GenerationPromptMode::ContinueFinalAssistant
-        )
-    }
-}
-
-/// Tool-choice semantics supported by `vllm-chat`.
-#[derive(Debug, Clone, Default, PartialEq, Eq, Serialize, Deserialize)]
-#[serde(rename_all = "snake_case")]
-pub enum ChatToolChoice {
-    #[default]
-    None,
-    Auto,
-    Required,
-    Function {
-        name: String,
-    },
-}

 /// One chat request ready to be rendered into a prompt and lowered into a
 /// generate request.
@@ -578,120 +146,12 @@ impl ChatRequest {
    }
 }

-impl ChatRole {
-    /// Return the chat-template role string used by the current text-only chat
-    /// backend.
-    pub fn as_str(&self) -> &'static str {
-        match self {
-            Self::System => "system",
-            Self::Developer => "developer",
-            Self::User => "user",
-            Self::Assistant => "assistant",
-            Self::ToolResponse => "tool_response",
-        }
-    }
-}
-
 #[cfg(test)]
 mod tests {
-    use serde_json::{json, to_value};
+    use serde_json::json;

-    use super::{ChatContent, ChatContentPart, ChatMessage, ChatRequest, ChatRole, ChatTool};
+    use super::ChatRequest;
    use crate::Error;
-    use crate::event::AssistantContentBlock;
-
-    #[test]
-    fn chat_content_deserializes_from_raw_string() {
-        let content: ChatContent = serde_json::from_value(json!("hello")).unwrap();
-        assert_eq!(content, ChatContent::Text("hello".to_string()));
-    }
-
-    #[test]
-    fn chat_content_video_url_part_round_trips_through_serde() {
-        let content = ChatContent::Parts(vec![ChatContentPart::VideoUrl {
-            video_url: "https://example.com/demo.mp4".to_string(),
-            uuid: Some("video-1".to_string()),
-        }]);
-
-        let value = to_value(&content).unwrap();
-        assert_eq!(
-            value,
-            json!([{
-                "type": "video_url",
-                "video_url": "https://example.com/demo.mp4",
-                "uuid": "video-1",
-            }])
-        );
-        let decoded: ChatContent = serde_json::from_value(value).unwrap();
-        assert_eq!(decoded, content);
-    }
-
-    #[test]
-    fn chat_content_deserializes_from_openai_text_blocks() {
-        let content: ChatContent =
-            serde_json::from_value(json!([{ "type": "text", "text": "hello" }])).unwrap();
-        assert_eq!(
-            content,
-            ChatContent::Parts(vec![ChatContentPart::text("hello")])
-        );
-    }
-
-    #[test]
-    fn chat_content_from_string_like_values_builds_text() {
-        assert_eq!(
-            ChatContent::from("hello"),
-            ChatContent::Text("hello".to_string())
-        );
-        assert_eq!(
-            ChatContent::from("hello".to_string()),
-            ChatContent::Text("hello".to_string())
-        );
-    }
-
-    #[test]
-    fn chat_content_try_flattens_text_parts_without_separators() {
-        let content = ChatContent::Parts(vec![
-            ChatContentPart::text("hello"),
-            ChatContentPart::text(" world"),
-        ]);
-        assert_eq!(content.try_flatten_to_text().unwrap(), "hello world");
-    }
-
-    #[test]
-    fn assistant_message_collects_visible_and_reasoning_text() {
-        let message = ChatMessage::assistant_blocks(vec![
-            AssistantContentBlock::Reasoning {
-                text: "inner".to_string(),
-            },
-            AssistantContentBlock::Text {
-                text: "outer".to_string(),
-            },
-        ]);
-
-        assert_eq!(message.role(), ChatRole::Assistant);
-        assert_eq!(message.text_content().unwrap(), "outer");
-        assert_eq!(message.reasoning_content().as_deref(), Some("inner"));
-    }
-
-    #[test]
-    fn developer_message_round_trips_through_serde() {
-        let message = ChatMessage::developer(
-            "hello",
-            Some(vec![ChatTool {
-                name: "get_weather".to_string(),
-                description: Some("Get weather".to_string()),
-                parameters: json!({
-                    "type": "object",
-                    "properties": {"city": {"type": "string"}},
-                }),
-                strict: Some(true),
-            }]),
-        );
-
-        let value = to_value(&message).unwrap();
-        let decoded: ChatMessage = serde_json::from_value(value).unwrap();
-        assert_eq!(decoded, message);
-    }

    #[test]
    fn enable_thinking_is_none_when_no_kwargs_are_present() {
@@ -11,7 +11,7 @@ use tokio::time::timeout;
 use vllm_chat::{
    AssistantBlockKind, AssistantContentBlock, AssistantMessageExt as _, ChatBackend, ChatEvent,
    ChatLlm, ChatMessage, ChatRenderer, ChatRequest, ChatRole, ChatTextBackend, ChatTool,
-    ChatToolChoice, DefaultChatOutputProcessor, DynChatOutputProcessor, DynChatRenderer,
+    ChatToolChoice, DefaultChatOutputProcessor, DynChatOutputProcessor, DynChatRenderer, Error,
    FinishReason, GenerationPromptMode, NewChatOutputProcessorOptions, ParserSelection,
    RenderedPrompt, SamplingParams,
 };
@@ -257,7 +257,7 @@ impl ChatRenderer for FakeChatBackend {
        for message in &request.messages {
            prompt.push_str(message.role().as_str());
            prompt.push_str(": ");
-            prompt.push_str(&message.text_content()?);
+            prompt.push_str(&message.text_content().map_err(Error::UnsupportedMultimodalContent)?);
            prompt.push('\n');
        }
        if request.chat_options.add_generation_prompt() {
@@ -9,10 +9,10 @@ test-util = []

 [dependencies]
 easy-ext.workspace = true
-serde.workspace = true
 serde_json.workspace = true
 thiserror.workspace = true
 thiserror-ext.workspace = true
+vllm-chat-types.workspace = true
 vllm-tokenizer.workspace = true
 winnow.workspace = true
 xgrammar-structural-tag.workspace = true
@@ -34,21 +34,11 @@ pub use minimax_m2::MinimaxM2ToolParser;
 pub use minimax_m3::MinimaxM3ToolParser;
 pub use qwen_coder::Qwen3CoderToolParser;
 pub use seed_oss::SeedOssToolParser;
-use serde::{Deserialize, Serialize};
-use serde_json::Value;
+pub use vllm_chat_types::Tool;
 pub use xgrammar_structural_tag::builders::StructuralTagBuilder;

 use crate::utils;

-/// One function-style tool made available to the model.
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
-pub struct Tool {
-    pub name: String,
-    pub description: Option<String>,
-    pub parameters: Value,
-    pub strict: Option<bool>,
-}
-
 /// One tool-call update emitted while parsing assistant text.
 #[derive(Debug, Clone, PartialEq, Eq)]
 pub struct ToolCallDelta {
@@ -8,7 +8,7 @@ use tonic_health::ServingStatus;
 use tonic_health::server::HealthReporter;
 use tracing::{info, warn};

-use super::GenerateGrpcService;
+use super::{ControlGrpcService, GenerateGrpcService};

 pub(crate) async fn monitor_health(
    mut health_reporter: HealthReporter,
@@ -16,21 +16,18 @@ pub(crate) async fn monitor_health(
    shutdown: CancellationToken,
 ) {
    let generate_service = GenerateGrpcService::NAME;
+    let control_service = ControlGrpcService::NAME;
    let status = ServingStatus::NotServing;
    let health_event_first = tokio::select! {
        result = engine_health.wait_for(|healthy| !*healthy) => {
            match result {
                Ok(_) => warn!(
-                    generate_service,
-                    overall_service = true,
                    status = ?status,
                    reason = "engine_unhealthy",
                    "marking gRPC health services as not serving"
                ),
                Err(error) => warn!(
                    %error,
-                    generate_service,
-                    overall_service = true,
                    status = ?status,
                    reason = "health_channel_closed",
                    "engine health channel closed; marking gRPC health services as not serving"
@@ -40,8 +37,6 @@ pub(crate) async fn monitor_health(
        }
        _ = shutdown.cancelled() => {
            info!(
-                generate_service,
-                overall_service = true,
                status = ?status,
                reason = "server_shutdown",
                "server shutting down; marking gRPC health services as not serving"
@@ -51,20 +46,20 @@ pub(crate) async fn monitor_health(
    };

    health_reporter.set_not_serving::<GenerateGrpcService>().await;
-    // Generate is currently the only engine-backed gRPC service, so overall
-    // server health intentionally mirrors it.
+    health_reporter.set_not_serving::<ControlGrpcService>().await;
+    // Both gRPC services use the same engine client, so overall server health
+    // mirrors their shared engine health.
    health_reporter.set_service_status("", status).await;

    if health_event_first {
        shutdown.cancelled().await;
        info!(
-            generate_service,
-            overall_service = true,
            reason = "server_shutdown",
            "server shutting down; closing gRPC health watches"
        );
    }

    health_reporter.clear_service_status(generate_service).await;
+    health_reporter.clear_service_status(control_service).await;
    health_reporter.clear_service_status("").await;
 }
@@ -26,8 +26,10 @@ pub mod pb {
 }

 pub(crate) use health::monitor_health;
+pub use pb::control_server::ControlServer;
 pub use pb::generate_server::GenerateServer;

+pub(crate) type ControlGrpcService = ControlServer<ControlServiceImpl>;
 pub(crate) type GenerateGrpcService = GenerateServer<GenerateServiceImpl>;

 #[cfg(test)]
@@ -44,6 +46,36 @@ impl GenerateServiceImpl {
    }
 }

+/// gRPC control service backed by the shared application state.
+pub struct ControlServiceImpl {
+    state: Arc<AppState>,
+}
+
+impl ControlServiceImpl {
+    pub fn new(state: Arc<AppState>) -> Self {
+        Self { state }
+    }
+}
+
+#[tonic::async_trait]
+impl pb::control_server::Control for ControlServiceImpl {
+    async fn abort(
+        &self,
+        request: Request<pb::AbortRequest>,
+    ) -> Result<Response<pb::AbortResponse>, Status> {
+        let request_ids = request.into_inner().request_ids;
+        if request_ids.is_empty() {
+            return Ok(Response::new(pb::AbortResponse {}));
+        }
+        self.state
+            .chat
+            .abort(&request_ids)
+            .await
+            .map_err(|error| Status::internal(error.to_report_string()))?;
+        Ok(Response::new(pb::AbortResponse {}))
+    }
+}
+
 #[tonic::async_trait]
 impl pb::generate_server::Generate for GenerateServiceImpl {
    type GenerateStreamStream =
@@ -38,8 +38,9 @@ use vllm_tokenizer::test_utils::TestTokenizer;
 use zeromq::prelude::{SocketRecv, SocketSend};
 use zeromq::{DealerSocket, PushSocket, ZmqMessage};

+use super::pb::control_client::ControlClient;
 use super::pb::generate_client::GenerateClient;
-use super::{GenerateServer, GenerateServiceImpl, pb};
+use super::{ControlServer, ControlServiceImpl, GenerateServer, GenerateServiceImpl, pb};
 use crate::listener::{Listener, MaybeTlsListener};
 use crate::state::AppState;
 use crate::tls;
@@ -153,10 +154,6 @@ async fn recv_engine_message(dealer: &mut DealerSocket) -> Vec<bytes::Bytes> {
    dealer.recv().await.expect("recv engine message").into_vec()
 }

-fn test_llm(client: EngineCoreClient) -> Llm {
-    Llm::new(client).with_request_id_randomization(false)
-}
-
 #[derive(Clone, Debug)]
 struct FakeTextBackend;

@@ -206,6 +203,7 @@ async fn setup_grpc_service(
    output_specs: Vec<(Vec<u32>, Option<EngineCoreFinishReason>)>,
 ) -> (
    GenerateServer<GenerateServiceImpl>,
+    ControlServer<ControlServiceImpl>,
    tokio::sync::watch::Receiver<bool>,
    MockEngineTask,
 ) {
@@ -243,12 +241,13 @@ async fn setup_grpc_service(
    let engine_health = client.subscribe_health();

    let chat = ChatLlm::from_shared_backend(
-        test_llm(client),
+        Llm::new(client),
        Arc::new(FakeTextBackend) as Arc<dyn ChatTextBackend>,
    );
    let state = Arc::new(AppState::new(vec!["test-model".to_string()], chat));
    (
-        GenerateServer::new(GenerateServiceImpl::new(state)),
+        GenerateServer::new(GenerateServiceImpl::new(state.clone())),
+        ControlServer::new(ControlServiceImpl::new(state)),
        engine_health,
        engine_task,
    )
@@ -264,9 +263,11 @@ async fn grpc_test_server(
    tokio::task::JoinHandle<()>,
    MockEngineTask,
 ) {
-    let (svc, engine_health, engine_task) = setup_grpc_service(engine_id, output_specs).await;
+    let (generate_service, control_service, engine_health, engine_task) =
+        setup_grpc_service(engine_id, output_specs).await;
    let (channel, server_task) = start_grpc_test_server(
-        svc,
+        generate_service,
+        control_service,
        engine_health,
        tokio_util::sync::CancellationToken::new(),
    )
@@ -276,11 +277,13 @@ async fn grpc_test_server(

 async fn start_grpc_test_server(
    generate_service: GenerateServer<GenerateServiceImpl>,
+    control_service: ControlServer<ControlServiceImpl>,
    engine_health: tokio::sync::watch::Receiver<bool>,
    shutdown: tokio_util::sync::CancellationToken,
 ) -> (Channel, tokio::task::JoinHandle<()>) {
    let (health_reporter, health_service) = health_reporter();
    health_reporter.set_serving::<GenerateServer<GenerateServiceImpl>>().await;
+    health_reporter.set_serving::<ControlServer<ControlServiceImpl>>().await;

    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.expect("bind grpc listener");
    let addr = listener.local_addr().expect("local addr");
@@ -289,6 +292,7 @@ async fn start_grpc_test_server(
        let incoming = MaybeTlsListener::plain(Listener::Tcp(listener));
        let server = TonicServer::builder()
            .add_service(health_service)
+            .add_service(control_service)
            .add_service(generate_service)
            .serve_with_incoming_shutdown(incoming, shutdown.clone().cancelled_owned());
        let health_monitor =
@@ -319,7 +323,8 @@ async fn grpc_tls_test_server(
    certs: &TestCerts,
    cert_reqs: i32,
 ) -> (String, tokio::task::JoinHandle<()>, MockEngineTask) {
-    let (svc, _engine_health, engine_task) = setup_grpc_service(engine_id, output_specs).await;
+    let (generate_service, control_service, _engine_health, engine_task) =
+        setup_grpc_service(engine_id, output_specs).await;
    let context = tls::build_grpc_server_config(&server_tls(certs, cert_reqs))
        .expect("build grpc tls config");

@@ -329,7 +334,8 @@ async fn grpc_tls_test_server(
    let server_task = tokio::spawn(async move {
        let incoming = MaybeTlsListener::tls(Listener::Tcp(listener), context);
        TonicServer::builder()
-            .add_service(svc)
+            .add_service(control_service)
+            .add_service(generate_service)
            .serve_with_incoming(incoming)
            .await
            .expect("grpc tls server");
@@ -409,7 +415,7 @@ async fn grpc_server_with_keepalive(
    engine_id: impl Into<EngineId>,
    keepalive: Option<Duration>,
 ) -> (String, tokio::task::JoinHandle<()>, MockEngineTask) {
-    let (svc, _engine_health, engine_task) =
+    let (generate_service, control_service, _engine_health, engine_task) =
        setup_grpc_service(engine_id, default_stream_output_specs()).await;

    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.expect("bind grpc listener");
@@ -425,7 +431,8 @@ async fn grpc_server_with_keepalive(
    let server_task = tokio::spawn(async move {
        let incoming = MaybeTlsListener::plain(Listener::Tcp(listener));
        builder
-            .add_service(svc)
+            .add_service(control_service)
+            .add_service(generate_service)
            .serve_with_incoming(incoming)
            .await
            .expect("grpc server");
@@ -1073,14 +1080,106 @@ async fn grpc_without_keepalive_keeps_unresponsive_connection_open() {
    server_task.abort();
 }

+#[tokio::test(flavor = "multi_thread", worker_threads = 2)]
+#[serial]
+async fn control_abort_resolves_external_id_and_empty_is_noop() {
+    let (generate_service, control_service, engine_health, engine_task) =
+        setup_grpc_service(b"engine-grpc-abort-active", vec![(vec![b'h' as u32], None)]).await;
+    let (channel, server_task) = start_grpc_test_server(
+        generate_service,
+        control_service,
+        engine_health,
+        tokio_util::sync::CancellationToken::new(),
+    )
+    .await;
+    let mut generate_client = GenerateClient::new(channel.clone());
+    let mut control_client = ControlClient::new(channel);
+    let request_id = "test-abort-active";
+
+    let mut stream = generate_client
+        .generate_stream(pb::GenerateRequest {
+            request_id: request_id.to_string(),
+            model: "test-model".to_string(),
+            prompt: Some(pb::generate_request::Prompt::Text("hello".to_string())),
+            stopping: Some(pb::StoppingCriteria {
+                max_new_tokens: 10,
+                ..Default::default()
+            }),
+            ..Default::default()
+        })
+        .await
+        .expect("start generation")
+        .into_inner();
+
+    loop {
+        let response = tokio::time::timeout(Duration::from_secs(2), stream.message())
+            .await
+            .expect("timed out waiting for active generation output")
+            .expect("read active generation output")
+            .expect("generation ended before producing output");
+        if let Some(output) = response.outputs {
+            assert!(
+                output.finish_info.is_none(),
+                "generation finished before abort behavior was exercised"
+            );
+            break;
+        }
+    }
+
+    control_client
+        .abort(pb::AbortRequest::default())
+        .await
+        .expect("empty abort should be a no-op");
+    assert!(
+        tokio::time::timeout(Duration::from_millis(100), stream.message())
+            .await
+            .is_err(),
+        "empty abort unexpectedly ended the active generation"
+    );
+
+    control_client
+        .abort(pb::AbortRequest {
+            request_ids: vec![
+                request_id.to_string(),
+                request_id.to_string(),
+                "unknown".to_string(),
+            ],
+        })
+        .await
+        .expect("abort active generation");
+
+    let finish_reason = loop {
+        let response = tokio::time::timeout(Duration::from_secs(2), stream.message())
+            .await
+            .expect("timed out waiting for aborted generation")
+            .expect("read aborted generation")
+            .expect("generation ended without an aborted response");
+        if let Some(finish_info) = response.outputs.and_then(|output| output.finish_info) {
+            break finish_info.finish_reason;
+        }
+    };
+    assert_eq!(finish_reason, pb::finish_info::FinishReason::Aborted as i32);
+
+    control_client
+        .abort(pb::AbortRequest {
+            request_ids: vec![request_id.to_string()],
+        })
+        .await
+        .expect("repeated abort should be idempotent");
+
+    engine_task.await.expect("mock engine task");
+    server_task.abort();
+}
+
 #[tokio::test(flavor = "multi_thread", worker_threads = 2)]
 #[serial]
 async fn grpc_health_transitions_to_not_serving_when_engine_becomes_unhealthy() {
-    let (generate_service, _connected_engine_health, _engine_task) =
+    let (generate_service, control_service, _connected_engine_health, _engine_task) =
        setup_grpc_service(b"engine-grpc-health-failure", default_stream_output_specs()).await;
    let (engine_health_tx, engine_health) = tokio::sync::watch::channel(true);
    let (channel, server_task) = start_grpc_test_server(
        generate_service,
+        control_service,
        engine_health,
        tokio_util::sync::CancellationToken::new(),
    )
@@ -1088,7 +1187,7 @@ async fn grpc_health_transitions_to_not_serving_when_engine_becomes_unhealthy()
    let mut health_client = HealthClient::new(channel);

    let mut health_streams = Vec::new();
-    for service in ["vllm.Generate", ""] {
+    for service in ["vllm.Generate", "vllm.Control", ""] {
        let service_label = if service.is_empty() {
            "overall"
        } else {
@@ -1143,14 +1242,19 @@ async fn grpc_health_transitions_to_not_serving_when_engine_becomes_unhealthy()
 #[tokio::test(flavor = "multi_thread", worker_threads = 2)]
 #[serial]
 async fn grpc_health_watch_closes_on_graceful_shutdown() {
-    let (generate_service, engine_health, _engine_task) = setup_grpc_service(
+    let (generate_service, control_service, engine_health, _engine_task) = setup_grpc_service(
        b"engine-grpc-health-shutdown",
        default_stream_output_specs(),
    )
    .await;
    let shutdown = tokio_util::sync::CancellationToken::new();
-    let (channel, server_task) =
-        start_grpc_test_server(generate_service, engine_health, shutdown.clone()).await;
+    let (channel, server_task) = start_grpc_test_server(
+        generate_service,
+        control_service,
+        engine_health,
+        shutdown.clone(),
+    )
+    .await;
    let mut health_client = HealthClient::new(channel);
    let mut stream = health_client
        .watch(HealthCheckRequest {
@@ -207,6 +207,9 @@ where
        let (health_reporter, health_service) = health_reporter();
        let engine_health = state.engine_core_client().subscribe_health();
        health_reporter.set_serving::<grpc::GenerateGrpcService>().await;
+        health_reporter.set_serving::<grpc::ControlGrpcService>().await;
+        let control_service =
+            grpc::ControlGrpcService::new(grpc::ControlServiceImpl::new(state.clone()));
        let generate_service =
            grpc::GenerateGrpcService::new(grpc::GenerateServiceImpl::new(state.clone()));
        let svc = TonicServer::builder()
@@ -214,6 +217,7 @@ where
            .http2_keepalive_timeout(Some(GRPC_KEEPALIVE_TIMEOUT))
            .layer(middleware::request_runtime_layer(state.clone()))
            .add_service(health_service)
+            .add_service(control_service)
            .add_service(generate_service);
        info!(%addr, tls = grpc_tls.is_some(), "starting gRPC server");
        Some((grpc_listener, svc, grpc_tls, health_reporter, engine_health))
@@ -19,7 +19,7 @@ use futures::StreamExt as _;
 use serial_test::serial;
 use vllm_chat::{
    ChatBackend, ChatLlm, ChatRenderer, ChatRequest, ChatTextBackend, DefaultChatOutputProcessor,
-    DynChatOutputProcessor, DynChatRenderer, NewChatOutputProcessorOptions, RenderedPrompt,
+    DynChatOutputProcessor, DynChatRenderer, Error, NewChatOutputProcessorOptions, RenderedPrompt,
 };
 use vllm_engine_core_client::protocol::output::{
    EngineCoreFinishReason, EngineCoreOutput, EngineCoreOutputs, RequestBatchOutputs,
@@ -188,7 +188,7 @@ impl ChatRenderer for FakeChatBackend {
        for message in &request.messages {
            prompt.push_str(message.role().as_str());
            prompt.push_str(": ");
-            prompt.push_str(&message.text_content()?);
+            prompt.push_str(&message.text_content().map_err(Error::UnsupportedMultimodalContent)?);
            prompt.push('\n');
        }
        if request.chat_options.add_generation_prompt() {
@@ -399,11 +399,10 @@ mod tests {

    use axum::http::HeaderMap;
    use expect_test::expect;
-    use llm_multimodal::ImageDetail;
    use serde_json::json;
    use vllm_chat::{
        AssistantContentBlock, AssistantToolCall, ChatContentPart, ChatMessage as VllmChatMessage,
-        ChatTool as VllmChatTool, ChatToolChoice, GenerationPromptMode,
+        ChatTool as VllmChatTool, ChatToolChoice, GenerationPromptMode, ImageDetail,
        SamplingParams as VllmSamplingParams,
    };
    use vllm_text::output::TextDecodeOptions;
@@ -4,9 +4,9 @@
 use std::collections::HashMap;
 use std::slice;

-use llm_multimodal::ImageDetail;
 use serde::{Deserialize, Serialize};
 use serde_json::Value;
+use vllm_chat::ImageDetail;
 use vllm_llm::TokenUsage;

 // ============================================================================
@@ -23,7 +23,7 @@ use serial_test::serial;
 use tower::{Service as _, ServiceExt as _};
 use vllm_chat::{
    ChatBackend, ChatContent, ChatContentPart, ChatLlm, ChatMessage, ChatRenderer, ChatRequest,
-    ChatTextBackend, DefaultChatOutputProcessor, DynChatOutputProcessor, DynChatRenderer,
+    ChatTextBackend, DefaultChatOutputProcessor, DynChatOutputProcessor, DynChatRenderer, Error,
    NewChatOutputProcessorOptions,
 };
 use vllm_engine_core_client::mock_engine::default_ready_response;
@@ -539,7 +539,9 @@ fn render_fake_message_content(
        | ChatMessage::Developer { content, .. }
        | ChatMessage::User { content }
        | ChatMessage::ToolResponse { content, .. } => render_fake_content(content, placeholder),
-        ChatMessage::Assistant { .. } => message.text_content(),
+        ChatMessage::Assistant { .. } => {
+            message.text_content().map_err(Error::UnsupportedMultimodalContent)
+        }
    }
 }

@@ -38,32 +38,22 @@ trait_set! {
    pub trait TextOutputStream = Stream<Item = Result<DecodedTextEvent>> + Send + 'static;
 }

-/// Raw text facade above [`Llm`].
-///
-/// This layer stays below chat semantics: prompt text or prompt token IDs flow
-/// in, decoded text deltas and terminal metadata flow out.
-pub struct TextLlm {
-    /// Generate-only client owned by this text facade.
-    llm: Llm,
+/// Text request preparation shared by inference and render-only frontends.
+pub struct TextRequestProcessor {
    /// Tokenizer/model metadata backend responsible for prompt encode/decode
    /// and sampling hints.
    backend: DynTextBackend,
    /// Runtime context window size reported by the engine startup handshake.
+    /// Render-only frontends supply the downstream engine's effective value.
    max_model_len: u32,
    /// Maximum number of top log probabilities accepted by this text facade.
    max_logprobs: i32,
 }

-impl TextLlm {
-    /// Create a new text-generation facade from a shared LLM client plus a text
-    /// backend.
-    pub fn new(llm: Llm, backend: DynTextBackend) -> Self {
-        // The engine-reported value reflects the post-profiling, auto-fitted
-        // KV cache limit used at runtime.
-        let max_model_len = llm.engine_core_client().max_model_len();
-
+impl TextRequestProcessor {
+    /// Create a processor with the effective model context length.
+    pub fn new(backend: DynTextBackend, max_model_len: u32) -> Self {
        Self {
-            llm,
            backend,
            max_model_len,
            max_logprobs: SamplingLimits::DEFAULT_MAX_LOGPROBS,
@@ -78,61 +68,18 @@ impl TextLlm {
        self
    }

-    /// Return the backend model ID.
-    pub fn model_id(&self) -> &str {
-        self.backend.model_id()
-    }
-
-    /// Expose the underlying engine-core client for low-level utility/admin
-    /// calls.
-    pub fn engine_core_client(&self) -> &EngineCoreClient {
-        self.llm.engine_core_client()
-    }
-
-    /// Return the tokenizer used by this text backend.
+    /// Return the tokenizer used by this processor.
    pub fn tokenizer(&self) -> DynTokenizer {
        self.backend.tokenizer()
    }

-    /// Tokenizer vocabulary size (the number of tokens the tokenizer knows),
-    /// used to bound `allowed_token_ids` like the Python frontend `len(tokenizer)`.
-    pub fn tokenizer_vocab_size(&self) -> usize {
-        self.backend.tokenizer_vocab_size()
+    /// Return the effective model context length.
+    pub fn max_model_len(&self) -> u32 {
+        self.max_model_len
    }

-    /// Model vocabulary size from the model config, used to bound generated
-    /// token IDs and logits-domain sampling controls.
-    pub fn model_vocab_size(&self) -> usize {
-        self.backend.model_vocab_size()
-    }
-
-    /// Tokenize if needed, lower to a generate request, and return the raw
-    /// token stream.
-    pub async fn generate_raw(&self, request: TextRequest) -> Result<GenerateOutputStream> {
-        let (_, raw_stream) = self.generate_inner(request).await?;
-        Ok(raw_stream)
-    }
-
-    /// Tokenize if needed, lower to a generate request, and stream
-    /// incrementally decoded text.
-    pub async fn generate(&self, request: TextRequest) -> Result<impl TextOutputStream> {
-        let (text_request, raw_stream) = self.generate_inner(request).await?;
-        let tokenizer = self.backend.tokenizer();
-        let decoded_stream = output::decoded_text_event_stream(
-            text_request.request_id,
-            tokenizer,
-            raw_stream,
-            text_request.decode_options,
-            text_request.intermediate,
-        );
-
-        Ok(decoded_stream)
-    }
-
-    async fn generate_inner(
-        &self,
-        mut request: TextRequest,
-    ) -> Result<(TextRequest, GenerateOutputStream)> {
+    /// Tokenize and lower one request without submitting it to an engine.
+    pub fn prepare(&self, mut request: TextRequest) -> Result<PreparedTextRequest> {
        request.validate()?;

        if request.arrival_time.is_none() {
@@ -146,7 +93,6 @@ impl TextLlm {
            // and infra workloads bypass chat rendering and tokenizer overhead entirely.
            Prompt::TokenIds(token_ids) => token_ids,
        };
-
        let sampling_hints = self.backend.sampling_hints()?;
        let sampling_limits = SamplingLimits {
            max_model_len: self.max_model_len,
@@ -155,16 +101,106 @@ impl TextLlm {
            tokenizer_vocab_size: self.backend.tokenizer_vocab_size(),
        };

-        let PreparedTextRequest {
-            text_request,
-            generate_request,
-        } = lower_text_request(
+        lower_text_request(
            request,
            prompt_token_ids,
            sampling_hints,
            sampling_limits,
-            &*tokenizer,
-        )?;
+            tokenizer.as_ref(),
+        )
+    }
+}
+
+/// Raw text facade above [`Llm`].
+///
+/// This layer stays below chat semantics: prompt text or prompt token IDs flow
+/// in, decoded text deltas and terminal metadata flow out.
+pub struct TextLlm {
+    /// Generate-only client owned by this text facade.
+    llm: Llm,
+    /// Shared engine-free request preparation.
+    processor: TextRequestProcessor,
+}
+
+impl TextLlm {
+    /// Create a new text-generation facade from a shared LLM client plus a text
+    /// backend.
+    pub fn new(llm: Llm, backend: DynTextBackend) -> Self {
+        // The engine-reported value reflects the post-profiling, auto-fitted
+        // KV cache limit used at runtime.
+        let max_model_len = llm.engine_core_client().max_model_len();
+
+        Self {
+            llm,
+            processor: TextRequestProcessor::new(backend, max_model_len),
+        }
+    }
+
+    /// Override the maximum accepted logprobs count.
+    pub fn with_max_logprobs(mut self, max_logprobs: Option<i32>) -> Self {
+        self.processor = self.processor.with_max_logprobs(max_logprobs);
+        self
+    }
+
+    /// Return the backend model ID.
+    pub fn model_id(&self) -> &str {
+        self.processor.backend.model_id()
+    }
+
+    /// Expose the underlying engine-core client for low-level utility/admin
+    /// calls.
+    pub fn engine_core_client(&self) -> &EngineCoreClient {
+        self.llm.engine_core_client()
+    }
+
+    /// Return the tokenizer used by this text backend.
+    pub fn tokenizer(&self) -> DynTokenizer {
+        self.processor.tokenizer()
+    }
+
+    /// Tokenizer vocabulary size (the number of tokens the tokenizer knows),
+    /// used to bound `allowed_token_ids` like the Python frontend `len(tokenizer)`.
+    pub fn tokenizer_vocab_size(&self) -> usize {
+        self.processor.backend.tokenizer_vocab_size()
+    }
+
+    /// Model vocabulary size from the model config, used to bound generated
+    /// token IDs and logits-domain sampling controls.
+    pub fn model_vocab_size(&self) -> usize {
+        self.processor.backend.model_vocab_size()
+    }
+
+    /// Tokenize if needed, lower to a generate request, and return the raw
+    /// token stream.
+    pub async fn generate_raw(&self, request: TextRequest) -> Result<GenerateOutputStream> {
+        let (_, raw_stream) = self.generate_inner(request).await?;
+        Ok(raw_stream)
+    }
+
+    /// Tokenize if needed, lower to a generate request, and stream
+    /// incrementally decoded text.
+    pub async fn generate(&self, request: TextRequest) -> Result<impl TextOutputStream> {
+        let (text_request, raw_stream) = self.generate_inner(request).await?;
+        let tokenizer = self.processor.tokenizer();
+        let decoded_stream = output::decoded_text_event_stream(
+            text_request.request_id,
+            tokenizer,
+            raw_stream,
+            text_request.decode_options,
+            text_request.intermediate,
+        );
+
+        Ok(decoded_stream)
+    }
+
+    async fn generate_inner(
+        &self,
+        request: TextRequest,
+    ) -> Result<(TextRequest, GenerateOutputStream)> {
+        let PreparedTextRequest {
+            text_request,
+            generate_request,
+        } = self.processor.prepare(request)?;

        let raw_stream = self.llm.generate(generate_request).await?;
        Ok((text_request, raw_stream))
@@ -214,3 +214,65 @@ def test_cache_config_hash_ignores_kv_cache_sizing_knobs():
    base_hash = CacheConfig().compute_hash()
    assert CacheConfig(kv_cache_memory_bytes=1 << 30).compute_hash() == base_hash
    assert CacheConfig(gpu_memory_utilization=0.5).compute_hash() == base_hash
+
+
+def test_envs_compile_factors_relocation_invariant(tmp_path):
+    """Relocating HOME or the XDG roots must not change the compile-cache
+    env hash.
+
+    Location-derived env vars (VLLM_XLA_CACHE_PATH from XDG_CACHE_HOME,
+    VLLM_CONFIG_ROOT from XDG_CONFIG_HOME/HOME) carry no information about
+    compiled artifacts, only about where directories live. When they leak
+    into compile_factors(), a cache produced under one HOME/XDG layout
+    silently misses under another - which defeats copying or pre-baking a
+    compile cache into a container image.
+    """
+    import os
+    import subprocess
+    import sys
+
+    code = """
+import sys
+import logging
+logging.disable(logging.CRITICAL)
+from vllm import envs
+from vllm.config.utils import hash_factors
+print(hash_factors(envs.compile_factors()))
+"""
+
+    def hash_with(extra_env):
+        env = {**dict(os.environ), "VLLM_LOGGING_LEVEL": "ERROR"}
+        # Drop explicit overrides so the derived defaults are what is
+        # exercised, then apply the relocation under test.
+        for key in ("VLLM_XLA_CACHE_PATH", "VLLM_CONFIG_ROOT", "VLLM_CACHE_ROOT"):
+            env.pop(key, None)
+        env.update(extra_env)
+        result = subprocess.run(
+            [sys.executable, "-c", code],
+            capture_output=True,
+            text=True,
+            check=True,
+            env=env,
+        )
+        return result.stdout.strip()
+
+    xdg_cache = tmp_path / "relocated-xdg-cache"
+    xdg_config = tmp_path / "relocated-xdg-config"
+    new_home = tmp_path / "relocated-home"
+    for d in (xdg_cache, xdg_config, new_home):
+        d.mkdir()
+
+    base = hash_with({})
+    relocated_xdg = hash_with(
+        {"XDG_CACHE_HOME": str(xdg_cache), "XDG_CONFIG_HOME": str(xdg_config)}
+    )
+    relocated_home = hash_with({"HOME": str(new_home)})
+
+    assert relocated_xdg == base, (
+        "XDG_CACHE_HOME/XDG_CONFIG_HOME relocation changed the compile-cache "
+        "env hash - a location-only derived var is leaking into the key"
+    )
+    assert relocated_home == base, (
+        "HOME relocation changed the compile-cache env hash - a "
+        "location-only derived var is leaking into the key"
+    )
@@ -43,6 +43,15 @@ def test_language_model_only_affects_model_hash():
    assert base_hash != lm_only_hash


+@pytest.mark.parametrize("backend_arg", ["video_backend", "backend"])
+def test_use_gpu_video_backend_from_media_io_kwargs(backend_arg: str):
+    config = MultiModalConfig(
+        media_io_kwargs={"video": {backend_arg: "pynvvideocodec"}}
+    )
+
+    assert config.use_gpu_video_backend()
+
+
 def test_mm_encoder_fp8_scale_path_requires_fp8():
    with pytest.raises(ValueError, match="mm_encoder_attn_dtype"):
        MultiModalConfig(mm_encoder_fp8_scale_path="/tmp/scales.json")
@@ -254,8 +254,8 @@ def test_multiproc_executor_shutdown_cleanup():
    for worker in executor.workers:
        assert not worker.proc.is_alive(), "Worker processes should be terminated"

-    # Verify shutdown event is set
-    assert executor.shutdown_event.is_set(), "Shutdown event should be set"
+    # Verify shutdown flag is set
+    assert executor.shutting_down, "Shutdown flag should be set"

    # Multiple shutdowns should be safe (idempotent)
    executor.shutdown()
@@ -292,7 +292,6 @@ def test_multiproc_executor_pipeline_parallel():
            "Max concurrent batches should follow the configured PP/async "
            "scheduling policy"
        )
-
    finally:
        # Clean up
        executor.shutdown()
@@ -338,6 +337,10 @@ def test_multiproc_executor_multi_node():
    - Node 1 (rank 1): Uses GPUs 2,3 (CUDA_VISIBLE_DEVICES=2,3) with TP=2
    Total world_size = 4, nnodes = 2
    """
+    # Python 3.14+ changed default multiprocessing start method to 'forkserver'
+    # which cannot pickle nested functions. Use 'fork' for this test.
+    mp_ctx = multiprocessing.get_context("fork")
+
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        s.bind(("", 0))
        port = s.getsockname()[1]
@@ -405,12 +408,12 @@ def test_multiproc_executor_multi_node():
                executor.shutdown()

    # Create a queue to collect results from both processes
-    result_queue: multiprocessing.Queue[dict[str, int | bool]] = multiprocessing.Queue()
+    result_queue: multiprocessing.Queue[dict[str, int | bool]] = mp_ctx.Queue()

    # Start both node processes
    processes = []
    for node_rank in range(2):
-        p = multiprocessing.Process(
+        p = mp_ctx.Process(
            target=run_node,
            args=(node_rank, result_queue, port),
            name=f"Node{node_rank}",
@@ -9,8 +9,6 @@ from pydantic import TypeAdapter, ValidationError
 from vllm import PoolingParams
 from vllm.entrypoints.pooling.embed.io_processor import EmbedIOProcessor
 from vllm.entrypoints.pooling.embed.protocol import (
-    CohereEmbedContent,
-    CohereEmbedInput,
    CohereEmbedRequest,
    EmbeddingBatchChatInputRequest,
    EmbeddingBatchChatRequest,
@@ -19,7 +17,10 @@ from vllm.entrypoints.pooling.embed.protocol import (
    EmbeddingCompletionRequest,
    EmbeddingRequest,
 )
-from vllm.entrypoints.pooling.typing import PoolingServeContext
+from vllm.entrypoints.pooling.typing import (
+    PoolingEngineInput,
+    PoolingServeContext,
+)
 from vllm.outputs import PoolingOutput, PoolingRequestOutput


@@ -410,6 +411,7 @@ class TestChunkedEmbeddingProcessing:
    def _make_handler(cls):
        handler = object.__new__(EmbedIOProcessor)
        handler.model_config = cls._FakeModelConfig()
+        handler.enable_chunked_processing = True
        return handler

    @staticmethod
@@ -421,15 +423,29 @@ class TestChunkedEmbeddingProcessing:
            }
        )
        assert isinstance(request, EmbeddingCompletionRequest)
+        pooling_params = PoolingParams()
        return PoolingServeContext(
            request=request,
-            pooling_params=PoolingParams(),
+            pooling_params=pooling_params,
            model_name="test",
            request_id="embd-client-prompt-999-chunk-888",
            engine_inputs=[
-                {"prompt_token_ids": [0, 1, 2, 3, 4]},
-                {"prompt_token_ids": [10, 11]},
+                PoolingEngineInput(
+                    prompts={"prompt_token_ids": [0, 1, 2, 3, 4]},
+                    params=pooling_params,
+                    lora_requests=None,
+                    priorities=0,
+                ),
+                PoolingEngineInput(
+                    prompts={"prompt_token_ids": [10, 11]},
+                    params=pooling_params,
+                    lora_requests=None,
+                    priorities=0,
+                ),
            ],
+            lora_request=None,
+            priorities=0,
+            prompt_extras=None,
        )

    @staticmethod
@@ -450,7 +466,7 @@ class TestChunkedEmbeddingProcessing:
        handler = self._make_handler()
        ctx = self._make_context()

-        handler._pre_process_chunked(ctx)
+        handler.maybe_pre_process_chunked(ctx)

        assert ctx.prompt_request_ids == [
            "embd-client-prompt-999-chunk-888-prompt-0-chunk-0",
@@ -488,227 +504,3 @@ class TestChunkedEmbeddingProcessing:
            ctx.final_res_batch[1].outputs.data,
            torch.tensor([9.0, 9.0]),
        )
-
-
-class TestPreProcessCohereOnline:
-    """Unit tests for EmbedIOProcessor._pre_process_cohere_online."""
-
-    @staticmethod
-    def _make_context(**request_kwargs) -> PoolingServeContext[CohereEmbedRequest]:
-        return PoolingServeContext(
-            request=CohereEmbedRequest(model="test", **request_kwargs),
-            pooling_params=PoolingParams(),
-            model_name="test",
-            request_id="embd-test",
-        )
-
-    @staticmethod
-    def _make_handler():
-        handler = object.__new__(EmbedIOProcessor)
-        handler._validate_input_type = lambda _input_type: None
-        return handler
-
-    def test_text_only_without_task_prefix_uses_completion_path(self):
-        handler = self._make_handler()
-        ctx = self._make_context(texts=["hello"])
-        calls: list[tuple[str, object]] = []
-
-        def preprocess_cmpl_online(request, prompt_input, prompt_embeds):
-            calls.append(("completion", prompt_input))
-            return ["completion"]
-
-        handler._get_task_instruction_prefix = lambda _input_type: None
-        handler._has_chat_template = lambda: False
-        handler._preprocess_cmpl_online = preprocess_cmpl_online
-        handler._batch_render_chat = lambda *_args, **_kwargs: pytest.fail(
-            "text-only request should not require chat rendering"
-        )
-
-        handler._pre_process_cohere_online(ctx)
-
-        assert ctx.engine_inputs == ["completion"]
-        assert calls == [("completion", ["hello"])]
-
-    def test_text_only_falls_back_to_prefixed_completion_without_template(self):
-        handler = self._make_handler()
-        ctx = self._make_context(texts=["hello"], input_type="query")
-        calls: list[tuple[str, object]] = []
-
-        def preprocess_cmpl(request, prompt_input, prompt_embeds):
-            calls.append(("completion", prompt_input))
-            return ["fallback"]
-
-        handler._get_task_instruction_prefix = lambda _input_type: "query: "
-        handler._has_chat_template = lambda: False
-        handler._batch_render_chat = lambda *_args, **_kwargs: pytest.fail(
-            "chat rendering should be skipped without a template"
-        )
-        handler._preprocess_cmpl_online = preprocess_cmpl
-
-        handler._pre_process_cohere_online(ctx)
-
-        assert ctx.engine_inputs == ["fallback"]
-        assert calls == [("completion", ["query: hello"])]
-
-    def test_text_only_with_template_uses_chat_path(self):
-        handler = self._make_handler()
-        ctx = self._make_context(texts=["hello"], input_type="query")
-        calls: list[tuple[str, object]] = []
-
-        def batch_render_chat(
-            request,
-            all_messages,
-            truncate_prompt_tokens,
-            truncation_side,
-        ):
-            calls.append(
-                (
-                    "chat",
-                    {
-                        "request": request,
-                        "all_messages": all_messages,
-                        "truncate_prompt_tokens": truncate_prompt_tokens,
-                        "truncation_side": truncation_side,
-                    },
-                )
-            )
-            return ["chat"]
-
-        handler._get_task_instruction_prefix = lambda _input_type: "query: "
-        handler._has_chat_template = lambda: True
-        handler._batch_render_chat = batch_render_chat
-        handler._preprocess_cmpl_online = lambda *_args, **_kwargs: pytest.fail(
-            "completion path should be skipped when a template exists"
-        )
-
-        handler._pre_process_cohere_online(ctx)
-
-        assert ctx.engine_inputs == ["chat"]
-        assert calls == [
-            (
-                "chat",
-                {
-                    "request": ctx.request,
-                    "all_messages": [
-                        handler._mixed_input_to_messages(
-                            CohereEmbedInput(
-                                content=[CohereEmbedContent(type="text", text="hello")]
-                            ),
-                            task_prefix="query: ",
-                        )
-                    ],
-                    "truncate_prompt_tokens": -1,
-                    "truncation_side": None,
-                },
-            )
-        ]
-
-
-class TestPreProcessOpenAIEmbeddingChatOnline:
-    """Unit tests for OpenAI embedding chat preprocessing."""
-
-    class _FakeModelConfig:
-        max_model_len = 128
-        encoder_config: dict[str, object] = {}
-        pooler_config = None
-        multimodal_config = None
-        is_encoder_decoder = False
-
-    class _FakeRenderer:
-        tokenizer = object()
-
-        def __init__(self):
-            self.calls = []
-
-        def render_chat(
-            self,
-            all_messages,
-            chat_params,
-            tok_params,
-            prompt_extras=None,
-        ):
-            self.calls.append(
-                {
-                    "all_messages": all_messages,
-                    "chat_params": chat_params,
-                    "tok_params": tok_params,
-                    "prompt_extras": prompt_extras,
-                }
-            )
-            return all_messages, [
-                {"prompt_token_ids": [index]} for index, _ in enumerate(all_messages)
-            ]
-
-    @classmethod
-    def _make_handler(cls, renderer):
-        handler = object.__new__(EmbedIOProcessor)
-        handler.renderer = renderer
-        handler.model_config = cls._FakeModelConfig()
-        handler.chat_template = "template"
-        handler.chat_template_content_format = "auto"
-        handler.trust_request_chat_template = False
-        handler.enable_chunked_processing = False
-        return handler
-
-    @staticmethod
-    def _make_context(
-        request: (
-            EmbeddingChatRequest
-            | EmbeddingBatchChatRequest
-            | EmbeddingChatInputRequest
-            | EmbeddingBatchChatInputRequest
-        ),
-    ) -> PoolingServeContext[
-        EmbeddingChatRequest
-        | EmbeddingBatchChatRequest
-        | EmbeddingChatInputRequest
-        | EmbeddingBatchChatInputRequest
-    ]:
-        return PoolingServeContext(
-            request=request,
-            pooling_params=PoolingParams(),
-            model_name="test",
-            request_id="embd-test",
-        )
-
-    def test_chat_template_kwargs_forwarded_for_batched_input_messages(self):
-        request = TypeAdapter(EmbeddingRequest).validate_python(
-            {
-                "model": "test",
-                "input": [
-                    [{"role": "user", "content": "hello"}],
-                    [{"role": "user", "content": "goodbye"}],
-                ],
-                "add_generation_prompt": True,
-                "chat_template_kwargs": {"instruction": "Represent the query: "},
-                "mm_processor_kwargs": {"max_pixels": 1},
-                "cache_salt": "salt",
-            }
-        )
-        assert isinstance(request, EmbeddingBatchChatInputRequest)
-
-        renderer = self._FakeRenderer()
-        handler = self._make_handler(renderer)
-        ctx = self._make_context(request)
-
-        handler.pre_process_online(ctx)
-
-        assert ctx.engine_inputs == [
-            {"prompt_token_ids": [0]},
-            {"prompt_token_ids": [1]},
-        ]
-        assert len(renderer.calls) == 1
-
-        call = renderer.calls[0]
-        assert call["all_messages"] == request.messages
-        assert call["prompt_extras"] == {
-            "mm_processor_kwargs": {"max_pixels": 1},
-            "cache_salt": "salt",
-        }
-
-        chat_template_kwargs = call["chat_params"].chat_template_kwargs
-        assert chat_template_kwargs["instruction"] == "Represent the query: "
-        assert chat_template_kwargs["add_generation_prompt"] is True
-        assert chat_template_kwargs["continue_final_message"] is False
-        assert "tools" not in chat_template_kwargs
-        assert chat_template_kwargs["tokenize"] is False
@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project

 import weakref
-from types import SimpleNamespace

 import pytest
 import torch
@@ -10,10 +9,7 @@ import torch
 from tests.models.utils import softmax
 from vllm import LLM, PoolingParams
 from vllm.distributed import cleanup_dist_env_and_memory
-from vllm.entrypoints.pooling.scoring.io_processor import CrossEncoderIOProcessor
-from vllm.entrypoints.pooling.scoring.typing import ScoringData
 from vllm.platforms import current_platform
-from vllm.renderers import TokenizeParams

 MODEL_NAME = "tomaarsen/Qwen3-Reranker-0.6B-seq-cls"
 PROMPT = "The chef prepared a delicious meal."
@@ -145,45 +141,6 @@ def test_max_tokens_per_doc(llm: LLM):
    assert with_limit_tokens < no_limit_tokens


-def test_token_type_ids_follow_post_tokenization():
-    processor = object.__new__(CrossEncoderIOProcessor)
-    processor.tokenizer = SimpleNamespace(truncation_side="right", pad_token_id=-1)
-    processor.renderer = SimpleNamespace(process_for_engine=lambda prompt, _: prompt)
-    processor.model_config = None
-    processor.get_score_prompt = lambda **_: (
-        "",
-        {
-            "prompt_token_ids": list(range(32)),
-            "token_type_ids": [0] * 16 + [1] * 16,
-        },
-    )
-
-    engine_inputs, pooling_params = processor._pre_process(
-        ScoringData(data_1=["query"], data_2=["document"]),
-        TokenizeParams(
-            max_total_tokens=None,
-            truncate_prompt_tokens=16,
-            truncation_side="left",
-        ),
-        PoolingParams(task="classify", extra_kwargs={"cache_salt": "salt"}),
-    )
-
-    assert engine_inputs[0]["prompt_token_ids"] == list(range(16, 32))
-    assert pooling_params[0].extra_kwargs == {
-        "cache_salt": "salt",
-        "compressed_token_type_ids": 0,
-    }
-
-    engine_inputs, pooling_params = processor._pre_process(
-        ScoringData(data_1=["query"], data_2=["document"]),
-        TokenizeParams(max_total_tokens=None, pad_prompt_tokens=40),
-        PoolingParams(task="classify"),
-    )
-
-    assert engine_inputs[0]["prompt_token_ids"] == list(range(32)) + [-1] * 8
-    assert pooling_params[0].extra_kwargs == {"compressed_token_type_ids": 16}
-
-
 def test_pooling_params(llm: LLM):
    def get_outputs(use_activation):
        outputs = llm.score(
@@ -161,7 +161,9 @@ def _make_pooling_serving(lora_name: str) -> _ConcretePoolingServing:
    return serving


-def _make_pooling_ctx(model_name: str) -> PoolingServeContext:
+def _make_pooling_ctx(
+    model_name: str, serving: PoolingBaseServing
+) -> PoolingServeContext:
    mock_request = MagicMock()
    mock_request.model = model_name
    return PoolingServeContext(
@@ -169,6 +171,9 @@ def _make_pooling_ctx(model_name: str) -> PoolingServeContext:
        model_name=MODEL_NAME,
        request_id="test-id",
        pooling_params=PoolingParams(),
+        lora_request=serving._maybe_get_adapters(mock_request),
+        priorities=0,
+        prompt_extras=None,
    )


@@ -176,9 +181,7 @@ def test_pooling_maybe_get_adapters_lora_name_sets_lora_request():
    """LoRA adapter name must populate ctx.lora_request without raising."""
    lora_name = "bot-embed-lora"
    serving = _make_pooling_serving(lora_name)
-    ctx = _make_pooling_ctx(lora_name)
-
-    ctx.lora_request = serving._maybe_get_adapters(ctx.request)
+    ctx = _make_pooling_ctx(lora_name, serving)

    assert ctx.lora_request is not None
    assert ctx.lora_request.lora_name == lora_name
@@ -187,7 +190,6 @@ def test_pooling_maybe_get_adapters_lora_name_sets_lora_request():
 def test_pooling_maybe_get_adapters_unknown_model_raises():
    """An unrecognised model name must still raise VLLMNotFoundError."""
    serving = _make_pooling_serving("some-lora")
-    ctx = _make_pooling_ctx("unknown-model")

    with pytest.raises(VLLMNotFoundError):
-        serving._maybe_get_adapters(ctx.request)
+        _make_pooling_ctx("unknown-model", serving)
@@ -6,6 +6,7 @@ max_concurrency: 100
 server_args: >-
  --enforce-eager
  --max-model-len 4096
+  --max-num-batched-tokens 32768
  --safetensors-load-strategy prefetch
  --moe-backend flashinfer_cutlass
  --prefill-context-parallel-size 4
@@ -6,6 +6,7 @@ max_concurrency: 100
 server_args: >-
  --enforce-eager
  --max-model-len 4096
+  --max-num-batched-tokens 32768
  --safetensors-load-strategy prefetch
  --moe-backend flashinfer_cutlass
  --tensor-parallel-size 2
@@ -0,0 +1,9 @@
+model_name: "poolside/Laguna-XS.2-NVFP4"
+accuracy_threshold: 0.86
+num_questions: 1319
+num_fewshot: 5
+startup_max_wait_seconds: 1200
+server_args: >-
+  --enforce-eager
+  --max-model-len 4096
+  --trust-remote-code
@@ -2,4 +2,7 @@ model_name: "google/gemma-4-E4B-it-qat-mobile-ct"
 accuracy_threshold: 0.50
 num_questions: 1319
 num_fewshot: 5
-server_args: "--enforce-eager --max-model-len 4096"
+server_args: >-
+  --enforce-eager
+  --max-model-len 4096
+  --speculative-config '{"method":"mtp","model":"google/gemma-4-E4B-it-assistant","num_speculative_tokens":4}'
@@ -3,3 +3,4 @@ Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
 Qwen1.5-MoE-W4A16-CT.yaml
 DeepSeek-V2-Lite-Instruct-FP8.yaml
 Qwen3-30B-A3B-NVFP4.yaml
+Laguna-XS.2-NVFP4.yaml
@@ -546,8 +546,11 @@ def test_flash_attn_accepts_handled_fp8_variants(
 ):
    """FlashAttentionBackend must accept the two fp8 dtypes it can actually
    handle: 'fp8' (alias for fp8_e4m3fn) and 'fp8_e4m3'."""
-    import vllm.v1.attention.backends.flash_attn as fa_mod
+    import vllm.v1.attention.backends.fa_utils as fa_utils_mod
    from vllm.v1.attention.backends.flash_attn import FlashAttentionBackend

-    monkeypatch.setattr(fa_mod.current_platform, "is_xpu", lambda: True)
+    # The fp8 decision is made in fa_utils, using its own current_platform
+    # binding, so patch is_xpu there (not on flash_attn's) to stay robust to
+    # import order across earlier tests that patch vllm.platforms.current_platform.
+    monkeypatch.setattr(fa_utils_mod.current_platform, "is_xpu", lambda: True)
    assert FlashAttentionBackend.supports_kv_cache_dtype(kv_cache_dtype)
@@ -11,6 +11,9 @@ from vllm._custom_ops import (
    scaled_fp8_quant,
 )
 from vllm.platforms import current_platform
+from vllm.v1.attention.ops.triton_merge_attn_states import (
+    mask_empty_context,
+)
 from vllm.v1.attention.ops.triton_merge_attn_states import (
    merge_attn_states as merge_attn_states_triton,
 )
@@ -73,6 +76,59 @@ DTYPES = [torch.float32, torch.half, torch.bfloat16]
 all_case_info: list[tuple] = []


+def test_mask_empty_context() -> None:
+    query_lens = torch.tensor([2] + [1] * 31 + [131, 1], dtype=torch.int32)
+    query_start_loc = torch.cat(
+        (torch.zeros(1, dtype=torch.int32), query_lens.cumsum(0))
+    ).cuda()
+    context_lens = torch.tensor([4] * 32 + [0, 3], dtype=torch.int32)
+    context_start_loc = torch.cat(
+        (torch.zeros(1, dtype=torch.int32), context_lens.cumsum(0))
+    ).cuda()
+    num_heads, num_tokens, head_dim = 4, 165, 16
+    lse = torch.randn(num_heads, num_tokens, device="cuda")
+    output = torch.randn(num_tokens, num_heads, head_dim, device="cuda")
+    # Empty-context rows carry undefined (possibly non-finite) attention output.
+    output[33:164] = float("nan")
+
+    expected_lse = lse.clone()
+    expected_lse[:, 33:164] = float("-inf")
+    expected_output = output.clone()
+    expected_output[33:164] = 0.0
+
+    mask_empty_context(lse, output, query_start_loc, context_start_loc)
+
+    torch.testing.assert_close(lse, expected_lse)
+    torch.testing.assert_close(output, expected_output)
+
+
+@pytest.mark.parametrize("merge_fn", [merge_attn_states_cuda, merge_attn_states_triton])
+@pytest.mark.parametrize("output_dtype", [torch.float32, torch.half, torch.bfloat16])
+def test_merge_attn_states_both_empty(merge_fn, output_dtype) -> None:
+    """When a token is empty on both sides (both LSE -inf), the 0/0 softmax
+    scales must not surface as NaN in the merged output."""
+    num_tokens, num_heads, head_size = 6, 8, 128
+    prefix_output = torch.zeros(
+        num_tokens, num_heads, head_size, device="cuda", dtype=output_dtype
+    )
+    prefix_lse = torch.randn(num_heads, num_tokens, device="cuda")
+    suffix_output = torch.zeros(
+        num_tokens, num_heads, head_size, device="cuda", dtype=output_dtype
+    )
+    suffix_lse = torch.randn(num_heads, num_tokens, device="cuda")
+
+    # Tokens 2 and 3 are empty on both sides (mask_empty_context already zeroed
+    # their outputs and set both LSEs to -inf).
+    empty = slice(2, 4)
+    prefix_lse[:, empty] = float("-inf")
+    suffix_lse[:, empty] = float("-inf")
+
+    output = torch.empty_like(prefix_output)
+    merge_fn(output, prefix_output, prefix_lse, suffix_output, suffix_lse)
+
+    assert not output.isnan().any()
+
+
 def generate_markdown_table():
    global all_case_info
    table_header = (
@@ -40,10 +40,11 @@ BATCH_SIZE = 128
 CONTEXT_LEN = 8192
 PAGE_SIZE = 1

-# Expected dtypes for this fold path: bf16 model dtype -> bf16 query; fp8
-# KV-cache -> fp8_e4m3 kv.
-EXPECTED_Q_DTYPE = torch.bfloat16
-EXPECTED_KV_DTYPE = torch.float8_e4m3fn
+# On the fp8 KV-cache path the builder forwards the platform fp8 dtype (aiter
+# dtypes.fp8) for both q and kv. Mirror it via current_platform.fp8_dtype()
+# instead of hardcoding a literal (see #47276).
+EXPECTED_Q_DTYPE = current_platform.fp8_dtype()
+EXPECTED_KV_DTYPE = current_platform.fp8_dtype()

 # The split/reduce content tensors filled by get_mla_metadata_v1. work_meta_data
 # is excluded: it holds raw device pointers, never equal across allocations.
@@ -0,0 +1,15 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+
+
+@pytest.fixture(autouse=True)
+def reset_default_torch_device():
+    """Several kernel tests call torch.set_default_device without restoring
+    it, which poisons subsequent tests in the same pytest run (e.g. CPU
+    tensors silently created on CUDA). Restore the factory default after
+    every test.
+    """
+    yield
+    torch.set_default_device(None)
@@ -33,12 +33,10 @@ from vllm.third_party.flash_linear_attention.ops.index import (  # noqa: E402
@pytest.mark.parametrize("num_seqs", [1, 5, 257])
@pytest.mark.parametrize("state_dtype", [torch.bfloat16, torch.float32])
 def test_gdn_chunk_cutedsl_correctness(num_seqs: int, state_dtype: torch.dtype):
-    seq_lens = torch.randint(
-        1,
-        130,
-        (num_seqs,),
-        dtype=torch.int32,
-    )
+    rng_cpu = torch.Generator("cpu").manual_seed(1234)
+    rng = torch.Generator("cuda").manual_seed(2345)
+
+    seq_lens = torch.randint(1, 130, (num_seqs,), dtype=torch.int32, generator=rng_cpu)
    cu_seqlens = torch.zeros(num_seqs + 1, device="cuda", dtype=torch.int32)
    cu_seqlens[1:] = seq_lens.to(device="cuda").cumsum(0)
    total_tokens = int(cu_seqlens[-1].item())
@@ -56,8 +54,9 @@ def test_gdn_chunk_cutedsl_correctness(num_seqs: int, state_dtype: torch.dtype):
        head_k_dim,
        device="cuda",
        dtype=dtype,
+        generator=rng,
    )
-    k = torch.randn_like(q)
+    k = torch.randn_like(q, generator=rng)
    v = torch.randn(
        1,
        total_tokens,
@@ -65,29 +64,24 @@ def test_gdn_chunk_cutedsl_correctness(num_seqs: int, state_dtype: torch.dtype):
        head_v_dim,
        device="cuda",
        dtype=dtype,
+        generator=rng,
    )
    q = F.normalize(q.float(), p=2, dim=-1).to(dtype)
    k = F.normalize(k.float(), p=2, dim=-1).to(dtype)
    a = torch.randn(
-        1,
-        total_tokens,
-        num_v_heads,
-        device="cuda",
-        dtype=dtype,
+        1, total_tokens, num_v_heads, device="cuda", dtype=dtype, generator=rng
    )
    b = torch.randn(
-        1,
-        total_tokens,
-        num_v_heads,
-        device="cuda",
-        dtype=dtype,
+        1, total_tokens, num_v_heads, device="cuda", dtype=dtype, generator=rng
    )
    # Match upstream FLA GatedDeltaNet synthetic initialization:
    # https://github.com/fla-org/flash-linear-attention/blob/main/fla/layers/gated_deltanet.py
-    A = torch.empty(num_v_heads, device="cuda", dtype=torch.float32).uniform_(0, 16)
+    A = torch.empty(num_v_heads, device="cuda", dtype=torch.float32).uniform_(
+        0, 16, generator=rng
+    )
    A_log = torch.log(A)
    dt = torch.exp(
-        torch.rand(num_v_heads, device="cuda", dtype=torch.float32)
+        torch.rand(num_v_heads, device="cuda", dtype=torch.float32, generator=rng)
        * (math.log(0.1) - math.log(0.001))
        + math.log(0.001)
    )
@@ -105,6 +99,7 @@ def test_gdn_chunk_cutedsl_correctness(num_seqs: int, state_dtype: torch.dtype):
            head_k_dim,
            device="cuda",
            dtype=state_dtype,
+            generator=rng,
        )
        * 0.05
    )
@@ -6,6 +6,7 @@ import pytest
 import torch
 import torch.nn.functional as F

+import vllm._custom_ops as ops
 from vllm.model_executor.layers.fused_moe.config import (
    RoutingMethodType,
    get_routing_method_type,
@@ -231,3 +232,119 @@ def test_dsv4_fast_topk(
        atol=2e-5,
        rtol=2e-5,
    )
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="This test is skipped on non-CUDA platform.",
+)
+@pytest.mark.parametrize("use_hash", [False, True])
+@pytest.mark.parametrize("use_bias", [False, True])
+@pytest.mark.parametrize("use_padding_mask", [False, True])
+@pytest.mark.parametrize("pad_with_nan", [False, True])
+@pytest.mark.parametrize("num_experts", [128, 256, 384])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.half, torch.float32])
+def test_fused_topk_softplus_sqrt_padding(
+    use_hash: bool,
+    use_bias: bool,
+    use_padding_mask: bool,
+    pad_with_nan: bool,
+    num_experts: int,
+    dtype: torch.dtype,
+):
+    """Verify explicit padding and NaN-padded rows do not affect real rows."""
+    torch.manual_seed(0)
+    num_tokens = 8
+    topk = 6
+    indices_dtype = torch.int32
+
+    gating_output = torch.randn((num_tokens, num_experts), dtype=dtype, device="cuda")
+
+    padding_rows = torch.zeros(num_tokens, dtype=torch.bool, device="cuda")
+    padding_rows[1::2] = True
+    if pad_with_nan:
+        gating_output[padding_rows] = float("nan")
+    is_padding = padding_rows if use_padding_mask else None
+
+    # A negative correction bias makes explicit pad rows look selectable unless
+    # the kernel uses the is_padding guard.
+    e_score_correction_bias = None
+    if use_bias:
+        e_score_correction_bias = (
+            -torch.rand((num_experts,), dtype=torch.float32, device="cuda") - 1.0
+        )
+
+    input_ids = None
+    hash_indices_table = None
+    if use_hash:
+        vocab_size = 64
+        hash_indices_table = torch.stack(
+            [torch.randperm(num_experts)[:topk] for _ in range(vocab_size)]
+        ).to(device="cuda", dtype=indices_dtype)
+        input_ids = torch.randint(
+            0, vocab_size, (num_tokens,), dtype=indices_dtype, device="cuda"
+        )
+
+    topk_weights = torch.empty(num_tokens, topk, dtype=torch.float32, device="cuda")
+    topk_ids = torch.empty(num_tokens, topk, dtype=indices_dtype, device="cuda")
+    token_expert_indices = torch.empty(
+        num_tokens, topk, dtype=torch.int32, device="cuda"
+    )
+
+    ops.topk_hash_softplus_sqrt(
+        topk_weights,
+        topk_ids,
+        token_expert_indices,
+        gating_output,
+        renormalize=True,
+        routed_scaling_factor=1.0,
+        e_score_correction_bias=e_score_correction_bias,
+        input_tokens=input_ids,
+        hash_indices_table=hash_indices_table,
+        is_padding=is_padding,
+    )
+
+    if use_padding_mask:
+        pad_ids = topk_ids[padding_rows]
+        pad_weights = topk_weights[padding_rows]
+        assert torch.equal(pad_ids, torch.full_like(pad_ids, -1)), (
+            f"Explicit pad rows should contain only -1 ids, got {pad_ids.tolist()}"
+        )
+        assert (pad_weights == 0).all(), (
+            "Explicit pad rows should have all-zero weights, "
+            f"got {pad_weights.tolist()}"
+        )
+
+    if pad_with_nan:
+        nan_pad_weights = topk_weights[padding_rows]
+        assert torch.isfinite(nan_pad_weights).all(), (
+            f"NaN-padded rows have non-finite weights, got {nan_pad_weights.tolist()}"
+        )
+        assert (nan_pad_weights == 0).all(), (
+            "NaN-padded rows should have all-zero weights, "
+            f"got {nan_pad_weights.tolist()}"
+        )
+
+    topk_weights_ref, topk_ids_ref = _torch_topk_softplus_sqrt(
+        gating_output=gating_output,
+        topk=topk,
+        renormalize=True,
+        routed_scaling_factor=1.0,
+        e_score_correction_bias=e_score_correction_bias,
+        input_ids=input_ids,
+        hash_indices_table=hash_indices_table,
+    )
+
+    rows_to_compare = torch.ones(num_tokens, dtype=torch.bool, device="cuda")
+    if use_padding_mask or pad_with_nan:
+        rows_to_compare = ~padding_rows
+
+    sorted_ref_ids, idx_ref = topk_ids_ref[rows_to_compare].sort(dim=-1)
+    sorted_ids, idx_ops = topk_ids[rows_to_compare].sort(dim=-1)
+    torch.testing.assert_close(
+        sorted_ref_ids, sorted_ids.to(sorted_ref_ids.dtype), atol=0, rtol=0
+    )
+
+    sorted_w_ref = topk_weights_ref[rows_to_compare].gather(1, idx_ref)
+    sorted_w = topk_weights[rows_to_compare].gather(1, idx_ops)
+    torch.testing.assert_close(sorted_w_ref, sorted_w, atol=2e-2, rtol=1e-2)
@@ -21,9 +21,9 @@ def test_gather_cache_oob():
    seq_starts causes the block_table offset to read out of bounds.
    """

-    batch_size = 1
    block_size = 64
-    entry_size = 128
+    # The kernel only supports the MLA entry sizes.
+    entry_size = 576

    block_table = torch.tensor([[1, 2]], dtype=torch.int32, device="cuda")

@@ -34,6 +34,7 @@ def test_gather_cache_oob():

    seq_len = 65
    cu_seq_lens = torch.tensor([0, seq_len], dtype=torch.int32, device="cuda")
+    token_to_seq = torch.zeros(seq_len, dtype=torch.int32, device="cuda")

    # src_cache: [num_blocks, block_size, entry_size]
    num_blocks = 5
@@ -51,7 +52,8 @@ def test_gather_cache_oob():
        dst,
        block_table,
        cu_seq_lens,
-        batch_size,
+        token_to_seq,
+        seq_len,
        "auto",  # kv_cache_dtype
        scale,
        seq_starts,
@@ -13,6 +13,7 @@ These tests cover:
 """

 import math
+from types import SimpleNamespace

 import pytest
 import torch
@@ -27,6 +28,7 @@ from vllm.models.deepseek_v4.common.ops.fused_compress_quant_cache import (
    _fused_kv_compress_norm_rope_insert_indexer_mxfp4_attn,
    _launch_two_stage_sparse_attn_compressor,
 )
+from vllm.models.deepseek_v4.compressor import _get_c128_boundary
 from vllm.platforms import current_platform

 from .test_fused_indexer_q_rope_quant import quantize_to_mxfp4
@@ -58,6 +60,25 @@ def _ue8m0_reference(x: torch.Tensor, block_size: int, fp8_max: float):
    return x_fp8, scales


+@pytest.mark.parametrize(
+    ("starts", "query_start_loc", "expected"),
+    [
+        ([0], [0, 127], False),
+        ([0], [0, 128], True),
+        ([127], [0, 1], True),
+        ([128], [0, 127], False),
+        ([1, 255], [0, 1, 2], True),
+        (None, [0, 1], None),
+    ],
+)
+def test_get_c128_boundary(starts, query_start_loc, expected):
+    metadata = SimpleNamespace(
+        _num_computed_tokens_cpu=None if starts is None else torch.tensor(starts),
+        query_start_loc_cpu=torch.tensor(query_start_loc),
+    )
+    assert _get_c128_boundary(metadata) is expected
+
+
 # ── Test A: DeepseekV4 Attention path ──────────────────────────────────────────────


@@ -264,7 +264,7 @@ def test_block_mask_direct_vs_slow_path():
    device = torch.device("cuda")

    vllm_config = create_vllm_config(
-        model_name="meta-llama/Meta-Llama-3-8B", block_size=16, max_model_len=1024
+        model_name="Qwen/Qwen2.5-1.5B-Instruct", block_size=16, max_model_len=1024
    )
    kv_cache_spec = create_standard_kv_cache_spec(vllm_config)

@@ -726,14 +726,19 @@ def test_einsum_end_to_end(num_tokens, num_heads, n_groups):
    This catches stride/layout bugs that only manifest when the einsum
    kernel actually consumes the quantized activations.
    """
-    from deep_gemm.utils.math import ceil_div
-
    from vllm.utils.deep_gemm import (
        fp8_einsum,
+        is_deep_gemm_supported,
        per_block_cast_to_fp8,
        transform_sf_into_required_layout,
    )

+    if not is_deep_gemm_supported():
+        pytest.skip("DeepGEMM not supported on this platform")
+
+    def ceil_div(a: int, b: int) -> int:
+        return (a + b - 1) // b
+
    heads_per_group = num_heads // n_groups
    d = heads_per_group * HEAD_DIM
    o_lora_rank = 1024
@@ -809,8 +814,12 @@ def test_einsum_end_to_end(num_tokens, num_heads, n_groups):
    # -- Checks --
    # Einsum output: Triton and CUDA both rotate in fp32 now, so diffs
    # come from fp32 ordering and UE8M0 boundary shifts only.
-    # Use relative diff (same metric as test_fp8_einsum.py).
-    from deep_gemm.testing import calc_diff
+    # Use relative diff (same metric as deep_gemm.testing.calc_diff).
+    def calc_diff(x, y):
+        x, y = x.double(), y.double()
+        denominator = (x * x + y * y).sum()
+        sim = 2 * (x * y).sum() / denominator
+        return 1 - sim

    z_diff = calc_diff(z_fused, z_ref)
    assert z_diff < 0.01, (
@@ -84,6 +84,22 @@ def norm_rope_ref(x, weight, positions, cos_sin_cache, eps):
    return roped


+def assert_fp8_cache_close(kv_cache, expected_kv_cache):
+    """Compare two e4m3 caches allowing 1 ulp.
+
+    On CUDA the fused kernel quantizes K from its fp32 intermediate, while the
+    reshape_and_cache_flash reference quantizes the bf16-materialized value, so
+    rounding-boundary values may differ by one e4m3 code.
+    """
+    byte_diff = (kv_cache.int() - expected_kv_cache.int()).abs()
+    got = kv_cache.view(torch.float8_e4m3fn).float()
+    exp = expected_kv_cache.view(torch.float8_e4m3fn).float()
+    ok = (byte_diff <= 1) | ((got == 0) & (exp == 0))
+    assert bool(ok.all()), (
+        f"fp8 cache differs by more than 1 ulp in {int((~ok).sum())} elements"
+    )
+
+
 # ── Test 1: dense mode (norm+rope only, no index, no insert) ─────────────────


@@ -265,7 +281,7 @@ def test_sparse_full(num_tokens, block_size, kv_cache_dtype):
            scale,
            scale,
        )
-        torch.testing.assert_close(kv_cache, expected_kv_cache, rtol=0, atol=0)
+        assert_fp8_cache_close(kv_cache, expected_kv_cache)
    else:
        for t in range(num_tokens):
            s = slot_mapping[t].item()
@@ -383,7 +399,7 @@ def test_sparse_skip_index_branch(num_tokens, block_size, kv_cache_dtype):
            scale,
            scale,
        )
-        torch.testing.assert_close(kv_cache, expected_kv_cache, rtol=0, atol=0)
+        assert_fp8_cache_close(kv_cache, expected_kv_cache)
    else:
        k_ref_h = k_ref.view(num_tokens, num_kv_heads, HEAD_DIM)
        v_ref_h = v_in.view(num_tokens, num_kv_heads, HEAD_DIM)
@@ -41,11 +41,12 @@ def test_fused_recurrent_packed_decode_matches_reference(
    A_log = torch.randn((HV,), device=device, dtype=dtype)
    dt_bias = torch.randn((HV,), device=device, dtype=dtype)

-    # Continuous batching indices (include PAD_SLOT_ID=-1 cases).
-    ssm_state_indices = torch.arange(B, device=device, dtype=torch.int32)
+    # Continuous batching indices (include PAD_SLOT_ID=-1 cases). Index 0 is
+    # reserved as NULL_BLOCK_ID (CUDA graph padding), so valid slots start at 1.
+    ssm_state_indices = torch.arange(1, B + 1, device=device, dtype=torch.int32)
    ssm_state_indices[-3:] = -1

-    state0 = torch.randn((B, HV, V, K), device=device, dtype=dtype)
+    state0 = torch.randn((B + 1, HV, V, K), device=device, dtype=dtype)
    state_ref = state0.clone()
    state_packed = state0.clone()

@@ -94,5 +95,8 @@ def test_fused_recurrent_packed_decode_matches_reference(

    atol = 2e-2 if dtype != torch.float32 else 1e-4
    rtol = 1e-2 if dtype != torch.float32 else 1e-4
-    torch.testing.assert_close(out_packed, out_ref, rtol=rtol, atol=atol)
+    # Output rows for PAD_SLOT_ID entries are never written (uninitialized in
+    # both paths), so compare only the valid rows.
+    valid = ssm_state_indices > 0
+    torch.testing.assert_close(out_packed[valid], out_ref[valid], rtol=rtol, atol=atol)
    torch.testing.assert_close(state_packed, state_ref, rtol=rtol, atol=atol)
@@ -58,10 +58,12 @@ def test_fused_sigmoid_gating_delta_rule_update_non_spec(
    dt_bias = torch.rand(num_v_heads // tp_size, dtype=dtype)
    a = torch.rand(num_tokens, num_v_heads, dtype=dtype)
    b = torch.rand(num_tokens, num_v_heads, dtype=dtype)
+    # Entry 0 is reserved as NULL_BLOCK_ID (CUDA graph padding), so valid
+    # state indices start at 1.
    ssm_state = torch.rand(
-        total_entries, num_v_heads, head_k_dim, head_v_dim, dtype=dtype
+        total_entries + 1, num_v_heads, head_k_dim, head_v_dim, dtype=dtype
    )
-    state_indices = torch.randperm(total_entries, dtype=torch.int32)[:num_tokens]
+    state_indices = (torch.randperm(total_entries, dtype=torch.int32) + 1)[:num_tokens]
    cu_seqlens = torch.arange(0, num_tokens + 1, dtype=torch.int32)

    beta = b.sigmoid()
@@ -144,13 +146,14 @@ def test_fused_sigmoid_gating_delta_rule_update_spec(
    dt_bias = torch.rand(num_v_heads // tp_size, dtype=dtype)
    a = torch.rand(num_tokens, num_v_heads, dtype=dtype)
    b = torch.rand(num_tokens, num_v_heads, dtype=dtype)
+    # Entry 0 is reserved as NULL_BLOCK_ID (CUDA graph padding), so valid
+    # state indices start at 1.
    ssm_state = torch.rand(
-        total_entries, num_v_heads, head_k_dim, head_v_dim, dtype=dtype
+        total_entries + 1, num_v_heads, head_k_dim, head_v_dim, dtype=dtype
    )
-    state_indices = torch.randperm(
-        total_entries,
-        dtype=torch.int32,
-    )[:num_tokens].view(num_reqs, num_speculative_tokens + 1)
+    state_indices = (torch.randperm(total_entries, dtype=torch.int32) + 1)[
+        :num_tokens
+    ].view(num_reqs, num_speculative_tokens + 1)
    num_accepted_tokens = torch.randint(
        1, num_speculative_tokens + 1, (num_reqs,), dtype=torch.int32
    )
@@ -0,0 +1,19 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.model_executor.model_loader.mtp_validation import (
+    disable_mtp_completeness_check,
+    is_mtp_completeness_check_enabled,
+)
+
+
+def test_disable_mtp_completeness_check_is_scoped():
+    assert is_mtp_completeness_check_enabled()
+
+    with pytest.raises(RuntimeError), disable_mtp_completeness_check():
+        assert not is_mtp_completeness_check_enabled()
+        raise RuntimeError
+
+    assert is_mtp_completeness_check_enabled()
@@ -6,6 +6,7 @@ import pytest

 from vllm.config.compilation import CompilationConfig, CUDAGraphMode
 from vllm.models.inkling.common.mm_preprocess import InklingMultiModalDataParser
+from vllm.models.inkling.common.towers import plan_out_scales
 from vllm.models.inkling.configs import (
    InklingAudioConfig,
    InklingModelConfig,
@@ -17,6 +18,22 @@ from vllm.models.inkling.nvidia.sconv_swa_attn import (
 from vllm.v1.attention.backend import AttentionCGSupport


+def test_vision_scale_plan_matches_released_config():
+    assert plan_out_scales(2, 40, 4) == [
+        (1, 1, 1, 3),
+        (1, 5, 5, 128),
+        (1, 10, 10, 320),
+        (1, 40, 40, 4800),
+        (2, 40, 40, 9600),
+    ]
+
+
+def test_vision_scale_plan_breaks_assignment_ties_in_order():
+    reductions = [np.prod(scale[:-1]) for scale in plan_out_scales(2, 52, 4)]
+
+    assert reductions == sorted(set(reductions))
+
+
@pytest.mark.parametrize(
    ("config_cls", "kwargs", "missing"),
    [
@@ -6,15 +6,43 @@ import time

 import pytest

+import vllm.config.multimodal as multimodal_config_module
+from vllm.config.multimodal import MultiModalConfig
 from vllm.multimodal.gpu_ipc_memory import (
    MultiModalGPUMemoryPool,
    get_mm_gpu_ipc_pool,
    maybe_init_mm_gpu_ipc_pool,
+    reserve_mm_ipc_gpu_memory,
    set_mm_gpu_ipc_pool,
 )
+from vllm.multimodal.video import (
+    PYNVVIDEOCODEC_CUDA_CONTEXT_BYTES,
+    PYNVVIDEOCODEC_DECODER_GPU_MEMORY_BYTES,
+    PYNVVIDEOCODEC_MAX_RETAINED_DECODERS,
+    PYNVVIDEOCODEC_VIDEO_BACKEND,
+)
 from vllm.utils.mem_constants import GiB_bytes


+def _mm_config(
+    *,
+    mm_ipc_gpu_memory_gb: float = 0,
+    video_backend: str | None = None,
+) -> MultiModalConfig:
+    video_kwargs = {} if video_backend is None else {"video_backend": video_backend}
+    return MultiModalConfig(
+        mm_ipc_gpu_memory_gb=mm_ipc_gpu_memory_gb,
+        media_io_kwargs={"video": video_kwargs} if video_kwargs else {},
+    )
+
+
+def _pynvvideocodec_decoder_budget(api_process_count: int = 1) -> int:
+    return api_process_count * (
+        PYNVVIDEOCODEC_DECODER_GPU_MEMORY_BYTES * PYNVVIDEOCODEC_MAX_RETAINED_DECODERS
+        + PYNVVIDEOCODEC_CUDA_CONTEXT_BYTES
+    )
+
+
 def test_acquire_release_accounting():
    pool = MultiModalGPUMemoryPool(total_bytes=100)
    assert pool.available_bytes == 100
@@ -143,3 +171,72 @@ def test_global_pool_splits_budget_across_api_processes():
 def test_global_pool_rejects_invalid_api_process_count():
    with pytest.raises(ValueError):
        maybe_init_mm_gpu_ipc_pool(2, api_process_count=0)
+
+
+@pytest.mark.parametrize("video_backend", [None, "opencv"])
+def test_reserve_mm_ipc_gpu_memory_raw_frame_budget_only(
+    monkeypatch: pytest.MonkeyPatch,
+    video_backend: str | None,
+):
+    monkeypatch.setattr(
+        multimodal_config_module.envs,
+        "VLLM_VIDEO_LOADER_BACKEND",
+        "opencv",
+    )
+    mm_config = _mm_config(
+        mm_ipc_gpu_memory_gb=0.25,
+        video_backend=video_backend,
+    )
+
+    assert reserve_mm_ipc_gpu_memory(GiB_bytes, mm_config) == int(0.75 * GiB_bytes)
+
+
+def test_reserve_mm_ipc_gpu_memory_includes_pynvvideocodec_decoder_budget(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    monkeypatch.setattr(
+        multimodal_config_module.envs,
+        "VLLM_VIDEO_LOADER_BACKEND",
+        "opencv",
+    )
+    mm_config = _mm_config(
+        mm_ipc_gpu_memory_gb=0.25,
+        video_backend=PYNVVIDEOCODEC_VIDEO_BACKEND,
+    )
+    available_bytes = 4 * GiB_bytes
+
+    assert reserve_mm_ipc_gpu_memory(available_bytes, mm_config) == (
+        available_bytes - int(0.25 * GiB_bytes) - _pynvvideocodec_decoder_budget()
+    )
+
+
+def test_reserve_mm_ipc_gpu_memory_uses_env_video_backend(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    monkeypatch.setattr(
+        multimodal_config_module.envs,
+        "VLLM_VIDEO_LOADER_BACKEND",
+        PYNVVIDEOCODEC_VIDEO_BACKEND,
+    )
+    available_bytes = 4 * GiB_bytes
+
+    assert reserve_mm_ipc_gpu_memory(available_bytes, _mm_config()) == (
+        available_bytes - _pynvvideocodec_decoder_budget()
+    )
+
+
+def test_reserve_mm_ipc_gpu_memory_scales_decoder_budget_by_api_servers(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    monkeypatch.setattr(
+        multimodal_config_module.envs,
+        "VLLM_VIDEO_LOADER_BACKEND",
+        PYNVVIDEOCODEC_VIDEO_BACKEND,
+    )
+    available_bytes = 8 * GiB_bytes
+
+    assert reserve_mm_ipc_gpu_memory(
+        available_bytes,
+        _mm_config(),
+        api_process_count=3,
+    ) == available_bytes - _pynvvideocodec_decoder_budget(api_process_count=3)
@@ -920,3 +920,45 @@ class TestDelegatingParserLargeDelta:
        assert output.tool_calls[0]["name"] == "get_weather"
        args = json.loads(output.tool_calls[0]["arguments"])
        assert args == {"location": "Berlin"}
+
+    @pytest.mark.parametrize(
+        "chunk_size",
+        [1, 2, 3, 5, None],
+        ids=lambda c: f"chunk={c}",
+    )
+    def test_eos_not_leaked_when_reasoning_never_ends(self, chunk_size):
+        """EOS must not leak into reasoning_content when the model never
+        emits </think> (generation ends while still in REASONING state)."""
+        eos_text = "<｜end▁of▁sentence｜>"
+        eos_id = 128801
+        vocab = {
+            **_DSV4_FULL_VOCAB,
+            eos_text: eos_id,
+        }
+
+        reasoning_text = "Good morning! How can I help you today?"
+        tokens: list[tuple[int, str]] = []
+        tid = 100
+        for word in reasoning_text.split(" "):
+            prefix = " " if tokens else ""
+            tokens.append((tid, prefix + word))
+            tid += 1
+        tokens.append((eos_id, eos_text))
+
+        tokenizer = MockTokenizer(vocab=vocab, tokens=tokens)
+        parser = _DeepSeekV4Delegating(
+            tokenizer,
+            chat_template_kwargs={"thinking": True},
+        )
+        deltas = replay_streaming(
+            parser,
+            tokens,
+            chunk_size=chunk_size,
+            finished_on_last=True,
+        )
+        output = collect_output(deltas)
+
+        assert reasoning_text in output.reasoning
+        assert eos_text not in output.reasoning
+        assert output.content == ""
+        assert output.tool_calls == []
@@ -1624,19 +1624,41 @@ class TestDropSpecialTokens:
        assert delta is not None
        assert "<bos>" in delta.reasoning

-    def test_drops_suppressed_with_skip_tool_parsing(self):
-        """When skip_tool_parsing is active, drop tokens are preserved
-        as content so a later tool-call pass can see them."""
+    def test_drops_applied_with_skip_tool_parsing(self):
+        """Drop tokens are always dropped, even with skip_tool_parsing.
+        DROP_TERMINALs have no transitions by construction, so no parser
+        pass can use them."""
+        for initial_state in (ParserState.REASONING, ParserState.CONTENT):
+            engine = _make_engine(
+                vocab=_DROP_VOCAB,
+                special_tokens=list(_DROP_VOCAB.keys()),
+            )
+            engine._engine.skip_tool_parsing = True
+            engine._engine.reset(initial_state=initial_state)
+            events = engine._engine.feed("hello<bos>world", [72, 204, 73])
+            delta = engine._events_to_delta(events)
+            assert delta is not None
+            output = (delta.reasoning or "") + (delta.content or "")
+            assert "<bos>" not in output, f"<bos> leaked in state {initial_state}"
+
+    def test_transitions_unaffected_by_drop_in_reasoning_with_skip_tool_parsing(self):
+        """With skip_tool_parsing in REASONING state, drop tokens are
+        removed but configured terminals still fire their transitions."""
        engine = _make_engine(
            vocab=_DROP_VOCAB,
            special_tokens=list(_DROP_VOCAB.keys()),
        )
        engine._engine.skip_tool_parsing = True
        engine._engine.reset()
-        events = engine._engine.feed("hello<bos>world", [72, 204, 73])
-        delta = engine._events_to_delta(events)
-        assert delta is not None
-        assert "<bos>" in delta.reasoning
+        events = engine._engine.feed("thought<bos></think>answer", [72, 204, 201, 73])
+        types = [e.type for e in events]
+        assert EventType.REASONING_CHUNK in types
+        assert EventType.REASONING_END in types
+        assert EventType.TEXT_CHUNK in types
+        reasoning_text = "".join(
+            e.value for e in events if e.type == EventType.REASONING_CHUNK
+        )
+        assert "<bos>" not in reasoning_text

    def test_drops_in_tool_args_state(self):
        """Drop tokens in TOOL_ARGS state are silently discarded."""
@@ -50,7 +50,7 @@ def test_oot_registration_embedding(
        m.setenv("VLLM_PLUGINS", "register_dummy_model")
        prompts = ["Hello, my name is", "The text does not matter"]
        llm = LLM(
-            model=dummy_gemma2_embedding_path, load_format="dummy", max_model_len=2048
+            model=dummy_gemma2_embedding_path, load_format="dummy", max_model_len=512
        )
        outputs = llm.embed(prompts)

@@ -5,13 +5,17 @@
 Run `pytest tests/quantization/test_auto_gptq.py -v -s`.
 """

+from types import SimpleNamespace
+
 import pytest
 import torch

 from tests.quantization.utils import is_quant_method_supported
+from vllm.model_executor.layers.fused_moe import RoutedExperts
 from vllm.model_executor.layers.quantization.auto_gptq import (
    AutoGPTQConfig,
    AutoGPTQLinearMethod,
+    AutoGPTQMoEMethod,
 )

 PROMPT = "On the surface of Mars, we found"
@@ -54,3 +58,78 @@ def test_auto_gptq_quantization_method(vllm_runner, model_id: str, monkeypatch):
 def test_auto_gptq_config_get_name():
    """Test that AutoGPTQConfig.get_name() returns 'auto_gptq'."""
    assert AutoGPTQConfig.get_name() == "auto_gptq"
+
+
+def test_auto_gptq_moe_creates_zero_initialized_expert_biases():
+    method = object.__new__(AutoGPTQMoEMethod)
+    method.quant_config = AutoGPTQConfig(4, 128, False, True, False, {}, {})
+    method.input_dtype = None
+    method.experts_cls = None
+    layer = torch.nn.Module()
+
+    method.create_weights(
+        layer=layer,
+        num_experts=2,
+        hidden_size=8,
+        intermediate_size_per_partition=4,
+        params_dtype=torch.float16,
+        intermediate_size_full=4,
+        weight_loader=lambda *args, **kwargs: None,
+    )
+
+    assert layer.w13_bias.shape == (2, 8)
+    assert layer.w2_bias.shape == (2, 8)
+    assert torch.count_nonzero(layer.w13_bias) == 0
+    assert torch.count_nonzero(layer.w2_bias) == 0
+
+
+def test_routed_experts_loads_per_expert_biases():
+    class Loader:
+        quant_config = None
+        quant_method = object()
+        moe_config = SimpleNamespace(
+            is_act_and_mul=True,
+            tp_rank=0,
+            moe_parallel_config=SimpleNamespace(tp_size=1),
+        )
+        _get_hidden_dim = staticmethod(RoutedExperts._get_hidden_dim)
+        _narrow_expert_data_for_padding = staticmethod(
+            RoutedExperts._narrow_expert_data_for_padding
+        )
+        _load_w13 = RoutedExperts._load_w13
+        _loaded_expert_biases = set()
+
+        @staticmethod
+        def _map_global_expert_id_to_local_expert_id(expert_id):
+            return expert_id
+
+    loader = Loader()
+    w13_bias = torch.nn.Parameter(torch.zeros(1, 8), requires_grad=False)
+    w2_bias = torch.nn.Parameter(torch.zeros(1, 4), requires_grad=False)
+
+    for shard_id, loaded in (
+        ("w1", torch.tensor([1.0, 2.0, 3.0, 4.0])),
+        ("w3", torch.tensor([5.0, 6.0, 7.0, 8.0])),
+    ):
+        assert RoutedExperts.weight_loader(
+            loader,
+            w13_bias,
+            loaded,
+            weight_name="model.layers.0.mlp.experts.w13_bias",
+            shard_id=shard_id,
+            expert_id=0,
+            return_success=True,
+        )
+
+    assert RoutedExperts.weight_loader(
+        loader,
+        w2_bias,
+        torch.tensor([9.0, 10.0, 11.0, 12.0]),
+        weight_name="model.layers.0.mlp.experts.w2_bias",
+        shard_id="w2",
+        expert_id=0,
+        return_success=True,
+    )
+    assert torch.equal(w13_bias, torch.arange(1, 9, dtype=torch.float32).reshape(1, 8))
+    assert torch.equal(w2_bias, torch.arange(9, 13, dtype=torch.float32).reshape(1, 4))
+    assert loader._loaded_expert_biases == {"w13_bias", "w2_bias"}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Bugen Zhao	97cbc212b4	[Rust Frontend] Extract shared chat types into `vllm-chat-types` Signed-off-by: Bugen Zhao <i@bugenzhao.com>	2026-07-23 10:16:32 +00:00
Rehan KhanandGitHub	f83de6d44c	[CPU][Docs] Update docs and dockerfile for s390x (#49523 ) Signed-off-by: Rehan Khan <Rehan.Khan7@ibm.com>	2026-07-23 07:17:24 +00:00
Umut PolatandGitHub	239fc73553	[Misc] Use VLLMValidationError in chat_utils content-part validation (#49217 ) Signed-off-by: Umut Polat <52835619+umut-polat@users.noreply.github.com>	2026-07-23 05:48:37 +00:00
Mike GandGitHub	76bf55240c	[Bugfix] Fix DeepSeek-V4 DSpark draft shared-expert padding for TP > 8 (#49415 ) Signed-off-by: Mike G <180722391+mikekg@users.noreply.github.com>	2026-07-23 05:06:21 +00:00
Mike G GitHub Michael Gschwind OpenAI Codex	9a698f3255	[Performance][Model] Avoid transient Inkling result allocations (performance, and OOM prevention on smaller memory configurations) (#49487 ) Signed-off-by: Michael Gschwind <mgschwind@nvidia.com> Co-authored-by: Michael Gschwind <mgschwind@nvidia.com> Co-authored-by: OpenAI Codex <codex@openai.com>	2026-07-23 05:03:34 +00:00
Mike G GitHub Michael Gschwind OpenAI Codex	4080263bb2	[Bugfix][Model] Remove SciPy dependency from Inkling scale planning (#49485 ) Signed-off-by: Michael Gschwind <mgschwind@nvidia.com> Co-authored-by: Michael Gschwind <mgschwind@nvidia.com> Co-authored-by: OpenAI Codex <codex@openai.com>	2026-07-23 04:34:57 +00:00
jcotant-inferactandGitHub	fc5fda105f	[Docs] Re-add Reo.dev analytics beacon (#49474 )	2026-07-23 03:03:32 +00:00
Matej SirovatkaandGitHub	b07ec92faa	[Bugfix] Make shared NVFP4 MoE scales writable (#49489 ) Signed-off-by: S1ro1 <matej.sirovatka@gmail.com>	2026-07-22 19:17:53 -07:00
Colin Z GitHubmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>	27ffbfde8d	Fused Shared Expert Support for AMD Quark DeepSeek-V4 Model Checkpoints (#48044 ) Signed-off-by: Colin Zeng <Colin.Zeng@amd.com> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>	2026-07-23 00:34:17 +00:00
Nick HillandGitHub	229e01e9e1	[BugFix] Handle per-group prefix-hit divergence for hybrid models with KV connector (#48425 )	2026-07-22 17:19:11 -07:00
Michael Goin GitHub OpenAI Codex	191146dba5	Add quantization label automation (#49492 ) Signed-off-by: mgoin <mgoin64@gmail.com> Co-authored-by: OpenAI Codex <codex@openai.com>	2026-07-22 19:59:15 -04:00
Summer YangandGitHub	f3a920a076	[Core][DSV4] Compact MXFP4 indexer KV cache and packed group overlays (#48993 )	2026-07-22 16:58:44 -07:00
Nils Matteson GitHubmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>	149daf0d72	[Bugfix] Exclude location-derived path vars from torch.compile cache factors (#47573 ) Signed-off-by: Nils Matteson <nilsmatteson@icloud.com> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>	2026-07-22 16:56:09 -07:00
Michael GoinandGitHub	917fdb5bf7	[Bugfix] Fix DeepGEMM warmup when using `FlashInferFp8DeepGEMMDynamicBlockScaledKernel` (#49467 ) Signed-off-by: mgoin <mgoin64@gmail.com>	2026-07-22 16:28:49 -07:00
stefankoncarevicandGitHub	4b594b4aa1	[Bugfix][CI] Fix `topk_softplus_sqrt` no-op on non-XPU platforms (#49452 ) Signed-off-by: Stefan Koncarevic <stefan.koncarevic@amd.com>	2026-07-22 15:36:17 -07:00
peizhang56 GitHub Claude	7d10a4cfce	[Bugfix] Retry config read to survive concurrent HF cache refresh (#49001 ) Signed-off-by: pei.zhang <pei.zhang@amd.com> Co-authored-by: Claude <noreply@anthropic.com>	2026-07-22 15:35:38 -07:00
Nick HillandGitHub	910cc8543a	[Bugfix] Restore `gather_and_maybe_dequant_cache` OOB guard (#49427 ) Signed-off-by: Nick Hill <nickhill123@gmail.com>	2026-07-22 13:47:37 -07:00
Nick HillandGitHub	431934522b	[CI] Fix stale/fragile untethered kernels-root tests (#49423 ) Signed-off-by: Nick Hill <nickhill123@gmail.com>	2026-07-22 14:43:07 -06:00
Wei Zhao GitHub Wei Zhao Cyrus Leung	61a09532f2	Bump Flashinfer version to 0.6.15 (#48914 ) Signed-off-by: wzhao18 <wzhao18.sz@gmail.com> Signed-off-by: Wei Zhao <weizha@oci-aga-slurm-1-vscode-02.cm.cluster> Co-authored-by: Wei Zhao <weizha@oci-aga-slurm-1-vscode-02.cm.cluster> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>	2026-07-22 13:32:05 -07:00
Ben Browning GitHubmergify[bot] <37929162+mergify[bot]@users.noreply.github.com>	3de4b2bf3c	[Bugfix][Parser] Fix special tokens (EOS/BOS) leaking into reasoning content (#48748 ) Signed-off-by: Ben Browning <56071+bbrowning@users.noreply.github.com> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>	2026-07-22 16:10:55 -04:00
Thien Tran GitHub Codex	b44311b6ef	[CI] stabilize GDN prefill CuTeDSL test (#49388 ) Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg> Co-authored-by: Codex <noreply@openai.com>	2026-07-22 09:03:46 -07:00
Nick HillandGitHub	b0d7875180	[CI] Increase timeout of pytorch-compilation-unit-tests (#49450 ) Signed-off-by: Nick Hill <nickhill123@gmail.com>	2026-07-22 15:39:09 +00:00
Divakar VermaandGitHub	53c2f20dd9	[ROCm][CI] skip moe weight padding for eplb (#49350 ) Signed-off-by: Divakar Verma <divakar.verma@amd.com>	2026-07-22 10:18:01 -05:00
Wentao YeandGitHub	37e370fe93	[DSv4 Perf] Skip empty c128 kernel launch, around 2x kernel performance improvement. (#48957 ) Signed-off-by: yewentao256 <zhyanwentao@126.com>	2026-07-22 10:55:20 -04:00
Guan-Ming ChiuandGitHub	2dc5a72e7e	[Bugfix][Renderer] Rebuild vision chunk UUIDs in async render path (#49400 ) Signed-off-by: Guan-Ming (Wesley) Chiu <105915352+guan404ming@users.noreply.github.com>	2026-07-22 14:11:24 +00:00
Andrey TalmanandGitHub	c79ff5f918	[Build] Bump vllm-flash-attn to C++20-compatible commit for torch-nightly (#49326 ) Signed-off-by: Andrey Talman <atalman@fb.com>	2026-07-22 13:51:59 +00:00
Teresa ChenandGitHub	1a659a0c37	Upgrade tpu-inference to v0.25.0 (#49431 )	2026-07-22 11:52:56 +00:00
SageandGitHub	0f6cf7f628	[Rust Frontend] Extract request preparation from the inference path (#49045 ) Signed-off-by: Sage Ahrac <sagiahrak@gmail.com>	2026-07-22 11:31:36 +00:00
Connor Carpenter GitHub OpenAI Codex	c79ad3ae21	[Rust Frontend][gRPC] Add abort control RPC (#49255 ) Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Connor Carpenter <connorc@nvidia.com>	2026-07-22 11:31:01 +00:00
wang.yuqiandGitHub	61c9ef986a	[Frontend] Parallelize preprocessing within the same request for pooling models online serving. (#49153 ) Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>	2026-07-22 10:56:23 +00:00
LiangqiusongandGitHub	d6dbdb9b0d	[XPU] WA of topk_softplus_sqrt arg mismatch on XPU (#49408 ) Signed-off-by: xiaolong <xiaolong.guo@intel.com>	2026-07-22 16:16:13 +08:00
liuzhenweiandGitHub	06da482fb4	[XPU] WA of topk_softmax arg mismatch on XPU (#49395 ) Signed-off-by: zhenwei-intel <zhenwei.liu@intel.com>	2026-07-22 01:05:01 -07:00
Kevin H. Luu GitHub OpenAI Codex	2f75e7f712	[CI] Increase timeouts for jobs exceeding current limits (#49374 ) Signed-off-by: khluu <khluu000@gmail.com> Co-authored-by: OpenAI Codex <codex@openai.com>	2026-07-22 00:16:16 -07:00
Ziming HuangandGitHub	7c21548ce3	[PD][Bugfix] Fix NIXL hybrid MLA+mamba heterogeneous TP (#49297 ) Signed-off-by: ZeldaHuang <zelda.huanghuang@gmail.com>	2026-07-22 07:11:39 +00:00
Guan-Ming ChiuandGitHub	9df2f91232	[Renderer] Offload derender CPU work to renderer thread pool (#49396 ) Signed-off-by: Guan-Ming (Wesley) Chiu <105915352+guan404ming@users.noreply.github.com>	2026-07-22 06:55:03 +00:00
stefankoncarevic GitHub Andreas Karatzas	387189c429	[ROCm] Remove redundant AITER fused_qk_rmsnorm probe (avoids config-time HIP init) (#47992 ) Signed-off-by: Stefan Koncarevic <stefan.koncarevic@amd.com> Co-authored-by: Andreas Karatzas <akaratza@amd.com>	2026-07-21 22:58:14 -05:00
Kunshang JiandGitHub	75576c63be	Add auto label for xpu relate issue (#49398 ) Signed-off-by: Kunshang Ji <kunshang.ji@intel.com>	2026-07-22 03:33:20 +00:00
Andreas KaratzasandGitHub	16aca639b7	[ROCm] Upgrade NIXL and UCX (#49251 ) Signed-off-by: Andreas Karatzas <Andreas.Karatzas@amd.com>	2026-07-21 22:30:02 -05:00
Woosuk KwonandGitHub	6049424b7e	[MRV2] Always build attn metadata at capture time (#49364 ) Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>	2026-07-21 19:27:16 -07:00
Lucas Wilkinson GitHub OpenAI Codex	060b5f61dc	[Bugfix][Attention] Ignore empty MLA context chunks during merge (#49294 ) Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Co-authored-by: OpenAI Codex <codex@openai.com>	2026-07-22 02:23:48 +00:00
bnellnm GitHub Claude mgoin	ec59c1579f	[MoE Refactor] Migrate MoeWNA16Method quantization method over to using the new MK oracle scheme. (#44120 ) Signed-off-by: Bill Nell <bnell@redhat.com> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: mgoin <mgoin64@gmail.com>	2026-07-21 19:20:30 -07:00
Isotr0pyandGitHub	1750e443f2	[Misc] Move PyNvVideoCodec stuff out of gpu worker (#49322 ) Signed-off-by: Isotr0py <Isotr0py@outlook.com>	2026-07-22 10:11:56 +08:00
aoshen02 GitHub aoshen02	ba18929079	[Bugfix][SpecDecode] Scope MTP completeness checks outside bucketed updates (#49178 ) Signed-off-by: aoshen02 <aoshen02@users.noreply.github.com> Co-authored-by: aoshen02 <aoshen02@users.noreply.github.com>	2026-07-21 18:56:39 -07:00
peizhang56 GitHub Claude	0500ca6a58	[CI][Bugfix] Fix ROCm FP8 KV cache dtype in attention backend test (#49380 ) Signed-off-by: pei.zhang <pei.zhang@amd.com> Co-authored-by: Claude <noreply@anthropic.com>	2026-07-22 01:34:49 +00:00
Nick Hill GitHub Claude Opus 4.8	a1c15bcb0f	[CI][Bugfix] Fix and wire streaming-input tests (#49356 ) Signed-off-by: Nick Hill <nickhill123@gmail.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-07-22 00:35:23 +00:00
Nick HillandGitHub	4809de7317	[Misc] Fix terminal output logo coloring (#49344 ) Signed-off-by: Nick Hill <nickhill123@gmail.com>	2026-07-22 00:21:29 +00:00
stefankoncarevicandGitHub	05781e21dd	[ROCm][CI] Fix order-dependent failure in test_flash_attn_accepts_handled_fp8_variants (MI355) (#49329 ) Signed-off-by: Stefan Koncarevic <Stefan.Koncarevic@amd.com>	2026-07-21 18:54:33 -05:00
gnovackandGitHub	85f638a2b8	skip cudagraph/DP padding in topk (#48979 ) Signed-off-by: gnovack <novackgm@gmail.com>	2026-07-21 15:20:30 -07:00
Nick HillandGitHub	08e5067561	[CI] Bump timeout of `entrypoints-integration-api-server-openai-part-2` (#49359 ) Signed-off-by: Nick Hill <nickhill123@gmail.com>	2026-07-21 14:54:03 -07:00
Michael Goin GitHub Codex	a7d00ec051	[Bugfix] DFlash fc sized wrong when num_target_layers != num_hidden_layers (#48524 ) Signed-off-by: mgoin <mgoin64@gmail.com> Co-authored-by: Codex <noreply@openai.com>	2026-07-21 14:42:52 -07:00
Michael GoinandGitHub	b8fb56d970	[CI] Add gemma-4-E4B-it-assistant to CI gsm8k for GemmaMTP (#49243 ) Signed-off-by: mgoin <mgoin64@gmail.com>	2026-07-21 16:51:04 -04:00
rongfu.leng GitHub Yongye Zhu	96a739289e	[Bugfix] fix cutalss version upgrade bug, need update MSG new commit (#49016 ) Signed-off-by: lengrongfu <lenronfu@gmail.com> Co-authored-by: Yongye Zhu <zyy1102000@gmail.com>	2026-07-21 13:42:14 -07:00
rasmith GitHub Andreas Karatzas	60d443f738	[CI/Build][The Rock][BugFix] Use fork method in test_multiproc_executor_multi_node for py 3.14 compat and fix test_multiproc_executor_shutdown_cleanup (#48655 ) Signed-off-by: Randall Smith <Randall.Smith@amd.com> Co-authored-by: Andreas Karatzas <akaratza@amd.com>	2026-07-21 15:40:29 -05:00
Nick Hill GitHub Claude Opus 4.8	1dca300653	[CI] Fix and wire encoder/manager cudagraph unit tests (#49339 ) Signed-off-by: Nick Hill <nickhill123@gmail.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-07-21 20:41:16 +01:00
Flora FengandGitHub	fca252d59e	[CI][Bugfix] Reduce max_model_len in OOT embedding test to fix KV-cache OOM on small GPUs (#49351 ) Signed-off-by: sfeng33 <4florafeng@gmail.com>	2026-07-21 15:00:06 -04:00
Aritra Roy Gosthipaty GitHub Harry Mellor	33178f9006	Fix Qwen3-VL M-RoPE on the Transformers modeling backend (grids + compile) (#49292 ) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>	2026-07-21 18:28:39 +00:00
Evan Takahashi GitHub Michael Goin	b2b8f679d0	[Bugfix][Spec Decode] Restrict embedding-width share guard to EAGLE drafts (#47953 ) Signed-off-by: Evan Takahashi <evan10takahashi@gmail.com> Co-authored-by: Michael Goin <mgoin64@gmail.com>	2026-07-21 10:53:37 -07:00
Nick Hill GitHub Claude Opus 4.8	de6ec294ef	[Bugfix] Fix DSA crash under breakable piecewise cudagraphs (#49302 ) Signed-off-by: Nick Hill <nickhill123@gmail.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-07-21 18:28:13 +01:00
stefankoncarevicandGitHub	61e10f0116	[ROCm][CI] Fix AITER MLA fp8 decode metadata regression test (#48845 ) Signed-off-by: Stefan Koncarevic <Stefan.Koncarevic@amd.com>	2026-07-21 12:19:45 -05:00
Fangzhou Ai GitHub OpenAI Codex Andreas Karatzas	6e96891ba0	[ROCm] Bump AITER to v0.1.16.post5 (#48683 ) Signed-off-by: Fangzhou Ai <fangzhouai@gmail.com> Signed-off-by: Fangzhou Ai <31551580+Fangzhou-Ai@users.noreply.github.com> Co-authored-by: OpenAI Codex <codex@openai.com> Co-authored-by: Andreas Karatzas <akaratza@amd.com>	2026-07-21 10:39:11 -05:00
Michael Goin GitHub Joe Rowell	47f1b47a73	Ci/add laguna xs gsm8k (#49241 ) Signed-off-by: Joe Rowell <joerowell4@gmail.com> Signed-off-by: mgoin <mgoin64@gmail.com> Co-authored-by: Joe Rowell <joerowell4@gmail.com>	2026-07-21 11:30:42 -04:00
Nick Hill GitHub Claude Opus 4.8	5aab491bc9	[CI] Wire tests/models/inkling into a B200 job (#49325 ) Signed-off-by: Nick Hill <nickhill123@gmail.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-07-21 15:43:20 +01:00
Stefano Castagnetta GitHub Benjamin Chislett	5812e1a66b	[Test] Add DeepSeek MTP parallel-load tests (#41653 ) Signed-off-by: Stefano Castagnetta <scastagnetta@nvidia.com> Co-authored-by: Benjamin Chislett <bchislett@nvidia.com>	2026-07-21 10:38:20 -04:00
Seiji Eicher GitHub Lixi Zhang harjothkhara Claude Opus 4.8 Nick Hill	8950394e0a	[Bugfix] Prefix-cache metrics double-counted when a KV connector defers requests (#48860 ) Signed-off-by: Seiji Eicher <58963096+eicherseiji@users.noreply.github.com> Signed-off-by: Seiji Eicher <seiji@anyscale.com> Signed-off-by: Nick Hill <nickhill123@gmail.com> Co-authored-by: Lixi Zhang <muxixibbb@gmail.com> Co-authored-by: harjothkhara <harjoth.khara@gmail.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: Nick Hill <nickhill123@gmail.com>	2026-07-21 15:02:03 +01:00
Roberto L. CastroandGitHub	7bb49be4d1	[Bugfix] Handle MLA fallback during FA4 JIT warmup (#49306 )	2026-07-21 13:58:05 +00:00
Avery Miao GitHub Kunshang Ji	c67650f04b	[XPU][DeepSeekV4]Add DeepSeek-V4 fuse_index_q SYCL kernel path (#45991 ) Signed-off-by: Avery Miao <avery.miao@intel.com> Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>	2026-07-21 21:22:34 +08:00
Ilya Markov GitHub Markov Ilya	f890e1dbe2	[BugFix] Set graph_pool_id before FULL CUDA graph capture in ModelRunner V2 (#48843 ) Signed-off-by: Markov Ilya <markovilya19@gmail.com> Co-authored-by: Markov Ilya <markovilya19@gmail.com>	2026-07-21 08:59:26 -04:00